diff --git a/MatlabFiles/a0asfun.m b/MatlabFiles/a0asfun.m
index 5054ea5f1c20ab121f1be6f547fc60076a73a0ee..7672e9f589b7967848a9f7e8bc4f65199c7df3d3 100644
--- a/MatlabFiles/a0asfun.m
+++ b/MatlabFiles/a0asfun.m
@@ -1,50 +1,49 @@
-function of = a0asfun(x,s,nobs,nvar,a0indx)
-% General program to setup A0 matrix with asymmetric prior (as) and compute the posterior
-% function of = a0asfun(x,s,nobs,nvar,a0indx) -- negative logPosterior
-% Note: columns correspond to equations
-% x (parameter vector),
-% s (diag(S1,...,Sm)): note, as in "a0lhfun", already divided by "nobs"
-% nobs (no of obs),
-% nvar (no of variables),
-% a0indx (matrix indicating the free parameters in A0, and each column in A0 corresponds
-% to an equation)
-%
-% Copyright (c) December 1997 by Tao Zha
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-a0 = zeros(nvar);
-a0(a0indx) = x;
-% Note: each column in a0 corresponds to an equation!!
-%
-%%ada = chol(a0'*a0);
-%%ada = log(abs(diag(ada)));
-%%ada = sum(ada);
-% ** TZ, 10/15/96, the above two lines can be improved by the following three lines
-[a0l,a0u] = lu(a0);
-%ada=diag(abs(a0u));
-%ada=sum(log(ada));
-ada = sum(log(abs(diag(a0u))));
-
-%
-%tra = sum(i=1:m){a0(:,i)'*Si*a0(:,i)}
-tra = 0.0;
-for i=1:nvar
- tra = tra + a0(:,i)'*s{i}*a0(:,i);
-end
-
-of = -nobs*ada + nobs*.5*tra;
\ No newline at end of file
+function of = a0asfun(x,s,nobs,nvar,a0indx)
+% General program to setup A0 matrix with asymmetric prior (as) and compute the posterior
+% function of = a0asfun(x,s,nobs,nvar,a0indx) -- negative logPosterior
+% Note: columns correspond to equations
+% x (parameter vector),
+% s (diag(S1,...,Sm)): note, as in "a0lhfun", already divided by "nobs"
+% nobs (no of obs),
+% nvar (no of variables),
+% a0indx (matrix indicating the free parameters in A0, and each column in A0 corresponds
+% to an equation)
+%
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+a0 = zeros(nvar);
+a0(a0indx) = x;
+% Note: each column in a0 corresponds to an equation!!
+%
+%%ada = chol(a0'*a0);
+%%ada = log(abs(diag(ada)));
+%%ada = sum(ada);
+% ** TZ, 10/15/96, the above two lines can be improved by the following three lines
+[a0l,a0u] = lu(a0);
+%ada=diag(abs(a0u));
+%ada=sum(log(ada));
+ada = sum(log(abs(diag(a0u))));
+
+%
+%tra = sum(i=1:m){a0(:,i)'*Si*a0(:,i)}
+tra = 0.0;
+for i=1:nvar
+ tra = tra + a0(:,i)'*s{i}*a0(:,i);
+end
+
+of = -nobs*ada + nobs*.5*tra;
diff --git a/MatlabFiles/a0asgrad.m b/MatlabFiles/a0asgrad.m
index 581b85aec71120b212ca96e50100eaf198cee45a..0a5252297dc14c8706aa4c3e4b90b0043ba6c2f5 100644
--- a/MatlabFiles/a0asgrad.m
+++ b/MatlabFiles/a0asgrad.m
@@ -1,42 +1,42 @@
-function [g,badg] = a0asgrad(x,s,nobs,nvar,a0indx);
-% Computes analytical gradient for use in csminwel.m
-% function [g,badg] = a0asgrad(x,s,nobs,nvar,a0indx);
-%
-% x (parameter vector),
-% s (diag(S1,...,Sm)): note, as in "a0lhfun", already divided by "nobs"
-% nobs (no of obs),
-% nvar (no of variables),
-% a0indx (matrix indicating the free parameters in A0, and each column in A0 corresponds
-% to an equation)
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-a0 = zeros(nvar);
-%%g = zeros(nvar*nvar,1); % 4/27/97: not necessary.
-badg = 0;
-
-a0(a0indx) = x;
-
-b1=-nobs*inv(a0');
-b2 = zeros(nvar,nvar);
-for i=1:nvar
- b2(:,i) = nobs*s{i}*a0(:,i);
-end
-
-%b = -nobs*inv(a') + nobs*s*a;
-b = b1(:)+b2(:);
-g = b(a0indx);
\ No newline at end of file
+function [g,badg] = a0asgrad(x,s,nobs,nvar,a0indx);
+% Computes analytical gradient for use in csminwel.m
+% function [g,badg] = a0asgrad(x,s,nobs,nvar,a0indx);
+%
+% x (parameter vector),
+% s (diag(S1,...,Sm)): note, as in "a0lhfun", already divided by "nobs"
+% nobs (no of obs),
+% nvar (no of variables),
+% a0indx (matrix indicating the free parameters in A0, and each column in A0 corresponds
+% to an equation)
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+a0 = zeros(nvar);
+%%g = zeros(nvar*nvar,1); % 4/27/97: not necessary.
+badg = 0;
+
+a0(a0indx) = x;
+
+b1=-nobs*inv(a0');
+b2 = zeros(nvar,nvar);
+for i=1:nvar
+ b2(:,i) = nobs*s{i}*a0(:,i);
+end
+
+%b = -nobs*inv(a') + nobs*s*a;
+b = b1(:)+b2(:);
+g = b(a0indx);
diff --git a/MatlabFiles/a0freefun.m b/MatlabFiles/a0freefun.m
index d625e40a9bc2af3c15c431c82bafbc3ac66d6372..5c7d5b200274e57fe771dc985e869250ff837395 100644
--- a/MatlabFiles/a0freefun.m
+++ b/MatlabFiles/a0freefun.m
@@ -1,61 +1,61 @@
-function of = a0freefun(b,Ui,nvar,n0,fss,H0inv)
-% of = a0freefun(b,Ui,nvar,n0,fss,H0inv)
-%
-% Negative logPosterior function for squeesed A0 free parameters, which are b's in the WZ notation
-% Note: columns correspond to equations
-%
-% b: sum(n0)-by-1 vector of A0 free parameters
-% Ui: nvar-by-1 cell. In each cell, nvar-by-qi orthonormal basis for the null of the ith
-% equation contemporaneous restriction matrix where qi is the number of free parameters.
-% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
-% of total original parameters and bi is a vector of free parameters. When no
-% restrictions are imposed, we have Ui = I. There must be at least one free
-% parameter left for the ith equation.
-% nvar: number of endogeous variables
-% n0: nvar-by-1, ith element represents the number of free A0 parameters in ith equation
-% fss: nSample-lags (plus ndobs if dummies are included)
-% H0inv: cell(nvar,1). In each cell, posterior inverse of covariance inv(H0) for the ith equation,
-% resembling old SpH in the exponent term in posterior of A0, but not divided by T yet.
-%----------------
-% of: objective function (negative logPosterior)
-%
-% Tao Zha, February 2000
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-b=b(:);
-
-A0 = zeros(nvar);
-n0cum = cumsum(n0);
-tra = 0.0;
-for kj = 1:nvar
- if kj==1
- bj = b(1:n0(kj));
- A0(:,kj) = Ui{kj}*bj;
- tra = tra + 0.5*bj'*H0inv{kj}*bj; % negative exponential term
- else
- bj = b(n0cum(kj-1)+1:n0cum(kj));
- A0(:,kj) = Ui{kj}*bj;
- tra = tra + 0.5*bj'*H0inv{kj}*bj; % negative exponential term
- end
-end
-
-[A0l,A0u] = lu(A0);
-
-ada = -fss*sum(log(abs(diag(A0u)))); % negative log determinant of A0 raised to power T
-
-of = ada + tra;
+function of = a0freefun(b,Ui,nvar,n0,fss,H0inv)
+% of = a0freefun(b,Ui,nvar,n0,fss,H0inv)
+%
+% Negative logPosterior function for squeesed A0 free parameters, which are b's in the WZ notation
+% Note: columns correspond to equations
+%
+% b: sum(n0)-by-1 vector of A0 free parameters
+% Ui: nvar-by-1 cell. In each cell, nvar-by-qi orthonormal basis for the null of the ith
+% equation contemporaneous restriction matrix where qi is the number of free parameters.
+% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
+% of total original parameters and bi is a vector of free parameters. When no
+% restrictions are imposed, we have Ui = I. There must be at least one free
+% parameter left for the ith equation.
+% nvar: number of endogeous variables
+% n0: nvar-by-1, ith element represents the number of free A0 parameters in ith equation
+% fss: nSample-lags (plus ndobs if dummies are included)
+% H0inv: cell(nvar,1). In each cell, posterior inverse of covariance inv(H0) for the ith equation,
+% resembling old SpH in the exponent term in posterior of A0, but not divided by T yet.
+%----------------
+% of: objective function (negative logPosterior)
+%
+% Tao Zha, February 2000
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+b=b(:);
+
+A0 = zeros(nvar);
+n0cum = cumsum(n0);
+tra = 0.0;
+for kj = 1:nvar
+ if kj==1
+ bj = b(1:n0(kj));
+ A0(:,kj) = Ui{kj}*bj;
+ tra = tra + 0.5*bj'*H0inv{kj}*bj; % negative exponential term
+ else
+ bj = b(n0cum(kj-1)+1:n0cum(kj));
+ A0(:,kj) = Ui{kj}*bj;
+ tra = tra + 0.5*bj'*H0inv{kj}*bj; % negative exponential term
+ end
+end
+
+[A0l,A0u] = lu(A0);
+
+ada = -fss*sum(log(abs(diag(A0u)))); % negative log determinant of A0 raised to power T
+
+of = ada + tra;
diff --git a/MatlabFiles/a0freegrad.m b/MatlabFiles/a0freegrad.m
index 7f1255156e5470e6e7292980f2c9bcf661054769..4a86e491e381c4177946b0f72b0a0de6ca6e8369 100644
--- a/MatlabFiles/a0freegrad.m
+++ b/MatlabFiles/a0freegrad.m
@@ -1,66 +1,66 @@
-function [g,badg] = a0freegrad(b,Ui,nvar,n0,fss,H0inv)
-% [g,badg] = a0freegrad(b,Ui,nvar,n0,fss,H0inv)
-%
-% Analytical gradient for a0freefun.m in use of csminwel.m
-% b: sum(n0)-by-1 vector of A0 free parameters
-% Ui: nvar-by-1 cell. In each cell, nvar-by-qi orthonormal basis for the null of the ith
-% equation contemporaneous restriction matrix where qi is the number of free parameters.
-% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
-% of total original parameters and bi is a vector of free parameters. When no
-% restrictions are imposed, we have Ui = I. There must be at least one free
-% parameter left for the ith equation.
-% nvar: number of endogeous variables
-% n0: nvar-by-1, ith element represents the number of free A0 parameters in ith equation
-% fss: nSample-lags (plus ndobs if dummies are included)
-% H0inv: cell(nvar,1). In each cell, posterior inverse of covariance inv(H0) for the ith equation,
-% resembling old SpH in the exponent term in posterior of A0, but not divided by T yet.
-%---------------
-% g: sum(n0)-by-1 analytical gradient for a0freefun.m
-% badg: 0, the value that is used in csminwel.m
-%
-% Tao Zha, February 2000
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-b=b(:);
-
-A0 = zeros(nvar);
-n0cum = cumsum(n0);
-g = zeros(n0cum(end),1);
-badg = 0;
-
-for kj = 1:nvar
- if kj==1
- bj = b(1:n0(kj));
- g(1:n0(kj)) = H0inv{kj}*bj;
- A0(:,kj) = Ui{kj}*bj;
- else
- bj = b(n0cum(kj-1)+1:n0cum(kj));
- g(n0cum(kj-1)+1:n0cum(kj)) = H0inv{kj}*bj;
- A0(:,kj) = Ui{kj}*bj;
- end
-end
-B=inv(A0');
-
-for ki = 1:sum(n0)
- if ki<=n0(1)
- g(ki) = g(ki) - fss*B(:,1)'*Ui{1}(:,ki);
- else
- n = max(find( (ki-n0cum)>0 ))+1; % note, 1<n<nvar
- g(ki) = g(ki) - fss*B(:,n)'*Ui{n}(:,ki-n0cum(n-1));
- end
-end
+function [g,badg] = a0freegrad(b,Ui,nvar,n0,fss,H0inv)
+% [g,badg] = a0freegrad(b,Ui,nvar,n0,fss,H0inv)
+%
+% Analytical gradient for a0freefun.m in use of csminwel.m
+% b: sum(n0)-by-1 vector of A0 free parameters
+% Ui: nvar-by-1 cell. In each cell, nvar-by-qi orthonormal basis for the null of the ith
+% equation contemporaneous restriction matrix where qi is the number of free parameters.
+% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
+% of total original parameters and bi is a vector of free parameters. When no
+% restrictions are imposed, we have Ui = I. There must be at least one free
+% parameter left for the ith equation.
+% nvar: number of endogeous variables
+% n0: nvar-by-1, ith element represents the number of free A0 parameters in ith equation
+% fss: nSample-lags (plus ndobs if dummies are included)
+% H0inv: cell(nvar,1). In each cell, posterior inverse of covariance inv(H0) for the ith equation,
+% resembling old SpH in the exponent term in posterior of A0, but not divided by T yet.
+%---------------
+% g: sum(n0)-by-1 analytical gradient for a0freefun.m
+% badg: 0, the value that is used in csminwel.m
+%
+% Tao Zha, February 2000
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+b=b(:);
+
+A0 = zeros(nvar);
+n0cum = cumsum(n0);
+g = zeros(n0cum(end),1);
+badg = 0;
+
+for kj = 1:nvar
+ if kj==1
+ bj = b(1:n0(kj));
+ g(1:n0(kj)) = H0inv{kj}*bj;
+ A0(:,kj) = Ui{kj}*bj;
+ else
+ bj = b(n0cum(kj-1)+1:n0cum(kj));
+ g(n0cum(kj-1)+1:n0cum(kj)) = H0inv{kj}*bj;
+ A0(:,kj) = Ui{kj}*bj;
+ end
+end
+B=inv(A0');
+
+for ki = 1:sum(n0)
+ if ki<=n0(1)
+ g(ki) = g(ki) - fss*B(:,1)'*Ui{1}(:,ki);
+ else
+ n = max(find( (ki-n0cum)>0 ))+1; % note, 1<n<nvar
+ g(ki) = g(ki) - fss*B(:,n)'*Ui{n}(:,ki-n0cum(n-1));
+ end
+end
diff --git a/MatlabFiles/a0impsmp.m b/MatlabFiles/a0impsmp.m
index d3b1a92c101901e3b7d6ef987e33a0a0b8895ef1..8b5ceaf31163ea568e1a75f2eb52b245a9b1200e 100644
--- a/MatlabFiles/a0impsmp.m
+++ b/MatlabFiles/a0impsmp.m
@@ -1,99 +1,99 @@
-function [xdraw,mphv,sm,timeminutes] = a0impsmp(xinput)
-% [xdraw,mphv,sm,timeminutes] = a0impsmp(xinput)
-% Export the simulated pdfs of draws of only selected parameters
-% with importance sampling techniques
-%
-% xinput{1}: nfp -- total number of free parameters
-% xinput{2}: nvar -- number of variables
-% xinput{3}: xhat -- ML estimate of free parameters in A0
-% xinput{4}: hess1 -- Hessian of -logLH for importance sampling
-% xinput{5}:Indxv -- index for selected variables of interest; normall first 2 are of our interest
-% to select variables, always check idmat0 to make sure.
-% When Indxv=[], xdraw is empty as well. When IndxGgraph=1, it plots
-% (1) pdf of 1st v for every buffer, (2) scattered plot of 1st and 2nd for every buffer,
-% (3) pdf of 1st v for all sequences; (4) scattered plot of 3rd and 4th for all sequences
-% (5) scattered plot of 1st and 2nd for al sequences.
-% xinput{6}: imndraws=nstarts*ndraws2
-% xinput{7}: a0indx -- index number for non-zero elements in A0
-% xinput{8}: tdf -- degrees of freedom for t-distribution
-% xinput{9}: nbuffer -- interval for printing, plotting, and saving
-% xinput{10}: Sbd -- nvar-by-nvar S{1}, ..., S{n} -- kind of covariance matrix for each simultaneous equation
-% Already divided by "fss."
-% xinput{11}: scf -- reduction scale factor for Metropolis jumping kernel
-% xinput{12}: Hsr1 -- square root of covariance matrix for free elements in A0 (nfp-by-nfp)
-% for importance sampling.
-% xinput{13}: fss -- effective sample size == nSample-lags+# of dummy observations
-%------------------
-% xdraw: imndraws-by-length(Indxv) matrix of draws;
-% empty if Indxv is empty.
-% timeminutes: minutes used for this simulation
-% mphv: imndraws-by-1 vector of unscaled weights
-% sm: sum of the weights
-%
-% Written by Tao Zha 1999
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-nfp=xinput{1}; nvar=xinput{2}; xhat=xinput{3}; hess1=xinput{4}; Indxv=xinput{5};
-imndraws=xinput{6}; a0indx=xinput{7}; tdf=xinput{8}; nbuffer=xinput{9};
-Sbd=xinput{10}; scf=xinput{11}; Hsr1=xinput{12}; fss=xinput{13};
-
-
-sm = 0.0;
-mphv=zeros(imndraws,1);
-if isempty(Indxv)
- xdraw=[];
-else
- xdraw=zeros(imndraws,length(Indxv)); %<<>> draws of selected variables
-end
-
-tic
-for draws=1:imndraws
- %
- if ~mod(draws,nbuffer)
- draws
- % fwriteid = fopen('outA0.bin','a');
- % count = fwrite(fwriteid,A0hatw,'double');
- % status = fclose('all');
- end
-
-
- %** draw free elements Avh in A0 and hyperparameters from t-dist
- Avhz1 = Hsr1\randn(nfp,1); % normal draws
- csq=randn(tdf,1);
- csq=sum(csq .* csq);
- Avhz = xhat+Avhz1/sqrt(csq/tdf); % Robert, p.382
-
- % *** compute weights ***
- % * t-dist density, having taken log
- tdhz = -0.5*(length(Avhz)+tdf)*log(1+(Avhz-xhat)'*(hess1*(Avhz-xhat))/tdf);
- % * actual density, having taken log
- hAvhz = a0asfun(Avhz,Sbd,fss,nvar,a0indx);
- hAvhz = -hAvhz; % converted to logLH
-
- % * mph: m prim hat in Kloek & van Dijk, p.5
- mph = exp(hAvhz-tdhz-scf); % scf: scaling factor to prevent overflow
- mphv(draws) = mph;
-
- sm=sm+mph;
- % * matrix of draws for selected variables
- if ~isempty(Indxv)
- xdraw(draws,:) = Avhz(Indxv)';
- end
- %
-end
-timeminutes = toc/60
+function [xdraw,mphv,sm,timeminutes] = a0impsmp(xinput)
+% [xdraw,mphv,sm,timeminutes] = a0impsmp(xinput)
+% Export the simulated pdfs of draws of only selected parameters
+% with importance sampling techniques
+%
+% xinput{1}: nfp -- total number of free parameters
+% xinput{2}: nvar -- number of variables
+% xinput{3}: xhat -- ML estimate of free parameters in A0
+% xinput{4}: hess1 -- Hessian of -logLH for importance sampling
+% xinput{5}:Indxv -- index for selected variables of interest; normall first 2 are of our interest
+% to select variables, always check idmat0 to make sure.
+% When Indxv=[], xdraw is empty as well. When IndxGgraph=1, it plots
+% (1) pdf of 1st v for every buffer, (2) scattered plot of 1st and 2nd for every buffer,
+% (3) pdf of 1st v for all sequences; (4) scattered plot of 3rd and 4th for all sequences
+% (5) scattered plot of 1st and 2nd for al sequences.
+% xinput{6}: imndraws=nstarts*ndraws2
+% xinput{7}: a0indx -- index number for non-zero elements in A0
+% xinput{8}: tdf -- degrees of freedom for t-distribution
+% xinput{9}: nbuffer -- interval for printing, plotting, and saving
+% xinput{10}: Sbd -- nvar-by-nvar S{1}, ..., S{n} -- kind of covariance matrix for each simultaneous equation
+% Already divided by "fss."
+% xinput{11}: scf -- reduction scale factor for Metropolis jumping kernel
+% xinput{12}: Hsr1 -- square root of covariance matrix for free elements in A0 (nfp-by-nfp)
+% for importance sampling.
+% xinput{13}: fss -- effective sample size == nSample-lags+# of dummy observations
+%------------------
+% xdraw: imndraws-by-length(Indxv) matrix of draws;
+% empty if Indxv is empty.
+% timeminutes: minutes used for this simulation
+% mphv: imndraws-by-1 vector of unscaled weights
+% sm: sum of the weights
+%
+% Written by Tao Zha 1999
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+nfp=xinput{1}; nvar=xinput{2}; xhat=xinput{3}; hess1=xinput{4}; Indxv=xinput{5};
+imndraws=xinput{6}; a0indx=xinput{7}; tdf=xinput{8}; nbuffer=xinput{9};
+Sbd=xinput{10}; scf=xinput{11}; Hsr1=xinput{12}; fss=xinput{13};
+
+
+sm = 0.0;
+mphv=zeros(imndraws,1);
+if isempty(Indxv)
+ xdraw=[];
+else
+ xdraw=zeros(imndraws,length(Indxv)); %<<>> draws of selected variables
+end
+
+tic
+for draws=1:imndraws
+ %
+ if ~mod(draws,nbuffer)
+ draws
+ % fwriteid = fopen('outA0.bin','a');
+ % count = fwrite(fwriteid,A0hatw,'double');
+ % status = fclose('all');
+ end
+
+
+ %** draw free elements Avh in A0 and hyperparameters from t-dist
+ Avhz1 = Hsr1\randn(nfp,1); % normal draws
+ csq=randn(tdf,1);
+ csq=sum(csq .* csq);
+ Avhz = xhat+Avhz1/sqrt(csq/tdf); % Robert, p.382
+
+ % *** compute weights ***
+ % * t-dist density, having taken log
+ tdhz = -0.5*(length(Avhz)+tdf)*log(1+(Avhz-xhat)'*(hess1*(Avhz-xhat))/tdf);
+ % * actual density, having taken log
+ hAvhz = a0asfun(Avhz,Sbd,fss,nvar,a0indx);
+ hAvhz = -hAvhz; % converted to logLH
+
+ % * mph: m prim hat in Kloek & van Dijk, p.5
+ mph = exp(hAvhz-tdhz-scf); % scf: scaling factor to prevent overflow
+ mphv(draws) = mph;
+
+ sm=sm+mph;
+ % * matrix of draws for selected variables
+ if ~isempty(Indxv)
+ xdraw(draws,:) = Avhz(Indxv)';
+ end
+ %
+end
+timeminutes = toc/60
diff --git a/MatlabFiles/a0lhfun.m b/MatlabFiles/a0lhfun.m
index a4f7e824af3afab2ef99c2bff69f06bc513fc594..80b392db634e49cd128b0b6202e8a7548c94ff53 100644
--- a/MatlabFiles/a0lhfun.m
+++ b/MatlabFiles/a0lhfun.m
@@ -1,55 +1,55 @@
-function of = a0lhfun(x,s,nobs,nvar,a0indx)
-% of = a0lhfun(x,s,nobs,nvar,a0indx) -- negative logLH
-% Note: columns correpond to equations
-% general program to setup A0 matrix and compute the likelihood
-% requires
-% x (parameter vector),
-% s (covariance matrix of innovations): note already divided by "nobs"
-% nobs (no of obs),
-% nvar (no of variables),
-% a0indx (matrix indicating the free parameters in A0, and each column in A0 corresponds
-% to an equation)
-% written by Eric Leeper
-% Copyright (C) 1997-2012 Eric Leeper
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-a0 = zeros(nvar);
-a0(a0indx) = x;
-% Note: each column in a0 corresponds to an equation!!
-%
-%%ada = chol(a0'*a0);
-%%ada = log(abs(diag(ada)));
-%%ada = sum(ada);
-% ** TZ, 10/15/96, the above two lines can be improved by the following three lines
-[a0l,a0u] = lu(a0);
-%ada=diag(abs(a0u));
-%ada=sum(log(ada));
-ada = sum(log(abs(diag(a0u))));
-
-%
-%tra = trace(a0'*s*a0);
-tra = reshape(s,nvar*nvar,1)'*reshape(a0*a0',nvar*nvar,1);
-%if ada == 0
-% of = 1.0;
-% else
-% of = -nobs*ada + nobs*.5*tra;
-%end
-% commented out by T.Z. for there appears no sense of using "if-end"
-
-
-of = -nobs*ada + nobs*.5*tra;
\ No newline at end of file
+function of = a0lhfun(x,s,nobs,nvar,a0indx)
+% of = a0lhfun(x,s,nobs,nvar,a0indx) -- negative logLH
+% Note: columns correpond to equations
+% general program to setup A0 matrix and compute the likelihood
+% requires
+% x (parameter vector),
+% s (covariance matrix of innovations): note already divided by "nobs"
+% nobs (no of obs),
+% nvar (no of variables),
+% a0indx (matrix indicating the free parameters in A0, and each column in A0 corresponds
+% to an equation)
+% written by Eric Leeper
+%
+% Copyright (C) 1997-2012 Eric Leeper
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+a0 = zeros(nvar);
+a0(a0indx) = x;
+% Note: each column in a0 corresponds to an equation!!
+%
+%%ada = chol(a0'*a0);
+%%ada = log(abs(diag(ada)));
+%%ada = sum(ada);
+% ** TZ, 10/15/96, the above two lines can be improved by the following three lines
+[a0l,a0u] = lu(a0);
+%ada=diag(abs(a0u));
+%ada=sum(log(ada));
+ada = sum(log(abs(diag(a0u))));
+
+%
+%tra = trace(a0'*s*a0);
+tra = reshape(s,nvar*nvar,1)'*reshape(a0*a0',nvar*nvar,1);
+%if ada == 0
+% of = 1.0;
+% else
+% of = -nobs*ada + nobs*.5*tra;
+%end
+% commented out by T.Z. for there appears no sense of using "if-end"
+
+
+of = -nobs*ada + nobs*.5*tra;
diff --git a/MatlabFiles/a0lhgh.m b/MatlabFiles/a0lhgh.m
index 42390d267bc3ac86108040198fee0e9140cb3e02..0b8b6e7184bd942446607e9898ff38e3069afcca 100644
--- a/MatlabFiles/a0lhgh.m
+++ b/MatlabFiles/a0lhgh.m
@@ -1,34 +1,34 @@
-function g = a0lhgh(x0,s,nobs,nvar,a0indx);
-%function g = a0lhgh(x0,s,nobs,nvar,a0indx);
-% computes the hessian with analytical gradient provided
-% x0 (parameter vector),
-% s (covariance matrix of innovations): note divided by "nobs"
-% nobs (no of obs),
-% nvar (no of variables),
-% a0indx (matrix indicating the free parameters in A0, and each column in A0 corresponds
-% to an equation)
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-%%a = zeros(nvar*nvar,1);
-a = zeros(nvar);
-g = zeros(nvar*nvar,1);
-badg = 0;
-a(a0indx) = x0;
-b = -nobs*inv(a') + nobs*s*a;
-b = reshape(b,nvar*nvar,1);
-g = b(a0indx);
\ No newline at end of file
+function g = a0lhgh(x0,s,nobs,nvar,a0indx);
+%function g = a0lhgh(x0,s,nobs,nvar,a0indx);
+% computes the hessian with analytical gradient provided
+% x0 (parameter vector),
+% s (covariance matrix of innovations): note divided by "nobs"
+% nobs (no of obs),
+% nvar (no of variables),
+% a0indx (matrix indicating the free parameters in A0, and each column in A0 corresponds
+% to an equation)
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+%%a = zeros(nvar*nvar,1);
+a = zeros(nvar);
+g = zeros(nvar*nvar,1);
+badg = 0;
+a(a0indx) = x0;
+b = -nobs*inv(a') + nobs*s*a;
+b = reshape(b,nvar*nvar,1);
+g = b(a0indx);
diff --git a/MatlabFiles/a0lhgrad.m b/MatlabFiles/a0lhgrad.m
index 4f5478b1331fb1b3e28232546da099200855ac1c..fba69e8de7c9a8363d3344ba98f4e42c3f2ac88a 100644
--- a/MatlabFiles/a0lhgrad.m
+++ b/MatlabFiles/a0lhgrad.m
@@ -1,36 +1,36 @@
-function [g,badg] = a0lhgrad(x0,s,nobs,nvar,a0indx);
-%function [g,badg] = a0lhgrad(x0,s,nobs,nvar,a0indx);
-% computes analytical gradient for use in csminwel.m
-% x0 (parameter vector),
-% s (covariance matrix of innovations): note divided by "nobs"
-% nobs (no of obs),
-% nvar (no of variables),
-% a0indx (matrix indicating the free parameters in A0, and each column in A0 corresponds
-% to an equation)
-%
-% Written by Tao Zha
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-%%a = zeros(nvar*nvar,1);
-a = zeros(nvar);
-%%g = zeros(nvar*nvar,1); % 4/27/97: not necessary. TAZ
-badg = 0;
-a(a0indx) = x0;
-b = -nobs*inv(a') + nobs*s*a;
-b = reshape(b,nvar*nvar,1);
-g = b(a0indx);
+function [g,badg] = a0lhgrad(x0,s,nobs,nvar,a0indx);
+%function [g,badg] = a0lhgrad(x0,s,nobs,nvar,a0indx);
+% computes analytical gradient for use in csminwel.m
+% x0 (parameter vector),
+% s (covariance matrix of innovations): note divided by "nobs"
+% nobs (no of obs),
+% nvar (no of variables),
+% a0indx (matrix indicating the free parameters in A0, and each column in A0 corresponds
+% to an equation)
+%
+% Written by Tao Zha
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+%%a = zeros(nvar*nvar,1);
+a = zeros(nvar);
+%%g = zeros(nvar*nvar,1); % 4/27/97: not necessary. TAZ
+badg = 0;
+a(a0indx) = x0;
+b = -nobs*inv(a') + nobs*s*a;
+b = reshape(b,nvar*nvar,1);
+g = b(a0indx);
diff --git a/MatlabFiles/a0onlysim.m b/MatlabFiles/a0onlysim.m
index b152bedc9e5404b38a3e88aa911a332a1ead7436..8975c97413b070765da1106cc21d18c6ec7f8dd0 100644
--- a/MatlabFiles/a0onlysim.m
+++ b/MatlabFiles/a0onlysim.m
@@ -1,286 +1,286 @@
-function [xdraw,timeminutes,nswitch] = a0onlysim(xinput)
-% [xdraw,timeminutes,nswitch] = a0onlysim(xinput)
-% Export and plot the simulated pdfs of draws of only selected parameters;
-% Print and save Gelman's measures of B and W for all parameters;
-% Ref: Waggoner and Zha "Does Normalization Matter for Inference"
-% See note Forecast (2)
-%
-% xinput{1}: nfp -- total number of free parameters
-% xinput{2}: nvar -- number of variables
-% xinput{3}: xhat -- ML estimate of free parameters in A0
-% xinput{4}: hess1 -- Hessian of -logLH
-% xinput{5}:Indxv -- index for selected variables of interest; normall first 2 are of our interest
-% to select variables, always check idmat0 to make sure.
-% When Indxv=[], xdraw is empty as well. When IndxGgraph=1, it plots
-% (1) pdf of 1st v for every buffer, (2) scattered plot of 1st and 2nd for every buffer,
-% (3) pdf of 1st v for all sequences; (4) scattered plot of 3rd and 4th for all sequences
-% (5) scattered plot of 1st and 2nd for al sequences.
-% xinput{6}: IndxGraph - 1: plot graphs; 0: no graphs
-% xinput{7}: idmat0 -- Index for non-zero elements in A0 with column to equation
-% xinput{8}: nstarts -- # of starting points in Gibbs sampling
-% xinput{9}: ndraws1 -- # of 1st loop to be discarded
-% xinput{10}: ndraws2 -- # of 2nd loop for saving A0 draws
-% xinput{11}: imndraws=nstarts*ndraws2
-% xinput{12}: a0indx -- index number for non-zero elements in A0
-% xinput{13}: tdf -- degrees of freedom for t-distribution
-% xinput{14}: nbuffer -- interval for printing, plotting, and saving
-% xinput{15}: Sbd -- nvar-by-nvar S{1}, ..., S{n} -- kind of covariance matrix for each simultaneous equation
-% Already divided by "fss."
-% xinput{16}: nSample -- the original sample size including lags
-% xinput{17}: IndxNmlr -- index for which normalization rule to choose
-% xinput{18}: IndxGibbs -- index for WZ Gibbs; 1; Gibbs; 0: Metropolis
-% xinput{19}: scf -- reduction scale factor for Metropolis jumping kernel
-% xinput{20}: H_sr -- square root of the inverse of the covariance matrix
-% for free elements in A0 (nfp-by-nfp)
-% xinput{21}: fss -- effective sample size == nSample-lags+# of dummy observations
-%------------------
-% xdraw: nstarts*ndraws2-by-length(Indxv) matrix of draws;
-% empty if Indxv=[]
-% timeminutes: minutes used for this simulation
-%
-% Written by Tao Zha 1999
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-nfp=xinput{1}; nvar=xinput{2}; xhat=xinput{3}; hess=xinput{4}; Indxv=xinput{5};
-IndxGraph=xinput{6}; idmat0=xinput{7}; nstarts=xinput{8}; ndraws1=xinput{9}; ndraws2=xinput{10};
-imndraws=xinput{11}; a0indx=xinput{12}; tdf=xinput{13}; nbuffer=xinput{14}; Sbd=xinput{15};
-nSample = xinput{16}; IndxNmlr=xinput{17}; IndxGibbs=xinput{18}; scf=xinput{19}; H_sr=xinput{20};
-fss=xinput{21};
-
-if IndxGraph>length(Indxv)
- disp(' ')
- warning('when IndxGraph=1, Indxv must have at least 1 elements')
- disp('Press ctrl-c to abort')
- pause
-end
-
-Avhx_bs = zeros(nfp,1);
-Avhx_bm = zeros(nfp,1);
-Avhx_bj = zeros(nfp,1);
-Avhx_cj = zeros(nfp,1);
-Avhx_cm = zeros(nfp,1);
-A0_h = zeros(nvar);
-A0gbs = A0_h; % drawn A0 in Gibbs sampling
-Avhxm = zeros(nfp,1);
-Avhxs = Avhxm;
-A0xhat = zeros(nvar);
-A0xhat(a0indx) = xhat;
-% A0hatw = zeros(nvar^2,nbuffer);
-
-countJump = zeros(nstarts,1);
-
-
-%===================================
-% Here begins with the big loop
-%===================================
-H1 = chol(hess); % upper triangular so that H1' is a lower triangular decomp
-baseW = H_sr; %inv(H1); %H_sr; % covariance matrix without scaling
-nswitch=0; %<<>> total number of sign switches
-A0inxhat = inv(A0xhat); % inverse of ML estimate
-a0indx0 = find(idmat0==0); % index for all zero's in A0;
-if isempty(Indxv)
- xdraw=[];
-else
- xdraw=zeros(imndraws,length(Indxv)); %<<>> draws of selected variables
-end
-
-
-[cT,vR,kdf] = gibbsglb(Sbd,idmat0,nvar,fss);
-
-tic
-for starts = 1:nstarts
- starts
- if starts == 1
- A0gbs(a0indx) = xhat; % from "load ..."
- if ~IndxGibbs % Metropolist
- Avhx = xhat;
- hAvhx = a0asfun(Avhx,Sbd,fss,nvar,a0indx);
- hAvhx = -hAvhx; % converted to logLH
- end
- else
- Avhx = baseW*randn(nfp,1); %H_sr*randn(nfp,1); % D: discarded sequence
- csq=randn(tdf,1);
- csq=sum(csq .* csq);
- Avhx = xhat+Avhx/sqrt(csq/tdf);
- %** Normalization by the choice of IndxNmlr
- A0gbs(a0indx) = Avhx;
- if ~IndxNmlr(5)
- [A0gbs,jnk] = nmlzvar(A0gbs,A0xhat,A0inxhat,IndxNmlr,nswitch,[]);
- else
- A0ingbs = inv(A0gbs);
- [A0gbs,jnk,jnk1] = nmlzvar(A0gbs,A0xhat,A0inxhat,IndxNmlr,nswitch,A0ingbs);
- end
- %
- if ~IndxGibbs % Metropolist
- Avhx = A0gbs(a0indx);
- hAvhx = a0asfun(Avhx,Sbd,fss,nvar,a0indx);
- hAvhx = -hAvhx; % converted to logLH
- end
- end
- %
- Avhxmm = zeros(nfp,1);
- Avhxss = zeros(nfp,1);
- cJump = 0;
-
- for draws = 1:ndraws1
- if IndxGibbs
- A0gbs = gibbsvar(A0gbs,cT,vR,nvar,fss,kdf);
- else % Metropolis
- [Avhx,hAvhx,cJump] = smtplis(Avhx,hAvhx,tdf,cJump,scf,...
- baseW,nfp,Sbd,fss,nvar,a0indx);
- end
- end
-
- wdraws=(starts-1)*ndraws2+0;
- for draws = 1:ndraws2
- drawsc = (starts-1)*ndraws2+draws;
- if IndxGibbs
- A0gbs = gibbsvar(A0gbs,cT,vR,nvar,fss,kdf);
- A0gbs(a0indx0) = 0; % set all zeros in A0gbs clean to avoid possible cumulative round-off errors
- else % Metropolis
- [Avhx,hAvhx,cJump] = smtplis(Avhx,hAvhx,tdf,cJump,scf,...
- baseW,nfp,Sbd,fss,nvar,a0indx);
- A0gbs(a0indx) = Avhx;
- end
-
- %*** call normalization so that A0_h is normalized
- if ~IndxNmlr(5)
- [A0_h,nswitch] = nmlzvar(A0gbs,A0xhat,A0inxhat,IndxNmlr,nswitch,[]);
- else
- A0ingbs = inv(A0gbs);
- [A0_h,nswitch,A0_hin] = nmlzvar(A0gbs,A0xhat,A0inxhat,IndxNmlr,nswitch,A0ingbs);
- end
- Avhx_norm = A0_h(a0indx);
-
- Avhxm = Avhxm + Avhx_norm; % 1st step to overall mean of parameter
- Avhxs = Avhxs + Avhx_norm.^2; % 1st step to overall 2nd moment of parameter
-
- %* compute the mean and 2nd moment
- %** Getting average of variances W and variance of means B/n -- B_n
- %* see Gelman, p.331, my Shock(0), 12-13, and my Forecast (2), 28-31
- if (nstarts>1)
- Avhxmm = Avhxmm + Avhx_norm; % n*(phi_.j)
- Avhxss = Avhxss + Avhx_norm.^2; % 1st step to (phi_ij) for fixed j
- end
-
- if ~isempty(Indxv)
- xdraw((starts-1)*ndraws2+draws,:) = Avhx_norm(Indxv)'; %<<>>
- end
- % A0hatw(:,drawsc-wdraws) = A0_h(:);
- if ~mod(draws,nbuffer)
- starts
- draws
- wdraws=drawsc
- % fwriteid = fopen('outA0.bin','a');
- % count = fwrite(fwriteid,A0hatw,'double');
- % status = fclose('all');
- if IndxGraph
- %** 1-d pdf plot
- % figure(2)
- % histpdfg(xdraw((starts-1)*ndraws2+(1:draws),1),50,[],[],[]);
- % pause(1)
-
- %** 2-d scatterplot
- figure(3)
- plot(xdraw((starts-1)*ndraws2+(1:draws),1),...
- xdraw((starts-1)*ndraws2+(1:draws),2),'.');
- drawnow
- end
- end
- end
- %
- if ~IndxGibbs
- countJump(starts,1) = cJump;
- end
- %
- %** Getting average of variances W and variance of means B/n -- B_n
- %** see Gelman, p.331, my Shock(0), pp.12-13, and my Forecast (2), pp.28-31
- if (nstarts>1)
- Avhx_aj = Avhxmm/ndraws2; % (phi_.j)
- Avhx_bs = Avhx_bs + Avhx_aj.^2; % 1st step to 2nd moment of (phi_.j)
- Avhx_bm = Avhx_bm + Avhx_aj; % 1st step to (phi_..)
- %
- Avhx_bj = Avhxss/ndraws2; % 2nd moment of (phi_ij) for fixed j
- Avhx_cj = Avhx_bj - Avhx_aj.^2; % ((n-1)/n)*S^2_j
- Avhx_cm = Avhx_cm + Avhx_cj; % sum of ((n-1)/n)*S^2_j across j
- end
-end
-timend = toc
-
-if ~IndxGibbs
- countJump = countJump/ndraws2
-end
-
-Avhxm = Avhxm/(imndraws);
-Avhxs = Avhxs/(imndraws);
-Avhxv = Avhxs - Avhxm.^2;
-Avhxs = sqrt(Avhxv); % stardard deviation
-A0hm = zeros(nvar);
-A0hm(a0indx) = Avhxm % mean
-A0hv = zeros(nvar);
-A0hv(a0indx) = Avhxv; % varaince matrix
-A0hs = zeros(nvar);
-A0hs(a0indx) = Avhxs; % standar deviation
-
-%**** Getting Within-Sequence W and Between-Sequence B_n
-% see Gelman, p.331, my Shock(0), pp.12-13, and my Forecast (2), pp.28-31
-if (nstarts>1)
- AvhxW = (ndraws2/(ndraws2-1))*Avhx_cm/nstarts;
- % W: average of j within-sequence variances
- AvhxB_n = (nstarts/(nstarts-1)) * ( Avhx_bs/nstarts - (Avhx_bm/nstarts).^2 );
- % B/n: variance of J within-sequence means
- AvhxB = ndraws2*AvhxB_n; % B
- %
- B_W1 = AvhxB ./ AvhxW;
- B1 = AvhxB;
- W1 = AvhxW;
- GR1 = sqrt((ndraws2-1)/ndraws2 + B_W1/ndraws2);
- % measure of Gelman reduction; need not be 1 to be accurate,
- % contrary to what Gelman claims
- save outB_W B_W1 B1 W1 GR1 nstarts ndraws2 imndraws timend Avhxs ...
- A0xhat A0hm A0hs A0hv IndxGibbs countJump nswitch
-
- titstr = ['J ' num2str(nstarts) ' n1 ' num2str(ndraws1) ...
- ' n2 ' num2str(ndraws2) ' timend minutes ' num2str(timend/60)];
- disp(' ')
- disp(titstr)
- disp('B/W sqrt(B) sqrt(W) Std(A0) GR')
- format short g
- [B_W1 sqrt(B1) sqrt(W1) Avhxs GR1]
-else
- save outB_W nstarts ndraws2 imndraws timend Avhxs ...
- A0xhat A0hm A0hs A0hv IndxGibbs countJump nswitch
-end
-
-disp(' ')
-disp('nswitch nswitch/imndraws -- # of sign switches')
-[nswitch nswitch/imndraws]
-disp(' ')
-disp('timend/60 minutes')
-timeminutes = timend/60
-nswitch=nswitch/imndraws;
-
-if IndxGraph
- %
- % figure(4)
- % histpdfg(xdraw(:,1),50,[],[],[]);
- % figure(5)
- % plot(xdraw(:,3),xdraw(:,4),'.');
- % figure(6)
- % plot(xdraw(:,1),xdraw(:,2),'.');
-end
\ No newline at end of file
+function [xdraw,timeminutes,nswitch] = a0onlysim(xinput)
+% [xdraw,timeminutes,nswitch] = a0onlysim(xinput)
+% Export and plot the simulated pdfs of draws of only selected parameters;
+% Print and save Gelman's measures of B and W for all parameters;
+% Ref: Waggoner and Zha "Does Normalization Matter for Inference"
+% See note Forecast (2)
+%
+% xinput{1}: nfp -- total number of free parameters
+% xinput{2}: nvar -- number of variables
+% xinput{3}: xhat -- ML estimate of free parameters in A0
+% xinput{4}: hess1 -- Hessian of -logLH
+% xinput{5}:Indxv -- index for selected variables of interest; normall first 2 are of our interest
+% to select variables, always check idmat0 to make sure.
+% When Indxv=[], xdraw is empty as well. When IndxGgraph=1, it plots
+% (1) pdf of 1st v for every buffer, (2) scattered plot of 1st and 2nd for every buffer,
+% (3) pdf of 1st v for all sequences; (4) scattered plot of 3rd and 4th for all sequences
+% (5) scattered plot of 1st and 2nd for al sequences.
+% xinput{6}: IndxGraph - 1: plot graphs; 0: no graphs
+% xinput{7}: idmat0 -- Index for non-zero elements in A0 with column to equation
+% xinput{8}: nstarts -- # of starting points in Gibbs sampling
+% xinput{9}: ndraws1 -- # of 1st loop to be discarded
+% xinput{10}: ndraws2 -- # of 2nd loop for saving A0 draws
+% xinput{11}: imndraws=nstarts*ndraws2
+% xinput{12}: a0indx -- index number for non-zero elements in A0
+% xinput{13}: tdf -- degrees of freedom for t-distribution
+% xinput{14}: nbuffer -- interval for printing, plotting, and saving
+% xinput{15}: Sbd -- nvar-by-nvar S{1}, ..., S{n} -- kind of covariance matrix for each simultaneous equation
+% Already divided by "fss."
+% xinput{16}: nSample -- the original sample size including lags
+% xinput{17}: IndxNmlr -- index for which normalization rule to choose
+% xinput{18}: IndxGibbs -- index for WZ Gibbs; 1; Gibbs; 0: Metropolis
+% xinput{19}: scf -- reduction scale factor for Metropolis jumping kernel
+% xinput{20}: H_sr -- square root of the inverse of the covariance matrix
+% for free elements in A0 (nfp-by-nfp)
+% xinput{21}: fss -- effective sample size == nSample-lags+# of dummy observations
+%------------------
+% xdraw: nstarts*ndraws2-by-length(Indxv) matrix of draws;
+% empty if Indxv=[]
+% timeminutes: minutes used for this simulation
+%
+% Written by Tao Zha 1999
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+nfp=xinput{1}; nvar=xinput{2}; xhat=xinput{3}; hess=xinput{4}; Indxv=xinput{5};
+IndxGraph=xinput{6}; idmat0=xinput{7}; nstarts=xinput{8}; ndraws1=xinput{9}; ndraws2=xinput{10};
+imndraws=xinput{11}; a0indx=xinput{12}; tdf=xinput{13}; nbuffer=xinput{14}; Sbd=xinput{15};
+nSample = xinput{16}; IndxNmlr=xinput{17}; IndxGibbs=xinput{18}; scf=xinput{19}; H_sr=xinput{20};
+fss=xinput{21};
+
+if IndxGraph>length(Indxv)
+ disp(' ')
+ warning('when IndxGraph=1, Indxv must have at least 1 elements')
+ disp('Press ctrl-c to abort')
+ pause
+end
+
+Avhx_bs = zeros(nfp,1);
+Avhx_bm = zeros(nfp,1);
+Avhx_bj = zeros(nfp,1);
+Avhx_cj = zeros(nfp,1);
+Avhx_cm = zeros(nfp,1);
+A0_h = zeros(nvar);
+A0gbs = A0_h; % drawn A0 in Gibbs sampling
+Avhxm = zeros(nfp,1);
+Avhxs = Avhxm;
+A0xhat = zeros(nvar);
+A0xhat(a0indx) = xhat;
+% A0hatw = zeros(nvar^2,nbuffer);
+
+countJump = zeros(nstarts,1);
+
+
+%===================================
+% Here begins with the big loop
+%===================================
+H1 = chol(hess); % upper triangular so that H1' is a lower triangular decomp
+baseW = H_sr; %inv(H1); %H_sr; % covariance matrix without scaling
+nswitch=0; %<<>> total number of sign switches
+A0inxhat = inv(A0xhat); % inverse of ML estimate
+a0indx0 = find(idmat0==0); % index for all zero's in A0;
+if isempty(Indxv)
+ xdraw=[];
+else
+ xdraw=zeros(imndraws,length(Indxv)); %<<>> draws of selected variables
+end
+
+
+[cT,vR,kdf] = gibbsglb(Sbd,idmat0,nvar,fss);
+
+tic
+for starts = 1:nstarts
+ starts
+ if starts == 1
+ A0gbs(a0indx) = xhat; % from "load ..."
+ if ~IndxGibbs % Metropolist
+ Avhx = xhat;
+ hAvhx = a0asfun(Avhx,Sbd,fss,nvar,a0indx);
+ hAvhx = -hAvhx; % converted to logLH
+ end
+ else
+ Avhx = baseW*randn(nfp,1); %H_sr*randn(nfp,1); % D: discarded sequence
+ csq=randn(tdf,1);
+ csq=sum(csq .* csq);
+ Avhx = xhat+Avhx/sqrt(csq/tdf);
+ %** Normalization by the choice of IndxNmlr
+ A0gbs(a0indx) = Avhx;
+ if ~IndxNmlr(5)
+ [A0gbs,jnk] = nmlzvar(A0gbs,A0xhat,A0inxhat,IndxNmlr,nswitch,[]);
+ else
+ A0ingbs = inv(A0gbs);
+ [A0gbs,jnk,jnk1] = nmlzvar(A0gbs,A0xhat,A0inxhat,IndxNmlr,nswitch,A0ingbs);
+ end
+ %
+ if ~IndxGibbs % Metropolist
+ Avhx = A0gbs(a0indx);
+ hAvhx = a0asfun(Avhx,Sbd,fss,nvar,a0indx);
+ hAvhx = -hAvhx; % converted to logLH
+ end
+ end
+ %
+ Avhxmm = zeros(nfp,1);
+ Avhxss = zeros(nfp,1);
+ cJump = 0;
+
+ for draws = 1:ndraws1
+ if IndxGibbs
+ A0gbs = gibbsvar(A0gbs,cT,vR,nvar,fss,kdf);
+ else % Metropolis
+ [Avhx,hAvhx,cJump] = smtplis(Avhx,hAvhx,tdf,cJump,scf,...
+ baseW,nfp,Sbd,fss,nvar,a0indx);
+ end
+ end
+
+ wdraws=(starts-1)*ndraws2+0;
+ for draws = 1:ndraws2
+ drawsc = (starts-1)*ndraws2+draws;
+ if IndxGibbs
+ A0gbs = gibbsvar(A0gbs,cT,vR,nvar,fss,kdf);
+ A0gbs(a0indx0) = 0; % set all zeros in A0gbs clean to avoid possible cumulative round-off errors
+ else % Metropolis
+ [Avhx,hAvhx,cJump] = smtplis(Avhx,hAvhx,tdf,cJump,scf,...
+ baseW,nfp,Sbd,fss,nvar,a0indx);
+ A0gbs(a0indx) = Avhx;
+ end
+
+ %*** call normalization so that A0_h is normalized
+ if ~IndxNmlr(5)
+ [A0_h,nswitch] = nmlzvar(A0gbs,A0xhat,A0inxhat,IndxNmlr,nswitch,[]);
+ else
+ A0ingbs = inv(A0gbs);
+ [A0_h,nswitch,A0_hin] = nmlzvar(A0gbs,A0xhat,A0inxhat,IndxNmlr,nswitch,A0ingbs);
+ end
+ Avhx_norm = A0_h(a0indx);
+
+ Avhxm = Avhxm + Avhx_norm; % 1st step to overall mean of parameter
+ Avhxs = Avhxs + Avhx_norm.^2; % 1st step to overall 2nd moment of parameter
+
+ %* compute the mean and 2nd moment
+ %** Getting average of variances W and variance of means B/n -- B_n
+ %* see Gelman, p.331, my Shock(0), 12-13, and my Forecast (2), 28-31
+ if (nstarts>1)
+ Avhxmm = Avhxmm + Avhx_norm; % n*(phi_.j)
+ Avhxss = Avhxss + Avhx_norm.^2; % 1st step to (phi_ij) for fixed j
+ end
+
+ if ~isempty(Indxv)
+ xdraw((starts-1)*ndraws2+draws,:) = Avhx_norm(Indxv)'; %<<>>
+ end
+ % A0hatw(:,drawsc-wdraws) = A0_h(:);
+ if ~mod(draws,nbuffer)
+ starts
+ draws
+ wdraws=drawsc
+ % fwriteid = fopen('outA0.bin','a');
+ % count = fwrite(fwriteid,A0hatw,'double');
+ % status = fclose('all');
+ if IndxGraph
+ %** 1-d pdf plot
+ % figure(2)
+ % histpdfg(xdraw((starts-1)*ndraws2+(1:draws),1),50,[],[],[]);
+ % pause(1)
+
+ %** 2-d scatterplot
+ figure(3)
+ plot(xdraw((starts-1)*ndraws2+(1:draws),1),...
+ xdraw((starts-1)*ndraws2+(1:draws),2),'.');
+ drawnow
+ end
+ end
+ end
+ %
+ if ~IndxGibbs
+ countJump(starts,1) = cJump;
+ end
+ %
+ %** Getting average of variances W and variance of means B/n -- B_n
+ %** see Gelman, p.331, my Shock(0), pp.12-13, and my Forecast (2), pp.28-31
+ if (nstarts>1)
+ Avhx_aj = Avhxmm/ndraws2; % (phi_.j)
+ Avhx_bs = Avhx_bs + Avhx_aj.^2; % 1st step to 2nd moment of (phi_.j)
+ Avhx_bm = Avhx_bm + Avhx_aj; % 1st step to (phi_..)
+ %
+ Avhx_bj = Avhxss/ndraws2; % 2nd moment of (phi_ij) for fixed j
+ Avhx_cj = Avhx_bj - Avhx_aj.^2; % ((n-1)/n)*S^2_j
+ Avhx_cm = Avhx_cm + Avhx_cj; % sum of ((n-1)/n)*S^2_j across j
+ end
+end
+timend = toc
+
+if ~IndxGibbs
+ countJump = countJump/ndraws2
+end
+
+Avhxm = Avhxm/(imndraws);
+Avhxs = Avhxs/(imndraws);
+Avhxv = Avhxs - Avhxm.^2;
+Avhxs = sqrt(Avhxv); % stardard deviation
+A0hm = zeros(nvar);
+A0hm(a0indx) = Avhxm % mean
+A0hv = zeros(nvar);
+A0hv(a0indx) = Avhxv; % varaince matrix
+A0hs = zeros(nvar);
+A0hs(a0indx) = Avhxs; % standar deviation
+
+%**** Getting Within-Sequence W and Between-Sequence B_n
+% see Gelman, p.331, my Shock(0), pp.12-13, and my Forecast (2), pp.28-31
+if (nstarts>1)
+ AvhxW = (ndraws2/(ndraws2-1))*Avhx_cm/nstarts;
+ % W: average of j within-sequence variances
+ AvhxB_n = (nstarts/(nstarts-1)) * ( Avhx_bs/nstarts - (Avhx_bm/nstarts).^2 );
+ % B/n: variance of J within-sequence means
+ AvhxB = ndraws2*AvhxB_n; % B
+ %
+ B_W1 = AvhxB ./ AvhxW;
+ B1 = AvhxB;
+ W1 = AvhxW;
+ GR1 = sqrt((ndraws2-1)/ndraws2 + B_W1/ndraws2);
+ % measure of Gelman reduction; need not be 1 to be accurate,
+ % contrary to what Gelman claims
+ save outB_W B_W1 B1 W1 GR1 nstarts ndraws2 imndraws timend Avhxs ...
+ A0xhat A0hm A0hs A0hv IndxGibbs countJump nswitch
+
+ titstr = ['J ' num2str(nstarts) ' n1 ' num2str(ndraws1) ...
+ ' n2 ' num2str(ndraws2) ' timend minutes ' num2str(timend/60)];
+ disp(' ')
+ disp(titstr)
+ disp('B/W sqrt(B) sqrt(W) Std(A0) GR')
+ format short g
+ [B_W1 sqrt(B1) sqrt(W1) Avhxs GR1]
+else
+ save outB_W nstarts ndraws2 imndraws timend Avhxs ...
+ A0xhat A0hm A0hs A0hv IndxGibbs countJump nswitch
+end
+
+disp(' ')
+disp('nswitch nswitch/imndraws -- # of sign switches')
+[nswitch nswitch/imndraws]
+disp(' ')
+disp('timend/60 minutes')
+timeminutes = timend/60
+nswitch=nswitch/imndraws;
+
+if IndxGraph
+ %
+ % figure(4)
+ % histpdfg(xdraw(:,1),50,[],[],[]);
+ % figure(5)
+ % plot(xdraw(:,3),xdraw(:,4),'.');
+ % figure(6)
+ % plot(xdraw(:,1),xdraw(:,2),'.');
+end
diff --git a/MatlabFiles/adapt.m b/MatlabFiles/adapt.m
deleted file mode 100644
index 1f8ba86a753062ea6895c3b76f49995415326404..0000000000000000000000000000000000000000
--- a/MatlabFiles/adapt.m
+++ /dev/null
@@ -1,49 +0,0 @@
-function Q = adapt(f,a,b,tol,trace,varargin)
-%ADAPT Numerically evaluate integral using adaptive Simpson rule.
-%
-% Q = ADAPT('F',A,B) approximates the integral of F(X) from A to B
-% to machine precision. 'F' is a string containing the name of the
-% function. Function F must return a vector of output values if given
-% a vector of input values.
-%
-% Q = ADAPT('F',A,B,TOL) integrates to a relative error of TOL.
-%
-% Q = ADAPT('F',A,B,TOL,TRACE) displays the left end point of the
-% current interval, the interval length and the partial integral.
-%
-% Q = ADAPT('F',A,B,TOL,TRACE,P1,P2,...) allows coefficients P1, ...
-% to be passed directly to function F: G = F(X,P1,P2,...).
-% To use default values for TOL or TRACE, you may pass in the empty
-% matrix ([]).
-%
-% See also QUAD, QUAD8, DBLQUAD.
-
-% % Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-if (nargin < 4), tol = []; end;
-if (nargin < 5), trace = []; end;
-if (isempty(tol)), tol = 10*eps; end;
-if (isempty(trace)), trace = 0; end;
-
-x = [a (a+b)/2 b];
-y = feval(f, x, varargin{:});
-fa = y(1); fm = y(2); fb = y(3);
-is = (b - a)/6 * (fa + 4*fm + fb);
-s = sign(is); if (s == 0), s = 1; end;
-is = s*(abs(is) + b - a)/2*tol/eps;
-Q = adaptstp(f, a, b, fa, fm, fb, is, trace, varargin{:});
\ No newline at end of file
diff --git a/MatlabFiles/adaptstp.m b/MatlabFiles/adaptstp.m
deleted file mode 100644
index 6075b7993bfeda37e7d82e1aa1944c02772c28e6..0000000000000000000000000000000000000000
--- a/MatlabFiles/adaptstp.m
+++ /dev/null
@@ -1,43 +0,0 @@
-function Q = adaptstp (f, a, b, fa, fm, fb, is, trace, varargin)
-%ADAPTSTP Recursive function used by ADAPT.
-%
-% Q = ADAPTSTP('F',A,B,FA,FM,FB,IS,TRACE) tries to
-% approximate the integral of f(x) from a to b to within a
-% relative error of IS/int(..)*eps. F is a string containing
-% the name of f. The remaining arguments are generated by adapt
-% or by the recursion.
-%
-% See also ADAPT.
-
-% Author: Walter Gander, 05/20/97
-% Copyright (C) 1997 Walter Gander
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-m = (a + b)/2; h = (b - a)/4;
-x = [a + h, b - h];
-y = feval(f, x, varargin{:});
-fml = y(1); fmr = y(2);
-i1 = h/1.5 * (fa + 4*fm + fb);
-i2 = h/3 * (fa + 4*(fml + fmr) + 2*fm + fb);
-i1 = (16*i2 - i1)/15;
-if (is + (i1-i2) == is),
- Q = i1;
- if (trace), disp([a b-a Q]), end;
-else
- Q = adaptstp (f, a, m, fa, fml, fm, is, trace, varargin{:}) + ...
- adaptstp (f, m, b, fm, fmr, fb, is, trace, varargin{:});
-end;
\ No newline at end of file
diff --git a/MatlabFiles/betapar.m b/MatlabFiles/betapar.m
index e79604d42cf4cf770ca311c7369c0da93ec62efc..a0c141d57f985ad9ae46f6a6f0b1ad61a8aab5fc 100644
--- a/MatlabFiles/betapar.m
+++ b/MatlabFiles/betapar.m
@@ -1,26 +1,26 @@
-function f = betapar(ab, XLO, XUP, PLO, PUP);
-
-% The function takes as inputs the parameters ab=[a, b] of the Beta
-% distribution, the bounds of the support [XLO, XUP], the the corresponding
-% probability of the bounds [PLO, PUP] and returns the residual value f.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-a = ab(1); b = ab(2);
-f1 = PLO - betacdf(XLO, a, b);
-f2 = PUP - betacdf(XUP, a, b);
-f = [f1, f2];
+function f = betapar(ab, XLO, XUP, PLO, PUP);
+
+% The function takes as inputs the parameters ab=[a, b] of the Beta
+% distribution, the bounds of the support [XLO, XUP], the the corresponding
+% probability of the bounds [PLO, PUP] and returns the residual value f.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+a = ab(1); b = ab(2);
+f1 = PLO - betacdf(XLO, a, b);
+f2 = PUP - betacdf(XUP, a, b);
+f = [f1, f2];
diff --git a/MatlabFiles/bfgsi.m b/MatlabFiles/bfgsi.m
index b56fe3662be6d5ec4dc88906c886cdc072e78e5e..7d94746726f8d13e30af7f96cfe241e5f578dae6 100644
--- a/MatlabFiles/bfgsi.m
+++ b/MatlabFiles/bfgsi.m
@@ -1,46 +1,44 @@
-function H = bfgsi(H0,dg,dx)
-% H = bfgsi(H0,dg,dx)
-% dg is previous change in gradient; dx is previous change in x;
-% 6/8/93 version that updates inverse hessian instead of hessian
-% itself.
-% Copyright by Christopher Sims 1996. This material may be freely
-% reproduced and modified.
-
-% Copyright (C) 1996 Christopher Sims
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-dispIndx = 1; % 1: turn on all the diplays on the screen; 0: turn off (Added by T. Zha)
-
-if size(dg,2)>1
- dg=dg';
-end
-if size(dx,2)>1
- dx=dx';
-end
-Hdg = H0*dg;
-dgdx = dg'*dx;
-if (abs(dgdx) >1e-12)
- H = H0 + (1+(dg'*Hdg)/dgdx)*(dx*dx')/dgdx - (dx*Hdg'+Hdg*dx')/dgdx;
-else
- if dispIndx
- disp('bfgs update failed.')
- disp(['|dg| = ' num2str(sqrt(dg'*dg)) '|dx| = ' num2str(sqrt(dx'*dx))]);
- disp(['dg''*dx = ' num2str(dgdx)])
- disp(['|H*dg| = ' num2str(Hdg'*Hdg)])
- end
- H=H0;
-end
-save H.dat H
+function H = bfgsi(H0,dg,dx)
+% H = bfgsi(H0,dg,dx)
+% dg is previous change in gradient; dx is previous change in x;
+% 6/8/93 version that updates inverse hessian instead of hessian
+% itself.
+%
+%
+% Copyright (C) 1996 Christopher A. Sims
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+dispIndx = 1; % 1: turn on all the diplays on the screen; 0: turn off (Added by T. Zha)
+
+if size(dg,2)>1
+ dg=dg';
+end
+if size(dx,2)>1
+ dx=dx';
+end
+Hdg = H0*dg;
+dgdx = dg'*dx;
+if (abs(dgdx) >1e-12)
+ H = H0 + (1+(dg'*Hdg)/dgdx)*(dx*dx')/dgdx - (dx*Hdg'+Hdg*dx')/dgdx;
+else
+ if dispIndx
+ disp('bfgs update failed.')
+ disp(['|dg| = ' num2str(sqrt(dg'*dg)) '|dx| = ' num2str(sqrt(dx'*dx))]);
+ disp(['dg''*dx = ' num2str(dgdx)])
+ disp(['|H*dg| = ' num2str(Hdg'*Hdg)])
+ end
+ H=H0;
+end
+save H.dat H
diff --git a/MatlabFiles/binread.m b/MatlabFiles/binread.m
index d9f6c930b9466a5d07336ea27a88c8ec06566857..806f2fce8e0da0aa8bbecfc4baeac3a4ac164c48 100644
--- a/MatlabFiles/binread.m
+++ b/MatlabFiles/binread.m
@@ -1,31 +1,31 @@
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-nbuffer = 10;
-ndraws=3*nbuffer;
-
-n=3;
-yhatw = zeros(n^2,ndraws);
-seldraw = [3 7];
-
-[fid,message]=fopen('outyhat.bin')
-%[xd,count]=fread(fid,[n^2,length(seldraw)],'double'); % (1) working
-[xd,count]=fread(fid,inf,'single'); % (2) working. with 'single' or 'double'
-fid
-message
-count
-%xdd=reshape(xd,n^2,length(seldraw)) % (1) working
-xdd=reshape(xd,n^2,ndraws) % (2) working
\ No newline at end of file
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+nbuffer = 10;
+ndraws=3*nbuffer;
+
+n=3;
+yhatw = zeros(n^2,ndraws);
+seldraw = [3 7];
+
+[fid,message]=fopen('outyhat.bin')
+%[xd,count]=fread(fid,[n^2,length(seldraw)],'double'); % (1) working
+[xd,count]=fread(fid,inf,'single'); % (2) working. with 'single' or 'double'
+fid
+message
+count
+%xdd=reshape(xd,n^2,length(seldraw)) % (1) working
+xdd=reshape(xd,n^2,ndraws) % (2) working
diff --git a/MatlabFiles/binwrite.m b/MatlabFiles/binwrite.m
index 20e46d64baaa062bfb321406fca8b2af9de2dd38..3607ac4ec0a6364e2ef773cf2119eafd15200256 100644
--- a/MatlabFiles/binwrite.m
+++ b/MatlabFiles/binwrite.m
@@ -1,36 +1,36 @@
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-nbuffer = 10;
-ndraws=3*nbuffer;
-
-n=3;
-yhatw = zeros(n^2,nbuffer);
-
-wdraws=0;
-for draws=1:ndraws
- tmp = draws*ones(n);
- yhatw(:,draws-wdraws) = tmp(:);
-
- if ~mod(draws,nbuffer)
- wdraws=draws
-
- fwriteid = fopen('outyhat.bin','a');
- count = fwrite(fwriteid,yhatw,'single'); % 'single' or 'double'
- status = fclose('all');
- end
-end
-
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+nbuffer = 10;
+ndraws=3*nbuffer;
+
+n=3;
+yhatw = zeros(n^2,nbuffer);
+
+wdraws=0;
+for draws=1:ndraws
+ tmp = draws*ones(n);
+ yhatw(:,draws-wdraws) = tmp(:);
+
+ if ~mod(draws,nbuffer)
+ wdraws=draws
+
+ fwriteid = fopen('outyhat.bin','a');
+ count = fwrite(fwriteid,yhatw,'single'); % 'single' or 'double'
+ status = fclose('all');
+ end
+end
+
diff --git a/MatlabFiles/calyrqm.m b/MatlabFiles/calyrqm.m
index 5c94aa34feb37157e794b8ca804646cd1ed6c599..69512a1caabdac0d0b0d76e70d3e0f45bf3126fa 100644
--- a/MatlabFiles/calyrqm.m
+++ b/MatlabFiles/calyrqm.m
@@ -1,74 +1,74 @@
-function [Myrqm,nMyrqm] = calyrqm(q_m,Byrqm,Eyrqm)
-% [Myrqm,nMyrqm] = calyrqm(q_m,Byrqm,Eyrqm)
-%
-% Given the beginning and end years and quarters (months), export a matrix of all years and
-% quarters (months) for these years and in between
-%
-% q_m: 4 if quarterly and 12 if monthly
-% Byrqm: [year quarter(month)] -- all integers, the begining year and quarter (month)
-% Eyrqm: [year quarter(month)] -- all integers, the end year and quarter (month)
-%-------------------
-% Myrqm: matrix of all years and quarters (months) between and incl. Byrqm and Eyrqm
-% nMyrqm: number of data points incl. Byrqm and Eyrqm
-%
-% Tao Zha, April 2000
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-if ~isempty(find(Byrqm-round(Byrqm))) | (q_m-round(q_m)) | ~isempty(find(Byrqm-round(Byrqm)))
- error('argin qm, Byrqm, or Eyrqm must of integer')
-elseif Byrqm(1)>Eyrqm(1)
- error('Eyrqm(1) must be equal to or greater than Byrqm(1)')
-elseif Byrqm(1)==Eyrqm(1)
- if Byrqm(2)>Eyrqm(2)
- error('Eyrqm(2) must be equal to or greater than Byrqm(2) because of the same year')
- end
-end
-
-
-Yr = Byrqm(1)+[0:Eyrqm(1)-Byrqm(1)]';
-
-if length(Yr)>=2 % there are years and quarters (months) between Byrqm and Eyrqm
- n=length(Yr)-2;
- C=zeros(n*q_m,2);
- C(:,1) = kron(Yr(2:end-1),ones(q_m,1));
- C(:,2) = kron(ones(n,1),[1:q_m]');
-
- %* initialize a matrix of years and quarters (months) including Byrqm and Eyrqm
- Myrqm = zeros((q_m-Byrqm(2)+1)+Eyrqm(2)+n*q_m,2);
-
- %* Years in between
- n1=q_m-Byrqm(2)+1;
- n2=Eyrqm(2);
- Myrqm(n1+1:end-n2,:) = C;
- %* Beginning year
- for k=1:n1
- Myrqm(k,:) = [Byrqm(1) Byrqm(2)+k-1];
- end
- %* End year
- for k=1:n2
- Myrqm(end-Eyrqm(2)+k,:) = [Eyrqm(1) k];
- end
-else %* all the data are in the same calendar year
- n1=Eyrqm(2)-Byrqm(2)+1;
- Myrqm = zeros(n1,2);
- for k=1:n1
- Myrqm(k,:) = [Byrqm(1) Byrqm(2)+k-1];
- end
-end
-
-nMyrqm = size(Myrqm,1);
+function [Myrqm,nMyrqm] = calyrqm(q_m,Byrqm,Eyrqm)
+% [Myrqm,nMyrqm] = calyrqm(q_m,Byrqm,Eyrqm)
+%
+% Given the beginning and end years and quarters (months), export a matrix of all years and
+% quarters (months) for these years and in between
+%
+% q_m: 4 if quarterly and 12 if monthly
+% Byrqm: [year quarter(month)] -- all integers, the begining year and quarter (month)
+% Eyrqm: [year quarter(month)] -- all integers, the end year and quarter (month)
+%-------------------
+% Myrqm: matrix of all years and quarters (months) between and incl. Byrqm and Eyrqm
+% nMyrqm: number of data points incl. Byrqm and Eyrqm
+%
+% Tao Zha, April 2000
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+if ~isempty(find(Byrqm-round(Byrqm))) | (q_m-round(q_m)) | ~isempty(find(Byrqm-round(Byrqm)))
+ error('argin qm, Byrqm, or Eyrqm must of integer')
+elseif Byrqm(1)>Eyrqm(1)
+ error('Eyrqm(1) must be equal to or greater than Byrqm(1)')
+elseif Byrqm(1)==Eyrqm(1)
+ if Byrqm(2)>Eyrqm(2)
+ error('Eyrqm(2) must be equal to or greater than Byrqm(2) because of the same year')
+ end
+end
+
+
+Yr = Byrqm(1)+[0:Eyrqm(1)-Byrqm(1)]';
+
+if length(Yr)>=2 % there are years and quarters (months) between Byrqm and Eyrqm
+ n=length(Yr)-2;
+ C=zeros(n*q_m,2);
+ C(:,1) = kron(Yr(2:end-1),ones(q_m,1));
+ C(:,2) = kron(ones(n,1),[1:q_m]');
+
+ %* initialize a matrix of years and quarters (months) including Byrqm and Eyrqm
+ Myrqm = zeros((q_m-Byrqm(2)+1)+Eyrqm(2)+n*q_m,2);
+
+ %* Years in between
+ n1=q_m-Byrqm(2)+1;
+ n2=Eyrqm(2);
+ Myrqm(n1+1:end-n2,:) = C;
+ %* Beginning year
+ for k=1:n1
+ Myrqm(k,:) = [Byrqm(1) Byrqm(2)+k-1];
+ end
+ %* End year
+ for k=1:n2
+ Myrqm(end-Eyrqm(2)+k,:) = [Eyrqm(1) k];
+ end
+else %* all the data are in the same calendar year
+ n1=Eyrqm(2)-Byrqm(2)+1;
+ Myrqm = zeros(n1,2);
+ for k=1:n1
+ Myrqm(k,:) = [Byrqm(1) Byrqm(2)+k-1];
+ end
+end
+
+nMyrqm = size(Myrqm,1);
diff --git a/MatlabFiles/cfore.m b/MatlabFiles/cfore.m
index c3e06510aa2a4edaf39eb6cd465fa8f619dbc1dc..b37cb7962024fa679201df609e479a155ff8504d 100644
--- a/MatlabFiles/cfore.m
+++ b/MatlabFiles/cfore.m
@@ -1,481 +1,481 @@
-% 10/24/97
-% Distance Method of Waggoner and Zha
-% Modified from Sims and Zha's code
-% Copyright (c) 1997 Tao Zha
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-% ** ONLY UNDER UNIX SYSTEM
-%path(path,'/usr2/f1taz14/mymatlab')
-
-
-%global xxhp Hm1t Hm1 Hm SpH FRESHFUNCTION
-%
-%* =================================================
-%* ====== Beginning of the script ==================
-%* =================================================
-%
-%* The available data considered
-%
-q_m = 12; % quarters or months
-yrBin=59; % beginning of the year
-qmBin=1; % begining of the quarter or month
-yrFin=97; % final year
-qmFin=12; % final quarter
-%tnvar = 2; % total number of variables
-nData=(yrFin-yrBin)*q_m + (qmFin-qmBin+1);
-% total number of the available data -- this is all you have
-%
-%* Load data and series
-%
-load xd24a % the default name for the variable is "xdd".
-[nt,ndv]=size(xdd);
-if nt~=nData
- warning(sprintf('nt=%d, Caution: not equal to the length in the data',nt));
- %disp(sprintf('nt=%d, Caution: not equal to the length in the data',nt));
- return
-end
-%1 CPI-U
-%2 FFR
-%3 T-bill3
-%4 Treasure note 10
-%5 M2
-%6 M1
-%7 Nominal PCE
-%8 real PCE
-%9 Unemployment Rate
-%10 IMF Commodity Price Index
-%11 Civilians Employed: 16 & over
-%12 Nonfarm Payroll Employment
-%13 IP
-%14 Retail Sales (Nominal)
-%15 NAPM Composit Index
-%16 Total Reserves
-%17 PPI-finished goods
-%18 PPI-Crude materials
-%19 PPI-Crude materials less energy
-%20 CRB Spot Commodity Index -- all commodities
-%21 CRB Spot Commodity Index -- raw industrials
-%22 PCE price index
-%23 rgdpmon Real GDP (monthly, chain $92)
-%24 dgdpmon Deflator GDP (monthly, chain $92)
-
-
-%1 IMF CP
-%2 M2
-%3 FFR
-%3 real GDP
-%4 CPI-U
-%5 U
-
-%>>>>>>>>>>>>>>>>>>
-logindx = [1 5:8 10:14 16:24];
-xdd(:,logindx) = log(xdd(:,logindx));
-pctindx = [2:4 9 15];
-xdd(:,pctindx)=.01*xdd(:,pctindx); % make it a general term for the following use
-%
-vlist = [10 5 2 23 1 9]; % regarding "xdd", IMF-CP, M2, FFR, GDP, CPI, U
-vlistlog = [1 2 4 5]; % subset of "vlist"
-vlistper = [3 6]; % subset of "vlist"
-%<<<<<<<<<<<<<<<<<<<
-
-idfile='iden6';
-
-xlab = {'Inf'
- 'MS'
- 'FFR'
- 'y'
- 'P'
- 'U'};
-
-ylab = {'Pcm'
- 'M2'
- 'FFR'
- 'y'
- 'P'
- 'U'};
-
-xdd_per = xdd(:,vlist);
-
-x1 = 'Pcm';
-x2 = 'M2';
-x3 = 'FFR';
-x4 = 'GDP';
-x5 = 'CPI';
-x6 = 'U';
-x7 = 'R10';
-
-
-baddata = find(isnan(xdd_per));
-if ~isempty(baddata)
- warning('Some data are actually unavailable.')
- disp('Hit any key to continue, or ctrl-c to abort')
- pause
-end
-%
-
-%* A specific sample is considered for estimation
-%* Sample period 59:7-82:9, forecast period 82:10-84:9
-yrStart=59;
-qmStart=1;
-[yrEnd,qmEnd,forep,forepy,forelabel] = pararc;
-nSample=(yrEnd-yrStart)*q_m + (qmEnd-qmStart+1);
-if qmEnd == q_m % end of the year
- nSampleCal=nSample; % Cal: calendar year
-else
- nSampleCal=(yrEnd-1-yrStart)*q_m + (q_m-qmStart+1); % Cal: calendar year
-end
-
-%* More script variables
-%
-lags = 13; % <<>>
-% automatic decay code (monthly data), only two options: lags = 6 or 13
-forepq = forep/3; % quarterly
-actup = 5*48; % <<>> actual periods before forecasting (20 years)
-%actup = 12*floor(nSample/12); % <<>> actual periods before forecasting (8 years)
-actup = 48; % <<>> actual periods before forecasting (4 years)
-actupq = actup/3; % quarterly
-actupy = actup/12; % four years
-imstp = 48; % <<>> impulse responses (4 years)
-ninv = 1000; % the number of intervals for counting impulse responses
-nhp = 6; % <<>> number of hyperparameters for estimation
-%%scf = 2.4/sqrt(nvar); % scf^2*Sigma (covaraince)
-scf = 0.25; % scf^2*Sigma (covaraince)
-ndraws1=15; % 1500, 1st part of Monte Carlo draws
-ndraws2=2*ndraws1; % 2nd part of Monte Carlo draws
-ndraws=3*ndraws2 % a total number of Monte Carlo draws
-nstarts=3; % number of starting points
-imndraws = nstarts*ndraws2;
-tdf = 3; % degrees of freedom for t-dist
-ga = tdf/2; % asymmetry parameter in Gamma
-gb = 2/tdf; % normalized parameter in Gamma
-%
-%* =================================================
-%* ====== End of the script ========================
-%* =================================================
-
-
-if (q_m==12)
- nStart=(yrStart-yrBin)*12+qmStart-qmBin; % positive number of months at the start
- nEnd=(yrEnd-yrFin)*12+qmEnd-qmFin; % negative number of months towards end
-elseif (q_m==4)
- nStart=(yrStart-yrBin)*4+qmStart-qmBin; % positive number of months at the start
- nEnd=(yrEnd-yrFin)*4+qmEnd-qmFin; % negative number of months towards end
-else
- disp('Warning: this code is only good for monthly/quarterly data!!!')
- return
-end
-%
-if nEnd>0 | nStart<0
- disp('Warning: this particular sample consider is out of bounds of the data!!!')
- return
-end
-%
-xdgel=xdd(nStart+1:nData+nEnd,vlist); % gel: general term for selected xdd
-xdata=xdd(nStart+1:nData,vlist);
-
-[Gb,Sbd,Bh,SpH,fss,ndobs,phi,y,nvar,ncoef,xxhpc,a0indx,na0p,...
- idmat0,idmatpp] = szasbvar(idfile,q_m,lags,nSample,nhp,xdgel);
-
-% * the largest matrix in this file <<>>
-yforew = zeros(ndraws,forep*nvar); % preallocating
-yforeqgw = zeros(ndraws,forepq*nvar); % preallocating
-yforeCalygw = zeros(ndraws,forepy*nvar); % preallocating
-% * the largest matrix in this file <<>>
-%%imfcnt = zeros(ninv+2,imstp*nvar^2); % cnt: count
-
-load idenml % xhat ghat fhat, etc.
-%load outiden % xhat ghat fhat
-
-%==================
-% Impulse responses first
-%==================
-%A0 = zeros(nvar);
-%A0(a0indx)=xhat;
-%A0(4,2) = -xhat(7); % output in MD
-%A0(5,2)=-xhat(7); % price in MD
-A0in = inv(A0);
-swish = A0in'; % each row corresponds to an equation
-%Bh = Hm1t; % no longer have Hm1t in this new szasbvar
-
-% ** impulse responses
-nn = [nvar lags imstp];
-imf = zimpulse(Bh,swish,nn); % in the form that is congenial to RATS
-%[vd,str,imf] = errors(Bh,swish,nn);
-
-scaleout = imcgraph(imf,nvar,imstp,xlab,ylab)
-
-
-%%%%
-%$$$ Out-of-sample forecasts. Note: Hm1t does not change with A0.
-%%%%
-%
-% ** out-of-sample forecast, from 82:4 to 84:3 (flp+1:flp+forep)
-% * updating the last row of X (phi) with the current (last row of) y.
-phil = phi(size(phi,1),:);
-phil(nvar+1:ncoef-1) = phil(1:ncoef-1-nvar);
-phil(1:nvar) = y(size(y,1),:);
-ylast = y(size(y,1),:);
-indx12 = size(y,1)-q_m+1:size(y,1);
-ylast12 = y(indx12,:); % last 12 months data
-nn = [nvar lags forep];
-%
-yfore = forecast(Bh,phil,nn); % forep-by-nvar
-
-
-%>>>>>>>>>>>>>>>
-yforel=yfore;
-yforel(:,vlistlog) = exp(yfore(:,vlistlog));
-figure;
-t2=1:forep;
-for i = 1:nvar
- subplot(nvar/2,2,i)
- plot(t2,yforel(:,i),'--')
- %title('solid-actual, dotted-forecast');
- %title(eval(['forelabel']));
- %ylabel(eval(['x' int2str(i)]));
- ylabel(char(ylab(i)))
-end
-%<<<<<<<<<<<<<<<
-
-%%%%%%%%
-%
-%% See Zha's note "Forecast (1)" p. 5, RATS manual (some errors in RATS), etc.
-%
-%% Some notations: y(t+1) = y(t)B1 + e(t+1)inv(A0). e(t+1) is 1-by-n.
-%% Let r(t+1)=e(t+1)inv(A0) + e(t+2)C + .... where inv(A0) is impulse
-%% response at t=1, C at t=2, etc. The row of inv(A0) or C is
-%% all responses to one shock.
-%% Let r be q-by-1 (such as r(1) = r(t+1)
-%% = y(t+1) (constrained) - y(t+1) (forecast)).
-%% Use impulse responses to find out R (k-by-q) where k=nvar*nsteps
-%% where nsteps the largest constrained step. The key of the program
-%% is to creat R using impulse responses
-%% Optimal solution for shock e where R'*e=r and e is k-by-1 is
-%% e = R*inv(R'*R)*r.
-%
-%%%%%%%%
-
-nconstr=4; % q: 4 years -- 4*12 months
-eq_ms = 2; % location of MS equation
-%eq_ms = []; % all shocks
-%*** initializing
-stepcon=cell(nconstr,1); % initializing, value y conditioned
-valuecon=zeros(nconstr,1); % initializing, value y conditioned
-varcon=zeros(nconstr,1); % initializing, endogous variables conditioned
-%
-stepcon{1}=[1:12]'; % average over 12 months.
-stepcon{2}=[13:24]'; % average over 12 months.
-stepcon{3}=[25:36]'; % average over 12 months.
-stepcon{4}=[37:48]'; % average over 12 months.
-%
-%for i=1:nconstr
-% stepcon{i}=i;
-%end
-%
-chk1 = mean(yfore(stepcon{1},3))
-chk2 = mean(yfore(stepcon{2},3))
-chk3 = mean(yfore(stepcon{3},3))
-chk4 = mean(yfore(stepcon{4},3))
-Ro=[chk1 chk2 chk3 chk4];
-%
-chk1 = exp( (sum(yfore(stepcon{1},5))-sum(ylast12(:,5))) ./ q_m )
-chk2 = exp( (sum(yfore(stepcon{2},5))-sum(yfore(stepcon{1},5))) ./ q_m )
-chk3 = exp( (sum(yfore(stepcon{3},5))-sum(yfore(stepcon{2},5))) ./ q_m )
-chk4 = exp( (sum(yfore(stepcon{4},5))-sum(yfore(stepcon{3},5))) ./ q_m )
-%
-%valuecon(:)=0.055;
-%
-%>>>>>>>>>>>>>>>>> E: Condition on funds rate path >>>>>>>>>>>>> Toggle
-%delta=0.0010;
-%valuecon(1) = mean(yfore(stepcon{1},3))+2*delta;
-%valuecon(2) = mean(yfore(stepcon{2},3))+2*delta;
-%valuecon(3) = mean(yfore(stepcon{3},3))-2*delta;
-%valuecon(4) = mean(yfore(stepcon{4},3))-2*delta;
-%valuecon(1) = 0.055;
-%valuecon(2) = 0.050;
-%valuecon(3) = 0.0475;
-%valuecon(4) = 0.045;
-%>>>>>>>>>>>>>>>>> E: Condition on funds rate path >>>>>>>>>>>>>
-%
-%<<<<<<<<<<<<<<<< B: Condition on inflation path <<<<<<<<<< Toggle
-%delta=0.0010;
-%valuecon(1)=mean(ylast12(:,5))+log(chk1-0*delta);
-%valuecon(2)=valuecon(1)+log(chk2-2*delta);
-%valuecon(3)=valuecon(2)+log(chk3-6*delta);
-%valuecon(4)=valuecon(3)+log(chk4-12*delta);
-% % 5: CPI; 2.5%: annual inflation over 12 months, geometric means
-%$$$ very good results -- following
-valuecon(1)=mean(ylast12(:,5))+log(chk1);
-valuecon(2)=valuecon(1)+log(1.020);
-valuecon(3)=valuecon(2)+log(1.02);
-valuecon(4)=valuecon(3)+log(1.02);
-% % 5: CPI; 2.5%: annual inflation over 12 months, geometric means
-%<<<<<<<<<<<<<<<< E: Condition on inflation path <<<<<<<<<<
-
-nstepsm = 0; % initializing, the maximum step in all constraints
-for i=1:nconstr
- nstepsm = max([nstepsm max(stepcon{i})]);
-end
-varcon(:)=5; % 3: FFR; 5: CPI
-%
-imf3=reshape(imf,size(imf,1),nvar,nvar);
- % imf3: row-steps, column-nvar responses, 3rd dimension-nvar shocks
-imf3s=permute(imf3,[1 3 2]);
- % imf3s: permuted so that row-steps, column-nvar shocks,
- % 3rd dimension-nvar responses
-
-[yhat,Estr] = fidencond(valuecon,stepcon,varcon,nconstr,nstepsm,nvar,lags,...
- yfore,imf3s,phil,Bh,eq_ms);
-
-chk1 = mean(yhat(stepcon{1},3))
-chk2 = mean(yhat(stepcon{2},3))
-chk3 = mean(yhat(stepcon{3},3))
-chk4 = mean(yhat(stepcon{4},3))
-Rh=[chk1 chk2 chk3 chk4];
-
-chk1 = exp( (sum(yhat(stepcon{1},5))-sum(ylast12(:,5))) ./ q_m )
-chk2 = exp( (sum(yhat(stepcon{2},5))-sum(yhat(stepcon{1},5))) ./ q_m )
-chk3 = exp( (sum(yhat(stepcon{3},5))-sum(yhat(stepcon{2},5))) ./ q_m )
-chk4 = exp( (sum(yhat(stepcon{4},5))-sum(yhat(stepcon{3},5))) ./ q_m )
-
-%chk1 = mean(yhat(1:12,3))
-%chk2 = mean(yhat(13:24,3))
-%chk3 = mean(yhat(25:36,3))
-%chk4 = mean(yhat(36:48,3))
-%Rh=[chk1 chk2 chk3 chk4];
-
-%chk1 = exp( (sum(yhat(1:12,5))-sum(ylast12(:,5))) ./ q_m )
-%chk2 = exp( (sum(yhat(13:24,5))-sum(yhat(1:12,5))) ./ q_m )
-%chk3 = exp( (sum(yhat(25:36,5))-sum(yhat(13:24,5))) ./ q_m )
-%chk4 = exp( (sum(yhat(37:48,5))-sum(yhat(25:36,5))) ./ q_m )
-
-
-idiff=mean(yfore(:,3))-mean(yhat(:,3))
-mean(yfore(:,3))
-mean(yhat(:,3))
-figure
-plot(1:48,yfore(:,3),1:48,yhat(:,3),':')
-figure
-plot(1:4,Ro,1:4,Rh,':')
-
-%>>>>>>>>>>>>>>>
-yhatl=yhat;
-yhatl(:,vlistlog) = exp(yhat(:,vlistlog));
-figure;
-t2=1:forep;
-for i = 1:nvar
- subplot(nvar/2,2,i)
- plot(t2,yhatl(:,i),'--')
- %title('solid-actual, dotted-forecast');
- %title(eval(['forelabel']));
- %ylabel(eval(['x' int2str(i)]));
- ylabel(char(ylab(i)))
-end
-%<<<<<<<<<<<<<<<
-
-
-% inputs needed.
-%yfore=yhat;
-
-%===================================================
-%%% Converting to calendar years and all at level
-%===================================================
-[yforeml,yforeqgml,yforeCalygml] = fore_cal(yhat,xdata,nvar,nSample,...
- nSampleCal,forep,forepq,forepy,q_m,qmEnd,vlist,vlistlog);
-
-%==================
-% Note
-%=================
-% ? 1--median; l--lower bound; h--upper bound: between the bound: 2/3 probability
-% yfore? % monthly, level
-% yforeqg? % quarterly, growth rate
-% yforeCalyg? % calendar year, growth rate
-
-%save outw ndraws yfore1 yforeqg1 yforeCalyg1 yforel yforeqgl yforeh yforeqgh ...
-% yforeCalygl yforeCalygh IUbeta
-
-yforeCalygml
-
-
-%----------------------------------------------------------------------
-%================= Graphics =====================
-%----------------------------------------------------------------------
-%
-
-%[yactCalyg,yforeCalygml,yAg,yFg] = fore_gh(xdata,nvar,nSample,nSampleCal,...
-% yforeml,yforeqgml,yforeCalygml,actup,actupq,actupy,...
-% vlist,vlistlog,vlistper,q_m,forep,ylab);
-
-xinput = cell(16,1);
-xinput{1}=xdata; xinput{2}=nvar; xinput{3}=nSample; xinput{4}=nSampleCal;
-xinput{5}=yforeml; xinput{6}=yforeqgml; xinput{7}=yforeCalygml; xinput{8}=actup;
-xinput{9}=actupq; xinput{10}=actupy; xinput{11}=vlist; xinput{12}=vlistlog;
-xinput{13}=vlistper; xinput{14}=q_m; xinput{15}=forep; xinput{16}=ylab;
-[yactCalyg,yforeCalygml,yAg,yFg] = fore_gh(xinput);
-
-
-% Key Macroeconomic Variables: GDP, CPI, U
-%******* From Goldbook July 1-2, 1997 FOMC
-yforeBlue = [
- 3.6 2.4 5.0
- 2.5 2.6 5.0
- ]; % real GDP, CPI-U, U,
-yforeMacro = [
- 3.7 2.3 4.9
- 2.1 2.4 4.9
- ]; % real GDP, CPI-U, U
-yforeGold = [
- 3.8 2.4 5.0
- 2.5 2.5 4.8
- 2.4 2.7 4.7
- ]; % real GDP, CPI-U, U
-t3 = yAg+1:yAg+length(yforeBlue(:,1));
-t4 = yAg+1:yAg+length(yforeGold(:,1));
-%
-keyindx = [4:nvar 3 2]; % GdP, CPI, U, FFR, M2
-count=0;
-t1 = 1:yAg;
-t2 = yAg:yAg+yFg;
-for i = keyindx
- count = count+1;
- subplot(3,2,count)
- %plot(t1,yactqg(:,i),t2,yforeqg(:,i),':')
- if (i==3) | (i==2)
- plot(t1,yactCalyg(:,i),t2,[yactCalyg(length(t1),i);yforeCalygml(:,i)],'--')
- %title('solid-actual, dotted-forecast');
- %xlabel(eval(['forelabel']));
- %ylabel(eval(['x' int2str(i)]));
- ylabel(char(ylab(i)))
- else
- plot(t1,yactCalyg(:,i),t2,[yactCalyg(length(t1),i);yforeCalygml(:,i)],'--',...
- t3,yforeBlue(:,count),'o',t3,yforeMacro(:,count),'d',...
- t4,yforeGold(:,count),'^')
- %title('solid-actual, dotted-forecast');
- %xlabel(eval(['forelabel']));
- %ylabel(eval(['x' int2str(i)]));
- ylabel(char(ylab(i)))
- end
-end
-
-actual=yactCalyg(:,keyindx) % GDP, CPI, U, M2
-mode=yforeCalygml(:,keyindx) % GDP, CPI, U, M2
-%low=yforeCalygl(:,keyindx)
-%high=yforeCalygh(:,keyindx)
-yforeBlue
-yforeMacro
-yforeGold
\ No newline at end of file
+% 10/24/97
+% Distance Method of Waggoner and Zha
+% Modified from Sims and Zha's code
+%
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+% ** ONLY UNDER UNIX SYSTEM
+%path(path,'/usr2/f1taz14/mymatlab')
+
+
+%global xxhp Hm1t Hm1 Hm SpH FRESHFUNCTION
+%
+%* =================================================
+%* ====== Beginning of the script ==================
+%* =================================================
+%
+%* The available data considered
+%
+q_m = 12; % quarters or months
+yrBin=59; % beginning of the year
+qmBin=1; % begining of the quarter or month
+yrFin=97; % final year
+qmFin=12; % final quarter
+%tnvar = 2; % total number of variables
+nData=(yrFin-yrBin)*q_m + (qmFin-qmBin+1);
+% total number of the available data -- this is all you have
+%
+%* Load data and series
+%
+load xd24a % the default name for the variable is "xdd".
+[nt,ndv]=size(xdd);
+if nt~=nData
+ warning(sprintf('nt=%d, Caution: not equal to the length in the data',nt));
+ %disp(sprintf('nt=%d, Caution: not equal to the length in the data',nt));
+ return
+end
+%1 CPI-U
+%2 FFR
+%3 T-bill3
+%4 Treasure note 10
+%5 M2
+%6 M1
+%7 Nominal PCE
+%8 real PCE
+%9 Unemployment Rate
+%10 IMF Commodity Price Index
+%11 Civilians Employed: 16 & over
+%12 Nonfarm Payroll Employment
+%13 IP
+%14 Retail Sales (Nominal)
+%15 NAPM Composit Index
+%16 Total Reserves
+%17 PPI-finished goods
+%18 PPI-Crude materials
+%19 PPI-Crude materials less energy
+%20 CRB Spot Commodity Index -- all commodities
+%21 CRB Spot Commodity Index -- raw industrials
+%22 PCE price index
+%23 rgdpmon Real GDP (monthly, chain $92)
+%24 dgdpmon Deflator GDP (monthly, chain $92)
+
+
+%1 IMF CP
+%2 M2
+%3 FFR
+%3 real GDP
+%4 CPI-U
+%5 U
+
+%>>>>>>>>>>>>>>>>>>
+logindx = [1 5:8 10:14 16:24];
+xdd(:,logindx) = log(xdd(:,logindx));
+pctindx = [2:4 9 15];
+xdd(:,pctindx)=.01*xdd(:,pctindx); % make it a general term for the following use
+%
+vlist = [10 5 2 23 1 9]; % regarding "xdd", IMF-CP, M2, FFR, GDP, CPI, U
+vlistlog = [1 2 4 5]; % subset of "vlist"
+vlistper = [3 6]; % subset of "vlist"
+%<<<<<<<<<<<<<<<<<<<
+
+idfile='iden6';
+
+xlab = {'Inf'
+ 'MS'
+ 'FFR'
+ 'y'
+ 'P'
+ 'U'};
+
+ylab = {'Pcm'
+ 'M2'
+ 'FFR'
+ 'y'
+ 'P'
+ 'U'};
+
+xdd_per = xdd(:,vlist);
+
+x1 = 'Pcm';
+x2 = 'M2';
+x3 = 'FFR';
+x4 = 'GDP';
+x5 = 'CPI';
+x6 = 'U';
+x7 = 'R10';
+
+
+baddata = find(isnan(xdd_per));
+if ~isempty(baddata)
+ warning('Some data are actually unavailable.')
+ disp('Hit any key to continue, or ctrl-c to abort')
+ pause
+end
+%
+
+%* A specific sample is considered for estimation
+%* Sample period 59:7-82:9, forecast period 82:10-84:9
+yrStart=59;
+qmStart=1;
+[yrEnd,qmEnd,forep,forepy,forelabel] = pararc;
+nSample=(yrEnd-yrStart)*q_m + (qmEnd-qmStart+1);
+if qmEnd == q_m % end of the year
+ nSampleCal=nSample; % Cal: calendar year
+else
+ nSampleCal=(yrEnd-1-yrStart)*q_m + (q_m-qmStart+1); % Cal: calendar year
+end
+
+%* More script variables
+%
+lags = 13; % <<>>
+% automatic decay code (monthly data), only two options: lags = 6 or 13
+forepq = forep/3; % quarterly
+actup = 5*48; % <<>> actual periods before forecasting (20 years)
+%actup = 12*floor(nSample/12); % <<>> actual periods before forecasting (8 years)
+actup = 48; % <<>> actual periods before forecasting (4 years)
+actupq = actup/3; % quarterly
+actupy = actup/12; % four years
+imstp = 48; % <<>> impulse responses (4 years)
+ninv = 1000; % the number of intervals for counting impulse responses
+nhp = 6; % <<>> number of hyperparameters for estimation
+%%scf = 2.4/sqrt(nvar); % scf^2*Sigma (covaraince)
+scf = 0.25; % scf^2*Sigma (covaraince)
+ndraws1=15; % 1500, 1st part of Monte Carlo draws
+ndraws2=2*ndraws1; % 2nd part of Monte Carlo draws
+ndraws=3*ndraws2 % a total number of Monte Carlo draws
+nstarts=3; % number of starting points
+imndraws = nstarts*ndraws2;
+tdf = 3; % degrees of freedom for t-dist
+ga = tdf/2; % asymmetry parameter in Gamma
+gb = 2/tdf; % normalized parameter in Gamma
+%
+%* =================================================
+%* ====== End of the script ========================
+%* =================================================
+
+
+if (q_m==12)
+ nStart=(yrStart-yrBin)*12+qmStart-qmBin; % positive number of months at the start
+ nEnd=(yrEnd-yrFin)*12+qmEnd-qmFin; % negative number of months towards end
+elseif (q_m==4)
+ nStart=(yrStart-yrBin)*4+qmStart-qmBin; % positive number of months at the start
+ nEnd=(yrEnd-yrFin)*4+qmEnd-qmFin; % negative number of months towards end
+else
+ disp('Warning: this code is only good for monthly/quarterly data!!!')
+ return
+end
+%
+if nEnd>0 | nStart<0
+ disp('Warning: this particular sample consider is out of bounds of the data!!!')
+ return
+end
+%
+xdgel=xdd(nStart+1:nData+nEnd,vlist); % gel: general term for selected xdd
+xdata=xdd(nStart+1:nData,vlist);
+
+[Gb,Sbd,Bh,SpH,fss,ndobs,phi,y,nvar,ncoef,xxhpc,a0indx,na0p,...
+ idmat0,idmatpp] = szasbvar(idfile,q_m,lags,nSample,nhp,xdgel);
+
+% * the largest matrix in this file <<>>
+yforew = zeros(ndraws,forep*nvar); % preallocating
+yforeqgw = zeros(ndraws,forepq*nvar); % preallocating
+yforeCalygw = zeros(ndraws,forepy*nvar); % preallocating
+% * the largest matrix in this file <<>>
+%%imfcnt = zeros(ninv+2,imstp*nvar^2); % cnt: count
+
+load idenml % xhat ghat fhat, etc.
+%load outiden % xhat ghat fhat
+
+%==================
+% Impulse responses first
+%==================
+%A0 = zeros(nvar);
+%A0(a0indx)=xhat;
+%A0(4,2) = -xhat(7); % output in MD
+%A0(5,2)=-xhat(7); % price in MD
+A0in = inv(A0);
+swish = A0in'; % each row corresponds to an equation
+%Bh = Hm1t; % no longer have Hm1t in this new szasbvar
+
+% ** impulse responses
+nn = [nvar lags imstp];
+imf = zimpulse(Bh,swish,nn); % in the form that is congenial to RATS
+%[vd,str,imf] = errors(Bh,swish,nn);
+
+scaleout = imcgraph(imf,nvar,imstp,xlab,ylab)
+
+
+%%%%
+%$$$ Out-of-sample forecasts. Note: Hm1t does not change with A0.
+%%%%
+%
+% ** out-of-sample forecast, from 82:4 to 84:3 (flp+1:flp+forep)
+% * updating the last row of X (phi) with the current (last row of) y.
+phil = phi(size(phi,1),:);
+phil(nvar+1:ncoef-1) = phil(1:ncoef-1-nvar);
+phil(1:nvar) = y(size(y,1),:);
+ylast = y(size(y,1),:);
+indx12 = size(y,1)-q_m+1:size(y,1);
+ylast12 = y(indx12,:); % last 12 months data
+nn = [nvar lags forep];
+%
+yfore = forecast(Bh,phil,nn); % forep-by-nvar
+
+
+%>>>>>>>>>>>>>>>
+yforel=yfore;
+yforel(:,vlistlog) = exp(yfore(:,vlistlog));
+figure;
+t2=1:forep;
+for i = 1:nvar
+ subplot(nvar/2,2,i)
+ plot(t2,yforel(:,i),'--')
+ %title('solid-actual, dotted-forecast');
+ %title(eval(['forelabel']));
+ %ylabel(eval(['x' int2str(i)]));
+ ylabel(char(ylab(i)))
+end
+%<<<<<<<<<<<<<<<
+
+%%%%%%%%
+%
+%% See Zha's note "Forecast (1)" p. 5, RATS manual (some errors in RATS), etc.
+%
+%% Some notations: y(t+1) = y(t)B1 + e(t+1)inv(A0). e(t+1) is 1-by-n.
+%% Let r(t+1)=e(t+1)inv(A0) + e(t+2)C + .... where inv(A0) is impulse
+%% response at t=1, C at t=2, etc. The row of inv(A0) or C is
+%% all responses to one shock.
+%% Let r be q-by-1 (such as r(1) = r(t+1)
+%% = y(t+1) (constrained) - y(t+1) (forecast)).
+%% Use impulse responses to find out R (k-by-q) where k=nvar*nsteps
+%% where nsteps the largest constrained step. The key of the program
+%% is to creat R using impulse responses
+%% Optimal solution for shock e where R'*e=r and e is k-by-1 is
+%% e = R*inv(R'*R)*r.
+%
+%%%%%%%%
+
+nconstr=4; % q: 4 years -- 4*12 months
+eq_ms = 2; % location of MS equation
+%eq_ms = []; % all shocks
+%*** initializing
+stepcon=cell(nconstr,1); % initializing, value y conditioned
+valuecon=zeros(nconstr,1); % initializing, value y conditioned
+varcon=zeros(nconstr,1); % initializing, endogous variables conditioned
+%
+stepcon{1}=[1:12]'; % average over 12 months.
+stepcon{2}=[13:24]'; % average over 12 months.
+stepcon{3}=[25:36]'; % average over 12 months.
+stepcon{4}=[37:48]'; % average over 12 months.
+%
+%for i=1:nconstr
+% stepcon{i}=i;
+%end
+%
+chk1 = mean(yfore(stepcon{1},3))
+chk2 = mean(yfore(stepcon{2},3))
+chk3 = mean(yfore(stepcon{3},3))
+chk4 = mean(yfore(stepcon{4},3))
+Ro=[chk1 chk2 chk3 chk4];
+%
+chk1 = exp( (sum(yfore(stepcon{1},5))-sum(ylast12(:,5))) ./ q_m )
+chk2 = exp( (sum(yfore(stepcon{2},5))-sum(yfore(stepcon{1},5))) ./ q_m )
+chk3 = exp( (sum(yfore(stepcon{3},5))-sum(yfore(stepcon{2},5))) ./ q_m )
+chk4 = exp( (sum(yfore(stepcon{4},5))-sum(yfore(stepcon{3},5))) ./ q_m )
+%
+%valuecon(:)=0.055;
+%
+%>>>>>>>>>>>>>>>>> E: Condition on funds rate path >>>>>>>>>>>>> Toggle
+%delta=0.0010;
+%valuecon(1) = mean(yfore(stepcon{1},3))+2*delta;
+%valuecon(2) = mean(yfore(stepcon{2},3))+2*delta;
+%valuecon(3) = mean(yfore(stepcon{3},3))-2*delta;
+%valuecon(4) = mean(yfore(stepcon{4},3))-2*delta;
+%valuecon(1) = 0.055;
+%valuecon(2) = 0.050;
+%valuecon(3) = 0.0475;
+%valuecon(4) = 0.045;
+%>>>>>>>>>>>>>>>>> E: Condition on funds rate path >>>>>>>>>>>>>
+%
+%<<<<<<<<<<<<<<<< B: Condition on inflation path <<<<<<<<<< Toggle
+%delta=0.0010;
+%valuecon(1)=mean(ylast12(:,5))+log(chk1-0*delta);
+%valuecon(2)=valuecon(1)+log(chk2-2*delta);
+%valuecon(3)=valuecon(2)+log(chk3-6*delta);
+%valuecon(4)=valuecon(3)+log(chk4-12*delta);
+% % 5: CPI; 2.5%: annual inflation over 12 months, geometric means
+%$$$ very good results -- following
+valuecon(1)=mean(ylast12(:,5))+log(chk1);
+valuecon(2)=valuecon(1)+log(1.020);
+valuecon(3)=valuecon(2)+log(1.02);
+valuecon(4)=valuecon(3)+log(1.02);
+% % 5: CPI; 2.5%: annual inflation over 12 months, geometric means
+%<<<<<<<<<<<<<<<< E: Condition on inflation path <<<<<<<<<<
+
+nstepsm = 0; % initializing, the maximum step in all constraints
+for i=1:nconstr
+ nstepsm = max([nstepsm max(stepcon{i})]);
+end
+varcon(:)=5; % 3: FFR; 5: CPI
+%
+imf3=reshape(imf,size(imf,1),nvar,nvar);
+ % imf3: row-steps, column-nvar responses, 3rd dimension-nvar shocks
+imf3s=permute(imf3,[1 3 2]);
+ % imf3s: permuted so that row-steps, column-nvar shocks,
+ % 3rd dimension-nvar responses
+
+[yhat,Estr] = fidencond(valuecon,stepcon,varcon,nconstr,nstepsm,nvar,lags,...
+ yfore,imf3s,phil,Bh,eq_ms);
+
+chk1 = mean(yhat(stepcon{1},3))
+chk2 = mean(yhat(stepcon{2},3))
+chk3 = mean(yhat(stepcon{3},3))
+chk4 = mean(yhat(stepcon{4},3))
+Rh=[chk1 chk2 chk3 chk4];
+
+chk1 = exp( (sum(yhat(stepcon{1},5))-sum(ylast12(:,5))) ./ q_m )
+chk2 = exp( (sum(yhat(stepcon{2},5))-sum(yhat(stepcon{1},5))) ./ q_m )
+chk3 = exp( (sum(yhat(stepcon{3},5))-sum(yhat(stepcon{2},5))) ./ q_m )
+chk4 = exp( (sum(yhat(stepcon{4},5))-sum(yhat(stepcon{3},5))) ./ q_m )
+
+%chk1 = mean(yhat(1:12,3))
+%chk2 = mean(yhat(13:24,3))
+%chk3 = mean(yhat(25:36,3))
+%chk4 = mean(yhat(36:48,3))
+%Rh=[chk1 chk2 chk3 chk4];
+
+%chk1 = exp( (sum(yhat(1:12,5))-sum(ylast12(:,5))) ./ q_m )
+%chk2 = exp( (sum(yhat(13:24,5))-sum(yhat(1:12,5))) ./ q_m )
+%chk3 = exp( (sum(yhat(25:36,5))-sum(yhat(13:24,5))) ./ q_m )
+%chk4 = exp( (sum(yhat(37:48,5))-sum(yhat(25:36,5))) ./ q_m )
+
+
+idiff=mean(yfore(:,3))-mean(yhat(:,3))
+mean(yfore(:,3))
+mean(yhat(:,3))
+figure
+plot(1:48,yfore(:,3),1:48,yhat(:,3),':')
+figure
+plot(1:4,Ro,1:4,Rh,':')
+
+%>>>>>>>>>>>>>>>
+yhatl=yhat;
+yhatl(:,vlistlog) = exp(yhat(:,vlistlog));
+figure;
+t2=1:forep;
+for i = 1:nvar
+ subplot(nvar/2,2,i)
+ plot(t2,yhatl(:,i),'--')
+ %title('solid-actual, dotted-forecast');
+ %title(eval(['forelabel']));
+ %ylabel(eval(['x' int2str(i)]));
+ ylabel(char(ylab(i)))
+end
+%<<<<<<<<<<<<<<<
+
+
+% inputs needed.
+%yfore=yhat;
+
+%===================================================
+%%% Converting to calendar years and all at level
+%===================================================
+[yforeml,yforeqgml,yforeCalygml] = fore_cal(yhat,xdata,nvar,nSample,...
+ nSampleCal,forep,forepq,forepy,q_m,qmEnd,vlist,vlistlog);
+
+%==================
+% Note
+%=================
+% ? 1--median; l--lower bound; h--upper bound: between the bound: 2/3 probability
+% yfore? % monthly, level
+% yforeqg? % quarterly, growth rate
+% yforeCalyg? % calendar year, growth rate
+
+%save outw ndraws yfore1 yforeqg1 yforeCalyg1 yforel yforeqgl yforeh yforeqgh ...
+% yforeCalygl yforeCalygh IUbeta
+
+yforeCalygml
+
+
+%----------------------------------------------------------------------
+%================= Graphics =====================
+%----------------------------------------------------------------------
+%
+
+%[yactCalyg,yforeCalygml,yAg,yFg] = fore_gh(xdata,nvar,nSample,nSampleCal,...
+% yforeml,yforeqgml,yforeCalygml,actup,actupq,actupy,...
+% vlist,vlistlog,vlistper,q_m,forep,ylab);
+
+xinput = cell(16,1);
+xinput{1}=xdata; xinput{2}=nvar; xinput{3}=nSample; xinput{4}=nSampleCal;
+xinput{5}=yforeml; xinput{6}=yforeqgml; xinput{7}=yforeCalygml; xinput{8}=actup;
+xinput{9}=actupq; xinput{10}=actupy; xinput{11}=vlist; xinput{12}=vlistlog;
+xinput{13}=vlistper; xinput{14}=q_m; xinput{15}=forep; xinput{16}=ylab;
+[yactCalyg,yforeCalygml,yAg,yFg] = fore_gh(xinput);
+
+
+% Key Macroeconomic Variables: GDP, CPI, U
+%******* From Goldbook July 1-2, 1997 FOMC
+yforeBlue = [
+ 3.6 2.4 5.0
+ 2.5 2.6 5.0
+ ]; % real GDP, CPI-U, U,
+yforeMacro = [
+ 3.7 2.3 4.9
+ 2.1 2.4 4.9
+ ]; % real GDP, CPI-U, U
+yforeGold = [
+ 3.8 2.4 5.0
+ 2.5 2.5 4.8
+ 2.4 2.7 4.7
+ ]; % real GDP, CPI-U, U
+t3 = yAg+1:yAg+length(yforeBlue(:,1));
+t4 = yAg+1:yAg+length(yforeGold(:,1));
+%
+keyindx = [4:nvar 3 2]; % GdP, CPI, U, FFR, M2
+count=0;
+t1 = 1:yAg;
+t2 = yAg:yAg+yFg;
+for i = keyindx
+ count = count+1;
+ subplot(3,2,count)
+ %plot(t1,yactqg(:,i),t2,yforeqg(:,i),':')
+ if (i==3) | (i==2)
+ plot(t1,yactCalyg(:,i),t2,[yactCalyg(length(t1),i);yforeCalygml(:,i)],'--')
+ %title('solid-actual, dotted-forecast');
+ %xlabel(eval(['forelabel']));
+ %ylabel(eval(['x' int2str(i)]));
+ ylabel(char(ylab(i)))
+ else
+ plot(t1,yactCalyg(:,i),t2,[yactCalyg(length(t1),i);yforeCalygml(:,i)],'--',...
+ t3,yforeBlue(:,count),'o',t3,yforeMacro(:,count),'d',...
+ t4,yforeGold(:,count),'^')
+ %title('solid-actual, dotted-forecast');
+ %xlabel(eval(['forelabel']));
+ %ylabel(eval(['x' int2str(i)]));
+ ylabel(char(ylab(i)))
+ end
+end
+
+actual=yactCalyg(:,keyindx) % GDP, CPI, U, M2
+mode=yforeCalygml(:,keyindx) % GDP, CPI, U, M2
+%low=yforeCalygl(:,keyindx)
+%high=yforeCalygh(:,keyindx)
+yforeBlue
+yforeMacro
+yforeGold
diff --git a/MatlabFiles/chol2.m b/MatlabFiles/chol2.m
index 619c829f0b00e132bd3de4a359ac10514dfee09a..e9024ec30cb2f929ee226e807cbf43dd189d80b1 100644
--- a/MatlabFiles/chol2.m
+++ b/MatlabFiles/chol2.m
@@ -1,30 +1,30 @@
-function R = chol2(A)
-% R = chol2(A)
-%
-% Returns an upper triangular R such that % R * R' is a factorization of
-% a symmetric, positive definite A
-%
-% Written by Tao Zha, July 1996
-% Copyright (C) 1996-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-%* The following two lines give another expression of the same result
-%%function [R,p] = chol2(A)
-%%[R,p] = chol(fliplr(flipud(A)));
-
-R = chol(fliplr(flipud(A)));
-R = fliplr(flipud(R))';
+function R = chol2(A)
+% R = chol2(A)
+%
+% Returns an upper triangular R such that % R * R' is a factorization of
+% a symmetric, positive definite A
+%
+% Written by Tao Zha, July 1996
+%
+% Copyright (C) 1996-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+%* The following two lines give another expression of the same result
+%%function [R,p] = chol2(A)
+%%[R,p] = chol(fliplr(flipud(A)));
+
+R = chol(fliplr(flipud(A)));
+R = fliplr(flipud(R))';
diff --git a/MatlabFiles/clgls.m b/MatlabFiles/clgls.m
index b6bf9c63db99d157af3529185aa802660e3ab185..9435297ecee38791402a755680e1626760b98111 100644
--- a/MatlabFiles/clgls.m
+++ b/MatlabFiles/clgls.m
@@ -1,66 +1,66 @@
-function [bhat,Vq,ACt,uqhat,q2] = clgls(a,yq,Xq,C,qm,mT,qT)
-%function [bhat,Vq,ACt,uqhat,q2] = clgls(a,yq,Xq,C,qm,mT,qT)
-% bhat = inv[Xq' * inv(Vq) * Xq] * Xq' * inv(Vq) * yq
-% Vq = C*A*C'
-% ACt = A*C'
-% uqhat = yq - Xq * bhat
-%
-% Written by E.M. Leeper
-% Modified by T. Zha, 5/6/97
-% Copyright (C) 1997-2012 Eric Leeper and Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-%*** The following creation of Ahat may be inefficient, T.Z., 5/7/97
-Ahat = eye(mT,mT);
-i = 1;
-arow = zeros(1,mT-1);
-while i <= mT-1
- arow(1,i) = a^i;
- i = i + 1;
-end
-i = 1;
-while i <= mT-1
- Ahat(i,i+1:mT) = arow(1,1:mT-i);
- i = i + 1;
-end
-Ahat = Ahat + Ahat' - eye(mT,mT);
-
-
-
-%*** GLS to estimate bhat
-ACt = Ahat*C';
-Vq = C*ACt;
-Xqt = Xq';
-%CACinv = inv(CAhatC); commented out by T.Z., not necessary
-%XqCACinv = Xq' * CACinv; commented out by T.Z., not necessary
-bhat = ((Xqt/Vq)*Xq)\(Xqt*(Vq\yq));
-uqhat = yq - Xq * bhat;
-
-%*** compute the new quarterly coefficeint "q2"
-uqlag = zeros(qT-1,1);
-uqlag = uqhat(1:qT-1,1);
-uqc = uqhat(2:qT,1);
-[u d v]=svd(uqlag,0); %trial
-dinv = 1./diag(d); % inv(diag(d))
-vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
-vdinv=v.*(ones(size(v,2),1)*dinv'); %trial
-xtxinv = vdinv*vdinv';
-uy = u'*uqc; %trial
-xty = vd*uy; %trial
-%Bh = xtx\xty; %inv(X'X)*(X'Y), k*m (ncoe*nvar).
-q2 = xtxinv*xty; % initial q
\ No newline at end of file
+function [bhat,Vq,ACt,uqhat,q2] = clgls(a,yq,Xq,C,qm,mT,qT)
+%function [bhat,Vq,ACt,uqhat,q2] = clgls(a,yq,Xq,C,qm,mT,qT)
+% bhat = inv[Xq' * inv(Vq) * Xq] * Xq' * inv(Vq) * yq
+% Vq = C*A*C'
+% ACt = A*C'
+% uqhat = yq - Xq * bhat
+%
+% Written by E.M. Leeper
+% Modified by T. Zha, 5/6/97
+%
+% Copyright (C) 1997-2012 Eric Leeper and Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+%*** The following creation of Ahat may be inefficient, T.Z., 5/7/97
+Ahat = eye(mT,mT);
+i = 1;
+arow = zeros(1,mT-1);
+while i <= mT-1
+ arow(1,i) = a^i;
+ i = i + 1;
+end
+i = 1;
+while i <= mT-1
+ Ahat(i,i+1:mT) = arow(1,1:mT-i);
+ i = i + 1;
+end
+Ahat = Ahat + Ahat' - eye(mT,mT);
+
+
+
+%*** GLS to estimate bhat
+ACt = Ahat*C';
+Vq = C*ACt;
+Xqt = Xq';
+%CACinv = inv(CAhatC); commented out by T.Z., not necessary
+%XqCACinv = Xq' * CACinv; commented out by T.Z., not necessary
+bhat = ((Xqt/Vq)*Xq)\(Xqt*(Vq\yq));
+uqhat = yq - Xq * bhat;
+
+%*** compute the new quarterly coefficeint "q2"
+uqlag = zeros(qT-1,1);
+uqlag = uqhat(1:qT-1,1);
+uqc = uqhat(2:qT,1);
+[u d v]=svd(uqlag,0); %trial
+dinv = 1./diag(d); % inv(diag(d))
+vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
+vdinv=v.*(ones(size(v,2),1)*dinv'); %trial
+xtxinv = vdinv*vdinv';
+uy = u'*uqc; %trial
+xty = vd*uy; %trial
+%Bh = xtx\xty; %inv(X'X)*(X'Y), k*m (ncoe*nvar).
+q2 = xtxinv*xty; % initial q
diff --git a/MatlabFiles/clmonq.m b/MatlabFiles/clmonq.m
index a0d86c98d196ade7c1dab65d6c5d6a524bdd46f7..93c9e11dfffaecc9f0f8eeaf7922947b8a127dbe 100644
--- a/MatlabFiles/clmonq.m
+++ b/MatlabFiles/clmonq.m
@@ -1,41 +1,41 @@
-function a = clmonq(q)
-% function a = monq(q)
-% Find the monthly AR coefficient for interpolated data given
-% an estimate of the quarterly AR coefficient
-% Written by E.M. Leeper
-%
-% Copyright (C) 1997-2012 E. M. Leeper and Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-% First take the quarterly AR coefficient, q, and then
-% seek the root, a, that solves
-% q = (a^5 + 2 a^4 + 3 a^3 + 2 a^2 + a) / (2 a^2 + 4 a + 3) (1)
-
-% if n = numerator poly and d = denominator poly in (1)
-n = [1 2 3 2 1 0];
-d = [2 4 3];
-ln = length(n);
-ld = length(d);
-pad = ln - ld;
-dd = [zeros(1,pad) d];
-qdd = q.*dd;
-newpol = n - qdd;
-r = roots(newpol);
-for i = 1:length(r)
- if imag(r(i)) == 0
- a = r(i);
- end
-end
+function a = clmonq(q)
+% function a = monq(q)
+% Find the monthly AR coefficient for interpolated data given
+% an estimate of the quarterly AR coefficient
+% Written by E.M. Leeper
+%
+%
+% Copyright (C) 1997-2012 E. M. Leeper and Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+% First take the quarterly AR coefficient, q, and then
+% seek the root, a, that solves
+% q = (a^5 + 2 a^4 + 3 a^3 + 2 a^2 + a) / (2 a^2 + 4 a + 3) (1)
+
+% if n = numerator poly and d = denominator poly in (1)
+n = [1 2 3 2 1 0];
+d = [2 4 3];
+ln = length(n);
+ld = length(d);
+pad = ln - ld;
+dd = [zeros(1,pad) d];
+qdd = q.*dd;
+newpol = n - qdd;
+r = roots(newpol);
+for i = 1:length(r)
+ if imag(r(i)) == 0
+ a = r(i);
+ end
+end
diff --git a/MatlabFiles/contents.m b/MatlabFiles/contents.m
deleted file mode 100644
index ae8be2ca59647a3a224bd1887d43173b3d77cd41..0000000000000000000000000000000000000000
--- a/MatlabFiles/contents.m
+++ /dev/null
@@ -1,21 +0,0 @@
-% Chris Sims and Tao Zha's .m files.
-% Vertion 1.1 March 1998
-% optimization, impulse, forecast, hessian, gradient
-%
-% Copyright (c) by Sims and Zha
-% Copyright (C) 1997-2012 Christopher Sims and Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
\ No newline at end of file
diff --git a/MatlabFiles/csminit.m b/MatlabFiles/csminit.m
index 6f790c17b0dd1c04381be456e83e481e351b5de6..ec75890c1447193a88034f132f54d477e019c78a 100644
--- a/MatlabFiles/csminit.m
+++ b/MatlabFiles/csminit.m
@@ -1,215 +1,215 @@
-function [fhat,xhat,fcount,retcode] = csminit(fcn,x0,f0,g0,badg,H0,varargin)
-% [fhat,xhat,fcount,retcode] = csminit(fcn,x0,f0,g0,badg,H0,...
-% P1,P2,P3,P4,P5,P6,P7,P8)
-% retcodes: 0, normal step. 5, largest step still improves too fast.
-% 4,2 back and forth adjustment of stepsize didn't finish. 3, smallest
-% stepsize still improves too slow. 6, no improvement found. 1, zero
-% gradient.
-%---------------------
-% Modified 7/22/96 to omit variable-length P list, for efficiency and compilation.
-% Places where the number of P's need to be altered or the code could be returned to
-% its old form are marked with ARGLIST comments.
-%
-% Fixed 7/17/93 to use inverse-hessian instead of hessian itself in bfgs
-% update.
-%
-% Fixed 7/19/93 to flip eigenvalues of H to get better performance when
-% it's not psd.
-%
-% Fixed 02/19/05 to correct for low angle problems.
-%
-%tailstr = ')';
-%for i=nargin-6:-1:1
-% tailstr=[ ',P' num2str(i) tailstr];
-%end
-% Copyright (C) 1997-2012 Christopher A. Sims
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-dispIndx = 1; % 1: turn on all the diplays on the screen; 0: turn off (Added by T. Zha)
-
-
-%ANGLE = .03; % when output of this variable becomes negative, we have wrong analytical graident
-ANGLE = .005; % works for identified VARs and OLS
-%THETA = .03;
-THETA = .3; %(0<THETA<.5) THETA near .5 makes long line searches, possibly fewer iterations.
-FCHANGE = 1000;
-MINLAMB = 1e-9;
-% fixed 7/15/94
-% MINDX = .0001;
-% MINDX = 1e-6;
-MINDFAC = .01;
-fcount=0;
-lambda=1;
-xhat=x0;
-f=f0;
-fhat=f0;
-g = g0;
-gnorm = norm(g);
-%
-if (gnorm < 1.e-12) & ~badg % put ~badg 8/4/94
- retcode =1;
- dxnorm=0;
- % gradient convergence
-else
- % with badg true, we don't try to match rate of improvement to directional
- % derivative. We're satisfied just to get some improvement in f.
- %
- %if(badg)
- % dx = -g*FCHANGE/(gnorm*gnorm);
- % dxnorm = norm(dx);
- % if dxnorm > 1e12
- % disp('Bad, small gradient problem.')
- % dx = dx*FCHANGE/dxnorm;
- % end
- %else
- % Gauss-Newton step;
- %---------- Start of 7/19/93 mod ---------------
- %[v d] = eig(H0);
- %toc
- %d=max(1e-10,abs(diag(d)));
- %d=abs(diag(d));
- %dx = -(v.*(ones(size(v,1),1)*d'))*(v'*g);
-% toc
- dx = -H0*g;
-% toc
- dxnorm = norm(dx);
- if dxnorm > 1e12
- if dispIndx, disp('Near-singular H problem.'), end
- dx = dx*FCHANGE/dxnorm;
- end
- dfhat = dx'*g0;
- %end
- %
- %
- if ~badg
- % test for alignment of dx with gradient and fix if necessary
- a = -dfhat/(gnorm*dxnorm);
- if a<ANGLE
- dx = dx - (ANGLE*dxnorm/gnorm+dfhat/(gnorm*gnorm))*g;
- % suggested alternate code: ---------------------
- dx = dx*dxnorm/norm(dx); % Added 02/19/05 by CAS. This keeps scale invariant to the angle correction
- % ------------------------------------------------
- dfhat = dx'*g;
- % dxnorm = norm(dx); % Removed 02/19/05 by CAS. This line unnecessary with modification that keeps scale invariant
- if dispIndx, disp(sprintf('Correct for low angle: %g',a)), end
- end
- end
- if dispIndx, disp(sprintf('Predicted improvement: %18.9f',-dfhat/2)), end
- %
- % Have OK dx, now adjust length of step (lambda) until min and
- % max improvement rate criteria are met.
- done=0;
- factor=3;
- shrink=1;
- lambdaMin=0;
- lambdaMax=inf;
- lambdaPeak=0;
- fPeak=f0;
- lambdahat=0;
- while ~done
- if size(x0,2)>1
- dxtest=x0+dx'*lambda;
- else
- dxtest=x0+dx*lambda;
- end
- % home
- f = feval(fcn,dxtest,varargin{:});
- %ARGLIST
- %f = feval(fcn,dxtest,P1,P2,P3,P4,P5,P6,P7,P8,P9,P10,P11,P12,P13);
- % f = feval(fcn,x0+dx*lambda,P1,P2,P3,P4,P5,P6,P7,P8);
- if dispIndx, disp(sprintf('lambda = %10.5g; f = %20.7f',lambda,f )), end
- %debug
- %disp(sprintf('Improvement too great? f0-f: %g, criterion: %g',f0-f,-(1-THETA)*dfhat*lambda))
- if f<fhat
- fhat=f;
- xhat=dxtest;
- lambdahat = lambda;
- end
- fcount=fcount+1;
- shrinkSignal = (~badg & (f0-f < max([-THETA*dfhat*lambda 0]))) | (badg & (f0-f) < 0) ;
- growSignal = ~badg & ( (lambda > 0) & (f0-f > -(1-THETA)*dfhat*lambda) );
- if shrinkSignal & ( (lambda>lambdaPeak) | (lambda<0) )
- if (lambda>0) & ((~shrink) | (lambda/factor <= lambdaPeak))
- shrink=1;
- factor=factor^.6;
- while lambda/factor <= lambdaPeak
- factor=factor^.6;
- end
- %if (abs(lambda)*(factor-1)*dxnorm < MINDX) | (abs(lambda)*(factor-1) < MINLAMB)
- if abs(factor-1)<MINDFAC
- if abs(lambda)<4
- retcode=2;
- else
- retcode=7;
- end
- done=1;
- end
- end
- if (lambda<lambdaMax) & (lambda>lambdaPeak)
- lambdaMax=lambda;
- end
- lambda=lambda/factor;
- if abs(lambda) < MINLAMB
- if (lambda > 0) & (f0 <= fhat)
- % try going against gradient, which may be inaccurate
- if dispIndx, lambda = -lambda*factor^6, end
- else
- if lambda < 0
- retcode = 6;
- else
- retcode = 3;
- end
- done = 1;
- end
- end
- elseif (growSignal & lambda>0) | (shrinkSignal & ((lambda <= lambdaPeak) & (lambda>0)))
- if shrink
- shrink=0;
- factor = factor^.6;
- %if ( abs(lambda)*(factor-1)*dxnorm< MINDX ) | ( abs(lambda)*(factor-1)< MINLAMB)
- if abs(factor-1)<MINDFAC
- if abs(lambda)<4
- retcode=4;
- else
- retcode=7;
- end
- done=1;
- end
- end
- if ( f<fPeak ) & (lambda>0)
- fPeak=f;
- lambdaPeak=lambda;
- if lambdaMax<=lambdaPeak
- lambdaMax=lambdaPeak*factor*factor;
- end
- end
- lambda=lambda*factor;
- if abs(lambda) > 1e20;
- retcode = 5;
- done =1;
- end
- else
- done=1;
- if factor < 1.2
- retcode=7;
- else
- retcode=0;
- end
- end
- end
-end
-if dispIndx, disp(sprintf('Norm of dx %10.5g', dxnorm)), end
+function [fhat,xhat,fcount,retcode] = csminit(fcn,x0,f0,g0,badg,H0,varargin)
+% [fhat,xhat,fcount,retcode] = csminit(fcn,x0,f0,g0,badg,H0,...
+% P1,P2,P3,P4,P5,P6,P7,P8)
+% retcodes: 0, normal step. 5, largest step still improves too fast.
+% 4,2 back and forth adjustment of stepsize didn't finish. 3, smallest
+% stepsize still improves too slow. 6, no improvement found. 1, zero
+% gradient.
+%---------------------
+% Modified 7/22/96 to omit variable-length P list, for efficiency and compilation.
+% Places where the number of P's need to be altered or the code could be returned to
+% its old form are marked with ARGLIST comments.
+%
+% Fixed 7/17/93 to use inverse-hessian instead of hessian itself in bfgs
+% update.
+%
+% Fixed 7/19/93 to flip eigenvalues of H to get better performance when
+% it's not psd.
+%
+% Fixed 02/19/05 to correct for low angle problems.
+%
+%tailstr = ')';
+%for i=nargin-6:-1:1
+% tailstr=[ ',P' num2str(i) tailstr];
+%end
+%
+% Copyright (C) 1997-2012 Christopher A. Sims
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+dispIndx = 1; % 1: turn on all the diplays on the screen; 0: turn off (Added by T. Zha)
+
+
+%ANGLE = .03; % when output of this variable becomes negative, we have wrong analytical graident
+ANGLE = .005; % works for identified VARs and OLS
+%THETA = .03;
+THETA = .3; %(0<THETA<.5) THETA near .5 makes long line searches, possibly fewer iterations.
+FCHANGE = 1000;
+MINLAMB = 1e-9;
+% fixed 7/15/94
+% MINDX = .0001;
+% MINDX = 1e-6;
+MINDFAC = .01;
+fcount=0;
+lambda=1;
+xhat=x0;
+f=f0;
+fhat=f0;
+g = g0;
+gnorm = norm(g);
+%
+if (gnorm < 1.e-12) & ~badg % put ~badg 8/4/94
+ retcode =1;
+ dxnorm=0;
+ % gradient convergence
+else
+ % with badg true, we don't try to match rate of improvement to directional
+ % derivative. We're satisfied just to get some improvement in f.
+ %
+ %if(badg)
+ % dx = -g*FCHANGE/(gnorm*gnorm);
+ % dxnorm = norm(dx);
+ % if dxnorm > 1e12
+ % disp('Bad, small gradient problem.')
+ % dx = dx*FCHANGE/dxnorm;
+ % end
+ %else
+ % Gauss-Newton step;
+ %---------- Start of 7/19/93 mod ---------------
+ %[v d] = eig(H0);
+ %toc
+ %d=max(1e-10,abs(diag(d)));
+ %d=abs(diag(d));
+ %dx = -(v.*(ones(size(v,1),1)*d'))*(v'*g);
+% toc
+ dx = -H0*g;
+% toc
+ dxnorm = norm(dx);
+ if dxnorm > 1e12
+ if dispIndx, disp('Near-singular H problem.'), end
+ dx = dx*FCHANGE/dxnorm;
+ end
+ dfhat = dx'*g0;
+ %end
+ %
+ %
+ if ~badg
+ % test for alignment of dx with gradient and fix if necessary
+ a = -dfhat/(gnorm*dxnorm);
+ if a<ANGLE
+ dx = dx - (ANGLE*dxnorm/gnorm+dfhat/(gnorm*gnorm))*g;
+ % suggested alternate code: ---------------------
+ dx = dx*dxnorm/norm(dx); % Added 02/19/05 by CAS. This keeps scale invariant to the angle correction
+ % ------------------------------------------------
+ dfhat = dx'*g;
+ % dxnorm = norm(dx); % Removed 02/19/05 by CAS. This line unnecessary with modification that keeps scale invariant
+ if dispIndx, disp(sprintf('Correct for low angle: %g',a)), end
+ end
+ end
+ if dispIndx, disp(sprintf('Predicted improvement: %18.9f',-dfhat/2)), end
+ %
+ % Have OK dx, now adjust length of step (lambda) until min and
+ % max improvement rate criteria are met.
+ done=0;
+ factor=3;
+ shrink=1;
+ lambdaMin=0;
+ lambdaMax=inf;
+ lambdaPeak=0;
+ fPeak=f0;
+ lambdahat=0;
+ while ~done
+ if size(x0,2)>1
+ dxtest=x0+dx'*lambda;
+ else
+ dxtest=x0+dx*lambda;
+ end
+ % home
+ f = feval(fcn,dxtest,varargin{:});
+ %ARGLIST
+ %f = feval(fcn,dxtest,P1,P2,P3,P4,P5,P6,P7,P8,P9,P10,P11,P12,P13);
+ % f = feval(fcn,x0+dx*lambda,P1,P2,P3,P4,P5,P6,P7,P8);
+ if dispIndx, disp(sprintf('lambda = %10.5g; f = %20.7f',lambda,f )), end
+ %debug
+ %disp(sprintf('Improvement too great? f0-f: %g, criterion: %g',f0-f,-(1-THETA)*dfhat*lambda))
+ if f<fhat
+ fhat=f;
+ xhat=dxtest;
+ lambdahat = lambda;
+ end
+ fcount=fcount+1;
+ shrinkSignal = (~badg & (f0-f < max([-THETA*dfhat*lambda 0]))) | (badg & (f0-f) < 0) ;
+ growSignal = ~badg & ( (lambda > 0) & (f0-f > -(1-THETA)*dfhat*lambda) );
+ if shrinkSignal & ( (lambda>lambdaPeak) | (lambda<0) )
+ if (lambda>0) & ((~shrink) | (lambda/factor <= lambdaPeak))
+ shrink=1;
+ factor=factor^.6;
+ while lambda/factor <= lambdaPeak
+ factor=factor^.6;
+ end
+ %if (abs(lambda)*(factor-1)*dxnorm < MINDX) | (abs(lambda)*(factor-1) < MINLAMB)
+ if abs(factor-1)<MINDFAC
+ if abs(lambda)<4
+ retcode=2;
+ else
+ retcode=7;
+ end
+ done=1;
+ end
+ end
+ if (lambda<lambdaMax) & (lambda>lambdaPeak)
+ lambdaMax=lambda;
+ end
+ lambda=lambda/factor;
+ if abs(lambda) < MINLAMB
+ if (lambda > 0) & (f0 <= fhat)
+ % try going against gradient, which may be inaccurate
+ if dispIndx, lambda = -lambda*factor^6, end
+ else
+ if lambda < 0
+ retcode = 6;
+ else
+ retcode = 3;
+ end
+ done = 1;
+ end
+ end
+ elseif (growSignal & lambda>0) | (shrinkSignal & ((lambda <= lambdaPeak) & (lambda>0)))
+ if shrink
+ shrink=0;
+ factor = factor^.6;
+ %if ( abs(lambda)*(factor-1)*dxnorm< MINDX ) | ( abs(lambda)*(factor-1)< MINLAMB)
+ if abs(factor-1)<MINDFAC
+ if abs(lambda)<4
+ retcode=4;
+ else
+ retcode=7;
+ end
+ done=1;
+ end
+ end
+ if ( f<fPeak ) & (lambda>0)
+ fPeak=f;
+ lambdaPeak=lambda;
+ if lambdaMax<=lambdaPeak
+ lambdaMax=lambdaPeak*factor*factor;
+ end
+ end
+ lambda=lambda*factor;
+ if abs(lambda) > 1e20;
+ retcode = 5;
+ done =1;
+ end
+ else
+ done=1;
+ if factor < 1.2
+ retcode=7;
+ else
+ retcode=0;
+ end
+ end
+ end
+end
+if dispIndx, disp(sprintf('Norm of dx %10.5g', dxnorm)), end
diff --git a/MatlabFiles/csminitworksuntiil0205.m b/MatlabFiles/csminitworksuntiil0205.m
index 6e1e2805bcc9998db727d9f916b957034736d570..52b1b5c67fb3583d4f41b5c934ef7c33cfed0687 100644
--- a/MatlabFiles/csminitworksuntiil0205.m
+++ b/MatlabFiles/csminitworksuntiil0205.m
@@ -1,214 +1,214 @@
-function [fhat,xhat,fcount,retcode] = csminit(fcn,x0,f0,g0,badg,H0,varargin)
-% [fhat,xhat,fcount,retcode] = csminit(fcn,x0,f0,g0,badg,H0,...
-% P1,P2,P3,P4,P5,P6,P7,P8)
-% retcodes: 0, normal step. 5, largest step still improves too fast.
-% 4,2 back and forth adjustment of stepsize didn't finish. 3, smallest
-% stepsize still improves too slow. 6, no improvement found. 1, zero
-% gradient.
-%---------------------
-% Modified 7/22/96 to omit variable-length P list, for efficiency and compilation.
-% Places where the number of P's need to be altered or the code could be returned to
-% its old form are marked with ARGLIST comments.
-%
-% Fixed 7/17/93 to use inverse-hessian instead of hessian itself in bfgs
-% update.
-%
-% Fixed 7/19/93 to flip eigenvalues of H to get better performance when
-% it's not psd.
-% NOTE: The display on screen can be turned off by seeting dispIndx=0 in this
-% function. This option is used for the loop operation. T. Zha, 2 May 2000
-
-% Copyright (C) 1997-2012 C. A. Sims
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-dispIndx = 0; % 1: turn on all the diplays on the screen; 0: turn off (Added by T. Zha)
-
-%
-%tailstr = ')';
-%for i=nargin-6:-1:1
-% tailstr=[ ',P' num2str(i) tailstr];
-%end
-
-%ANGLE = .01; % when output of this variable becomes negative, we have wrong analytical graident
-ANGLE = .005; % works for identified VARs and OLS
-%THETA = .1; % works for OLS or other nonlinear functions
-THETA = .3; %(0<THETA<.5) THETA near .5 makes long line searches, possibly fewer iterations.
- % workds for identified VARs
-FCHANGE = 1000;
-MINLAMB = 1e-9;
-% fixed 7/15/94
-% MINDX = .0001;
-% MINDX = 1e-6;
-MINDFAC = .01;
-fcount=0;
-lambda=1;
-xhat=x0;
-f=f0;
-fhat=f0;
-g = g0;
-gnorm = norm(g);
-%
-if (gnorm < 1.e-12) & ~badg % put ~badg 8/4/94
- retcode =1;
- dxnorm=0;
- % gradient convergence
-else
- % with badg true, we don't try to match rate of improvement to directional
- % derivative. We're satisfied just to get some improvement in f.
- %
- %if(badg)
- % dx = -g*FCHANGE/(gnorm*gnorm);
- % dxnorm = norm(dx);
- % if dxnorm > 1e12
- % disp('Bad, small gradient problem.')
- % dx = dx*FCHANGE/dxnorm;
- % end
- %else
- % Gauss-Newton step;
- %---------- Start of 7/19/93 mod ---------------
- %[v d] = eig(H0);
- %toc
- %d=max(1e-10,abs(diag(d)));
- %d=abs(diag(d));
- %dx = -(v.*(ones(size(v,1),1)*d'))*(v'*g);
-% toc
- dx = -H0*g;
-% toc
- dxnorm = norm(dx);
- if dxnorm > 1e12
- if dispIndx, disp('Near-singular H problem.'), end
- dx = dx*FCHANGE/dxnorm;
- end
- dfhat = dx'*g0;
- %end
- %
- %
- if ~badg
- % test for alignment of dx with gradient and fix if necessary
- a = -dfhat/(gnorm*dxnorm);
- if a<ANGLE
- dx = dx - (ANGLE*dxnorm/gnorm+dfhat/(gnorm*gnorm))*g;
- dfhat = dx'*g;
- dxnorm = norm(dx);
- if dispIndx, disp(sprintf('Correct for low angle: %g',a)), end
- end
- end
- if dispIndx, disp(sprintf('Predicted improvement: %18.9f',-dfhat/2)), end
- %
- % Have OK dx, now adjust length of step (lambda) until min and
- % max improvement rate criteria are met.
- done=0;
- factor=3;
- shrink=1;
- lambdaMin=0;
- lambdaMax=inf;
- lambdaPeak=0;
- fPeak=f0;
- lambdahat=0;
- while ~done
- if size(x0,2)>1
- dxtest=x0+dx'*lambda;
- else
- dxtest=x0+dx*lambda;
- end
- % home
- f = eval([fcn '(dxtest,varargin{:})']);
- %ARGLIST
- %f = feval(fcn,dxtest,P1,P2,P3,P4,P5,P6,P7,P8,P9,P10,P11,P12,P13);
- % f = feval(fcn,x0+dx*lambda,P1,P2,P3,P4,P5,P6,P7,P8);
- if dispIndx, disp(sprintf('lambda = %10.5g; f = %20.7f',lambda,f )), end
- %debug
- %disp(sprintf('Improvement too great? f0-f: %g, criterion: %g',f0-f,-(1-THETA)*dfhat*lambda))
- if f<fhat
- fhat=f;
- xhat=dxtest;
- lambdahat = lambda;
- end
- fcount=fcount+1;
- shrinkSignal = (~badg & (f0-f < max([-THETA*dfhat*lambda 0]))) | (badg & (f0-f) < 0) ;
- growSignal = ~badg & ( (lambda > 0) & (f0-f > -(1-THETA)*dfhat*lambda) );
- if shrinkSignal & ( (lambda>lambdaPeak) | (lambda<0) )
- if (lambda>0) & ((~shrink) | (lambda/factor <= lambdaPeak))
- shrink=1;
- factor=factor^.6;
- while lambda/factor <= lambdaPeak
- factor=factor^.6;
- end
- %if (abs(lambda)*(factor-1)*dxnorm < MINDX) | (abs(lambda)*(factor-1) < MINLAMB)
- if abs(factor-1) < MINDFAC
- if abs(lambda)<4
- retcode = 2;
- else
- retcode=7;
- end
- done=1;
- end
- end
- if (lambda<lambdaMax) & (lambda>lambdaPeak)
- lambdaMax=lambda;
- end
- lambda=lambda/factor;
- if abs(lambda) < MINLAMB
- if (lambda > 0) & (f0 <= fhat)
- % try going against gradient, which may be inaccurate
- lambda = -lambda*factor^6
- else
- if lambda < 0
- retcode = 6;
- else
- retcode = 3;
- end
- done = 1;
- end
- end
- elseif (growSignal & lambda>0) | (shrinkSignal & ((lambda <= lambdaPeak) & (lambda>0)))
- if shrink
- shrink=0;
- factor = factor^.6;
- %if ( abs(lambda)*(factor-1)*dxnorm< MINDX ) | ( abs(lambda)*(factor-1)< MINLAMB)
- if abs(factor-1) < MINDFAC
- if abs(lambda)<4
- retcode = 4;
- else
- retcode=7;
- end
- done=1;
- end
- end
- if ( f<fPeak ) & (lambda>0)
- fPeak=f;
- lambdaPeak=lambda;
- if lambdaMax<=lambdaPeak
- lambdaMax=lambdaPeak*factor*factor;
- end
- end
- lambda=lambda*factor;
- if abs(lambda) > 1e20;
- retcode = 5;
- done =1;
- end
- else
- done=1;
- if factor < 1.2
- retcode=7;
- else
- retcode=0;
- end
- end
- end
-end
-if dispIndx, disp(sprintf('Norm of dx %10.5g', dxnorm)), end
+function [fhat,xhat,fcount,retcode] = csminit(fcn,x0,f0,g0,badg,H0,varargin)
+% [fhat,xhat,fcount,retcode] = csminit(fcn,x0,f0,g0,badg,H0,...
+% P1,P2,P3,P4,P5,P6,P7,P8)
+% retcodes: 0, normal step. 5, largest step still improves too fast.
+% 4,2 back and forth adjustment of stepsize didn't finish. 3, smallest
+% stepsize still improves too slow. 6, no improvement found. 1, zero
+% gradient.
+%---------------------
+% Modified 7/22/96 to omit variable-length P list, for efficiency and compilation.
+% Places where the number of P's need to be altered or the code could be returned to
+% its old form are marked with ARGLIST comments.
+%
+% Fixed 7/17/93 to use inverse-hessian instead of hessian itself in bfgs
+% update.
+%
+% Fixed 7/19/93 to flip eigenvalues of H to get better performance when
+% it's not psd.
+% NOTE: The display on screen can be turned off by seeting dispIndx=0 in this
+% function. This option is used for the loop operation. T. Zha, 2 May 2000
+
+%
+% Copyright (C) 1997-2012 C. A. Sims
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+dispIndx = 0; % 1: turn on all the diplays on the screen; 0: turn off (Added by T. Zha)
+
+%
+%tailstr = ')';
+%for i=nargin-6:-1:1
+% tailstr=[ ',P' num2str(i) tailstr];
+%end
+
+%ANGLE = .01; % when output of this variable becomes negative, we have wrong analytical graident
+ANGLE = .005; % works for identified VARs and OLS
+%THETA = .1; % works for OLS or other nonlinear functions
+THETA = .3; %(0<THETA<.5) THETA near .5 makes long line searches, possibly fewer iterations.
+ % workds for identified VARs
+FCHANGE = 1000;
+MINLAMB = 1e-9;
+% fixed 7/15/94
+% MINDX = .0001;
+% MINDX = 1e-6;
+MINDFAC = .01;
+fcount=0;
+lambda=1;
+xhat=x0;
+f=f0;
+fhat=f0;
+g = g0;
+gnorm = norm(g);
+%
+if (gnorm < 1.e-12) & ~badg % put ~badg 8/4/94
+ retcode =1;
+ dxnorm=0;
+ % gradient convergence
+else
+ % with badg true, we don't try to match rate of improvement to directional
+ % derivative. We're satisfied just to get some improvement in f.
+ %
+ %if(badg)
+ % dx = -g*FCHANGE/(gnorm*gnorm);
+ % dxnorm = norm(dx);
+ % if dxnorm > 1e12
+ % disp('Bad, small gradient problem.')
+ % dx = dx*FCHANGE/dxnorm;
+ % end
+ %else
+ % Gauss-Newton step;
+ %---------- Start of 7/19/93 mod ---------------
+ %[v d] = eig(H0);
+ %toc
+ %d=max(1e-10,abs(diag(d)));
+ %d=abs(diag(d));
+ %dx = -(v.*(ones(size(v,1),1)*d'))*(v'*g);
+% toc
+ dx = -H0*g;
+% toc
+ dxnorm = norm(dx);
+ if dxnorm > 1e12
+ if dispIndx, disp('Near-singular H problem.'), end
+ dx = dx*FCHANGE/dxnorm;
+ end
+ dfhat = dx'*g0;
+ %end
+ %
+ %
+ if ~badg
+ % test for alignment of dx with gradient and fix if necessary
+ a = -dfhat/(gnorm*dxnorm);
+ if a<ANGLE
+ dx = dx - (ANGLE*dxnorm/gnorm+dfhat/(gnorm*gnorm))*g;
+ dfhat = dx'*g;
+ dxnorm = norm(dx);
+ if dispIndx, disp(sprintf('Correct for low angle: %g',a)), end
+ end
+ end
+ if dispIndx, disp(sprintf('Predicted improvement: %18.9f',-dfhat/2)), end
+ %
+ % Have OK dx, now adjust length of step (lambda) until min and
+ % max improvement rate criteria are met.
+ done=0;
+ factor=3;
+ shrink=1;
+ lambdaMin=0;
+ lambdaMax=inf;
+ lambdaPeak=0;
+ fPeak=f0;
+ lambdahat=0;
+ while ~done
+ if size(x0,2)>1
+ dxtest=x0+dx'*lambda;
+ else
+ dxtest=x0+dx*lambda;
+ end
+ % home
+ f = eval([fcn '(dxtest,varargin{:})']);
+ %ARGLIST
+ %f = feval(fcn,dxtest,P1,P2,P3,P4,P5,P6,P7,P8,P9,P10,P11,P12,P13);
+ % f = feval(fcn,x0+dx*lambda,P1,P2,P3,P4,P5,P6,P7,P8);
+ if dispIndx, disp(sprintf('lambda = %10.5g; f = %20.7f',lambda,f )), end
+ %debug
+ %disp(sprintf('Improvement too great? f0-f: %g, criterion: %g',f0-f,-(1-THETA)*dfhat*lambda))
+ if f<fhat
+ fhat=f;
+ xhat=dxtest;
+ lambdahat = lambda;
+ end
+ fcount=fcount+1;
+ shrinkSignal = (~badg & (f0-f < max([-THETA*dfhat*lambda 0]))) | (badg & (f0-f) < 0) ;
+ growSignal = ~badg & ( (lambda > 0) & (f0-f > -(1-THETA)*dfhat*lambda) );
+ if shrinkSignal & ( (lambda>lambdaPeak) | (lambda<0) )
+ if (lambda>0) & ((~shrink) | (lambda/factor <= lambdaPeak))
+ shrink=1;
+ factor=factor^.6;
+ while lambda/factor <= lambdaPeak
+ factor=factor^.6;
+ end
+ %if (abs(lambda)*(factor-1)*dxnorm < MINDX) | (abs(lambda)*(factor-1) < MINLAMB)
+ if abs(factor-1) < MINDFAC
+ if abs(lambda)<4
+ retcode = 2;
+ else
+ retcode=7;
+ end
+ done=1;
+ end
+ end
+ if (lambda<lambdaMax) & (lambda>lambdaPeak)
+ lambdaMax=lambda;
+ end
+ lambda=lambda/factor;
+ if abs(lambda) < MINLAMB
+ if (lambda > 0) & (f0 <= fhat)
+ % try going against gradient, which may be inaccurate
+ lambda = -lambda*factor^6
+ else
+ if lambda < 0
+ retcode = 6;
+ else
+ retcode = 3;
+ end
+ done = 1;
+ end
+ end
+ elseif (growSignal & lambda>0) | (shrinkSignal & ((lambda <= lambdaPeak) & (lambda>0)))
+ if shrink
+ shrink=0;
+ factor = factor^.6;
+ %if ( abs(lambda)*(factor-1)*dxnorm< MINDX ) | ( abs(lambda)*(factor-1)< MINLAMB)
+ if abs(factor-1) < MINDFAC
+ if abs(lambda)<4
+ retcode = 4;
+ else
+ retcode=7;
+ end
+ done=1;
+ end
+ end
+ if ( f<fPeak ) & (lambda>0)
+ fPeak=f;
+ lambdaPeak=lambda;
+ if lambdaMax<=lambdaPeak
+ lambdaMax=lambdaPeak*factor*factor;
+ end
+ end
+ lambda=lambda*factor;
+ if abs(lambda) > 1e20;
+ retcode = 5;
+ done =1;
+ end
+ else
+ done=1;
+ if factor < 1.2
+ retcode=7;
+ else
+ retcode=0;
+ end
+ end
+ end
+end
+if dispIndx, disp(sprintf('Norm of dx %10.5g', dxnorm)), end
diff --git a/MatlabFiles/csminwel.m b/MatlabFiles/csminwel.m
index 04485eef2300d5f637314c98100d7a1b580e452e..e5a6ee61599509a586191e31cba87533607b6768 100644
--- a/MatlabFiles/csminwel.m
+++ b/MatlabFiles/csminwel.m
@@ -1,311 +1,311 @@
-function [fh,xh,gh,H,itct,fcount,retcodeh] = csminwel(fcn,x0,H0,grad,crit,nit,varargin)
-%[fhat,xhat,ghat,Hhat,itct,fcount,retcodehat] = csminwel(fcn,x0,H0,grad,crit,nit,varargin)
-% fcn: string naming the objective function to be minimized
-% x0: initial value of the parameter vector
-% H0: initial value for the inverse Hessian. Must be positive definite.
-% grad: Either a string naming a function that calculates the gradient, or the null matrix.
-% If it's null, the program calculates a numerical gradient. In this case fcn must
-% be written so that it can take a matrix argument and produce a row vector of values.
-% crit: Convergence criterion. Iteration will cease when it proves impossible to improve the
-% function value by more than crit.
-% nit: Maximum number of iterations.
-% varargin: A list of optional length of additional parameters that get handed off to fcn each
-% time it is called.
-% Note that if the program ends abnormally, it is possible to retrieve the current x,
-% f, and H from the files g1.mat and H.mat that are written at each iteration and at each
-% hessian update, respectively. (When the routine hits certain kinds of difficulty, it
-% write g2.mat and g3.mat as well. If all were written at about the same time, any of them
-% may be a decent starting point. One can also start from the one with best function value.)
-% NOTE: The display on screen can be turned off by seeting dispIndx=0 in this
-% function. This option is used for the loop operation. T. Zha, 2 May 2000
-% NOTE: You may want to change stps to 1.0e-02 or 1.0e-03 to get a better convergence. August, 2006
-% Copyright (C) 1997-2012 Christopher Sims and Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-Verbose = 1; % 1: turn on all the diplays on the screen; 0: turn off (Added by T. Zha)
-dispIndx = 1; % 1: turn on all the diplays on the screen; 0: turn off (Added by T. Zha)
-
-[nx,no]=size(x0);
-nx=max(nx,no);
-NumGrad= ( ~isstr(grad) | length(grad)==0);
-done=0;
-itct=0;
-fcount=0;
-snit=100;
-%tailstr = ')';
-%stailstr = [];
-% Lines below make the number of Pi's optional. This is inefficient, though, and precludes
-% use of the matlab compiler. Without them, we use feval and the number of Pi's must be
-% changed with the editor for each application. Places where this is required are marked
-% with ARGLIST comments
-%for i=nargin-6:-1:1
-% tailstr=[ ',P' num2str(i) tailstr];
-% stailstr=[' P' num2str(i) stailstr];
-%end
-if ischar(fcn)
- f0 = eval([fcn '(x0,varargin{:})']);
-else
- f0 = fcn(x0,varargin{:});
-end
-%ARGLIST
-%f0 = feval(fcn,x0,P1,P2,P3,P4,P5,P6,P7,P8,P9,P10,P11,P12,P13);
-% disp('first fcn in csminwel.m ----------------') % Jinill on 9/5/95
-if f0 > 1e50, disp('Bad initial parameter.'), return, end
-if NumGrad
- if length(grad)==0
- [g badg] = numgradcd(fcn,x0, varargin{:});
- %ARGLIST
- %[g badg] = numgradcd(fcn,x0,P1,P2,P3,P4,P5,P6,P7,P8,P9,P10,P11,P12,P13);
- else
- badg=any(find(grad==0));
- g=grad;
- end
- %numgradcd(fcn,x0,P1,P2,P3,P4);
-else
- [g badg] = eval([grad '(x0,varargin{:})']);
- %ARGLIST
- %[g badg] = feval(grad,x0,P1,P2,P3,P4,P5,P6,P7,P8,P9,P10,P11,P12,P13);
-end
-retcode3=101;
-x=x0;
-f=f0;
-H=H0;
-cliff=0;
-while ~done
- g1=[]; g2=[]; g3=[];
- %addition fj. 7/6/94 for control
- if dispIndx
- disp('-----------------')
- disp('-----------------')
- %disp('f and x at the beginning of new iteration')
- disp(sprintf('f at the beginning of new iteration, %20.10f',f))
- %-----------Comment out this line if the x vector is long----------------
- disp([sprintf('x = ') sprintf('%15.8g%15.8g%15.8g%15.8g%15.8g\n',x)]);
- end
- %-------------------------
- itct=itct+1;
- [f1 x1 fc retcode1] = csminit(fcn,x,f,g,badg,H,varargin{:});
- %ARGLIST
- %[f1 x1 fc retcode1] = csminit(fcn,x,f,g,badg,H,P1,P2,P3,P4,P5,P6,P7,...
- % P8,P9,P10,P11,P12,P13);
- % itct=itct+1;
- fcount = fcount+fc;
- % erased on 8/4/94
- % if (retcode == 1) | (abs(f1-f) < crit)
- % done=1;
- % end
- % if itct > nit
- % done = 1;
- % retcode = -retcode;
- % end
- if retcode1 ~= 1
- if retcode1==2 | retcode1==4
- wall1=1; badg1=1;
- else
- if NumGrad
- [g1 badg1] = numgradcd(fcn, x1,varargin{:});
- %ARGLIST
- %[g1 badg1] = numgradcd(fcn, x1,P1,P2,P3,P4,P5,P6,P7,P8,P9,...
- % P10,P11,P12,P13);
- else
- [g1 badg1] = eval([grad '(x1,varargin{:})']);
- %ARGLIST
- %[g1 badg1] = feval(grad, x1,P1,P2,P3,P4,P5,P6,P7,P8,P9,...
- % P10,P11,P12,P13);
- end
- wall1=badg1;
- % g1
- save g1 g1 x1 f1 varargin;
- %ARGLIST
- %save g1 g1 x1 f1 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13;
- end
- if wall1 % & (~done) by Jinill
- % Bad gradient or back and forth on step length. Possibly at
- % cliff edge. Try perturbing search direction.
- %
- %fcliff=fh;xcliff=xh;
- if dispIndx
- disp(' ')
- disp('************************* Random search. *****************************************')
- disp('************************* Random search. *****************************************')
- disp(' ')
- pause(1.0)
- end
- Hcliff=H+diag(diag(H).*rand(nx,1));
- if dispIndx, disp('Cliff. Perturbing search direction.'), end
- [f2 x2 fc retcode2] = csminit(fcn,x,f,g,badg,Hcliff,varargin{:});
- %ARGLIST
- %[f2 x2 fc retcode2] = csminit(fcn,x,f,g,badg,Hcliff,P1,P2,P3,P4,...
- % P5,P6,P7,P8,P9,P10,P11,P12,P13);
- fcount = fcount+fc; % put by Jinill
- if f2 < f
- if retcode2==2 | retcode2==4
- wall2=1; badg2=1;
- else
- if NumGrad
- [g2 badg2] = numgradcd(fcn, x2,varargin{:});
- %ARGLIST
- %[g2 badg2] = numgradcd(fcn, x2,P1,P2,P3,P4,P5,P6,P7,P8,...
- % P9,P10,P11,P12,P13);
- else
- [g2 badg2] = eval([grad '(x2,varargin{:})']);
- %ARGLIST
- %[g2 badg2] = feval(grad,x2,P1,P2,P3,P4,P5,P6,P7,P8,...
- % P9,P10,P11,P12,P13);
- end
- wall2=badg2;
- % g2
- if dispIndx, badg2, end
- save g2 g2 x2 f2 varargin
- %ARGLIST
- %save g2 g2 x2 f2 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13;
- end
- if wall2
- if dispIndx, disp('Cliff again. Try traversing'), end
- if norm(x2-x1) < 1e-13
- f3=f; x3=x; badg3=1;retcode3=101;
- else
- gcliff=((f2-f1)/((norm(x2-x1))^2))*(x2-x1);
- [f3 x3 fc retcode3] = csminit(fcn,x,f,gcliff,0,eye(nx),varargin{:});
- %ARGLIST
- %[f3 x3 fc retcode3] = csminit(fcn,x,f,gcliff,0,eye(nx),P1,P2,P3,...
- % P4,P5,P6,P7,P8,...
- % P9,P10,P11,P12,P13);
- fcount = fcount+fc; % put by Jinill
- if retcode3==2 | retcode3==4
- wall3=1; badg3=1;
- else
- if NumGrad
- [g3 badg3] = numgradcd(fcn, x3,varargin{:});
- %ARGLIST
- %[g3 badg3] = numgradcd(fcn, x3,P1,P2,P3,P4,P5,P6,P7,P8,...
- % P9,P10,P11,P12,P13);
- else
- [g3 badg3] = eval([grad '(x3,varargin{:})']);
- %ARGLIST
- %[g3 badg3] = feval(grad,x3,P1,P2,P3,P4,P5,P6,P7,P8,...
- % P9,P10,P11,P12,P13);
- end
- wall3=badg3;
- % g3
- if dispIndx, badg3, end
- save g3 g3 x3 f3 varargin;
- %ARGLIST
- %save g3 g3 x3 f3 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13;
- end
- end
- else
- f3=f; x3=x; badg3=1; retcode3=101;
- end
- else
- f3=f; x3=x; badg3=1;retcode3=101;
- end
- else
- % normal iteration, no walls, or else we're finished here.
- f2=f; f3=f; badg2=1; badg3=1; retcode2=101; retcode3=101;
- end
- else
- f1=f; f2=f; f3=f; retcode2=retcode1; retcode3=retcode1;
- end
- %how to pick gh and xh
- if f3<f & badg3==0
- if dispIndx, ih=3, end
- fh=f3;xh=x3;gh=g3;badgh=badg3;retcodeh=retcode3;
- elseif f2<f & badg2==0
- if dispIndx, ih=2, end
- fh=f2;xh=x2;gh=g2;badgh=badg2;retcodeh=retcode2;
- elseif f1<f & badg1==0
- if dispIndx, ih=1, end
- fh=f1;xh=x1;gh=g1;badgh=badg1;retcodeh=retcode1;
- else
- [fh,ih] = min([f1,f2,f3]);
- if dispIndx, disp(sprintf('ih = %d',ih)), end
- %eval(['xh=x' num2str(ih) ';'])
- switch ih
- case 1
- xh=x1;
- case 2
- xh=x2;
- case 3
- xh=x3;
- end %case
- %eval(['gh=g' num2str(ih) ';'])
- %eval(['retcodeh=retcode' num2str(ih) ';'])
- retcodei=[retcode1,retcode2,retcode3];
- retcodeh=retcodei(ih);
- if exist('gh')
- nogh=isempty(gh);
- else
- nogh=1;
- end
- if nogh
- if NumGrad
- [gh badgh] = numgradcd(fcn, xh,varargin{:}); %Pointed out by Junior Maih.
- %[gh badgh] = feval('numgrad',fcn,xh,varargin{:});
- else
- [gh badgh] = numgradcd(fcn, xh,varargin{:}); %Pointed out by Junior Maih.
- %[gh badgh] = feval('grad', xh,varargin{:});
- end
- end
- badgh=1;
- end
- %end of picking
- %ih
- %fh
- %xh
- %gh
- %badgh
- stuck = (abs(fh-f) < crit);
- if (~badg)&(~badgh)&(~stuck)
- H = bfgsi(H,gh-g,xh-x);
- end
- if Verbose
- if dispIndx
- disp('----')
- disp(sprintf('Improvement on iteration %d = %18.9f',itct,f-fh))
- end
- end
-
- if itct > nit
- if dispIndx, disp('iteration count termination'), end
- done = 1;
- elseif stuck
- if dispIndx, disp('improvement < crit termination'), end
- done = 1;
- end
- rc=retcodeh;
- if rc == 1
- if dispIndx, disp('zero gradient'), end
- elseif rc == 6
- if dispIndx, disp('smallest step still improving too slow, reversed gradient'), end
- elseif rc == 5
- if dispIndx, disp('largest step still improving too fast'), end
- elseif (rc == 4) | (rc==2)
- if dispIndx, disp('back and forth on step length never finished'), end
- elseif rc == 3
- if dispIndx, disp('smallest step still improving too slow'), end
- elseif rc == 7
- if dispIndx, disp('warning: possible inaccuracy in H matrix'), end
- end
-
- f=fh;
- x=xh;
- g=gh;
- badg=badgh;
-end
-% what about making an m-file of 10 lines including numgrad.m
-% since it appears three times in csminwel.m
+function [fh,xh,gh,H,itct,fcount,retcodeh] = csminwel(fcn,x0,H0,grad,crit,nit,varargin)
+%[fhat,xhat,ghat,Hhat,itct,fcount,retcodehat] = csminwel(fcn,x0,H0,grad,crit,nit,varargin)
+% fcn: string naming the objective function to be minimized
+% x0: initial value of the parameter vector
+% H0: initial value for the inverse Hessian. Must be positive definite.
+% grad: Either a string naming a function that calculates the gradient, or the null matrix.
+% If it's null, the program calculates a numerical gradient. In this case fcn must
+% be written so that it can take a matrix argument and produce a row vector of values.
+% crit: Convergence criterion. Iteration will cease when it proves impossible to improve the
+% function value by more than crit.
+% nit: Maximum number of iterations.
+% varargin: A list of optional length of additional parameters that get handed off to fcn each
+% time it is called.
+% Note that if the program ends abnormally, it is possible to retrieve the current x,
+% f, and H from the files g1.mat and H.mat that are written at each iteration and at each
+% hessian update, respectively. (When the routine hits certain kinds of difficulty, it
+% write g2.mat and g3.mat as well. If all were written at about the same time, any of them
+% may be a decent starting point. One can also start from the one with best function value.)
+% NOTE: The display on screen can be turned off by seeting dispIndx=0 in this
+% function. This option is used for the loop operation. T. Zha, 2 May 2000
+% NOTE: You may want to change stps to 1.0e-02 or 1.0e-03 to get a better convergence. August, 2006
+%
+% Copyright (C) 1997-2012 Christopher Sims and Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+Verbose = 1; % 1: turn on all the diplays on the screen; 0: turn off (Added by T. Zha)
+dispIndx = 1; % 1: turn on all the diplays on the screen; 0: turn off (Added by T. Zha)
+
+[nx,no]=size(x0);
+nx=max(nx,no);
+NumGrad= ( ~isstr(grad) | length(grad)==0);
+done=0;
+itct=0;
+fcount=0;
+snit=100;
+%tailstr = ')';
+%stailstr = [];
+% Lines below make the number of Pi's optional. This is inefficient, though, and precludes
+% use of the matlab compiler. Without them, we use feval and the number of Pi's must be
+% changed with the editor for each application. Places where this is required are marked
+% with ARGLIST comments
+%for i=nargin-6:-1:1
+% tailstr=[ ',P' num2str(i) tailstr];
+% stailstr=[' P' num2str(i) stailstr];
+%end
+if ischar(fcn)
+ f0 = eval([fcn '(x0,varargin{:})']);
+else
+ f0 = fcn(x0,varargin{:});
+end
+%ARGLIST
+%f0 = feval(fcn,x0,P1,P2,P3,P4,P5,P6,P7,P8,P9,P10,P11,P12,P13);
+% disp('first fcn in csminwel.m ----------------') % Jinill on 9/5/95
+if f0 > 1e50, disp('Bad initial parameter.'), return, end
+if NumGrad
+ if length(grad)==0
+ [g badg] = numgradcd(fcn,x0, varargin{:});
+ %ARGLIST
+ %[g badg] = numgradcd(fcn,x0,P1,P2,P3,P4,P5,P6,P7,P8,P9,P10,P11,P12,P13);
+ else
+ badg=any(find(grad==0));
+ g=grad;
+ end
+ %numgradcd(fcn,x0,P1,P2,P3,P4);
+else
+ [g badg] = eval([grad '(x0,varargin{:})']);
+ %ARGLIST
+ %[g badg] = feval(grad,x0,P1,P2,P3,P4,P5,P6,P7,P8,P9,P10,P11,P12,P13);
+end
+retcode3=101;
+x=x0;
+f=f0;
+H=H0;
+cliff=0;
+while ~done
+ g1=[]; g2=[]; g3=[];
+ %addition fj. 7/6/94 for control
+ if dispIndx
+ disp('-----------------')
+ disp('-----------------')
+ %disp('f and x at the beginning of new iteration')
+ disp(sprintf('f at the beginning of new iteration, %20.10f',f))
+ %-----------Comment out this line if the x vector is long----------------
+ disp([sprintf('x = ') sprintf('%15.8g%15.8g%15.8g%15.8g%15.8g\n',x)]);
+ end
+ %-------------------------
+ itct=itct+1;
+ [f1 x1 fc retcode1] = csminit(fcn,x,f,g,badg,H,varargin{:});
+ %ARGLIST
+ %[f1 x1 fc retcode1] = csminit(fcn,x,f,g,badg,H,P1,P2,P3,P4,P5,P6,P7,...
+ % P8,P9,P10,P11,P12,P13);
+ % itct=itct+1;
+ fcount = fcount+fc;
+ % erased on 8/4/94
+ % if (retcode == 1) | (abs(f1-f) < crit)
+ % done=1;
+ % end
+ % if itct > nit
+ % done = 1;
+ % retcode = -retcode;
+ % end
+ if retcode1 ~= 1
+ if retcode1==2 | retcode1==4
+ wall1=1; badg1=1;
+ else
+ if NumGrad
+ [g1 badg1] = numgradcd(fcn, x1,varargin{:});
+ %ARGLIST
+ %[g1 badg1] = numgradcd(fcn, x1,P1,P2,P3,P4,P5,P6,P7,P8,P9,...
+ % P10,P11,P12,P13);
+ else
+ [g1 badg1] = eval([grad '(x1,varargin{:})']);
+ %ARGLIST
+ %[g1 badg1] = feval(grad, x1,P1,P2,P3,P4,P5,P6,P7,P8,P9,...
+ % P10,P11,P12,P13);
+ end
+ wall1=badg1;
+ % g1
+ save g1 g1 x1 f1 varargin;
+ %ARGLIST
+ %save g1 g1 x1 f1 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13;
+ end
+ if wall1 % & (~done) by Jinill
+ % Bad gradient or back and forth on step length. Possibly at
+ % cliff edge. Try perturbing search direction.
+ %
+ %fcliff=fh;xcliff=xh;
+ if dispIndx
+ disp(' ')
+ disp('************************* Random search. *****************************************')
+ disp('************************* Random search. *****************************************')
+ disp(' ')
+ pause(1.0)
+ end
+ Hcliff=H+diag(diag(H).*rand(nx,1));
+ if dispIndx, disp('Cliff. Perturbing search direction.'), end
+ [f2 x2 fc retcode2] = csminit(fcn,x,f,g,badg,Hcliff,varargin{:});
+ %ARGLIST
+ %[f2 x2 fc retcode2] = csminit(fcn,x,f,g,badg,Hcliff,P1,P2,P3,P4,...
+ % P5,P6,P7,P8,P9,P10,P11,P12,P13);
+ fcount = fcount+fc; % put by Jinill
+ if f2 < f
+ if retcode2==2 | retcode2==4
+ wall2=1; badg2=1;
+ else
+ if NumGrad
+ [g2 badg2] = numgradcd(fcn, x2,varargin{:});
+ %ARGLIST
+ %[g2 badg2] = numgradcd(fcn, x2,P1,P2,P3,P4,P5,P6,P7,P8,...
+ % P9,P10,P11,P12,P13);
+ else
+ [g2 badg2] = eval([grad '(x2,varargin{:})']);
+ %ARGLIST
+ %[g2 badg2] = feval(grad,x2,P1,P2,P3,P4,P5,P6,P7,P8,...
+ % P9,P10,P11,P12,P13);
+ end
+ wall2=badg2;
+ % g2
+ if dispIndx, badg2, end
+ save g2 g2 x2 f2 varargin
+ %ARGLIST
+ %save g2 g2 x2 f2 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13;
+ end
+ if wall2
+ if dispIndx, disp('Cliff again. Try traversing'), end
+ if norm(x2-x1) < 1e-13
+ f3=f; x3=x; badg3=1;retcode3=101;
+ else
+ gcliff=((f2-f1)/((norm(x2-x1))^2))*(x2-x1);
+ [f3 x3 fc retcode3] = csminit(fcn,x,f,gcliff,0,eye(nx),varargin{:});
+ %ARGLIST
+ %[f3 x3 fc retcode3] = csminit(fcn,x,f,gcliff,0,eye(nx),P1,P2,P3,...
+ % P4,P5,P6,P7,P8,...
+ % P9,P10,P11,P12,P13);
+ fcount = fcount+fc; % put by Jinill
+ if retcode3==2 | retcode3==4
+ wall3=1; badg3=1;
+ else
+ if NumGrad
+ [g3 badg3] = numgradcd(fcn, x3,varargin{:});
+ %ARGLIST
+ %[g3 badg3] = numgradcd(fcn, x3,P1,P2,P3,P4,P5,P6,P7,P8,...
+ % P9,P10,P11,P12,P13);
+ else
+ [g3 badg3] = eval([grad '(x3,varargin{:})']);
+ %ARGLIST
+ %[g3 badg3] = feval(grad,x3,P1,P2,P3,P4,P5,P6,P7,P8,...
+ % P9,P10,P11,P12,P13);
+ end
+ wall3=badg3;
+ % g3
+ if dispIndx, badg3, end
+ save g3 g3 x3 f3 varargin;
+ %ARGLIST
+ %save g3 g3 x3 f3 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13;
+ end
+ end
+ else
+ f3=f; x3=x; badg3=1; retcode3=101;
+ end
+ else
+ f3=f; x3=x; badg3=1;retcode3=101;
+ end
+ else
+ % normal iteration, no walls, or else we're finished here.
+ f2=f; f3=f; badg2=1; badg3=1; retcode2=101; retcode3=101;
+ end
+ else
+ f1=f; f2=f; f3=f; retcode2=retcode1; retcode3=retcode1;
+ end
+ %how to pick gh and xh
+ if f3<f & badg3==0
+ if dispIndx, ih=3, end
+ fh=f3;xh=x3;gh=g3;badgh=badg3;retcodeh=retcode3;
+ elseif f2<f & badg2==0
+ if dispIndx, ih=2, end
+ fh=f2;xh=x2;gh=g2;badgh=badg2;retcodeh=retcode2;
+ elseif f1<f & badg1==0
+ if dispIndx, ih=1, end
+ fh=f1;xh=x1;gh=g1;badgh=badg1;retcodeh=retcode1;
+ else
+ [fh,ih] = min([f1,f2,f3]);
+ if dispIndx, disp(sprintf('ih = %d',ih)), end
+ %eval(['xh=x' num2str(ih) ';'])
+ switch ih
+ case 1
+ xh=x1;
+ case 2
+ xh=x2;
+ case 3
+ xh=x3;
+ end %case
+ %eval(['gh=g' num2str(ih) ';'])
+ %eval(['retcodeh=retcode' num2str(ih) ';'])
+ retcodei=[retcode1,retcode2,retcode3];
+ retcodeh=retcodei(ih);
+ if exist('gh')
+ nogh=isempty(gh);
+ else
+ nogh=1;
+ end
+ if nogh
+ if NumGrad
+ [gh badgh] = numgradcd(fcn, xh,varargin{:}); %Pointed out by Junior Maih.
+ %[gh badgh] = feval('numgrad',fcn,xh,varargin{:});
+ else
+ [gh badgh] = numgradcd(fcn, xh,varargin{:}); %Pointed out by Junior Maih.
+ %[gh badgh] = feval('grad', xh,varargin{:});
+ end
+ end
+ badgh=1;
+ end
+ %end of picking
+ %ih
+ %fh
+ %xh
+ %gh
+ %badgh
+ stuck = (abs(fh-f) < crit);
+ if (~badg)&(~badgh)&(~stuck)
+ H = bfgsi(H,gh-g,xh-x);
+ end
+ if Verbose
+ if dispIndx
+ disp('----')
+ disp(sprintf('Improvement on iteration %d = %18.9f',itct,f-fh))
+ end
+ end
+
+ if itct > nit
+ if dispIndx, disp('iteration count termination'), end
+ done = 1;
+ elseif stuck
+ if dispIndx, disp('improvement < crit termination'), end
+ done = 1;
+ end
+ rc=retcodeh;
+ if rc == 1
+ if dispIndx, disp('zero gradient'), end
+ elseif rc == 6
+ if dispIndx, disp('smallest step still improving too slow, reversed gradient'), end
+ elseif rc == 5
+ if dispIndx, disp('largest step still improving too fast'), end
+ elseif (rc == 4) | (rc==2)
+ if dispIndx, disp('back and forth on step length never finished'), end
+ elseif rc == 3
+ if dispIndx, disp('smallest step still improving too slow'), end
+ elseif rc == 7
+ if dispIndx, disp('warning: possible inaccuracy in H matrix'), end
+ end
+
+ f=fh;
+ x=xh;
+ g=gh;
+ badg=badgh;
+end
+% what about making an m-file of 10 lines including numgrad.m
+% since it appears three times in csminwel.m
diff --git a/MatlabFiles/datactcon.m b/MatlabFiles/datactcon.m
index fcc9b6492c9c31019ef407e2ef86526ecf290be3..85d48bbb0db422d2295d84f53340618a2f842d06 100644
--- a/MatlabFiles/datactcon.m
+++ b/MatlabFiles/datactcon.m
@@ -1,133 +1,133 @@
-function [yact,yactmg,yactqg,yactCalyg,yactCal] = datactcon(xinput)
-% [yact,yactmg,yactqg,yactCalyg,yactCal] = datactcon(xinput)
-% Data-actual-conditions. Take the raw data xdata to mlog, mg, qg, and yg.
-% Note yact(mlog), yactqg, and yactCalyg corr. to myears, qyears, and yearsCal
-%
-% xdata=xinput{1}: data, all logged except R, U, etc.
-% nSample=xinput{2}: sample size (including lags or initial periods)
-% nSampleCal=xinput{3}: converting nSample to sample marked by calendar years;
-% actup=xinput{4}: periods for actual data (monthly)
-% actupq=xinput{5}: periods for actual data (quarterly)
-% actupy=xinput{6}: periods for actual data (calendar yearly)
-% vlist=xinput{7}: list of variables
-% vlistlog=xinput{8}: sub list of variables that are in log
-% vlistper=xinput{9}: sub list of variables that are in percent
-% q_m=xinput{10}: month or quarter in the model
-% forep=xinput{11}: forecast periods (monthly)
-% forepq=xinput{12}: quarters in the forecast period
-% forepy=xinput{13}: calendar years in the forecast period
-% Psuedo=xinput{14}: 1: Psuedo out-of-sample; 0: real time out-of-sample
-% qmEnd=xinput{15}: last month (or quarter) of the sample
-%-------------
-% yact: (actup+forep*Psuedo)-by-nvar; log(y) except R, ect. (monthly).
-% Begin: nSample-actup+1; End: nSample+forep*Psuedo. Match dates myears
-% yactmg: (size(yact,1)-1)-by-nvar; month-to-month annualized growth for actual data.
-% Begin: nSample-actup+2; End: nSample+forep*Psuedo. Match dates "myears(2:end)"
-% yactqg: prior-quarter annualized growth for actual data
-% Match dates "qyears."
-% yactCalyg: annual growth for actual data
-% Match dates "yearsCal."
-% yactCal: weirdo -- seldom used. Same as yact but ends at the end of the
-% last calendar year. If Psuedo, it includes some actual data in
-% the 1st forecast calendar year. I haven't found the use of this weirdo.
-%
-% Copyright (c) March 1998 by Tao Zha
-% Revised, October 1998
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-xdata=xinput{1}; nSample=xinput{2}; nSampleCal=xinput{3};
-actup=xinput{4}; actupq=xinput{5}; actupy=xinput{6}; vlist=xinput{7};
-vlistlog=xinput{8}; vlistper=xinput{9}; q_m=xinput{10}; forep=xinput{11};
-forepq=xinput{12}; forepy=xinput{13}; Psuedo=xinput{14}; qmEnd=xinput{15};
-
-%--------------------------------------
-% Monthly log, acutal data
-% yact the way it is and yactCal ends at the end of calendar year
-%----------------------------------------
-yact = xdata(nSample-actup+1:nSample+forep*Psuedo,:);
- % actup (not calendar) months + forep
-yactCal = xdata(nSampleCal-actupy*q_m+1:nSampleCal+forepy*q_m*Psuedo,:);
- % calendar years
-
-
-
-
-%---------------------------------------------------------------
-% Actual monthly growth rate, Prior quarter, and calendar yearly change
-%---------------------------------------------------------------
-%
-%@@@ Monthly change (annaluized rate)
-%
-yactm = yact;
-mT1 = length(yact(:,1));
-%
-yactmg = yactm(2:mT1,:); % start at second month to get growth rate
-yactmg(:,vlistlog) = (yactm(2:mT1,vlistlog) - yactm(1:mT1-1,vlistlog)) .* q_m;
- % monthly, 12*log(1+growth rate), annualized growth rate
-yactmg(:,vlistlog) = 100*(exp(yactmg(:,vlistlog))-1);
-yactmg(:,vlistper) = 100*yactmg(:,vlistper);
-
-%
-%@@@ Prior Quarter change (annaluized rate)
-%
-yactQ = xdata(nSample-mod(qmEnd,3)-actupq*3+1:nSample-mod(qmEnd,3)+forepq*3*Psuedo,:);
-yactq = zeros(actupq+forepq*Psuedo,length(vlist));
-%
-qT1 = length(yactQ(:,1))/3;
-qT1a = length(yactq(:,1));
-if qT1 ~= qT1a
- warning('line #')
- error('qT1: Beginings or ends of monthly and quarterly series do not match!')
-end
-%
-for i = 1:qT1
- i1 = 1+3*(i-1);
- i2 = 3*i;
- yactq(i,:) = sum(yactQ(i1:i2,:)) ./ 3;
-end
-%
-yactqg = yactq(5:qT1,:); % 4+1=5 where 4 means 4 quarters
-yactqg(:,vlistlog) = (yactq(5:qT1,vlistlog) - yactq(4:qT1-1,vlistlog)) .* 4;
- % quarterly, 4*log(1+growth rate), annualized growth rate
-yactqg(:,vlistlog) = 100*(exp(yactqg(:,vlistlog))-1);
-yactqg(:,vlistper) = 100*yactqg(:,vlistper);
-
-%
-%@@@ Calendar year-over-year change
-%
-yactCaly = zeros(actupy+forepy*Psuedo,length(vlist));
- % past "actupy" calendar years + forepy*Psuedo
-yT1 = length(yactCal(:,1))/q_m;
-yT1a = length(yactCaly(:,1));
-if yT1 ~= yT1a
- warning('Line #')
- error('yT1: Beginings or ends of monthly and quarterly series are not the same!')
-end
-%
-for i = 1:yT1
- i1 = 1+q_m*(i-1);
- i2 = q_m*i;
- yactCaly(i,:) = sum(yactCal(i1:i2,:)) ./ q_m;
-end
-%
-yactCalyg = yactCaly(2:yT1,:); % 1+1=2 where 1 means 1 year
-yactCalyg(:,vlistlog) = (yactCaly(2:yT1,vlistlog) - yactCaly(1:yT1-1,vlistlog));
- % annual rate: log(1+growth rate)
-yactCalyg(:,vlistlog) = 100*(exp(yactCalyg(:,vlistlog))-1);
-yactCalyg(:,vlistper) = 100*yactCalyg(:,vlistper);
+function [yact,yactmg,yactqg,yactCalyg,yactCal] = datactcon(xinput)
+% [yact,yactmg,yactqg,yactCalyg,yactCal] = datactcon(xinput)
+% Data-actual-conditions. Take the raw data xdata to mlog, mg, qg, and yg.
+% Note yact(mlog), yactqg, and yactCalyg corr. to myears, qyears, and yearsCal
+%
+% xdata=xinput{1}: data, all logged except R, U, etc.
+% nSample=xinput{2}: sample size (including lags or initial periods)
+% nSampleCal=xinput{3}: converting nSample to sample marked by calendar years;
+% actup=xinput{4}: periods for actual data (monthly)
+% actupq=xinput{5}: periods for actual data (quarterly)
+% actupy=xinput{6}: periods for actual data (calendar yearly)
+% vlist=xinput{7}: list of variables
+% vlistlog=xinput{8}: sub list of variables that are in log
+% vlistper=xinput{9}: sub list of variables that are in percent
+% q_m=xinput{10}: month or quarter in the model
+% forep=xinput{11}: forecast periods (monthly)
+% forepq=xinput{12}: quarters in the forecast period
+% forepy=xinput{13}: calendar years in the forecast period
+% Psuedo=xinput{14}: 1: Psuedo out-of-sample; 0: real time out-of-sample
+% qmEnd=xinput{15}: last month (or quarter) of the sample
+%-------------
+% yact: (actup+forep*Psuedo)-by-nvar; log(y) except R, ect. (monthly).
+% Begin: nSample-actup+1; End: nSample+forep*Psuedo. Match dates myears
+% yactmg: (size(yact,1)-1)-by-nvar; month-to-month annualized growth for actual data.
+% Begin: nSample-actup+2; End: nSample+forep*Psuedo. Match dates "myears(2:end)"
+% yactqg: prior-quarter annualized growth for actual data
+% Match dates "qyears."
+% yactCalyg: annual growth for actual data
+% Match dates "yearsCal."
+% yactCal: weirdo -- seldom used. Same as yact but ends at the end of the
+% last calendar year. If Psuedo, it includes some actual data in
+% the 1st forecast calendar year. I haven't found the use of this weirdo.
+%
+% Written by Tao Zha March 1998
+% Revised, October 1998
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+xdata=xinput{1}; nSample=xinput{2}; nSampleCal=xinput{3};
+actup=xinput{4}; actupq=xinput{5}; actupy=xinput{6}; vlist=xinput{7};
+vlistlog=xinput{8}; vlistper=xinput{9}; q_m=xinput{10}; forep=xinput{11};
+forepq=xinput{12}; forepy=xinput{13}; Psuedo=xinput{14}; qmEnd=xinput{15};
+
+%--------------------------------------
+% Monthly log, acutal data
+% yact the way it is and yactCal ends at the end of calendar year
+%----------------------------------------
+yact = xdata(nSample-actup+1:nSample+forep*Psuedo,:);
+ % actup (not calendar) months + forep
+yactCal = xdata(nSampleCal-actupy*q_m+1:nSampleCal+forepy*q_m*Psuedo,:);
+ % calendar years
+
+
+
+
+%---------------------------------------------------------------
+% Actual monthly growth rate, Prior quarter, and calendar yearly change
+%---------------------------------------------------------------
+%
+%@@@ Monthly change (annaluized rate)
+%
+yactm = yact;
+mT1 = length(yact(:,1));
+%
+yactmg = yactm(2:mT1,:); % start at second month to get growth rate
+yactmg(:,vlistlog) = (yactm(2:mT1,vlistlog) - yactm(1:mT1-1,vlistlog)) .* q_m;
+ % monthly, 12*log(1+growth rate), annualized growth rate
+yactmg(:,vlistlog) = 100*(exp(yactmg(:,vlistlog))-1);
+yactmg(:,vlistper) = 100*yactmg(:,vlistper);
+
+%
+%@@@ Prior Quarter change (annaluized rate)
+%
+yactQ = xdata(nSample-mod(qmEnd,3)-actupq*3+1:nSample-mod(qmEnd,3)+forepq*3*Psuedo,:);
+yactq = zeros(actupq+forepq*Psuedo,length(vlist));
+%
+qT1 = length(yactQ(:,1))/3;
+qT1a = length(yactq(:,1));
+if qT1 ~= qT1a
+ warning('line #')
+ error('qT1: Beginings or ends of monthly and quarterly series do not match!')
+end
+%
+for i = 1:qT1
+ i1 = 1+3*(i-1);
+ i2 = 3*i;
+ yactq(i,:) = sum(yactQ(i1:i2,:)) ./ 3;
+end
+%
+yactqg = yactq(5:qT1,:); % 4+1=5 where 4 means 4 quarters
+yactqg(:,vlistlog) = (yactq(5:qT1,vlistlog) - yactq(4:qT1-1,vlistlog)) .* 4;
+ % quarterly, 4*log(1+growth rate), annualized growth rate
+yactqg(:,vlistlog) = 100*(exp(yactqg(:,vlistlog))-1);
+yactqg(:,vlistper) = 100*yactqg(:,vlistper);
+
+%
+%@@@ Calendar year-over-year change
+%
+yactCaly = zeros(actupy+forepy*Psuedo,length(vlist));
+ % past "actupy" calendar years + forepy*Psuedo
+yT1 = length(yactCal(:,1))/q_m;
+yT1a = length(yactCaly(:,1));
+if yT1 ~= yT1a
+ warning('Line #')
+ error('yT1: Beginings or ends of monthly and quarterly series are not the same!')
+end
+%
+for i = 1:yT1
+ i1 = 1+q_m*(i-1);
+ i2 = q_m*i;
+ yactCaly(i,:) = sum(yactCal(i1:i2,:)) ./ q_m;
+end
+%
+yactCalyg = yactCaly(2:yT1,:); % 1+1=2 where 1 means 1 year
+yactCalyg(:,vlistlog) = (yactCaly(2:yT1,vlistlog) - yactCaly(1:yT1-1,vlistlog));
+ % annual rate: log(1+growth rate)
+yactCalyg(:,vlistlog) = 100*(exp(yactCalyg(:,vlistlog))-1);
+yactCalyg(:,vlistper) = 100*yactCalyg(:,vlistper);
diff --git a/MatlabFiles/dataext.m b/MatlabFiles/dataext.m
index 1301c1c9b45f45683536407bc7228cbb111336d5..d6d56a251d473ba7ea7ffeaa94514425824deac2 100644
--- a/MatlabFiles/dataext.m
+++ b/MatlabFiles/dataext.m
@@ -1,61 +1,61 @@
-function [xdsube,Brow,Erow] = dataext(Byrqm,Eyrqm,xdatae)
-% xdsube = dataext(Byrqm,Eyrqm,xdatae)
-%
-% Extract subset of xdatae, given Byrqm and Eyrqm
-%
-% Byrqm: [year quarter(month)]: beginning year and period. If Byqm(2)=0, we get annual data.
-% Eyrqm: [year period]: end year and period. If Byrqm(2)=0, it must be Eyrqm(2)=0.
-% xdatae: all data (some of which may be NaN) with the first 2 columns indicating years and periods.
-%----------
-% xdsube: subset of xdatae.
-% Brow: the row number in xdatee that marks the first row of xdsube.
-% Erow: the row number in xdatee that marks the last row of xdsube.
-%
-% Tao Zha, April 2000
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-if (Byrqm(2)==0) & (Eyrqm(2)~=0)
- error('If annual data, make sure both Byrqm(2) and Eyrqm(2) are zero')
-end
-
-Brow = min(find(xdatae(:,1)==Byrqm(1)));
-if isempty(Brow)
- error('Byrqm is outside the date range indicated by xdatae(:,1:2)!')
-end
-if Byrqm(2)>0
- nadt=Byrqm(2)-xdatae(Brow,2);
- if nadt<0
- error('Byrqm is outside the date range indicated by xdatae(:,1:2)!')
- end
- Brow=Brow+nadt;
-end
-%
-Erow = min(find(xdatae(:,1)==Eyrqm(1)));
-if isempty(Erow)
- error('Eyrqm is outside the date range indicated by xdatae(:,1:2)!')
-end
-if Eyrqm(2)>0
- nadt2=Eyrqm(2)-xdatae(Erow,2);
- if nadt<0
- error('Eyrqm is outside the date range indicated by xdatae(:,1:2)!')
- end
- Erow=Erow+nadt2;
-end
-%
-xdsube = xdatae(Brow:Erow,:); % with dates
\ No newline at end of file
+function [xdsube,Brow,Erow] = dataext(Byrqm,Eyrqm,xdatae)
+% xdsube = dataext(Byrqm,Eyrqm,xdatae)
+%
+% Extract subset of xdatae, given Byrqm and Eyrqm
+%
+% Byrqm: [year quarter(month)]: beginning year and period. If Byqm(2)=0, we get annual data.
+% Eyrqm: [year period]: end year and period. If Byrqm(2)=0, it must be Eyrqm(2)=0.
+% xdatae: all data (some of which may be NaN) with the first 2 columns indicating years and periods.
+%----------
+% xdsube: subset of xdatae.
+% Brow: the row number in xdatee that marks the first row of xdsube.
+% Erow: the row number in xdatee that marks the last row of xdsube.
+%
+% Tao Zha, April 2000
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+if (Byrqm(2)==0) & (Eyrqm(2)~=0)
+ error('If annual data, make sure both Byrqm(2) and Eyrqm(2) are zero')
+end
+
+Brow = min(find(xdatae(:,1)==Byrqm(1)));
+if isempty(Brow)
+ error('Byrqm is outside the date range indicated by xdatae(:,1:2)!')
+end
+if Byrqm(2)>0
+ nadt=Byrqm(2)-xdatae(Brow,2);
+ if nadt<0
+ error('Byrqm is outside the date range indicated by xdatae(:,1:2)!')
+ end
+ Brow=Brow+nadt;
+end
+%
+Erow = min(find(xdatae(:,1)==Eyrqm(1)));
+if isempty(Erow)
+ error('Eyrqm is outside the date range indicated by xdatae(:,1:2)!')
+end
+if Eyrqm(2)>0
+ nadt2=Eyrqm(2)-xdatae(Erow,2);
+ if nadt<0
+ error('Eyrqm is outside the date range indicated by xdatae(:,1:2)!')
+ end
+ Erow=Erow+nadt2;
+end
+%
+xdsube = xdatae(Brow:Erow,:); % with dates
diff --git a/MatlabFiles/datana.m b/MatlabFiles/datana.m
index 3d7052e3faaa9b655fb15b1de4921a3b5aae5e0a..d7658c5a0122b4ed8f6101d1db31e217b33f8d9c 100644
--- a/MatlabFiles/datana.m
+++ b/MatlabFiles/datana.m
@@ -1,113 +1,113 @@
-function [yactyrge,yactyre,yactqmyge,yactqmge,yactqme] = datana(xdatae,q_m,vlistlog,vlistper,Byrqm,Eyrqm)
-% [yactyrge,yactyre,yactqmyge,yactqmge,yactqme] = datana(Byrqm,Eyrqm,xdatae,q_m,vlistlog,vlistper)
-%
-% Generate prior period, period-to-period-in-last-year, and annual growth rates
-% For annual rates, works for both calendar and any annual years, depending on Byrqm and Eyrqm
-% If monthly data, we haven't got time to get quarterly growth rates yet.
-%
-% xdatae: all data (some of which may be NaN) with the first 2 columns indicating years and periods.
-% q_m: quarter or month period
-% vlistlog: sublist for logged variables
-% vlistper: sublists for percent variables
-% Byrqm: [year quarter(month)]: beginning year and period. If Byqm(2)~=1, we don't get
-% calendar annual rates. In other words, the first column of yactyge (which
-% indicates years) does not mean calendar years
-% Eyrqm: [year period]: end year and period
-% NOTE: if no inputs Byrqm and Eyrqm are specified, all growth rates begin at xdatae(1,1:2).
-%----------
-% yactyrge: annual growth rates with dates in the first 2 columns.
-% yactyre: annual average logged level with dates in the 1st 2 columns.
-% yactqmyge: period-to-period-in-last-year annual growth rates with dates in the first 2 columns.
-% yactqmge: prior-period annualized growth rates with dates in the first 2 columns.
-% yactqme: logged data with dates in the first 2 columns.
-%
-% Tao Zha, April 2000
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-if size(xdatae,1)<2*q_m
- error('We need at least two years of xdatae to get annual rates. Check xdatae!!')
-end
-
-if nargin==4
- Brow=1; Erow=length(xdatae(:,1));
- nyr = floor((Erow-Brow+1)/q_m);
- yrsg = [xdatae(q_m+1,1):xdatae(q_m+1,1)+nyr-2]'; % for annual growth later on
-else
- if Byrqm(2)<1 | Eyrqm(2)<1
- error('This function requires both years and months (quarters) in Byrqm and Eyrqm')
- end
-
- Brow = min(find(xdatae(:,1)==Byrqm(1)));
- if isempty(Brow)
- error('Byrqm is outside the date range of xdatae(:,1:2)!')
- end
- nadt=Byrqm(2)-xdatae(Brow,2);
- if nadt<0
- error('Byrqm is outside the date range indicated by xdatae(:,1:2)!')
- end
- Brow=Brow+nadt;
- %
- Erow = min(find(xdatae(:,1)==Eyrqm(1)));
- if isempty(Brow)
- error('Eyrqm is outside the date range of xdatae(:,1:2)!')
- end
- nadt2=Eyrqm(2)-xdatae(Erow,2);
- if nadt<0
- error('Eyrqm is outside the date range indicated by xdatae(:,1:2)!')
- end
- Erow=Erow+nadt2;
-
- nyr = floor((Erow-Brow+1)/q_m);
- yrsg = [Byrqm(1)+1:Byrqm(1)+nyr-1]'; % for annual growth later on, which will
- % start at Byrqm(1) instead of Byrqm(1)+1
-end
-%
-yactqme = xdatae(Brow:Erow,:); % with dates
-yactqm = yactqme(:,3:end); % only data
-
-%======== prior period change (annaluized rate)
-yactqmg = yactqm(2:end,:); % start at second period to get growth rate
-yactqmg(:,vlistlog) = (yactqm(2:end,vlistlog) - yactqm(1:end-1,vlistlog)) .* q_m;
- % monthly, 12*log(1+growth rate), annualized growth rate
-yactqmg(:,vlistlog) = 100*(exp(yactqmg(:,vlistlog))-1);
-yactqmg(:,vlistper) = 100*yactqmg(:,vlistper);
-yactqmge = [yactqme(2:end,1:2) yactqmg];
-
-%======== change from the last year
-yactqmyg = yactqm(q_m+1:end,:); % start at the last-year period to get growth rate
-yactqmyg(:,vlistlog) = (yactqm(q_m+1:end,vlistlog) - yactqm(1:end-q_m,vlistlog));
-yactqmyg(:,vlistlog) = 100*(exp(yactqmyg(:,vlistlog))-1);
-yactqmyg(:,vlistper) = 100*yactqmyg(:,vlistper);
-yactqmyge = [yactqme(q_m+1:end,1:2) yactqmyg];
-
-%======== annual growth rates
-nvar = length(xdatae(1,3:end));
-ygmts = yactqm(1:nyr*q_m,:); % converted to the multiplication of q_m
-ygmts1 = reshape(ygmts,q_m,nyr,nvar);
-ygmts2 = sum(ygmts1,1) ./ q_m;
-ygmts3 = reshape(ygmts2,nyr,nvar); % converted to annual average series
-%
-yactyrg = ygmts3(2:end,:); % start at the last-year period to get growth rate
-yactyrg(:,vlistlog) = ygmts3(2:end,vlistlog) - ygmts3(1:end-1,vlistlog);
- % annual rate: log(1+growth rate)
-yactyrg(:,vlistlog) = 100*(exp(yactyrg(:,vlistlog))-1);
-yactyrg(:,vlistper) = 100*yactyrg(:,vlistper);
-yactyrge = [yrsg zeros(nyr-1,1) yactyrg];
-yrsg1=[yrsg(1)-1:yrsg(end)]';
-yactyre = [yrsg1 zeros(nyr,1) ygmts3];
\ No newline at end of file
+function [yactyrge,yactyre,yactqmyge,yactqmge,yactqme] = datana(xdatae,q_m,vlistlog,vlistper,Byrqm,Eyrqm)
+% [yactyrge,yactyre,yactqmyge,yactqmge,yactqme] = datana(Byrqm,Eyrqm,xdatae,q_m,vlistlog,vlistper)
+%
+% Generate prior period, period-to-period-in-last-year, and annual growth rates
+% For annual rates, works for both calendar and any annual years, depending on Byrqm and Eyrqm
+% If monthly data, we haven't got time to get quarterly growth rates yet.
+%
+% xdatae: all data (some of which may be NaN) with the first 2 columns indicating years and periods.
+% q_m: quarter or month period
+% vlistlog: sublist for logged variables
+% vlistper: sublists for percent variables
+% Byrqm: [year quarter(month)]: beginning year and period. If Byqm(2)~=1, we don't get
+% calendar annual rates. In other words, the first column of yactyge (which
+% indicates years) does not mean calendar years
+% Eyrqm: [year period]: end year and period
+% NOTE: if no inputs Byrqm and Eyrqm are specified, all growth rates begin at xdatae(1,1:2).
+%----------
+% yactyrge: annual growth rates with dates in the first 2 columns.
+% yactyre: annual average logged level with dates in the 1st 2 columns.
+% yactqmyge: period-to-period-in-last-year annual growth rates with dates in the first 2 columns.
+% yactqmge: prior-period annualized growth rates with dates in the first 2 columns.
+% yactqme: logged data with dates in the first 2 columns.
+%
+% Tao Zha, April 2000
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+if size(xdatae,1)<2*q_m
+ error('We need at least two years of xdatae to get annual rates. Check xdatae!!')
+end
+
+if nargin==4
+ Brow=1; Erow=length(xdatae(:,1));
+ nyr = floor((Erow-Brow+1)/q_m);
+ yrsg = [xdatae(q_m+1,1):xdatae(q_m+1,1)+nyr-2]'; % for annual growth later on
+else
+ if Byrqm(2)<1 | Eyrqm(2)<1
+ error('This function requires both years and months (quarters) in Byrqm and Eyrqm')
+ end
+
+ Brow = min(find(xdatae(:,1)==Byrqm(1)));
+ if isempty(Brow)
+ error('Byrqm is outside the date range of xdatae(:,1:2)!')
+ end
+ nadt=Byrqm(2)-xdatae(Brow,2);
+ if nadt<0
+ error('Byrqm is outside the date range indicated by xdatae(:,1:2)!')
+ end
+ Brow=Brow+nadt;
+ %
+ Erow = min(find(xdatae(:,1)==Eyrqm(1)));
+ if isempty(Brow)
+ error('Eyrqm is outside the date range of xdatae(:,1:2)!')
+ end
+ nadt2=Eyrqm(2)-xdatae(Erow,2);
+ if nadt<0
+ error('Eyrqm is outside the date range indicated by xdatae(:,1:2)!')
+ end
+ Erow=Erow+nadt2;
+
+ nyr = floor((Erow-Brow+1)/q_m);
+ yrsg = [Byrqm(1)+1:Byrqm(1)+nyr-1]'; % for annual growth later on, which will
+ % start at Byrqm(1) instead of Byrqm(1)+1
+end
+%
+yactqme = xdatae(Brow:Erow,:); % with dates
+yactqm = yactqme(:,3:end); % only data
+
+%======== prior period change (annaluized rate)
+yactqmg = yactqm(2:end,:); % start at second period to get growth rate
+yactqmg(:,vlistlog) = (yactqm(2:end,vlistlog) - yactqm(1:end-1,vlistlog)) .* q_m;
+ % monthly, 12*log(1+growth rate), annualized growth rate
+yactqmg(:,vlistlog) = 100*(exp(yactqmg(:,vlistlog))-1);
+yactqmg(:,vlistper) = 100*yactqmg(:,vlistper);
+yactqmge = [yactqme(2:end,1:2) yactqmg];
+
+%======== change from the last year
+yactqmyg = yactqm(q_m+1:end,:); % start at the last-year period to get growth rate
+yactqmyg(:,vlistlog) = (yactqm(q_m+1:end,vlistlog) - yactqm(1:end-q_m,vlistlog));
+yactqmyg(:,vlistlog) = 100*(exp(yactqmyg(:,vlistlog))-1);
+yactqmyg(:,vlistper) = 100*yactqmyg(:,vlistper);
+yactqmyge = [yactqme(q_m+1:end,1:2) yactqmyg];
+
+%======== annual growth rates
+nvar = length(xdatae(1,3:end));
+ygmts = yactqm(1:nyr*q_m,:); % converted to the multiplication of q_m
+ygmts1 = reshape(ygmts,q_m,nyr,nvar);
+ygmts2 = sum(ygmts1,1) ./ q_m;
+ygmts3 = reshape(ygmts2,nyr,nvar); % converted to annual average series
+%
+yactyrg = ygmts3(2:end,:); % start at the last-year period to get growth rate
+yactyrg(:,vlistlog) = ygmts3(2:end,vlistlog) - ygmts3(1:end-1,vlistlog);
+ % annual rate: log(1+growth rate)
+yactyrg(:,vlistlog) = 100*(exp(yactyrg(:,vlistlog))-1);
+yactyrg(:,vlistper) = 100*yactyrg(:,vlistper);
+yactyrge = [yrsg zeros(nyr-1,1) yactyrg];
+yrsg1=[yrsg(1)-1:yrsg(end)]';
+yactyre = [yrsg1 zeros(nyr,1) ygmts3];
diff --git a/MatlabFiles/dataxy.m b/MatlabFiles/dataxy.m
index 639e78d49f16ff4538e491e385738d2ec4f9f426..25abb6cefd01b41655252ed34269881eaaf5c2c2 100644
--- a/MatlabFiles/dataxy.m
+++ b/MatlabFiles/dataxy.m
@@ -1,160 +1,160 @@
-function [xtx,xty,yty,fss,phi,y,ncoef,xr,Bh,e] = dataxy(nvar,lags,z,mu,indxDummy,nexo)
-% [xtx,xty,yty,fss,phi,y,ncoef,xr,Bh,e] = dataxy(nvar,lags,z,mu,indxDummy,nexo)
-%
-% Export arranged data matrices for future estimation for DM models.
-% See Wagonner and Zha's Gibbs sampling paper.
-%
-% nvar: number of endogenous variables.
-% lags: the maximum length of lag
-% nhp: number of haperparameters
-% z: T*(nvar+(nexo-1)) matrix of raw or original data (no manipulation involved)
-% with sample size including lags.
-% mu: 6-by-1 vector of hyperparameters (the following numbers for Atlanta Fed's forecast)
-% mu(1): overall tightness and also for A0; (0.57)
-% mu(2): relative tightness for A+; (0.13)
-% mu(3): relative tightness for the constant term; (0.1)
-% mu(4): tightness on lag decay; (1)
-% mu(5): weight on nvar sums of coeffs dummy observations (unit roots); (5)
-% mu(6): weight on single dummy initial observation including constant
-% (cointegration, unit roots, and stationarity); (5)
-% Here, only mu(5) and mu(6) are used for dummy observations
-% indxDummy = 1; % 1: add dummy observations to the data; 0: no dummy added.
-% nexo: number of exogenous variables. Besides constant terms, we have nexo-1 exogenous variables.
-% -------------------
-% xtx: X'X: k-by-k where k=ncoef
-% xty: X'Y: k-by-nvar
-% yty: Y'Y: nvar-by-nvar
-% fss: T: sample size excluding lags. With dummyies, fss=nSample-lags+ndobs.
-% phi: X; T-by-k; column: [nvar for 1st lag, ..., nvar for last lag, const]
-% y: Y: T-by-nvar where T=fss
-% ncoef: number of coefficients in *each* equation. RHS coefficients only, nvar*lags+ncoef
-% xr: the economy size (ncoef-by-ncoef) in qr(phi) so that xr=chol(X'*X) or xr'*xr=X'*X
-% Bh: ncoef-by-nvar estimated reduced-form parameter; column: nvar;
-% row: ncoef=[nvar for 1st lag, ..., nvar for last lag, exogenous variables, const]
-% e: estimated residual e = y -x*Bh, T-by-nvar
-%
-% Tao Zha, February 2000
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-if nargin == 5
- nexo=1; % default for constant term
-elseif nexo<1
- error('We need at least one exogenous term so nexo must >= 1')
-end
-
-%*** original sample dimension without dummy prior
-nSample = size(z,1); % the sample size (including lags, of course)
-sb = lags+1; % original beginning without dummies
-ncoef = nvar*lags+nexo; % number of coefficients in *each* equation, RHS coefficients only.
-
-if indxDummy % prior dummy prior
-
- %*** expanded sample dimension by dummy prior
- ndobs=nvar+1; % number of dummy observations
- fss = nSample+ndobs-lags;
-
- %
- % **** nvar prior dummy observations with the sum of coefficients
- % ** construct X for Y = X*B + U where phi = X: (T-lags)*k, Y: (T-lags)*nvar
- % ** columns: k = # of [nvar for 1st lag, ..., nvar for last lag, exo var, const]
- % ** Now, T=T+ndobs -- added with "ndobs" dummy observations
- %
- phi = zeros(fss,ncoef);
- %* constant term
- const = ones(fss,1);
- const(1:nvar) = zeros(nvar,1);
- phi(:,ncoef) = const; % the first nvar periods: no or zero constant!
- %* other exogenous (than) constant term
- phi(ndobs+1:end,ncoef-nexo+1:ncoef-1) = z(lags+1:end,nvar+1:nvar+nexo-1);
- exox = zeros(ndobs,nexo);
- phi(1:ndobs,ncoef-nexo+1:ncoef-1) = exox(:,1:nexo-1);
- % this = [] when nexo=1 (no other exogenous than constant)
-
- xdgel = z(:,1:nvar); % endogenous variable matrix
- xdgelint = mean(xdgel(1:lags,:),1); % mean of the first lags initial conditions
- %* Dummies
- for k=1:nvar
- for m=1:lags
- phi(ndobs,nvar*(m-1)+k) = xdgelint(k);
- phi(k,nvar*(m-1)+k) = xdgelint(k);
- % <<>> multiply hyperparameter later
- end
- end
- %* True data
- for k=1:lags
- phi(ndobs+1:fss,nvar*(k-1)+1:nvar*k) = xdgel(sb-k:nSample-k,:);
- % row: T-lags; column: [nvar for 1st lag, ..., nvar for last lag, exo var, const]
- % Thus, # of columns is nvar*lags+nexo = ncoef.
- end
- %
- % ** Y with "ndobs" dummies added
- y = zeros(fss,nvar);
- %* Dummies
- for k=1:nvar
- y(ndobs,k) = xdgelint(k);
- y(k,k) = xdgelint(k);
- % multiply hyperparameter later
- end
- %* True data
- y(ndobs+1:fss,:) = xdgel(sb:nSample,:);
-
- phi(1:nvar,:) = 1*mu(5)*phi(1:nvar,:); % standard Sims and Zha prior
- y(1:nvar,:) = mu(5)*y(1:nvar,:); % standard Sims and Zha prior
- phi(nvar+1,:) = mu(6)*phi(nvar+1,:);
- y(nvar+1,:) = mu(6)*y(nvar+1,:);
-
- [xq,xr]=qr(phi,0);
- xtx=xr'*xr;
- xty=phi'*y;
- [yq,yr]=qr(y,0);
- yty=yr'*yr;
- Bh = xr\(xr'\xty); % xtx\xty where inv(X'X)*(X'Y)
- e=y-phi*Bh;
-else
- fss = nSample-lags;
- %
- % ** construct X for Y = X*B + U where phi = X: (T-lags)*k, Y: (T-lags)*nvar
- % ** columns: k = # of [nvar for 1st lag, ..., nvar for last lag, exo var, const]
- %
- phi = zeros(fss,ncoef);
- %* constant term
- const = ones(fss,1);
- phi(:,ncoef) = const; % the first nvar periods: no or zero constant!
- %* other exogenous (than) constant term
- phi(:,ncoef-nexo+1:ncoef-1) = z(lags+1:end,nvar+1:nvar+nexo-1);
- % this = [] when nexo=1 (no other exogenous than constant)
-
- xdgel = z(:,1:nvar); % endogenous variable matrix
- %* True data
- for k=1:lags
- phi(:,nvar*(k-1)+1:nvar*k) = xdgel(sb-k:nSample-k,:);
- % row: T-lags; column: [nvar for 1st lag, ..., nvar for last lag, exo var, const]
- % Thus, # of columns is nvar*lags+nexo = ncoef.
- end
- %
- y = xdgel(sb:nSample,:);
-
- [xq,xr]=qr(phi,0);
- xtx=xr'*xr;
- xty=phi'*y;
- [yq,yr]=qr(y,0);
- yty=yr'*yr;
- Bh = xr\(xr'\xty); % xtx\xty where inv(X'X)*(X'Y)
- e=y-phi*Bh;
-end
+function [xtx,xty,yty,fss,phi,y,ncoef,xr,Bh,e] = dataxy(nvar,lags,z,mu,indxDummy,nexo)
+% [xtx,xty,yty,fss,phi,y,ncoef,xr,Bh,e] = dataxy(nvar,lags,z,mu,indxDummy,nexo)
+%
+% Export arranged data matrices for future estimation for DM models.
+% See Wagonner and Zha's Gibbs sampling paper.
+%
+% nvar: number of endogenous variables.
+% lags: the maximum length of lag
+% nhp: number of haperparameters
+% z: T*(nvar+(nexo-1)) matrix of raw or original data (no manipulation involved)
+% with sample size including lags.
+% mu: 6-by-1 vector of hyperparameters (the following numbers for Atlanta Fed's forecast)
+% mu(1): overall tightness and also for A0; (0.57)
+% mu(2): relative tightness for A+; (0.13)
+% mu(3): relative tightness for the constant term; (0.1)
+% mu(4): tightness on lag decay; (1)
+% mu(5): weight on nvar sums of coeffs dummy observations (unit roots); (5)
+% mu(6): weight on single dummy initial observation including constant
+% (cointegration, unit roots, and stationarity); (5)
+% Here, only mu(5) and mu(6) are used for dummy observations
+% indxDummy = 1; % 1: add dummy observations to the data; 0: no dummy added.
+% nexo: number of exogenous variables. Besides constant terms, we have nexo-1 exogenous variables.
+% -------------------
+% xtx: X'X: k-by-k where k=ncoef
+% xty: X'Y: k-by-nvar
+% yty: Y'Y: nvar-by-nvar
+% fss: T: sample size excluding lags. With dummyies, fss=nSample-lags+ndobs.
+% phi: X; T-by-k; column: [nvar for 1st lag, ..., nvar for last lag, const]
+% y: Y: T-by-nvar where T=fss
+% ncoef: number of coefficients in *each* equation. RHS coefficients only, nvar*lags+ncoef
+% xr: the economy size (ncoef-by-ncoef) in qr(phi) so that xr=chol(X'*X) or xr'*xr=X'*X
+% Bh: ncoef-by-nvar estimated reduced-form parameter; column: nvar;
+% row: ncoef=[nvar for 1st lag, ..., nvar for last lag, exogenous variables, const]
+% e: estimated residual e = y -x*Bh, T-by-nvar
+%
+% Tao Zha, February 2000
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+if nargin == 5
+ nexo=1; % default for constant term
+elseif nexo<1
+ error('We need at least one exogenous term so nexo must >= 1')
+end
+
+%*** original sample dimension without dummy prior
+nSample = size(z,1); % the sample size (including lags, of course)
+sb = lags+1; % original beginning without dummies
+ncoef = nvar*lags+nexo; % number of coefficients in *each* equation, RHS coefficients only.
+
+if indxDummy % prior dummy prior
+
+ %*** expanded sample dimension by dummy prior
+ ndobs=nvar+1; % number of dummy observations
+ fss = nSample+ndobs-lags;
+
+ %
+ % **** nvar prior dummy observations with the sum of coefficients
+ % ** construct X for Y = X*B + U where phi = X: (T-lags)*k, Y: (T-lags)*nvar
+ % ** columns: k = # of [nvar for 1st lag, ..., nvar for last lag, exo var, const]
+ % ** Now, T=T+ndobs -- added with "ndobs" dummy observations
+ %
+ phi = zeros(fss,ncoef);
+ %* constant term
+ const = ones(fss,1);
+ const(1:nvar) = zeros(nvar,1);
+ phi(:,ncoef) = const; % the first nvar periods: no or zero constant!
+ %* other exogenous (than) constant term
+ phi(ndobs+1:end,ncoef-nexo+1:ncoef-1) = z(lags+1:end,nvar+1:nvar+nexo-1);
+ exox = zeros(ndobs,nexo);
+ phi(1:ndobs,ncoef-nexo+1:ncoef-1) = exox(:,1:nexo-1);
+ % this = [] when nexo=1 (no other exogenous than constant)
+
+ xdgel = z(:,1:nvar); % endogenous variable matrix
+ xdgelint = mean(xdgel(1:lags,:),1); % mean of the first lags initial conditions
+ %* Dummies
+ for k=1:nvar
+ for m=1:lags
+ phi(ndobs,nvar*(m-1)+k) = xdgelint(k);
+ phi(k,nvar*(m-1)+k) = xdgelint(k);
+ % <<>> multiply hyperparameter later
+ end
+ end
+ %* True data
+ for k=1:lags
+ phi(ndobs+1:fss,nvar*(k-1)+1:nvar*k) = xdgel(sb-k:nSample-k,:);
+ % row: T-lags; column: [nvar for 1st lag, ..., nvar for last lag, exo var, const]
+ % Thus, # of columns is nvar*lags+nexo = ncoef.
+ end
+ %
+ % ** Y with "ndobs" dummies added
+ y = zeros(fss,nvar);
+ %* Dummies
+ for k=1:nvar
+ y(ndobs,k) = xdgelint(k);
+ y(k,k) = xdgelint(k);
+ % multiply hyperparameter later
+ end
+ %* True data
+ y(ndobs+1:fss,:) = xdgel(sb:nSample,:);
+
+ phi(1:nvar,:) = 1*mu(5)*phi(1:nvar,:); % standard Sims and Zha prior
+ y(1:nvar,:) = mu(5)*y(1:nvar,:); % standard Sims and Zha prior
+ phi(nvar+1,:) = mu(6)*phi(nvar+1,:);
+ y(nvar+1,:) = mu(6)*y(nvar+1,:);
+
+ [xq,xr]=qr(phi,0);
+ xtx=xr'*xr;
+ xty=phi'*y;
+ [yq,yr]=qr(y,0);
+ yty=yr'*yr;
+ Bh = xr\(xr'\xty); % xtx\xty where inv(X'X)*(X'Y)
+ e=y-phi*Bh;
+else
+ fss = nSample-lags;
+ %
+ % ** construct X for Y = X*B + U where phi = X: (T-lags)*k, Y: (T-lags)*nvar
+ % ** columns: k = # of [nvar for 1st lag, ..., nvar for last lag, exo var, const]
+ %
+ phi = zeros(fss,ncoef);
+ %* constant term
+ const = ones(fss,1);
+ phi(:,ncoef) = const; % the first nvar periods: no or zero constant!
+ %* other exogenous (than) constant term
+ phi(:,ncoef-nexo+1:ncoef-1) = z(lags+1:end,nvar+1:nvar+nexo-1);
+ % this = [] when nexo=1 (no other exogenous than constant)
+
+ xdgel = z(:,1:nvar); % endogenous variable matrix
+ %* True data
+ for k=1:lags
+ phi(:,nvar*(k-1)+1:nvar*k) = xdgel(sb-k:nSample-k,:);
+ % row: T-lags; column: [nvar for 1st lag, ..., nvar for last lag, exo var, const]
+ % Thus, # of columns is nvar*lags+nexo = ncoef.
+ end
+ %
+ y = xdgel(sb:nSample,:);
+
+ [xq,xr]=qr(phi,0);
+ xtx=xr'*xr;
+ xty=phi'*y;
+ [yq,yr]=qr(y,0);
+ yty=yr'*yr;
+ Bh = xr\(xr'\xty); % xtx\xty where inv(X'X)*(X'Y)
+ e=y-phi*Bh;
+end
diff --git a/MatlabFiles/demarcate.m b/MatlabFiles/demarcate.m
index c83940772b9976fee9329b7a1c58e3e5eed200ab..d3b030a8aeed7e8e2b86109fa6198b417e19c0bc 100644
--- a/MatlabFiles/demarcate.m
+++ b/MatlabFiles/demarcate.m
@@ -1,115 +1,114 @@
-function [imfl,imfh,imfl1,imfh1] = demarcate(imfcnt,imndraws,forep,nvar,...
- ninv,invc,Am)
-% [imfl,imfh,imfl1,imfh1] = demarcate(imfcnt,imndraws,forep,nvar,...
-% ninv,invc,Am)
-% imfcnt: 2+ninv-by-forep*nvar. Counted logcnt, yhatqgcnt, or yhatCalygcnt.
-% In the case of impulse responses, forep=imstp and nvar=nvar^2
-% imndraws: total number of draws allocated to "imfcnt"
-% forep: the number of forecast periods (for both impulse responses and forecasting)
-% nvar: the number of variables.
-% ninv: the number of small interior intervals for sorting.
-% invc: 1-by-forep*nvar. Whole inverval lenghth from min to max for one of
-% (yhat, yhatqg, or yhatCalyg)
-% Am: 1-by-forep*nvar. Range5{i}(:,:,1) is lowest range for for one of
-% (yhat, yhatqg, or yhatCalyg)
-%-----------------
-% imfl: lower .95 bound, forep-by-nvar
-% imfh: higher .95 bound, forep-by-nvar
-% imfl1: lower .68 bound, forep-by-nvar
-% imfh1: higher .68 bound, forep-by-nvar
-%
-% Copyright (c) March 1998 Tao Zha
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-
-
-%$$$ .68 and .95 probability bands
-imfl = zeros(forep*nvar,1); % preallocating
-imfh = zeros(forep*nvar,1); % preallocating
-imfl1 = zeros(forep*nvar,1); % preallocating
-imfh1 = zeros(forep*nvar,1); % preallocating
-imfpo = zeros(4,forep*nvar); % 4 positions: l,h,l1,h1.
-%
-%tic
-tem = cumsum(imfcnt) ./ imndraws; % cumulative probability
-tem = tem .* 100;
-clear imfcnt
-
-uptail1=5; % 2.5, interval
-lowtail1=95; % 97.5
-
-%t_cum = toc
-%
-%tic
-%
-%@@@ the following operations are valid only because tem are increasing!
-for k = 1:forep*nvar
- %
- %@@@ the following operations are valid only because tem are increasing!
- %** 2.5% low tail
- if isempty(max(find(tem(:,k)<uptail1)))
- imfpo(1,k) = 1;
- else
- imfpo(1,k) = max(find(tem(:,k)<uptail1));
- end
- %** 16% low tail
- if isempty(max(find(tem(:,k)<16)))
- imfpo(2,k) = 1;
- else
- imfpo(2,k) = max(find(tem(:,k)<16));
- end
- %** 2.5% high tail
- imfpo(3,k) = min(find(tem(:,k)>lowtail1));
- %** 16% low tail
- imfpo(4,k) = min(find(tem(:,k)>84));
-end
-
-tem = imfpo';
-%save outprob.txt tem -ascii
-%
-
-imfpo = imfpo - 2; % the first step to put back to original form
-% * 2.5% low tail
-imfs = ((imfpo(1,:) .* invc) ./ ninv) + Am;
- % the final step to put back to original form of impulse responses
-imfl = imfs';
-% * 16% low tail
-imfs = ((imfpo(2,:) .* invc) ./ ninv) + Am;
- % the final step to put back to original form of impulse responses
-imfl1 = imfs';
-% * 2.5% high tail
-imfs = ((imfpo(3,:) .* invc) ./ ninv) + Am;
- % the final step to put back to original form of impulse responses
-imfh = imfs';
-% * 16% high tail
-imfs = ((imfpo(4,:) .* invc) ./ ninv) + Am;
- % the final step to put back to original form of impulse responses
-imfh1 = imfs';
-%
-%e_t = toc
-
-
-% *** write out final results
-clear tem
-imfl = reshape(imfl,forep,nvar);
-imfh = reshape(imfh,forep,nvar);
-imfl1 = reshape(imfl1,forep,nvar);
-imfh1 = reshape(imfh1,forep,nvar);
-%
-%
\ No newline at end of file
+function [imfl,imfh,imfl1,imfh1] = demarcate(imfcnt,imndraws,forep,nvar,...
+ ninv,invc,Am)
+% [imfl,imfh,imfl1,imfh1] = demarcate(imfcnt,imndraws,forep,nvar,...
+% ninv,invc,Am)
+% imfcnt: 2+ninv-by-forep*nvar. Counted logcnt, yhatqgcnt, or yhatCalygcnt.
+% In the case of impulse responses, forep=imstp and nvar=nvar^2
+% imndraws: total number of draws allocated to "imfcnt"
+% forep: the number of forecast periods (for both impulse responses and forecasting)
+% nvar: the number of variables.
+% ninv: the number of small interior intervals for sorting.
+% invc: 1-by-forep*nvar. Whole inverval lenghth from min to max for one of
+% (yhat, yhatqg, or yhatCalyg)
+% Am: 1-by-forep*nvar. Range5{i}(:,:,1) is lowest range for for one of
+% (yhat, yhatqg, or yhatCalyg)
+%-----------------
+% imfl: lower .95 bound, forep-by-nvar
+% imfh: higher .95 bound, forep-by-nvar
+% imfl1: lower .68 bound, forep-by-nvar
+% imfh1: higher .68 bound, forep-by-nvar
+%
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+
+
+%$$$ .68 and .95 probability bands
+imfl = zeros(forep*nvar,1); % preallocating
+imfh = zeros(forep*nvar,1); % preallocating
+imfl1 = zeros(forep*nvar,1); % preallocating
+imfh1 = zeros(forep*nvar,1); % preallocating
+imfpo = zeros(4,forep*nvar); % 4 positions: l,h,l1,h1.
+%
+%tic
+tem = cumsum(imfcnt) ./ imndraws; % cumulative probability
+tem = tem .* 100;
+clear imfcnt
+
+uptail1=5; % 2.5, interval
+lowtail1=95; % 97.5
+
+%t_cum = toc
+%
+%tic
+%
+%@@@ the following operations are valid only because tem are increasing!
+for k = 1:forep*nvar
+ %
+ %@@@ the following operations are valid only because tem are increasing!
+ %** 2.5% low tail
+ if isempty(max(find(tem(:,k)<uptail1)))
+ imfpo(1,k) = 1;
+ else
+ imfpo(1,k) = max(find(tem(:,k)<uptail1));
+ end
+ %** 16% low tail
+ if isempty(max(find(tem(:,k)<16)))
+ imfpo(2,k) = 1;
+ else
+ imfpo(2,k) = max(find(tem(:,k)<16));
+ end
+ %** 2.5% high tail
+ imfpo(3,k) = min(find(tem(:,k)>lowtail1));
+ %** 16% low tail
+ imfpo(4,k) = min(find(tem(:,k)>84));
+end
+
+tem = imfpo';
+%save outprob.txt tem -ascii
+%
+
+imfpo = imfpo - 2; % the first step to put back to original form
+% * 2.5% low tail
+imfs = ((imfpo(1,:) .* invc) ./ ninv) + Am;
+ % the final step to put back to original form of impulse responses
+imfl = imfs';
+% * 16% low tail
+imfs = ((imfpo(2,:) .* invc) ./ ninv) + Am;
+ % the final step to put back to original form of impulse responses
+imfl1 = imfs';
+% * 2.5% high tail
+imfs = ((imfpo(3,:) .* invc) ./ ninv) + Am;
+ % the final step to put back to original form of impulse responses
+imfh = imfs';
+% * 16% high tail
+imfs = ((imfpo(4,:) .* invc) ./ ninv) + Am;
+ % the final step to put back to original form of impulse responses
+imfh1 = imfs';
+%
+%e_t = toc
+
+
+% *** write out final results
+clear tem
+imfl = reshape(imfl,forep,nvar);
+imfh = reshape(imfh,forep,nvar);
+imfl1 = reshape(imfl1,forep,nvar);
+imfh1 = reshape(imfh1,forep,nvar);
+%
+%
diff --git a/MatlabFiles/demarw.m b/MatlabFiles/demarw.m
index f354059495b1273ae92563101200bf39452a93d6..ac28a39cc646570a822b5ab819bda65d1057c193 100644
--- a/MatlabFiles/demarw.m
+++ b/MatlabFiles/demarw.m
@@ -1,65 +1,64 @@
-function [imfl,imfh,imfl1,imfh1] = demarw(xpo,xprob)
-% [imfl,imfh,imfl1,imfh1] = demarw(xpo,xprob)
-% Demarcate the .68 and .95 error bands given prob. (weights) and positions
-%
-% xpo: ndraws-by-nvar. Centered position
-% xprob: ndraws-by-nvar. Properly scaled probabilities corresponding to xpo.
-%-----------------
-% imfl: lower .95 bound, nvar-by-1
-% imfh: higher .95 bound, nvar-by-1
-% imfl1: lower .68 bound, nvar-by-1
-% imfh1: higher .68 bound, nvar-by-1
-%
-% Copyright (c) August 1999 Tao Zha
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-[ndraws,nvar]=size(xpo);
-
-%$$$ .68 and .95 probability bands
-imfl = zeros(nvar,1); % preallocating
-imfh = zeros(nvar,1); % preallocating
-imfl1 = zeros(nvar,1); % preallocating
-imfh1 = zeros(nvar,1); % preallocating
-imfpo = zeros(4,nvar); % 4 positions: l,h,l1,h1.
-%
-%tic
-tem = cumsum(xprob); % cumulative probability
-tem = tem .* 100;
-%
-%@@@ the following operations are valid only because tem are increasing!
-for k = 1:nvar
- %
- %@@@ the following operations are valid only because tem are increasing!
- %** 2.5% low tail
- if isempty(max(find(tem(:,k)<2.5)))
- imfl(k) = xpo(1,k);
- else
- imfl(k) = xpo(max(find(tem(:,k)<2.5)),k);
- end
- %** 16% low tail
- if isempty(max(find(tem(:,k)<16)))
- imfl1(k) = xpo(1,k);
- else
- imfl1(k) = xpo(max(find(tem(:,k)<16)),k);
- end
- %** 2.5% high tail
- imfh(k) = xpo(min(find(tem(:,k)>97.5)),k);
- %** 16% low tail
- imfh1(k) = xpo(min(find(tem(:,k)>84)),k);
-end
+function [imfl,imfh,imfl1,imfh1] = demarw(xpo,xprob)
+% [imfl,imfh,imfl1,imfh1] = demarw(xpo,xprob)
+% Demarcate the .68 and .95 error bands given prob. (weights) and positions
+%
+% xpo: ndraws-by-nvar. Centered position
+% xprob: ndraws-by-nvar. Properly scaled probabilities corresponding to xpo.
+%-----------------
+% imfl: lower .95 bound, nvar-by-1
+% imfh: higher .95 bound, nvar-by-1
+% imfl1: lower .68 bound, nvar-by-1
+% imfh1: higher .68 bound, nvar-by-1
+%
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+[ndraws,nvar]=size(xpo);
+
+%$$$ .68 and .95 probability bands
+imfl = zeros(nvar,1); % preallocating
+imfh = zeros(nvar,1); % preallocating
+imfl1 = zeros(nvar,1); % preallocating
+imfh1 = zeros(nvar,1); % preallocating
+imfpo = zeros(4,nvar); % 4 positions: l,h,l1,h1.
+%
+%tic
+tem = cumsum(xprob); % cumulative probability
+tem = tem .* 100;
+%
+%@@@ the following operations are valid only because tem are increasing!
+for k = 1:nvar
+ %
+ %@@@ the following operations are valid only because tem are increasing!
+ %** 2.5% low tail
+ if isempty(max(find(tem(:,k)<2.5)))
+ imfl(k) = xpo(1,k);
+ else
+ imfl(k) = xpo(max(find(tem(:,k)<2.5)),k);
+ end
+ %** 16% low tail
+ if isempty(max(find(tem(:,k)<16)))
+ imfl1(k) = xpo(1,k);
+ else
+ imfl1(k) = xpo(max(find(tem(:,k)<16)),k);
+ end
+ %** 2.5% high tail
+ imfh(k) = xpo(min(find(tem(:,k)>97.5)),k);
+ %** 16% low tail
+ imfh1(k) = xpo(min(find(tem(:,k)>84)),k);
+end
diff --git a/MatlabFiles/dlrpostr.m b/MatlabFiles/dlrpostr.m
index 52b7ba54cbbd0f6e22ba2d7d67de77a9cf0a9c8d..b462f8dae5a1384524ae55e6b506ba5bd6727e48 100644
--- a/MatlabFiles/dlrpostr.m
+++ b/MatlabFiles/dlrpostr.m
@@ -1,59 +1,59 @@
-function [P,H0inv,Hpinv] = dlrpostr(xtx,xty,yty,Ui,Vi)
-% [P,H0inv,Hpinv] = dlrpostr(xtx,xty,yty,Ui,Vi)
-%
-% Exporting deterministic (hard restrictions) Bayesian posterior matrices with linear restrictions
-% See Waggoner and Zha's Gibbs sampling paper
-%
-% xtx: X'X: k-by-k where k=ncoef
-% xty: X'Y: k-by-nvar
-% yty: Y'Y: nvar-by-nvar
-% Ui: nvar-by-1 cell. In each cell, nvar-by-qi orthonormal basis for the null of the ith
-% equation contemporaneous restriction matrix where qi is the number of free parameters.
-% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
-% of total original parameters and bi is a vector of free parameters. When no
-% restrictions are imposed, we have Ui = I. There must be at least one free
-% parameter left for the ith equation. Imported from dnrprior.m.
-% Vi: nvar-by-1 cell. In each cell, k-by-ri orthonormal basis for the null of the ith
-% equation lagged restriction matrix where k (ncoef) is a total number of RHS variables and
-% ri is the number of free parameters. With this transformation, we have fi = Vi*gi
-% or Vi'*fi = gi where fi is a vector of total original parameters and gi is a
-% vector of free parameters. There must be at least one free parameter left for
-% the ith equation. Imported from dnrprior.m.
-%-----------------
-% P: cell(nvar,1). In each cell, posterior linear transformation for random walk prior for the ith equation % tld: tilda
-% H0inv: cell(nvar,1). In each cell, posterior inverse of covariance inv(H0) for the ith equation,
-% resembling old SpH in the exponent term in posterior of A0, but not divided by T yet.
-% Hpinv: cell(nvar,1). In each cell, posterior inv(Hp) for the ith equation.
-%
-% Tao Zha, February 2000
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-nvar = size(yty,1);
-
-P = cell(nvar,1); % tld: tilda
-H0inv = cell(nvar,1); % posterior inv(H0), resemble old SpH, but not divided by T yet.
-Hpinv = cell(nvar,1); % posterior inv(Hp).
-
-for n=1:nvar % one for each equation
- Hpinv{n} = Vi{n}'*xtx*Vi{n};
- P1 = Vi{n}'*xty*Ui{n};
- P{n} = Hpinv{n}\P1;
- H0inv{n} = Ui{n}'*yty*Ui{n} - P1'*(Hpinv{n}\P1);
-end
-
+function [P,H0inv,Hpinv] = dlrpostr(xtx,xty,yty,Ui,Vi)
+% [P,H0inv,Hpinv] = dlrpostr(xtx,xty,yty,Ui,Vi)
+%
+% Exporting deterministic (hard restrictions) Bayesian posterior matrices with linear restrictions
+% See Waggoner and Zha's Gibbs sampling paper
+%
+% xtx: X'X: k-by-k where k=ncoef
+% xty: X'Y: k-by-nvar
+% yty: Y'Y: nvar-by-nvar
+% Ui: nvar-by-1 cell. In each cell, nvar-by-qi orthonormal basis for the null of the ith
+% equation contemporaneous restriction matrix where qi is the number of free parameters.
+% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
+% of total original parameters and bi is a vector of free parameters. When no
+% restrictions are imposed, we have Ui = I. There must be at least one free
+% parameter left for the ith equation. Imported from dnrprior.m.
+% Vi: nvar-by-1 cell. In each cell, k-by-ri orthonormal basis for the null of the ith
+% equation lagged restriction matrix where k (ncoef) is a total number of RHS variables and
+% ri is the number of free parameters. With this transformation, we have fi = Vi*gi
+% or Vi'*fi = gi where fi is a vector of total original parameters and gi is a
+% vector of free parameters. There must be at least one free parameter left for
+% the ith equation. Imported from dnrprior.m.
+%-----------------
+% P: cell(nvar,1). In each cell, posterior linear transformation for random walk prior for the ith equation % tld: tilda
+% H0inv: cell(nvar,1). In each cell, posterior inverse of covariance inv(H0) for the ith equation,
+% resembling old SpH in the exponent term in posterior of A0, but not divided by T yet.
+% Hpinv: cell(nvar,1). In each cell, posterior inv(Hp) for the ith equation.
+%
+% Tao Zha, February 2000
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+nvar = size(yty,1);
+
+P = cell(nvar,1); % tld: tilda
+H0inv = cell(nvar,1); % posterior inv(H0), resemble old SpH, but not divided by T yet.
+Hpinv = cell(nvar,1); % posterior inv(Hp).
+
+for n=1:nvar % one for each equation
+ Hpinv{n} = Vi{n}'*xtx*Vi{n};
+ P1 = Vi{n}'*xty*Ui{n};
+ P{n} = Hpinv{n}\P1;
+ H0inv{n} = Ui{n}'*yty*Ui{n} - P1'*(Hpinv{n}\P1);
+end
+
diff --git a/MatlabFiles/eigsort.m b/MatlabFiles/eigsort.m
index 771d7b0d7018be1fee38611648d575a4e99ffdbb..43877703176968bc1436d166460debdc24bccaba 100644
--- a/MatlabFiles/eigsort.m
+++ b/MatlabFiles/eigsort.m
@@ -1,46 +1,46 @@
-function [vU2,dU2] = eigsort(U,desI)
-% E = EIGSORT(X,desI) is a vector containing the eigenvalues of a square
-% matrix X, where E is sorted descendingly if desI=1 or ascendingly otherwise
-%
-% [V,D] = EIG(X) produces a diagonal matrix D of eigenvalues and a
-% full matrix V whose columns are the corresponding eigenvectors so
-% that X*V = V*D.
-%
-% Written 9/6/98 by Tao Zha
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-[vU,dU]=eig(U);
-dUvec = diag(dU); % If dU is SPD, use "abs" in front of diag. This is because,
- % even though dU is supposed to be positive, the numerical solution
- % for a near-zero eigenvalue can be negative.
- % *******************
- % Second thought: "abs" is not necessary, I think. If negative, it
- % implies zero already -- thus the smallest number
-[dUvec2,dUI2] = sort(dUvec); % ascending
-if desI==1 % if descending is required
- dUvec2 = flipud(dUvec2);
- dUI2 = flipud(dUI2);
-end
-
-if nargout==1
- vU2 = dUvec2;
- dU2 = NaN;
-else
- vU2 = vU(:,dUI2);
- dU2 = diag(dUvec2); % put it back to matrix form
-end
\ No newline at end of file
+function [vU2,dU2] = eigsort(U,desI)
+% E = EIGSORT(X,desI) is a vector containing the eigenvalues of a square
+% matrix X, where E is sorted descendingly if desI=1 or ascendingly otherwise
+%
+% [V,D] = EIG(X) produces a diagonal matrix D of eigenvalues and a
+% full matrix V whose columns are the corresponding eigenvectors so
+% that X*V = V*D.
+%
+% Written 9/6/98 by Tao Zha
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+[vU,dU]=eig(U);
+dUvec = diag(dU); % If dU is SPD, use "abs" in front of diag. This is because,
+ % even though dU is supposed to be positive, the numerical solution
+ % for a near-zero eigenvalue can be negative.
+ % *******************
+ % Second thought: "abs" is not necessary, I think. If negative, it
+ % implies zero already -- thus the smallest number
+[dUvec2,dUI2] = sort(dUvec); % ascending
+if desI==1 % if descending is required
+ dUvec2 = flipud(dUvec2);
+ dUI2 = flipud(dUI2);
+end
+
+if nargout==1
+ vU2 = dUvec2;
+ dU2 = NaN;
+else
+ vU2 = vU(:,dUI2);
+ dU2 = diag(dUvec2); % put it back to matrix form
+end
diff --git a/MatlabFiles/ellipse.m b/MatlabFiles/ellipse.m
index 5ef3f6b56a3fd0c5d7d3ff9b025afca3210c4a1d..d80b6865a7dc930b0f98bb94533f4be1b851c862 100644
--- a/MatlabFiles/ellipse.m
+++ b/MatlabFiles/ellipse.m
@@ -1,86 +1,86 @@
-function [x,y,z] = ellipse(sigma,mu,k)
-%ELLIPSE Creates an ellipsoid
-%[X,Y,Z] = ELLIPSE(SIGMA,MU,K) Creates an arbitrary
-%ellipsoid in 2 or 3 dimensions. The ellipsoid represents
-%the solutions to the quadratic equation:
-%
-% (x-MU)'*SIGMA*(x-MU) = K
-%
-%SIGMA is a positive definate matrix of size 2x2 or 3x3.
-%SIGMA determines the shape of the ellipsoid.
-%MU is a vector conformable with SIGMA; either 2x1 or 3x1.
-%MU determines the location of the ellipsoid.
-%K is a real constant.
-%K determines the size of the ellipsoid.
-%The vector x=[X;Y] in 2D or x=[X,Y,Z] in 3D.
-%
-%If no output aruments are specified, the resulting
-%ellipsoid is plotted. One can create these plots manually
-%using PLOT(X,Y) in the 2 dimensional case and
-%using MESH(X,Y,Z) in the 3 dimensional case.
-
-% QPLOT was written by Clark A. Burdick of the research
-% department of the Federal Reserve Bank of Atlanta.
-% Original: August 19, 1997
-% Last Modified: August 19, 1997
-% Copyright (C) 1997-2012 Clark A. Burdick
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-% TO BE DONE:
-% I'm open to suggestions.
-
-n=50; % <<>>
-theta=pi*(-n:2:n)/n;
-phi=(pi/2)*(-n:2:n)'/n;
-
-R=chol(sigma);
-rho=sqrt(k);
-
-if length(mu) == 2
- X = rho*cos(theta);
- Y = rho*sin(theta);
-
- for i = 1:length(theta)
- circlevec = [X(i);Y(i)];
- ellipsevec = R\circlevec; % T.A. Zha, 8/16/98
- x(i) = mu(1) + ellipsevec(1);
- y(i) = mu(2) + ellipsevec(2);
- end
- if nargout == 0
- plot(x,y)
- end
- z='This was only a 2 dimensional ellipse';
-end
-
-if length(mu) == 3
- X = rho*cos(phi)*cos(theta);
- Y = rho*cos(phi)*sin(theta);
- Z = rho*sin(phi)*ones(size(theta));
-
- for i = 1:length(phi)
- for j = 1:length(theta)
- spherevec = [X(i,j); Y(i,j); Z(i,j)];
- ellipsevec = R\spherevec; % T.A. Zha, 8/16/98
- x(i,j) = mu(1) + ellipsevec(1);
- y(i,j) = mu(2) + ellipsevec(2);
- z(i,j) = mu(3) + ellipsevec(3);
- end
- end
- if nargout == 0
- mesh(x,y,z)
- end
-end
\ No newline at end of file
+function [x,y,z] = ellipse(sigma,mu,k)
+%ELLIPSE Creates an ellipsoid
+%[X,Y,Z] = ELLIPSE(SIGMA,MU,K) Creates an arbitrary
+%ellipsoid in 2 or 3 dimensions. The ellipsoid represents
+%the solutions to the quadratic equation:
+%
+% (x-MU)'*SIGMA*(x-MU) = K
+%
+%SIGMA is a positive definate matrix of size 2x2 or 3x3.
+%SIGMA determines the shape of the ellipsoid.
+%MU is a vector conformable with SIGMA; either 2x1 or 3x1.
+%MU determines the location of the ellipsoid.
+%K is a real constant.
+%K determines the size of the ellipsoid.
+%The vector x=[X;Y] in 2D or x=[X,Y,Z] in 3D.
+%
+%If no output aruments are specified, the resulting
+%ellipsoid is plotted. One can create these plots manually
+%using PLOT(X,Y) in the 2 dimensional case and
+%using MESH(X,Y,Z) in the 3 dimensional case.
+
+% QPLOT was written by Clark A. Burdick of the research
+% department of the Federal Reserve Bank of Atlanta.
+% Original: August 19, 1997
+% Last Modified: August 19, 1997
+%
+% Copyright (C) 1997-2012 Clark A. Burdick
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+% TO BE DONE:
+% I'm open to suggestions.
+
+n=50; % <<>>
+theta=pi*(-n:2:n)/n;
+phi=(pi/2)*(-n:2:n)'/n;
+
+R=chol(sigma);
+rho=sqrt(k);
+
+if length(mu) == 2
+ X = rho*cos(theta);
+ Y = rho*sin(theta);
+
+ for i = 1:length(theta)
+ circlevec = [X(i);Y(i)];
+ ellipsevec = R\circlevec; % T.A. Zha, 8/16/98
+ x(i) = mu(1) + ellipsevec(1);
+ y(i) = mu(2) + ellipsevec(2);
+ end
+ if nargout == 0
+ plot(x,y)
+ end
+ z='This was only a 2 dimensional ellipse';
+end
+
+if length(mu) == 3
+ X = rho*cos(phi)*cos(theta);
+ Y = rho*cos(phi)*sin(theta);
+ Z = rho*sin(phi)*ones(size(theta));
+
+ for i = 1:length(phi)
+ for j = 1:length(theta)
+ spherevec = [X(i,j); Y(i,j); Z(i,j)];
+ ellipsevec = R\spherevec; % T.A. Zha, 8/16/98
+ x(i,j) = mu(1) + ellipsevec(1);
+ y(i,j) = mu(2) + ellipsevec(2);
+ z(i,j) = mu(3) + ellipsevec(3);
+ end
+ end
+ if nargout == 0
+ mesh(x,y,z)
+ end
+end
diff --git a/MatlabFiles/empdfsort.m b/MatlabFiles/empdfsort.m
index c9f36273705aaa75b878d7babdd694f1fa27daa5..68282b7df68b8e2b0a592abc301b17b2bee29b06 100644
--- a/MatlabFiles/empdfsort.m
+++ b/MatlabFiles/empdfsort.m
@@ -1,47 +1,47 @@
-function imfcnt = empdfsort(imfcnt,imf_h,imfloor,hbin,ninv)
-% imfcnt = empdfsort(imfcnt,imf_h,imfloor,hbin,ninv)
-% Variablewise (but multivariate) empirical probability distribution with counts
-% sorted into bins variablewise
-% Note that since a particular draw (imf_h) can be below "imfloor" and above "imceiling"
-% (=(imceiling-imfloor)*hbin), this function allows ninv+2 bins for each variable
-%
-% imfcnt: initial ninv+2-by-k matrix that records counts in each bin given a column in imfcnt
-% if k==1, then only one variable is considered.
-% imf_h: particular draw, needed not to be a 1-by-k row vector
-% imfloor: the low value of imf but imf_h can be below "imfloor" and above "imceiling"
-% hbin: bin size = (imceilling-imfloor)/ninv
-% ninv: number of bins between "imfloor" and "imceiling"
-%------------------
-% imfcnt: updated ninv+2-by-k matrix of counts (probability)
-%
-% January 1999 TAZ
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-%** find the bin locations and arrange them to the order of 1, 2, ...
-countInt = floor( (imf_h-imfloor) ./ hbin ); % imstp-by-nvar^2
- % bin locations from <0, 0, 1,..., ninv-1, >=ninv, a total of ninv+2 bins
-countInt = 2+countInt(:)'; % row vector, 1-by-imstp*nvar^2, see my shock (1), pp.1
- % move everyting by 2 so as to take account of <0
-
-countInt(find(countInt<2)) = 1; % set <0 or -infinity at 1 to start
-countInt(find(countInt>=ninv+2)) = ninv+2; % set >=ninv+2 or +infinity at ninv+2 to end
-countInt = countInt + (0:length(countInt)-1)*(ninv+2); % index to fill the matrix with prob. (counts)
- % The term after "+": with every count, skip ninv+2 to keep
- % each column in "imfcnt" with only one element (which is probability)
-imfcnt(countInt) = imfcnt(countInt) + 1;
+function imfcnt = empdfsort(imfcnt,imf_h,imfloor,hbin,ninv)
+% imfcnt = empdfsort(imfcnt,imf_h,imfloor,hbin,ninv)
+% Variablewise (but multivariate) empirical probability distribution with counts
+% sorted into bins variablewise
+% Note that since a particular draw (imf_h) can be below "imfloor" and above "imceiling"
+% (=(imceiling-imfloor)*hbin), this function allows ninv+2 bins for each variable
+%
+% imfcnt: initial ninv+2-by-k matrix that records counts in each bin given a column in imfcnt
+% if k==1, then only one variable is considered.
+% imf_h: particular draw, needed not to be a 1-by-k row vector
+% imfloor: the low value of imf but imf_h can be below "imfloor" and above "imceiling"
+% hbin: bin size = (imceilling-imfloor)/ninv
+% ninv: number of bins between "imfloor" and "imceiling"
+%------------------
+% imfcnt: updated ninv+2-by-k matrix of counts (probability)
+%
+% January 1999 TAZ
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+%** find the bin locations and arrange them to the order of 1, 2, ...
+countInt = floor( (imf_h-imfloor) ./ hbin ); % imstp-by-nvar^2
+ % bin locations from <0, 0, 1,..., ninv-1, >=ninv, a total of ninv+2 bins
+countInt = 2+countInt(:)'; % row vector, 1-by-imstp*nvar^2, see my shock (1), pp.1
+ % move everyting by 2 so as to take account of <0
+
+countInt(find(countInt<2)) = 1; % set <0 or -infinity at 1 to start
+countInt(find(countInt>=ninv+2)) = ninv+2; % set >=ninv+2 or +infinity at ninv+2 to end
+countInt = countInt + (0:length(countInt)-1)*(ninv+2); % index to fill the matrix with prob. (counts)
+ % The term after "+": with every count, skip ninv+2 to keep
+ % each column in "imfcnt" with only one element (which is probability)
+imfcnt(countInt) = imfcnt(countInt) + 1;
diff --git a/MatlabFiles/errors.m b/MatlabFiles/errors.m
index 09a5dd6f076aef483c335117d1bc8b0201c2635c..bcbd57b7c4185f3988125cf28805c51a2c1e3a29 100644
--- a/MatlabFiles/errors.m
+++ b/MatlabFiles/errors.m
@@ -1,102 +1,102 @@
-function [vd,str,imf] = errors(Bh,swish,nn)
-% Computing variance decompositions and impulse functions with
-% [vd,str,imf] = errors(Bh,swish,nn)
-% where imf and vd is of the same format as in RATS, that is to say:
-% Column: nvar responses to 1st shock,
-% nvar responses to 2nd shock, and so on.
-% Row: steps of impulse responses.
-% vd: variance decompositions
-% str: standard errors of each variable, steps-by-nvar
-% imf: impulse response functions
-% Bh is the estimated reduced form coefficient in the form
-% Y(T*nvar) = XB + U, X: T*k, B: k*nvar. The matrix
-% form or dimension is the same as "Bh" from the function "sye";
-% swish is the inv(A0) in the structural model A(L)y(t) = e(t).
-% nn is the numbers of inputs [nvar,lags,# of impulse responses].
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-nvar = nn(1);
-lags = nn(2);
-imstep = nn(3); % number of steps for impulse responses
-
-Ah = Bh';
-% Row: nvar equations
-% Column: 1st lag (with nvar variables) to lags (with nvar variables) + const = k.
-
-imf = zeros(imstep,nvar*nvar);
-vd = imf;
-% Column: nvar responses to 1st shock, nvar responses to 2nd shock, and so on.
-% Row: steps of impulse responses.
-str = zeros(imstep,nvar); % initializing standard errors of each equation
-M = zeros(nvar*(lags+1),nvar);
-% Stack lags M's in the order of, e.g., [Mlags, ..., M2,M1;M0]
-M(1:nvar,:) = swish';
-Mtem = M(1:nvar,:); % temporary M -- impulse responses.
-%
-Mvd = Mtem.^2; % saved for the cumulative sum later
-Mvds = (sum(Mvd'))';
-str(1,:) = sqrt(Mvds'); % standard errors of each equation
-Mvds = Mvds(:,ones(size(Mvds,1),1));
-Mvdtem = (100.0*Mvd) ./ Mvds; % tempoary Mvd -- variance decompositions
-% first or initial responses to
-% one standard deviation shock (or forecast errors).
-% Row: responses; Column: shocks
-%
-% * put in the form of "imf"
-imf(1,:) = Mtem(:)';
-vd(1,:) = Mvdtem(:)';
-
-t = 1;
-ims1 = min([imstep-1 lags]);
-while t <= ims1
- Mtem = Ah(:,1:nvar*t)*M(1:nvar*t,:);
- % Row: nvar equations, each for the nvar variables at tth lag
- M(nvar+1:nvar*(t+1),:)=M(1:nvar*t,:);
- M(1:nvar,:) = Mtem;
- % ** impulse response functions
- imf(t+1,:) = Mtem(:)';
- % stack imf with each step, Row: 6 var to 1st shock, 6 var to 2nd shock, etc.
- % ** variance decompositions
- Mvd = Mvd + Mtem.^2; % saved for the cumulative sum later
- Mvds = (sum(Mvd'))';
- str(t+1,:) = sqrt(Mvds'); % standard errors of each equation
- Mvds = Mvds(:,ones(size(Mvds,1),1));
- Mvdtem = (100.0*Mvd) ./ Mvds; % tempoary Mvd -- variance decompositions
- vd(t+1,:) = Mvdtem(:)';
- % stack vd with each step, Row: 6 var to 1st shock, 6 var to 2nd shock, etc.
- t= t+1;
-end
-
-for t = lags+1:imstep-1
- Mtem = Ah(:,1:nvar*lags)*M(1:nvar*lags,:);
- % Row: nvar equations, each for the nvar variables at tth lag
- M(nvar+1:nvar*(t+1),:) = M(1:nvar*t,:);
- M(1:nvar,:)=Mtem;
- % ** impulse response functions
- imf(t+1,:) = Mtem(:)';
- % stack imf with each step, Row: 6 var to 1st shock, 6 var to 2nd shock, etc.
- % ** variance decompositions
- Mvd = Mvd + Mtem.^2; % saved for the cumulative sum later
- Mvds = (sum(Mvd'))';
- str(t+1,:) = sqrt(Mvds'); % standard errors of each equation
- Mvds = Mvds(:,ones(size(Mvds,1),1));
- Mvdtem = (100.0*Mvd) ./ Mvds; % tempoary Mvd -- variance decompositions
- vd(t+1,:) = Mvdtem(:)';
- % stack vd with each step, Row: 6 var to 1st shock, 6 var to 2nd shock, etc.
-end
-
+function [vd,str,imf] = errors(Bh,swish,nn)
+% Computing variance decompositions and impulse functions with
+% [vd,str,imf] = errors(Bh,swish,nn)
+% where imf and vd is of the same format as in RATS, that is to say:
+% Column: nvar responses to 1st shock,
+% nvar responses to 2nd shock, and so on.
+% Row: steps of impulse responses.
+% vd: variance decompositions
+% str: standard errors of each variable, steps-by-nvar
+% imf: impulse response functions
+% Bh is the estimated reduced form coefficient in the form
+% Y(T*nvar) = XB + U, X: T*k, B: k*nvar. The matrix
+% form or dimension is the same as "Bh" from the function "sye";
+% swish is the inv(A0) in the structural model A(L)y(t) = e(t).
+% nn is the numbers of inputs [nvar,lags,# of impulse responses].
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+nvar = nn(1);
+lags = nn(2);
+imstep = nn(3); % number of steps for impulse responses
+
+Ah = Bh';
+% Row: nvar equations
+% Column: 1st lag (with nvar variables) to lags (with nvar variables) + const = k.
+
+imf = zeros(imstep,nvar*nvar);
+vd = imf;
+% Column: nvar responses to 1st shock, nvar responses to 2nd shock, and so on.
+% Row: steps of impulse responses.
+str = zeros(imstep,nvar); % initializing standard errors of each equation
+M = zeros(nvar*(lags+1),nvar);
+% Stack lags M's in the order of, e.g., [Mlags, ..., M2,M1;M0]
+M(1:nvar,:) = swish';
+Mtem = M(1:nvar,:); % temporary M -- impulse responses.
+%
+Mvd = Mtem.^2; % saved for the cumulative sum later
+Mvds = (sum(Mvd'))';
+str(1,:) = sqrt(Mvds'); % standard errors of each equation
+Mvds = Mvds(:,ones(size(Mvds,1),1));
+Mvdtem = (100.0*Mvd) ./ Mvds; % tempoary Mvd -- variance decompositions
+% first or initial responses to
+% one standard deviation shock (or forecast errors).
+% Row: responses; Column: shocks
+%
+% * put in the form of "imf"
+imf(1,:) = Mtem(:)';
+vd(1,:) = Mvdtem(:)';
+
+t = 1;
+ims1 = min([imstep-1 lags]);
+while t <= ims1
+ Mtem = Ah(:,1:nvar*t)*M(1:nvar*t,:);
+ % Row: nvar equations, each for the nvar variables at tth lag
+ M(nvar+1:nvar*(t+1),:)=M(1:nvar*t,:);
+ M(1:nvar,:) = Mtem;
+ % ** impulse response functions
+ imf(t+1,:) = Mtem(:)';
+ % stack imf with each step, Row: 6 var to 1st shock, 6 var to 2nd shock, etc.
+ % ** variance decompositions
+ Mvd = Mvd + Mtem.^2; % saved for the cumulative sum later
+ Mvds = (sum(Mvd'))';
+ str(t+1,:) = sqrt(Mvds'); % standard errors of each equation
+ Mvds = Mvds(:,ones(size(Mvds,1),1));
+ Mvdtem = (100.0*Mvd) ./ Mvds; % tempoary Mvd -- variance decompositions
+ vd(t+1,:) = Mvdtem(:)';
+ % stack vd with each step, Row: 6 var to 1st shock, 6 var to 2nd shock, etc.
+ t= t+1;
+end
+
+for t = lags+1:imstep-1
+ Mtem = Ah(:,1:nvar*lags)*M(1:nvar*lags,:);
+ % Row: nvar equations, each for the nvar variables at tth lag
+ M(nvar+1:nvar*(t+1),:) = M(1:nvar*t,:);
+ M(1:nvar,:)=Mtem;
+ % ** impulse response functions
+ imf(t+1,:) = Mtem(:)';
+ % stack imf with each step, Row: 6 var to 1st shock, 6 var to 2nd shock, etc.
+ % ** variance decompositions
+ Mvd = Mvd + Mtem.^2; % saved for the cumulative sum later
+ Mvds = (sum(Mvd'))';
+ str(t+1,:) = sqrt(Mvds'); % standard errors of each equation
+ Mvds = Mvds(:,ones(size(Mvds,1),1));
+ Mvdtem = (100.0*Mvd) ./ Mvds; % tempoary Mvd -- variance decompositions
+ vd(t+1,:) = Mvdtem(:)';
+ % stack vd with each step, Row: 6 var to 1st shock, 6 var to 2nd shock, etc.
+end
+
diff --git a/MatlabFiles/fcstidcnd.m b/MatlabFiles/fcstidcnd.m
index edce12ff627fafc1876e8773ffaf91b52c4ab303..e2bc7898746551c51f95177fd88726e47c305271 100644
--- a/MatlabFiles/fcstidcnd.m
+++ b/MatlabFiles/fcstidcnd.m
@@ -1,321 +1,322 @@
-function [yhat,Estr,rcon,Rcon,u,v,d] = fcstidcnd(valuecon,stepcon,varcon,nstepsm,...
- nconstr,eq_ms,nvar,lags,phil,Aband,Sband,yfore_h,imf3s_h,A0_h,Bh_h,...
- forep,TLindx,TLnumber,nCms,eq_Cms)
-% [yhat,Estr,rcon,Rcon,u,v,d] = fcstidcnd(valuecon,stepcon,varcon,nstepsm,...
-% nconstr,eq_ms,nvar,lags,phil,Aband,Sband,yfore_h,imf3s_h,A0_h,Bh_h,...
-% forep,TLindx,TLnumber,nCms,eq_Cms)
-%
-% Conditional forecasting in the identified model with or without error bands
-% It handles conditions on average values as well, so "valuecon" must be
-% expressed at average (NOT sum) level.
-% Aband is used only once when nconstr>0 and Bband=1 where Gibbs sampler may be used
-% Unconditional forecast when imf3s_h, etc is fixed and nconstr=0.
-%
-% valuecon: vector of values conditioned
-% stepcon: sequence (cell) of steps conditioned; if length(stepcon{i}) > 1, the condition
-% is then an arithmetic average of log(y) over the stepcon{i} period.
-% varcon: vector of variables conditioned
-% nconstr: number of DLS constraints
-% nstepsm: maximum number of steps in all DLS constraints
-% nvar: number of variables in the BVAR model
-% lags: number of lags in the BVAR model
-% phil: the 1-by-(nvar*lags+1) data matrix where k=nvar*lags+1
-% (last period plus lags before the beginning of forecast)
-% Aband: 1: draws from A0 and Bh; 0: no draws
-% Sband: 1: draws from random shocks E; 0: no random shocks
-% yfore_h: uncondtional forecasts: forep-by-nvar. Never used when nconstr=0.
-% In this case, set it to [];
-% imf3s_h: 3-dimensional impulse responses matrix: impsteps-by-nvar shocks-by-nvar responses
-% Never used when nconstr=0. In this case, set it to [];
-% A0_h: A0 contemporaneous parameter matrix
-% Bh_h: reduced-form parameter matrix: k-by-nvar, y(t) = X(t)*Bh+e(t)
-% where X(t) is k-by-nvar and y(t) is 1-by-nvar
-% forep: # of forecast periods (e.g., monthly for a monthly model)
-% TLindx: 1-by-nCms vector of 1's and 0's, indicating tight or loose; 1: tighter, 0: looser
-% Used only when /* (MS draws) is activated. Right now, MS shocks are deterministic.
-% TLnumber: 1-by-nCms vector, lower bound for tight and upper bound for loose
-% nCms: # of LZ conditions
-% eq_Cms: equation location of MS shocks
-% ------
-% yhat: conditional forecasts: forep-by-nvar
-% Estr: backed-out structural shocks (from N(0,1))
-% rcon: vector - the difference between valuecon and log(yfore) (unconditional forecasts)
-% Rcon: k-by-q (q constranits and k=nvar*max(nsteps)) so that
-% Rcon'*e = rcon where e is k-by-1
-% [u,d,v]: svd(Rcon,0)
-%
-%% See Zha's note "Forecast (1)" p. 5, RATS manual (some errors in RATS), etc.
-%
-%% Some notations: y(t+1) = y(t)B1 + e(t+1)inv(A0). e(t+1) is 1-by-n.
-%% Let r(t+1)=e(t+1)inv(A0) + e(t+2)C + .... where inv(A0) is impulse
-%% response at t=1, C at t=2, etc. The row of inv(A0) or C is
-%% all responses to one shock.
-%% Let r be q-by-1 (such as r(1) = r(t+1)
-%% = y(t+1) (constrained) - y(t+1) (forecast)).
-%% Use impulse responses to find out R (k-by-q) where k=nvar*nsteps
-%% where nsteps the largest constrained step. The key of the program
-%% is to creat R using impulse responses
-%% Optimal solution for shock e where R'*e=r and e is k-by-1 is
-%% e = R*inv(R'*R)*r and k>=q
-%
-% Copyright (c) March 1998 by Tao Zha. Revised November 1998;
-% 3/20/99 Disenabled draws of MS shcoks. To enable it, activate /* part
-% 3/20/99 Added A0_h and forep and deleted Cms as input argument. Previous
-% programs may not be compatible.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-DLSIdShock = ~isempty(eq_ms); % if not empty, the MS shock is identified as in DLS
-
-impsteps=size(imf3s_h,1);
-if (forep<nstepsm) | (impsteps<nstepsm)
- disp('Increase # of forecast or impulse steps!!')
- disp('Or decrease # of constraints (nconstr) or constrained steps (stepcon(i))!!')
- error('Maximum of conditional steps > # of forecast or impulse steps!!')
-end
-kts = nvar*nstepsm; % k -- ts: total shocks some of which are restricted and others
- % are free.
-%*** initializing
-Rcon = zeros(kts,nconstr); % R: k-by-q
-Econ = zeros(kts,1); % E: k-by-1
-rcon = zeros(nconstr,1); % r: q-by-1
-%rcon=valuecon-diag(yfore(stepcon,varcon)); % another way is to use "loop" below.
-tcwc = nvar*lags; % total coefficients without constant
-phi=phil;
-
-
-
-%----------------------------------------------------
-% Form rcon, Rcon, and Econ (the mean of structural shocks)
-%----------------------------------------------------
-if nconstr
- A0in = reshape(imf3s_h(1,:,:),nvar,nvar); % nvar shocks-by-nvar responses
- for i=1:nconstr
- rcon(i)=length(stepcon{i})*valuecon(i) - ...
- sum(yfore_h(stepcon{i},varcon(i)),1); %<<>>
- Rmat = zeros(nstepsm,nvar);
- r2mat = zeros(nstepsm,1); % simply one identified equation
- % Must be here inside the loop because it's matrix of one column of Rcon
- for j=1:length(stepcon{i})
- if DLSIdShock % Assuming the Fed can't see all other shocks within a month
- Rmat(1:stepcon{i}(j),eq_ms) = Rmat(1:stepcon{i}(j),eq_ms) + ...
- imf3s_h(stepcon{i}(j):-1:1,eq_ms,varcon(i));
- % Rmat: row--nstepsm, column--nvar shocks (here all shocks except
- % the identified one are set to zero) for a particular
- % endogenous variable 'varcon(i)'. See Zha Forcast (1), pp.6-7
- else % Rcon random with (A0,A+)
- Rmat(1:stepcon{i}(j),:) = Rmat(1:stepcon{i}(j),:) + ...
- imf3s_h(stepcon{i}(j):-1:1,:,varcon(i));
- % Rmat: row--nstepsm, column--nvar shocks (here all shocks are
- % *not* set to zero) for a particular endogenous
- % variable 'varcon(i)'. See Zha Forcast (1), pp.6-7
- end
- end
- Rmatt = Rmat'; % Now, nvar-by-nstepsm. I think here is where RATS has an error
- % i.e. "OVERR" is not transposed when overlaid to "CAPR"
- Rcon(:,i)=Rmatt(:); % Rcon: k-by-q where q=nconstr
- end
-
- [u d v]=svd(Rcon,0); %trial
- %???? Can we reduce the time by computing inv(R'*R) directly?
- % rtr = Rcon'*Rcon; %trial
- % rtrinv = inv(Rcon'*Rcon); %trial
- vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
- dinv = 1./diag(d); % inv(diag(d))
- vdinv=v.*(ones(size(v,2),1)*dinv'); %trial
- rtr=vd*vd'; % R'*R
- rtrinv = vdinv*vdinv'; % inv(R'*R)
-
- Econ = Rcon*rtrinv*rcon; % E = R*inv(R'R)*r; the mean of structural shocks
-else
- Econ = zeros(kts,1); % the mean of shocks is zero under no variable condition
- Rcon = NaN;
- rcon = NaN;
- u = NaN;
- d = NaN;
- v = NaN;
-end
-
-
-
-%---------------------------------------
-% No uncertainty at all or only random (A0,A+)
-% In other words, no future shocks
-%---------------------------------------
-if (~Sband) %| (nconstr & (length(eq_ms)==1))
- % length(eq_ms)==1 implies one-one mapping between MS shocks and, say, FFR
- % if nstepsm==nconstr. If this condition does not hold, this procedure
- % is incorrect. I don't have time to fix it now (3/20/99). So I use
- % this as a proximation
- Estr = reshape(Econ,nvar,nstepsm);
- Estr = Estr'; % transpose so that
- % Estr: structural shocks. Row--steps, Column--n shocks
- Estr = [Estr;zeros(forep-nstepsm,nvar)];
- % Now, forep-by-nvar -- ready for forecasts
- Estr(1:nCms,eq_Cms) = TLnumber(:);
- Ures = Estr/A0_h; % nstepsm-by-nvar
- % Ures: reduced-form residuals. Row--steps; Column--n shocks
-
- % ** reconstruct x(t) for y(t+h) = x(t+h-1)*B
- % ** where phi = x(t+h-1) with last column being constant
- %
- yhat = zeros(forep,nvar);
- for k=1:forep
- yhat(k,:) = phi*Bh_h + Ures(k,:);
- phi(1,nvar+1:tcwc) = phi(1,1:tcwc-nvar);
- phi(1,1:nvar) = yhat(k,:);
- %
- end
-
-%---------------------------------------
-% With random future shocks and possibly (A0,A+) depending
-% on if imf3s_h is random
-%---------------------------------------
-else
- %--------------
- % Condition on variables and A random
- %--------------
- if nconstr & Aband
- warning(' ')
- disp('This situation (both E and A random) is still under construction')
- disp('It is closely related to Waggoner and Zha ReStat Gibbs sampling method')
- disp('Please press ctrl-c to abort')
- pause
- elseif nconstr
- %--------------
- % Condition on variables and DLS MS shock, no A random but S random
- %--------------
- if DLSIdShock % other shocks are indepedent of the eq_ms shock
- % 3/20/99 The following may be problematic because Osk should depend
- % on u (A0_h and Bh_h) in general. I haven't worked out any good version
- %/*
- % Osk = randn(kts,1); % other shocks
- % for j=1:nstepsm
- % Osk(nvar*(j-1)+eq_ms)=0; % no shock to the MS or identified equation
- % end
- % Estr = Econ + Osk; % Econ is non zero only at position
- % % eq_ms*j where j=1:nstepsm
- % Estr = reshape(Estr,nvar,nstepsm);
- % Estr = Estr'; % transpose so that
- % % Estr: structural shocks. Row--steps, Column--n shocks
- % Estr = [Estr;randn(forep-nstepsm,nvar)];
- % % Now, forep-by-nvar -- ready for forecasts
- %
- disp('DLS')
- Ome = eye(kts) - u*u'; % note, I-u*u' = I - R*inv(R'*R)*R'
- %[u1 d1 v1] = svd(Ome); % too slow
- [u1 d1] = eig(Ome);
- Stdcon = u1*diag(sqrt(diag(abs(d1)))); % lower triagular chol of conditional variance
- % see Zha's forecast (1), p.17
- tmp1=zeros(nvar,nstepsm);
- tmp1(eq_ms,:)=randn(1,nstepsm);
- tmp2=tmp1(:);
- %Estr1 = Econ + Stdcon*randn(kts,1);
- %jnk = reshape(Stdcon*tmp2,nvar,nstepsm)
- Estr1 = Econ + Stdcon*tmp2;
- Estr2 = reshape(Estr1,nvar,nstepsm);
- Estr2 = Estr2'; % transpose so that
- % Estr2: structural shocks. Row--nstepsm, Column--n shocks
- Estr = [Estr2;randn(forep-nstepsm,nvar)];
- % Now, forep-by-nvar -- ready for forecasts
- else
- Ome = eye(kts) - u*u'; % note, I-u*u' = I - R*inv(R'*R)*R'
- %[u1 d1 v1] = svd(Ome); % too slow
- [u1 d1] = eig(Ome);
- Stdcon = u1*diag(sqrt(diag(abs(d1)))); % lower triagular chol of conditional variance
- % see Zha's forecast (1), p.17
- %--------------
- % Condition on variables and LZ MS shock, no A random but S random
- % This section has not be tested yet, 10/14/98
- %--------------
- if nCms
- Estr1 = Econ + Stdcon*randn(kts,1);
- Estr2 = reshape(Estr1,nvar,nstepsm);
- Estr2 = Estr2'; % transpose so that
- % Estr2: structural shocks. Row--nstepsm, Column--n shocks
- Estr = [Estr2;randn(forep-nstepsm,nvar)];
- % Now, forep-by-nvar -- ready for forecasts
- Estr(1:nCms,eq_Cms) = TLnumber(:);
-
- %/* draw MS shocks
- % for k=1:nCms
- % if TLindx(k) % tighter
- % while (Estr(k,eq_Cms)<TLnumber(k))
- % Estr(k,eq_Cms) = randn(1,1);
- % end
- % else % looser
- % while (Estr(k,eq_Cms)>TLnumber(k))
- % Estr(k,eq_Cms) = randn(1,1);
- % end
- % end
- % end
- %--------------
- % Condition on variables only, no A random but S random
- %--------------
- else
- Estr1 = Econ + Stdcon*randn(kts,1);
- Estr2 = reshape(Estr1,nvar,nstepsm);
- Estr2 = Estr2'; % transpose so that
- % Estr2: structural shocks. Row--nstepsm, Column--n shocks
- Estr = [Estr2;randn(forep-nstepsm,nvar)];
- % Now, forep-by-nvar -- ready for forecasts
- end
- end
- %--------------
- % Condition on LZ MS shocks only, S random and possibly A random depending on
- % if A0_h and Bh_h are random
- %--------------
- else
- if nCms
- Estr = randn(forep,nvar);
- % Now, forep-by-nvar -- ready for forecasts
- Estr(1:nCms,eq_Cms) = TLnumber(:);
-
- %/* draw MS shocks
- % for k=1:nCms
- % if TLindx(k) % tighter
- % while (Estr(k,eq_Cms)<TLnumber(k))
- % Estr(k,eq_Cms) = randn(1,1);
- % end
- % else % looser
- % while (Estr(k,eq_Cms)>TLnumber(k))
- % Estr(k,eq_Cms) = randn(1,1);
- % end
- % end
- % end
- else
- Estr = randn(forep,nvar); % Unconditional forecast
- % Now, forep-by-nvar -- ready for forecasts
- end
- end
- %
-
-
- Ures = Estr/A0_h; % nstepsm-by-nvar
- % Ures: reduced-form residuals. Row--steps; Column--n shocks
-
- % ** reconstruct x(t) for y(t+h) = x(t+h-1)*B
- % ** where phi = x(t+h-1) with last column being constant
- %
- yhat = zeros(forep,nvar);
- for k=1:forep
- yhat(k,:) = phi*Bh_h + Ures(k,:);
- phi(1,nvar+1:tcwc) = phi(1,1:tcwc-nvar);
- phi(1,1:nvar) = yhat(k,:);
- end
-end
+function [yhat,Estr,rcon,Rcon,u,v,d] = fcstidcnd(valuecon,stepcon,varcon,nstepsm,...
+ nconstr,eq_ms,nvar,lags,phil,Aband,Sband,yfore_h,imf3s_h,A0_h,Bh_h,...
+ forep,TLindx,TLnumber,nCms,eq_Cms)
+% [yhat,Estr,rcon,Rcon,u,v,d] = fcstidcnd(valuecon,stepcon,varcon,nstepsm,...
+% nconstr,eq_ms,nvar,lags,phil,Aband,Sband,yfore_h,imf3s_h,A0_h,Bh_h,...
+% forep,TLindx,TLnumber,nCms,eq_Cms)
+%
+% Conditional forecasting in the identified model with or without error bands
+% It handles conditions on average values as well, so "valuecon" must be
+% expressed at average (NOT sum) level.
+% Aband is used only once when nconstr>0 and Bband=1 where Gibbs sampler may be used
+% Unconditional forecast when imf3s_h, etc is fixed and nconstr=0.
+%
+% valuecon: vector of values conditioned
+% stepcon: sequence (cell) of steps conditioned; if length(stepcon{i}) > 1, the condition
+% is then an arithmetic average of log(y) over the stepcon{i} period.
+% varcon: vector of variables conditioned
+% nconstr: number of DLS constraints
+% nstepsm: maximum number of steps in all DLS constraints
+% nvar: number of variables in the BVAR model
+% lags: number of lags in the BVAR model
+% phil: the 1-by-(nvar*lags+1) data matrix where k=nvar*lags+1
+% (last period plus lags before the beginning of forecast)
+% Aband: 1: draws from A0 and Bh; 0: no draws
+% Sband: 1: draws from random shocks E; 0: no random shocks
+% yfore_h: uncondtional forecasts: forep-by-nvar. Never used when nconstr=0.
+% In this case, set it to [];
+% imf3s_h: 3-dimensional impulse responses matrix: impsteps-by-nvar shocks-by-nvar responses
+% Never used when nconstr=0. In this case, set it to [];
+% A0_h: A0 contemporaneous parameter matrix
+% Bh_h: reduced-form parameter matrix: k-by-nvar, y(t) = X(t)*Bh+e(t)
+% where X(t) is k-by-nvar and y(t) is 1-by-nvar
+% forep: # of forecast periods (e.g., monthly for a monthly model)
+% TLindx: 1-by-nCms vector of 1's and 0's, indicating tight or loose; 1: tighter, 0: looser
+% Used only when /* (MS draws) is activated. Right now, MS shocks are deterministic.
+% TLnumber: 1-by-nCms vector, lower bound for tight and upper bound for loose
+% nCms: # of LZ conditions
+% eq_Cms: equation location of MS shocks
+% ------
+% yhat: conditional forecasts: forep-by-nvar
+% Estr: backed-out structural shocks (from N(0,1))
+% rcon: vector - the difference between valuecon and log(yfore) (unconditional forecasts)
+% Rcon: k-by-q (q constranits and k=nvar*max(nsteps)) so that
+% Rcon'*e = rcon where e is k-by-1
+% [u,d,v]: svd(Rcon,0)
+%
+%% See Zha's note "Forecast (1)" p. 5, RATS manual (some errors in RATS), etc.
+%
+%% Some notations: y(t+1) = y(t)B1 + e(t+1)inv(A0). e(t+1) is 1-by-n.
+%% Let r(t+1)=e(t+1)inv(A0) + e(t+2)C + .... where inv(A0) is impulse
+%% response at t=1, C at t=2, etc. The row of inv(A0) or C is
+%% all responses to one shock.
+%% Let r be q-by-1 (such as r(1) = r(t+1)
+%% = y(t+1) (constrained) - y(t+1) (forecast)).
+%% Use impulse responses to find out R (k-by-q) where k=nvar*nsteps
+%% where nsteps the largest constrained step. The key of the program
+%% is to creat R using impulse responses
+%% Optimal solution for shock e where R'*e=r and e is k-by-1 is
+%% e = R*inv(R'*R)*r and k>=q
+%
+% Written by Tao Zha. Revised November 1998;
+% 3/20/99 Disenabled draws of MS shcoks. To enable it, activate /* part
+% 3/20/99 Added A0_h and forep and deleted Cms as input argument. Previous
+% programs may not be compatible.
+%
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+DLSIdShock = ~isempty(eq_ms); % if not empty, the MS shock is identified as in DLS
+
+impsteps=size(imf3s_h,1);
+if (forep<nstepsm) | (impsteps<nstepsm)
+ disp('Increase # of forecast or impulse steps!!')
+ disp('Or decrease # of constraints (nconstr) or constrained steps (stepcon(i))!!')
+ error('Maximum of conditional steps > # of forecast or impulse steps!!')
+end
+kts = nvar*nstepsm; % k -- ts: total shocks some of which are restricted and others
+ % are free.
+%*** initializing
+Rcon = zeros(kts,nconstr); % R: k-by-q
+Econ = zeros(kts,1); % E: k-by-1
+rcon = zeros(nconstr,1); % r: q-by-1
+%rcon=valuecon-diag(yfore(stepcon,varcon)); % another way is to use "loop" below.
+tcwc = nvar*lags; % total coefficients without constant
+phi=phil;
+
+
+
+%----------------------------------------------------
+% Form rcon, Rcon, and Econ (the mean of structural shocks)
+%----------------------------------------------------
+if nconstr
+ A0in = reshape(imf3s_h(1,:,:),nvar,nvar); % nvar shocks-by-nvar responses
+ for i=1:nconstr
+ rcon(i)=length(stepcon{i})*valuecon(i) - ...
+ sum(yfore_h(stepcon{i},varcon(i)),1); %<<>>
+ Rmat = zeros(nstepsm,nvar);
+ r2mat = zeros(nstepsm,1); % simply one identified equation
+ % Must be here inside the loop because it's matrix of one column of Rcon
+ for j=1:length(stepcon{i})
+ if DLSIdShock % Assuming the Fed can't see all other shocks within a month
+ Rmat(1:stepcon{i}(j),eq_ms) = Rmat(1:stepcon{i}(j),eq_ms) + ...
+ imf3s_h(stepcon{i}(j):-1:1,eq_ms,varcon(i));
+ % Rmat: row--nstepsm, column--nvar shocks (here all shocks except
+ % the identified one are set to zero) for a particular
+ % endogenous variable 'varcon(i)'. See Zha Forcast (1), pp.6-7
+ else % Rcon random with (A0,A+)
+ Rmat(1:stepcon{i}(j),:) = Rmat(1:stepcon{i}(j),:) + ...
+ imf3s_h(stepcon{i}(j):-1:1,:,varcon(i));
+ % Rmat: row--nstepsm, column--nvar shocks (here all shocks are
+ % *not* set to zero) for a particular endogenous
+ % variable 'varcon(i)'. See Zha Forcast (1), pp.6-7
+ end
+ end
+ Rmatt = Rmat'; % Now, nvar-by-nstepsm. I think here is where RATS has an error
+ % i.e. "OVERR" is not transposed when overlaid to "CAPR"
+ Rcon(:,i)=Rmatt(:); % Rcon: k-by-q where q=nconstr
+ end
+
+ [u d v]=svd(Rcon,0); %trial
+ %???? Can we reduce the time by computing inv(R'*R) directly?
+ % rtr = Rcon'*Rcon; %trial
+ % rtrinv = inv(Rcon'*Rcon); %trial
+ vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
+ dinv = 1./diag(d); % inv(diag(d))
+ vdinv=v.*(ones(size(v,2),1)*dinv'); %trial
+ rtr=vd*vd'; % R'*R
+ rtrinv = vdinv*vdinv'; % inv(R'*R)
+
+ Econ = Rcon*rtrinv*rcon; % E = R*inv(R'R)*r; the mean of structural shocks
+else
+ Econ = zeros(kts,1); % the mean of shocks is zero under no variable condition
+ Rcon = NaN;
+ rcon = NaN;
+ u = NaN;
+ d = NaN;
+ v = NaN;
+end
+
+
+
+%---------------------------------------
+% No uncertainty at all or only random (A0,A+)
+% In other words, no future shocks
+%---------------------------------------
+if (~Sband) %| (nconstr & (length(eq_ms)==1))
+ % length(eq_ms)==1 implies one-one mapping between MS shocks and, say, FFR
+ % if nstepsm==nconstr. If this condition does not hold, this procedure
+ % is incorrect. I don't have time to fix it now (3/20/99). So I use
+ % this as a proximation
+ Estr = reshape(Econ,nvar,nstepsm);
+ Estr = Estr'; % transpose so that
+ % Estr: structural shocks. Row--steps, Column--n shocks
+ Estr = [Estr;zeros(forep-nstepsm,nvar)];
+ % Now, forep-by-nvar -- ready for forecasts
+ Estr(1:nCms,eq_Cms) = TLnumber(:);
+ Ures = Estr/A0_h; % nstepsm-by-nvar
+ % Ures: reduced-form residuals. Row--steps; Column--n shocks
+
+ % ** reconstruct x(t) for y(t+h) = x(t+h-1)*B
+ % ** where phi = x(t+h-1) with last column being constant
+ %
+ yhat = zeros(forep,nvar);
+ for k=1:forep
+ yhat(k,:) = phi*Bh_h + Ures(k,:);
+ phi(1,nvar+1:tcwc) = phi(1,1:tcwc-nvar);
+ phi(1,1:nvar) = yhat(k,:);
+ %
+ end
+
+%---------------------------------------
+% With random future shocks and possibly (A0,A+) depending
+% on if imf3s_h is random
+%---------------------------------------
+else
+ %--------------
+ % Condition on variables and A random
+ %--------------
+ if nconstr & Aband
+ warning(' ')
+ disp('This situation (both E and A random) is still under construction')
+ disp('It is closely related to Waggoner and Zha ReStat Gibbs sampling method')
+ disp('Please press ctrl-c to abort')
+ pause
+ elseif nconstr
+ %--------------
+ % Condition on variables and DLS MS shock, no A random but S random
+ %--------------
+ if DLSIdShock % other shocks are indepedent of the eq_ms shock
+ % 3/20/99 The following may be problematic because Osk should depend
+ % on u (A0_h and Bh_h) in general. I haven't worked out any good version
+ %/*
+ % Osk = randn(kts,1); % other shocks
+ % for j=1:nstepsm
+ % Osk(nvar*(j-1)+eq_ms)=0; % no shock to the MS or identified equation
+ % end
+ % Estr = Econ + Osk; % Econ is non zero only at position
+ % % eq_ms*j where j=1:nstepsm
+ % Estr = reshape(Estr,nvar,nstepsm);
+ % Estr = Estr'; % transpose so that
+ % % Estr: structural shocks. Row--steps, Column--n shocks
+ % Estr = [Estr;randn(forep-nstepsm,nvar)];
+ % % Now, forep-by-nvar -- ready for forecasts
+ %
+ disp('DLS')
+ Ome = eye(kts) - u*u'; % note, I-u*u' = I - R*inv(R'*R)*R'
+ %[u1 d1 v1] = svd(Ome); % too slow
+ [u1 d1] = eig(Ome);
+ Stdcon = u1*diag(sqrt(diag(abs(d1)))); % lower triagular chol of conditional variance
+ % see Zha's forecast (1), p.17
+ tmp1=zeros(nvar,nstepsm);
+ tmp1(eq_ms,:)=randn(1,nstepsm);
+ tmp2=tmp1(:);
+ %Estr1 = Econ + Stdcon*randn(kts,1);
+ %jnk = reshape(Stdcon*tmp2,nvar,nstepsm)
+ Estr1 = Econ + Stdcon*tmp2;
+ Estr2 = reshape(Estr1,nvar,nstepsm);
+ Estr2 = Estr2'; % transpose so that
+ % Estr2: structural shocks. Row--nstepsm, Column--n shocks
+ Estr = [Estr2;randn(forep-nstepsm,nvar)];
+ % Now, forep-by-nvar -- ready for forecasts
+ else
+ Ome = eye(kts) - u*u'; % note, I-u*u' = I - R*inv(R'*R)*R'
+ %[u1 d1 v1] = svd(Ome); % too slow
+ [u1 d1] = eig(Ome);
+ Stdcon = u1*diag(sqrt(diag(abs(d1)))); % lower triagular chol of conditional variance
+ % see Zha's forecast (1), p.17
+ %--------------
+ % Condition on variables and LZ MS shock, no A random but S random
+ % This section has not be tested yet, 10/14/98
+ %--------------
+ if nCms
+ Estr1 = Econ + Stdcon*randn(kts,1);
+ Estr2 = reshape(Estr1,nvar,nstepsm);
+ Estr2 = Estr2'; % transpose so that
+ % Estr2: structural shocks. Row--nstepsm, Column--n shocks
+ Estr = [Estr2;randn(forep-nstepsm,nvar)];
+ % Now, forep-by-nvar -- ready for forecasts
+ Estr(1:nCms,eq_Cms) = TLnumber(:);
+
+ %/* draw MS shocks
+ % for k=1:nCms
+ % if TLindx(k) % tighter
+ % while (Estr(k,eq_Cms)<TLnumber(k))
+ % Estr(k,eq_Cms) = randn(1,1);
+ % end
+ % else % looser
+ % while (Estr(k,eq_Cms)>TLnumber(k))
+ % Estr(k,eq_Cms) = randn(1,1);
+ % end
+ % end
+ % end
+ %--------------
+ % Condition on variables only, no A random but S random
+ %--------------
+ else
+ Estr1 = Econ + Stdcon*randn(kts,1);
+ Estr2 = reshape(Estr1,nvar,nstepsm);
+ Estr2 = Estr2'; % transpose so that
+ % Estr2: structural shocks. Row--nstepsm, Column--n shocks
+ Estr = [Estr2;randn(forep-nstepsm,nvar)];
+ % Now, forep-by-nvar -- ready for forecasts
+ end
+ end
+ %--------------
+ % Condition on LZ MS shocks only, S random and possibly A random depending on
+ % if A0_h and Bh_h are random
+ %--------------
+ else
+ if nCms
+ Estr = randn(forep,nvar);
+ % Now, forep-by-nvar -- ready for forecasts
+ Estr(1:nCms,eq_Cms) = TLnumber(:);
+
+ %/* draw MS shocks
+ % for k=1:nCms
+ % if TLindx(k) % tighter
+ % while (Estr(k,eq_Cms)<TLnumber(k))
+ % Estr(k,eq_Cms) = randn(1,1);
+ % end
+ % else % looser
+ % while (Estr(k,eq_Cms)>TLnumber(k))
+ % Estr(k,eq_Cms) = randn(1,1);
+ % end
+ % end
+ % end
+ else
+ Estr = randn(forep,nvar); % Unconditional forecast
+ % Now, forep-by-nvar -- ready for forecasts
+ end
+ end
+ %
+
+
+ Ures = Estr/A0_h; % nstepsm-by-nvar
+ % Ures: reduced-form residuals. Row--steps; Column--n shocks
+
+ % ** reconstruct x(t) for y(t+h) = x(t+h-1)*B
+ % ** where phi = x(t+h-1) with last column being constant
+ %
+ yhat = zeros(forep,nvar);
+ for k=1:forep
+ yhat(k,:) = phi*Bh_h + Ures(k,:);
+ phi(1,nvar+1:tcwc) = phi(1,1:tcwc-nvar);
+ phi(1,1:nvar) = yhat(k,:);
+ end
+end
diff --git a/MatlabFiles/fcstidcnd2.m b/MatlabFiles/fcstidcnd2.m
index 081c52049838c6f557a29143efe4fa4e0f62a2d6..aa21a74156a801ffd6f9e293dc13371f941f89e8 100644
--- a/MatlabFiles/fcstidcnd2.m
+++ b/MatlabFiles/fcstidcnd2.m
@@ -1,358 +1,358 @@
-function [yhat,Estr,rcon,Rcon,u,v,d] = fcstidcnd2(valuecon,stepcon,varcon,nstepsm,...
- nconstr,eq_ms,nvar,lags,phil,Aband,Sband,yfore_h,imf3s_h,A0_h,Bh_h,...
- forep,TLindx,TLnumber,nCms,eq_Cms,nconadd,eq_oth,radd,Radd)
-% [yhat,Estr,rcon,Rcon,u,v,d] = fcstidcnd2(valuecon,stepcon,varcon,nstepsm,...
-% nconstr,eq_ms,nvar,lags,phil,Aband,Sband,yfore_h,imf3s_h,A0_h,Bh_h,...
-% forep,TLindx,TLnumber,nCms,eq_Cms,nconadd,eq_oth,radd,Radd)
-%
-% 5/2/99: This one is much, much slower than fidcnderr.m when eq_ms=[] and
-% nconstr>0 and length(eq_ms)*nstepsm>nconstr. Seems to me that fcstidcnd2.m
-% is designed for the situation which eq_ms=2. The slow speed is due to
-% "Radd" added. When I have time, I need to check to see if I can speed up
-% this M file.
-%
-% Conditional forecasting in the identified model with or without error bands
-% It handles conditions on average values as well, so "valuecon" must be
-% expressed at average (NOT sum) level. Including unconditional forecasts
-% when nconstr=nCms=0. Maybe slower than "fcstidcnd.m" when length(eq_ms)*nstepsm=nconstr
-% because of Radd added. But "fsctidcnd.m" is incorrect when length(eq_ms)
-% *nstepsm>nconstr (situations like the average annual FFR constraint). 3/22/99
-% Ref.: Zha Forecast (I), pp. 17-17c.
-%
-% valuecon: vector of values conditioned
-% stepcon: sequence (cell) of steps conditioned; if length(stepcon{i}) > 1, the condition
-% is then an arithmetic average of log(y) over the stepcon{i} period.
-% varcon: vector of variables conditioned
-% nconstr: number of DLS constraints
-% nstepsm: maximum number of steps in all DLS constraints
-% nvar: number of variables in the BVAR model
-% lags: number of lags in the BVAR model
-% phil: the 1-by-(nvar*lags+1) data matrix where k=nvar*lags+1
-% (last period plus lags before the beginning of forecast)
-% eq_ms: identified equation or shock (in terms of number). If equ_ms=[], then "fidencond" or
-% 'fcstidcond' is, similar to RATS, to compute forecasts with *all* shocks.
-% Aband: 1: draws from A0 and Bh; 0: no draws
-% Sband: 1: draws from random shocks E; 0: no random shocks
-% yfore_h: uncondtional forecasts: forep-by-nvar. Never used when nconstr=0.
-% In this case, set it to [];
-% imf3s_h: 3-dimensional impulse responses matrix: impsteps-by-nvar shocks-by-nvar responses
-% Never used when nconstr=0. In this case, set it to [];
-% A0_h: A0 contemporaneous parameter matrix
-% Bh_h: reduced-form parameter matrix: k-by-nvar, y(t) = X(t)*Bh+e(t)
-% where X(t) is k-by-nvar and y(t) is 1-by-nvar
-% forep: # of forecast periods (e.g., monthly for a monthly model)
-% TLindx: 1-by-nCms vector of 1's and 0's, indicating tight or loose; 1: tighter, 0: looser
-% Used only when /* (MS draws) is activated. Right now, MS shocks are deterministic.
-% TLnumber: 1-by-nCms vector, lower bound for tight and upper bound for loose
-% nCms: # of LZ conditions
-% eq_Cms: equation location of MS shocks
-% nconadd: number of DLS added constraints DIRECTLY on structural shocks
-% for identified version where eq_ms is not empty. Note
-% nconadd=0 when eq_ms is empty.
-% eq_oth: index for other structural shocks than those in eq_ms.
-% If eq_ms=[], eq_oth=[]. Note length(eq_oth)+length(eq_ms)=nvar
-% radd: sparse nconadd-by-1; nconadd values added to rcon in the text later
-% Radd: sparce nvar*nstepsm-by-nconadd; added to Rcon in the text later
-% ------
-% yhat: conditional forecasts: forep-by-nvar
-% Estr: backed-out structural shocks (from N(0,1))
-% rcon: vector - the difference between valuecon and log(yfore) (unconditional forecasts)
-% Rcon: k-by-q (q constranits and k=nvar*max(nsteps)) so that
-% Rcon'*e = rcon where e is k-by-1
-% [u,d,v]: svd(Rcon,0)
-%
-% Copyright (c) March 1998 by Tao Zha. Revised November 1998;
-% 3/20/99 Disenabled draws of MS shcoks. To enable it, activate /* part
-% 3/20/99 Added A0_h and forep and deleted Cms as input argument.
-% 3/21/99 Added nconadd and eq_oth.
-% Previous programs may not be compatible.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-
-%
-%% See Zha's note "Forecast (1)" p. 5, RATS manual (some errors in RATS), etc.
-%% See also Zha Forecast (I), pp. 17-17c.
-%
-%% Some notations: y(t+1) = y(t)B1 + e(t+1)inv(A0). e(t+1) is 1-by-n.
-%% Let r(t+1)=e(t+1)inv(A0) + e(t+2)C + .... where inv(A0) is impulse
-%% response at t=1, C at t=2, etc. The row of inv(A0) or C is
-%% all responses to one shock.
-%% Let r be q-by-1 (such as r(1) = r(t+1)
-%% = y(t+1) (constrained) - y(t+1) (forecast)).
-%% Use impulse responses to find out R (k-by-q) where k=nvar*nsteps
-%% where nsteps the largest constrained step. The key of the program
-%% is to creat R using impulse responses
-%% Optimal solution for shock e where R'*e=r and e is k-by-1 is
-%% e = R*inv(R'*R)*r and k>=q
-%
-
-
-DLSIdShock = ~isempty(eq_ms); % if not empty, the MS shock is identified as in DLS
-
-impsteps=size(imf3s_h,1);
-if (forep<nstepsm) | (impsteps<nstepsm)
- disp('Increase # of forecast or impulse steps!!')
- disp('Or decrease # of constraints (nconstr) or constrained steps (stepcon(i))!!')
- error('Maximum of conditional steps > # of forecast or impulse steps!!')
-end
-kts = nvar*nstepsm; % k -- ts: total shocks some of which are restricted and others
- % are free.
-%*** initializing
-Rcon = zeros(kts,nconstr); % R: k-by-q where q include possible additional
- % constraints directly on structural shocks for identified models.
- % These addtional constraints will be added later.
-Econ = sparse(zeros(kts,1)); % E: k-by-1
-rcon = zeros(nconstr,1); % r: q-by-1
-%rcon=valuecon-diag(yfore(stepcon,varcon)); % another way is to use "loop" below.
-tcwc = nvar*lags; % total coefficients without constant
-phi=phil;
-
-
-
-%----------------------------------------------------
-% Form rcon, Rcon, and Econ (the mean of structural shocks)
-%----------------------------------------------------
-if nconstr
- A0in = reshape(imf3s_h(1,:,:),nvar,nvar); % nvar shocks-by-nvar responses
- for i=1:nconstr
- rcon(i)=length(stepcon{i})*valuecon(i) - ...
- sum(yfore_h(stepcon{i},varcon(i)),1); %<<>>
- Rmat = zeros(nstepsm,nvar);
- % Must be here inside the loop because it's matrix of one column of Rcon
- for j=1:length(stepcon{i})
- if DLSIdShock % Assuming the Fed can't see all other shocks within a month
- Rmat(1:stepcon{i}(j),:) = Rmat(1:stepcon{i}(j),:) + ...
- imf3s_h(stepcon{i}(j):-1:1,:,varcon(i));
- % Rmat: row--nstepsm, column--nvar shocks (here all shocks except
- % the identified one are set to zero) for a particular
- % endogenous variable 'varcon(i)'. See Zha Forcast (1), pp.6-7
- else % Rcon random with (A0,A+)
- Rmat(1:stepcon{i}(j),:) = Rmat(1:stepcon{i}(j),:) + ...
- imf3s_h(stepcon{i}(j):-1:1,:,varcon(i));
- % Rmat: row--nstepsm, column--nvar shocks (here all shocks are
- % *not* set to zero) for a particular endogenous
- % variable 'varcon(i)'. See Zha Forcast (1), pp.6-7
- end
- end
- Rmatt = Rmat'; % Now, nvar-by-nstepsm. I think here is where RATS has an error
- % i.e. "OVERR" is not transposed when overlaid to "CAPR"
- Rcon(:,i)=Rmatt(:); % Rcon: k-by-q where q=nconstr
- end
-
- rcon = [rcon;radd]; % added nconadd constrained values for identified model
- % sparse because radd is sparse
- Rcon = [Rcon Radd]; % added constraints on shocks for identified version
- % sparse because Radd is sparse
- Rcont=Rcon';
- rinvrtr=Rcon/(Rcont*Rcon);
- Econ = rinvrtr*rcon;
-
-
- %/* Too slow, I believe, when q is large. 3/21/99
- % [u d v]=svd(Rcon,0); %trial
- % %???? Can we reduce the time by computing inv(R'*R) directly?
- % % rtr = Rcon'*Rcon; %trial
- % % rtrinv = inv(Rcon'*Rcon); %trial
- % vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
- % dinv = 1./diag(d); % inv(diag(d))
- % vdinv=v.*(ones(size(v,2),1)*dinv'); %trial
- % rtr=vd*vd'; % R'*R
- % rtrinv = vdinv*vdinv'; % inv(R'*R)
- %
- % Econ = Rcon*rtrinv*rcon; % E = R*inv(R'R)*r; the mean of structural shocks
-else
- Econ = sparse(zeros(kts,1)); % the mean of shocks is zero under no variable condition
- Rcon = NaN;
- rcon = NaN;
- u = NaN;
- d = NaN;
- v = NaN;
-end
-
-
-
-%---------------------------------------
-% No uncertainty at all or only random (A0,A+)
-% In other words, no future shocks
-%---------------------------------------
-if (~Sband) | (nconstr & length(eq_ms)*nstepsm==nconstr)
- % length(eq_ms)==1 implies one-one mapping between MS shocks and, say, FFR
- % if nstepsm==nconstr. If this condition does not hold, this procedure
- % is incorrect. I don't have time to fix it now (3/20/99). So I use
- % this as a proximation
- Estr = reshape(Econ,nvar,nstepsm);
- Estr = Estr'; % transpose so that
- % Estr: structural shocks. Row--steps, Column--n shocks
- Estr = [Estr;zeros(forep-nstepsm,nvar)];
- % Now, forep-by-nvar -- ready for forecasts
- Estr(1:nCms,eq_Cms) = TLnumber(:);
- Ures = Estr/A0_h; % nstepsm-by-nvar
- % Ures: reduced-form residuals. Row--steps; Column--n shocks
-
- % ** reconstruct x(t) for y(t+h) = x(t+h-1)*B
- % ** where phi = x(t+h-1) with last column being constant
- %
- yhat = zeros(forep,nvar);
- for k=1:forep
- yhat(k,:) = phi*Bh_h + Ures(k,:);
- phi(1,nvar+1:tcwc) = phi(1,1:tcwc-nvar);
- phi(1,1:nvar) = yhat(k,:);
- %
- end
-
-%---------------------------------------
-% With random future shocks and possibly (A0,A+) depending
-% on if imf3s_h is random
-%---------------------------------------
-else
- %--------------
- % Condition on variables and A random
- %--------------
- if nconstr & Aband
- warning(' ')
- disp('This situation (both E and A random) is still under construction')
- disp('It is closely related to Waggoner and Zha ReStat Gibbs sampling method')
- disp('and related to "if DLSIdShock" in the following')
- disp('Please press ctrl-c to abort')
- pause
- elseif nconstr
- %--------------
- % Condition on variables and DLS MS shock, no A random but S random
- %--------------
- if DLSIdShock % other shocks are indepedent of the eq_ms shock
- Ome=speye(kts)-rinvrtr*Rcont;
- Ome(find(abs(Ome)<1e-10))=0; % tighter the tolerance is, the longer it
- % takes for svds to compute. E.g., size(Ome,1)*eps is perhaps very
- % tight. After all, when the number in Ome is very small relative to
- % 1 (which is the variance of structural shocks Estr), we can treat it
- % as zero.
- [u1 d1 v1] = svds(Ome,kts-nconstr-nconadd);
- % We have exactly nconstr+nconadd zero singular values
- Stdcon = u1*diag(sqrt(diag(abs(d1)))); % find a square root of Ome
- % see Zha's forecast (1), p.17
- Stdcon = [Stdcon sparse(zeros(kts,kts-size(d1,1)))];
- Estr1 = Econ + Stdcon*randn(kts,1); % We must have rand(kts,1). Assigning
- % some of randn(kts,1) to be zero according to Radd is WRONG.
- % For this argument, see Zha Forecast (I) p.17a
- Estr2 = reshape(Estr1,nvar,nstepsm);
- % nvar-by-nstepsm; Needed to be transposed later
- Estr = [Estr2';randn(forep-nstepsm,nvar)]; % transpose so that
- % Estr2: structural shocks. Row--nstepsm, Column--n shocks
- % Now, forep-by-nvar -- ready for forecasts
- else
- Ome=speye(kts)-rinvrtr*Rcont;
- Ome(find(abs(Ome)<1e-10))=0; % tighter the tolerance is, the longer it
- % takes for svds to compute. E.g., size(Ome,1)*eps is perhaps very
- % tight. After all, when the number in Ome is very small relative to
- % 1 (which is the variance of structural shocks Estr), we can treat it
- % as zero.
- [u1 d1 v1] = svds(Ome,kts-nconstr-nconadd);
- % We have exactly nconstr+nconadd zero singular values
- Stdcon = u1*diag(sqrt(diag(abs(d1)))); % find a square root of Ome
- % see Zha's forecast (1), p.17
- Stdcon = [Stdcon sparse(zeros(kts,nconstr+nconadd))];
-
- %*** The following very inefficient
- % Ome = eye(kts) - u*u'; % note, I-u*u' = I - R*inv(R'*R)*R'
- % %[u1 d1 v1] = svd(Ome); % too slow
- % [u1 d1] = eig(Ome);
- % Stdcon = u1*diag(sqrt(diag(abs(d1)))); % lower triagular chol of conditional variance
- % % see Zha's forecast (1), p.17
-
- %--------------
- % Condition on variables and LZ MS shock, no A random but S random
- % This section has not be tested yet, 10/14/98
- %--------------
- if nCms
- Estr1 = Econ + Stdcon*randn(kts,1);
- Estr2 = reshape(Estr1,nvar,nstepsm);
- Estr2 = Estr2'; % transpose so that
- % Estr2: structural shocks. Row--nstepsm, Column--n shocks
- Estr = [Estr2;randn(forep-nstepsm,nvar)];
- % Now, forep-by-nvar -- ready for forecasts
- Estr(1:nCms,eq_Cms) = TLnumber(:);
-
- %/* draw MS shocks
- % for k=1:nCms
- % if TLindx(k) % tighter
- % while (Estr(k,eq_Cms)<TLnumber(k))
- % Estr(k,eq_Cms) = randn(1,1);
- % end
- % else % looser
- % while (Estr(k,eq_Cms)>TLnumber(k))
- % Estr(k,eq_Cms) = randn(1,1);
- % end
- % end
- % end
- %--------------
- % Condition on variables only, no A random but S random
- %--------------
- else
- Estr1 = Econ + Stdcon*randn(kts,1);
- Estr2 = reshape(Estr1,nvar,nstepsm);
- Estr2 = Estr2'; % transpose so that
- % Estr2: structural shocks. Row--nstepsm, Column--n shocks
- Estr = [Estr2;randn(forep-nstepsm,nvar)];
- % Now, forep-by-nvar -- ready for forecasts
- end
- end
- %--------------
- % Condition on LZ MS shocks only, S random and possibly A random depending on
- % if A0_h and Bh_h are random
- %--------------
- else
- if nCms
- Estr = randn(forep,nvar);
- % Now, forep-by-nvar -- ready for forecasts
- Estr(:,eq_Cms)=0;
- Estr(1:nCms,eq_Cms) = TLnumber(:);
-
- %/* draw MS shocks
- % for k=1:nCms
- % if TLindx(k) % tighter
- % while (Estr(k,eq_Cms)<TLnumber(k))
- % Estr(k,eq_Cms) = randn(1,1);
- % end
- % else % looser
- % while (Estr(k,eq_Cms)>TLnumber(k))
- % Estr(k,eq_Cms) = randn(1,1);
- % end
- % end
- % end
- else
- Estr = randn(forep,nvar); % Unconditional forecast
- % Now, forep-by-nvar -- ready for forecasts
- end
- end
- %
-
-
- Ures = Estr/A0_h; % nstepsm-by-nvar
- % Ures: reduced-form residuals. Row--steps; Column--n shocks
-
- % ** reconstruct x(t) for y(t+h) = x(t+h-1)*B
- % ** where phi = x(t+h-1) with last column being constant
- %
- yhat = zeros(forep,nvar);
- for k=1:forep
- yhat(k,:) = phi*Bh_h + Ures(k,:);
- phi(1,nvar+1:tcwc) = phi(1,1:tcwc-nvar);
- phi(1,1:nvar) = yhat(k,:);
- end
-end
\ No newline at end of file
+function [yhat,Estr,rcon,Rcon,u,v,d] = fcstidcnd2(valuecon,stepcon,varcon,nstepsm,...
+ nconstr,eq_ms,nvar,lags,phil,Aband,Sband,yfore_h,imf3s_h,A0_h,Bh_h,...
+ forep,TLindx,TLnumber,nCms,eq_Cms,nconadd,eq_oth,radd,Radd)
+% [yhat,Estr,rcon,Rcon,u,v,d] = fcstidcnd2(valuecon,stepcon,varcon,nstepsm,...
+% nconstr,eq_ms,nvar,lags,phil,Aband,Sband,yfore_h,imf3s_h,A0_h,Bh_h,...
+% forep,TLindx,TLnumber,nCms,eq_Cms,nconadd,eq_oth,radd,Radd)
+%
+% 5/2/99: This one is much, much slower than fidcnderr.m when eq_ms=[] and
+% nconstr>0 and length(eq_ms)*nstepsm>nconstr. Seems to me that fcstidcnd2.m
+% is designed for the situation which eq_ms=2. The slow speed is due to
+% "Radd" added. When I have time, I need to check to see if I can speed up
+% this M file.
+%
+% Conditional forecasting in the identified model with or without error bands
+% It handles conditions on average values as well, so "valuecon" must be
+% expressed at average (NOT sum) level. Including unconditional forecasts
+% when nconstr=nCms=0. Maybe slower than "fcstidcnd.m" when length(eq_ms)*nstepsm=nconstr
+% because of Radd added. But "fsctidcnd.m" is incorrect when length(eq_ms)
+% *nstepsm>nconstr (situations like the average annual FFR constraint). 3/22/99
+% Ref.: Zha Forecast (I), pp. 17-17c.
+%
+% valuecon: vector of values conditioned
+% stepcon: sequence (cell) of steps conditioned; if length(stepcon{i}) > 1, the condition
+% is then an arithmetic average of log(y) over the stepcon{i} period.
+% varcon: vector of variables conditioned
+% nconstr: number of DLS constraints
+% nstepsm: maximum number of steps in all DLS constraints
+% nvar: number of variables in the BVAR model
+% lags: number of lags in the BVAR model
+% phil: the 1-by-(nvar*lags+1) data matrix where k=nvar*lags+1
+% (last period plus lags before the beginning of forecast)
+% eq_ms: identified equation or shock (in terms of number). If equ_ms=[], then "fidencond" or
+% 'fcstidcond' is, similar to RATS, to compute forecasts with *all* shocks.
+% Aband: 1: draws from A0 and Bh; 0: no draws
+% Sband: 1: draws from random shocks E; 0: no random shocks
+% yfore_h: uncondtional forecasts: forep-by-nvar. Never used when nconstr=0.
+% In this case, set it to [];
+% imf3s_h: 3-dimensional impulse responses matrix: impsteps-by-nvar shocks-by-nvar responses
+% Never used when nconstr=0. In this case, set it to [];
+% A0_h: A0 contemporaneous parameter matrix
+% Bh_h: reduced-form parameter matrix: k-by-nvar, y(t) = X(t)*Bh+e(t)
+% where X(t) is k-by-nvar and y(t) is 1-by-nvar
+% forep: # of forecast periods (e.g., monthly for a monthly model)
+% TLindx: 1-by-nCms vector of 1's and 0's, indicating tight or loose; 1: tighter, 0: looser
+% Used only when /* (MS draws) is activated. Right now, MS shocks are deterministic.
+% TLnumber: 1-by-nCms vector, lower bound for tight and upper bound for loose
+% nCms: # of LZ conditions
+% eq_Cms: equation location of MS shocks
+% nconadd: number of DLS added constraints DIRECTLY on structural shocks
+% for identified version where eq_ms is not empty. Note
+% nconadd=0 when eq_ms is empty.
+% eq_oth: index for other structural shocks than those in eq_ms.
+% If eq_ms=[], eq_oth=[]. Note length(eq_oth)+length(eq_ms)=nvar
+% radd: sparse nconadd-by-1; nconadd values added to rcon in the text later
+% Radd: sparce nvar*nstepsm-by-nconadd; added to Rcon in the text later
+% ------
+% yhat: conditional forecasts: forep-by-nvar
+% Estr: backed-out structural shocks (from N(0,1))
+% rcon: vector - the difference between valuecon and log(yfore) (unconditional forecasts)
+% Rcon: k-by-q (q constranits and k=nvar*max(nsteps)) so that
+% Rcon'*e = rcon where e is k-by-1
+% [u,d,v]: svd(Rcon,0)
+%
+% Written by Tao Zha March 1998. Revised November 1998;
+% 3/20/99 Disenabled draws of MS shcoks. To enable it, activate /* part
+% 3/20/99 Added A0_h and forep and deleted Cms as input argument.
+% 3/21/99 Added nconadd and eq_oth.
+% Previous programs may not be compatible.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+
+%
+%% See Zha's note "Forecast (1)" p. 5, RATS manual (some errors in RATS), etc.
+%% See also Zha Forecast (I), pp. 17-17c.
+%
+%% Some notations: y(t+1) = y(t)B1 + e(t+1)inv(A0). e(t+1) is 1-by-n.
+%% Let r(t+1)=e(t+1)inv(A0) + e(t+2)C + .... where inv(A0) is impulse
+%% response at t=1, C at t=2, etc. The row of inv(A0) or C is
+%% all responses to one shock.
+%% Let r be q-by-1 (such as r(1) = r(t+1)
+%% = y(t+1) (constrained) - y(t+1) (forecast)).
+%% Use impulse responses to find out R (k-by-q) where k=nvar*nsteps
+%% where nsteps the largest constrained step. The key of the program
+%% is to creat R using impulse responses
+%% Optimal solution for shock e where R'*e=r and e is k-by-1 is
+%% e = R*inv(R'*R)*r and k>=q
+%
+
+
+DLSIdShock = ~isempty(eq_ms); % if not empty, the MS shock is identified as in DLS
+
+impsteps=size(imf3s_h,1);
+if (forep<nstepsm) | (impsteps<nstepsm)
+ disp('Increase # of forecast or impulse steps!!')
+ disp('Or decrease # of constraints (nconstr) or constrained steps (stepcon(i))!!')
+ error('Maximum of conditional steps > # of forecast or impulse steps!!')
+end
+kts = nvar*nstepsm; % k -- ts: total shocks some of which are restricted and others
+ % are free.
+%*** initializing
+Rcon = zeros(kts,nconstr); % R: k-by-q where q include possible additional
+ % constraints directly on structural shocks for identified models.
+ % These addtional constraints will be added later.
+Econ = sparse(zeros(kts,1)); % E: k-by-1
+rcon = zeros(nconstr,1); % r: q-by-1
+%rcon=valuecon-diag(yfore(stepcon,varcon)); % another way is to use "loop" below.
+tcwc = nvar*lags; % total coefficients without constant
+phi=phil;
+
+
+
+%----------------------------------------------------
+% Form rcon, Rcon, and Econ (the mean of structural shocks)
+%----------------------------------------------------
+if nconstr
+ A0in = reshape(imf3s_h(1,:,:),nvar,nvar); % nvar shocks-by-nvar responses
+ for i=1:nconstr
+ rcon(i)=length(stepcon{i})*valuecon(i) - ...
+ sum(yfore_h(stepcon{i},varcon(i)),1); %<<>>
+ Rmat = zeros(nstepsm,nvar);
+ % Must be here inside the loop because it's matrix of one column of Rcon
+ for j=1:length(stepcon{i})
+ if DLSIdShock % Assuming the Fed can't see all other shocks within a month
+ Rmat(1:stepcon{i}(j),:) = Rmat(1:stepcon{i}(j),:) + ...
+ imf3s_h(stepcon{i}(j):-1:1,:,varcon(i));
+ % Rmat: row--nstepsm, column--nvar shocks (here all shocks except
+ % the identified one are set to zero) for a particular
+ % endogenous variable 'varcon(i)'. See Zha Forcast (1), pp.6-7
+ else % Rcon random with (A0,A+)
+ Rmat(1:stepcon{i}(j),:) = Rmat(1:stepcon{i}(j),:) + ...
+ imf3s_h(stepcon{i}(j):-1:1,:,varcon(i));
+ % Rmat: row--nstepsm, column--nvar shocks (here all shocks are
+ % *not* set to zero) for a particular endogenous
+ % variable 'varcon(i)'. See Zha Forcast (1), pp.6-7
+ end
+ end
+ Rmatt = Rmat'; % Now, nvar-by-nstepsm. I think here is where RATS has an error
+ % i.e. "OVERR" is not transposed when overlaid to "CAPR"
+ Rcon(:,i)=Rmatt(:); % Rcon: k-by-q where q=nconstr
+ end
+
+ rcon = [rcon;radd]; % added nconadd constrained values for identified model
+ % sparse because radd is sparse
+ Rcon = [Rcon Radd]; % added constraints on shocks for identified version
+ % sparse because Radd is sparse
+ Rcont=Rcon';
+ rinvrtr=Rcon/(Rcont*Rcon);
+ Econ = rinvrtr*rcon;
+
+
+ %/* Too slow, I believe, when q is large. 3/21/99
+ % [u d v]=svd(Rcon,0); %trial
+ % %???? Can we reduce the time by computing inv(R'*R) directly?
+ % % rtr = Rcon'*Rcon; %trial
+ % % rtrinv = inv(Rcon'*Rcon); %trial
+ % vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
+ % dinv = 1./diag(d); % inv(diag(d))
+ % vdinv=v.*(ones(size(v,2),1)*dinv'); %trial
+ % rtr=vd*vd'; % R'*R
+ % rtrinv = vdinv*vdinv'; % inv(R'*R)
+ %
+ % Econ = Rcon*rtrinv*rcon; % E = R*inv(R'R)*r; the mean of structural shocks
+else
+ Econ = sparse(zeros(kts,1)); % the mean of shocks is zero under no variable condition
+ Rcon = NaN;
+ rcon = NaN;
+ u = NaN;
+ d = NaN;
+ v = NaN;
+end
+
+
+
+%---------------------------------------
+% No uncertainty at all or only random (A0,A+)
+% In other words, no future shocks
+%---------------------------------------
+if (~Sband) | (nconstr & length(eq_ms)*nstepsm==nconstr)
+ % length(eq_ms)==1 implies one-one mapping between MS shocks and, say, FFR
+ % if nstepsm==nconstr. If this condition does not hold, this procedure
+ % is incorrect. I don't have time to fix it now (3/20/99). So I use
+ % this as a proximation
+ Estr = reshape(Econ,nvar,nstepsm);
+ Estr = Estr'; % transpose so that
+ % Estr: structural shocks. Row--steps, Column--n shocks
+ Estr = [Estr;zeros(forep-nstepsm,nvar)];
+ % Now, forep-by-nvar -- ready for forecasts
+ Estr(1:nCms,eq_Cms) = TLnumber(:);
+ Ures = Estr/A0_h; % nstepsm-by-nvar
+ % Ures: reduced-form residuals. Row--steps; Column--n shocks
+
+ % ** reconstruct x(t) for y(t+h) = x(t+h-1)*B
+ % ** where phi = x(t+h-1) with last column being constant
+ %
+ yhat = zeros(forep,nvar);
+ for k=1:forep
+ yhat(k,:) = phi*Bh_h + Ures(k,:);
+ phi(1,nvar+1:tcwc) = phi(1,1:tcwc-nvar);
+ phi(1,1:nvar) = yhat(k,:);
+ %
+ end
+
+%---------------------------------------
+% With random future shocks and possibly (A0,A+) depending
+% on if imf3s_h is random
+%---------------------------------------
+else
+ %--------------
+ % Condition on variables and A random
+ %--------------
+ if nconstr & Aband
+ warning(' ')
+ disp('This situation (both E and A random) is still under construction')
+ disp('It is closely related to Waggoner and Zha ReStat Gibbs sampling method')
+ disp('and related to "if DLSIdShock" in the following')
+ disp('Please press ctrl-c to abort')
+ pause
+ elseif nconstr
+ %--------------
+ % Condition on variables and DLS MS shock, no A random but S random
+ %--------------
+ if DLSIdShock % other shocks are indepedent of the eq_ms shock
+ Ome=speye(kts)-rinvrtr*Rcont;
+ Ome(find(abs(Ome)<1e-10))=0; % tighter the tolerance is, the longer it
+ % takes for svds to compute. E.g., size(Ome,1)*eps is perhaps very
+ % tight. After all, when the number in Ome is very small relative to
+ % 1 (which is the variance of structural shocks Estr), we can treat it
+ % as zero.
+ [u1 d1 v1] = svds(Ome,kts-nconstr-nconadd);
+ % We have exactly nconstr+nconadd zero singular values
+ Stdcon = u1*diag(sqrt(diag(abs(d1)))); % find a square root of Ome
+ % see Zha's forecast (1), p.17
+ Stdcon = [Stdcon sparse(zeros(kts,kts-size(d1,1)))];
+ Estr1 = Econ + Stdcon*randn(kts,1); % We must have rand(kts,1). Assigning
+ % some of randn(kts,1) to be zero according to Radd is WRONG.
+ % For this argument, see Zha Forecast (I) p.17a
+ Estr2 = reshape(Estr1,nvar,nstepsm);
+ % nvar-by-nstepsm; Needed to be transposed later
+ Estr = [Estr2';randn(forep-nstepsm,nvar)]; % transpose so that
+ % Estr2: structural shocks. Row--nstepsm, Column--n shocks
+ % Now, forep-by-nvar -- ready for forecasts
+ else
+ Ome=speye(kts)-rinvrtr*Rcont;
+ Ome(find(abs(Ome)<1e-10))=0; % tighter the tolerance is, the longer it
+ % takes for svds to compute. E.g., size(Ome,1)*eps is perhaps very
+ % tight. After all, when the number in Ome is very small relative to
+ % 1 (which is the variance of structural shocks Estr), we can treat it
+ % as zero.
+ [u1 d1 v1] = svds(Ome,kts-nconstr-nconadd);
+ % We have exactly nconstr+nconadd zero singular values
+ Stdcon = u1*diag(sqrt(diag(abs(d1)))); % find a square root of Ome
+ % see Zha's forecast (1), p.17
+ Stdcon = [Stdcon sparse(zeros(kts,nconstr+nconadd))];
+
+ %*** The following very inefficient
+ % Ome = eye(kts) - u*u'; % note, I-u*u' = I - R*inv(R'*R)*R'
+ % %[u1 d1 v1] = svd(Ome); % too slow
+ % [u1 d1] = eig(Ome);
+ % Stdcon = u1*diag(sqrt(diag(abs(d1)))); % lower triagular chol of conditional variance
+ % % see Zha's forecast (1), p.17
+
+ %--------------
+ % Condition on variables and LZ MS shock, no A random but S random
+ % This section has not be tested yet, 10/14/98
+ %--------------
+ if nCms
+ Estr1 = Econ + Stdcon*randn(kts,1);
+ Estr2 = reshape(Estr1,nvar,nstepsm);
+ Estr2 = Estr2'; % transpose so that
+ % Estr2: structural shocks. Row--nstepsm, Column--n shocks
+ Estr = [Estr2;randn(forep-nstepsm,nvar)];
+ % Now, forep-by-nvar -- ready for forecasts
+ Estr(1:nCms,eq_Cms) = TLnumber(:);
+
+ %/* draw MS shocks
+ % for k=1:nCms
+ % if TLindx(k) % tighter
+ % while (Estr(k,eq_Cms)<TLnumber(k))
+ % Estr(k,eq_Cms) = randn(1,1);
+ % end
+ % else % looser
+ % while (Estr(k,eq_Cms)>TLnumber(k))
+ % Estr(k,eq_Cms) = randn(1,1);
+ % end
+ % end
+ % end
+ %--------------
+ % Condition on variables only, no A random but S random
+ %--------------
+ else
+ Estr1 = Econ + Stdcon*randn(kts,1);
+ Estr2 = reshape(Estr1,nvar,nstepsm);
+ Estr2 = Estr2'; % transpose so that
+ % Estr2: structural shocks. Row--nstepsm, Column--n shocks
+ Estr = [Estr2;randn(forep-nstepsm,nvar)];
+ % Now, forep-by-nvar -- ready for forecasts
+ end
+ end
+ %--------------
+ % Condition on LZ MS shocks only, S random and possibly A random depending on
+ % if A0_h and Bh_h are random
+ %--------------
+ else
+ if nCms
+ Estr = randn(forep,nvar);
+ % Now, forep-by-nvar -- ready for forecasts
+ Estr(:,eq_Cms)=0;
+ Estr(1:nCms,eq_Cms) = TLnumber(:);
+
+ %/* draw MS shocks
+ % for k=1:nCms
+ % if TLindx(k) % tighter
+ % while (Estr(k,eq_Cms)<TLnumber(k))
+ % Estr(k,eq_Cms) = randn(1,1);
+ % end
+ % else % looser
+ % while (Estr(k,eq_Cms)>TLnumber(k))
+ % Estr(k,eq_Cms) = randn(1,1);
+ % end
+ % end
+ % end
+ else
+ Estr = randn(forep,nvar); % Unconditional forecast
+ % Now, forep-by-nvar -- ready for forecasts
+ end
+ end
+ %
+
+
+ Ures = Estr/A0_h; % nstepsm-by-nvar
+ % Ures: reduced-form residuals. Row--steps; Column--n shocks
+
+ % ** reconstruct x(t) for y(t+h) = x(t+h-1)*B
+ % ** where phi = x(t+h-1) with last column being constant
+ %
+ yhat = zeros(forep,nvar);
+ for k=1:forep
+ yhat(k,:) = phi*Bh_h + Ures(k,:);
+ phi(1,nvar+1:tcwc) = phi(1,1:tcwc-nvar);
+ phi(1,1:nvar) = yhat(k,:);
+ end
+end
diff --git a/MatlabFiles/fidcnderr.m b/MatlabFiles/fidcnderr.m
index 979ae4e3a39cb348d158df4f777c9ea6e5db8b8a..5587dbf1ad5ba50ab571fefdb1368ee3917538fb 100644
--- a/MatlabFiles/fidcnderr.m
+++ b/MatlabFiles/fidcnderr.m
@@ -1,266 +1,265 @@
-function [yhat,Estr,rcon,Rcon,u,v,d] = fidcnderr(valuecon,stepcon,varcon,nconstr,...
- nstepsm,nvar,lags,phil,eq_iden,Aband,Sband,yforeml,imf3sml,Bhml,...
- yfore_h,imf3s_h,Bh_h,Cms,TLindx,TLnumber,nCms,eq_Cms)
-%function [yhat,Estr,rcon,Rcon,u,v,d] = fidcnderr(valuecon,stepcon,varcon,nconstr,...
-% nstepsm,nvar,lags,phil,eq_iden,Aband,Sband,yforeml,imf3sml,Bhml,...
-% yfore_h,imf3s_h,Bh_h,Cms,TLindx,TLnumber,nCms,eq_Cms)
-% To be done (8/25/98): (1) yforeml, imf3sml, and Bhml should not be there.
-% (2) MS stuff need to be generalized.
-% (3) perhaps, imf3s_h needs to be re-examined.
-% Conditional forecasting in the identified model with one draw for error bands
-%
-% valuecon: vector of values conditioned
-% stepcon: sequence (cell) of steps conditioned; if length(stepcon{i}) > 1, the condition
-% is then an arithmetic average of log(y) over the stepcon{i} period.
-% varcon: vector of variables conditioned
-% nconstr: number of constraints
-% nstepsm: maximum number of steps in all constraints
-% nvar: number of variables in the BVAR model
-% lags: number of lags in the BVAR model
-% phil: the 1-by-(nvar*lags+1) data matrix where k=nvar*lags+1
-% (last period plus lags before the beginning of forecast)
-% eq_iden: identified equation or shock (in terms of number). If equ_iden=[], then
-% 'fidencond' is, similar to RATS, to compute forecasts with *all* shocks.
-% Sband: 1: generate error bands from random shocks E; 0: no random shocks
-% yfore: uncondtional forecasts: forep-by-nvar
-% imf3s: 3-dimensional impulse responses matrix:
-% impsteps-by-nvar shocks-by-nvar responses
-% Bh: reduced-form parameter matrix: k-by-nvar, y(t) = X(t)*Bh+e(t)
-% where X(t) is k-by-nvar and y(t) is 1-by-nvar
-% ------
-% yhat: conditional forecasts: forep-by-nvar
-% Estr: backed-out structural shocks (from N(0,1))
-% rcon: vector - the difference between valuecon and log(yfore) (unconditional forecasts)
-% Rcon: k-by-q (q constranits and k=nvar*max(nsteps)) so that
-% Rcon'*e = rcon where e is k-by-1
-% [u,d,v]: svd(Rcon,0)
-%
-%% See Zha's note "Forecast (1)" p. 5, RATS manual (some errors in RATS), etc.
-%
-%% Some notations: y(t+1) = y(t)B1 + e(t+1)inv(A0). e(t+1) is 1-by-n.
-%% Let r(t+1)=e(t+1)inv(A0) + e(t+2)C + .... where inv(A0) is impulse
-%% response at t=1, C at t=2, etc. The row of inv(A0) or C is
-%% all responses to one shock.
-%% Let r be q-by-1 (such as r(1) = r(t+1)
-%% = y(t+1) (constrained) - y(t+1) (forecast)).
-%% Use impulse responses to find out R (k-by-q) where k=nvar*nsteps
-%% where nsteps the largest constrained step. The key of the program
-%% is to creat R using impulse responses
-%% Optimal solution for shock e where R'*e=r and e is k-by-1 is
-%% e = R*inv(R'*R)*r.
-%
-% Copyright (c) March 1998 by Tao Zha
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-
-IdenShock = ~isempty(eq_iden); % if not empty, the shock is identified
-
-forep=size(yfore_h,1);
-impsteps=size(imf3s_h,1);
-if (forep<nstepsm) | (impsteps<nstepsm)
- disp('Increase # of forecast or impulse steps!!')
- disp('Or decrease # of constraints (nconstr) or constrained steps (stepcon(i))!!')
- error('Maximum of conditional steps > # of forecast or impulse steps!!')
-end
-kfs = nvar*nstepsm; % k -- fs: free shocks
-%*** initializing
-Rcon = zeros(kfs,nconstr); % R: k-by-q
-Econ = zeros(kfs,1); % E: k-by-1
-rcon = zeros(nconstr,1); % r: q-by-1
-%rcon=valuecon-diag(yfore(stepcon,varcon)); % another way is to use "loop" below.
-A0in = reshape(imf3s_h(1,:,:),nvar,nvar); % nvar shocks-by-nvar responses
-
-
-for i=1:nconstr
- if IdenShock
- rcon(i)=length(stepcon{i})*valuecon(i) - ...
- sum(yforeml(stepcon{i},varcon(i)),1); % <<>>
- else
- rcon(i)=length(stepcon{i})*valuecon(i) - ...
- sum(yfore_h(stepcon{i},varcon(i)),1); %<<>>
- end
- Rmat = zeros(nstepsm,nvar);
- r2mat = zeros(nstepsm,1); % simply one identified equation
- % Must be here inside the loop because it's matrix of one column of Rcon
- for j=1:length(stepcon{i})
- if IdenShock % Rcon only at ML; assuming the Fed can't see all other shocks
- Rmat(1:stepcon{i}(j),eq_iden) = Rmat(1:stepcon{i}(j),eq_iden) + ...
- imf3sml(stepcon{i}(j):-1:1,eq_iden,varcon(i));
- % Rmat: row--nstepsm, column--nvar shocks (here all shocks except
- % the identified one are set to zero) for a particular
- % endogenous variable 'varcon(i)'. See Zha Forcast (1), pp.6-7
- else % Rcon random with (A0,A+)
- Rmat(1:stepcon{i}(j),:) = Rmat(1:stepcon{i}(j),:) + ...
- imf3s_h(stepcon{i}(j):-1:1,:,varcon(i));
- % Rmat: row--nstepsm, column--nvar shocks (here all shocks are
- % *not* set to zero) for a particular endogenous
- % variable 'varcon(i)'. See Zha Forcast (1), pp.6-7
- end
- end
- Rmatt = Rmat'; % Now, nvar-by-nstepsm. I think here is where RATS has an error
- % i.e. "OVERR" is not transposed when overlaid to "CAPR"
- Rcon(:,i)=Rmatt(:); % Rcon: k-by-q where q=nconstr
-end
-
-if nconstr
- [u d v]=svd(Rcon,0); %trial
- % rtr = Rcon'*Rcon; %trial
- % rtrinv = inv(Rcon'*Rcon); %trial
- vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
- dinv = 1./diag(d); % inv(diag(d))
- vdinv=v.*(ones(size(v,2),1)*dinv'); %trial
- rtr=vd*vd'; % R'*R
- rtrinv = vdinv*vdinv'; % inv(R'*R)
-
- Econ = Rcon*rtrinv*rcon; % E = R*inv(R'R)*r; mean
-else
- Econ = zeros(kfs,1);
- Rcon = NaN;
- rcon = NaN;
- u = NaN;
- d = NaN;
- v = NaN;
-end
-
-
-tcwc = nvar*lags; % total coefficients without constant
-phi=phil;
-phis=phil; % for exact backed out shocks
-%
-if (Sband==0) % no random or random only from (A0,A+)
- Estr = reshape(Econ,nvar,nstepsm);
- Estr = Estr'; % transpose so that
- % Estr: structural shocks. Row--steps, Column--n shocks
- Estr = [Estr;randn(forep-nstepsm,nvar)];
- % Now, forep-by-nvar -- ready for forecasts
- Ures = Estr*A0in; % nstepsm-by-nvar
- % Ures: reduced-form residuals. Row--steps; Column--n shocks
-
- % ** reconstruct x(t) for y(t+h) = x(t+h-1)*B
- % ** where phi = x(t+h-1) with last column being constant
- %
- yhat = zeros(forep,nvar);
- for k=1:forep
- yhat(k,:) = phi*Bh_h + Ures(k,:);
- phi(1,nvar+1:tcwc) = phi(1,1:tcwc-nvar);
- phi(1,1:nvar) = yhat(k,:);
- %
- end
-else % random from shocks E and possibly (A0,A+) depending on if imf3s is random
- if nconstr
- if IdenShock % other shocks are indepedent of the eq_iden shock
- Osk = randn(kfs,1); % other shocks
- for j=1:nstepsm
- Osk(nvar*(j-1)+eq_iden)=0; % no shock to the MS or identified equation
- end
- Estr = Econ + Osk; % Econ is non zero only at position
- % eq_iden*j where j=1:nstepsm
- else
- Ome = eye(kfs) - u*u'; % note, I-u*u' = I - R*inv(R'*R)*R'
- %[u1 d1 v1] = svd(Ome); % too slow
- [u1 d1] = eig(Ome);
- Stdcon = u1*diag(sqrt(diag(abs(d1)))); % lower triagular chol of conditional variance
- % see Zha's forecast (1), p.17
- if Cms
- if TLindx % tight
- Estr1 = Econ + Stdcon*randn(kfs,1);
- Estr2 = reshape(Estr1,nvar,nstepsm);
- Estr2 = Estr2'; % transpose so that
- % Estr2: structural shocks. Row--nstepsm, Column--n shocks
- Estr = [Estr2;randn(forep-nstepsm,nvar)];
- % Now, forep-by-nvar -- ready for forecasts
- Sindx = find(Estr(1:nCms,eq_Cms)<TLnumber);
- while ~isempty(Sindx)
- Estr1 = Econ + Stdcon*randn(kfs,1);
- Estr2 = reshape(Estr1,nvar,nstepsm);
- Estr2 = Estr2'; % transpose so that
- % Estr2: structural shocks. Row--nstepsm, Column--n shocks
- Estr = [Estr2;randn(forep-nstepsm,nvar)];
- % Now, forep-by-nvar -- ready for forecasts
- Sindx = find(Estr(1:nCms,eq_Cms)<TLnumber);
- end
- else % loose
- Estr1 = Econ + Stdcon*randn(kfs,1);
- Estr2 = reshape(Estr1,nvar,nstepsm);
- Estr2 = Estr2'; % transpose so that
- % Estr2: structural shocks. Row--nstepsm, Column--n shocks
- Estr = [Estr2;randn(forep-nstepsm,nvar)];
- % Now, forep-by-nvar -- ready for forecasts
- Sindx = find(Estr(1:nCms,eq_Cms)>TLnumber);
- while ~isempty(Sindx)
- Estr1 = Econ + Stdcon*randn(kfs,1);
- Estr2 = reshape(Estr1,nvar,nstepsm);
- Estr2 = Estr2'; % transpose so that
- % Estr2: structural shocks. Row--nstepsm, Column--n shocks
- Estr = [Estr2;randn(forep-nstepsm,nvar)];
- % Now, forep-by-nvar -- ready for forecasts
- Sindx = find(Estr(1:nCms,eq_Cms)>TLnumber);
- end
- end
- else
- Estr1 = Econ + Stdcon*randn(kfs,1);
- Estr2 = reshape(Estr1,nvar,nstepsm);
- Estr2 = Estr2'; % transpose so that
- % Estr2: structural shocks. Row--nstepsm, Column--n shocks
- Estr = [Estr2;randn(forep-nstepsm,nvar)];
- % Now, forep-by-nvar -- ready for forecasts
- end
- end
- else
- if Cms
- if TLindx % tight
- Estr = randn(forep,nvar);
- % Now, forep-by-nvar -- ready for forecasts
- Sindx = find(Estr(1:nCms,eq_Cms)<TLnumber);
- while ~isempty(Sindx)
- Estr = randn(forep,nvar);
- % Now, forep-by-nvar -- ready for forecasts
- Sindx = find(Estr(1:nCms,eq_Cms)<TLnumber);
- end
- else % loose
- Estr = randn(forep,nvar);
- % Now, forep-by-nvar -- ready for forecasts
- Sindx = find(Estr(1:nCms,eq_Cms)>TLnumber);
- while ~isempty(Sindx)
- Estr = randn(forep,nvar);
- % Now, forep-by-nvar -- ready for forecasts
- Sindx = find(Estr(1:nCms,eq_Cms)>TLnumber);
- end
- end
- else
- Estr = randn(forep,nvar);
- % Now, forep-by-nvar -- ready for forecasts
- end
- end
- %
- %disp('HERE')
-
- Ures = Estr*A0in; % nstepsm-by-nvar
- % Ures: reduced-form residuals. Row--steps; Column--n shocks
-
- % ** reconstruct x(t) for y(t+h) = x(t+h-1)*B
- % ** where phi = x(t+h-1) with last column being constant
- %
- yhat = zeros(forep,nvar);
- for k=1:forep
- yhat(k,:) = phi*Bh_h + Ures(k,:);
- phi(1,nvar+1:tcwc) = phi(1,1:tcwc-nvar);
- phi(1,1:nvar) = yhat(k,:);
- end
-end
\ No newline at end of file
+function [yhat,Estr,rcon,Rcon,u,v,d] = fidcnderr(valuecon,stepcon,varcon,nconstr,...
+ nstepsm,nvar,lags,phil,eq_iden,Aband,Sband,yforeml,imf3sml,Bhml,...
+ yfore_h,imf3s_h,Bh_h,Cms,TLindx,TLnumber,nCms,eq_Cms)
+%function [yhat,Estr,rcon,Rcon,u,v,d] = fidcnderr(valuecon,stepcon,varcon,nconstr,...
+% nstepsm,nvar,lags,phil,eq_iden,Aband,Sband,yforeml,imf3sml,Bhml,...
+% yfore_h,imf3s_h,Bh_h,Cms,TLindx,TLnumber,nCms,eq_Cms)
+% To be done (8/25/98): (1) yforeml, imf3sml, and Bhml should not be there.
+% (2) MS stuff need to be generalized.
+% (3) perhaps, imf3s_h needs to be re-examined.
+% Conditional forecasting in the identified model with one draw for error bands
+%
+% valuecon: vector of values conditioned
+% stepcon: sequence (cell) of steps conditioned; if length(stepcon{i}) > 1, the condition
+% is then an arithmetic average of log(y) over the stepcon{i} period.
+% varcon: vector of variables conditioned
+% nconstr: number of constraints
+% nstepsm: maximum number of steps in all constraints
+% nvar: number of variables in the BVAR model
+% lags: number of lags in the BVAR model
+% phil: the 1-by-(nvar*lags+1) data matrix where k=nvar*lags+1
+% (last period plus lags before the beginning of forecast)
+% eq_iden: identified equation or shock (in terms of number). If equ_iden=[], then
+% 'fidencond' is, similar to RATS, to compute forecasts with *all* shocks.
+% Sband: 1: generate error bands from random shocks E; 0: no random shocks
+% yfore: uncondtional forecasts: forep-by-nvar
+% imf3s: 3-dimensional impulse responses matrix:
+% impsteps-by-nvar shocks-by-nvar responses
+% Bh: reduced-form parameter matrix: k-by-nvar, y(t) = X(t)*Bh+e(t)
+% where X(t) is k-by-nvar and y(t) is 1-by-nvar
+% ------
+% yhat: conditional forecasts: forep-by-nvar
+% Estr: backed-out structural shocks (from N(0,1))
+% rcon: vector - the difference between valuecon and log(yfore) (unconditional forecasts)
+% Rcon: k-by-q (q constranits and k=nvar*max(nsteps)) so that
+% Rcon'*e = rcon where e is k-by-1
+% [u,d,v]: svd(Rcon,0)
+%
+%% See Zha's note "Forecast (1)" p. 5, RATS manual (some errors in RATS), etc.
+%
+%% Some notations: y(t+1) = y(t)B1 + e(t+1)inv(A0). e(t+1) is 1-by-n.
+%% Let r(t+1)=e(t+1)inv(A0) + e(t+2)C + .... where inv(A0) is impulse
+%% response at t=1, C at t=2, etc. The row of inv(A0) or C is
+%% all responses to one shock.
+%% Let r be q-by-1 (such as r(1) = r(t+1)
+%% = y(t+1) (constrained) - y(t+1) (forecast)).
+%% Use impulse responses to find out R (k-by-q) where k=nvar*nsteps
+%% where nsteps the largest constrained step. The key of the program
+%% is to creat R using impulse responses
+%% Optimal solution for shock e where R'*e=r and e is k-by-1 is
+%% e = R*inv(R'*R)*r.
+%
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+
+IdenShock = ~isempty(eq_iden); % if not empty, the shock is identified
+
+forep=size(yfore_h,1);
+impsteps=size(imf3s_h,1);
+if (forep<nstepsm) | (impsteps<nstepsm)
+ disp('Increase # of forecast or impulse steps!!')
+ disp('Or decrease # of constraints (nconstr) or constrained steps (stepcon(i))!!')
+ error('Maximum of conditional steps > # of forecast or impulse steps!!')
+end
+kfs = nvar*nstepsm; % k -- fs: free shocks
+%*** initializing
+Rcon = zeros(kfs,nconstr); % R: k-by-q
+Econ = zeros(kfs,1); % E: k-by-1
+rcon = zeros(nconstr,1); % r: q-by-1
+%rcon=valuecon-diag(yfore(stepcon,varcon)); % another way is to use "loop" below.
+A0in = reshape(imf3s_h(1,:,:),nvar,nvar); % nvar shocks-by-nvar responses
+
+
+for i=1:nconstr
+ if IdenShock
+ rcon(i)=length(stepcon{i})*valuecon(i) - ...
+ sum(yforeml(stepcon{i},varcon(i)),1); % <<>>
+ else
+ rcon(i)=length(stepcon{i})*valuecon(i) - ...
+ sum(yfore_h(stepcon{i},varcon(i)),1); %<<>>
+ end
+ Rmat = zeros(nstepsm,nvar);
+ r2mat = zeros(nstepsm,1); % simply one identified equation
+ % Must be here inside the loop because it's matrix of one column of Rcon
+ for j=1:length(stepcon{i})
+ if IdenShock % Rcon only at ML; assuming the Fed can't see all other shocks
+ Rmat(1:stepcon{i}(j),eq_iden) = Rmat(1:stepcon{i}(j),eq_iden) + ...
+ imf3sml(stepcon{i}(j):-1:1,eq_iden,varcon(i));
+ % Rmat: row--nstepsm, column--nvar shocks (here all shocks except
+ % the identified one are set to zero) for a particular
+ % endogenous variable 'varcon(i)'. See Zha Forcast (1), pp.6-7
+ else % Rcon random with (A0,A+)
+ Rmat(1:stepcon{i}(j),:) = Rmat(1:stepcon{i}(j),:) + ...
+ imf3s_h(stepcon{i}(j):-1:1,:,varcon(i));
+ % Rmat: row--nstepsm, column--nvar shocks (here all shocks are
+ % *not* set to zero) for a particular endogenous
+ % variable 'varcon(i)'. See Zha Forcast (1), pp.6-7
+ end
+ end
+ Rmatt = Rmat'; % Now, nvar-by-nstepsm. I think here is where RATS has an error
+ % i.e. "OVERR" is not transposed when overlaid to "CAPR"
+ Rcon(:,i)=Rmatt(:); % Rcon: k-by-q where q=nconstr
+end
+
+if nconstr
+ [u d v]=svd(Rcon,0); %trial
+ % rtr = Rcon'*Rcon; %trial
+ % rtrinv = inv(Rcon'*Rcon); %trial
+ vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
+ dinv = 1./diag(d); % inv(diag(d))
+ vdinv=v.*(ones(size(v,2),1)*dinv'); %trial
+ rtr=vd*vd'; % R'*R
+ rtrinv = vdinv*vdinv'; % inv(R'*R)
+
+ Econ = Rcon*rtrinv*rcon; % E = R*inv(R'R)*r; mean
+else
+ Econ = zeros(kfs,1);
+ Rcon = NaN;
+ rcon = NaN;
+ u = NaN;
+ d = NaN;
+ v = NaN;
+end
+
+
+tcwc = nvar*lags; % total coefficients without constant
+phi=phil;
+phis=phil; % for exact backed out shocks
+%
+if (Sband==0) % no random or random only from (A0,A+)
+ Estr = reshape(Econ,nvar,nstepsm);
+ Estr = Estr'; % transpose so that
+ % Estr: structural shocks. Row--steps, Column--n shocks
+ Estr = [Estr;randn(forep-nstepsm,nvar)];
+ % Now, forep-by-nvar -- ready for forecasts
+ Ures = Estr*A0in; % nstepsm-by-nvar
+ % Ures: reduced-form residuals. Row--steps; Column--n shocks
+
+ % ** reconstruct x(t) for y(t+h) = x(t+h-1)*B
+ % ** where phi = x(t+h-1) with last column being constant
+ %
+ yhat = zeros(forep,nvar);
+ for k=1:forep
+ yhat(k,:) = phi*Bh_h + Ures(k,:);
+ phi(1,nvar+1:tcwc) = phi(1,1:tcwc-nvar);
+ phi(1,1:nvar) = yhat(k,:);
+ %
+ end
+else % random from shocks E and possibly (A0,A+) depending on if imf3s is random
+ if nconstr
+ if IdenShock % other shocks are indepedent of the eq_iden shock
+ Osk = randn(kfs,1); % other shocks
+ for j=1:nstepsm
+ Osk(nvar*(j-1)+eq_iden)=0; % no shock to the MS or identified equation
+ end
+ Estr = Econ + Osk; % Econ is non zero only at position
+ % eq_iden*j where j=1:nstepsm
+ else
+ Ome = eye(kfs) - u*u'; % note, I-u*u' = I - R*inv(R'*R)*R'
+ %[u1 d1 v1] = svd(Ome); % too slow
+ [u1 d1] = eig(Ome);
+ Stdcon = u1*diag(sqrt(diag(abs(d1)))); % lower triagular chol of conditional variance
+ % see Zha's forecast (1), p.17
+ if Cms
+ if TLindx % tight
+ Estr1 = Econ + Stdcon*randn(kfs,1);
+ Estr2 = reshape(Estr1,nvar,nstepsm);
+ Estr2 = Estr2'; % transpose so that
+ % Estr2: structural shocks. Row--nstepsm, Column--n shocks
+ Estr = [Estr2;randn(forep-nstepsm,nvar)];
+ % Now, forep-by-nvar -- ready for forecasts
+ Sindx = find(Estr(1:nCms,eq_Cms)<TLnumber);
+ while ~isempty(Sindx)
+ Estr1 = Econ + Stdcon*randn(kfs,1);
+ Estr2 = reshape(Estr1,nvar,nstepsm);
+ Estr2 = Estr2'; % transpose so that
+ % Estr2: structural shocks. Row--nstepsm, Column--n shocks
+ Estr = [Estr2;randn(forep-nstepsm,nvar)];
+ % Now, forep-by-nvar -- ready for forecasts
+ Sindx = find(Estr(1:nCms,eq_Cms)<TLnumber);
+ end
+ else % loose
+ Estr1 = Econ + Stdcon*randn(kfs,1);
+ Estr2 = reshape(Estr1,nvar,nstepsm);
+ Estr2 = Estr2'; % transpose so that
+ % Estr2: structural shocks. Row--nstepsm, Column--n shocks
+ Estr = [Estr2;randn(forep-nstepsm,nvar)];
+ % Now, forep-by-nvar -- ready for forecasts
+ Sindx = find(Estr(1:nCms,eq_Cms)>TLnumber);
+ while ~isempty(Sindx)
+ Estr1 = Econ + Stdcon*randn(kfs,1);
+ Estr2 = reshape(Estr1,nvar,nstepsm);
+ Estr2 = Estr2'; % transpose so that
+ % Estr2: structural shocks. Row--nstepsm, Column--n shocks
+ Estr = [Estr2;randn(forep-nstepsm,nvar)];
+ % Now, forep-by-nvar -- ready for forecasts
+ Sindx = find(Estr(1:nCms,eq_Cms)>TLnumber);
+ end
+ end
+ else
+ Estr1 = Econ + Stdcon*randn(kfs,1);
+ Estr2 = reshape(Estr1,nvar,nstepsm);
+ Estr2 = Estr2'; % transpose so that
+ % Estr2: structural shocks. Row--nstepsm, Column--n shocks
+ Estr = [Estr2;randn(forep-nstepsm,nvar)];
+ % Now, forep-by-nvar -- ready for forecasts
+ end
+ end
+ else
+ if Cms
+ if TLindx % tight
+ Estr = randn(forep,nvar);
+ % Now, forep-by-nvar -- ready for forecasts
+ Sindx = find(Estr(1:nCms,eq_Cms)<TLnumber);
+ while ~isempty(Sindx)
+ Estr = randn(forep,nvar);
+ % Now, forep-by-nvar -- ready for forecasts
+ Sindx = find(Estr(1:nCms,eq_Cms)<TLnumber);
+ end
+ else % loose
+ Estr = randn(forep,nvar);
+ % Now, forep-by-nvar -- ready for forecasts
+ Sindx = find(Estr(1:nCms,eq_Cms)>TLnumber);
+ while ~isempty(Sindx)
+ Estr = randn(forep,nvar);
+ % Now, forep-by-nvar -- ready for forecasts
+ Sindx = find(Estr(1:nCms,eq_Cms)>TLnumber);
+ end
+ end
+ else
+ Estr = randn(forep,nvar);
+ % Now, forep-by-nvar -- ready for forecasts
+ end
+ end
+ %
+ %disp('HERE')
+
+ Ures = Estr*A0in; % nstepsm-by-nvar
+ % Ures: reduced-form residuals. Row--steps; Column--n shocks
+
+ % ** reconstruct x(t) for y(t+h) = x(t+h-1)*B
+ % ** where phi = x(t+h-1) with last column being constant
+ %
+ yhat = zeros(forep,nvar);
+ for k=1:forep
+ yhat(k,:) = phi*Bh_h + Ures(k,:);
+ phi(1,nvar+1:tcwc) = phi(1,1:tcwc-nvar);
+ phi(1,1:nvar) = yhat(k,:);
+ end
+end
diff --git a/MatlabFiles/fidcndexa.m b/MatlabFiles/fidcndexa.m
index 1e4e478b93939ac1fd3e250c8f8ced30d8b04353..681a8365195800fdc4243ee422403aff93230c89 100644
--- a/MatlabFiles/fidcndexa.m
+++ b/MatlabFiles/fidcndexa.m
@@ -1,62 +1,62 @@
-function Estrexa = fidcndexa(yexa,phil,A0_h,Bh_h,nvar,lags)
-% Estrexa = fidcndexa(yexa,phil,A0_h,Bh_h,nvar,lags)
-% Exact structural shocks e(t) (backed out by conditioning on the path "yexa")
-%
-% yexa: actup-by-nvar. Actual data (all log except R, U, etc.) begin
-% at nSample-actup+1 and ends at nSample, where nSample is the period
-% for actual data (excluding dummy observations)
-% phil: the 1-by-(nvar*lags+1) data matrix where k=nvar*lags+1
-% (last period plus lags, prior to nSample-actup+1)
-% A0_h: A0, column-equation; in the form of y(t)A0=X(t)A+ + e(t)
-% Bh_h: reduced-form parameter matrix: k-by-nvar, y(t) = X(t)*Bh+u(t)
-% where X(t) is k-by-nvar and y(t) is 1-by-nvar
-% nvar: number of variables in the BVAR model
-% lags: number of lags in the BVAR model
-% ------
-% Estrexa: actup-by-nvar -- backed out exact shocks given parameter values
-% where actup = length(yexa(:,1)).
-%
-%% See Zha's note "Forecast (2)" p.11
-%
-%% Some notations: y(t+1) = y(t)B1 + e(t+1)inv(A0). e(t+1) is 1-by-n.
-%% Let r(t+1)=e(t+1)inv(A0) + e(t+2)C + .... where inv(A0) is impulse
-%% response at t=1, C at t=2, etc. The row of inv(A0) or C is
-%% all responses to one shock.
-%% Let r be q-by-1 (such as r(1) = r(t+1)
-%% = y(t+1) (constrained) - y(t+1) (forecast)).
-%% Use impulse responses to find out R (k-by-q) where k=nvar*nsteps
-%% where nsteps the largest constrained step. The key of the program
-%% is to creat R using impulse responses
-%% Optimal solution for shock e where R'*e=r and e is k-by-1 is
-%% e = R*inv(R'*R)*r.
-%
-% Copyright (c) April 1998 by Tao Zha
-% Change the input arguments so that old programs may not be compatible, 03/19/98.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-tcwc = nvar*lags; % total coefficients without constant
-actup = length(yexa(:,1)); % periods considered for structural shocks.
-phis=phil; % for exact backed out shocks
-
-Estrsexa=zeros(actup,nvar); % backed out exact shocks
-
-for k=1:actup
- Estrexa(k,:) = (yexa(k,:)-phis*Bh_h) * A0_h;
- phis(nvar+1:tcwc) = phis(1:tcwc-nvar);
- phis(1:nvar) = yexa(k,:);
-end
+function Estrexa = fidcndexa(yexa,phil,A0_h,Bh_h,nvar,lags)
+% Estrexa = fidcndexa(yexa,phil,A0_h,Bh_h,nvar,lags)
+% Exact structural shocks e(t) (backed out by conditioning on the path "yexa")
+%
+% yexa: actup-by-nvar. Actual data (all log except R, U, etc.) begin
+% at nSample-actup+1 and ends at nSample, where nSample is the period
+% for actual data (excluding dummy observations)
+% phil: the 1-by-(nvar*lags+1) data matrix where k=nvar*lags+1
+% (last period plus lags, prior to nSample-actup+1)
+% A0_h: A0, column-equation; in the form of y(t)A0=X(t)A+ + e(t)
+% Bh_h: reduced-form parameter matrix: k-by-nvar, y(t) = X(t)*Bh+u(t)
+% where X(t) is k-by-nvar and y(t) is 1-by-nvar
+% nvar: number of variables in the BVAR model
+% lags: number of lags in the BVAR model
+% ------
+% Estrexa: actup-by-nvar -- backed out exact shocks given parameter values
+% where actup = length(yexa(:,1)).
+%
+%% See Zha's note "Forecast (2)" p.11
+%
+%% Some notations: y(t+1) = y(t)B1 + e(t+1)inv(A0). e(t+1) is 1-by-n.
+%% Let r(t+1)=e(t+1)inv(A0) + e(t+2)C + .... where inv(A0) is impulse
+%% response at t=1, C at t=2, etc. The row of inv(A0) or C is
+%% all responses to one shock.
+%% Let r be q-by-1 (such as r(1) = r(t+1)
+%% = y(t+1) (constrained) - y(t+1) (forecast)).
+%% Use impulse responses to find out R (k-by-q) where k=nvar*nsteps
+%% where nsteps the largest constrained step. The key of the program
+%% is to creat R using impulse responses
+%% Optimal solution for shock e where R'*e=r and e is k-by-1 is
+%% e = R*inv(R'*R)*r.
+%
+% Written by Tao Zha April 1998
+% Change the input arguments so that old programs may not be compatible, 03/19/98.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+tcwc = nvar*lags; % total coefficients without constant
+actup = length(yexa(:,1)); % periods considered for structural shocks.
+phis=phil; % for exact backed out shocks
+
+Estrsexa=zeros(actup,nvar); % backed out exact shocks
+
+for k=1:actup
+ Estrexa(k,:) = (yexa(k,:)-phis*Bh_h) * A0_h;
+ phis(nvar+1:tcwc) = phis(1:tcwc-nvar);
+ phis(1:nvar) = yexa(k,:);
+end
diff --git a/MatlabFiles/fidencond.m b/MatlabFiles/fidencond.m
index 4a5cb95fa4a1e619308513f7f44c8346341cd65d..5aa2c57d03a08356c29a547682c60a1ec6405847 100644
--- a/MatlabFiles/fidencond.m
+++ b/MatlabFiles/fidencond.m
@@ -1,192 +1,192 @@
-function [yhat,Estr,rcon,Ome,Rmat,u,v,d] = fidencond(valuecon,stepcon,varcon,nconstr,...
- nstepsm,nvar,lags,yfore,imf3s,phil,Bh,eq_iden,steps_iden)
-% Estimating conditional forecasting in the identified model
-%function [yhat,Estr,rcon,Ome,Rmat,u,v,d] = fidencond(valuecon,stepcon,varcon,nconstr,...
-% nstepsm,nvar,lags,yfore,imf3s,phil,Bh,eq_iden,steps_iden)
-%
-% valuecon: vector of values conditioned
-% stepcon: sequence (cell) of steps conditioned; if length(stepcon{i}) > 1, the condition
-% is then an arithmetic average of log(y) over the stepcon{i} period.
-% varcon: vector of variables conditioned
-% nconstr: number of constraints
-% nstepsm: maximum number of steps in all constraints
-% nvar: number of variables in the BVAR model
-% lags: number of lags in the BVAR model
-% yfore: uncondtional forecasts: forep-by-nvar
-% imf3s: 3-dimensional impulse responses matrix:
-% impsteps-by-nvar shocks-by-nvar responses
-% phil: the 1-by-(nvar*lags+1) data matrix where k=nvar*lags+1
-% (last period plus lags before the beginning of forecast)
-% Bh: reduced-form parameter matrix: k-by-nvar, y(t) = X(t)*Bh+e(t)
-% where X(t) is k-by-nvar and y(t) is 1-by-nvar
-% eq_iden: identified equation or shock (in terms of number).
-% If eq_iden=[], then'fidencond' is, similar to RATS, to compute
-% forecasts with *all* shocks.
-% If length(eq_iden)=1, compute forecasts with only "MS" shocks.
-% If eq_iden = [a b c], a is # of constraints for all shocks (<=nconstr),
-% b is location of MS, and c is max steps for all shocks.
-% My recall is that this option has never been tested 9/18/98
-% steps_iden: a vector (set) of steps for identified shocks. Valid only
-% if length(eq_iden)=1. Note, length(steps_iden) must nconstr.
-% ------
-% yhat: conditional forecasts: forep-by-nvar
-% Estr: backed-out structural shocks (from N(0,1))
-% rcon: vector - the difference between valuecon and log(yfore) (unconditional forecasts)
-% Rcon: k-by-q (q constranits and k=nvar*max(nsteps)) so that
-% Rcon'*e = rcon where e is k-by-1, where k=nvar*nstepm
-% Rmat: nstepsm-by-nvar (shocks), for only one constraint at a time. See Zha's
-% Forecast (1), pp.5-6
-% Ome: k-by-k: covariance matrix of vectorized structural shocks vec(Estr)
-%
-% [u,d,v]: svd(Rcon,0)
-%
-%% See Zha's note "Forecast (1)" pp.5-7, RATS manual (some errors in RATS), etc.
-%
-%% Some notations: y(t+1) = y(t)B1 + e(t+1)inv(A0). e(t+1) is 1-by-n.
-%% Let r(t+1)=e(t+1)inv(A0) + e(t+2)C + .... where inv(A0) is impulse
-%% response at t=1, C at t=2, etc. The row of inv(A0) or C is
-%% all responses to one shock.
-%% Let r be q-by-1 (such as r(1) = r(t+1)
-%% = y(t+1) (constrained) - y(t+1) (forecast)).
-%% Use impulse responses to find out R (k-by-q) where k=nvar*nsteps
-%% where nsteps the largest constrained step. The key of the program
-%% is to creat R using impulse responses
-%% Optimal solution for shock e where R'*e=r and e is k-by-1 is
-%% e = R*inv(R'*R)*r.
-%
-% Copyright (c) February 1998 by Tao Zha
-% NOTE: the code needs to be improved, 10/19/98 (use lzpaper/fcstidcnd.m for the time being).
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-%IdenShock = ~isempty(eq_iden); % if not empty, the shock is identified
-forep=size(yfore,1);
-impsteps=size(imf3s,1);
-if (forep<nstepsm) | (impsteps<nstepsm)
- disp('Increase # of forecast or impulse steps!!')
- disp('Or decrease # of constraints (nconstr) or constrained steps (stepcon(i))!!')
- error('Maximum of conditional steps > # of forecast or impulse steps!!')
- %warning
- %return
-end
-if max(steps_iden) < nconstr
- disp('Increase # of identified steps or decrease # of constraints')
- error('length(steps_iden) > nconstr')
-end
-
-kfs = nvar*nstepsm; % k -- fs: free shocks
-%*** initializing
-Rcon = zeros(kfs,nconstr); % R: k-by-q
-Econ = zeros(kfs,1); % E: k-by-1
-rcon = zeros(nconstr,1); % r: q-by-1
-%rcon=valuecon-diag(yfore(stepcon,varcon)); % another way is to use "loop" below.
-A0in = reshape(imf3s(1,:,:),nvar,nvar); % nvar shocks-by-nvar responses
-
-
-for i=1:nconstr
- rcon(i)=valuecon(i)-mean(yfore(stepcon{i},varcon(i)));
- %rcon(i)=valuecon(i)-sum(yfore(stepcon{i},varcon(i)));
- Rmat = zeros(nstepsm,nvar);
- % Rmat: row--nstepsm, column--nvar shocks (here all shocks except
- % the identified one are set to zero) for a particular
- % endogenous variable 'varcon(i)'. See Zha Forcast (1), pp.6-7
- r2mat = zeros(nstepsm,1); % simply one identified equation
- % Must be here inside the loop because it's matrix of one column of Rcon
- for j=1:length(stepcon{i})
- if (length(eq_iden)==1)
- r2mat(1:stepcon{i}(j)) = r2mat(1:stepcon{i}(j)) + ...
- imf3s(stepcon{i}(j):-1:1,eq_iden,varcon(i));
- elseif (length(eq_iden)>1)
- if (i<=eq_iden(1))
- Rmat(1:stepcon{i}(j),:) = Rmat(1:stepcon{i}(j),:) + ...
- imf3s(stepcon{i}(j):-1:1,:,varcon(i));
- else
- Rmat(1:eq_iden(3),:) = Rmat(1:eq_iden(3),:) + ...
- imf3s(stepcon{i}(j):-1:stepcon{i}(j)-eq_iden(3)+1,:,varcon(i));
-
- Rmat(eq_iden(3)+1:stepcon{i}(j),eq_iden(2)) = ...
- Rmat(eq_iden(3)+1:stepcon{i}(j),eq_iden(2)) + ...
- imf3s(stepcon{i}(j)-eq_iden(3):-1:1,eq_iden(2),varcon(i));
- end
- else
- Rmat(1:stepcon{i}(j),:) = Rmat(1:stepcon{i}(j),:) + ...
- imf3s(stepcon{i}(j):-1:1,:,varcon(i));
- % Rmat: row--nstepsm, column--nvar shocks (here all shocks are
- % *not* set to zero) for a particular endogenous
- % variable 'varcon(i)'. See Zha Forcast (1), pp.6-7
- end
- end
- %
- if (length(eq_iden)==1)
- Rmat(steps_iden,eq_iden) = r2mat(steps_iden); % for only one constraint at a time
- end
- Rmat=Rmat/length(stepcon{i}); % <<>>
- Rmatt = Rmat'; % Now, nvar-by-nstepsm. I think here is where RATS has an error
- % i.e. "OVERR" is not transposed when overlaid to "CAPR"
- Rcon(:,i)=Rmatt(:); % Rcon: k-by-q where q=nconstr
-end
-
-if nconstr
- [u d v]=svd(Rcon,0); %trial. Rcon: k-by-q; u: k-by-q
- % rtr = Rcon'*Rcon; %trial
- % rtrinv = inv(Rcon'*Rcon); %trial
- vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
- dinv = 1./diag(d); % inv(diag(d))
- vdinv=v.*(ones(size(v,2),1)*dinv'); %trial
- rtr=vd*vd'; % R'*R
- rtrinv = vdinv*vdinv'; % inv(R'*R)
- Ome = eye(kfs) - u*u'; % note: I-u*u' = I - R*inv(R'*R)*R'; k-by-k
-
- Econ = Rcon*rtrinv*rcon; % E = R*inv(R'R)*r; mean
-else
- Econ = zeros(kfs,1);
- Rcon = NaN;
- rcon = NaN;
- u = NaN;
- d = NaN;
- v = NaN;
-end
-
-
-Estr = reshape(Econ,nvar,nstepsm);
-Estr = Estr'; % transpose so that
- % Estr: structural shocks. Row--steps, Column--n shocks
-Ures = Estr*A0in; % nstepsm-by-nvar
- % Ures: reduced-form residuals. Row--steps; Column--n shocks
-%Ures = Estr*A0in;
-
-% ** reconstruct x(t) for y(t+h) = x(t+h-1)*B
-% ** where phi = x(t+h-1) with last column being constant
-%
-tcwc = nvar*lags; % total coefficients without constant
-phi=phil;
-%
-yhat = zeros(forep,nvar);
-for k=1:forep
- if (k<=nstepsm)
- epsl = Ures(k,:);
- yhat(k,:) = phi*Bh + epsl;
- %yhat(k,:) = phi*Bh;
- phi(1,nvar+1:tcwc) = phi(1,1:tcwc-nvar);
- phi(1,1:nvar) = yhat(k,:);
- else
- yhat(k,:) = phi*Bh;
- phi(1,nvar+1:tcwc) = phi(1,1:tcwc-nvar);
- phi(1,1:nvar) = yhat(k,:);
- end
-end
+function [yhat,Estr,rcon,Ome,Rmat,u,v,d] = fidencond(valuecon,stepcon,varcon,nconstr,...
+ nstepsm,nvar,lags,yfore,imf3s,phil,Bh,eq_iden,steps_iden)
+% Estimating conditional forecasting in the identified model
+%function [yhat,Estr,rcon,Ome,Rmat,u,v,d] = fidencond(valuecon,stepcon,varcon,nconstr,...
+% nstepsm,nvar,lags,yfore,imf3s,phil,Bh,eq_iden,steps_iden)
+%
+% valuecon: vector of values conditioned
+% stepcon: sequence (cell) of steps conditioned; if length(stepcon{i}) > 1, the condition
+% is then an arithmetic average of log(y) over the stepcon{i} period.
+% varcon: vector of variables conditioned
+% nconstr: number of constraints
+% nstepsm: maximum number of steps in all constraints
+% nvar: number of variables in the BVAR model
+% lags: number of lags in the BVAR model
+% yfore: uncondtional forecasts: forep-by-nvar
+% imf3s: 3-dimensional impulse responses matrix:
+% impsteps-by-nvar shocks-by-nvar responses
+% phil: the 1-by-(nvar*lags+1) data matrix where k=nvar*lags+1
+% (last period plus lags before the beginning of forecast)
+% Bh: reduced-form parameter matrix: k-by-nvar, y(t) = X(t)*Bh+e(t)
+% where X(t) is k-by-nvar and y(t) is 1-by-nvar
+% eq_iden: identified equation or shock (in terms of number).
+% If eq_iden=[], then'fidencond' is, similar to RATS, to compute
+% forecasts with *all* shocks.
+% If length(eq_iden)=1, compute forecasts with only "MS" shocks.
+% If eq_iden = [a b c], a is # of constraints for all shocks (<=nconstr),
+% b is location of MS, and c is max steps for all shocks.
+% My recall is that this option has never been tested 9/18/98
+% steps_iden: a vector (set) of steps for identified shocks. Valid only
+% if length(eq_iden)=1. Note, length(steps_iden) must nconstr.
+% ------
+% yhat: conditional forecasts: forep-by-nvar
+% Estr: backed-out structural shocks (from N(0,1))
+% rcon: vector - the difference between valuecon and log(yfore) (unconditional forecasts)
+% Rcon: k-by-q (q constranits and k=nvar*max(nsteps)) so that
+% Rcon'*e = rcon where e is k-by-1, where k=nvar*nstepm
+% Rmat: nstepsm-by-nvar (shocks), for only one constraint at a time. See Zha's
+% Forecast (1), pp.5-6
+% Ome: k-by-k: covariance matrix of vectorized structural shocks vec(Estr)
+%
+% [u,d,v]: svd(Rcon,0)
+%
+%% See Zha's note "Forecast (1)" pp.5-7, RATS manual (some errors in RATS), etc.
+%
+%% Some notations: y(t+1) = y(t)B1 + e(t+1)inv(A0). e(t+1) is 1-by-n.
+%% Let r(t+1)=e(t+1)inv(A0) + e(t+2)C + .... where inv(A0) is impulse
+%% response at t=1, C at t=2, etc. The row of inv(A0) or C is
+%% all responses to one shock.
+%% Let r be q-by-1 (such as r(1) = r(t+1)
+%% = y(t+1) (constrained) - y(t+1) (forecast)).
+%% Use impulse responses to find out R (k-by-q) where k=nvar*nsteps
+%% where nsteps the largest constrained step. The key of the program
+%% is to creat R using impulse responses
+%% Optimal solution for shock e where R'*e=r and e is k-by-1 is
+%% e = R*inv(R'*R)*r.
+%
+% NOTE: the code needs to be improved, 10/19/98 (use lzpaper/fcstidcnd.m for the time being).
+%
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+%IdenShock = ~isempty(eq_iden); % if not empty, the shock is identified
+forep=size(yfore,1);
+impsteps=size(imf3s,1);
+if (forep<nstepsm) | (impsteps<nstepsm)
+ disp('Increase # of forecast or impulse steps!!')
+ disp('Or decrease # of constraints (nconstr) or constrained steps (stepcon(i))!!')
+ error('Maximum of conditional steps > # of forecast or impulse steps!!')
+ %warning
+ %return
+end
+if max(steps_iden) < nconstr
+ disp('Increase # of identified steps or decrease # of constraints')
+ error('length(steps_iden) > nconstr')
+end
+
+kfs = nvar*nstepsm; % k -- fs: free shocks
+%*** initializing
+Rcon = zeros(kfs,nconstr); % R: k-by-q
+Econ = zeros(kfs,1); % E: k-by-1
+rcon = zeros(nconstr,1); % r: q-by-1
+%rcon=valuecon-diag(yfore(stepcon,varcon)); % another way is to use "loop" below.
+A0in = reshape(imf3s(1,:,:),nvar,nvar); % nvar shocks-by-nvar responses
+
+
+for i=1:nconstr
+ rcon(i)=valuecon(i)-mean(yfore(stepcon{i},varcon(i)));
+ %rcon(i)=valuecon(i)-sum(yfore(stepcon{i},varcon(i)));
+ Rmat = zeros(nstepsm,nvar);
+ % Rmat: row--nstepsm, column--nvar shocks (here all shocks except
+ % the identified one are set to zero) for a particular
+ % endogenous variable 'varcon(i)'. See Zha Forcast (1), pp.6-7
+ r2mat = zeros(nstepsm,1); % simply one identified equation
+ % Must be here inside the loop because it's matrix of one column of Rcon
+ for j=1:length(stepcon{i})
+ if (length(eq_iden)==1)
+ r2mat(1:stepcon{i}(j)) = r2mat(1:stepcon{i}(j)) + ...
+ imf3s(stepcon{i}(j):-1:1,eq_iden,varcon(i));
+ elseif (length(eq_iden)>1)
+ if (i<=eq_iden(1))
+ Rmat(1:stepcon{i}(j),:) = Rmat(1:stepcon{i}(j),:) + ...
+ imf3s(stepcon{i}(j):-1:1,:,varcon(i));
+ else
+ Rmat(1:eq_iden(3),:) = Rmat(1:eq_iden(3),:) + ...
+ imf3s(stepcon{i}(j):-1:stepcon{i}(j)-eq_iden(3)+1,:,varcon(i));
+
+ Rmat(eq_iden(3)+1:stepcon{i}(j),eq_iden(2)) = ...
+ Rmat(eq_iden(3)+1:stepcon{i}(j),eq_iden(2)) + ...
+ imf3s(stepcon{i}(j)-eq_iden(3):-1:1,eq_iden(2),varcon(i));
+ end
+ else
+ Rmat(1:stepcon{i}(j),:) = Rmat(1:stepcon{i}(j),:) + ...
+ imf3s(stepcon{i}(j):-1:1,:,varcon(i));
+ % Rmat: row--nstepsm, column--nvar shocks (here all shocks are
+ % *not* set to zero) for a particular endogenous
+ % variable 'varcon(i)'. See Zha Forcast (1), pp.6-7
+ end
+ end
+ %
+ if (length(eq_iden)==1)
+ Rmat(steps_iden,eq_iden) = r2mat(steps_iden); % for only one constraint at a time
+ end
+ Rmat=Rmat/length(stepcon{i}); % <<>>
+ Rmatt = Rmat'; % Now, nvar-by-nstepsm. I think here is where RATS has an error
+ % i.e. "OVERR" is not transposed when overlaid to "CAPR"
+ Rcon(:,i)=Rmatt(:); % Rcon: k-by-q where q=nconstr
+end
+
+if nconstr
+ [u d v]=svd(Rcon,0); %trial. Rcon: k-by-q; u: k-by-q
+ % rtr = Rcon'*Rcon; %trial
+ % rtrinv = inv(Rcon'*Rcon); %trial
+ vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
+ dinv = 1./diag(d); % inv(diag(d))
+ vdinv=v.*(ones(size(v,2),1)*dinv'); %trial
+ rtr=vd*vd'; % R'*R
+ rtrinv = vdinv*vdinv'; % inv(R'*R)
+ Ome = eye(kfs) - u*u'; % note: I-u*u' = I - R*inv(R'*R)*R'; k-by-k
+
+ Econ = Rcon*rtrinv*rcon; % E = R*inv(R'R)*r; mean
+else
+ Econ = zeros(kfs,1);
+ Rcon = NaN;
+ rcon = NaN;
+ u = NaN;
+ d = NaN;
+ v = NaN;
+end
+
+
+Estr = reshape(Econ,nvar,nstepsm);
+Estr = Estr'; % transpose so that
+ % Estr: structural shocks. Row--steps, Column--n shocks
+Ures = Estr*A0in; % nstepsm-by-nvar
+ % Ures: reduced-form residuals. Row--steps; Column--n shocks
+%Ures = Estr*A0in;
+
+% ** reconstruct x(t) for y(t+h) = x(t+h-1)*B
+% ** where phi = x(t+h-1) with last column being constant
+%
+tcwc = nvar*lags; % total coefficients without constant
+phi=phil;
+%
+yhat = zeros(forep,nvar);
+for k=1:forep
+ if (k<=nstepsm)
+ epsl = Ures(k,:);
+ yhat(k,:) = phi*Bh + epsl;
+ %yhat(k,:) = phi*Bh;
+ phi(1,nvar+1:tcwc) = phi(1,1:tcwc-nvar);
+ phi(1,1:nvar) = yhat(k,:);
+ else
+ yhat(k,:) = phi*Bh;
+ phi(1,nvar+1:tcwc) = phi(1,1:tcwc-nvar);
+ phi(1,1:nvar) = yhat(k,:);
+ end
+end
diff --git a/MatlabFiles/find_betapar.m b/MatlabFiles/find_betapar.m
index 7345cc4b7e8539118cc75879d36e1e4c65f959cd..bad7d275c66afdebeda366cde6fcc7dd8f1a0e76 100644
--- a/MatlabFiles/find_betapar.m
+++ b/MatlabFiles/find_betapar.m
@@ -1,59 +1,59 @@
-function [a, b, XLO, XUP] = find_betapar(XLO, XUP, PLO, PUP, a0, b0);
-
-% This function takes as inputs the bounds [XLO, XUP] in the support of the
-% Beta distribution (with unknown parameters a and b), the
-% probabilities of the bounds [PLO, PUP], and the initial values for ab=[a0, b0]
-% and returns the estimates of a and b (as well as XLO and XUP)
-% by solving the non-linear functions in betapar(ab, XLO, XUP, PLO, PUP).
-
-%-----------------------------------------------------------------------------------
-%------------------------------- Beta distribution --------------------------------%
-%--- p(x) = ( Gamma(a+b)/(Gamma(a)*Gamma(b)) ) x^(a-1) (1-x)^(b-1)
-% = (1/Beta(a, b))x^(a-1) (1-x)^(b-1) for a>0 and b>0, and
-% 0<=x<=1.
-%--- E(x) = a/(a+b); var(x) = a*b/( (a+b)^2*(a+b+1) );
-%--- The density is finite if a,b>=1.
-%--- Noninformative density: (1) a=b=1; (2) a=b=0.5; or (3) a=b=0.
-%-----------------------------------------------------------------------------------
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-if XLO >= XUP;
- error('the lower bound needs to be smaller than the upper bound')
-elseif XLO<=0 or XUP >=1;
- error('the support for Beta distribution needs to be between 0 and 1');
-end;
-
-if a0 <= 0 || b0 <= 0;
- error('the values for a and b need to be positive');
-end;
-
-disp(' ')
-disp('*************** Convergence results for beta density ***************')
-options = optimset('Display', 'on','TolFun', 1.0e-10, 'TolX', 1.0e-10);
-ab_values = fsolve('betapar', [a0, b0], options, XLO, XUP, PLO, PUP);
-a = ab_values(1); b = ab_values(2);
-
-% Alternatively, it is possible to constrain the search for the values of a
-% and b in the positive range (with 0 being the explicit lower bound) by
-% using the lsqnonlin function (see below) instead of the fsolve. The
-% tradeoff is that lsqnonlin is typically slower than fsolve.
-% LB_a = 0; LB_b = 0; UB_a = Inf; UB_b = Inf;
-% ab_values = lsqnonlin('betapar', [a0, b0], [LB_a, LB_b], [UB_a, UB_b],...
-% options, XLO, XUP, PLO, PUP);
-% a = ab_values(1); b = ab_values(2);
+function [a, b, XLO, XUP] = find_betapar(XLO, XUP, PLO, PUP, a0, b0);
+
+% This function takes as inputs the bounds [XLO, XUP] in the support of the
+% Beta distribution (with unknown parameters a and b), the
+% probabilities of the bounds [PLO, PUP], and the initial values for ab=[a0, b0]
+% and returns the estimates of a and b (as well as XLO and XUP)
+% by solving the non-linear functions in betapar(ab, XLO, XUP, PLO, PUP).
+
+%-----------------------------------------------------------------------------------
+%------------------------------- Beta distribution --------------------------------%
+%--- p(x) = ( Gamma(a+b)/(Gamma(a)*Gamma(b)) ) x^(a-1) (1-x)^(b-1)
+% = (1/Beta(a, b))x^(a-1) (1-x)^(b-1) for a>0 and b>0, and
+% 0<=x<=1.
+%--- E(x) = a/(a+b); var(x) = a*b/( (a+b)^2*(a+b+1) );
+%--- The density is finite if a,b>=1.
+%--- Noninformative density: (1) a=b=1; (2) a=b=0.5; or (3) a=b=0.
+%-----------------------------------------------------------------------------------
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+if XLO >= XUP;
+ error('the lower bound needs to be smaller than the upper bound')
+elseif XLO<=0 or XUP >=1;
+ error('the support for Beta distribution needs to be between 0 and 1');
+end;
+
+if a0 <= 0 || b0 <= 0;
+ error('the values for a and b need to be positive');
+end;
+
+disp(' ')
+disp('*************** Convergence results for beta density ***************')
+options = optimset('Display', 'on','TolFun', 1.0e-10, 'TolX', 1.0e-10);
+ab_values = fsolve('betapar', [a0, b0], options, XLO, XUP, PLO, PUP);
+a = ab_values(1); b = ab_values(2);
+
+% Alternatively, it is possible to constrain the search for the values of a
+% and b in the positive range (with 0 being the explicit lower bound) by
+% using the lsqnonlin function (see below) instead of the fsolve. The
+% tradeoff is that lsqnonlin is typically slower than fsolve.
+% LB_a = 0; LB_b = 0; UB_a = Inf; UB_b = Inf;
+% ab_values = lsqnonlin('betapar', [a0, b0], [LB_a, LB_b], [UB_a, UB_b],...
+% options, XLO, XUP, PLO, PUP);
+% a = ab_values(1); b = ab_values(2);
diff --git a/MatlabFiles/find_gampar.m b/MatlabFiles/find_gampar.m
index e008085db2b8843296fd4100a5740ef9ed657dcd..2659681ac68c73dd74c0c566dedcfb3a745dbac7 100644
--- a/MatlabFiles/find_gampar.m
+++ b/MatlabFiles/find_gampar.m
@@ -1,56 +1,56 @@
-function [a, b, XLO, XUP] = find_gampar(XLO, XUP, PLO, PUP, a0, b0);
-
-% This function takes as inputs the bounds [XLO, XUP] in the support of the
-% gamma distribution (with parameters a and b), the probabilities of the
-% bounds [PLO, PUP], and the initial values for ab=[a0, b0]
-% and returns the estimates of a and b (as well as XLO and XUP)
-% by solving the non-linear functions in gampar(ab, XLO, XUP, PLO, PUP).\
-
-%---------------------------- Gamma distribution ----------------------------------%
-%--- p(x) = ( 1/(b^a Gamma(a) ) x^(a-1) exp(-x/b) for a>0, b>0, and x>=0.
-%--- where a is the shape and b is the scale parameter.
-%--- E(x) = a b; var(x) = a b^2;
-%--- Noninformative distribution: a,b -> 0.
-%--- The density function is finite if a >= 1.
-%-----------------------------------------------------------------------------------
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-if XLO >= XUP;
- error('the lower bound needs to be smaller than the upper bound')
-elseif XLO<= 0;
- error('the support for Gamma distribution needs to be non-negative');
-end;
-
-if a0 <= 0 || b0 <= 0;
- error('the values for a0 and b0 need to be positive');
-end;
-
-disp(' ')
-disp('*************** Convergence results for gamma density ***************')
-options = optimset('Display', 'on','TolFun', 1.0e-10, 'TolX', 1.0e-10);
-ab_values = fsolve('gampar', [a0, b0], options, XLO, XUP, PLO, PUP);
-a = ab_values(1); b = ab_values(2);
-
-% Alternatively, it is possible to constrain the search for the values of a
-% and b in the positive range (with 0 being the explicit lower bound) by
-% using the lsqnonlin function (see below) instead of the fsolve. The
-% tradeoff is that lsqnonlin is typically slower than fsolve.
-% LB_a = 0; LB_b = 0; UB_a = Inf; UB_b = Inf;
-% ab_values = lsqnonlin('gampar', [a0, b0], [LB_a, LB_b], [UB_a, UB_b],...
-% options, XLO, XUP, PLO, PUP);
-% a = ab_values(1); b = ab_values(2);
+function [a, b, XLO, XUP] = find_gampar(XLO, XUP, PLO, PUP, a0, b0);
+
+% This function takes as inputs the bounds [XLO, XUP] in the support of the
+% gamma distribution (with parameters a and b), the probabilities of the
+% bounds [PLO, PUP], and the initial values for ab=[a0, b0]
+% and returns the estimates of a and b (as well as XLO and XUP)
+% by solving the non-linear functions in gampar(ab, XLO, XUP, PLO, PUP).\
+
+%---------------------------- Gamma distribution ----------------------------------%
+%--- p(x) = ( 1/(b^a Gamma(a) ) x^(a-1) exp(-x/b) for a>0, b>0, and x>=0.
+%--- where a is the shape and b is the scale parameter.
+%--- E(x) = a b; var(x) = a b^2;
+%--- Noninformative distribution: a,b -> 0.
+%--- The density function is finite if a >= 1.
+%-----------------------------------------------------------------------------------
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+if XLO >= XUP;
+ error('the lower bound needs to be smaller than the upper bound')
+elseif XLO<= 0;
+ error('the support for Gamma distribution needs to be non-negative');
+end;
+
+if a0 <= 0 || b0 <= 0;
+ error('the values for a0 and b0 need to be positive');
+end;
+
+disp(' ')
+disp('*************** Convergence results for gamma density ***************')
+options = optimset('Display', 'on','TolFun', 1.0e-10, 'TolX', 1.0e-10);
+ab_values = fsolve('gampar', [a0, b0], options, XLO, XUP, PLO, PUP);
+a = ab_values(1); b = ab_values(2);
+
+% Alternatively, it is possible to constrain the search for the values of a
+% and b in the positive range (with 0 being the explicit lower bound) by
+% using the lsqnonlin function (see below) instead of the fsolve. The
+% tradeoff is that lsqnonlin is typically slower than fsolve.
+% LB_a = 0; LB_b = 0; UB_a = Inf; UB_b = Inf;
+% ab_values = lsqnonlin('gampar', [a0, b0], [LB_a, LB_b], [UB_a, UB_b],...
+% options, XLO, XUP, PLO, PUP);
+% a = ab_values(1); b = ab_values(2);
diff --git a/MatlabFiles/find_invgampar.m b/MatlabFiles/find_invgampar.m
index 56e27a872714396db7663f7ee35310532e11e2ad..30b3338609f36c989cdff94a6d90a5306254a0da 100644
--- a/MatlabFiles/find_invgampar.m
+++ b/MatlabFiles/find_invgampar.m
@@ -1,57 +1,57 @@
-function [a, b, XLO, XUP] = find_invgampar(XLO, XUP, PLO, PUP, a0, b0);
-
-% This function takes as inputs the bounds [XLO, XUP] in the support of the
-% Inverse Gamma distribution (with parameters a and b), the probabilities of the
-% bounds [PLO, PUP], and the initial values for ab=[a0, b0]
-% and returns the estimates of a and b (as well as XLO and XUP)
-% by solving the non-linear functions in invgampar(ab, XLO, XUP, PLO, PUP).\
-
-%-----------------------------------------------------------------------------------
-%------------------------ Inverse-Gamma distribution ------------------------------%
-%--- p(x) = ( b^a/Gamma(a) ) x^(-a-1) exp(-b/x) for a>0 and b>0.
-%--- where a is shape and b is scale parameter.
-%--- E(x) = b/(a-1) for a>1; var(x) = b^2/( (a-1)^2*(a-2) ) for a>2;
-%--- Noninformative distribution: a,b -> 0.
-%--- How to draw: (1) draw z from Gamma(a,b); (2) let x=1/z.
-%-----------------------------------------------------------------------------------
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-if XLO >= XUP;
- error('the lower bound needs to be smaller than the upper bound')
-elseif XLO<= 0;
- error('the support for Gamma distribution needs to be non-negative');
-end;
-
-if a0 <= 0 || b0 <= 0;
- error('the values for a and b need to be positive');
-end;
-
-disp(' ')
-disp('*************** Convergence results for inverse gamma density ***************')
-options = optimset('Display', 'on','TolFun', 1.0e-10, 'TolX', 1.0e-10);
-ab_values = fsolve('invgampar', [a0, b0], options, XLO, XUP, PLO, PUP);
-a = ab_values(1); b = ab_values(2);
-
-% Alternatively, it is possible to constrain the search for the values of a
-% and b in the positive range (with 0 being the explicit lower bound) by
-% using the lsqnonlin function (see below) instead of the fsolve. The
-% tradeoff is that lsqnonlin is typically slower than fsolve.
-% LB_a = 0; LB_b = 0; UB_a = Inf; UB_b = Inf;
-% ab_values = lsqnonlin('invgampar', [a0, b0], [LB_a, LB_b], [UB_a, UB_b],...
-% options, XLO, XUP, PLO, PUP);
-% a = ab_values(1); b = ab_values(2);
+function [a, b, XLO, XUP] = find_invgampar(XLO, XUP, PLO, PUP, a0, b0);
+
+% This function takes as inputs the bounds [XLO, XUP] in the support of the
+% Inverse Gamma distribution (with parameters a and b), the probabilities of the
+% bounds [PLO, PUP], and the initial values for ab=[a0, b0]
+% and returns the estimates of a and b (as well as XLO and XUP)
+% by solving the non-linear functions in invgampar(ab, XLO, XUP, PLO, PUP).\
+
+%-----------------------------------------------------------------------------------
+%------------------------ Inverse-Gamma distribution ------------------------------%
+%--- p(x) = ( b^a/Gamma(a) ) x^(-a-1) exp(-b/x) for a>0 and b>0.
+%--- where a is shape and b is scale parameter.
+%--- E(x) = b/(a-1) for a>1; var(x) = b^2/( (a-1)^2*(a-2) ) for a>2;
+%--- Noninformative distribution: a,b -> 0.
+%--- How to draw: (1) draw z from Gamma(a,b); (2) let x=1/z.
+%-----------------------------------------------------------------------------------
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+if XLO >= XUP;
+ error('the lower bound needs to be smaller than the upper bound')
+elseif XLO<= 0;
+ error('the support for Gamma distribution needs to be non-negative');
+end;
+
+if a0 <= 0 || b0 <= 0;
+ error('the values for a and b need to be positive');
+end;
+
+disp(' ')
+disp('*************** Convergence results for inverse gamma density ***************')
+options = optimset('Display', 'on','TolFun', 1.0e-10, 'TolX', 1.0e-10);
+ab_values = fsolve('invgampar', [a0, b0], options, XLO, XUP, PLO, PUP);
+a = ab_values(1); b = ab_values(2);
+
+% Alternatively, it is possible to constrain the search for the values of a
+% and b in the positive range (with 0 being the explicit lower bound) by
+% using the lsqnonlin function (see below) instead of the fsolve. The
+% tradeoff is that lsqnonlin is typically slower than fsolve.
+% LB_a = 0; LB_b = 0; UB_a = Inf; UB_b = Inf;
+% ab_values = lsqnonlin('invgampar', [a0, b0], [LB_a, LB_b], [UB_a, UB_b],...
+% options, XLO, XUP, PLO, PUP);
+% a = ab_values(1); b = ab_values(2);
diff --git a/MatlabFiles/find_normpar.m b/MatlabFiles/find_normpar.m
index c39b7432102df739618a03814fe9bd73be8f3c81..b5ce545ac6d54b980a882bf88594bb6e00a7c182 100644
--- a/MatlabFiles/find_normpar.m
+++ b/MatlabFiles/find_normpar.m
@@ -1,47 +1,47 @@
-function [a, b, XLO, XUP] = find_normpar(XLO, XUP, PLO, PUP, a0, b0);
-
-% This function takes as inputs the bounds [XLO, XUP] in the support of the
-% Normal distribution (with unknown parameters a = mean and b=standard deviation), the
-% probabilities of the bounds [PLO, PUP], and the initial values for ab=[a0, b0]
-% and returns the estimates of a and b (as well as XLO and XUP)
-% by solving the non-linear functions in normpar(ab, XLO, XUP, PLO, PUP).
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-if XLO >= XUP;
- error('the lower bound needs to be smaller than the upper bound')
-end;
-
-if b0 <= 0;
- error('the values for the standard deviation needs to be positive');
-end;
-
-
-disp(' ')
-disp('*************** Convergence results for normal density ***************')
-options = optimset('Display', 'on','TolFun', 1.0e-10, 'TolX', 1.0e-10);
-ab_values = fsolve('normpar', [a0, b0], options, XLO, XUP, PLO, PUP);
-a = ab_values(1); b = ab_values(2);
-
-% Alternatively, it is possible to constrain the search for the values of a
-% and b in the positive range (with 0 being the explicit lower bound) by
-% using the lsqnonlin function (see below) instead of the fsolve. The
-% tradeoff is that lsqnonlin is typically slower than fsolve.
-% LB_a = 0; LB_b = 0; UB_a = Inf; UB_b = Inf;
-% ab_values = lsqnonlin('normpar', [a0, b0], [LB_a, LB_b], [UB_a, UB_b],...
-% options, XLO, XUP, PLO, PUP);
-% a = ab_values(1); b = ab_values(2);
+function [a, b, XLO, XUP] = find_normpar(XLO, XUP, PLO, PUP, a0, b0);
+
+% This function takes as inputs the bounds [XLO, XUP] in the support of the
+% Normal distribution (with unknown parameters a = mean and b=standard deviation), the
+% probabilities of the bounds [PLO, PUP], and the initial values for ab=[a0, b0]
+% and returns the estimates of a and b (as well as XLO and XUP)
+% by solving the non-linear functions in normpar(ab, XLO, XUP, PLO, PUP).
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+if XLO >= XUP;
+ error('the lower bound needs to be smaller than the upper bound')
+end;
+
+if b0 <= 0;
+ error('the values for the standard deviation needs to be positive');
+end;
+
+
+disp(' ')
+disp('*************** Convergence results for normal density ***************')
+options = optimset('Display', 'on','TolFun', 1.0e-10, 'TolX', 1.0e-10);
+ab_values = fsolve('normpar', [a0, b0], options, XLO, XUP, PLO, PUP);
+a = ab_values(1); b = ab_values(2);
+
+% Alternatively, it is possible to constrain the search for the values of a
+% and b in the positive range (with 0 being the explicit lower bound) by
+% using the lsqnonlin function (see below) instead of the fsolve. The
+% tradeoff is that lsqnonlin is typically slower than fsolve.
+% LB_a = 0; LB_b = 0; UB_a = Inf; UB_b = Inf;
+% ab_values = lsqnonlin('normpar', [a0, b0], [LB_a, LB_b], [UB_a, UB_b],...
+% options, XLO, XUP, PLO, PUP);
+% a = ab_values(1); b = ab_values(2);
diff --git a/MatlabFiles/fn_a0cfreefun_tv.m b/MatlabFiles/fn_a0cfreefun_tv.m
index d4a189ecf510b900b0214fb7d12d415929eaa19b..79feb2e01fdc2a4cb598ba1c7a756cc11b966a35 100644
--- a/MatlabFiles/fn_a0cfreefun_tv.m
+++ b/MatlabFiles/fn_a0cfreefun_tv.m
@@ -1,77 +1,77 @@
-function of = fn_a0cfreefun_tv(b,nvar,nStates,n0cumsum,Ui,Tkave,Del00invcell_ave,dpDelp0cell_ave)
-% of = fn_a0cfreefun_tv(b,nvar,nStates,n0cumsum,Ui,Tkave,Del00invcell_ave,dpDelp0cell_ave)
-% Negative logPosterior function for squeesed free A0 parameters (bar tv structural variances) in
-% a new SZ time-varying model, which are vectorized as b's in the WZ notation. In other words,
-% A0 parameters can be fully time varying, but the time varying structural variances will be
-% excluded. It differs from fn_a0sfreefun*.m in several aspects:
-% (a) can deal with equations with only structural variances time varying;
-% (b) take care of lag restrictions;
-% (c) conditional on the values of all other parameters including A+.
-% Note: (1) columns correspond to equations; (2) c stands for constant or for an exclusion of
-% time varying structural variances.
-% See TBVAR NOTE p. 61.
-%
-% b: n0cumsum(end)-by-1 vector of free constant A0 parameters, vectorized from b_ihatcell.
-% nvar: Number of endogeous variables.
-% nStates: Number of states.
-% n0cumsum: [0;cumsum(n0)] where n0 is nvar-by-1 and its ith element represents the number of
-% free constant A0 parameters in ith equation for *all states*.
-% Ui: nvar-by-1 cell. In each cell, nvar*nStates-by-(qi+si) orthonormal basis for the null of the ith
-% equation contemporaneous restriction matrix where qi is the number of free parameters
-% within the state and si is the number of free parameters across the states.
-% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
-% of total original parameters and bi is a vector of free parameters. When no
-% restrictions are imposed, we have Ui = I. There must be at least one free
-% parameter left for the ith equation.
-% Tkave: nStates-by-1 of sample sizes (excluding lags but including dummies if provided) for different states k,
-% averaged (ave) over E-step draws. For T_k. See p.61. In the Gibbs-Metropolis step, there is
-% no need to make Tk the same as Tkave (See Chib and Jeliazkov 2001).
-% Del00invcell_ave: Quardratic term for b0_j in each cell. See p.61. When fn_a0cfreegrad_tv.m is used,
-% always make this systemetric by using (Del00invcell_ave'+Del00invcell_ave)/2.
-% Also note that in the Gibbs-Metropolis step, there is no need to make Del00invcell the same
-% as Del00invcell_ave (See Chib and Jeliazkov 2001).
-% dpDelp0cell_ave: Cross d+_j and b0_j term in each cell. See p.61. In the Gibbs-Metropolis step, there is no need
-% to make dpDelp0cell the same as dpDelp0cell_ave (See Chib and Jeliazkov 2001).
-%----------------
-% of: Objective function (negative logPosterior).
-%
-% This function is called by szeml*.m which is a bettern program than tveml*.m. Thus,
-% use this function in place of fn_a0sfreefun2.m if possible.
-% See fn_a0cfreegrad_tv.m for analytical gradient for this function.
-%
-% Tao Zha, September 2001
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-
-A0_shat=zeros(nvar,nvar,nStates); % Arranged A0 matrix across states.
-
-tra = 0.0;
-for kj = 1:nvar
- bj = b(n0cumsum(kj)+1:n0cumsum(kj+1));
- A0_shat(:,kj,:) = reshape(Ui{kj}*bj,nvar,nStates);
- tra = tra+0.5*( (bj'*Del00invcell_ave{kj})*bj - 2*dpDelp0cell_ave{kj}*bj ); % Negative exponential term
-end
-
-ada=0.0;
-for si=1:nStates % See p.34a.
- [A0l,A0u] = lu(A0_shat(:,:,si));
- ada = ada - Tkave(si)*sum(log(abs(diag(A0u)))); % Negative log determinant of A0 raised to power T
-end
-
-of = ada + tra;
+function of = fn_a0cfreefun_tv(b,nvar,nStates,n0cumsum,Ui,Tkave,Del00invcell_ave,dpDelp0cell_ave)
+% of = fn_a0cfreefun_tv(b,nvar,nStates,n0cumsum,Ui,Tkave,Del00invcell_ave,dpDelp0cell_ave)
+% Negative logPosterior function for squeesed free A0 parameters (bar tv structural variances) in
+% a new SZ time-varying model, which are vectorized as b's in the WZ notation. In other words,
+% A0 parameters can be fully time varying, but the time varying structural variances will be
+% excluded. It differs from fn_a0sfreefun*.m in several aspects:
+% (a) can deal with equations with only structural variances time varying;
+% (b) take care of lag restrictions;
+% (c) conditional on the values of all other parameters including A+.
+% Note: (1) columns correspond to equations; (2) c stands for constant or for an exclusion of
+% time varying structural variances.
+% See TBVAR NOTE p. 61.
+%
+% b: n0cumsum(end)-by-1 vector of free constant A0 parameters, vectorized from b_ihatcell.
+% nvar: Number of endogeous variables.
+% nStates: Number of states.
+% n0cumsum: [0;cumsum(n0)] where n0 is nvar-by-1 and its ith element represents the number of
+% free constant A0 parameters in ith equation for *all states*.
+% Ui: nvar-by-1 cell. In each cell, nvar*nStates-by-(qi+si) orthonormal basis for the null of the ith
+% equation contemporaneous restriction matrix where qi is the number of free parameters
+% within the state and si is the number of free parameters across the states.
+% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
+% of total original parameters and bi is a vector of free parameters. When no
+% restrictions are imposed, we have Ui = I. There must be at least one free
+% parameter left for the ith equation.
+% Tkave: nStates-by-1 of sample sizes (excluding lags but including dummies if provided) for different states k,
+% averaged (ave) over E-step draws. For T_k. See p.61. In the Gibbs-Metropolis step, there is
+% no need to make Tk the same as Tkave (See Chib and Jeliazkov 2001).
+% Del00invcell_ave: Quardratic term for b0_j in each cell. See p.61. When fn_a0cfreegrad_tv.m is used,
+% always make this systemetric by using (Del00invcell_ave'+Del00invcell_ave)/2.
+% Also note that in the Gibbs-Metropolis step, there is no need to make Del00invcell the same
+% as Del00invcell_ave (See Chib and Jeliazkov 2001).
+% dpDelp0cell_ave: Cross d+_j and b0_j term in each cell. See p.61. In the Gibbs-Metropolis step, there is no need
+% to make dpDelp0cell the same as dpDelp0cell_ave (See Chib and Jeliazkov 2001).
+%----------------
+% of: Objective function (negative logPosterior).
+%
+% This function is called by szeml*.m which is a bettern program than tveml*.m. Thus,
+% use this function in place of fn_a0sfreefun2.m if possible.
+% See fn_a0cfreegrad_tv.m for analytical gradient for this function.
+%
+% Tao Zha, September 2001
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+
+A0_shat=zeros(nvar,nvar,nStates); % Arranged A0 matrix across states.
+
+tra = 0.0;
+for kj = 1:nvar
+ bj = b(n0cumsum(kj)+1:n0cumsum(kj+1));
+ A0_shat(:,kj,:) = reshape(Ui{kj}*bj,nvar,nStates);
+ tra = tra+0.5*( (bj'*Del00invcell_ave{kj})*bj - 2*dpDelp0cell_ave{kj}*bj ); % Negative exponential term
+end
+
+ada=0.0;
+for si=1:nStates % See p.34a.
+ [A0l,A0u] = lu(A0_shat(:,:,si));
+ ada = ada - Tkave(si)*sum(log(abs(diag(A0u)))); % Negative log determinant of A0 raised to power T
+end
+
+of = ada + tra;
diff --git a/MatlabFiles/fn_a0cfreegrad_tv.m b/MatlabFiles/fn_a0cfreegrad_tv.m
index 5bea42ff1ca68995303776cbef069195614ea673..29b0493a52abd58db424efdc4ad2549ce3e401cc 100644
--- a/MatlabFiles/fn_a0cfreegrad_tv.m
+++ b/MatlabFiles/fn_a0cfreegrad_tv.m
@@ -1,72 +1,72 @@
-function [g,badg] = fn_a0cfreegrad_tv(b,nvar,nStates,n0cumsum,Ui,Tkave,Del00invcell_ave,dpDelp0cell_ave)
-% [g,badg] = fn_a0cfreegrad_tv(b,nvar,nStates,n0cumsum,Ui,Tkave,Del00invcell_ave,dpDelp0cell_ave)
-% Analytical gradient for fn_a0cfreegrad_tv.m when using csminwel.m.
-% Note: (1) columns correspond to equations; (2) c stands for constant.
-% See TBVAR NOTE pp. 61-62, 34a-34c.
-%
-% b: n0cumsum(end)-by-1 vector of free constant A0 parameters, vectorized from b_ihatcell.
-% nvar: Number of endogeous variables.
-% nStates: Number of states.
-% n0cumsum: [0;cumsum(n0)] where n0 is nvar-by-1 and its ith element represents the number of
-% free constant A0 parameters in ith equation for *all states*.
-% Ui: nvar-by-1 cell. In each cell, nvar*nStates-by-(qi+si) orthonormal basis for the null of the ith
-% equation contemporaneous restriction matrix where qi is the number of free parameters
-% within the state and si is the number of free parameters across the states.
-% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
-% of total original parameters and bi is a vector of free parameters. When no
-% restrictions are imposed, we have Ui = I. There must be at least one free
-% parameter left for the ith equation.
-% Tkave: nStates-by-1 of sample sizes (excluding lags but including dummies if provided) for different states k,
-% averaged (ave) over E-step draws. For T_k. See p.61.
-% Del00invcell_ave: Quardratic term for b0_j in each cell. See p.61.
-% dpDelp0cell_ave: Cross d+_j and b0_j term in each cell. See p.61.
-%----------------
-% g: n0cumsum(end)-by-1 analytical gradient for fn_a0cfreegrad_tv.m.
-% badg: 0, the value that is used in csminwel.m.
-%
-% Tao Zha, September 2001
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-A0_shat=zeros(nvar,nvar,nStates); % Arranged A0 matrix across states.
-B_shat=zeros(nvar,nvar,nStates); % inv(A0_shat);
-
-badg = 0;
-g = zeros(size(b(:)));
-
-%**** The derivative of the exponential term w.r.t. each free constanta A0 paramater. See pp. 34a and 62.
-for kj = 1:nvar
- indxn0j = [n0cumsum(kj)+1:n0cumsum(kj+1)]; % Index for the parameters in the ith equation.
- bj = b(indxn0j);
- g(indxn0j) = Del00invcell_ave{kj}*bj - dpDelp0cell_ave{kj}';
- A0_shat(:,kj,:) = reshape(Ui{kj}*bj,nvar,nStates);
-end
-
-
-%for si=1:nStates % See p.34a.
-
-%end
-%**** Add the derivative of -Tk_ave*log|A0(k)| w.r.t. each free constanta A0 paramater. See pp. 62 and 34a.
-for kj = 1:nvar
- indxn0j = [n0cumsum(kj)+1:n0cumsum(kj+1)]; % Index for the parameters in the ith equation.
- for si=1:nStates % See p.34a.
- B_shat(:,:,si)=inv(A0_shat(:,:,si)); % See p.62.
- g(indxn0j) = g(indxn0j) - Tkave(si)*( B_shat(kj,:,si)*Ui{kj}((si-1)*nvar+1:si*nvar,:) )'; % See p.62.
- end
-end
-
+function [g,badg] = fn_a0cfreegrad_tv(b,nvar,nStates,n0cumsum,Ui,Tkave,Del00invcell_ave,dpDelp0cell_ave)
+% [g,badg] = fn_a0cfreegrad_tv(b,nvar,nStates,n0cumsum,Ui,Tkave,Del00invcell_ave,dpDelp0cell_ave)
+% Analytical gradient for fn_a0cfreegrad_tv.m when using csminwel.m.
+% Note: (1) columns correspond to equations; (2) c stands for constant.
+% See TBVAR NOTE pp. 61-62, 34a-34c.
+%
+% b: n0cumsum(end)-by-1 vector of free constant A0 parameters, vectorized from b_ihatcell.
+% nvar: Number of endogeous variables.
+% nStates: Number of states.
+% n0cumsum: [0;cumsum(n0)] where n0 is nvar-by-1 and its ith element represents the number of
+% free constant A0 parameters in ith equation for *all states*.
+% Ui: nvar-by-1 cell. In each cell, nvar*nStates-by-(qi+si) orthonormal basis for the null of the ith
+% equation contemporaneous restriction matrix where qi is the number of free parameters
+% within the state and si is the number of free parameters across the states.
+% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
+% of total original parameters and bi is a vector of free parameters. When no
+% restrictions are imposed, we have Ui = I. There must be at least one free
+% parameter left for the ith equation.
+% Tkave: nStates-by-1 of sample sizes (excluding lags but including dummies if provided) for different states k,
+% averaged (ave) over E-step draws. For T_k. See p.61.
+% Del00invcell_ave: Quardratic term for b0_j in each cell. See p.61.
+% dpDelp0cell_ave: Cross d+_j and b0_j term in each cell. See p.61.
+%----------------
+% g: n0cumsum(end)-by-1 analytical gradient for fn_a0cfreegrad_tv.m.
+% badg: 0, the value that is used in csminwel.m.
+%
+% Tao Zha, September 2001
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+A0_shat=zeros(nvar,nvar,nStates); % Arranged A0 matrix across states.
+B_shat=zeros(nvar,nvar,nStates); % inv(A0_shat);
+
+badg = 0;
+g = zeros(size(b(:)));
+
+%**** The derivative of the exponential term w.r.t. each free constanta A0 paramater. See pp. 34a and 62.
+for kj = 1:nvar
+ indxn0j = [n0cumsum(kj)+1:n0cumsum(kj+1)]; % Index for the parameters in the ith equation.
+ bj = b(indxn0j);
+ g(indxn0j) = Del00invcell_ave{kj}*bj - dpDelp0cell_ave{kj}';
+ A0_shat(:,kj,:) = reshape(Ui{kj}*bj,nvar,nStates);
+end
+
+
+%for si=1:nStates % See p.34a.
+
+%end
+%**** Add the derivative of -Tk_ave*log|A0(k)| w.r.t. each free constanta A0 paramater. See pp. 62 and 34a.
+for kj = 1:nvar
+ indxn0j = [n0cumsum(kj)+1:n0cumsum(kj+1)]; % Index for the parameters in the ith equation.
+ for si=1:nStates % See p.34a.
+ B_shat(:,:,si)=inv(A0_shat(:,:,si)); % See p.62.
+ g(indxn0j) = g(indxn0j) - Tkave(si)*( B_shat(kj,:,si)*Ui{kj}((si-1)*nvar+1:si*nvar,:) )'; % See p.62.
+ end
+end
+
diff --git a/MatlabFiles/fn_a0freefun.m b/MatlabFiles/fn_a0freefun.m
index 9c1cbb415fb6e99a9ddff3599304767e9054ad01..0e776e420a9965f3470028ab5232aea9288e3dc1 100644
--- a/MatlabFiles/fn_a0freefun.m
+++ b/MatlabFiles/fn_a0freefun.m
@@ -1,55 +1,55 @@
-function of = fn_a0freefun(b,Ui,nvar,n0,fss,H0inv)
-% of = fn_a0freefun(b,Ui,nvar,n0,fss,H0inv)
-%
-% Negative logPosterior function for squeesed A0 free parameters, which are b's in the WZ notation
-% Note: columns correspond to equations
-%
-% b: sum(n0)-by-1 vector of A0 free parameters
-% Ui: nvar-by-1 cell. In each cell, nvar-by-qi orthonormal basis for the null of the ith
-% equation contemporaneous restriction matrix where qi is the number of free parameters.
-% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
-% of total original parameters and bi is a vector of free parameters. When no
-% restrictions are imposed, we have Ui = I. There must be at least one free
-% parameter left for the ith equation.
-% nvar: number of endogeous variables
-% n0: nvar-by-1, ith element represents the number of free A0 parameters in ith equation
-% fss: nSample-lags (plus ndobs if dummies are included)
-% H0inv: cell(nvar,1). In each cell, posterior inverse of covariance inv(H0) for the ith equation,
-% resembling old SpH in the exponent term in posterior of A0, but not divided by T yet.
-%----------------
-% of: objective function (negative logPosterior)
-%
-% Tao Zha, February 2000
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-b=b(:); n0=n0(:);
-
-A0 = zeros(nvar);
-n0cum = [0;cumsum(n0)];
-tra = 0.0;
-for kj = 1:nvar
- bj = b(n0cum(kj)+1:n0cum(kj+1));
- A0(:,kj) = Ui{kj}*bj;
- tra = tra + 0.5*bj'*H0inv{kj}*bj; % negative exponential term
-end
-
-[A0l,A0u] = lu(A0);
-
-ada = -fss*sum(log(abs(diag(A0u)))); % negative log determinant of A0 raised to power T
-
-of = ada + tra;
+function of = fn_a0freefun(b,Ui,nvar,n0,fss,H0inv)
+% of = fn_a0freefun(b,Ui,nvar,n0,fss,H0inv)
+%
+% Negative logPosterior function for squeesed A0 free parameters, which are b's in the WZ notation
+% Note: columns correspond to equations
+%
+% b: sum(n0)-by-1 vector of A0 free parameters
+% Ui: nvar-by-1 cell. In each cell, nvar-by-qi orthonormal basis for the null of the ith
+% equation contemporaneous restriction matrix where qi is the number of free parameters.
+% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
+% of total original parameters and bi is a vector of free parameters. When no
+% restrictions are imposed, we have Ui = I. There must be at least one free
+% parameter left for the ith equation.
+% nvar: number of endogeous variables
+% n0: nvar-by-1, ith element represents the number of free A0 parameters in ith equation
+% fss: nSample-lags (plus ndobs if dummies are included)
+% H0inv: cell(nvar,1). In each cell, posterior inverse of covariance inv(H0) for the ith equation,
+% resembling old SpH in the exponent term in posterior of A0, but not divided by T yet.
+%----------------
+% of: objective function (negative logPosterior)
+%
+% Tao Zha, February 2000
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+b=b(:); n0=n0(:);
+
+A0 = zeros(nvar);
+n0cum = [0;cumsum(n0)];
+tra = 0.0;
+for kj = 1:nvar
+ bj = b(n0cum(kj)+1:n0cum(kj+1));
+ A0(:,kj) = Ui{kj}*bj;
+ tra = tra + 0.5*bj'*H0inv{kj}*bj; % negative exponential term
+end
+
+[A0l,A0u] = lu(A0);
+
+ada = -fss*sum(log(abs(diag(A0u)))); % negative log determinant of A0 raised to power T
+
+of = ada + tra;
diff --git a/MatlabFiles/fn_a0freegrad.m b/MatlabFiles/fn_a0freegrad.m
index 638db31417d89aef616c41d15f75f9532b05e4bd..ea3d1a05d2f995e3a8b895ae9e724c5ab5f9addb 100644
--- a/MatlabFiles/fn_a0freegrad.m
+++ b/MatlabFiles/fn_a0freegrad.m
@@ -1,59 +1,59 @@
-function [g,badg] = fn_a0freegrad(b,Ui,nvar,n0,fss,H0inv)
-% [g,badg] = a0freegrad(b,Ui,nvar,n0,fss,H0inv)
-% Analytical gradient for a0freefun.m in use of csminwel.m. See Dhrymes's book.
-%
-% b: sum(n0)-by-1 vector of A0 free parameters
-% Ui: nvar-by-1 cell. In each cell, nvar-by-qi orthonormal basis for the null of the ith
-% equation contemporaneous restriction matrix where qi is the number of free parameters.
-% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
-% of total original parameters and bi is a vector of free parameters. When no
-% restrictions are imposed, we have Ui = I. There must be at least one free
-% parameter left for the ith equation.
-% nvar: number of endogeous variables
-% n0: nvar-by-1, ith element represents the number of free A0 parameters in ith equation
-% fss: nSample-lags (plus ndobs if dummies are included)
-% H0inv: cell(nvar,1). In each cell, posterior inverse of covariance inv(H0) for the ith equation,
-% resembling old SpH in the exponent term in posterior of A0, but not divided by T yet.
-%---------------
-% g: sum(n0)-by-1 analytical gradient for a0freefun.m
-% badg: 0, the value that is used in csminwel.m
-%
-% Tao Zha, February 2000. Revised, August 2000
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-b=b(:); n0 = n0(:);
-
-A0 = zeros(nvar);
-n0cum = [0;cumsum(n0)];
-g = zeros(n0cum(end),1);
-badg = 0;
-
-%*** The derivative of the exponential term w.r.t. each free paramater
-for kj = 1:nvar
- bj = b(n0cum(kj)+1:n0cum(kj+1));
- g(n0cum(kj)+1:n0cum(kj+1)) = H0inv{kj}*bj;
- A0(:,kj) = Ui{kj}*bj;
-end
-B=inv(A0');
-
-%*** Add the derivative of -Tlog|A0| w.r.t. each free paramater
-for ki = 1:sum(n0)
- n = max(find( (ki-n0cum)>0 )); % note, 1<=n<=nvar
- g(ki) = g(ki) - fss*B(:,n)'*Ui{n}(:,ki-n0cum(n));
-end
+function [g,badg] = fn_a0freegrad(b,Ui,nvar,n0,fss,H0inv)
+% [g,badg] = a0freegrad(b,Ui,nvar,n0,fss,H0inv)
+% Analytical gradient for a0freefun.m in use of csminwel.m. See Dhrymes's book.
+%
+% b: sum(n0)-by-1 vector of A0 free parameters
+% Ui: nvar-by-1 cell. In each cell, nvar-by-qi orthonormal basis for the null of the ith
+% equation contemporaneous restriction matrix where qi is the number of free parameters.
+% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
+% of total original parameters and bi is a vector of free parameters. When no
+% restrictions are imposed, we have Ui = I. There must be at least one free
+% parameter left for the ith equation.
+% nvar: number of endogeous variables
+% n0: nvar-by-1, ith element represents the number of free A0 parameters in ith equation
+% fss: nSample-lags (plus ndobs if dummies are included)
+% H0inv: cell(nvar,1). In each cell, posterior inverse of covariance inv(H0) for the ith equation,
+% resembling old SpH in the exponent term in posterior of A0, but not divided by T yet.
+%---------------
+% g: sum(n0)-by-1 analytical gradient for a0freefun.m
+% badg: 0, the value that is used in csminwel.m
+%
+% Tao Zha, February 2000. Revised, August 2000
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+b=b(:); n0 = n0(:);
+
+A0 = zeros(nvar);
+n0cum = [0;cumsum(n0)];
+g = zeros(n0cum(end),1);
+badg = 0;
+
+%*** The derivative of the exponential term w.r.t. each free paramater
+for kj = 1:nvar
+ bj = b(n0cum(kj)+1:n0cum(kj+1));
+ g(n0cum(kj)+1:n0cum(kj+1)) = H0inv{kj}*bj;
+ A0(:,kj) = Ui{kj}*bj;
+end
+B=inv(A0');
+
+%*** Add the derivative of -Tlog|A0| w.r.t. each free paramater
+for ki = 1:sum(n0)
+ n = max(find( (ki-n0cum)>0 )); % note, 1<=n<=nvar
+ g(ki) = g(ki) - fss*B(:,n)'*Ui{n}(:,ki-n0cum(n));
+end
diff --git a/MatlabFiles/fn_a0sfreefun.m b/MatlabFiles/fn_a0sfreefun.m
index 3f5144f65da3f762ea146faba7a501d35f342622..1f75812a620b82f98780e66baa9f0e0d178e9648 100644
--- a/MatlabFiles/fn_a0sfreefun.m
+++ b/MatlabFiles/fn_a0sfreefun.m
@@ -1,76 +1,76 @@
-function of = fn_a0sfreefun(b,Uistar,Uibar,nvar,nStates,n0,Tkave,Sgm0tldinvave)
-% of = fn_a0sfreefun(b,Uistar,Uibar,nvar,nStates,n0,Tkave,Sgm0tldinvave)
-% Negative logPosterior function for regime-switching squeesed free A0 parameters,
-% which are b's in the WZ notation. The case of no asymmetric prior and no lag restrictions.
-% Note: (1) columns correspond to equations; s stands for state.
-% See TBVAR NOTE pp.34-34a,38.
-%
-% b: sum(n0)*nStates-by-1 vector of free A0 parameters, vectorized from the sum(n0)-by-nStates matrix.
-% Uistar: cell(nvar,1). In each cell, nvar*nSates-by-qi orthonormal basis for the null of the ith
-% equation contemporaneous restriction matrix where qi is the number of free parameters.
-% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
-% of total original parameters and bi is a vector of free parameters. When no
-% restrictions are imposed, we have Ui = I. There must be at least one free
-% parameter left for the ith equation. See p.33.
-% Uibar: cell(nvar,1). In each cell, we have nvar*nStates-by-qi*nStates, rearranged
-% from Uistar or Ui. See p.33. This is NOT used for this function, but serves as an argument
-% to keep it compatible with fn_a0sfreegrad.m which uses it.
-% nvar: Number of endogeous variables.
-% nStates: NUmber of states.
-% n0: nvar-by-1, ith element represents the number of free A0 parameters in ith equation for each state.
-% Tkave: nStates-by-1 of sample sizes (excluding lags but including dummies if provided) for different states k,
-% averaged (ave) over E-step draws. For T_k. See p.38.
-% Sgm0tldinvave: nvar*nStates-by-nvar*nStates. The matrix inv(\Sigma~_0) averaged (ave)
-% over E-step draws. Same for all equations because of no asymmetric prior and no lag
-% restrictions. Resembles old SpH in the exponent term in posterior of A0,
-% but NOT divided by fss (T) yet. See p.38.
-%----------------
-% of: Objective function (negative logPosterior).
-%
-% This function is a special case of fn_a0sfreefun2.m.
-% Tao Zha, March 2001
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-
-n0=n0(:);
-A0=zeros(nvar,nvar,nStates);
-n0cum = [0;cumsum(n0)];
-b=reshape(b,n0cum(end),nStates);
-
-tra = 0.0;
-for kj = 1:nvar
- lenbjs=length(n0cum(kj)+1:n0cum(kj+1));
- bj = zeros(nStates*lenbjs,1);
- a0j = zeros(nStates*nvar,1); % See p.33.
- for si=1:nStates
- bj((si-1)*lenbjs+1:si*lenbjs) = b(n0cum(kj)+1:n0cum(kj+1),si); % bj(si). See p.34a.
- A0(:,kj,si) = Uistar{kj}((si-1)*nvar+1:si*nvar,:)*b(n0cum(kj)+1:n0cum(kj+1),si);
- a0j((si-1)*nvar+1:si*nvar) = A0(:,kj,si); % a0j(si). See p.33.
- end
- %tra = tra + 0.5*a0j'*Sgm0tldinvave*a0j; % negative exponential term
- tra = tra+0.5*bj'*(Uibar{kj}'*Sgm0tldinvave*Uibar{kj})*bj;
-end
-
-ada=0.0;
-for si=1:nStates % See p.34a.
- [A0l,A0u] = lu(A0(:,:,si));
- ada = ada - Tkave(si)*sum(log(abs(diag(A0u)))); % negative log determinant of A0 raised to power T
-end
-
-of = ada + tra;
+function of = fn_a0sfreefun(b,Uistar,Uibar,nvar,nStates,n0,Tkave,Sgm0tldinvave)
+% of = fn_a0sfreefun(b,Uistar,Uibar,nvar,nStates,n0,Tkave,Sgm0tldinvave)
+% Negative logPosterior function for regime-switching squeesed free A0 parameters,
+% which are b's in the WZ notation. The case of no asymmetric prior and no lag restrictions.
+% Note: (1) columns correspond to equations; s stands for state.
+% See TBVAR NOTE pp.34-34a,38.
+%
+% b: sum(n0)*nStates-by-1 vector of free A0 parameters, vectorized from the sum(n0)-by-nStates matrix.
+% Uistar: cell(nvar,1). In each cell, nvar*nSates-by-qi orthonormal basis for the null of the ith
+% equation contemporaneous restriction matrix where qi is the number of free parameters.
+% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
+% of total original parameters and bi is a vector of free parameters. When no
+% restrictions are imposed, we have Ui = I. There must be at least one free
+% parameter left for the ith equation. See p.33.
+% Uibar: cell(nvar,1). In each cell, we have nvar*nStates-by-qi*nStates, rearranged
+% from Uistar or Ui. See p.33. This is NOT used for this function, but serves as an argument
+% to keep it compatible with fn_a0sfreegrad.m which uses it.
+% nvar: Number of endogeous variables.
+% nStates: NUmber of states.
+% n0: nvar-by-1, ith element represents the number of free A0 parameters in ith equation for each state.
+% Tkave: nStates-by-1 of sample sizes (excluding lags but including dummies if provided) for different states k,
+% averaged (ave) over E-step draws. For T_k. See p.38.
+% Sgm0tldinvave: nvar*nStates-by-nvar*nStates. The matrix inv(\Sigma~_0) averaged (ave)
+% over E-step draws. Same for all equations because of no asymmetric prior and no lag
+% restrictions. Resembles old SpH in the exponent term in posterior of A0,
+% but NOT divided by fss (T) yet. See p.38.
+%----------------
+% of: Objective function (negative logPosterior).
+%
+% This function is a special case of fn_a0sfreefun2.m.
+% Tao Zha, March 2001
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+
+n0=n0(:);
+A0=zeros(nvar,nvar,nStates);
+n0cum = [0;cumsum(n0)];
+b=reshape(b,n0cum(end),nStates);
+
+tra = 0.0;
+for kj = 1:nvar
+ lenbjs=length(n0cum(kj)+1:n0cum(kj+1));
+ bj = zeros(nStates*lenbjs,1);
+ a0j = zeros(nStates*nvar,1); % See p.33.
+ for si=1:nStates
+ bj((si-1)*lenbjs+1:si*lenbjs) = b(n0cum(kj)+1:n0cum(kj+1),si); % bj(si). See p.34a.
+ A0(:,kj,si) = Uistar{kj}((si-1)*nvar+1:si*nvar,:)*b(n0cum(kj)+1:n0cum(kj+1),si);
+ a0j((si-1)*nvar+1:si*nvar) = A0(:,kj,si); % a0j(si). See p.33.
+ end
+ %tra = tra + 0.5*a0j'*Sgm0tldinvave*a0j; % negative exponential term
+ tra = tra+0.5*bj'*(Uibar{kj}'*Sgm0tldinvave*Uibar{kj})*bj;
+end
+
+ada=0.0;
+for si=1:nStates % See p.34a.
+ [A0l,A0u] = lu(A0(:,:,si));
+ ada = ada - Tkave(si)*sum(log(abs(diag(A0u)))); % negative log determinant of A0 raised to power T
+end
+
+of = ada + tra;
diff --git a/MatlabFiles/fn_a0sfreefun2.m b/MatlabFiles/fn_a0sfreefun2.m
index ffc2f441ecd76456bbf78df22740b5d50498d759..e6ce14645f32e24d4c08043c4229dc8d803c73cf 100644
--- a/MatlabFiles/fn_a0sfreefun2.m
+++ b/MatlabFiles/fn_a0sfreefun2.m
@@ -1,97 +1,97 @@
-function of = fn_a0sfreefun2(b,Uistar,Uibar,nvar,nStates,n0,n0cumsum,tvstate,tvtot,constot,...
- tvinx,constinx,indxTV,indxConst,Tkave,Sgm0tldinvaveConst,Sgm0tldinvaveTV)
-% Negative logPosterior function for regime-switching vectorized free A0 parameters,
-% which are b's in the WZ notation. The case of no asymmetric prior and no lag restrictions.
-% It improves fn_a0sfreefun2.m in several aspects:
-% (a) allows some equations to have constant parameters.
-% It differs from fn_a0cfreefun_tv.m in several aspects:
-% (a) does not deal with equations with only structural variances time varying;
-% (b) cannot deal with lag restrictions;
-% (c) only deal with the marginal distribution of A0_s.
-% Note: (1) columns correspond to equations; (2) s stands for state.
-% See Time-Varying BVAR NOTE pp.34-34c,40.
-%
-% b: sum(n0)*nStates-by-1 vector of free A0 parameters, vectorized from the sum(n0)-by-nStates matrix.
-% Uistar: cell(nvar,1). In each cell, nvar*nSates-by-qi orthonormal basis for the null of the ith
-% equation contemporaneous restriction matrix where qi is the number of free parameters.
-% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
-% of total original parameters and bi is a vector of free parameters. When no
-% restrictions are imposed, we have Ui = I. There must be at least one free
-% parameter left for the ith equation. See p.33.
-% Uibar: cell(nvar,1). In each cell, we have nvar*nStates-by-qi*nStates, rearranged
-% from Uistar or Ui. See p.33.
-% nvar: Number of endogeous variables.
-% nStates: NUmber of states.
-% n0: nvar-by-1, ith element represents the number of free A0 parameters in ith equation for each state.
-% n0cumsum: Equal to [0;cumsum(n0)];
-% tvstate: Number of time-varying parameters for all equations for each state.
-% tvtot: Total number of time-varying parameters.
-% constot: Total number of constant parameters.
-% tvinx: Vectorized index to include time-varying parameters for all equations under *each* state.
-% constinx: Vectorized index to include constant parameters for all equations.
-% indxTV: Index of acending order for equations with time-varying parameters.
-% indxConst: Index of ascending order for equations with constant parameters. When [], all equations
-% are time varying; when [1:nvar], all equations have constant parameters.
-% Tkave: nStates-by-1 of sample sizes (excluding lags but including dummies if provided) for different states k,
-% averaged (ave) over E-step draws. For T_k. See p.40.
-% Sgm0tldinvaveConst
-% Sgm0tldinvaveTV
-% Sgm0tldinvave: nvar*nStates-by-nvar*nStates. The matrix inv(\Sigma~_0) averaged (ave)
-% over E-step draws. Same for all equations because of no asymmetric prior and no lag
-% restrictions. Resembles old SpH in the exponent term in posterior of A0,
-% but NOT divided by fss (T) yet. See p.40.
-%----------------
-% of: Objective function (negative logPosterior).
-%
-% This function is called by tveml*.m which is an old program compared with szeml*.m. Thus,
-% use fn_a0cfreefun_tv.m if possible.
-% Tao Zha, August 2001.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-n0=n0(:);
-A0=zeros(nvar,nvar,nStates);
-b_shat = zeros(n0cumsum(end),nStates);
-b_shat(tvinx,:) = reshape(b(1:tvtot),tvstate,nStates); % Time-varying parameter matrix.
-b_shat(constinx,:) = repmat(b(tvtot+1:end), [1 nStates]); % Constant parameter matrix.
-
-tra = 0.0;
-for kj = 1:nvar
- lenbjs=length(n0cumsum(kj)+1:n0cumsum(kj+1));
- bj = zeros(nStates*lenbjs,1);
- a0j = zeros(nStates*nvar,1); % See p.33.
- for si=1:nStates
- bj((si-1)*lenbjs+1:si*lenbjs) = b_shat(n0cumsum(kj)+1:n0cumsum(kj+1),si); % bj(si). See p.34a.
- A0(:,kj,si) = Uistar{kj}((si-1)*nvar+1:si*nvar,:)*b_shat(n0cumsum(kj)+1:n0cumsum(kj+1),si);
- a0j((si-1)*nvar+1:si*nvar) = A0(:,kj,si); % a0j(si). See p.33.
- end
- %tra = tra + 0.5*a0j'*Sgm0tldinvave*a0j; % negative exponential term
- if find(kj==indxTV) % For time-varying equations.
- tra = tra+0.5*bj'*(Uibar{kj}'*Sgm0tldinvaveTV*Uibar{kj})*bj;
- else % % For constant parameter equations.
- tra = tra+0.5*bj'*(Uibar{kj}'*Sgm0tldinvaveConst*Uibar{kj})*bj;
- end
-end
-
-ada=0.0;
-for si=1:nStates % See p.34a.
- [A0l,A0u] = lu(A0(:,:,si));
- ada = ada - Tkave(si)*sum(log(abs(diag(A0u)))); % negative log determinant of A0 raised to power T
-end
-
-of = ada + tra;
+function of = fn_a0sfreefun2(b,Uistar,Uibar,nvar,nStates,n0,n0cumsum,tvstate,tvtot,constot,...
+ tvinx,constinx,indxTV,indxConst,Tkave,Sgm0tldinvaveConst,Sgm0tldinvaveTV)
+% Negative logPosterior function for regime-switching vectorized free A0 parameters,
+% which are b's in the WZ notation. The case of no asymmetric prior and no lag restrictions.
+% It improves fn_a0sfreefun2.m in several aspects:
+% (a) allows some equations to have constant parameters.
+% It differs from fn_a0cfreefun_tv.m in several aspects:
+% (a) does not deal with equations with only structural variances time varying;
+% (b) cannot deal with lag restrictions;
+% (c) only deal with the marginal distribution of A0_s.
+% Note: (1) columns correspond to equations; (2) s stands for state.
+% See Time-Varying BVAR NOTE pp.34-34c,40.
+%
+% b: sum(n0)*nStates-by-1 vector of free A0 parameters, vectorized from the sum(n0)-by-nStates matrix.
+% Uistar: cell(nvar,1). In each cell, nvar*nSates-by-qi orthonormal basis for the null of the ith
+% equation contemporaneous restriction matrix where qi is the number of free parameters.
+% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
+% of total original parameters and bi is a vector of free parameters. When no
+% restrictions are imposed, we have Ui = I. There must be at least one free
+% parameter left for the ith equation. See p.33.
+% Uibar: cell(nvar,1). In each cell, we have nvar*nStates-by-qi*nStates, rearranged
+% from Uistar or Ui. See p.33.
+% nvar: Number of endogeous variables.
+% nStates: NUmber of states.
+% n0: nvar-by-1, ith element represents the number of free A0 parameters in ith equation for each state.
+% n0cumsum: Equal to [0;cumsum(n0)];
+% tvstate: Number of time-varying parameters for all equations for each state.
+% tvtot: Total number of time-varying parameters.
+% constot: Total number of constant parameters.
+% tvinx: Vectorized index to include time-varying parameters for all equations under *each* state.
+% constinx: Vectorized index to include constant parameters for all equations.
+% indxTV: Index of acending order for equations with time-varying parameters.
+% indxConst: Index of ascending order for equations with constant parameters. When [], all equations
+% are time varying; when [1:nvar], all equations have constant parameters.
+% Tkave: nStates-by-1 of sample sizes (excluding lags but including dummies if provided) for different states k,
+% averaged (ave) over E-step draws. For T_k. See p.40.
+% Sgm0tldinvaveConst
+% Sgm0tldinvaveTV
+% Sgm0tldinvave: nvar*nStates-by-nvar*nStates. The matrix inv(\Sigma~_0) averaged (ave)
+% over E-step draws. Same for all equations because of no asymmetric prior and no lag
+% restrictions. Resembles old SpH in the exponent term in posterior of A0,
+% but NOT divided by fss (T) yet. See p.40.
+%----------------
+% of: Objective function (negative logPosterior).
+%
+% This function is called by tveml*.m which is an old program compared with szeml*.m. Thus,
+% use fn_a0cfreefun_tv.m if possible.
+% Tao Zha, August 2001.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+n0=n0(:);
+A0=zeros(nvar,nvar,nStates);
+b_shat = zeros(n0cumsum(end),nStates);
+b_shat(tvinx,:) = reshape(b(1:tvtot),tvstate,nStates); % Time-varying parameter matrix.
+b_shat(constinx,:) = repmat(b(tvtot+1:end), [1 nStates]); % Constant parameter matrix.
+
+tra = 0.0;
+for kj = 1:nvar
+ lenbjs=length(n0cumsum(kj)+1:n0cumsum(kj+1));
+ bj = zeros(nStates*lenbjs,1);
+ a0j = zeros(nStates*nvar,1); % See p.33.
+ for si=1:nStates
+ bj((si-1)*lenbjs+1:si*lenbjs) = b_shat(n0cumsum(kj)+1:n0cumsum(kj+1),si); % bj(si). See p.34a.
+ A0(:,kj,si) = Uistar{kj}((si-1)*nvar+1:si*nvar,:)*b_shat(n0cumsum(kj)+1:n0cumsum(kj+1),si);
+ a0j((si-1)*nvar+1:si*nvar) = A0(:,kj,si); % a0j(si). See p.33.
+ end
+ %tra = tra + 0.5*a0j'*Sgm0tldinvave*a0j; % negative exponential term
+ if find(kj==indxTV) % For time-varying equations.
+ tra = tra+0.5*bj'*(Uibar{kj}'*Sgm0tldinvaveTV*Uibar{kj})*bj;
+ else % % For constant parameter equations.
+ tra = tra+0.5*bj'*(Uibar{kj}'*Sgm0tldinvaveConst*Uibar{kj})*bj;
+ end
+end
+
+ada=0.0;
+for si=1:nStates % See p.34a.
+ [A0l,A0u] = lu(A0(:,:,si));
+ ada = ada - Tkave(si)*sum(log(abs(diag(A0u)))); % negative log determinant of A0 raised to power T
+end
+
+of = ada + tra;
diff --git a/MatlabFiles/fn_a0sfreegrad.m b/MatlabFiles/fn_a0sfreegrad.m
index 4d09f2396db5c9fdaee12eed43fa0e86d37e95c8..9f4b272b0d2ef7eb4493c43c4b121bcee44e05cf 100644
--- a/MatlabFiles/fn_a0sfreegrad.m
+++ b/MatlabFiles/fn_a0sfreegrad.m
@@ -1,82 +1,82 @@
-function [g,badg] = fn_a0sfreegrad(b,Uistar,Uibar,nvar,nStates,n0,Tkave,Sgm0tldinvave)
-% [g,badg] = fn_a0sfreegrad(b,Uistar,Uibar,nvar,nStates,n0,Tkave,Sgm0tldinvave)
-% Analytical gradient for fn_a0sfreefun.m when using csminwel.m.
-% The case of no asymmetric prior and no lag restrictions.
-% Note: (1) columns correspond to equations; s stands for state.
-% See TBVAR NOTE p.34a.
-%
-% b: sum(n0)*nStates-by-1 vector of free A0 parameters, vectorized from the sum(n0)-by-nStates matrix.
-% Uistar: cell(nvar,1). In each cell, nvar*nStates-by-qi orthonormal basis for the null of the ith
-% equation contemporaneous restriction matrix where qi is the number of free parameters.
-% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
-% of total original parameters and bi is a vector of free parameters. When no
-% restrictions are imposed, we have Ui = I. There must be at least one free
-% parameter left for the ith equation. See p.33.
-% Uibar: cell(nvar,1). In each cell, we have nvar*nStates-by-qi*nStates, rearranged
-% from Uistar or Ui. See p.33.
-% nvar: Number of endogeous variables.
-% nStates: NUmber of states.
-% n0: nvar-by-1. n0(i)=qi where ith element represents the number of free A0 parameters in ith equation for each state.
-% Tkave: nStates-by-1 of sample sizes (excluding lags but including dummies if provided) for different states k,
-% averaged (ave) over E-step draws. For T_k. See p.38.
-% Sgm0tldinvave: nvar*nStates-by-nvar*nStates. The matrix inv(\Sigma~_0) averaged (ave)
-% over E-step draws. Same for all equations because of no asymmetric prior and no lag
-% restrictions. Resembles old SpH in the exponent term in posterior of A0,
-% but NOT divided by fss (T) yet. See p.38.
-%----------------
-% g: sum(n0)*nStates-by-1 analytical gradient for fn_a0sfreefun.m.
-% Vectorized frmo the sum(n0)-by-nStates matrix.
-% badg: 0, the value that is used in csminwel.m.
-%
-% Tao Zha, March 2001
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-
-n0=n0(:);
-A0=zeros(nvar,nvar,nStates);
-n0cum = [0;cumsum(n0)];
-b=reshape(b,n0cum(end),nStates);
-
-g = zeros(n0cum(end),nStates); % which will be vectorized later.
-badg = 0;
-
-
-%*** The derivative of the exponential term w.r.t. each free paramater
-for kj = 1:nvar
- lenbjs=length(n0cum(kj)+1:n0cum(kj+1));
- bj = zeros(nStates*lenbjs,1);
- for si=1:nStates
- bj((si-1)*lenbjs+1:si*lenbjs) = b(n0cum(kj)+1:n0cum(kj+1),si); % bj(si). See p.34a.
- A0(:,kj,si) = Uistar{kj}((si-1)*nvar+1:si*nvar,:)*b(n0cum(kj)+1:n0cum(kj+1),si);
- end
- gj = (Uibar{kj}'*Sgm0tldinvave*Uibar{kj})*bj;
- for si=1:nStates
- g(n0cum(kj)+1:n0cum(kj+1),si) = gj((si-1)*lenbjs+1:si*lenbjs);
- end
-end
-
-%*** Add the derivative of -T_klog|A0(k)| w.r.t. each free paramater
-for si=1:nStates % See p.34a.
- B=inv(A0(:,:,si)');
- for ki = 1:n0cum(end) % from 1 to sum(q_i)
- n = max(find( (ki-n0cum)>0 )); % note, 1<=n<=nvar equations.
- g(ki,si) = g(ki,si) - Tkave(si)*B(:,n)'*Uistar{n}((si-1)*nvar+1:si*nvar,ki-n0cum(n)); % See p.34a.
- end
-end
-g = g(:); % vectorized the same way as b is vectorized.
+function [g,badg] = fn_a0sfreegrad(b,Uistar,Uibar,nvar,nStates,n0,Tkave,Sgm0tldinvave)
+% [g,badg] = fn_a0sfreegrad(b,Uistar,Uibar,nvar,nStates,n0,Tkave,Sgm0tldinvave)
+% Analytical gradient for fn_a0sfreefun.m when using csminwel.m.
+% The case of no asymmetric prior and no lag restrictions.
+% Note: (1) columns correspond to equations; s stands for state.
+% See TBVAR NOTE p.34a.
+%
+% b: sum(n0)*nStates-by-1 vector of free A0 parameters, vectorized from the sum(n0)-by-nStates matrix.
+% Uistar: cell(nvar,1). In each cell, nvar*nStates-by-qi orthonormal basis for the null of the ith
+% equation contemporaneous restriction matrix where qi is the number of free parameters.
+% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
+% of total original parameters and bi is a vector of free parameters. When no
+% restrictions are imposed, we have Ui = I. There must be at least one free
+% parameter left for the ith equation. See p.33.
+% Uibar: cell(nvar,1). In each cell, we have nvar*nStates-by-qi*nStates, rearranged
+% from Uistar or Ui. See p.33.
+% nvar: Number of endogeous variables.
+% nStates: NUmber of states.
+% n0: nvar-by-1. n0(i)=qi where ith element represents the number of free A0 parameters in ith equation for each state.
+% Tkave: nStates-by-1 of sample sizes (excluding lags but including dummies if provided) for different states k,
+% averaged (ave) over E-step draws. For T_k. See p.38.
+% Sgm0tldinvave: nvar*nStates-by-nvar*nStates. The matrix inv(\Sigma~_0) averaged (ave)
+% over E-step draws. Same for all equations because of no asymmetric prior and no lag
+% restrictions. Resembles old SpH in the exponent term in posterior of A0,
+% but NOT divided by fss (T) yet. See p.38.
+%----------------
+% g: sum(n0)*nStates-by-1 analytical gradient for fn_a0sfreefun.m.
+% Vectorized frmo the sum(n0)-by-nStates matrix.
+% badg: 0, the value that is used in csminwel.m.
+%
+% Tao Zha, March 2001
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+
+n0=n0(:);
+A0=zeros(nvar,nvar,nStates);
+n0cum = [0;cumsum(n0)];
+b=reshape(b,n0cum(end),nStates);
+
+g = zeros(n0cum(end),nStates); % which will be vectorized later.
+badg = 0;
+
+
+%*** The derivative of the exponential term w.r.t. each free paramater
+for kj = 1:nvar
+ lenbjs=length(n0cum(kj)+1:n0cum(kj+1));
+ bj = zeros(nStates*lenbjs,1);
+ for si=1:nStates
+ bj((si-1)*lenbjs+1:si*lenbjs) = b(n0cum(kj)+1:n0cum(kj+1),si); % bj(si). See p.34a.
+ A0(:,kj,si) = Uistar{kj}((si-1)*nvar+1:si*nvar,:)*b(n0cum(kj)+1:n0cum(kj+1),si);
+ end
+ gj = (Uibar{kj}'*Sgm0tldinvave*Uibar{kj})*bj;
+ for si=1:nStates
+ g(n0cum(kj)+1:n0cum(kj+1),si) = gj((si-1)*lenbjs+1:si*lenbjs);
+ end
+end
+
+%*** Add the derivative of -T_klog|A0(k)| w.r.t. each free paramater
+for si=1:nStates % See p.34a.
+ B=inv(A0(:,:,si)');
+ for ki = 1:n0cum(end) % from 1 to sum(q_i)
+ n = max(find( (ki-n0cum)>0 )); % note, 1<=n<=nvar equations.
+ g(ki,si) = g(ki,si) - Tkave(si)*B(:,n)'*Uistar{n}((si-1)*nvar+1:si*nvar,ki-n0cum(n)); % See p.34a.
+ end
+end
+g = g(:); % vectorized the same way as b is vectorized.
diff --git a/MatlabFiles/fn_a0sfreegrad2.m b/MatlabFiles/fn_a0sfreegrad2.m
index bc7eb939a3026265a53c009dcd3dab44182db193..a88760bede55065b31a0e10e798292ab11b3d055 100644
--- a/MatlabFiles/fn_a0sfreegrad2.m
+++ b/MatlabFiles/fn_a0sfreegrad2.m
@@ -1,141 +1,141 @@
-function [g,badg] = fn_a0sfreegrad2(b,Uistar,Uibar,nvar,nStates,n0,n0cumsum,tvstate,tvtot,constot,...
- tvinx,constinx,indxTV,indxConst,Tkave,Sgm0tldinvaveConst,Sgm0tldinvaveTV)
-% Analytical gradient for both time-varying and constant parameters for fn_a0sfreefun2.m when using csminwel.m.
-% The case of no asymmetric prior and no lag restrictions.
-% Note: (1) columns correspond to equations; s stands for state.
-% See Time-Varying BVAR NOTE pp.34a-34c.
-%
-% b: tvtot+constot-by-1 vector of free A0 parameters, vectorized from b_shat for both time-varying and constant parameter matrices.
-% Uistar: cell(nvar,1). In each cell, nvar*nStates-by-qi orthonormal basis for the null of the ith
-% equation contemporaneous restriction matrix where qi is the number of free parameters
-% where in each state, nvar-by-qi is the same. With this transformation, we have
-% ai = Ui*bi or Ui'*ai = bi where ai is a vector of total original parameters and
-% bi is a vector of free parameters. When no restrictions are imposed, we have Ui = I.
-% There must be at least one free parameter left for the ith equation. See p.33.
-% Uibar: cell(nvar,1). In each cell, we have nvar*nStates-by-qi*nStates, rearranged
-% from Uistar or Ui. See p.33.
-% nvar: Number of endogenous variables.
-% nStates: NUmber of states.
-% n0: nvar-by-1. n0(i)=qi where ith element represents the number of free A0 parameters in ith equation for each state.
-% n0cumsum=[0;cumsum(n0)]: nvar+1-by-1. Cumulative sums of n0 with zero added.
-% tvstate=length(tvinx): Number of time-varying parameters for all equations under *each* state.
-% constot=length(constinx): Total number of constant parameters for all equations.
-% tvtot = tvstate*nStates: Total number of time-varying parameters for all equations under all states.
-% tvinx: Vectorized index to include time-varying parameters for all equations under *each* state.
-% constinx: Vectorized index to include constant parameters for all equations.
-% indxTV: Index vector of ascending order for equations with time-varying parameters.
-% indxConst: Index vector of ascending order for equations with constant parameters. When [], all equations
-% are time varying; when [1:nvar], all equations have constant parameters.
-% Tkave: nStates-by-1 of sample sizes (excluding lags but including dummies if provided) for different states k,
-% averaged (ave) over E-step draws. For T_k. See p.40.
-% Sgm0tldinvaveConst
-% Sgm0tldinvaveTV
-% Sgm0tldinvave: nvar*nStates-by-nvar*nStates. The matrix inv(\Sigma~_0) averaged (ave)
-% over E-step draws. Same for all equations because of no asymmetric prior and no lag
-% restrictions. Resembles old SpH in the exponent term in posterior of A0,
-% but NOT divided by fss (T) yet. See p.40.
-%----------------
-% g: tvtot+constot-by-1 analytical gradient for fn_a0sfreefun2.m. Vectorized first from the
-% time-varying parameter matrix and then from constant parameter and constant parameter matrix.
-% badg: Always 0 --- the value that is used in csminwel.m.
-%
-% Tao Zha, August 2001.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-n0=n0(:);
-A0=zeros(nvar,nvar,nStates);
-B0=A0;
-b_shat = zeros(n0cumsum(end),nStates);
-b_shat(tvinx,:) = reshape(b(1:tvtot),tvstate,nStates); % Time-varying parameter matrix.
-b_shat(constinx,:) = repmat(b(tvtot+1:end), [1 nStates]); % Constant parameter matrix.
-%
-gmix = zeros(n0cumsum(end),nStates);
- % Gradient matrix mixing time-varying and constant parameters.
- % Rows of gmix, indexed by constinx, will not be used but instead treated
- % different under "for kj=indxConst."
-gtv = zeros(tvstate,nStates); % Gradient wrt time-varying parameters, which will be vectorized later.
-gconst1 = zeros(constot,1); % The first gradient wrt constant parameters for the exponetial term.
-gconst2=zeros(constot,1); % The second gradient wrt constant parameters for -T_klog|A0(k)|.
-
-
-%---------------------------------------------------
-% Time-varying situation. See p. 34a.
-%---------------------------------------------------
-%*** The derivative of the exponential term w.r.t. each free time-varying paramater.
-for kj = 1:nvar % For all equations
- lenbjs=length(n0cumsum(kj)+1:n0cumsum(kj+1));
- bj = zeros(nStates*lenbjs,1);
- for si=1:nStates
- bj((si-1)*lenbjs+1:si*lenbjs) = b_shat(n0cumsum(kj)+1:n0cumsum(kj+1),si); % bj(si). See p.34a.
- A0(:,kj,si) = Uistar{kj}((si-1)*nvar+1:si*nvar,:)*b_shat(n0cumsum(kj)+1:n0cumsum(kj+1),si);
- end
- gj = (Uibar{kj}'*Sgm0tldinvaveTV*Uibar{kj})*bj;
- if find(kj==indxTV) % For time-varying equations.
- for si=1:nStates
- gmix(n0cumsum(kj)+1:n0cumsum(kj+1),si) = gj((si-1)*lenbjs+1:si*lenbjs);
- end
- end
-end
-
-%*** Add the derivative of -T_klog|A0(k)| w.r.t. each free time-varying paramater.
-for si=1:nStates % See p.34a.
- B0(:,:,si)=inv(A0(:,:,si)');
- for ki = tvinx % Those from 1 to sum(q_i) that pertain to time-varying parameters.
- n = max(find( (ki-n0cumsum)>0 )); % note, 1<=n<=nvar equations.
- gmix(ki,si) = gmix(ki,si) - Tkave(si)*B0(:,n,si)'*Uistar{n}((si-1)*nvar+1:si*nvar,ki-n0cumsum(n)); % See p.34a.
- end
-end
-
-%---------------------------------------------------
-% Constant-parameter situation. See pp. 34b-34c.
-%---------------------------------------------------
-kcount = 0; % Counts.
-for kj = indxConst % Equations
- klocat=kcount+n0(kj); % Losition for the last constant element in the jth equation.
- Bbar = repmat(eye(n0(kj)),[1 nStates]); % See p.34b.
- lenbjs=length(n0cumsum(kj)+1:n0cumsum(kj+1));
- bj = zeros(nStates*lenbjs,1);
- for si=1:nStates
- bj((si-1)*lenbjs+1:si*lenbjs) = b_shat(n0cumsum(kj)+1:n0cumsum(kj+1),si); % bj(si). See p.34a.
- A0(:,kj,si) = Uistar{kj}((si-1)*nvar+1:si*nvar,:)*b_shat(n0cumsum(kj)+1:n0cumsum(kj+1),si);
- end
- gconst1(kcount+1:klocat) = Bbar*( (Uibar{kj}'*Sgm0tldinvaveConst*Uibar{kj})*bj );
- kcount=klocat;
-end
-%*** Add the derivative of -T_klog|A0(k)| w.r.t. each free time-varying paramater.
-kcount = 0; % Counts.
-for ki = constinx % Those from 1 to sum(q_i) that pertain to constant parameters.
- kcount=kcount+1;
- n = max(find( (ki-n0cumsum)>0 )); % note, 1<=n<=nvar equations.
- gi=0; % Initializes a gradient summed across the states. See p. 34c.
- for si=1:nStates % See p.34c.
- gi = gi - Tkave(si)*B0(:,n,si)'*Uistar{n}((si-1)*nvar+1:si*nvar,ki-n0cumsum(n)); % See p. 34a.
- end
- gconst2(kcount)=gi;
-end
-
-
-%---------------------------------------------------
-% Conclusion.
-%---------------------------------------------------
-gtv = gmix(tvinx,:); % Extract the gradient wrt time-varying parameters.
-gconst = gconst1+gconst2; % Gradient wrt to constant parameters.
-%
-g = [gtv(:);gconst]; % Vectorized the same way as b is vectorized.
-badg = 0; % This term is used for Sims's csminwel program.
+function [g,badg] = fn_a0sfreegrad2(b,Uistar,Uibar,nvar,nStates,n0,n0cumsum,tvstate,tvtot,constot,...
+ tvinx,constinx,indxTV,indxConst,Tkave,Sgm0tldinvaveConst,Sgm0tldinvaveTV)
+% Analytical gradient for both time-varying and constant parameters for fn_a0sfreefun2.m when using csminwel.m.
+% The case of no asymmetric prior and no lag restrictions.
+% Note: (1) columns correspond to equations; s stands for state.
+% See Time-Varying BVAR NOTE pp.34a-34c.
+%
+% b: tvtot+constot-by-1 vector of free A0 parameters, vectorized from b_shat for both time-varying and constant parameter matrices.
+% Uistar: cell(nvar,1). In each cell, nvar*nStates-by-qi orthonormal basis for the null of the ith
+% equation contemporaneous restriction matrix where qi is the number of free parameters
+% where in each state, nvar-by-qi is the same. With this transformation, we have
+% ai = Ui*bi or Ui'*ai = bi where ai is a vector of total original parameters and
+% bi is a vector of free parameters. When no restrictions are imposed, we have Ui = I.
+% There must be at least one free parameter left for the ith equation. See p.33.
+% Uibar: cell(nvar,1). In each cell, we have nvar*nStates-by-qi*nStates, rearranged
+% from Uistar or Ui. See p.33.
+% nvar: Number of endogenous variables.
+% nStates: NUmber of states.
+% n0: nvar-by-1. n0(i)=qi where ith element represents the number of free A0 parameters in ith equation for each state.
+% n0cumsum=[0;cumsum(n0)]: nvar+1-by-1. Cumulative sums of n0 with zero added.
+% tvstate=length(tvinx): Number of time-varying parameters for all equations under *each* state.
+% constot=length(constinx): Total number of constant parameters for all equations.
+% tvtot = tvstate*nStates: Total number of time-varying parameters for all equations under all states.
+% tvinx: Vectorized index to include time-varying parameters for all equations under *each* state.
+% constinx: Vectorized index to include constant parameters for all equations.
+% indxTV: Index vector of ascending order for equations with time-varying parameters.
+% indxConst: Index vector of ascending order for equations with constant parameters. When [], all equations
+% are time varying; when [1:nvar], all equations have constant parameters.
+% Tkave: nStates-by-1 of sample sizes (excluding lags but including dummies if provided) for different states k,
+% averaged (ave) over E-step draws. For T_k. See p.40.
+% Sgm0tldinvaveConst
+% Sgm0tldinvaveTV
+% Sgm0tldinvave: nvar*nStates-by-nvar*nStates. The matrix inv(\Sigma~_0) averaged (ave)
+% over E-step draws. Same for all equations because of no asymmetric prior and no lag
+% restrictions. Resembles old SpH in the exponent term in posterior of A0,
+% but NOT divided by fss (T) yet. See p.40.
+%----------------
+% g: tvtot+constot-by-1 analytical gradient for fn_a0sfreefun2.m. Vectorized first from the
+% time-varying parameter matrix and then from constant parameter and constant parameter matrix.
+% badg: Always 0 --- the value that is used in csminwel.m.
+%
+% Tao Zha, August 2001.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+n0=n0(:);
+A0=zeros(nvar,nvar,nStates);
+B0=A0;
+b_shat = zeros(n0cumsum(end),nStates);
+b_shat(tvinx,:) = reshape(b(1:tvtot),tvstate,nStates); % Time-varying parameter matrix.
+b_shat(constinx,:) = repmat(b(tvtot+1:end), [1 nStates]); % Constant parameter matrix.
+%
+gmix = zeros(n0cumsum(end),nStates);
+ % Gradient matrix mixing time-varying and constant parameters.
+ % Rows of gmix, indexed by constinx, will not be used but instead treated
+ % different under "for kj=indxConst."
+gtv = zeros(tvstate,nStates); % Gradient wrt time-varying parameters, which will be vectorized later.
+gconst1 = zeros(constot,1); % The first gradient wrt constant parameters for the exponetial term.
+gconst2=zeros(constot,1); % The second gradient wrt constant parameters for -T_klog|A0(k)|.
+
+
+%---------------------------------------------------
+% Time-varying situation. See p. 34a.
+%---------------------------------------------------
+%*** The derivative of the exponential term w.r.t. each free time-varying paramater.
+for kj = 1:nvar % For all equations
+ lenbjs=length(n0cumsum(kj)+1:n0cumsum(kj+1));
+ bj = zeros(nStates*lenbjs,1);
+ for si=1:nStates
+ bj((si-1)*lenbjs+1:si*lenbjs) = b_shat(n0cumsum(kj)+1:n0cumsum(kj+1),si); % bj(si). See p.34a.
+ A0(:,kj,si) = Uistar{kj}((si-1)*nvar+1:si*nvar,:)*b_shat(n0cumsum(kj)+1:n0cumsum(kj+1),si);
+ end
+ gj = (Uibar{kj}'*Sgm0tldinvaveTV*Uibar{kj})*bj;
+ if find(kj==indxTV) % For time-varying equations.
+ for si=1:nStates
+ gmix(n0cumsum(kj)+1:n0cumsum(kj+1),si) = gj((si-1)*lenbjs+1:si*lenbjs);
+ end
+ end
+end
+
+%*** Add the derivative of -T_klog|A0(k)| w.r.t. each free time-varying paramater.
+for si=1:nStates % See p.34a.
+ B0(:,:,si)=inv(A0(:,:,si)');
+ for ki = tvinx % Those from 1 to sum(q_i) that pertain to time-varying parameters.
+ n = max(find( (ki-n0cumsum)>0 )); % note, 1<=n<=nvar equations.
+ gmix(ki,si) = gmix(ki,si) - Tkave(si)*B0(:,n,si)'*Uistar{n}((si-1)*nvar+1:si*nvar,ki-n0cumsum(n)); % See p.34a.
+ end
+end
+
+%---------------------------------------------------
+% Constant-parameter situation. See pp. 34b-34c.
+%---------------------------------------------------
+kcount = 0; % Counts.
+for kj = indxConst % Equations
+ klocat=kcount+n0(kj); % Losition for the last constant element in the jth equation.
+ Bbar = repmat(eye(n0(kj)),[1 nStates]); % See p.34b.
+ lenbjs=length(n0cumsum(kj)+1:n0cumsum(kj+1));
+ bj = zeros(nStates*lenbjs,1);
+ for si=1:nStates
+ bj((si-1)*lenbjs+1:si*lenbjs) = b_shat(n0cumsum(kj)+1:n0cumsum(kj+1),si); % bj(si). See p.34a.
+ A0(:,kj,si) = Uistar{kj}((si-1)*nvar+1:si*nvar,:)*b_shat(n0cumsum(kj)+1:n0cumsum(kj+1),si);
+ end
+ gconst1(kcount+1:klocat) = Bbar*( (Uibar{kj}'*Sgm0tldinvaveConst*Uibar{kj})*bj );
+ kcount=klocat;
+end
+%*** Add the derivative of -T_klog|A0(k)| w.r.t. each free time-varying paramater.
+kcount = 0; % Counts.
+for ki = constinx % Those from 1 to sum(q_i) that pertain to constant parameters.
+ kcount=kcount+1;
+ n = max(find( (ki-n0cumsum)>0 )); % note, 1<=n<=nvar equations.
+ gi=0; % Initializes a gradient summed across the states. See p. 34c.
+ for si=1:nStates % See p.34c.
+ gi = gi - Tkave(si)*B0(:,n,si)'*Uistar{n}((si-1)*nvar+1:si*nvar,ki-n0cumsum(n)); % See p. 34a.
+ end
+ gconst2(kcount)=gi;
+end
+
+
+%---------------------------------------------------
+% Conclusion.
+%---------------------------------------------------
+gtv = gmix(tvinx,:); % Extract the gradient wrt time-varying parameters.
+gconst = gconst1+gconst2; % Gradient wrt to constant parameters.
+%
+g = [gtv(:);gconst]; % Vectorized the same way as b is vectorized.
+badg = 0; % This term is used for Sims's csminwel program.
diff --git a/MatlabFiles/fn_calyrqm.m b/MatlabFiles/fn_calyrqm.m
index 5025449e9797c9dc12995b8fb59310558b6cd7a1..65b7b5ac5efe628f8df1e1e2d0371958c3adc0e7 100644
--- a/MatlabFiles/fn_calyrqm.m
+++ b/MatlabFiles/fn_calyrqm.m
@@ -1,75 +1,75 @@
-function [Myrqm,nMyrqm] = fn_calyrqm(q_m,Byrqm,Eyrqm)
-% [Myrqm,nMyrqm] = fn_calyrqm(q_m,Byrqm,Eyrqm)
-%
-% Given the beginning and end years and quarters (months), export a matrix of all years and
-% quarters (months) for these years and in between
-%
-% q_m: 4 if quarterly and 12 if monthly
-% Byrqm: [year quarter(month)] -- all integers, the begining year and quarter (month)
-% Eyrqm: [year quarter(month)] -- all integers, the end year and quarter (month)
-%-------------------
-% Myrqm: matrix of all years and quarters (months) between and incl. Byrqm and Eyrqm
-% nMyrqm: number of data points incl. Byrqm and Eyrqm
-%
-% Tao Zha, April 2000
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-if ~isempty(find(Byrqm-round(Byrqm))) | (q_m-round(q_m)) | ~isempty(find(Byrqm-round(Byrqm)))
- error('argin qm, Byrqm, or Eyrqm must of integer')
-elseif Byrqm(1)>Eyrqm(1)
- error('Eyrqm(1) must be equal to or greater than Byrqm(1)')
-elseif Byrqm(1)==Eyrqm(1)
- if Byrqm(2)>Eyrqm(2)
- error('Eyrqm(2) must be equal to or greater than Byrqm(2) because of the same year')
- end
-end
-
-
-Yr = Byrqm(1)+[0:Eyrqm(1)-Byrqm(1)]';
-
-if length(Yr)>=2 % there are years and quarters (months) between Byrqm and Eyrqm
- n=length(Yr)-2;
- C=zeros(n*q_m,2);
- C(:,1) = kron(Yr(2:end-1),ones(q_m,1));
- C(:,2) = kron(ones(n,1),[1:q_m]');
-
- %* initialize a matrix of years and quarters (months) including Byrqm and Eyrqm
- Myrqm = zeros((q_m-Byrqm(2)+1)+Eyrqm(2)+n*q_m,2);
-
- %* Years in between
- n1=q_m-Byrqm(2)+1;
- n2=Eyrqm(2);
- Myrqm(n1+1:end-n2,:) = C;
- %* Beginning year
- for k=1:n1
- Myrqm(k,:) = [Byrqm(1) Byrqm(2)+k-1];
- end
- %* End year
- for k=1:n2
- Myrqm(end-Eyrqm(2)+k,:) = [Eyrqm(1) k];
- end
-else %* all the data are in the same calendar year
- n1=Eyrqm(2)-Byrqm(2)+1;
- Myrqm = zeros(n1,2);
- for k=1:n1
- Myrqm(k,:) = [Byrqm(1) Byrqm(2)+k-1];
- end
-end
-
-nMyrqm = size(Myrqm,1);
+function [Myrqm,nMyrqm] = fn_calyrqm(q_m,Byrqm,Eyrqm)
+% [Myrqm,nMyrqm] = fn_calyrqm(q_m,Byrqm,Eyrqm)
+%
+% Given the beginning and end years and quarters (months), export a matrix of all years and
+% quarters (months) for these years and in between
+%
+% q_m: 4 if quarterly and 12 if monthly
+% Byrqm: [year quarter(month)] -- all integers, the begining year and quarter (month)
+% Eyrqm: [year quarter(month)] -- all integers, the end year and quarter (month)
+%-------------------
+% Myrqm: matrix of all years and quarters (months) between and incl. Byrqm and Eyrqm
+% nMyrqm: number of data points incl. Byrqm and Eyrqm
+%
+% Tao Zha, April 2000
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+if ~isempty(find(Byrqm-round(Byrqm))) | (q_m-round(q_m)) | ~isempty(find(Byrqm-round(Byrqm)))
+ error('argin qm, Byrqm, or Eyrqm must of integer')
+elseif Byrqm(1)>Eyrqm(1)
+ error('Eyrqm(1) must be equal to or greater than Byrqm(1)')
+elseif Byrqm(1)==Eyrqm(1)
+ if Byrqm(2)>Eyrqm(2)
+ error('Eyrqm(2) must be equal to or greater than Byrqm(2) because of the same year')
+ end
+end
+
+
+Yr = Byrqm(1)+[0:Eyrqm(1)-Byrqm(1)]';
+
+if length(Yr)>=2 % there are years and quarters (months) between Byrqm and Eyrqm
+ n=length(Yr)-2;
+ C=zeros(n*q_m,2);
+ C(:,1) = kron(Yr(2:end-1),ones(q_m,1));
+ C(:,2) = kron(ones(n,1),[1:q_m]');
+
+ %* initialize a matrix of years and quarters (months) including Byrqm and Eyrqm
+ Myrqm = zeros((q_m-Byrqm(2)+1)+Eyrqm(2)+n*q_m,2);
+
+ %* Years in between
+ n1=q_m-Byrqm(2)+1;
+ n2=Eyrqm(2);
+ Myrqm(n1+1:end-n2,:) = C;
+ %* Beginning year
+ for k=1:n1
+ Myrqm(k,:) = [Byrqm(1) Byrqm(2)+k-1];
+ end
+ %* End year
+ for k=1:n2
+ Myrqm(end-Eyrqm(2)+k,:) = [Eyrqm(1) k];
+ end
+else %* all the data are in the same calendar year
+ n1=Eyrqm(2)-Byrqm(2)+1;
+ Myrqm = zeros(n1,2);
+ for k=1:n1
+ Myrqm(k,:) = [Byrqm(1) Byrqm(2)+k-1];
+ end
+end
+
+nMyrqm = size(Myrqm,1);
diff --git a/MatlabFiles/fn_construct_cov.m b/MatlabFiles/fn_construct_cov.m
index 47f58887a60aebbbe8ccdc9ca5a715b3af1cb6a9..76bd1b5e5c68554e8fac39b6bf50d5da9c33c80d 100644
--- a/MatlabFiles/fn_construct_cov.m
+++ b/MatlabFiles/fn_construct_cov.m
@@ -1,36 +1,36 @@
-function covM = fn_construct_cov(diagcovM, corM)
-% function corM = corr(covM)
-% diagcovM: diag of covariance matrix (input)
-% corM: correlation matrix (input)
-% covM: covariance matrix (output)
-% 9 May 2008
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-[nvar,jnk]=size(corM);
-if nvar~=jnk
- error('The input matrix must be square!!')
-end
-covM=zeros(nvar);
-
-for i=1:nvar
- for j=1:nvar
- covM(i,j)= corM(i,j) * sqrt( diagcovM(i) * diagcovM(j) );
- end
-end
-
+function covM = fn_construct_cov(diagcovM, corM)
+% function corM = corr(covM)
+% diagcovM: diag of covariance matrix (input)
+% corM: correlation matrix (input)
+% covM: covariance matrix (output)
+% 9 May 2008
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+[nvar,jnk]=size(corM);
+if nvar~=jnk
+ error('The input matrix must be square!!')
+end
+covM=zeros(nvar);
+
+for i=1:nvar
+ for j=1:nvar
+ covM(i,j)= corM(i,j) * sqrt( diagcovM(i) * diagcovM(j) );
+ end
+end
+
diff --git a/MatlabFiles/fn_corr.m b/MatlabFiles/fn_corr.m
index 165f329143b19094774bb129d94522f6f86440c0..581a1cd5cfa6384c7d94bbc9068b030807e71f63 100644
--- a/MatlabFiles/fn_corr.m
+++ b/MatlabFiles/fn_corr.m
@@ -1,35 +1,35 @@
-function corM = fn_corr(covM)
-% function corM = fn_corr(covM)
-%
-% covM: covariance matrix (input)
-% corM: correlation matrix (output)
-% 6 September 1998
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-[nvar,jnk]=size(covM);
-if nvar~=jnk
- error('The input matrix must be square!!')
-end
-corM=zeros(nvar);
-for i=1:nvar
- for j=1:nvar
- corM(i,j)=covM(i,j) / sqrt( covM(i,i) * covM(j,j));
- end
-end
-
+function corM = fn_corr(covM)
+% function corM = fn_corr(covM)
+%
+% covM: covariance matrix (input)
+% corM: correlation matrix (output)
+% 6 September 1998
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+[nvar,jnk]=size(covM);
+if nvar~=jnk
+ error('The input matrix must be square!!')
+end
+corM=zeros(nvar);
+for i=1:nvar
+ for j=1:nvar
+ corM(i,j)=covM(i,j) / sqrt( covM(i,i) * covM(j,j));
+ end
+end
+
diff --git a/MatlabFiles/fn_dataext.m b/MatlabFiles/fn_dataext.m
index 0366fdf0fbb1530927b7dbe426cde78865e956d6..0069eff4bda69b0eeec9da31b2963d24b4a28960 100644
--- a/MatlabFiles/fn_dataext.m
+++ b/MatlabFiles/fn_dataext.m
@@ -1,60 +1,60 @@
-function [xdsube,Brow,Erow] = fn_dataext(Byrqm,Eyrqm,xdatae)
-% xdsube = dataext(Byrqm,Eyrqm,xdatae)
-% Extract subset of xdatae, given Byrqm and Eyrqm
-%
-% Byrqm: [year quarter(month)]: beginning year and period. If Byqm(2)=0, we get annual data.
-% Eyrqm: [year period]: end year and period. If Byrqm(2)=0, it must be Eyrqm(2)=0.
-% xdatae: all data (some of which may be NaN) with the first 2 columns indicating years and periods.
-%----------
-% xdsube: subset of xdatae.
-% Brow: the row number in xdatee that marks the first row of xdsube.
-% Erow: the row number in xdatee that marks the last row of xdsube.
-%
-% Tao Zha, April 2000
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-if (Byrqm(2)==0) & (Eyrqm(2)~=0)
- error('If annual data, make sure both Byrqm(2) and Eyrqm(2) are zero')
-end
-
-Brow = min(find(xdatae(:,1)==Byrqm(1)));
-if isempty(Brow)
- error('Byrqm is outside the date range indicated by xdatae(:,1:2)!')
-end
-if Byrqm(2)>0
- nadt=Byrqm(2)-xdatae(Brow,2);
- if nadt<0
- error('Byrqm is outside the date range indicated by xdatae(:,1:2)!')
- end
- Brow=Brow+nadt;
-end
-%
-Erow = min(find(xdatae(:,1)==Eyrqm(1)));
-if isempty(Erow)
- error('Eyrqm is outside the date range indicated by xdatae(:,1:2)!')
-end
-if Eyrqm(2)>0
- nadt2=Eyrqm(2)-xdatae(Erow,2);
- if nadt<0
- error('Eyrqm is outside the date range indicated by xdatae(:,1:2)!')
- end
- Erow=Erow+nadt2;
-end
-%
-xdsube = xdatae(Brow:Erow,:); % with dates
+function [xdsube,Brow,Erow] = fn_dataext(Byrqm,Eyrqm,xdatae)
+% xdsube = dataext(Byrqm,Eyrqm,xdatae)
+% Extract subset of xdatae, given Byrqm and Eyrqm
+%
+% Byrqm: [year quarter(month)]: beginning year and period. If Byqm(2)=0, we get annual data.
+% Eyrqm: [year period]: end year and period. If Byrqm(2)=0, it must be Eyrqm(2)=0.
+% xdatae: all data (some of which may be NaN) with the first 2 columns indicating years and periods.
+%----------
+% xdsube: subset of xdatae.
+% Brow: the row number in xdatee that marks the first row of xdsube.
+% Erow: the row number in xdatee that marks the last row of xdsube.
+%
+% Tao Zha, April 2000
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+if (Byrqm(2)==0) & (Eyrqm(2)~=0)
+ error('If annual data, make sure both Byrqm(2) and Eyrqm(2) are zero')
+end
+
+Brow = min(find(xdatae(:,1)==Byrqm(1)));
+if isempty(Brow)
+ error('Byrqm is outside the date range indicated by xdatae(:,1:2)!')
+end
+if Byrqm(2)>0
+ nadt=Byrqm(2)-xdatae(Brow,2);
+ if nadt<0
+ error('Byrqm is outside the date range indicated by xdatae(:,1:2)!')
+ end
+ Brow=Brow+nadt;
+end
+%
+Erow = min(find(xdatae(:,1)==Eyrqm(1)));
+if isempty(Erow)
+ error('Eyrqm is outside the date range indicated by xdatae(:,1:2)!')
+end
+if Eyrqm(2)>0
+ nadt2=Eyrqm(2)-xdatae(Erow,2);
+ if nadt<0
+ error('Eyrqm is outside the date range indicated by xdatae(:,1:2)!')
+ end
+ Erow=Erow+nadt2;
+end
+%
+xdsube = xdatae(Brow:Erow,:); % with dates
diff --git a/MatlabFiles/fn_dataext2.m b/MatlabFiles/fn_dataext2.m
index dc9abb99af9550462ce9395c118be242ea4bdc4a..09ce79b201503311f12fb8781c596b2b889952a5 100644
--- a/MatlabFiles/fn_dataext2.m
+++ b/MatlabFiles/fn_dataext2.m
@@ -1,73 +1,73 @@
-function [xdsube,Brow,Erow] = fn_dataext2(Byrqm,Eyrqm,xdatae)
-% xdsube = dataext(Byrqm,Eyrqm,xdatae)
-% Extract subset of xdatae, given Byrqm and Eyrqm
-%
-% Byrqm: [year quarter(month)]: beginning year and period. If Byqm(2)=0, we get annual data.
-% Eyrqm: [year period]: end year and period. If Byrqm(2)=0, it must be Eyrqm(2)=0.
-% xdatae: all data (some of which may be NaN) with the first 2 columns indicating years and periods.
-%----------
-% xdsube: subset of xdatae.
-% Brow: the row number in xdatee that marks the first row of xdsube.
-% Erow: the row number in xdatee that marks the last row of xdsube.
-%
-% Tao Zha, April 2000
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-if (Byrqm(2)==0)
- Eyrqm(2)=0;
- q_m1=find(xdatae(:,2)==xdatae(1,2)); % Locating quarter or month
- if length(q_m1)<2
- error('The matrix xdatae must at least have a cycle of q_m periods for possibly one calendar year')
- end
- q_m=q_m1(2)-1;
-end
-
-Brow = min(find(xdatae(:,1)==Byrqm(1)));
-if isempty(Brow)
- error('Byrqm is outside the date range indicated by xdatae(:,1:2)!')
-end
-if Byrqm(2)>0
- nadt=Byrqm(2)-xdatae(Brow,2);
- if nadt<0
- error('Byrqm is outside the date range indicated by xdatae(:,1:2)!')
- end
- Brow=Brow+nadt;
-end
-%
-Erow = min(find(xdatae(:,1)==Eyrqm(1)));
-if isempty(Erow)
- error('Eyrqm is outside the date range indicated by xdatae(:,1:2)!')
-end
-if Eyrqm(2)>0
- nadt2=Eyrqm(2)-xdatae(Erow,2);
- if nadt<0
- error('Eyrqm is outside the date range indicated by xdatae(:,1:2)!')
- end
- Erow=Erow+nadt2;
-end
-%
-
-if (Byrqm(2)==0) % By calendar years.
- if (xdatae(Brow,2)~=1) | (xdatae(Erow,2)~=1)
- error('Calendar years extracted here must have q_m periods')
- end
- Erow=Erow+q_m-1;
-end
-
-xdsube = xdatae(Brow:Erow,:); % with dates
+function [xdsube,Brow,Erow] = fn_dataext2(Byrqm,Eyrqm,xdatae)
+% xdsube = dataext(Byrqm,Eyrqm,xdatae)
+% Extract subset of xdatae, given Byrqm and Eyrqm
+%
+% Byrqm: [year quarter(month)]: beginning year and period. If Byqm(2)=0, we get annual data.
+% Eyrqm: [year period]: end year and period. If Byrqm(2)=0, it must be Eyrqm(2)=0.
+% xdatae: all data (some of which may be NaN) with the first 2 columns indicating years and periods.
+%----------
+% xdsube: subset of xdatae.
+% Brow: the row number in xdatee that marks the first row of xdsube.
+% Erow: the row number in xdatee that marks the last row of xdsube.
+%
+% Tao Zha, April 2000
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+if (Byrqm(2)==0)
+ Eyrqm(2)=0;
+ q_m1=find(xdatae(:,2)==xdatae(1,2)); % Locating quarter or month
+ if length(q_m1)<2
+ error('The matrix xdatae must at least have a cycle of q_m periods for possibly one calendar year')
+ end
+ q_m=q_m1(2)-1;
+end
+
+Brow = min(find(xdatae(:,1)==Byrqm(1)));
+if isempty(Brow)
+ error('Byrqm is outside the date range indicated by xdatae(:,1:2)!')
+end
+if Byrqm(2)>0
+ nadt=Byrqm(2)-xdatae(Brow,2);
+ if nadt<0
+ error('Byrqm is outside the date range indicated by xdatae(:,1:2)!')
+ end
+ Brow=Brow+nadt;
+end
+%
+Erow = min(find(xdatae(:,1)==Eyrqm(1)));
+if isempty(Erow)
+ error('Eyrqm is outside the date range indicated by xdatae(:,1:2)!')
+end
+if Eyrqm(2)>0
+ nadt2=Eyrqm(2)-xdatae(Erow,2);
+ if nadt<0
+ error('Eyrqm is outside the date range indicated by xdatae(:,1:2)!')
+ end
+ Erow=Erow+nadt2;
+end
+%
+
+if (Byrqm(2)==0) % By calendar years.
+ if (xdatae(Brow,2)~=1) | (xdatae(Erow,2)~=1)
+ error('Calendar years extracted here must have q_m periods')
+ end
+ Erow=Erow+q_m-1;
+end
+
+xdsube = xdatae(Brow:Erow,:); % with dates
diff --git a/MatlabFiles/fn_datana.m b/MatlabFiles/fn_datana.m
index 64c6e4baf50aa42d56dd9bc6ccbfc4007cdd8e65..cf4f8d5d17d70926b170867d0236aa08ed3715d7 100644
--- a/MatlabFiles/fn_datana.m
+++ b/MatlabFiles/fn_datana.m
@@ -1,117 +1,117 @@
-function [yactyrge,yactyre,yactqmyge,yactqmge,yactqme] = fn_datana(xdatae,q_m,vlistlog,vlistper,Byrqm,Eyrqm)
-% [yactyrge,yactyre,yactqmyge,yactqmge,yactqme] = fn_datana(Byrqm,Eyrqm,xdatae,q_m,vlistlog,vlistper)
-%
-% Generate prior period, period-to-period-in-last-year, and annual growth rates
-% For annual rates, works for both calendar and any annual years, depending on Byrqm and Eyrqm
-% If monthly data, we haven't got time to get quarterly growth rates yet.
-%
-% xdatae: all data (logged levels or interest rates/100, some of which may be NaN) with the first
-% 2 columns indicating years and periods.
-% q_m: quarter or month period
-% vlistlog: sublist for logged variables
-% vlistper: sublists for percent variables
-% Byrqm: [year quarter(month)]: beginning year and period. If Byqm(2)~=1, we don't get
-% calendar annual rates. In other words, the first column of yactyge (which
-% indicates years) does not mean calendar years. Byqm(2) must be specified; in other
-% words, it must be not set to 0 as in, say, fn_dataext.
-% Eyrqm: [year period]: end year and period. Eyqm(2) must be specified; in other words, it
-% must be not set to 0 as in, say, fn_dataext.
-% NOTE: if no inputs Byrqm and Eyrqm are specified, all growth rates begin at xdatae(1,1:2).
-%----------
-% yactyrge: annual growth rates with dates in the first 2 columns.
-% yactyre: annual average logged level with dates in the 1st 2 columns.
-% yactqmyge: period-to-period-in-last-year annual growth rates with dates in the first 2 columns.
-% yactqmge: prior-period annualized growth rates with dates in the first 2 columns.
-% yactqme: data (logged levels or interest rates/100) with dates in the first 2 columns.
-% Same as xdatae but with Brow:Erow.
-%
-% Tao Zha, April 2000.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-if size(xdatae,1)<2*q_m
- error('We need at least two years of xdatae to get annual rates. Check xdatae!!')
-end
-
-if nargin==4
- Brow=1; Erow=length(xdatae(:,1));
- nyr = floor((Erow-Brow+1)/q_m);
- yrsg = [xdatae(q_m+1,1):xdatae(q_m+1,1)+nyr-2]'; % for annual growth later on
-else
- if Byrqm(2)<1 | Eyrqm(2)<1
- error('This function requires specifying both years and months (quarters) in Byrqm and Eyrqm')
- end
-
- Brow = min(find(xdatae(:,1)==Byrqm(1)));
- if isempty(Brow)
- error('Byrqm is outside the date range of xdatae(:,1:2)!')
- end
- nadt=Byrqm(2)-xdatae(Brow,2);
- if nadt<0
- error('Byrqm is outside the date range indicated by xdatae(:,1:2)!')
- end
- Brow=Brow+nadt;
- %
- Erow = min(find(xdatae(:,1)==Eyrqm(1)));
- if isempty(Brow)
- error('Eyrqm is outside the date range of xdatae(:,1:2)!')
- end
- nadt2=Eyrqm(2)-xdatae(Erow,2);
- if nadt<0
- error('Eyrqm is outside the date range indicated by xdatae(:,1:2)!')
- end
- Erow=Erow+nadt2;
-
- nyr = floor((Erow-Brow+1)/q_m);
- yrsg = [Byrqm(1)+1:Byrqm(1)+nyr-1]'; % for annual growth later on, which will
- % start at Byrqm(1) instead of Byrqm(1)+1
-end
-%
-yactqme = xdatae(Brow:Erow,:); % with dates
-yactqm = yactqme(:,3:end); % only data
-
-%======== prior period change (annaluized rate)
-yactqmg = yactqm(2:end,:); % start at second period to get growth rate
-yactqmg(:,vlistlog) = (yactqm(2:end,vlistlog) - yactqm(1:end-1,vlistlog)) .* q_m;
- % monthly, 12*log(1+growth rate), annualized growth rate
-yactqmg(:,vlistlog) = 100*(exp(yactqmg(:,vlistlog))-1);
-yactqmg(:,vlistper) = 100*yactqmg(:,vlistper);
-yactqmge = [yactqme(2:end,1:2) yactqmg];
-
-%======== change from the last year
-yactqmyg = yactqm(q_m+1:end,:); % start at the last-year period to get growth rate
-yactqmyg(:,vlistlog) = (yactqm(q_m+1:end,vlistlog) - yactqm(1:end-q_m,vlistlog));
-yactqmyg(:,vlistlog) = 100*(exp(yactqmyg(:,vlistlog))-1);
-yactqmyg(:,vlistper) = 100*yactqmyg(:,vlistper);
-yactqmyge = [yactqme(q_m+1:end,1:2) yactqmyg];
-
-%======== annual growth rates
-nvar = length(xdatae(1,3:end));
-ygmts = yactqm(1:nyr*q_m,:); % converted to the multiplication of q_m
-ygmts1 = reshape(ygmts,q_m,nyr,nvar);
-ygmts2 = sum(ygmts1,1) ./ q_m;
-ygmts3 = reshape(ygmts2,nyr,nvar); % converted to annual average series
-%
-yactyrg = ygmts3(2:end,:); % start at the last-year period to get growth rate
-yactyrg(:,vlistlog) = ygmts3(2:end,vlistlog) - ygmts3(1:end-1,vlistlog);
- % annual rate: log(1+growth rate)
-yactyrg(:,vlistlog) = 100*(exp(yactyrg(:,vlistlog))-1);
-yactyrg(:,vlistper) = 100*yactyrg(:,vlistper);
-yactyrge = [yrsg zeros(nyr-1,1) yactyrg];
-yrsg1=[yrsg(1)-1:yrsg(end)]';
-yactyre = [yrsg1 zeros(nyr,1) ygmts3];
+function [yactyrge,yactyre,yactqmyge,yactqmge,yactqme] = fn_datana(xdatae,q_m,vlistlog,vlistper,Byrqm,Eyrqm)
+% [yactyrge,yactyre,yactqmyge,yactqmge,yactqme] = fn_datana(Byrqm,Eyrqm,xdatae,q_m,vlistlog,vlistper)
+%
+% Generate prior period, period-to-period-in-last-year, and annual growth rates
+% For annual rates, works for both calendar and any annual years, depending on Byrqm and Eyrqm
+% If monthly data, we haven't got time to get quarterly growth rates yet.
+%
+% xdatae: all data (logged levels or interest rates/100, some of which may be NaN) with the first
+% 2 columns indicating years and periods.
+% q_m: quarter or month period
+% vlistlog: sublist for logged variables
+% vlistper: sublists for percent variables
+% Byrqm: [year quarter(month)]: beginning year and period. If Byqm(2)~=1, we don't get
+% calendar annual rates. In other words, the first column of yactyge (which
+% indicates years) does not mean calendar years. Byqm(2) must be specified; in other
+% words, it must be not set to 0 as in, say, fn_dataext.
+% Eyrqm: [year period]: end year and period. Eyqm(2) must be specified; in other words, it
+% must be not set to 0 as in, say, fn_dataext.
+% NOTE: if no inputs Byrqm and Eyrqm are specified, all growth rates begin at xdatae(1,1:2).
+%----------
+% yactyrge: annual growth rates with dates in the first 2 columns.
+% yactyre: annual average logged level with dates in the 1st 2 columns.
+% yactqmyge: period-to-period-in-last-year annual growth rates with dates in the first 2 columns.
+% yactqmge: prior-period annualized growth rates with dates in the first 2 columns.
+% yactqme: data (logged levels or interest rates/100) with dates in the first 2 columns.
+% Same as xdatae but with Brow:Erow.
+%
+% Tao Zha, April 2000.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+if size(xdatae,1)<2*q_m
+ error('We need at least two years of xdatae to get annual rates. Check xdatae!!')
+end
+
+if nargin==4
+ Brow=1; Erow=length(xdatae(:,1));
+ nyr = floor((Erow-Brow+1)/q_m);
+ yrsg = [xdatae(q_m+1,1):xdatae(q_m+1,1)+nyr-2]'; % for annual growth later on
+else
+ if Byrqm(2)<1 | Eyrqm(2)<1
+ error('This function requires specifying both years and months (quarters) in Byrqm and Eyrqm')
+ end
+
+ Brow = min(find(xdatae(:,1)==Byrqm(1)));
+ if isempty(Brow)
+ error('Byrqm is outside the date range of xdatae(:,1:2)!')
+ end
+ nadt=Byrqm(2)-xdatae(Brow,2);
+ if nadt<0
+ error('Byrqm is outside the date range indicated by xdatae(:,1:2)!')
+ end
+ Brow=Brow+nadt;
+ %
+ Erow = min(find(xdatae(:,1)==Eyrqm(1)));
+ if isempty(Brow)
+ error('Eyrqm is outside the date range of xdatae(:,1:2)!')
+ end
+ nadt2=Eyrqm(2)-xdatae(Erow,2);
+ if nadt<0
+ error('Eyrqm is outside the date range indicated by xdatae(:,1:2)!')
+ end
+ Erow=Erow+nadt2;
+
+ nyr = floor((Erow-Brow+1)/q_m);
+ yrsg = [Byrqm(1)+1:Byrqm(1)+nyr-1]'; % for annual growth later on, which will
+ % start at Byrqm(1) instead of Byrqm(1)+1
+end
+%
+yactqme = xdatae(Brow:Erow,:); % with dates
+yactqm = yactqme(:,3:end); % only data
+
+%======== prior period change (annaluized rate)
+yactqmg = yactqm(2:end,:); % start at second period to get growth rate
+yactqmg(:,vlistlog) = (yactqm(2:end,vlistlog) - yactqm(1:end-1,vlistlog)) .* q_m;
+ % monthly, 12*log(1+growth rate), annualized growth rate
+yactqmg(:,vlistlog) = 100*(exp(yactqmg(:,vlistlog))-1);
+yactqmg(:,vlistper) = 100*yactqmg(:,vlistper);
+yactqmge = [yactqme(2:end,1:2) yactqmg];
+
+%======== change from the last year
+yactqmyg = yactqm(q_m+1:end,:); % start at the last-year period to get growth rate
+yactqmyg(:,vlistlog) = (yactqm(q_m+1:end,vlistlog) - yactqm(1:end-q_m,vlistlog));
+yactqmyg(:,vlistlog) = 100*(exp(yactqmyg(:,vlistlog))-1);
+yactqmyg(:,vlistper) = 100*yactqmyg(:,vlistper);
+yactqmyge = [yactqme(q_m+1:end,1:2) yactqmyg];
+
+%======== annual growth rates
+nvar = length(xdatae(1,3:end));
+ygmts = yactqm(1:nyr*q_m,:); % converted to the multiplication of q_m
+ygmts1 = reshape(ygmts,q_m,nyr,nvar);
+ygmts2 = sum(ygmts1,1) ./ q_m;
+ygmts3 = reshape(ygmts2,nyr,nvar); % converted to annual average series
+%
+yactyrg = ygmts3(2:end,:); % start at the last-year period to get growth rate
+yactyrg(:,vlistlog) = ygmts3(2:end,vlistlog) - ygmts3(1:end-1,vlistlog);
+ % annual rate: log(1+growth rate)
+yactyrg(:,vlistlog) = 100*(exp(yactyrg(:,vlistlog))-1);
+yactyrg(:,vlistper) = 100*yactyrg(:,vlistper);
+yactyrge = [yrsg zeros(nyr-1,1) yactyrg];
+yrsg1=[yrsg(1)-1:yrsg(end)]';
+yactyre = [yrsg1 zeros(nyr,1) ygmts3];
diff --git a/MatlabFiles/fn_datana2.m b/MatlabFiles/fn_datana2.m
index 6a78d07537764973aad225318be1ff0904f47299..c67531e06b7496d0e0f814960a1e36f164db4279 100644
--- a/MatlabFiles/fn_datana2.m
+++ b/MatlabFiles/fn_datana2.m
@@ -1,193 +1,193 @@
-function [yactyrge,yactyre,yactqmyge,yactqmge,yactqge,yacteoqyge,yactqme] = fn_datana2(xdatae,q_m,vlistlog,vlistper,Byrqm,Eyrqm)
-% [yactyrge,yactyre,yactqmyge,yactqmge,yactqge,yacteoqyge,yactqme] = fn_datana2(xdatae,q_m,vlistlog,vlistper,Byrqm,Eyrqm)
-%
-% Generate prior period, period-this-year-to-period-last-year, and other growth rates.
-% For annual rates, works for both calendar and any annual years, depending on Byrqm and Eyrqm
-%
-% xdatae: all data (logged levels or interest rates/100, some of which may be NaN) with the first
-% 2 columns indicating years and periods.
-% q_m: quarter or month period
-% vlistlog: sublist for logged variables
-% vlistper: sublists for percent variables
-% Byrqm: [year quarter(month)] -- beginning year and period. Optional. If Byqm(2)~=1, we don't get
-% calendar annual rates. In other words, the first column of yactyge (which
-% indicates years) does not mean calendar years. Byqm(2) must be specified; in other
-% words, it must be not set to 0 as in, say, fn_dataext.
-% Eyrqm: [year period] -- end year and period. Optional. Eyqm(2) must be specified; in other words, it
-% must be not set to 0 as in, say, fn_dataext.
-% NOTE: if no inputs Byrqm and Eyrqm are specified, all growth rates begin at xdatae(1,1:2).
-%----------
-% yactyrge: annual growth rates with dates in the first 2 columns.
-% yactyre: annual average logged level with dates in the 1st 2 columns.
-% yactqmyge: period-this-year-to-period-last-year annual growth rates with dates in the first 2 columns.
-% yactqmge: prior-period annualized growth rates with dates in the first 2 columns.
-% yactqge: if monthly data, prior-quarter annualized growth rate with dates in the first 2 columns;
-% if not, yactqge = NaN.
-% yacteoqyge: if monthly data, EOQ is last month of quarter. EOQ-this-year-to-EOQ-last-year annual growth rates with dates in first 2 columns.
-% If not monthly data, yacteoqyge = NaN.
-% Note that yacteoqyge(:,1:2) = yactqge(4:end,1:2).
-% yactqme: data (logged levels or interest rates/100) with dates in the first 2 columns.
-% Same as xdatae but with Brow:Erow.
-%
-% Tao Zha, April 2000.
-% See the old but useful function fore_mqy.m.
-% Added yactqge in February 2004.
-% Added yacteoqyge in March 2004.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-if size(xdatae,1)<2*q_m
- error('We need at least two years of xdatae to get annual rates. Check xdatae!!')
-end
-
-if nargin==4
- Brow=1; Erow=length(xdatae(:,1));
- nyr = floor((Erow-Brow+1)/q_m);
- yrsg = [xdatae(q_m+1,1):xdatae(q_m+1,1)+nyr-2]'; % for annual growth later on
-else
- if Byrqm(2)<1 | Eyrqm(2)<1
- error('This function requires specifying both years and months (quarters) in Byrqm and Eyrqm')
- end
-
- Brow = min(find(xdatae(:,1)==Byrqm(1)));
- if isempty(Brow)
- error('Byrqm is outside the date range of xdatae(:,1:2)!')
- end
- nadt=Byrqm(2)-xdatae(Brow,2);
- if nadt<0
- error('Byrqm is outside the date range indicated by xdatae(:,1:2)!')
- end
- Brow=Brow+nadt;
- %
- Erow = min(find(xdatae(:,1)==Eyrqm(1)));
- if isempty(Brow)
- error('Eyrqm is outside the date range of xdatae(:,1:2)!')
- end
- nadt2=Eyrqm(2)-xdatae(Erow,2);
- if nadt<0
- error('Eyrqm is outside the date range indicated by xdatae(:,1:2)!')
- end
- Erow=Erow+nadt2;
-
- nyr = floor((Erow-Brow+1)/q_m);
- yrsg = [Byrqm(1)+1:Byrqm(1)+nyr-1]'; % for annual growth later on, which will
- % start at Byrqm(1) instead of Byrqm(1)+1
-end
-%
-yactqme = xdatae(Brow:Erow,:); % with dates
-yactqm = yactqme(:,3:end); % only data
-
-%======== prior period change (annaluized rate)
-yactqmg = yactqm(2:end,:); % start at second period to get growth rate
-yactqmg(:,vlistlog) = (yactqm(2:end,vlistlog) - yactqm(1:end-1,vlistlog)) .* q_m;
- % monthly, 12*log(1+growth rate), annualized growth rate
-yactqmg(:,vlistlog) = 100*(exp(yactqmg(:,vlistlog))-1);
-yactqmg(:,vlistper) = 100*yactqmg(:,vlistper);
-yactqmge = [yactqme(2:end,1:2) yactqmg];
-
-%======== change from the last year
-yactqmyg = yactqm(q_m+1:end,:); % start at the last-year period to get growth rate
-yactqmyg(:,vlistlog) = (yactqm(q_m+1:end,vlistlog) - yactqm(1:end-q_m,vlistlog));
-yactqmyg(:,vlistlog) = 100*(exp(yactqmyg(:,vlistlog))-1);
-yactqmyg(:,vlistper) = 100*yactqmyg(:,vlistper);
-yactqmyge = [yactqme(q_m+1:end,1:2) yactqmyg];
-
-%======== annual growth rates
-nvar = length(xdatae(1,3:end));
-ygmts = yactqm(1:nyr*q_m,:); % converted to the multiplication of q_m
-ygmts1 = reshape(ygmts,q_m,nyr,nvar);
-ygmts2 = sum(ygmts1,1) ./ q_m;
-ygmts3 = reshape(ygmts2,nyr,nvar); % converted to annual average series
-%
-yactyrg = ygmts3(2:end,:); % start at the last-year period to get growth rate
-yactyrg(:,vlistlog) = ygmts3(2:end,vlistlog) - ygmts3(1:end-1,vlistlog);
- % annual rate: log(1+growth rate)
-yactyrg(:,vlistlog) = 100*(exp(yactyrg(:,vlistlog))-1);
-yactyrg(:,vlistper) = 100*yactyrg(:,vlistper);
-yactyrge = [yrsg zeros(nyr-1,1) yactyrg];
-yrsg1=[yrsg(1)-1:yrsg(end)]';
-yactyre = [yrsg1 zeros(nyr,1) ygmts3];
-
-
-%======== Quarter-to-last quarter annualized growth rates.
-if (q_m==12)
- %=== Beginning row.
- mcnt = mod(xdatae(1,2),3); %Begining month.
- if (mcnt==0)
- QrowBin = 2; % Reset the beginning month to match a quarter.
- elseif (mcnt==1)
- QrowBin = 1; % Reset the beginning month to match a quarter.
- elseif (mcnt==2)
- QrowBin = 3; % Reset the beginning month to match a quarter.
- else
- error('.../fn_datana2.m: Number for months in the dates must be between 1 and 12 inclusive');
- end
- Qcnt = (xdatae(QrowBin,2)+2)/3;
- if (xdatae(1,2)<11) % Up to October.
- QyearBin = xdatae(1,1);
- else % November and December.
- QyearBin = xdatae(1,1) + 1;
- end;%if
- %=== Ending row.
- QrowEnd = size(xdatae, 1) - mod(xdatae(end,2),3); % Reset the ending month to match a quarter.
- nqs = (QrowEnd - QrowBin + 1)/3;
-
-
- %=== Getting last month of the quarter (end of quarter: eoq).
- yacteoq = xdatae(QrowBin:QrowEnd,3:end); % Without dates.
- yacteoq1 = reshape(yacteoq, 3, nqs, nvar);
- yacteoq2 = zeros(1, nqs, nvar);
- yacteoq2(:,:,vlistper) = yacteoq1(3,:,vlistper);
- yacteoq2(:,:,vlistlog) = yacteoq1(3,:,vlistlog);
- yacteoq3 = reshape(yacteoq2, nqs, nvar);
- %=== EOQ-this-year-to-EOQ-last-year annual growth rates.
- yacteoqyg = yacteoq3(5:end,:);
- yacteoqyg(:,vlistlog) = 100*(exp(yacteoq3(5:end, vlistlog) - yacteoq3(1:end-4, vlistlog))-1);
- yacteoqyg(:,vlistper) = 100*yacteoqyg(:,vlistper);
-
-
- %=== Getting quarterly averages.
- yactq = xdatae(QrowBin:QrowEnd,3:end); % Without dates.
- nqs = (QrowEnd - QrowBin + 1)/3;
- yactq1 = reshape(yactq, 3, nqs, nvar);
- yactq2 = zeros(1, nqs, nvar);
- yactq2(:,:,vlistper) = sum(yactq1(:,:,vlistper), 1) ./ 3;
- yactq2(:,:,vlistlog) = sum(exp(yactq1(:,:,vlistlog)), 1) ./ 3;
- yactq3 = reshape(yactq2, nqs, nvar);
- %=== Quarterly (to prior-quarter) annualized growth rates.
- yactqg = yactq3(2:end,:);
- yactqg(:,vlistlog) = 100*((yactq3(2:end, vlistlog) ./ yactq3(1:end-1, vlistlog)).^4-1);
- yactqg(:,vlistper) = 100*yactqg(:,vlistper);
-
-
- %======= Adding quarterly dates. =======
- qdates = zeros(nqs,2);
- for k=1:nqs
- qdates(k,1) = floor(QyearBin + (k+Qcnt-2)/4);
- tmpi = mod(k+Qcnt-1, 4);
- if (tmpi==0)
- qdates(k,2) = 4;
- else
- qdates(k,2) = tmpi;
- end
- end
- yacteoqyge = [qdates(5:end,:) yacteoqyg];
- yactqge = [qdates(2:end,:) yactqg];
-else
- yacteoqyge = NaN;
- yactqge = NaN;
-end
+function [yactyrge,yactyre,yactqmyge,yactqmge,yactqge,yacteoqyge,yactqme] = fn_datana2(xdatae,q_m,vlistlog,vlistper,Byrqm,Eyrqm)
+% [yactyrge,yactyre,yactqmyge,yactqmge,yactqge,yacteoqyge,yactqme] = fn_datana2(xdatae,q_m,vlistlog,vlistper,Byrqm,Eyrqm)
+%
+% Generate prior period, period-this-year-to-period-last-year, and other growth rates.
+% For annual rates, works for both calendar and any annual years, depending on Byrqm and Eyrqm
+%
+% xdatae: all data (logged levels or interest rates/100, some of which may be NaN) with the first
+% 2 columns indicating years and periods.
+% q_m: quarter or month period
+% vlistlog: sublist for logged variables
+% vlistper: sublists for percent variables
+% Byrqm: [year quarter(month)] -- beginning year and period. Optional. If Byqm(2)~=1, we don't get
+% calendar annual rates. In other words, the first column of yactyge (which
+% indicates years) does not mean calendar years. Byqm(2) must be specified; in other
+% words, it must be not set to 0 as in, say, fn_dataext.
+% Eyrqm: [year period] -- end year and period. Optional. Eyqm(2) must be specified; in other words, it
+% must be not set to 0 as in, say, fn_dataext.
+% NOTE: if no inputs Byrqm and Eyrqm are specified, all growth rates begin at xdatae(1,1:2).
+%----------
+% yactyrge: annual growth rates with dates in the first 2 columns.
+% yactyre: annual average logged level with dates in the 1st 2 columns.
+% yactqmyge: period-this-year-to-period-last-year annual growth rates with dates in the first 2 columns.
+% yactqmge: prior-period annualized growth rates with dates in the first 2 columns.
+% yactqge: if monthly data, prior-quarter annualized growth rate with dates in the first 2 columns;
+% if not, yactqge = NaN.
+% yacteoqyge: if monthly data, EOQ is last month of quarter. EOQ-this-year-to-EOQ-last-year annual growth rates with dates in first 2 columns.
+% If not monthly data, yacteoqyge = NaN.
+% Note that yacteoqyge(:,1:2) = yactqge(4:end,1:2).
+% yactqme: data (logged levels or interest rates/100) with dates in the first 2 columns.
+% Same as xdatae but with Brow:Erow.
+%
+% Tao Zha, April 2000.
+% See the old but useful function fore_mqy.m.
+% Added yactqge in February 2004.
+% Added yacteoqyge in March 2004.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+if size(xdatae,1)<2*q_m
+ error('We need at least two years of xdatae to get annual rates. Check xdatae!!')
+end
+
+if nargin==4
+ Brow=1; Erow=length(xdatae(:,1));
+ nyr = floor((Erow-Brow+1)/q_m);
+ yrsg = [xdatae(q_m+1,1):xdatae(q_m+1,1)+nyr-2]'; % for annual growth later on
+else
+ if Byrqm(2)<1 | Eyrqm(2)<1
+ error('This function requires specifying both years and months (quarters) in Byrqm and Eyrqm')
+ end
+
+ Brow = min(find(xdatae(:,1)==Byrqm(1)));
+ if isempty(Brow)
+ error('Byrqm is outside the date range of xdatae(:,1:2)!')
+ end
+ nadt=Byrqm(2)-xdatae(Brow,2);
+ if nadt<0
+ error('Byrqm is outside the date range indicated by xdatae(:,1:2)!')
+ end
+ Brow=Brow+nadt;
+ %
+ Erow = min(find(xdatae(:,1)==Eyrqm(1)));
+ if isempty(Brow)
+ error('Eyrqm is outside the date range of xdatae(:,1:2)!')
+ end
+ nadt2=Eyrqm(2)-xdatae(Erow,2);
+ if nadt<0
+ error('Eyrqm is outside the date range indicated by xdatae(:,1:2)!')
+ end
+ Erow=Erow+nadt2;
+
+ nyr = floor((Erow-Brow+1)/q_m);
+ yrsg = [Byrqm(1)+1:Byrqm(1)+nyr-1]'; % for annual growth later on, which will
+ % start at Byrqm(1) instead of Byrqm(1)+1
+end
+%
+yactqme = xdatae(Brow:Erow,:); % with dates
+yactqm = yactqme(:,3:end); % only data
+
+%======== prior period change (annaluized rate)
+yactqmg = yactqm(2:end,:); % start at second period to get growth rate
+yactqmg(:,vlistlog) = (yactqm(2:end,vlistlog) - yactqm(1:end-1,vlistlog)) .* q_m;
+ % monthly, 12*log(1+growth rate), annualized growth rate
+yactqmg(:,vlistlog) = 100*(exp(yactqmg(:,vlistlog))-1);
+yactqmg(:,vlistper) = 100*yactqmg(:,vlistper);
+yactqmge = [yactqme(2:end,1:2) yactqmg];
+
+%======== change from the last year
+yactqmyg = yactqm(q_m+1:end,:); % start at the last-year period to get growth rate
+yactqmyg(:,vlistlog) = (yactqm(q_m+1:end,vlistlog) - yactqm(1:end-q_m,vlistlog));
+yactqmyg(:,vlistlog) = 100*(exp(yactqmyg(:,vlistlog))-1);
+yactqmyg(:,vlistper) = 100*yactqmyg(:,vlistper);
+yactqmyge = [yactqme(q_m+1:end,1:2) yactqmyg];
+
+%======== annual growth rates
+nvar = length(xdatae(1,3:end));
+ygmts = yactqm(1:nyr*q_m,:); % converted to the multiplication of q_m
+ygmts1 = reshape(ygmts,q_m,nyr,nvar);
+ygmts2 = sum(ygmts1,1) ./ q_m;
+ygmts3 = reshape(ygmts2,nyr,nvar); % converted to annual average series
+%
+yactyrg = ygmts3(2:end,:); % start at the last-year period to get growth rate
+yactyrg(:,vlistlog) = ygmts3(2:end,vlistlog) - ygmts3(1:end-1,vlistlog);
+ % annual rate: log(1+growth rate)
+yactyrg(:,vlistlog) = 100*(exp(yactyrg(:,vlistlog))-1);
+yactyrg(:,vlistper) = 100*yactyrg(:,vlistper);
+yactyrge = [yrsg zeros(nyr-1,1) yactyrg];
+yrsg1=[yrsg(1)-1:yrsg(end)]';
+yactyre = [yrsg1 zeros(nyr,1) ygmts3];
+
+
+%======== Quarter-to-last quarter annualized growth rates.
+if (q_m==12)
+ %=== Beginning row.
+ mcnt = mod(xdatae(1,2),3); %Begining month.
+ if (mcnt==0)
+ QrowBin = 2; % Reset the beginning month to match a quarter.
+ elseif (mcnt==1)
+ QrowBin = 1; % Reset the beginning month to match a quarter.
+ elseif (mcnt==2)
+ QrowBin = 3; % Reset the beginning month to match a quarter.
+ else
+ error('.../fn_datana2.m: Number for months in the dates must be between 1 and 12 inclusive');
+ end
+ Qcnt = (xdatae(QrowBin,2)+2)/3;
+ if (xdatae(1,2)<11) % Up to October.
+ QyearBin = xdatae(1,1);
+ else % November and December.
+ QyearBin = xdatae(1,1) + 1;
+ end;%if
+ %=== Ending row.
+ QrowEnd = size(xdatae, 1) - mod(xdatae(end,2),3); % Reset the ending month to match a quarter.
+ nqs = (QrowEnd - QrowBin + 1)/3;
+
+
+ %=== Getting last month of the quarter (end of quarter: eoq).
+ yacteoq = xdatae(QrowBin:QrowEnd,3:end); % Without dates.
+ yacteoq1 = reshape(yacteoq, 3, nqs, nvar);
+ yacteoq2 = zeros(1, nqs, nvar);
+ yacteoq2(:,:,vlistper) = yacteoq1(3,:,vlistper);
+ yacteoq2(:,:,vlistlog) = yacteoq1(3,:,vlistlog);
+ yacteoq3 = reshape(yacteoq2, nqs, nvar);
+ %=== EOQ-this-year-to-EOQ-last-year annual growth rates.
+ yacteoqyg = yacteoq3(5:end,:);
+ yacteoqyg(:,vlistlog) = 100*(exp(yacteoq3(5:end, vlistlog) - yacteoq3(1:end-4, vlistlog))-1);
+ yacteoqyg(:,vlistper) = 100*yacteoqyg(:,vlistper);
+
+
+ %=== Getting quarterly averages.
+ yactq = xdatae(QrowBin:QrowEnd,3:end); % Without dates.
+ nqs = (QrowEnd - QrowBin + 1)/3;
+ yactq1 = reshape(yactq, 3, nqs, nvar);
+ yactq2 = zeros(1, nqs, nvar);
+ yactq2(:,:,vlistper) = sum(yactq1(:,:,vlistper), 1) ./ 3;
+ yactq2(:,:,vlistlog) = sum(exp(yactq1(:,:,vlistlog)), 1) ./ 3;
+ yactq3 = reshape(yactq2, nqs, nvar);
+ %=== Quarterly (to prior-quarter) annualized growth rates.
+ yactqg = yactq3(2:end,:);
+ yactqg(:,vlistlog) = 100*((yactq3(2:end, vlistlog) ./ yactq3(1:end-1, vlistlog)).^4-1);
+ yactqg(:,vlistper) = 100*yactqg(:,vlistper);
+
+
+ %======= Adding quarterly dates. =======
+ qdates = zeros(nqs,2);
+ for k=1:nqs
+ qdates(k,1) = floor(QyearBin + (k+Qcnt-2)/4);
+ tmpi = mod(k+Qcnt-1, 4);
+ if (tmpi==0)
+ qdates(k,2) = 4;
+ else
+ qdates(k,2) = tmpi;
+ end
+ end
+ yacteoqyge = [qdates(5:end,:) yacteoqyg];
+ yactqge = [qdates(2:end,:) yactqg];
+else
+ yacteoqyge = NaN;
+ yactqge = NaN;
+end
diff --git a/MatlabFiles/fn_dataxy.m b/MatlabFiles/fn_dataxy.m
index 128038a58ec47c7fcd1dcd6c112e7d6451da138b..e8bce7e7d85a9668052a213cddcd07f7e3e9c346 100644
--- a/MatlabFiles/fn_dataxy.m
+++ b/MatlabFiles/fn_dataxy.m
@@ -1,164 +1,164 @@
-function [xtx,xty,yty,fss,phi,y,ncoef,xr,Bh,e] = fn_dataxy(nvar,lags,z,mu,indxDummy,nexo)
-% [xtx,xty,yty,fss,phi,y,ncoef,xr,Bh,e] = fn_dataxy(nvar,lags,z,mu,indxDummy,nexo)
-%
-% Export arranged data matrices for future estimation of the VAR.
-% If indxDummy=0, no mu's are used at all and Bh is the OLS estimate.
-% If indxDummy=1, only mu(5) and mu(6) as dummy observations. See fn_rnrprior*.m for using mu(1)-mu(4).
-% See Wagonner and Zha's Gibbs sampling paper.
-%
-% nvar: number of endogenous variables.
-% lags: the maximum length of lag
-% z: T*(nvar+(nexo-1)) matrix of raw or original data (no manipulation involved)
-% with sample size including lags and with exogenous variables other than a constant.
-% Order of columns: (1) nvar endogenous variables; (2) (nexo-1) exogenous variables;
-% (3) constants will be automatically put in the last column.
-% mu: 6-by-1 vector of hyperparameters (the following numbers for Atlanta Fed's forecast), where
-% only mu(5) and mu(6) are used for dummy observations in this function (i.e.,
-% mu(1)-mu(4) are irrelevant here). See fn_rnrprior*.m for using mu(1)-mu(4).
-% mu(1): overall tightness and also for A0; (0.57)
-% mu(2): relative tightness for A+; (0.13)
-% mu(3): relative tightness for the constant term; (0.1)
-% mu(4): tightness on lag decay; (1)
-% mu(5): weight on nvar sums of coeffs dummy observations (unit roots); (5)
-% mu(6): weight on single dummy initial observation including constant
-% (cointegration, unit roots, and stationarity); (5)
-% indxDummy: 1: add dummy observations to the data; 0: no dummy added.
-% nexo: number of exogenous variables. The constant term is the default setting. Besides this term,
-% we have nexo-1 exogenous variables. Optional. If left blank, nexo is set to 1.
-% -------------------
-% xtx: X'X: k-by-k where k=ncoef
-% xty: X'Y: k-by-nvar
-% yty: Y'Y: nvar-by-nvar
-% fss: T: sample size excluding lags. With dummyies, fss=nSample-lags+ndobs.
-% phi: X; T-by-k; column: [nvar for 1st lag, ..., nvar for last lag, other exogenous terms, const term]
-% y: Y: T-by-nvar where T=fss
-% ncoef: number of coefficients in *each* equation. RHS coefficients only, nvar*lags+nexo
-% xr: the economy size (ncoef-by-ncoef) in qr(phi) so that xr=chol(X'*X) or xr'*xr=X'*X
-% Bh: ncoef-by-nvar estimated reduced-form parameter; column: nvar;
-% row: ncoef=[nvar for 1st lag, ..., nvar for last lag, other exogenous terms, const term]
-% e: estimated residual e = y -x*Bh, T-by-nvar
-%
-% Tao Zha, February 2000.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-if nargin == 5
- nexo=1; % default for constant term
-elseif nexo<1
- error('We need at least one exogenous term so nexo must >= 1')
-end
-
-%*** original sample dimension without dummy prior
-nSample = size(z,1); % the sample size (including lags, of course)
-sb = lags+1; % original beginning without dummies
-ncoef = nvar*lags+nexo; % number of coefficients in *each* equation, RHS coefficients only.
-
-if indxDummy % prior dummy prior
- %*** expanded sample dimension by dummy prior
- ndobs=nvar+1; % number of dummy observations
- fss = nSample+ndobs-lags;
-
- %
- % **** nvar prior dummy observations with the sum of coefficients
- % ** construct X for Y = X*B + U where phi = X: (T-lags)*k, Y: (T-lags)*nvar
- % ** columns: k = # of [nvar for 1st lag, ..., nvar for last lag, exo var, const]
- % ** Now, T=T+ndobs -- added with "ndobs" dummy observations
- %
- phi = zeros(fss,ncoef);
- %* constant term
- const = ones(fss,1);
- const(1:nvar) = zeros(nvar,1);
- phi(:,ncoef) = const; % the first nvar periods: no or zero constant!
- %* other exogenous (than) constant term
- phi(ndobs+1:end,ncoef-nexo+1:ncoef-1) = z(lags+1:end,nvar+1:nvar+nexo-1);
- exox = zeros(ndobs,nexo);
- phi(1:ndobs,ncoef-nexo+1:ncoef-1) = exox(:,1:nexo-1);
- % this = [] when nexo=1 (no other exogenous than constant)
-
- xdgel = z(:,1:nvar); % endogenous variable matrix
- xdgelint = mean(xdgel(1:lags,:),1); % mean of the first lags initial conditions
- %* Dummies
- for k=1:nvar
- for m=1:lags
- phi(ndobs,nvar*(m-1)+k) = xdgelint(k);
- phi(k,nvar*(m-1)+k) = xdgelint(k);
- % <<>> multiply hyperparameter later
- end
- end
- %* True data
- for k=1:lags
- phi(ndobs+1:fss,nvar*(k-1)+1:nvar*k) = xdgel(sb-k:nSample-k,:);
- % row: T-lags; column: [nvar for 1st lag, ..., nvar for last lag, exo var, const]
- % Thus, # of columns is nvar*lags+nexo = ncoef.
- end
- %
- % ** Y with "ndobs" dummies added
- y = zeros(fss,nvar);
- %* Dummies
- for k=1:nvar
- y(ndobs,k) = xdgelint(k);
- y(k,k) = xdgelint(k);
- % multiply hyperparameter later
- end
- %* True data
- y(ndobs+1:fss,:) = xdgel(sb:nSample,:);
-
- phi(1:nvar,:) = 1*mu(5)*phi(1:nvar,:); % standard Sims and Zha prior
- y(1:nvar,:) = mu(5)*y(1:nvar,:); % standard Sims and Zha prior
- phi(nvar+1,:) = mu(6)*phi(nvar+1,:);
- y(nvar+1,:) = mu(6)*y(nvar+1,:);
-
- [xq,xr]=qr(phi,0);
- xtx=xr'*xr;
- xty=phi'*y;
- [yq,yr]=qr(y,0);
- yty=yr'*yr;
- Bh = xr\(xr'\xty); % xtx\xty where inv(X'X)*(X'Y)
- e=y-phi*Bh;
-else
- fss = nSample-lags;
- %
- % ** construct X for Y = X*B + U where phi = X: (T-lags)*k, Y: (T-lags)*nvar
- % ** columns: k = # of [nvar for 1st lag, ..., nvar for last lag, exo var, const]
- %
- phi = zeros(fss,ncoef);
- %* constant term
- const = ones(fss,1);
- phi(:,ncoef) = const; % the first nvar periods: no or zero constant!
- %* other exogenous (than) constant term
- phi(:,ncoef-nexo+1:ncoef-1) = z(lags+1:end,nvar+1:nvar+nexo-1);
- % this = [] when nexo=1 (no other exogenous than constant)
-
- xdgel = z(:,1:nvar); % endogenous variable matrix
- %* True data
- for k=1:lags
- phi(:,nvar*(k-1)+1:nvar*k) = xdgel(sb-k:nSample-k,:);
- % row: T-lags; column: [nvar for 1st lag, ..., nvar for last lag, exo var, const]
- % Thus, # of columns is nvar*lags+nexo = ncoef.
- end
- %
- y = xdgel(sb:nSample,:);
-
- [xq,xr]=qr(phi,0);
- xtx=xr'*xr;
- xty=phi'*y;
- [yq,yr]=qr(y,0);
- yty=yr'*yr;
- Bh = xr\(xr'\xty); % xtx\xty where inv(X'X)*(X'Y)
- e=y-phi*Bh;
-end
+function [xtx,xty,yty,fss,phi,y,ncoef,xr,Bh,e] = fn_dataxy(nvar,lags,z,mu,indxDummy,nexo)
+% [xtx,xty,yty,fss,phi,y,ncoef,xr,Bh,e] = fn_dataxy(nvar,lags,z,mu,indxDummy,nexo)
+%
+% Export arranged data matrices for future estimation of the VAR.
+% If indxDummy=0, no mu's are used at all and Bh is the OLS estimate.
+% If indxDummy=1, only mu(5) and mu(6) as dummy observations. See fn_rnrprior*.m for using mu(1)-mu(4).
+% See Wagonner and Zha's Gibbs sampling paper.
+%
+% nvar: number of endogenous variables.
+% lags: the maximum length of lag
+% z: T*(nvar+(nexo-1)) matrix of raw or original data (no manipulation involved)
+% with sample size including lags and with exogenous variables other than a constant.
+% Order of columns: (1) nvar endogenous variables; (2) (nexo-1) exogenous variables;
+% (3) constants will be automatically put in the last column.
+% mu: 6-by-1 vector of hyperparameters (the following numbers for Atlanta Fed's forecast), where
+% only mu(5) and mu(6) are used for dummy observations in this function (i.e.,
+% mu(1)-mu(4) are irrelevant here). See fn_rnrprior*.m for using mu(1)-mu(4).
+% mu(1): overall tightness and also for A0; (0.57)
+% mu(2): relative tightness for A+; (0.13)
+% mu(3): relative tightness for the constant term; (0.1)
+% mu(4): tightness on lag decay; (1)
+% mu(5): weight on nvar sums of coeffs dummy observations (unit roots); (5)
+% mu(6): weight on single dummy initial observation including constant
+% (cointegration, unit roots, and stationarity); (5)
+% indxDummy: 1: add dummy observations to the data; 0: no dummy added.
+% nexo: number of exogenous variables. The constant term is the default setting. Besides this term,
+% we have nexo-1 exogenous variables. Optional. If left blank, nexo is set to 1.
+% -------------------
+% xtx: X'X: k-by-k where k=ncoef
+% xty: X'Y: k-by-nvar
+% yty: Y'Y: nvar-by-nvar
+% fss: T: sample size excluding lags. With dummyies, fss=nSample-lags+ndobs.
+% phi: X; T-by-k; column: [nvar for 1st lag, ..., nvar for last lag, other exogenous terms, const term]
+% y: Y: T-by-nvar where T=fss
+% ncoef: number of coefficients in *each* equation. RHS coefficients only, nvar*lags+nexo
+% xr: the economy size (ncoef-by-ncoef) in qr(phi) so that xr=chol(X'*X) or xr'*xr=X'*X
+% Bh: ncoef-by-nvar estimated reduced-form parameter; column: nvar;
+% row: ncoef=[nvar for 1st lag, ..., nvar for last lag, other exogenous terms, const term]
+% e: estimated residual e = y -x*Bh, T-by-nvar
+%
+% Tao Zha, February 2000.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+if nargin == 5
+ nexo=1; % default for constant term
+elseif nexo<1
+ error('We need at least one exogenous term so nexo must >= 1')
+end
+
+%*** original sample dimension without dummy prior
+nSample = size(z,1); % the sample size (including lags, of course)
+sb = lags+1; % original beginning without dummies
+ncoef = nvar*lags+nexo; % number of coefficients in *each* equation, RHS coefficients only.
+
+if indxDummy % prior dummy prior
+ %*** expanded sample dimension by dummy prior
+ ndobs=nvar+1; % number of dummy observations
+ fss = nSample+ndobs-lags;
+
+ %
+ % **** nvar prior dummy observations with the sum of coefficients
+ % ** construct X for Y = X*B + U where phi = X: (T-lags)*k, Y: (T-lags)*nvar
+ % ** columns: k = # of [nvar for 1st lag, ..., nvar for last lag, exo var, const]
+ % ** Now, T=T+ndobs -- added with "ndobs" dummy observations
+ %
+ phi = zeros(fss,ncoef);
+ %* constant term
+ const = ones(fss,1);
+ const(1:nvar) = zeros(nvar,1);
+ phi(:,ncoef) = const; % the first nvar periods: no or zero constant!
+ %* other exogenous (than) constant term
+ phi(ndobs+1:end,ncoef-nexo+1:ncoef-1) = z(lags+1:end,nvar+1:nvar+nexo-1);
+ exox = zeros(ndobs,nexo);
+ phi(1:ndobs,ncoef-nexo+1:ncoef-1) = exox(:,1:nexo-1);
+ % this = [] when nexo=1 (no other exogenous than constant)
+
+ xdgel = z(:,1:nvar); % endogenous variable matrix
+ xdgelint = mean(xdgel(1:lags,:),1); % mean of the first lags initial conditions
+ %* Dummies
+ for k=1:nvar
+ for m=1:lags
+ phi(ndobs,nvar*(m-1)+k) = xdgelint(k);
+ phi(k,nvar*(m-1)+k) = xdgelint(k);
+ % <<>> multiply hyperparameter later
+ end
+ end
+ %* True data
+ for k=1:lags
+ phi(ndobs+1:fss,nvar*(k-1)+1:nvar*k) = xdgel(sb-k:nSample-k,:);
+ % row: T-lags; column: [nvar for 1st lag, ..., nvar for last lag, exo var, const]
+ % Thus, # of columns is nvar*lags+nexo = ncoef.
+ end
+ %
+ % ** Y with "ndobs" dummies added
+ y = zeros(fss,nvar);
+ %* Dummies
+ for k=1:nvar
+ y(ndobs,k) = xdgelint(k);
+ y(k,k) = xdgelint(k);
+ % multiply hyperparameter later
+ end
+ %* True data
+ y(ndobs+1:fss,:) = xdgel(sb:nSample,:);
+
+ phi(1:nvar,:) = 1*mu(5)*phi(1:nvar,:); % standard Sims and Zha prior
+ y(1:nvar,:) = mu(5)*y(1:nvar,:); % standard Sims and Zha prior
+ phi(nvar+1,:) = mu(6)*phi(nvar+1,:);
+ y(nvar+1,:) = mu(6)*y(nvar+1,:);
+
+ [xq,xr]=qr(phi,0);
+ xtx=xr'*xr;
+ xty=phi'*y;
+ [yq,yr]=qr(y,0);
+ yty=yr'*yr;
+ Bh = xr\(xr'\xty); % xtx\xty where inv(X'X)*(X'Y)
+ e=y-phi*Bh;
+else
+ fss = nSample-lags;
+ %
+ % ** construct X for Y = X*B + U where phi = X: (T-lags)*k, Y: (T-lags)*nvar
+ % ** columns: k = # of [nvar for 1st lag, ..., nvar for last lag, exo var, const]
+ %
+ phi = zeros(fss,ncoef);
+ %* constant term
+ const = ones(fss,1);
+ phi(:,ncoef) = const; % the first nvar periods: no or zero constant!
+ %* other exogenous (than) constant term
+ phi(:,ncoef-nexo+1:ncoef-1) = z(lags+1:end,nvar+1:nvar+nexo-1);
+ % this = [] when nexo=1 (no other exogenous than constant)
+
+ xdgel = z(:,1:nvar); % endogenous variable matrix
+ %* True data
+ for k=1:lags
+ phi(:,nvar*(k-1)+1:nvar*k) = xdgel(sb-k:nSample-k,:);
+ % row: T-lags; column: [nvar for 1st lag, ..., nvar for last lag, exo var, const]
+ % Thus, # of columns is nvar*lags+nexo = ncoef.
+ end
+ %
+ y = xdgel(sb:nSample,:);
+
+ [xq,xr]=qr(phi,0);
+ xtx=xr'*xr;
+ xty=phi'*y;
+ [yq,yr]=qr(y,0);
+ yty=yr'*yr;
+ Bh = xr\(xr'\xty); % xtx\xty where inv(X'X)*(X'Y)
+ e=y-phi*Bh;
+end
diff --git a/MatlabFiles/fn_dataxy7982.m b/MatlabFiles/fn_dataxy7982.m
index c08e79cbb39313a65516845d46e47f8b2607f355..334b4186ca38d43c246c9e4efd9154956539d7f7 100644
--- a/MatlabFiles/fn_dataxy7982.m
+++ b/MatlabFiles/fn_dataxy7982.m
@@ -1,183 +1,183 @@
-function [xtx,xty,yty,fss,phi,y,ncoef,xr,Bh,e] = fn_dataxy7982(nvar,lags,z,mu,indxDummy,...
- q_m,SpBin,SkipBin,SkipFin,nexo)
-% [xtx,xty,yty,fss,phi,y,ncoef,xr,Bh,e] = fn_dataxy7982(nvar,lags,z,mu,indxDummy,...
-% q_m,SpBin,SkipBin,SkipFin,nexo)
-% Export arranged data matrices for future estimation for DM models with the period from
-% SkipBin to SkipFin to be skiped.
-% See Wagonner and Zha's Gibbs sampling paper.
-%
-% nvar: number of endogenous variables.
-% lags: the maximum length of lag
-% z: T*(nvar+(nexo-1)) matrix of raw or original data (no manipulation involved)
-% with sample size including lags and with exogenous variables other than a constant.
-% Order of columns: (1) nvar endogenous variables; (2) (nexo-1) exogenous variables;
-% (3) constants are automatically put in the last column.
-% mu: 6-by-1 vector of hyperparameters (the following numbers for Atlanta Fed's forecast)
-% mu(1): overall tightness and also for A0; (0.57)
-% mu(2): relative tightness for A+; (0.13)
-% mu(3): relative tightness for the constant term; (0.1)
-% mu(4): tightness on lag decay; (1)
-% mu(5): weight on nvar sums of coeffs dummy observations (unit roots); (5)
-% mu(6): weight on single dummy initial observation including constant
-% (cointegration, unit roots, and stationarity); (5)
-% Here, only mu(5) and mu(6) are used for dummy observations
-% indxDummy = 1; % 1: add dummy observations to the data; 0: no dummy added.
-% q_m: 12 (monthly) or 4 (quarterly).
-% SpBin: [year month] at the begining of the sample (including lags).
-% SkipBin: [year month] at the begining of the skipped period.
-% SkipFin: [year month] at the end of the skipped period.
-% nexo: number of exogenous variables. The constant term is the default setting. Besides this term,
-% we have nexo-1 exogenous variables.
-% -------------------
-% xtx: X'X: k-by-k where k=ncoef
-% xty: X'Y: k-by-nvar
-% yty: Y'Y: nvar-by-nvar
-% fss: T: sample size excluding lags. With dummyies, fss=nSample-lags+ndobs.
-% phi: X; T-by-k; column: [nvar for 1st lag, ..., nvar for last lag, other exogenous terms, const term]
-% y: Y: T-by-nvar where T=fss
-% ncoef: number of coefficients in *each* equation. RHS coefficients only, nvar*lags+nexo
-% xr: the economy size (ncoef-by-ncoef) in qr(phi) so that xr=chol(X'*X) or xr'*xr=X'*X
-% Bh: ncoef-by-nvar estimated reduced-form parameter; column: nvar;
-% row: ncoef=[nvar for 1st lag, ..., nvar for last lag, other exogenous terms, const term]
-% e: estimated residual e = y -x*Bh, T-by-nvar
-%
-% Tao Zha, February 2000. Revised, May 2001.
-% See fn_dataxy.m.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-if nargin == 9
- nexo=1; % default for constant term
-elseif nexo<1
- error('We need at least one exogenous term so nexo must >= 1')
-end
-
-%*** original sample dimension without dummy prior
-nSample = size(z,1); % the sample size (including lags, of course)
-sb = lags+1; % original beginning without dummies
-ncoef = nvar*lags+nexo; % number of coefficients in *each* equation, RHS coefficients only.
-
-if indxDummy % prior dummy prior
-
- %*** expanded sample dimension by dummy prior
- ndobs=nvar+1; % number of dummy observations
- fss = nSample+ndobs-lags;
-
- %
- % **** nvar prior dummy observations with the sum of coefficients
- % ** construct X for Y = X*B + U where phi = X: (T-lags)*k, Y: (T-lags)*nvar
- % ** columns: k = # of [nvar for 1st lag, ..., nvar for last lag, exo var, const]
- % ** Now, T=T+ndobs -- added with "ndobs" dummy observations
- %
- phi = zeros(fss,ncoef);
- %* constant term
- const = ones(fss,1);
- const(1:nvar) = zeros(nvar,1);
- phi(:,ncoef) = const; % the first nvar periods: no or zero constant!
- %* other exogenous (than) constant term
- phi(ndobs+1:end,ncoef-nexo+1:ncoef-1) = z(lags+1:end,nvar+1:nvar+nexo-1);
- exox = zeros(ndobs,nexo);
- phi(1:ndobs,ncoef-nexo+1:ncoef-1) = exox(:,1:nexo-1);
- % this = [] when nexo=1 (no other exogenous than constant)
-
- xdgel = z(:,1:nvar); % endogenous variable matrix
- xdgelint = mean(xdgel(1:lags,:),1); % mean of the first lags initial conditions
- %* Dummies
- for k=1:nvar
- for m=1:lags
- phi(ndobs,nvar*(m-1)+k) = xdgelint(k);
- phi(k,nvar*(m-1)+k) = xdgelint(k);
- % <<>> multiply hyperparameter later
- end
- end
- %* True data
- for k=1:lags
- phi(ndobs+1:fss,nvar*(k-1)+1:nvar*k) = xdgel(sb-k:nSample-k,:);
- % row: T-lags; column: [nvar for 1st lag, ..., nvar for last lag, exo var, const]
- % Thus, # of columns is nvar*lags+nexo = ncoef.
- end
- %
- % ** Y with "ndobs" dummies added
- y = zeros(fss,nvar);
- %* Dummies
- for k=1:nvar
- y(ndobs,k) = xdgelint(k);
- y(k,k) = xdgelint(k);
- % multiply hyperparameter later
- end
- %* True data
- y(ndobs+1:fss,:) = xdgel(sb:nSample,:);
-
- phi(1:nvar,:) = 1*mu(5)*phi(1:nvar,:); % standard Sims and Zha prior
- y(1:nvar,:) = mu(5)*y(1:nvar,:); % standard Sims and Zha prior
- phi(nvar+1,:) = mu(6)*phi(nvar+1,:);
- y(nvar+1,:) = mu(6)*y(nvar+1,:);
-
- %-------------- Taking the 79:10-83:12 period out ----------------------
- outper1=(SkipBin(1)-SpBin(1))*q_m+(SkipBin(2)-SpBin(2)+1) + ndobs-lags;
- outper2=(SkipFin(1)-SpBin(1))*q_m+(SkipFin(2)-SpBin(2)+1) + ndobs-lags;
- phi(outper1:outper2,:)=[];
- y(outper1:outper2,:)=[];
- fss=size(phi,1);
-
- [xq,xr]=qr(phi,0);
- xtx=xr'*xr;
- xty=phi'*y;
- [yq,yr]=qr(y,0);
- yty=yr'*yr;
- Bh = xr\(xr'\xty); % xtx\xty where inv(X'X)*(X'Y)
- e=y-phi*Bh;
-else
- fss = nSample-lags;
- %
- % ** construct X for Y = X*B + U where phi = X: (T-lags)*k, Y: (T-lags)*nvar
- % ** columns: k = # of [nvar for 1st lag, ..., nvar for last lag, exo var, const]
- %
- phi = zeros(fss,ncoef);
- %* constant term
- const = ones(fss,1);
- phi(:,ncoef) = const; % the first nvar periods: no or zero constant!
- %* other exogenous (than) constant term
- phi(:,ncoef-nexo+1:ncoef-1) = z(lags+1:end,nvar+1:nvar+nexo-1);
- % this = [] when nexo=1 (no other exogenous than constant)
-
- xdgel = z(:,1:nvar); % endogenous variable matrix
- %* True data
- for k=1:lags
- phi(:,nvar*(k-1)+1:nvar*k) = xdgel(sb-k:nSample-k,:);
- % row: T-lags; column: [nvar for 1st lag, ..., nvar for last lag, exo var, const]
- % Thus, # of columns is nvar*lags+nexo = ncoef.
- end
- %
- y = xdgel(sb:nSample,:);
-
- %-------------- Taking the 79:10-83:12 period out ----------------------
- outper1=(SkipBin(1)-SpBin(1))*q_m+(SkipBin(2)-SpBin(2)+1) - lags;
- outper2=(SkipFin(1)-SpBin(1))*q_m+(SkipFin(2)-SpBin(2)+1) - lags;
- phi(outper1:outper2,:)=[];
- y(outper1:outper2,:)=[];
- fss=size(phi,1);
-
- [xq,xr]=qr(phi,0);
- xtx=xr'*xr;
- xty=phi'*y;
- [yq,yr]=qr(y,0);
- yty=yr'*yr;
- Bh = xr\(xr'\xty); % xtx\xty where inv(X'X)*(X'Y)
- e=y-phi*Bh;
-end
+function [xtx,xty,yty,fss,phi,y,ncoef,xr,Bh,e] = fn_dataxy7982(nvar,lags,z,mu,indxDummy,...
+ q_m,SpBin,SkipBin,SkipFin,nexo)
+% [xtx,xty,yty,fss,phi,y,ncoef,xr,Bh,e] = fn_dataxy7982(nvar,lags,z,mu,indxDummy,...
+% q_m,SpBin,SkipBin,SkipFin,nexo)
+% Export arranged data matrices for future estimation for DM models with the period from
+% SkipBin to SkipFin to be skiped.
+% See Wagonner and Zha's Gibbs sampling paper.
+%
+% nvar: number of endogenous variables.
+% lags: the maximum length of lag
+% z: T*(nvar+(nexo-1)) matrix of raw or original data (no manipulation involved)
+% with sample size including lags and with exogenous variables other than a constant.
+% Order of columns: (1) nvar endogenous variables; (2) (nexo-1) exogenous variables;
+% (3) constants are automatically put in the last column.
+% mu: 6-by-1 vector of hyperparameters (the following numbers for Atlanta Fed's forecast)
+% mu(1): overall tightness and also for A0; (0.57)
+% mu(2): relative tightness for A+; (0.13)
+% mu(3): relative tightness for the constant term; (0.1)
+% mu(4): tightness on lag decay; (1)
+% mu(5): weight on nvar sums of coeffs dummy observations (unit roots); (5)
+% mu(6): weight on single dummy initial observation including constant
+% (cointegration, unit roots, and stationarity); (5)
+% Here, only mu(5) and mu(6) are used for dummy observations
+% indxDummy = 1; % 1: add dummy observations to the data; 0: no dummy added.
+% q_m: 12 (monthly) or 4 (quarterly).
+% SpBin: [year month] at the begining of the sample (including lags).
+% SkipBin: [year month] at the begining of the skipped period.
+% SkipFin: [year month] at the end of the skipped period.
+% nexo: number of exogenous variables. The constant term is the default setting. Besides this term,
+% we have nexo-1 exogenous variables.
+% -------------------
+% xtx: X'X: k-by-k where k=ncoef
+% xty: X'Y: k-by-nvar
+% yty: Y'Y: nvar-by-nvar
+% fss: T: sample size excluding lags. With dummyies, fss=nSample-lags+ndobs.
+% phi: X; T-by-k; column: [nvar for 1st lag, ..., nvar for last lag, other exogenous terms, const term]
+% y: Y: T-by-nvar where T=fss
+% ncoef: number of coefficients in *each* equation. RHS coefficients only, nvar*lags+nexo
+% xr: the economy size (ncoef-by-ncoef) in qr(phi) so that xr=chol(X'*X) or xr'*xr=X'*X
+% Bh: ncoef-by-nvar estimated reduced-form parameter; column: nvar;
+% row: ncoef=[nvar for 1st lag, ..., nvar for last lag, other exogenous terms, const term]
+% e: estimated residual e = y -x*Bh, T-by-nvar
+%
+% Tao Zha, February 2000. Revised, May 2001.
+% See fn_dataxy.m.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+if nargin == 9
+ nexo=1; % default for constant term
+elseif nexo<1
+ error('We need at least one exogenous term so nexo must >= 1')
+end
+
+%*** original sample dimension without dummy prior
+nSample = size(z,1); % the sample size (including lags, of course)
+sb = lags+1; % original beginning without dummies
+ncoef = nvar*lags+nexo; % number of coefficients in *each* equation, RHS coefficients only.
+
+if indxDummy % prior dummy prior
+
+ %*** expanded sample dimension by dummy prior
+ ndobs=nvar+1; % number of dummy observations
+ fss = nSample+ndobs-lags;
+
+ %
+ % **** nvar prior dummy observations with the sum of coefficients
+ % ** construct X for Y = X*B + U where phi = X: (T-lags)*k, Y: (T-lags)*nvar
+ % ** columns: k = # of [nvar for 1st lag, ..., nvar for last lag, exo var, const]
+ % ** Now, T=T+ndobs -- added with "ndobs" dummy observations
+ %
+ phi = zeros(fss,ncoef);
+ %* constant term
+ const = ones(fss,1);
+ const(1:nvar) = zeros(nvar,1);
+ phi(:,ncoef) = const; % the first nvar periods: no or zero constant!
+ %* other exogenous (than) constant term
+ phi(ndobs+1:end,ncoef-nexo+1:ncoef-1) = z(lags+1:end,nvar+1:nvar+nexo-1);
+ exox = zeros(ndobs,nexo);
+ phi(1:ndobs,ncoef-nexo+1:ncoef-1) = exox(:,1:nexo-1);
+ % this = [] when nexo=1 (no other exogenous than constant)
+
+ xdgel = z(:,1:nvar); % endogenous variable matrix
+ xdgelint = mean(xdgel(1:lags,:),1); % mean of the first lags initial conditions
+ %* Dummies
+ for k=1:nvar
+ for m=1:lags
+ phi(ndobs,nvar*(m-1)+k) = xdgelint(k);
+ phi(k,nvar*(m-1)+k) = xdgelint(k);
+ % <<>> multiply hyperparameter later
+ end
+ end
+ %* True data
+ for k=1:lags
+ phi(ndobs+1:fss,nvar*(k-1)+1:nvar*k) = xdgel(sb-k:nSample-k,:);
+ % row: T-lags; column: [nvar for 1st lag, ..., nvar for last lag, exo var, const]
+ % Thus, # of columns is nvar*lags+nexo = ncoef.
+ end
+ %
+ % ** Y with "ndobs" dummies added
+ y = zeros(fss,nvar);
+ %* Dummies
+ for k=1:nvar
+ y(ndobs,k) = xdgelint(k);
+ y(k,k) = xdgelint(k);
+ % multiply hyperparameter later
+ end
+ %* True data
+ y(ndobs+1:fss,:) = xdgel(sb:nSample,:);
+
+ phi(1:nvar,:) = 1*mu(5)*phi(1:nvar,:); % standard Sims and Zha prior
+ y(1:nvar,:) = mu(5)*y(1:nvar,:); % standard Sims and Zha prior
+ phi(nvar+1,:) = mu(6)*phi(nvar+1,:);
+ y(nvar+1,:) = mu(6)*y(nvar+1,:);
+
+ %-------------- Taking the 79:10-83:12 period out ----------------------
+ outper1=(SkipBin(1)-SpBin(1))*q_m+(SkipBin(2)-SpBin(2)+1) + ndobs-lags;
+ outper2=(SkipFin(1)-SpBin(1))*q_m+(SkipFin(2)-SpBin(2)+1) + ndobs-lags;
+ phi(outper1:outper2,:)=[];
+ y(outper1:outper2,:)=[];
+ fss=size(phi,1);
+
+ [xq,xr]=qr(phi,0);
+ xtx=xr'*xr;
+ xty=phi'*y;
+ [yq,yr]=qr(y,0);
+ yty=yr'*yr;
+ Bh = xr\(xr'\xty); % xtx\xty where inv(X'X)*(X'Y)
+ e=y-phi*Bh;
+else
+ fss = nSample-lags;
+ %
+ % ** construct X for Y = X*B + U where phi = X: (T-lags)*k, Y: (T-lags)*nvar
+ % ** columns: k = # of [nvar for 1st lag, ..., nvar for last lag, exo var, const]
+ %
+ phi = zeros(fss,ncoef);
+ %* constant term
+ const = ones(fss,1);
+ phi(:,ncoef) = const; % the first nvar periods: no or zero constant!
+ %* other exogenous (than) constant term
+ phi(:,ncoef-nexo+1:ncoef-1) = z(lags+1:end,nvar+1:nvar+nexo-1);
+ % this = [] when nexo=1 (no other exogenous than constant)
+
+ xdgel = z(:,1:nvar); % endogenous variable matrix
+ %* True data
+ for k=1:lags
+ phi(:,nvar*(k-1)+1:nvar*k) = xdgel(sb-k:nSample-k,:);
+ % row: T-lags; column: [nvar for 1st lag, ..., nvar for last lag, exo var, const]
+ % Thus, # of columns is nvar*lags+nexo = ncoef.
+ end
+ %
+ y = xdgel(sb:nSample,:);
+
+ %-------------- Taking the 79:10-83:12 period out ----------------------
+ outper1=(SkipBin(1)-SpBin(1))*q_m+(SkipBin(2)-SpBin(2)+1) - lags;
+ outper2=(SkipFin(1)-SpBin(1))*q_m+(SkipFin(2)-SpBin(2)+1) - lags;
+ phi(outper1:outper2,:)=[];
+ y(outper1:outper2,:)=[];
+ fss=size(phi,1);
+
+ [xq,xr]=qr(phi,0);
+ xtx=xr'*xr;
+ xty=phi'*y;
+ [yq,yr]=qr(y,0);
+ yty=yr'*yr;
+ Bh = xr\(xr'\xty); % xtx\xty where inv(X'X)*(X'Y)
+ e=y-phi*Bh;
+end
diff --git a/MatlabFiles/fn_dataxyoldver.m b/MatlabFiles/fn_dataxyoldver.m
index d7056fe8dff3a4b1d40b85579e3d8458914a15a4..5621eb2608778f01556020f7ee69759369e0264e 100644
--- a/MatlabFiles/fn_dataxyoldver.m
+++ b/MatlabFiles/fn_dataxyoldver.m
@@ -1,157 +1,157 @@
-function [xtx,xty,yty,fss,phi,y,ncoef,xr,Bh,e] = fn_dataxyOldVer(nvar,lags,z,mu5,mu6,indxDummy,nexo)
-% Export arranged data matrices (including dummy obs priors) for estimation for DM models.
-% See Wagonner and Zha's Gibbs sampling paper.
-%
-% nvar: number of endogenous variables.
-% lags: the maximum length of lag
-% z: T*(nvar+(nexo-1)) matrix of raw or original data (no manipulation involved)
-% with sample size including lags and with exogenous variables other than a constant.
-% Order of columns: (1) nvar endogenous variables; (2) (nexo-1) exogenous variables;
-% (3) constants are automatically put in the last column.
-% mu5: nvar-by-1 weights on nvar sums of coeffs dummy observations (unit roots); (all equal 5--Atlanta model setting)
-% mu6: weight on single dummy initial observation including constant
-% (cointegration, unit roots, and stationarity); (5--Atlanta model number)
-% indxDummy = 1; % 1: add dummy observations to the data; 0: no dummy added.
-% nexo: number of exogenous variables. The constant term is the default setting. Besides this term,
-% we have nexo-1 exogenous variables.
-% -------------------
-% xtx: X'X: k-by-k where k=ncoef
-% xty: X'Y: k-by-nvar
-% yty: Y'Y: nvar-by-nvar
-% fss: T: sample size excluding lags. With dummyies, fss=nSample-lags+ndobs.
-% phi: X; T-by-k; column: [nvar for 1st lag, ..., nvar for last lag, other exogenous terms, const term]
-% y: Y: T-by-nvar where T=fss
-% ncoef: number of coefficients in *each* equation. RHS coefficients only, nvar*lags+nexo
-% xr: the economy size (ncoef-by-ncoef) in qr(phi) so that xr=chol(X'*X) or xr'*xr=X'*X
-% Bh: ncoef-by-nvar estimated reduced-form parameter; column: nvar;
-% row: ncoef=[nvar for 1st lag, ..., nvar for last lag, other exogenous terms, const term]
-% e: estimated residual e = y -x*Bh, T-by-nvar
-%
-% Tao Zha, February 2000
-% See fn_rnrprior2.m for the base prior.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-if nargin == 6
- nexo=1; % default for constant term
-elseif nexo<1
- error('We need at least one exogenous term so nexo must >= 1')
-end
-
-%*** original sample dimension without dummy prior
-nSample = size(z,1); % the sample size (including lags, of course)
-sb = lags+1; % original beginning without dummies
-ncoef = nvar*lags+nexo; % number of coefficients in *each* equation, RHS coefficients only.
-
-if indxDummy % prior dummy prior
-
- %*** expanded sample dimension by dummy prior
- ndobs=nvar+1; % number of dummy observations
- fss = nSample+ndobs-lags;
-
- %
- % **** nvar prior dummy observations with the sum of coefficients
- % ** construct X for Y = X*B + U where phi = X: (T-lags)*k, Y: (T-lags)*nvar
- % ** columns: k = # of [nvar for 1st lag, ..., nvar for last lag, exo var, const]
- % ** Now, T=T+ndobs -- added with "ndobs" dummy observations
- %
- phi = zeros(fss,ncoef);
- %* constant term
- const = ones(fss,1);
- const(1:nvar) = zeros(nvar,1);
- phi(:,ncoef) = const; % the first nvar periods: no or zero constant!
- %* other exogenous (than) constant term
- phi(ndobs+1:end,ncoef-nexo+1:ncoef-1) = z(lags+1:end,nvar+1:nvar+nexo-1);
- exox = zeros(ndobs,nexo);
- phi(1:ndobs,ncoef-nexo+1:ncoef-1) = exox(:,1:nexo-1);
- % this = [] when nexo=1 (no other exogenous than constant)
-
- xdgel = z(:,1:nvar); % endogenous variable matrix
- xdgelint = mean(xdgel(1:lags,:),1); % mean of the first lags initial conditions
- %* Dummies
- for k=1:nvar
- for m=1:lags
- phi(ndobs,nvar*(m-1)+k) = xdgelint(k);
- phi(k,nvar*(m-1)+k) = xdgelint(k);
- % <<>> multiply hyperparameter later
- end
- end
- %* True data
- for k=1:lags
- phi(ndobs+1:fss,nvar*(k-1)+1:nvar*k) = xdgel(sb-k:nSample-k,:);
- % row: T-lags; column: [nvar for 1st lag, ..., nvar for last lag, exo var, const]
- % Thus, # of columns is nvar*lags+nexo = ncoef.
- end
- %
- % ** Y with "ndobs" dummies added
- y = zeros(fss,nvar);
- %* Dummies
- for k=1:nvar
- y(ndobs,k) = xdgelint(k);
- y(k,k) = xdgelint(k);
- % multiply hyperparameter later
- end
- %* True data
- y(ndobs+1:fss,:) = xdgel(sb:nSample,:);
-
- for ki=1:nvar
- phi(ki,:) = 1*mu5(ki)*phi(ki,:); % standard Sims and Zha prior
- y(ki,:) = mu5(ki)*y(ki,:); % standard Sims and Zha prior
- end
- phi(nvar+1,:) = mu6*phi(nvar+1,:);
- y(nvar+1,:) = mu6*y(nvar+1,:);
-
- [xq,xr]=qr(phi,0);
- xtx=xr'*xr;
- xty=phi'*y;
- [yq,yr]=qr(y,0);
- yty=yr'*yr;
- Bh = xr\(xr'\xty); % xtx\xty where inv(X'X)*(X'Y)
- e=y-phi*Bh;
-else
- fss = nSample-lags;
- %
- % ** construct X for Y = X*B + U where phi = X: (T-lags)*k, Y: (T-lags)*nvar
- % ** columns: k = # of [nvar for 1st lag, ..., nvar for last lag, exo var, const]
- %
- phi = zeros(fss,ncoef);
- %* constant term
- const = ones(fss,1);
- phi(:,ncoef) = const; % the first nvar periods: no or zero constant!
- %* other exogenous (than) constant term
- phi(:,ncoef-nexo+1:ncoef-1) = z(lags+1:end,nvar+1:nvar+nexo-1);
- % this = [] when nexo=1 (no other exogenous than constant)
-
- xdgel = z(:,1:nvar); % endogenous variable matrix
- %* True data
- for k=1:lags
- phi(:,nvar*(k-1)+1:nvar*k) = xdgel(sb-k:nSample-k,:);
- % row: T-lags; column: [nvar for 1st lag, ..., nvar for last lag, exo var, const]
- % Thus, # of columns is nvar*lags+nexo = ncoef.
- end
- %
- y = xdgel(sb:nSample,:);
-
- [xq,xr]=qr(phi,0);
- xtx=xr'*xr;
- xty=phi'*y;
- [yq,yr]=qr(y,0);
- yty=yr'*yr;
- Bh = xr\(xr'\xty); % xtx\xty where inv(X'X)*(X'Y)
- e=y-phi*Bh;
-end
+function [xtx,xty,yty,fss,phi,y,ncoef,xr,Bh,e] = fn_dataxyOldVer(nvar,lags,z,mu5,mu6,indxDummy,nexo)
+% Export arranged data matrices (including dummy obs priors) for estimation for DM models.
+% See Wagonner and Zha's Gibbs sampling paper.
+%
+% nvar: number of endogenous variables.
+% lags: the maximum length of lag
+% z: T*(nvar+(nexo-1)) matrix of raw or original data (no manipulation involved)
+% with sample size including lags and with exogenous variables other than a constant.
+% Order of columns: (1) nvar endogenous variables; (2) (nexo-1) exogenous variables;
+% (3) constants are automatically put in the last column.
+% mu5: nvar-by-1 weights on nvar sums of coeffs dummy observations (unit roots); (all equal 5--Atlanta model setting)
+% mu6: weight on single dummy initial observation including constant
+% (cointegration, unit roots, and stationarity); (5--Atlanta model number)
+% indxDummy = 1; % 1: add dummy observations to the data; 0: no dummy added.
+% nexo: number of exogenous variables. The constant term is the default setting. Besides this term,
+% we have nexo-1 exogenous variables.
+% -------------------
+% xtx: X'X: k-by-k where k=ncoef
+% xty: X'Y: k-by-nvar
+% yty: Y'Y: nvar-by-nvar
+% fss: T: sample size excluding lags. With dummyies, fss=nSample-lags+ndobs.
+% phi: X; T-by-k; column: [nvar for 1st lag, ..., nvar for last lag, other exogenous terms, const term]
+% y: Y: T-by-nvar where T=fss
+% ncoef: number of coefficients in *each* equation. RHS coefficients only, nvar*lags+nexo
+% xr: the economy size (ncoef-by-ncoef) in qr(phi) so that xr=chol(X'*X) or xr'*xr=X'*X
+% Bh: ncoef-by-nvar estimated reduced-form parameter; column: nvar;
+% row: ncoef=[nvar for 1st lag, ..., nvar for last lag, other exogenous terms, const term]
+% e: estimated residual e = y -x*Bh, T-by-nvar
+%
+% Tao Zha, February 2000
+% See fn_rnrprior2.m for the base prior.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+if nargin == 6
+ nexo=1; % default for constant term
+elseif nexo<1
+ error('We need at least one exogenous term so nexo must >= 1')
+end
+
+%*** original sample dimension without dummy prior
+nSample = size(z,1); % the sample size (including lags, of course)
+sb = lags+1; % original beginning without dummies
+ncoef = nvar*lags+nexo; % number of coefficients in *each* equation, RHS coefficients only.
+
+if indxDummy % prior dummy prior
+
+ %*** expanded sample dimension by dummy prior
+ ndobs=nvar+1; % number of dummy observations
+ fss = nSample+ndobs-lags;
+
+ %
+ % **** nvar prior dummy observations with the sum of coefficients
+ % ** construct X for Y = X*B + U where phi = X: (T-lags)*k, Y: (T-lags)*nvar
+ % ** columns: k = # of [nvar for 1st lag, ..., nvar for last lag, exo var, const]
+ % ** Now, T=T+ndobs -- added with "ndobs" dummy observations
+ %
+ phi = zeros(fss,ncoef);
+ %* constant term
+ const = ones(fss,1);
+ const(1:nvar) = zeros(nvar,1);
+ phi(:,ncoef) = const; % the first nvar periods: no or zero constant!
+ %* other exogenous (than) constant term
+ phi(ndobs+1:end,ncoef-nexo+1:ncoef-1) = z(lags+1:end,nvar+1:nvar+nexo-1);
+ exox = zeros(ndobs,nexo);
+ phi(1:ndobs,ncoef-nexo+1:ncoef-1) = exox(:,1:nexo-1);
+ % this = [] when nexo=1 (no other exogenous than constant)
+
+ xdgel = z(:,1:nvar); % endogenous variable matrix
+ xdgelint = mean(xdgel(1:lags,:),1); % mean of the first lags initial conditions
+ %* Dummies
+ for k=1:nvar
+ for m=1:lags
+ phi(ndobs,nvar*(m-1)+k) = xdgelint(k);
+ phi(k,nvar*(m-1)+k) = xdgelint(k);
+ % <<>> multiply hyperparameter later
+ end
+ end
+ %* True data
+ for k=1:lags
+ phi(ndobs+1:fss,nvar*(k-1)+1:nvar*k) = xdgel(sb-k:nSample-k,:);
+ % row: T-lags; column: [nvar for 1st lag, ..., nvar for last lag, exo var, const]
+ % Thus, # of columns is nvar*lags+nexo = ncoef.
+ end
+ %
+ % ** Y with "ndobs" dummies added
+ y = zeros(fss,nvar);
+ %* Dummies
+ for k=1:nvar
+ y(ndobs,k) = xdgelint(k);
+ y(k,k) = xdgelint(k);
+ % multiply hyperparameter later
+ end
+ %* True data
+ y(ndobs+1:fss,:) = xdgel(sb:nSample,:);
+
+ for ki=1:nvar
+ phi(ki,:) = 1*mu5(ki)*phi(ki,:); % standard Sims and Zha prior
+ y(ki,:) = mu5(ki)*y(ki,:); % standard Sims and Zha prior
+ end
+ phi(nvar+1,:) = mu6*phi(nvar+1,:);
+ y(nvar+1,:) = mu6*y(nvar+1,:);
+
+ [xq,xr]=qr(phi,0);
+ xtx=xr'*xr;
+ xty=phi'*y;
+ [yq,yr]=qr(y,0);
+ yty=yr'*yr;
+ Bh = xr\(xr'\xty); % xtx\xty where inv(X'X)*(X'Y)
+ e=y-phi*Bh;
+else
+ fss = nSample-lags;
+ %
+ % ** construct X for Y = X*B + U where phi = X: (T-lags)*k, Y: (T-lags)*nvar
+ % ** columns: k = # of [nvar for 1st lag, ..., nvar for last lag, exo var, const]
+ %
+ phi = zeros(fss,ncoef);
+ %* constant term
+ const = ones(fss,1);
+ phi(:,ncoef) = const; % the first nvar periods: no or zero constant!
+ %* other exogenous (than) constant term
+ phi(:,ncoef-nexo+1:ncoef-1) = z(lags+1:end,nvar+1:nvar+nexo-1);
+ % this = [] when nexo=1 (no other exogenous than constant)
+
+ xdgel = z(:,1:nvar); % endogenous variable matrix
+ %* True data
+ for k=1:lags
+ phi(:,nvar*(k-1)+1:nvar*k) = xdgel(sb-k:nSample-k,:);
+ % row: T-lags; column: [nvar for 1st lag, ..., nvar for last lag, exo var, const]
+ % Thus, # of columns is nvar*lags+nexo = ncoef.
+ end
+ %
+ y = xdgel(sb:nSample,:);
+
+ [xq,xr]=qr(phi,0);
+ xtx=xr'*xr;
+ xty=phi'*y;
+ [yq,yr]=qr(y,0);
+ yty=yr'*yr;
+ Bh = xr\(xr'\xty); % xtx\xty where inv(X'X)*(X'Y)
+ e=y-phi*Bh;
+end
diff --git a/MatlabFiles/fn_demarw.m b/MatlabFiles/fn_demarw.m
index 08acf55affa27e681aaef7dfcecdaa7d0d25aa39..6530780049aa847ae07e9832ab53cab98a3f8aca 100644
--- a/MatlabFiles/fn_demarw.m
+++ b/MatlabFiles/fn_demarw.m
@@ -1,75 +1,75 @@
-function [imfl,imfh,imfl1,imfh1, imfmedian] = demarw(xpo,xprob)
-% [imfl,imfh,imfl1,imfh1] = fn_demarw(xpo,xprob)
-% Demarcate the .68 and .90 error bands given positions and properly scaled probabilities (weights).
-% Calls fn_histwpdfg() first.
-%
-% xpo: ndraws-by-n. Centered position
-% xprob: ndraws-by-n. Must be properly scaled probabilities corresponding to xpo.
-%-----------------
-% imfl: lower .90 bound, n-by-1
-% imfh: higher .90 bound, n-by-1
-% imfl1: lower .68 bound, n-by-1
-% imfh1: higher .68 bound, n-by-1
-% imfmedian: .50 estimate, n-by-1.
-%
-% Note: fn_histwpdfg() must be called first to get xpo and xprob.
-% August 1999 Tao Zha; Revised, August 2000
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-[ndraws,n]=size(xpo);
-
-%$$$ .68 and .95 probability bands
-imfl = zeros(n,1); % preallocating
-imfh = zeros(n,1); % preallocating
-imfl1 = zeros(n,1); % preallocating
-imfh1 = zeros(n,1); % preallocating
-imfmedian = zeros(n,1); % preallocating
-imfpo = zeros(4,n); % 4 positions: l,h,l1,h1.
-%
-%tic
-tem = cumsum(xprob); % cumulative probability
-tem = tem .* 100;
-%
-%@@@ the following operations are valid only because tem are increasing!
-for k = 1:n
- %
- %@@@ the following operations are valid only because tem are increasing!
- %** 5% low tail
- if isempty(max(find(tem(:,k)<5)))
- imfl(k) = xpo(1,k);
- else
- imfl(k) = xpo(max(find(tem(:,k)<5)),k);
- end
- %** 16% low tail
- if isempty(max(find(tem(:,k)<16)))
- imfl1(k) = xpo(1,k);
- else
- imfl1(k) = xpo(max(find(tem(:,k)<16)),k);
- end
- %** 50% estimate
- if isempty(max(find(tem(:,k)<50)))
- imfmedian(k) = xpo(1,k);
- else
- imfmedian(k) = xpo(max(find(tem(:,k)<50)),k);
- end
- %** 5% high tail
- imfh(k) = xpo(min(find(tem(:,k)>95)),k);
- %** 16% low tail
- imfh1(k) = xpo(min(find(tem(:,k)>84)),k);
-end
+function [imfl,imfh,imfl1,imfh1, imfmedian] = demarw(xpo,xprob)
+% [imfl,imfh,imfl1,imfh1] = fn_demarw(xpo,xprob)
+% Demarcate the .68 and .90 error bands given positions and properly scaled probabilities (weights).
+% Calls fn_histwpdfg() first.
+%
+% xpo: ndraws-by-n. Centered position
+% xprob: ndraws-by-n. Must be properly scaled probabilities corresponding to xpo.
+%-----------------
+% imfl: lower .90 bound, n-by-1
+% imfh: higher .90 bound, n-by-1
+% imfl1: lower .68 bound, n-by-1
+% imfh1: higher .68 bound, n-by-1
+% imfmedian: .50 estimate, n-by-1.
+%
+% Note: fn_histwpdfg() must be called first to get xpo and xprob.
+% August 1999 Tao Zha; Revised, August 2000
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+[ndraws,n]=size(xpo);
+
+%$$$ .68 and .95 probability bands
+imfl = zeros(n,1); % preallocating
+imfh = zeros(n,1); % preallocating
+imfl1 = zeros(n,1); % preallocating
+imfh1 = zeros(n,1); % preallocating
+imfmedian = zeros(n,1); % preallocating
+imfpo = zeros(4,n); % 4 positions: l,h,l1,h1.
+%
+%tic
+tem = cumsum(xprob); % cumulative probability
+tem = tem .* 100;
+%
+%@@@ the following operations are valid only because tem are increasing!
+for k = 1:n
+ %
+ %@@@ the following operations are valid only because tem are increasing!
+ %** 5% low tail
+ if isempty(max(find(tem(:,k)<5)))
+ imfl(k) = xpo(1,k);
+ else
+ imfl(k) = xpo(max(find(tem(:,k)<5)),k);
+ end
+ %** 16% low tail
+ if isempty(max(find(tem(:,k)<16)))
+ imfl1(k) = xpo(1,k);
+ else
+ imfl1(k) = xpo(max(find(tem(:,k)<16)),k);
+ end
+ %** 50% estimate
+ if isempty(max(find(tem(:,k)<50)))
+ imfmedian(k) = xpo(1,k);
+ else
+ imfmedian(k) = xpo(max(find(tem(:,k)<50)),k);
+ end
+ %** 5% high tail
+ imfh(k) = xpo(min(find(tem(:,k)>95)),k);
+ %** 16% low tail
+ imfh1(k) = xpo(min(find(tem(:,k)>84)),k);
+end
diff --git a/MatlabFiles/fn_dirichprior.m b/MatlabFiles/fn_dirichprior.m
index 6c903d434e708f27faeb1cc48fa54724c91041e5..6f660c49300b47e951cc2504a30516e42397fbb6 100644
--- a/MatlabFiles/fn_dirichprior.m
+++ b/MatlabFiles/fn_dirichprior.m
@@ -1,95 +1,95 @@
-function [Galpha,stdthe] = fn_dirichprior(Glamda,std_maxv)
-% [Galpha,stdthe] = fn_dirichprior(Glamda,std_maxv)
-% Setup of the Dirichlet prior on the transition matrix P. Given the mode of each random
-% variable theta_j, solve for the (hyper)parameter \alpha where one of \alpha, say, \alpha_j
-% is free for controlling the overall variance of \alpha consisdent with the given modes.
-%
-% Glamda: h-by-1 vector of prior modes for the random variables \theta.
-% For each column of P (transitional matrix), Glamda'll be different with a large weight on a diaganol.
-% std_maxv: prior standard deviation for the selected random variable
-% (here the one with the maximum (largest) mode).
-%----------
-% Galpha: h-by-1 vector of parameters' values for the Dirichlet distribtuion. If one of the
-% elements in Galpha is Inf, we mean a degenerate case where stdthe is set to zeros.
-% stdthe: h-by-1 vector of standard deviations for all random variables \theta.
-%
-% See Gelman, et al p. 476 and TVBvar Note pp. 24-31
-% Tao Zha, March 2001
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-Glamda = Glamda(:); % Column vector! Prior modes for the random variables \theta.
-[maxlam,indxlam] = max(Glamda); % index for the selected hyperparameter that is freely set to match the variance of the corresponding random variable.
-ndiri = length(Glamda); % the number of random variables in the Dirichlet distribution.
-Galpha=zeros(ndiri,1); % \alpha's: hyperparameters that need to be choosen. Must be all greater than 1.
-stdthe = zeros(ndiri,1); % Standard deviations for each \theta
-if indxlam>ndiri
- warning('Make sure indxlam is less than the total number of parameters in the Dirichlet')
- error(' ')
-end
-if (maxlam==1) % We treat this as a degenerate case for convenient coding.
- Galpha(indxlam)=Inf; % Note the use of Inf, NOT NaN. Because
- % 0(or any finite number)/Inf=0 while 0/NaN gives a nonsense (i.e., NaN again).
- % See tveml.m and pp. 27a-27b for more information.
- return % The function stops here.
-end
-
-
-%*** Given std_maxv, find the corresponding hyperparameter Galpha_j by soloving
-% a polynomial of order 3. See Note (*) page 30.
-vj=std_maxv^2; % variance of the selected random variable \theta.
-Gsi = (1-Glamda(indxlam))/Glamda(indxlam); % unchanged value, given Glamda.
-coevec = zeros(4,1); % 4 because we're going to consider a polynomial of order 3 (+ constant).
-coevec(1) = vj*(1+Gsi)^3;
-coevec(2) = vj*(1+Gsi)^2*(3*(ndiri-Gsi)-2) - Gsi;
-coevec(3) = (ndiri-Gsi-1)*(vj*(1+Gsi)*(3*(ndiri-Gsi)-1)-1);
-coevec(4) = vj*(ndiri-Gsi)*(ndiri-Gsi-1)^2;
-
-Galphaj = max(roots(coevec)); % j: jth element, set to match the variance. Must be greater than 1.
-if (Galphaj<=1)
- disp(' ')
- warning('Error! Make sure std_maxv is small enough to have a large Galphaj > 1')
- error(' ')
-end
-
-%*** Solve for the rest of \alpha's. See Note page 27.
-A = eye(ndiri) - repmat(Glamda,[1 ndiri]);
-C = ones(ndiri,1) + (Galphaj-ndiri)*Glamda;
-A1=A; C1=C;
-A1(indxlam,:)=[]; A1(:,indxlam)=[]; C1(indxlam)=[];
-indxrest=1:ndiri; indxrest(indxlam)=[];
-Galpha(indxrest) = A1\C1;
-Galpha(indxlam) = Galphaj;
-
-%========= Debugging or checking the properties ==========
-%disp(' ')
-%disp('2nd-to-first-or-rest ratio: it should not change with the last element of \alpha')
-%(Galpha(2)-1)/(Galpha(1)-1)
-%(Galpha(2)-1)/(sum(Galpha([1 3]))-2)
-%disp(' ')
-%disp('Solution for \alpha')
-%Galpha
-
-for k=1:ndiri
- tmp=Galpha;
- tmp(k)=[];
- stdthe(k)=sqrt( Galpha(k)*sum(tmp)/(sum(Galpha)^2*(sum(Galpha)+1)) );
-end
-%disp(' ')
-%disp('Standard deviations for each \theta')
-%stdthe
+function [Galpha,stdthe] = fn_dirichprior(Glamda,std_maxv)
+% [Galpha,stdthe] = fn_dirichprior(Glamda,std_maxv)
+% Setup of the Dirichlet prior on the transition matrix P. Given the mode of each random
+% variable theta_j, solve for the (hyper)parameter \alpha where one of \alpha, say, \alpha_j
+% is free for controlling the overall variance of \alpha consisdent with the given modes.
+%
+% Glamda: h-by-1 vector of prior modes for the random variables \theta.
+% For each column of P (transitional matrix), Glamda'll be different with a large weight on a diaganol.
+% std_maxv: prior standard deviation for the selected random variable
+% (here the one with the maximum (largest) mode).
+%----------
+% Galpha: h-by-1 vector of parameters' values for the Dirichlet distribtuion. If one of the
+% elements in Galpha is Inf, we mean a degenerate case where stdthe is set to zeros.
+% stdthe: h-by-1 vector of standard deviations for all random variables \theta.
+%
+% See Gelman, et al p. 476 and TVBvar Note pp. 24-31
+% Tao Zha, March 2001
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+Glamda = Glamda(:); % Column vector! Prior modes for the random variables \theta.
+[maxlam,indxlam] = max(Glamda); % index for the selected hyperparameter that is freely set to match the variance of the corresponding random variable.
+ndiri = length(Glamda); % the number of random variables in the Dirichlet distribution.
+Galpha=zeros(ndiri,1); % \alpha's: hyperparameters that need to be choosen. Must be all greater than 1.
+stdthe = zeros(ndiri,1); % Standard deviations for each \theta
+if indxlam>ndiri
+ warning('Make sure indxlam is less than the total number of parameters in the Dirichlet')
+ error(' ')
+end
+if (maxlam==1) % We treat this as a degenerate case for convenient coding.
+ Galpha(indxlam)=Inf; % Note the use of Inf, NOT NaN. Because
+ % 0(or any finite number)/Inf=0 while 0/NaN gives a nonsense (i.e., NaN again).
+ % See tveml.m and pp. 27a-27b for more information.
+ return % The function stops here.
+end
+
+
+%*** Given std_maxv, find the corresponding hyperparameter Galpha_j by soloving
+% a polynomial of order 3. See Note (*) page 30.
+vj=std_maxv^2; % variance of the selected random variable \theta.
+Gsi = (1-Glamda(indxlam))/Glamda(indxlam); % unchanged value, given Glamda.
+coevec = zeros(4,1); % 4 because we're going to consider a polynomial of order 3 (+ constant).
+coevec(1) = vj*(1+Gsi)^3;
+coevec(2) = vj*(1+Gsi)^2*(3*(ndiri-Gsi)-2) - Gsi;
+coevec(3) = (ndiri-Gsi-1)*(vj*(1+Gsi)*(3*(ndiri-Gsi)-1)-1);
+coevec(4) = vj*(ndiri-Gsi)*(ndiri-Gsi-1)^2;
+
+Galphaj = max(roots(coevec)); % j: jth element, set to match the variance. Must be greater than 1.
+if (Galphaj<=1)
+ disp(' ')
+ warning('Error! Make sure std_maxv is small enough to have a large Galphaj > 1')
+ error(' ')
+end
+
+%*** Solve for the rest of \alpha's. See Note page 27.
+A = eye(ndiri) - repmat(Glamda,[1 ndiri]);
+C = ones(ndiri,1) + (Galphaj-ndiri)*Glamda;
+A1=A; C1=C;
+A1(indxlam,:)=[]; A1(:,indxlam)=[]; C1(indxlam)=[];
+indxrest=1:ndiri; indxrest(indxlam)=[];
+Galpha(indxrest) = A1\C1;
+Galpha(indxlam) = Galphaj;
+
+%========= Debugging or checking the properties ==========
+%disp(' ')
+%disp('2nd-to-first-or-rest ratio: it should not change with the last element of \alpha')
+%(Galpha(2)-1)/(Galpha(1)-1)
+%(Galpha(2)-1)/(sum(Galpha([1 3]))-2)
+%disp(' ')
+%disp('Solution for \alpha')
+%Galpha
+
+for k=1:ndiri
+ tmp=Galpha;
+ tmp(k)=[];
+ stdthe(k)=sqrt( Galpha(k)*sum(tmp)/(sum(Galpha)^2*(sum(Galpha)+1)) );
+end
+%disp(' ')
+%disp('Standard deviations for each \theta')
+%stdthe
diff --git a/MatlabFiles/fn_dlrpostr.m b/MatlabFiles/fn_dlrpostr.m
index 27a8628de1811b8e3d1548e720070ce53a6d6c73..3b57e8b62b88f31aa3d81245990819cfea48651e 100644
--- a/MatlabFiles/fn_dlrpostr.m
+++ b/MatlabFiles/fn_dlrpostr.m
@@ -1,61 +1,61 @@
-function [P,H0inv,Hpinv] = fn_dlrpostr(xtx,xty,yty,Ui,Vi)
-% [P,H0inv,Hpinv] = fn_dlrpostr(xtx,xty,yty,Ui,Vi)
-%
-% Exporting deterministic (no random prior) Bayesian posterior matrices with linear restrictions
-% See Waggoner and Zha's Gibbs sampling paper
-%
-% xtx: X'X: k-by-k where k=ncoef
-% xty: X'Y: k-by-nvar
-% yty: Y'Y: nvar-by-nvar
-% Ui: nvar-by-1 cell. In each cell, nvar-by-qi orthonormal basis for the null of the ith
-% equation contemporaneous restriction matrix where qi is the number of free parameters.
-% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
-% of total original parameters and bi is a vector of free parameters. When no
-% restrictions are imposed, we have Ui = I. There must be at least one free
-% parameter left for the ith equation. Imported from dnrprior.m.
-% Vi: nvar-by-1 cell. In each cell, k-by-ri orthonormal basis for the null of the ith
-% equation lagged restriction matrix where k (ncoef) is a total number of RHS variables and
-% ri is the number of free parameters. With this transformation, we have fi = Vi*gi
-% or Vi'*fi = gi where fi is a vector of total original parameters and gi is a
-% vector of free parameters. There must be at least one free parameter left for
-% the ith equation. Imported from dnrprior.m.
-%-----------------
-% P: cell(nvar,1). In each cell, the transformation matrix that affects the posterior mean of A+ conditional on A0.
-% H0inv: cell(nvar,1). In each cell, posterior inverse of covariance matrix (i.e., the exponential
-% term is a_i'*H0*a_i) for the ith equation (not divided by T yet). It resembles
-% old SpH in the exponent term in posterior of A0, but not divided by T yet.
-% Hpinv: cell(nvar,1). In each cell, posterior inverse of covariance matrix Hp (A+) for the
-% ith equation.
-%
-% Tao Zha, February 2000
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-nvar = size(yty,1);
-
-P = cell(nvar,1); % tld: tilda
-H0inv = cell(nvar,1); % posterior inv(H0), resemble old SpH, but not divided by T yet.
-Hpinv = cell(nvar,1); % posterior inv(Hp).
-
-for n=1:nvar % one for each equation
- Hpinv{n} = Vi{n}'*xtx*Vi{n};
- P1 = Vi{n}'*xty*Ui{n};
- P{n} = Hpinv{n}\P1;
- H0inv{n} = Ui{n}'*yty*Ui{n} - P1'*(Hpinv{n}\P1);
-end
-
+function [P,H0inv,Hpinv] = fn_dlrpostr(xtx,xty,yty,Ui,Vi)
+% [P,H0inv,Hpinv] = fn_dlrpostr(xtx,xty,yty,Ui,Vi)
+%
+% Exporting deterministic (no random prior) Bayesian posterior matrices with linear restrictions
+% See Waggoner and Zha's Gibbs sampling paper
+%
+% xtx: X'X: k-by-k where k=ncoef
+% xty: X'Y: k-by-nvar
+% yty: Y'Y: nvar-by-nvar
+% Ui: nvar-by-1 cell. In each cell, nvar-by-qi orthonormal basis for the null of the ith
+% equation contemporaneous restriction matrix where qi is the number of free parameters.
+% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
+% of total original parameters and bi is a vector of free parameters. When no
+% restrictions are imposed, we have Ui = I. There must be at least one free
+% parameter left for the ith equation. Imported from dnrprior.m.
+% Vi: nvar-by-1 cell. In each cell, k-by-ri orthonormal basis for the null of the ith
+% equation lagged restriction matrix where k (ncoef) is a total number of RHS variables and
+% ri is the number of free parameters. With this transformation, we have fi = Vi*gi
+% or Vi'*fi = gi where fi is a vector of total original parameters and gi is a
+% vector of free parameters. There must be at least one free parameter left for
+% the ith equation. Imported from dnrprior.m.
+%-----------------
+% P: cell(nvar,1). In each cell, the transformation matrix that affects the posterior mean of A+ conditional on A0.
+% H0inv: cell(nvar,1). In each cell, posterior inverse of covariance matrix (i.e., the exponential
+% term is a_i'*H0*a_i) for the ith equation (not divided by T yet). It resembles
+% old SpH in the exponent term in posterior of A0, but not divided by T yet.
+% Hpinv: cell(nvar,1). In each cell, posterior inverse of covariance matrix Hp (A+) for the
+% ith equation.
+%
+% Tao Zha, February 2000
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+nvar = size(yty,1);
+
+P = cell(nvar,1); % tld: tilda
+H0inv = cell(nvar,1); % posterior inv(H0), resemble old SpH, but not divided by T yet.
+Hpinv = cell(nvar,1); % posterior inv(Hp).
+
+for n=1:nvar % one for each equation
+ Hpinv{n} = Vi{n}'*xtx*Vi{n};
+ P1 = Vi{n}'*xty*Ui{n};
+ P{n} = Hpinv{n}\P1;
+ H0inv{n} = Ui{n}'*yty*Ui{n} - P1'*(Hpinv{n}\P1);
+end
+
diff --git a/MatlabFiles/fn_empdfsort.m b/MatlabFiles/fn_empdfsort.m
index ce2f41936c69c1843d96979ce2050c9625823fe8..eeca73fca4c83a3057ab23bdc7c96ef828798021 100644
--- a/MatlabFiles/fn_empdfsort.m
+++ b/MatlabFiles/fn_empdfsort.m
@@ -1,49 +1,49 @@
-function imfcnt = fn_empdfsort(imfcnt,imf_h,imfloor,hbin,ninv)
-% imfcnt = fn_empdfsort(imfcnt,imf_h,imfloor,hbin,ninv)
-% Variablewise (but multivariate) empirical probability distribution with counts
-% sorted into bins variablewise
-% Note that since a particular draw (imf_h) can be below "imfloor" and above "imceiling"
-% (=(imceiling-imfloor)*hbin), this function allows ninv+2 bins for each variable
-%
-% imfcnt: initial ninv+2-by-k matrix that records counts in each bin given a column in imfcnt
-% if k==1, then only one variable is considered.
-% imf_h: k-element vector of particular draws. Need not be a 1-by-k row vector.
-% imfloor: k-element vector of low values of imf but imf_h can be below "imfloor" and above "imceiling"
-% hbin: k-element vector of bin lengths = (imceilling-imfloor)/ninv.
-% ninv: number of bins between "imfloor" and "imceiling"
-%------------------
-% imfcnt: updated ninv+2-by-k matrix of counts (probability)
-%
-% January 1999 TZ; Revised, August 2000.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-%** find the bin locations and arrange them to the order of 1, 2, ...
-imf_h=imf_h(:); imfloor=imfloor(:); hbin=hbin(:);
-countInt = floor( (imf_h-imfloor) ./ hbin ); % k-by-1
- % bin locations from <0, 0, 1,..., ninv-1, >=ninv, a total of ninv+2 bins
-countInt = 2+countInt'; % 1-by-k row vector, see my shock (1), pp.1
- % move everyting by 2 so as to take account of <0
-
-countInt(find(countInt<2)) = 1; % set <0 or -infinity at 1 to start
-countInt(find(countInt>=ninv+2)) = ninv+2; % set >=ninv+2 or +infinity at ninv+2 to end
-countInt = countInt + (0:length(countInt)-1)*(ninv+2);
- % 1-by-k*(ninv+2) index vector to fill the matrix with prob. (counts)
- % The term after "+" implies that with every count, we skip ninv+2 to keep
- % each column in "imfcnt" with only one element (which is probability)
-imfcnt(countInt) = imfcnt(countInt) + 1;
+function imfcnt = fn_empdfsort(imfcnt,imf_h,imfloor,hbin,ninv)
+% imfcnt = fn_empdfsort(imfcnt,imf_h,imfloor,hbin,ninv)
+% Variablewise (but multivariate) empirical probability distribution with counts
+% sorted into bins variablewise
+% Note that since a particular draw (imf_h) can be below "imfloor" and above "imceiling"
+% (=(imceiling-imfloor)*hbin), this function allows ninv+2 bins for each variable
+%
+% imfcnt: initial ninv+2-by-k matrix that records counts in each bin given a column in imfcnt
+% if k==1, then only one variable is considered.
+% imf_h: k-element vector of particular draws. Need not be a 1-by-k row vector.
+% imfloor: k-element vector of low values of imf but imf_h can be below "imfloor" and above "imceiling"
+% hbin: k-element vector of bin lengths = (imceilling-imfloor)/ninv.
+% ninv: number of bins between "imfloor" and "imceiling"
+%------------------
+% imfcnt: updated ninv+2-by-k matrix of counts (probability)
+%
+% January 1999 TZ; Revised, August 2000.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+%** find the bin locations and arrange them to the order of 1, 2, ...
+imf_h=imf_h(:); imfloor=imfloor(:); hbin=hbin(:);
+countInt = floor( (imf_h-imfloor) ./ hbin ); % k-by-1
+ % bin locations from <0, 0, 1,..., ninv-1, >=ninv, a total of ninv+2 bins
+countInt = 2+countInt'; % 1-by-k row vector, see my shock (1), pp.1
+ % move everyting by 2 so as to take account of <0
+
+countInt(find(countInt<2)) = 1; % set <0 or -infinity at 1 to start
+countInt(find(countInt>=ninv+2)) = ninv+2; % set >=ninv+2 or +infinity at ninv+2 to end
+countInt = countInt + (0:length(countInt)-1)*(ninv+2);
+ % 1-by-k*(ninv+2) index vector to fill the matrix with prob. (counts)
+ % The term after "+" implies that with every count, we skip ninv+2 to keep
+ % each column in "imfcnt" with only one element (which is probability)
+imfcnt(countInt) = imfcnt(countInt) + 1;
diff --git a/MatlabFiles/fn_ergodp.m b/MatlabFiles/fn_ergodp.m
index 93cef5da493ae5095ea3953444264cc4e37665ae..660cf1704c370b9f2c8da1bc26a8af5df64d5c7e 100644
--- a/MatlabFiles/fn_ergodp.m
+++ b/MatlabFiles/fn_ergodp.m
@@ -1,33 +1,33 @@
-function gpi = fn_ergodp(P)
-% gpi = fn_ergodp(P)
-% Compute the ergodic probabilities. See Hamilton p.681.
-%
-% P: n-by-n matrix of transition matrix where all elements in each column sum up to 1.
-%-----
-% gpi: n-by-1 vector of ergodic probabilities.
-%
-% Tao Zha August 2000
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-[gpim,gpid] = eig(P); % m: matrix; d: diagonal
-[gpidv,gpidvinx] = sort(diag(gpid));
-gpidv = fliplr(gpidv);
-gpidvinx = flipud(gpidvinx);
-gpim = gpim(:,gpidvinx);
-gpi = gpim(:,1)/sum(gpim(:,1));
+function gpi = fn_ergodp(P)
+% gpi = fn_ergodp(P)
+% Compute the ergodic probabilities. See Hamilton p.681.
+%
+% P: n-by-n matrix of transition matrix where all elements in each column sum up to 1.
+%-----
+% gpi: n-by-1 vector of ergodic probabilities.
+%
+% Tao Zha August 2000
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+[gpim,gpid] = eig(P); % m: matrix; d: diagonal
+[gpidv,gpidvinx] = sort(diag(gpid));
+gpidv = fliplr(gpidv);
+gpidvinx = flipud(gpidvinx);
+gpim = gpim(:,gpidvinx);
+gpi = gpim(:,1)/sum(gpim(:,1));
diff --git a/MatlabFiles/fn_fcstcnd.m b/MatlabFiles/fn_fcstcnd.m
index 68a246153c48dbaced423d6af48766954f35f628..74602005e21e9bb03ad672a6b12366c7b2937588 100644
--- a/MatlabFiles/fn_fcstcnd.m
+++ b/MatlabFiles/fn_fcstcnd.m
@@ -1,252 +1,253 @@
-function [yhat,Estr,rcon,Rcon,u,v,d] = fn_fcstcnd(valuecon,stepcon,varcon,nstepsm,...
- nconstr,eq_ms,nvar,lags,phil,Sband,yfore_h,imf3s_h,Bh_h,forep)
-% ******* There are some BUG problems when calling this fucntion. ******* 3/15/2004.
-% [yhat,Estr,rcon,Rcon,u,v,d] = fn_fcstcnd(valuecon,stepcon,varcon,nstepsm,...
-% nconstr,eq_ms,nvar,lags,phil,Sband,yfore_h,imf3s_h,Bh_h,forep)
-%
-% Conditional forecasting in the identified model with or without error bands
-% It handles conditions on average values as well, so "valuecon" must be
-% expressed at average (NOT sum) levels (i.e., arithmetic averages of log(y) over
-% the period of stepcon{i}. 5/22/01.
-% Unconditional forecast when imf3s_h, etc is fixed and nconstr=0.
-% Note that length(eq_ms)==1 implies one-one mapping between MS shocks and, say, FFR
-% if nstepsm==nconstr. If this condition does not hold, this procedure is incorrect.
-% I don't have time to fix it now (3/20/99). Meantime, consult or use the old code
-% fidencond.m.
-%
-% valuecon: vector of values conditioned
-% stepcon: sequence (cell) of steps conditioned; if length(stepcon{i}) > 1, the condition
-% is then an arithmetic average of log(y) over the stepcon{i} period.
-% varcon: vector of variables conditioned
-% nstepsm: maximum number of steps in all DLS constraints
-% nconstr: number of DLS constraints
-% eq_ms: Equation location of MS shocks. If [], all shocks.
-% nvar: number of variables in the BVAR model
-% lags: number of lags in the BVAR model
-% phil: the 1-by-(nvar*lags+1) data matrix where k=nvar*lags+1
-% (last period plus lags before the beginning of forecast)
-% Sband: 1: draws from random shocks E; 0: no random shocks For now (4/27/01), no option
-% for Aband because I don't think it works best to do both Aband and Sband in one function.
-% yfore_h: uncondtional forecasts: forep-by-nvar. Never used when nconstr=0.
-% In this case, may set it to [];
-% imf3s_h: 3-dimensional impulse responses matrix: impsteps-by-nvar shocks-by-nvar responses
-% Never used when nconstr=0. In this case, may set it to [];
-% Bh_h: reduced-form parameter matrix: k-by-nvar, y(t) = X(t)*Bh+e(t)
-% where X(t) is k-by-nvar and y(t) is 1-by-nvar
-% forep: # of forecast periods (e.g., monthly for a monthly model)
-% eq_Cms: equation location of MS shocks
-% ------
-% yhat: conditional forecasts: forep-by-nvar
-% Estr: backed-out structural shocks (from constrained Gaussians)
-% rcon: vector - the difference between valuecon and log(yfore) (unconditional forecasts)
-% Rcon: k-by-q (q constranits and k=nvar*max(nsteps)) so that
-% Rcon'*e = rcon where e is k-by-1
-% [u,d,v]: svd(Rcon,0)
-%
-%% See Zha's note "Forecast (1)" p. 5, RATS manual (some errors in RATS), etc.
-%% Some notations: y(t+1) = y(t)B1 + e(t+1)inv(A0). e(t+1) is 1-by-n.
-%% Let r(t+1)=e(t+1)inv(A0) + e(t+2)C + .... where inv(A0) is impulse
-%% response at t=1, C at t=2, etc. The row of inv(A0) or C is
-%% all responses to one shock.
-%% Let r be q-by-1 (such as r(1) = r(t+1)
-%% = y(t+1) (constrained) - y(t+1) (forecast)).
-%% Use impulse responses to find out R (k-by-q) where k=nvar*nsteps
-%% where nsteps the largest constrained step. The key of the program
-%% is to creat R using impulse responses
-%% Optimal solution for shock e where R'*e=r and e is k-by-1 is
-%% e = R*inv(R'*R)*r and k>=q
-%
-% See the old code fidencond.m. I wond't use fn_fcstidcnd?.m, 5/22/01.
-% Copyright (c) March 1998 by Tao Zha. Revised November 1998, May 2001 (Delete A0_h as
-% input arg so that previous programs may not be compatible).
-
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-DLSIdShock = ~isempty(eq_ms); % if not empty, the MS shock is identified as in DLS
-
-impsteps=size(imf3s_h,1);
-if (forep<nstepsm) | (impsteps<nstepsm)
- disp('Increase # of forecast or impulse steps!!')
- disp('Or decrease # of constraints (nconstr) or constrained steps (stepcon(i))!!')
- error('Maximum of conditional steps > # of forecast or impulse steps!!')
-end
-kts = nvar*nstepsm; % k -- ts: total shocks some of which are restricted and others
- % are free.
-%*** initializing
-Rcon = zeros(kts,nconstr); % R: k-by-q
-Econ = zeros(kts,1); % E: k-by-1
-rcon = zeros(nconstr,1); % r: q-by-1
-%rcon=valuecon-diag(yfore(stepcon,varcon)); % another way is to use "loop" below.
-tcwc = nvar*lags; % total coefficients without constant
-phi=phil;
-
-
-
-%----------------------------------------------------
-% Form rcon, Rcon, and Econ (the mean of structural shocks)
-%----------------------------------------------------
-A0in = reshape(imf3s_h(1,:,:),nvar,nvar); % <<>>1 nvar shocks-by-nvar responses
-if nconstr % Conditional forecasts.
- for i=1:nconstr
- rcon(i)=length(stepcon{i})*valuecon(i) - sum(yfore_h(stepcon{i},varcon(i)),1);
- %<<>>2 Automatically taking care of average conditions.
- Rmat = zeros(nstepsm,nvar);
- r2mat = zeros(nstepsm,1); % simply one identified equation
- % Must be here inside the loop because it's matrix of one column of Rcon
- for j=1:length(stepcon{i})
- if DLSIdShock % Assuming the Fed can't see all other shocks within a month
- Rmat(1:stepcon{i}(j),eq_ms) = Rmat(1:stepcon{i}(j),eq_ms) + ...
- imf3s_h(stepcon{i}(j):-1:1,eq_ms,varcon(i));
- % Rmat: row--nstepsm, column--nvar shocks (here all shocks except
- % the identified one are set to zero) for a particular
- % endogenous variable 'varcon(i)'. See Zha Forcast (1), pp.6-7
- else % Rcon random with (A0,A+)
- Rmat(1:stepcon{i}(j),:) = Rmat(1:stepcon{i}(j),:) + ...
- imf3s_h(stepcon{i}(j):-1:1,:,varcon(i));
- % Rmat: row--nstepsm, column--nvar shocks (here all shocks are
- % *not* set to zero) for a particular endogenous
- % variable 'varcon(i)'. See Zha Forcast (1), pp.6-7
- end
- end
- Rmatt = Rmat'; % Now, nvar-by-nstepsm. I think here is where RATS has an error
- % i.e. "OVERR" is not transposed when overlaid to "CAPR"
- Rcon(:,i)=Rmatt(:); % Rcon: k-by-q where q=nconstr
- end
- %
- [u d v]=svd(Rcon,0); %trial
- vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
- dinv = 1./diag(d); % inv(diag(d))
- vdinv=v.*(ones(size(v,2),1)*dinv'); %trial
- rtr=vd*vd'; % R'*R
- rtrinv = vdinv*vdinv'; % inv(R'*R)
-
- Econ = Rcon*rtrinv*rcon; % E = R*inv(R'R)*r; the mean of structural shocks
-else % Unconditional forecasts.
- Econ = zeros(kts,1); % the mean of shocks is zero under no variable condition
- Rcon = NaN;
- rcon = NaN;
- u = NaN;
- d = NaN;
- v = NaN;
-end
-
-
-
-%---------------------------------------
-% No uncertainty at all. In other words, no future shocks.
-%---------------------------------------
-if (~Sband) %| (nconstr & (length(eq_ms)==1))
- % length(eq_ms)==1 implies one-one mapping between MS shocks and, say, FFR
- % if nstepsm==nconstr. If this condition does not hold, this procedure
- % is incorrect. I don't have time to fix it now (3/20/99). So I use
- % this as a proximation
- Estr = reshape(Econ,nvar,nstepsm);
- Estr = Estr'; % transpose so that
- % Estr: structural shocks. Row--steps, Column--n shocks
- Estr = [Estr;zeros(forep-nstepsm,nvar)];
- % Now, forep-by-nvar -- ready for forecasts
- Ures = Estr*A0in; % <<>>1 nstepsm-by-nvar
- % Ures: reduced-form residuals. Row--steps; Column--n shocks
- % Note: We don't use /A0_h so as to eliminate small discrepancies to be
- % completely compatible with the computation of Rmat and Estr, which uses A0in.
-
- % ** reconstruct x(t) for y(t+h) = x(t+h-1)*B
- % ** where phi = x(t+h-1) with last column being constant
- %
- yhat = zeros(forep,nvar);
- for k=1:forep
- yhat(k,:) = phi*Bh_h + Ures(k,:);
- phi(1,nvar+1:tcwc) = phi(1,1:tcwc-nvar);
- phi(1,1:nvar) = yhat(k,:);
- end
-%---------------------------------------
-% With random future shocks.
-%---------------------------------------
-else
- if nconstr % Conditional forecasts.
- %--------------
- % Condition on variables with all shocks backed out. Straight DLS forecasts. No A random but S random.
- %--------------
- if ~DLSIdShock
- Ome = eye(kts) - u*u'; % note, I-u*u' = I - R*inv(R'*R)*R'
- %[u1 d1 v1] = svd(Ome); % too slow
- [u1 d1] = eig(Ome);
- Stdcon = u1*diag(sqrt(diag(abs(d1)))); % lower triagular chol of conditional variance
- % see Zha's forecast (1), p.17
- Estr1 = Econ + Stdcon*randn(kts,1); % Draws of constrained (conditioned) shocks.
- Estr2 = reshape(Estr1,nvar,nstepsm);
- Estr2 = Estr2'; % transpose so that
- % Estr2: structural shocks. Row--nstepsm, Column--n shocks
- Estr = [Estr2;randn(forep-nstepsm,nvar)]; % Second concatenated part: draws of free shocks.
- % Now, forep-by-nvar -- ready for forecasts
- %--------------
- % Condition on variables with identified MS shocks backed out, no A random but S random.
- %--------------
- else % other shocks are indepedent of the eq_ms shock
- % 3/20/99 The following may be problematic because Osk should depend
- % on u (A0_h and Bh_h) in general. I have NOT worked out any good version.
- %/*
- % Osk = randn(kts,1); % other shocks
- % for j=1:nstepsm
- % Osk(nvar*(j-1)+eq_ms)=0; % no shock to the MS or identified equation
- % end
- % Estr = Econ + Osk; % Econ is non zero only at position
- % % eq_ms*j where j=1:nstepsm
- % Estr = reshape(Estr,nvar,nstepsm);
- % Estr = Estr'; % transpose so that
- % % Estr: structural shocks. Row--steps, Column--n shocks
- % Estr = [Estr;randn(forep-nstepsm,nvar)];
- % % Now, forep-by-nvar -- ready for forecasts
- %
- disp('DLS')
- Ome = eye(kts) - u*u'; % note, I-u*u' = I - R*inv(R'*R)*R'
- %[u1 d1 v1] = svd(Ome); % too slow
- [u1 d1] = eig(Ome);
- Stdcon = u1*diag(sqrt(diag(abs(d1)))); % lower triagular chol of conditional variance
- % see Zha's forecast (1), p.17
- tmp1=zeros(nvar,nstepsm);
- tmp1(eq_ms,:)=randn(1,nstepsm);
- tmp2=tmp1(:);
- %Estr1 = Econ + Stdcon*randn(kts,1);
- %jnk = reshape(Stdcon*tmp2,nvar,nstepsm)
- Estr1 = Econ + Stdcon*tmp2;
- Estr2 = reshape(Estr1,nvar,nstepsm);
- Estr2 = Estr2'; % transpose so that
- % Estr2: structural shocks. Row--nstepsm, Column--n shocks
- Estr = [Estr2;randn(forep-nstepsm,nvar)];
- % Now, forep-by-nvar -- ready for forecasts
- end
- else % Unconditional forecasts.
- Estr = randn(forep,nvar); % Unconditional draws.
- end
-
- Ures = Estr*A0in; % <<>>1 nstepsm-by-nvar
- % Ures: reduced-form residuals. Row--steps; Column--n shocks
- % Note: We don't use /A0_h so as to eliminate small discrepancies to be
- % completely compatible with the computation of Rmat and Estr, which uses A0in.
-
- % ** reconstruct x(t) for y(t+h) = x(t+h-1)*B
- % ** where phi = x(t+h-1) with last column being constant
- %
- yhat = zeros(forep,nvar);
- for k=1:forep
- yhat(k,:) = phi*Bh_h + Ures(k,:);
- phi(1,nvar+1:tcwc) = phi(1,1:tcwc-nvar);
- phi(1,1:nvar) = yhat(k,:);
- end
-end
+function [yhat,Estr,rcon,Rcon,u,v,d] = fn_fcstcnd(valuecon,stepcon,varcon,nstepsm,...
+ nconstr,eq_ms,nvar,lags,phil,Sband,yfore_h,imf3s_h,Bh_h,forep)
+% ******* There are some BUG problems when calling this fucntion. ******* 3/15/2004.
+% [yhat,Estr,rcon,Rcon,u,v,d] = fn_fcstcnd(valuecon,stepcon,varcon,nstepsm,...
+% nconstr,eq_ms,nvar,lags,phil,Sband,yfore_h,imf3s_h,Bh_h,forep)
+%
+% Conditional forecasting in the identified model with or without error bands
+% It handles conditions on average values as well, so "valuecon" must be
+% expressed at average (NOT sum) levels (i.e., arithmetic averages of log(y) over
+% the period of stepcon{i}. 5/22/01.
+% Unconditional forecast when imf3s_h, etc is fixed and nconstr=0.
+% Note that length(eq_ms)==1 implies one-one mapping between MS shocks and, say, FFR
+% if nstepsm==nconstr. If this condition does not hold, this procedure is incorrect.
+% I don't have time to fix it now (3/20/99). Meantime, consult or use the old code
+% fidencond.m.
+%
+% valuecon: vector of values conditioned
+% stepcon: sequence (cell) of steps conditioned; if length(stepcon{i}) > 1, the condition
+% is then an arithmetic average of log(y) over the stepcon{i} period.
+% varcon: vector of variables conditioned
+% nstepsm: maximum number of steps in all DLS constraints
+% nconstr: number of DLS constraints
+% eq_ms: Equation location of MS shocks. If [], all shocks.
+% nvar: number of variables in the BVAR model
+% lags: number of lags in the BVAR model
+% phil: the 1-by-(nvar*lags+1) data matrix where k=nvar*lags+1
+% (last period plus lags before the beginning of forecast)
+% Sband: 1: draws from random shocks E; 0: no random shocks For now (4/27/01), no option
+% for Aband because I don't think it works best to do both Aband and Sband in one function.
+% yfore_h: uncondtional forecasts: forep-by-nvar. Never used when nconstr=0.
+% In this case, may set it to [];
+% imf3s_h: 3-dimensional impulse responses matrix: impsteps-by-nvar shocks-by-nvar responses
+% Never used when nconstr=0. In this case, may set it to [];
+% Bh_h: reduced-form parameter matrix: k-by-nvar, y(t) = X(t)*Bh+e(t)
+% where X(t) is k-by-nvar and y(t) is 1-by-nvar
+% forep: # of forecast periods (e.g., monthly for a monthly model)
+% eq_Cms: equation location of MS shocks
+% ------
+% yhat: conditional forecasts: forep-by-nvar
+% Estr: backed-out structural shocks (from constrained Gaussians)
+% rcon: vector - the difference between valuecon and log(yfore) (unconditional forecasts)
+% Rcon: k-by-q (q constranits and k=nvar*max(nsteps)) so that
+% Rcon'*e = rcon where e is k-by-1
+% [u,d,v]: svd(Rcon,0)
+%
+%% See Zha's note "Forecast (1)" p. 5, RATS manual (some errors in RATS), etc.
+%% Some notations: y(t+1) = y(t)B1 + e(t+1)inv(A0). e(t+1) is 1-by-n.
+%% Let r(t+1)=e(t+1)inv(A0) + e(t+2)C + .... where inv(A0) is impulse
+%% response at t=1, C at t=2, etc. The row of inv(A0) or C is
+%% all responses to one shock.
+%% Let r be q-by-1 (such as r(1) = r(t+1)
+%% = y(t+1) (constrained) - y(t+1) (forecast)).
+%% Use impulse responses to find out R (k-by-q) where k=nvar*nsteps
+%% where nsteps the largest constrained step. The key of the program
+%% is to creat R using impulse responses
+%% Optimal solution for shock e where R'*e=r and e is k-by-1 is
+%% e = R*inv(R'*R)*r and k>=q
+%
+% See the old code fidencond.m. I wond't use fn_fcstidcnd?.m, 5/22/01.
+% Written by Tao Zha March 1998 .
+% Revised November 1998, May 2001 (Delete A0_h as
+% input arg so that previous programs may not be compatible).
+
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+DLSIdShock = ~isempty(eq_ms); % if not empty, the MS shock is identified as in DLS
+
+impsteps=size(imf3s_h,1);
+if (forep<nstepsm) | (impsteps<nstepsm)
+ disp('Increase # of forecast or impulse steps!!')
+ disp('Or decrease # of constraints (nconstr) or constrained steps (stepcon(i))!!')
+ error('Maximum of conditional steps > # of forecast or impulse steps!!')
+end
+kts = nvar*nstepsm; % k -- ts: total shocks some of which are restricted and others
+ % are free.
+%*** initializing
+Rcon = zeros(kts,nconstr); % R: k-by-q
+Econ = zeros(kts,1); % E: k-by-1
+rcon = zeros(nconstr,1); % r: q-by-1
+%rcon=valuecon-diag(yfore(stepcon,varcon)); % another way is to use "loop" below.
+tcwc = nvar*lags; % total coefficients without constant
+phi=phil;
+
+
+
+%----------------------------------------------------
+% Form rcon, Rcon, and Econ (the mean of structural shocks)
+%----------------------------------------------------
+A0in = reshape(imf3s_h(1,:,:),nvar,nvar); % <<>>1 nvar shocks-by-nvar responses
+if nconstr % Conditional forecasts.
+ for i=1:nconstr
+ rcon(i)=length(stepcon{i})*valuecon(i) - sum(yfore_h(stepcon{i},varcon(i)),1);
+ %<<>>2 Automatically taking care of average conditions.
+ Rmat = zeros(nstepsm,nvar);
+ r2mat = zeros(nstepsm,1); % simply one identified equation
+ % Must be here inside the loop because it's matrix of one column of Rcon
+ for j=1:length(stepcon{i})
+ if DLSIdShock % Assuming the Fed can't see all other shocks within a month
+ Rmat(1:stepcon{i}(j),eq_ms) = Rmat(1:stepcon{i}(j),eq_ms) + ...
+ imf3s_h(stepcon{i}(j):-1:1,eq_ms,varcon(i));
+ % Rmat: row--nstepsm, column--nvar shocks (here all shocks except
+ % the identified one are set to zero) for a particular
+ % endogenous variable 'varcon(i)'. See Zha Forcast (1), pp.6-7
+ else % Rcon random with (A0,A+)
+ Rmat(1:stepcon{i}(j),:) = Rmat(1:stepcon{i}(j),:) + ...
+ imf3s_h(stepcon{i}(j):-1:1,:,varcon(i));
+ % Rmat: row--nstepsm, column--nvar shocks (here all shocks are
+ % *not* set to zero) for a particular endogenous
+ % variable 'varcon(i)'. See Zha Forcast (1), pp.6-7
+ end
+ end
+ Rmatt = Rmat'; % Now, nvar-by-nstepsm. I think here is where RATS has an error
+ % i.e. "OVERR" is not transposed when overlaid to "CAPR"
+ Rcon(:,i)=Rmatt(:); % Rcon: k-by-q where q=nconstr
+ end
+ %
+ [u d v]=svd(Rcon,0); %trial
+ vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
+ dinv = 1./diag(d); % inv(diag(d))
+ vdinv=v.*(ones(size(v,2),1)*dinv'); %trial
+ rtr=vd*vd'; % R'*R
+ rtrinv = vdinv*vdinv'; % inv(R'*R)
+
+ Econ = Rcon*rtrinv*rcon; % E = R*inv(R'R)*r; the mean of structural shocks
+else % Unconditional forecasts.
+ Econ = zeros(kts,1); % the mean of shocks is zero under no variable condition
+ Rcon = NaN;
+ rcon = NaN;
+ u = NaN;
+ d = NaN;
+ v = NaN;
+end
+
+
+
+%---------------------------------------
+% No uncertainty at all. In other words, no future shocks.
+%---------------------------------------
+if (~Sband) %| (nconstr & (length(eq_ms)==1))
+ % length(eq_ms)==1 implies one-one mapping between MS shocks and, say, FFR
+ % if nstepsm==nconstr. If this condition does not hold, this procedure
+ % is incorrect. I don't have time to fix it now (3/20/99). So I use
+ % this as a proximation
+ Estr = reshape(Econ,nvar,nstepsm);
+ Estr = Estr'; % transpose so that
+ % Estr: structural shocks. Row--steps, Column--n shocks
+ Estr = [Estr;zeros(forep-nstepsm,nvar)];
+ % Now, forep-by-nvar -- ready for forecasts
+ Ures = Estr*A0in; % <<>>1 nstepsm-by-nvar
+ % Ures: reduced-form residuals. Row--steps; Column--n shocks
+ % Note: We don't use /A0_h so as to eliminate small discrepancies to be
+ % completely compatible with the computation of Rmat and Estr, which uses A0in.
+
+ % ** reconstruct x(t) for y(t+h) = x(t+h-1)*B
+ % ** where phi = x(t+h-1) with last column being constant
+ %
+ yhat = zeros(forep,nvar);
+ for k=1:forep
+ yhat(k,:) = phi*Bh_h + Ures(k,:);
+ phi(1,nvar+1:tcwc) = phi(1,1:tcwc-nvar);
+ phi(1,1:nvar) = yhat(k,:);
+ end
+%---------------------------------------
+% With random future shocks.
+%---------------------------------------
+else
+ if nconstr % Conditional forecasts.
+ %--------------
+ % Condition on variables with all shocks backed out. Straight DLS forecasts. No A random but S random.
+ %--------------
+ if ~DLSIdShock
+ Ome = eye(kts) - u*u'; % note, I-u*u' = I - R*inv(R'*R)*R'
+ %[u1 d1 v1] = svd(Ome); % too slow
+ [u1 d1] = eig(Ome);
+ Stdcon = u1*diag(sqrt(diag(abs(d1)))); % lower triagular chol of conditional variance
+ % see Zha's forecast (1), p.17
+ Estr1 = Econ + Stdcon*randn(kts,1); % Draws of constrained (conditioned) shocks.
+ Estr2 = reshape(Estr1,nvar,nstepsm);
+ Estr2 = Estr2'; % transpose so that
+ % Estr2: structural shocks. Row--nstepsm, Column--n shocks
+ Estr = [Estr2;randn(forep-nstepsm,nvar)]; % Second concatenated part: draws of free shocks.
+ % Now, forep-by-nvar -- ready for forecasts
+ %--------------
+ % Condition on variables with identified MS shocks backed out, no A random but S random.
+ %--------------
+ else % other shocks are indepedent of the eq_ms shock
+ % 3/20/99 The following may be problematic because Osk should depend
+ % on u (A0_h and Bh_h) in general. I have NOT worked out any good version.
+ %/*
+ % Osk = randn(kts,1); % other shocks
+ % for j=1:nstepsm
+ % Osk(nvar*(j-1)+eq_ms)=0; % no shock to the MS or identified equation
+ % end
+ % Estr = Econ + Osk; % Econ is non zero only at position
+ % % eq_ms*j where j=1:nstepsm
+ % Estr = reshape(Estr,nvar,nstepsm);
+ % Estr = Estr'; % transpose so that
+ % % Estr: structural shocks. Row--steps, Column--n shocks
+ % Estr = [Estr;randn(forep-nstepsm,nvar)];
+ % % Now, forep-by-nvar -- ready for forecasts
+ %
+ disp('DLS')
+ Ome = eye(kts) - u*u'; % note, I-u*u' = I - R*inv(R'*R)*R'
+ %[u1 d1 v1] = svd(Ome); % too slow
+ [u1 d1] = eig(Ome);
+ Stdcon = u1*diag(sqrt(diag(abs(d1)))); % lower triagular chol of conditional variance
+ % see Zha's forecast (1), p.17
+ tmp1=zeros(nvar,nstepsm);
+ tmp1(eq_ms,:)=randn(1,nstepsm);
+ tmp2=tmp1(:);
+ %Estr1 = Econ + Stdcon*randn(kts,1);
+ %jnk = reshape(Stdcon*tmp2,nvar,nstepsm)
+ Estr1 = Econ + Stdcon*tmp2;
+ Estr2 = reshape(Estr1,nvar,nstepsm);
+ Estr2 = Estr2'; % transpose so that
+ % Estr2: structural shocks. Row--nstepsm, Column--n shocks
+ Estr = [Estr2;randn(forep-nstepsm,nvar)];
+ % Now, forep-by-nvar -- ready for forecasts
+ end
+ else % Unconditional forecasts.
+ Estr = randn(forep,nvar); % Unconditional draws.
+ end
+
+ Ures = Estr*A0in; % <<>>1 nstepsm-by-nvar
+ % Ures: reduced-form residuals. Row--steps; Column--n shocks
+ % Note: We don't use /A0_h so as to eliminate small discrepancies to be
+ % completely compatible with the computation of Rmat and Estr, which uses A0in.
+
+ % ** reconstruct x(t) for y(t+h) = x(t+h-1)*B
+ % ** where phi = x(t+h-1) with last column being constant
+ %
+ yhat = zeros(forep,nvar);
+ for k=1:forep
+ yhat(k,:) = phi*Bh_h + Ures(k,:);
+ phi(1,nvar+1:tcwc) = phi(1,1:tcwc-nvar);
+ phi(1,1:nvar) = yhat(k,:);
+ end
+end
diff --git a/MatlabFiles/fn_fcstidcnd.m b/MatlabFiles/fn_fcstidcnd.m
index 0bf66715fc968ca6385a7c9fe25b5300d6b93768..e77a1bf7c9b11523867a703c42800b3ac9090a3c 100644
--- a/MatlabFiles/fn_fcstidcnd.m
+++ b/MatlabFiles/fn_fcstidcnd.m
@@ -1,326 +1,327 @@
-function [yhat,Estr,rcon,Rcon,u,v,d] = fn_fcstidcnd(valuecon,stepcon,varcon,nstepsm,...
- nconstr,eq_ms,nvar,lags,phil,Aband,Sband,yfore_h,imf3s_h,A0_h,Bh_h,...
- forep,TLindx,TLnumber,nCms,eq_Cms)
-% [yhat,Estr,rcon,Rcon,u,v,d] = fn_fcstidcnd(valuecon,stepcon,varcon,nstepsm,...
-% nconstr,eq_ms,nvar,lags,phil,Aband,Sband,yfore_h,imf3s_h,A0_h,Bh_h,...
-% forep,TLindx,TLnumber,nCms,eq_Cms)
-%
-% Note that the case Aband=1 is not finished yet.
-%
-% Conditional forecasting in the identified model with or without error bands
-% It handles conditions on average values as well, so "valuecon" must be
-% expressed at average (NOT sum) level.
-% Aband is used only once when nconstr>0 and Aband=1, where Gibbs sampler may be used. NOT yet finished.
-% Unconditional forecast when imf3s_h, etc is fixed and nconstr=0, where yfore_h must set to [].
-%
-% valuecon: vector of values conditioned
-% stepcon: sequence (cell) of steps conditioned; if length(stepcon{i}) > 1, the condition
-% is then an arithmetic average of log(y) over the stepcon{i} period.
-% varcon: vector of variables conditioned
-% nconstr: number of DLS constraints. If zero, it leads to unconditional forecasts.
-% nstepsm: maximum number of steps in all DLS constraints
-% nvar: number of variables in the BVAR model
-% lags: number of lags in the BVAR model
-% phil: the 1-by-(nvar*lags+1) data matrix where k=nvar*lags+1
-% (last period plus lags before the beginning of forecast)
-% Aband: 1: draws from A0 and Bh; 0: no draws. The case Aband=1 and nconstr>0 has NOT been finished yet.
-% Sband: 1: draws from random shocks E; 0: no random shocks
-% yfore_h: uncondtional forecasts: forep-by-nvar. Never used when nconstr=0.
-% In this case, set it to [];
-% imf3s_h: 3-dimensional impulse responses matrix: impsteps-by-nvar shocks-by-nvar responses
-% Never used when nconstr=0. In this case, set it to [];
-% A0_h: A0 contemporaneous parameter matrix
-% Bh_h: reduced-form parameter matrix: k-by-nvar, y(t) = X(t)*Bh+e(t)
-% where X(t) is k-by-nvar and y(t) is 1-by-nvar
-% forep: # of forecast periods (e.g., monthly for a monthly model)
-% TLindx: 1-by-nCms vector of 1's and 0's, indicating tight or loose; 1: tighter, 0: looser
-% Used only when /* (MS draws) is activated. Right now, MS shocks are deterministic.
-% TLnumber: 1-by-nCms vector, lower bound for tight and upper bound for loose
-% nCms: # of LZ conditions
-% eq_Cms: equation location of MS shocks
-% ------
-% yhat: conditional forecasts: forep-by-nvar
-% Estr: backed-out structural shocks (from N(0,1))
-% rcon: vector - the difference between valuecon and log(yfore) (unconditional forecasts)
-% Rcon: k-by-q (q constranits and k=nvar*max(nsteps)) so that
-% Rcon'*e = rcon where e is k-by-1
-% [u,d,v]: svd(Rcon,0)
-%
-%% See Zha's note "Forecast (1)" p. 5, RATS manual (some errors in RATS), etc.
-%
-%% Some notations: y(t+1) = y(t)B1 + e(t+1)inv(A0). e(t+1) is 1-by-n.
-%% Let r(t+1)=e(t+1)inv(A0) + e(t+2)C + .... where inv(A0) is impulse
-%% response at t=1, C at t=2, etc. The row of inv(A0) or C is
-%% all responses to one shock.
-%% Let r be q-by-1 (such as r(1) = r(t+1)
-%% = y(t+1) (constrained) - y(t+1) (forecast)).
-%% Use impulse responses to find out R (k-by-q) where k=nvar*nsteps
-%% where nsteps the largest constrained step. The key of the program
-%% is to creat R using impulse responses
-%% Optimal solution for shock e where R'*e=r and e is k-by-1 is
-%% e = R*inv(R'*R)*r and k>=q
-%
-% Copyright (c) March 1998 by Tao Zha. Revised November 1998;
-% 3/20/99 Disenabled draws of MS shcoks. To enable it, activate /* part
-% 3/20/99 Added A0_h and forep and deleted Cms as input argument. Previous
-% programs may not be compatible.
-% 3/15/2004 There are some BUG problems when calling fn_fcstcnd.m().
-%
-% Comparing with the version fn_fcstidcnd2.m, this is a better version.
-% See the note in fn_fcstidcnd2.m for explanation. April, 2009. TZ.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-DLSIdShock = ~isempty(eq_ms); % if not empty, the MS shock is identified as in DLS
-
-impsteps=size(imf3s_h,1);
-if (forep<nstepsm) | (impsteps<nstepsm)
- disp('Increase # of forecast or impulse steps!!')
- disp('Or decrease # of constraints (nconstr) or constrained steps (stepcon(i))!!')
- error('Maximum of conditional steps > # of forecast or impulse steps!!')
-end
-kts = nvar*nstepsm; % k -- ts: total shocks some of which are restricted and others
- % are free.
-%*** initializing
-Rcon = zeros(kts,nconstr); % R: k-by-q
-Econ = zeros(kts,1); % E: k-by-1
-rcon = zeros(nconstr,1); % r: q-by-1
-%rcon=valuecon-diag(yfore(stepcon,varcon)); % another way is to use "loop" below.
-tcwc = nvar*lags; % total coefficients without constant
-phi=phil;
-
-
-
-%----------------------------------------------------
-% Form rcon, Rcon, and Econ (the mean of structural shocks)
-%----------------------------------------------------
-if nconstr
- A0in = reshape(imf3s_h(1,:,:),nvar,nvar); % nvar shocks-by-nvar responses
- for i=1:nconstr
- rcon(i)=length(stepcon{i})*valuecon(i) - ...
- sum(yfore_h(stepcon{i},varcon(i)),1); %<<>>
- Rmat = zeros(nstepsm,nvar);
- r2mat = zeros(nstepsm,1); % simply one identified equation
- % Must be here inside the loop because it's matrix of one column of Rcon
- for j=1:length(stepcon{i})
- if DLSIdShock % Assuming the Fed can't see all other shocks within a month
- Rmat(1:stepcon{i}(j),eq_ms) = Rmat(1:stepcon{i}(j),eq_ms) + ...
- imf3s_h(stepcon{i}(j):-1:1,eq_ms,varcon(i));
- % Rmat: row--nstepsm, column--nvar shocks (here all shocks except
- % the identified one are set to zero) for a particular
- % endogenous variable 'varcon(i)'. See Zha Forcast (1), pp.6-7
- else % Rcon random with (A0,A+)
- Rmat(1:stepcon{i}(j),:) = Rmat(1:stepcon{i}(j),:) + ...
- imf3s_h(stepcon{i}(j):-1:1,:,varcon(i));
- % Rmat: row--nstepsm, column--nvar shocks (here all shocks are
- % *not* set to zero) for a particular endogenous
- % variable 'varcon(i)'. See Zha Forcast (1), pp.6-7
- end
- end
- Rmatt = Rmat'; % Now, nvar-by-nstepsm. I think here is where RATS has an error
- % i.e. "OVERR" is not transposed when overlaid to "CAPR"
- Rcon(:,i)=Rmatt(:); % Rcon: k-by-q where q=nconstr
- end
-
- [u d v]=svd(Rcon,0); %trial
- %???? Can we reduce the time by computing inv(R'*R) directly?
- % rtr = Rcon'*Rcon; %trial
- % rtrinv = inv(Rcon'*Rcon); %trial
- vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
- dinv = 1./diag(d); % inv(diag(d))
- vdinv=v.*(ones(size(v,2),1)*dinv'); %trial
- rtr=vd*vd'; % R'*R
- rtrinv = vdinv*vdinv'; % inv(R'*R)
-
- Econ = Rcon*rtrinv*rcon; % E = R*inv(R'R)*r; the mean of structural shocks
-else
- Econ = zeros(kts,1); % the mean of shocks is zero under no variable condition
- Rcon = NaN;
- rcon = NaN;
- u = NaN;
- d = NaN;
- v = NaN;
-end
-
-
-
-%---------------------------------------
-% No uncertainty at all or only random (A0,A+)
-% In other words, no future shocks
-%---------------------------------------
-if (~Sband) %| (nconstr & (length(eq_ms)==1))
- % length(eq_ms)==1 implies one-one mapping between MS shocks and, say, FFR
- % if nstepsm==nconstr. If this condition does not hold, this procedure
- % is incorrect. I don't have time to fix it now (3/20/99). So I use
- % this as a proximation
- Estr = reshape(Econ,nvar,nstepsm);
- Estr = Estr'; % transpose so that
- % Estr: structural shocks. Row--steps, Column--n shocks
- Estr = [Estr;zeros(forep-nstepsm,nvar)];
- % Now, forep-by-nvar -- ready for forecasts
- Estr(1:nCms,eq_Cms) = TLnumber(:);
- Ures = Estr/A0_h; % nstepsm-by-nvar
- % Ures: reduced-form residuals. Row--steps; Column--n shocks
-
- % ** reconstruct x(t) for y(t+h) = x(t+h-1)*B
- % ** where phi = x(t+h-1) with last column being constant
- %
- yhat = zeros(forep,nvar);
- for k=1:forep
- yhat(k,:) = phi*Bh_h + Ures(k,:);
- phi(1,nvar+1:tcwc) = phi(1,1:tcwc-nvar);
- phi(1,1:nvar) = yhat(k,:);
- %
- end
-
-%---------------------------------------
-% With random future shocks and possibly (A0,A+) depending
-% on if imf3s_h is random
-%---------------------------------------
-else
- %--------------
- % Condition on variables and A random
- %--------------
- if nconstr & Aband
- warning(' ')
- disp('This situation (both E and A random) is still under construction')
- disp('It is closely related to Waggoner and Zha ReStat Gibbs sampling method')
- disp('Please press ctrl-c to abort')
- pause
- elseif nconstr
- %--------------
- % Condition on variables and DLS MS shock, no A random but S random
- %--------------
- if DLSIdShock % other shocks are indepedent of the eq_ms shock
- % 3/20/99 The following may be problematic because Osk should depend
- % on u (A0_h and Bh_h) in general. I haven't worked out any good version
- %/*
- % Osk = randn(kts,1); % other shocks
- % for j=1:nstepsm
- % Osk(nvar*(j-1)+eq_ms)=0; % no shock to the MS or identified equation
- % end
- % Estr = Econ + Osk; % Econ is non zero only at position
- % % eq_ms*j where j=1:nstepsm
- % Estr = reshape(Estr,nvar,nstepsm);
- % Estr = Estr'; % transpose so that
- % % Estr: structural shocks. Row--steps, Column--n shocks
- % Estr = [Estr;randn(forep-nstepsm,nvar)];
- % % Now, forep-by-nvar -- ready for forecasts
- %
- disp('DLS')
- Ome = eye(kts) - u*u'; % note, I-u*u' = I - R*inv(R'*R)*R'
- %[u1 d1 v1] = svd(Ome); % too slow
- [u1 d1] = eig(Ome);
- Stdcon = u1*diag(sqrt(diag(abs(d1)))); % lower triagular chol of conditional variance
- % see Zha's forecast (1), p.17
- tmp1=zeros(nvar,nstepsm);
- tmp1(eq_ms,:)=randn(1,nstepsm);
- tmp2=tmp1(:);
- %Estr1 = Econ + Stdcon*randn(kts,1);
- %jnk = reshape(Stdcon*tmp2,nvar,nstepsm)
- Estr1 = Econ + Stdcon*tmp2;
- Estr2 = reshape(Estr1,nvar,nstepsm);
- Estr2 = Estr2'; % transpose so that
- % Estr2: structural shocks. Row--nstepsm, Column--n shocks
- Estr = [Estr2;randn(forep-nstepsm,nvar)];
- % Now, forep-by-nvar -- ready for forecasts
- else
- Ome = eye(kts) - u*u'; % note, I-u*u' = I - R*inv(R'*R)*R'
- %[u1 d1 v1] = svd(Ome); % too slow
- [u1 d1] = eig(Ome);
- Stdcon = u1*diag(sqrt(diag(abs(d1)))); % lower triagular chol of conditional variance
- % see Zha's forecast (1), p.17
- %--------------
- % Condition on variables and LZ MS shock, no A random but S random
- % This section has not be tested yet, 10/14/98
- %--------------
- if nCms
- Estr1 = Econ + Stdcon*randn(kts,1);
- Estr2 = reshape(Estr1,nvar,nstepsm);
- Estr2 = Estr2'; % transpose so that
- % Estr2: structural shocks. Row--nstepsm, Column--n shocks
- Estr = [Estr2;randn(forep-nstepsm,nvar)];
- % Now, forep-by-nvar -- ready for forecasts
- Estr(1:nCms,eq_Cms) = TLnumber(:);
-
- %/* draw MS shocks
- % for k=1:nCms
- % if TLindx(k) % tighter
- % while (Estr(k,eq_Cms)<TLnumber(k))
- % Estr(k,eq_Cms) = randn(1,1);
- % end
- % else % looser
- % while (Estr(k,eq_Cms)>TLnumber(k))
- % Estr(k,eq_Cms) = randn(1,1);
- % end
- % end
- % end
- %--------------
- % Condition on variables only, no A random but S random
- %--------------
- else
- Estr1 = Econ + Stdcon*randn(kts,1);
- Estr2 = reshape(Estr1,nvar,nstepsm);
- Estr2 = Estr2'; % transpose so that
- % Estr2: structural shocks. Row--nstepsm, Column--n shocks
- Estr = [Estr2;randn(forep-nstepsm,nvar)];
- % Now, forep-by-nvar -- ready for forecasts
- end
- end
- %--------------
- % Condition on LZ MS shocks only, S random and possibly A random depending on
- % if A0_h and Bh_h are random
- %--------------
- else
- if nCms
- Estr = randn(forep,nvar);
- % Now, forep-by-nvar -- ready for forecasts
- Estr(1:nCms,eq_Cms) = TLnumber(:);
-
- %/* draw MS shocks
- % for k=1:nCms
- % if TLindx(k) % tighter
- % while (Estr(k,eq_Cms)<TLnumber(k))
- % Estr(k,eq_Cms) = randn(1,1);
- % end
- % else % looser
- % while (Estr(k,eq_Cms)>TLnumber(k))
- % Estr(k,eq_Cms) = randn(1,1);
- % end
- % end
- % end
- else
- Estr = randn(forep,nvar); % Unconditional forecast
- % Now, forep-by-nvar -- ready for forecasts
- end
- end
- %
-
-
- Ures = Estr/A0_h; % nstepsm-by-nvar
- % Ures: reduced-form residuals. Row--steps; Column--n shocks
-
- % ** reconstruct x(t) for y(t+h) = x(t+h-1)*B
- % ** where phi = x(t+h-1) with last column being constant
- %
- yhat = zeros(forep,nvar);
- for k=1:forep
- yhat(k,:) = phi*Bh_h + Ures(k,:);
- phi(1,nvar+1:tcwc) = phi(1,1:tcwc-nvar);
- phi(1,1:nvar) = yhat(k,:);
- end
-end
+function [yhat,Estr,rcon,Rcon,u,v,d] = fn_fcstidcnd(valuecon,stepcon,varcon,nstepsm,...
+ nconstr,eq_ms,nvar,lags,phil,Aband,Sband,yfore_h,imf3s_h,A0_h,Bh_h,...
+ forep,TLindx,TLnumber,nCms,eq_Cms)
+% [yhat,Estr,rcon,Rcon,u,v,d] = fn_fcstidcnd(valuecon,stepcon,varcon,nstepsm,...
+% nconstr,eq_ms,nvar,lags,phil,Aband,Sband,yfore_h,imf3s_h,A0_h,Bh_h,...
+% forep,TLindx,TLnumber,nCms,eq_Cms)
+%
+% Note that the case Aband=1 is not finished yet.
+%
+% Conditional forecasting in the identified model with or without error bands
+% It handles conditions on average values as well, so "valuecon" must be
+% expressed at average (NOT sum) level.
+% Aband is used only once when nconstr>0 and Aband=1, where Gibbs sampler may be used. NOT yet finished.
+% Unconditional forecast when imf3s_h, etc is fixed and nconstr=0, where yfore_h must set to [].
+%
+% valuecon: vector of values conditioned
+% stepcon: sequence (cell) of steps conditioned; if length(stepcon{i}) > 1, the condition
+% is then an arithmetic average of log(y) over the stepcon{i} period.
+% varcon: vector of variables conditioned
+% nconstr: number of DLS constraints. If zero, it leads to unconditional forecasts.
+% nstepsm: maximum number of steps in all DLS constraints
+% nvar: number of variables in the BVAR model
+% lags: number of lags in the BVAR model
+% phil: the 1-by-(nvar*lags+1) data matrix where k=nvar*lags+1
+% (last period plus lags before the beginning of forecast)
+% Aband: 1: draws from A0 and Bh; 0: no draws. The case Aband=1 and nconstr>0 has NOT been finished yet.
+% Sband: 1: draws from random shocks E; 0: no random shocks
+% yfore_h: uncondtional forecasts: forep-by-nvar. Never used when nconstr=0.
+% In this case, set it to [];
+% imf3s_h: 3-dimensional impulse responses matrix: impsteps-by-nvar shocks-by-nvar responses
+% Never used when nconstr=0. In this case, set it to [];
+% A0_h: A0 contemporaneous parameter matrix
+% Bh_h: reduced-form parameter matrix: k-by-nvar, y(t) = X(t)*Bh+e(t)
+% where X(t) is k-by-nvar and y(t) is 1-by-nvar
+% forep: # of forecast periods (e.g., monthly for a monthly model)
+% TLindx: 1-by-nCms vector of 1's and 0's, indicating tight or loose; 1: tighter, 0: looser
+% Used only when /* (MS draws) is activated. Right now, MS shocks are deterministic.
+% TLnumber: 1-by-nCms vector, lower bound for tight and upper bound for loose
+% nCms: # of LZ conditions
+% eq_Cms: equation location of MS shocks
+% ------
+% yhat: conditional forecasts: forep-by-nvar
+% Estr: backed-out structural shocks (from N(0,1))
+% rcon: vector - the difference between valuecon and log(yfore) (unconditional forecasts)
+% Rcon: k-by-q (q constranits and k=nvar*max(nsteps)) so that
+% Rcon'*e = rcon where e is k-by-1
+% [u,d,v]: svd(Rcon,0)
+%
+%% See Zha's note "Forecast (1)" p. 5, RATS manual (some errors in RATS), etc.
+%
+%% Some notations: y(t+1) = y(t)B1 + e(t+1)inv(A0). e(t+1) is 1-by-n.
+%% Let r(t+1)=e(t+1)inv(A0) + e(t+2)C + .... where inv(A0) is impulse
+%% response at t=1, C at t=2, etc. The row of inv(A0) or C is
+%% all responses to one shock.
+%% Let r be q-by-1 (such as r(1) = r(t+1)
+%% = y(t+1) (constrained) - y(t+1) (forecast)).
+%% Use impulse responses to find out R (k-by-q) where k=nvar*nsteps
+%% where nsteps the largest constrained step. The key of the program
+%% is to creat R using impulse responses
+%% Optimal solution for shock e where R'*e=r and e is k-by-1 is
+%% e = R*inv(R'*R)*r and k>=q
+%
+% Revised November 1998;
+% 3/20/99 Disenabled draws of MS shcoks. To enable it, activate /* part
+% 3/20/99 Added A0_h and forep and deleted Cms as input argument. Previous
+% programs may not be compatible.
+% 3/15/2004 There are some BUG problems when calling fn_fcstcnd.m().
+%
+% Comparing with the version fn_fcstidcnd2.m, this is a better version.
+% See the note in fn_fcstidcnd2.m for explanation. April, 2009. TZ.
+%
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+DLSIdShock = ~isempty(eq_ms); % if not empty, the MS shock is identified as in DLS
+
+impsteps=size(imf3s_h,1);
+if (forep<nstepsm) | (impsteps<nstepsm)
+ disp('Increase # of forecast or impulse steps!!')
+ disp('Or decrease # of constraints (nconstr) or constrained steps (stepcon(i))!!')
+ error('Maximum of conditional steps > # of forecast or impulse steps!!')
+end
+kts = nvar*nstepsm; % k -- ts: total shocks some of which are restricted and others
+ % are free.
+%*** initializing
+Rcon = zeros(kts,nconstr); % R: k-by-q
+Econ = zeros(kts,1); % E: k-by-1
+rcon = zeros(nconstr,1); % r: q-by-1
+%rcon=valuecon-diag(yfore(stepcon,varcon)); % another way is to use "loop" below.
+tcwc = nvar*lags; % total coefficients without constant
+phi=phil;
+
+
+
+%----------------------------------------------------
+% Form rcon, Rcon, and Econ (the mean of structural shocks)
+%----------------------------------------------------
+if nconstr
+ A0in = reshape(imf3s_h(1,:,:),nvar,nvar); % nvar shocks-by-nvar responses
+ for i=1:nconstr
+ rcon(i)=length(stepcon{i})*valuecon(i) - ...
+ sum(yfore_h(stepcon{i},varcon(i)),1); %<<>>
+ Rmat = zeros(nstepsm,nvar);
+ r2mat = zeros(nstepsm,1); % simply one identified equation
+ % Must be here inside the loop because it's matrix of one column of Rcon
+ for j=1:length(stepcon{i})
+ if DLSIdShock % Assuming the Fed can't see all other shocks within a month
+ Rmat(1:stepcon{i}(j),eq_ms) = Rmat(1:stepcon{i}(j),eq_ms) + ...
+ imf3s_h(stepcon{i}(j):-1:1,eq_ms,varcon(i));
+ % Rmat: row--nstepsm, column--nvar shocks (here all shocks except
+ % the identified one are set to zero) for a particular
+ % endogenous variable 'varcon(i)'. See Zha Forcast (1), pp.6-7
+ else % Rcon random with (A0,A+)
+ Rmat(1:stepcon{i}(j),:) = Rmat(1:stepcon{i}(j),:) + ...
+ imf3s_h(stepcon{i}(j):-1:1,:,varcon(i));
+ % Rmat: row--nstepsm, column--nvar shocks (here all shocks are
+ % *not* set to zero) for a particular endogenous
+ % variable 'varcon(i)'. See Zha Forcast (1), pp.6-7
+ end
+ end
+ Rmatt = Rmat'; % Now, nvar-by-nstepsm. I think here is where RATS has an error
+ % i.e. "OVERR" is not transposed when overlaid to "CAPR"
+ Rcon(:,i)=Rmatt(:); % Rcon: k-by-q where q=nconstr
+ end
+
+ [u d v]=svd(Rcon,0); %trial
+ %???? Can we reduce the time by computing inv(R'*R) directly?
+ % rtr = Rcon'*Rcon; %trial
+ % rtrinv = inv(Rcon'*Rcon); %trial
+ vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
+ dinv = 1./diag(d); % inv(diag(d))
+ vdinv=v.*(ones(size(v,2),1)*dinv'); %trial
+ rtr=vd*vd'; % R'*R
+ rtrinv = vdinv*vdinv'; % inv(R'*R)
+
+ Econ = Rcon*rtrinv*rcon; % E = R*inv(R'R)*r; the mean of structural shocks
+else
+ Econ = zeros(kts,1); % the mean of shocks is zero under no variable condition
+ Rcon = NaN;
+ rcon = NaN;
+ u = NaN;
+ d = NaN;
+ v = NaN;
+end
+
+
+
+%---------------------------------------
+% No uncertainty at all or only random (A0,A+)
+% In other words, no future shocks
+%---------------------------------------
+if (~Sband) %| (nconstr & (length(eq_ms)==1))
+ % length(eq_ms)==1 implies one-one mapping between MS shocks and, say, FFR
+ % if nstepsm==nconstr. If this condition does not hold, this procedure
+ % is incorrect. I don't have time to fix it now (3/20/99). So I use
+ % this as a proximation
+ Estr = reshape(Econ,nvar,nstepsm);
+ Estr = Estr'; % transpose so that
+ % Estr: structural shocks. Row--steps, Column--n shocks
+ Estr = [Estr;zeros(forep-nstepsm,nvar)];
+ % Now, forep-by-nvar -- ready for forecasts
+ Estr(1:nCms,eq_Cms) = TLnumber(:);
+ Ures = Estr/A0_h; % nstepsm-by-nvar
+ % Ures: reduced-form residuals. Row--steps; Column--n shocks
+
+ % ** reconstruct x(t) for y(t+h) = x(t+h-1)*B
+ % ** where phi = x(t+h-1) with last column being constant
+ %
+ yhat = zeros(forep,nvar);
+ for k=1:forep
+ yhat(k,:) = phi*Bh_h + Ures(k,:);
+ phi(1,nvar+1:tcwc) = phi(1,1:tcwc-nvar);
+ phi(1,1:nvar) = yhat(k,:);
+ %
+ end
+
+%---------------------------------------
+% With random future shocks and possibly (A0,A+) depending
+% on if imf3s_h is random
+%---------------------------------------
+else
+ %--------------
+ % Condition on variables and A random
+ %--------------
+ if nconstr & Aband
+ warning(' ')
+ disp('This situation (both E and A random) is still under construction')
+ disp('It is closely related to Waggoner and Zha ReStat Gibbs sampling method')
+ disp('Please press ctrl-c to abort')
+ pause
+ elseif nconstr
+ %--------------
+ % Condition on variables and DLS MS shock, no A random but S random
+ %--------------
+ if DLSIdShock % other shocks are indepedent of the eq_ms shock
+ % 3/20/99 The following may be problematic because Osk should depend
+ % on u (A0_h and Bh_h) in general. I haven't worked out any good version
+ %/*
+ % Osk = randn(kts,1); % other shocks
+ % for j=1:nstepsm
+ % Osk(nvar*(j-1)+eq_ms)=0; % no shock to the MS or identified equation
+ % end
+ % Estr = Econ + Osk; % Econ is non zero only at position
+ % % eq_ms*j where j=1:nstepsm
+ % Estr = reshape(Estr,nvar,nstepsm);
+ % Estr = Estr'; % transpose so that
+ % % Estr: structural shocks. Row--steps, Column--n shocks
+ % Estr = [Estr;randn(forep-nstepsm,nvar)];
+ % % Now, forep-by-nvar -- ready for forecasts
+ %
+ disp('DLS')
+ Ome = eye(kts) - u*u'; % note, I-u*u' = I - R*inv(R'*R)*R'
+ %[u1 d1 v1] = svd(Ome); % too slow
+ [u1 d1] = eig(Ome);
+ Stdcon = u1*diag(sqrt(diag(abs(d1)))); % lower triagular chol of conditional variance
+ % see Zha's forecast (1), p.17
+ tmp1=zeros(nvar,nstepsm);
+ tmp1(eq_ms,:)=randn(1,nstepsm);
+ tmp2=tmp1(:);
+ %Estr1 = Econ + Stdcon*randn(kts,1);
+ %jnk = reshape(Stdcon*tmp2,nvar,nstepsm)
+ Estr1 = Econ + Stdcon*tmp2;
+ Estr2 = reshape(Estr1,nvar,nstepsm);
+ Estr2 = Estr2'; % transpose so that
+ % Estr2: structural shocks. Row--nstepsm, Column--n shocks
+ Estr = [Estr2;randn(forep-nstepsm,nvar)];
+ % Now, forep-by-nvar -- ready for forecasts
+ else
+ Ome = eye(kts) - u*u'; % note, I-u*u' = I - R*inv(R'*R)*R'
+ %[u1 d1 v1] = svd(Ome); % too slow
+ [u1 d1] = eig(Ome);
+ Stdcon = u1*diag(sqrt(diag(abs(d1)))); % lower triagular chol of conditional variance
+ % see Zha's forecast (1), p.17
+ %--------------
+ % Condition on variables and LZ MS shock, no A random but S random
+ % This section has not be tested yet, 10/14/98
+ %--------------
+ if nCms
+ Estr1 = Econ + Stdcon*randn(kts,1);
+ Estr2 = reshape(Estr1,nvar,nstepsm);
+ Estr2 = Estr2'; % transpose so that
+ % Estr2: structural shocks. Row--nstepsm, Column--n shocks
+ Estr = [Estr2;randn(forep-nstepsm,nvar)];
+ % Now, forep-by-nvar -- ready for forecasts
+ Estr(1:nCms,eq_Cms) = TLnumber(:);
+
+ %/* draw MS shocks
+ % for k=1:nCms
+ % if TLindx(k) % tighter
+ % while (Estr(k,eq_Cms)<TLnumber(k))
+ % Estr(k,eq_Cms) = randn(1,1);
+ % end
+ % else % looser
+ % while (Estr(k,eq_Cms)>TLnumber(k))
+ % Estr(k,eq_Cms) = randn(1,1);
+ % end
+ % end
+ % end
+ %--------------
+ % Condition on variables only, no A random but S random
+ %--------------
+ else
+ Estr1 = Econ + Stdcon*randn(kts,1);
+ Estr2 = reshape(Estr1,nvar,nstepsm);
+ Estr2 = Estr2'; % transpose so that
+ % Estr2: structural shocks. Row--nstepsm, Column--n shocks
+ Estr = [Estr2;randn(forep-nstepsm,nvar)];
+ % Now, forep-by-nvar -- ready for forecasts
+ end
+ end
+ %--------------
+ % Condition on LZ MS shocks only, S random and possibly A random depending on
+ % if A0_h and Bh_h are random
+ %--------------
+ else
+ if nCms
+ Estr = randn(forep,nvar);
+ % Now, forep-by-nvar -- ready for forecasts
+ Estr(1:nCms,eq_Cms) = TLnumber(:);
+
+ %/* draw MS shocks
+ % for k=1:nCms
+ % if TLindx(k) % tighter
+ % while (Estr(k,eq_Cms)<TLnumber(k))
+ % Estr(k,eq_Cms) = randn(1,1);
+ % end
+ % else % looser
+ % while (Estr(k,eq_Cms)>TLnumber(k))
+ % Estr(k,eq_Cms) = randn(1,1);
+ % end
+ % end
+ % end
+ else
+ Estr = randn(forep,nvar); % Unconditional forecast
+ % Now, forep-by-nvar -- ready for forecasts
+ end
+ end
+ %
+
+
+ Ures = Estr/A0_h; % nstepsm-by-nvar
+ % Ures: reduced-form residuals. Row--steps; Column--n shocks
+
+ % ** reconstruct x(t) for y(t+h) = x(t+h-1)*B
+ % ** where phi = x(t+h-1) with last column being constant
+ %
+ yhat = zeros(forep,nvar);
+ for k=1:forep
+ yhat(k,:) = phi*Bh_h + Ures(k,:);
+ phi(1,nvar+1:tcwc) = phi(1,1:tcwc-nvar);
+ phi(1,1:nvar) = yhat(k,:);
+ end
+end
diff --git a/MatlabFiles/fn_fcstidcnd2.m b/MatlabFiles/fn_fcstidcnd2.m
index f88fe953b492bfe781bae9128b8391521c9bf72d..50e5972525e8e64d164cd0ec1187243f55f2d096 100644
--- a/MatlabFiles/fn_fcstidcnd2.m
+++ b/MatlabFiles/fn_fcstidcnd2.m
@@ -1,365 +1,365 @@
-function [yhat,Estr,rcon,Rcon,u,v,d] = fn_fcstidcnd2(valuecon,stepcon,varcon,nstepsm,...
- nconstr,eq_ms,nvar,lags,phil,Aband,Sband,yfore_h,imf3s_h,A0_h,Bh_h,...
- forep,TLindx,TLnumber,nCms,eq_Cms,nconadd,eq_oth,radd,Radd)
-% [yhat,Estr,rcon,Rcon,u,v,d] = fn_fcstidcnd2(valuecon,stepcon,varcon,nstepsm,...
-% nconstr,eq_ms,nvar,lags,phil,Aband,Sband,yfore_h,imf3s_h,A0_h,Bh_h,...
-% forep,TLindx,TLnumber,nCms,eq_Cms,nconadd,eq_oth,radd,Radd)
-%
-% 5/2/99: This one is much, much slower than fidcnderr.m when eq_ms=[] and
-% nconstr>0 and length(eq_ms)*nstepsm>nconstr. Seems to me that fcstidcnd2.m
-% is designed for the situation which eq_ms=2. The slow speed is due to
-% "Radd" added. When I have time, I need to check to see if I can speed up
-% this M file.
-%
-% Note that the case Aband=1 is not finished yet.
-%
-% Conditional forecasting in the identified model with or without error bands
-% It handles conditions on average values as well, so "valuecon" must be
-% expressed at average (NOT sum) level. Including unconditional forecasts
-% when nconstr=nCms=0. Maybe slower than "fcstidcnd.m" when length(eq_ms)*nstepsm=nconstr
-% because of Radd added. But "fsctidcnd.m" is incorrect when length(eq_ms)
-% *nstepsm>nconstr (situations like the average annual FFR constraint). 3/22/99
-% Ref.: Zha Forecast (I), pp. 17-17c.
-%
-% valuecon: vector of values conditioned
-% stepcon: sequence (cell) of steps conditioned; if length(stepcon{i}) > 1, the condition
-% is then an arithmetic average of log(y) over the stepcon{i} period.
-% varcon: vector of variables conditioned
-% nconstr: number of DLS constraints
-% nstepsm: maximum number of steps in all DLS constraints
-% nvar: number of variables in the BVAR model
-% lags: number of lags in the BVAR model
-% phil: the 1-by-(nvar*lags+1) data matrix where k=nvar*lags+1
-% (last period plus lags before the beginning of forecast)
-% eq_ms: identified equation or shock (in terms of number). If equ_ms=[], then "fidencond" or
-% 'fcstidcond' is, similar to RATS, to compute forecasts with *all* shocks.
-% Aband: 1: draws from A0 and Bh; 0: no draws
-% Sband: 1: draws from random shocks E; 0: no random shocks
-% yfore_h: uncondtional forecasts: forep-by-nvar. Never used when nconstr=0.
-% In this case, set it to [];
-% imf3s_h: 3-dimensional impulse responses matrix: impsteps-by-nvar shocks-by-nvar responses
-% Never used when nconstr=0. In this case, set it to [];
-% A0_h: A0 contemporaneous parameter matrix
-% Bh_h: reduced-form parameter matrix: k-by-nvar, y(t) = X(t)*Bh+e(t)
-% where X(t) is k-by-nvar and y(t) is 1-by-nvar
-% forep: # of forecast periods (e.g., monthly for a monthly model)
-% TLindx: 1-by-nCms vector of 1's and 0's, indicating tight or loose; 1: tighter, 0: looser
-% Used only when /* (MS draws) is activated. Right now, MS shocks are deterministic.
-% TLnumber: 1-by-nCms vector, lower bound for tight and upper bound for loose
-% nCms: # of LZ conditions
-% eq_Cms: equation location of MS shocks
-% nconadd: number of DLS added constraints DIRECTLY on structural shocks
-% for identified version where eq_ms is not empty. Note
-% nconadd=0 when eq_ms is empty.
-% eq_oth: index for other structural shocks than those in eq_ms.
-% If eq_ms=[], eq_oth=[]. Note length(eq_oth)+length(eq_ms)=nvar
-% radd: sparse nconadd-by-1; nconadd values added to rcon in the text later
-% Radd: sparce nvar*nstepsm-by-nconadd; added to Rcon in the text later
-% ------
-% yhat: conditional forecasts: forep-by-nvar
-% Estr: backed-out structural shocks (from N(0,1))
-% rcon: vector - the difference between valuecon and log(yfore) (unconditional forecasts)
-% Rcon: k-by-q (q constranits and k=nvar*max(nsteps)) so that
-% Rcon'*e = rcon where e is k-by-1
-% [u,d,v]: svd(Rcon,0)
-%
-% Copyright (c) March 1998 by Tao Zha. Revised November 1998;
-% 3/20/99 Disenabled draws of MS shcoks. To enable it, activate /* part
-% 3/20/99 Added A0_h and forep and deleted Cms as input argument.
-% 3/21/99 Added nconadd and eq_oth.
-% Previous programs may not be compatible.
-
-%
-% This version differs from "fn_fcstidcnd.m" because nconadd, radd, and Radd are added to allow for
-% both DLS-WZ conditions and structural-shock conditions to exist at the same time. My recognition
-% is that it does not work well at all. So one should use the version "fn_fcstidcnd.m."" April, 2009 by TZ.
-
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-%
-%% See Zha's note "Forecast (1)" p. 5, RATS manual (some errors in RATS), etc.
-%% See also Zha Forecast (I), pp. 17-17c.
-%
-%% Some notations: y(t+1) = y(t)B1 + e(t+1)inv(A0). e(t+1) is 1-by-n.
-%% Let r(t+1)=e(t+1)inv(A0) + e(t+2)C + .... where inv(A0) is impulse
-%% response at t=1, C at t=2, etc. The row of inv(A0) or C is
-%% all responses to one shock.
-%% Let r be q-by-1 (such as r(1) = r(t+1)
-%% = y(t+1) (constrained) - y(t+1) (forecast)).
-%% Use impulse responses to find out R (k-by-q) where k=nvar*nsteps
-%% where nsteps the largest constrained step. The key of the program
-%% is to creat R using impulse responses
-%% Optimal solution for shock e where R'*e=r and e is k-by-1 is
-%% e = R*inv(R'*R)*r and k>=q
-%
-
-
-DLSIdShock = ~isempty(eq_ms); % if not empty, the MS shock is identified as in DLS
-
-impsteps=size(imf3s_h,1);
-if (forep<nstepsm) | (impsteps<nstepsm)
- disp('Increase # of forecast or impulse steps!!')
- disp('Or decrease # of constraints (nconstr) or constrained steps (stepcon(i))!!')
- error('Maximum of conditional steps > # of forecast or impulse steps!!')
-end
-kts = nvar*nstepsm; % k -- ts: total shocks some of which are restricted and others
- % are free.
-%*** initializing
-Rcon = zeros(kts,nconstr); % R: k-by-q where q include possible additional
- % constraints directly on structural shocks for identified models.
- % These addtional constraints will be added later.
-Econ = sparse(zeros(kts,1)); % E: k-by-1
-rcon = zeros(nconstr,1); % r: q-by-1
-%rcon=valuecon-diag(yfore(stepcon,varcon)); % another way is to use "loop" below.
-tcwc = nvar*lags; % total coefficients without constant
-phi=phil;
-
-
-
-%----------------------------------------------------
-% Form rcon, Rcon, and Econ (the mean of structural shocks)
-%----------------------------------------------------
-if nconstr
- A0in = reshape(imf3s_h(1,:,:),nvar,nvar); % nvar shocks-by-nvar responses
- for i=1:nconstr
- rcon(i)=length(stepcon{i})*valuecon(i) - ...
- sum(yfore_h(stepcon{i},varcon(i)),1); %<<>>
- Rmat = zeros(nstepsm,nvar);
- % Must be here inside the loop because it's matrix of one column of Rcon
- for j=1:length(stepcon{i})
- if DLSIdShock % Assuming the Fed can't see all other shocks within a month
- Rmat(1:stepcon{i}(j),:) = Rmat(1:stepcon{i}(j),:) + ...
- imf3s_h(stepcon{i}(j):-1:1,:,varcon(i));
- % Rmat: row--nstepsm, column--nvar shocks (here all shocks except
- % the identified one are set to zero) for a particular
- % endogenous variable 'varcon(i)'. See Zha Forcast (1), pp.6-7
- else % Rcon random with (A0,A+)
- Rmat(1:stepcon{i}(j),:) = Rmat(1:stepcon{i}(j),:) + ...
- imf3s_h(stepcon{i}(j):-1:1,:,varcon(i));
- % Rmat: row--nstepsm, column--nvar shocks (here all shocks are
- % *not* set to zero) for a particular endogenous
- % variable 'varcon(i)'. See Zha Forcast (1), pp.6-7
- end
- end
- Rmatt = Rmat'; % Now, nvar-by-nstepsm. I think here is where RATS has an error
- % i.e. "OVERR" is not transposed when overlaid to "CAPR"
- Rcon(:,i)=Rmatt(:); % Rcon: k-by-q where q=nconstr
- end
-
- rcon = [rcon;radd]; % added nconadd constrained values for identified model
- % sparse because radd is sparse
- Rcon = [Rcon Radd]; % added constraints on shocks for identified version
- % sparse because Radd is sparse
- Rcont=Rcon';
- rinvrtr=Rcon/(Rcont*Rcon);
- Econ = rinvrtr*rcon;
-
-
- %/* Too slow, I believe, when q is large. 3/21/99
- % [u d v]=svd(Rcon,0); %trial
- % %???? Can we reduce the time by computing inv(R'*R) directly?
- % % rtr = Rcon'*Rcon; %trial
- % % rtrinv = inv(Rcon'*Rcon); %trial
- % vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
- % dinv = 1./diag(d); % inv(diag(d))
- % vdinv=v.*(ones(size(v,2),1)*dinv'); %trial
- % rtr=vd*vd'; % R'*R
- % rtrinv = vdinv*vdinv'; % inv(R'*R)
- %
- % Econ = Rcon*rtrinv*rcon; % E = R*inv(R'R)*r; the mean of structural shocks
-else
- Econ = sparse(zeros(kts,1)); % the mean of shocks is zero under no variable condition
- Rcon = NaN;
- rcon = NaN;
- u = NaN;
- d = NaN;
- v = NaN;
-end
-
-
-
-%---------------------------------------
-% No uncertainty at all or only random (A0,A+)
-% In other words, no future shocks
-%---------------------------------------
-if (~Sband) | (nconstr & length(eq_ms)*nstepsm==nconstr)
- % length(eq_ms)==1 implies one-one mapping between MS shocks and, say, FFR
- % if nstepsm==nconstr. If this condition does not hold, this procedure
- % is incorrect. I don't have time to fix it now (3/20/99). So I use
- % this as a proximation
- Estr = reshape(Econ,nvar,nstepsm);
- Estr = Estr'; % transpose so that
- % Estr: structural shocks. Row--steps, Column--n shocks
- Estr = [Estr;zeros(forep-nstepsm,nvar)];
- % Now, forep-by-nvar -- ready for forecasts
- Estr(1:nCms,eq_Cms) = TLnumber(:);
- Ures = Estr/A0_h; % nstepsm-by-nvar
- % Ures: reduced-form residuals. Row--steps; Column--n shocks
-
- % ** reconstruct x(t) for y(t+h) = x(t+h-1)*B
- % ** where phi = x(t+h-1) with last column being constant
- %
- yhat = zeros(forep,nvar);
- for k=1:forep
- yhat(k,:) = phi*Bh_h + Ures(k,:);
- phi(1,nvar+1:tcwc) = phi(1,1:tcwc-nvar);
- phi(1,1:nvar) = yhat(k,:);
- %
- end
-
-%---------------------------------------
-% With random future shocks and possibly (A0,A+) depending
-% on if imf3s_h is random
-%---------------------------------------
-else
- %--------------
- % Condition on variables and A random
- %--------------
- if nconstr & Aband
- warning(' ')
- disp('This situation (both E and A random) is still under construction')
- disp('It is closely related to Waggoner and Zha ReStat Gibbs sampling method')
- disp('and related to "if DLSIdShock" in the following')
- disp('Please press ctrl-c to abort')
- pause
- elseif nconstr
- %--------------
- % Condition on variables and DLS MS shock, no A random but S random
- %--------------
- if DLSIdShock % other shocks are indepedent of the eq_ms shock
- Ome=speye(kts)-rinvrtr*Rcont;
- Ome(find(abs(Ome)<1e-10))=0; % tighter the tolerance is, the longer it
- % takes for svds to compute. E.g., size(Ome,1)*eps is perhaps very
- % tight. After all, when the number in Ome is very small relative to
- % 1 (which is the variance of structural shocks Estr), we can treat it
- % as zero.
- [u1 d1 v1] = svds(Ome,kts-nconstr-nconadd);
- % We have exactly nconstr+nconadd zero singular values
- Stdcon = u1*diag(sqrt(diag(abs(d1)))); % find a square root of Ome
- % see Zha's forecast (1), p.17
- Stdcon = [Stdcon sparse(zeros(kts,kts-size(d1,1)))];
- Estr1 = Econ + Stdcon*randn(kts,1); % We must have rand(kts,1). Assigning
- % some of randn(kts,1) to be zero according to Radd is WRONG.
- % For this argument, see Zha Forecast (I) p.17a
- Estr2 = reshape(Estr1,nvar,nstepsm);
- % nvar-by-nstepsm; Needed to be transposed later
- Estr = [Estr2';randn(forep-nstepsm,nvar)]; % transpose so that
- % Estr2: structural shocks. Row--nstepsm, Column--n shocks
- % Now, forep-by-nvar -- ready for forecasts
- else
- Ome=speye(kts)-rinvrtr*Rcont;
- Ome(find(abs(Ome)<1e-10))=0; % tighter the tolerance is, the longer it
- % takes for svds to compute. E.g., size(Ome,1)*eps is perhaps very
- % tight. After all, when the number in Ome is very small relative to
- % 1 (which is the variance of structural shocks Estr), we can treat it
- % as zero.
- [u1 d1 v1] = svds(Ome,kts-nconstr-nconadd);
- % We have exactly nconstr+nconadd zero singular values
- Stdcon = u1*diag(sqrt(diag(abs(d1)))); % find a square root of Ome
- % see Zha's forecast (1), p.17
- Stdcon = [Stdcon sparse(zeros(kts,nconstr+nconadd))];
-
- %*** The following very inefficient
- % Ome = eye(kts) - u*u'; % note, I-u*u' = I - R*inv(R'*R)*R'
- % %[u1 d1 v1] = svd(Ome); % too slow
- % [u1 d1] = eig(Ome);
- % Stdcon = u1*diag(sqrt(diag(abs(d1)))); % lower triagular chol of conditional variance
- % % see Zha's forecast (1), p.17
-
- %--------------
- % Condition on variables and LZ MS shock, no A random but S random
- % This section has not be tested yet, 10/14/98
- %--------------
- if nCms
- Estr1 = Econ + Stdcon*randn(kts,1);
- Estr2 = reshape(Estr1,nvar,nstepsm);
- Estr2 = Estr2'; % transpose so that
- % Estr2: structural shocks. Row--nstepsm, Column--n shocks
- Estr = [Estr2;randn(forep-nstepsm,nvar)];
- % Now, forep-by-nvar -- ready for forecasts
- Estr(1:nCms,eq_Cms) = TLnumber(:);
-
- %/* draw MS shocks
- % for k=1:nCms
- % if TLindx(k) % tighter
- % while (Estr(k,eq_Cms)<TLnumber(k))
- % Estr(k,eq_Cms) = randn(1,1);
- % end
- % else % looser
- % while (Estr(k,eq_Cms)>TLnumber(k))
- % Estr(k,eq_Cms) = randn(1,1);
- % end
- % end
- % end
- %--------------
- % Condition on variables only, no A random but S random
- %--------------
- else
- Estr1 = Econ + Stdcon*randn(kts,1);
- Estr2 = reshape(Estr1,nvar,nstepsm);
- Estr2 = Estr2'; % transpose so that
- % Estr2: structural shocks. Row--nstepsm, Column--n shocks
- Estr = [Estr2;randn(forep-nstepsm,nvar)];
- % Now, forep-by-nvar -- ready for forecasts
- end
- end
- %--------------
- % Condition on LZ MS shocks only, S random and possibly A random depending on
- % if A0_h and Bh_h are random
- %--------------
- else
- if nCms
- Estr = randn(forep,nvar);
- % Now, forep-by-nvar -- ready for forecasts
- Estr(:,eq_Cms)=0;
- Estr(1:nCms,eq_Cms) = TLnumber(:);
-
- %/* draw MS shocks
- % for k=1:nCms
- % if TLindx(k) % tighter
- % while (Estr(k,eq_Cms)<TLnumber(k))
- % Estr(k,eq_Cms) = randn(1,1);
- % end
- % else % looser
- % while (Estr(k,eq_Cms)>TLnumber(k))
- % Estr(k,eq_Cms) = randn(1,1);
- % end
- % end
- % end
- else
- Estr = randn(forep,nvar); % Unconditional forecast
- % Now, forep-by-nvar -- ready for forecasts
- end
- end
- %
-
-
- Ures = Estr/A0_h; % nstepsm-by-nvar
- % Ures: reduced-form residuals. Row--steps; Column--n shocks
-
- % ** reconstruct x(t) for y(t+h) = x(t+h-1)*B
- % ** where phi = x(t+h-1) with last column being constant
- %
- yhat = zeros(forep,nvar);
- for k=1:forep
- yhat(k,:) = phi*Bh_h + Ures(k,:);
- phi(1,nvar+1:tcwc) = phi(1,1:tcwc-nvar);
- phi(1,1:nvar) = yhat(k,:);
- end
-end
\ No newline at end of file
+function [yhat,Estr,rcon,Rcon,u,v,d] = fn_fcstidcnd2(valuecon,stepcon,varcon,nstepsm,...
+ nconstr,eq_ms,nvar,lags,phil,Aband,Sband,yfore_h,imf3s_h,A0_h,Bh_h,...
+ forep,TLindx,TLnumber,nCms,eq_Cms,nconadd,eq_oth,radd,Radd)
+% [yhat,Estr,rcon,Rcon,u,v,d] = fn_fcstidcnd2(valuecon,stepcon,varcon,nstepsm,...
+% nconstr,eq_ms,nvar,lags,phil,Aband,Sband,yfore_h,imf3s_h,A0_h,Bh_h,...
+% forep,TLindx,TLnumber,nCms,eq_Cms,nconadd,eq_oth,radd,Radd)
+%
+% 5/2/99: This one is much, much slower than fidcnderr.m when eq_ms=[] and
+% nconstr>0 and length(eq_ms)*nstepsm>nconstr. Seems to me that fcstidcnd2.m
+% is designed for the situation which eq_ms=2. The slow speed is due to
+% "Radd" added. When I have time, I need to check to see if I can speed up
+% this M file.
+%
+% Note that the case Aband=1 is not finished yet.
+%
+% Conditional forecasting in the identified model with or without error bands
+% It handles conditions on average values as well, so "valuecon" must be
+% expressed at average (NOT sum) level. Including unconditional forecasts
+% when nconstr=nCms=0. Maybe slower than "fcstidcnd.m" when length(eq_ms)*nstepsm=nconstr
+% because of Radd added. But "fsctidcnd.m" is incorrect when length(eq_ms)
+% *nstepsm>nconstr (situations like the average annual FFR constraint). 3/22/99
+% Ref.: Zha Forecast (I), pp. 17-17c.
+%
+% valuecon: vector of values conditioned
+% stepcon: sequence (cell) of steps conditioned; if length(stepcon{i}) > 1, the condition
+% is then an arithmetic average of log(y) over the stepcon{i} period.
+% varcon: vector of variables conditioned
+% nconstr: number of DLS constraints
+% nstepsm: maximum number of steps in all DLS constraints
+% nvar: number of variables in the BVAR model
+% lags: number of lags in the BVAR model
+% phil: the 1-by-(nvar*lags+1) data matrix where k=nvar*lags+1
+% (last period plus lags before the beginning of forecast)
+% eq_ms: identified equation or shock (in terms of number). If equ_ms=[], then "fidencond" or
+% 'fcstidcond' is, similar to RATS, to compute forecasts with *all* shocks.
+% Aband: 1: draws from A0 and Bh; 0: no draws
+% Sband: 1: draws from random shocks E; 0: no random shocks
+% yfore_h: uncondtional forecasts: forep-by-nvar. Never used when nconstr=0.
+% In this case, set it to [];
+% imf3s_h: 3-dimensional impulse responses matrix: impsteps-by-nvar shocks-by-nvar responses
+% Never used when nconstr=0. In this case, set it to [];
+% A0_h: A0 contemporaneous parameter matrix
+% Bh_h: reduced-form parameter matrix: k-by-nvar, y(t) = X(t)*Bh+e(t)
+% where X(t) is k-by-nvar and y(t) is 1-by-nvar
+% forep: # of forecast periods (e.g., monthly for a monthly model)
+% TLindx: 1-by-nCms vector of 1's and 0's, indicating tight or loose; 1: tighter, 0: looser
+% Used only when /* (MS draws) is activated. Right now, MS shocks are deterministic.
+% TLnumber: 1-by-nCms vector, lower bound for tight and upper bound for loose
+% nCms: # of LZ conditions
+% eq_Cms: equation location of MS shocks
+% nconadd: number of DLS added constraints DIRECTLY on structural shocks
+% for identified version where eq_ms is not empty. Note
+% nconadd=0 when eq_ms is empty.
+% eq_oth: index for other structural shocks than those in eq_ms.
+% If eq_ms=[], eq_oth=[]. Note length(eq_oth)+length(eq_ms)=nvar
+% radd: sparse nconadd-by-1; nconadd values added to rcon in the text later
+% Radd: sparce nvar*nstepsm-by-nconadd; added to Rcon in the text later
+% ------
+% yhat: conditional forecasts: forep-by-nvar
+% Estr: backed-out structural shocks (from N(0,1))
+% rcon: vector - the difference between valuecon and log(yfore) (unconditional forecasts)
+% Rcon: k-by-q (q constranits and k=nvar*max(nsteps)) so that
+% Rcon'*e = rcon where e is k-by-1
+% [u,d,v]: svd(Rcon,0)
+%
+% Written by Tao Zha March 1998. Revised November 1998;
+% 3/20/99 Disenabled draws of MS shcoks. To enable it, activate /* part
+% 3/20/99 Added A0_h and forep and deleted Cms as input argument.
+% 3/21/99 Added nconadd and eq_oth.
+% Previous programs may not be compatible.
+
+%
+% This version differs from "fn_fcstidcnd.m" because nconadd, radd, and Radd are added to allow for
+% both DLS-WZ conditions and structural-shock conditions to exist at the same time. My recognition
+% is that it does not work well at all. So one should use the version "fn_fcstidcnd.m."" April, 2009 by TZ.
+
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+%
+%% See Zha's note "Forecast (1)" p. 5, RATS manual (some errors in RATS), etc.
+%% See also Zha Forecast (I), pp. 17-17c.
+%
+%% Some notations: y(t+1) = y(t)B1 + e(t+1)inv(A0). e(t+1) is 1-by-n.
+%% Let r(t+1)=e(t+1)inv(A0) + e(t+2)C + .... where inv(A0) is impulse
+%% response at t=1, C at t=2, etc. The row of inv(A0) or C is
+%% all responses to one shock.
+%% Let r be q-by-1 (such as r(1) = r(t+1)
+%% = y(t+1) (constrained) - y(t+1) (forecast)).
+%% Use impulse responses to find out R (k-by-q) where k=nvar*nsteps
+%% where nsteps the largest constrained step. The key of the program
+%% is to creat R using impulse responses
+%% Optimal solution for shock e where R'*e=r and e is k-by-1 is
+%% e = R*inv(R'*R)*r and k>=q
+%
+
+
+DLSIdShock = ~isempty(eq_ms); % if not empty, the MS shock is identified as in DLS
+
+impsteps=size(imf3s_h,1);
+if (forep<nstepsm) | (impsteps<nstepsm)
+ disp('Increase # of forecast or impulse steps!!')
+ disp('Or decrease # of constraints (nconstr) or constrained steps (stepcon(i))!!')
+ error('Maximum of conditional steps > # of forecast or impulse steps!!')
+end
+kts = nvar*nstepsm; % k -- ts: total shocks some of which are restricted and others
+ % are free.
+%*** initializing
+Rcon = zeros(kts,nconstr); % R: k-by-q where q include possible additional
+ % constraints directly on structural shocks for identified models.
+ % These addtional constraints will be added later.
+Econ = sparse(zeros(kts,1)); % E: k-by-1
+rcon = zeros(nconstr,1); % r: q-by-1
+%rcon=valuecon-diag(yfore(stepcon,varcon)); % another way is to use "loop" below.
+tcwc = nvar*lags; % total coefficients without constant
+phi=phil;
+
+
+
+%----------------------------------------------------
+% Form rcon, Rcon, and Econ (the mean of structural shocks)
+%----------------------------------------------------
+if nconstr
+ A0in = reshape(imf3s_h(1,:,:),nvar,nvar); % nvar shocks-by-nvar responses
+ for i=1:nconstr
+ rcon(i)=length(stepcon{i})*valuecon(i) - ...
+ sum(yfore_h(stepcon{i},varcon(i)),1); %<<>>
+ Rmat = zeros(nstepsm,nvar);
+ % Must be here inside the loop because it's matrix of one column of Rcon
+ for j=1:length(stepcon{i})
+ if DLSIdShock % Assuming the Fed can't see all other shocks within a month
+ Rmat(1:stepcon{i}(j),:) = Rmat(1:stepcon{i}(j),:) + ...
+ imf3s_h(stepcon{i}(j):-1:1,:,varcon(i));
+ % Rmat: row--nstepsm, column--nvar shocks (here all shocks except
+ % the identified one are set to zero) for a particular
+ % endogenous variable 'varcon(i)'. See Zha Forcast (1), pp.6-7
+ else % Rcon random with (A0,A+)
+ Rmat(1:stepcon{i}(j),:) = Rmat(1:stepcon{i}(j),:) + ...
+ imf3s_h(stepcon{i}(j):-1:1,:,varcon(i));
+ % Rmat: row--nstepsm, column--nvar shocks (here all shocks are
+ % *not* set to zero) for a particular endogenous
+ % variable 'varcon(i)'. See Zha Forcast (1), pp.6-7
+ end
+ end
+ Rmatt = Rmat'; % Now, nvar-by-nstepsm. I think here is where RATS has an error
+ % i.e. "OVERR" is not transposed when overlaid to "CAPR"
+ Rcon(:,i)=Rmatt(:); % Rcon: k-by-q where q=nconstr
+ end
+
+ rcon = [rcon;radd]; % added nconadd constrained values for identified model
+ % sparse because radd is sparse
+ Rcon = [Rcon Radd]; % added constraints on shocks for identified version
+ % sparse because Radd is sparse
+ Rcont=Rcon';
+ rinvrtr=Rcon/(Rcont*Rcon);
+ Econ = rinvrtr*rcon;
+
+
+ %/* Too slow, I believe, when q is large. 3/21/99
+ % [u d v]=svd(Rcon,0); %trial
+ % %???? Can we reduce the time by computing inv(R'*R) directly?
+ % % rtr = Rcon'*Rcon; %trial
+ % % rtrinv = inv(Rcon'*Rcon); %trial
+ % vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
+ % dinv = 1./diag(d); % inv(diag(d))
+ % vdinv=v.*(ones(size(v,2),1)*dinv'); %trial
+ % rtr=vd*vd'; % R'*R
+ % rtrinv = vdinv*vdinv'; % inv(R'*R)
+ %
+ % Econ = Rcon*rtrinv*rcon; % E = R*inv(R'R)*r; the mean of structural shocks
+else
+ Econ = sparse(zeros(kts,1)); % the mean of shocks is zero under no variable condition
+ Rcon = NaN;
+ rcon = NaN;
+ u = NaN;
+ d = NaN;
+ v = NaN;
+end
+
+
+
+%---------------------------------------
+% No uncertainty at all or only random (A0,A+)
+% In other words, no future shocks
+%---------------------------------------
+if (~Sband) | (nconstr & length(eq_ms)*nstepsm==nconstr)
+ % length(eq_ms)==1 implies one-one mapping between MS shocks and, say, FFR
+ % if nstepsm==nconstr. If this condition does not hold, this procedure
+ % is incorrect. I don't have time to fix it now (3/20/99). So I use
+ % this as a proximation
+ Estr = reshape(Econ,nvar,nstepsm);
+ Estr = Estr'; % transpose so that
+ % Estr: structural shocks. Row--steps, Column--n shocks
+ Estr = [Estr;zeros(forep-nstepsm,nvar)];
+ % Now, forep-by-nvar -- ready for forecasts
+ Estr(1:nCms,eq_Cms) = TLnumber(:);
+ Ures = Estr/A0_h; % nstepsm-by-nvar
+ % Ures: reduced-form residuals. Row--steps; Column--n shocks
+
+ % ** reconstruct x(t) for y(t+h) = x(t+h-1)*B
+ % ** where phi = x(t+h-1) with last column being constant
+ %
+ yhat = zeros(forep,nvar);
+ for k=1:forep
+ yhat(k,:) = phi*Bh_h + Ures(k,:);
+ phi(1,nvar+1:tcwc) = phi(1,1:tcwc-nvar);
+ phi(1,1:nvar) = yhat(k,:);
+ %
+ end
+
+%---------------------------------------
+% With random future shocks and possibly (A0,A+) depending
+% on if imf3s_h is random
+%---------------------------------------
+else
+ %--------------
+ % Condition on variables and A random
+ %--------------
+ if nconstr & Aband
+ warning(' ')
+ disp('This situation (both E and A random) is still under construction')
+ disp('It is closely related to Waggoner and Zha ReStat Gibbs sampling method')
+ disp('and related to "if DLSIdShock" in the following')
+ disp('Please press ctrl-c to abort')
+ pause
+ elseif nconstr
+ %--------------
+ % Condition on variables and DLS MS shock, no A random but S random
+ %--------------
+ if DLSIdShock % other shocks are indepedent of the eq_ms shock
+ Ome=speye(kts)-rinvrtr*Rcont;
+ Ome(find(abs(Ome)<1e-10))=0; % tighter the tolerance is, the longer it
+ % takes for svds to compute. E.g., size(Ome,1)*eps is perhaps very
+ % tight. After all, when the number in Ome is very small relative to
+ % 1 (which is the variance of structural shocks Estr), we can treat it
+ % as zero.
+ [u1 d1 v1] = svds(Ome,kts-nconstr-nconadd);
+ % We have exactly nconstr+nconadd zero singular values
+ Stdcon = u1*diag(sqrt(diag(abs(d1)))); % find a square root of Ome
+ % see Zha's forecast (1), p.17
+ Stdcon = [Stdcon sparse(zeros(kts,kts-size(d1,1)))];
+ Estr1 = Econ + Stdcon*randn(kts,1); % We must have rand(kts,1). Assigning
+ % some of randn(kts,1) to be zero according to Radd is WRONG.
+ % For this argument, see Zha Forecast (I) p.17a
+ Estr2 = reshape(Estr1,nvar,nstepsm);
+ % nvar-by-nstepsm; Needed to be transposed later
+ Estr = [Estr2';randn(forep-nstepsm,nvar)]; % transpose so that
+ % Estr2: structural shocks. Row--nstepsm, Column--n shocks
+ % Now, forep-by-nvar -- ready for forecasts
+ else
+ Ome=speye(kts)-rinvrtr*Rcont;
+ Ome(find(abs(Ome)<1e-10))=0; % tighter the tolerance is, the longer it
+ % takes for svds to compute. E.g., size(Ome,1)*eps is perhaps very
+ % tight. After all, when the number in Ome is very small relative to
+ % 1 (which is the variance of structural shocks Estr), we can treat it
+ % as zero.
+ [u1 d1 v1] = svds(Ome,kts-nconstr-nconadd);
+ % We have exactly nconstr+nconadd zero singular values
+ Stdcon = u1*diag(sqrt(diag(abs(d1)))); % find a square root of Ome
+ % see Zha's forecast (1), p.17
+ Stdcon = [Stdcon sparse(zeros(kts,nconstr+nconadd))];
+
+ %*** The following very inefficient
+ % Ome = eye(kts) - u*u'; % note, I-u*u' = I - R*inv(R'*R)*R'
+ % %[u1 d1 v1] = svd(Ome); % too slow
+ % [u1 d1] = eig(Ome);
+ % Stdcon = u1*diag(sqrt(diag(abs(d1)))); % lower triagular chol of conditional variance
+ % % see Zha's forecast (1), p.17
+
+ %--------------
+ % Condition on variables and LZ MS shock, no A random but S random
+ % This section has not be tested yet, 10/14/98
+ %--------------
+ if nCms
+ Estr1 = Econ + Stdcon*randn(kts,1);
+ Estr2 = reshape(Estr1,nvar,nstepsm);
+ Estr2 = Estr2'; % transpose so that
+ % Estr2: structural shocks. Row--nstepsm, Column--n shocks
+ Estr = [Estr2;randn(forep-nstepsm,nvar)];
+ % Now, forep-by-nvar -- ready for forecasts
+ Estr(1:nCms,eq_Cms) = TLnumber(:);
+
+ %/* draw MS shocks
+ % for k=1:nCms
+ % if TLindx(k) % tighter
+ % while (Estr(k,eq_Cms)<TLnumber(k))
+ % Estr(k,eq_Cms) = randn(1,1);
+ % end
+ % else % looser
+ % while (Estr(k,eq_Cms)>TLnumber(k))
+ % Estr(k,eq_Cms) = randn(1,1);
+ % end
+ % end
+ % end
+ %--------------
+ % Condition on variables only, no A random but S random
+ %--------------
+ else
+ Estr1 = Econ + Stdcon*randn(kts,1);
+ Estr2 = reshape(Estr1,nvar,nstepsm);
+ Estr2 = Estr2'; % transpose so that
+ % Estr2: structural shocks. Row--nstepsm, Column--n shocks
+ Estr = [Estr2;randn(forep-nstepsm,nvar)];
+ % Now, forep-by-nvar -- ready for forecasts
+ end
+ end
+ %--------------
+ % Condition on LZ MS shocks only, S random and possibly A random depending on
+ % if A0_h and Bh_h are random
+ %--------------
+ else
+ if nCms
+ Estr = randn(forep,nvar);
+ % Now, forep-by-nvar -- ready for forecasts
+ Estr(:,eq_Cms)=0;
+ Estr(1:nCms,eq_Cms) = TLnumber(:);
+
+ %/* draw MS shocks
+ % for k=1:nCms
+ % if TLindx(k) % tighter
+ % while (Estr(k,eq_Cms)<TLnumber(k))
+ % Estr(k,eq_Cms) = randn(1,1);
+ % end
+ % else % looser
+ % while (Estr(k,eq_Cms)>TLnumber(k))
+ % Estr(k,eq_Cms) = randn(1,1);
+ % end
+ % end
+ % end
+ else
+ Estr = randn(forep,nvar); % Unconditional forecast
+ % Now, forep-by-nvar -- ready for forecasts
+ end
+ end
+ %
+
+
+ Ures = Estr/A0_h; % nstepsm-by-nvar
+ % Ures: reduced-form residuals. Row--steps; Column--n shocks
+
+ % ** reconstruct x(t) for y(t+h) = x(t+h-1)*B
+ % ** where phi = x(t+h-1) with last column being constant
+ %
+ yhat = zeros(forep,nvar);
+ for k=1:forep
+ yhat(k,:) = phi*Bh_h + Ures(k,:);
+ phi(1,nvar+1:tcwc) = phi(1,1:tcwc-nvar);
+ phi(1,1:nvar) = yhat(k,:);
+ end
+end
diff --git a/MatlabFiles/fn_forecast.m b/MatlabFiles/fn_forecast.m
index 86c50bbcde9efdcaf1fa85e854664cc1de475721..f5ac10173cf087372784bae4a51138950e5c8215 100644
--- a/MatlabFiles/fn_forecast.m
+++ b/MatlabFiles/fn_forecast.m
@@ -1,74 +1,74 @@
-function yhat = fn_forecast(Bh,phi,nn,nexo,Xfexo)
-% yhat = fn_forecast(Bh,phi,nn,nexo,Xfexo)
-% Unconditional forecating without shocks.
-% y_hat(t+h) = c + x_hat(t+h-1)*Bh, X: 1*k; Bh: k*nvar; y_hat: 1*nvar
-%
-% Bh: k-by-nvar, the (posterior) estimate of B.
-% phi: the 1-by-(nvar*lags+nexo) data matrix X where k=nvar*lags+1
-% (last period plus lags before the beginning of forecast).
-% nn: [nvar,lags,nfqm], nfqm: forecast periods (months or quarters).
-% nexo: number of exogenous variables. The constant term is the default setting.
-% Besides this term, we have nexo-1 exogenous variables.
-% Xfexo: nfqm-by-nexo-1 vector of exoenous variables in the forecast horizon where
-% nfqm: number of forecast periods.
-%-----------
-% yhat: nfqm-by-nvar forecast.
-%
-% See fn_forecastsim.m with shocks; fn_forecaststre.m.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-if nargin == 3
- nexo=1; % default for constant term
-elseif nexo<1
- error('We need at least one exogenous term so nexo must >= 1')
-end
-
-% ** setup
-nvar = nn(1);
-lags = nn(2);
-nfqm = nn(3);
-tcwx = nvar*lags; % total coefficeint without exogenous variables
-
-if nexo>1
- if (nfqm > size(Xfexo,1))
- disp(' ')
- warning('Make sure the forecast horizon in the exogenous variable matrix Xfexo > forecast periods')
- disp('Press ctrl-c to abort')
- pause
- elseif ((nexo-1) ~= size(Xfexo,2))
- disp(' ')
- warning('Make sure that nexo matchs the exogenous variable matrix Xfexo')
- disp('Press ctrl-c to abort')
- pause
- end
-end
-
-% ** reconstruct x(t) for y(t+h) = x(t+h-1)*B
-% ** where phi = x(t+h-1) with last column being constant
-yhat = zeros(nfqm,nvar);
-for k=1:nfqm
- yhat(k,:) = phi*Bh;
- %*** lagged endogenous variables
- phi(1,nvar+1:tcwx) = phi(1,1:tcwx-nvar);
- phi(1,1:nvar) = yhat(k,:);
- %*** exogenous variables excluding constant terms
- if (nexo>1)
- phi(1,tcwx+1:tcwx+nexo-1) = Xfexo(k,:);
- end
-end
+function yhat = fn_forecast(Bh,phi,nn,nexo,Xfexo)
+% yhat = fn_forecast(Bh,phi,nn,nexo,Xfexo)
+% Unconditional forecating without shocks.
+% y_hat(t+h) = c + x_hat(t+h-1)*Bh, X: 1*k; Bh: k*nvar; y_hat: 1*nvar
+%
+% Bh: k-by-nvar, the (posterior) estimate of B.
+% phi: the 1-by-(nvar*lags+nexo) data matrix X where k=nvar*lags+1
+% (last period plus lags before the beginning of forecast).
+% nn: [nvar,lags,nfqm], nfqm: forecast periods (months or quarters).
+% nexo: number of exogenous variables. The constant term is the default setting.
+% Besides this term, we have nexo-1 exogenous variables.
+% Xfexo: nfqm-by-nexo-1 vector of exoenous variables in the forecast horizon where
+% nfqm: number of forecast periods.
+%-----------
+% yhat: nfqm-by-nvar forecast.
+%
+% See fn_forecastsim.m with shocks; fn_forecaststre.m.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+if nargin == 3
+ nexo=1; % default for constant term
+elseif nexo<1
+ error('We need at least one exogenous term so nexo must >= 1')
+end
+
+% ** setup
+nvar = nn(1);
+lags = nn(2);
+nfqm = nn(3);
+tcwx = nvar*lags; % total coefficeint without exogenous variables
+
+if nexo>1
+ if (nfqm > size(Xfexo,1))
+ disp(' ')
+ warning('Make sure the forecast horizon in the exogenous variable matrix Xfexo > forecast periods')
+ disp('Press ctrl-c to abort')
+ pause
+ elseif ((nexo-1) ~= size(Xfexo,2))
+ disp(' ')
+ warning('Make sure that nexo matchs the exogenous variable matrix Xfexo')
+ disp('Press ctrl-c to abort')
+ pause
+ end
+end
+
+% ** reconstruct x(t) for y(t+h) = x(t+h-1)*B
+% ** where phi = x(t+h-1) with last column being constant
+yhat = zeros(nfqm,nvar);
+for k=1:nfqm
+ yhat(k,:) = phi*Bh;
+ %*** lagged endogenous variables
+ phi(1,nvar+1:tcwx) = phi(1,1:tcwx-nvar);
+ phi(1,1:nvar) = yhat(k,:);
+ %*** exogenous variables excluding constant terms
+ if (nexo>1)
+ phi(1,tcwx+1:tcwx+nexo-1) = Xfexo(k,:);
+ end
+end
diff --git a/MatlabFiles/fn_forecastfixe.m b/MatlabFiles/fn_forecastfixe.m
index e378dc4617edecacd837d0b97e355fff366211f2..3facb577faedde27ea9aa7359c78022067417eb1 100644
--- a/MatlabFiles/fn_forecastfixe.m
+++ b/MatlabFiles/fn_forecastfixe.m
@@ -1,78 +1,78 @@
-function yhat = fn_forecastfixe(Bh,A0h,phi,nn,Estr,nexo,Xfexo)
-% yhat = fn_forecastfixe(Bh,A0h,phi,nn,Estr,nexo,Xfexo)
-% Forecasts conditional on fixed structural shocks
-% y_hat(t+h) = c + x_hat(t+h-1)*Bh + Estr(t+h,:), X: 1*k; Bh: k*nvar; y_hat: 1*nvar
-%
-% Bh: k-by-nvar, the (posterior) estimate of B;
-% A0h: nvar-by-nvar, columns correponding to equations
-% phi: the 1-by-(nvar*lags+1) data matrix where k=nvar*lags+1
-% (last period plus lags before the beginning of forecast).
-% nn: [nvar,lags,nfqm], nfqm: forecast periods (months or quarters).
-% Estr: nfqm-by-nvar, each column corresponds to strcutural shocks from a particular
-% source such as MS;
-% nexo: number of exogenous variables. The constant term is the default setting.
-% Besides this term, we have nexo-1 exogenous variables.
-% Xfexo: nfqm-by-nexo-1 vector of exoenous variables in the forecast horizon where
-% nfqm: number of forecast periods.
-%-----------------
-% yhat: nfqm*nvar matrix of forecasts.
-%
-% See also fn_forecast.m and fn_forecastsim.m.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-if nargin == 5
- nexo=1; % default for constant term
-elseif nexo<1
- error('We need at least one exogenous term so nexo must >= 1')
-end
-
-% ** setup
-nvar = nn(1);
-lags = nn(2);
-nfqm = nn(3);
-tcwx = nvar*lags; % total coefficeint without exogenous variables
-
-if nexo>1
- if (nfqm > size(Xfexo,1))
- disp(' ')
- warning('Make sure the forecast horizon in the exogenous variable matrix Xfexo > forecast periods')
- disp('Press ctrl-c to abort')
- pause
- elseif ((nexo-1) ~= size(Xfexo,2))
- disp(' ')
- warning('Make sure that nexo matchs the exogenous variable matrix Xfexo')
- disp('Press ctrl-c to abort')
- pause
- end
-end
-
-% ** reconstruct x(t) for y(t+h) = x(t+h-1)*B
-% ** where phi = x(t+h-1) with last column being constant
-Ures = Estr/A0h;
-yhat = zeros(nfqm,nvar);
-for k=1:nfqm
- yhat(k,:) = phi*Bh + Ures(k,:);
- %*** lagged endogenous variables
- phi(1,nvar+1:tcwx) = phi(1,1:tcwx-nvar);
- phi(1,1:nvar) = yhat(k,:);
- %*** exogenous variables excluding constant terms
- if (nexo>1)
- phi(1,tcwx+1:tcwx+nexo-1) = Xfexo(k,:);
- end
-end
+function yhat = fn_forecastfixe(Bh,A0h,phi,nn,Estr,nexo,Xfexo)
+% yhat = fn_forecastfixe(Bh,A0h,phi,nn,Estr,nexo,Xfexo)
+% Forecasts conditional on fixed structural shocks
+% y_hat(t+h) = c + x_hat(t+h-1)*Bh + Estr(t+h,:), X: 1*k; Bh: k*nvar; y_hat: 1*nvar
+%
+% Bh: k-by-nvar, the (posterior) estimate of B;
+% A0h: nvar-by-nvar, columns correponding to equations
+% phi: the 1-by-(nvar*lags+1) data matrix where k=nvar*lags+1
+% (last period plus lags before the beginning of forecast).
+% nn: [nvar,lags,nfqm], nfqm: forecast periods (months or quarters).
+% Estr: nfqm-by-nvar, each column corresponds to strcutural shocks from a particular
+% source such as MS;
+% nexo: number of exogenous variables. The constant term is the default setting.
+% Besides this term, we have nexo-1 exogenous variables.
+% Xfexo: nfqm-by-nexo-1 vector of exoenous variables in the forecast horizon where
+% nfqm: number of forecast periods.
+%-----------------
+% yhat: nfqm*nvar matrix of forecasts.
+%
+% See also fn_forecast.m and fn_forecastsim.m.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+if nargin == 5
+ nexo=1; % default for constant term
+elseif nexo<1
+ error('We need at least one exogenous term so nexo must >= 1')
+end
+
+% ** setup
+nvar = nn(1);
+lags = nn(2);
+nfqm = nn(3);
+tcwx = nvar*lags; % total coefficeint without exogenous variables
+
+if nexo>1
+ if (nfqm > size(Xfexo,1))
+ disp(' ')
+ warning('Make sure the forecast horizon in the exogenous variable matrix Xfexo > forecast periods')
+ disp('Press ctrl-c to abort')
+ pause
+ elseif ((nexo-1) ~= size(Xfexo,2))
+ disp(' ')
+ warning('Make sure that nexo matchs the exogenous variable matrix Xfexo')
+ disp('Press ctrl-c to abort')
+ pause
+ end
+end
+
+% ** reconstruct x(t) for y(t+h) = x(t+h-1)*B
+% ** where phi = x(t+h-1) with last column being constant
+Ures = Estr/A0h;
+yhat = zeros(nfqm,nvar);
+for k=1:nfqm
+ yhat(k,:) = phi*Bh + Ures(k,:);
+ %*** lagged endogenous variables
+ phi(1,nvar+1:tcwx) = phi(1,1:tcwx-nvar);
+ phi(1,1:nvar) = yhat(k,:);
+ %*** exogenous variables excluding constant terms
+ if (nexo>1)
+ phi(1,tcwx+1:tcwx+nexo-1) = Xfexo(k,:);
+ end
+end
diff --git a/MatlabFiles/fn_forecastsim.m b/MatlabFiles/fn_forecastsim.m
index 79a04c27fad04ec95748f2f7f03862a5d0e568e1..c3382dd38062f3e8355033d95134afcf420c0df0 100644
--- a/MatlabFiles/fn_forecastsim.m
+++ b/MatlabFiles/fn_forecastsim.m
@@ -1,79 +1,79 @@
-function yhat = fn_forecastsim(Bh,A0h,phi,nn,nexo,Xfexo)
-% yhat = fn_forecastsim(Bh,A0h,phi,nn,nexo,Xfexo)
-% Unconditional forecasts with simulated shocks, not depending on reduced-form or structural shocks.
-% y_hat(t+h) = c + x_hat(t+h-1)*Bh + u, X: 1*k; Bh: k*nvar; y_hat: 1*nvar; u: simulated shocks.
-%
-% Bh: k-by-nvar, the (posterior) estimate of B;
-% A0h: nvar-by-nvar, columns correponding to equations
-% phi: the 1-by-(nvar*lags+1) data matrix where k=nvar*lags+1
-% (last period plus lags before the beginning of forecast).
-% nn: [nvar,lags,nfqm], nfqm: forecast periods (months or quarters).
-% nexo: number of exogenous variables. The constant term is the default setting.
-% Besides this term, we have nexo-1 exogenous variables.
-% Xfexo: nfqm-by-nexo-1 vector of exoenous variables in the forecast horizon where
-% nfqm: number of forecast periods.
-%-----------------
-% yhat: nfqm*nvar matrix of forecasts.
-%
-% See fn_forecast.m without shocks and forecasterr.m when A0h_in (instead of A0h) is used;
-% fn_forecaststre.m.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-if nargin == 4
- nexo=1; % default for constant term
-elseif nexo<1
- error('We need at least one exogenous term so nexo must >= 1')
-end
-
-% ** setup
-nvar = nn(1);
-lags = nn(2);
-nfqm = nn(3);
-tcwx = nvar*lags; % total coefficeint without exogenous variables
-
-if nexo>1
- if (nfqm > size(Xfexo,1))
- disp(' ')
- warning('Make sure the forecast horizon in the exogenous variable matrix Xfexo > forecast periods')
- disp('Press ctrl-c to abort')
- pause
- elseif ((nexo-1) ~= size(Xfexo,2))
- disp(' ')
- warning('Make sure that nexo matchs the exogenous variable matrix Xfexo')
- disp('Press ctrl-c to abort')
- pause
- end
-end
-
-% ** reconstruct x(t) for y(t+h) = x(t+h-1)*B
-% ** where phi = x(t+h-1) with last column being constant
-Ures = randn(nfqm,nvar)/A0h; % Unconditional forecast
- % Now, nfqm-by-nvar -- ready for forecasts
- % Ures: reduced-form residuals. Row--steps; Column--n shocks
-yhat = zeros(nfqm,nvar);
-for k=1:nfqm
- yhat(k,:) = phi*Bh + Ures(k,:);;
- %*** lagged endogenous variables
- phi(1,nvar+1:tcwx) = phi(1,1:tcwx-nvar);
- phi(1,1:nvar) = yhat(k,:);
- %*** exogenous variables excluding constant terms
- if (nexo>1)
- phi(1,tcwx+1:tcwx+nexo-1) = Xfexo(k,:);
- end
-end
+function yhat = fn_forecastsim(Bh,A0h,phi,nn,nexo,Xfexo)
+% yhat = fn_forecastsim(Bh,A0h,phi,nn,nexo,Xfexo)
+% Unconditional forecasts with simulated shocks, not depending on reduced-form or structural shocks.
+% y_hat(t+h) = c + x_hat(t+h-1)*Bh + u, X: 1*k; Bh: k*nvar; y_hat: 1*nvar; u: simulated shocks.
+%
+% Bh: k-by-nvar, the (posterior) estimate of B;
+% A0h: nvar-by-nvar, columns correponding to equations
+% phi: the 1-by-(nvar*lags+1) data matrix where k=nvar*lags+1
+% (last period plus lags before the beginning of forecast).
+% nn: [nvar,lags,nfqm], nfqm: forecast periods (months or quarters).
+% nexo: number of exogenous variables. The constant term is the default setting.
+% Besides this term, we have nexo-1 exogenous variables.
+% Xfexo: nfqm-by-nexo-1 vector of exoenous variables in the forecast horizon where
+% nfqm: number of forecast periods.
+%-----------------
+% yhat: nfqm*nvar matrix of forecasts.
+%
+% See fn_forecast.m without shocks and forecasterr.m when A0h_in (instead of A0h) is used;
+% fn_forecaststre.m.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+if nargin == 4
+ nexo=1; % default for constant term
+elseif nexo<1
+ error('We need at least one exogenous term so nexo must >= 1')
+end
+
+% ** setup
+nvar = nn(1);
+lags = nn(2);
+nfqm = nn(3);
+tcwx = nvar*lags; % total coefficeint without exogenous variables
+
+if nexo>1
+ if (nfqm > size(Xfexo,1))
+ disp(' ')
+ warning('Make sure the forecast horizon in the exogenous variable matrix Xfexo > forecast periods')
+ disp('Press ctrl-c to abort')
+ pause
+ elseif ((nexo-1) ~= size(Xfexo,2))
+ disp(' ')
+ warning('Make sure that nexo matchs the exogenous variable matrix Xfexo')
+ disp('Press ctrl-c to abort')
+ pause
+ end
+end
+
+% ** reconstruct x(t) for y(t+h) = x(t+h-1)*B
+% ** where phi = x(t+h-1) with last column being constant
+Ures = randn(nfqm,nvar)/A0h; % Unconditional forecast
+ % Now, nfqm-by-nvar -- ready for forecasts
+ % Ures: reduced-form residuals. Row--steps; Column--n shocks
+yhat = zeros(nfqm,nvar);
+for k=1:nfqm
+ yhat(k,:) = phi*Bh + Ures(k,:);;
+ %*** lagged endogenous variables
+ phi(1,nvar+1:tcwx) = phi(1,1:tcwx-nvar);
+ phi(1,1:nvar) = yhat(k,:);
+ %*** exogenous variables excluding constant terms
+ if (nexo>1)
+ phi(1,tcwx+1:tcwx+nexo-1) = Xfexo(k,:);
+ end
+end
diff --git a/MatlabFiles/fn_foregraph.m b/MatlabFiles/fn_foregraph.m
index 424870f2481a7746ff4f5845f2f05163825719a8..0af4377773e5f830732a8b2809937e550cb8ce39 100644
--- a/MatlabFiles/fn_foregraph.m
+++ b/MatlabFiles/fn_foregraph.m
@@ -1,66 +1,66 @@
-function fn_foregraph(yfore,yacte,keyindx,rnum,cnum,q_m,ylab,forelabel,conlab)
-%
-% Graph annual (or calendar year) forecasts vs actual (all data from "msstart.m")
-%
-% yfore: actual and forecast annual growth data with dates.
-% yacte: actual annual growth data with dates.
-% keyindx: index for the variables to be graphed
-% rnum: number of rows in subplot
-% cnum: number of columns in subplot
-% q_m: if 4 or 12, quarterly or monthly data
-% ylab: string array for the length(keyindx)-by-1 variables
-% forelabel: title label for as of time of forecast
-% conlab: label for what conditions imposed; e.g., conlab = 'All bar MS shocks inspl'
-%-------------
-% No output argument for this graph file
-% See fn_seriesgraph.m, fn_forerrgraph.m.
-%
-% Tao Zha, March 2000
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-vyrs = yfore(:,1);
-hornum = cell(length(vyrs),1); % horizontal year (number)
-count=0;
-for k=vyrs'
- count=count+1;
- jnk=num2str(k);
- hornum{count}=jnk(3:4); % e.g., with '1990', we have '90'
-end
-
-count=0;
-for i = keyindx
- count = count+1;
- subplot(rnum,cnum,count)
- plot(yacte(:,1)+yacte(:,2)/q_m,yacte(:,2+i),yfore(:,1)+yfore(:,2)/q_m,yfore(:,2+i),'--')
-
- if (yfore(1,2)==0) % only for annual growth rates (not for, say, monthly annualized rates)
- set(gca,'XLim',[vyrs(1) vyrs(end)])
- set(gca,'XTick',vyrs)
- set(gca,'XTickLabel',char(hornum))
- end
-
- if i==keyindx(1)
- title(forelabel)
- elseif i>=length(keyindx) %i>=length(keyindx)-1
- xlabel(conlab)
- end
- %
- grid
- ylabel(char(ylab(i)))
-end
+function fn_foregraph(yfore,yacte,keyindx,rnum,cnum,q_m,ylab,forelabel,conlab)
+%
+% Graph annual (or calendar year) forecasts vs actual (all data from "msstart.m")
+%
+% yfore: actual and forecast annual growth data with dates.
+% yacte: actual annual growth data with dates.
+% keyindx: index for the variables to be graphed
+% rnum: number of rows in subplot
+% cnum: number of columns in subplot
+% q_m: if 4 or 12, quarterly or monthly data
+% ylab: string array for the length(keyindx)-by-1 variables
+% forelabel: title label for as of time of forecast
+% conlab: label for what conditions imposed; e.g., conlab = 'All bar MS shocks inspl'
+%-------------
+% No output argument for this graph file
+% See fn_seriesgraph.m, fn_forerrgraph.m.
+%
+% Tao Zha, March 2000
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+vyrs = yfore(:,1);
+hornum = cell(length(vyrs),1); % horizontal year (number)
+count=0;
+for k=vyrs'
+ count=count+1;
+ jnk=num2str(k);
+ hornum{count}=jnk(3:4); % e.g., with '1990', we have '90'
+end
+
+count=0;
+for i = keyindx
+ count = count+1;
+ subplot(rnum,cnum,count)
+ plot(yacte(:,1)+yacte(:,2)/q_m,yacte(:,2+i),yfore(:,1)+yfore(:,2)/q_m,yfore(:,2+i),'--')
+
+ if (yfore(1,2)==0) % only for annual growth rates (not for, say, monthly annualized rates)
+ set(gca,'XLim',[vyrs(1) vyrs(end)])
+ set(gca,'XTick',vyrs)
+ set(gca,'XTickLabel',char(hornum))
+ end
+
+ if i==keyindx(1)
+ title(forelabel)
+ elseif i>=length(keyindx) %i>=length(keyindx)-1
+ xlabel(conlab)
+ end
+ %
+ grid
+ ylabel(char(ylab(i)))
+end
diff --git a/MatlabFiles/fn_forerrgraph.m b/MatlabFiles/fn_forerrgraph.m
index b2fc21a9dd3fc9090a5d69c98fb44397e0787832..0a6beada1559f7669fa22c2b9be7ce8c417b6114 100644
--- a/MatlabFiles/fn_forerrgraph.m
+++ b/MatlabFiles/fn_forerrgraph.m
@@ -1,71 +1,71 @@
-function fn_forerrgraph(yfore,yforel,yforeh,yacte,keyindx,rnum,cnum,q_m,ylab,forelabel,conlab)
-%
-% Graph annual (or calendar year) forecasts with one error band vs actual
-% Import some data from "msstart.m"
-%
-% yfore: both actual and forecast annual growth data with dates in the first 2 columns
-% in the order of year and month (or quarter).
-% yforel: lower bound of the forecast.
-% yforeh: high bound of the forecast.
-% yacte: actual annual growth data with dates.
-% keyindx: index for the variables to be graphed
-% rnum: number of rows in subplot
-% cnum: number of columns in subplot
-% q_m: if 4 or 12, quarterly or monthly data.
-% ylab: string array for the length(keyindx)-by-1 variables
-% forelabel: title label for as of time of forecast
-% conlab: x-axis label for what conditions imposed; e.g., conlab = 'All bar MS shocks inspl'
-%-------------
-% No output argument for this graph file.
-% See fn_seriesgraph.m, fn_foregraph.m.
-%
-% Tao Zha, August 2000
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-vyrs = yfore(:,1); % vectorized
-hornum = cell(length(vyrs),1); % horizontal year (number)
-count=0;
-for k=vyrs'
- count=count+1;
- jnk=num2str(k);
- hornum{count}=jnk(3:4); % e.g., with '1990', we have '90'
-end
-
-count=0;
-for i = keyindx
- count = count+1;
- subplot(rnum,cnum,count)
- plot(yacte(:,1)+yacte(:,2)/q_m,yacte(:,2+i),yfore(:,1)+yfore(:,2)/q_m,yfore(:,2+i),'--',...
- yforel(:,1)+yforel(:,2)/q_m,yforel(:,2+i),'-.',yforeh(:,1)+yforeh(:,2)/q_m,yforeh(:,2+i),'-.')
-
- if (yfore(1,2)==0) % only for annual growth rates (not for, say, monthly annualized rates)
- set(gca,'XLim',[vyrs(1) vyrs(end)])
- set(gca,'XTick',vyrs)
- set(gca,'XTickLabel',char(hornum))
- end
-
- if i==keyindx(1)
- title(forelabel)
- elseif i>=length(keyindx) %i>=length(keyindx)-1
- xlabel(conlab)
- end
- %
- grid
- ylabel(char(ylab(i)))
-end
+function fn_forerrgraph(yfore,yforel,yforeh,yacte,keyindx,rnum,cnum,q_m,ylab,forelabel,conlab)
+%
+% Graph annual (or calendar year) forecasts with one error band vs actual
+% Import some data from "msstart.m"
+%
+% yfore: both actual and forecast annual growth data with dates in the first 2 columns
+% in the order of year and month (or quarter).
+% yforel: lower bound of the forecast.
+% yforeh: high bound of the forecast.
+% yacte: actual annual growth data with dates.
+% keyindx: index for the variables to be graphed
+% rnum: number of rows in subplot
+% cnum: number of columns in subplot
+% q_m: if 4 or 12, quarterly or monthly data.
+% ylab: string array for the length(keyindx)-by-1 variables
+% forelabel: title label for as of time of forecast
+% conlab: x-axis label for what conditions imposed; e.g., conlab = 'All bar MS shocks inspl'
+%-------------
+% No output argument for this graph file.
+% See fn_seriesgraph.m, fn_foregraph.m.
+%
+% Tao Zha, August 2000
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+vyrs = yfore(:,1); % vectorized
+hornum = cell(length(vyrs),1); % horizontal year (number)
+count=0;
+for k=vyrs'
+ count=count+1;
+ jnk=num2str(k);
+ hornum{count}=jnk(3:4); % e.g., with '1990', we have '90'
+end
+
+count=0;
+for i = keyindx
+ count = count+1;
+ subplot(rnum,cnum,count)
+ plot(yacte(:,1)+yacte(:,2)/q_m,yacte(:,2+i),yfore(:,1)+yfore(:,2)/q_m,yfore(:,2+i),'--',...
+ yforel(:,1)+yforel(:,2)/q_m,yforel(:,2+i),'-.',yforeh(:,1)+yforeh(:,2)/q_m,yforeh(:,2+i),'-.')
+
+ if (yfore(1,2)==0) % only for annual growth rates (not for, say, monthly annualized rates)
+ set(gca,'XLim',[vyrs(1) vyrs(end)])
+ set(gca,'XTick',vyrs)
+ set(gca,'XTickLabel',char(hornum))
+ end
+
+ if i==keyindx(1)
+ title(forelabel)
+ elseif i>=length(keyindx) %i>=length(keyindx)-1
+ xlabel(conlab)
+ end
+ %
+ grid
+ ylabel(char(ylab(i)))
+end
diff --git a/MatlabFiles/fn_fprintmatrix.m b/MatlabFiles/fn_fprintmatrix.m
index 2bd5cc2376706ed67b2263ac5d4059100040ee7e..2e9598f0a4ca59803dd0098a1f74c5688a240811 100644
--- a/MatlabFiles/fn_fprintmatrix.m
+++ b/MatlabFiles/fn_fprintmatrix.m
@@ -1,55 +1,55 @@
-function fn_fprintmatrix(fid, M, nrows, ncols, indxFloat)
-% Prints the matrix to an ascii file indexed by fid.
-%
-% Inputs:
-% fid: Ascii file id. Example: fid = fopen('outdatainp_3s_stv_tvms6lags.prn','a');
-% M: The matrix to be written to the file.
-% nrows: Number of rows of M.
-% ncols: Number of columns of M.
-% indxFloat: 1 if double;
-% 2 if single;
-% 3 if only 3 significant digits
-% 0 if integer.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-%
-if nrows~=size(M,1)
- error('fn_fprintmatrix(): Make sure the row number supplied match that of the matrix');
-end
-if ncols~=size(M,2)
- error('fn_fprintmatrix(): Make sure the column number supplied match that of the matrix');
-end
-for ki=1:nrows
- for kj=1:ncols
- if (indxFloat == 1)
- fprintf(fid,' %.16e ',M((kj-1)*nrows+ki));
- elseif (indxFloat == 2)
- fprintf(fid,' %.8e ',M((kj-1)*nrows+ki));
- elseif (indxFloat == 3)
- fprintf(fid,' %.3e ',M((kj-1)*nrows+ki));
- else
- fprintf(fid,' %d ',M((kj-1)*nrows+ki));
- end
- if (kj==ncols)
- fprintf(fid,'\n');
- end
- end
- if (ki==nrows)
- fprintf(fid,'\n\n');
- end
-end
+function fn_fprintmatrix(fid, M, nrows, ncols, indxFloat)
+% Prints the matrix to an ascii file indexed by fid.
+%
+% Inputs:
+% fid: Ascii file id. Example: fid = fopen('outdatainp_3s_stv_tvms6lags.prn','a');
+% M: The matrix to be written to the file.
+% nrows: Number of rows of M.
+% ncols: Number of columns of M.
+% indxFloat: 1 if double;
+% 2 if single;
+% 3 if only 3 significant digits
+% 0 if integer.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+%
+if nrows~=size(M,1)
+ error('fn_fprintmatrix(): Make sure the row number supplied match that of the matrix');
+end
+if ncols~=size(M,2)
+ error('fn_fprintmatrix(): Make sure the column number supplied match that of the matrix');
+end
+for ki=1:nrows
+ for kj=1:ncols
+ if (indxFloat == 1)
+ fprintf(fid,' %.16e ',M((kj-1)*nrows+ki));
+ elseif (indxFloat == 2)
+ fprintf(fid,' %.8e ',M((kj-1)*nrows+ki));
+ elseif (indxFloat == 3)
+ fprintf(fid,' %.3e ',M((kj-1)*nrows+ki));
+ else
+ fprintf(fid,' %d ',M((kj-1)*nrows+ki));
+ end
+ if (kj==ncols)
+ fprintf(fid,'\n');
+ end
+ end
+ if (ki==nrows)
+ fprintf(fid,'\n\n');
+ end
+end
diff --git a/MatlabFiles/fn_fprintvector.m b/MatlabFiles/fn_fprintvector.m
index 2329c6c76a6ca65da3da24ff1336ca41c5dac246..c7f9752a561ea95ada5451b60bfe1852ec27673e 100644
--- a/MatlabFiles/fn_fprintvector.m
+++ b/MatlabFiles/fn_fprintvector.m
@@ -1,46 +1,46 @@
-function fn_fprintvector(fid, vec, ncols, indxFloat)
-% Prints a row vector to an ascii file indexed by fid without any spacing.
-%
-% Inputs:
-% fid: Ascii file id. Example: fid = fopen('outdatainp_3s_stv_tvms6lags.prn','a');
-% vec: A row vector to be written to the file.
-% ncols: Number of columns of vec.
-% indxFloat: 1 if double;
-% 2 if single;
-% 0 if integer.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-%
-if size(vec,1)~=1
- error('fn_fprintvector(): The vector must be a row vector');
-end
-if ncols~=size(vec,2)
- error('fn_fprintvector(): The column number supplied match that of the vector');
-end
-for kj=1:ncols
- if (indxFloat == 1)
- fprintf(fid,' %.16e ',vec(kj));
- elseif (indxFloat == 2)
- fprintf(fid,' %.8e ',vec(kj));
- else
- fprintf(fid,' %d ',vec(kj));
- end
- if (kj==ncols)
- fprintf(fid,'\n');
- end
-end
+function fn_fprintvector(fid, vec, ncols, indxFloat)
+% Prints a row vector to an ascii file indexed by fid without any spacing.
+%
+% Inputs:
+% fid: Ascii file id. Example: fid = fopen('outdatainp_3s_stv_tvms6lags.prn','a');
+% vec: A row vector to be written to the file.
+% ncols: Number of columns of vec.
+% indxFloat: 1 if double;
+% 2 if single;
+% 0 if integer.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+%
+if size(vec,1)~=1
+ error('fn_fprintvector(): The vector must be a row vector');
+end
+if ncols~=size(vec,2)
+ error('fn_fprintvector(): The column number supplied match that of the vector');
+end
+for kj=1:ncols
+ if (indxFloat == 1)
+ fprintf(fid,' %.16e ',vec(kj));
+ elseif (indxFloat == 2)
+ fprintf(fid,' %.8e ',vec(kj));
+ else
+ fprintf(fid,' %d ',vec(kj));
+ end
+ if (kj==ncols)
+ fprintf(fid,'\n');
+ end
+end
diff --git a/MatlabFiles/fn_gfmean.m b/MatlabFiles/fn_gfmean.m
index b0dd67b61f6ae5a0d48e107f5519714f62d5ab7d..34c4b605e9f338407c9f1448a264418d39dad151 100644
--- a/MatlabFiles/fn_gfmean.m
+++ b/MatlabFiles/fn_gfmean.m
@@ -1,58 +1,58 @@
-function [Fmat,gvec] = fn_gfmean(b,P,Vi,nvar,ncoef,n0,np)
-% [Fmat,gvec] = fn_gfmean(b,P,Vi,nvar,ncoef,n0,np)
-%
-% Mean of free lagged parameters g and original lagged parameters F, conditional on comtemporaneous b's
-% See Waggoner and Zha's Gibbs sampling
-%
-% b: sum(n0)-element vector of mean estimate of A0 free parameters
-% P: cell(nvar,1). In each cell, the transformation matrix that affects the posterior mean of A+ conditional on A0.
-% Vi: nvar-by-1 cell. In each cell, k-by-ri orthonormal basis for the null of the ith
-% equation lagged restriction matrix where k is a total of exogenous variables and
-% ri is the number of free parameters. With this transformation, we have fi = Vi*gi
-% or Vi'*fi = gi where fi is a vector of total original parameters and gi is a
-% vector of free parameters. There must be at least one free parameter left for
-% the ith equation.
-% nvar: number of endogeous variables
-% ncoef: number of original lagged variables per equation
-% n0: nvar-element vector, ith element represents the number of free A0 parameters in ith equation
-% np: nvar-element vector, ith element represents the number of free A+ parameters in ith equation
-%---------------
-% Fmat: ncoef-by-nvar matrix of original lagged parameters A+. Column corresponding to equation.
-% gvec: sum(np)-by-1 stacked vector of all free lagged parameters A+.
-%
-% Tao Zha, February 2000. Revised, August 2000.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-b=b(:); n0=n0(:); np=np(:);
-
-n0cum = [0;cumsum(n0)];
-npcum = [0;cumsum(np)];
-gvec = zeros(npcum(end),1);
-Fmat = zeros(ncoef,nvar); % ncoef: maximum original lagged parameters per equation
-
-if ~(length(b)==n0cum(end))
- error('Make inputs n0 and length(b) match exactly')
-end
-
-for kj=1:nvar
- bj = b(n0cum(kj)+1:n0cum(kj+1));
- gj = P{kj}*bj;
- gvec(npcum(kj)+1:npcum(kj+1)) = gj;
- Fmat(:,kj) = Vi{kj}*gj;
-end
+function [Fmat,gvec] = fn_gfmean(b,P,Vi,nvar,ncoef,n0,np)
+% [Fmat,gvec] = fn_gfmean(b,P,Vi,nvar,ncoef,n0,np)
+%
+% Mean of free lagged parameters g and original lagged parameters F, conditional on comtemporaneous b's
+% See Waggoner and Zha's Gibbs sampling
+%
+% b: sum(n0)-element vector of mean estimate of A0 free parameters
+% P: cell(nvar,1). In each cell, the transformation matrix that affects the posterior mean of A+ conditional on A0.
+% Vi: nvar-by-1 cell. In each cell, k-by-ri orthonormal basis for the null of the ith
+% equation lagged restriction matrix where k is a total of exogenous variables and
+% ri is the number of free parameters. With this transformation, we have fi = Vi*gi
+% or Vi'*fi = gi where fi is a vector of total original parameters and gi is a
+% vector of free parameters. There must be at least one free parameter left for
+% the ith equation.
+% nvar: number of endogeous variables
+% ncoef: number of original lagged variables per equation
+% n0: nvar-element vector, ith element represents the number of free A0 parameters in ith equation
+% np: nvar-element vector, ith element represents the number of free A+ parameters in ith equation
+%---------------
+% Fmat: ncoef-by-nvar matrix of original lagged parameters A+. Column corresponding to equation.
+% gvec: sum(np)-by-1 stacked vector of all free lagged parameters A+.
+%
+% Tao Zha, February 2000. Revised, August 2000.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+b=b(:); n0=n0(:); np=np(:);
+
+n0cum = [0;cumsum(n0)];
+npcum = [0;cumsum(np)];
+gvec = zeros(npcum(end),1);
+Fmat = zeros(ncoef,nvar); % ncoef: maximum original lagged parameters per equation
+
+if ~(length(b)==n0cum(end))
+ error('Make inputs n0 and length(b) match exactly')
+end
+
+for kj=1:nvar
+ bj = b(n0cum(kj)+1:n0cum(kj+1));
+ gj = P{kj}*bj;
+ gvec(npcum(kj)+1:npcum(kj+1)) = gj;
+ Fmat(:,kj) = Vi{kj}*gj;
+end
diff --git a/MatlabFiles/fn_gibbsglb.m b/MatlabFiles/fn_gibbsglb.m
index cace135b1279b447be12035f3155929e196685e5..bb6277fa8db6a514e6ecb8ef81042bd5ec118e1b 100644
--- a/MatlabFiles/fn_gibbsglb.m
+++ b/MatlabFiles/fn_gibbsglb.m
@@ -1,73 +1,72 @@
-function [cT,vR,kdf] = fn_gibbsglb(Sbd,idmat0,nvar,fss)
-% [cT,vR,kdf] = gibbsglb(Sbd,idmat0,nvar,fss)
-% Global setup outside the Gibbs loop -- c.f. gibbsvar
-% Ref.: D.F. Waggoner and T.A. Zha: "Does Normalization Matter for Inference?"
-% See Note Forecast (2) pp. 44-51
-%
-% Sbd: cell(nvar,1). Sbd=diag(S(1), ..., S(m)). Already divided by 'fss' for the
-% posterior of a0 or A0(:) in Waggoner and Zha when one has asymmetric prior.
-% Note,"bd" stands for block diagonal.
-% idmat0: identification matrix for A0 with asymmetric prior; column -- equation.
-% nvar: rank of A0 or # of variables
-% fss: effective sample size (in the exponential term) --
-% # of observations + # of dummy observations
-% (or nSample - lags + # of dummy observations)
-%-------------
-% cT{i}: nvar-by-nvar where T'*T=Sbd{i} which is kind of covariance martrix
-% divided by fss already
-% vR{i}: nvar-by-q{i} -- orthonormral basis for T*R, which is obtained through
-% single value decomposition of Q*inv(T)
-% kdf: the polynomial power in the Gamma or 1d Wishart distribution, used in
-% "gibbsvar.m"
-%
-% Written by Tao Zha; Copyright (c) 1999 by Waggoner and Zha
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-kdf = fss; %2; %fss;
-
-cT = cell(nvar,1);
-for k=1:nvar
- cT{k} = chol(Sbd{k}); % upper triangular but lower triangular Choleski
-end
-
-Q = cell(nvar,1);
- % Q{i}: nvar-by-nvar with rank nvar-q(i) with ith equation a and
- % q(i) which is # of non-zero elements in a. Note: Q*a=0.
-
-vR = cell(nvar,1);
-for k=1:nvar
- Q{k} = diag(idmat0(:,k)==0); % constructing Q{k}
- %
- [jnk1,d1,v1] = svd(Q{k}/cT{k});
- d1max=max(diag(d1));
- if d1max==0
- Idxk=1:nvar;
- else
- Idxk = find(diag(d1)<eps*d1max);
- end
- lenk = length(find(idmat0(:,k)));
- if ( length(Idxk)<lenk )
- warning('Dimension of non-zero A0(:,k) is different from svd(Q*inv(T))')
- disp('Press ctrl-c to abort')
- pause
- else
- jnk1 = v1(:,Idxk);
- vR{k} = jnk1(:,1:lenk); % orthonormal basis for T*R
- end
-end
+function [cT,vR,kdf] = fn_gibbsglb(Sbd,idmat0,nvar,fss)
+% [cT,vR,kdf] = gibbsglb(Sbd,idmat0,nvar,fss)
+% Global setup outside the Gibbs loop -- c.f. gibbsvar
+% Ref.: D.F. Waggoner and T.A. Zha: "Does Normalization Matter for Inference?"
+% See Note Forecast (2) pp. 44-51
+%
+% Sbd: cell(nvar,1). Sbd=diag(S(1), ..., S(m)). Already divided by 'fss' for the
+% posterior of a0 or A0(:) in Waggoner and Zha when one has asymmetric prior.
+% Note,"bd" stands for block diagonal.
+% idmat0: identification matrix for A0 with asymmetric prior; column -- equation.
+% nvar: rank of A0 or # of variables
+% fss: effective sample size (in the exponential term) --
+% # of observations + # of dummy observations
+% (or nSample - lags + # of dummy observations)
+%-------------
+% cT{i}: nvar-by-nvar where T'*T=Sbd{i} which is kind of covariance martrix
+% divided by fss already
+% vR{i}: nvar-by-q{i} -- orthonormral basis for T*R, which is obtained through
+% single value decomposition of Q*inv(T)
+% kdf: the polynomial power in the Gamma or 1d Wishart distribution, used in
+% "gibbsvar.m"
+%
+%
+% Copyright (C) 1997-2012 Daniel Waggoner and Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+kdf = fss; %2; %fss;
+
+cT = cell(nvar,1);
+for k=1:nvar
+ cT{k} = chol(Sbd{k}); % upper triangular but lower triangular Choleski
+end
+
+Q = cell(nvar,1);
+ % Q{i}: nvar-by-nvar with rank nvar-q(i) with ith equation a and
+ % q(i) which is # of non-zero elements in a. Note: Q*a=0.
+
+vR = cell(nvar,1);
+for k=1:nvar
+ Q{k} = diag(idmat0(:,k)==0); % constructing Q{k}
+ %
+ [jnk1,d1,v1] = svd(Q{k}/cT{k});
+ d1max=max(diag(d1));
+ if d1max==0
+ Idxk=1:nvar;
+ else
+ Idxk = find(diag(d1)<eps*d1max);
+ end
+ lenk = length(find(idmat0(:,k)));
+ if ( length(Idxk)<lenk )
+ warning('Dimension of non-zero A0(:,k) is different from svd(Q*inv(T))')
+ disp('Press ctrl-c to abort')
+ pause
+ else
+ jnk1 = v1(:,Idxk);
+ vR{k} = jnk1(:,1:lenk); % orthonormal basis for T*R
+ end
+end
diff --git a/MatlabFiles/fn_gibbsrvar.m b/MatlabFiles/fn_gibbsrvar.m
index 6e0d2e253aa7105bc0002a8dd22251bcf3893b75..bbbbd65898d39ecdb75091c05b1d4bce9aaaaee0 100644
--- a/MatlabFiles/fn_gibbsrvar.m
+++ b/MatlabFiles/fn_gibbsrvar.m
@@ -1,83 +1,83 @@
-function [A0gbs, Wcell] = fn_gibbsrvar(A0gbs,UT,nvar,fss,n0,Indxcol)
-% [A0gbs, Wcell] = fn_gibbsrvar(A0gbs,UT,nvar,fss,n0,Indxcol)
-% One-step Gibbs sampler for restricted VARs in the structural form (including over-identified cases).
-% Reference: "A Gibbs sampler for structural VARs" by D.F. Waggoner and T. Zha, ``
-% Journal of Economic Dynamics & Control (JEDC) 28 (2003) 349-366.
-% See Note Forecast (2) pp. 44-51, 70-71, and Theorem 1 and Section 3.1 in the WZ JEDC paper.
-%
-% A0gbs: the last draw of A0 matrix
-% UT: cell(nvar,1) -- U_i*T_i in the proof of Theorem 1 where
-% (1) a_i = U_i*b_i with b_i being a vector of free parameters
-% (2) T_i (q_i-by-q_i) is from T_i*T_i'= S_i = inv(H0inv{i}/T) where H0inv is the inverse of
-% the covariance martrix NOT divided by fss and S_i is defined in (14) on p.355 of the WZ JEDC paper.
-% nvar: rank of A0 or # of variables
-% fss: effective sample size == nSample (T)-lags+# of dummy observations
-% n0: nvar-by-1, ith element represents the number of free A0 parameters in ith equation
-% Indxcol: a row vector indicating random columns this Gibbs draws.
-% When this input is not supplied, the Gibbs draws all columns
-%------------------
-% A0bgs: nvar-by-nvar. New draw of A0 matrix in this Gibbs step
-% Wcell: cell(nvar,1). In each cell, columns of Wcell{i} form an orthonormal basis w_1,...,w_qi in Section 3.1 in the WZ paper. Added 9/04.
-%
-% Written by Tao Zha, August 2000. Revised, September 2004.
-% Copyright (c) by Waggoner and Zha
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-if (nargin==5), Indxcol=[1:nvar]; end
-
-%---------------- Local loop for Gibbs given last A0gbs ----------
-Wcell = cell(length(Indxcol),1);
-w = zeros(nvar,1);
-for ki=Indxcol % given last A0gbs and generate new A0bgs
- X = A0gbs; % WZ's Section 4.3
- X(:,ki) = 0; % want to find non-zero sw s.t., X'*w=0
-
-
- %*** Solving for w and getting an orthonormal basis. See Theorem 1 and also p.48 in Forecast II
- [jL,Ux] = lu(X');
- jIx0 = min(find(abs(diag(Ux))<eps)); % if isempty(jIx0), then something is wrong here
- w(jIx0+1:end) = 0; % if jIx0+1>end, no effect on w0
- w(jIx0) = 1;
- jA = Ux(1:jIx0-1,1:jIx0-1);
- jb = Ux(1:jIx0-1,jIx0);
- jy = -jA\jb;
- w(1:jIx0-1) = jy;
- % Note: if jIx0=1 (which almost never happens for numerical stability reasons), no effect on w.
-
- %*** Constructing orthonormal basis w_1, ..., w_qi at each Gibbs step
- w0 = UT{ki}'*w;
- w1 = w0/sqrt(sum(w0.^2));
- [W,jnk] = qr(w1); % Columns of W form an orthonormal basis w_1,...,w_qi in Section 3.1 in the WZ paper
-
- %*** Draw beta's according to Theorem 1 in the WZ JEDC paper.
- gkbeta = zeros(n0(ki),1); % qi-by-1: greak beta's
- jstd = sqrt(1/fss);
- gkbeta(2:end) = jstd*randn(n0(ki)-1,1); % for beta_2, ..., beta_qi
- %--- Unnormalized (i.e., not normalized) gamma or 1-d Wishart draw of beta_1 in Theorem 1 of the WZ JEDC paper.
- jr = jstd*randn(fss+1,1);
- if rand(1)<0.5
- gkbeta(1) = sqrt(jr'*jr);
- else
- gkbeta(1) = -sqrt(jr'*jr);
- end
-
- %*** Getting a new ki_th column in A0
- A0gbs(:,ki) = UT{ki}*(W*gkbeta); % n-by-1: U_i*T_i*W*beta;
- Wcell{ki} = W; %q_i-by-1.
-end
+function [A0gbs, Wcell] = fn_gibbsrvar(A0gbs,UT,nvar,fss,n0,Indxcol)
+% [A0gbs, Wcell] = fn_gibbsrvar(A0gbs,UT,nvar,fss,n0,Indxcol)
+% One-step Gibbs sampler for restricted VARs in the structural form (including over-identified cases).
+% Reference: "A Gibbs sampler for structural VARs" by D.F. Waggoner and T. Zha, ``
+% Journal of Economic Dynamics & Control (JEDC) 28 (2003) 349-366.
+% See Note Forecast (2) pp. 44-51, 70-71, and Theorem 1 and Section 3.1 in the WZ JEDC paper.
+%
+% A0gbs: the last draw of A0 matrix
+% UT: cell(nvar,1) -- U_i*T_i in the proof of Theorem 1 where
+% (1) a_i = U_i*b_i with b_i being a vector of free parameters
+% (2) T_i (q_i-by-q_i) is from T_i*T_i'= S_i = inv(H0inv{i}/T) where H0inv is the inverse of
+% the covariance martrix NOT divided by fss and S_i is defined in (14) on p.355 of the WZ JEDC paper.
+% nvar: rank of A0 or # of variables
+% fss: effective sample size == nSample (T)-lags+# of dummy observations
+% n0: nvar-by-1, ith element represents the number of free A0 parameters in ith equation
+% Indxcol: a row vector indicating random columns this Gibbs draws.
+% When this input is not supplied, the Gibbs draws all columns
+%------------------
+% A0bgs: nvar-by-nvar. New draw of A0 matrix in this Gibbs step
+% Wcell: cell(nvar,1). In each cell, columns of Wcell{i} form an orthonormal basis w_1,...,w_qi in Section 3.1 in the WZ paper. Added 9/04.
+%
+% Written by Tao Zha, August 2000. Revised, September 2004.
+%
+%
+% Copyright (C) 1997-2012 Daniel Waggoner and Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+if (nargin==5), Indxcol=[1:nvar]; end
+
+%---------------- Local loop for Gibbs given last A0gbs ----------
+Wcell = cell(length(Indxcol),1);
+w = zeros(nvar,1);
+for ki=Indxcol % given last A0gbs and generate new A0bgs
+ X = A0gbs; % WZ's Section 4.3
+ X(:,ki) = 0; % want to find non-zero sw s.t., X'*w=0
+
+
+ %*** Solving for w and getting an orthonormal basis. See Theorem 1 and also p.48 in Forecast II
+ [jL,Ux] = lu(X');
+ jIx0 = min(find(abs(diag(Ux))<eps)); % if isempty(jIx0), then something is wrong here
+ w(jIx0+1:end) = 0; % if jIx0+1>end, no effect on w0
+ w(jIx0) = 1;
+ jA = Ux(1:jIx0-1,1:jIx0-1);
+ jb = Ux(1:jIx0-1,jIx0);
+ jy = -jA\jb;
+ w(1:jIx0-1) = jy;
+ % Note: if jIx0=1 (which almost never happens for numerical stability reasons), no effect on w.
+
+ %*** Constructing orthonormal basis w_1, ..., w_qi at each Gibbs step
+ w0 = UT{ki}'*w;
+ w1 = w0/sqrt(sum(w0.^2));
+ [W,jnk] = qr(w1); % Columns of W form an orthonormal basis w_1,...,w_qi in Section 3.1 in the WZ paper
+
+ %*** Draw beta's according to Theorem 1 in the WZ JEDC paper.
+ gkbeta = zeros(n0(ki),1); % qi-by-1: greak beta's
+ jstd = sqrt(1/fss);
+ gkbeta(2:end) = jstd*randn(n0(ki)-1,1); % for beta_2, ..., beta_qi
+ %--- Unnormalized (i.e., not normalized) gamma or 1-d Wishart draw of beta_1 in Theorem 1 of the WZ JEDC paper.
+ jr = jstd*randn(fss+1,1);
+ if rand(1)<0.5
+ gkbeta(1) = sqrt(jr'*jr);
+ else
+ gkbeta(1) = -sqrt(jr'*jr);
+ end
+
+ %*** Getting a new ki_th column in A0
+ A0gbs(:,ki) = UT{ki}*(W*gkbeta); % n-by-1: U_i*T_i*W*beta;
+ Wcell{ki} = W; %q_i-by-1.
+end
diff --git a/MatlabFiles/fn_gibbsrvar_setup.m b/MatlabFiles/fn_gibbsrvar_setup.m
index 2c3049829de4c90fcb9f868b2885ff499e45b0d3..8f3046d5c827cdc3f8d91de1bf939a46902e6cb4 100644
--- a/MatlabFiles/fn_gibbsrvar_setup.m
+++ b/MatlabFiles/fn_gibbsrvar_setup.m
@@ -1,75 +1,75 @@
-function [Tinv,UT,VHphalf,PU,VPU] = fn_gibbsrvar_setup(H0inv, Ui, Hpinv, Pmat, Vi, nvar, fss)
-% [Tinv,UT,VHphalf,PU,VPU] = fn_gibbsrvar_setup.m(H0inv, Ui, Hpinv, Pmat, Vi, fss, nvar)
-% Global setup outside the Gibbs loop to be used by fn_gibbsvar().
-% Reference: "A Gibbs sampler for structural VARs" by D.F. Waggoner and T. Zha, ``
-% Journal of Economic Dynamics & Control (JEDC) 28 (2003) 349-366.
-% See Note Forecast (2) pp. 44-51, 70-71, and Theorem 1 and Section 3.1 in the WZ JEDC paper.
-%
-% H0inv: cell(nvar,1). Not divided by T yet. In each cell, inverse of posterior covariance matrix H0.
-% The exponential term is b_i'*inv(H0)*b_i for the ith equation where b_i = U_i*a0_i.
-% It resembles old SpH or Sbd in the exponent term in posterior of A0, but not divided by T yet.
-% Ui: nvar-by-1 cell. In each cell, nvar-by-qi orthonormal basis for the null of the ith
-% equation contemporaneous restriction matrix where qi is the number of free parameters.
-% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
-% of total original parameters and bi is a vector of free parameters. When no
-% restrictions are imposed, we have Ui = I. There must be at least one free
-% parameter left for the ith equation. Imported from dnrprior.m.
-% Hpinv: cell(nvar,1). In each cell, posterior inverse of covariance matrix Hp (A+) for the free parameters
-% g_i = V_i*A+(:,i) in the ith equation.
-% Pmat: cell(nvar,1). In each cell, the transformation matrix that affects the posterior mean of A+ conditional on A0.
-% In other words, the posterior mean (of g_i) = Pmat{i}*b_i where g_i is a column vector of free parameters
-% of A+(:,i)) given b_i (b_i is a column vector of free parameters of A0(:,i)).
-% Vi: nvar-by-1 cell. In each cell, k-by-ri orthonormal basis for the null of the ith
-% equation lagged restriction matrix where k (ncoef) is a total number of RHS variables and
-% ri is the number of free parameters. With this transformation, we have fi = Vi*gi
-% or Vi'*fi = gi where fi is a vector of total original parameters and gi is a
-% vector of free parameters. There must be at least one free parameter left for
-% the ith equation. Imported from dnrprior.m.
-% nvar: number of endogenous variables or rank of A0.
-% fss: effective sample size (in the exponential term) = nSample - lags + ndobs (ndobs = # of dummy observations
-% is set to 0 when fn_rnrprior_covres_dobs() is used where dummy observations are included as part of the explicit prior.
-%-------------
-% Tinv: cell(nvar,1). In each cell, inv(T_i) for T_iT_i'=S_i where S_i is defined on p.355 of the WZ JEDC paper.
-% UT: cell(nvar,1). In each cell, U_i*T_i.
-% VHphalf: cell(nvar,1). In each cell, V_i*sqrt(Hp_i).
-% PU: cell(nvar,1). In each cell, Pmat{i}*U_i where Pmat{i} = P_i defined in (13) on p.353 of the WZ JEDC paper.
-% VPU: cell(nvar,1). In each cell, V_i*P_i*U_i
-%
-% Written by Tao Zha, September 2004.
-% Copyright (c) 2004 by Waggoner and Zha
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-
-%--- For A0.
-Tinv = cell(nvar,1); % in each cell, inv(T_i) for T_iT_i'=S_i where S_i is defined on p.355 of the WZ JEDC paper.
-UT = cell(nvar,1); % in each cell, U_i*T_i.
-%--- For A+.
-VHphalf = cell(nvar,1); % in each cell, V_i*sqrt(Hp_i).
-PU = cell(nvar,1); % in each cell, Pmat{i}*U_i where Pmat{i} = P_i defined in (13) on p.353 of the WZ JEDC paper.
-VPU = cell(nvar,1); % in each cell, V_i*P_i*U_i
-%
-for ki=1:nvar
- %--- For A0.
- Tinv{ki} = chol(H0inv{ki}/fss); % Tinv_i'*Tinv_i = inv(S_i) ==> T_i*T_i' = S_i where S_i = H0inv{i}/fss is defined on p.355 of the WZ JEDC paper.
- UT{ki} = Ui{ki}/Tinv{ki}; % n-by-qi: U_i*T_i in (14) on p. 255 of the WZ JEDC paper.
- %--- For A+.
- VHphalf{ki} = Vi{ki}/chol(Hpinv{ki}); % where chol(Hpinv_i)*chol(Hpinv_i)'=Hpinv_i.
- PU{ki} = Pmat{ki}*Ui{ki}';
- VPU{ki} = Vi{ki}*PU{ki};
-end
+function [Tinv,UT,VHphalf,PU,VPU] = fn_gibbsrvar_setup(H0inv, Ui, Hpinv, Pmat, Vi, nvar, fss)
+% [Tinv,UT,VHphalf,PU,VPU] = fn_gibbsrvar_setup.m(H0inv, Ui, Hpinv, Pmat, Vi, fss, nvar)
+% Global setup outside the Gibbs loop to be used by fn_gibbsvar().
+% Reference: "A Gibbs sampler for structural VARs" by D.F. Waggoner and T. Zha, ``
+% Journal of Economic Dynamics & Control (JEDC) 28 (2003) 349-366.
+% See Note Forecast (2) pp. 44-51, 70-71, and Theorem 1 and Section 3.1 in the WZ JEDC paper.
+%
+% H0inv: cell(nvar,1). Not divided by T yet. In each cell, inverse of posterior covariance matrix H0.
+% The exponential term is b_i'*inv(H0)*b_i for the ith equation where b_i = U_i*a0_i.
+% It resembles old SpH or Sbd in the exponent term in posterior of A0, but not divided by T yet.
+% Ui: nvar-by-1 cell. In each cell, nvar-by-qi orthonormal basis for the null of the ith
+% equation contemporaneous restriction matrix where qi is the number of free parameters.
+% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
+% of total original parameters and bi is a vector of free parameters. When no
+% restrictions are imposed, we have Ui = I. There must be at least one free
+% parameter left for the ith equation. Imported from dnrprior.m.
+% Hpinv: cell(nvar,1). In each cell, posterior inverse of covariance matrix Hp (A+) for the free parameters
+% g_i = V_i*A+(:,i) in the ith equation.
+% Pmat: cell(nvar,1). In each cell, the transformation matrix that affects the posterior mean of A+ conditional on A0.
+% In other words, the posterior mean (of g_i) = Pmat{i}*b_i where g_i is a column vector of free parameters
+% of A+(:,i)) given b_i (b_i is a column vector of free parameters of A0(:,i)).
+% Vi: nvar-by-1 cell. In each cell, k-by-ri orthonormal basis for the null of the ith
+% equation lagged restriction matrix where k (ncoef) is a total number of RHS variables and
+% ri is the number of free parameters. With this transformation, we have fi = Vi*gi
+% or Vi'*fi = gi where fi is a vector of total original parameters and gi is a
+% vector of free parameters. There must be at least one free parameter left for
+% the ith equation. Imported from dnrprior.m.
+% nvar: number of endogenous variables or rank of A0.
+% fss: effective sample size (in the exponential term) = nSample - lags + ndobs (ndobs = # of dummy observations
+% is set to 0 when fn_rnrprior_covres_dobs() is used where dummy observations are included as part of the explicit prior.
+%-------------
+% Tinv: cell(nvar,1). In each cell, inv(T_i) for T_iT_i'=S_i where S_i is defined on p.355 of the WZ JEDC paper.
+% UT: cell(nvar,1). In each cell, U_i*T_i.
+% VHphalf: cell(nvar,1). In each cell, V_i*sqrt(Hp_i).
+% PU: cell(nvar,1). In each cell, Pmat{i}*U_i where Pmat{i} = P_i defined in (13) on p.353 of the WZ JEDC paper.
+% VPU: cell(nvar,1). In each cell, V_i*P_i*U_i
+%
+% Written by Tao Zha, September 2004.
+%
+%
+% Copyright (C) 1997-2012 Daniel Waggoner and Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+
+%--- For A0.
+Tinv = cell(nvar,1); % in each cell, inv(T_i) for T_iT_i'=S_i where S_i is defined on p.355 of the WZ JEDC paper.
+UT = cell(nvar,1); % in each cell, U_i*T_i.
+%--- For A+.
+VHphalf = cell(nvar,1); % in each cell, V_i*sqrt(Hp_i).
+PU = cell(nvar,1); % in each cell, Pmat{i}*U_i where Pmat{i} = P_i defined in (13) on p.353 of the WZ JEDC paper.
+VPU = cell(nvar,1); % in each cell, V_i*P_i*U_i
+%
+for ki=1:nvar
+ %--- For A0.
+ Tinv{ki} = chol(H0inv{ki}/fss); % Tinv_i'*Tinv_i = inv(S_i) ==> T_i*T_i' = S_i where S_i = H0inv{i}/fss is defined on p.355 of the WZ JEDC paper.
+ UT{ki} = Ui{ki}/Tinv{ki}; % n-by-qi: U_i*T_i in (14) on p. 255 of the WZ JEDC paper.
+ %--- For A+.
+ VHphalf{ki} = Vi{ki}/chol(Hpinv{ki}); % where chol(Hpinv_i)*chol(Hpinv_i)'=Hpinv_i.
+ PU{ki} = Pmat{ki}*Ui{ki}';
+ VPU{ki} = Vi{ki}*PU{ki};
+end
diff --git a/MatlabFiles/fn_gibbsrvaroldworks.m b/MatlabFiles/fn_gibbsrvaroldworks.m
index ed20ecc93a81e8da5787049c030705edc461d0bf..1469ac22a3d9549c2d50de196ce58d9ace8f135d 100644
--- a/MatlabFiles/fn_gibbsrvaroldworks.m
+++ b/MatlabFiles/fn_gibbsrvaroldworks.m
@@ -1,79 +1,80 @@
-function A0gbs = fn_gibbsrvar(A0gbs,UT,nvar,fss,n0,Indxcol)
-% A0gbs = fn_gibbsrvar(A0gbs,UT,nvar,fss,n0,Indxcol)
-% One-step Gibbs sampler for restricted VARs in the structural form
-% Ref.: D.F. Waggoner and T. Zha: ``A Gibbs sampler for structural VARs''
-% See Note Forecast (2) pp. 44-51 and Theorem 1 and Section 3 in the WZ paper.
-%
-% A0gbs: the last draw of A0 matrix
-% UT: cell(nvar,1) -- U_i*T_i in the proof of Theorem 1 where
-% (1) a_i = U_i*b_i with b_i being a vector of free parameters
-% (2) T_i (q_i-by-q_i) is from T_i*T_i'=H0inv{i}/T. Note that H0inv is the inverse of
-% the covariance martrix NOT divided by fss. See Theorem 1.
-% nvar: rank of A0 or # of variables
-% fss: effective sample size == nSample (T)-lags+# of dummy observations
-% n0: nvar-by-1, ith element represents the number of free A0 parameters in ith equation
-% Indxcol: a row vector indicating random columns this Gibbs draws.
-% When this input is not supplied, the Gibbs draws all columns
-%------------------
-% A0bgs: new draw of A0 matrix in this Gibbs step
-%
-% Written by Tao Zha, August 2000; Copyright (c) by Waggoner and Zha
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-if (nargin==5), Indxcol=[1:nvar]; end
-
-%---------------- Local loop for Gibbs given last A0gbs ----------
-%
-w = zeros(nvar,1);
-for ki=Indxcol % given last A0gbs and generate new A0bgs
- X = A0gbs; % WZ's Section 4.3
- X(:,ki) = 0; % want to find non-zero sw s.t., X'*w=0
-
-
- %*** Solving for w and getting an orthonormal basis. See Theorem 1 and also p.48 in Forecast II
- [jL,Ux] = lu(X');
- jIx0 = min(find(abs(diag(Ux))<eps)); % if isempty(jIx0), then something is wrong here
- w(jIx0+1:end) = 0; % if jIx0+1>end, no effect on w0
- w(jIx0) = 1;
- jA = Ux(1:jIx0-1,1:jIx0-1);
- jb = Ux(1:jIx0-1,jIx0);
- jy = -jA\jb;
- w(1:jIx0-1) = jy;
- % Note: if jIx0=1 (which almost never happens for numerical stability reasons), no effect on w.
-
- %*** Constructing orthonormal basis w_1, ..., w_qi at each Gibbs step
- w0 = UT{ki}'*w;
- w1 = w0/sqrt(sum(w0.^2));
- [W,jnk] = qr(w1); % columns of W form an orthonormal basis w_1,...,w_qi in Section 4.2 in the WZ paper
-
- %*** Draw beta's in Theorem 1
- gkbeta = zeros(n0(ki),1); % qi-by-1: greak beta's
- jstd = sqrt(1/fss);
- gkbeta(2:end) = jstd*randn(n0(ki)-1,1); % for beta_2, ..., beta_qi
- %--- Unnormalized (i.e., not normalized) gamma or 1-d Wishart draw of beta_1 in Theorem 1.
- %* gamma or 1-d Wishart draw of beta_1
- jr = jstd*randn(fss+1,1);
- if rand(1)<0.5
- gkbeta(1) = sqrt(jr'*jr);
- else
- gkbeta(1) = -sqrt(jr'*jr);
- end
-
- %*** Getting a new ki_th column in A0
- A0gbs(:,ki) = UT{ki}*(W*gkbeta); % n-by-1
-end
+function A0gbs = fn_gibbsrvar(A0gbs,UT,nvar,fss,n0,Indxcol)
+% A0gbs = fn_gibbsrvar(A0gbs,UT,nvar,fss,n0,Indxcol)
+% One-step Gibbs sampler for restricted VARs in the structural form
+% Ref.: D.F. Waggoner and T. Zha: ``A Gibbs sampler for structural VARs''
+% See Note Forecast (2) pp. 44-51 and Theorem 1 and Section 3 in the WZ paper.
+%
+% A0gbs: the last draw of A0 matrix
+% UT: cell(nvar,1) -- U_i*T_i in the proof of Theorem 1 where
+% (1) a_i = U_i*b_i with b_i being a vector of free parameters
+% (2) T_i (q_i-by-q_i) is from T_i*T_i'=H0inv{i}/T. Note that H0inv is the inverse of
+% the covariance martrix NOT divided by fss. See Theorem 1.
+% nvar: rank of A0 or # of variables
+% fss: effective sample size == nSample (T)-lags+# of dummy observations
+% n0: nvar-by-1, ith element represents the number of free A0 parameters in ith equation
+% Indxcol: a row vector indicating random columns this Gibbs draws.
+% When this input is not supplied, the Gibbs draws all columns
+%------------------
+% A0bgs: new draw of A0 matrix in this Gibbs step
+%
+% Written by Tao Zha, August 2000
+%
+%
+% Copyright (C) 1997-2012 Daniel Waggoner and Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+if (nargin==5), Indxcol=[1:nvar]; end
+
+%---------------- Local loop for Gibbs given last A0gbs ----------
+%
+w = zeros(nvar,1);
+for ki=Indxcol % given last A0gbs and generate new A0bgs
+ X = A0gbs; % WZ's Section 4.3
+ X(:,ki) = 0; % want to find non-zero sw s.t., X'*w=0
+
+
+ %*** Solving for w and getting an orthonormal basis. See Theorem 1 and also p.48 in Forecast II
+ [jL,Ux] = lu(X');
+ jIx0 = min(find(abs(diag(Ux))<eps)); % if isempty(jIx0), then something is wrong here
+ w(jIx0+1:end) = 0; % if jIx0+1>end, no effect on w0
+ w(jIx0) = 1;
+ jA = Ux(1:jIx0-1,1:jIx0-1);
+ jb = Ux(1:jIx0-1,jIx0);
+ jy = -jA\jb;
+ w(1:jIx0-1) = jy;
+ % Note: if jIx0=1 (which almost never happens for numerical stability reasons), no effect on w.
+
+ %*** Constructing orthonormal basis w_1, ..., w_qi at each Gibbs step
+ w0 = UT{ki}'*w;
+ w1 = w0/sqrt(sum(w0.^2));
+ [W,jnk] = qr(w1); % columns of W form an orthonormal basis w_1,...,w_qi in Section 4.2 in the WZ paper
+
+ %*** Draw beta's in Theorem 1
+ gkbeta = zeros(n0(ki),1); % qi-by-1: greak beta's
+ jstd = sqrt(1/fss);
+ gkbeta(2:end) = jstd*randn(n0(ki)-1,1); % for beta_2, ..., beta_qi
+ %--- Unnormalized (i.e., not normalized) gamma or 1-d Wishart draw of beta_1 in Theorem 1.
+ %* gamma or 1-d Wishart draw of beta_1
+ jr = jstd*randn(fss+1,1);
+ if rand(1)<0.5
+ gkbeta(1) = sqrt(jr'*jr);
+ else
+ gkbeta(1) = -sqrt(jr'*jr);
+ end
+
+ %*** Getting a new ki_th column in A0
+ A0gbs(:,ki) = UT{ki}*(W*gkbeta); % n-by-1
+end
diff --git a/MatlabFiles/fn_gradcd.m b/MatlabFiles/fn_gradcd.m
index 276c9e966ace6bdc5c27ccc1c0b6810552d6996a..e1bd79e6aa61eab406a816e59d71086e9723338f 100644
--- a/MatlabFiles/fn_gradcd.m
+++ b/MatlabFiles/fn_gradcd.m
@@ -1,86 +1,86 @@
-function grdd = fn_gradcd(fcn,x0,grdh,P1,P2,P3,P4,P5,P6,P7,P8,P9,...
- P10,P11,P12,P13,P14,P15,P16,P17,P18,P19,P20)
-%function grdd = fn_gradcd(fcn,x0,grdh,P1,P2,P3,P4,P5,P6,P7,P8,P9,...
-% P10,P11,P12,P13,P14,P15,P16,P17,P18,P19,P20)
-% computes numerical gradient of a single-valued function or Jacobian
-% matrix of a vector-valued function using a central difference with
-% function grdd = gradcd(fcn,x0,grdh)
-%
-% fcn: a string naming a vector-valued function (f:n-by-1 -> k-by-1).
-% x0: a column vector n-by-1, at which point the hessian is evaluated.
-% grdh: step size, n*1. Set as follows
-% step = eps^(1/3);
-% %step = 1e-04;
-% grdh = step * (max([abs(x) ones(length(x),1)]'))' .* (abs(x) ./ x);
-%--------------------
-% grdd: n-by-k Jacobian matrix (gradients).
-%
-% Written by Tao Zha, 2002.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-stps = eps^(1/3);
-% eps: floating point relative accuracy or machine precision: 2.22e-16
-% stps: step size recommended by Dennis and Schnabel: 6.006e-6
-
-x0 = x0(:);
-tailstr = ')';
-for i=nargin-3:-1:1
- tailstr=[ ',P' num2str(i) tailstr];
-end
-f0 = eval([fcn '(x0' tailstr]);
-
-% ** initializations
-n = length(x0);
-k = length(f0); % dimension of "fcn"
-
-% ** Computation of stepsize (dh)
-if all(grdh)
- dh = grdh;
-else
- ax0 = abs(x0);
- if all(x0)
- dax0 = x0 ./ ax0;
- else
- dax0 = 1;
- end
- dh = stps * (max([ax0 ones(n,1)]'))' .* dax0;
-end
-
-xdh = x0 + dh;
-dh = xdh - x0; % This increases precision slightly
-%
-argplus = x0(:,ones(n,1));
-argminus = argplus;
-dnum = 1:n+1:n^2; % positions of diagonals in vec(argplus).
-argplus(dnum) = xdh; % replace the diagonals of "argplus" by "xdh".
-argminus(dnum) = x0-dh; % replace the diagonals of "argplus" by "xdh".
-
-grdd = zeros(k,n); % preallocate to speed the loop.
-i = 0;
-while i ~= n
- i = i+1;
- fp = eval([fcn '(argplus(:,i)' tailstr]);
- fm = eval([fcn '(argminus(:,i)' tailstr]);
- grdd(:,i) = fp - fm;
-end
-dhm = dh(:,ones(k,1));
-dhm = dhm'; % k*n
-grdd = grdd ./ (2*dhm); % k-by-n.
-grdd = grdd'; % n-by-k.
-
+function grdd = fn_gradcd(fcn,x0,grdh,P1,P2,P3,P4,P5,P6,P7,P8,P9,...
+ P10,P11,P12,P13,P14,P15,P16,P17,P18,P19,P20)
+%function grdd = fn_gradcd(fcn,x0,grdh,P1,P2,P3,P4,P5,P6,P7,P8,P9,...
+% P10,P11,P12,P13,P14,P15,P16,P17,P18,P19,P20)
+% computes numerical gradient of a single-valued function or Jacobian
+% matrix of a vector-valued function using a central difference with
+% function grdd = gradcd(fcn,x0,grdh)
+%
+% fcn: a string naming a vector-valued function (f:n-by-1 -> k-by-1).
+% x0: a column vector n-by-1, at which point the hessian is evaluated.
+% grdh: step size, n*1. Set as follows
+% step = eps^(1/3);
+% %step = 1e-04;
+% grdh = step * (max([abs(x) ones(length(x),1)]'))' .* (abs(x) ./ x);
+%--------------------
+% grdd: n-by-k Jacobian matrix (gradients).
+%
+% Written by Tao Zha, 2002.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+stps = eps^(1/3);
+% eps: floating point relative accuracy or machine precision: 2.22e-16
+% stps: step size recommended by Dennis and Schnabel: 6.006e-6
+
+x0 = x0(:);
+tailstr = ')';
+for i=nargin-3:-1:1
+ tailstr=[ ',P' num2str(i) tailstr];
+end
+f0 = eval([fcn '(x0' tailstr]);
+
+% ** initializations
+n = length(x0);
+k = length(f0); % dimension of "fcn"
+
+% ** Computation of stepsize (dh)
+if all(grdh)
+ dh = grdh;
+else
+ ax0 = abs(x0);
+ if all(x0)
+ dax0 = x0 ./ ax0;
+ else
+ dax0 = 1;
+ end
+ dh = stps * (max([ax0 ones(n,1)]'))' .* dax0;
+end
+
+xdh = x0 + dh;
+dh = xdh - x0; % This increases precision slightly
+%
+argplus = x0(:,ones(n,1));
+argminus = argplus;
+dnum = 1:n+1:n^2; % positions of diagonals in vec(argplus).
+argplus(dnum) = xdh; % replace the diagonals of "argplus" by "xdh".
+argminus(dnum) = x0-dh; % replace the diagonals of "argplus" by "xdh".
+
+grdd = zeros(k,n); % preallocate to speed the loop.
+i = 0;
+while i ~= n
+ i = i+1;
+ fp = eval([fcn '(argplus(:,i)' tailstr]);
+ fm = eval([fcn '(argminus(:,i)' tailstr]);
+ grdd(:,i) = fp - fm;
+end
+dhm = dh(:,ones(k,1));
+dhm = dhm'; % k*n
+grdd = grdd ./ (2*dhm); % k-by-n.
+grdd = grdd'; % n-by-k.
+
diff --git a/MatlabFiles/fn_gradcd2.m b/MatlabFiles/fn_gradcd2.m
index 7712ac8935bd81689f4f50c9ce500bc81a5b3c2d..e7d433152e6eba28b5115595ec236b1acea46b03 100644
--- a/MatlabFiles/fn_gradcd2.m
+++ b/MatlabFiles/fn_gradcd2.m
@@ -1,98 +1,98 @@
-function grdd = fn_gradcd2(fcn,x0,grdh,P1,P2,P3,P4,P5,P6,P7,P8,P9,...
- P10,P11,P12,P13,P14,P15,P16,P17,P18,P19,P20)
-%function grdd = fn_gradcd(fcn,x0,grdh,P1,P2,P3,P4,P5,P6,P7,P8,P9,...
-% P10,P11,P12,P13,P14,P15,P16,P17,P18,P19,P20)
-% computes numerical gradient of a single-valued function or Jacobian
-% matrix of a vector-valued function using a central difference with
-% function grdd = gradcd(fcn,x0,grdh)
-%
-% fcn: a string naming a vector-valued function (f:n-by-1 -> k-by-1).
-% x0: a column vector n-by-1, at which point the hessian is evaluated.
-% grdh: step size, n*1. Set as follows
-% step = eps^(1/3);
-% %step = 1e-04;
-% grdh = step * (max([abs(x) ones(length(x),1)]'))' .* (abs(x) ./ x);
-%--------------------
-% grdd: n-by-k Jacobian matrix (gradients).
-%
-% Written by Tao Zha, 2002.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-stps = eps^(1/3);
-% eps: floating point relative accuracy or machine precision: 2.22e-16
-% stps: step size recommended by Dennis and Schnabel: 6.006e-6
-
-NEARINFINITY = 1.0e+100;
-GRADMANUAL = 0.0; %Arbitrarily (manually) set gradient.
-
-
-x0 = x0(:);
-tailstr = ')';
-for i=nargin-3:-1:1
- tailstr=[ ',P' num2str(i) tailstr];
-end
-f0 = eval([fcn '(x0' tailstr]);
-
-% ** initializations
-n = length(x0);
-k = length(f0); % dimension of "fcn"
-
-% ** Computation of stepsize (dh)
-if all(grdh)
- dh = grdh;
-else
- ax0 = abs(x0);
- if all(x0)
- dax0 = x0 ./ ax0;
- else
- dax0 = 1;
- end
- dh = stps * (max([ax0 ones(n,1)]'))' .* dax0;
-end
-
-xdh = x0 + dh;
-dh = xdh - x0; % This increases precision slightly
-%
-argplus = x0(:,ones(n,1));
-argminus = argplus;
-dnum = 1:n+1:n^2; % positions of diagonals in vec(argplus).
-argplus(dnum) = xdh; % replace the diagonals of "argplus" by "xdh".
-argminus(dnum) = x0-dh; % replace the diagonals of "argplus" by "xdh".
-
-grdd = zeros(k,n); % preallocate to speed the loop.
-i = 0;
-while i ~= n
- i = i+1;
- fp = eval([fcn '(argplus(:,i)' tailstr]);
- fm = eval([fcn '(argminus(:,i)' tailstr]);
- if ((fp < NEARINFINITY) && (fm < NEARINFINITY))
- grdd(:,i) = (fp - fm) ./ (2*dh(i));
- elseif (fp < NEARINFINITY)
- grdd(:,i) = (fp - f0) ./ (dh(i));
- elseif (fm < NEARINFINITY)
- grdd(:,i) = (f0 - fm) ./ (dh(i));
- else
- grdd(:,i) = GRADMANUAL; %Arbitrarily (manually) set gradient.
- end
-end
-%dhm = dh(:,ones(k,1));
-%dhm = dhm'; % k*n
-%grdd = grdd ./ (2*dhm); % k-by-n.
-grdd = grdd'; % n-by-k.
-
-
+function grdd = fn_gradcd2(fcn,x0,grdh,P1,P2,P3,P4,P5,P6,P7,P8,P9,...
+ P10,P11,P12,P13,P14,P15,P16,P17,P18,P19,P20)
+%function grdd = fn_gradcd(fcn,x0,grdh,P1,P2,P3,P4,P5,P6,P7,P8,P9,...
+% P10,P11,P12,P13,P14,P15,P16,P17,P18,P19,P20)
+% computes numerical gradient of a single-valued function or Jacobian
+% matrix of a vector-valued function using a central difference with
+% function grdd = gradcd(fcn,x0,grdh)
+%
+% fcn: a string naming a vector-valued function (f:n-by-1 -> k-by-1).
+% x0: a column vector n-by-1, at which point the hessian is evaluated.
+% grdh: step size, n*1. Set as follows
+% step = eps^(1/3);
+% %step = 1e-04;
+% grdh = step * (max([abs(x) ones(length(x),1)]'))' .* (abs(x) ./ x);
+%--------------------
+% grdd: n-by-k Jacobian matrix (gradients).
+%
+% Written by Tao Zha, 2002.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+stps = eps^(1/3);
+% eps: floating point relative accuracy or machine precision: 2.22e-16
+% stps: step size recommended by Dennis and Schnabel: 6.006e-6
+
+NEARINFINITY = 1.0e+100;
+GRADMANUAL = 0.0; %Arbitrarily (manually) set gradient.
+
+
+x0 = x0(:);
+tailstr = ')';
+for i=nargin-3:-1:1
+ tailstr=[ ',P' num2str(i) tailstr];
+end
+f0 = eval([fcn '(x0' tailstr]);
+
+% ** initializations
+n = length(x0);
+k = length(f0); % dimension of "fcn"
+
+% ** Computation of stepsize (dh)
+if all(grdh)
+ dh = grdh;
+else
+ ax0 = abs(x0);
+ if all(x0)
+ dax0 = x0 ./ ax0;
+ else
+ dax0 = 1;
+ end
+ dh = stps * (max([ax0 ones(n,1)]'))' .* dax0;
+end
+
+xdh = x0 + dh;
+dh = xdh - x0; % This increases precision slightly
+%
+argplus = x0(:,ones(n,1));
+argminus = argplus;
+dnum = 1:n+1:n^2; % positions of diagonals in vec(argplus).
+argplus(dnum) = xdh; % replace the diagonals of "argplus" by "xdh".
+argminus(dnum) = x0-dh; % replace the diagonals of "argplus" by "xdh".
+
+grdd = zeros(k,n); % preallocate to speed the loop.
+i = 0;
+while i ~= n
+ i = i+1;
+ fp = eval([fcn '(argplus(:,i)' tailstr]);
+ fm = eval([fcn '(argminus(:,i)' tailstr]);
+ if ((fp < NEARINFINITY) && (fm < NEARINFINITY))
+ grdd(:,i) = (fp - fm) ./ (2*dh(i));
+ elseif (fp < NEARINFINITY)
+ grdd(:,i) = (fp - f0) ./ (dh(i));
+ elseif (fm < NEARINFINITY)
+ grdd(:,i) = (f0 - fm) ./ (dh(i));
+ else
+ grdd(:,i) = GRADMANUAL; %Arbitrarily (manually) set gradient.
+ end
+end
+%dhm = dh(:,ones(k,1));
+%dhm = dhm'; % k*n
+%grdd = grdd ./ (2*dhm); % k-by-n.
+grdd = grdd'; % n-by-k.
+
+
diff --git a/MatlabFiles/fn_gyrfore.m b/MatlabFiles/fn_gyrfore.m
index 6218ae685c4d8d9e0cf110e564b44e9b3176e8de..967291249dcdbbed8cb4ce0227c4d47a534c2f66 100644
--- a/MatlabFiles/fn_gyrfore.m
+++ b/MatlabFiles/fn_gyrfore.m
@@ -1,61 +1,61 @@
-function fn_gyrfore(yfore,yacte,keyindx,rnum,cnum,ylab,forelabel,conlab)
-%
-% Graph annual (or calendar year) forecasts vs actual (all data from "msstart.m")
-%
-% yfore: actual and forecast annual growth data with dates.
-% yacte: actual annual growth data with dates.
-% keyindx: index for the variables to be graphed
-% rnum: number of rows in subplot
-% cnum: number of columns in subplot
-% ylab: label for the variables
-% forelabel: title label for as of time of forecast
-% conlab: label for what conditions imposed; e.g., conlab = 'All bar MS shocks inspl'
-%-------------
-% No output argument for this graph file
-%
-% Tao Zha, March 2000
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-vyrs = yfore(:,1);
-hornum = cell(length(vyrs),1); % horizontal year (number)
-count=0;
-for k=vyrs'
- count=count+1;
- jnk=num2str(k);
- hornum{count}=jnk(3:4); % e.g., with '1990', we have '90'
-end
-
-count=0;
-for i = keyindx
- count = count+1;
- subplot(rnum,cnum,count)
- %
- plot(yacte(:,1),yacte(:,2+i),vyrs,yfore(:,2+i),'--')
- set(gca,'XLim',[vyrs(1) vyrs(end)])
- set(gca,'XTick',vyrs)
- set(gca,'XTickLabel',char(hornum))
- if i==keyindx(1)
- title(forelabel)
- elseif i>=length(keyindx) %i>=length(keyindx)-1
- xlabel(conlab)
- end
- %
- grid
- ylabel(char(ylab(i)))
-end
+function fn_gyrfore(yfore,yacte,keyindx,rnum,cnum,ylab,forelabel,conlab)
+%
+% Graph annual (or calendar year) forecasts vs actual (all data from "msstart.m")
+%
+% yfore: actual and forecast annual growth data with dates.
+% yacte: actual annual growth data with dates.
+% keyindx: index for the variables to be graphed
+% rnum: number of rows in subplot
+% cnum: number of columns in subplot
+% ylab: label for the variables
+% forelabel: title label for as of time of forecast
+% conlab: label for what conditions imposed; e.g., conlab = 'All bar MS shocks inspl'
+%-------------
+% No output argument for this graph file
+%
+% Tao Zha, March 2000
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+vyrs = yfore(:,1);
+hornum = cell(length(vyrs),1); % horizontal year (number)
+count=0;
+for k=vyrs'
+ count=count+1;
+ jnk=num2str(k);
+ hornum{count}=jnk(3:4); % e.g., with '1990', we have '90'
+end
+
+count=0;
+for i = keyindx
+ count = count+1;
+ subplot(rnum,cnum,count)
+ %
+ plot(yacte(:,1),yacte(:,2+i),vyrs,yfore(:,2+i),'--')
+ set(gca,'XLim',[vyrs(1) vyrs(end)])
+ set(gca,'XTick',vyrs)
+ set(gca,'XTickLabel',char(hornum))
+ if i==keyindx(1)
+ title(forelabel)
+ elseif i>=length(keyindx) %i>=length(keyindx)-1
+ xlabel(conlab)
+ end
+ %
+ grid
+ ylabel(char(ylab(i)))
+end
diff --git a/MatlabFiles/fn_hesscd.m b/MatlabFiles/fn_hesscd.m
index a46716b77e30096bc9c788ece3b795b9f21a126c..88889b9b60232af70210efdb38e41d5bbbba4b37 100644
--- a/MatlabFiles/fn_hesscd.m
+++ b/MatlabFiles/fn_hesscd.m
@@ -1,82 +1,82 @@
-function grdd = fn_hesscd(fcn,x0,grdh,P1,P2,P3,P4,P5,P6,P7,P8,P9,...
- P10,P11,P12,P13,P14,P15,P16,P17,P18,P19,P20)
-% Computing numerical hessian using a central difference with
-% function grdd = hesscd(fcn,x0,grdh,Passed variables1)
-%
-% fcn: a string naming the objective function.
-% x0: a column vector n*1, at which point the hessian is evaluated.
-% grdh: step size.
-% grdd: hessian matrix (second derivative), n*n.
-%
-% Written by Tao Zha, May 1998. Revised June 1002.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-stps = eps^(1/3);
-% eps: floating point relative accuracy or machine precision: 2.22e-16
-% stps: step size recommended by Dennis and Schnabel: 6.006e-6
-
-x0 = x0(:);
-tailstr = ')';
-for i=nargin-3:-1:1
- tailstr=[ ',P' num2str(i) tailstr];
-end
-f0 = eval([fcn '(x0' tailstr]);
-
-% ** initializations
-k = length(x0);
-grdd = zeros(k);
-
-% ** Computation of stepsize (dh)
-if all(grdh)
- dh = grdh;
-else
- ax0 = abs(x0);
- if all(x0)
- dax0 = x0 ./ ax0;
- else
- dax0 = 1;
- end
- dh = stps * (max([ax0 (1e-2)*ones(k,1)]'))' .* dax0;
-end
-
-xdh = x0 + dh;
-dh = xdh - x0; % This increases precision slightly
-dhm = dh(:,ones(k,1));
-ee = eye(k) .* dhm;
-
-i = 1;
-while i <= k
- j = i;
- while j <= k
-
- fune1 = eval([fcn '(x0 + ee(:,i) + ee(:,j)' tailstr]);
- fune2 = eval([fcn '(x0 - ee(:,i) + ee(:,j)' tailstr]);
- fune3 = eval([fcn '(x0 + ee(:,i) - ee(:,j)' tailstr]);
- fune4 = eval([fcn '(x0 - ee(:,i) - ee(:,j)' tailstr]);
- grdd(i,j) = (fune1 - fune2 - fune3 + fune4) / (4 * dh(i) * dh(j));
-
- if i ~= j
- grdd(j,i) = grdd(i,j);
- end
-
- j = j+1;
- end
- i = i+1;
-end
-
+function grdd = fn_hesscd(fcn,x0,grdh,P1,P2,P3,P4,P5,P6,P7,P8,P9,...
+ P10,P11,P12,P13,P14,P15,P16,P17,P18,P19,P20)
+% Computing numerical hessian using a central difference with
+% function grdd = hesscd(fcn,x0,grdh,Passed variables1)
+%
+% fcn: a string naming the objective function.
+% x0: a column vector n*1, at which point the hessian is evaluated.
+% grdh: step size.
+% grdd: hessian matrix (second derivative), n*n.
+%
+% Written by Tao Zha, May 1998. Revised June 1002.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+stps = eps^(1/3);
+% eps: floating point relative accuracy or machine precision: 2.22e-16
+% stps: step size recommended by Dennis and Schnabel: 6.006e-6
+
+x0 = x0(:);
+tailstr = ')';
+for i=nargin-3:-1:1
+ tailstr=[ ',P' num2str(i) tailstr];
+end
+f0 = eval([fcn '(x0' tailstr]);
+
+% ** initializations
+k = length(x0);
+grdd = zeros(k);
+
+% ** Computation of stepsize (dh)
+if all(grdh)
+ dh = grdh;
+else
+ ax0 = abs(x0);
+ if all(x0)
+ dax0 = x0 ./ ax0;
+ else
+ dax0 = 1;
+ end
+ dh = stps * (max([ax0 (1e-2)*ones(k,1)]'))' .* dax0;
+end
+
+xdh = x0 + dh;
+dh = xdh - x0; % This increases precision slightly
+dhm = dh(:,ones(k,1));
+ee = eye(k) .* dhm;
+
+i = 1;
+while i <= k
+ j = i;
+ while j <= k
+
+ fune1 = eval([fcn '(x0 + ee(:,i) + ee(:,j)' tailstr]);
+ fune2 = eval([fcn '(x0 - ee(:,i) + ee(:,j)' tailstr]);
+ fune3 = eval([fcn '(x0 + ee(:,i) - ee(:,j)' tailstr]);
+ fune4 = eval([fcn '(x0 - ee(:,i) - ee(:,j)' tailstr]);
+ grdd(i,j) = (fune1 - fune2 - fune3 + fune4) / (4 * dh(i) * dh(j));
+
+ if i ~= j
+ grdd(j,i) = grdd(i,j);
+ end
+
+ j = j+1;
+ end
+ i = i+1;
+end
+
diff --git a/MatlabFiles/fn_histpdfcnt.m b/MatlabFiles/fn_histpdfcnt.m
index 007fb892b4ee702a9619efcba19cef097cc58667..31a662b06704afe2f19e40235babb950719fb06d 100644
--- a/MatlabFiles/fn_histpdfcnt.m
+++ b/MatlabFiles/fn_histpdfcnt.m
@@ -1,95 +1,95 @@
-function [imfpdf,imfpo,imfprob] = fn_histpdfcnt(imfcnt,ndrawscnt,imfloor,hbin,ninv,...
- gIndx,ncom,kIndx,lval,hval)
-% [imfpdf,imfpo,imfprob] = fn_histpdfcnt(imfcnt,ndrawscnt,imfloor,hbin,ninv,gIndx,ncom,kIndx,lval,hval)
-% From already ordered and binned (cnt: count) series (not pdf yet).
-% Produce (1) the dataset for generating p.d.f.;
-% (2) the dataset for probability (NOT density) at each bin;
-% (3) with option gIndx=1, graph density functions.
-%
-% imfcnt: 2+ninv-by-k. Counted structural parameters, qmygcnt, ygcnt, or impulse responses.
-% ndrawscnt: a total number of draws used in imfcnt
-% imfloor: k-element vector of low values of imf but imf_h can be below "imfloor" and above "imceiling"
-% hbin: k-element vector of bin lengths = (imceilling-imfloor)/ninv. Need not be a 1-by-k row vector.
-% ninv: the number of bins (small interior intervals) between ``imfloor'' and ``imfceiling''
-% gIndx: 1: plot graphs of pdf's; 0: no plot.
-% If gIndx=0, ncom, kIndx, lval, and hval are irrelevant and no densities will be plotted.
-% ncom: number of combined intervals. Large ncom implies large size of the bin.
-% Must set ncom so that ninv can be divided
-% kIndx: index for selected variables. Length(kIndx)<=k.
-% If kIndx=[], lval and hval are irrelevant and no densities will be plotted.
-% lval: length(kIndx)-element vector of the lowest values on the axis;
-% The vector corresponds to kIndx. This option is disenabled by setting it to [].
-% hval: length(kIndx)-element vector of the highest values on the axis;
-% The vector corresponds to kIndx. This option is disenabled by setting it to [].
-%-----------------
-% imfpdf: 2+ninv-by-k. Density (NOT probability)
-% imfpo: 2+ninv-by-k. Bin position (x-axis) in relation to imfs3
-% imfprob: 2+ninv-by-k. Probability (NOT density) at each bin
-%
-% 27 August 1998 Tao Zha
-% Revised, October 1998, March 1999, August 2000.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-if (nargin==5), gIndx=0; end
-if (nargin==8), lval=[]; hval=[]; end
-
-imfloor=imfloor(:); hbin=hbin(:);
-k=size(imfcnt,2);
-invlengthM = repmat(hbin',[2+ninv,1]);
-%invlengthM([1 2+ninv],:) = 1; % August 2000. I comment this out because it leads to an extorted picture
- % when the rest of invlengthM is much smaller or larger than 1.
- % For the first interval (-inf, low bound) and last interval (high bound, +inf),
- % the length is set at 1. Of course, theoretically, the interval length should be set
- % to infinity. Adjust low and high bounds if 1 is not large enough compared with hbin.
-imfprob = imfcnt ./ ndrawscnt; % probability (NOT density)
-imfpdf = imfprob ./ invlengthM; % density
-
-imfpo = [1:2+ninv]'; % positions for each forecast
-imfpo = imfpo - 2; % the first step to put back to original form
-imfpo = repmat(imfpo,[1,k]);
-imfloorM = repmat(imfloor',[2+ninv,1]);
-imfpo = (imfpo .* invlengthM) + imfloorM; % 2+ninv-by-k
- % the final step to put back to original form of impulse responses
-
-if gIndx
- if mod(ninv,ncom)
- warning('Set ncom so that ninv can be divided')
- return
- end
- %
- ninv2=ninv/ncom;
- imfpdfn=zeros(2+ninv2,size(imfcnt,2)); %<<>>
- imfpon=imfpdfn;
- %
- for ik=1:ninv2 % from 2 to 1+ninv2 for imfpdfn and imfpon
- imfpdfn(1+ik,:) = sum(imfpdf(1+(ik-1)*ncom+1:1+ik*ncom,:))/ncom;
- imfpon(1+ik,:) = mean(imfpo(1+(ik-1)*ncom+1:1+ik*ncom,:));
- end
-
- ck1=0;
- for k1=kIndx
- ck1=ck1+1;
- figure
- plot(imfpon(2:1+ninv2,k1),imfpdfn(2:1+ninv2,k1)) % do not plot the values at -infty and +infty
- if ~isempty(lval) & ~isempty(hval)
- set(gca,'XLim',[lval(ck1) hval(ck1)])
- end
- grid
- end
-end
+function [imfpdf,imfpo,imfprob] = fn_histpdfcnt(imfcnt,ndrawscnt,imfloor,hbin,ninv,...
+ gIndx,ncom,kIndx,lval,hval)
+% [imfpdf,imfpo,imfprob] = fn_histpdfcnt(imfcnt,ndrawscnt,imfloor,hbin,ninv,gIndx,ncom,kIndx,lval,hval)
+% From already ordered and binned (cnt: count) series (not pdf yet).
+% Produce (1) the dataset for generating p.d.f.;
+% (2) the dataset for probability (NOT density) at each bin;
+% (3) with option gIndx=1, graph density functions.
+%
+% imfcnt: 2+ninv-by-k. Counted structural parameters, qmygcnt, ygcnt, or impulse responses.
+% ndrawscnt: a total number of draws used in imfcnt
+% imfloor: k-element vector of low values of imf but imf_h can be below "imfloor" and above "imceiling"
+% hbin: k-element vector of bin lengths = (imceilling-imfloor)/ninv. Need not be a 1-by-k row vector.
+% ninv: the number of bins (small interior intervals) between ``imfloor'' and ``imfceiling''
+% gIndx: 1: plot graphs of pdf's; 0: no plot.
+% If gIndx=0, ncom, kIndx, lval, and hval are irrelevant and no densities will be plotted.
+% ncom: number of combined intervals. Large ncom implies large size of the bin.
+% Must set ncom so that ninv can be divided
+% kIndx: index for selected variables. Length(kIndx)<=k.
+% If kIndx=[], lval and hval are irrelevant and no densities will be plotted.
+% lval: length(kIndx)-element vector of the lowest values on the axis;
+% The vector corresponds to kIndx. This option is disenabled by setting it to [].
+% hval: length(kIndx)-element vector of the highest values on the axis;
+% The vector corresponds to kIndx. This option is disenabled by setting it to [].
+%-----------------
+% imfpdf: 2+ninv-by-k. Density (NOT probability)
+% imfpo: 2+ninv-by-k. Bin position (x-axis) in relation to imfs3
+% imfprob: 2+ninv-by-k. Probability (NOT density) at each bin
+%
+% 27 August 1998 Tao Zha
+% Revised, October 1998, March 1999, August 2000.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+if (nargin==5), gIndx=0; end
+if (nargin==8), lval=[]; hval=[]; end
+
+imfloor=imfloor(:); hbin=hbin(:);
+k=size(imfcnt,2);
+invlengthM = repmat(hbin',[2+ninv,1]);
+%invlengthM([1 2+ninv],:) = 1; % August 2000. I comment this out because it leads to an extorted picture
+ % when the rest of invlengthM is much smaller or larger than 1.
+ % For the first interval (-inf, low bound) and last interval (high bound, +inf),
+ % the length is set at 1. Of course, theoretically, the interval length should be set
+ % to infinity. Adjust low and high bounds if 1 is not large enough compared with hbin.
+imfprob = imfcnt ./ ndrawscnt; % probability (NOT density)
+imfpdf = imfprob ./ invlengthM; % density
+
+imfpo = [1:2+ninv]'; % positions for each forecast
+imfpo = imfpo - 2; % the first step to put back to original form
+imfpo = repmat(imfpo,[1,k]);
+imfloorM = repmat(imfloor',[2+ninv,1]);
+imfpo = (imfpo .* invlengthM) + imfloorM; % 2+ninv-by-k
+ % the final step to put back to original form of impulse responses
+
+if gIndx
+ if mod(ninv,ncom)
+ warning('Set ncom so that ninv can be divided')
+ return
+ end
+ %
+ ninv2=ninv/ncom;
+ imfpdfn=zeros(2+ninv2,size(imfcnt,2)); %<<>>
+ imfpon=imfpdfn;
+ %
+ for ik=1:ninv2 % from 2 to 1+ninv2 for imfpdfn and imfpon
+ imfpdfn(1+ik,:) = sum(imfpdf(1+(ik-1)*ncom+1:1+ik*ncom,:))/ncom;
+ imfpon(1+ik,:) = mean(imfpo(1+(ik-1)*ncom+1:1+ik*ncom,:));
+ end
+
+ ck1=0;
+ for k1=kIndx
+ ck1=ck1+1;
+ figure
+ plot(imfpon(2:1+ninv2,k1),imfpdfn(2:1+ninv2,k1)) % do not plot the values at -infty and +infty
+ if ~isempty(lval) & ~isempty(hval)
+ set(gca,'XLim',[lval(ck1) hval(ck1)])
+ end
+ grid
+ end
+end
diff --git a/MatlabFiles/fn_histwpdfg.m b/MatlabFiles/fn_histwpdfg.m
index 288fa60df22be932fa63db2026f6c109e1a6767f..ced2d72c38b2e7b3dd22a1146606c3df12d7900e 100644
--- a/MatlabFiles/fn_histwpdfg.m
+++ b/MatlabFiles/fn_histwpdfg.m
@@ -1,84 +1,84 @@
-function [xpd,xc,xp,w,bw] = fn_histwpdfg(s,nbin,gIdx,w,xlabstr,ylabstr,titstr)
-% [xpd,xc,xp,w,bw] = fn_histwpdfg(s,nbin,gIdx,w,xlabstr,ylabstr,titstr)
-% Generate probabilities and plot scaled densitys, given the unsorted draws and weights
-%
-% s: ndraws-by-n draws where ndraws is # of draws and n is # of series
-% nbin: the number of bins (the maximum is ndraws)
-% gIdx: 1 if plotting the pdf; 0 if no graph
-% w (optional): ndraws-by-n (unscaled) weights where ndraws is # of draws and n is # of series
-% xlabstr (optional): xlabel string
-% ylabstr (optional): ylabel string
-% titstr (optional): title string
-%-------------
-% xpd: nbin-by-n: density or pdf (not probability) in the centered bin on x-axis.
-% xc: nbin-by-n: the position of centered bin on x-axis, from top to bottom.
-% All columns are identical
-% xp: nbin-by-n: probability (not density) in the centered bin on x-axis.
-% NOTE: sum(xp) must be 1 for each column
-% w: ndraws-by-n scaled weights so that sum(w)=1 for each column
-% bw: bandwidth
-%
-% August 1999 by Tao Zha
-% July 18 2000. Place w after gIdx so that previous programs need modifications accordingly.
-% Oct 1 2000. Change the order of xpd, xc, and xp so that previous programs need modifications accordingly.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-if (nargin<=4), titstr=[]; xlabstr=[]; ylabstr=[]; end
-[m,n] = size(s);
-if (nargin<=3)
- w=ones(m,n)/m;
-else
- %** normalized to 1 for the probability
- wsum = repmat(sum(w), [m 1]);
- w = w ./ wsum;
-end
-
-%if min(size(s))==1, s=s(:); w=w(:); end % making sure they are column vectors
-
-%*** the position of the center of the bin on the x-axis
-mins = min(min(s));
-maxs = max(max(s));
-bw = (maxs-mins)/nbin; % binwidth for x-axis
-x = mins + bw*(0:nbin);
-x(end) = maxs; % in case nbin is not an integer
-xc = x(1:end-1) + bw/2; % the position of the center of the x-bin
-xc = xc'; % nbin-by-1 row vector: from top to bottom
-xc = repmat(xc,[1 n]); % nbin-by-n, same for each column
-
-
-%*** the probability at each bin on the x-axis
-nbin = nbin+1; % + 1 to get the difference for getting probability xp
-nn = zeros(nbin,n);
-for i=2:nbin
- for k=1:n
- xidx = find(s(:,k) <= x(i)); % index for the positions
- nn(i,k) = sum(w(xidx,k));
- end
-end
-xp = nn(2:nbin,:) - nn(1:nbin-1,:);
-if (bw<eps)
- xpd=Inf*ones(size(xp));
-else
- xpd = xp/bw; % the density, NOT the probability as xp
-end
-
-if gIdx
- plot(xc,xpd)
- %set(gca,'XLim',[-5 5]) % put the limit only on the y-axis
- title(titstr), xlabel(xlabstr), ylabel(ylabstr);
-end
+function [xpd,xc,xp,w,bw] = fn_histwpdfg(s,nbin,gIdx,w,xlabstr,ylabstr,titstr)
+% [xpd,xc,xp,w,bw] = fn_histwpdfg(s,nbin,gIdx,w,xlabstr,ylabstr,titstr)
+% Generate probabilities and plot scaled densitys, given the unsorted draws and weights
+%
+% s: ndraws-by-n draws where ndraws is # of draws and n is # of series
+% nbin: the number of bins (the maximum is ndraws)
+% gIdx: 1 if plotting the pdf; 0 if no graph
+% w (optional): ndraws-by-n (unscaled) weights where ndraws is # of draws and n is # of series
+% xlabstr (optional): xlabel string
+% ylabstr (optional): ylabel string
+% titstr (optional): title string
+%-------------
+% xpd: nbin-by-n: density or pdf (not probability) in the centered bin on x-axis.
+% xc: nbin-by-n: the position of centered bin on x-axis, from top to bottom.
+% All columns are identical
+% xp: nbin-by-n: probability (not density) in the centered bin on x-axis.
+% NOTE: sum(xp) must be 1 for each column
+% w: ndraws-by-n scaled weights so that sum(w)=1 for each column
+% bw: bandwidth
+%
+% August 1999 by Tao Zha
+% July 18 2000. Place w after gIdx so that previous programs need modifications accordingly.
+% Oct 1 2000. Change the order of xpd, xc, and xp so that previous programs need modifications accordingly.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+if (nargin<=4), titstr=[]; xlabstr=[]; ylabstr=[]; end
+[m,n] = size(s);
+if (nargin<=3)
+ w=ones(m,n)/m;
+else
+ %** normalized to 1 for the probability
+ wsum = repmat(sum(w), [m 1]);
+ w = w ./ wsum;
+end
+
+%if min(size(s))==1, s=s(:); w=w(:); end % making sure they are column vectors
+
+%*** the position of the center of the bin on the x-axis
+mins = min(min(s));
+maxs = max(max(s));
+bw = (maxs-mins)/nbin; % binwidth for x-axis
+x = mins + bw*(0:nbin);
+x(end) = maxs; % in case nbin is not an integer
+xc = x(1:end-1) + bw/2; % the position of the center of the x-bin
+xc = xc'; % nbin-by-1 row vector: from top to bottom
+xc = repmat(xc,[1 n]); % nbin-by-n, same for each column
+
+
+%*** the probability at each bin on the x-axis
+nbin = nbin+1; % + 1 to get the difference for getting probability xp
+nn = zeros(nbin,n);
+for i=2:nbin
+ for k=1:n
+ xidx = find(s(:,k) <= x(i)); % index for the positions
+ nn(i,k) = sum(w(xidx,k));
+ end
+end
+xp = nn(2:nbin,:) - nn(1:nbin-1,:);
+if (bw<eps)
+ xpd=Inf*ones(size(xp));
+else
+ xpd = xp/bw; % the density, NOT the probability as xp
+end
+
+if gIdx
+ plot(xc,xpd)
+ %set(gca,'XLim',[-5 5]) % put the limit only on the y-axis
+ title(titstr), xlabel(xlabstr), ylabel(ylabstr);
+end
diff --git a/MatlabFiles/fn_histwpdfg2d.m b/MatlabFiles/fn_histwpdfg2d.m
index 1e203a6725f28f29fc0731a12ba395b26e5dd1c7..f7d6dcbc8e84a9a65b5b8e276f5b64d016c7a83a 100644
--- a/MatlabFiles/fn_histwpdfg2d.m
+++ b/MatlabFiles/fn_histwpdfg2d.m
@@ -1,134 +1,134 @@
-function [xc,yc,z,zn] = fn_histwpdfg2D(s,n,w,ditp,xlabstr,ylabstr)
-% [xc,yc,z,zn] = histwpdfg2D(s,w,n,ditp,xlabstr,ylabstr)
-% Given uneven weights and 2D draws, generate 2D pdf and probabilites by scaling 2D histogram
-%
-% s: ndraws-by-2 matrix where ndraws is # of draws and 2 variables
-% n: 1-by-2 vector -- # of bins (integers in general) for both x-axis and y-axis
-% w: ndraws-by-1 vector of (uneven) weights. Optional. If [] or no entry, even weights are given.
-% ditp: Postive number -- degrees of bicubic interpolation. Bigger ditp is, more points for z with
-% finer the (x,y) plance are interpolated. Optional. If [] or no entry, no interpolation.
-% xlabstr: xlabel string. Optional. If [] or no entry, no x label.
-% ylabstr: ylabel string. Optional. If [] or no entry, no y label.
-%---------------------------------
-% xc: the position of centered bin on x-axis, from left to right. All rows are identical
-% yc: the position of centered bin on y-axis, from top to bottom. All columns are identical
-% z: size(xc,2)-by-size(yc,1) -- the pdf value in each rectangular cell
-% zn: size(xc,2)-by-size(yc,1) -- the probability value in each rectangular cell
-% if nargout==0, plot 2D p.d.f. graphics
-%
-% January 1999 by Tao Zha. Revised, March 2002.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-if (size(s,2)~=2)
- error('The size of 1st argument must be *-by-2 (i.e., 2 columns)')
-end
-
-if (nargin<3), w=[]; ditp=[]; xlabstr=[]; ylabstr=[]; end
-if (nargin<4), ditp=[]; xlabstr=[]; ylabstr=[]; end
-if (nargin<5), xlabstr=[]; ylabstr=[]; end
-if (nargin<6), ylabstr=[]; end
-
-if (length(n(:))~=2)
- error('2nd argument must have exactly 2 elements')
-end
-
-if (min(n)<3)
- error('2nd argument -- bin size -- must have at least 3 for each axis')
-end
-
-ndraws=size(s,1);
-
-%** normalized to 1 for the probability
-if isempty(w)
- w = ones(ndraws,1)/ndraws;
-else
- w=w(:); % make sure it's a column vector
- w = w/sum(w);
-end
-
-
-%*** x-axis
-minx = min(min(s(:,1)));
-maxx = max(max(s(:,1)));
-h1 = (maxx-minx)/n(1); % binwidth for x-axis
-x = minx + h1*(0:n(1));
-xlen = length(x); % n(1)+1
-x(xlen) = maxx; % in case n(1) is not an integer
-xc = x(1:xlen-1) + h1/2; % the position of the center of the x-bin
- % 1-by-n(1) row vector: from left to right
-xc = repmat(xc,[n(2) 1]);
-
-%*** y-axis
-miny = min(min(s(:,2)));
-maxy = max(max(s(:,2)));
-h2 = (maxy-miny)/n(2); % binwidth for y-axis
-y = miny + h2*(0:n(2));
-ylen = length(y);
-y(ylen) = maxy; % in case n(2) is not an integer
-yc = y(1:ylen-1) + h2/2; % the position of the center of the y-bin
-yc = yc(:); % n(2)-by-1 column vector: from top to bottom
-yc = repmat(yc,[1 n(1)]);
-
-
-zn = zeros(n(2),n(1)); % the reverse of x and y is designed for mesh.
- % see meshgrid to understand this.
-tic
-for draws=1:ndraws
- k1 = floor((s(draws,1)-minx)/h1)+1;
- k2 = floor((s(draws,2)-miny)/h2)+1;
- %
- % if k1==0
- % k1=1;
- % end
- % if k2==0;
- % k2=1;
- % end
- %
- if k1>n(1)
- k1=n(1);
- end
- if k2>n(2)
- k2=n(2)
- end
- zn(k2,k1) = zn(k2,k1)+w(draws); % probability in each rectangular cell
-end
-timeloop=toc;
-disp(['Loop time -- minutes ' num2str(timeloop/60)])
-
-z=zn/(h1*h2); % converted to the height of the p.d.f.
-
-
-%*** scaled 2D histogram or p.d.f.
-%figure(1)
-%*** interpolation
-if isempty(ditp)
- colormap(zeros(1,3)); % Set color to black
- mesh(xc,yc,z)
- title('Scaled histogram or p.d.f.')
- xlabel(xlabstr)
- ylabel(ylabstr)
-else
- [xi,yi]=meshgrid(minx:h1/ditp:maxx,miny:h2/ditp:maxy);
- zi = interp2(xc,yc,z,xi,yi,'bicubic');
- figure(30)
- colormap(zeros(1,3)); % Set color to black
- mesh(xi,yi,zi) % or mesh
- title('Interpolated p.d.f.')
- xlabel(xlabstr)
- ylabel(ylabstr)
-end
+function [xc,yc,z,zn] = fn_histwpdfg2D(s,n,w,ditp,xlabstr,ylabstr)
+% [xc,yc,z,zn] = histwpdfg2D(s,w,n,ditp,xlabstr,ylabstr)
+% Given uneven weights and 2D draws, generate 2D pdf and probabilites by scaling 2D histogram
+%
+% s: ndraws-by-2 matrix where ndraws is # of draws and 2 variables
+% n: 1-by-2 vector -- # of bins (integers in general) for both x-axis and y-axis
+% w: ndraws-by-1 vector of (uneven) weights. Optional. If [] or no entry, even weights are given.
+% ditp: Postive number -- degrees of bicubic interpolation. Bigger ditp is, more points for z with
+% finer the (x,y) plance are interpolated. Optional. If [] or no entry, no interpolation.
+% xlabstr: xlabel string. Optional. If [] or no entry, no x label.
+% ylabstr: ylabel string. Optional. If [] or no entry, no y label.
+%---------------------------------
+% xc: the position of centered bin on x-axis, from left to right. All rows are identical
+% yc: the position of centered bin on y-axis, from top to bottom. All columns are identical
+% z: size(xc,2)-by-size(yc,1) -- the pdf value in each rectangular cell
+% zn: size(xc,2)-by-size(yc,1) -- the probability value in each rectangular cell
+% if nargout==0, plot 2D p.d.f. graphics
+%
+% January 1999 by Tao Zha. Revised, March 2002.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+if (size(s,2)~=2)
+ error('The size of 1st argument must be *-by-2 (i.e., 2 columns)')
+end
+
+if (nargin<3), w=[]; ditp=[]; xlabstr=[]; ylabstr=[]; end
+if (nargin<4), ditp=[]; xlabstr=[]; ylabstr=[]; end
+if (nargin<5), xlabstr=[]; ylabstr=[]; end
+if (nargin<6), ylabstr=[]; end
+
+if (length(n(:))~=2)
+ error('2nd argument must have exactly 2 elements')
+end
+
+if (min(n)<3)
+ error('2nd argument -- bin size -- must have at least 3 for each axis')
+end
+
+ndraws=size(s,1);
+
+%** normalized to 1 for the probability
+if isempty(w)
+ w = ones(ndraws,1)/ndraws;
+else
+ w=w(:); % make sure it's a column vector
+ w = w/sum(w);
+end
+
+
+%*** x-axis
+minx = min(min(s(:,1)));
+maxx = max(max(s(:,1)));
+h1 = (maxx-minx)/n(1); % binwidth for x-axis
+x = minx + h1*(0:n(1));
+xlen = length(x); % n(1)+1
+x(xlen) = maxx; % in case n(1) is not an integer
+xc = x(1:xlen-1) + h1/2; % the position of the center of the x-bin
+ % 1-by-n(1) row vector: from left to right
+xc = repmat(xc,[n(2) 1]);
+
+%*** y-axis
+miny = min(min(s(:,2)));
+maxy = max(max(s(:,2)));
+h2 = (maxy-miny)/n(2); % binwidth for y-axis
+y = miny + h2*(0:n(2));
+ylen = length(y);
+y(ylen) = maxy; % in case n(2) is not an integer
+yc = y(1:ylen-1) + h2/2; % the position of the center of the y-bin
+yc = yc(:); % n(2)-by-1 column vector: from top to bottom
+yc = repmat(yc,[1 n(1)]);
+
+
+zn = zeros(n(2),n(1)); % the reverse of x and y is designed for mesh.
+ % see meshgrid to understand this.
+tic
+for draws=1:ndraws
+ k1 = floor((s(draws,1)-minx)/h1)+1;
+ k2 = floor((s(draws,2)-miny)/h2)+1;
+ %
+ % if k1==0
+ % k1=1;
+ % end
+ % if k2==0;
+ % k2=1;
+ % end
+ %
+ if k1>n(1)
+ k1=n(1);
+ end
+ if k2>n(2)
+ k2=n(2)
+ end
+ zn(k2,k1) = zn(k2,k1)+w(draws); % probability in each rectangular cell
+end
+timeloop=toc;
+disp(['Loop time -- minutes ' num2str(timeloop/60)])
+
+z=zn/(h1*h2); % converted to the height of the p.d.f.
+
+
+%*** scaled 2D histogram or p.d.f.
+%figure(1)
+%*** interpolation
+if isempty(ditp)
+ colormap(zeros(1,3)); % Set color to black
+ mesh(xc,yc,z)
+ title('Scaled histogram or p.d.f.')
+ xlabel(xlabstr)
+ ylabel(ylabstr)
+else
+ [xi,yi]=meshgrid(minx:h1/ditp:maxx,miny:h2/ditp:maxy);
+ zi = interp2(xc,yc,z,xi,yi,'bicubic');
+ figure(30)
+ colormap(zeros(1,3)); % Set color to black
+ mesh(xi,yi,zi) % or mesh
+ title('Interpolated p.d.f.')
+ xlabel(xlabstr)
+ ylabel(ylabstr)
+end
diff --git a/MatlabFiles/fn_histwpdfg_bound.m b/MatlabFiles/fn_histwpdfg_bound.m
index b017cd7a7271741f104a866e4368b960fac8b5f5..679f9ebe02bd08e26a40c4d4a6bb70b8f3359b58 100644
--- a/MatlabFiles/fn_histwpdfg_bound.m
+++ b/MatlabFiles/fn_histwpdfg_bound.m
@@ -1,85 +1,85 @@
-function [xpd,xc,xp,w,bw] = fn_histwpdfg_bound(s,nbin,gIdx,bound,w,xlabstr,ylabstr,titstr)
-% [xpd,xc,xp,w,bw] = fn_histwpdfg_bound(s,nbin,gIdx,bound,w,xlabstr,ylabstr,titstr)
-% Generate probabilities and plot scaled densitys, given the unsorted draws and weights
-%
-% s: ndraws-by-n draws where ndraws is # of draws and n is # of series
-% nbin: the number of bins (the maximum is ndraws)
-% gIdx: 1 if plotting the pdf; 0 if no graph
-% bound: bounds for XLim. Example: bound = [-2 2];
-% w (optional): ndraws-by-n (unscaled) weights where ndraws is # of draws and n is # of series
-% xlabstr (optional): xlabel string
-% ylabstr (optional): ylabel string
-% titstr (optional): title string
-%-------------
-% xpd: nbin-by-n: density or pdf (not probability) in the centered bin on x-axis.
-% xc: nbin-by-n: the position of centered bin on x-axis, from top to bottom.
-% All columns are identical
-% xp: nbin-by-n: probability (not density) in the centered bin on x-axis.
-% NOTE: sum(xp) must be 1 for each column
-% w: ndraws-by-n scaled weights so that sum(w)=1 for each column
-% bw: bandwidth
-%
-% August 1999 by Tao Zha
-% July 18 2000. Place w after gIdx so that previous programs need modifications accordingly.
-% Oct 1 2000. Change the order of xpd, xc, and xp so that previous programs need modifications accordingly.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-if (nargin<=5), titstr=[]; xlabstr=[]; ylabstr=[]; end
-[m,n] = size(s);
-if (nargin<=4)
- w=ones(m,n)/m;
-else
- %** normalized to 1 for the probability
- wsum = repmat(sum(w), [m 1]);
- w = w ./ wsum;
-end
-
-%if min(size(s))==1, s=s(:); w=w(:); end % making sure they are column vectors
-
-%*** the position of the center of the bin on the x-axis
-mins = min(min(s));
-maxs = max(max(s));
-bw = (maxs-mins)/nbin; % binwidth for x-axis
-x = mins + bw*(0:nbin);
-x(end) = maxs; % in case nbin is not an integer
-xc = x(1:end-1) + bw/2; % the position of the center of the x-bin
-xc = xc'; % nbin-by-1 row vector: from top to bottom
-xc = repmat(xc,[1 n]); % nbin-by-n, same for each column
-
-
-%*** the probability at each bin on the x-axis
-nbin = nbin+1; % + 1 to get the difference for getting probability xp
-nn = zeros(nbin,n);
-for i=2:nbin
- for k=1:n
- xidx = find(s(:,k) <= x(i)); % index for the positions
- nn(i,k) = sum(w(xidx,k));
- end
-end
-xp = nn(2:nbin,:) - nn(1:nbin-1,:);
-if (bw<eps)
- xpd=Inf*ones(size(xp));
-else
- xpd = xp/bw; % the density, NOT the probability as xp
-end
-
-if gIdx
- plot(xc,xpd)
- set(gca,'XLim',bound) % put the limit only on the y-axis
- title(titstr), xlabel(xlabstr), ylabel(ylabstr);
-end
+function [xpd,xc,xp,w,bw] = fn_histwpdfg_bound(s,nbin,gIdx,bound,w,xlabstr,ylabstr,titstr)
+% [xpd,xc,xp,w,bw] = fn_histwpdfg_bound(s,nbin,gIdx,bound,w,xlabstr,ylabstr,titstr)
+% Generate probabilities and plot scaled densitys, given the unsorted draws and weights
+%
+% s: ndraws-by-n draws where ndraws is # of draws and n is # of series
+% nbin: the number of bins (the maximum is ndraws)
+% gIdx: 1 if plotting the pdf; 0 if no graph
+% bound: bounds for XLim. Example: bound = [-2 2];
+% w (optional): ndraws-by-n (unscaled) weights where ndraws is # of draws and n is # of series
+% xlabstr (optional): xlabel string
+% ylabstr (optional): ylabel string
+% titstr (optional): title string
+%-------------
+% xpd: nbin-by-n: density or pdf (not probability) in the centered bin on x-axis.
+% xc: nbin-by-n: the position of centered bin on x-axis, from top to bottom.
+% All columns are identical
+% xp: nbin-by-n: probability (not density) in the centered bin on x-axis.
+% NOTE: sum(xp) must be 1 for each column
+% w: ndraws-by-n scaled weights so that sum(w)=1 for each column
+% bw: bandwidth
+%
+% August 1999 by Tao Zha
+% July 18 2000. Place w after gIdx so that previous programs need modifications accordingly.
+% Oct 1 2000. Change the order of xpd, xc, and xp so that previous programs need modifications accordingly.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+if (nargin<=5), titstr=[]; xlabstr=[]; ylabstr=[]; end
+[m,n] = size(s);
+if (nargin<=4)
+ w=ones(m,n)/m;
+else
+ %** normalized to 1 for the probability
+ wsum = repmat(sum(w), [m 1]);
+ w = w ./ wsum;
+end
+
+%if min(size(s))==1, s=s(:); w=w(:); end % making sure they are column vectors
+
+%*** the position of the center of the bin on the x-axis
+mins = min(min(s));
+maxs = max(max(s));
+bw = (maxs-mins)/nbin; % binwidth for x-axis
+x = mins + bw*(0:nbin);
+x(end) = maxs; % in case nbin is not an integer
+xc = x(1:end-1) + bw/2; % the position of the center of the x-bin
+xc = xc'; % nbin-by-1 row vector: from top to bottom
+xc = repmat(xc,[1 n]); % nbin-by-n, same for each column
+
+
+%*** the probability at each bin on the x-axis
+nbin = nbin+1; % + 1 to get the difference for getting probability xp
+nn = zeros(nbin,n);
+for i=2:nbin
+ for k=1:n
+ xidx = find(s(:,k) <= x(i)); % index for the positions
+ nn(i,k) = sum(w(xidx,k));
+ end
+end
+xp = nn(2:nbin,:) - nn(1:nbin-1,:);
+if (bw<eps)
+ xpd=Inf*ones(size(xp));
+else
+ xpd = xp/bw; % the density, NOT the probability as xp
+end
+
+if gIdx
+ plot(xc,xpd)
+ set(gca,'XLim',bound) % put the limit only on the y-axis
+ title(titstr), xlabel(xlabstr), ylabel(ylabstr);
+end
diff --git a/MatlabFiles/fn_imc2errgraph.m b/MatlabFiles/fn_imc2errgraph.m
index f19134c686473c1dff47af40c1521ec77ac376c9..1ea73b4435d0ff906ce4a3925a0fc4b45217edc1 100644
--- a/MatlabFiles/fn_imc2errgraph.m
+++ b/MatlabFiles/fn_imc2errgraph.m
@@ -1,132 +1,132 @@
-function scaleout = imc2errgraph(imf,firstl1,firsth1,...
- firstl,firsth,nvar,imstp,xlab,ylab,indxGimfml,xTick)
-% imc2errgraph: impulse, c (column: shock 1 to N), 2 error bands, graph
-%
-% imf: imstp-by-nvar^2 matrix of impulse responses. Column (responses to 1st shock, responses to 2nd shock
-% etc), Row (impusle steps),
-% firstl1: lower band, .68
-% highth1: high band, .68
-% firstl: lower band, .90
-% highth: high band, .90
-% nvar: number of variables
-% imstp: number of steps of impulse responses
-% xlab,ylab: labels
-% indxGimfml: 1, graph; 0, no graph
-% xTick: optional. Eg: [12 24 36].
-%---------------
-% scaleout: column 1 represents maximums; column 2 minimums. Rows: nvar variables.
-%
-% See imrgraph, fn_imcerrgraph, imrerrgraph
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-if nargin < 11, xTick = []; end
-
-t = 1:imstp;
-temp1=zeros(nvar,1);
-temp2=zeros(nvar,1);
-maxval=zeros(nvar,1);
-minval=zeros(nvar,1);
-for i = 1:nvar
- for j = 1:nvar
- jnk1=max(firsth(:,(j-1)*nvar+i));
- jnk2=max(firstl(:,(j-1)*nvar+i));
- jnk3=max(firsth1(:,(j-1)*nvar+i));
- jnk4=max(firstl1(:,(j-1)*nvar+i));
- jnk5=max(imf(:,(j-1)*nvar+i));
-
- temp1(j)=max([jnk1 jnk2 jnk3 jnk4 jnk5]);
- %
- jnk1=min(firstl(:,(j-1)*nvar+i));
- jnk2=min(firsth(:,(j-1)*nvar+i));
- jnk3=min(firstl1(:,(j-1)*nvar+i));
- jnk4=min(firsth1(:,(j-1)*nvar+i));
- jnk5=min(imf(:,(j-1)*nvar+i));
-
- temp2(j)=min([jnk1 jnk2 jnk3 jnk4 jnk5]);
- end
- maxval(i)=max(temp1);
- minval(i)=min(temp2);
-end
-
-scaleout = [maxval(:) minval(:)];
-
-%--------------
-% Column j: Shock 1 to N; Row i: Responses to
-%-------------
-if indxGimfml
- %figure
- rowlabel = 1;
- for i = 1:nvar
- columnlabel = 1;
-
- if minval(i)<0
- if maxval(i)<=0
- yt=[minval(i) 0];
- else
- yt=[minval(i) 0 maxval(i)];
- end
- else % (minval(i) >=0)
- if maxval(i) > 0
- yt=[0 maxval(i)];
- else % (identically zero responses)
- yt=[-1 0 1];
- end
- end
-
- scale=[1 imstp minval(i) maxval(i)];
- for j = 1:nvar
- k1=(i-1)*nvar+j;
- k2=(j-1)*nvar+i;
- subplot(nvar,nvar,k1)
- plot(t,imf(:,k2),t,[firstl1(:,k2) firsth1(:,k2)],'--',...
- t,[firstl(:,k2) firsth(:,k2)],'-.',t,zeros(length(imf(:,k2)),1),'-');
-
- set(gca,'XTick',xTick)
- set(gca,'YTick',yt)
- grid;
-
- axis(scale); % put limits on both axes.
- %set(gca,'YLim',[minval(i) maxval(i)])
- if 1 % no numbers on axes
- set(gca,'XTickLabel',' ');
- set(gca,'YTickLabel',' ');
- else % put numbers on both axes
- if i<nvar
- set(gca,'XTickLabelMode','manual','XTickLabel',[])
- end
- if j>1
- set(gca,'YTickLabel',' ');
- end
- end
-
- if rowlabel == 1
- %title(['x' num2str(j)])
- %title(eval(['x' num2str(j)]))
- title(char(xlab(j)))
- end
- if columnlabel == 1
- %ylabel(['x' num2str(i)])
- %ylabel(eval(['x' num2str(i)]))
- ylabel(char(ylab(i)))
- end
- columnlabel = 0;
- end
- rowlabel = 0;
- end
-end
+function scaleout = imc2errgraph(imf,firstl1,firsth1,...
+ firstl,firsth,nvar,imstp,xlab,ylab,indxGimfml,xTick)
+% imc2errgraph: impulse, c (column: shock 1 to N), 2 error bands, graph
+%
+% imf: imstp-by-nvar^2 matrix of impulse responses. Column (responses to 1st shock, responses to 2nd shock
+% etc), Row (impusle steps),
+% firstl1: lower band, .68
+% highth1: high band, .68
+% firstl: lower band, .90
+% highth: high band, .90
+% nvar: number of variables
+% imstp: number of steps of impulse responses
+% xlab,ylab: labels
+% indxGimfml: 1, graph; 0, no graph
+% xTick: optional. Eg: [12 24 36].
+%---------------
+% scaleout: column 1 represents maximums; column 2 minimums. Rows: nvar variables.
+%
+% See imrgraph, fn_imcerrgraph, imrerrgraph
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+if nargin < 11, xTick = []; end
+
+t = 1:imstp;
+temp1=zeros(nvar,1);
+temp2=zeros(nvar,1);
+maxval=zeros(nvar,1);
+minval=zeros(nvar,1);
+for i = 1:nvar
+ for j = 1:nvar
+ jnk1=max(firsth(:,(j-1)*nvar+i));
+ jnk2=max(firstl(:,(j-1)*nvar+i));
+ jnk3=max(firsth1(:,(j-1)*nvar+i));
+ jnk4=max(firstl1(:,(j-1)*nvar+i));
+ jnk5=max(imf(:,(j-1)*nvar+i));
+
+ temp1(j)=max([jnk1 jnk2 jnk3 jnk4 jnk5]);
+ %
+ jnk1=min(firstl(:,(j-1)*nvar+i));
+ jnk2=min(firsth(:,(j-1)*nvar+i));
+ jnk3=min(firstl1(:,(j-1)*nvar+i));
+ jnk4=min(firsth1(:,(j-1)*nvar+i));
+ jnk5=min(imf(:,(j-1)*nvar+i));
+
+ temp2(j)=min([jnk1 jnk2 jnk3 jnk4 jnk5]);
+ end
+ maxval(i)=max(temp1);
+ minval(i)=min(temp2);
+end
+
+scaleout = [maxval(:) minval(:)];
+
+%--------------
+% Column j: Shock 1 to N; Row i: Responses to
+%-------------
+if indxGimfml
+ %figure
+ rowlabel = 1;
+ for i = 1:nvar
+ columnlabel = 1;
+
+ if minval(i)<0
+ if maxval(i)<=0
+ yt=[minval(i) 0];
+ else
+ yt=[minval(i) 0 maxval(i)];
+ end
+ else % (minval(i) >=0)
+ if maxval(i) > 0
+ yt=[0 maxval(i)];
+ else % (identically zero responses)
+ yt=[-1 0 1];
+ end
+ end
+
+ scale=[1 imstp minval(i) maxval(i)];
+ for j = 1:nvar
+ k1=(i-1)*nvar+j;
+ k2=(j-1)*nvar+i;
+ subplot(nvar,nvar,k1)
+ plot(t,imf(:,k2),t,[firstl1(:,k2) firsth1(:,k2)],'--',...
+ t,[firstl(:,k2) firsth(:,k2)],'-.',t,zeros(length(imf(:,k2)),1),'-');
+
+ set(gca,'XTick',xTick)
+ set(gca,'YTick',yt)
+ grid;
+
+ axis(scale); % put limits on both axes.
+ %set(gca,'YLim',[minval(i) maxval(i)])
+ if 1 % no numbers on axes
+ set(gca,'XTickLabel',' ');
+ set(gca,'YTickLabel',' ');
+ else % put numbers on both axes
+ if i<nvar
+ set(gca,'XTickLabelMode','manual','XTickLabel',[])
+ end
+ if j>1
+ set(gca,'YTickLabel',' ');
+ end
+ end
+
+ if rowlabel == 1
+ %title(['x' num2str(j)])
+ %title(eval(['x' num2str(j)]))
+ title(char(xlab(j)))
+ end
+ if columnlabel == 1
+ %ylabel(['x' num2str(i)])
+ %ylabel(eval(['x' num2str(i)]))
+ ylabel(char(ylab(i)))
+ end
+ columnlabel = 0;
+ end
+ rowlabel = 0;
+ end
+end
diff --git a/MatlabFiles/fn_imcerrgraph.m b/MatlabFiles/fn_imcerrgraph.m
index 36501975f049e5df2b090adc0186efdf9a3e37d9..b5888cd312870947cebe3d306b5676b99a44679c 100644
--- a/MatlabFiles/fn_imcerrgraph.m
+++ b/MatlabFiles/fn_imcerrgraph.m
@@ -1,123 +1,123 @@
-function scaleout = fn_imcerrgraph(imf,firstl,firsth,nvar,imstp,xlab,ylab,indxGimfml,xTick,indx_num)
-% scaleout = fn_imcerrgraph(imf,firstl,firsth,nvar,imstp,xlab,ylab,indxGimfml,xTick)
-% imcerrgraph: impulse, c (column: shock 1 to N), 1 error band, graph
-%
-% imf: imstp-by-nvar^2 matrix of impulse responses. Column (responses to 1st shock, responses to 2nd shock
-% etc), Row (impusle steps),
-% firstl: lower band
-% highth: high band
-% nvar: number of variables
-% imstp: number of steps of impulse responses
-% xlab,ylab: labels
-% indxGimfml: 1, graph; 0, no graph
-% xTick: optional. Eg: [12 24 36].
-% indx_num: 0: no number on either axis (default), 1: number on both x-axis and y-axis.
-%---------------
-% scaleout: column 1 represents maximums; column 2 minimums. Rows: nvar variables.
-%
-% See fn_imcgraph, fn_imc2errgraph, imrerrgraph
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-if nargin < 9, xTick = []; end
-if nargin < 10, indx_num = 0; end
-
-t = 1:imstp;
-temp1=zeros(nvar,1);
-temp2=zeros(nvar,1);
-maxval=zeros(nvar,1);
-minval=zeros(nvar,1);
-for i = 1:nvar
- for j = 1:nvar
- temp1(j)=max(firsth(:,(j-1)*nvar+i));
- temp2(j)=min(firstl(:,(j-1)*nvar+i));
- end
- maxval(i)=max(temp1);
- minval(i)=min(temp2);
-end
-
-scaleout = [maxval(:) minval(:)];
-
-%--------------
-% Column j: Shock 1 to N; Row i: Responses to
-%-------------
-if indxGimfml
- %figure
- rowlabel = 1;
- for i = 1:nvar
- columnlabel = 1;
-
- if minval(i)<0
- if maxval(i)<=0
- yt=[minval(i) 0];
- else
- yt=[minval(i) 0 maxval(i)];
- end
- else % (minval(i) >=0)
- if maxval(i) > 0
- yt=[0 maxval(i)];
- else % (identically zero responses)
- yt=[-1 0 1];
- end
- end
-
- scale=[1 imstp minval(i) maxval(i)];
- for j = 1:nvar
- k1=(i-1)*nvar+j;
- k2=(j-1)*nvar+i;
- subplot(nvar,nvar,k1)
- plot(t,imf(:,k2),t,[firstl(:,k2) firsth(:,k2)],'--',...
- t,zeros(length(imf(:,k2)),1),'-');
-
- set(gca,'XTick',xTick)
- set(gca,'YTick',yt)
- grid
-
- axis(scale); % put limits on both axes.
- %set(gca,'YLim',[minval(i) maxval(i)])
- if (indx_num==0) % no numbers on axes
- set(gca,'XTickLabel',' ');
- set(gca,'YTickLabel',' ');
-% elseif (indx_num==1) %numbers on x-axis.
-% if i<nvar
-% set(gca,'XTickLabelMode','manual','XTickLabel',[])
-% end
- else % put numbers on both axes
- if i<nvar
- set(gca,'XTickLabelMode','manual','XTickLabel',[])
- end
- if j>1
- set(gca,'YTickLabel',' ');
- end
- end
-
- if rowlabel == 1
- %title(['x' num2str(j)])
- %title(eval(['x' num2str(j)]))
- title(char(xlab(j)))
- end
- if columnlabel == 1
- %ylabel(['x' num2str(i)])
- %ylabel(eval(['x' num2str(i)]))
- ylabel(char(ylab(i)))
- end
- columnlabel = 0;
- end
- rowlabel = 0;
- end
-end
+function scaleout = fn_imcerrgraph(imf,firstl,firsth,nvar,imstp,xlab,ylab,indxGimfml,xTick,indx_num)
+% scaleout = fn_imcerrgraph(imf,firstl,firsth,nvar,imstp,xlab,ylab,indxGimfml,xTick)
+% imcerrgraph: impulse, c (column: shock 1 to N), 1 error band, graph
+%
+% imf: imstp-by-nvar^2 matrix of impulse responses. Column (responses to 1st shock, responses to 2nd shock
+% etc), Row (impusle steps),
+% firstl: lower band
+% highth: high band
+% nvar: number of variables
+% imstp: number of steps of impulse responses
+% xlab,ylab: labels
+% indxGimfml: 1, graph; 0, no graph
+% xTick: optional. Eg: [12 24 36].
+% indx_num: 0: no number on either axis (default), 1: number on both x-axis and y-axis.
+%---------------
+% scaleout: column 1 represents maximums; column 2 minimums. Rows: nvar variables.
+%
+% See fn_imcgraph, fn_imc2errgraph, imrerrgraph
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+if nargin < 9, xTick = []; end
+if nargin < 10, indx_num = 0; end
+
+t = 1:imstp;
+temp1=zeros(nvar,1);
+temp2=zeros(nvar,1);
+maxval=zeros(nvar,1);
+minval=zeros(nvar,1);
+for i = 1:nvar
+ for j = 1:nvar
+ temp1(j)=max(firsth(:,(j-1)*nvar+i));
+ temp2(j)=min(firstl(:,(j-1)*nvar+i));
+ end
+ maxval(i)=max(temp1);
+ minval(i)=min(temp2);
+end
+
+scaleout = [maxval(:) minval(:)];
+
+%--------------
+% Column j: Shock 1 to N; Row i: Responses to
+%-------------
+if indxGimfml
+ %figure
+ rowlabel = 1;
+ for i = 1:nvar
+ columnlabel = 1;
+
+ if minval(i)<0
+ if maxval(i)<=0
+ yt=[minval(i) 0];
+ else
+ yt=[minval(i) 0 maxval(i)];
+ end
+ else % (minval(i) >=0)
+ if maxval(i) > 0
+ yt=[0 maxval(i)];
+ else % (identically zero responses)
+ yt=[-1 0 1];
+ end
+ end
+
+ scale=[1 imstp minval(i) maxval(i)];
+ for j = 1:nvar
+ k1=(i-1)*nvar+j;
+ k2=(j-1)*nvar+i;
+ subplot(nvar,nvar,k1)
+ plot(t,imf(:,k2),t,[firstl(:,k2) firsth(:,k2)],'--',...
+ t,zeros(length(imf(:,k2)),1),'-');
+
+ set(gca,'XTick',xTick)
+ set(gca,'YTick',yt)
+ grid
+
+ axis(scale); % put limits on both axes.
+ %set(gca,'YLim',[minval(i) maxval(i)])
+ if (indx_num==0) % no numbers on axes
+ set(gca,'XTickLabel',' ');
+ set(gca,'YTickLabel',' ');
+% elseif (indx_num==1) %numbers on x-axis.
+% if i<nvar
+% set(gca,'XTickLabelMode','manual','XTickLabel',[])
+% end
+ else % put numbers on both axes
+ if i<nvar
+ set(gca,'XTickLabelMode','manual','XTickLabel',[])
+ end
+ if j>1
+ set(gca,'YTickLabel',' ');
+ end
+ end
+
+ if rowlabel == 1
+ %title(['x' num2str(j)])
+ %title(eval(['x' num2str(j)]))
+ title(char(xlab(j)))
+ end
+ if columnlabel == 1
+ %ylabel(['x' num2str(i)])
+ %ylabel(eval(['x' num2str(i)]))
+ ylabel(char(ylab(i)))
+ end
+ columnlabel = 0;
+ end
+ rowlabel = 0;
+ end
+end
diff --git a/MatlabFiles/fn_imcerrgraph_scl.m b/MatlabFiles/fn_imcerrgraph_scl.m
index 733d3700d75d3c699ca24c26570ead903927d9d2..46c9953ad45cbf9d49a4292af5ff0de01533f652 100644
--- a/MatlabFiles/fn_imcerrgraph_scl.m
+++ b/MatlabFiles/fn_imcerrgraph_scl.m
@@ -1,122 +1,122 @@
-function scaleout = fn_imcerrgraph_scl(imf,firstl,firsth,nvar,imstp,scaleout,xlab,ylab,indxGimfml,xTick)
-% scaleout = fn_imcerrgraph(imf,firstl,firsth,nvar,imstp,xlab,ylab,indxGimfml,xTick)
-% imcerrgraph: impulse, c (column: shock 1 to N), 1 error band, graph
-%
-% imf: imstp-by-nvar^2 matrix of impulse responses. Column (responses to 1st shock, responses to 2nd shock
-% etc), Row (impusle steps),
-% firstl: lower band
-% highth: high band
-% nvar: number of variables
-% imstp: number of steps of impulse responses
-% xlab,ylab: labels
-% indxGimfml: 1, graph; 0, no graph
-% xTick: optional. Eg: [12 24 36].
-%---------------
-% scaleout: column 1 represents maximums; column 2 minimums. Rows: nvar variables.
-%
-% See fn_imcgraph, fn_imc2errgraph, imrerrgraph
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-if nargin < 10, xTick = []; end
-
-t = 1:imstp;
-if isempty(scaleout)
- temp1=zeros(nvar,1);
- temp2=zeros(nvar,1);
- maxval=zeros(nvar,1);
- minval=zeros(nvar,1);
- for i = 1:nvar
- for j = 1:nvar
- temp1(j)=max(firsth(:,(j-1)*nvar+i));
- temp2(j)=min(firstl(:,(j-1)*nvar+i));
- end
- maxval(i)=max(temp1);
- minval(i)=min(temp2);
- end
- scaleout = [maxval(:) minval(:)];
-else
- maxval = scaleout(:,1);
- minval = scaleout(:,2);
-end
-
-
-%--------------
-% Column j: Shock 1 to N; Row i: Responses to
-%-------------
-if indxGimfml
- %figure
- rowlabel = 1;
- for i = 1:nvar
- columnlabel = 1;
-
- if minval(i)<0
- if maxval(i)<=0
- yt=[minval(i) 0];
- else
- yt=[minval(i) 0 maxval(i)];
- end
- else % (minval(i) >=0)
- if maxval(i) > 0
- yt=[0 maxval(i)];
- else % (identically zero responses)
- yt=[-1 0 1];
- end
- end
-
- scale=[1 imstp minval(i) maxval(i)];
- for j = 1:nvar
- k1=(i-1)*nvar+j;
- k2=(j-1)*nvar+i;
- subplot(nvar,nvar,k1)
- plot(t,imf(:,k2),t,[firstl(:,k2) firsth(:,k2)],'--',...
- t,zeros(length(imf(:,k2)),1),'-');
-
- set(gca,'XTick',xTick)
- set(gca,'YTick',yt)
- grid
-
- axis(scale); % put limits on both axes.
- %set(gca,'YLim',[minval(i) maxval(i)])
- if 1 % no numbers on axes
- set(gca,'XTickLabel',' ');
- set(gca,'YTickLabel',' ');
- else % put numbers on both axes
- if i<nvar
- set(gca,'XTickLabelMode','manual','XTickLabel',[])
- end
- if j>1
- set(gca,'YTickLabel',' ');
- end
- end
-
- if rowlabel == 1
- %title(['x' num2str(j)])
- %title(eval(['x' num2str(j)]))
- title(char(xlab(j)))
- end
- if columnlabel == 1
- %ylabel(['x' num2str(i)])
- %ylabel(eval(['x' num2str(i)]))
- ylabel(char(ylab(i)))
- end
- columnlabel = 0;
- end
- rowlabel = 0;
- end
-end
+function scaleout = fn_imcerrgraph_scl(imf,firstl,firsth,nvar,imstp,scaleout,xlab,ylab,indxGimfml,xTick)
+% scaleout = fn_imcerrgraph(imf,firstl,firsth,nvar,imstp,xlab,ylab,indxGimfml,xTick)
+% imcerrgraph: impulse, c (column: shock 1 to N), 1 error band, graph
+%
+% imf: imstp-by-nvar^2 matrix of impulse responses. Column (responses to 1st shock, responses to 2nd shock
+% etc), Row (impusle steps),
+% firstl: lower band
+% highth: high band
+% nvar: number of variables
+% imstp: number of steps of impulse responses
+% xlab,ylab: labels
+% indxGimfml: 1, graph; 0, no graph
+% xTick: optional. Eg: [12 24 36].
+%---------------
+% scaleout: column 1 represents maximums; column 2 minimums. Rows: nvar variables.
+%
+% See fn_imcgraph, fn_imc2errgraph, imrerrgraph
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+if nargin < 10, xTick = []; end
+
+t = 1:imstp;
+if isempty(scaleout)
+ temp1=zeros(nvar,1);
+ temp2=zeros(nvar,1);
+ maxval=zeros(nvar,1);
+ minval=zeros(nvar,1);
+ for i = 1:nvar
+ for j = 1:nvar
+ temp1(j)=max(firsth(:,(j-1)*nvar+i));
+ temp2(j)=min(firstl(:,(j-1)*nvar+i));
+ end
+ maxval(i)=max(temp1);
+ minval(i)=min(temp2);
+ end
+ scaleout = [maxval(:) minval(:)];
+else
+ maxval = scaleout(:,1);
+ minval = scaleout(:,2);
+end
+
+
+%--------------
+% Column j: Shock 1 to N; Row i: Responses to
+%-------------
+if indxGimfml
+ %figure
+ rowlabel = 1;
+ for i = 1:nvar
+ columnlabel = 1;
+
+ if minval(i)<0
+ if maxval(i)<=0
+ yt=[minval(i) 0];
+ else
+ yt=[minval(i) 0 maxval(i)];
+ end
+ else % (minval(i) >=0)
+ if maxval(i) > 0
+ yt=[0 maxval(i)];
+ else % (identically zero responses)
+ yt=[-1 0 1];
+ end
+ end
+
+ scale=[1 imstp minval(i) maxval(i)];
+ for j = 1:nvar
+ k1=(i-1)*nvar+j;
+ k2=(j-1)*nvar+i;
+ subplot(nvar,nvar,k1)
+ plot(t,imf(:,k2),t,[firstl(:,k2) firsth(:,k2)],'--',...
+ t,zeros(length(imf(:,k2)),1),'-');
+
+ set(gca,'XTick',xTick)
+ set(gca,'YTick',yt)
+ grid
+
+ axis(scale); % put limits on both axes.
+ %set(gca,'YLim',[minval(i) maxval(i)])
+ if 1 % no numbers on axes
+ set(gca,'XTickLabel',' ');
+ set(gca,'YTickLabel',' ');
+ else % put numbers on both axes
+ if i<nvar
+ set(gca,'XTickLabelMode','manual','XTickLabel',[])
+ end
+ if j>1
+ set(gca,'YTickLabel',' ');
+ end
+ end
+
+ if rowlabel == 1
+ %title(['x' num2str(j)])
+ %title(eval(['x' num2str(j)]))
+ title(char(xlab(j)))
+ end
+ if columnlabel == 1
+ %ylabel(['x' num2str(i)])
+ %ylabel(eval(['x' num2str(i)]))
+ ylabel(char(ylab(i)))
+ end
+ columnlabel = 0;
+ end
+ rowlabel = 0;
+ end
+end
diff --git a/MatlabFiles/fn_imcgraph.m b/MatlabFiles/fn_imcgraph.m
index cdc7cfb9e2906b7e9aef79825c1a9599aecba7a0..e42b97f52ede418954938fa0ab966542f366bcd6 100644
--- a/MatlabFiles/fn_imcgraph.m
+++ b/MatlabFiles/fn_imcgraph.m
@@ -1,123 +1,123 @@
-function scaleout = fn_imcgraph(imf,nvar,imstp,xlab,ylab,indxGimfml,xTick)
-% scaleout = fn_imcgraph(imf,nvar,imstp,xlab,ylab,indxGimfml,xTick)
-% imcgraph: impulse, c (column: shock 1 to N), graph the ML point impulse response
-%
-% imf: imstp-by-nvar^2 matrix of impulse responses, column (responses to 1st shock, responses to 2nd shock
-% etc), row (impusle steps),
-% nvar: number of variables
-% imstp: number of steps of impulse responses
-% xlab,ylab: labels
-% indxGimfml: 1, graph; 0, no graph
-% xTick: optional. Eg: [12 24 36].
-%---------------
-% scaleout: column 1 represents maximums; column 2 minimums. Rows: nvar variables.
-%
-% NOTE: I added "indxGimfml" so this function may not be compatible with programs
-% older than 03/06/99, TZ
-%
-% See imrgraph, fn_imcerrgraph, fn_imc2errgraph, imrerrgraph, fn_gyrfore in RVARcode
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-if nargin < 7, xTick = []; end
-
-t = 1:imstp;
-temp1=zeros(nvar,1);
-temp2=zeros(nvar,1);
-maxval=zeros(nvar,1);
-minval=zeros(nvar,1);
-for i = 1:nvar
- for j = 1:nvar
- temp1(j)=max(imf(:,(j-1)*nvar+i));
- temp2(j)=min(imf(:,(j-1)*nvar+i));
- end
- maxval(i)=max(temp1);
- minval(i)=min(temp2);
-end
-
-scaleout = [maxval(:) minval(:)];
-
-%--------------
-% Column j: Shock 1 to N; Row i: Responses to
-%-------------
-if indxGimfml
- %figure
- rowlabel = 1;
- for i = 1:nvar % Responses of
- columnlabel = 1;
-
- if minval(i)<0
- if maxval(i)<=0
- yt=[minval(i) 0];
- else
- yt=[minval(i) 0 maxval(i)];
- end
- else % (minval(i) >=0)
- if maxval(i) > 0
- yt=[0 maxval(i)];
- else % (identically zero responses)
- yt=[-1 0 1];
- end
- end
-
-
- scale=[1 imstp minval(i) maxval(i)];
- for j = 1:nvar % To shocks
- k1=(i-1)*nvar+j;
- k2=(j-1)*nvar+i;
- subplot(nvar,nvar,k1)
- plot(t,imf(:,k2),t,zeros(length(imf(:,k2)),1),'r:');
-
- set(gca,'XTick',xTick)
- set(gca,'YTick',yt)
- grid
-
- axis(scale); % put limits on both axes.
- %
- % if maxval(i)>minval(i)
- % set(gca,'YLim',[minval(i) maxval(i)])
- % end
-
-
- if isempty(xTick) %1 % No numbers on both axes
- set(gca,'XTickLabel',' ');
- set(gca,'YTickLabel',' ');
- else % Put numbers on both axes
- if i<nvar
- set(gca,'XTickLabelMode','manual','XTickLabel',[])
- end
- if j>1
- set(gca,'YTickLabel',' ');
- end
- end
-
-
- if rowlabel == 1
- %title(['x' num2str(j)])
- %title(eval(['x' num2str(j)]))
- title(char(xlab(j)))
- end
- if columnlabel == 1
- %ylabel(['x' num2str(i)])
- %ylabel(eval(['x' num2str(i)]))
- ylabel(char(ylab(i)))
- end
- columnlabel = 0;
- end
- rowlabel = 0;
- end
-end
+function scaleout = fn_imcgraph(imf,nvar,imstp,xlab,ylab,indxGimfml,xTick)
+% scaleout = fn_imcgraph(imf,nvar,imstp,xlab,ylab,indxGimfml,xTick)
+% imcgraph: impulse, c (column: shock 1 to N), graph the ML point impulse response
+%
+% imf: imstp-by-nvar^2 matrix of impulse responses, column (responses to 1st shock, responses to 2nd shock
+% etc), row (impusle steps),
+% nvar: number of variables
+% imstp: number of steps of impulse responses
+% xlab,ylab: labels
+% indxGimfml: 1, graph; 0, no graph
+% xTick: optional. Eg: [12 24 36].
+%---------------
+% scaleout: column 1 represents maximums; column 2 minimums. Rows: nvar variables.
+%
+% NOTE: I added "indxGimfml" so this function may not be compatible with programs
+% older than 03/06/99, TZ
+%
+% See imrgraph, fn_imcerrgraph, fn_imc2errgraph, imrerrgraph, fn_gyrfore in RVARcode
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+if nargin < 7, xTick = []; end
+
+t = 1:imstp;
+temp1=zeros(nvar,1);
+temp2=zeros(nvar,1);
+maxval=zeros(nvar,1);
+minval=zeros(nvar,1);
+for i = 1:nvar
+ for j = 1:nvar
+ temp1(j)=max(imf(:,(j-1)*nvar+i));
+ temp2(j)=min(imf(:,(j-1)*nvar+i));
+ end
+ maxval(i)=max(temp1);
+ minval(i)=min(temp2);
+end
+
+scaleout = [maxval(:) minval(:)];
+
+%--------------
+% Column j: Shock 1 to N; Row i: Responses to
+%-------------
+if indxGimfml
+ %figure
+ rowlabel = 1;
+ for i = 1:nvar % Responses of
+ columnlabel = 1;
+
+ if minval(i)<0
+ if maxval(i)<=0
+ yt=[minval(i) 0];
+ else
+ yt=[minval(i) 0 maxval(i)];
+ end
+ else % (minval(i) >=0)
+ if maxval(i) > 0
+ yt=[0 maxval(i)];
+ else % (identically zero responses)
+ yt=[-1 0 1];
+ end
+ end
+
+
+ scale=[1 imstp minval(i) maxval(i)];
+ for j = 1:nvar % To shocks
+ k1=(i-1)*nvar+j;
+ k2=(j-1)*nvar+i;
+ subplot(nvar,nvar,k1)
+ plot(t,imf(:,k2),t,zeros(length(imf(:,k2)),1),'r:');
+
+ set(gca,'XTick',xTick)
+ set(gca,'YTick',yt)
+ grid
+
+ axis(scale); % put limits on both axes.
+ %
+ % if maxval(i)>minval(i)
+ % set(gca,'YLim',[minval(i) maxval(i)])
+ % end
+
+
+ if isempty(xTick) %1 % No numbers on both axes
+ set(gca,'XTickLabel',' ');
+ set(gca,'YTickLabel',' ');
+ else % Put numbers on both axes
+ if i<nvar
+ set(gca,'XTickLabelMode','manual','XTickLabel',[])
+ end
+ if j>1
+ set(gca,'YTickLabel',' ');
+ end
+ end
+
+
+ if rowlabel == 1
+ %title(['x' num2str(j)])
+ %title(eval(['x' num2str(j)]))
+ title(char(xlab(j)))
+ end
+ if columnlabel == 1
+ %ylabel(['x' num2str(i)])
+ %ylabel(eval(['x' num2str(i)]))
+ ylabel(char(ylab(i)))
+ end
+ columnlabel = 0;
+ end
+ rowlabel = 0;
+ end
+end
diff --git a/MatlabFiles/fn_imcgraph_scl.m b/MatlabFiles/fn_imcgraph_scl.m
index 6fd2acfc499df92a71c1a1a5abef1a163ae1ab4d..4fc5e0a8161783f6652afd24805d1b3919e87ef6 100644
--- a/MatlabFiles/fn_imcgraph_scl.m
+++ b/MatlabFiles/fn_imcgraph_scl.m
@@ -1,128 +1,128 @@
-function scaleout = fn_imcgraph_scl(imf,nvar,imstp,scaleout,xlab,ylab,indxGimfml,xTick)
-% scaleout = fn_imcgraph(imf,nvar,imstp,xlab,ylab,indxGimfml,xTick)
-% imcgraph: impulse, c (column: shock 1 to N), graph the ML point impulse response
-%
-% imf: imstp-by-nvar^2 matrix of impulse responses, column (responses to 1st shock, responses to 2nd shock
-% etc), row (impusle steps),
-% nvar: number of variables
-% imstp: number of steps of impulse responses
-% xlab,ylab: labels
-% indxGimfml: 1, graph; 0, no graph
-% xTick: optional. Eg: [12 24 36].
-%---------------
-% scaleout: column 1 represents maximums; column 2 minimums. Rows: nvar variables.
-%
-% NOTE: I added "indxGimfml" so this function may not be compatible with programs
-% older than 03/06/99, TZ
-%
-% See imrgraph, fn_imcerrgraph, fn_imc2errgraph, imrerrgraph, fn_gyrfore in RVARcode
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-if nargin < 7, xTick = []; end
-
-t = 1:imstp;
-if isempty(scaleout)
- temp1=zeros(nvar,1);
- temp2=zeros(nvar,1);
- maxval=zeros(nvar,1);
- minval=zeros(nvar,1);
- for i = 1:nvar
- for j = 1:nvar
- temp1(j)=max(imf(:,(j-1)*nvar+i));
- temp2(j)=min(imf(:,(j-1)*nvar+i));
- end
- maxval(i)=max(temp1);
- minval(i)=min(temp2);
- end
- scaleout = [maxval(:) minval(:)];
-else
- maxval = scaleout(:,1);
- minval = scaleout(:,2);
-end
-
-
-%--------------
-% Column j: Shock 1 to N; Row i: Responses to
-%-------------
-if indxGimfml
- %figure
- rowlabel = 1;
- for i = 1:nvar % Responses of
- columnlabel = 1;
-
- if minval(i)<0
- if maxval(i)<=0
- yt=[minval(i) 0];
- else
- yt=[minval(i) 0 maxval(i)];
- end
- else % (minval(i) >=0)
- if maxval(i) > 0
- yt=[0 maxval(i)];
- else % (identically zero responses)
- yt=[-1 0 1];
- end
- end
-
-
- scale=[1 imstp minval(i) maxval(i)];
- for j = 1:nvar % To shocks
- k1=(i-1)*nvar+j;
- k2=(j-1)*nvar+i;
- subplot(nvar,nvar,k1)
- plot(t,imf(:,k2),t,zeros(length(imf(:,k2)),1),'r:');
-
- set(gca,'XTick',xTick)
- set(gca,'YTick',yt)
- grid
-
- axis(scale); % put limits on both axes.
- %
- % if maxval(i)>minval(i)
- % set(gca,'YLim',[minval(i) maxval(i)])
- % end
-
-
- if isempty(xTick) %1 % No numbers on both axes
- set(gca,'XTickLabel',' ');
- set(gca,'YTickLabel',' ');
- else % Put numbers on both axes
- if i<nvar
- set(gca,'XTickLabelMode','manual','XTickLabel',[])
- end
- if j>1
- set(gca,'YTickLabel',' ');
- end
- end
-
-
- if rowlabel == 1
- %title(['x' num2str(j)])
- %title(eval(['x' num2str(j)]))
- title(char(xlab(j)))
- end
- if columnlabel == 1
- %ylabel(['x' num2str(i)])
- %ylabel(eval(['x' num2str(i)]))
- ylabel(char(ylab(i)))
- end
- columnlabel = 0;
- end
- rowlabel = 0;
- end
-end
+function scaleout = fn_imcgraph_scl(imf,nvar,imstp,scaleout,xlab,ylab,indxGimfml,xTick)
+% scaleout = fn_imcgraph(imf,nvar,imstp,xlab,ylab,indxGimfml,xTick)
+% imcgraph: impulse, c (column: shock 1 to N), graph the ML point impulse response
+%
+% imf: imstp-by-nvar^2 matrix of impulse responses, column (responses to 1st shock, responses to 2nd shock
+% etc), row (impusle steps),
+% nvar: number of variables
+% imstp: number of steps of impulse responses
+% xlab,ylab: labels
+% indxGimfml: 1, graph; 0, no graph
+% xTick: optional. Eg: [12 24 36].
+%---------------
+% scaleout: column 1 represents maximums; column 2 minimums. Rows: nvar variables.
+%
+% NOTE: I added "indxGimfml" so this function may not be compatible with programs
+% older than 03/06/99, TZ
+%
+% See imrgraph, fn_imcerrgraph, fn_imc2errgraph, imrerrgraph, fn_gyrfore in RVARcode
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+if nargin < 7, xTick = []; end
+
+t = 1:imstp;
+if isempty(scaleout)
+ temp1=zeros(nvar,1);
+ temp2=zeros(nvar,1);
+ maxval=zeros(nvar,1);
+ minval=zeros(nvar,1);
+ for i = 1:nvar
+ for j = 1:nvar
+ temp1(j)=max(imf(:,(j-1)*nvar+i));
+ temp2(j)=min(imf(:,(j-1)*nvar+i));
+ end
+ maxval(i)=max(temp1);
+ minval(i)=min(temp2);
+ end
+ scaleout = [maxval(:) minval(:)];
+else
+ maxval = scaleout(:,1);
+ minval = scaleout(:,2);
+end
+
+
+%--------------
+% Column j: Shock 1 to N; Row i: Responses to
+%-------------
+if indxGimfml
+ %figure
+ rowlabel = 1;
+ for i = 1:nvar % Responses of
+ columnlabel = 1;
+
+ if minval(i)<0
+ if maxval(i)<=0
+ yt=[minval(i) 0];
+ else
+ yt=[minval(i) 0 maxval(i)];
+ end
+ else % (minval(i) >=0)
+ if maxval(i) > 0
+ yt=[0 maxval(i)];
+ else % (identically zero responses)
+ yt=[-1 0 1];
+ end
+ end
+
+
+ scale=[1 imstp minval(i) maxval(i)];
+ for j = 1:nvar % To shocks
+ k1=(i-1)*nvar+j;
+ k2=(j-1)*nvar+i;
+ subplot(nvar,nvar,k1)
+ plot(t,imf(:,k2),t,zeros(length(imf(:,k2)),1),'r:');
+
+ set(gca,'XTick',xTick)
+ set(gca,'YTick',yt)
+ grid
+
+ axis(scale); % put limits on both axes.
+ %
+ % if maxval(i)>minval(i)
+ % set(gca,'YLim',[minval(i) maxval(i)])
+ % end
+
+
+ if isempty(xTick) %1 % No numbers on both axes
+ set(gca,'XTickLabel',' ');
+ set(gca,'YTickLabel',' ');
+ else % Put numbers on both axes
+ if i<nvar
+ set(gca,'XTickLabelMode','manual','XTickLabel',[])
+ end
+ if j>1
+ set(gca,'YTickLabel',' ');
+ end
+ end
+
+
+ if rowlabel == 1
+ %title(['x' num2str(j)])
+ %title(eval(['x' num2str(j)]))
+ title(char(xlab(j)))
+ end
+ if columnlabel == 1
+ %ylabel(['x' num2str(i)])
+ %ylabel(eval(['x' num2str(i)]))
+ ylabel(char(ylab(i)))
+ end
+ columnlabel = 0;
+ end
+ rowlabel = 0;
+ end
+end
diff --git a/MatlabFiles/fn_impulse.m b/MatlabFiles/fn_impulse.m
index 77e618f97d307d7979c6f00c1c142592c161a7ca..7c1e7e26bbccea42c1e3c3efb6fff0100f2939bc 100644
--- a/MatlabFiles/fn_impulse.m
+++ b/MatlabFiles/fn_impulse.m
@@ -1,75 +1,75 @@
-function imf = fn_impulse(Bh,swish,nn)
-% Computing impulse functions with
-% imf = fn_impulse(Bh,swish,nn)
-% imf is in a format that is the SAME as in RATS.
-% Column: nvar responses to 1st shock,
-% nvar responses to 2nd shock, and so on.
-% Row: steps of impulse responses.
-%-----------------
-% Bh is the estimated reduced form coefficient in the form
-% Y(T*nvar) = XB + U, X: T*k (may include all exogenous terms), B: k*nvar.
-% The matrix form and dimension are the same as "Bh" from the function "sye.m";
-% Column: 1st lag (with nvar variables) to lags (with nvar variables) + const = k.
-% Note: columns correspond to equations.
-% swish is the inv(A0) in the structural model y'(t)*A0 = e'(t).
-% Note: columns corresponding to equations.
-% nn is the numbers of inputs [nvar,lags,# of steps of impulse responses].
-%
-% Written by Tao Zha.
-% Copyright (c) 1994 by Tao Zha
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-nvar = nn(1);
-lags = nn(2);
-imstep = nn(3); % number of steps for impulse responses
-
-Ah = Bh';
-% Row: nvar equations
-% Column: 1st lag (with nvar variables) to lags (with nvar variables) + const = k.
-
-imf = zeros(imstep,nvar*nvar);
-% Column: nvar responses to 1st shock, nvar responses to 2nd shock, and so on.
-% Row: steps of impulse responses.
-M = zeros(nvar*(lags+1),nvar);
-% Stack lags M's in the order of, e.g., [Mlags, ..., M2,M1;M0]
-M(1:nvar,:) = swish';
-Mtem = M(1:nvar,:); % temporary M.
-% first (initial) responses to 1 standard deviation shock. Row: responses; Column: shocks
-% * put in the form of "imf"
-imf(1,:) = Mtem(:)';
-
-t = 1;
-ims1 = min([imstep-1 lags]);
-while t <= ims1
- Mtem = Ah(:,1:nvar*t)*M(1:nvar*t,:);
- % Row: nvar equations, each for the nvar variables at tth lag
- M(nvar+1:nvar*(t+1),:)=M(1:nvar*t,:);
- M(1:nvar,:) = Mtem;
- imf(t+1,:) = Mtem(:)';
- % stack imf with each step, Row: 6 var to 1st shock, 6 var to 2nd shock, etc.
- t= t+1;
-end
-
-for t = lags+1:imstep-1
- Mtem = Ah(:,1:nvar*lags)*M(1:nvar*lags,:);
- % Row: nvar equations, each for the nvar variables at tth lag
- M(nvar+1:nvar*(t+1),:) = M(1:nvar*t,:);
- M(1:nvar,:)=Mtem;
- imf(t+1,:) = Mtem(:)';
- % stack imf with each step, Row: 6 var to 1st shock, 6 var to 2nd shock, etc.
-end
+function imf = fn_impulse(Bh,swish,nn)
+% Computing impulse functions with
+% imf = fn_impulse(Bh,swish,nn)
+% imf is in a format that is the SAME as in RATS.
+% Column: nvar responses to 1st shock,
+% nvar responses to 2nd shock, and so on.
+% Row: steps of impulse responses.
+%-----------------
+% Bh is the estimated reduced form coefficient in the form
+% Y(T*nvar) = XB + U, X: T*k (may include all exogenous terms), B: k*nvar.
+% The matrix form and dimension are the same as "Bh" from the function "sye.m";
+% Column: 1st lag (with nvar variables) to lags (with nvar variables) + const = k.
+% Note: columns correspond to equations.
+% swish is the inv(A0) in the structural model y'(t)*A0 = e'(t).
+% Note: columns corresponding to equations.
+% nn is the numbers of inputs [nvar,lags,# of steps of impulse responses].
+%
+% Written by Tao Zha.
+%
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+nvar = nn(1);
+lags = nn(2);
+imstep = nn(3); % number of steps for impulse responses
+
+Ah = Bh';
+% Row: nvar equations
+% Column: 1st lag (with nvar variables) to lags (with nvar variables) + const = k.
+
+imf = zeros(imstep,nvar*nvar);
+% Column: nvar responses to 1st shock, nvar responses to 2nd shock, and so on.
+% Row: steps of impulse responses.
+M = zeros(nvar*(lags+1),nvar);
+% Stack lags M's in the order of, e.g., [Mlags, ..., M2,M1;M0]
+M(1:nvar,:) = swish';
+Mtem = M(1:nvar,:); % temporary M.
+% first (initial) responses to 1 standard deviation shock. Row: responses; Column: shocks
+% * put in the form of "imf"
+imf(1,:) = Mtem(:)';
+
+t = 1;
+ims1 = min([imstep-1 lags]);
+while t <= ims1
+ Mtem = Ah(:,1:nvar*t)*M(1:nvar*t,:);
+ % Row: nvar equations, each for the nvar variables at tth lag
+ M(nvar+1:nvar*(t+1),:)=M(1:nvar*t,:);
+ M(1:nvar,:) = Mtem;
+ imf(t+1,:) = Mtem(:)';
+ % stack imf with each step, Row: 6 var to 1st shock, 6 var to 2nd shock, etc.
+ t= t+1;
+end
+
+for t = lags+1:imstep-1
+ Mtem = Ah(:,1:nvar*lags)*M(1:nvar*lags,:);
+ % Row: nvar equations, each for the nvar variables at tth lag
+ M(nvar+1:nvar*(t+1),:) = M(1:nvar*t,:);
+ M(1:nvar,:)=Mtem;
+ imf(t+1,:) = Mtem(:)';
+ % stack imf with each step, Row: 6 var to 1st shock, 6 var to 2nd shock, etc.
+end
diff --git a/MatlabFiles/fn_irf_var1.m b/MatlabFiles/fn_irf_var1.m
index 490a31f7ee7b4f03f19bbc18557869ca757b071e..00106de102a765c23682f796a3e8a9127f4861fe 100644
--- a/MatlabFiles/fn_irf_var1.m
+++ b/MatlabFiles/fn_irf_var1.m
@@ -1,51 +1,51 @@
-function irfs = fn_irf_var1(G1, impact, nsteps);
-%irfs = fn_irf_var1(G1, impact,nsteps);
-% Inputs:
-% G1: n-by-n;
-% impact: n-by-r;
-% nsteps: number of steps for impulse responses.
-%---
-% Outputs:
-% irfs: nsteps-by-n-by-r. For each shock of r shocks, impulse responses are nsteps-by-n.
-%
-% See fn_vds.m and fn_uncondfcst_var1.m.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-[n,r] = size(impact);
-
-[n1,n2] = size(G1);
-if (n1 ~= n2) || (n1 ~= n)
- error('fn_irf_var1.m: make sure that (1) G1 is square and (2) size(G1,1) = size(impact,1)');
-end
-
-irfs = zeros(nsteps,n,r);
-
-
-%---- Impuse responses at the first step.
-M = impact;
-for ri=1:r
- irfs(1,:,ri) = M(:,ri)';
-end
-
-for ti = 2:nsteps
- M = G1*M;
- for ri=1:r
- irfs(ti,:,ri) = M(:,ri)';
- end
-end
+function irfs = fn_irf_var1(G1, impact, nsteps);
+%irfs = fn_irf_var1(G1, impact,nsteps);
+% Inputs:
+% G1: n-by-n;
+% impact: n-by-r;
+% nsteps: number of steps for impulse responses.
+%---
+% Outputs:
+% irfs: nsteps-by-n-by-r. For each shock of r shocks, impulse responses are nsteps-by-n.
+%
+% See fn_vds.m and fn_uncondfcst_var1.m.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+[n,r] = size(impact);
+
+[n1,n2] = size(G1);
+if (n1 ~= n2) || (n1 ~= n)
+ error('fn_irf_var1.m: make sure that (1) G1 is square and (2) size(G1,1) = size(impact,1)');
+end
+
+irfs = zeros(nsteps,n,r);
+
+
+%---- Impuse responses at the first step.
+M = impact;
+for ri=1:r
+ irfs(1,:,ri) = M(:,ri)';
+end
+
+for ti = 2:nsteps
+ M = G1*M;
+ for ri=1:r
+ irfs(ti,:,ri) = M(:,ri)';
+ end
+end
diff --git a/MatlabFiles/fn_kalfil_tv.m b/MatlabFiles/fn_kalfil_tv.m
index 262a6a97e7d41bc85628d759c52f31183f4504bc..13badcfaf30d227469d2b9721862d9b292c2cfc0 100644
--- a/MatlabFiles/fn_kalfil_tv.m
+++ b/MatlabFiles/fn_kalfil_tv.m
@@ -1,154 +1,154 @@
-function [loglh,zt_tm1,Pt_tm1, loglh_t_allvalues] = fn_kalfil_tv(Y_T, a, H, R, G, b, F, V, indxIni,z0,P0)
-%[loglh,zt_tm1,Pt_tm1] = fn_kalfil_tv(Y_T, a, H, R, G, b, F, V, indxIni,z0,P0)
-%Time-varying Kalman filter (conditional on all the regimes in a Markov-switching model). It computes
-% a sequence of one-step predictions and their covariance matrices, and the log likelihood.
-% The function uses a forward recursion algorithm.
-%
-% Note: for b, F, V, zt_tm1, and Pt_tm1, there is not need for the T+1 length because the values at T+1 have
-% never been used in the likelihood function. See tz_kalfiltv() in kalman.c for an illustration.
-%
-% State space model is defined as follows:
-% y(t) = a(t) + H(t)*z(t) + eps(t) (observation or measurement equation)
-% z(t) = b(t) + F(t)*z(t) + eta(t) (state or transition equation)
-% where a(t), H(t), b(t), and F(t) depend on s_t that follows a Markov-chain process and are taken as given.
-%
-% Inputs are as follows:
-% Y_T is a n_y-by-T matrix containing data [y(1), ... , y(T)].
-% a is an n_y-by-T matrix of time-varying input vectors in the measurement equation.
-% H is an n_y-by-n_z-by-T 3-D of time-varying matrices in the measurement equation.
-% R is an n_y-by-n_y-by-T 3-D of time-varying covariance matrices for the error in the measurement equation.
-% G is an n_z-by-n_y-by-T 3-D of time-varying E(eta_t * eps_t').
-% ------
-% b is an n_z-by-(T+1) matrix of time-varying input vectors in the state equation with b(:,1) as an initial condition.
-% F is an n_z-by-n_z-by-(T+1) 3-D of time-varying transition matrices in the state equation with F(:,:,1) as an initial condition.
-% V is an n_z-by-n_z-by-(T+1) 3-D of time-varying covariance matrices for the error in the state equation with V(:,:,1) as an initial condition.
-% ------
-% indxIni: 1: using the initial condition with zt_tm1(:,1)=z0 and Pt_tm1(:,:,1)=P0;
-% 0: using the unconditional mean for any given regime at time 0.
-% z0 is an n_z-by-1 vector of initial condition when indxIni=1. (Do not enter if indxIni=0.)
-% P0 is an n_z-by-n_z matrix of initial condition when indxIni=1. (Do not enter if indxIni=0.)
-%
-% Outputs are as follows:
-% loglh is a value of the log likelihood function of the state-space model
-% under the assumption that errors are multivariate Gaussian.
-% zt_tm1 is an n_z-by-(T+1) matrices of one-step predicted state vectors with z0_0m1 as a initial condition.
-% Pt_tm1 is an n_z-by-n_z-by-(T+1) 3-D of covariance matrices of zt_tm1 with P0_0m1 as a initial condition.
-% loglh_t_allvalues is a T-by-1 vector of loglh at time t for t=1:T.
-%
-% The initial state vector and its covariance matrix are computed under the bounded (stationary) condition:
-% z0_0m1 = (I-F(:,:,1))\b(:,1)
-% vec(P0_0m1) = (I-kron(F(:,:,1),F(:,:,1)))\vec(V(:,:,1))
-% Note that all eigenvalues of the matrix F(:,:,1) are inside the unit circle when the state-space model is bounded (stationary).
-%
-% March 2007, written by Tao Zha
-% See Hamilton's book ([13.2.13] -- [13.2.22]), Harvey (pp.100-106), and LiuWZ Model I NOTES pp.001-003.
-%==========================================================================
-% Revision history:
-%
-%
-%
-%==========================================================================
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-Tp1 = size(Y_T,2) + 1; %T+1;
-n_y = size(a,1);
-n_z = size(b,1);
-%--- Checking input matrix dimensions
-if (size(Y_T,1)~=n_y)
- error('kalf_tv(): Y_T and a must have the same number of rows')
-end
-%--- Allocating memory.
-zt_tm1 = zeros(n_z,Tp1);
-Pt_tm1 = zeros(n_z,n_z,Tp1);
-loglh_t_allvalues = zeros(Tp1-1,1);
-%--- Initializing.
-loglh = 0.0;
-if (indxIni)
- zt_tm1(:,1) = z0;
- Pt_tm1(:,:,1) = P0;
-else
- eigmax4F = max(abs(eig(F(:,:,1))));
- format long e
- eigmax4F
- if (eigmax4F < 1.0)
- zt_tm1(:,1) = (eye(n_z)-F(:,:,1))\b(:,1);
- V1 = V(:,:,1);
- Pt_tm1(:,:,1) = reshape((eye(n_z^2)-kron(F(:,:,1),F(:,:,1)))\V1(:) ,n_z,n_z);
- else
- %--- Do NOT use the following option. It turns out that this will often generate explosive conditional liklihood
- %--- at the end of the sample, because Pt_tm1 shrinks to zero overtime due to the sigularity of the initila condition P_{1|0}.
- % zt_tm1(:,1) = zeros(size(zt_tm1(:,1)));
- % Pt_tm1(:,:,1) = V(:,:,1); %+eye(size(V(:,:,1)));
-
- nearinfinity = -1.0e+300
- loglh = nearinfinity;
- return; %Eearly exit.
- %error('kalf_tv(): For non-stationary solutions, the initial conditions must be supplied by, say, input arguments')
- end
-end
-
-
-%====== See p.002 in LiuWZ. ======
-indx_badlh = 0; %1: bad likelihood with, say, -infinity of the LH value.
-for t=2:Tp1
- tdata = t-1;
-
- %--- Setup.
- Htdata = H(:,:,tdata);
- Htdatatran = Htdata';
- ztdata = zt_tm1(:,tdata);
- Ptdata = Pt_tm1(:,:,tdata);
- PHtran_tdata = Ptdata*Htdatatran;
- Ft = F(:,:,t);
- Fttran = Ft';
-
- %--- Data.
- etdata = Y_T(:,tdata) - a(:,tdata) - Htdata*ztdata;
- etdatatran = etdata';
- Dtdata = Htdata*PHtran_tdata + R(:,:,tdata);
- Dtdata = 0.5*(Dtdata + Dtdata'); %Making it symmetric against some rounding errors.
- %This making-symmetric is very IMPORTANT; otherwise, we will get the matrix being singular message
- % and eigenvalues being negative for the SPD matrix, etc. Then the likelihood becomes either
- % a bad number or a complex number.
-
- %--- State (updating).
- Kt_tdata = (Ft*PHtran_tdata+G(:,:,tdata))/Dtdata;
- Kt_tdatatran = Kt_tdata';
- zt_tm1(:,t) = b(:,t) + Ft*zt_tm1(:,tdata) + Kt_tdata*etdata;
- Pt_tm1(:,:,t) = Ft*Ptdata*Fttran - Kt_tdata*Dtdata*Kt_tdatatran + V(:,:,t);
-
- %--- Forming the log likelihood.
- detDtdata = det(Dtdata);
- %if (~isfinite(detDtdata))
- if (detDtdata < realmin)
- indx_badlh = 1;
- break;
- else
- loglh_tdata = -(0.5*n_y)*log(2*pi) - 0.5*log(detDtdata) - 0.5*(etdatatran/Dtdata)*etdata;
- loglh = loglh + loglh_tdata;
- loglh_t_allvalues(tdata) = loglh_tdata;
- end
-end
-
-if (indx_badlh)
- nearinfinity = -1.0e+300;
- loglh = nearinfinity;
- loglh_t_allvalues(tdata) = nearinfinity;
-end
-
+function [loglh,zt_tm1,Pt_tm1, loglh_t_allvalues] = fn_kalfil_tv(Y_T, a, H, R, G, b, F, V, indxIni,z0,P0)
+%[loglh,zt_tm1,Pt_tm1] = fn_kalfil_tv(Y_T, a, H, R, G, b, F, V, indxIni,z0,P0)
+%Time-varying Kalman filter (conditional on all the regimes in a Markov-switching model). It computes
+% a sequence of one-step predictions and their covariance matrices, and the log likelihood.
+% The function uses a forward recursion algorithm.
+%
+% Note: for b, F, V, zt_tm1, and Pt_tm1, there is not need for the T+1 length because the values at T+1 have
+% never been used in the likelihood function. See tz_kalfiltv() in kalman.c for an illustration.
+%
+% State space model is defined as follows:
+% y(t) = a(t) + H(t)*z(t) + eps(t) (observation or measurement equation)
+% z(t) = b(t) + F(t)*z(t) + eta(t) (state or transition equation)
+% where a(t), H(t), b(t), and F(t) depend on s_t that follows a Markov-chain process and are taken as given.
+%
+% Inputs are as follows:
+% Y_T is a n_y-by-T matrix containing data [y(1), ... , y(T)].
+% a is an n_y-by-T matrix of time-varying input vectors in the measurement equation.
+% H is an n_y-by-n_z-by-T 3-D of time-varying matrices in the measurement equation.
+% R is an n_y-by-n_y-by-T 3-D of time-varying covariance matrices for the error in the measurement equation.
+% G is an n_z-by-n_y-by-T 3-D of time-varying E(eta_t * eps_t').
+% ------
+% b is an n_z-by-(T+1) matrix of time-varying input vectors in the state equation with b(:,1) as an initial condition.
+% F is an n_z-by-n_z-by-(T+1) 3-D of time-varying transition matrices in the state equation with F(:,:,1) as an initial condition.
+% V is an n_z-by-n_z-by-(T+1) 3-D of time-varying covariance matrices for the error in the state equation with V(:,:,1) as an initial condition.
+% ------
+% indxIni: 1: using the initial condition with zt_tm1(:,1)=z0 and Pt_tm1(:,:,1)=P0;
+% 0: using the unconditional mean for any given regime at time 0.
+% z0 is an n_z-by-1 vector of initial condition when indxIni=1. (Do not enter if indxIni=0.)
+% P0 is an n_z-by-n_z matrix of initial condition when indxIni=1. (Do not enter if indxIni=0.)
+%
+% Outputs are as follows:
+% loglh is a value of the log likelihood function of the state-space model
+% under the assumption that errors are multivariate Gaussian.
+% zt_tm1 is an n_z-by-(T+1) matrices of one-step predicted state vectors with z0_0m1 as a initial condition.
+% Pt_tm1 is an n_z-by-n_z-by-(T+1) 3-D of covariance matrices of zt_tm1 with P0_0m1 as a initial condition.
+% loglh_t_allvalues is a T-by-1 vector of loglh at time t for t=1:T.
+%
+% The initial state vector and its covariance matrix are computed under the bounded (stationary) condition:
+% z0_0m1 = (I-F(:,:,1))\b(:,1)
+% vec(P0_0m1) = (I-kron(F(:,:,1),F(:,:,1)))\vec(V(:,:,1))
+% Note that all eigenvalues of the matrix F(:,:,1) are inside the unit circle when the state-space model is bounded (stationary).
+%
+% March 2007, written by Tao Zha
+% See Hamilton's book ([13.2.13] -- [13.2.22]), Harvey (pp.100-106), and LiuWZ Model I NOTES pp.001-003.
+%==========================================================================
+% Revision history:
+%
+%
+%
+%==========================================================================
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+Tp1 = size(Y_T,2) + 1; %T+1;
+n_y = size(a,1);
+n_z = size(b,1);
+%--- Checking input matrix dimensions
+if (size(Y_T,1)~=n_y)
+ error('kalf_tv(): Y_T and a must have the same number of rows')
+end
+%--- Allocating memory.
+zt_tm1 = zeros(n_z,Tp1);
+Pt_tm1 = zeros(n_z,n_z,Tp1);
+loglh_t_allvalues = zeros(Tp1-1,1);
+%--- Initializing.
+loglh = 0.0;
+if (indxIni)
+ zt_tm1(:,1) = z0;
+ Pt_tm1(:,:,1) = P0;
+else
+ eigmax4F = max(abs(eig(F(:,:,1))));
+ format long e
+ eigmax4F
+ if (eigmax4F < 1.0)
+ zt_tm1(:,1) = (eye(n_z)-F(:,:,1))\b(:,1);
+ V1 = V(:,:,1);
+ Pt_tm1(:,:,1) = reshape((eye(n_z^2)-kron(F(:,:,1),F(:,:,1)))\V1(:) ,n_z,n_z);
+ else
+ %--- Do NOT use the following option. It turns out that this will often generate explosive conditional liklihood
+ %--- at the end of the sample, because Pt_tm1 shrinks to zero overtime due to the sigularity of the initila condition P_{1|0}.
+ % zt_tm1(:,1) = zeros(size(zt_tm1(:,1)));
+ % Pt_tm1(:,:,1) = V(:,:,1); %+eye(size(V(:,:,1)));
+
+ nearinfinity = -1.0e+300
+ loglh = nearinfinity;
+ return; %Eearly exit.
+ %error('kalf_tv(): For non-stationary solutions, the initial conditions must be supplied by, say, input arguments')
+ end
+end
+
+
+%====== See p.002 in LiuWZ. ======
+indx_badlh = 0; %1: bad likelihood with, say, -infinity of the LH value.
+for t=2:Tp1
+ tdata = t-1;
+
+ %--- Setup.
+ Htdata = H(:,:,tdata);
+ Htdatatran = Htdata';
+ ztdata = zt_tm1(:,tdata);
+ Ptdata = Pt_tm1(:,:,tdata);
+ PHtran_tdata = Ptdata*Htdatatran;
+ Ft = F(:,:,t);
+ Fttran = Ft';
+
+ %--- Data.
+ etdata = Y_T(:,tdata) - a(:,tdata) - Htdata*ztdata;
+ etdatatran = etdata';
+ Dtdata = Htdata*PHtran_tdata + R(:,:,tdata);
+ Dtdata = 0.5*(Dtdata + Dtdata'); %Making it symmetric against some rounding errors.
+ %This making-symmetric is very IMPORTANT; otherwise, we will get the matrix being singular message
+ % and eigenvalues being negative for the SPD matrix, etc. Then the likelihood becomes either
+ % a bad number or a complex number.
+
+ %--- State (updating).
+ Kt_tdata = (Ft*PHtran_tdata+G(:,:,tdata))/Dtdata;
+ Kt_tdatatran = Kt_tdata';
+ zt_tm1(:,t) = b(:,t) + Ft*zt_tm1(:,tdata) + Kt_tdata*etdata;
+ Pt_tm1(:,:,t) = Ft*Ptdata*Fttran - Kt_tdata*Dtdata*Kt_tdatatran + V(:,:,t);
+
+ %--- Forming the log likelihood.
+ detDtdata = det(Dtdata);
+ %if (~isfinite(detDtdata))
+ if (detDtdata < realmin)
+ indx_badlh = 1;
+ break;
+ else
+ loglh_tdata = -(0.5*n_y)*log(2*pi) - 0.5*log(detDtdata) - 0.5*(etdatatran/Dtdata)*etdata;
+ loglh = loglh + loglh_tdata;
+ loglh_t_allvalues(tdata) = loglh_tdata;
+ end
+end
+
+if (indx_badlh)
+ nearinfinity = -1.0e+300;
+ loglh = nearinfinity;
+ loglh_t_allvalues(tdata) = nearinfinity;
+end
+
diff --git a/MatlabFiles/fn_kalfil_tv2.m b/MatlabFiles/fn_kalfil_tv2.m
index 31003da7e073e811c325794fb0f20de406901731..2accf365a4886d496d9582d3115b9d50cb86c478 100644
--- a/MatlabFiles/fn_kalfil_tv2.m
+++ b/MatlabFiles/fn_kalfil_tv2.m
@@ -1,163 +1,163 @@
-function [loglh,zt_tm1,Pt_tm1, loglh_t_allvalues] = fn_kalfil_tv(Y_T, a, H, R, G, b, F, V, indxIni, indxDiffuse, z0,P0)
-%[loglh,zt_tm1,Pt_tm1, loglh_t_allvalues] = fn_kalfil_tv(Y_T, a, H, R, G, b, F, V, indxIni, indxDiffuse, z0,P0)
-%Time-varying Kalman filter (conditional on all the regimes in a Markov-switching model). It computes
-% a sequence of one-step predictions and their covariance matrices, and the log likelihood.
-% The function uses a forward recursion algorithm.
-%
-% Note: for b, F, V, zt_tm1, and Pt_tm1, there is not need for the T+1 length because the values at T+1 have
-% never been used in the likelihood function. See tz_kalfiltv() in kalman.c for an illustration.
-%
-% State space model is defined as follows:
-% y(t) = a(t) + H(t)*z(t) + eps(t) (observation or measurement equation)
-% z(t) = b(t) + F(t)*z(t) + eta(t) (state or transition equation)
-% where a(t), H(t), b(t), and F(t) depend on s_t that follows a Markov-chain process and are taken as given.
-%
-% Inputs are as follows:
-% Y_T is a n_y-by-T matrix containing data [y(1), ... , y(T)].
-% a is an n_y-by-T matrix of time-varying input vectors in the measurement equation.
-% H is an n_y-by-n_z-by-T 3-D of time-varying matrices in the measurement equation.
-% R is an n_y-by-n_y-by-T 3-D of time-varying covariance matrices for the error in the measurement equation.
-% G is an n_z-by-n_y-by-T 3-D of time-varying E(eta_t * eps_t').
-% ------
-% b is an n_z-by-(T+1) matrix of time-varying input vectors in the state equation with b(:,1) as an initial condition.
-% F is an n_z-by-n_z-by-(T+1) 3-D of time-varying transition matrices in the state equation with F(:,:,1) as an initial condition.
-% V is an n_z-by-n_z-by-(T+1) 3-D of time-varying covariance matrices for the error in the state equation with V(:,:,1) as an initial condition.
-% ------
-% indxIni: 1: using the initial condition with zt_tm1(:,1)=z0 and Pt_tm1(:,:,1)=P0
-% (thus, indxDiffuse is automatically not enativated).;
-% 0: creating the initial condition depending on indxDiffuse (thus, indxDiffuse is active).
-% indxDiffuse: 1: using the diffuse initial condition by setting P=100*I and z=0;
-% 0: using the unconditional mean and variance for any given regime at time 0.
-% z0 is an n_z-by-1 vector of initial condition when indxIni=1. (Do not enter if indxIni=0.)
-% P0 is an n_z-by-n_z matrix of initial condition when indxIni=1. (Do not enter if indxIni=0.)
-%
-% Outputs are as follows:
-% loglh is a value of the log likelihood function of the state-space model
-% under the assumption that errors are multivariate Gaussian.
-% zt_tm1 is an n_z-by-(T+1) matrices of one-step predicted state vectors with z0_0m1 as a initial condition.
-% Pt_tm1 is an n_z-by-n_z-by-(T+1) 3-D of covariance matrices of zt_tm1 with P0_0m1 as a initial condition.
-% loglh_t_allvalues is a T-by-1 vector of loglh at time t for t=1:T.
-%
-% The initial state vector and its covariance matrix are computed under the bounded (stationary) condition:
-% z0_0m1 = (I-F(:,:,1))\b(:,1)
-% vec(P0_0m1) = (I-kron(F(:,:,1),F(:,:,1)))\vec(V(:,:,1))
-% Note that all eigenvalues of the matrix F(:,:,1) are inside the unit circle when the state-space model is bounded (stationary).
-%
-% March 2007, written by Tao Zha
-% See Hamilton's book ([13.2.13] -- [13.2.22]), Harvey (pp.100-106), and LiuWZ Model I NOTES pp.001-003.
-%==========================================================================
-% Revision history:
-%
-%
-%
-%==========================================================================
-
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-Tp1 = size(Y_T,2) + 1; %T+1;
-n_y = size(a,1);
-n_z = size(b,1);
-%--- Checking input matrix dimensions
-if (size(Y_T,1)~=n_y)
- error('kalf_tv(): Y_T and a must have the same number of rows')
-end
-%--- Allocating memory.
-zt_tm1 = zeros(n_z,Tp1);
-Pt_tm1 = zeros(n_z,n_z,Tp1);
-loglh_t_allvalues = zeros(Tp1-1,1);
-%--- Initializing.
-loglh = 0.0;
-if (indxIni)
- zt_tm1(:,1) = z0;
- Pt_tm1(:,:,1) = P0;
-else
- if (indxDiffuse)
- %See Koopman and Durbin, "Filtering and Smoothing of State Vector for Diffuse State-Space Models," J. of Time Series Analysis, Vol 24(1), pp.85-99.
- zt_tm1(:,1) = zeros(n_z, 1);
- Pt_tm1(:,:,1) = 100*eye(n_z);
- else
- eigmax4F = max(abs(eig(F(:,:,1))));
- format long e
- eigmax4F
- if (eigmax4F < 1.0)
- zt_tm1(:,1) = (eye(n_z)-F(:,:,1))\b(:,1);
- V1 = V(:,:,1);
- Pt_tm1(:,:,1) = reshape((eye(n_z^2)-kron(F(:,:,1),F(:,:,1)))\V1(:) ,n_z,n_z);
- else
- %--- Do NOT use the following option. It turns out that this will often generate explosive conditional liklihood
- %--- at the end of the sample, because Pt_tm1 shrinks to zero overtime due to the sigularity of the initila condition P_{1|0}.
- % zt_tm1(:,1) = zeros(size(zt_tm1(:,1)));
- % Pt_tm1(:,:,1) = V(:,:,1); %+eye(size(V(:,:,1)));
-
- nearinfinity = -1.0e+300
- loglh = nearinfinity;
- return; %Eearly exit.
- %error('kalf_tv(): For non-stationary solutions, the initial conditions must be supplied by, say, input arguments')
- end
- end
-end
-
-
-%====== See p.002 in LiuWZ. ======
-indx_badlh = 0; %1: bad likelihood with, say, -infinity of the LH value.
-for t=2:Tp1
- tdata = t-1;
-
- %--- Setup.
- Htdata = H(:,:,tdata);
- Htdatatran = Htdata';
- ztdata = zt_tm1(:,tdata);
- Ptdata = Pt_tm1(:,:,tdata);
- PHtran_tdata = Ptdata*Htdatatran;
- Ft = F(:,:,t);
- Fttran = Ft';
-
- %--- Data.
- etdata = Y_T(:,tdata) - a(:,tdata) - Htdata*ztdata;
- etdatatran = etdata';
- Dtdata = Htdata*PHtran_tdata + R(:,:,tdata);
- Dtdata = 0.5*(Dtdata + Dtdata'); %Making it symmetric against some rounding errors.
- %This making-symmetric is very IMPORTANT; otherwise, we will get the matrix being singular message
- % and eigenvalues being negative for the SPD matrix, etc. Then the likelihood becomes either
- % a bad number or a complex number.
-
- %--- State (updating).
- Kt_tdata = (Ft*PHtran_tdata+G(:,:,tdata))/Dtdata;
- Kt_tdatatran = Kt_tdata';
- zt_tm1(:,t) = b(:,t) + Ft*zt_tm1(:,tdata) + Kt_tdata*etdata;
- Pt_tm1(:,:,t) = Ft*Ptdata*Fttran - Kt_tdata*Dtdata*Kt_tdatatran + V(:,:,t);
-
- %--- Forming the log likelihood.
- detDtdata = det(Dtdata);
- %if (~isfinite(detDtdata))
- if (detDtdata < realmin)
- indx_badlh = 1;
- break;
- else
- loglh_tdata = -(0.5*n_y)*log(2*pi) - 0.5*log(detDtdata) - 0.5*(etdatatran/Dtdata)*etdata;
- loglh = loglh + loglh_tdata;
- loglh_t_allvalues(tdata) = loglh_tdata;
- end
-end
-
-if (indx_badlh)
- nearinfinity = -1.0e+300;
- loglh = nearinfinity;
- loglh_t_allvalues(tdata) = nearinfinity;
-end
-
+function [loglh,zt_tm1,Pt_tm1, loglh_t_allvalues] = fn_kalfil_tv(Y_T, a, H, R, G, b, F, V, indxIni, indxDiffuse, z0,P0)
+%[loglh,zt_tm1,Pt_tm1, loglh_t_allvalues] = fn_kalfil_tv(Y_T, a, H, R, G, b, F, V, indxIni, indxDiffuse, z0,P0)
+%Time-varying Kalman filter (conditional on all the regimes in a Markov-switching model). It computes
+% a sequence of one-step predictions and their covariance matrices, and the log likelihood.
+% The function uses a forward recursion algorithm.
+%
+% Note: for b, F, V, zt_tm1, and Pt_tm1, there is not need for the T+1 length because the values at T+1 have
+% never been used in the likelihood function. See tz_kalfiltv() in kalman.c for an illustration.
+%
+% State space model is defined as follows:
+% y(t) = a(t) + H(t)*z(t) + eps(t) (observation or measurement equation)
+% z(t) = b(t) + F(t)*z(t) + eta(t) (state or transition equation)
+% where a(t), H(t), b(t), and F(t) depend on s_t that follows a Markov-chain process and are taken as given.
+%
+% Inputs are as follows:
+% Y_T is a n_y-by-T matrix containing data [y(1), ... , y(T)].
+% a is an n_y-by-T matrix of time-varying input vectors in the measurement equation.
+% H is an n_y-by-n_z-by-T 3-D of time-varying matrices in the measurement equation.
+% R is an n_y-by-n_y-by-T 3-D of time-varying covariance matrices for the error in the measurement equation.
+% G is an n_z-by-n_y-by-T 3-D of time-varying E(eta_t * eps_t').
+% ------
+% b is an n_z-by-(T+1) matrix of time-varying input vectors in the state equation with b(:,1) as an initial condition.
+% F is an n_z-by-n_z-by-(T+1) 3-D of time-varying transition matrices in the state equation with F(:,:,1) as an initial condition.
+% V is an n_z-by-n_z-by-(T+1) 3-D of time-varying covariance matrices for the error in the state equation with V(:,:,1) as an initial condition.
+% ------
+% indxIni: 1: using the initial condition with zt_tm1(:,1)=z0 and Pt_tm1(:,:,1)=P0
+% (thus, indxDiffuse is automatically not enativated).;
+% 0: creating the initial condition depending on indxDiffuse (thus, indxDiffuse is active).
+% indxDiffuse: 1: using the diffuse initial condition by setting P=100*I and z=0;
+% 0: using the unconditional mean and variance for any given regime at time 0.
+% z0 is an n_z-by-1 vector of initial condition when indxIni=1. (Do not enter if indxIni=0.)
+% P0 is an n_z-by-n_z matrix of initial condition when indxIni=1. (Do not enter if indxIni=0.)
+%
+% Outputs are as follows:
+% loglh is a value of the log likelihood function of the state-space model
+% under the assumption that errors are multivariate Gaussian.
+% zt_tm1 is an n_z-by-(T+1) matrices of one-step predicted state vectors with z0_0m1 as a initial condition.
+% Pt_tm1 is an n_z-by-n_z-by-(T+1) 3-D of covariance matrices of zt_tm1 with P0_0m1 as a initial condition.
+% loglh_t_allvalues is a T-by-1 vector of loglh at time t for t=1:T.
+%
+% The initial state vector and its covariance matrix are computed under the bounded (stationary) condition:
+% z0_0m1 = (I-F(:,:,1))\b(:,1)
+% vec(P0_0m1) = (I-kron(F(:,:,1),F(:,:,1)))\vec(V(:,:,1))
+% Note that all eigenvalues of the matrix F(:,:,1) are inside the unit circle when the state-space model is bounded (stationary).
+%
+% March 2007, written by Tao Zha
+% See Hamilton's book ([13.2.13] -- [13.2.22]), Harvey (pp.100-106), and LiuWZ Model I NOTES pp.001-003.
+%==========================================================================
+% Revision history:
+%
+%
+%
+%==========================================================================
+
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+Tp1 = size(Y_T,2) + 1; %T+1;
+n_y = size(a,1);
+n_z = size(b,1);
+%--- Checking input matrix dimensions
+if (size(Y_T,1)~=n_y)
+ error('kalf_tv(): Y_T and a must have the same number of rows')
+end
+%--- Allocating memory.
+zt_tm1 = zeros(n_z,Tp1);
+Pt_tm1 = zeros(n_z,n_z,Tp1);
+loglh_t_allvalues = zeros(Tp1-1,1);
+%--- Initializing.
+loglh = 0.0;
+if (indxIni)
+ zt_tm1(:,1) = z0;
+ Pt_tm1(:,:,1) = P0;
+else
+ if (indxDiffuse)
+ %See Koopman and Durbin, "Filtering and Smoothing of State Vector for Diffuse State-Space Models," J. of Time Series Analysis, Vol 24(1), pp.85-99.
+ zt_tm1(:,1) = zeros(n_z, 1);
+ Pt_tm1(:,:,1) = 100*eye(n_z);
+ else
+ eigmax4F = max(abs(eig(F(:,:,1))));
+ format long e
+ eigmax4F
+ if (eigmax4F < 1.0)
+ zt_tm1(:,1) = (eye(n_z)-F(:,:,1))\b(:,1);
+ V1 = V(:,:,1);
+ Pt_tm1(:,:,1) = reshape((eye(n_z^2)-kron(F(:,:,1),F(:,:,1)))\V1(:) ,n_z,n_z);
+ else
+ %--- Do NOT use the following option. It turns out that this will often generate explosive conditional liklihood
+ %--- at the end of the sample, because Pt_tm1 shrinks to zero overtime due to the sigularity of the initila condition P_{1|0}.
+ % zt_tm1(:,1) = zeros(size(zt_tm1(:,1)));
+ % Pt_tm1(:,:,1) = V(:,:,1); %+eye(size(V(:,:,1)));
+
+ nearinfinity = -1.0e+300
+ loglh = nearinfinity;
+ return; %Eearly exit.
+ %error('kalf_tv(): For non-stationary solutions, the initial conditions must be supplied by, say, input arguments')
+ end
+ end
+end
+
+
+%====== See p.002 in LiuWZ. ======
+indx_badlh = 0; %1: bad likelihood with, say, -infinity of the LH value.
+for t=2:Tp1
+ tdata = t-1;
+
+ %--- Setup.
+ Htdata = H(:,:,tdata);
+ Htdatatran = Htdata';
+ ztdata = zt_tm1(:,tdata);
+ Ptdata = Pt_tm1(:,:,tdata);
+ PHtran_tdata = Ptdata*Htdatatran;
+ Ft = F(:,:,t);
+ Fttran = Ft';
+
+ %--- Data.
+ etdata = Y_T(:,tdata) - a(:,tdata) - Htdata*ztdata;
+ etdatatran = etdata';
+ Dtdata = Htdata*PHtran_tdata + R(:,:,tdata);
+ Dtdata = 0.5*(Dtdata + Dtdata'); %Making it symmetric against some rounding errors.
+ %This making-symmetric is very IMPORTANT; otherwise, we will get the matrix being singular message
+ % and eigenvalues being negative for the SPD matrix, etc. Then the likelihood becomes either
+ % a bad number or a complex number.
+
+ %--- State (updating).
+ Kt_tdata = (Ft*PHtran_tdata+G(:,:,tdata))/Dtdata;
+ Kt_tdatatran = Kt_tdata';
+ zt_tm1(:,t) = b(:,t) + Ft*zt_tm1(:,tdata) + Kt_tdata*etdata;
+ Pt_tm1(:,:,t) = Ft*Ptdata*Fttran - Kt_tdata*Dtdata*Kt_tdatatran + V(:,:,t);
+
+ %--- Forming the log likelihood.
+ detDtdata = det(Dtdata);
+ %if (~isfinite(detDtdata))
+ if (detDtdata < realmin)
+ indx_badlh = 1;
+ break;
+ else
+ loglh_tdata = -(0.5*n_y)*log(2*pi) - 0.5*log(detDtdata) - 0.5*(etdatatran/Dtdata)*etdata;
+ loglh = loglh + loglh_tdata;
+ loglh_t_allvalues(tdata) = loglh_tdata;
+ end
+end
+
+if (indx_badlh)
+ nearinfinity = -1.0e+300;
+ loglh = nearinfinity;
+ loglh_t_allvalues(tdata) = nearinfinity;
+end
+
diff --git a/MatlabFiles/fn_logsum.m b/MatlabFiles/fn_logsum.m
index a891cf85147bc1b0124a4106fb52ecc200f9ae45..436905fdf502bcb5e3016d715756bf6effb767d8 100644
--- a/MatlabFiles/fn_logsum.m
+++ b/MatlabFiles/fn_logsum.m
@@ -1,38 +1,38 @@
-function [log_sum, log_max] = fn_logsum(log_sum, log_max, log_new)
-% [log_sum, log_max] = fn_logsum(log_sum, log_max, log_new)
-%
-%Outputs:
-% log_sum: updated log(sum of x_1, ..., x_{N+1})
-% log_max: updated max of log(x_1), ..., log(x_{N+1}).
-%--------------
-%Inputs:
-% log_sum: log(sum of x_1, ..., x_N)
-% log_max: max of log(x_1), ..., log(x_N).
-% log_new: log(x_{N+1}).
-%
-%Written by T. Zha; 12:20PM 06/28/2005
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-%=== Updates log_sumpdf with an additional pdf value. See TVBVAR Notes p.81a.
-if (log_max>=log_new)
- log_sum = log( exp(log_sum-log_max) + exp(log_new-log_max) ) + log_max;
-else
- log_sum = log( exp(log_sum-log_new) + 1.0 ) + log_new;
- log_max = log_new;
-end
+function [log_sum, log_max] = fn_logsum(log_sum, log_max, log_new)
+% [log_sum, log_max] = fn_logsum(log_sum, log_max, log_new)
+%
+%Outputs:
+% log_sum: updated log(sum of x_1, ..., x_{N+1})
+% log_max: updated max of log(x_1), ..., log(x_{N+1}).
+%--------------
+%Inputs:
+% log_sum: log(sum of x_1, ..., x_N)
+% log_max: max of log(x_1), ..., log(x_N).
+% log_new: log(x_{N+1}).
+%
+%Written by T. Zha; 12:20PM 06/28/2005
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+%=== Updates log_sumpdf with an additional pdf value. See TVBVAR Notes p.81a.
+if (log_max>=log_new)
+ log_sum = log( exp(log_sum-log_max) + exp(log_new-log_max) ) + log_max;
+else
+ log_sum = log( exp(log_sum-log_new) + 1.0 ) + log_new;
+ log_max = log_new;
+end
diff --git a/MatlabFiles/fn_mimfgraph.m b/MatlabFiles/fn_mimfgraph.m
index f21aecd167a6f5b655e4a74b5a7f1b2c3f61932a..2dc83dd0ed01efce8ecf606a1392235030347296 100644
--- a/MatlabFiles/fn_mimfgraph.m
+++ b/MatlabFiles/fn_mimfgraph.m
@@ -1,152 +1,152 @@
-function scaleout = fn_mimfgraph(imfe,nvar,q_m,imstp,xlab,ylab,tlab,xTick)
-% mimfgraph: multiple impulse functions plotted in subplots put in one chart.
-%
-% imfe: imstp-by-n^2+2-by-h series data with dates in the first 2 columns in the order of year and month (or quarter).
-% imstp: the number of impulse response steps.
-% nvar^2+2: n series plus the first 2 columns indicating dates. For the last nvar^2 columns,
-% the order is: nvar responses to the 1st shock, ..., nvar responses to the last shock.
-% h: The number of sereies on the 3rd dimension, put in the same subplot as in each of the n series.
-% The first 2 columns in the 3rd dimension can be NaN while some other columns
-% are also allowed to be NaN if no data are available.
-% The last 2 columns in the 3rd dimension must be used for error bands if "area"
-% is used to plot these bands.
-% nvar: number of variables
-% q_m: monthly (12) or quarterly (4)
-% imstp: number of steps of impulse responses
-% xlab,ylab,tlab: x-axis, y-axis, and title labels
-% xTick: optional. Eg: [12 24 36].
-%---------------
-% scaleout: column 1 represents maximums; column 2 minimums. Rows: nvar variables.
-%
-% See ftd_mseriesgraph.m and fn_mseriesgraph.m
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-if nargin < 7, xTick = []; end
-
-if nvar^2~=size(imfe,2)-2
- disp(' ')
- warning('The number of columns in imfe must be nvar^2+2 (columns of dates)')
- disp('Press ctrl-c to abort')
- pause
-end
-
-
-
-t = 1:imstp;
-temp1=zeros(nvar,1);
-temp2=zeros(nvar,1);
-minval=zeros(nvar,1); % for each variable to nvar shocks
-maxval=zeros(nvar,1); % for each variable to nvar shocks
-for i = 1:nvar % variable
- for j = 1:nvar % shock
- tmpimf = squeeze(imfe(:,2+(j-1)*nvar+i,:));
- temp1(j) = min(min(tmpimf));
- temp2(j) = max(max(tmpimf));
- end
- minval(i)=min(temp1);
- maxval(i)=max(temp2);
-end
-
-scaleout = [minval(:) maxval(:)];
-
-%--------------
-% Column j: Shocks 1 to N; Row i: Variable responses to
-%-------------
-
-rowlabel = 1;
-for i = 1:nvar % variable
- columnlabel = 1;
-
- if minval(i)<0
- if maxval(i)<=0
- yt=[minval(i) 0];
- else
- yt=[minval(i) 0 maxval(i)];
- end
- else % (minval(i) >=0)
- if maxval(i) > 0
- yt=[0 maxval(i)];
- else % (identically zero responses)
- yt=[-1 0 1];
- end
- end
-
- scale=[1 imstp minval(i) maxval(i)];
- for j = 1:nvar % shock
- k1=(i-1)*nvar+j;
- k2=(j-1)*nvar+i;
- subplot(nvar,nvar,k1)
- if 0 % Plot area for error bands at the last two 3-rd dimensions.
- area(imfe(:,1)+imfe(:,2)/q_m,squeeze(imfe(:,2+k2,end)),-100,'EdgeColor','none','FaceColor','y') % yellow
- hold on
- area(imfe(:,1)+imfe(:,2)/q_m,squeeze(imfe(:,2+k2,end-1)),-100,'EdgeColor','none','FaceColor',[1 1 1]) % white
- set(gca,'ColorOrder',[0 0 0]); % turn the color off and set it to black
- set(gca,'LineStyleOrder', '-|-.|--|:'); % cycle through the newly defined LineSytleOrder
- plot(t,squeeze(imfe(:,2+k2,1:end-2)),t,zeros(length(imfe(:,k2)),1),'-');
- set(gca,'Layer','top')
- hold off
- else % No error bands plotted
- %=== set color to black and cycle through the newly defined LineSytleOrder
- set(0,'DefaultAxesColorOrder',[0 0 0], ...
- 'DefaultAxesLineStyleOrder','-|-.|--|:')
- %set(gca,'ColorOrder',[0 0 0]); % turn the color off and set it to black
- %set(gca,'LineStyleOrder', '-|-.|--|:'); % cycle through the newly defined LineSytleOrder
- plot(t,squeeze(imfe(:,2+k2,1:end)),t,zeros(length(imfe(:,k2)),1),'-');
- end
- if 1 % Get legends
- if (i==2) & (j==3) % | (i==6)
- legend('Const','S1','S2','S3',0)
- % legend('Actual Data','Absent policy shocks',0)
- end
- end
-
- set(gca,'XTick',xTick)
- set(gca,'YTick',yt)
- grid
-
- axis(scale); % put limits on both axes.
- %set(gca,'YLim',[minval(i) maxval(i)]) % put the limit only on the y-axis
- if isempty(xTick) %1 % no numbers on axes
- set(gca,'XTickLabel',' ');
- set(gca,'YTickLabel',' ');
- else % put numbers on both axes
- if i<nvar
- set(gca,'XTickLabelMode','manual','XTickLabel',[])
- end
- if j>1
- set(gca,'YTickLabel',' ');
- end
- end
-
- if rowlabel == 1
- %title(['x' num2str(j)])
- %title(eval(['x' num2str(j)]))
- title(char(xlab(j)))
- end
- if columnlabel == 1
- %ylabel(['x' num2str(i)])
- %ylabel(eval(['x' num2str(i)]))
- ylabel(char(ylab(i)))
- end
- columnlabel = 0;
- end
- rowlabel = 0;
-end
-
-subtitle(tlab)
+function scaleout = fn_mimfgraph(imfe,nvar,q_m,imstp,xlab,ylab,tlab,xTick)
+% mimfgraph: multiple impulse functions plotted in subplots put in one chart.
+%
+% imfe: imstp-by-n^2+2-by-h series data with dates in the first 2 columns in the order of year and month (or quarter).
+% imstp: the number of impulse response steps.
+% nvar^2+2: n series plus the first 2 columns indicating dates. For the last nvar^2 columns,
+% the order is: nvar responses to the 1st shock, ..., nvar responses to the last shock.
+% h: The number of sereies on the 3rd dimension, put in the same subplot as in each of the n series.
+% The first 2 columns in the 3rd dimension can be NaN while some other columns
+% are also allowed to be NaN if no data are available.
+% The last 2 columns in the 3rd dimension must be used for error bands if "area"
+% is used to plot these bands.
+% nvar: number of variables
+% q_m: monthly (12) or quarterly (4)
+% imstp: number of steps of impulse responses
+% xlab,ylab,tlab: x-axis, y-axis, and title labels
+% xTick: optional. Eg: [12 24 36].
+%---------------
+% scaleout: column 1 represents maximums; column 2 minimums. Rows: nvar variables.
+%
+% See ftd_mseriesgraph.m and fn_mseriesgraph.m
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+if nargin < 7, xTick = []; end
+
+if nvar^2~=size(imfe,2)-2
+ disp(' ')
+ warning('The number of columns in imfe must be nvar^2+2 (columns of dates)')
+ disp('Press ctrl-c to abort')
+ pause
+end
+
+
+
+t = 1:imstp;
+temp1=zeros(nvar,1);
+temp2=zeros(nvar,1);
+minval=zeros(nvar,1); % for each variable to nvar shocks
+maxval=zeros(nvar,1); % for each variable to nvar shocks
+for i = 1:nvar % variable
+ for j = 1:nvar % shock
+ tmpimf = squeeze(imfe(:,2+(j-1)*nvar+i,:));
+ temp1(j) = min(min(tmpimf));
+ temp2(j) = max(max(tmpimf));
+ end
+ minval(i)=min(temp1);
+ maxval(i)=max(temp2);
+end
+
+scaleout = [minval(:) maxval(:)];
+
+%--------------
+% Column j: Shocks 1 to N; Row i: Variable responses to
+%-------------
+
+rowlabel = 1;
+for i = 1:nvar % variable
+ columnlabel = 1;
+
+ if minval(i)<0
+ if maxval(i)<=0
+ yt=[minval(i) 0];
+ else
+ yt=[minval(i) 0 maxval(i)];
+ end
+ else % (minval(i) >=0)
+ if maxval(i) > 0
+ yt=[0 maxval(i)];
+ else % (identically zero responses)
+ yt=[-1 0 1];
+ end
+ end
+
+ scale=[1 imstp minval(i) maxval(i)];
+ for j = 1:nvar % shock
+ k1=(i-1)*nvar+j;
+ k2=(j-1)*nvar+i;
+ subplot(nvar,nvar,k1)
+ if 0 % Plot area for error bands at the last two 3-rd dimensions.
+ area(imfe(:,1)+imfe(:,2)/q_m,squeeze(imfe(:,2+k2,end)),-100,'EdgeColor','none','FaceColor','y') % yellow
+ hold on
+ area(imfe(:,1)+imfe(:,2)/q_m,squeeze(imfe(:,2+k2,end-1)),-100,'EdgeColor','none','FaceColor',[1 1 1]) % white
+ set(gca,'ColorOrder',[0 0 0]); % turn the color off and set it to black
+ set(gca,'LineStyleOrder', '-|-.|--|:'); % cycle through the newly defined LineSytleOrder
+ plot(t,squeeze(imfe(:,2+k2,1:end-2)),t,zeros(length(imfe(:,k2)),1),'-');
+ set(gca,'Layer','top')
+ hold off
+ else % No error bands plotted
+ %=== set color to black and cycle through the newly defined LineSytleOrder
+ set(0,'DefaultAxesColorOrder',[0 0 0], ...
+ 'DefaultAxesLineStyleOrder','-|-.|--|:')
+ %set(gca,'ColorOrder',[0 0 0]); % turn the color off and set it to black
+ %set(gca,'LineStyleOrder', '-|-.|--|:'); % cycle through the newly defined LineSytleOrder
+ plot(t,squeeze(imfe(:,2+k2,1:end)),t,zeros(length(imfe(:,k2)),1),'-');
+ end
+ if 1 % Get legends
+ if (i==2) & (j==3) % | (i==6)
+ legend('Const','S1','S2','S3',0)
+ % legend('Actual Data','Absent policy shocks',0)
+ end
+ end
+
+ set(gca,'XTick',xTick)
+ set(gca,'YTick',yt)
+ grid
+
+ axis(scale); % put limits on both axes.
+ %set(gca,'YLim',[minval(i) maxval(i)]) % put the limit only on the y-axis
+ if isempty(xTick) %1 % no numbers on axes
+ set(gca,'XTickLabel',' ');
+ set(gca,'YTickLabel',' ');
+ else % put numbers on both axes
+ if i<nvar
+ set(gca,'XTickLabelMode','manual','XTickLabel',[])
+ end
+ if j>1
+ set(gca,'YTickLabel',' ');
+ end
+ end
+
+ if rowlabel == 1
+ %title(['x' num2str(j)])
+ %title(eval(['x' num2str(j)]))
+ title(char(xlab(j)))
+ end
+ if columnlabel == 1
+ %ylabel(['x' num2str(i)])
+ %ylabel(eval(['x' num2str(i)]))
+ ylabel(char(ylab(i)))
+ end
+ columnlabel = 0;
+ end
+ rowlabel = 0;
+end
+
+subtitle(tlab)
diff --git a/MatlabFiles/fn_mseriesgraph.m b/MatlabFiles/fn_mseriesgraph.m
index 78d3d5dcc774c5845cac74c6b59284f992852105..8c2c9f1cd03714e0e366a8103e142382007b43c4 100644
--- a/MatlabFiles/fn_mseriesgraph.m
+++ b/MatlabFiles/fn_mseriesgraph.m
@@ -1,83 +1,83 @@
-function fn_mseriesgraph(ydate,keyindx,rnum,cnum,q_m,xlab,ylab,tlab)
-% fn_mseriesgraph(ydate,keyindx,rnum,cnum,q_m,xlab,ylab,tlab)
-% Graph actual series or point forecasts or both (annual or monthly or quarterly)
-% with multiple series in one subplot.
-%
-% ydate: T-by-n+2-by-h series data with dates in the first 2 columns in the order of year and month (or quarter).
-% T: the length of an individual series.
-% n+2: n series plus the first 2 columns indicating dates.
-% h: the number of sereies on the 3rd dimension, put in the same subplot as in each of the n series.
-% Thus, the first 2 columns in the 3rd dimension can be NaN while
-% some elements are also allowed to be NaN if no data are available.
-% keyindx: index for the variables to be graphed
-% rnum: number of rows in subplot
-% cnum: number of columns in subplot
-% q_m: if 4 or 12, quarterly or monthly data
-% xlab: 1-by-1 x-axis label for what conditions imposed; e.g., conlab = 'All bar MS shocks inspl'
-% ylab: string array for the length(keyindx)-by-1 variables
-% tlab: 1-by-1 title label for (e.g., as of time of forecast)
-%-------------
-% No output argument for this graph file
-% See fn_foregraph.m, fn_forerrgraph.m.
-%
-% Tao Zha, September 2000
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-vyrs = ydate(:,1); % vector column
-hornum = cell(length(vyrs),1); % horizontal year (number)
-count=0;
-for k=vyrs'
- count=count+1;
- jnk=num2str(k);
- if length(jnk)==4 % years
- hornum{count}=jnk(3:4); % e.g., with '1990', we have '90'
- else
- hornum{count}=jnk; % e.g., with '1', '2', ...
- end
-end
-
-if rnum*cnum<length(keyindx)
- disp(' ')
- warning('The total number of subplots must be greater that the number of selected series for graphing!')
- disp('Press ctrl-c to abort')
- pause
-end
-
-count=0;
-for i = keyindx
- count = count+1;
- subplot(rnum,cnum,count)
- plot(ydate(:,1)+ydate(:,2)/q_m,squeeze(ydate(:,2+i,:)),...
- ydate(:,1)+ydate(:,2)/q_m,zeros(length(vyrs),1),'-')
-
- if (ydate(1,2)==0) & (length(num2str(ydate(1,1)))==4) % only for annual growth rates (not for, say, monthly annualized rates)
- set(gca,'XLim',[vyrs(1) vyrs(end)])
- set(gca,'XTick',vyrs)
- set(gca,'XTickLabel',char(hornum))
- end
-
- if i==keyindx(1)
- title(tlab)
- elseif i>=length(keyindx) %i>=length(keyindx)-1
- xlabel(xlab)
- end
- %
- grid
- ylabel(char(ylab(i)))
-end
+function fn_mseriesgraph(ydate,keyindx,rnum,cnum,q_m,xlab,ylab,tlab)
+% fn_mseriesgraph(ydate,keyindx,rnum,cnum,q_m,xlab,ylab,tlab)
+% Graph actual series or point forecasts or both (annual or monthly or quarterly)
+% with multiple series in one subplot.
+%
+% ydate: T-by-n+2-by-h series data with dates in the first 2 columns in the order of year and month (or quarter).
+% T: the length of an individual series.
+% n+2: n series plus the first 2 columns indicating dates.
+% h: the number of sereies on the 3rd dimension, put in the same subplot as in each of the n series.
+% Thus, the first 2 columns in the 3rd dimension can be NaN while
+% some elements are also allowed to be NaN if no data are available.
+% keyindx: index for the variables to be graphed
+% rnum: number of rows in subplot
+% cnum: number of columns in subplot
+% q_m: if 4 or 12, quarterly or monthly data
+% xlab: 1-by-1 x-axis label for what conditions imposed; e.g., conlab = 'All bar MS shocks inspl'
+% ylab: string array for the length(keyindx)-by-1 variables
+% tlab: 1-by-1 title label for (e.g., as of time of forecast)
+%-------------
+% No output argument for this graph file
+% See fn_foregraph.m, fn_forerrgraph.m.
+%
+% Tao Zha, September 2000
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+vyrs = ydate(:,1); % vector column
+hornum = cell(length(vyrs),1); % horizontal year (number)
+count=0;
+for k=vyrs'
+ count=count+1;
+ jnk=num2str(k);
+ if length(jnk)==4 % years
+ hornum{count}=jnk(3:4); % e.g., with '1990', we have '90'
+ else
+ hornum{count}=jnk; % e.g., with '1', '2', ...
+ end
+end
+
+if rnum*cnum<length(keyindx)
+ disp(' ')
+ warning('The total number of subplots must be greater that the number of selected series for graphing!')
+ disp('Press ctrl-c to abort')
+ pause
+end
+
+count=0;
+for i = keyindx
+ count = count+1;
+ subplot(rnum,cnum,count)
+ plot(ydate(:,1)+ydate(:,2)/q_m,squeeze(ydate(:,2+i,:)),...
+ ydate(:,1)+ydate(:,2)/q_m,zeros(length(vyrs),1),'-')
+
+ if (ydate(1,2)==0) & (length(num2str(ydate(1,1)))==4) % only for annual growth rates (not for, say, monthly annualized rates)
+ set(gca,'XLim',[vyrs(1) vyrs(end)])
+ set(gca,'XTick',vyrs)
+ set(gca,'XTickLabel',char(hornum))
+ end
+
+ if i==keyindx(1)
+ title(tlab)
+ elseif i>=length(keyindx) %i>=length(keyindx)-1
+ xlabel(xlab)
+ end
+ %
+ grid
+ ylabel(char(ylab(i)))
+end
diff --git a/MatlabFiles/fn_msv_sw.m b/MatlabFiles/fn_msv_sw.m
index 8d3756a5d9944dc357c9a94c67fe5044439e8a16..6cefecbfd6231e0d450e133ab529549da3c229f7 100644
--- a/MatlabFiles/fn_msv_sw.m
+++ b/MatlabFiles/fn_msv_sw.m
@@ -1,78 +1,78 @@
-function [G1cell_sw, err] = fn_msv_sw(G1cell_0, A11cell, A12cell, A21cell, A22cell, Hcell, P)
-%G1cell_sw = fn_msv_sw(G1cell_0, A11cell, A12cell, A21cell, A22cell, Hcell,P)
-%
-% Model: X_{t+1} = A11_{t+1} X_t + A12_{t+1} x_t + C_{t+1} episilon_{t+1}
-% E_t(H_{t+1} x_{t+1}) = A21_t X_t + A22_t x_t
-% Solution: x_t = G1_t X_t where G1_t is nx-by-nX.
-%
-%Output:
-% G1cell_sw: solution.
-% err: if the value > sqrt(eps), then not converged ==> no solution.
-%Inputs:
-% G1cell_0: initial guess (starting point) for the Svensson-William algorithm.
-% If G1cell_0 is empty, G1_0 will be randomly selected.
-% A11cell, A12cell, A21cell, A22cell, and Hcell are all ns-by-1 cells -- the model coefficients depending on regime.
-% In each cell, the dimensions must be
-% A11=zeros(nX,nX); A12=zeros(nX,nx);
-% A21=zeros(nx,nX); A22=zeros(nx,nx);
-% H=diag(ones(nx,1));
-% P: ns-by-ns transition matrix with each column summing up to 1.
-%
-% See ZhaNotes on FWZ MSV solution, p. AA.1. For the SW solution, see (7) on p. AA.1.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-%--- Convergence criteria
-tol = 1.0e-09;
-maxiter = 1e+04;
-
-%--- Dimensions.
-ns = length(A11cell); %number of states (regimes)
-nX = size(A11cell{1},1);
-nx = size(A22cell{1},1);
-
-%--- Initialization.
-if isempty(G1cell_0)
- G1cell_0 = cell(ns,1);
- for si=1:ns
- G1cell_0{si} = 1000.0*randn(nx,nX);
- end
-end
-
-%====== Iterative procedure. ======
-Gerr = zeros(ns,1);
-err = 1.0;
-iter = 1;
-while ( (iter<maxiter) && (err>tol) )
- for j=1:ns
- M1 = zeros(nx,nx);
- M2 = zeros(nx,nX);
- for k=1:ns
- HGk = Hcell{k}*G1cell_0{k};
- M1 = M1 + P(k,j)*HGk*A12cell{k};
- M2 = M2 + P(k,j)*HGk*A11cell{k};
- end % k loop
- Mden = A22cell{j} - M1;
- Mnum = M2 - A21cell{j};
- G1cell_sw{j} = Mden\Mnum;
- Gerr(j)=norm(G1cell_sw{j} - G1cell_0{j});
- G1cell_0{j} = G1cell_sw{j}; % Updated for the next iteration.
- end %j loop
- err=sum(Gerr);
- iter=iter+1;
-end % err loop
-
+function [G1cell_sw, err] = fn_msv_sw(G1cell_0, A11cell, A12cell, A21cell, A22cell, Hcell, P)
+%G1cell_sw = fn_msv_sw(G1cell_0, A11cell, A12cell, A21cell, A22cell, Hcell,P)
+%
+% Model: X_{t+1} = A11_{t+1} X_t + A12_{t+1} x_t + C_{t+1} episilon_{t+1}
+% E_t(H_{t+1} x_{t+1}) = A21_t X_t + A22_t x_t
+% Solution: x_t = G1_t X_t where G1_t is nx-by-nX.
+%
+%Output:
+% G1cell_sw: solution.
+% err: if the value > sqrt(eps), then not converged ==> no solution.
+%Inputs:
+% G1cell_0: initial guess (starting point) for the Svensson-William algorithm.
+% If G1cell_0 is empty, G1_0 will be randomly selected.
+% A11cell, A12cell, A21cell, A22cell, and Hcell are all ns-by-1 cells -- the model coefficients depending on regime.
+% In each cell, the dimensions must be
+% A11=zeros(nX,nX); A12=zeros(nX,nx);
+% A21=zeros(nx,nX); A22=zeros(nx,nx);
+% H=diag(ones(nx,1));
+% P: ns-by-ns transition matrix with each column summing up to 1.
+%
+% See ZhaNotes on FWZ MSV solution, p. AA.1. For the SW solution, see (7) on p. AA.1.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+%--- Convergence criteria
+tol = 1.0e-09;
+maxiter = 1e+04;
+
+%--- Dimensions.
+ns = length(A11cell); %number of states (regimes)
+nX = size(A11cell{1},1);
+nx = size(A22cell{1},1);
+
+%--- Initialization.
+if isempty(G1cell_0)
+ G1cell_0 = cell(ns,1);
+ for si=1:ns
+ G1cell_0{si} = 1000.0*randn(nx,nX);
+ end
+end
+
+%====== Iterative procedure. ======
+Gerr = zeros(ns,1);
+err = 1.0;
+iter = 1;
+while ( (iter<maxiter) && (err>tol) )
+ for j=1:ns
+ M1 = zeros(nx,nx);
+ M2 = zeros(nx,nX);
+ for k=1:ns
+ HGk = Hcell{k}*G1cell_0{k};
+ M1 = M1 + P(k,j)*HGk*A12cell{k};
+ M2 = M2 + P(k,j)*HGk*A11cell{k};
+ end % k loop
+ Mden = A22cell{j} - M1;
+ Mnum = M2 - A21cell{j};
+ G1cell_sw{j} = Mden\Mnum;
+ Gerr(j)=norm(G1cell_sw{j} - G1cell_0{j});
+ G1cell_0{j} = G1cell_sw{j}; % Updated for the next iteration.
+ end %j loop
+ err=sum(Gerr);
+ iter=iter+1;
+end % err loop
+
diff --git a/MatlabFiles/fn_mtpdf.m b/MatlabFiles/fn_mtpdf.m
index f4085fcb761dc24336c2e59007c41de220d10e58..4087aa9d8c16ded5afc030b7258e5d12def3fa60 100644
--- a/MatlabFiles/fn_mtpdf.m
+++ b/MatlabFiles/fn_mtpdf.m
@@ -1,54 +1,54 @@
-function y = fn_mtpdf(x,xm,C,v,covIx,constIx)
-% y = mtpdf(x,xm,C,v,covIx,constIx)
-% The pdf value for multivariate Student t distribution allowing many values (draws) stored in columns.
-%
-% x: p-by-draws matrix of values evaluated at where p=size(x,1) is # of variables
-% xm: p-by-draws matrix of the mean of x
-% C: p-by-p Choleski square root of PDS S, which is the covariance matrix in the normal case
-% so that S = C*C'. That is, C=chol(S)' (notice the transpose ' here).
-% v (>0): A scalar value for the degrees of freedom.
-% covIx: An index for a decomposition of the covariance matrix. If 1, C=chol(S)' (notice the transpose ');
-% if 0, C=chol(inv(S)) (note that there is no transpose here).
-% constIx: An index for the constant. 1: constant (normalized); 0: no constant (unnormalized)
-%----------
-% y: p-by-draws matrix of pdf's for multivariate Student t distribution with v degrees of freedom
-%
-% Christian P. Robert, "The Bayesian Choice," Springer-Verlag, New York, 1994,
-% p. 382.
-%
-% Tao Zha, December 1998; Revised, January 2002.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-[p,nx]=size(x);
-if covIx
- z = C\(x-xm);
-else
- z = C*(x-xm);
-end
-
-
-if constIx
- dSh=sum(log(diag(C))); % (detSigma)^(1/2)
- %* Use gammaln function to avoid overflows.
- term = exp(gammaln((v + p) / 2) - gammaln(v/2) - dSh);
- y = (term*(v*pi)^(-p/2)) * (1 + sum(z.*z,1)/v).^(-(v + p)/2);
- y = y';
-else
- y = (1 + sum(z.*z,1)/v).^(-(v + p)/2);
- y = y';
-end
+function y = fn_mtpdf(x,xm,C,v,covIx,constIx)
+% y = mtpdf(x,xm,C,v,covIx,constIx)
+% The pdf value for multivariate Student t distribution allowing many values (draws) stored in columns.
+%
+% x: p-by-draws matrix of values evaluated at where p=size(x,1) is # of variables
+% xm: p-by-draws matrix of the mean of x
+% C: p-by-p Choleski square root of PDS S, which is the covariance matrix in the normal case
+% so that S = C*C'. That is, C=chol(S)' (notice the transpose ' here).
+% v (>0): A scalar value for the degrees of freedom.
+% covIx: An index for a decomposition of the covariance matrix. If 1, C=chol(S)' (notice the transpose ');
+% if 0, C=chol(inv(S)) (note that there is no transpose here).
+% constIx: An index for the constant. 1: constant (normalized); 0: no constant (unnormalized)
+%----------
+% y: p-by-draws matrix of pdf's for multivariate Student t distribution with v degrees of freedom
+%
+% Christian P. Robert, "The Bayesian Choice," Springer-Verlag, New York, 1994,
+% p. 382.
+%
+% Tao Zha, December 1998; Revised, January 2002.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+[p,nx]=size(x);
+if covIx
+ z = C\(x-xm);
+else
+ z = C*(x-xm);
+end
+
+
+if constIx
+ dSh=sum(log(diag(C))); % (detSigma)^(1/2)
+ %* Use gammaln function to avoid overflows.
+ term = exp(gammaln((v + p) / 2) - gammaln(v/2) - dSh);
+ y = (term*(v*pi)^(-p/2)) * (1 + sum(z.*z,1)/v).^(-(v + p)/2);
+ y = y';
+else
+ y = (1 + sum(z.*z,1)/v).^(-(v + p)/2);
+ y = y';
+end
diff --git a/MatlabFiles/fn_multigraph1.m b/MatlabFiles/fn_multigraph1.m
index f8f96f56b9a2333af28d4b50e8a4f7d51a1786fe..a6435241401f0bea57379d1ddb7e0ba8b428c270 100644
--- a/MatlabFiles/fn_multigraph1.m
+++ b/MatlabFiles/fn_multigraph1.m
@@ -1,126 +1,126 @@
-function scaleout = fn_multigraph1(imf1,nrow,ncol,xlab,ylab,XTick,YTickIndx,scaleIndx)
-%Plot only one set of impulse responses in one graph. See fn_multigraph2.m.
-%imf1 -- each has 3 dimensions. Row: horizon; column: 'nrow' variables; 3rd dim: 'ncol' situations such as regimes 1 and 2.
-% imf: row: "nstp" time horizon (in the graphics), column: "nrow "variables (row in
-% the graphics), 3rd D: across "ncol" different situations (column in
-% the graphics)
-% nrow: # of rows for the graphics
-% ncol: # of columns for the graphics
-% YTickIndx: 1: enable YTick; 0: disable
-% scaleIndx: 1: enable scale along Y-axis; 0: disable
-%
-% See imrgraph, imcerrgraph, imrerrgraph
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-nstp = size(imf1,1);
-t = 1:nstp;
-nrow;
-ncol;
-
-temp1=zeros(ncol,1);
-temp2=zeros(ncol,1);
-maxval=zeros(nrow,1);
-minval=zeros(nrow,1);
-for i = 1:nrow
- for j = 1:ncol
- %jnk1=max(firsth(:,i,j));
- %jnk2=max(firstl(:,i,j));
- %jnk3=max(firsth1(:,i,j));
- %jnk4=max(imf2(:,i,j));
- jnk5=max(imf1(:,i,j));
-
- temp1(j)=max([jnk5]);
- %
- %jnk1=min(firstl(:,i,j));
- %jnk2=min(firsth(:,i,j));
- %jnk3=min(firstl1(:,i,j));
- %jnk4=min(imf2(:,i,j));
- jnk5=min(imf1(:,i,j));
-
- temp2(j)=min([jnk5]);
- end
- maxval(i)=max(temp1);
- minval(i)=min(temp2);
-end
-
-scaleout = [maxval(:) minval(:)];
-
-%--------------
-% Column j: situation 1 to N; Row i: variables (ie responses to)
-%-------------
-%figure
-rowlabel = 1;
-for i = 1:nrow % column: from top to bottom
- columnlabel = 1;
-
- if minval(i)<0
- if maxval(i)<=0
- yt=[minval(i) 0];
- else
- yt=[minval(i) 0 maxval(i)];
- end
- else % (minval(i) >=0)
- if maxval(i) > 0
- yt=[0 maxval(i)];
- else % (identically zero responses)
- yt=[-1 0 1];
- end
- end
-
-
- scale=[1 nstp minval(i) maxval(i)];
- for j = 1:ncol % row: from left to right
- k1=(i-1)*ncol+j;
- subplot(nrow,ncol,k1)
- plot(t,imf1(:,i,j)) %,t,imf2(:,i,j),'--')
- %t,[firstl(:,i,j) firsth(:,i,j)],':');
- grid;
- if scaleIndx
- axis(scale);
- end
- %set(gca,'YLim',[minval(i) maxval(i)])
- %
- set(gca,'XTick',XTick)
- if YTickIndx
- set(gca,'YTick',yt)
- end
-
- if i<nrow
- set(gca,'XTickLabelMode','manual','XTickLabel',[])
- end
- %set(gca,'XTickLabel',' ');
- if j>1
- set(gca,'YTickLabel',' ');
- end
- if rowlabel == 1
- %title(['x' num2str(j)])
- %title(eval(['x' num2str(j)]))
- title(char(xlab(j)))
- end
- if columnlabel == 1
- %ylabel(['x' num2str(i)])
- %ylabel(eval(['x' num2str(i)]))
- ylabel(char(ylab(i)))
- end
- if i==nrow
- xlabel('Quarters')
- end
- columnlabel = 0;
- end
- rowlabel = 0;
-end
+function scaleout = fn_multigraph1(imf1,nrow,ncol,xlab,ylab,XTick,YTickIndx,scaleIndx)
+%Plot only one set of impulse responses in one graph. See fn_multigraph2.m.
+%imf1 -- each has 3 dimensions. Row: horizon; column: 'nrow' variables; 3rd dim: 'ncol' situations such as regimes 1 and 2.
+% imf: row: "nstp" time horizon (in the graphics), column: "nrow "variables (row in
+% the graphics), 3rd D: across "ncol" different situations (column in
+% the graphics)
+% nrow: # of rows for the graphics
+% ncol: # of columns for the graphics
+% YTickIndx: 1: enable YTick; 0: disable
+% scaleIndx: 1: enable scale along Y-axis; 0: disable
+%
+% See imrgraph, imcerrgraph, imrerrgraph
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+nstp = size(imf1,1);
+t = 1:nstp;
+nrow;
+ncol;
+
+temp1=zeros(ncol,1);
+temp2=zeros(ncol,1);
+maxval=zeros(nrow,1);
+minval=zeros(nrow,1);
+for i = 1:nrow
+ for j = 1:ncol
+ %jnk1=max(firsth(:,i,j));
+ %jnk2=max(firstl(:,i,j));
+ %jnk3=max(firsth1(:,i,j));
+ %jnk4=max(imf2(:,i,j));
+ jnk5=max(imf1(:,i,j));
+
+ temp1(j)=max([jnk5]);
+ %
+ %jnk1=min(firstl(:,i,j));
+ %jnk2=min(firsth(:,i,j));
+ %jnk3=min(firstl1(:,i,j));
+ %jnk4=min(imf2(:,i,j));
+ jnk5=min(imf1(:,i,j));
+
+ temp2(j)=min([jnk5]);
+ end
+ maxval(i)=max(temp1);
+ minval(i)=min(temp2);
+end
+
+scaleout = [maxval(:) minval(:)];
+
+%--------------
+% Column j: situation 1 to N; Row i: variables (ie responses to)
+%-------------
+%figure
+rowlabel = 1;
+for i = 1:nrow % column: from top to bottom
+ columnlabel = 1;
+
+ if minval(i)<0
+ if maxval(i)<=0
+ yt=[minval(i) 0];
+ else
+ yt=[minval(i) 0 maxval(i)];
+ end
+ else % (minval(i) >=0)
+ if maxval(i) > 0
+ yt=[0 maxval(i)];
+ else % (identically zero responses)
+ yt=[-1 0 1];
+ end
+ end
+
+
+ scale=[1 nstp minval(i) maxval(i)];
+ for j = 1:ncol % row: from left to right
+ k1=(i-1)*ncol+j;
+ subplot(nrow,ncol,k1)
+ plot(t,imf1(:,i,j)) %,t,imf2(:,i,j),'--')
+ %t,[firstl(:,i,j) firsth(:,i,j)],':');
+ grid;
+ if scaleIndx
+ axis(scale);
+ end
+ %set(gca,'YLim',[minval(i) maxval(i)])
+ %
+ set(gca,'XTick',XTick)
+ if YTickIndx
+ set(gca,'YTick',yt)
+ end
+
+ if i<nrow
+ set(gca,'XTickLabelMode','manual','XTickLabel',[])
+ end
+ %set(gca,'XTickLabel',' ');
+ if j>1
+ set(gca,'YTickLabel',' ');
+ end
+ if rowlabel == 1
+ %title(['x' num2str(j)])
+ %title(eval(['x' num2str(j)]))
+ title(char(xlab(j)))
+ end
+ if columnlabel == 1
+ %ylabel(['x' num2str(i)])
+ %ylabel(eval(['x' num2str(i)]))
+ ylabel(char(ylab(i)))
+ end
+ if i==nrow
+ xlabel('Quarters')
+ end
+ columnlabel = 0;
+ end
+ rowlabel = 0;
+end
diff --git a/MatlabFiles/fn_multigraph1_ver2.m b/MatlabFiles/fn_multigraph1_ver2.m
index 46b422f5c156f5a67f5cd0d9c59da28eb8eebcd7..874f9307a7e10e334ca9f58391de6b8d7be01c47 100644
--- a/MatlabFiles/fn_multigraph1_ver2.m
+++ b/MatlabFiles/fn_multigraph1_ver2.m
@@ -1,139 +1,139 @@
-function scaleout = fn_multigraph1_ver2(imf1,xlab,ylab,tstring,XTick,YTickIndx,scaleIndx,nrowg,ncolg)
-%scaleout = fn_multigraph1_ver2(imf1,xlab,ylab,tstring,XTick,YTickIndx,scaleIndx,nrowg,ncolg)
-%
-% Version 2 for plotting only one set of impulse responses in one graph. See fn_multigraph2.m.
-% imf1 -- each has 3 dimensions. Row: horizon; column: 'nrow' variables; 3rd dim: 'ncol' situations such as shocks or regimes.
-% imf1: row: "nstp" time horizon (in the graphics), column: "nrow "variables (row in
-% the graphics), 3rd D: across "ncol" different situations such as shocks or regimes (column in the graphics)
-% xlab: x-axis labels on the top
-% ylab: y-axis labels on the left
-% tstring: string for time (e.g., month or quarter) -- x-axis labels at the bottom
-% YTickIndx: 1: enable YTick; 0: disable;
-% To get a better picture, it is sometimes better to set YtickIndx to zero.
-% scaleIndx: 1: enable scale along Y-axis; 0: disable
-% nrowg: number of rows in the graph
-% ncolg: number of columns in the graph
-%
-% See imrgraph, imcerrgraph, imrerrgraph
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-nstp = size(imf1,1);
-nrow = size(imf1,2);
-ncol = size(imf1,3);
-t = 1:nstp;
-
-if (nargin < 8)
- nrowg = nrow;
- ncolg = ncol;
-end
-if (nrowg*ncolg < nrow*ncol)
- error('fn_multigraph1_ver2.m: nrowg*ncolg must equal to or less than nrow*ncol')
-end
-
-temp1=zeros(ncol,1);
-temp2=zeros(ncol,1);
-maxval=zeros(nrow,1);
-minval=zeros(nrow,1);
-for i = 1:nrow
- for j = 1:ncol
- %jnk1=max(firsth(:,i,j));
- %jnk2=max(firstl(:,i,j));
- %jnk3=max(firsth1(:,i,j));
- %jnk4=max(imf2(:,i,j));
- jnk5=max(imf1(:,i,j));
-
- temp1(j)=max([jnk5]);
- %
- %jnk1=min(firstl(:,i,j));
- %jnk2=min(firsth(:,i,j));
- %jnk3=min(firstl1(:,i,j));
- %jnk4=min(imf2(:,i,j));
- jnk5=min(imf1(:,i,j));
-
- temp2(j)=min([jnk5]);
- end
- maxval(i)=max(temp1);
- minval(i)=min(temp2);
-end
-
-scaleout = [maxval(:) minval(:)];
-
-%--------------
-% Column j: situation 1 to N; Row i: variables (ie responses to)
-%-------------
-%figure
-rowlabel = 1;
-for i = 1:nrow % column: from top to bottom
- columnlabel = 1;
-
- if minval(i)<0
- if maxval(i)<=0
- yt=[minval(i) 0];
- else
- yt=[minval(i) 0 maxval(i)];
- end
- else % (minval(i) >=0)
- if maxval(i) > 0
- yt=[0 maxval(i)];
- else % (identically zero responses)
- yt=[-1 0 1];
- end
- end
-
-
- scale=[1 nstp minval(i) maxval(i)];
- for j = 1:ncol % row: from left to right
- k1=(i-1)*ncol+j;
- subplot(nrowg,ncolg,k1)
- plot(t,imf1(:,i,j)) %,t,imf2(:,i,j),'--')
- %t,[firstl(:,i,j) firsth(:,i,j)],':');
- grid;
- if scaleIndx
- axis(scale);
- end
- %set(gca,'YLim',[minval(i) maxval(i)])
- %
- set(gca,'XTick',XTick)
- if YTickIndx
- set(gca,'YTick',yt)
- end
-
- if i<nrow
- set(gca,'XTickLabelMode','manual','XTickLabel',[])
- end
- %set(gca,'XTickLabel',' ');
- if (scaleIndx) && (j>1)
- set(gca,'YTickLabel',' ');
- end
- if rowlabel == 1
- %title(['x' num2str(j)])
- %title(eval(['x' num2str(j)]))
- title(char(xlab(j)))
- end
- if columnlabel == 1
- %ylabel(['x' num2str(i)])
- %ylabel(eval(['x' num2str(i)]))
- ylabel(char(ylab(i)))
- end
- if i==nrow
- xlabel(tstring)
- end
- columnlabel = 0;
- end
- rowlabel = 0;
-end
+function scaleout = fn_multigraph1_ver2(imf1,xlab,ylab,tstring,XTick,YTickIndx,scaleIndx,nrowg,ncolg)
+%scaleout = fn_multigraph1_ver2(imf1,xlab,ylab,tstring,XTick,YTickIndx,scaleIndx,nrowg,ncolg)
+%
+% Version 2 for plotting only one set of impulse responses in one graph. See fn_multigraph2.m.
+% imf1 -- each has 3 dimensions. Row: horizon; column: 'nrow' variables; 3rd dim: 'ncol' situations such as shocks or regimes.
+% imf1: row: "nstp" time horizon (in the graphics), column: "nrow "variables (row in
+% the graphics), 3rd D: across "ncol" different situations such as shocks or regimes (column in the graphics)
+% xlab: x-axis labels on the top
+% ylab: y-axis labels on the left
+% tstring: string for time (e.g., month or quarter) -- x-axis labels at the bottom
+% YTickIndx: 1: enable YTick; 0: disable;
+% To get a better picture, it is sometimes better to set YtickIndx to zero.
+% scaleIndx: 1: enable scale along Y-axis; 0: disable
+% nrowg: number of rows in the graph
+% ncolg: number of columns in the graph
+%
+% See imrgraph, imcerrgraph, imrerrgraph
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+nstp = size(imf1,1);
+nrow = size(imf1,2);
+ncol = size(imf1,3);
+t = 1:nstp;
+
+if (nargin < 8)
+ nrowg = nrow;
+ ncolg = ncol;
+end
+if (nrowg*ncolg < nrow*ncol)
+ error('fn_multigraph1_ver2.m: nrowg*ncolg must equal to or less than nrow*ncol')
+end
+
+temp1=zeros(ncol,1);
+temp2=zeros(ncol,1);
+maxval=zeros(nrow,1);
+minval=zeros(nrow,1);
+for i = 1:nrow
+ for j = 1:ncol
+ %jnk1=max(firsth(:,i,j));
+ %jnk2=max(firstl(:,i,j));
+ %jnk3=max(firsth1(:,i,j));
+ %jnk4=max(imf2(:,i,j));
+ jnk5=max(imf1(:,i,j));
+
+ temp1(j)=max([jnk5]);
+ %
+ %jnk1=min(firstl(:,i,j));
+ %jnk2=min(firsth(:,i,j));
+ %jnk3=min(firstl1(:,i,j));
+ %jnk4=min(imf2(:,i,j));
+ jnk5=min(imf1(:,i,j));
+
+ temp2(j)=min([jnk5]);
+ end
+ maxval(i)=max(temp1);
+ minval(i)=min(temp2);
+end
+
+scaleout = [maxval(:) minval(:)];
+
+%--------------
+% Column j: situation 1 to N; Row i: variables (ie responses to)
+%-------------
+%figure
+rowlabel = 1;
+for i = 1:nrow % column: from top to bottom
+ columnlabel = 1;
+
+ if minval(i)<0
+ if maxval(i)<=0
+ yt=[minval(i) 0];
+ else
+ yt=[minval(i) 0 maxval(i)];
+ end
+ else % (minval(i) >=0)
+ if maxval(i) > 0
+ yt=[0 maxval(i)];
+ else % (identically zero responses)
+ yt=[-1 0 1];
+ end
+ end
+
+
+ scale=[1 nstp minval(i) maxval(i)];
+ for j = 1:ncol % row: from left to right
+ k1=(i-1)*ncol+j;
+ subplot(nrowg,ncolg,k1)
+ plot(t,imf1(:,i,j)) %,t,imf2(:,i,j),'--')
+ %t,[firstl(:,i,j) firsth(:,i,j)],':');
+ grid;
+ if scaleIndx
+ axis(scale);
+ end
+ %set(gca,'YLim',[minval(i) maxval(i)])
+ %
+ set(gca,'XTick',XTick)
+ if YTickIndx
+ set(gca,'YTick',yt)
+ end
+
+ if i<nrow
+ set(gca,'XTickLabelMode','manual','XTickLabel',[])
+ end
+ %set(gca,'XTickLabel',' ');
+ if (scaleIndx) && (j>1)
+ set(gca,'YTickLabel',' ');
+ end
+ if rowlabel == 1
+ %title(['x' num2str(j)])
+ %title(eval(['x' num2str(j)]))
+ title(char(xlab(j)))
+ end
+ if columnlabel == 1
+ %ylabel(['x' num2str(i)])
+ %ylabel(eval(['x' num2str(i)]))
+ ylabel(char(ylab(i)))
+ end
+ if i==nrow
+ xlabel(tstring)
+ end
+ columnlabel = 0;
+ end
+ rowlabel = 0;
+end
diff --git a/MatlabFiles/fn_multigraph1_ver2_all_labels.m b/MatlabFiles/fn_multigraph1_ver2_all_labels.m
index 4d359fcb1b73245efc98724ecb24d39deca9ade0..455e662d9021252dbf17825bce5bcbb92b1d37a9 100644
--- a/MatlabFiles/fn_multigraph1_ver2_all_labels.m
+++ b/MatlabFiles/fn_multigraph1_ver2_all_labels.m
@@ -1,144 +1,144 @@
-function scaleout = fn_multigraph1_ver2_all_labels(imf1,xlab,ylab,tstring,XTick,YTickIndx,scaleIndx,nrowg,ncolg)
-%scaleout = fn_multigraph1_ver2_all_labels(imf1,xlab,ylab,tstring,XTick,YTickIndx,scaleIndx,nrowg,ncolg)
-%
-% Version 2 for plotting only one set of impulse responses in one graph. See fn_multigraph1_ver2.
-% imf1 -- each has 3 dimensions. Row: horizon; column: 'nrow' variables; 3rd dim: 'ncol' situations such as shocks or regimes.
-% imf1: row: "nstp" time horizon (in the graphics), column: "nrow "variables (row in
-% the graphics), 3rd D: across "ncol" different situations such as shocks or regimes (column in the graphics)
-% xlab: x-axis labels on the top. Use [] if no label is needed.
-% ylab: y-axis labels on the left. Use [] if no label is needed.
-% tstring: string for time (e.g., month or quarter) -- x-axis labels at the bottom. Use [] if no label is needed.
-% YTickIndx: 1: enable YTick; 0: disable. Often set to zero to get a better picture.
-% scaleIndx: 1: enable scale along Y-axis; 0: disable
-% nrowg: number of rows in the graph
-% ncolg: number of columns in the graph
-%
-% See fn_multigraph1_ver2, imrgraph, imcerrgraph, imrerrgraph
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-nstp = size(imf1,1);
-nrow = size(imf1,2);
-ncol = size(imf1,3);
-t = 1:nstp;
-
-if (nargin < 8)
- nrowg = nrow;
- ncolg = ncol;
-end
-if (nrowg*ncolg < nrow*ncol)
- error('fn_multigraph1_ver2.m: nrowg*ncolg must equal to or greater than nrow*ncol')
-end
-
-temp1=zeros(ncol,1);
-temp2=zeros(ncol,1);
-maxval=zeros(nrow,1);
-minval=zeros(nrow,1);
-for i = 1:nrow
- for j = 1:ncol
- %jnk1=max(firsth(:,i,j));
- %jnk2=max(firstl(:,i,j));
- %jnk3=max(firsth1(:,i,j));
- %jnk4=max(imf2(:,i,j));
- jnk5=max(imf1(:,i,j));
-
- temp1(j)=max([jnk5]);
- %
- %jnk1=min(firstl(:,i,j));
- %jnk2=min(firsth(:,i,j));
- %jnk3=min(firstl1(:,i,j));
- %jnk4=min(imf2(:,i,j));
- jnk5=min(imf1(:,i,j));
-
- temp2(j)=min([jnk5]);
- end
- maxval(i)=max(temp1);
- minval(i)=min(temp2);
-end
-
-scaleout = [maxval(:) minval(:)];
-
-%--------------
-% Column j: situation 1 to N; Row i: variables (ie responses to)
-%-------------
-%figure
-rowlabel = 1;
-for i = 1:nrow % column: from top to bottom
- columnlabel = 1;
-
- if minval(i)<0
- if maxval(i)<=0
- yt=[minval(i) 0];
- else
- yt=[minval(i) 0 maxval(i)];
- end
- else % (minval(i) >=0)
- if maxval(i) > 0
- yt=[0 maxval(i)];
- else % (identically zero responses)
- yt=[-1 0 1];
- end
- end
-
-
- scale=[1 nstp minval(i) maxval(i)];
- for j = 1:ncol % row: from left to right
- k1=(i-1)*ncol+j;
- subplot(nrowg,ncolg,k1)
- plot(t,imf1(:,i,j)) %,t,imf2(:,i,j),'--')
- %t,[firstl(:,i,j) firsth(:,i,j)],':');
- grid;
- if scaleIndx
- axis(scale);
- end
- %set(gca,'YLim',[minval(i) maxval(i)])
- %
- set(gca,'XTick',XTick)
- if YTickIndx
- set(gca,'YTick',yt)
- end
-
- % if i<nrow
- % set(gca,'XTickLabelMode','manual','XTickLabel',[])
- % end
- %set(gca,'XTickLabel',' ');
- if (scaleIndx) && (j>1)
- set(gca,'YTickLabel',' ');
- end
- %if rowlabel == 1
- % %title(['x' num2str(j)])
- % %title(eval(['x' num2str(j)]))
- % title(char(xlab(j)))
- %end
- if (~isempty(xlab))
- title(char(xlab(i)))
- end
- %if columnlabel == 1
- % %ylabel(['x' num2str(i)])
- % %ylabel(eval(['x' num2str(i)]))
- % ylabel(char(ylab(i)))
- %end
- if (~isempty(ylab))
- ylabel(char(ylab(i)))
- end
- if (i==nrow) && (~isempty(tstring))
- xlabel(tstring)
- end
- columnlabel = 0;
- end
- rowlabel = 0;
-end
+function scaleout = fn_multigraph1_ver2_all_labels(imf1,xlab,ylab,tstring,XTick,YTickIndx,scaleIndx,nrowg,ncolg)
+%scaleout = fn_multigraph1_ver2_all_labels(imf1,xlab,ylab,tstring,XTick,YTickIndx,scaleIndx,nrowg,ncolg)
+%
+% Version 2 for plotting only one set of impulse responses in one graph. See fn_multigraph1_ver2.
+% imf1 -- each has 3 dimensions. Row: horizon; column: 'nrow' variables; 3rd dim: 'ncol' situations such as shocks or regimes.
+% imf1: row: "nstp" time horizon (in the graphics), column: "nrow "variables (row in
+% the graphics), 3rd D: across "ncol" different situations such as shocks or regimes (column in the graphics)
+% xlab: x-axis labels on the top. Use [] if no label is needed.
+% ylab: y-axis labels on the left. Use [] if no label is needed.
+% tstring: string for time (e.g., month or quarter) -- x-axis labels at the bottom. Use [] if no label is needed.
+% YTickIndx: 1: enable YTick; 0: disable. Often set to zero to get a better picture.
+% scaleIndx: 1: enable scale along Y-axis; 0: disable
+% nrowg: number of rows in the graph
+% ncolg: number of columns in the graph
+%
+% See fn_multigraph1_ver2, imrgraph, imcerrgraph, imrerrgraph
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+nstp = size(imf1,1);
+nrow = size(imf1,2);
+ncol = size(imf1,3);
+t = 1:nstp;
+
+if (nargin < 8)
+ nrowg = nrow;
+ ncolg = ncol;
+end
+if (nrowg*ncolg < nrow*ncol)
+ error('fn_multigraph1_ver2.m: nrowg*ncolg must equal to or greater than nrow*ncol')
+end
+
+temp1=zeros(ncol,1);
+temp2=zeros(ncol,1);
+maxval=zeros(nrow,1);
+minval=zeros(nrow,1);
+for i = 1:nrow
+ for j = 1:ncol
+ %jnk1=max(firsth(:,i,j));
+ %jnk2=max(firstl(:,i,j));
+ %jnk3=max(firsth1(:,i,j));
+ %jnk4=max(imf2(:,i,j));
+ jnk5=max(imf1(:,i,j));
+
+ temp1(j)=max([jnk5]);
+ %
+ %jnk1=min(firstl(:,i,j));
+ %jnk2=min(firsth(:,i,j));
+ %jnk3=min(firstl1(:,i,j));
+ %jnk4=min(imf2(:,i,j));
+ jnk5=min(imf1(:,i,j));
+
+ temp2(j)=min([jnk5]);
+ end
+ maxval(i)=max(temp1);
+ minval(i)=min(temp2);
+end
+
+scaleout = [maxval(:) minval(:)];
+
+%--------------
+% Column j: situation 1 to N; Row i: variables (ie responses to)
+%-------------
+%figure
+rowlabel = 1;
+for i = 1:nrow % column: from top to bottom
+ columnlabel = 1;
+
+ if minval(i)<0
+ if maxval(i)<=0
+ yt=[minval(i) 0];
+ else
+ yt=[minval(i) 0 maxval(i)];
+ end
+ else % (minval(i) >=0)
+ if maxval(i) > 0
+ yt=[0 maxval(i)];
+ else % (identically zero responses)
+ yt=[-1 0 1];
+ end
+ end
+
+
+ scale=[1 nstp minval(i) maxval(i)];
+ for j = 1:ncol % row: from left to right
+ k1=(i-1)*ncol+j;
+ subplot(nrowg,ncolg,k1)
+ plot(t,imf1(:,i,j)) %,t,imf2(:,i,j),'--')
+ %t,[firstl(:,i,j) firsth(:,i,j)],':');
+ grid;
+ if scaleIndx
+ axis(scale);
+ end
+ %set(gca,'YLim',[minval(i) maxval(i)])
+ %
+ set(gca,'XTick',XTick)
+ if YTickIndx
+ set(gca,'YTick',yt)
+ end
+
+ % if i<nrow
+ % set(gca,'XTickLabelMode','manual','XTickLabel',[])
+ % end
+ %set(gca,'XTickLabel',' ');
+ if (scaleIndx) && (j>1)
+ set(gca,'YTickLabel',' ');
+ end
+ %if rowlabel == 1
+ % %title(['x' num2str(j)])
+ % %title(eval(['x' num2str(j)]))
+ % title(char(xlab(j)))
+ %end
+ if (~isempty(xlab))
+ title(char(xlab(i)))
+ end
+ %if columnlabel == 1
+ % %ylabel(['x' num2str(i)])
+ % %ylabel(eval(['x' num2str(i)]))
+ % ylabel(char(ylab(i)))
+ %end
+ if (~isempty(ylab))
+ ylabel(char(ylab(i)))
+ end
+ if (i==nrow) && (~isempty(tstring))
+ xlabel(tstring)
+ end
+ columnlabel = 0;
+ end
+ rowlabel = 0;
+end
diff --git a/MatlabFiles/fn_multigraph2.m b/MatlabFiles/fn_multigraph2.m
index 4fe7e3f853a6644e95344be9895ad378bcd32e9d..fda226691fa46e4864f3c21c4382d9755ccdc808 100644
--- a/MatlabFiles/fn_multigraph2.m
+++ b/MatlabFiles/fn_multigraph2.m
@@ -1,126 +1,126 @@
-function scaleout = fn_multigraph2(imf1,imf2,...
- nrow,ncol,nstp,xlab,ylab,XTick,YTickIndx,scaleIndx)
-%Stacking two sets of impulse responses in one graph. See fn_multigraph1.m.
-%imf1, imf2 -- each has 3 dimensions. Row: horizon; column: 'nrow' variables; 3rd dim: 'ncol' situations such as regimes 1 and 2.
-% imf: row: "nstp" time horizon (in the graphics), column: "nrow "variables (row in
-% the graphics), 3rd D: across "ncol" different situations (column in
-% the graphics)
-% nrow: # of rows for the graphics
-% ncol: # of columns for the graphics
-% YTickIndx: 1: enable YTick; 0: disable
-% scaleIndx: 1: enable scale along Y-axis; 0: disable
-%
-% See imrgraph, imcerrgraph, imrerrgraph
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-t = 1:nstp;
-nrow
-ncol
-
-temp1=zeros(ncol,1);
-temp2=zeros(ncol,1);
-maxval=zeros(nrow,1);
-minval=zeros(nrow,1);
-for i = 1:nrow
- for j = 1:ncol
- %jnk1=max(firsth(:,i,j));
- %jnk2=max(firstl(:,i,j));
- %jnk3=max(firsth1(:,i,j));
- jnk4=max(imf2(:,i,j));
- jnk5=max(imf1(:,i,j));
-
- temp1(j)=max([jnk4 jnk5]);
- %
- %jnk1=min(firstl(:,i,j));
- %jnk2=min(firsth(:,i,j));
- %jnk3=min(firstl1(:,i,j));
- jnk4=min(imf2(:,i,j));
- jnk5=min(imf1(:,i,j));
-
- temp2(j)=min([jnk4 jnk5]);
- end
- maxval(i)=max(temp1);
- minval(i)=min(temp2);
-end
-
-scaleout = [maxval(:) minval(:)];
-
-%--------------
-% Column j: Shock 1 to N; Row i: Responses to
-%-------------
-figure
-rowlabel = 1;
-for i = 1:nrow % column: from top to bottom
- columnlabel = 1;
-
- if minval(i)<0
- if maxval(i)<=0
- yt=[minval(i) 0];
- else
- yt=[minval(i) 0 maxval(i)];
- end
- else % (minval(i) >=0)
- if maxval(i) > 0
- yt=[0 maxval(i)];
- else % (identically zero responses)
- yt=[-1 0 1];
- end
- end
-
-
- scale=[1 nstp minval(i) maxval(i)];
- for j = 1:ncol % row: from left to right
- k1=(i-1)*ncol+j;
- subplot(nrow,ncol,k1)
- plot(t,imf1(:,i,j),t,imf2(:,i,j),'--')
- %t,[firstl(:,i,j) firsth(:,i,j)],':');
- grid;
- if scaleIndx
- axis(scale);
- end
- %set(gca,'YLim',[minval(i) maxval(i)])
- %
- set(gca,'XTick',XTick)
- if YTickIndx
- set(gca,'YTick',yt)
- end
-
- if i<nrow
- set(gca,'XTickLabelMode','manual','XTickLabel',[])
- end
- %set(gca,'XTickLabel',' ');
- if j>1
- set(gca,'YTickLabel',' ');
- end
- if rowlabel == 1
- %title(['x' num2str(j)])
- %title(eval(['x' num2str(j)]))
- title(char(xlab(j)))
- end
- if columnlabel == 1
- %ylabel(['x' num2str(i)])
- %ylabel(eval(['x' num2str(i)]))
- ylabel(char(ylab(i)))
- end
- if i==nrow
- xlabel('Quarters')
- end
- columnlabel = 0;
- end
- rowlabel = 0;
-end
+function scaleout = fn_multigraph2(imf1,imf2,...
+ nrow,ncol,nstp,xlab,ylab,XTick,YTickIndx,scaleIndx)
+%Stacking two sets of impulse responses in one graph. See fn_multigraph1.m.
+%imf1, imf2 -- each has 3 dimensions. Row: horizon; column: 'nrow' variables; 3rd dim: 'ncol' situations such as regimes 1 and 2.
+% imf: row: "nstp" time horizon (in the graphics), column: "nrow "variables (row in
+% the graphics), 3rd D: across "ncol" different situations (column in
+% the graphics)
+% nrow: # of rows for the graphics
+% ncol: # of columns for the graphics
+% YTickIndx: 1: enable YTick; 0: disable
+% scaleIndx: 1: enable scale along Y-axis; 0: disable
+%
+% See imrgraph, imcerrgraph, imrerrgraph
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+t = 1:nstp;
+nrow
+ncol
+
+temp1=zeros(ncol,1);
+temp2=zeros(ncol,1);
+maxval=zeros(nrow,1);
+minval=zeros(nrow,1);
+for i = 1:nrow
+ for j = 1:ncol
+ %jnk1=max(firsth(:,i,j));
+ %jnk2=max(firstl(:,i,j));
+ %jnk3=max(firsth1(:,i,j));
+ jnk4=max(imf2(:,i,j));
+ jnk5=max(imf1(:,i,j));
+
+ temp1(j)=max([jnk4 jnk5]);
+ %
+ %jnk1=min(firstl(:,i,j));
+ %jnk2=min(firsth(:,i,j));
+ %jnk3=min(firstl1(:,i,j));
+ jnk4=min(imf2(:,i,j));
+ jnk5=min(imf1(:,i,j));
+
+ temp2(j)=min([jnk4 jnk5]);
+ end
+ maxval(i)=max(temp1);
+ minval(i)=min(temp2);
+end
+
+scaleout = [maxval(:) minval(:)];
+
+%--------------
+% Column j: Shock 1 to N; Row i: Responses to
+%-------------
+figure
+rowlabel = 1;
+for i = 1:nrow % column: from top to bottom
+ columnlabel = 1;
+
+ if minval(i)<0
+ if maxval(i)<=0
+ yt=[minval(i) 0];
+ else
+ yt=[minval(i) 0 maxval(i)];
+ end
+ else % (minval(i) >=0)
+ if maxval(i) > 0
+ yt=[0 maxval(i)];
+ else % (identically zero responses)
+ yt=[-1 0 1];
+ end
+ end
+
+
+ scale=[1 nstp minval(i) maxval(i)];
+ for j = 1:ncol % row: from left to right
+ k1=(i-1)*ncol+j;
+ subplot(nrow,ncol,k1)
+ plot(t,imf1(:,i,j),t,imf2(:,i,j),'--')
+ %t,[firstl(:,i,j) firsth(:,i,j)],':');
+ grid;
+ if scaleIndx
+ axis(scale);
+ end
+ %set(gca,'YLim',[minval(i) maxval(i)])
+ %
+ set(gca,'XTick',XTick)
+ if YTickIndx
+ set(gca,'YTick',yt)
+ end
+
+ if i<nrow
+ set(gca,'XTickLabelMode','manual','XTickLabel',[])
+ end
+ %set(gca,'XTickLabel',' ');
+ if j>1
+ set(gca,'YTickLabel',' ');
+ end
+ if rowlabel == 1
+ %title(['x' num2str(j)])
+ %title(eval(['x' num2str(j)]))
+ title(char(xlab(j)))
+ end
+ if columnlabel == 1
+ %ylabel(['x' num2str(i)])
+ %ylabel(eval(['x' num2str(i)]))
+ ylabel(char(ylab(i)))
+ end
+ if i==nrow
+ xlabel('Quarters')
+ end
+ columnlabel = 0;
+ end
+ rowlabel = 0;
+end
diff --git a/MatlabFiles/fn_multigraph2_ver2.m b/MatlabFiles/fn_multigraph2_ver2.m
index 3781941c587ba82047b6b8481586293fc156f116..6c5ae89d3d70d7759a164553fffa26eeed81eb17 100644
--- a/MatlabFiles/fn_multigraph2_ver2.m
+++ b/MatlabFiles/fn_multigraph2_ver2.m
@@ -1,139 +1,139 @@
-function scaleout = fn_multigraph2(imf1,imf2,...
- xlab,ylab,tstring,XTick,YTickIndx,scaleIndx,nrowg,ncolg)
-%Stacking two sets of impulse responses in one graph. See fn_multigraph1_ver2.m.
-%imf1, imf2 -- each has 3 dimensions. Row: horizon; column: 'nrow' variables; 3rd dim: 'ncol' situations such as shocks or regimes.
-% imf#: row: "nstp" time horizon (in the graphics), column: "nrow "variables (row in
-% the graphics), 3rd D: across "ncol" different situations (column in the graphics)
-% xlab: x-axis labels on the top
-% ylab: y-axis labels on the left
-% tstring: string for time (e.g., month or quarter) -- x-axis labels at the bottom
-% YTickIndx: 1: enable YTick; 0: disable;
-% To get a better picture, it is sometimes better to set YtickIndx to zero.
-% scaleIndx: 1: enable scale along Y-axis; 0: disable
-% nrowg: number of rows in the graph
-% ncolg: number of columns in the graph
-%
-% See imrgraph, imcerrgraph, imrerrgraph
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-nstp = size(imf1,1);
-nrow = size(imf1,2);
-ncol = size(imf1,3);
-t = 1:nstp;
-
-if (nargin < 9)
- nrowg = nrow;
- ncolg = ncol;
-end
-if (nrowg*ncolg ~= nrow*ncol)
- error('fn_multigraph2_ver2.m: nrowg*ncolg must equal nrow*ncol')
-end
-
-
-temp1=zeros(ncol,1);
-temp2=zeros(ncol,1);
-maxval=zeros(nrow,1);
-minval=zeros(nrow,1);
-for i = 1:nrow
- for j = 1:ncol
- %jnk1=max(firsth(:,i,j));
- %jnk2=max(firstl(:,i,j));
- %jnk3=max(firsth1(:,i,j));
- jnk4=max(imf2(:,i,j));
- jnk5=max(imf1(:,i,j));
-
- temp1(j)=max([jnk4 jnk5]);
- %
- %jnk1=min(firstl(:,i,j));
- %jnk2=min(firsth(:,i,j));
- %jnk3=min(firstl1(:,i,j));
- jnk4=min(imf2(:,i,j));
- jnk5=min(imf1(:,i,j));
-
- temp2(j)=min([jnk4 jnk5]);
- end
- maxval(i)=max(temp1);
- minval(i)=min(temp2);
-end
-
-scaleout = [maxval(:) minval(:)];
-
-%--------------
-% Column j: Shock 1 to N; Row i: Responses to
-%-------------
-%figure
-rowlabel = 1;
-for i = 1:nrow % column: from top to bottom
- columnlabel = 1;
-
- if minval(i)<0
- if maxval(i)<=0
- yt=[minval(i) 0];
- else
- yt=[minval(i) 0 maxval(i)];
- end
- else % (minval(i) >=0)
- if maxval(i) > 0
- yt=[0 maxval(i)];
- else % (identically zero responses)
- yt=[-1 0 1];
- end
- end
-
-
- scale=[1 nstp minval(i) maxval(i)];
- for j = 1:ncol % row: from left to right
- k1=(i-1)*ncol+j;
- subplot(nrowg,ncolg,k1)
- plot(t,imf1(:,i,j),t,imf2(:,i,j),'--')
- %t,[firstl(:,i,j) firsth(:,i,j)],':');
- grid;
- if scaleIndx
- axis(scale);
- end
- %set(gca,'YLim',[minval(i) maxval(i)])
- %
- set(gca,'XTick',XTick)
- if YTickIndx
- set(gca,'YTick',yt)
- end
-
- if i<nrow
- set(gca,'XTickLabelMode','manual','XTickLabel',[])
- end
- %set(gca,'XTickLabel',' ');
- if (scaleIndx) && (j>1)
- set(gca,'YTickLabel',' ');
- end
- if rowlabel == 1
- %title(['x' num2str(j)])
- %title(eval(['x' num2str(j)]))
- title(char(xlab(j)))
- end
- if columnlabel == 1
- %ylabel(['x' num2str(i)])
- %ylabel(eval(['x' num2str(i)]))
- ylabel(char(ylab(i)))
- end
- if i==nrow
- xlabel(tstring)
- end
- columnlabel = 0;
- end
- rowlabel = 0;
-end
+function scaleout = fn_multigraph2(imf1,imf2,...
+ xlab,ylab,tstring,XTick,YTickIndx,scaleIndx,nrowg,ncolg)
+%Stacking two sets of impulse responses in one graph. See fn_multigraph1_ver2.m.
+%imf1, imf2 -- each has 3 dimensions. Row: horizon; column: 'nrow' variables; 3rd dim: 'ncol' situations such as shocks or regimes.
+% imf#: row: "nstp" time horizon (in the graphics), column: "nrow "variables (row in
+% the graphics), 3rd D: across "ncol" different situations (column in the graphics)
+% xlab: x-axis labels on the top
+% ylab: y-axis labels on the left
+% tstring: string for time (e.g., month or quarter) -- x-axis labels at the bottom
+% YTickIndx: 1: enable YTick; 0: disable;
+% To get a better picture, it is sometimes better to set YtickIndx to zero.
+% scaleIndx: 1: enable scale along Y-axis; 0: disable
+% nrowg: number of rows in the graph
+% ncolg: number of columns in the graph
+%
+% See imrgraph, imcerrgraph, imrerrgraph
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+nstp = size(imf1,1);
+nrow = size(imf1,2);
+ncol = size(imf1,3);
+t = 1:nstp;
+
+if (nargin < 9)
+ nrowg = nrow;
+ ncolg = ncol;
+end
+if (nrowg*ncolg ~= nrow*ncol)
+ error('fn_multigraph2_ver2.m: nrowg*ncolg must equal nrow*ncol')
+end
+
+
+temp1=zeros(ncol,1);
+temp2=zeros(ncol,1);
+maxval=zeros(nrow,1);
+minval=zeros(nrow,1);
+for i = 1:nrow
+ for j = 1:ncol
+ %jnk1=max(firsth(:,i,j));
+ %jnk2=max(firstl(:,i,j));
+ %jnk3=max(firsth1(:,i,j));
+ jnk4=max(imf2(:,i,j));
+ jnk5=max(imf1(:,i,j));
+
+ temp1(j)=max([jnk4 jnk5]);
+ %
+ %jnk1=min(firstl(:,i,j));
+ %jnk2=min(firsth(:,i,j));
+ %jnk3=min(firstl1(:,i,j));
+ jnk4=min(imf2(:,i,j));
+ jnk5=min(imf1(:,i,j));
+
+ temp2(j)=min([jnk4 jnk5]);
+ end
+ maxval(i)=max(temp1);
+ minval(i)=min(temp2);
+end
+
+scaleout = [maxval(:) minval(:)];
+
+%--------------
+% Column j: Shock 1 to N; Row i: Responses to
+%-------------
+%figure
+rowlabel = 1;
+for i = 1:nrow % column: from top to bottom
+ columnlabel = 1;
+
+ if minval(i)<0
+ if maxval(i)<=0
+ yt=[minval(i) 0];
+ else
+ yt=[minval(i) 0 maxval(i)];
+ end
+ else % (minval(i) >=0)
+ if maxval(i) > 0
+ yt=[0 maxval(i)];
+ else % (identically zero responses)
+ yt=[-1 0 1];
+ end
+ end
+
+
+ scale=[1 nstp minval(i) maxval(i)];
+ for j = 1:ncol % row: from left to right
+ k1=(i-1)*ncol+j;
+ subplot(nrowg,ncolg,k1)
+ plot(t,imf1(:,i,j),t,imf2(:,i,j),'--')
+ %t,[firstl(:,i,j) firsth(:,i,j)],':');
+ grid;
+ if scaleIndx
+ axis(scale);
+ end
+ %set(gca,'YLim',[minval(i) maxval(i)])
+ %
+ set(gca,'XTick',XTick)
+ if YTickIndx
+ set(gca,'YTick',yt)
+ end
+
+ if i<nrow
+ set(gca,'XTickLabelMode','manual','XTickLabel',[])
+ end
+ %set(gca,'XTickLabel',' ');
+ if (scaleIndx) && (j>1)
+ set(gca,'YTickLabel',' ');
+ end
+ if rowlabel == 1
+ %title(['x' num2str(j)])
+ %title(eval(['x' num2str(j)]))
+ title(char(xlab(j)))
+ end
+ if columnlabel == 1
+ %ylabel(['x' num2str(i)])
+ %ylabel(eval(['x' num2str(i)]))
+ ylabel(char(ylab(i)))
+ end
+ if i==nrow
+ xlabel(tstring)
+ end
+ columnlabel = 0;
+ end
+ rowlabel = 0;
+end
diff --git a/MatlabFiles/fn_multigraph2_ver2_all_labels.m b/MatlabFiles/fn_multigraph2_ver2_all_labels.m
index 9dbcd7b322079f55690f814c8d1d805df572cf7f..f4fa3d5da7b593123158b771d12c57de232d5a73 100644
--- a/MatlabFiles/fn_multigraph2_ver2_all_labels.m
+++ b/MatlabFiles/fn_multigraph2_ver2_all_labels.m
@@ -1,150 +1,150 @@
-function scaleout = fn_multigraph2_ver2_all_labels(imf1,imf2,...
- xlab,ylab,tstring,XTick,YTickIndx,scaleIndx,nrowg,ncolg)
-%scaleout = fn_multigraph2_ver2_all_labels(imf1,imf2,...
-% xlab,ylab,tstring,XTick,YTickIndx,scaleIndx,nrowg,ncolg)
-%
-%Stacking two sets of impulse responses in one graph. See fn_multigraph2_ver2.m.
-% imf1, imf2: row: "nstp" time horizon (in the graphics),
-% column: "nrow "variables (row in the graphics),
-% 3rd dim: across "ncol" different situations (column in the graphics, but may be overwritten by ncolg).
-% If the 3rd D is only 1, then we can specifiy nrowg and ncolg.
-% xlab: x-axis labels on the top. Use [] if no label is needed.
-% ylab: y-axis labels on the left. Use [] if no label is needed.
-% XTick: ticks on the x-axis with grids on. Set to [] if no x-ticks and no grids.
-% tstring: string for time (e.g., month or quarter) -- x-axis labels at the bottom. Use [] if no label is needed.
-% YTickIndx: 1: enable YTick; 0: disable. Often set to zero to get a better picture.
-% scaleIndx: 1: enable scale along Y-axis; 0: disable
-% nrowg: number of rows in the graph (valid when the 3rd D is one)
-% ncolg: number of columns in the graph (valid when the 3rd D is one)
-%
-% See fn_multigraph2_ver2, imrgraph, imcerrgraph, imrerrgraph
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-nstp = size(imf1,1);
-nrow = size(imf1,2);
-ncol = size(imf1,3);
-t = 1:nstp;
-
-if (nargin < 9)
- nrowg = nrow;
- ncolg = ncol;
-end
-if (nrowg*ncolg < nrow*ncol)
- error('fn_multigraph2_ver2_all_labels.m: nrowg*ncolg must be greater or equal to nrow*ncol')
-end
-
-
-temp1=zeros(ncol,1);
-temp2=zeros(ncol,1);
-maxval=zeros(nrow,1);
-minval=zeros(nrow,1);
-for i = 1:nrow
- for j = 1:ncol
- %jnk1=max(firsth(:,i,j));
- %jnk2=max(firstl(:,i,j));
- %jnk3=max(firsth1(:,i,j));
- jnk4=max(imf2(:,i,j));
- jnk5=max(imf1(:,i,j));
-
- temp1(j)=max([jnk4 jnk5]);
- %
- %jnk1=min(firstl(:,i,j));
- %jnk2=min(firsth(:,i,j));
- %jnk3=min(firstl1(:,i,j));
- jnk4=min(imf2(:,i,j));
- jnk5=min(imf1(:,i,j));
-
- temp2(j)=min([jnk4 jnk5]);
- end
- maxval(i)=max(temp1);
- minval(i)=min(temp2);
-end
-
-scaleout = [maxval(:) minval(:)];
-
-%--------------
-% Column j: Shock 1 to N; Row i: Responses to
-%-------------
-%figure
-rowlabel = 1;
-for i = 1:nrow % column: from top to bottom
- columnlabel = 1;
-
- if minval(i)<0
- if maxval(i)<=0
- yt=[minval(i) 0];
- else
- yt=[minval(i) 0 maxval(i)];
- end
- else % (minval(i) >=0)
- if maxval(i) > 0
- yt=[0 maxval(i)];
- else % (identically zero responses)
- yt=[-1 0 1];
- end
- end
-
-
- scale=[1 nstp minval(i) maxval(i)];
- for j = 1:ncol % row: from left to right
- k1=(i-1)*ncol+j;
- subplot(nrowg,ncolg,k1)
- plot(t,imf1(:,i,j),'k-',t,imf2(:,i,j),'k--')
- %t,[firstl(:,i,j) firsth(:,i,j)],':');
- grid;
- if scaleIndx
- axis(scale);
- end
- %set(gca,'YLim',[minval(i) maxval(i)])
- %
- set(gca,'XTick',XTick)
- if YTickIndx
- set(gca,'YTick',yt)
- end
-
- % if i<nrow
- % set(gca,'XTickLabelMode','manual','XTickLabel',[])
- % end
- %set(gca,'XTickLabel',' ');
- if (scaleIndx) && (j>1)
- set(gca,'YTickLabel',' ');
- end
- % if rowlabel == 1
- % %title(['x' num2str(j)])
- % %title(eval(['x' num2str(j)]))
- % title(char(xlab(j)))
- % end
- if (~isempty(xlab))
- title(char(xlab(i)))
- end
- %
- % if columnlabel == 1
- % %ylabel(['x' num2str(i)])
- % %ylabel(eval(['x' num2str(i)]))
- % ylabel(char(ylab(i)))
- % end
- if (~isempty(ylab))
- ylabel(char(ylab(i)))
- end
- if (i==nrow) && (~isempty(tstring))
- xlabel(tstring)
- end
- columnlabel = 0;
- end
- rowlabel = 0;
-end
+function scaleout = fn_multigraph2_ver2_all_labels(imf1,imf2,...
+ xlab,ylab,tstring,XTick,YTickIndx,scaleIndx,nrowg,ncolg)
+%scaleout = fn_multigraph2_ver2_all_labels(imf1,imf2,...
+% xlab,ylab,tstring,XTick,YTickIndx,scaleIndx,nrowg,ncolg)
+%
+%Stacking two sets of impulse responses in one graph. See fn_multigraph2_ver2.m.
+% imf1, imf2: row: "nstp" time horizon (in the graphics),
+% column: "nrow "variables (row in the graphics),
+% 3rd dim: across "ncol" different situations (column in the graphics, but may be overwritten by ncolg).
+% If the 3rd D is only 1, then we can specifiy nrowg and ncolg.
+% xlab: x-axis labels on the top. Use [] if no label is needed.
+% ylab: y-axis labels on the left. Use [] if no label is needed.
+% XTick: ticks on the x-axis with grids on. Set to [] if no x-ticks and no grids.
+% tstring: string for time (e.g., month or quarter) -- x-axis labels at the bottom. Use [] if no label is needed.
+% YTickIndx: 1: enable YTick; 0: disable. Often set to zero to get a better picture.
+% scaleIndx: 1: enable scale along Y-axis; 0: disable
+% nrowg: number of rows in the graph (valid when the 3rd D is one)
+% ncolg: number of columns in the graph (valid when the 3rd D is one)
+%
+% See fn_multigraph2_ver2, imrgraph, imcerrgraph, imrerrgraph
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+nstp = size(imf1,1);
+nrow = size(imf1,2);
+ncol = size(imf1,3);
+t = 1:nstp;
+
+if (nargin < 9)
+ nrowg = nrow;
+ ncolg = ncol;
+end
+if (nrowg*ncolg < nrow*ncol)
+ error('fn_multigraph2_ver2_all_labels.m: nrowg*ncolg must be greater or equal to nrow*ncol')
+end
+
+
+temp1=zeros(ncol,1);
+temp2=zeros(ncol,1);
+maxval=zeros(nrow,1);
+minval=zeros(nrow,1);
+for i = 1:nrow
+ for j = 1:ncol
+ %jnk1=max(firsth(:,i,j));
+ %jnk2=max(firstl(:,i,j));
+ %jnk3=max(firsth1(:,i,j));
+ jnk4=max(imf2(:,i,j));
+ jnk5=max(imf1(:,i,j));
+
+ temp1(j)=max([jnk4 jnk5]);
+ %
+ %jnk1=min(firstl(:,i,j));
+ %jnk2=min(firsth(:,i,j));
+ %jnk3=min(firstl1(:,i,j));
+ jnk4=min(imf2(:,i,j));
+ jnk5=min(imf1(:,i,j));
+
+ temp2(j)=min([jnk4 jnk5]);
+ end
+ maxval(i)=max(temp1);
+ minval(i)=min(temp2);
+end
+
+scaleout = [maxval(:) minval(:)];
+
+%--------------
+% Column j: Shock 1 to N; Row i: Responses to
+%-------------
+%figure
+rowlabel = 1;
+for i = 1:nrow % column: from top to bottom
+ columnlabel = 1;
+
+ if minval(i)<0
+ if maxval(i)<=0
+ yt=[minval(i) 0];
+ else
+ yt=[minval(i) 0 maxval(i)];
+ end
+ else % (minval(i) >=0)
+ if maxval(i) > 0
+ yt=[0 maxval(i)];
+ else % (identically zero responses)
+ yt=[-1 0 1];
+ end
+ end
+
+
+ scale=[1 nstp minval(i) maxval(i)];
+ for j = 1:ncol % row: from left to right
+ k1=(i-1)*ncol+j;
+ subplot(nrowg,ncolg,k1)
+ plot(t,imf1(:,i,j),'k-',t,imf2(:,i,j),'k--')
+ %t,[firstl(:,i,j) firsth(:,i,j)],':');
+ grid;
+ if scaleIndx
+ axis(scale);
+ end
+ %set(gca,'YLim',[minval(i) maxval(i)])
+ %
+ set(gca,'XTick',XTick)
+ if YTickIndx
+ set(gca,'YTick',yt)
+ end
+
+ % if i<nrow
+ % set(gca,'XTickLabelMode','manual','XTickLabel',[])
+ % end
+ %set(gca,'XTickLabel',' ');
+ if (scaleIndx) && (j>1)
+ set(gca,'YTickLabel',' ');
+ end
+ % if rowlabel == 1
+ % %title(['x' num2str(j)])
+ % %title(eval(['x' num2str(j)]))
+ % title(char(xlab(j)))
+ % end
+ if (~isempty(xlab))
+ title(char(xlab(i)))
+ end
+ %
+ % if columnlabel == 1
+ % %ylabel(['x' num2str(i)])
+ % %ylabel(eval(['x' num2str(i)]))
+ % ylabel(char(ylab(i)))
+ % end
+ if (~isempty(ylab))
+ ylabel(char(ylab(i)))
+ end
+ if (i==nrow) && (~isempty(tstring))
+ xlabel(tstring)
+ end
+ columnlabel = 0;
+ end
+ rowlabel = 0;
+end
diff --git a/MatlabFiles/fn_multigraph2_ver2_all_labels_dates.m b/MatlabFiles/fn_multigraph2_ver2_all_labels_dates.m
index 1352c0db4008cab61d02ea13364b926b78bb945d..01d62eb9382c774f94a922082bb0dcdb8a46de7e 100644
--- a/MatlabFiles/fn_multigraph2_ver2_all_labels_dates.m
+++ b/MatlabFiles/fn_multigraph2_ver2_all_labels_dates.m
@@ -1,159 +1,159 @@
-function scaleout = fn_multigraph2_ver2_all_labels_dates(dates,imf1,imf2,...
- xlab,ylab,tstring,XTick,YTickIndx,scaleIndx,legend_inp, nrowg,ncolg)
-%scaleout = fn_multigraph2_ver2_all_labels_dates(dates,imf1,imf2,...
-% xlab,ylab,tstring,XTick,YTickIndx,scaleIndx,nrowg,ncolg)
-%
-%Stacking two sets of impulse responses in one graph. See fn_multigraph2_ver2_all_labels.m.
-% dates: time horizon in the graphics,
-% imf1, imf2: row: the same dimension as the dates (in the graphics),
-% column: "nrow "variables (row in the graphics),
-% 3rd dim: across "ncol" different situations (column in the graphics, but may be overwritten by ncolg).
-% If the 3rd D is only 1, then we can specifiy nrowg and ncolg.
-% xlab: x-axis labels on the top. Use [] if no label is needed.
-% ylab: y-axis labels on the left. Use [] if no label is needed.
-% tstring: string for time (e.g., month or quarter) -- x-axis labels at the bottom. Use [] if no label is needed.
-% XTick: ticks on the x-axis with grids on. If [], no x-ticks and no grids.
-% YTickIndx: 1: enable YTick; 0: disable. Often set to zero to get a better picture.
-% scaleIndx: 1: enable scale along Y-axis; 0: disable
-% legend_inp: legend inputs. If [], no legend.
-% nrowg: number of rows in the graph (valid when the 3rd D is one)
-% ncolg: number of columns in the graph (valid when the 3rd D is one)
-%
-% See fn_multigraph2_ver2_all_labels.m, fn_multigraph2_ver2, imrgraph, imcerrgraph, imrerrgraph
-
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-nstp = size(imf1,1);
-nrow = size(imf1,2);
-ncol = size(imf1,3);
-
-if (length(dates) ~= nstp)
- error('.../fn_multigraph2_ver2_all_labels_dates: dates must have the dimension as the row number of imf1 and imf2!')
-end
-
-if (nargin < 11)
- nrowg = nrow;
- ncolg = ncol;
-end
-if (nrowg*ncolg < nrow*ncol)
- error('fn_multigraph2_ver2_all_labels_dates.m: nrowg*ncolg must be greater or equal to nrow*ncol')
-end
-
-
-temp1=zeros(ncol,1);
-temp2=zeros(ncol,1);
-maxval=zeros(nrow,1);
-minval=zeros(nrow,1);
-for i = 1:nrow
- for j = 1:ncol
- %jnk1=max(firsth(:,i,j));
- %jnk2=max(firstl(:,i,j));
- %jnk3=max(firsth1(:,i,j));
- jnk4=max(imf2(:,i,j));
- jnk5=max(imf1(:,i,j));
-
- temp1(j)=max([jnk4 jnk5]);
- %
- %jnk1=min(firstl(:,i,j));
- %jnk2=min(firsth(:,i,j));
- %jnk3=min(firstl1(:,i,j));
- jnk4=min(imf2(:,i,j));
- jnk5=min(imf1(:,i,j));
-
- temp2(j)=min([jnk4 jnk5]);
- end
- maxval(i)=max(temp1);
- minval(i)=min(temp2);
-end
-
-scaleout = [maxval(:) minval(:)];
-
-%--------------
-% Column j: Shock 1 to N; Row i: Responses to
-%-------------
-%figure
-rowlabel = 1;
-for i = 1:nrow % column: from top to bottom
- columnlabel = 1;
-
- if minval(i)<0
- if maxval(i)<=0
- yt=[minval(i) 0];
- else
- yt=[minval(i) 0 maxval(i)];
- end
- else % (minval(i) >=0)
- if maxval(i) > 0
- yt=[0 maxval(i)];
- else % (identically zero responses)
- yt=[-1 0 1];
- end
- end
-
-
- scale=[dates(1) dates(end) minval(i) maxval(i)];
- for j = 1:ncol % row: from left to right
- k1=(i-1)*ncol+j;
- subplot(nrowg,ncolg,k1)
- plot(dates,imf1(:,i,j),'k-',dates,imf2(:,i,j),'k--')
- %t,[firstl(:,i,j) firsth(:,i,j)],':');
- grid;
- if (~isempty(legend_inp))
- legend(legend_inp)
- end
- if scaleIndx
- axis(scale);
- end
- %set(gca,'YLim',[minval(i) maxval(i)])
- %
- set(gca,'XTick',XTick)
- if YTickIndx
- set(gca,'YTick',yt)
- end
-
- % if i<nrow
- % set(gca,'XTickLabelMode','manual','XTickLabel',[])
- % end
- %set(gca,'XTickLabel',' ');
- if (scaleIndx) && (j>1)
- set(gca,'YTickLabel',' ');
- end
- % if rowlabel == 1
- % %title(['x' num2str(j)])
- % %title(eval(['x' num2str(j)]))
- % title(char(xlab(j)))
- % end
- if (~isempty(xlab))
- title(char(xlab(i)))
- end
- %
- % if columnlabel == 1
- % %ylabel(['x' num2str(i)])
- % %ylabel(eval(['x' num2str(i)]))
- % ylabel(char(ylab(i)))
- % end
- if (~isempty(ylab))
- ylabel(char(ylab(i)))
- end
- if (i==nrow) && (~isempty(tstring))
- xlabel(tstring)
- end
- columnlabel = 0;
- end
- rowlabel = 0;
-end
+function scaleout = fn_multigraph2_ver2_all_labels_dates(dates,imf1,imf2,...
+ xlab,ylab,tstring,XTick,YTickIndx,scaleIndx,legend_inp, nrowg,ncolg)
+%scaleout = fn_multigraph2_ver2_all_labels_dates(dates,imf1,imf2,...
+% xlab,ylab,tstring,XTick,YTickIndx,scaleIndx,nrowg,ncolg)
+%
+%Stacking two sets of impulse responses in one graph. See fn_multigraph2_ver2_all_labels.m.
+% dates: time horizon in the graphics,
+% imf1, imf2: row: the same dimension as the dates (in the graphics),
+% column: "nrow "variables (row in the graphics),
+% 3rd dim: across "ncol" different situations (column in the graphics, but may be overwritten by ncolg).
+% If the 3rd D is only 1, then we can specifiy nrowg and ncolg.
+% xlab: x-axis labels on the top. Use [] if no label is needed.
+% ylab: y-axis labels on the left. Use [] if no label is needed.
+% tstring: string for time (e.g., month or quarter) -- x-axis labels at the bottom. Use [] if no label is needed.
+% XTick: ticks on the x-axis with grids on. If [], no x-ticks and no grids.
+% YTickIndx: 1: enable YTick; 0: disable. Often set to zero to get a better picture.
+% scaleIndx: 1: enable scale along Y-axis; 0: disable
+% legend_inp: legend inputs. If [], no legend.
+% nrowg: number of rows in the graph (valid when the 3rd D is one)
+% ncolg: number of columns in the graph (valid when the 3rd D is one)
+%
+% See fn_multigraph2_ver2_all_labels.m, fn_multigraph2_ver2, imrgraph, imcerrgraph, imrerrgraph
+
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+nstp = size(imf1,1);
+nrow = size(imf1,2);
+ncol = size(imf1,3);
+
+if (length(dates) ~= nstp)
+ error('.../fn_multigraph2_ver2_all_labels_dates: dates must have the dimension as the row number of imf1 and imf2!')
+end
+
+if (nargin < 11)
+ nrowg = nrow;
+ ncolg = ncol;
+end
+if (nrowg*ncolg < nrow*ncol)
+ error('fn_multigraph2_ver2_all_labels_dates.m: nrowg*ncolg must be greater or equal to nrow*ncol')
+end
+
+
+temp1=zeros(ncol,1);
+temp2=zeros(ncol,1);
+maxval=zeros(nrow,1);
+minval=zeros(nrow,1);
+for i = 1:nrow
+ for j = 1:ncol
+ %jnk1=max(firsth(:,i,j));
+ %jnk2=max(firstl(:,i,j));
+ %jnk3=max(firsth1(:,i,j));
+ jnk4=max(imf2(:,i,j));
+ jnk5=max(imf1(:,i,j));
+
+ temp1(j)=max([jnk4 jnk5]);
+ %
+ %jnk1=min(firstl(:,i,j));
+ %jnk2=min(firsth(:,i,j));
+ %jnk3=min(firstl1(:,i,j));
+ jnk4=min(imf2(:,i,j));
+ jnk5=min(imf1(:,i,j));
+
+ temp2(j)=min([jnk4 jnk5]);
+ end
+ maxval(i)=max(temp1);
+ minval(i)=min(temp2);
+end
+
+scaleout = [maxval(:) minval(:)];
+
+%--------------
+% Column j: Shock 1 to N; Row i: Responses to
+%-------------
+%figure
+rowlabel = 1;
+for i = 1:nrow % column: from top to bottom
+ columnlabel = 1;
+
+ if minval(i)<0
+ if maxval(i)<=0
+ yt=[minval(i) 0];
+ else
+ yt=[minval(i) 0 maxval(i)];
+ end
+ else % (minval(i) >=0)
+ if maxval(i) > 0
+ yt=[0 maxval(i)];
+ else % (identically zero responses)
+ yt=[-1 0 1];
+ end
+ end
+
+
+ scale=[dates(1) dates(end) minval(i) maxval(i)];
+ for j = 1:ncol % row: from left to right
+ k1=(i-1)*ncol+j;
+ subplot(nrowg,ncolg,k1)
+ plot(dates,imf1(:,i,j),'k-',dates,imf2(:,i,j),'k--')
+ %t,[firstl(:,i,j) firsth(:,i,j)],':');
+ grid;
+ if (~isempty(legend_inp))
+ legend(legend_inp)
+ end
+ if scaleIndx
+ axis(scale);
+ end
+ %set(gca,'YLim',[minval(i) maxval(i)])
+ %
+ set(gca,'XTick',XTick)
+ if YTickIndx
+ set(gca,'YTick',yt)
+ end
+
+ % if i<nrow
+ % set(gca,'XTickLabelMode','manual','XTickLabel',[])
+ % end
+ %set(gca,'XTickLabel',' ');
+ if (scaleIndx) && (j>1)
+ set(gca,'YTickLabel',' ');
+ end
+ % if rowlabel == 1
+ % %title(['x' num2str(j)])
+ % %title(eval(['x' num2str(j)]))
+ % title(char(xlab(j)))
+ % end
+ if (~isempty(xlab))
+ title(char(xlab(i)))
+ end
+ %
+ % if columnlabel == 1
+ % %ylabel(['x' num2str(i)])
+ % %ylabel(eval(['x' num2str(i)]))
+ % ylabel(char(ylab(i)))
+ % end
+ if (~isempty(ylab))
+ ylabel(char(ylab(i)))
+ end
+ if (i==nrow) && (~isempty(tstring))
+ xlabel(tstring)
+ end
+ columnlabel = 0;
+ end
+ rowlabel = 0;
+end
diff --git a/MatlabFiles/fn_multigraphn_3g_shadedbands.m b/MatlabFiles/fn_multigraphn_3g_shadedbands.m
index 6b21b3a12e5952f75c731a07c9d321d66ccc2517..a92c96ae53f7bbcd5db8a2cfc2273ab2863061bd 100644
--- a/MatlabFiles/fn_multigraphn_3g_shadedbands.m
+++ b/MatlabFiles/fn_multigraphn_3g_shadedbands.m
@@ -1,188 +1,188 @@
-function scaleout = fn_multigraphn_3g_shadedbands(imfn,...
- xlab,ylab,tstring,XTick,YTickIndx,scaleIndx,nrowg,ncolg)
-%Searching <<>> for ad hoc and specific changes.
-%
-%Stacking n sets of impulse responses in one graph. See fn_multigraph2_ver2.m.
-% imfn: row: "nstp" time horizon (in the graphics),
-% column: "nrow "variables such as responses (row in the graphics),
-% 3rd D: across "ncol" different situations such as shocks (column in the graphics),
-% 4th D: low band, estimate, high band (in each graph).
-% xlab: x-axis labels on the top
-% ylab: y-axis labels on the left
-% tstring: string for time (e.g., month or quarter) -- x-axis labels at the bottom
-% YTickIndx: 1: enable YTick; 0: disable;
-% To get a better picture, it is sometimes better to set YtickIndx to zero.
-% scaleIndx: 1: enable scale along Y-axis; 0: disable
-% nrowg: number of rows in the graph
-% ncolg: number of columns in the graph
-%
-% See imrgraph, imcerrgraph, imrerrgraph
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-nstp = size(imfn,1);
-nrow = size(imfn,2);
-ncol = size(imfn,3);
-nmodels = size(imfn,4);
-t = 1:nstp;
-treverse = fliplr(t);
-
-if (nargin < 9)
- nrowg = nrow;
- ncolg = ncol;
-end
-if (nrowg*ncolg < nrow*ncol)
- nrowg
- ncolg
- nrow
- ncol
-
- error('fn_multigraphn_3g_shadedbands.m: nrowg*ncolg must be greater than nrow*ncol')
-end
-
-
-tempmax=zeros(ncol,1);
-tempmin=zeros(ncol,1);
-maxval=zeros(nrow,1);
-minval=zeros(nrow,1);
-for i = 1:nrow
- for j = 1:ncol
- tempmax(j) = -realmax;
- tempmin(j) = realmax;
- for k=1:nmodels
- jnk = max(imfn(:,i,j,k));
- tempmax(j) = max([jnk tempmax(j)]);
- %
- jnk = min(imfn(:,i,j,k));
- tempmin(j) = min([jnk tempmin(j)]);
- end
- end
- maxval(i)=max(tempmax);
- minval(i)=min(tempmin);
-end
-
-scaleout = [maxval(:) minval(:)];
-
-%--------------
-% Column j: Shock 1 to N; Row i: Responses to
-%-------------
-%figure
-
-
-rowlabel = 1;
-for i = 1:nrow % column: from top to bottom
- columnlabel = 1;
-
- if minval(i)<0
- if maxval(i)<=0
- yt=[minval(i) 0];
- else
- yt=[minval(i) 0 maxval(i)];
- end
- else % (minval(i) >=0)
- if maxval(i) > 0
- yt=[0 maxval(i)];
- else % (identically zero responses)
- yt=[-1 0 1];
- end
- end
-
-
- scale=[1 nstp minval(i) maxval(i)];
-
- for j = 1:ncol % row: from left to right
- k1=(i-1)*ncol+j;
- subplot(nrowg,ncolg,k1)
-
- %set(0,'DefaultAxesColorOrder',[1 0 0;0 1 0;0 0 1],...
- % 'DefaultAxesLineStyleOrder','-|--|:')
- %set(0,'DefaultAxesLineStyleOrder','-|--|:|-.')
- %---<<>>
- %set(0,'DefaultAxesColorOrder',[0 0 0],...
- % 'DefaultAxesLineStyleOrder','-.|-.|-|--|-.|-*|--o|:d')
-
- nseries = zeros(nstp, nmodels);
- for k=1:nmodels
- nseries(:,k) = imfn(:,i,j,k);
- end
-
- %---<<>>
- fill([t treverse],[nseries(:,[3])' fliplr(nseries(:,[1])')],[0.8,0.8,0.8],'EdgeColor','none');
- %plot(t,nseries(:,[1 3]), '-.k','LineWidth',1.0); %,'Color','k');
- hold on
- plot(t,nseries(:,[2]), '-k','LineWidth',1.5);
- %plot(t,nseries(:,[4]), '--k','LineWidth',1.6);
- hold off
-
- %set(gca,'LineStyleOrder','-|--|:|-.')
- %set(gca,'LineStyleOrder',{'-*',':','o'})
- grid;
- if scaleIndx
- axis(scale);
- end
- %set(gca,'YLim',[minval(i) maxval(i)])
- %
- set(gca,'XTick',XTick)
- if YTickIndx
- set(gca,'YTick',yt)
- end
-
- if i<nrow
- set(gca,'XTickLabelMode','manual','XTickLabel',[])
- end
- %set(gca,'XTickLabel',' ');
- if (scaleIndx) && (j>1)
- set(gca,'YTickLabel',' ');
- end
- if rowlabel == 1
- %title(['x' num2str(j)])
- %title(eval(['x' num2str(j)]))
- if (~isempty(xlab)), title(char(xlab(j))), end
- end
- if columnlabel == 1
- %ylabel(['x' num2str(i)])
- %ylabel(eval(['x' num2str(i)]))
- if (~isempty(ylab)), ylabel(char(ylab(i))), end
- end
- if (i==nrow) && (~isempty(tstring))
- xlabel(tstring)
- end
- columnlabel = 0;
- end
- rowlabel = 0;
-end
-
-
-%Order of line styles and markers used in a plot.
-%This property specifies which line styles and markers to use and in what order
-%when creating multiple-line plots. For example,set(gca,'LineStyleOrder', '-*|:|o')sets LineStyleOrder to solid line with asterisk
-%marker, dotted line, and hollow circle marker. The default is (-), which specifies
-%a solid line for all data plotted. Alternatively, you can create a cell array
-%of character strings to define the line styles:set(gca,'LineStyleOrder',{'-*',':','o'})MATLAB supports four line styles, which you can specify any number of
-%times in any order. MATLAB cycles through the line styles only after using
-%all colors defined by the ColorOrder property. For example,
-%the first eight lines plotted use the different colors defined by ColorOrder with
-%the first line style. MATLAB then cycles through the colors again, using the
-%second line style specified, and so on.You can also specify line style and color directly with the plot and plot3 functions
-%or by altering the properties of theline or
-%lineseries objects after creating the graph. High-Level Functions and LineStyleOrderNote that, if the axes NextPlot property is set
-%to replace (the default), high-level functions like plot reset
-%the LineStyleOrder property before determining the line
-%style to use. If you want MATLAB to use a LineStyleOrder that
-%is different from the default, set NextPlot to replacechildren. Specifying a Default LineStyleOrderYou can also specify your own default LineStyleOrder.
-%For example, this statementset(0,'DefaultAxesLineStyleOrder',{'-*',':','o'})
-%creates a default value for
+function scaleout = fn_multigraphn_3g_shadedbands(imfn,...
+ xlab,ylab,tstring,XTick,YTickIndx,scaleIndx,nrowg,ncolg)
+%Searching <<>> for ad hoc and specific changes.
+%
+%Stacking n sets of impulse responses in one graph. See fn_multigraph2_ver2.m.
+% imfn: row: "nstp" time horizon (in the graphics),
+% column: "nrow "variables such as responses (row in the graphics),
+% 3rd D: across "ncol" different situations such as shocks (column in the graphics),
+% 4th D: low band, estimate, high band (in each graph).
+% xlab: x-axis labels on the top
+% ylab: y-axis labels on the left
+% tstring: string for time (e.g., month or quarter) -- x-axis labels at the bottom
+% YTickIndx: 1: enable YTick; 0: disable;
+% To get a better picture, it is sometimes better to set YtickIndx to zero.
+% scaleIndx: 1: enable scale along Y-axis; 0: disable
+% nrowg: number of rows in the graph
+% ncolg: number of columns in the graph
+%
+% See imrgraph, imcerrgraph, imrerrgraph
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+nstp = size(imfn,1);
+nrow = size(imfn,2);
+ncol = size(imfn,3);
+nmodels = size(imfn,4);
+t = 1:nstp;
+treverse = fliplr(t);
+
+if (nargin < 9)
+ nrowg = nrow;
+ ncolg = ncol;
+end
+if (nrowg*ncolg < nrow*ncol)
+ nrowg
+ ncolg
+ nrow
+ ncol
+
+ error('fn_multigraphn_3g_shadedbands.m: nrowg*ncolg must be greater than nrow*ncol')
+end
+
+
+tempmax=zeros(ncol,1);
+tempmin=zeros(ncol,1);
+maxval=zeros(nrow,1);
+minval=zeros(nrow,1);
+for i = 1:nrow
+ for j = 1:ncol
+ tempmax(j) = -realmax;
+ tempmin(j) = realmax;
+ for k=1:nmodels
+ jnk = max(imfn(:,i,j,k));
+ tempmax(j) = max([jnk tempmax(j)]);
+ %
+ jnk = min(imfn(:,i,j,k));
+ tempmin(j) = min([jnk tempmin(j)]);
+ end
+ end
+ maxval(i)=max(tempmax);
+ minval(i)=min(tempmin);
+end
+
+scaleout = [maxval(:) minval(:)];
+
+%--------------
+% Column j: Shock 1 to N; Row i: Responses to
+%-------------
+%figure
+
+
+rowlabel = 1;
+for i = 1:nrow % column: from top to bottom
+ columnlabel = 1;
+
+ if minval(i)<0
+ if maxval(i)<=0
+ yt=[minval(i) 0];
+ else
+ yt=[minval(i) 0 maxval(i)];
+ end
+ else % (minval(i) >=0)
+ if maxval(i) > 0
+ yt=[0 maxval(i)];
+ else % (identically zero responses)
+ yt=[-1 0 1];
+ end
+ end
+
+
+ scale=[1 nstp minval(i) maxval(i)];
+
+ for j = 1:ncol % row: from left to right
+ k1=(i-1)*ncol+j;
+ subplot(nrowg,ncolg,k1)
+
+ %set(0,'DefaultAxesColorOrder',[1 0 0;0 1 0;0 0 1],...
+ % 'DefaultAxesLineStyleOrder','-|--|:')
+ %set(0,'DefaultAxesLineStyleOrder','-|--|:|-.')
+ %---<<>>
+ %set(0,'DefaultAxesColorOrder',[0 0 0],...
+ % 'DefaultAxesLineStyleOrder','-.|-.|-|--|-.|-*|--o|:d')
+
+ nseries = zeros(nstp, nmodels);
+ for k=1:nmodels
+ nseries(:,k) = imfn(:,i,j,k);
+ end
+
+ %---<<>>
+ fill([t treverse],[nseries(:,[3])' fliplr(nseries(:,[1])')],[0.8,0.8,0.8],'EdgeColor','none');
+ %plot(t,nseries(:,[1 3]), '-.k','LineWidth',1.0); %,'Color','k');
+ hold on
+ plot(t,nseries(:,[2]), '-k','LineWidth',1.5);
+ %plot(t,nseries(:,[4]), '--k','LineWidth',1.6);
+ hold off
+
+ %set(gca,'LineStyleOrder','-|--|:|-.')
+ %set(gca,'LineStyleOrder',{'-*',':','o'})
+ grid;
+ if scaleIndx
+ axis(scale);
+ end
+ %set(gca,'YLim',[minval(i) maxval(i)])
+ %
+ set(gca,'XTick',XTick)
+ if YTickIndx
+ set(gca,'YTick',yt)
+ end
+
+ if i<nrow
+ set(gca,'XTickLabelMode','manual','XTickLabel',[])
+ end
+ %set(gca,'XTickLabel',' ');
+ if (scaleIndx) && (j>1)
+ set(gca,'YTickLabel',' ');
+ end
+ if rowlabel == 1
+ %title(['x' num2str(j)])
+ %title(eval(['x' num2str(j)]))
+ if (~isempty(xlab)), title(char(xlab(j))), end
+ end
+ if columnlabel == 1
+ %ylabel(['x' num2str(i)])
+ %ylabel(eval(['x' num2str(i)]))
+ if (~isempty(ylab)), ylabel(char(ylab(i))), end
+ end
+ if (i==nrow) && (~isempty(tstring))
+ xlabel(tstring)
+ end
+ columnlabel = 0;
+ end
+ rowlabel = 0;
+end
+
+
+%Order of line styles and markers used in a plot.
+%This property specifies which line styles and markers to use and in what order
+%when creating multiple-line plots. For example,set(gca,'LineStyleOrder', '-*|:|o')sets LineStyleOrder to solid line with asterisk
+%marker, dotted line, and hollow circle marker. The default is (-), which specifies
+%a solid line for all data plotted. Alternatively, you can create a cell array
+%of character strings to define the line styles:set(gca,'LineStyleOrder',{'-*',':','o'})MATLAB supports four line styles, which you can specify any number of
+%times in any order. MATLAB cycles through the line styles only after using
+%all colors defined by the ColorOrder property. For example,
+%the first eight lines plotted use the different colors defined by ColorOrder with
+%the first line style. MATLAB then cycles through the colors again, using the
+%second line style specified, and so on.You can also specify line style and color directly with the plot and plot3 functions
+%or by altering the properties of theline or
+%lineseries objects after creating the graph. High-Level Functions and LineStyleOrderNote that, if the axes NextPlot property is set
+%to replace (the default), high-level functions like plot reset
+%the LineStyleOrder property before determining the line
+%style to use. If you want MATLAB to use a LineStyleOrder that
+%is different from the default, set NextPlot to replacechildren. Specifying a Default LineStyleOrderYou can also specify your own default LineStyleOrder.
+%For example, this statementset(0,'DefaultAxesLineStyleOrder',{'-*',':','o'})
+%creates a default value for
diff --git a/MatlabFiles/fn_multigraphn_ver2.m b/MatlabFiles/fn_multigraphn_ver2.m
index a6bd030e1604006f8a88b839bc2f8b28eaa22e7f..63e55c6fceccea9353ba07e9c0de12b939d06732 100644
--- a/MatlabFiles/fn_multigraphn_ver2.m
+++ b/MatlabFiles/fn_multigraphn_ver2.m
@@ -1,175 +1,175 @@
-function scaleout = fn_multigraphn_ver2(imfn,...
- xlab,ylab,tstring,XTick,YTickIndx,scaleIndx,nrowg,ncolg)
-%Stacking n sets of impulse responses in one graph. See fn_multigraph2_ver2.m.
-% imfn: row: "nstp" time horizon (in the graphics),
-% column: "nrow "variables such as responses (row in the graphics),
-% 3rd D: across "ncol" different situations such as shocks (column in the graphics),
-% 4th D: across different scenarios such as error bands or different models (in each graph).
-% xlab: x-axis labels on the top
-% ylab: y-axis labels on the left
-% tstring: string for time (e.g., month or quarter) -- x-axis labels at the bottom
-% YTickIndx: 1: enable YTick; 0: disable;
-% To get a better picture, it is sometimes better to set YtickIndx to zero.
-% scaleIndx: 1: enable scale along Y-axis; 0: disable
-% nrowg: number of rows in the graph
-% ncolg: number of columns in the graph
-%
-% See imrgraph, imcerrgraph, imrerrgraph
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-nstp = size(imfn,1);
-nrow = size(imfn,2);
-ncol = size(imfn,3);
-nmodels = size(imfn,4);
-t = 1:nstp;
-
-if (nargin < 9)
- nrowg = nrow;
- ncolg = ncol;
-end
-if (nrowg*ncolg < nrow*ncol)
- nrowg
- ncolg
- nrow
- ncol
-
- error('fn_multigraphn_ver2.m: nrowg*ncolg must be greater than nrow*ncol')
-end
-
-
-tempmax=zeros(ncol,1);
-tempmin=zeros(ncol,1);
-maxval=zeros(nrow,1);
-minval=zeros(nrow,1);
-for i = 1:nrow
- for j = 1:ncol
- tempmax(j) = -realmax;
- tempmin(j) = realmax;
- for k=1:nmodels
- jnk = max(imfn(:,i,j,k));
- tempmax(j) = max([jnk tempmax(j)]);
- %
- jnk = min(imfn(:,i,j,k));
- tempmin(j) = min([jnk tempmin(j)]);
- end
- end
- maxval(i)=max(tempmax);
- minval(i)=min(tempmin);
-end
-
-scaleout = [maxval(:) minval(:)];
-
-%--------------
-% Column j: Shock 1 to N; Row i: Responses to
-%-------------
-%figure
-
-
-rowlabel = 1;
-for i = 1:nrow % column: from top to bottom
- columnlabel = 1;
-
- if minval(i)<0
- if maxval(i)<=0
- yt=[minval(i) 0];
- else
- yt=[minval(i) 0 maxval(i)];
- end
- else % (minval(i) >=0)
- if maxval(i) > 0
- yt=[0 maxval(i)];
- else % (identically zero responses)
- yt=[-1 0 1];
- end
- end
-
-
- scale=[1 nstp minval(i) maxval(i)];
- for j = 1:ncol % row: from left to right
- k1=(i-1)*ncol+j;
- subplot(nrowg,ncolg,k1)
-
- %set(0,'DefaultAxesColorOrder',[1 0 0;0 1 0;0 0 1],...
- % 'DefaultAxesLineStyleOrder','-|--|:')
- %set(0,'DefaultAxesLineStyleOrder','-|--|:|-.')
- set(0,'DefaultAxesColorOrder',[0 0 0],...
- 'DefaultAxesLineStyleOrder','-|--|--|:*|-.|-*|--o|:d')
-
- nseries = zeros(nstp, nmodels);
- for k=1:nmodels
- nseries(:,k) = imfn(:,i,j,k);
- end
- plot(t,nseries, 'LineWidth',1.7); %,'Color','k');
- %set(gca,'LineStyleOrder','-|--|:|-.')
- %set(gca,'LineStyleOrder',{'-*',':','o'})
- grid;
- if scaleIndx
- axis(scale);
- end
- %set(gca,'YLim',[minval(i) maxval(i)])
- %
- set(gca,'XTick',XTick)
- if YTickIndx
- set(gca,'YTick',yt)
- end
-
- if i<nrow
- set(gca,'XTickLabelMode','manual','XTickLabel',[])
- end
- %set(gca,'XTickLabel',' ');
- if (scaleIndx) && (j>1)
- set(gca,'YTickLabel',' ');
- end
- if rowlabel == 1
- %title(['x' num2str(j)])
- %title(eval(['x' num2str(j)]))
- title(char(xlab(j)))
- end
- if columnlabel == 1
- %ylabel(['x' num2str(i)])
- %ylabel(eval(['x' num2str(i)]))
- ylabel(char(ylab(i)))
- end
- if i==nrow
- xlabel(tstring)
- end
- columnlabel = 0;
- end
- rowlabel = 0;
-end
-
-
-%Order of line styles and markers used in a plot.
-%This property specifies which line styles and markers to use and in what order
-%when creating multiple-line plots. For example,set(gca,'LineStyleOrder', '-*|:|o')sets LineStyleOrder to solid line with asterisk
-%marker, dotted line, and hollow circle marker. The default is (-), which specifies
-%a solid line for all data plotted. Alternatively, you can create a cell array
-%of character strings to define the line styles:set(gca,'LineStyleOrder',{'-*',':','o'})MATLAB supports four line styles, which you can specify any number of
-%times in any order. MATLAB cycles through the line styles only after using
-%all colors defined by the ColorOrder property. For example,
-%the first eight lines plotted use the different colors defined by ColorOrder with
-%the first line style. MATLAB then cycles through the colors again, using the
-%second line style specified, and so on.You can also specify line style and color directly with the plot and plot3 functions
-%or by altering the properties of theline or
-%lineseries objects after creating the graph. High-Level Functions and LineStyleOrderNote that, if the axes NextPlot property is set
-%to replace (the default), high-level functions like plot reset
-%the LineStyleOrder property before determining the line
-%style to use. If you want MATLAB to use a LineStyleOrder that
-%is different from the default, set NextPlot to replacechildren. Specifying a Default LineStyleOrderYou can also specify your own default LineStyleOrder.
-%For example, this statementset(0,'DefaultAxesLineStyleOrder',{'-*',':','o'})
-%creates a default value for
+function scaleout = fn_multigraphn_ver2(imfn,...
+ xlab,ylab,tstring,XTick,YTickIndx,scaleIndx,nrowg,ncolg)
+%Stacking n sets of impulse responses in one graph. See fn_multigraph2_ver2.m.
+% imfn: row: "nstp" time horizon (in the graphics),
+% column: "nrow "variables such as responses (row in the graphics),
+% 3rd D: across "ncol" different situations such as shocks (column in the graphics),
+% 4th D: across different scenarios such as error bands or different models (in each graph).
+% xlab: x-axis labels on the top
+% ylab: y-axis labels on the left
+% tstring: string for time (e.g., month or quarter) -- x-axis labels at the bottom
+% YTickIndx: 1: enable YTick; 0: disable;
+% To get a better picture, it is sometimes better to set YtickIndx to zero.
+% scaleIndx: 1: enable scale along Y-axis; 0: disable
+% nrowg: number of rows in the graph
+% ncolg: number of columns in the graph
+%
+% See imrgraph, imcerrgraph, imrerrgraph
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+nstp = size(imfn,1);
+nrow = size(imfn,2);
+ncol = size(imfn,3);
+nmodels = size(imfn,4);
+t = 1:nstp;
+
+if (nargin < 9)
+ nrowg = nrow;
+ ncolg = ncol;
+end
+if (nrowg*ncolg < nrow*ncol)
+ nrowg
+ ncolg
+ nrow
+ ncol
+
+ error('fn_multigraphn_ver2.m: nrowg*ncolg must be greater than nrow*ncol')
+end
+
+
+tempmax=zeros(ncol,1);
+tempmin=zeros(ncol,1);
+maxval=zeros(nrow,1);
+minval=zeros(nrow,1);
+for i = 1:nrow
+ for j = 1:ncol
+ tempmax(j) = -realmax;
+ tempmin(j) = realmax;
+ for k=1:nmodels
+ jnk = max(imfn(:,i,j,k));
+ tempmax(j) = max([jnk tempmax(j)]);
+ %
+ jnk = min(imfn(:,i,j,k));
+ tempmin(j) = min([jnk tempmin(j)]);
+ end
+ end
+ maxval(i)=max(tempmax);
+ minval(i)=min(tempmin);
+end
+
+scaleout = [maxval(:) minval(:)];
+
+%--------------
+% Column j: Shock 1 to N; Row i: Responses to
+%-------------
+%figure
+
+
+rowlabel = 1;
+for i = 1:nrow % column: from top to bottom
+ columnlabel = 1;
+
+ if minval(i)<0
+ if maxval(i)<=0
+ yt=[minval(i) 0];
+ else
+ yt=[minval(i) 0 maxval(i)];
+ end
+ else % (minval(i) >=0)
+ if maxval(i) > 0
+ yt=[0 maxval(i)];
+ else % (identically zero responses)
+ yt=[-1 0 1];
+ end
+ end
+
+
+ scale=[1 nstp minval(i) maxval(i)];
+ for j = 1:ncol % row: from left to right
+ k1=(i-1)*ncol+j;
+ subplot(nrowg,ncolg,k1)
+
+ %set(0,'DefaultAxesColorOrder',[1 0 0;0 1 0;0 0 1],...
+ % 'DefaultAxesLineStyleOrder','-|--|:')
+ %set(0,'DefaultAxesLineStyleOrder','-|--|:|-.')
+ set(0,'DefaultAxesColorOrder',[0 0 0],...
+ 'DefaultAxesLineStyleOrder','-|--|--|:*|-.|-*|--o|:d')
+
+ nseries = zeros(nstp, nmodels);
+ for k=1:nmodels
+ nseries(:,k) = imfn(:,i,j,k);
+ end
+ plot(t,nseries, 'LineWidth',1.7); %,'Color','k');
+ %set(gca,'LineStyleOrder','-|--|:|-.')
+ %set(gca,'LineStyleOrder',{'-*',':','o'})
+ grid;
+ if scaleIndx
+ axis(scale);
+ end
+ %set(gca,'YLim',[minval(i) maxval(i)])
+ %
+ set(gca,'XTick',XTick)
+ if YTickIndx
+ set(gca,'YTick',yt)
+ end
+
+ if i<nrow
+ set(gca,'XTickLabelMode','manual','XTickLabel',[])
+ end
+ %set(gca,'XTickLabel',' ');
+ if (scaleIndx) && (j>1)
+ set(gca,'YTickLabel',' ');
+ end
+ if rowlabel == 1
+ %title(['x' num2str(j)])
+ %title(eval(['x' num2str(j)]))
+ title(char(xlab(j)))
+ end
+ if columnlabel == 1
+ %ylabel(['x' num2str(i)])
+ %ylabel(eval(['x' num2str(i)]))
+ ylabel(char(ylab(i)))
+ end
+ if i==nrow
+ xlabel(tstring)
+ end
+ columnlabel = 0;
+ end
+ rowlabel = 0;
+end
+
+
+%Order of line styles and markers used in a plot.
+%This property specifies which line styles and markers to use and in what order
+%when creating multiple-line plots. For example,set(gca,'LineStyleOrder', '-*|:|o')sets LineStyleOrder to solid line with asterisk
+%marker, dotted line, and hollow circle marker. The default is (-), which specifies
+%a solid line for all data plotted. Alternatively, you can create a cell array
+%of character strings to define the line styles:set(gca,'LineStyleOrder',{'-*',':','o'})MATLAB supports four line styles, which you can specify any number of
+%times in any order. MATLAB cycles through the line styles only after using
+%all colors defined by the ColorOrder property. For example,
+%the first eight lines plotted use the different colors defined by ColorOrder with
+%the first line style. MATLAB then cycles through the colors again, using the
+%second line style specified, and so on.You can also specify line style and color directly with the plot and plot3 functions
+%or by altering the properties of theline or
+%lineseries objects after creating the graph. High-Level Functions and LineStyleOrderNote that, if the axes NextPlot property is set
+%to replace (the default), high-level functions like plot reset
+%the LineStyleOrder property before determining the line
+%style to use. If you want MATLAB to use a LineStyleOrder that
+%is different from the default, set NextPlot to replacechildren. Specifying a Default LineStyleOrderYou can also specify your own default LineStyleOrder.
+%For example, this statementset(0,'DefaultAxesLineStyleOrder',{'-*',':','o'})
+%creates a default value for
diff --git a/MatlabFiles/fn_nmlzvar.m b/MatlabFiles/fn_nmlzvar.m
index 51c7f25f9fee564967be491684b85dea8a76f477..e8244db8be16872d7b1617f1d4c605804a51ce7b 100644
--- a/MatlabFiles/fn_nmlzvar.m
+++ b/MatlabFiles/fn_nmlzvar.m
@@ -1,108 +1,108 @@
-function [A0n,a0dpindx,nswitch,A0inn] = fn_nmlzvar(A0u,A0xhat,A0inxhat,IndxNmlr,nswitch,A0inu)
-% [A0n,nswitch,A0inn] = nmlzvar(A0u,A0xhat,A0inxhat,IndxNmlr,nswitch,A0inu)
-% Export normalized new draw A0 (and inv(A0) only if IndxNmlr(5)) and the number of sign switches
-% Ref.: D.F. Waggoner and T.A. Zha: "Does Normalization Matter for Inference?"
-% See Note Forecast (2) pp. 52-53
-%
-% A0u: unnormalized A0; column--equation
-% A0xhat: ML estimate or posterior mode of A0
-% A0inxhat: inv(A0xhat)
-% IndxNmlr: index for which normalization rule to choose
-% Only one of the elments in IndxNmlr can be non-zero
-% IndxNmlr(1): ML A distance rule (supposed to be the best)
-% IndxNmlr(2): ML Ahat distance rule (to approximate IndxNmlr(1))
-% IndxNmlr(3): ML Euclidean distance rule (not invariant to scale)
-% IndxNmlr(4): Positive diagonal rule
-% IndxNmlr(5): Positive inv(A) diagonal rule (if ~IndxNmlr(5), no need for A0inu,
-% so we set A0inn=[])
-% IndxNmlr(6): Assigned postive rule (such as off-diagonal elements). Added 1/3/00
-% nswitch: # of sign switches
-% A0inu: unnormalized inv(A0); used only if IndxNmlr(5)
-%-----------------
-% A0n: normalized new A0; column--equation
-% a0dpindx: Index of the columns in A0 or A+ whose signs need to be switched.
-% nswitch: updated # of sign switches
-% A0inn: normalized inv(A0); used only if IndxNmlr(5)
-%
-% Written by Tao Zha
-% 1/3/00: added IndxNmlr(6) so that previous programs may not be compatible.
-% 10/11/01: added a0dpindx in front of nswitch as an output argument so that previous programs may not be compatible.
-
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-A0inn = []; % no output for normalized A0in unless IndxNmlr(5)
-
-if (length(find(IndxNmlr))>1)
- warning('You cannot choose more than one normalization rule at a time')
- disp('Press ctrl-c to abort')
- pause
-elseif isempty(find(IndxNmlr)) % no normalization
- A0n=A0u; nswitch=0;
-elseif IndxNmlr(1)
- a0dpindx = find(diag(A0u\A0xhat)<0);
- A0n = A0u;
- if ~isempty(a0dpindx)
- A0n(:,a0dpindx) = -A0u(:,a0dpindx); % normalized new draws
- nswitch = nswitch + 1; %<<>> # of sign switches
- end
-elseif IndxNmlr(2)
- a0dpindx = find(diag(A0inxhat*A0u)<0);
- A0n = A0u;
- if ~isempty(a0dpindx)
- A0n(:,a0dpindx) = -A0u(:,a0dpindx); % normalized new draws
- nswitch = nswitch + 1; %<<>> # of sign switches
- end
-elseif IndxNmlr(3)
- Adiff = (A0u - A0xhat).^2; % distance that may be far from axhat or A0xhat
- Adiffn = (-A0u - A0xhat).^2; % distance by chaning the sign of Atem
- cAdiff = sum(Adiff); % each column summed up
- cAdiffn = sum(Adiffn); % each column summed up
- cAindx = find(cAdiffn<cAdiff); % index for shorter distance
- A0n = A0u;
- if ~isempty(cAindx)
- A0n(:,cAindx) = -A0u(:,cAindx); % find the shortest or nearest distance
- nswitch = nswitch + 1; %<<>> # of sign switches
- end
-elseif IndxNmlr(4)
- a0dpindx = find(diag(A0u)<0);
- A0n = A0u;
- if ~isempty(a0dpindx)
- A0n(:,a0dpindx) = -A0u(:,a0dpindx); % normalized new draws
- nswitch = nswitch + 1; %<<>> # of sign switches
- end
-elseif IndxNmlr(5)
- a0dpindx = find(diag(A0inu)<0);
- A0n = A0u;
- A0inn = A0inu;
- if ~isempty(a0dpindx)
- A0n(:,a0dpindx) = -A0u(:,a0dpindx);
- A0inn(a0dpindx,:) = -A0inu(a0dpindx,:);
- nswitch = nswitch + 1; %<<>> # of sign switches
- end
-elseif IndxNmlr(6) %*** This one has to be MANUALLY handled
- [jnk,nvar]=size(A0u);
- A0dummy=A0u;
- A0dummy(:,1:2)=-A0u(:,1:2); % keep it to a sign that coincide with Brooking paper
- a0dpindx = find(A0dummy(nvar,:)<0); % the last row
- A0n = A0u;
- if ~isempty(a0dpindx)
- A0n(:,a0dpindx) = -A0u(:,a0dpindx); % normalized new draws
- nswitch = nswitch + 1; %<<>> # of sign switches
- end
-end
+function [A0n,a0dpindx,nswitch,A0inn] = fn_nmlzvar(A0u,A0xhat,A0inxhat,IndxNmlr,nswitch,A0inu)
+% [A0n,nswitch,A0inn] = nmlzvar(A0u,A0xhat,A0inxhat,IndxNmlr,nswitch,A0inu)
+% Export normalized new draw A0 (and inv(A0) only if IndxNmlr(5)) and the number of sign switches
+% Ref.: D.F. Waggoner and T.A. Zha: "Does Normalization Matter for Inference?"
+% See Note Forecast (2) pp. 52-53
+%
+% A0u: unnormalized A0; column--equation
+% A0xhat: ML estimate or posterior mode of A0
+% A0inxhat: inv(A0xhat)
+% IndxNmlr: index for which normalization rule to choose
+% Only one of the elments in IndxNmlr can be non-zero
+% IndxNmlr(1): ML A distance rule (supposed to be the best)
+% IndxNmlr(2): ML Ahat distance rule (to approximate IndxNmlr(1))
+% IndxNmlr(3): ML Euclidean distance rule (not invariant to scale)
+% IndxNmlr(4): Positive diagonal rule
+% IndxNmlr(5): Positive inv(A) diagonal rule (if ~IndxNmlr(5), no need for A0inu,
+% so we set A0inn=[])
+% IndxNmlr(6): Assigned postive rule (such as off-diagonal elements). Added 1/3/00
+% nswitch: # of sign switches
+% A0inu: unnormalized inv(A0); used only if IndxNmlr(5)
+%-----------------
+% A0n: normalized new A0; column--equation
+% a0dpindx: Index of the columns in A0 or A+ whose signs need to be switched.
+% nswitch: updated # of sign switches
+% A0inn: normalized inv(A0); used only if IndxNmlr(5)
+%
+% Written by Tao Zha
+% 1/3/00: added IndxNmlr(6) so that previous programs may not be compatible.
+% 10/11/01: added a0dpindx in front of nswitch as an output argument so that previous programs may not be compatible.
+
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+A0inn = []; % no output for normalized A0in unless IndxNmlr(5)
+
+if (length(find(IndxNmlr))>1)
+ warning('You cannot choose more than one normalization rule at a time')
+ disp('Press ctrl-c to abort')
+ pause
+elseif isempty(find(IndxNmlr)) % no normalization
+ A0n=A0u; nswitch=0;
+elseif IndxNmlr(1)
+ a0dpindx = find(diag(A0u\A0xhat)<0);
+ A0n = A0u;
+ if ~isempty(a0dpindx)
+ A0n(:,a0dpindx) = -A0u(:,a0dpindx); % normalized new draws
+ nswitch = nswitch + 1; %<<>> # of sign switches
+ end
+elseif IndxNmlr(2)
+ a0dpindx = find(diag(A0inxhat*A0u)<0);
+ A0n = A0u;
+ if ~isempty(a0dpindx)
+ A0n(:,a0dpindx) = -A0u(:,a0dpindx); % normalized new draws
+ nswitch = nswitch + 1; %<<>> # of sign switches
+ end
+elseif IndxNmlr(3)
+ Adiff = (A0u - A0xhat).^2; % distance that may be far from axhat or A0xhat
+ Adiffn = (-A0u - A0xhat).^2; % distance by chaning the sign of Atem
+ cAdiff = sum(Adiff); % each column summed up
+ cAdiffn = sum(Adiffn); % each column summed up
+ cAindx = find(cAdiffn<cAdiff); % index for shorter distance
+ A0n = A0u;
+ if ~isempty(cAindx)
+ A0n(:,cAindx) = -A0u(:,cAindx); % find the shortest or nearest distance
+ nswitch = nswitch + 1; %<<>> # of sign switches
+ end
+elseif IndxNmlr(4)
+ a0dpindx = find(diag(A0u)<0);
+ A0n = A0u;
+ if ~isempty(a0dpindx)
+ A0n(:,a0dpindx) = -A0u(:,a0dpindx); % normalized new draws
+ nswitch = nswitch + 1; %<<>> # of sign switches
+ end
+elseif IndxNmlr(5)
+ a0dpindx = find(diag(A0inu)<0);
+ A0n = A0u;
+ A0inn = A0inu;
+ if ~isempty(a0dpindx)
+ A0n(:,a0dpindx) = -A0u(:,a0dpindx);
+ A0inn(a0dpindx,:) = -A0inu(a0dpindx,:);
+ nswitch = nswitch + 1; %<<>> # of sign switches
+ end
+elseif IndxNmlr(6) %*** This one has to be MANUALLY handled
+ [jnk,nvar]=size(A0u);
+ A0dummy=A0u;
+ A0dummy(:,1:2)=-A0u(:,1:2); % keep it to a sign that coincide with Brooking paper
+ a0dpindx = find(A0dummy(nvar,:)<0); % the last row
+ A0n = A0u;
+ if ~isempty(a0dpindx)
+ A0n(:,a0dpindx) = -A0u(:,a0dpindx); % normalized new draws
+ nswitch = nswitch + 1; %<<>> # of sign switches
+ end
+end
diff --git a/MatlabFiles/fn_numstruct2numcell_nummatrix.m b/MatlabFiles/fn_numstruct2numcell_nummatrix.m
index 3f530d2ef95b8a60e4b4a190fe601ee9fbc430e5..0b8a9adbe5dab09c078627059bb54bba7c7e0b83 100644
--- a/MatlabFiles/fn_numstruct2numcell_nummatrix.m
+++ b/MatlabFiles/fn_numstruct2numcell_nummatrix.m
@@ -1,31 +1,31 @@
function [uniform_cell, uniform_matrix3D, names_fields] = fn_numstruct2numcell_nummatrix(uniform_ps)
%[uniform_cell, uniform_matrix3D, names_fields] = fn_numstruct2numcell_nummatrix(uniform_ps)
-%
+%
% Inputs:
% uniform_ps: a structure where each field has the same nrows-by-ncols matrix. This function does NOT work
% if each field has different data types.
% Outputs:
-% uniform_cell: nfields cells where each cell has a nrows-by-ncols matrix.
+% uniform_cell: nfields cells where each cell has a nrows-by-ncols matrix.
% uniform_matrix3D: 3-D array: nrows-by-ncols-by-nfields
-% Copyright (C) 1997-2012 Tao Zha
%
-% This file is part of Dynare.
+% Copyright (C) 1997-2012 Tao Zha
%
-% Dynare is free software: you can redistribute it and/or modify
+% This free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
-% Dynare is distributed in the hope that it will be useful,
+% It is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
names_fields = fieldnames(uniform_ps);
-nfields = length(names_fields);
+nfields = length(names_fields);
uniform_cell = struct2cell(uniform_ps);
nrows = size(uniform_cell{1},1);
ncols = size(uniform_cell{1},2);
@@ -33,4 +33,4 @@ ncols = size(uniform_cell{1},2);
uniform_matrix3D = zeros(nrows,ncols,nfields);
for (ni=1:nfields)
uniform_matrix3D(:,:,ni) = uniform_cell{ni};
-end
\ No newline at end of file
+end
diff --git a/MatlabFiles/fn_ols.m b/MatlabFiles/fn_ols.m
index 33d4c5762733e72dcdfa9c4cdcf807c25f25b281..9f7a85671f0a600718861455002e2150f6463c71 100644
--- a/MatlabFiles/fn_ols.m
+++ b/MatlabFiles/fn_ols.m
@@ -1,48 +1,48 @@
-function [Bh,e,xtx,xty] = fn_ols(y,phi)
-% [Bh,e,xtx,xty] = fn_ols(y,phi)
-% ols: estimate a system of equations: Y(T*nvar) = XB + u, X: T*k, B: k*nvar.
-%
-% y: Y: T-by-nvar
-% phi: X; T-by-k; column: number of r.h.s. variables (including
-%------------
-% Bh: the estimated B; column: nvar; row: number of r.h.s. variables.
-% e: estimated residual e = y -xBh, T*nvar
-% xtx: X'X: k-by-k
-% xty: X'Y: k-by-nvar
-% deterministic terms)%
-% See also sye.m and syed.m
-
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-% ** setup of orders and lengths **
-
-[u d v]=svd(phi,0); %trial
-%xtx = phi'*phi; % X'X, k*k (ncoe*ncoe)
-vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
-dinv = 1./diag(d); % inv(diag(d))
-vdinv=v.*(ones(size(v,2),1)*dinv'); %trial
-xtx=vd*vd';
-xtxinv = vdinv*vdinv';
-%xty = phi'*y; % X'Y
-uy = u'*y; %trial
-xty = vd*uy; %trial
-%Bh = xtx\xty; %inv(X'X)*(X'Y), k*m (ncoe*nvar).
-Bh = xtxinv*xty;
-%e = y - phi*Bh; % from Y = XB + U, e: (T-lags)*nvar
-e = y - u*uy;
+function [Bh,e,xtx,xty] = fn_ols(y,phi)
+% [Bh,e,xtx,xty] = fn_ols(y,phi)
+% ols: estimate a system of equations: Y(T*nvar) = XB + u, X: T*k, B: k*nvar.
+%
+% y: Y: T-by-nvar
+% phi: X; T-by-k; column: number of r.h.s. variables (including
+%------------
+% Bh: the estimated B; column: nvar; row: number of r.h.s. variables.
+% e: estimated residual e = y -xBh, T*nvar
+% xtx: X'X: k-by-k
+% xty: X'Y: k-by-nvar
+% deterministic terms)%
+% See also sye.m and syed.m
+
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+% ** setup of orders and lengths **
+
+[u d v]=svd(phi,0); %trial
+%xtx = phi'*phi; % X'X, k*k (ncoe*ncoe)
+vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
+dinv = 1./diag(d); % inv(diag(d))
+vdinv=v.*(ones(size(v,2),1)*dinv'); %trial
+xtx=vd*vd';
+xtxinv = vdinv*vdinv';
+%xty = phi'*y; % X'Y
+uy = u'*y; %trial
+xty = vd*uy; %trial
+%Bh = xtx\xty; %inv(X'X)*(X'Y), k*m (ncoe*nvar).
+Bh = xtxinv*xty;
+%e = y - phi*Bh; % from Y = XB + U, e: (T-lags)*nvar
+e = y - u*uy;
diff --git a/MatlabFiles/fn_plotrecessionshades57_01.m b/MatlabFiles/fn_plotrecessionshades57_01.m
index bebb443441579b75979f157d6bc0c4ed282212a9..fa872ee46d4ee3b258614c7d9d72ccaee2adb861 100644
--- a/MatlabFiles/fn_plotrecessionshades57_01.m
+++ b/MatlabFiles/fn_plotrecessionshades57_01.m
@@ -1,25 +1,25 @@
-function fn_PlotRecessionShades57_01(rec_dates, AxisDat)
-% Recession shades. For some reasons, Matlab code will only take pairs of rec_dates when using rectangle.
-% Inputs: rec_dates -- must be in pairs for rectangle to work.
-% AxisDat -- example: AxisDat=axis([1976 2010 -.2 .15]);
-% 1976 will automatically cut off the dates before 1976 in the following lines.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-for RecessionNumber=1:length(rec_dates);
- rectangle('Position', [rec_dates(RecessionNumber,1) AxisDat(3) (rec_dates(RecessionNumber,2)-rec_dates(RecessionNumber,1)) (AxisDat(4)-AxisDat(3))], 'FaceColor','y');
-end
+function fn_PlotRecessionShades57_01(rec_dates, AxisDat)
+% Recession shades. For some reasons, Matlab code will only take pairs of rec_dates when using rectangle.
+% Inputs: rec_dates -- must be in pairs for rectangle to work.
+% AxisDat -- example: AxisDat=axis([1976 2010 -.2 .15]);
+% 1976 will automatically cut off the dates before 1976 in the following lines.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+for RecessionNumber=1:length(rec_dates);
+ rectangle('Position', [rec_dates(RecessionNumber,1) AxisDat(3) (rec_dates(RecessionNumber,2)-rec_dates(RecessionNumber,1)) (AxisDat(4)-AxisDat(3))], 'FaceColor','y');
+end
diff --git a/MatlabFiles/fn_posteriorvariancedecomposition.m b/MatlabFiles/fn_posteriorvariancedecomposition.m
index b091ae1e61ba2da4e5e107efe3b05abbad64b44a..e203583af8ac940c54d1b850eab6b6a5d2604d38 100644
--- a/MatlabFiles/fn_posteriorvariancedecomposition.m
+++ b/MatlabFiles/fn_posteriorvariancedecomposition.m
@@ -10,55 +10,55 @@ function [VarXAll, E_YVarXGivenY, Var_YEXGivenY] = fn_PosteriorVarianceDecomposi
% Mathematically and numerically, it must be that VarXAll = E_YVarXGivenY + Var_YEXGivenY.
%
% Written by T. Zha, October 2009
-% Copyright (C) 1997-2012 Tao Zha
%
-% This file is part of Dynare.
+% Copyright (C) 1997-2012 Tao Zha
%
-% Dynare is free software: you can redistribute it and/or modify
+% This free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
-% Dynare is distributed in the hope that it will be useful,
+% It is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
n_ydraws = size(xydraws,2);
if (nargin==1), probs = (1/n_ydraws)*ones(n_ydraws,1); end
-
+
%---------------- Overall variance --------------------
-VarXAll1st = 0.0;
-VarXAll2nd = 0.0;
+VarXAll1st = 0.0;
+VarXAll2nd = 0.0;
xy1st = mean(mean(xydraws));
xy2nd = mean(mean(xydraws.^2));
-for (yi=1:n_ydraws)
+for (yi=1:n_ydraws)
xGiveny1st = mean(xydraws(:,yi));
xGiveny2nd = mean(xydraws(:,yi).^2);
VarXAll1st = VarXAll1st + probs(yi) * xGiveny1st;
VarXAll2nd = VarXAll2nd + probs(yi) * xGiveny2nd;
-end
-VarXAll = VarXAll2nd - VarXAll1st^2;
+end
+VarXAll = VarXAll2nd - VarXAll1st^2;
%-------------- The component E_YVarXGivenY --------------------
E_YVarXGivenY = 0.0;
-for (yi=1:n_ydraws)
+for (yi=1:n_ydraws)
VarXGivenY = mean(xydraws(:,yi).^2) - (mean(xydraws(:,yi)))^2;
E_YVarXGivenY = E_YVarXGivenY + probs(yi) * VarXGivenY;
-end
+end
E_YVarXGivenY = E_YVarXGivenY;
-%-------------- The component Var_YEXGivenY --------------------
-Var_YEXGivenY1st = 0.0;
-Var_YEXGivenY2nd = 0.0;
-for (yi=1:n_ydraws)
+%-------------- The component Var_YEXGivenY --------------------
+Var_YEXGivenY1st = 0.0;
+Var_YEXGivenY2nd = 0.0;
+for (yi=1:n_ydraws)
EXGivenY = mean(xydraws(:,yi));
Var_YEXGivenY1st = Var_YEXGivenY1st + probs(yi) * EXGivenY;
Var_YEXGivenY2nd = Var_YEXGivenY2nd + probs(yi) * EXGivenY^2;
-end
+end
Var_YEXGivenY = Var_YEXGivenY2nd - Var_YEXGivenY1st^2;
diff --git a/MatlabFiles/fn_printmatrix4tex.m b/MatlabFiles/fn_printmatrix4tex.m
index c7e8b432eb3eab23ee61ec38cccf98b9f75dada8..34acfa433c4df4f08247ec9fe27957adf02f2e2b 100644
--- a/MatlabFiles/fn_printmatrix4tex.m
+++ b/MatlabFiles/fn_printmatrix4tex.m
@@ -1,55 +1,55 @@
-function fn_printmatrix4tex(M, nrows, ncols, indxFloat)
-% Prints the matrix to a screen for a tex file.
-%
-% Inputs:
-% M: The matrix to be written to the file.
-% nrows: Number of rows of M.
-% ncols: Number of columns of M.
-% indxFloat: 1 if double;
-% 2 if single;
-% 3 if only 2 significant digits
-% 0 if integer.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-%
-if nrows~=size(M,1)
- error('fn_fprintmatrix(): Make sure the row number supplied match that of the matrix');
-end
-if ncols~=size(M,2)
- error('fn_fprintmatrix(): Make sure the column number supplied match that of the matrix');
-end
-for ki=1:nrows
- for kj=1:ncols
- fprintf(1,' & ');
- if (indxFloat == 1)
- fprintf(1,' %.16e ',M((kj-1)*nrows+ki));
- elseif (indxFloat == 2)
- fprintf(1,' %.8e ',M((kj-1)*nrows+ki));
- elseif (indxFloat == 3)
- fprintf(1,' %5.2f ',M((kj-1)*nrows+ki));
- else
- fprintf(1,' %d ',M((kj-1)*nrows+ki));
- end
- if (kj==ncols)
- fprintf(1,'\\\\\n');
- end
- end
- if (ki==nrows)
- fprintf(1,'\n\n');
- end
-end
+function fn_printmatrix4tex(M, nrows, ncols, indxFloat)
+% Prints the matrix to a screen for a tex file.
+%
+% Inputs:
+% M: The matrix to be written to the file.
+% nrows: Number of rows of M.
+% ncols: Number of columns of M.
+% indxFloat: 1 if double;
+% 2 if single;
+% 3 if only 2 significant digits
+% 0 if integer.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+%
+if nrows~=size(M,1)
+ error('fn_fprintmatrix(): Make sure the row number supplied match that of the matrix');
+end
+if ncols~=size(M,2)
+ error('fn_fprintmatrix(): Make sure the column number supplied match that of the matrix');
+end
+for ki=1:nrows
+ for kj=1:ncols
+ fprintf(1,' & ');
+ if (indxFloat == 1)
+ fprintf(1,' %.16e ',M((kj-1)*nrows+ki));
+ elseif (indxFloat == 2)
+ fprintf(1,' %.8e ',M((kj-1)*nrows+ki));
+ elseif (indxFloat == 3)
+ fprintf(1,' %5.2f ',M((kj-1)*nrows+ki));
+ else
+ fprintf(1,' %d ',M((kj-1)*nrows+ki));
+ end
+ if (kj==ncols)
+ fprintf(1,'\\\\\n');
+ end
+ end
+ if (ki==nrows)
+ fprintf(1,'\n\n');
+ end
+end
diff --git a/MatlabFiles/fn_printmatrix4tex_ver2.m b/MatlabFiles/fn_printmatrix4tex_ver2.m
index ef0cdc37c236c3164c12375f55f94df4c2554f3f..220325fca8408b1c1920685597eccabc2b7a618a 100644
--- a/MatlabFiles/fn_printmatrix4tex_ver2.m
+++ b/MatlabFiles/fn_printmatrix4tex_ver2.m
@@ -1,65 +1,65 @@
-function fn_printmatrix4tex_ver2(M, nrows, ncols, indxFloat)
-%fn_printmatrix4tex_ver2(M, nrows, ncols, indxFloat)
-%
-% Prints the matrix to a screen for a tex file.
-% Inputs:
-% M: The matrix to be written to the file.
-% nrows: Number of rows of M.
-% ncols: Number of columns of M.
-% indxFloat: 1 if only 1 digits after decimal point
-% 2 if only 2 digits after decimal point
-% 3 if only 3 digits after decimal point
-% 4 if only 4 digits after decimal point
-% 0 if integer.
-% 1001 if double and %.16e
-% 1002 if single and %.8e
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-%
-if nrows~=size(M,1)
- error('fn_fprintmatrix(): Make sure the row number supplied match that of the matrix');
-end
-if ncols~=size(M,2)
- error('fn_fprintmatrix(): Make sure the column number supplied match that of the matrix');
-end
-for ki=1:nrows
- for kj=1:ncols
- fprintf(1,' & ');
- if (indxFloat == 1001)
- fprintf(1,' %.16e ',M((kj-1)*nrows+ki));
- elseif (indxFloat == 1002)
- fprintf(1,' %.8e ',M((kj-1)*nrows+ki));
- elseif (indxFloat == 1)
- fprintf(1,' %5.1f ',M((kj-1)*nrows+ki));
- elseif (indxFloat == 2)
- fprintf(1,' %5.2f ',M((kj-1)*nrows+ki));
- elseif (indxFloat == 3)
- fprintf(1,' %5.3f ',M((kj-1)*nrows+ki));
- elseif (indxFloat == 4)
- fprintf(1,' %5.4f ',M((kj-1)*nrows+ki));
- else
- fprintf(1,' %d ',M((kj-1)*nrows+ki));
- end
- if (kj==ncols)
- fprintf(1,'\\\\\n');
- end
- end
- if (ki==nrows)
- fprintf(1,'\n\n');
- end
-end
+function fn_printmatrix4tex_ver2(M, nrows, ncols, indxFloat)
+%fn_printmatrix4tex_ver2(M, nrows, ncols, indxFloat)
+%
+% Prints the matrix to a screen for a tex file.
+% Inputs:
+% M: The matrix to be written to the file.
+% nrows: Number of rows of M.
+% ncols: Number of columns of M.
+% indxFloat: 1 if only 1 digits after decimal point
+% 2 if only 2 digits after decimal point
+% 3 if only 3 digits after decimal point
+% 4 if only 4 digits after decimal point
+% 0 if integer.
+% 1001 if double and %.16e
+% 1002 if single and %.8e
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+%
+if nrows~=size(M,1)
+ error('fn_fprintmatrix(): Make sure the row number supplied match that of the matrix');
+end
+if ncols~=size(M,2)
+ error('fn_fprintmatrix(): Make sure the column number supplied match that of the matrix');
+end
+for ki=1:nrows
+ for kj=1:ncols
+ fprintf(1,' & ');
+ if (indxFloat == 1001)
+ fprintf(1,' %.16e ',M((kj-1)*nrows+ki));
+ elseif (indxFloat == 1002)
+ fprintf(1,' %.8e ',M((kj-1)*nrows+ki));
+ elseif (indxFloat == 1)
+ fprintf(1,' %5.1f ',M((kj-1)*nrows+ki));
+ elseif (indxFloat == 2)
+ fprintf(1,' %5.2f ',M((kj-1)*nrows+ki));
+ elseif (indxFloat == 3)
+ fprintf(1,' %5.3f ',M((kj-1)*nrows+ki));
+ elseif (indxFloat == 4)
+ fprintf(1,' %5.4f ',M((kj-1)*nrows+ki));
+ else
+ fprintf(1,' %d ',M((kj-1)*nrows+ki));
+ end
+ if (kj==ncols)
+ fprintf(1,'\\\\\n');
+ end
+ end
+ if (ki==nrows)
+ fprintf(1,'\n\n');
+ end
+end
diff --git a/MatlabFiles/fn_readrecessiondates57_01.m b/MatlabFiles/fn_readrecessiondates57_01.m
index d75c90dfd38608f110490e2f1ac418ec34775faa..edc2827328814741f6ca8ccf3199234a95864c79 100644
--- a/MatlabFiles/fn_readrecessiondates57_01.m
+++ b/MatlabFiles/fn_readrecessiondates57_01.m
@@ -1,32 +1,32 @@
-function rec_dates = fn_ReadRecessionDates57_01()
-% Recession dates. For some reasons, Matlab code will only take pairs of rec_dates when using rectangle.
-% Note that -1 in the following is for the graph lie in the correct position.
-% For example, 1961+(1-1)/12 means Jan 1961 plotted right at 1961.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-rec_dates=[
- 1957+(7-1)/12 1958+(3-1)/12
- 1960+(3-1)/12 1961+(1-1)/12
- 1969+(11-1)/12 1970+(10-1)/12
- 1973+(10-1)/12 1975+(2-1)/12
- 1980+(1-1)/12 1980+(6-1)/12
- 1981+(6-1)/12 1982+(10-1)/12
- 1990+(6-1)/12 1991+(2-1)/12
- 2001+(2-1)/12 2001+(10-1)/12
- 2007+(12-1)/12 2008+(12-1)/12];
-
+function rec_dates = fn_ReadRecessionDates57_01()
+% Recession dates. For some reasons, Matlab code will only take pairs of rec_dates when using rectangle.
+% Note that -1 in the following is for the graph lie in the correct position.
+% For example, 1961+(1-1)/12 means Jan 1961 plotted right at 1961.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+rec_dates=[
+ 1957+(7-1)/12 1958+(3-1)/12
+ 1960+(3-1)/12 1961+(1-1)/12
+ 1969+(11-1)/12 1970+(10-1)/12
+ 1973+(10-1)/12 1975+(2-1)/12
+ 1980+(1-1)/12 1980+(6-1)/12
+ 1981+(6-1)/12 1982+(10-1)/12
+ 1990+(6-1)/12 1991+(2-1)/12
+ 2001+(2-1)/12 2001+(10-1)/12
+ 2007+(12-1)/12 2008+(12-1)/12];
+
diff --git a/MatlabFiles/fn_reset_ini_seed.m b/MatlabFiles/fn_reset_ini_seed.m
index f0db11e51d8cae2be7c5a7cf6233439da9f004b0..29053328f0b4fd91a9dc41b3982e34c6f509a4a8 100644
--- a/MatlabFiles/fn_reset_ini_seed.m
+++ b/MatlabFiles/fn_reset_ini_seed.m
@@ -1,29 +1,29 @@
-function fn_reset_ini_seed(seednumber)
-%fn_reset_ini_seed(seednumber)
-%
-% After Matlab starts, run this function to reset the seed number; otherwise, Matlab automatically resets to the same initial seed.
-% seednumber: 0 -- random state reset to the clock time;
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-if seednumber
- randn('state',seednumber);
- rand('state',seednumber);
-else
- randn('state',fix(100*sum(clock)));
- rand('state',fix(100*sum(clock)));
-end
+function fn_reset_ini_seed(seednumber)
+%fn_reset_ini_seed(seednumber)
+%
+% After Matlab starts, run this function to reset the seed number; otherwise, Matlab automatically resets to the same initial seed.
+% seednumber: 0 -- random state reset to the clock time;
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+if seednumber
+ randn('state',seednumber);
+ rand('state',seednumber);
+else
+ randn('state',fix(100*sum(clock)));
+ rand('state',fix(100*sum(clock)));
+end
diff --git a/MatlabFiles/fn_rlrpostr.m b/MatlabFiles/fn_rlrpostr.m
index 4350f99300455d7816e240aacad91251bd381147..50b3faf0299917c2bfe59f2aa175b961c62a6700 100644
--- a/MatlabFiles/fn_rlrpostr.m
+++ b/MatlabFiles/fn_rlrpostr.m
@@ -1,67 +1,67 @@
-function [P,H0inv,Hpinv] = fn_rlrpostr(xtx,xty,yty,Ptld,H0invtld,Hpinvtld,Ui,Vi)
-% [P,H0inv,Hpinv] = fn_rlrpostr(xtx,xty,yty,Ptld,H0tld,Hptld,Ui,Vi)
-%
-% Exporting random (i.e., random prior) Bayesian posterior matrices with linear restrictions
-% See Waggoner and Zha's Gibbs sampling paper
-%
-% xtx: X'X: k-by-k where k=ncoef
-% xty: X'Y: k-by-nvar
-% yty: Y'Y: nvar-by-nvar
-% Ptld: cell(nvar,1), transformation matrix that affects the (random walk) prior mean of A+ conditional on A0.
-% H0invtld: cell(nvar,1), transformed inv covaraince for free parameters in A0(:,i).
-% Hpinvtld: cell(nvar,1), transformed inv covaraince for free parameters in A+(:,i);
-% Ui: nvar-by-1 cell. In each cell, nvar-by-qi orthonormal basis for the null of the ith
-% equation contemporaneous restriction matrix where qi is the number of free parameters.
-% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
-% of total original parameters and bi is a vector of free parameters. When no
-% restrictions are imposed, we have Ui = I. There must be at least one free
-% parameter left for the ith equation. Imported from dnrprior.m.
-% Vi: nvar-by-1 cell. In each cell, k-by-ri orthonormal basis for the null of the ith
-% equation lagged restriction matrix where k (ncoef) is a total number of RHS variables and
-% ri is the number of free parameters. With this transformation, we have fi = Vi*gi
-% or Vi'*fi = gi where fi is a vector of total original parameters and gi is a
-% vector of free parameters. There must be at least one free parameter left for
-% the ith equation. Imported from dnrprior.m.
-%-----------------
-% P: cell(nvar,1). In each cell, the transformation matrix that affects the posterior mean of A+ conditional on A0.
-% In other words, the posterior mean (of g_i) = P{i}*b_i where g_i is a column vector of free parameters
-% of A+(:,i)) given b_i (b_i is a column vector of free parameters of A0(:,i)).
-% H0inv: cell(nvar,1). Not divided by T yet. In each cell, inverse of posterior covariance matrix H0.
-% The exponential term is b_i'*inv(H0)*b_i for the ith equation where b_i = U_i*a0_i.
-% It resembles old SpH or Sbd in the exponent term in posterior of A0, but not divided by T yet.
-% Hpinv: cell(nvar,1). In each cell, posterior inverse of covariance matrix Hp (A+) for the free parameters
-% g_i = V_i*A+(:,i) in the ith equation.
-%
-% Tao Zha, February 2000
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-nvar = size(yty,1);
-
-P = cell(nvar,1); % tld: tilda
-H0inv = cell(nvar,1); % posterior inv(H0), resemble old SpH, but not divided by T yet.
-Hpinv = cell(nvar,1); % posterior inv(Hp).
-
-
-for n=1:nvar % one for each equation
- Hpinv{n} = Vi{n}'*xtx*Vi{n} + Hpinvtld{n};
- P1 = Vi{n}'*xty*Ui{n} + Hpinvtld{n}*Ptld{n};
- P{n} = Hpinv{n}\P1;
- H0inv{n} = Ui{n}'*yty*Ui{n} + H0invtld{n} + Ptld{n}'*Hpinvtld{n}*Ptld{n} ...
- - P1'*P{n}; %P{n} = (Hpinv{n}\P1);
-end
+function [P,H0inv,Hpinv] = fn_rlrpostr(xtx,xty,yty,Ptld,H0invtld,Hpinvtld,Ui,Vi)
+% [P,H0inv,Hpinv] = fn_rlrpostr(xtx,xty,yty,Ptld,H0tld,Hptld,Ui,Vi)
+%
+% Exporting random (i.e., random prior) Bayesian posterior matrices with linear restrictions
+% See Waggoner and Zha's Gibbs sampling paper
+%
+% xtx: X'X: k-by-k where k=ncoef
+% xty: X'Y: k-by-nvar
+% yty: Y'Y: nvar-by-nvar
+% Ptld: cell(nvar,1), transformation matrix that affects the (random walk) prior mean of A+ conditional on A0.
+% H0invtld: cell(nvar,1), transformed inv covaraince for free parameters in A0(:,i).
+% Hpinvtld: cell(nvar,1), transformed inv covaraince for free parameters in A+(:,i);
+% Ui: nvar-by-1 cell. In each cell, nvar-by-qi orthonormal basis for the null of the ith
+% equation contemporaneous restriction matrix where qi is the number of free parameters.
+% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
+% of total original parameters and bi is a vector of free parameters. When no
+% restrictions are imposed, we have Ui = I. There must be at least one free
+% parameter left for the ith equation. Imported from dnrprior.m.
+% Vi: nvar-by-1 cell. In each cell, k-by-ri orthonormal basis for the null of the ith
+% equation lagged restriction matrix where k (ncoef) is a total number of RHS variables and
+% ri is the number of free parameters. With this transformation, we have fi = Vi*gi
+% or Vi'*fi = gi where fi is a vector of total original parameters and gi is a
+% vector of free parameters. There must be at least one free parameter left for
+% the ith equation. Imported from dnrprior.m.
+%-----------------
+% P: cell(nvar,1). In each cell, the transformation matrix that affects the posterior mean of A+ conditional on A0.
+% In other words, the posterior mean (of g_i) = P{i}*b_i where g_i is a column vector of free parameters
+% of A+(:,i)) given b_i (b_i is a column vector of free parameters of A0(:,i)).
+% H0inv: cell(nvar,1). Not divided by T yet. In each cell, inverse of posterior covariance matrix H0.
+% The exponential term is b_i'*inv(H0)*b_i for the ith equation where b_i = U_i*a0_i.
+% It resembles old SpH or Sbd in the exponent term in posterior of A0, but not divided by T yet.
+% Hpinv: cell(nvar,1). In each cell, posterior inverse of covariance matrix Hp (A+) for the free parameters
+% g_i = V_i*A+(:,i) in the ith equation.
+%
+% Tao Zha, February 2000
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+nvar = size(yty,1);
+
+P = cell(nvar,1); % tld: tilda
+H0inv = cell(nvar,1); % posterior inv(H0), resemble old SpH, but not divided by T yet.
+Hpinv = cell(nvar,1); % posterior inv(Hp).
+
+
+for n=1:nvar % one for each equation
+ Hpinv{n} = Vi{n}'*xtx*Vi{n} + Hpinvtld{n};
+ P1 = Vi{n}'*xty*Ui{n} + Hpinvtld{n}*Ptld{n};
+ P{n} = Hpinv{n}\P1;
+ H0inv{n} = Ui{n}'*yty*Ui{n} + H0invtld{n} + Ptld{n}'*Hpinvtld{n}*Ptld{n} ...
+ - P1'*P{n}; %P{n} = (Hpinv{n}\P1);
+end
diff --git a/MatlabFiles/fn_rlrprior.m b/MatlabFiles/fn_rlrprior.m
index ad7f0e5e3920a53e14961abaf2019418d884e458..626ec0e63f0e766e33b6c63ac2f19d601fd2d508 100644
--- a/MatlabFiles/fn_rlrprior.m
+++ b/MatlabFiles/fn_rlrprior.m
@@ -1,55 +1,55 @@
-function [Ptld,H0invtld,Hpinvtld] = fn_rlrprior(Ui,Vi,Pi,H0multi,Hpmulti,nvar)
-% [Ptld,H0invtld,Hpinvtld] = fn_rlrprior(Ui,Vi,Pi,H0multi,Hpmulti,nvar)
-%
-% Exporting random Bayesian prior with linear restrictions
-% See Waggoner and Zha's Gibbs sampling paper
-%
-% Ui: nvar-by-1 cell. In each cell, nvar-by-qi orthonormal basis for the null of the ith
-% equation contemporaneous restriction matrix where qi is the number of free parameters.
-% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
-% of total original parameters and bi is a vector of free parameters. When no
-% restrictions are imposed, we have Ui = I. There must be at least one free
-% parameter left for the ith equation. Imported from dnrprior.m.
-% Vi: nvar-by-1 cell. In each cell, k-by-ri orthonormal basis for the null of the ith
-% equation lagged restriction matrix where k (ncoef) is a total number of RHS variables and
-% ri is the number of free parameters. With this transformation, we have fi = Vi*gi
-% or Vi'*fi = gi where fi is a vector of total original parameters and gi is a
-% vector of free parameters. There must be at least one free parameter left for
-% the ith equation. Imported from dnrprior.m.
-% Pi: ncoef-by-nvar matrix for the ith equation under random walk. Same for all equations
-% H0multi: nvar-by-nvar-by-nvar; H0 for different equations under asymmetric prior
-% Hpmulti: ncoef-by-ncoef-by-nvar; H+ for different equations under asymmetric prior
-% nvar: number of endogenous variables
-% --------------------
-% Ptld: cell(nvar,1). The prior mean of g_i is Ptld{i}*b_i;
-% H0invtld: cell(nvar,1). Transformed inv covaraince for b_i, the free parameters in A0(:,i);
-% Hpinvtld: cell(nvar,1). Transformed inv covaraince for g_i, the free parameters in A+(:,i);
-%
-% Tao Zha, February 2000
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-Ptld = cell(nvar,1); % tld: tilda
-H0invtld = cell(nvar,1); % H0 for different equations under linear restrictions
-Hpinvtld = cell(nvar,1); % H+ for different equations under linear restrictions
-
-for n=1:nvar % one for each equation
- Hpinvtld{n} = Vi{n}'*(Hpmulti(:,:,n)\Vi{n});
- Ptld{n} = (Hpinvtld{n}\Vi{n}')*(Hpmulti(:,:,n)\Pi)*Ui{n};
- H0invtld{n} = Ui{n}'*(H0multi(:,:,n)\Ui{n}) + Ui{n}'*Pi'*(Hpmulti(:,:,n)\Pi)*Ui{n} ...
- - Ptld{n}'*Hpinvtld{n}*Ptld{n};
-end
+function [Ptld,H0invtld,Hpinvtld] = fn_rlrprior(Ui,Vi,Pi,H0multi,Hpmulti,nvar)
+% [Ptld,H0invtld,Hpinvtld] = fn_rlrprior(Ui,Vi,Pi,H0multi,Hpmulti,nvar)
+%
+% Exporting random Bayesian prior with linear restrictions
+% See Waggoner and Zha's Gibbs sampling paper
+%
+% Ui: nvar-by-1 cell. In each cell, nvar-by-qi orthonormal basis for the null of the ith
+% equation contemporaneous restriction matrix where qi is the number of free parameters.
+% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
+% of total original parameters and bi is a vector of free parameters. When no
+% restrictions are imposed, we have Ui = I. There must be at least one free
+% parameter left for the ith equation. Imported from dnrprior.m.
+% Vi: nvar-by-1 cell. In each cell, k-by-ri orthonormal basis for the null of the ith
+% equation lagged restriction matrix where k (ncoef) is a total number of RHS variables and
+% ri is the number of free parameters. With this transformation, we have fi = Vi*gi
+% or Vi'*fi = gi where fi is a vector of total original parameters and gi is a
+% vector of free parameters. There must be at least one free parameter left for
+% the ith equation. Imported from dnrprior.m.
+% Pi: ncoef-by-nvar matrix for the ith equation under random walk. Same for all equations
+% H0multi: nvar-by-nvar-by-nvar; H0 for different equations under asymmetric prior
+% Hpmulti: ncoef-by-ncoef-by-nvar; H+ for different equations under asymmetric prior
+% nvar: number of endogenous variables
+% --------------------
+% Ptld: cell(nvar,1). The prior mean of g_i is Ptld{i}*b_i;
+% H0invtld: cell(nvar,1). Transformed inv covaraince for b_i, the free parameters in A0(:,i);
+% Hpinvtld: cell(nvar,1). Transformed inv covaraince for g_i, the free parameters in A+(:,i);
+%
+% Tao Zha, February 2000
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+Ptld = cell(nvar,1); % tld: tilda
+H0invtld = cell(nvar,1); % H0 for different equations under linear restrictions
+Hpinvtld = cell(nvar,1); % H+ for different equations under linear restrictions
+
+for n=1:nvar % one for each equation
+ Hpinvtld{n} = Vi{n}'*(Hpmulti(:,:,n)\Vi{n});
+ Ptld{n} = (Hpinvtld{n}\Vi{n}')*(Hpmulti(:,:,n)\Pi)*Ui{n};
+ H0invtld{n} = Ui{n}'*(H0multi(:,:,n)\Ui{n}) + Ui{n}'*Pi'*(Hpmulti(:,:,n)\Pi)*Ui{n} ...
+ - Ptld{n}'*Hpinvtld{n}*Ptld{n};
+end
diff --git a/MatlabFiles/fn_rnrprior.m b/MatlabFiles/fn_rnrprior.m
index c431895989446b31d7feace5729f18a9dae2307a..c7fdf10e77acd2568de23a69df14b220356e4271 100644
--- a/MatlabFiles/fn_rnrprior.m
+++ b/MatlabFiles/fn_rnrprior.m
@@ -1,240 +1,240 @@
-function [Pi,H0multi,Hpmulti,H0invmulti,Hpinvmulti] ...
- = fn_rnrprior(nvar,q_m,lags,xdgel,mu,nexo,asym0,asymp)
-% [Pi,H0multi,Hpmulti,H0invmulti,Hpinvmulti] ...
-% = fn_rnrprior(nvar,q_m,lags,xdgel,mu,nexo,asym0,asymp)
-% Exporting random Bayesian prior of Sims and Zha with asymmetric rior (but no linear restrictions)
-% See Waggoner and Zha's Gibbs sampling paper
-%
-% nvar: number of endogenous variables
-% q_m: quarter or month
-% lags: the maximum length of lag
-% xdgel: the general matrix of the original data (no manipulation involved)
-% with sample size including lags. Used only to get variances of residuals for
-% the scaling purpose; NOT used for mu(5) and mu(6).
-% mu: 6-by-1 vector of hyperparameters (the following numbers for Atlanta Fed's forecast), where
-% mu(5) and mu(6) are NOT used here. See fn_dataxy.m for using mu(5) and mu(6).
-% mu(1): overall tightness and also for A0; (0.57)
-% mu(2): relative tightness for A+; (0.13)
-% mu(3): relative tightness for the constant term; (0.1). NOTE: for other
-% exogenous terms, the variance of each exogenous term must be taken into
-% acount to eliminate the scaling factor.
-% mu(4): tightness on lag decay; (1)
-% mu(5): weight on nvar sums of coeffs dummy observations (unit roots); (5)
-% mu(6): weight on single dummy initial observation including constant
-% (cointegration, unit roots, and stationarity); (5)
-% NOTE: for this subdirectory, mu(5) and mu(6) are not used.
-% asym0: nvar-by-nvar asymmetric prior on A0. Column -- equation.
-% If ones(nvar,nvar), symmetric prior; if not, relative (asymmetric) tightness on A0.
-% asymp: ncoef-1-by-nvar asymmetric prior on A+ bar constant. Column -- equation.
-% If ones(ncoef-1,nvar), symmetric prior; if not, relative (asymmetric) tightness on A+.
-% nexo: number of exogenous variables (if not specified, nexo=1 (constant) by default).
-% The constant term is always put to the last of all endogenous and exogenous variables.
-% --------------------
-% Pi: ncoef-by-nvar matrix for the ith equation under random walk. Same for all equations
-% H0multi: nvar-by-nvar-by-nvar; H0 for different equations under asymmetric prior
-% Hpmulti: ncoef-by-ncoef-by-nvar; H+ for different equations under asymmetric prior
-% H0invmulti: nvar-by-nvar-by-nvar; inv(H0) for different equations under asymmetric prior
-% Hpinvmulti: ncoef-by-ncoef-by-nvar; inv(H+) for different equations under asymmetric prior
-%
-% Tao Zha, February 2000. Revised, September 2000.
-% See fn_dataxy.m for using mu(5) and mu(6).
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-if nargin==5, nexo=1; end
-ncoef = nvar*lags+nexo; % number of coefficients in *each* equation, RHS coefficients only.
-
-H0multi=zeros(nvar,nvar,nvar); % H0 for different equations under asymmetric prior
-Hpmulti=zeros(ncoef,ncoef,nvar); % H+ for different equations under asymmetric prior
-H0invmulti=zeros(nvar,nvar,nvar); % inv(H0) for different equations under asymmetric prior
-Hpinvmulti=zeros(ncoef,ncoef,nvar); % inv(H+) for different equations under asymmetric prior
-
-%*** Constructing Pi for the ith equation under the random walk assumption
-Pi = zeros(ncoef,nvar); % same for all equations
-Pi(1:nvar,1:nvar) = eye(nvar); % random walk
-
-%
-%@@@ Prepared for Bayesian prior
-%
-%
-% ** monthly lag decay in order to match quarterly decay: a*exp(bl) where
-% ** l is the monthly lag. Suppose quarterly decay is 1/x where x=1,2,3,4.
-% ** Let the decay of l1 (a*exp(b*l1)) match that of x1 (say, beginning: 1/1)
-% ** and the decay of l2 (a*exp(b*l2)) match that of x2 (say, end: 1/5),
-% ** we can solve for a and b which are
-% ** b = (log_x1-log_x2)/(l1-l2), and a = x1*exp(-b*l1).
-if q_m==12
- l1 = 1; % 1st month == 1st quarter
- xx1 = 1; % 1st quarter
- l2 = lags; % last month
- xx2 = 1/((ceil(lags/3))^mu(4)); % last quarter
- %xx2 = 1/6; % last quarter
- % 3rd quarter: i.e., we intend to let decay of the 6th month match
- % that of the 3rd quarter, so that the 6th month decays a little
- % faster than the second quarter which is 1/2.
- if lags==1
- b = 0;
- else
- b = (log(xx1)-log(xx2))/(l1-l2);
- end
- a = xx1*exp(-b*l1);
-end
-
-
-
-%
-% *** specify the prior for each equation separately, SZ method,
-% ** get the residuals from univariate regressions.
-%
-sgh = zeros(nvar,1); % square root
-sgsh = sgh; % square
-nSample=size(xdgel,1); % sample size-lags
-yu = xdgel;
-C = ones(nSample,1);
-for k=1:nvar
- [Bk,ek,junk1,junk2,junk3,junk4] = sye([yu(:,k) C],lags);
- clear Bk junk1 junk2 junk3 junk4;
- sgsh(k) = ek'*ek/(nSample-lags);
- sgh(k) = sqrt(sgsh(k));
-end
-% ** prior variance for A0(:,1), same for all equations!!!
-sg0bid = zeros(nvar,1); % Sigma0_bar diagonal only for the ith equation
-for j=1:nvar
- sg0bid(j) = 1/sgsh(j); % sgsh = sigmai^2
-end
-% ** prior variance for lagged and exogeous variables, same for all equations
-sgpbid = zeros(ncoef,1); % Sigma_plus_bar, diagonal, for the ith equation
-for i = 1:lags
- if (q_m==12)
- lagdecay = a*exp(b*i*mu(4));
- end
- %
- for j = 1:nvar
- if (q_m==12)
- % exponential decay to match quarterly decay
- sgpbid((i-1)*nvar+j) = lagdecay^2/sgsh(j); % ith equation
- elseif (q_m==4)
- sgpbid((i-1)*nvar+j) = (1/i^mu(4))^2/sgsh(j); % ith equation
- else
- error('Incompatibility with lags, check the possible errors!!!')
- %warning('Incompatibility with lags, check the possible errors!!!')
- %return
- end
- end
-end
-%
-
-
-%=================================================
-% Computing the (prior) covariance matrix for the posterior of A0, no data yet
-%=================================================
-%
-%
-% ** set up the conditional prior variance sg0bi and sgpbi.
-sg0bida = mu(1)^2*sg0bid; % ith equation
-sgpbida = mu(1)^2*mu(2)^2*sgpbid;
-sgpbida(ncoef-nexo+1:ncoef) = mu(1)^2*mu(3)^2;
- %<<>> No scaling adjustment has been made for exogenous terms other than constant
-sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in a Sims-Zha paper
-
-Hptd = zeros(ncoef);
-Hptdi=Hptd;
-Hptd(ncoef,ncoef)=sgppbd(ncoef-nvar);
-Hptdinv(ncoef,ncoef)=1./sgppbd(ncoef-nvar);
- % condtional on A0i, H_plus_tilde
-
-
-if nargin<7 % the default is no asymmetric information
- asym0 = ones(nvar,nvar); % if not ones, then we have relative (asymmetric) tightness
- asymp = ones(ncoef-1,nvar); % for A+. Column -- equation
-end
-
-%**** Asymmetric Information
-%asym0 = ones(nvar,nvar); % if not ones, then we have relative (asymmetric) tightness
-%asymp = ones(ncoef-1,nvar); % pp: plus without constant. Column -- equation
-%>>>>>> B: asymmetric prior variance for asymp <<<<<<<<
-%
-%for i = 1:lags
-% rowif = (i-1)*nvar+1;
-% rowil = i*nvar;
-% idmatw0 = 0.5; % weight assigned to idmat0 in the formation of asymp
-% if (i==1)
-% asymp(rowif:rowil,:)=(1-idmatw0)*ones(nvar)+idmatw0*idmat0; % first lag
-% % note: idmat1 is already transposed. Column -- equation
-% else
-% %asymp(rowif:rowil,1:nvar) = (1-idmatw0)*ones(nvar)+idmatw0*idmat0;
-% % <<<<<<< toggle +
-% % Note: already transposed, since idmat0 is transposed.
-% % Meaning: column implies equation
-% asymp(rowif:rowil,1:nvar) = ones(nvar);
-% % >>>>>>> toggle -
-% end
-%end
-%
-%>>>>>> E: asymmetric prior variance for asymp <<<<<<<<
-
-
-%=================================================
-% Computing the final covariance matrix (S1,...,Sm) for the prior of A0,
-% and final Gb=(G1,...,Gm) for A+ if asymmetric prior or for
-% B if symmetric prior for A+
-%=================================================
-%
-for i = 1:nvar
- %------------------------------
- % Introduce prior information on which variables "belong" in various equations.
- % In this first trial, we just introduce this information here, in a model-specific way.
- % Eventually this info has to be passed parametricly. In our first shot, we just damp down
- % all coefficients except those on the diagonal.
-
- %*** For A0
- factor0=asym0(:,i);
- sg0bd = sg0bida.*factor0; % Note, this only works for the prior variance Sg(i)
- % of a0(i) being diagonal. If the prior variance Sg(i) is not
- % diagonal, we have to work on inv(Sg(i)) or sg0bdinv directly.
- sg0bdinv = 1./sg0bd;
- % * unconditional variance on A0+
- H0td = diag(sg0bd); % unconditional
- H0tdinv = diag(sg0bdinv);
- %
- H0multi(:,:,i)=H0td;
- H0invmulti(:,:,i)=H0tdinv;
-
-
- %*** For A+
- if ~(lags==0) % For A1 to remain random walk properties
- factor1=asymp(1:nvar,i);
- sg1bd = sgpbida(1:nvar).*factor1;
- sg1bdinv = 1./sg1bd;
- %
- Hptd(1:nvar,1:nvar)=diag(sg1bd);
- Hptdinv(1:nvar,1:nvar)=diag(sg1bdinv);
- if lags>1
- factorpp=asymp(nvar+1:ncoef-1,i);
- sgpp_cbd = sgppbd(1:ncoef-nvar-1) .* factorpp;
- sgpp_cbdinv = 1./sgpp_cbd;
- Hptd(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbd);
- Hptdinv(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbdinv);
- % condtional on A0i, H_plus_tilde
- end
- end
- Hpmulti(:,:,i)=Hptd;
- Hpinvmulti(:,:,i)=Hptdinv;
-end
-
-
+function [Pi,H0multi,Hpmulti,H0invmulti,Hpinvmulti] ...
+ = fn_rnrprior(nvar,q_m,lags,xdgel,mu,nexo,asym0,asymp)
+% [Pi,H0multi,Hpmulti,H0invmulti,Hpinvmulti] ...
+% = fn_rnrprior(nvar,q_m,lags,xdgel,mu,nexo,asym0,asymp)
+% Exporting random Bayesian prior of Sims and Zha with asymmetric rior (but no linear restrictions)
+% See Waggoner and Zha's Gibbs sampling paper
+%
+% nvar: number of endogenous variables
+% q_m: quarter or month
+% lags: the maximum length of lag
+% xdgel: the general matrix of the original data (no manipulation involved)
+% with sample size including lags. Used only to get variances of residuals for
+% the scaling purpose; NOT used for mu(5) and mu(6).
+% mu: 6-by-1 vector of hyperparameters (the following numbers for Atlanta Fed's forecast), where
+% mu(5) and mu(6) are NOT used here. See fn_dataxy.m for using mu(5) and mu(6).
+% mu(1): overall tightness and also for A0; (0.57)
+% mu(2): relative tightness for A+; (0.13)
+% mu(3): relative tightness for the constant term; (0.1). NOTE: for other
+% exogenous terms, the variance of each exogenous term must be taken into
+% acount to eliminate the scaling factor.
+% mu(4): tightness on lag decay; (1)
+% mu(5): weight on nvar sums of coeffs dummy observations (unit roots); (5)
+% mu(6): weight on single dummy initial observation including constant
+% (cointegration, unit roots, and stationarity); (5)
+% NOTE: for this subdirectory, mu(5) and mu(6) are not used.
+% asym0: nvar-by-nvar asymmetric prior on A0. Column -- equation.
+% If ones(nvar,nvar), symmetric prior; if not, relative (asymmetric) tightness on A0.
+% asymp: ncoef-1-by-nvar asymmetric prior on A+ bar constant. Column -- equation.
+% If ones(ncoef-1,nvar), symmetric prior; if not, relative (asymmetric) tightness on A+.
+% nexo: number of exogenous variables (if not specified, nexo=1 (constant) by default).
+% The constant term is always put to the last of all endogenous and exogenous variables.
+% --------------------
+% Pi: ncoef-by-nvar matrix for the ith equation under random walk. Same for all equations
+% H0multi: nvar-by-nvar-by-nvar; H0 for different equations under asymmetric prior
+% Hpmulti: ncoef-by-ncoef-by-nvar; H+ for different equations under asymmetric prior
+% H0invmulti: nvar-by-nvar-by-nvar; inv(H0) for different equations under asymmetric prior
+% Hpinvmulti: ncoef-by-ncoef-by-nvar; inv(H+) for different equations under asymmetric prior
+%
+% Tao Zha, February 2000. Revised, September 2000.
+% See fn_dataxy.m for using mu(5) and mu(6).
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+if nargin==5, nexo=1; end
+ncoef = nvar*lags+nexo; % number of coefficients in *each* equation, RHS coefficients only.
+
+H0multi=zeros(nvar,nvar,nvar); % H0 for different equations under asymmetric prior
+Hpmulti=zeros(ncoef,ncoef,nvar); % H+ for different equations under asymmetric prior
+H0invmulti=zeros(nvar,nvar,nvar); % inv(H0) for different equations under asymmetric prior
+Hpinvmulti=zeros(ncoef,ncoef,nvar); % inv(H+) for different equations under asymmetric prior
+
+%*** Constructing Pi for the ith equation under the random walk assumption
+Pi = zeros(ncoef,nvar); % same for all equations
+Pi(1:nvar,1:nvar) = eye(nvar); % random walk
+
+%
+%@@@ Prepared for Bayesian prior
+%
+%
+% ** monthly lag decay in order to match quarterly decay: a*exp(bl) where
+% ** l is the monthly lag. Suppose quarterly decay is 1/x where x=1,2,3,4.
+% ** Let the decay of l1 (a*exp(b*l1)) match that of x1 (say, beginning: 1/1)
+% ** and the decay of l2 (a*exp(b*l2)) match that of x2 (say, end: 1/5),
+% ** we can solve for a and b which are
+% ** b = (log_x1-log_x2)/(l1-l2), and a = x1*exp(-b*l1).
+if q_m==12
+ l1 = 1; % 1st month == 1st quarter
+ xx1 = 1; % 1st quarter
+ l2 = lags; % last month
+ xx2 = 1/((ceil(lags/3))^mu(4)); % last quarter
+ %xx2 = 1/6; % last quarter
+ % 3rd quarter: i.e., we intend to let decay of the 6th month match
+ % that of the 3rd quarter, so that the 6th month decays a little
+ % faster than the second quarter which is 1/2.
+ if lags==1
+ b = 0;
+ else
+ b = (log(xx1)-log(xx2))/(l1-l2);
+ end
+ a = xx1*exp(-b*l1);
+end
+
+
+
+%
+% *** specify the prior for each equation separately, SZ method,
+% ** get the residuals from univariate regressions.
+%
+sgh = zeros(nvar,1); % square root
+sgsh = sgh; % square
+nSample=size(xdgel,1); % sample size-lags
+yu = xdgel;
+C = ones(nSample,1);
+for k=1:nvar
+ [Bk,ek,junk1,junk2,junk3,junk4] = sye([yu(:,k) C],lags);
+ clear Bk junk1 junk2 junk3 junk4;
+ sgsh(k) = ek'*ek/(nSample-lags);
+ sgh(k) = sqrt(sgsh(k));
+end
+% ** prior variance for A0(:,1), same for all equations!!!
+sg0bid = zeros(nvar,1); % Sigma0_bar diagonal only for the ith equation
+for j=1:nvar
+ sg0bid(j) = 1/sgsh(j); % sgsh = sigmai^2
+end
+% ** prior variance for lagged and exogeous variables, same for all equations
+sgpbid = zeros(ncoef,1); % Sigma_plus_bar, diagonal, for the ith equation
+for i = 1:lags
+ if (q_m==12)
+ lagdecay = a*exp(b*i*mu(4));
+ end
+ %
+ for j = 1:nvar
+ if (q_m==12)
+ % exponential decay to match quarterly decay
+ sgpbid((i-1)*nvar+j) = lagdecay^2/sgsh(j); % ith equation
+ elseif (q_m==4)
+ sgpbid((i-1)*nvar+j) = (1/i^mu(4))^2/sgsh(j); % ith equation
+ else
+ error('Incompatibility with lags, check the possible errors!!!')
+ %warning('Incompatibility with lags, check the possible errors!!!')
+ %return
+ end
+ end
+end
+%
+
+
+%=================================================
+% Computing the (prior) covariance matrix for the posterior of A0, no data yet
+%=================================================
+%
+%
+% ** set up the conditional prior variance sg0bi and sgpbi.
+sg0bida = mu(1)^2*sg0bid; % ith equation
+sgpbida = mu(1)^2*mu(2)^2*sgpbid;
+sgpbida(ncoef-nexo+1:ncoef) = mu(1)^2*mu(3)^2;
+ %<<>> No scaling adjustment has been made for exogenous terms other than constant
+sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in a Sims-Zha paper
+
+Hptd = zeros(ncoef);
+Hptdi=Hptd;
+Hptd(ncoef,ncoef)=sgppbd(ncoef-nvar);
+Hptdinv(ncoef,ncoef)=1./sgppbd(ncoef-nvar);
+ % condtional on A0i, H_plus_tilde
+
+
+if nargin<7 % the default is no asymmetric information
+ asym0 = ones(nvar,nvar); % if not ones, then we have relative (asymmetric) tightness
+ asymp = ones(ncoef-1,nvar); % for A+. Column -- equation
+end
+
+%**** Asymmetric Information
+%asym0 = ones(nvar,nvar); % if not ones, then we have relative (asymmetric) tightness
+%asymp = ones(ncoef-1,nvar); % pp: plus without constant. Column -- equation
+%>>>>>> B: asymmetric prior variance for asymp <<<<<<<<
+%
+%for i = 1:lags
+% rowif = (i-1)*nvar+1;
+% rowil = i*nvar;
+% idmatw0 = 0.5; % weight assigned to idmat0 in the formation of asymp
+% if (i==1)
+% asymp(rowif:rowil,:)=(1-idmatw0)*ones(nvar)+idmatw0*idmat0; % first lag
+% % note: idmat1 is already transposed. Column -- equation
+% else
+% %asymp(rowif:rowil,1:nvar) = (1-idmatw0)*ones(nvar)+idmatw0*idmat0;
+% % <<<<<<< toggle +
+% % Note: already transposed, since idmat0 is transposed.
+% % Meaning: column implies equation
+% asymp(rowif:rowil,1:nvar) = ones(nvar);
+% % >>>>>>> toggle -
+% end
+%end
+%
+%>>>>>> E: asymmetric prior variance for asymp <<<<<<<<
+
+
+%=================================================
+% Computing the final covariance matrix (S1,...,Sm) for the prior of A0,
+% and final Gb=(G1,...,Gm) for A+ if asymmetric prior or for
+% B if symmetric prior for A+
+%=================================================
+%
+for i = 1:nvar
+ %------------------------------
+ % Introduce prior information on which variables "belong" in various equations.
+ % In this first trial, we just introduce this information here, in a model-specific way.
+ % Eventually this info has to be passed parametricly. In our first shot, we just damp down
+ % all coefficients except those on the diagonal.
+
+ %*** For A0
+ factor0=asym0(:,i);
+ sg0bd = sg0bida.*factor0; % Note, this only works for the prior variance Sg(i)
+ % of a0(i) being diagonal. If the prior variance Sg(i) is not
+ % diagonal, we have to work on inv(Sg(i)) or sg0bdinv directly.
+ sg0bdinv = 1./sg0bd;
+ % * unconditional variance on A0+
+ H0td = diag(sg0bd); % unconditional
+ H0tdinv = diag(sg0bdinv);
+ %
+ H0multi(:,:,i)=H0td;
+ H0invmulti(:,:,i)=H0tdinv;
+
+
+ %*** For A+
+ if ~(lags==0) % For A1 to remain random walk properties
+ factor1=asymp(1:nvar,i);
+ sg1bd = sgpbida(1:nvar).*factor1;
+ sg1bdinv = 1./sg1bd;
+ %
+ Hptd(1:nvar,1:nvar)=diag(sg1bd);
+ Hptdinv(1:nvar,1:nvar)=diag(sg1bdinv);
+ if lags>1
+ factorpp=asymp(nvar+1:ncoef-1,i);
+ sgpp_cbd = sgppbd(1:ncoef-nvar-1) .* factorpp;
+ sgpp_cbdinv = 1./sgpp_cbd;
+ Hptd(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbd);
+ Hptdinv(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbdinv);
+ % condtional on A0i, H_plus_tilde
+ end
+ end
+ Hpmulti(:,:,i)=Hptd;
+ Hpinvmulti(:,:,i)=Hptdinv;
+end
+
+
diff --git a/MatlabFiles/fn_rnrprior2.m b/MatlabFiles/fn_rnrprior2.m
index fd7c332d86d4972166e76eeb46cb472b73952a37..adc9fbdd176d130af2ff578830f1807e3aa16fbe 100644
--- a/MatlabFiles/fn_rnrprior2.m
+++ b/MatlabFiles/fn_rnrprior2.m
@@ -1,236 +1,236 @@
-function [Pi,H0multi,Hpmulti,H0invmulti,Hpinvmulti] ...
- = fn_rnrprior2(nvar,q_m,lags,xdgel,mu1_4,nexo,asym0,asymp)
-% [Pi,H0multi,Hpmulti,H0invmulti,Hpinvmulti] ...
-% = fn_rnrprior2(nvar,q_m,lags,xdgel,mu1_4,nexo,asym0,asymp)
-% Exporting random Bayesian prior of Sims and Zha with asymmetric rior (but no linear restrictions)
-% See Waggoner and Zha's Gibbs sampling paper
-%
-% nvar: number of endogenous variables
-% q_m: quarter or month
-% lags: the maximum length of lag
-% xdgel: the general matrix of the original data (no manipulation involved)
-% with sample size including lags. Used only to get variances of residuals for
-% the scaling purpose; NOT used for hyperparameters on dummy observations.
-% mu1_4: 4-by-1 vector of hyperparameters (the numbers for Atlanta Fed's forecast), where
-% mu(5) and mu(6) are NOT used here. See fn_dataxy2.m for using mu(5) and mu(6).
-% mu1_4(1): overall tightness and also for A0; (0.57)
-% mu1_4(2): relative tightness for A+; (0.13)
-% mu1_4(3): relative tightness for the constant term; (0.1). NOTE: for other
-% exogenous terms, the variance of each exogenous term must be taken into
-% acount to eliminate the scaling factor.
-% mu1_4(4): tightness on lag decay; (1)
-% asym0: nvar-by-nvar asymmetric prior on A0. Column -- equation.
-% If ones(nvar,nvar), symmetric prior; if not, relative (asymmetric) tightness on A0.
-% asymp: ncoef-1-by-nvar asymmetric prior on A+ bar constant. Column -- equation.
-% If ones(ncoef-1,nvar), symmetric prior; if not, relative (asymmetric) tightness on A+.
-% nexo: number of exogenous variables (if not specified, nexo=1 (constant) by default).
-% The constant term is always put to the last of all endogenous and exogenous variables.
-% --------------------
-% Pi: ncoef-by-nvar matrix for the ith equation under random walk. Same for all equations
-% H0multi: nvar-by-nvar-by-nvar; H0 for different equations under asymmetric prior
-% Hpmulti: ncoef-by-ncoef-by-nvar; H+ for different equations under asymmetric prior
-% H0invmulti: nvar-by-nvar-by-nvar; inv(H0) for different equations under asymmetric prior
-% Hpinvmulti: ncoef-by-ncoef-by-nvar; inv(H+) for different equations under asymmetric prior
-%
-% Tao Zha, February 2000. Revised, September 2000.
-% See fn_dataxy2.m for using dummy hyperparameters mu5 and mu6.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-if nargin==5, nexo=1; end
-ncoef = nvar*lags+nexo; % number of coefficients in *each* equation, RHS coefficients only.
-
-H0multi=zeros(nvar,nvar,nvar); % H0 for different equations under asymmetric prior
-Hpmulti=zeros(ncoef,ncoef,nvar); % H+ for different equations under asymmetric prior
-H0invmulti=zeros(nvar,nvar,nvar); % inv(H0) for different equations under asymmetric prior
-Hpinvmulti=zeros(ncoef,ncoef,nvar); % inv(H+) for different equations under asymmetric prior
-
-%*** Constructing Pi for the ith equation under the random walk assumption
-Pi = zeros(ncoef,nvar); % same for all equations
-Pi(1:nvar,1:nvar) = eye(nvar); % random walk
-
-%
-%@@@ Prepared for Bayesian prior
-%
-%
-% ** monthly lag decay in order to match quarterly decay: a*exp(bl) where
-% ** l is the monthly lag. Suppose quarterly decay is 1/x where x=1,2,3,4.
-% ** Let the decay of l1 (a*exp(b*l1)) match that of x1 (say, beginning: 1/1)
-% ** and the decay of l2 (a*exp(b*l2)) match that of x2 (say, end: 1/5),
-% ** we can solve for a and b which are
-% ** b = (log_x1-log_x2)/(l1-l2), and a = x1*exp(-b*l1).
-if q_m==12
- l1 = 1; % 1st month == 1st quarter
- xx1 = 1; % 1st quarter
- l2 = lags; % last month
- xx2 = 1/((ceil(lags/3))^mu1_4(4)); % last quarter
- %xx2 = 1/6; % last quarter
- % 3rd quarter: i.e., we intend to let decay of the 6th month match
- % that of the 3rd quarter, so that the 6th month decays a little
- % faster than the second quarter which is 1/2.
- if lags==1
- b = 0;
- else
- b = (log(xx1)-log(xx2))/(l1-l2);
- end
- a = xx1*exp(-b*l1);
-end
-
-
-
-%
-% *** specify the prior for each equation separately, SZ method,
-% ** get the residuals from univariate regressions.
-%
-sgh = zeros(nvar,1); % square root
-sgsh = sgh; % square
-nSample=size(xdgel,1); % sample size-lags
-yu = xdgel;
-C = ones(nSample,1);
-for k=1:nvar
- [Bk,ek,junk1,junk2,junk3,junk4] = sye([yu(:,k) C],lags);
- clear Bk junk1 junk2 junk3 junk4;
- sgsh(k) = ek'*ek/(nSample-lags);
- sgh(k) = sqrt(sgsh(k));
-end
-% ** prior variance for A0(:,1), same for all equations!!!
-sg0bid = zeros(nvar,1); % Sigma0_bar diagonal only for the ith equation
-for j=1:nvar
- sg0bid(j) = 1/sgsh(j); % sgsh = sigmai^2
-end
-% ** prior variance for lagged and exogeous variables, same for all equations
-sgpbid = zeros(ncoef,1); % Sigma_plus_bar, diagonal, for the ith equation
-for i = 1:lags
- if (q_m==12)
- lagdecay = a*exp(b*i*mu1_4(4));
- end
- %
- for j = 1:nvar
- if (q_m==12)
- % exponential decay to match quarterly decay
- sgpbid((i-1)*nvar+j) = lagdecay^2/sgsh(j); % ith equation
- elseif (q_m==4)
- sgpbid((i-1)*nvar+j) = (1/i^mu1_4(4))^2/sgsh(j); % ith equation
- else
- error('Incompatibility with lags, check the possible errors!!!')
- %warning('Incompatibility with lags, check the possible errors!!!')
- %return
- end
- end
-end
-%
-
-
-%=================================================
-% Computing the (prior) covariance matrix for the posterior of A0, no data yet
-%=================================================
-%
-%
-% ** set up the conditional prior variance sg0bi and sgpbi.
-sg0bida = mu1_4(1)^2*sg0bid; % ith equation
-sgpbida = mu1_4(1)^2*mu1_4(2)^2*sgpbid;
-sgpbida(ncoef-nexo+1:ncoef) = mu1_4(1)^2*mu1_4(3)^2;
- %<<>> No scaling adjustment has been made for exogenous terms other than constant
-sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in a Sims-Zha paper
-
-Hptd = zeros(ncoef);
-Hptdi=Hptd;
-Hptd(ncoef,ncoef)=sgppbd(ncoef-nvar);
-Hptdinv(ncoef,ncoef)=1./sgppbd(ncoef-nvar);
- % condtional on A0i, H_plus_tilde
-
-
-if nargin<7 % the default is no asymmetric information
- asym0 = ones(nvar,nvar); % if not ones, then we have relative (asymmetric) tightness
- asymp = ones(ncoef-1,nvar); % for A+. Column -- equation
-end
-
-%**** Asymmetric Information
-%asym0 = ones(nvar,nvar); % if not ones, then we have relative (asymmetric) tightness
-%asymp = ones(ncoef-1,nvar); % pp: plus without constant. Column -- equation
-%>>>>>> B: asymmetric prior variance for asymp <<<<<<<<
-%
-%for i = 1:lags
-% rowif = (i-1)*nvar+1;
-% rowil = i*nvar;
-% idmatw0 = 0.5; % weight assigned to idmat0 in the formation of asymp
-% if (i==1)
-% asymp(rowif:rowil,:)=(1-idmatw0)*ones(nvar)+idmatw0*idmat0; % first lag
-% % note: idmat1 is already transposed. Column -- equation
-% else
-% %asymp(rowif:rowil,1:nvar) = (1-idmatw0)*ones(nvar)+idmatw0*idmat0;
-% % <<<<<<< toggle +
-% % Note: already transposed, since idmat0 is transposed.
-% % Meaning: column implies equation
-% asymp(rowif:rowil,1:nvar) = ones(nvar);
-% % >>>>>>> toggle -
-% end
-%end
-%
-%>>>>>> E: asymmetric prior variance for asymp <<<<<<<<
-
-
-%=================================================
-% Computing the final covariance matrix (S1,...,Sm) for the prior of A0,
-% and final Gb=(G1,...,Gm) for A+ if asymmetric prior or for
-% B if symmetric prior for A+
-%=================================================
-%
-for i = 1:nvar
- %------------------------------
- % Introduce prior information on which variables "belong" in various equations.
- % In this first trial, we just introduce this information here, in a model-specific way.
- % Eventually this info has to be passed parametricly. In our first shot, we just damp down
- % all coefficients except those on the diagonal.
-
- %*** For A0
- factor0=asym0(:,i);
- sg0bd = sg0bida.*factor0; % Note, this only works for the prior variance Sg(i)
- % of a0(i) being diagonal. If the prior variance Sg(i) is not
- % diagonal, we have to work on inv(Sg(i)) or sg0bdinv directly.
- sg0bdinv = 1./sg0bd;
- % * unconditional variance on A0+
- H0td = diag(sg0bd); % unconditional
- H0tdinv = diag(sg0bdinv);
- %
- H0multi(:,:,i)=H0td;
- H0invmulti(:,:,i)=H0tdinv;
-
-
- %*** For A+
- if ~(lags==0) % For A1 to remain random walk properties
- factor1=asymp(1:nvar,i);
- sg1bd = sgpbida(1:nvar).*factor1;
- sg1bdinv = 1./sg1bd;
- %
- Hptd(1:nvar,1:nvar)=diag(sg1bd);
- Hptdinv(1:nvar,1:nvar)=diag(sg1bdinv);
- if lags>1
- factorpp=asymp(nvar+1:ncoef-1,i);
- sgpp_cbd = sgppbd(1:ncoef-nvar-1) .* factorpp;
- sgpp_cbdinv = 1./sgpp_cbd;
- Hptd(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbd);
- Hptdinv(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbdinv);
- % condtional on A0i, H_plus_tilde
- end
- end
- Hpmulti(:,:,i)=Hptd;
- Hpinvmulti(:,:,i)=Hptdinv;
-end
-
-
+function [Pi,H0multi,Hpmulti,H0invmulti,Hpinvmulti] ...
+ = fn_rnrprior2(nvar,q_m,lags,xdgel,mu1_4,nexo,asym0,asymp)
+% [Pi,H0multi,Hpmulti,H0invmulti,Hpinvmulti] ...
+% = fn_rnrprior2(nvar,q_m,lags,xdgel,mu1_4,nexo,asym0,asymp)
+% Exporting random Bayesian prior of Sims and Zha with asymmetric rior (but no linear restrictions)
+% See Waggoner and Zha's Gibbs sampling paper
+%
+% nvar: number of endogenous variables
+% q_m: quarter or month
+% lags: the maximum length of lag
+% xdgel: the general matrix of the original data (no manipulation involved)
+% with sample size including lags. Used only to get variances of residuals for
+% the scaling purpose; NOT used for hyperparameters on dummy observations.
+% mu1_4: 4-by-1 vector of hyperparameters (the numbers for Atlanta Fed's forecast), where
+% mu(5) and mu(6) are NOT used here. See fn_dataxy2.m for using mu(5) and mu(6).
+% mu1_4(1): overall tightness and also for A0; (0.57)
+% mu1_4(2): relative tightness for A+; (0.13)
+% mu1_4(3): relative tightness for the constant term; (0.1). NOTE: for other
+% exogenous terms, the variance of each exogenous term must be taken into
+% acount to eliminate the scaling factor.
+% mu1_4(4): tightness on lag decay; (1)
+% asym0: nvar-by-nvar asymmetric prior on A0. Column -- equation.
+% If ones(nvar,nvar), symmetric prior; if not, relative (asymmetric) tightness on A0.
+% asymp: ncoef-1-by-nvar asymmetric prior on A+ bar constant. Column -- equation.
+% If ones(ncoef-1,nvar), symmetric prior; if not, relative (asymmetric) tightness on A+.
+% nexo: number of exogenous variables (if not specified, nexo=1 (constant) by default).
+% The constant term is always put to the last of all endogenous and exogenous variables.
+% --------------------
+% Pi: ncoef-by-nvar matrix for the ith equation under random walk. Same for all equations
+% H0multi: nvar-by-nvar-by-nvar; H0 for different equations under asymmetric prior
+% Hpmulti: ncoef-by-ncoef-by-nvar; H+ for different equations under asymmetric prior
+% H0invmulti: nvar-by-nvar-by-nvar; inv(H0) for different equations under asymmetric prior
+% Hpinvmulti: ncoef-by-ncoef-by-nvar; inv(H+) for different equations under asymmetric prior
+%
+% Tao Zha, February 2000. Revised, September 2000.
+% See fn_dataxy2.m for using dummy hyperparameters mu5 and mu6.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+if nargin==5, nexo=1; end
+ncoef = nvar*lags+nexo; % number of coefficients in *each* equation, RHS coefficients only.
+
+H0multi=zeros(nvar,nvar,nvar); % H0 for different equations under asymmetric prior
+Hpmulti=zeros(ncoef,ncoef,nvar); % H+ for different equations under asymmetric prior
+H0invmulti=zeros(nvar,nvar,nvar); % inv(H0) for different equations under asymmetric prior
+Hpinvmulti=zeros(ncoef,ncoef,nvar); % inv(H+) for different equations under asymmetric prior
+
+%*** Constructing Pi for the ith equation under the random walk assumption
+Pi = zeros(ncoef,nvar); % same for all equations
+Pi(1:nvar,1:nvar) = eye(nvar); % random walk
+
+%
+%@@@ Prepared for Bayesian prior
+%
+%
+% ** monthly lag decay in order to match quarterly decay: a*exp(bl) where
+% ** l is the monthly lag. Suppose quarterly decay is 1/x where x=1,2,3,4.
+% ** Let the decay of l1 (a*exp(b*l1)) match that of x1 (say, beginning: 1/1)
+% ** and the decay of l2 (a*exp(b*l2)) match that of x2 (say, end: 1/5),
+% ** we can solve for a and b which are
+% ** b = (log_x1-log_x2)/(l1-l2), and a = x1*exp(-b*l1).
+if q_m==12
+ l1 = 1; % 1st month == 1st quarter
+ xx1 = 1; % 1st quarter
+ l2 = lags; % last month
+ xx2 = 1/((ceil(lags/3))^mu1_4(4)); % last quarter
+ %xx2 = 1/6; % last quarter
+ % 3rd quarter: i.e., we intend to let decay of the 6th month match
+ % that of the 3rd quarter, so that the 6th month decays a little
+ % faster than the second quarter which is 1/2.
+ if lags==1
+ b = 0;
+ else
+ b = (log(xx1)-log(xx2))/(l1-l2);
+ end
+ a = xx1*exp(-b*l1);
+end
+
+
+
+%
+% *** specify the prior for each equation separately, SZ method,
+% ** get the residuals from univariate regressions.
+%
+sgh = zeros(nvar,1); % square root
+sgsh = sgh; % square
+nSample=size(xdgel,1); % sample size-lags
+yu = xdgel;
+C = ones(nSample,1);
+for k=1:nvar
+ [Bk,ek,junk1,junk2,junk3,junk4] = sye([yu(:,k) C],lags);
+ clear Bk junk1 junk2 junk3 junk4;
+ sgsh(k) = ek'*ek/(nSample-lags);
+ sgh(k) = sqrt(sgsh(k));
+end
+% ** prior variance for A0(:,1), same for all equations!!!
+sg0bid = zeros(nvar,1); % Sigma0_bar diagonal only for the ith equation
+for j=1:nvar
+ sg0bid(j) = 1/sgsh(j); % sgsh = sigmai^2
+end
+% ** prior variance for lagged and exogeous variables, same for all equations
+sgpbid = zeros(ncoef,1); % Sigma_plus_bar, diagonal, for the ith equation
+for i = 1:lags
+ if (q_m==12)
+ lagdecay = a*exp(b*i*mu1_4(4));
+ end
+ %
+ for j = 1:nvar
+ if (q_m==12)
+ % exponential decay to match quarterly decay
+ sgpbid((i-1)*nvar+j) = lagdecay^2/sgsh(j); % ith equation
+ elseif (q_m==4)
+ sgpbid((i-1)*nvar+j) = (1/i^mu1_4(4))^2/sgsh(j); % ith equation
+ else
+ error('Incompatibility with lags, check the possible errors!!!')
+ %warning('Incompatibility with lags, check the possible errors!!!')
+ %return
+ end
+ end
+end
+%
+
+
+%=================================================
+% Computing the (prior) covariance matrix for the posterior of A0, no data yet
+%=================================================
+%
+%
+% ** set up the conditional prior variance sg0bi and sgpbi.
+sg0bida = mu1_4(1)^2*sg0bid; % ith equation
+sgpbida = mu1_4(1)^2*mu1_4(2)^2*sgpbid;
+sgpbida(ncoef-nexo+1:ncoef) = mu1_4(1)^2*mu1_4(3)^2;
+ %<<>> No scaling adjustment has been made for exogenous terms other than constant
+sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in a Sims-Zha paper
+
+Hptd = zeros(ncoef);
+Hptdi=Hptd;
+Hptd(ncoef,ncoef)=sgppbd(ncoef-nvar);
+Hptdinv(ncoef,ncoef)=1./sgppbd(ncoef-nvar);
+ % condtional on A0i, H_plus_tilde
+
+
+if nargin<7 % the default is no asymmetric information
+ asym0 = ones(nvar,nvar); % if not ones, then we have relative (asymmetric) tightness
+ asymp = ones(ncoef-1,nvar); % for A+. Column -- equation
+end
+
+%**** Asymmetric Information
+%asym0 = ones(nvar,nvar); % if not ones, then we have relative (asymmetric) tightness
+%asymp = ones(ncoef-1,nvar); % pp: plus without constant. Column -- equation
+%>>>>>> B: asymmetric prior variance for asymp <<<<<<<<
+%
+%for i = 1:lags
+% rowif = (i-1)*nvar+1;
+% rowil = i*nvar;
+% idmatw0 = 0.5; % weight assigned to idmat0 in the formation of asymp
+% if (i==1)
+% asymp(rowif:rowil,:)=(1-idmatw0)*ones(nvar)+idmatw0*idmat0; % first lag
+% % note: idmat1 is already transposed. Column -- equation
+% else
+% %asymp(rowif:rowil,1:nvar) = (1-idmatw0)*ones(nvar)+idmatw0*idmat0;
+% % <<<<<<< toggle +
+% % Note: already transposed, since idmat0 is transposed.
+% % Meaning: column implies equation
+% asymp(rowif:rowil,1:nvar) = ones(nvar);
+% % >>>>>>> toggle -
+% end
+%end
+%
+%>>>>>> E: asymmetric prior variance for asymp <<<<<<<<
+
+
+%=================================================
+% Computing the final covariance matrix (S1,...,Sm) for the prior of A0,
+% and final Gb=(G1,...,Gm) for A+ if asymmetric prior or for
+% B if symmetric prior for A+
+%=================================================
+%
+for i = 1:nvar
+ %------------------------------
+ % Introduce prior information on which variables "belong" in various equations.
+ % In this first trial, we just introduce this information here, in a model-specific way.
+ % Eventually this info has to be passed parametricly. In our first shot, we just damp down
+ % all coefficients except those on the diagonal.
+
+ %*** For A0
+ factor0=asym0(:,i);
+ sg0bd = sg0bida.*factor0; % Note, this only works for the prior variance Sg(i)
+ % of a0(i) being diagonal. If the prior variance Sg(i) is not
+ % diagonal, we have to work on inv(Sg(i)) or sg0bdinv directly.
+ sg0bdinv = 1./sg0bd;
+ % * unconditional variance on A0+
+ H0td = diag(sg0bd); % unconditional
+ H0tdinv = diag(sg0bdinv);
+ %
+ H0multi(:,:,i)=H0td;
+ H0invmulti(:,:,i)=H0tdinv;
+
+
+ %*** For A+
+ if ~(lags==0) % For A1 to remain random walk properties
+ factor1=asymp(1:nvar,i);
+ sg1bd = sgpbida(1:nvar).*factor1;
+ sg1bdinv = 1./sg1bd;
+ %
+ Hptd(1:nvar,1:nvar)=diag(sg1bd);
+ Hptdinv(1:nvar,1:nvar)=diag(sg1bdinv);
+ if lags>1
+ factorpp=asymp(nvar+1:ncoef-1,i);
+ sgpp_cbd = sgppbd(1:ncoef-nvar-1) .* factorpp;
+ sgpp_cbdinv = 1./sgpp_cbd;
+ Hptd(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbd);
+ Hptdinv(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbdinv);
+ % condtional on A0i, H_plus_tilde
+ end
+ end
+ Hpmulti(:,:,i)=Hptd;
+ Hpinvmulti(:,:,i)=Hptdinv;
+end
+
+
diff --git a/MatlabFiles/fn_rnrprior_covres.m b/MatlabFiles/fn_rnrprior_covres.m
index 9d84cc8873762daae7b5273354729fbbffbe6f65..f600f9258d3183206c0ad86676259021620d8923 100644
--- a/MatlabFiles/fn_rnrprior_covres.m
+++ b/MatlabFiles/fn_rnrprior_covres.m
@@ -1,259 +1,259 @@
-function [Pi,H0multi,Hpmulti,H0invmulti,Hpinvmulti] ...
- = fn_rnrprior_covres(nvar,q_m,lags,xdgel,mu,hpmsmd,indxmsmdeqn,nexo,asym0,asymp)
-% [Pi,H0multi,Hpmulti,H0invmulti,Hpinvmulti] ...
-% = fn_rnrprior_covres(nvar,q_m,lags,xdgel,mu,hpmsmd,indxmsmdeqn,nexo,asym0,asymp)
-%
-% More general than fn_rnrprior.m because when hpmsmd=0, fn_rnrprior_covres() is the same as fn_rnrprior().
-% Allows for prior covariances for the MS and MD equations to achieve liquidity effects.
-% Exports random Bayesian prior of Sims and Zha with asymmetric rior (but no linear restrictions yet)
-% See Waggoner and Zha's Gibbs sampling paper and TVBVAR NOTES p. 71k.0.
-%
-% nvar: number of endogenous variables
-% q_m: quarter or month
-% lags: the maximum length of lag
-% xdgel: the general matrix of the original data (no manipulation involved)
-% with sample size including lags. Used only to get variances of residuals for
-% the scaling purpose; NOT used for mu(5) and mu(6).
-% mu: 6-by-1 vector of hyperparameters (the following numbers for Atlanta Fed's forecast), where
-% mu(5) and mu(6) are NOT used here. See fn_dataxy.m for using mu(5) and mu(6).
-% mu(1): overall tightness and also for A0; (0.57)
-% mu(2): relative tightness for A+; (0.13)
-% mu(3): relative tightness for the constant term; (0.1). NOTE: for other
-% exogenous terms, the variance of each exogenous term must be taken into
-% acount to eliminate the scaling factor.
-% mu(4): tightness on lag decay; (1)
-% mu(5): weight on nvar sums of coeffs dummy observations (unit roots); (5)
-% mu(6): weight on single dummy initial observation including constant
-% (cointegration, unit roots, and stationarity); (5)
-% NOTE: for this subdirectory, mu(5) and mu(6) are not used. See fn_dataxy.m for using mu(5) and mu(6).
-% hpmsmd: 2-by-1 hyperparameters with -1<h1=hpmsmd(1)<=0 for the MS equation and 0<=h2=hpmsmd(2)<1 the MD equation. Consider a1*R + a2*M.
-% The term h1*var(a1)*var(a2) is the prior covariance of a1 and a2 for MS, equivalent to penalizing the same sign of a1 and a2.
-% The term h2*var(a1)*var(a2) is the prior covariance of a1 and a2 for MD, equivalent to penalizing opposite signs of a1 and a2.
-% This will give us a liquidity effect.
-% indxmsmdeqn: 4-by-1 index for the locations of the MS and MD equation and for the locations of M and R.
-% indxmsmdeqn(1) for MS and indxmsmdeqn(2) for MD.
-% indxmsmdeqn(3) for M and indxmsmdeqn(4) for R.
-% nexo: number of exogenous variables (if not specified, nexo=1 (constant) by default).
-% The constant term is always put to the last of all endogenous and exogenous variables.
-% asym0: nvar-by-nvar asymmetric prior on A0. Column -- equation.
-% If ones(nvar,nvar), symmetric prior; if not, relative (asymmetric) tightness on A0.
-% asymp: ncoef-1-by-nvar asymmetric prior on A+ bar constant. Column -- equation.
-% If ones(ncoef-1,nvar), symmetric prior; if not, relative (asymmetric) tightness on A+.
-% --------------------
-% Pi: ncoef-by-nvar matrix for the ith equation under random walk. Same for all equations
-% H0multi: nvar-by-nvar-by-nvar; H0 for different equations under asymmetric prior
-% Hpmulti: ncoef-by-ncoef-by-nvar; H+ for different equations under asymmetric prior
-% H0invmulti: nvar-by-nvar-by-nvar; inv(H0) for different equations under asymmetric prior
-% Hpinvmulti: ncoef-by-ncoef-by-nvar; inv(H+) for different equations under asymmetric prior
-%
-% Tao Zha, February 2000. Revised, September 2000, February 2003.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-
-if nargin==7, nexo=1; end
-ncoef = nvar*lags+nexo; % number of coefficients in *each* equation, RHS coefficients only.
-
-H0multi=zeros(nvar,nvar,nvar); % H0 for different equations under asymmetric prior
-Hpmulti=zeros(ncoef,ncoef,nvar); % H+ for different equations under asymmetric prior
-H0invmulti=zeros(nvar,nvar,nvar); % inv(H0) for different equations under asymmetric prior
-Hpinvmulti=zeros(ncoef,ncoef,nvar); % inv(H+) for different equations under asymmetric prior
-
-%*** Constructing Pi for the ith equation under the random walk assumption
-Pi = zeros(ncoef,nvar); % same for all equations
-Pi(1:nvar,1:nvar) = eye(nvar); % random walk
-
-%
-%@@@ Prepared for Bayesian prior
-%
-%
-% ** monthly lag decay in order to match quarterly decay: a*exp(bl) where
-% ** l is the monthly lag. Suppose quarterly decay is 1/x where x=1,2,3,4.
-% ** Let the decay of l1 (a*exp(b*l1)) match that of x1 (say, beginning: 1/1)
-% ** and the decay of l2 (a*exp(b*l2)) match that of x2 (say, end: 1/5),
-% ** we can solve for a and b which are
-% ** b = (log_x1-log_x2)/(l1-l2), and a = x1*exp(-b*l1).
-if q_m==12
- l1 = 1; % 1st month == 1st quarter
- xx1 = 1; % 1st quarter
- l2 = lags; % last month
- xx2 = 1/((ceil(lags/3))^mu(4)); % last quarter
- %xx2 = 1/6; % last quarter
- % 3rd quarter: i.e., we intend to let decay of the 6th month match
- % that of the 3rd quarter, so that the 6th month decays a little
- % faster than the second quarter which is 1/2.
- if lags==1
- b = 0;
- else
- b = (log(xx1)-log(xx2))/(l1-l2);
- end
- a = xx1*exp(-b*l1);
-end
-
-
-
-%
-% *** specify the prior for each equation separately, SZ method,
-% ** get the residuals from univariate regressions.
-%
-sgh = zeros(nvar,1); % square root
-sgsh = sgh; % square
-nSample=size(xdgel,1); % sample size-lags
-yu = xdgel;
-C = ones(nSample,1);
-for k=1:nvar
- [Bk,ek,junk1,junk2,junk3,junk4] = sye([yu(:,k) C],lags);
- clear Bk junk1 junk2 junk3 junk4;
- sgsh(k) = ek'*ek/(nSample-lags);
- sgh(k) = sqrt(sgsh(k));
-end
-% ** prior variance for A0(:,1), same for all equations!!!
-sg0bid = zeros(nvar,1); % Sigma0_bar diagonal only for the ith equation
-for j=1:nvar
- sg0bid(j) = 1/sgsh(j); % sgsh = sigmai^2
-end
-% ** prior variance for lagged and exogeous variables, same for all equations
-sgpbid = zeros(ncoef,1); % Sigma_plus_bar, diagonal, for the ith equation
-for i = 1:lags
- if (q_m==12)
- lagdecay = a*exp(b*i*mu(4));
- end
- %
- for j = 1:nvar
- if (q_m==12)
- % exponential decay to match quarterly decay
- sgpbid((i-1)*nvar+j) = lagdecay^2/sgsh(j); % ith equation
- elseif (q_m==4)
- sgpbid((i-1)*nvar+j) = (1/i^mu(4))^2/sgsh(j); % ith equation
- else
- error('Incompatibility with lags, check the possible errors!!!')
- %warning('Incompatibility with lags, check the possible errors!!!')
- %return
- end
- end
-end
-%
-
-
-%=================================================
-% Computing the (prior) covariance matrix for the posterior of A0, no data yet
-%=================================================
-%
-%
-% ** set up the conditional prior variance sg0bi and sgpbi.
-sg0bida = mu(1)^2*sg0bid; % ith equation
-sgpbida = mu(1)^2*mu(2)^2*sgpbid;
-sgpbida(ncoef-nexo+1:ncoef) = mu(1)^2*mu(3)^2;
- %<<>> No scaling adjustment has been made for exogenous terms other than constant
-sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in a Sims-Zha paper
-
-Hptd = zeros(ncoef);
-Hptdi=Hptd;
-Hptd(ncoef,ncoef)=sgppbd(ncoef-nvar);
-Hptdinv(ncoef,ncoef)=1./sgppbd(ncoef-nvar);
- % condtional on A0i, H_plus_tilde
-
-
-if nargin<9 % the default is no asymmetric information
- asym0 = ones(nvar,nvar); % if not ones, then we have relative (asymmetric) tightness
- asymp = ones(ncoef-1,nvar); % for A+. Column -- equation
-end
-
-%**** Asymmetric Information
-%asym0 = ones(nvar,nvar); % if not ones, then we have relative (asymmetric) tightness
-%asymp = ones(ncoef-1,nvar); % pp: plus without constant. Column -- equation
-%>>>>>> B: asymmetric prior variance for asymp <<<<<<<<
-%
-%for i = 1:lags
-% rowif = (i-1)*nvar+1;
-% rowil = i*nvar;
-% idmatw0 = 0.5; % weight assigned to idmat0 in the formation of asymp
-% if (i==1)
-% asymp(rowif:rowil,:)=(1-idmatw0)*ones(nvar)+idmatw0*idmat0; % first lag
-% % note: idmat1 is already transposed. Column -- equation
-% else
-% %asymp(rowif:rowil,1:nvar) = (1-idmatw0)*ones(nvar)+idmatw0*idmat0;
-% % <<<<<<< toggle +
-% % Note: already transposed, since idmat0 is transposed.
-% % Meaning: column implies equation
-% asymp(rowif:rowil,1:nvar) = ones(nvar);
-% % >>>>>>> toggle -
-% end
-%end
-%
-%>>>>>> E: asymmetric prior variance for asymp <<<<<<<<
-
-
-%=================================================
-% Computing the final covariance matrix (S1,...,Sm) for the prior of A0,
-% and final Gb=(G1,...,Gm) for A+ if asymmetric prior or for
-% B if symmetric prior for A+
-%=================================================
-%
-for i = 1:nvar
- %------------------------------
- % Introduce prior information on which variables "belong" in various equations.
- % In this first trial, we just introduce this information here, in a model-specific way.
- % Eventually this info has to be passed parametricly. In our first shot, we just damp down
- % all coefficients except those on the diagonal.
-
- %*** For A0
- factor0=asym0(:,i);
- sg0bd = sg0bida.*factor0; % Note, this only works for the prior variance Sg(i)
- % of a0(i) being diagonal. If the prior variance Sg(i) is not
- % diagonal, we have to the inverse to get inv(Sg(i)).
- %sg0bdinv = 1./sg0bd;
- % * unconditional variance on A0+
- H0td = diag(sg0bd); % unconditional
- %=== Correlation in the MS equation to get a liquidity effect.
- if (i==indxmsmdeqn(1))
- H0td(indxmsmdeqn(3),indxmsmdeqn(4)) = hpmsmd(1)*sqrt(sg0bida(indxmsmdeqn(3))*sg0bida(indxmsmdeqn(4)));
- H0td(indxmsmdeqn(4),indxmsmdeqn(3)) = hpmsmd(1)*sqrt(sg0bida(indxmsmdeqn(3))*sg0bida(indxmsmdeqn(4)));
- else
- H0td(indxmsmdeqn(3),indxmsmdeqn(4)) = hpmsmd(2)*sqrt(sg0bida(indxmsmdeqn(3))*sg0bida(indxmsmdeqn(4)));
- H0td(indxmsmdeqn(4),indxmsmdeqn(3)) = hpmsmd(2)*sqrt(sg0bida(indxmsmdeqn(3))*sg0bida(indxmsmdeqn(4)));
- end
- H0tdinv = inv(H0td);
- %H0tdinv = diag(sg0bdinv);
- %
- H0multi(:,:,i)=H0td;
- H0invmulti(:,:,i)=H0tdinv;
-
-
- %*** For A+
- if ~(lags==0) % For A1 to remain random walk properties
- factor1=asymp(1:nvar,i);
- sg1bd = sgpbida(1:nvar).*factor1;
- sg1bdinv = 1./sg1bd;
- %
- Hptd(1:nvar,1:nvar)=diag(sg1bd);
- Hptdinv(1:nvar,1:nvar)=diag(sg1bdinv);
- if lags>1
- factorpp=asymp(nvar+1:ncoef-1,i);
- sgpp_cbd = sgppbd(1:ncoef-nvar-1) .* factorpp;
- sgpp_cbdinv = 1./sgpp_cbd;
- Hptd(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbd);
- Hptdinv(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbdinv);
- % condtional on A0i, H_plus_tilde
- end
- end
- Hpmulti(:,:,i)=Hptd;
- Hpinvmulti(:,:,i)=Hptdinv;
-end
-
-
+function [Pi,H0multi,Hpmulti,H0invmulti,Hpinvmulti] ...
+ = fn_rnrprior_covres(nvar,q_m,lags,xdgel,mu,hpmsmd,indxmsmdeqn,nexo,asym0,asymp)
+% [Pi,H0multi,Hpmulti,H0invmulti,Hpinvmulti] ...
+% = fn_rnrprior_covres(nvar,q_m,lags,xdgel,mu,hpmsmd,indxmsmdeqn,nexo,asym0,asymp)
+%
+% More general than fn_rnrprior.m because when hpmsmd=0, fn_rnrprior_covres() is the same as fn_rnrprior().
+% Allows for prior covariances for the MS and MD equations to achieve liquidity effects.
+% Exports random Bayesian prior of Sims and Zha with asymmetric rior (but no linear restrictions yet)
+% See Waggoner and Zha's Gibbs sampling paper and TVBVAR NOTES p. 71k.0.
+%
+% nvar: number of endogenous variables
+% q_m: quarter or month
+% lags: the maximum length of lag
+% xdgel: the general matrix of the original data (no manipulation involved)
+% with sample size including lags. Used only to get variances of residuals for
+% the scaling purpose; NOT used for mu(5) and mu(6).
+% mu: 6-by-1 vector of hyperparameters (the following numbers for Atlanta Fed's forecast), where
+% mu(5) and mu(6) are NOT used here. See fn_dataxy.m for using mu(5) and mu(6).
+% mu(1): overall tightness and also for A0; (0.57)
+% mu(2): relative tightness for A+; (0.13)
+% mu(3): relative tightness for the constant term; (0.1). NOTE: for other
+% exogenous terms, the variance of each exogenous term must be taken into
+% acount to eliminate the scaling factor.
+% mu(4): tightness on lag decay; (1)
+% mu(5): weight on nvar sums of coeffs dummy observations (unit roots); (5)
+% mu(6): weight on single dummy initial observation including constant
+% (cointegration, unit roots, and stationarity); (5)
+% NOTE: for this subdirectory, mu(5) and mu(6) are not used. See fn_dataxy.m for using mu(5) and mu(6).
+% hpmsmd: 2-by-1 hyperparameters with -1<h1=hpmsmd(1)<=0 for the MS equation and 0<=h2=hpmsmd(2)<1 the MD equation. Consider a1*R + a2*M.
+% The term h1*var(a1)*var(a2) is the prior covariance of a1 and a2 for MS, equivalent to penalizing the same sign of a1 and a2.
+% The term h2*var(a1)*var(a2) is the prior covariance of a1 and a2 for MD, equivalent to penalizing opposite signs of a1 and a2.
+% This will give us a liquidity effect.
+% indxmsmdeqn: 4-by-1 index for the locations of the MS and MD equation and for the locations of M and R.
+% indxmsmdeqn(1) for MS and indxmsmdeqn(2) for MD.
+% indxmsmdeqn(3) for M and indxmsmdeqn(4) for R.
+% nexo: number of exogenous variables (if not specified, nexo=1 (constant) by default).
+% The constant term is always put to the last of all endogenous and exogenous variables.
+% asym0: nvar-by-nvar asymmetric prior on A0. Column -- equation.
+% If ones(nvar,nvar), symmetric prior; if not, relative (asymmetric) tightness on A0.
+% asymp: ncoef-1-by-nvar asymmetric prior on A+ bar constant. Column -- equation.
+% If ones(ncoef-1,nvar), symmetric prior; if not, relative (asymmetric) tightness on A+.
+% --------------------
+% Pi: ncoef-by-nvar matrix for the ith equation under random walk. Same for all equations
+% H0multi: nvar-by-nvar-by-nvar; H0 for different equations under asymmetric prior
+% Hpmulti: ncoef-by-ncoef-by-nvar; H+ for different equations under asymmetric prior
+% H0invmulti: nvar-by-nvar-by-nvar; inv(H0) for different equations under asymmetric prior
+% Hpinvmulti: ncoef-by-ncoef-by-nvar; inv(H+) for different equations under asymmetric prior
+%
+% Tao Zha, February 2000. Revised, September 2000, February 2003.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+
+if nargin==7, nexo=1; end
+ncoef = nvar*lags+nexo; % number of coefficients in *each* equation, RHS coefficients only.
+
+H0multi=zeros(nvar,nvar,nvar); % H0 for different equations under asymmetric prior
+Hpmulti=zeros(ncoef,ncoef,nvar); % H+ for different equations under asymmetric prior
+H0invmulti=zeros(nvar,nvar,nvar); % inv(H0) for different equations under asymmetric prior
+Hpinvmulti=zeros(ncoef,ncoef,nvar); % inv(H+) for different equations under asymmetric prior
+
+%*** Constructing Pi for the ith equation under the random walk assumption
+Pi = zeros(ncoef,nvar); % same for all equations
+Pi(1:nvar,1:nvar) = eye(nvar); % random walk
+
+%
+%@@@ Prepared for Bayesian prior
+%
+%
+% ** monthly lag decay in order to match quarterly decay: a*exp(bl) where
+% ** l is the monthly lag. Suppose quarterly decay is 1/x where x=1,2,3,4.
+% ** Let the decay of l1 (a*exp(b*l1)) match that of x1 (say, beginning: 1/1)
+% ** and the decay of l2 (a*exp(b*l2)) match that of x2 (say, end: 1/5),
+% ** we can solve for a and b which are
+% ** b = (log_x1-log_x2)/(l1-l2), and a = x1*exp(-b*l1).
+if q_m==12
+ l1 = 1; % 1st month == 1st quarter
+ xx1 = 1; % 1st quarter
+ l2 = lags; % last month
+ xx2 = 1/((ceil(lags/3))^mu(4)); % last quarter
+ %xx2 = 1/6; % last quarter
+ % 3rd quarter: i.e., we intend to let decay of the 6th month match
+ % that of the 3rd quarter, so that the 6th month decays a little
+ % faster than the second quarter which is 1/2.
+ if lags==1
+ b = 0;
+ else
+ b = (log(xx1)-log(xx2))/(l1-l2);
+ end
+ a = xx1*exp(-b*l1);
+end
+
+
+
+%
+% *** specify the prior for each equation separately, SZ method,
+% ** get the residuals from univariate regressions.
+%
+sgh = zeros(nvar,1); % square root
+sgsh = sgh; % square
+nSample=size(xdgel,1); % sample size-lags
+yu = xdgel;
+C = ones(nSample,1);
+for k=1:nvar
+ [Bk,ek,junk1,junk2,junk3,junk4] = sye([yu(:,k) C],lags);
+ clear Bk junk1 junk2 junk3 junk4;
+ sgsh(k) = ek'*ek/(nSample-lags);
+ sgh(k) = sqrt(sgsh(k));
+end
+% ** prior variance for A0(:,1), same for all equations!!!
+sg0bid = zeros(nvar,1); % Sigma0_bar diagonal only for the ith equation
+for j=1:nvar
+ sg0bid(j) = 1/sgsh(j); % sgsh = sigmai^2
+end
+% ** prior variance for lagged and exogeous variables, same for all equations
+sgpbid = zeros(ncoef,1); % Sigma_plus_bar, diagonal, for the ith equation
+for i = 1:lags
+ if (q_m==12)
+ lagdecay = a*exp(b*i*mu(4));
+ end
+ %
+ for j = 1:nvar
+ if (q_m==12)
+ % exponential decay to match quarterly decay
+ sgpbid((i-1)*nvar+j) = lagdecay^2/sgsh(j); % ith equation
+ elseif (q_m==4)
+ sgpbid((i-1)*nvar+j) = (1/i^mu(4))^2/sgsh(j); % ith equation
+ else
+ error('Incompatibility with lags, check the possible errors!!!')
+ %warning('Incompatibility with lags, check the possible errors!!!')
+ %return
+ end
+ end
+end
+%
+
+
+%=================================================
+% Computing the (prior) covariance matrix for the posterior of A0, no data yet
+%=================================================
+%
+%
+% ** set up the conditional prior variance sg0bi and sgpbi.
+sg0bida = mu(1)^2*sg0bid; % ith equation
+sgpbida = mu(1)^2*mu(2)^2*sgpbid;
+sgpbida(ncoef-nexo+1:ncoef) = mu(1)^2*mu(3)^2;
+ %<<>> No scaling adjustment has been made for exogenous terms other than constant
+sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in a Sims-Zha paper
+
+Hptd = zeros(ncoef);
+Hptdi=Hptd;
+Hptd(ncoef,ncoef)=sgppbd(ncoef-nvar);
+Hptdinv(ncoef,ncoef)=1./sgppbd(ncoef-nvar);
+ % condtional on A0i, H_plus_tilde
+
+
+if nargin<9 % the default is no asymmetric information
+ asym0 = ones(nvar,nvar); % if not ones, then we have relative (asymmetric) tightness
+ asymp = ones(ncoef-1,nvar); % for A+. Column -- equation
+end
+
+%**** Asymmetric Information
+%asym0 = ones(nvar,nvar); % if not ones, then we have relative (asymmetric) tightness
+%asymp = ones(ncoef-1,nvar); % pp: plus without constant. Column -- equation
+%>>>>>> B: asymmetric prior variance for asymp <<<<<<<<
+%
+%for i = 1:lags
+% rowif = (i-1)*nvar+1;
+% rowil = i*nvar;
+% idmatw0 = 0.5; % weight assigned to idmat0 in the formation of asymp
+% if (i==1)
+% asymp(rowif:rowil,:)=(1-idmatw0)*ones(nvar)+idmatw0*idmat0; % first lag
+% % note: idmat1 is already transposed. Column -- equation
+% else
+% %asymp(rowif:rowil,1:nvar) = (1-idmatw0)*ones(nvar)+idmatw0*idmat0;
+% % <<<<<<< toggle +
+% % Note: already transposed, since idmat0 is transposed.
+% % Meaning: column implies equation
+% asymp(rowif:rowil,1:nvar) = ones(nvar);
+% % >>>>>>> toggle -
+% end
+%end
+%
+%>>>>>> E: asymmetric prior variance for asymp <<<<<<<<
+
+
+%=================================================
+% Computing the final covariance matrix (S1,...,Sm) for the prior of A0,
+% and final Gb=(G1,...,Gm) for A+ if asymmetric prior or for
+% B if symmetric prior for A+
+%=================================================
+%
+for i = 1:nvar
+ %------------------------------
+ % Introduce prior information on which variables "belong" in various equations.
+ % In this first trial, we just introduce this information here, in a model-specific way.
+ % Eventually this info has to be passed parametricly. In our first shot, we just damp down
+ % all coefficients except those on the diagonal.
+
+ %*** For A0
+ factor0=asym0(:,i);
+ sg0bd = sg0bida.*factor0; % Note, this only works for the prior variance Sg(i)
+ % of a0(i) being diagonal. If the prior variance Sg(i) is not
+ % diagonal, we have to the inverse to get inv(Sg(i)).
+ %sg0bdinv = 1./sg0bd;
+ % * unconditional variance on A0+
+ H0td = diag(sg0bd); % unconditional
+ %=== Correlation in the MS equation to get a liquidity effect.
+ if (i==indxmsmdeqn(1))
+ H0td(indxmsmdeqn(3),indxmsmdeqn(4)) = hpmsmd(1)*sqrt(sg0bida(indxmsmdeqn(3))*sg0bida(indxmsmdeqn(4)));
+ H0td(indxmsmdeqn(4),indxmsmdeqn(3)) = hpmsmd(1)*sqrt(sg0bida(indxmsmdeqn(3))*sg0bida(indxmsmdeqn(4)));
+ else
+ H0td(indxmsmdeqn(3),indxmsmdeqn(4)) = hpmsmd(2)*sqrt(sg0bida(indxmsmdeqn(3))*sg0bida(indxmsmdeqn(4)));
+ H0td(indxmsmdeqn(4),indxmsmdeqn(3)) = hpmsmd(2)*sqrt(sg0bida(indxmsmdeqn(3))*sg0bida(indxmsmdeqn(4)));
+ end
+ H0tdinv = inv(H0td);
+ %H0tdinv = diag(sg0bdinv);
+ %
+ H0multi(:,:,i)=H0td;
+ H0invmulti(:,:,i)=H0tdinv;
+
+
+ %*** For A+
+ if ~(lags==0) % For A1 to remain random walk properties
+ factor1=asymp(1:nvar,i);
+ sg1bd = sgpbida(1:nvar).*factor1;
+ sg1bdinv = 1./sg1bd;
+ %
+ Hptd(1:nvar,1:nvar)=diag(sg1bd);
+ Hptdinv(1:nvar,1:nvar)=diag(sg1bdinv);
+ if lags>1
+ factorpp=asymp(nvar+1:ncoef-1,i);
+ sgpp_cbd = sgppbd(1:ncoef-nvar-1) .* factorpp;
+ sgpp_cbdinv = 1./sgpp_cbd;
+ Hptd(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbd);
+ Hptdinv(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbdinv);
+ % condtional on A0i, H_plus_tilde
+ end
+ end
+ Hpmulti(:,:,i)=Hptd;
+ Hpinvmulti(:,:,i)=Hptdinv;
+end
+
+
diff --git a/MatlabFiles/fn_rnrprior_covres_dobs.m b/MatlabFiles/fn_rnrprior_covres_dobs.m
index e0f7b3b4ae55c9b639e09b4f7f6240e112e22170..1586290a18011efe8de2d3f59e1262bcfa501c49 100644
--- a/MatlabFiles/fn_rnrprior_covres_dobs.m
+++ b/MatlabFiles/fn_rnrprior_covres_dobs.m
@@ -1,297 +1,297 @@
-function [Pi,H0multi,Hpmulti,H0invmulti,Hpinvmulti,sgh] ...
- = fn_rnrprior_covres_dobs(nvar,q_m,lags,xdgel,mu,indxDummy,hpmsmd,indxmsmdeqn,nexo,asym0,asymp)
-% Differs from fn_rnrprior_covres_dobs_tv(): no linear restrictions (Ui and Vi) have applied yet to this function, but
-% linear restrictions are incorported in fn_rnrprior_covres_dobs_tv().
-%
-% Only works for the nexo=1 (constant term) case. To extend this to other exogenous variables, see fn_dataxy.m. 01/14/03.
-% Differs from fn_rnrprior_covres.m in that dummy observations are included as part of the explicit prior. See Forcast II, pp.68-69b.
-% More general than fn_rnrprior.m because when hpmsmd=0, fn_rnrprior_covres() is the same as fn_rnrprior().
-% Allows for prior covariances for the MS and MD equations to achieve liquidity effects.
-% Exports random Bayesian prior of Sims and Zha with asymmetric rior (but no linear restrictions yet)
-% See Waggoner and Zha's Gibbs sampling paper and TVBVAR NOTES p. 71k.0.
-%
-% nvar: number of endogenous variables
-% q_m: quarter or month
-% lags: the maximum length of lag
-% xdgel: T*nvar endogenous-variable matrix of raw or original data (no manipulation involved) with sample size including lags.
-% Order of columns: (1) nvar endogenous variables; (2) constants will be automatically put in the last column.
-% Used only to get variances of residuals for mu(1)-mu(5) and for dummy observations mu(5) and mu(6).
-% mu: 6-by-1 vector of hyperparameters (the following numbers for Atlanta Fed's forecast), where
-% mu(5) and mu(6) are NOT used here. See fn_dataxy.m for using mu(5) and mu(6).
-% mu(1): overall tightness and also for A0; (0.57)
-% mu(2): relative tightness for A+; (0.13)
-% mu(3): relative tightness for the constant term; (0.1). NOTE: for other
-% exogenous terms, the variance of each exogenous term must be taken into
-% acount to eliminate the scaling factor.
-% mu(4): tightness on lag decay; (1)
-% mu(5): weight on nvar sums of coeffs dummy observations (unit roots); (5)
-% mu(6): weight on single dummy initial observation including constant
-% (cointegration, unit roots, and stationarity); (5)
-% indxDummy: 1: uses dummy observations to form part of an explicit prior; 0: no dummy observations as part of the prior.
-% hpmsmd: 2-by-1 hyperparameters with -1<h1=hpmsmd(1)<=0 for the MS equation and 0<=h2=hpmsmd(2)<1 the MD equation. Consider a1*R + a2*M.
-% The term h1*var(a1)*var(a2) is the prior covariance of a1 and a2 for MS, equivalent to penalizing the same sign of a1 and a2.
-% The term h2*var(a1)*var(a2) is the prior covariance of a1 and a2 for MD, equivalent to penalizing opposite signs of a1 and a2.
-% This will give us a liquidity effect.
-% indxmsmdeqn: 4-by-1 index for the locations of the MS and MD equation and for the locations of M and R.
-% indxmsmdeqn(1) for MS and indxmsmdeqn(2) for MD.
-% indxmsmdeqn(3) for M and indxmsmdeqn(4) for R.
-% nexo: number of exogenous variables (if not specified, nexo=1 (constant) by default).
-% The constant term is always put to the last of all endogenous and exogenous variables.
-% asym0: nvar-by-nvar asymmetric prior on A0. Column -- equation.
-% If ones(nvar,nvar), symmetric prior; if not, relative (asymmetric) tightness on A0.
-% asymp: ncoef-1-by-nvar asymmetric prior on A+ bar constant. Column -- equation.
-% If ones(ncoef-1,nvar), symmetric prior; if not, relative (asymmetric) tightness on A+.
-% --------------------
-% Pi: ncoef-by-nvar matrix for the ith equation under random walk. Same for all equations
-% H0multi: nvar-by-nvar-by-nvar; H0 for different equations under asymmetric prior
-% Hpmulti: ncoef-by-ncoef-by-nvar; H+ for different equations under asymmetric prior
-% H0invmulti: nvar-by-nvar-by-nvar; inv(H0) for different equations under asymmetric prior
-% Hpinvmulti: ncoef-by-ncoef-by-nvar; inv(H+) for different equations under asymmetric prior
-% sgh: nvar-by-1 standard deviations of residuals for each equation.
-%
-% Tao Zha, February 2000. Revised, September 2000, February, May 2003.
-
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-if (nargin<=8), nexo=1; end
-ncoef = nvar*lags+nexo; % number of coefficients in *each* equation, RHS coefficients only.
-
-H0multi=zeros(nvar,nvar,nvar); % H0 for different equations under asymmetric prior
-Hpmulti=zeros(ncoef,ncoef,nvar); % H+ for different equations under asymmetric prior
-H0invmulti=zeros(nvar,nvar,nvar); % inv(H0) for different equations under asymmetric prior
-Hpinvmulti=zeros(ncoef,ncoef,nvar); % inv(H+) for different equations under asymmetric prior
-
-%*** Constructing Pi for the ith equation under the random walk assumption
-Pi = zeros(ncoef,nvar); % same for all equations
-Pi(1:nvar,1:nvar) = eye(nvar); % random walk
-
-%
-%@@@ Prepared for Bayesian prior
-%
-%
-% ** monthly lag decay in order to match quarterly decay: a*exp(bl) where
-% ** l is the monthly lag. Suppose quarterly decay is 1/x where x=1,2,3,4.
-% ** Let the decay of l1 (a*exp(b*l1)) match that of x1 (say, beginning: 1/1)
-% ** and the decay of l2 (a*exp(b*l2)) match that of x2 (say, end: 1/5),
-% ** we can solve for a and b which are
-% ** b = (log_x1-log_x2)/(l1-l2), and a = x1*exp(-b*l1).
-if q_m==12
- l1 = 1; % 1st month == 1st quarter
- xx1 = 1; % 1st quarter
- l2 = lags; % last month
- xx2 = 1/((ceil(lags/3))^mu(4)); % last quarter
- %xx2 = 1/6; % last quarter
- % 3rd quarter: i.e., we intend to let decay of the 6th month match
- % that of the 3rd quarter, so that the 6th month decays a little
- % faster than the second quarter which is 1/2.
- if lags==1
- b = 0;
- else
- b = (log(xx1)-log(xx2))/(l1-l2);
- end
- a = xx1*exp(-b*l1);
-end
-
-
-
-%
-% *** specify the prior for each equation separately, SZ method,
-% ** get the residuals from univariate regressions.
-%
-sgh = zeros(nvar,1); % square root
-sgsh = sgh; % square
-nSample=size(xdgel,1); % sample size-lags
-yu = xdgel;
-C = ones(nSample,1);
-for k=1:nvar
- [Bk,ek,junk1,junk2,junk3,junk4] = sye([yu(:,k) C],lags);
- clear Bk junk1 junk2 junk3 junk4;
- sgsh(k) = ek'*ek/(nSample-lags);
- sgh(k) = sqrt(sgsh(k));
-end
-% ** prior variance for A0(:,1), same for all equations!!!
-sg0bid = zeros(nvar,1); % Sigma0_bar diagonal only for the ith equation
-for j=1:nvar
- sg0bid(j) = 1/sgsh(j); % sgsh = sigmai^2
-end
-% ** prior variance for lagged and exogeous variables, same for all equations
-sgpbid = zeros(ncoef,1); % Sigma_plus_bar, diagonal, for the ith equation
-for i = 1:lags
- if (q_m==12)
- lagdecay = a*exp(b*i*mu(4));
- end
- %
- for j = 1:nvar
- if (q_m==12)
- % exponential decay to match quarterly decay
- sgpbid((i-1)*nvar+j) = lagdecay^2/sgsh(j); % ith equation
- elseif (q_m==4)
- sgpbid((i-1)*nvar+j) = (1/i^mu(4))^2/sgsh(j); % ith equation
- else
- error('Incompatibility with lags, check the possible errors!!!')
- %warning('Incompatibility with lags, check the possible errors!!!')
- %return
- end
- end
-end
-%
-
-if indxDummy % Dummy observations as part of the explicit prior.
- ndobs=nvar+1; % Number of dummy observations: nvar unit roots and 1 cointegration prior.
- phibar = zeros(ndobs,ncoef);
- %* constant term
- const = ones(nvar+1,1);
- const(1:nvar) = 0.0;
- phibar(:,ncoef) = const; % the first nvar periods: no or zero constant!
-
- xdgelint = mean(xdgel(1:lags,:),1); % mean of the first lags initial conditions
- %* Dummies
- for k=1:nvar
- for m=1:lags
- phibar(ndobs,nvar*(m-1)+k) = xdgelint(k);
- phibar(k,nvar*(m-1)+k) = xdgelint(k);
- % <<>> multiply hyperparameter later
- end
- end
- phibar(1:nvar,:) = 1*mu(5)*phibar(1:nvar,:); % standard Sims and Zha prior
- phibar(ndobs,:) = mu(6)*phibar(ndobs,:);
- [phiq,phir]=qr(phibar,0);
- xtxbar=phir'*phir; % phibar'*phibar. ncoef-by-ncoef. Reduced (not full) rank. See Forcast II, pp.69-69b.
-end
-
-%=================================================
-% Computing the (prior) covariance matrix for A0, no data yet.
-% As proved in pp.69a-69b, Forecast II, the following prior covariance of A0
-% will remain the same after the dummy observations prior is incorporated.
-% The dummy observation prior only affects the prior covariance of A+|A0.
-% See pp.69a-69b for the proof.
-%=================================================
-%
-%
-% ** set up the conditional prior variance sg0bi and sgpbi.
-sg0bida = mu(1)^2*sg0bid; % ith equation
-sgpbida = mu(1)^2*mu(2)^2*sgpbid;
-sgpbida(ncoef-nexo+1:ncoef) = mu(1)^2*mu(3)^2;
- %<<>> No scaling adjustment has been made for exogenous terms other than constant
-sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in a Sims-Zha paper
-
-Hptd = zeros(ncoef);
-Hptdi=Hptd;
-Hptd(ncoef,ncoef)=sgppbd(ncoef-nvar);
-Hptdinv(ncoef,ncoef)=1./sgppbd(ncoef-nvar);
- % condtional on A0i, H_plus_tilde
-
-
-if nargin<10 % the default is no asymmetric information
- asym0 = ones(nvar,nvar); % if not ones, then we have relative (asymmetric) tightness
- asymp = ones(ncoef-1,nvar); % for A+. Column -- equation
-end
-
-%**** Asymmetric Information
-%asym0 = ones(nvar,nvar); % if not ones, then we have relative (asymmetric) tightness
-%asymp = ones(ncoef-1,nvar); % pp: plus without constant. Column -- equation
-%>>>>>> B: asymmetric prior variance for asymp <<<<<<<<
-%
-%for i = 1:lags
-% rowif = (i-1)*nvar+1;
-% rowil = i*nvar;
-% idmatw0 = 0.5; % weight assigned to idmat0 in the formation of asymp
-% if (i==1)
-% asymp(rowif:rowil,:)=(1-idmatw0)*ones(nvar)+idmatw0*idmat0; % first lag
-% % note: idmat1 is already transposed. Column -- equation
-% else
-% %asymp(rowif:rowil,1:nvar) = (1-idmatw0)*ones(nvar)+idmatw0*idmat0;
-% % <<<<<<< toggle +
-% % Note: already transposed, since idmat0 is transposed.
-% % Meaning: column implies equation
-% asymp(rowif:rowil,1:nvar) = ones(nvar);
-% % >>>>>>> toggle -
-% end
-%end
-%
-%>>>>>> E: asymmetric prior variance for asymp <<<<<<<<
-
-
-%=================================================
-% Computing the final covariance matrix (S1,...,Sm) for the prior of A0,
-% and final Gb=(G1,...,Gm) for A+ if asymmetric prior or for
-% B if symmetric prior for A+
-%=================================================
-%
-for i = 1:nvar
- %------------------------------
- % Introduce prior information on which variables "belong" in various equations.
- % In this first trial, we just introduce this information here, in a model-specific way.
- % Eventually this info has to be passed parametricly. In our first shot, we just damp down
- % all coefficients except those on the diagonal.
-
- %*** For A0
- factor0=asym0(:,i);
- sg0bd = sg0bida.*factor0; % Note, this only works for the prior variance Sg(i)
- % of a0(i) being diagonal. If the prior variance Sg(i) is not
- % diagonal, we have to the inverse to get inv(Sg(i)).
- %sg0bdinv = 1./sg0bd;
- % * unconditional variance on A0+
- H0td = diag(sg0bd); % unconditional
- %=== Correlation in the MS equation to get a liquidity effect.
- if (i==indxmsmdeqn(1))
- H0td(indxmsmdeqn(3),indxmsmdeqn(4)) = hpmsmd(1)*sqrt(sg0bida(indxmsmdeqn(3))*sg0bida(indxmsmdeqn(4)));
- H0td(indxmsmdeqn(4),indxmsmdeqn(3)) = hpmsmd(1)*sqrt(sg0bida(indxmsmdeqn(3))*sg0bida(indxmsmdeqn(4)));
- elseif (i==indxmsmdeqn(2))
- H0td(indxmsmdeqn(3),indxmsmdeqn(4)) = hpmsmd(2)*sqrt(sg0bida(indxmsmdeqn(3))*sg0bida(indxmsmdeqn(4)));
- H0td(indxmsmdeqn(4),indxmsmdeqn(3)) = hpmsmd(2)*sqrt(sg0bida(indxmsmdeqn(3))*sg0bida(indxmsmdeqn(4)));
- end
- H0tdinv = inv(H0td);
- %H0tdinv = diag(sg0bdinv);
- %
- H0multi(:,:,i)=H0td;
- H0invmulti(:,:,i)=H0tdinv;
-
-
- %*** For A+
- if ~(lags==0) % For A1 to remain random walk properties
- factor1=asymp(1:nvar,i);
- sg1bd = sgpbida(1:nvar).*factor1;
- sg1bdinv = 1./sg1bd;
- %
- Hptd(1:nvar,1:nvar)=diag(sg1bd);
- Hptdinv(1:nvar,1:nvar)=diag(sg1bdinv);
- if lags>1
- factorpp=asymp(nvar+1:ncoef-1,i);
- sgpp_cbd = sgppbd(1:ncoef-nvar-1) .* factorpp;
- sgpp_cbdinv = 1./sgpp_cbd;
- Hptd(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbd);
- Hptdinv(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbdinv);
- % condtional on A0i, H_plus_tilde
- end
- end
- %---------------
- % The dummy observation prior affects only the prior covariance of A+|A0,
- % but not the covariance of A0. See pp.69a-69b for the proof.
- %---------------
- if indxDummy % Dummy observations as part of the explicit prior.
- Hpinvmulti(:,:,i)=Hptdinv + xtxbar;
- Hpmulti(:,:,i) = inv(Hpinvmulti(:,:,i));
- else
- Hpmulti(:,:,i)=Hptd;
- Hpinvmulti(:,:,i)=Hptdinv;
- end
-end
-
-
+function [Pi,H0multi,Hpmulti,H0invmulti,Hpinvmulti,sgh] ...
+ = fn_rnrprior_covres_dobs(nvar,q_m,lags,xdgel,mu,indxDummy,hpmsmd,indxmsmdeqn,nexo,asym0,asymp)
+% Differs from fn_rnrprior_covres_dobs_tv(): no linear restrictions (Ui and Vi) have applied yet to this function, but
+% linear restrictions are incorported in fn_rnrprior_covres_dobs_tv().
+%
+% Only works for the nexo=1 (constant term) case. To extend this to other exogenous variables, see fn_dataxy.m. 01/14/03.
+% Differs from fn_rnrprior_covres.m in that dummy observations are included as part of the explicit prior. See Forcast II, pp.68-69b.
+% More general than fn_rnrprior.m because when hpmsmd=0, fn_rnrprior_covres() is the same as fn_rnrprior().
+% Allows for prior covariances for the MS and MD equations to achieve liquidity effects.
+% Exports random Bayesian prior of Sims and Zha with asymmetric rior (but no linear restrictions yet)
+% See Waggoner and Zha's Gibbs sampling paper and TVBVAR NOTES p. 71k.0.
+%
+% nvar: number of endogenous variables
+% q_m: quarter or month
+% lags: the maximum length of lag
+% xdgel: T*nvar endogenous-variable matrix of raw or original data (no manipulation involved) with sample size including lags.
+% Order of columns: (1) nvar endogenous variables; (2) constants will be automatically put in the last column.
+% Used only to get variances of residuals for mu(1)-mu(5) and for dummy observations mu(5) and mu(6).
+% mu: 6-by-1 vector of hyperparameters (the following numbers for Atlanta Fed's forecast), where
+% mu(5) and mu(6) are NOT used here. See fn_dataxy.m for using mu(5) and mu(6).
+% mu(1): overall tightness and also for A0; (0.57)
+% mu(2): relative tightness for A+; (0.13)
+% mu(3): relative tightness for the constant term; (0.1). NOTE: for other
+% exogenous terms, the variance of each exogenous term must be taken into
+% acount to eliminate the scaling factor.
+% mu(4): tightness on lag decay; (1)
+% mu(5): weight on nvar sums of coeffs dummy observations (unit roots); (5)
+% mu(6): weight on single dummy initial observation including constant
+% (cointegration, unit roots, and stationarity); (5)
+% indxDummy: 1: uses dummy observations to form part of an explicit prior; 0: no dummy observations as part of the prior.
+% hpmsmd: 2-by-1 hyperparameters with -1<h1=hpmsmd(1)<=0 for the MS equation and 0<=h2=hpmsmd(2)<1 the MD equation. Consider a1*R + a2*M.
+% The term h1*var(a1)*var(a2) is the prior covariance of a1 and a2 for MS, equivalent to penalizing the same sign of a1 and a2.
+% The term h2*var(a1)*var(a2) is the prior covariance of a1 and a2 for MD, equivalent to penalizing opposite signs of a1 and a2.
+% This will give us a liquidity effect.
+% indxmsmdeqn: 4-by-1 index for the locations of the MS and MD equation and for the locations of M and R.
+% indxmsmdeqn(1) for MS and indxmsmdeqn(2) for MD.
+% indxmsmdeqn(3) for M and indxmsmdeqn(4) for R.
+% nexo: number of exogenous variables (if not specified, nexo=1 (constant) by default).
+% The constant term is always put to the last of all endogenous and exogenous variables.
+% asym0: nvar-by-nvar asymmetric prior on A0. Column -- equation.
+% If ones(nvar,nvar), symmetric prior; if not, relative (asymmetric) tightness on A0.
+% asymp: ncoef-1-by-nvar asymmetric prior on A+ bar constant. Column -- equation.
+% If ones(ncoef-1,nvar), symmetric prior; if not, relative (asymmetric) tightness on A+.
+% --------------------
+% Pi: ncoef-by-nvar matrix for the ith equation under random walk. Same for all equations
+% H0multi: nvar-by-nvar-by-nvar; H0 for different equations under asymmetric prior
+% Hpmulti: ncoef-by-ncoef-by-nvar; H+ for different equations under asymmetric prior
+% H0invmulti: nvar-by-nvar-by-nvar; inv(H0) for different equations under asymmetric prior
+% Hpinvmulti: ncoef-by-ncoef-by-nvar; inv(H+) for different equations under asymmetric prior
+% sgh: nvar-by-1 standard deviations of residuals for each equation.
+%
+% Tao Zha, February 2000. Revised, September 2000, February, May 2003.
+
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+if (nargin<=8), nexo=1; end
+ncoef = nvar*lags+nexo; % number of coefficients in *each* equation, RHS coefficients only.
+
+H0multi=zeros(nvar,nvar,nvar); % H0 for different equations under asymmetric prior
+Hpmulti=zeros(ncoef,ncoef,nvar); % H+ for different equations under asymmetric prior
+H0invmulti=zeros(nvar,nvar,nvar); % inv(H0) for different equations under asymmetric prior
+Hpinvmulti=zeros(ncoef,ncoef,nvar); % inv(H+) for different equations under asymmetric prior
+
+%*** Constructing Pi for the ith equation under the random walk assumption
+Pi = zeros(ncoef,nvar); % same for all equations
+Pi(1:nvar,1:nvar) = eye(nvar); % random walk
+
+%
+%@@@ Prepared for Bayesian prior
+%
+%
+% ** monthly lag decay in order to match quarterly decay: a*exp(bl) where
+% ** l is the monthly lag. Suppose quarterly decay is 1/x where x=1,2,3,4.
+% ** Let the decay of l1 (a*exp(b*l1)) match that of x1 (say, beginning: 1/1)
+% ** and the decay of l2 (a*exp(b*l2)) match that of x2 (say, end: 1/5),
+% ** we can solve for a and b which are
+% ** b = (log_x1-log_x2)/(l1-l2), and a = x1*exp(-b*l1).
+if q_m==12
+ l1 = 1; % 1st month == 1st quarter
+ xx1 = 1; % 1st quarter
+ l2 = lags; % last month
+ xx2 = 1/((ceil(lags/3))^mu(4)); % last quarter
+ %xx2 = 1/6; % last quarter
+ % 3rd quarter: i.e., we intend to let decay of the 6th month match
+ % that of the 3rd quarter, so that the 6th month decays a little
+ % faster than the second quarter which is 1/2.
+ if lags==1
+ b = 0;
+ else
+ b = (log(xx1)-log(xx2))/(l1-l2);
+ end
+ a = xx1*exp(-b*l1);
+end
+
+
+
+%
+% *** specify the prior for each equation separately, SZ method,
+% ** get the residuals from univariate regressions.
+%
+sgh = zeros(nvar,1); % square root
+sgsh = sgh; % square
+nSample=size(xdgel,1); % sample size-lags
+yu = xdgel;
+C = ones(nSample,1);
+for k=1:nvar
+ [Bk,ek,junk1,junk2,junk3,junk4] = sye([yu(:,k) C],lags);
+ clear Bk junk1 junk2 junk3 junk4;
+ sgsh(k) = ek'*ek/(nSample-lags);
+ sgh(k) = sqrt(sgsh(k));
+end
+% ** prior variance for A0(:,1), same for all equations!!!
+sg0bid = zeros(nvar,1); % Sigma0_bar diagonal only for the ith equation
+for j=1:nvar
+ sg0bid(j) = 1/sgsh(j); % sgsh = sigmai^2
+end
+% ** prior variance for lagged and exogeous variables, same for all equations
+sgpbid = zeros(ncoef,1); % Sigma_plus_bar, diagonal, for the ith equation
+for i = 1:lags
+ if (q_m==12)
+ lagdecay = a*exp(b*i*mu(4));
+ end
+ %
+ for j = 1:nvar
+ if (q_m==12)
+ % exponential decay to match quarterly decay
+ sgpbid((i-1)*nvar+j) = lagdecay^2/sgsh(j); % ith equation
+ elseif (q_m==4)
+ sgpbid((i-1)*nvar+j) = (1/i^mu(4))^2/sgsh(j); % ith equation
+ else
+ error('Incompatibility with lags, check the possible errors!!!')
+ %warning('Incompatibility with lags, check the possible errors!!!')
+ %return
+ end
+ end
+end
+%
+
+if indxDummy % Dummy observations as part of the explicit prior.
+ ndobs=nvar+1; % Number of dummy observations: nvar unit roots and 1 cointegration prior.
+ phibar = zeros(ndobs,ncoef);
+ %* constant term
+ const = ones(nvar+1,1);
+ const(1:nvar) = 0.0;
+ phibar(:,ncoef) = const; % the first nvar periods: no or zero constant!
+
+ xdgelint = mean(xdgel(1:lags,:),1); % mean of the first lags initial conditions
+ %* Dummies
+ for k=1:nvar
+ for m=1:lags
+ phibar(ndobs,nvar*(m-1)+k) = xdgelint(k);
+ phibar(k,nvar*(m-1)+k) = xdgelint(k);
+ % <<>> multiply hyperparameter later
+ end
+ end
+ phibar(1:nvar,:) = 1*mu(5)*phibar(1:nvar,:); % standard Sims and Zha prior
+ phibar(ndobs,:) = mu(6)*phibar(ndobs,:);
+ [phiq,phir]=qr(phibar,0);
+ xtxbar=phir'*phir; % phibar'*phibar. ncoef-by-ncoef. Reduced (not full) rank. See Forcast II, pp.69-69b.
+end
+
+%=================================================
+% Computing the (prior) covariance matrix for A0, no data yet.
+% As proved in pp.69a-69b, Forecast II, the following prior covariance of A0
+% will remain the same after the dummy observations prior is incorporated.
+% The dummy observation prior only affects the prior covariance of A+|A0.
+% See pp.69a-69b for the proof.
+%=================================================
+%
+%
+% ** set up the conditional prior variance sg0bi and sgpbi.
+sg0bida = mu(1)^2*sg0bid; % ith equation
+sgpbida = mu(1)^2*mu(2)^2*sgpbid;
+sgpbida(ncoef-nexo+1:ncoef) = mu(1)^2*mu(3)^2;
+ %<<>> No scaling adjustment has been made for exogenous terms other than constant
+sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in a Sims-Zha paper
+
+Hptd = zeros(ncoef);
+Hptdi=Hptd;
+Hptd(ncoef,ncoef)=sgppbd(ncoef-nvar);
+Hptdinv(ncoef,ncoef)=1./sgppbd(ncoef-nvar);
+ % condtional on A0i, H_plus_tilde
+
+
+if nargin<10 % the default is no asymmetric information
+ asym0 = ones(nvar,nvar); % if not ones, then we have relative (asymmetric) tightness
+ asymp = ones(ncoef-1,nvar); % for A+. Column -- equation
+end
+
+%**** Asymmetric Information
+%asym0 = ones(nvar,nvar); % if not ones, then we have relative (asymmetric) tightness
+%asymp = ones(ncoef-1,nvar); % pp: plus without constant. Column -- equation
+%>>>>>> B: asymmetric prior variance for asymp <<<<<<<<
+%
+%for i = 1:lags
+% rowif = (i-1)*nvar+1;
+% rowil = i*nvar;
+% idmatw0 = 0.5; % weight assigned to idmat0 in the formation of asymp
+% if (i==1)
+% asymp(rowif:rowil,:)=(1-idmatw0)*ones(nvar)+idmatw0*idmat0; % first lag
+% % note: idmat1 is already transposed. Column -- equation
+% else
+% %asymp(rowif:rowil,1:nvar) = (1-idmatw0)*ones(nvar)+idmatw0*idmat0;
+% % <<<<<<< toggle +
+% % Note: already transposed, since idmat0 is transposed.
+% % Meaning: column implies equation
+% asymp(rowif:rowil,1:nvar) = ones(nvar);
+% % >>>>>>> toggle -
+% end
+%end
+%
+%>>>>>> E: asymmetric prior variance for asymp <<<<<<<<
+
+
+%=================================================
+% Computing the final covariance matrix (S1,...,Sm) for the prior of A0,
+% and final Gb=(G1,...,Gm) for A+ if asymmetric prior or for
+% B if symmetric prior for A+
+%=================================================
+%
+for i = 1:nvar
+ %------------------------------
+ % Introduce prior information on which variables "belong" in various equations.
+ % In this first trial, we just introduce this information here, in a model-specific way.
+ % Eventually this info has to be passed parametricly. In our first shot, we just damp down
+ % all coefficients except those on the diagonal.
+
+ %*** For A0
+ factor0=asym0(:,i);
+ sg0bd = sg0bida.*factor0; % Note, this only works for the prior variance Sg(i)
+ % of a0(i) being diagonal. If the prior variance Sg(i) is not
+ % diagonal, we have to the inverse to get inv(Sg(i)).
+ %sg0bdinv = 1./sg0bd;
+ % * unconditional variance on A0+
+ H0td = diag(sg0bd); % unconditional
+ %=== Correlation in the MS equation to get a liquidity effect.
+ if (i==indxmsmdeqn(1))
+ H0td(indxmsmdeqn(3),indxmsmdeqn(4)) = hpmsmd(1)*sqrt(sg0bida(indxmsmdeqn(3))*sg0bida(indxmsmdeqn(4)));
+ H0td(indxmsmdeqn(4),indxmsmdeqn(3)) = hpmsmd(1)*sqrt(sg0bida(indxmsmdeqn(3))*sg0bida(indxmsmdeqn(4)));
+ elseif (i==indxmsmdeqn(2))
+ H0td(indxmsmdeqn(3),indxmsmdeqn(4)) = hpmsmd(2)*sqrt(sg0bida(indxmsmdeqn(3))*sg0bida(indxmsmdeqn(4)));
+ H0td(indxmsmdeqn(4),indxmsmdeqn(3)) = hpmsmd(2)*sqrt(sg0bida(indxmsmdeqn(3))*sg0bida(indxmsmdeqn(4)));
+ end
+ H0tdinv = inv(H0td);
+ %H0tdinv = diag(sg0bdinv);
+ %
+ H0multi(:,:,i)=H0td;
+ H0invmulti(:,:,i)=H0tdinv;
+
+
+ %*** For A+
+ if ~(lags==0) % For A1 to remain random walk properties
+ factor1=asymp(1:nvar,i);
+ sg1bd = sgpbida(1:nvar).*factor1;
+ sg1bdinv = 1./sg1bd;
+ %
+ Hptd(1:nvar,1:nvar)=diag(sg1bd);
+ Hptdinv(1:nvar,1:nvar)=diag(sg1bdinv);
+ if lags>1
+ factorpp=asymp(nvar+1:ncoef-1,i);
+ sgpp_cbd = sgppbd(1:ncoef-nvar-1) .* factorpp;
+ sgpp_cbdinv = 1./sgpp_cbd;
+ Hptd(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbd);
+ Hptdinv(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbdinv);
+ % condtional on A0i, H_plus_tilde
+ end
+ end
+ %---------------
+ % The dummy observation prior affects only the prior covariance of A+|A0,
+ % but not the covariance of A0. See pp.69a-69b for the proof.
+ %---------------
+ if indxDummy % Dummy observations as part of the explicit prior.
+ Hpinvmulti(:,:,i)=Hptdinv + xtxbar;
+ Hpmulti(:,:,i) = inv(Hpinvmulti(:,:,i));
+ else
+ Hpmulti(:,:,i)=Hptd;
+ Hpinvmulti(:,:,i)=Hptdinv;
+ end
+end
+
+
diff --git a/MatlabFiles/fn_rnrprior_covres_dobs_tv.m b/MatlabFiles/fn_rnrprior_covres_dobs_tv.m
index e89ccd480a5d8a78d680f383a0829c56c568f86c..1a9c9c3eb9e5af7c79a6ec8e4ad6e9adabf699b2 100644
--- a/MatlabFiles/fn_rnrprior_covres_dobs_tv.m
+++ b/MatlabFiles/fn_rnrprior_covres_dobs_tv.m
@@ -1,310 +1,310 @@
-function [Pi_bar,H0tldcell_inv,Hptldcell_inv] ...
- = fn_rnrprior_covres_dobs_tv(nvar,nStates,q_m,lags,xdgel,mu,indxDummy,Ui,Vi,hpmsmd,indxmsmdeqn,nexo,asym0,asymp)
-% Differs from fn_rnrprior_covres_dobs_tv(): no linear restrictions (Ui and Vi) have applied yet to this function, but
-% linear restrictions are incorported in fn_rnrprior_covres_dobs_tv().
-%
-% Only works for the nexo=1 (constant term) case. To extend this to other exogenous variables, see fn_dataxy.m. 01/14/03.
-% Differs from fn_rnrprior_covres_tv.m in that dummy observations are included as part of the explicit prior. See Forcast II, pp.68-69b.
-% Exports random Bayesian prior of Sims and Zha with asymmetric rior with linear restrictions already applied
-% and with dummy observations (i.e., mu(5) and mu(6)) used as part of an explicit prior.
-% This function allows for prior covariances for the MS and MD equations to achieve liquidity effects.
-% See Waggoner and Zha's Gibbs sampling paper and TVBVAR NOTES pp. 71k.0 and 50-61.
-%
-% nvar: number of endogenous variables
-% nStates: Number of states.
-% q_m: quarter or month
-% lags: the maximum length of lag
-% xdgel: T*nvar endogenous-variable matrix of raw or original data (no manipulation involved) with sample size including lags.
-% Order of columns: (1) nvar endogenous variables; (2) constants will be automatically put in the last column.
-% Used only to get variances of residuals for mu(1)-mu(5) and for dummy observations mu(5) and mu(6).
-% mu: 6-by-1 vector of hyperparameters (the following numbers for Atlanta Fed's forecast), where
-% mu(5) and mu(6) are NOT used here. See fn_dataxy.m for using mu(5) and mu(6).
-% mu(1): overall tightness and also for A0; (0.57)
-% mu(2): relative tightness for A+; (0.13)
-% mu(3): relative tightness for the constant term; (0.1). NOTE: for other
-% exogenous terms, the variance of each exogenous term must be taken into
-% acount to eliminate the scaling factor.
-% mu(4): tightness on lag decay; (1)
-% mu(5): weight on nvar sums of coeffs dummy observations (unit roots); (5)
-% mu(6): weight on single dummy initial observation including constant
-% (cointegration, unit roots, and stationarity); (5)
-% NOTE: for this function, mu(5) and mu(6) are not used. See fn_dataxy.m for using mu(5) and mu(6).
-% indxDummy: 1: uses dummy observations to form part of an explicit prior; 0: no dummy observations as part of the prior.
-% Ui: nvar-by-1 cell. In each cell, nvar-by-qi*si orthonormal basis for the null of the ith
-% equation contemporaneous restriction matrix where qi is the number of free parameters
-% within the state and si is the number of free states.
-% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
-% of total original parameters and bi is a vector of free parameters. When no
-% restrictions are imposed, we have Ui = I. There must be at least one free
-% parameter left for the ith equation in the order of [a_i for 1st state, ..., a_i for last state].
-% Vi: nvar-by-1 cell. In each cell, k-by-ri*ti orthonormal basis for the null of the ith
-% equation lagged restriction matrix where k is a total of exogenous variables and
-% ri is the number of free parameters within the state and ti is the number of free states.
-% With this transformation, we have fi = Vi*gi
-% or Vi'*fi = gi where fi is a vector of total original parameters and gi is a
-% vector of free parameters. The ith equation is in the order of [nvar variables
-% for 1st lag and 1st state, ..., nvar variables for last lag and 1st state, const for 1st state, nvar
-% variables for 1st lag and 2nd state, nvar variables for last lag and 2nd state, const for 2nd state, and so on].
-% hpmsmd: 2-by-1 hyperparameters with -1<h1=hpmsmd(1)<=0 for the MS equation and 0<=h2=hpmsmd(2)<1 the MD equation. Consider a1*R + a2*M.
-% The term h1*var(a1)*var(a2) is the prior covariance of a1 and a2 for MS, equivalent to penalizing the same sign of a1 and a2.
-% The term h2*var(a1)*var(a2) is the prior covariance of a1 and a2 for MD, equivalent to penalizing opposite signs of a1 and a2.
-% This will give us a liquidity effect. If hpmsmd=0, no such restrictions will be imposed.
-% indxmsmdeqn: 4-by-1 index for the locations of the MS and MD equation and for the locations of M and R.
-% indxmsmdeqn(1) for MS and indxmsmdeqn(2) for MD.
-% indxmsmdeqn(3) for M and indxmsmdeqn(4) for R.
-% nexo: number of exogenous variables (if not specified, nexo=1 (constant) by default).
-% The constant term is always put to the last of all endogenous and exogenous variables.
-% asym0: nvar-by-nvar asymmetric prior on A0. Column -- equation.
-% If ones(nvar,nvar), symmetric prior; if not, relative (asymmetric) tightness on A0.
-% asymp: ncoef-1-by-nvar asymmetric prior on A+ bar constant. Column -- equation.
-% If ones(ncoef-1,nvar), symmetric prior; if not, relative (asymmetric) tightness on A+.
-% --------------------
-% Pi_bar: ncoef-by-nvar matrix for the ith equation under random walk. Same for all equations
-% H0tldcell_inv: cell(nvar,1). The ith cell represents the ith equation, where the dim is
-% qi*si-by-qi*si. The inverse of H0tld on p.60.
-% Hptldcell_inv: cell(nvar,1). The ith cell represents the ith equation, where the dim is
-% ri*ti-by-ri*ti.The inverse of Hptld on p.60.
-%
-% Tao Zha, February 2000. Revised, September 2000, 2001, February, May 2003.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-if nargin<=11, nexo=1; end % <<>>1
-ncoef = nvar*lags+nexo; % Number of coefficients in *each* equation for each state, RHS coefficients only.
-ncoefsts = nStates*ncoef; % Number of coefficients in *each* equation in all states, RHS coefficients only.
-
-H0tldcell_inv=cell(nvar,1); % inv(H0tilde) for different equations under asymmetric prior.
-Hptldcell_inv=cell(nvar,1); % inv(H+tilde) for different equations under asymmetric prior.
-
-%*** Constructing Pi_bar for the ith equation under the random walk assumption
-Pi_bar = zeros(ncoef,nvar); % same for all equations
-Pi_bar(1:nvar,1:nvar) = eye(nvar); % random walk
-
-%
-%@@@ Prepared for Bayesian prior
-%
-%
-% ** monthly lag decay in order to match quarterly decay: a*exp(bl) where
-% ** l is the monthly lag. Suppose quarterly decay is 1/x where x=1,2,3,4.
-% ** Let the decay of l1 (a*exp(b*l1)) match that of x1 (say, beginning: 1/1)
-% ** and the decay of l2 (a*exp(b*l2)) match that of x2 (say, end: 1/5),
-% ** we can solve for a and b which are
-% ** b = (log_x1-log_x2)/(l1-l2), and a = x1*exp(-b*l1).
-if q_m==12
- l1 = 1; % 1st month == 1st quarter
- xx1 = 1; % 1st quarter
- l2 = lags; % last month
- xx2 = 1/((ceil(lags/3))^mu(4)); % last quarter
- %xx2 = 1/6; % last quarter
- % 3rd quarter: i.e., we intend to let decay of the 6th month match
- % that of the 3rd quarter, so that the 6th month decays a little
- % faster than the second quarter which is 1/2.
- if lags==1
- b = 0;
- else
- b = (log(xx1)-log(xx2))/(l1-l2);
- end
- a = xx1*exp(-b*l1);
-end
-
-
-
-%
-% *** specify the prior for each equation separately, SZ method,
-% ** get the residuals from univariate regressions.
-%
-sgh = zeros(nvar,1); % square root
-sgsh = sgh; % square
-nSample=size(xdgel,1); % sample size-lags
-yu = xdgel;
-C = ones(nSample,1);
-for k=1:nvar
- [Bk,ek,junk1,junk2,junk3,junk4] = sye([yu(:,k) C],lags);
- clear Bk junk1 junk2 junk3 junk4;
- sgsh(k) = ek'*ek/(nSample-lags);
- sgh(k) = sqrt(sgsh(k));
-end
-% ** prior variance for A0(:,1), same for all equations!!!
-sg0bid = zeros(nvar,1); % Sigma0_bar diagonal only for the ith equation
-for j=1:nvar
- sg0bid(j) = 1/sgsh(j); % sgsh = sigmai^2
-end
-% ** prior variance for lagged and exogeous variables, same for all equations
-sgpbid = zeros(ncoef,1); % Sigma_plus_bar, diagonal, for the ith equation
-for i = 1:lags
- if (q_m==12)
- lagdecay = a*exp(b*i*mu(4));
- end
- %
- for j = 1:nvar
- if (q_m==12)
- % exponential decay to match quarterly decay
- sgpbid((i-1)*nvar+j) = lagdecay^2/sgsh(j); % ith equation
- elseif (q_m==4)
- sgpbid((i-1)*nvar+j) = (1/i^mu(4))^2/sgsh(j); % ith equation
- else
- error('Incompatibility with lags, check the possible errors!!!')
- %warning('Incompatibility with lags, check the possible errors!!!')
- %return
- end
- end
-end
-%
-if indxDummy % Dummy observations as part of the explicit prior.
- ndobs=nvar+1; % Number of dummy observations: nvar unit roots and 1 cointegration prior.
- phibar = zeros(ndobs,ncoef);
- %* constant term
- const = ones(nvar+1,1);
- const(1:nvar) = 0.0;
- phibar(:,ncoef) = const; % the first nvar periods: no or zero constant!
-
- xdgelint = mean(xdgel(1:lags,:),1); % mean of the first lags initial conditions
- %* Dummies
- for k=1:nvar
- for m=1:lags
- phibar(ndobs,nvar*(m-1)+k) = xdgelint(k);
- phibar(k,nvar*(m-1)+k) = xdgelint(k);
- % <<>> multiply hyperparameter later
- end
- end
- phibar(1:nvar,:) = 1*mu(5)*phibar(1:nvar,:); % standard Sims and Zha prior
- phibar(ndobs,:) = mu(6)*phibar(ndobs,:);
- [phiq,phir]=qr(phibar,0);
- xtxbar=phir'*phir; % phibar'*phibar. ncoef-by-ncoef. Reduced (not full) rank. See Forcast II, pp.69-69b.
-end
-
-
-
-
-
-%=================================================
-% Computing the (prior) covariance matrix for the posterior of A0, no data yet
-%=================================================
-%
-%
-% ** set up the conditional prior variance sg0bi and sgpbi.
-sg0bida = mu(1)^2*sg0bid; % ith equation
-sgpbida = mu(1)^2*mu(2)^2*sgpbid;
-sgpbida(ncoef-nexo+1:ncoef) = mu(1)^2*mu(3)^2;
- %<<>> No scaling adjustment has been made for exogenous terms other than constant
-sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in a Sims-Zha paper
-
-Hptd = zeros(ncoef);
-Hptdi=Hptd;
-Hptd(ncoef,ncoef)=sgppbd(ncoef-nvar);
-Hptdinv(ncoef,ncoef)=1./sgppbd(ncoef-nvar);
- % condtional on A0i, H_plus_tilde
-
-
-if nargin<13 % <<>>1 Default is no asymmetric information
- asym0 = ones(nvar,nvar); % if not ones, then we have relative (asymmetric) tightness
- asymp = ones(ncoef-1,nvar); % for A+. Column -- equation
-end
-
-%**** Asymmetric Information
-%asym0 = ones(nvar,nvar); % if not ones, then we have relative (asymmetric) tightness
-%asymp = ones(ncoef-1,nvar); % pp: plus without constant. Column -- equation
-%>>>>>> B: asymmetric prior variance for asymp <<<<<<<<
-%
-%for i = 1:lags
-% rowif = (i-1)*nvar+1;
-% rowil = i*nvar;
-% idmatw0 = 0.5; % weight assigned to idmat0 in the formation of asymp
-% if (i==1)
-% asymp(rowif:rowil,:)=(1-idmatw0)*ones(nvar)+idmatw0*idmat0; % first lag
-% % note: idmat1 is already transposed. Column -- equation
-% else
-% %asymp(rowif:rowil,1:nvar) = (1-idmatw0)*ones(nvar)+idmatw0*idmat0;
-% % <<<<<<< toggle +
-% % Note: already transposed, since idmat0 is transposed.
-% % Meaning: column implies equation
-% asymp(rowif:rowil,1:nvar) = ones(nvar);
-% % >>>>>>> toggle -
-% end
-%end
-%
-%>>>>>> E: asymmetric prior variance for asymp <<<<<<<<
-
-
-%=================================================
-% Computing the final covariance matrix (S1,...,Sm) for the prior of A0,
-% and final Gb=(G1,...,Gm) for A+ if asymmetric prior or for
-% B if symmetric prior for A+
-%=================================================
-%
-for i = 1:nvar
- %------------------------------
- % Introduce prior information on which variables "belong" in various equations.
- % In this first trial, we just introduce this information here, in a model-specific way.
- % Eventually this info has to be passed parametricly. In our first shot, we just damp down
- % all coefficients except those on the diagonal.
-
- %*** For A0
- factor0=asym0(:,i);
-
- sg0bd = sg0bida.*factor0; % Note, this only works for the prior variance Sg(i)
- % of a0(i) being diagonal. If the prior variance Sg(i) is not
- % diagonal, we have to the inverse to get inv(Sg(i)).
- %sg0bdinv = 1./sg0bd;
- % * unconditional variance on A0+
- H0td = diag(sg0bd); % unconditional
- %=== Correlation in the MS equation to get a liquidity effect.
- if (i==indxmsmdeqn(1))
- H0td(indxmsmdeqn(3),indxmsmdeqn(4)) = hpmsmd(1)*sqrt(sg0bida(indxmsmdeqn(3))*sg0bida(indxmsmdeqn(4)));
- H0td(indxmsmdeqn(4),indxmsmdeqn(3)) = hpmsmd(1)*sqrt(sg0bida(indxmsmdeqn(3))*sg0bida(indxmsmdeqn(4)));
- elseif (i==indxmsmdeqn(2))
- H0td(indxmsmdeqn(3),indxmsmdeqn(4)) = hpmsmd(2)*sqrt(sg0bida(indxmsmdeqn(3))*sg0bida(indxmsmdeqn(4)));
- H0td(indxmsmdeqn(4),indxmsmdeqn(3)) = hpmsmd(2)*sqrt(sg0bida(indxmsmdeqn(3))*sg0bida(indxmsmdeqn(4)));
- end
- H0tdinv = inv(H0td);
- %H0tdinv = diag(sg0bdinv);
- %
- H0tldcell_inv{i}=(Ui{i}'*kron(eye(nStates),H0tdinv/nStates))*Ui{i};
-
-
- %*** For A+
- if ~(lags==0) % For A1 to remain random walk properties
- factor1=asymp(1:nvar,i);
- sg1bd = sgpbida(1:nvar).*factor1;
- sg1bdinv = 1./sg1bd;
- %
- Hptd(1:nvar,1:nvar)=diag(sg1bd);
- Hptdinv(1:nvar,1:nvar)=diag(sg1bdinv);
- if lags>1
- factorpp=asymp(nvar+1:ncoef-1,i);
- sgpp_cbd = sgppbd(1:ncoef-nvar-1) .* factorpp;
- sgpp_cbdinv = 1./sgpp_cbd;
- Hptd(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbd);
- Hptdinv(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbdinv);
- % condtional on A0i, H_plus_tilde
- end
- end
- %---------------
- % The dummy observation prior affects only the prior covariance of A+|A0,
- % but not the covariance of A0. See pp.69a-69b for the proof.
- %---------------
- if indxDummy % Dummy observations as part of the explicit prior.
- Hptdinv2 = Hptdinv + xtxbar; % Rename Hptdinv to Hptdinv2 because we want to keep Hptdinv diagonal in the next loop of i.
- else
- Hptdinv2 = Hptdinv;
- end
- Hptldcell_inv{i}=(Vi{i}'*kron(eye(nStates),Hptdinv2/nStates))*Vi{i};
- %Hptdinv_3 = kron(eye(nStates),Hptdinv); % ?????
-end
-
-
+function [Pi_bar,H0tldcell_inv,Hptldcell_inv] ...
+ = fn_rnrprior_covres_dobs_tv(nvar,nStates,q_m,lags,xdgel,mu,indxDummy,Ui,Vi,hpmsmd,indxmsmdeqn,nexo,asym0,asymp)
+% Differs from fn_rnrprior_covres_dobs_tv(): no linear restrictions (Ui and Vi) have applied yet to this function, but
+% linear restrictions are incorported in fn_rnrprior_covres_dobs_tv().
+%
+% Only works for the nexo=1 (constant term) case. To extend this to other exogenous variables, see fn_dataxy.m. 01/14/03.
+% Differs from fn_rnrprior_covres_tv.m in that dummy observations are included as part of the explicit prior. See Forcast II, pp.68-69b.
+% Exports random Bayesian prior of Sims and Zha with asymmetric rior with linear restrictions already applied
+% and with dummy observations (i.e., mu(5) and mu(6)) used as part of an explicit prior.
+% This function allows for prior covariances for the MS and MD equations to achieve liquidity effects.
+% See Waggoner and Zha's Gibbs sampling paper and TVBVAR NOTES pp. 71k.0 and 50-61.
+%
+% nvar: number of endogenous variables
+% nStates: Number of states.
+% q_m: quarter or month
+% lags: the maximum length of lag
+% xdgel: T*nvar endogenous-variable matrix of raw or original data (no manipulation involved) with sample size including lags.
+% Order of columns: (1) nvar endogenous variables; (2) constants will be automatically put in the last column.
+% Used only to get variances of residuals for mu(1)-mu(5) and for dummy observations mu(5) and mu(6).
+% mu: 6-by-1 vector of hyperparameters (the following numbers for Atlanta Fed's forecast), where
+% mu(5) and mu(6) are NOT used here. See fn_dataxy.m for using mu(5) and mu(6).
+% mu(1): overall tightness and also for A0; (0.57)
+% mu(2): relative tightness for A+; (0.13)
+% mu(3): relative tightness for the constant term; (0.1). NOTE: for other
+% exogenous terms, the variance of each exogenous term must be taken into
+% acount to eliminate the scaling factor.
+% mu(4): tightness on lag decay; (1)
+% mu(5): weight on nvar sums of coeffs dummy observations (unit roots); (5)
+% mu(6): weight on single dummy initial observation including constant
+% (cointegration, unit roots, and stationarity); (5)
+% NOTE: for this function, mu(5) and mu(6) are not used. See fn_dataxy.m for using mu(5) and mu(6).
+% indxDummy: 1: uses dummy observations to form part of an explicit prior; 0: no dummy observations as part of the prior.
+% Ui: nvar-by-1 cell. In each cell, nvar-by-qi*si orthonormal basis for the null of the ith
+% equation contemporaneous restriction matrix where qi is the number of free parameters
+% within the state and si is the number of free states.
+% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
+% of total original parameters and bi is a vector of free parameters. When no
+% restrictions are imposed, we have Ui = I. There must be at least one free
+% parameter left for the ith equation in the order of [a_i for 1st state, ..., a_i for last state].
+% Vi: nvar-by-1 cell. In each cell, k-by-ri*ti orthonormal basis for the null of the ith
+% equation lagged restriction matrix where k is a total of exogenous variables and
+% ri is the number of free parameters within the state and ti is the number of free states.
+% With this transformation, we have fi = Vi*gi
+% or Vi'*fi = gi where fi is a vector of total original parameters and gi is a
+% vector of free parameters. The ith equation is in the order of [nvar variables
+% for 1st lag and 1st state, ..., nvar variables for last lag and 1st state, const for 1st state, nvar
+% variables for 1st lag and 2nd state, nvar variables for last lag and 2nd state, const for 2nd state, and so on].
+% hpmsmd: 2-by-1 hyperparameters with -1<h1=hpmsmd(1)<=0 for the MS equation and 0<=h2=hpmsmd(2)<1 the MD equation. Consider a1*R + a2*M.
+% The term h1*var(a1)*var(a2) is the prior covariance of a1 and a2 for MS, equivalent to penalizing the same sign of a1 and a2.
+% The term h2*var(a1)*var(a2) is the prior covariance of a1 and a2 for MD, equivalent to penalizing opposite signs of a1 and a2.
+% This will give us a liquidity effect. If hpmsmd=0, no such restrictions will be imposed.
+% indxmsmdeqn: 4-by-1 index for the locations of the MS and MD equation and for the locations of M and R.
+% indxmsmdeqn(1) for MS and indxmsmdeqn(2) for MD.
+% indxmsmdeqn(3) for M and indxmsmdeqn(4) for R.
+% nexo: number of exogenous variables (if not specified, nexo=1 (constant) by default).
+% The constant term is always put to the last of all endogenous and exogenous variables.
+% asym0: nvar-by-nvar asymmetric prior on A0. Column -- equation.
+% If ones(nvar,nvar), symmetric prior; if not, relative (asymmetric) tightness on A0.
+% asymp: ncoef-1-by-nvar asymmetric prior on A+ bar constant. Column -- equation.
+% If ones(ncoef-1,nvar), symmetric prior; if not, relative (asymmetric) tightness on A+.
+% --------------------
+% Pi_bar: ncoef-by-nvar matrix for the ith equation under random walk. Same for all equations
+% H0tldcell_inv: cell(nvar,1). The ith cell represents the ith equation, where the dim is
+% qi*si-by-qi*si. The inverse of H0tld on p.60.
+% Hptldcell_inv: cell(nvar,1). The ith cell represents the ith equation, where the dim is
+% ri*ti-by-ri*ti.The inverse of Hptld on p.60.
+%
+% Tao Zha, February 2000. Revised, September 2000, 2001, February, May 2003.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+if nargin<=11, nexo=1; end % <<>>1
+ncoef = nvar*lags+nexo; % Number of coefficients in *each* equation for each state, RHS coefficients only.
+ncoefsts = nStates*ncoef; % Number of coefficients in *each* equation in all states, RHS coefficients only.
+
+H0tldcell_inv=cell(nvar,1); % inv(H0tilde) for different equations under asymmetric prior.
+Hptldcell_inv=cell(nvar,1); % inv(H+tilde) for different equations under asymmetric prior.
+
+%*** Constructing Pi_bar for the ith equation under the random walk assumption
+Pi_bar = zeros(ncoef,nvar); % same for all equations
+Pi_bar(1:nvar,1:nvar) = eye(nvar); % random walk
+
+%
+%@@@ Prepared for Bayesian prior
+%
+%
+% ** monthly lag decay in order to match quarterly decay: a*exp(bl) where
+% ** l is the monthly lag. Suppose quarterly decay is 1/x where x=1,2,3,4.
+% ** Let the decay of l1 (a*exp(b*l1)) match that of x1 (say, beginning: 1/1)
+% ** and the decay of l2 (a*exp(b*l2)) match that of x2 (say, end: 1/5),
+% ** we can solve for a and b which are
+% ** b = (log_x1-log_x2)/(l1-l2), and a = x1*exp(-b*l1).
+if q_m==12
+ l1 = 1; % 1st month == 1st quarter
+ xx1 = 1; % 1st quarter
+ l2 = lags; % last month
+ xx2 = 1/((ceil(lags/3))^mu(4)); % last quarter
+ %xx2 = 1/6; % last quarter
+ % 3rd quarter: i.e., we intend to let decay of the 6th month match
+ % that of the 3rd quarter, so that the 6th month decays a little
+ % faster than the second quarter which is 1/2.
+ if lags==1
+ b = 0;
+ else
+ b = (log(xx1)-log(xx2))/(l1-l2);
+ end
+ a = xx1*exp(-b*l1);
+end
+
+
+
+%
+% *** specify the prior for each equation separately, SZ method,
+% ** get the residuals from univariate regressions.
+%
+sgh = zeros(nvar,1); % square root
+sgsh = sgh; % square
+nSample=size(xdgel,1); % sample size-lags
+yu = xdgel;
+C = ones(nSample,1);
+for k=1:nvar
+ [Bk,ek,junk1,junk2,junk3,junk4] = sye([yu(:,k) C],lags);
+ clear Bk junk1 junk2 junk3 junk4;
+ sgsh(k) = ek'*ek/(nSample-lags);
+ sgh(k) = sqrt(sgsh(k));
+end
+% ** prior variance for A0(:,1), same for all equations!!!
+sg0bid = zeros(nvar,1); % Sigma0_bar diagonal only for the ith equation
+for j=1:nvar
+ sg0bid(j) = 1/sgsh(j); % sgsh = sigmai^2
+end
+% ** prior variance for lagged and exogeous variables, same for all equations
+sgpbid = zeros(ncoef,1); % Sigma_plus_bar, diagonal, for the ith equation
+for i = 1:lags
+ if (q_m==12)
+ lagdecay = a*exp(b*i*mu(4));
+ end
+ %
+ for j = 1:nvar
+ if (q_m==12)
+ % exponential decay to match quarterly decay
+ sgpbid((i-1)*nvar+j) = lagdecay^2/sgsh(j); % ith equation
+ elseif (q_m==4)
+ sgpbid((i-1)*nvar+j) = (1/i^mu(4))^2/sgsh(j); % ith equation
+ else
+ error('Incompatibility with lags, check the possible errors!!!')
+ %warning('Incompatibility with lags, check the possible errors!!!')
+ %return
+ end
+ end
+end
+%
+if indxDummy % Dummy observations as part of the explicit prior.
+ ndobs=nvar+1; % Number of dummy observations: nvar unit roots and 1 cointegration prior.
+ phibar = zeros(ndobs,ncoef);
+ %* constant term
+ const = ones(nvar+1,1);
+ const(1:nvar) = 0.0;
+ phibar(:,ncoef) = const; % the first nvar periods: no or zero constant!
+
+ xdgelint = mean(xdgel(1:lags,:),1); % mean of the first lags initial conditions
+ %* Dummies
+ for k=1:nvar
+ for m=1:lags
+ phibar(ndobs,nvar*(m-1)+k) = xdgelint(k);
+ phibar(k,nvar*(m-1)+k) = xdgelint(k);
+ % <<>> multiply hyperparameter later
+ end
+ end
+ phibar(1:nvar,:) = 1*mu(5)*phibar(1:nvar,:); % standard Sims and Zha prior
+ phibar(ndobs,:) = mu(6)*phibar(ndobs,:);
+ [phiq,phir]=qr(phibar,0);
+ xtxbar=phir'*phir; % phibar'*phibar. ncoef-by-ncoef. Reduced (not full) rank. See Forcast II, pp.69-69b.
+end
+
+
+
+
+
+%=================================================
+% Computing the (prior) covariance matrix for the posterior of A0, no data yet
+%=================================================
+%
+%
+% ** set up the conditional prior variance sg0bi and sgpbi.
+sg0bida = mu(1)^2*sg0bid; % ith equation
+sgpbida = mu(1)^2*mu(2)^2*sgpbid;
+sgpbida(ncoef-nexo+1:ncoef) = mu(1)^2*mu(3)^2;
+ %<<>> No scaling adjustment has been made for exogenous terms other than constant
+sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in a Sims-Zha paper
+
+Hptd = zeros(ncoef);
+Hptdi=Hptd;
+Hptd(ncoef,ncoef)=sgppbd(ncoef-nvar);
+Hptdinv(ncoef,ncoef)=1./sgppbd(ncoef-nvar);
+ % condtional on A0i, H_plus_tilde
+
+
+if nargin<13 % <<>>1 Default is no asymmetric information
+ asym0 = ones(nvar,nvar); % if not ones, then we have relative (asymmetric) tightness
+ asymp = ones(ncoef-1,nvar); % for A+. Column -- equation
+end
+
+%**** Asymmetric Information
+%asym0 = ones(nvar,nvar); % if not ones, then we have relative (asymmetric) tightness
+%asymp = ones(ncoef-1,nvar); % pp: plus without constant. Column -- equation
+%>>>>>> B: asymmetric prior variance for asymp <<<<<<<<
+%
+%for i = 1:lags
+% rowif = (i-1)*nvar+1;
+% rowil = i*nvar;
+% idmatw0 = 0.5; % weight assigned to idmat0 in the formation of asymp
+% if (i==1)
+% asymp(rowif:rowil,:)=(1-idmatw0)*ones(nvar)+idmatw0*idmat0; % first lag
+% % note: idmat1 is already transposed. Column -- equation
+% else
+% %asymp(rowif:rowil,1:nvar) = (1-idmatw0)*ones(nvar)+idmatw0*idmat0;
+% % <<<<<<< toggle +
+% % Note: already transposed, since idmat0 is transposed.
+% % Meaning: column implies equation
+% asymp(rowif:rowil,1:nvar) = ones(nvar);
+% % >>>>>>> toggle -
+% end
+%end
+%
+%>>>>>> E: asymmetric prior variance for asymp <<<<<<<<
+
+
+%=================================================
+% Computing the final covariance matrix (S1,...,Sm) for the prior of A0,
+% and final Gb=(G1,...,Gm) for A+ if asymmetric prior or for
+% B if symmetric prior for A+
+%=================================================
+%
+for i = 1:nvar
+ %------------------------------
+ % Introduce prior information on which variables "belong" in various equations.
+ % In this first trial, we just introduce this information here, in a model-specific way.
+ % Eventually this info has to be passed parametricly. In our first shot, we just damp down
+ % all coefficients except those on the diagonal.
+
+ %*** For A0
+ factor0=asym0(:,i);
+
+ sg0bd = sg0bida.*factor0; % Note, this only works for the prior variance Sg(i)
+ % of a0(i) being diagonal. If the prior variance Sg(i) is not
+ % diagonal, we have to the inverse to get inv(Sg(i)).
+ %sg0bdinv = 1./sg0bd;
+ % * unconditional variance on A0+
+ H0td = diag(sg0bd); % unconditional
+ %=== Correlation in the MS equation to get a liquidity effect.
+ if (i==indxmsmdeqn(1))
+ H0td(indxmsmdeqn(3),indxmsmdeqn(4)) = hpmsmd(1)*sqrt(sg0bida(indxmsmdeqn(3))*sg0bida(indxmsmdeqn(4)));
+ H0td(indxmsmdeqn(4),indxmsmdeqn(3)) = hpmsmd(1)*sqrt(sg0bida(indxmsmdeqn(3))*sg0bida(indxmsmdeqn(4)));
+ elseif (i==indxmsmdeqn(2))
+ H0td(indxmsmdeqn(3),indxmsmdeqn(4)) = hpmsmd(2)*sqrt(sg0bida(indxmsmdeqn(3))*sg0bida(indxmsmdeqn(4)));
+ H0td(indxmsmdeqn(4),indxmsmdeqn(3)) = hpmsmd(2)*sqrt(sg0bida(indxmsmdeqn(3))*sg0bida(indxmsmdeqn(4)));
+ end
+ H0tdinv = inv(H0td);
+ %H0tdinv = diag(sg0bdinv);
+ %
+ H0tldcell_inv{i}=(Ui{i}'*kron(eye(nStates),H0tdinv/nStates))*Ui{i};
+
+
+ %*** For A+
+ if ~(lags==0) % For A1 to remain random walk properties
+ factor1=asymp(1:nvar,i);
+ sg1bd = sgpbida(1:nvar).*factor1;
+ sg1bdinv = 1./sg1bd;
+ %
+ Hptd(1:nvar,1:nvar)=diag(sg1bd);
+ Hptdinv(1:nvar,1:nvar)=diag(sg1bdinv);
+ if lags>1
+ factorpp=asymp(nvar+1:ncoef-1,i);
+ sgpp_cbd = sgppbd(1:ncoef-nvar-1) .* factorpp;
+ sgpp_cbdinv = 1./sgpp_cbd;
+ Hptd(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbd);
+ Hptdinv(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbdinv);
+ % condtional on A0i, H_plus_tilde
+ end
+ end
+ %---------------
+ % The dummy observation prior affects only the prior covariance of A+|A0,
+ % but not the covariance of A0. See pp.69a-69b for the proof.
+ %---------------
+ if indxDummy % Dummy observations as part of the explicit prior.
+ Hptdinv2 = Hptdinv + xtxbar; % Rename Hptdinv to Hptdinv2 because we want to keep Hptdinv diagonal in the next loop of i.
+ else
+ Hptdinv2 = Hptdinv;
+ end
+ Hptldcell_inv{i}=(Vi{i}'*kron(eye(nStates),Hptdinv2/nStates))*Vi{i};
+ %Hptdinv_3 = kron(eye(nStates),Hptdinv); % ?????
+end
+
+
diff --git a/MatlabFiles/fn_rnrprior_covres_dobs_tv2.m b/MatlabFiles/fn_rnrprior_covres_dobs_tv2.m
index 745f8adb678aaf4794771ad6a1351ddba9af9886..b1798897b442c3116c13bb6394b269c1a4a57b1c 100644
--- a/MatlabFiles/fn_rnrprior_covres_dobs_tv2.m
+++ b/MatlabFiles/fn_rnrprior_covres_dobs_tv2.m
@@ -1,325 +1,325 @@
-function [Pi_bar,H0tldcell_inv,Hptldcell_inv] ...
- = fn_rnrprior_covres_dobs_tv2(nvar,nStates,indxScaleStates,q_m,lags,xdgel,mu,indxDummy,Ui,Vi,hpmsmd,indxmsmdeqn,nexo,asym0,asymp)
-% Differs from fn_rnrprior_covres_dobs(): linear restrictions (Ui and Vi) have been incorported in fn_rnrprior_covres_dobs_tv?().
-% Differs from fn_rnrprior_covres_dobs_tv(): allows an option to scale up the prior variance by nStates or not scale at all,
-% so that the prior value is the same as the constant VAR when the parameters in all states are the same.
-%
-% Only works for the nexo=1 (constant term) case. To extend this to other exogenous variables, see fn_dataxy.m. 01/14/03.
-% Differs from fn_rnrprior_covres_tv.m in that dummy observations are included as part of the explicit prior. See Forcast II, pp.68-69b.
-% Exports random Bayesian prior of Sims and Zha with asymmetric rior with linear restrictions already applied
-% and with dummy observations (i.e., mu(5) and mu(6)) used as part of an explicit prior.
-% This function allows for prior covariances for the MS and MD equations to achieve liquidity effects.
-% See Waggoner and Zha's Gibbs sampling paper and TVBVAR NOTES pp. 71k.0 and 50-61.
-%
-% nvar: number of endogenous variables
-% nStates: Number of states.
-% indxScaleStates: if 0, no scale adjustment in the prior variance for the number of states in the function fn_rnrprior_covres_dobs_tv2();
-% if 1: allows a scale adjustment, marking the prior variance bigger by the number of states.
-% q_m: quarter or month
-% lags: the maximum length of lag
-% xdgel: T*nvar endogenous-variable matrix of raw or original data (no manipulation involved) with sample size including lags.
-% Order of columns: (1) nvar endogenous variables; (2) constants will be automatically put in the last column.
-% Used only to get variances of residuals for mu(1)-mu(5) and for dummy observations mu(5) and mu(6).
-% mu: 6-by-1 vector of hyperparameters (the following numbers for Atlanta Fed's forecast), where
-% mu(5) and mu(6) are NOT used here. See fn_dataxy.m for using mu(5) and mu(6).
-% mu(1): overall tightness and also for A0; (0.57)
-% mu(2): relative tightness for A+; (0.13)
-% mu(3): relative tightness for the constant term; (0.1). NOTE: for other
-% exogenous terms, the variance of each exogenous term must be taken into
-% acount to eliminate the scaling factor.
-% mu(4): tightness on lag decay; (1)
-% mu(5): weight on nvar sums of coeffs dummy observations (unit roots); (5)
-% mu(6): weight on single dummy initial observation including constant
-% (cointegration, unit roots, and stationarity); (5)
-% NOTE: for this function, mu(5) and mu(6) are not used. See fn_dataxy.m for using mu(5) and mu(6).
-% indxDummy: 1: uses dummy observations to form part of an explicit prior; 0: no dummy observations as part of the prior.
-% Ui: nvar-by-1 cell. In each cell, nvar-by-qi*si orthonormal basis for the null of the ith
-% equation contemporaneous restriction matrix where qi is the number of free parameters
-% within the state and si is the number of free states.
-% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
-% of total original parameters and bi is a vector of free parameters. When no
-% restrictions are imposed, we have Ui = I. There must be at least one free
-% parameter left for the ith equation in the order of [a_i for 1st state, ..., a_i for last state].
-% Vi: nvar-by-1 cell. In each cell, k-by-ri*ti orthonormal basis for the null of the ith
-% equation lagged restriction matrix where k is a total of exogenous variables and
-% ri is the number of free parameters within the state and ti is the number of free states.
-% With this transformation, we have fi = Vi*gi
-% or Vi'*fi = gi where fi is a vector of total original parameters and gi is a
-% vector of free parameters. The ith equation is in the order of [nvar variables
-% for 1st lag and 1st state, ..., nvar variables for last lag and 1st state, const for 1st state, nvar
-% variables for 1st lag and 2nd state, nvar variables for last lag and 2nd state, const for 2nd state, and so on].
-% hpmsmd: 2-by-1 hyperparameters with -1<h1=hpmsmd(1)<=0 for the MS equation and 0<=h2=hpmsmd(2)<1 the MD equation. Consider a1*R + a2*M.
-% The term h1*var(a1)*var(a2) is the prior covariance of a1 and a2 for MS, equivalent to penalizing the same sign of a1 and a2.
-% The term h2*var(a1)*var(a2) is the prior covariance of a1 and a2 for MD, equivalent to penalizing opposite signs of a1 and a2.
-% This will give us a liquidity effect. If hpmsmd=0, no such restrictions will be imposed.
-% indxmsmdeqn: 4-by-1 index for the locations of the MS and MD equation and for the locations of M and R.
-% indxmsmdeqn(1) for MS and indxmsmdeqn(2) for MD.
-% indxmsmdeqn(3) for M and indxmsmdeqn(4) for R.
-% nexo: number of exogenous variables (if not specified, nexo=1 (constant) by default).
-% The constant term is always put to the last of all endogenous and exogenous variables.
-% asym0: nvar-by-nvar asymmetric prior on A0. Column -- equation.
-% If ones(nvar,nvar), symmetric prior; if not, relative (asymmetric) tightness on A0.
-% asymp: ncoef-1-by-nvar asymmetric prior on A+ bar constant. Column -- equation.
-% If ones(ncoef-1,nvar), symmetric prior; if not, relative (asymmetric) tightness on A+.
-% --------------------
-% Pi_bar: ncoef-by-nvar matrix for the ith equation under random walk. Same for all equations
-% H0tldcell_inv: cell(nvar,1). The ith cell represents the ith equation, where the dim is
-% qi*si-by-qi*si. The inverse of H0tld on p.60.
-% Hptldcell_inv: cell(nvar,1). The ith cell represents the ith equation, where the dim is
-% ri*ti-by-ri*ti.The inverse of Hptld on p.60.
-%
-% Differs from fn_rnrprior_covres_dobs(): linear restrictions (Ui and Vi) have been incorported in fn_rnrprior_covres_dobs_tv?().
-% Differs from fn_rnrprior_covres_dobs_tv(): allows an option to scale up the prior variance by nStates or not scale at all.
-% so that the prior value is the same as the constant VAR when the parameters in all states are the same.
-% Tao Zha, February 2000. Revised, September 2000, 2001, February, May 2003, May 2004.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-if nargin<=12, nexo=1; end % <<>>1
-ncoef = nvar*lags+nexo; % Number of coefficients in *each* equation for each state, RHS coefficients only.
-ncoefsts = nStates*ncoef; % Number of coefficients in *each* equation in all states, RHS coefficients only.
-
-H0tldcell_inv=cell(nvar,1); % inv(H0tilde) for different equations under asymmetric prior.
-Hptldcell_inv=cell(nvar,1); % inv(H+tilde) for different equations under asymmetric prior.
-
-%*** Constructing Pi_bar for the ith equation under the random walk assumption
-Pi_bar = zeros(ncoef,nvar); % same for all equations
-Pi_bar(1:nvar,1:nvar) = eye(nvar); % random walk
-
-%
-%@@@ Prepared for Bayesian prior
-%
-%
-% ** monthly lag decay in order to match quarterly decay: a*exp(bl) where
-% ** l is the monthly lag. Suppose quarterly decay is 1/x where x=1,2,3,4.
-% ** Let the decay of l1 (a*exp(b*l1)) match that of x1 (say, beginning: 1/1)
-% ** and the decay of l2 (a*exp(b*l2)) match that of x2 (say, end: 1/5),
-% ** we can solve for a and b which are
-% ** b = (log_x1-log_x2)/(l1-l2), and a = x1*exp(-b*l1).
-if q_m==12
- l1 = 1; % 1st month == 1st quarter
- xx1 = 1; % 1st quarter
- l2 = lags; % last month
- xx2 = 1/((ceil(lags/3))^mu(4)); % last quarter
- %xx2 = 1/6; % last quarter
- % 3rd quarter: i.e., we intend to let decay of the 6th month match
- % that of the 3rd quarter, so that the 6th month decays a little
- % faster than the second quarter which is 1/2.
- if lags==1
- b = 0;
- else
- b = (log(xx1)-log(xx2))/(l1-l2);
- end
- a = xx1*exp(-b*l1);
-end
-
-
-
-%
-% *** specify the prior for each equation separately, SZ method,
-% ** get the residuals from univariate regressions.
-%
-sgh = zeros(nvar,1); % square root
-sgsh = sgh; % square
-nSample=size(xdgel,1); % sample size-lags
-yu = xdgel;
-C = ones(nSample,1);
-for k=1:nvar
- [Bk,ek,junk1,junk2,junk3,junk4] = sye([yu(:,k) C],lags);
- clear Bk junk1 junk2 junk3 junk4;
- sgsh(k) = ek'*ek/(nSample-lags);
- sgh(k) = sqrt(sgsh(k));
-end
-% ** prior variance for A0(:,1), same for all equations!!!
-sg0bid = zeros(nvar,1); % Sigma0_bar diagonal only for the ith equation
-for j=1:nvar
- sg0bid(j) = 1/sgsh(j); % sgsh = sigmai^2
-end
-% ** prior variance for lagged and exogeous variables, same for all equations
-sgpbid = zeros(ncoef,1); % Sigma_plus_bar, diagonal, for the ith equation
-for i = 1:lags
- if (q_m==12)
- lagdecay = a*exp(b*i*mu(4));
- end
- %
- for j = 1:nvar
- if (q_m==12)
- % exponential decay to match quarterly decay
- sgpbid((i-1)*nvar+j) = lagdecay^2/sgsh(j); % ith equation
- elseif (q_m==4)
- sgpbid((i-1)*nvar+j) = (1/i^mu(4))^2/sgsh(j); % ith equation
- else
- error('Incompatibility with lags, check the possible errors!!!')
- %warning('Incompatibility with lags, check the possible errors!!!')
- %return
- end
- end
-end
-%
-if indxDummy % Dummy observations as part of the explicit prior.
- ndobs=nvar+1; % Number of dummy observations: nvar unit roots and 1 cointegration prior.
- phibar = zeros(ndobs,ncoef);
- %* constant term
- const = ones(nvar+1,1);
- const(1:nvar) = 0.0;
- phibar(:,ncoef) = const; % the first nvar periods: no or zero constant!
-
- xdgelint = mean(xdgel(1:lags,:),1); % mean of the first lags initial conditions
- %* Dummies
- for k=1:nvar
- for m=1:lags
- phibar(ndobs,nvar*(m-1)+k) = xdgelint(k);
- phibar(k,nvar*(m-1)+k) = xdgelint(k);
- % <<>> multiply hyperparameter later
- end
- end
- phibar(1:nvar,:) = 1*mu(5)*phibar(1:nvar,:); % standard Sims and Zha prior
- phibar(ndobs,:) = mu(6)*phibar(ndobs,:);
- [phiq,phir]=qr(phibar,0);
- xtxbar=phir'*phir; % phibar'*phibar. ncoef-by-ncoef. Reduced (not full) rank. See Forcast II, pp.69-69b.
-end
-
-
-
-
-
-%=================================================
-% Computing the (prior) covariance matrix for the posterior of A0, no data yet
-%=================================================
-%
-%
-% ** set up the conditional prior variance sg0bi and sgpbi.
-sg0bida = mu(1)^2*sg0bid; % ith equation
-sgpbida = mu(1)^2*mu(2)^2*sgpbid;
-sgpbida(ncoef-nexo+1:ncoef) = mu(1)^2*mu(3)^2;
- %<<>> No scaling adjustment has been made for exogenous terms other than constant
-sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in a Sims-Zha paper
-
-Hptd = zeros(ncoef);
-Hptdi=Hptd;
-Hptd(ncoef,ncoef)=sgppbd(ncoef-nvar);
-Hptdinv(ncoef,ncoef)=1./sgppbd(ncoef-nvar);
- % condtional on A0i, H_plus_tilde
-
-
-if nargin<14 % <<>>1 Default is no asymmetric information
- asym0 = ones(nvar,nvar); % if not ones, then we have relative (asymmetric) tightness
- asymp = ones(ncoef-1,nvar); % for A+. Column -- equation
-end
-
-%**** Asymmetric Information
-%asym0 = ones(nvar,nvar); % if not ones, then we have relative (asymmetric) tightness
-%asymp = ones(ncoef-1,nvar); % pp: plus without constant. Column -- equation
-%>>>>>> B: asymmetric prior variance for asymp <<<<<<<<
-%
-%for i = 1:lags
-% rowif = (i-1)*nvar+1;
-% rowil = i*nvar;
-% idmatw0 = 0.5; % weight assigned to idmat0 in the formation of asymp
-% if (i==1)
-% asymp(rowif:rowil,:)=(1-idmatw0)*ones(nvar)+idmatw0*idmat0; % first lag
-% % note: idmat1 is already transposed. Column -- equation
-% else
-% %asymp(rowif:rowil,1:nvar) = (1-idmatw0)*ones(nvar)+idmatw0*idmat0;
-% % <<<<<<< toggle +
-% % Note: already transposed, since idmat0 is transposed.
-% % Meaning: column implies equation
-% asymp(rowif:rowil,1:nvar) = ones(nvar);
-% % >>>>>>> toggle -
-% end
-%end
-%
-%>>>>>> E: asymmetric prior variance for asymp <<<<<<<<
-
-
-%=================================================
-% Computing the final covariance matrix (S1,...,Sm) for the prior of A0,
-% and final Gb=(G1,...,Gm) for A+ if asymmetric prior or for
-% B if symmetric prior for A+
-%=================================================
-%
-for i = 1:nvar
- %------------------------------
- % Introduce prior information on which variables "belong" in various equations.
- % In this first trial, we just introduce this information here, in a model-specific way.
- % Eventually this info has to be passed parametricly. In our first shot, we just damp down
- % all coefficients except those on the diagonal.
-
- %*** For A0
- factor0=asym0(:,i);
-
- sg0bd = sg0bida.*factor0; % Note, this only works for the prior variance Sg(i)
- % of a0(i) being diagonal. If the prior variance Sg(i) is not
- % diagonal, we have to the inverse to get inv(Sg(i)).
- %sg0bdinv = 1./sg0bd;
- % * unconditional variance on A0+
- H0td = diag(sg0bd); % unconditional
- %=== Correlation in the MS equation to get a liquidity effect.
- if (i==indxmsmdeqn(1))
- H0td(indxmsmdeqn(3),indxmsmdeqn(4)) = hpmsmd(1)*sqrt(sg0bida(indxmsmdeqn(3))*sg0bida(indxmsmdeqn(4)));
- H0td(indxmsmdeqn(4),indxmsmdeqn(3)) = hpmsmd(1)*sqrt(sg0bida(indxmsmdeqn(3))*sg0bida(indxmsmdeqn(4)));
- elseif (i==indxmsmdeqn(2))
- H0td(indxmsmdeqn(3),indxmsmdeqn(4)) = hpmsmd(2)*sqrt(sg0bida(indxmsmdeqn(3))*sg0bida(indxmsmdeqn(4)));
- H0td(indxmsmdeqn(4),indxmsmdeqn(3)) = hpmsmd(2)*sqrt(sg0bida(indxmsmdeqn(3))*sg0bida(indxmsmdeqn(4)));
- end
- H0tdinv = inv(H0td);
- %H0tdinv = diag(sg0bdinv);
- %
- if indxScaleStates
- H0tldcell_inv{i}=(Ui{i}'*kron(eye(nStates),H0tdinv/nStates))*Ui{i};
- else
- H0tldcell_inv{i}=(Ui{i}'*kron(eye(nStates),H0tdinv))*Ui{i};
- end
-
-
-
- %*** For A+
- if ~(lags==0) % For A1 to remain random walk properties
- factor1=asymp(1:nvar,i);
- sg1bd = sgpbida(1:nvar).*factor1;
- sg1bdinv = 1./sg1bd;
- %
- Hptd(1:nvar,1:nvar)=diag(sg1bd);
- Hptdinv(1:nvar,1:nvar)=diag(sg1bdinv);
- if lags>1
- factorpp=asymp(nvar+1:ncoef-1,i);
- sgpp_cbd = sgppbd(1:ncoef-nvar-1) .* factorpp;
- sgpp_cbdinv = 1./sgpp_cbd;
- Hptd(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbd);
- Hptdinv(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbdinv);
- % condtional on A0i, H_plus_tilde
- end
- end
- %---------------
- % The dummy observation prior affects only the prior covariance of A+|A0,
- % but not the covariance of A0. See pp.69a-69b for the proof.
- %---------------
- if indxDummy % Dummy observations as part of the explicit prior.
- Hptdinv2 = Hptdinv + xtxbar; % Rename Hptdinv to Hptdinv2 because we want to keep Hptdinv diagonal in the next loop of i.
- else
- Hptdinv2 = Hptdinv;
- end
- if (indxScaleStates)
- Hptldcell_inv{i}=(Vi{i}'*kron(eye(nStates),Hptdinv2/nStates))*Vi{i};
- else
- Hptldcell_inv{i}=(Vi{i}'*kron(eye(nStates),Hptdinv2))*Vi{i};
- end
- %Hptdinv_3 = kron(eye(nStates),Hptdinv); % ?????
-end
-
-
+function [Pi_bar,H0tldcell_inv,Hptldcell_inv] ...
+ = fn_rnrprior_covres_dobs_tv2(nvar,nStates,indxScaleStates,q_m,lags,xdgel,mu,indxDummy,Ui,Vi,hpmsmd,indxmsmdeqn,nexo,asym0,asymp)
+% Differs from fn_rnrprior_covres_dobs(): linear restrictions (Ui and Vi) have been incorported in fn_rnrprior_covres_dobs_tv?().
+% Differs from fn_rnrprior_covres_dobs_tv(): allows an option to scale up the prior variance by nStates or not scale at all,
+% so that the prior value is the same as the constant VAR when the parameters in all states are the same.
+%
+% Only works for the nexo=1 (constant term) case. To extend this to other exogenous variables, see fn_dataxy.m. 01/14/03.
+% Differs from fn_rnrprior_covres_tv.m in that dummy observations are included as part of the explicit prior. See Forcast II, pp.68-69b.
+% Exports random Bayesian prior of Sims and Zha with asymmetric rior with linear restrictions already applied
+% and with dummy observations (i.e., mu(5) and mu(6)) used as part of an explicit prior.
+% This function allows for prior covariances for the MS and MD equations to achieve liquidity effects.
+% See Waggoner and Zha's Gibbs sampling paper and TVBVAR NOTES pp. 71k.0 and 50-61.
+%
+% nvar: number of endogenous variables
+% nStates: Number of states.
+% indxScaleStates: if 0, no scale adjustment in the prior variance for the number of states in the function fn_rnrprior_covres_dobs_tv2();
+% if 1: allows a scale adjustment, marking the prior variance bigger by the number of states.
+% q_m: quarter or month
+% lags: the maximum length of lag
+% xdgel: T*nvar endogenous-variable matrix of raw or original data (no manipulation involved) with sample size including lags.
+% Order of columns: (1) nvar endogenous variables; (2) constants will be automatically put in the last column.
+% Used only to get variances of residuals for mu(1)-mu(5) and for dummy observations mu(5) and mu(6).
+% mu: 6-by-1 vector of hyperparameters (the following numbers for Atlanta Fed's forecast), where
+% mu(5) and mu(6) are NOT used here. See fn_dataxy.m for using mu(5) and mu(6).
+% mu(1): overall tightness and also for A0; (0.57)
+% mu(2): relative tightness for A+; (0.13)
+% mu(3): relative tightness for the constant term; (0.1). NOTE: for other
+% exogenous terms, the variance of each exogenous term must be taken into
+% acount to eliminate the scaling factor.
+% mu(4): tightness on lag decay; (1)
+% mu(5): weight on nvar sums of coeffs dummy observations (unit roots); (5)
+% mu(6): weight on single dummy initial observation including constant
+% (cointegration, unit roots, and stationarity); (5)
+% NOTE: for this function, mu(5) and mu(6) are not used. See fn_dataxy.m for using mu(5) and mu(6).
+% indxDummy: 1: uses dummy observations to form part of an explicit prior; 0: no dummy observations as part of the prior.
+% Ui: nvar-by-1 cell. In each cell, nvar-by-qi*si orthonormal basis for the null of the ith
+% equation contemporaneous restriction matrix where qi is the number of free parameters
+% within the state and si is the number of free states.
+% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
+% of total original parameters and bi is a vector of free parameters. When no
+% restrictions are imposed, we have Ui = I. There must be at least one free
+% parameter left for the ith equation in the order of [a_i for 1st state, ..., a_i for last state].
+% Vi: nvar-by-1 cell. In each cell, k-by-ri*ti orthonormal basis for the null of the ith
+% equation lagged restriction matrix where k is a total of exogenous variables and
+% ri is the number of free parameters within the state and ti is the number of free states.
+% With this transformation, we have fi = Vi*gi
+% or Vi'*fi = gi where fi is a vector of total original parameters and gi is a
+% vector of free parameters. The ith equation is in the order of [nvar variables
+% for 1st lag and 1st state, ..., nvar variables for last lag and 1st state, const for 1st state, nvar
+% variables for 1st lag and 2nd state, nvar variables for last lag and 2nd state, const for 2nd state, and so on].
+% hpmsmd: 2-by-1 hyperparameters with -1<h1=hpmsmd(1)<=0 for the MS equation and 0<=h2=hpmsmd(2)<1 the MD equation. Consider a1*R + a2*M.
+% The term h1*var(a1)*var(a2) is the prior covariance of a1 and a2 for MS, equivalent to penalizing the same sign of a1 and a2.
+% The term h2*var(a1)*var(a2) is the prior covariance of a1 and a2 for MD, equivalent to penalizing opposite signs of a1 and a2.
+% This will give us a liquidity effect. If hpmsmd=0, no such restrictions will be imposed.
+% indxmsmdeqn: 4-by-1 index for the locations of the MS and MD equation and for the locations of M and R.
+% indxmsmdeqn(1) for MS and indxmsmdeqn(2) for MD.
+% indxmsmdeqn(3) for M and indxmsmdeqn(4) for R.
+% nexo: number of exogenous variables (if not specified, nexo=1 (constant) by default).
+% The constant term is always put to the last of all endogenous and exogenous variables.
+% asym0: nvar-by-nvar asymmetric prior on A0. Column -- equation.
+% If ones(nvar,nvar), symmetric prior; if not, relative (asymmetric) tightness on A0.
+% asymp: ncoef-1-by-nvar asymmetric prior on A+ bar constant. Column -- equation.
+% If ones(ncoef-1,nvar), symmetric prior; if not, relative (asymmetric) tightness on A+.
+% --------------------
+% Pi_bar: ncoef-by-nvar matrix for the ith equation under random walk. Same for all equations
+% H0tldcell_inv: cell(nvar,1). The ith cell represents the ith equation, where the dim is
+% qi*si-by-qi*si. The inverse of H0tld on p.60.
+% Hptldcell_inv: cell(nvar,1). The ith cell represents the ith equation, where the dim is
+% ri*ti-by-ri*ti.The inverse of Hptld on p.60.
+%
+% Differs from fn_rnrprior_covres_dobs(): linear restrictions (Ui and Vi) have been incorported in fn_rnrprior_covres_dobs_tv?().
+% Differs from fn_rnrprior_covres_dobs_tv(): allows an option to scale up the prior variance by nStates or not scale at all.
+% so that the prior value is the same as the constant VAR when the parameters in all states are the same.
+% Tao Zha, February 2000. Revised, September 2000, 2001, February, May 2003, May 2004.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+if nargin<=12, nexo=1; end % <<>>1
+ncoef = nvar*lags+nexo; % Number of coefficients in *each* equation for each state, RHS coefficients only.
+ncoefsts = nStates*ncoef; % Number of coefficients in *each* equation in all states, RHS coefficients only.
+
+H0tldcell_inv=cell(nvar,1); % inv(H0tilde) for different equations under asymmetric prior.
+Hptldcell_inv=cell(nvar,1); % inv(H+tilde) for different equations under asymmetric prior.
+
+%*** Constructing Pi_bar for the ith equation under the random walk assumption
+Pi_bar = zeros(ncoef,nvar); % same for all equations
+Pi_bar(1:nvar,1:nvar) = eye(nvar); % random walk
+
+%
+%@@@ Prepared for Bayesian prior
+%
+%
+% ** monthly lag decay in order to match quarterly decay: a*exp(bl) where
+% ** l is the monthly lag. Suppose quarterly decay is 1/x where x=1,2,3,4.
+% ** Let the decay of l1 (a*exp(b*l1)) match that of x1 (say, beginning: 1/1)
+% ** and the decay of l2 (a*exp(b*l2)) match that of x2 (say, end: 1/5),
+% ** we can solve for a and b which are
+% ** b = (log_x1-log_x2)/(l1-l2), and a = x1*exp(-b*l1).
+if q_m==12
+ l1 = 1; % 1st month == 1st quarter
+ xx1 = 1; % 1st quarter
+ l2 = lags; % last month
+ xx2 = 1/((ceil(lags/3))^mu(4)); % last quarter
+ %xx2 = 1/6; % last quarter
+ % 3rd quarter: i.e., we intend to let decay of the 6th month match
+ % that of the 3rd quarter, so that the 6th month decays a little
+ % faster than the second quarter which is 1/2.
+ if lags==1
+ b = 0;
+ else
+ b = (log(xx1)-log(xx2))/(l1-l2);
+ end
+ a = xx1*exp(-b*l1);
+end
+
+
+
+%
+% *** specify the prior for each equation separately, SZ method,
+% ** get the residuals from univariate regressions.
+%
+sgh = zeros(nvar,1); % square root
+sgsh = sgh; % square
+nSample=size(xdgel,1); % sample size-lags
+yu = xdgel;
+C = ones(nSample,1);
+for k=1:nvar
+ [Bk,ek,junk1,junk2,junk3,junk4] = sye([yu(:,k) C],lags);
+ clear Bk junk1 junk2 junk3 junk4;
+ sgsh(k) = ek'*ek/(nSample-lags);
+ sgh(k) = sqrt(sgsh(k));
+end
+% ** prior variance for A0(:,1), same for all equations!!!
+sg0bid = zeros(nvar,1); % Sigma0_bar diagonal only for the ith equation
+for j=1:nvar
+ sg0bid(j) = 1/sgsh(j); % sgsh = sigmai^2
+end
+% ** prior variance for lagged and exogeous variables, same for all equations
+sgpbid = zeros(ncoef,1); % Sigma_plus_bar, diagonal, for the ith equation
+for i = 1:lags
+ if (q_m==12)
+ lagdecay = a*exp(b*i*mu(4));
+ end
+ %
+ for j = 1:nvar
+ if (q_m==12)
+ % exponential decay to match quarterly decay
+ sgpbid((i-1)*nvar+j) = lagdecay^2/sgsh(j); % ith equation
+ elseif (q_m==4)
+ sgpbid((i-1)*nvar+j) = (1/i^mu(4))^2/sgsh(j); % ith equation
+ else
+ error('Incompatibility with lags, check the possible errors!!!')
+ %warning('Incompatibility with lags, check the possible errors!!!')
+ %return
+ end
+ end
+end
+%
+if indxDummy % Dummy observations as part of the explicit prior.
+ ndobs=nvar+1; % Number of dummy observations: nvar unit roots and 1 cointegration prior.
+ phibar = zeros(ndobs,ncoef);
+ %* constant term
+ const = ones(nvar+1,1);
+ const(1:nvar) = 0.0;
+ phibar(:,ncoef) = const; % the first nvar periods: no or zero constant!
+
+ xdgelint = mean(xdgel(1:lags,:),1); % mean of the first lags initial conditions
+ %* Dummies
+ for k=1:nvar
+ for m=1:lags
+ phibar(ndobs,nvar*(m-1)+k) = xdgelint(k);
+ phibar(k,nvar*(m-1)+k) = xdgelint(k);
+ % <<>> multiply hyperparameter later
+ end
+ end
+ phibar(1:nvar,:) = 1*mu(5)*phibar(1:nvar,:); % standard Sims and Zha prior
+ phibar(ndobs,:) = mu(6)*phibar(ndobs,:);
+ [phiq,phir]=qr(phibar,0);
+ xtxbar=phir'*phir; % phibar'*phibar. ncoef-by-ncoef. Reduced (not full) rank. See Forcast II, pp.69-69b.
+end
+
+
+
+
+
+%=================================================
+% Computing the (prior) covariance matrix for the posterior of A0, no data yet
+%=================================================
+%
+%
+% ** set up the conditional prior variance sg0bi and sgpbi.
+sg0bida = mu(1)^2*sg0bid; % ith equation
+sgpbida = mu(1)^2*mu(2)^2*sgpbid;
+sgpbida(ncoef-nexo+1:ncoef) = mu(1)^2*mu(3)^2;
+ %<<>> No scaling adjustment has been made for exogenous terms other than constant
+sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in a Sims-Zha paper
+
+Hptd = zeros(ncoef);
+Hptdi=Hptd;
+Hptd(ncoef,ncoef)=sgppbd(ncoef-nvar);
+Hptdinv(ncoef,ncoef)=1./sgppbd(ncoef-nvar);
+ % condtional on A0i, H_plus_tilde
+
+
+if nargin<14 % <<>>1 Default is no asymmetric information
+ asym0 = ones(nvar,nvar); % if not ones, then we have relative (asymmetric) tightness
+ asymp = ones(ncoef-1,nvar); % for A+. Column -- equation
+end
+
+%**** Asymmetric Information
+%asym0 = ones(nvar,nvar); % if not ones, then we have relative (asymmetric) tightness
+%asymp = ones(ncoef-1,nvar); % pp: plus without constant. Column -- equation
+%>>>>>> B: asymmetric prior variance for asymp <<<<<<<<
+%
+%for i = 1:lags
+% rowif = (i-1)*nvar+1;
+% rowil = i*nvar;
+% idmatw0 = 0.5; % weight assigned to idmat0 in the formation of asymp
+% if (i==1)
+% asymp(rowif:rowil,:)=(1-idmatw0)*ones(nvar)+idmatw0*idmat0; % first lag
+% % note: idmat1 is already transposed. Column -- equation
+% else
+% %asymp(rowif:rowil,1:nvar) = (1-idmatw0)*ones(nvar)+idmatw0*idmat0;
+% % <<<<<<< toggle +
+% % Note: already transposed, since idmat0 is transposed.
+% % Meaning: column implies equation
+% asymp(rowif:rowil,1:nvar) = ones(nvar);
+% % >>>>>>> toggle -
+% end
+%end
+%
+%>>>>>> E: asymmetric prior variance for asymp <<<<<<<<
+
+
+%=================================================
+% Computing the final covariance matrix (S1,...,Sm) for the prior of A0,
+% and final Gb=(G1,...,Gm) for A+ if asymmetric prior or for
+% B if symmetric prior for A+
+%=================================================
+%
+for i = 1:nvar
+ %------------------------------
+ % Introduce prior information on which variables "belong" in various equations.
+ % In this first trial, we just introduce this information here, in a model-specific way.
+ % Eventually this info has to be passed parametricly. In our first shot, we just damp down
+ % all coefficients except those on the diagonal.
+
+ %*** For A0
+ factor0=asym0(:,i);
+
+ sg0bd = sg0bida.*factor0; % Note, this only works for the prior variance Sg(i)
+ % of a0(i) being diagonal. If the prior variance Sg(i) is not
+ % diagonal, we have to the inverse to get inv(Sg(i)).
+ %sg0bdinv = 1./sg0bd;
+ % * unconditional variance on A0+
+ H0td = diag(sg0bd); % unconditional
+ %=== Correlation in the MS equation to get a liquidity effect.
+ if (i==indxmsmdeqn(1))
+ H0td(indxmsmdeqn(3),indxmsmdeqn(4)) = hpmsmd(1)*sqrt(sg0bida(indxmsmdeqn(3))*sg0bida(indxmsmdeqn(4)));
+ H0td(indxmsmdeqn(4),indxmsmdeqn(3)) = hpmsmd(1)*sqrt(sg0bida(indxmsmdeqn(3))*sg0bida(indxmsmdeqn(4)));
+ elseif (i==indxmsmdeqn(2))
+ H0td(indxmsmdeqn(3),indxmsmdeqn(4)) = hpmsmd(2)*sqrt(sg0bida(indxmsmdeqn(3))*sg0bida(indxmsmdeqn(4)));
+ H0td(indxmsmdeqn(4),indxmsmdeqn(3)) = hpmsmd(2)*sqrt(sg0bida(indxmsmdeqn(3))*sg0bida(indxmsmdeqn(4)));
+ end
+ H0tdinv = inv(H0td);
+ %H0tdinv = diag(sg0bdinv);
+ %
+ if indxScaleStates
+ H0tldcell_inv{i}=(Ui{i}'*kron(eye(nStates),H0tdinv/nStates))*Ui{i};
+ else
+ H0tldcell_inv{i}=(Ui{i}'*kron(eye(nStates),H0tdinv))*Ui{i};
+ end
+
+
+
+ %*** For A+
+ if ~(lags==0) % For A1 to remain random walk properties
+ factor1=asymp(1:nvar,i);
+ sg1bd = sgpbida(1:nvar).*factor1;
+ sg1bdinv = 1./sg1bd;
+ %
+ Hptd(1:nvar,1:nvar)=diag(sg1bd);
+ Hptdinv(1:nvar,1:nvar)=diag(sg1bdinv);
+ if lags>1
+ factorpp=asymp(nvar+1:ncoef-1,i);
+ sgpp_cbd = sgppbd(1:ncoef-nvar-1) .* factorpp;
+ sgpp_cbdinv = 1./sgpp_cbd;
+ Hptd(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbd);
+ Hptdinv(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbdinv);
+ % condtional on A0i, H_plus_tilde
+ end
+ end
+ %---------------
+ % The dummy observation prior affects only the prior covariance of A+|A0,
+ % but not the covariance of A0. See pp.69a-69b for the proof.
+ %---------------
+ if indxDummy % Dummy observations as part of the explicit prior.
+ Hptdinv2 = Hptdinv + xtxbar; % Rename Hptdinv to Hptdinv2 because we want to keep Hptdinv diagonal in the next loop of i.
+ else
+ Hptdinv2 = Hptdinv;
+ end
+ if (indxScaleStates)
+ Hptldcell_inv{i}=(Vi{i}'*kron(eye(nStates),Hptdinv2/nStates))*Vi{i};
+ else
+ Hptldcell_inv{i}=(Vi{i}'*kron(eye(nStates),Hptdinv2))*Vi{i};
+ end
+ %Hptdinv_3 = kron(eye(nStates),Hptdinv); % ?????
+end
+
+
diff --git a/MatlabFiles/fn_rnrprior_covres_tv.m b/MatlabFiles/fn_rnrprior_covres_tv.m
index eb5935f9448e57de0c4361bd29c4e4c571579986..8082206b38d45482a81debe64b1380593fd42464 100644
--- a/MatlabFiles/fn_rnrprior_covres_tv.m
+++ b/MatlabFiles/fn_rnrprior_covres_tv.m
@@ -1,274 +1,274 @@
-function [Pi_bar,H0tldcell_inv,Hptldcell_inv] ...
- = fn_rnrprior_covres_tv(nvar,nStates,q_m,lags,xdgel,mu,Ui,Vi,hpmsmd,indxmsmdeqn,nexo,asym0,asymp)
-% [Pi_bar,H0tldcell_inv,Hptldcell_inv] ...
-% = fn_rnrprior_covres_tv(nvar,nStates,q_m,lags,xdgel,mu,Ui,Vi,hpmsmd,indxmsmdeqn,nexo,asym0,asymp)
-%
-% More general than fn_rnrprior_tv() because, when hpmsmd=0, fn_rnrprior_covres_tv() is the same as fn_rnrprior_tv().
-% Exports random Bayesian prior of Sims and Zha with asymmetric rior with linear restrictions already applied
-% but without dummy observations (i.e., mu(5) and mu(6)) yet.
-% This function allows for prior covariances for the MS and MD equations to achieve liquidity effects.
-% See Waggoner and Zha's Gibbs sampling paper and TVBVAR NOTES pp. 71k.0 and 50-61.
-%
-% nvar: number of endogenous variables
-% nStates: Number of states.
-% q_m: quarter or month
-% lags: the maximum length of lag
-% xdgel: the general matrix of the original data (no manipulation involved)
-% with sample size including lags. Used only to get variances of residuals for
-% the scaling purpose; NOT used for mu(5) and mu(6).
-% mu: 6-by-1 vector of hyperparameters (the following numbers for Atlanta Fed's forecast), where
-% mu(5) and mu(6) are NOT used here. See fn_dataxy.m for using mu(5) and mu(6).
-% mu(1): overall tightness and also for A0; (0.57)
-% mu(2): relative tightness for A+; (0.13)
-% mu(3): relative tightness for the constant term; (0.1). NOTE: for other
-% exogenous terms, the variance of each exogenous term must be taken into
-% acount to eliminate the scaling factor.
-% mu(4): tightness on lag decay; (1)
-% mu(5): weight on nvar sums of coeffs dummy observations (unit roots); (5)
-% mu(6): weight on single dummy initial observation including constant
-% (cointegration, unit roots, and stationarity); (5)
-% NOTE: for this function, mu(5) and mu(6) are not used. See fn_dataxy.m for using mu(5) and mu(6).
-% Ui: nvar-by-1 cell. In each cell, nvar-by-qi*si orthonormal basis for the null of the ith
-% equation contemporaneous restriction matrix where qi is the number of free parameters
-% within the state and si is the number of free states.
-% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
-% of total original parameters and bi is a vector of free parameters. When no
-% restrictions are imposed, we have Ui = I. There must be at least one free
-% parameter left for the ith equation in the order of [a_i for 1st state, ..., a_i for last state].
-% Vi: nvar-by-1 cell. In each cell, k-by-ri*ti orthonormal basis for the null of the ith
-% equation lagged restriction matrix where k is a total of exogenous variables and
-% ri is the number of free parameters within the state and ti is the number of free states.
-% With this transformation, we have fi = Vi*gi
-% or Vi'*fi = gi where fi is a vector of total original parameters and gi is a
-% vector of free parameters. The ith equation is in the order of [nvar variables
-% for 1st lag and 1st state, ..., nvar variables for last lag and 1st state, const for 1st state, nvar
-% variables for 1st lag and 2nd state, nvar variables for last lag and 2nd state, const for 2nd state, and so on].
-% hpmsmd: 2-by-1 hyperparameters with -1<h1=hpmsmd(1)<=0 for the MS equation and 0<=h2=hpmsmd(2)<1 the MD equation. Consider a1*R + a2*M.
-% The term h1*var(a1)*var(a2) is the prior covariance of a1 and a2 for MS, equivalent to penalizing the same sign of a1 and a2.
-% The term h2*var(a1)*var(a2) is the prior covariance of a1 and a2 for MD, equivalent to penalizing opposite signs of a1 and a2.
-% This will give us a liquidity effect. If hpmsmd=0, no such restrictions will be imposed.
-% indxmsmdeqn: 4-by-1 index for the locations of the MS and MD equation and for the locations of M and R.
-% indxmsmdeqn(1) for MS and indxmsmdeqn(2) for MD.
-% indxmsmdeqn(3) for M and indxmsmdeqn(4) for R.
-% nexo: number of exogenous variables (if not specified, nexo=1 (constant) by default).
-% The constant term is always put to the last of all endogenous and exogenous variables.
-% asym0: nvar-by-nvar asymmetric prior on A0. Column -- equation.
-% If ones(nvar,nvar), symmetric prior; if not, relative (asymmetric) tightness on A0.
-% asymp: ncoef-1-by-nvar asymmetric prior on A+ bar constant. Column -- equation.
-% If ones(ncoef-1,nvar), symmetric prior; if not, relative (asymmetric) tightness on A+.
-% --------------------
-% Pi_bar: ncoef-by-nvar matrix for the ith equation under random walk. Same for all equations
-% H0tldcell_inv: cell(nvar,1). The ith cell represents the ith equation, where the dim is
-% qi*si-by-qi*si. The inverse of H0tld on p.60.
-% Hptldcell_inv: cell(nvar,1). The ith cell represents the ith equation, where the dim is
-% ri*ti-by-ri*ti.The inverse of Hptld on p.60.
-%
-% Tao Zha, February 2000. Revised, September 2000, 2001, February, May 2003.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-if nargin==10, nexo=1; end % <<>>1
-ncoef = nvar*lags+nexo; % Number of coefficients in *each* equation for each state, RHS coefficients only.
-ncoefsts = nStates*ncoef; % Number of coefficients in *each* equation in all states, RHS coefficients only.
-
-H0tldcell_inv=cell(nvar,1); % inv(H0tilde) for different equations under asymmetric prior.
-Hptldcell_inv=cell(nvar,1); % inv(H+tilde) for different equations under asymmetric prior.
-
-%*** Constructing Pi_bar for the ith equation under the random walk assumption
-Pi_bar = zeros(ncoef,nvar); % same for all equations
-Pi_bar(1:nvar,1:nvar) = eye(nvar); % random walk
-
-%
-%@@@ Prepared for Bayesian prior
-%
-%
-% ** monthly lag decay in order to match quarterly decay: a*exp(bl) where
-% ** l is the monthly lag. Suppose quarterly decay is 1/x where x=1,2,3,4.
-% ** Let the decay of l1 (a*exp(b*l1)) match that of x1 (say, beginning: 1/1)
-% ** and the decay of l2 (a*exp(b*l2)) match that of x2 (say, end: 1/5),
-% ** we can solve for a and b which are
-% ** b = (log_x1-log_x2)/(l1-l2), and a = x1*exp(-b*l1).
-if q_m==12
- l1 = 1; % 1st month == 1st quarter
- xx1 = 1; % 1st quarter
- l2 = lags; % last month
- xx2 = 1/((ceil(lags/3))^mu(4)); % last quarter
- %xx2 = 1/6; % last quarter
- % 3rd quarter: i.e., we intend to let decay of the 6th month match
- % that of the 3rd quarter, so that the 6th month decays a little
- % faster than the second quarter which is 1/2.
- if lags==1
- b = 0;
- else
- b = (log(xx1)-log(xx2))/(l1-l2);
- end
- a = xx1*exp(-b*l1);
-end
-
-
-
-%
-% *** specify the prior for each equation separately, SZ method,
-% ** get the residuals from univariate regressions.
-%
-sgh = zeros(nvar,1); % square root
-sgsh = sgh; % square
-nSample=size(xdgel,1); % sample size-lags
-yu = xdgel;
-C = ones(nSample,1);
-for k=1:nvar
- [Bk,ek,junk1,junk2,junk3,junk4] = sye([yu(:,k) C],lags);
- clear Bk junk1 junk2 junk3 junk4;
- sgsh(k) = ek'*ek/(nSample-lags);
- sgh(k) = sqrt(sgsh(k));
-end
-% ** prior variance for A0(:,1), same for all equations!!!
-sg0bid = zeros(nvar,1); % Sigma0_bar diagonal only for the ith equation
-for j=1:nvar
- sg0bid(j) = 1/sgsh(j); % sgsh = sigmai^2
-end
-% ** prior variance for lagged and exogeous variables, same for all equations
-sgpbid = zeros(ncoef,1); % Sigma_plus_bar, diagonal, for the ith equation
-for i = 1:lags
- if (q_m==12)
- lagdecay = a*exp(b*i*mu(4));
- end
- %
- for j = 1:nvar
- if (q_m==12)
- % exponential decay to match quarterly decay
- sgpbid((i-1)*nvar+j) = lagdecay^2/sgsh(j); % ith equation
- elseif (q_m==4)
- sgpbid((i-1)*nvar+j) = (1/i^mu(4))^2/sgsh(j); % ith equation
- else
- error('Incompatibility with lags, check the possible errors!!!')
- %warning('Incompatibility with lags, check the possible errors!!!')
- %return
- end
- end
-end
-%
-
-
-%=================================================
-% Computing the (prior) covariance matrix for the posterior of A0, no data yet
-%=================================================
-%
-%
-% ** set up the conditional prior variance sg0bi and sgpbi.
-sg0bida = mu(1)^2*sg0bid; % ith equation
-sgpbida = mu(1)^2*mu(2)^2*sgpbid;
-sgpbida(ncoef-nexo+1:ncoef) = mu(1)^2*mu(3)^2;
- %<<>> No scaling adjustment has been made for exogenous terms other than constant
-sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in a Sims-Zha paper
-
-Hptd = zeros(ncoef);
-Hptdi=Hptd;
-Hptd(ncoef,ncoef)=sgppbd(ncoef-nvar);
-Hptdinv(ncoef,ncoef)=1./sgppbd(ncoef-nvar);
- % condtional on A0i, H_plus_tilde
-
-
-if nargin<12 % <<>>1 Default is no asymmetric information
- asym0 = ones(nvar,nvar); % if not ones, then we have relative (asymmetric) tightness
- asymp = ones(ncoef-1,nvar); % for A+. Column -- equation
-end
-
-%**** Asymmetric Information
-%asym0 = ones(nvar,nvar); % if not ones, then we have relative (asymmetric) tightness
-%asymp = ones(ncoef-1,nvar); % pp: plus without constant. Column -- equation
-%>>>>>> B: asymmetric prior variance for asymp <<<<<<<<
-%
-%for i = 1:lags
-% rowif = (i-1)*nvar+1;
-% rowil = i*nvar;
-% idmatw0 = 0.5; % weight assigned to idmat0 in the formation of asymp
-% if (i==1)
-% asymp(rowif:rowil,:)=(1-idmatw0)*ones(nvar)+idmatw0*idmat0; % first lag
-% % note: idmat1 is already transposed. Column -- equation
-% else
-% %asymp(rowif:rowil,1:nvar) = (1-idmatw0)*ones(nvar)+idmatw0*idmat0;
-% % <<<<<<< toggle +
-% % Note: already transposed, since idmat0 is transposed.
-% % Meaning: column implies equation
-% asymp(rowif:rowil,1:nvar) = ones(nvar);
-% % >>>>>>> toggle -
-% end
-%end
-%
-%>>>>>> E: asymmetric prior variance for asymp <<<<<<<<
-
-
-%=================================================
-% Computing the final covariance matrix (S1,...,Sm) for the prior of A0,
-% and final Gb=(G1,...,Gm) for A+ if asymmetric prior or for
-% B if symmetric prior for A+
-%=================================================
-%
-for i = 1:nvar
- %------------------------------
- % Introduce prior information on which variables "belong" in various equations.
- % In this first trial, we just introduce this information here, in a model-specific way.
- % Eventually this info has to be passed parametricly. In our first shot, we just damp down
- % all coefficients except those on the diagonal.
-
- %*** For A0
- factor0=asym0(:,i);
-
- sg0bd = sg0bida.*factor0; % Note, this only works for the prior variance Sg(i)
- % of a0(i) being diagonal. If the prior variance Sg(i) is not
- % diagonal, we have to the inverse to get inv(Sg(i)).
- %sg0bdinv = 1./sg0bd;
- % * unconditional variance on A0+
- H0td = diag(sg0bd); % unconditional
- %=== Correlation in the MS equation to get a liquidity effect.
- if (i==indxmsmdeqn(1))
- H0td(indxmsmdeqn(3),indxmsmdeqn(4)) = hpmsmd(1)*sqrt(sg0bida(indxmsmdeqn(3))*sg0bida(indxmsmdeqn(4)));
- H0td(indxmsmdeqn(4),indxmsmdeqn(3)) = hpmsmd(1)*sqrt(sg0bida(indxmsmdeqn(3))*sg0bida(indxmsmdeqn(4)));
- elseif (i==indxmsmdeqn(2))
- H0td(indxmsmdeqn(3),indxmsmdeqn(4)) = hpmsmd(2)*sqrt(sg0bida(indxmsmdeqn(3))*sg0bida(indxmsmdeqn(4)));
- H0td(indxmsmdeqn(4),indxmsmdeqn(3)) = hpmsmd(2)*sqrt(sg0bida(indxmsmdeqn(3))*sg0bida(indxmsmdeqn(4)));
- end
- H0tdinv = inv(H0td);
- %H0tdinv = diag(sg0bdinv);
- %
- H0tldcell_inv{i}=(Ui{i}'*kron(eye(nStates),H0tdinv/nStates))*Ui{i};
-
-
- %*** For A+
- if ~(lags==0) % For A1 to remain random walk properties
- factor1=asymp(1:nvar,i);
- sg1bd = sgpbida(1:nvar).*factor1;
- sg1bdinv = 1./sg1bd;
- %
- Hptd(1:nvar,1:nvar)=diag(sg1bd);
- Hptdinv(1:nvar,1:nvar)=diag(sg1bdinv);
- if lags>1
- factorpp=asymp(nvar+1:ncoef-1,i);
- sgpp_cbd = sgppbd(1:ncoef-nvar-1) .* factorpp;
- sgpp_cbdinv = 1./sgpp_cbd;
- Hptd(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbd);
- Hptdinv(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbdinv);
- % condtional on A0i, H_plus_tilde
- end
- end
- Hptldcell_inv{i}=(Vi{i}'*kron(eye(nStates),Hptdinv/nStates))*Vi{i};
- %Hptdinv_3 = kron(eye(nStates),Hptdinv); % ?????
-end
-
-
+function [Pi_bar,H0tldcell_inv,Hptldcell_inv] ...
+ = fn_rnrprior_covres_tv(nvar,nStates,q_m,lags,xdgel,mu,Ui,Vi,hpmsmd,indxmsmdeqn,nexo,asym0,asymp)
+% [Pi_bar,H0tldcell_inv,Hptldcell_inv] ...
+% = fn_rnrprior_covres_tv(nvar,nStates,q_m,lags,xdgel,mu,Ui,Vi,hpmsmd,indxmsmdeqn,nexo,asym0,asymp)
+%
+% More general than fn_rnrprior_tv() because, when hpmsmd=0, fn_rnrprior_covres_tv() is the same as fn_rnrprior_tv().
+% Exports random Bayesian prior of Sims and Zha with asymmetric rior with linear restrictions already applied
+% but without dummy observations (i.e., mu(5) and mu(6)) yet.
+% This function allows for prior covariances for the MS and MD equations to achieve liquidity effects.
+% See Waggoner and Zha's Gibbs sampling paper and TVBVAR NOTES pp. 71k.0 and 50-61.
+%
+% nvar: number of endogenous variables
+% nStates: Number of states.
+% q_m: quarter or month
+% lags: the maximum length of lag
+% xdgel: the general matrix of the original data (no manipulation involved)
+% with sample size including lags. Used only to get variances of residuals for
+% the scaling purpose; NOT used for mu(5) and mu(6).
+% mu: 6-by-1 vector of hyperparameters (the following numbers for Atlanta Fed's forecast), where
+% mu(5) and mu(6) are NOT used here. See fn_dataxy.m for using mu(5) and mu(6).
+% mu(1): overall tightness and also for A0; (0.57)
+% mu(2): relative tightness for A+; (0.13)
+% mu(3): relative tightness for the constant term; (0.1). NOTE: for other
+% exogenous terms, the variance of each exogenous term must be taken into
+% acount to eliminate the scaling factor.
+% mu(4): tightness on lag decay; (1)
+% mu(5): weight on nvar sums of coeffs dummy observations (unit roots); (5)
+% mu(6): weight on single dummy initial observation including constant
+% (cointegration, unit roots, and stationarity); (5)
+% NOTE: for this function, mu(5) and mu(6) are not used. See fn_dataxy.m for using mu(5) and mu(6).
+% Ui: nvar-by-1 cell. In each cell, nvar-by-qi*si orthonormal basis for the null of the ith
+% equation contemporaneous restriction matrix where qi is the number of free parameters
+% within the state and si is the number of free states.
+% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
+% of total original parameters and bi is a vector of free parameters. When no
+% restrictions are imposed, we have Ui = I. There must be at least one free
+% parameter left for the ith equation in the order of [a_i for 1st state, ..., a_i for last state].
+% Vi: nvar-by-1 cell. In each cell, k-by-ri*ti orthonormal basis for the null of the ith
+% equation lagged restriction matrix where k is a total of exogenous variables and
+% ri is the number of free parameters within the state and ti is the number of free states.
+% With this transformation, we have fi = Vi*gi
+% or Vi'*fi = gi where fi is a vector of total original parameters and gi is a
+% vector of free parameters. The ith equation is in the order of [nvar variables
+% for 1st lag and 1st state, ..., nvar variables for last lag and 1st state, const for 1st state, nvar
+% variables for 1st lag and 2nd state, nvar variables for last lag and 2nd state, const for 2nd state, and so on].
+% hpmsmd: 2-by-1 hyperparameters with -1<h1=hpmsmd(1)<=0 for the MS equation and 0<=h2=hpmsmd(2)<1 the MD equation. Consider a1*R + a2*M.
+% The term h1*var(a1)*var(a2) is the prior covariance of a1 and a2 for MS, equivalent to penalizing the same sign of a1 and a2.
+% The term h2*var(a1)*var(a2) is the prior covariance of a1 and a2 for MD, equivalent to penalizing opposite signs of a1 and a2.
+% This will give us a liquidity effect. If hpmsmd=0, no such restrictions will be imposed.
+% indxmsmdeqn: 4-by-1 index for the locations of the MS and MD equation and for the locations of M and R.
+% indxmsmdeqn(1) for MS and indxmsmdeqn(2) for MD.
+% indxmsmdeqn(3) for M and indxmsmdeqn(4) for R.
+% nexo: number of exogenous variables (if not specified, nexo=1 (constant) by default).
+% The constant term is always put to the last of all endogenous and exogenous variables.
+% asym0: nvar-by-nvar asymmetric prior on A0. Column -- equation.
+% If ones(nvar,nvar), symmetric prior; if not, relative (asymmetric) tightness on A0.
+% asymp: ncoef-1-by-nvar asymmetric prior on A+ bar constant. Column -- equation.
+% If ones(ncoef-1,nvar), symmetric prior; if not, relative (asymmetric) tightness on A+.
+% --------------------
+% Pi_bar: ncoef-by-nvar matrix for the ith equation under random walk. Same for all equations
+% H0tldcell_inv: cell(nvar,1). The ith cell represents the ith equation, where the dim is
+% qi*si-by-qi*si. The inverse of H0tld on p.60.
+% Hptldcell_inv: cell(nvar,1). The ith cell represents the ith equation, where the dim is
+% ri*ti-by-ri*ti.The inverse of Hptld on p.60.
+%
+% Tao Zha, February 2000. Revised, September 2000, 2001, February, May 2003.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+if nargin==10, nexo=1; end % <<>>1
+ncoef = nvar*lags+nexo; % Number of coefficients in *each* equation for each state, RHS coefficients only.
+ncoefsts = nStates*ncoef; % Number of coefficients in *each* equation in all states, RHS coefficients only.
+
+H0tldcell_inv=cell(nvar,1); % inv(H0tilde) for different equations under asymmetric prior.
+Hptldcell_inv=cell(nvar,1); % inv(H+tilde) for different equations under asymmetric prior.
+
+%*** Constructing Pi_bar for the ith equation under the random walk assumption
+Pi_bar = zeros(ncoef,nvar); % same for all equations
+Pi_bar(1:nvar,1:nvar) = eye(nvar); % random walk
+
+%
+%@@@ Prepared for Bayesian prior
+%
+%
+% ** monthly lag decay in order to match quarterly decay: a*exp(bl) where
+% ** l is the monthly lag. Suppose quarterly decay is 1/x where x=1,2,3,4.
+% ** Let the decay of l1 (a*exp(b*l1)) match that of x1 (say, beginning: 1/1)
+% ** and the decay of l2 (a*exp(b*l2)) match that of x2 (say, end: 1/5),
+% ** we can solve for a and b which are
+% ** b = (log_x1-log_x2)/(l1-l2), and a = x1*exp(-b*l1).
+if q_m==12
+ l1 = 1; % 1st month == 1st quarter
+ xx1 = 1; % 1st quarter
+ l2 = lags; % last month
+ xx2 = 1/((ceil(lags/3))^mu(4)); % last quarter
+ %xx2 = 1/6; % last quarter
+ % 3rd quarter: i.e., we intend to let decay of the 6th month match
+ % that of the 3rd quarter, so that the 6th month decays a little
+ % faster than the second quarter which is 1/2.
+ if lags==1
+ b = 0;
+ else
+ b = (log(xx1)-log(xx2))/(l1-l2);
+ end
+ a = xx1*exp(-b*l1);
+end
+
+
+
+%
+% *** specify the prior for each equation separately, SZ method,
+% ** get the residuals from univariate regressions.
+%
+sgh = zeros(nvar,1); % square root
+sgsh = sgh; % square
+nSample=size(xdgel,1); % sample size-lags
+yu = xdgel;
+C = ones(nSample,1);
+for k=1:nvar
+ [Bk,ek,junk1,junk2,junk3,junk4] = sye([yu(:,k) C],lags);
+ clear Bk junk1 junk2 junk3 junk4;
+ sgsh(k) = ek'*ek/(nSample-lags);
+ sgh(k) = sqrt(sgsh(k));
+end
+% ** prior variance for A0(:,1), same for all equations!!!
+sg0bid = zeros(nvar,1); % Sigma0_bar diagonal only for the ith equation
+for j=1:nvar
+ sg0bid(j) = 1/sgsh(j); % sgsh = sigmai^2
+end
+% ** prior variance for lagged and exogeous variables, same for all equations
+sgpbid = zeros(ncoef,1); % Sigma_plus_bar, diagonal, for the ith equation
+for i = 1:lags
+ if (q_m==12)
+ lagdecay = a*exp(b*i*mu(4));
+ end
+ %
+ for j = 1:nvar
+ if (q_m==12)
+ % exponential decay to match quarterly decay
+ sgpbid((i-1)*nvar+j) = lagdecay^2/sgsh(j); % ith equation
+ elseif (q_m==4)
+ sgpbid((i-1)*nvar+j) = (1/i^mu(4))^2/sgsh(j); % ith equation
+ else
+ error('Incompatibility with lags, check the possible errors!!!')
+ %warning('Incompatibility with lags, check the possible errors!!!')
+ %return
+ end
+ end
+end
+%
+
+
+%=================================================
+% Computing the (prior) covariance matrix for the posterior of A0, no data yet
+%=================================================
+%
+%
+% ** set up the conditional prior variance sg0bi and sgpbi.
+sg0bida = mu(1)^2*sg0bid; % ith equation
+sgpbida = mu(1)^2*mu(2)^2*sgpbid;
+sgpbida(ncoef-nexo+1:ncoef) = mu(1)^2*mu(3)^2;
+ %<<>> No scaling adjustment has been made for exogenous terms other than constant
+sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in a Sims-Zha paper
+
+Hptd = zeros(ncoef);
+Hptdi=Hptd;
+Hptd(ncoef,ncoef)=sgppbd(ncoef-nvar);
+Hptdinv(ncoef,ncoef)=1./sgppbd(ncoef-nvar);
+ % condtional on A0i, H_plus_tilde
+
+
+if nargin<12 % <<>>1 Default is no asymmetric information
+ asym0 = ones(nvar,nvar); % if not ones, then we have relative (asymmetric) tightness
+ asymp = ones(ncoef-1,nvar); % for A+. Column -- equation
+end
+
+%**** Asymmetric Information
+%asym0 = ones(nvar,nvar); % if not ones, then we have relative (asymmetric) tightness
+%asymp = ones(ncoef-1,nvar); % pp: plus without constant. Column -- equation
+%>>>>>> B: asymmetric prior variance for asymp <<<<<<<<
+%
+%for i = 1:lags
+% rowif = (i-1)*nvar+1;
+% rowil = i*nvar;
+% idmatw0 = 0.5; % weight assigned to idmat0 in the formation of asymp
+% if (i==1)
+% asymp(rowif:rowil,:)=(1-idmatw0)*ones(nvar)+idmatw0*idmat0; % first lag
+% % note: idmat1 is already transposed. Column -- equation
+% else
+% %asymp(rowif:rowil,1:nvar) = (1-idmatw0)*ones(nvar)+idmatw0*idmat0;
+% % <<<<<<< toggle +
+% % Note: already transposed, since idmat0 is transposed.
+% % Meaning: column implies equation
+% asymp(rowif:rowil,1:nvar) = ones(nvar);
+% % >>>>>>> toggle -
+% end
+%end
+%
+%>>>>>> E: asymmetric prior variance for asymp <<<<<<<<
+
+
+%=================================================
+% Computing the final covariance matrix (S1,...,Sm) for the prior of A0,
+% and final Gb=(G1,...,Gm) for A+ if asymmetric prior or for
+% B if symmetric prior for A+
+%=================================================
+%
+for i = 1:nvar
+ %------------------------------
+ % Introduce prior information on which variables "belong" in various equations.
+ % In this first trial, we just introduce this information here, in a model-specific way.
+ % Eventually this info has to be passed parametricly. In our first shot, we just damp down
+ % all coefficients except those on the diagonal.
+
+ %*** For A0
+ factor0=asym0(:,i);
+
+ sg0bd = sg0bida.*factor0; % Note, this only works for the prior variance Sg(i)
+ % of a0(i) being diagonal. If the prior variance Sg(i) is not
+ % diagonal, we have to the inverse to get inv(Sg(i)).
+ %sg0bdinv = 1./sg0bd;
+ % * unconditional variance on A0+
+ H0td = diag(sg0bd); % unconditional
+ %=== Correlation in the MS equation to get a liquidity effect.
+ if (i==indxmsmdeqn(1))
+ H0td(indxmsmdeqn(3),indxmsmdeqn(4)) = hpmsmd(1)*sqrt(sg0bida(indxmsmdeqn(3))*sg0bida(indxmsmdeqn(4)));
+ H0td(indxmsmdeqn(4),indxmsmdeqn(3)) = hpmsmd(1)*sqrt(sg0bida(indxmsmdeqn(3))*sg0bida(indxmsmdeqn(4)));
+ elseif (i==indxmsmdeqn(2))
+ H0td(indxmsmdeqn(3),indxmsmdeqn(4)) = hpmsmd(2)*sqrt(sg0bida(indxmsmdeqn(3))*sg0bida(indxmsmdeqn(4)));
+ H0td(indxmsmdeqn(4),indxmsmdeqn(3)) = hpmsmd(2)*sqrt(sg0bida(indxmsmdeqn(3))*sg0bida(indxmsmdeqn(4)));
+ end
+ H0tdinv = inv(H0td);
+ %H0tdinv = diag(sg0bdinv);
+ %
+ H0tldcell_inv{i}=(Ui{i}'*kron(eye(nStates),H0tdinv/nStates))*Ui{i};
+
+
+ %*** For A+
+ if ~(lags==0) % For A1 to remain random walk properties
+ factor1=asymp(1:nvar,i);
+ sg1bd = sgpbida(1:nvar).*factor1;
+ sg1bdinv = 1./sg1bd;
+ %
+ Hptd(1:nvar,1:nvar)=diag(sg1bd);
+ Hptdinv(1:nvar,1:nvar)=diag(sg1bdinv);
+ if lags>1
+ factorpp=asymp(nvar+1:ncoef-1,i);
+ sgpp_cbd = sgppbd(1:ncoef-nvar-1) .* factorpp;
+ sgpp_cbdinv = 1./sgpp_cbd;
+ Hptd(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbd);
+ Hptdinv(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbdinv);
+ % condtional on A0i, H_plus_tilde
+ end
+ end
+ Hptldcell_inv{i}=(Vi{i}'*kron(eye(nStates),Hptdinv/nStates))*Vi{i};
+ %Hptdinv_3 = kron(eye(nStates),Hptdinv); % ?????
+end
+
+
diff --git a/MatlabFiles/fn_rnrprior_tv.m b/MatlabFiles/fn_rnrprior_tv.m
index 3b7c4414a730fc8eca5e053b18c4c95cfdb4d4b4..d408bbf7d4b0c41199b8f5ed0ca3fe1c2d4fe6ea 100644
--- a/MatlabFiles/fn_rnrprior_tv.m
+++ b/MatlabFiles/fn_rnrprior_tv.m
@@ -1,252 +1,252 @@
-function [Pi_bar,H0tldcell_inv,Hptldcell_inv] ...
- = fn_rnrprior_tv(nvar,nStates,q_m,lags,xdgel,mu,Ui,Vi,nexo,asym0,asymp)
-% Exports random Bayesian prior of Sims and Zha with linear restrictions applied, allowing for
-% possibly with asymmetric prior.
-% See Waggoner and Zha's Gibbs sampling paper and TVBVAR NOTE pp.50-61.
-%
-% nvar: number of endogenous variables
-% nStates: Number of states.
-% q_m: quarter or month
-% lags: the maximum length of lag
-% xdgel: the general matrix of the original data (no manipulation involved)
-% with sample size including lags. Used only to get variances of residuals for
-% the scaling purpose; NOT used for mu(5) and mu(6).
-% mu: 6-by-1 vector of hyperparameters (the following numbers for Atlanta Fed's forecast), where
-% mu(5) and mu(6) are NOT used here. See fn_dataxy.m for using mu(5) and mu(6).
-% mu(1): overall tightness and also for A0; (0.57)
-% mu(2): relative tightness for A+; (0.13)
-% mu(3): relative tightness for the constant term; (0.1). NOTE: for other
-% exogenous terms, the variance of each exogenous term must be taken into
-% acount to eliminate the scaling factor.
-% mu(4): tightness on lag decay; (1)
-% mu(5): weight on nvar sums of coeffs dummy observations (unit roots); (5)
-% mu(6): weight on single dummy initial observation including constant
-% (cointegration, unit roots, and stationarity); (5)
-% NOTE: for this function, mu(5) and mu(6) are not used.
-% Ui: nvar-by-1 cell. In each cell, nvar-by-(qi+si) orthonormal basis for the null of the ith
-% equation contemporaneous restriction matrix where qi is the number of free parameters
-% within the state and si is the number of free parameters across the states.
-% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
-% of total original parameters and bi is a vector of free parameters. When no
-% restrictions are imposed, we have Ui = I. There must be at least one free
-% parameter left for the ith equation.
-% Vi: nvar-by-1 cell. In each cell, k-by-(ri+ti) orthonormal basis for the null of the ith
-% equation lagged restriction matrix where k is a total of exogenous variables and
-% ri is the number of free parameters within the state and ti is the number of free
-% parameters across the states. With this transformation, we have fi = Vi*gi
-% or Vi'*fi = gi where fi is a vector of total original parameters and gi is a
-% vector of free parameters. The ith equation is in the order of [nvar for 1st lag,
-% ..., nvar for last lag, const].
-% nexo: number of exogenous variables (if not specified, nexo=1 (constant) by default).
-% The constant term is always put to the last of all endogenous and exogenous variables.
-% asym0: nvar-by-nvar asymmetric prior on A0. Column -- equation.
-% If ones(nvar,nvar), symmetric prior; if not, relative (asymmetric) tightness on A0.
-% asymp: ncoef-1-by-nvar asymmetric prior on A+ bar constant. Column -- equation.
-% If ones(ncoef-1,nvar), symmetric prior; if not, relative (asymmetric) tightness on A+.
-% --------------------
-% Pi_bar: ncoef-by-nvar matrix for the ith equation under random walk. Same for all equations
-% H0tldcell_inv: cell(nvar,1). The ith cell represents the ith equation, where the dim is
-% (qi+si)-by-(qi+si). The inverse of H0tld on p.60.
-% Hptldcell_inv: cell(nvar,1). The ith cell represents the ith equation, where the dim is
-% (ri+ti)-by-(ri+ti).The inverse of Hptld on p.60.
-%
-% Tao Zha, February 2000. Revised, September 2000, 2001.
-% See fn_dataxy.m for using mu(5) and mu(6).
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-if nargin==8, nexo=1; end % <<>>1
-ncoef = nvar*lags+nexo; % Number of coefficients in *each* equation for each state, RHS coefficients only.
-ncoefsts = nStates*ncoef; % Number of coefficients in *each* equation in all states, RHS coefficients only.
-
-H0tldcell_inv=cell(nvar,1); % inv(H0tilde) for different equations under asymmetric prior.
-Hptldcell_inv=cell(nvar,1); % inv(H+tilde) for different equations under asymmetric prior.
-
-%*** Constructing Pi_bar for the ith equation under the random walk assumption
-Pi_bar = zeros(ncoef,nvar); % same for all equations
-Pi_bar(1:nvar,1:nvar) = eye(nvar); % random walk
-
-%
-%@@@ Prepared for Bayesian prior
-%
-%
-% ** monthly lag decay in order to match quarterly decay: a*exp(bl) where
-% ** l is the monthly lag. Suppose quarterly decay is 1/x where x=1,2,3,4.
-% ** Let the decay of l1 (a*exp(b*l1)) match that of x1 (say, beginning: 1/1)
-% ** and the decay of l2 (a*exp(b*l2)) match that of x2 (say, end: 1/5),
-% ** we can solve for a and b which are
-% ** b = (log_x1-log_x2)/(l1-l2), and a = x1*exp(-b*l1).
-if q_m==12
- l1 = 1; % 1st month == 1st quarter
- xx1 = 1; % 1st quarter
- l2 = lags; % last month
- xx2 = 1/((ceil(lags/3))^mu(4)); % last quarter
- %xx2 = 1/6; % last quarter
- % 3rd quarter: i.e., we intend to let decay of the 6th month match
- % that of the 3rd quarter, so that the 6th month decays a little
- % faster than the second quarter which is 1/2.
- if lags==1
- b = 0;
- else
- b = (log(xx1)-log(xx2))/(l1-l2);
- end
- a = xx1*exp(-b*l1);
-end
-
-
-
-%
-% *** specify the prior for each equation separately, SZ method,
-% ** get the residuals from univariate regressions.
-%
-sgh = zeros(nvar,1); % square root
-sgsh = sgh; % square
-nSample=size(xdgel,1); % sample size-lags
-yu = xdgel;
-C = ones(nSample,1);
-for k=1:nvar
- [Bk,ek,junk1,junk2,junk3,junk4] = sye([yu(:,k) C],lags);
- clear Bk junk1 junk2 junk3 junk4;
- sgsh(k) = ek'*ek/(nSample-lags);
- sgh(k) = sqrt(sgsh(k));
-end
-% ** prior variance for A0(:,1), same for all equations!!!
-sg0bid = zeros(nvar,1); % Sigma0_bar diagonal only for the ith equation
-for j=1:nvar
- sg0bid(j) = 1/sgsh(j); % sgsh = sigmai^2
-end
-% ** prior variance for lagged and exogeous variables, same for all equations
-sgpbid = zeros(ncoef,1); % Sigma_plus_bar, diagonal, for the ith equation
-for i = 1:lags
- if (q_m==12)
- lagdecay = a*exp(b*i*mu(4));
- end
- %
- for j = 1:nvar
- if (q_m==12)
- % exponential decay to match quarterly decay
- sgpbid((i-1)*nvar+j) = lagdecay^2/sgsh(j); % ith equation
- elseif (q_m==4)
- sgpbid((i-1)*nvar+j) = (1/i^mu(4))^2/sgsh(j); % ith equation
- else
- error('Incompatibility with lags, check the possible errors!!!')
- %warning('Incompatibility with lags, check the possible errors!!!')
- %return
- end
- end
-end
-%
-
-
-%=================================================
-% Computing the (prior) covariance matrix for the posterior of A0, no data yet
-%=================================================
-%
-%
-% ** set up the conditional prior variance sg0bi and sgpbi.
-sg0bida = mu(1)^2*sg0bid; % ith equation
-sgpbida = mu(1)^2*mu(2)^2*sgpbid;
-sgpbida(ncoef-nexo+1:ncoef) = mu(1)^2*mu(3)^2;
- %<<>> No scaling adjustment has been made for exogenous terms other than constant
-sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in a Sims-Zha paper
-
-Hptd = zeros(ncoef);
-Hptdi=Hptd;
-Hptd(ncoef,ncoef)=sgppbd(ncoef-nvar);
-Hptdinv(ncoef,ncoef)=1./sgppbd(ncoef-nvar);
- % condtional on A0i, H_plus_tilde
-
-
-if nargin<10 % <<>>1 Default is no asymmetric information
- asym0 = ones(nvar,nvar); % if not ones, then we have relative (asymmetric) tightness
- asymp = ones(ncoef-1,nvar); % for A+. Column -- equation
-end
-
-%**** Asymmetric Information
-%asym0 = ones(nvar,nvar); % if not ones, then we have relative (asymmetric) tightness
-%asymp = ones(ncoef-1,nvar); % pp: plus without constant. Column -- equation
-%>>>>>> B: asymmetric prior variance for asymp <<<<<<<<
-%
-%for i = 1:lags
-% rowif = (i-1)*nvar+1;
-% rowil = i*nvar;
-% idmatw0 = 0.5; % weight assigned to idmat0 in the formation of asymp
-% if (i==1)
-% asymp(rowif:rowil,:)=(1-idmatw0)*ones(nvar)+idmatw0*idmat0; % first lag
-% % note: idmat1 is already transposed. Column -- equation
-% else
-% %asymp(rowif:rowil,1:nvar) = (1-idmatw0)*ones(nvar)+idmatw0*idmat0;
-% % <<<<<<< toggle +
-% % Note: already transposed, since idmat0 is transposed.
-% % Meaning: column implies equation
-% asymp(rowif:rowil,1:nvar) = ones(nvar);
-% % >>>>>>> toggle -
-% end
-%end
-%
-%>>>>>> E: asymmetric prior variance for asymp <<<<<<<<
-
-
-%=================================================
-% Computing the final covariance matrix (S1,...,Sm) for the prior of A0,
-% and final Gb=(G1,...,Gm) for A+ if asymmetric prior or for
-% B if symmetric prior for A+
-%=================================================
-%
-for i = 1:nvar
- %------------------------------
- % Introduce prior information on which variables "belong" in various equations.
- % In this first trial, we just introduce this information here, in a model-specific way.
- % Eventually this info has to be passed parametricly. In our first shot, we just damp down
- % all coefficients except those on the diagonal.
-
- %*** For A0
- factor0=asym0(:,i);
- sg0bd = sg0bida.*factor0; % Note, this only works for the prior variance Sg(i)
- % of a0(i) being diagonal. If the prior variance Sg(i) is not
- % diagonal, we have to work on inv(Sg(i)) or sg0bdinv directly.
- sg0bdinv = 1./sg0bd;
- % * unconditional variance on A0+
- H0td = diag(sg0bd); % unconditional
- H0tdinv = diag(sg0bdinv);
- %
- H0tldcell_inv{i}=(Ui{i}'*kron(eye(nStates),H0tdinv/nStates))*Ui{i};
-
-
- %*** For A+
- if ~(lags==0) % For A1 to remain random walk properties
- factor1=asymp(1:nvar,i);
- sg1bd = sgpbida(1:nvar).*factor1;
- sg1bdinv = 1./sg1bd;
- %
- Hptd(1:nvar,1:nvar)=diag(sg1bd);
- Hptdinv(1:nvar,1:nvar)=diag(sg1bdinv);
- if lags>1
- factorpp=asymp(nvar+1:ncoef-1,i);
- sgpp_cbd = sgppbd(1:ncoef-nvar-1) .* factorpp;
- sgpp_cbdinv = 1./sgpp_cbd;
- Hptd(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbd);
- Hptdinv(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbdinv);
- % condtional on A0i, H_plus_tilde
- end
- end
- Hptldcell_inv{i}=(Vi{i}'*kron(eye(nStates),Hptdinv/nStates))*Vi{i};
- %Hptdinv_3 = kron(eye(nStates),Hptdinv); % ?????
-end
-
-
+function [Pi_bar,H0tldcell_inv,Hptldcell_inv] ...
+ = fn_rnrprior_tv(nvar,nStates,q_m,lags,xdgel,mu,Ui,Vi,nexo,asym0,asymp)
+% Exports random Bayesian prior of Sims and Zha with linear restrictions applied, allowing for
+% possibly with asymmetric prior.
+% See Waggoner and Zha's Gibbs sampling paper and TVBVAR NOTE pp.50-61.
+%
+% nvar: number of endogenous variables
+% nStates: Number of states.
+% q_m: quarter or month
+% lags: the maximum length of lag
+% xdgel: the general matrix of the original data (no manipulation involved)
+% with sample size including lags. Used only to get variances of residuals for
+% the scaling purpose; NOT used for mu(5) and mu(6).
+% mu: 6-by-1 vector of hyperparameters (the following numbers for Atlanta Fed's forecast), where
+% mu(5) and mu(6) are NOT used here. See fn_dataxy.m for using mu(5) and mu(6).
+% mu(1): overall tightness and also for A0; (0.57)
+% mu(2): relative tightness for A+; (0.13)
+% mu(3): relative tightness for the constant term; (0.1). NOTE: for other
+% exogenous terms, the variance of each exogenous term must be taken into
+% acount to eliminate the scaling factor.
+% mu(4): tightness on lag decay; (1)
+% mu(5): weight on nvar sums of coeffs dummy observations (unit roots); (5)
+% mu(6): weight on single dummy initial observation including constant
+% (cointegration, unit roots, and stationarity); (5)
+% NOTE: for this function, mu(5) and mu(6) are not used.
+% Ui: nvar-by-1 cell. In each cell, nvar-by-(qi+si) orthonormal basis for the null of the ith
+% equation contemporaneous restriction matrix where qi is the number of free parameters
+% within the state and si is the number of free parameters across the states.
+% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
+% of total original parameters and bi is a vector of free parameters. When no
+% restrictions are imposed, we have Ui = I. There must be at least one free
+% parameter left for the ith equation.
+% Vi: nvar-by-1 cell. In each cell, k-by-(ri+ti) orthonormal basis for the null of the ith
+% equation lagged restriction matrix where k is a total of exogenous variables and
+% ri is the number of free parameters within the state and ti is the number of free
+% parameters across the states. With this transformation, we have fi = Vi*gi
+% or Vi'*fi = gi where fi is a vector of total original parameters and gi is a
+% vector of free parameters. The ith equation is in the order of [nvar for 1st lag,
+% ..., nvar for last lag, const].
+% nexo: number of exogenous variables (if not specified, nexo=1 (constant) by default).
+% The constant term is always put to the last of all endogenous and exogenous variables.
+% asym0: nvar-by-nvar asymmetric prior on A0. Column -- equation.
+% If ones(nvar,nvar), symmetric prior; if not, relative (asymmetric) tightness on A0.
+% asymp: ncoef-1-by-nvar asymmetric prior on A+ bar constant. Column -- equation.
+% If ones(ncoef-1,nvar), symmetric prior; if not, relative (asymmetric) tightness on A+.
+% --------------------
+% Pi_bar: ncoef-by-nvar matrix for the ith equation under random walk. Same for all equations
+% H0tldcell_inv: cell(nvar,1). The ith cell represents the ith equation, where the dim is
+% (qi+si)-by-(qi+si). The inverse of H0tld on p.60.
+% Hptldcell_inv: cell(nvar,1). The ith cell represents the ith equation, where the dim is
+% (ri+ti)-by-(ri+ti).The inverse of Hptld on p.60.
+%
+% Tao Zha, February 2000. Revised, September 2000, 2001.
+% See fn_dataxy.m for using mu(5) and mu(6).
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+if nargin==8, nexo=1; end % <<>>1
+ncoef = nvar*lags+nexo; % Number of coefficients in *each* equation for each state, RHS coefficients only.
+ncoefsts = nStates*ncoef; % Number of coefficients in *each* equation in all states, RHS coefficients only.
+
+H0tldcell_inv=cell(nvar,1); % inv(H0tilde) for different equations under asymmetric prior.
+Hptldcell_inv=cell(nvar,1); % inv(H+tilde) for different equations under asymmetric prior.
+
+%*** Constructing Pi_bar for the ith equation under the random walk assumption
+Pi_bar = zeros(ncoef,nvar); % same for all equations
+Pi_bar(1:nvar,1:nvar) = eye(nvar); % random walk
+
+%
+%@@@ Prepared for Bayesian prior
+%
+%
+% ** monthly lag decay in order to match quarterly decay: a*exp(bl) where
+% ** l is the monthly lag. Suppose quarterly decay is 1/x where x=1,2,3,4.
+% ** Let the decay of l1 (a*exp(b*l1)) match that of x1 (say, beginning: 1/1)
+% ** and the decay of l2 (a*exp(b*l2)) match that of x2 (say, end: 1/5),
+% ** we can solve for a and b which are
+% ** b = (log_x1-log_x2)/(l1-l2), and a = x1*exp(-b*l1).
+if q_m==12
+ l1 = 1; % 1st month == 1st quarter
+ xx1 = 1; % 1st quarter
+ l2 = lags; % last month
+ xx2 = 1/((ceil(lags/3))^mu(4)); % last quarter
+ %xx2 = 1/6; % last quarter
+ % 3rd quarter: i.e., we intend to let decay of the 6th month match
+ % that of the 3rd quarter, so that the 6th month decays a little
+ % faster than the second quarter which is 1/2.
+ if lags==1
+ b = 0;
+ else
+ b = (log(xx1)-log(xx2))/(l1-l2);
+ end
+ a = xx1*exp(-b*l1);
+end
+
+
+
+%
+% *** specify the prior for each equation separately, SZ method,
+% ** get the residuals from univariate regressions.
+%
+sgh = zeros(nvar,1); % square root
+sgsh = sgh; % square
+nSample=size(xdgel,1); % sample size-lags
+yu = xdgel;
+C = ones(nSample,1);
+for k=1:nvar
+ [Bk,ek,junk1,junk2,junk3,junk4] = sye([yu(:,k) C],lags);
+ clear Bk junk1 junk2 junk3 junk4;
+ sgsh(k) = ek'*ek/(nSample-lags);
+ sgh(k) = sqrt(sgsh(k));
+end
+% ** prior variance for A0(:,1), same for all equations!!!
+sg0bid = zeros(nvar,1); % Sigma0_bar diagonal only for the ith equation
+for j=1:nvar
+ sg0bid(j) = 1/sgsh(j); % sgsh = sigmai^2
+end
+% ** prior variance for lagged and exogeous variables, same for all equations
+sgpbid = zeros(ncoef,1); % Sigma_plus_bar, diagonal, for the ith equation
+for i = 1:lags
+ if (q_m==12)
+ lagdecay = a*exp(b*i*mu(4));
+ end
+ %
+ for j = 1:nvar
+ if (q_m==12)
+ % exponential decay to match quarterly decay
+ sgpbid((i-1)*nvar+j) = lagdecay^2/sgsh(j); % ith equation
+ elseif (q_m==4)
+ sgpbid((i-1)*nvar+j) = (1/i^mu(4))^2/sgsh(j); % ith equation
+ else
+ error('Incompatibility with lags, check the possible errors!!!')
+ %warning('Incompatibility with lags, check the possible errors!!!')
+ %return
+ end
+ end
+end
+%
+
+
+%=================================================
+% Computing the (prior) covariance matrix for the posterior of A0, no data yet
+%=================================================
+%
+%
+% ** set up the conditional prior variance sg0bi and sgpbi.
+sg0bida = mu(1)^2*sg0bid; % ith equation
+sgpbida = mu(1)^2*mu(2)^2*sgpbid;
+sgpbida(ncoef-nexo+1:ncoef) = mu(1)^2*mu(3)^2;
+ %<<>> No scaling adjustment has been made for exogenous terms other than constant
+sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in a Sims-Zha paper
+
+Hptd = zeros(ncoef);
+Hptdi=Hptd;
+Hptd(ncoef,ncoef)=sgppbd(ncoef-nvar);
+Hptdinv(ncoef,ncoef)=1./sgppbd(ncoef-nvar);
+ % condtional on A0i, H_plus_tilde
+
+
+if nargin<10 % <<>>1 Default is no asymmetric information
+ asym0 = ones(nvar,nvar); % if not ones, then we have relative (asymmetric) tightness
+ asymp = ones(ncoef-1,nvar); % for A+. Column -- equation
+end
+
+%**** Asymmetric Information
+%asym0 = ones(nvar,nvar); % if not ones, then we have relative (asymmetric) tightness
+%asymp = ones(ncoef-1,nvar); % pp: plus without constant. Column -- equation
+%>>>>>> B: asymmetric prior variance for asymp <<<<<<<<
+%
+%for i = 1:lags
+% rowif = (i-1)*nvar+1;
+% rowil = i*nvar;
+% idmatw0 = 0.5; % weight assigned to idmat0 in the formation of asymp
+% if (i==1)
+% asymp(rowif:rowil,:)=(1-idmatw0)*ones(nvar)+idmatw0*idmat0; % first lag
+% % note: idmat1 is already transposed. Column -- equation
+% else
+% %asymp(rowif:rowil,1:nvar) = (1-idmatw0)*ones(nvar)+idmatw0*idmat0;
+% % <<<<<<< toggle +
+% % Note: already transposed, since idmat0 is transposed.
+% % Meaning: column implies equation
+% asymp(rowif:rowil,1:nvar) = ones(nvar);
+% % >>>>>>> toggle -
+% end
+%end
+%
+%>>>>>> E: asymmetric prior variance for asymp <<<<<<<<
+
+
+%=================================================
+% Computing the final covariance matrix (S1,...,Sm) for the prior of A0,
+% and final Gb=(G1,...,Gm) for A+ if asymmetric prior or for
+% B if symmetric prior for A+
+%=================================================
+%
+for i = 1:nvar
+ %------------------------------
+ % Introduce prior information on which variables "belong" in various equations.
+ % In this first trial, we just introduce this information here, in a model-specific way.
+ % Eventually this info has to be passed parametricly. In our first shot, we just damp down
+ % all coefficients except those on the diagonal.
+
+ %*** For A0
+ factor0=asym0(:,i);
+ sg0bd = sg0bida.*factor0; % Note, this only works for the prior variance Sg(i)
+ % of a0(i) being diagonal. If the prior variance Sg(i) is not
+ % diagonal, we have to work on inv(Sg(i)) or sg0bdinv directly.
+ sg0bdinv = 1./sg0bd;
+ % * unconditional variance on A0+
+ H0td = diag(sg0bd); % unconditional
+ H0tdinv = diag(sg0bdinv);
+ %
+ H0tldcell_inv{i}=(Ui{i}'*kron(eye(nStates),H0tdinv/nStates))*Ui{i};
+
+
+ %*** For A+
+ if ~(lags==0) % For A1 to remain random walk properties
+ factor1=asymp(1:nvar,i);
+ sg1bd = sgpbida(1:nvar).*factor1;
+ sg1bdinv = 1./sg1bd;
+ %
+ Hptd(1:nvar,1:nvar)=diag(sg1bd);
+ Hptdinv(1:nvar,1:nvar)=diag(sg1bdinv);
+ if lags>1
+ factorpp=asymp(nvar+1:ncoef-1,i);
+ sgpp_cbd = sgppbd(1:ncoef-nvar-1) .* factorpp;
+ sgpp_cbdinv = 1./sgpp_cbd;
+ Hptd(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbd);
+ Hptdinv(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbdinv);
+ % condtional on A0i, H_plus_tilde
+ end
+ end
+ Hptldcell_inv{i}=(Vi{i}'*kron(eye(nStates),Hptdinv/nStates))*Vi{i};
+ %Hptdinv_3 = kron(eye(nStates),Hptdinv); % ?????
+end
+
+
diff --git a/MatlabFiles/fn_seriesgraph.m b/MatlabFiles/fn_seriesgraph.m
index 2a252e8d97e6ecd4b93df691ebff1688a49b2c52..53cd72d54bf076d9cd501bc483edb243504e8e38 100644
--- a/MatlabFiles/fn_seriesgraph.m
+++ b/MatlabFiles/fn_seriesgraph.m
@@ -1,65 +1,65 @@
-function fn_seriesgraph(ydate,keyindx,rnum,cnum,q_m,xlab,ylab,tlab)
-%
-% Graph actual series or point forecasts or both (annual or monthly or quarterly)
-%
-% ydate: series data with dates in the first 2 columns in the order of year and month (or quarter).
-% Some elements are allowed to be NaN if no data are available.
-% keyindx: index for the variables to be graphed
-% rnum: number of rows in subplot
-% cnum: number of columns in subplot
-% q_m: if 4 or 12, quarterly or monthly data
-% xlab: x-axis label for what conditions imposed; e.g., conlab = 'All bar MS shocks inspl'
-% ylab: string array for the length(keyindx)-by-1 variables
-% tlab: title label for (e.g., as of time of forecast)
-%-------------
-% No output argument for this graph file
-% See fn_foregraph.m, fn_forerrgraph.m.
-%
-% Tao Zha, September 2000
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-vyrs = ydate(:,1); % vectorized
-hornum = cell(length(vyrs),1); % horizontal year (number)
-count=0;
-for k=vyrs'
- count=count+1;
- jnk=num2str(k);
- hornum{count}=jnk(3:4); % e.g., with '1990', we have '90'
-end
-
-count=0;
-for i = keyindx
- count = count+1;
- subplot(rnum,cnum,count)
- plot(ydate(:,1)+ydate(:,2)/q_m,ydate(:,2+i))
-
- if (ydate(1,2)==0) % only for annual growth rates (not for, say, monthly annualized rates)
- set(gca,'XLim',[vyrs(1) vyrs(end)])
- set(gca,'XTick',vyrs)
- set(gca,'XTickLabel',char(hornum))
- end
-
- if i==keyindx(1)
- title(tlab)
- elseif i>=length(keyindx) %i>=length(keyindx)-1
- xlabel(xlab)
- end
- %
- grid
- ylabel(char(ylab(i)))
-end
+function fn_seriesgraph(ydate,keyindx,rnum,cnum,q_m,xlab,ylab,tlab)
+%
+% Graph actual series or point forecasts or both (annual or monthly or quarterly)
+%
+% ydate: series data with dates in the first 2 columns in the order of year and month (or quarter).
+% Some elements are allowed to be NaN if no data are available.
+% keyindx: index for the variables to be graphed
+% rnum: number of rows in subplot
+% cnum: number of columns in subplot
+% q_m: if 4 or 12, quarterly or monthly data
+% xlab: x-axis label for what conditions imposed; e.g., conlab = 'All bar MS shocks inspl'
+% ylab: string array for the length(keyindx)-by-1 variables
+% tlab: title label for (e.g., as of time of forecast)
+%-------------
+% No output argument for this graph file
+% See fn_foregraph.m, fn_forerrgraph.m.
+%
+% Tao Zha, September 2000
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+vyrs = ydate(:,1); % vectorized
+hornum = cell(length(vyrs),1); % horizontal year (number)
+count=0;
+for k=vyrs'
+ count=count+1;
+ jnk=num2str(k);
+ hornum{count}=jnk(3:4); % e.g., with '1990', we have '90'
+end
+
+count=0;
+for i = keyindx
+ count = count+1;
+ subplot(rnum,cnum,count)
+ plot(ydate(:,1)+ydate(:,2)/q_m,ydate(:,2+i))
+
+ if (ydate(1,2)==0) % only for annual growth rates (not for, say, monthly annualized rates)
+ set(gca,'XLim',[vyrs(1) vyrs(end)])
+ set(gca,'XTick',vyrs)
+ set(gca,'XTickLabel',char(hornum))
+ end
+
+ if i==keyindx(1)
+ title(tlab)
+ elseif i>=length(keyindx) %i>=length(keyindx)-1
+ xlabel(xlab)
+ end
+ %
+ grid
+ ylabel(char(ylab(i)))
+end
diff --git a/MatlabFiles/fn_simul.m b/MatlabFiles/fn_simul.m
index 9d3d8d83105c6e76a5e1d3fe854ff06c2c23fbd2..7a77db5a08a8bfd18a68f3dc8ba2cf4f0af56728 100644
--- a/MatlabFiles/fn_simul.m
+++ b/MatlabFiles/fn_simul.m
@@ -1,42 +1,42 @@
-function simul = fn_simul(G1, impact, nsim, shocks, x0);
-% Inputs:
-% G1: n-by-n;
-% impact: n-by-r;
-% nsim: length of time series for simulation.
-% shocks: r-by-nsim, exogenous driving processes
-% x0: n-by-1, initial values for the variables of interest
-%---
-% Outputs:
-% simul: nsim-by-n.
-%
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-[n,r] = size(impact);
-
-[n1,n2] = size(G1);
-if (n1 ~= n2) || (n1 ~= n)
- error('fn_simul.m: make sure that (1) G1 is square and (2) size(G1,1) = size(impact,1)');
-end
-
-simul_tmp = zeros(n, nsim);
-
-simul_tmp(:, 1) = x0;
-for ti = 2:nsim;
- simul_tmp(:, ti) = G1*simul_tmp(:, ti-1) + impact*shocks(:, ti);
-end;
-simul = simul_tmp';
+function simul = fn_simul(G1, impact, nsim, shocks, x0);
+% Inputs:
+% G1: n-by-n;
+% impact: n-by-r;
+% nsim: length of time series for simulation.
+% shocks: r-by-nsim, exogenous driving processes
+% x0: n-by-1, initial values for the variables of interest
+%---
+% Outputs:
+% simul: nsim-by-n.
+%
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+[n,r] = size(impact);
+
+[n1,n2] = size(G1);
+if (n1 ~= n2) || (n1 ~= n)
+ error('fn_simul.m: make sure that (1) G1 is square and (2) size(G1,1) = size(impact,1)');
+end
+
+simul_tmp = zeros(n, nsim);
+
+simul_tmp(:, 1) = x0;
+for ti = 2:nsim;
+ simul_tmp(:, ti) = G1*simul_tmp(:, ti-1) + impact*shocks(:, ti);
+end;
+simul = simul_tmp';
diff --git a/MatlabFiles/fn_tran_a2b.m b/MatlabFiles/fn_tran_a2b.m
index b8ca8ddd690073a0d76ba7ccd26e08351e2a6110..51fe994b07abafbb61309e66c478126e5d7cdf61 100644
--- a/MatlabFiles/fn_tran_a2b.m
+++ b/MatlabFiles/fn_tran_a2b.m
@@ -1,41 +1,41 @@
-function b = fn_tran_a2b(A0,Ui,nvar,n0)
-% b = fn_tran_a2b(A0,Ui,nvar,n0)
-% Transform A0 to free parameters b's. Note: columns correspond to equations
-% See Waggoner and Zha's ``A Gibbs sampler for structural VARs''
-%
-% A0: nvar-by-nvar, contempareous matrix (columns correspond to equations)
-% Ui: nvar-by-1 cell. In each cell, nvar-by-qi orthonormal basis for the null of the ith
-% equation contemporaneous restriction matrix where qi is the number of free parameters.
-% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
-% of total original parameters and bi is a vector of free parameters. When no
-% restrictions are imposed, we have Ui = I. There must be at least one free
-% parameter left for the ith equation.
-% nvar: number of endogeous variables
-% n0: nvar-by-1, ith element represents the number of free A0 parameters in ith equation
-%----------------
-% b: sum(n0)-by-1 vector of A0 free parameters
-%
-% Tao Zha, February 2000. Revised, August 2000
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-n0=n0(:);
-n0cum = [0; cumsum(n0)];
-b=zeros(n0cum(end),1);
-for kj = 1:nvar
- b(n0cum(kj)+1:n0cum(kj+1))=Ui{kj}'*A0(:,kj);
-end
+function b = fn_tran_a2b(A0,Ui,nvar,n0)
+% b = fn_tran_a2b(A0,Ui,nvar,n0)
+% Transform A0 to free parameters b's. Note: columns correspond to equations
+% See Waggoner and Zha's ``A Gibbs sampler for structural VARs''
+%
+% A0: nvar-by-nvar, contempareous matrix (columns correspond to equations)
+% Ui: nvar-by-1 cell. In each cell, nvar-by-qi orthonormal basis for the null of the ith
+% equation contemporaneous restriction matrix where qi is the number of free parameters.
+% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
+% of total original parameters and bi is a vector of free parameters. When no
+% restrictions are imposed, we have Ui = I. There must be at least one free
+% parameter left for the ith equation.
+% nvar: number of endogeous variables
+% n0: nvar-by-1, ith element represents the number of free A0 parameters in ith equation
+%----------------
+% b: sum(n0)-by-1 vector of A0 free parameters
+%
+% Tao Zha, February 2000. Revised, August 2000
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+n0=n0(:);
+n0cum = [0; cumsum(n0)];
+b=zeros(n0cum(end),1);
+for kj = 1:nvar
+ b(n0cum(kj)+1:n0cum(kj+1))=Ui{kj}'*A0(:,kj);
+end
diff --git a/MatlabFiles/fn_tran_b2a.m b/MatlabFiles/fn_tran_b2a.m
index 4cc95b6623c7d4f49d1f17f7a99c549dc052a4da..00645e957ff59fc7f64dbee8d3f3c82a37889085 100644
--- a/MatlabFiles/fn_tran_b2a.m
+++ b/MatlabFiles/fn_tran_b2a.m
@@ -1,40 +1,40 @@
-function A0 = fn_tran_b2a(b,Ui,nvar,n0)
-% A0 = fn_tran_b2a(b,Ui,nvar,n0)
-% Transform free parameters b's to A0. Note: columns correspond to equations
-%
-% b: sum(n0)-by-1 vector of A0 free parameters
-% Ui: nvar-by-1 cell. In each cell, nvar-by-qi orthonormal basis for the null of the ith
-% equation contemporaneous restriction matrix where qi is the number of free parameters.
-% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
-% of total original parameters and bi is a vector of free parameters. When no
-% restrictions are imposed, we have Ui = I. There must be at least one free
-% parameter left for the ith equation.
-% nvar: number of endogeous variables
-% n0: nvar-by-1, ith element represents the number of free A0 parameters in ith equation
-%----------------
-% A0: nvar-by-nvar, contempareous matrix (columns correspond to equations)
-%
-% Tao Zha, February 2000. Revised, August 2000.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-b=b(:); n0=n0(:);
-A0 = zeros(nvar);
-n0cum = [0; cumsum(n0)];
-for kj = 1:nvar
- A0(:,kj) = Ui{kj}*b(n0cum(kj)+1:n0cum(kj+1));
-end
+function A0 = fn_tran_b2a(b,Ui,nvar,n0)
+% A0 = fn_tran_b2a(b,Ui,nvar,n0)
+% Transform free parameters b's to A0. Note: columns correspond to equations
+%
+% b: sum(n0)-by-1 vector of A0 free parameters
+% Ui: nvar-by-1 cell. In each cell, nvar-by-qi orthonormal basis for the null of the ith
+% equation contemporaneous restriction matrix where qi is the number of free parameters.
+% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
+% of total original parameters and bi is a vector of free parameters. When no
+% restrictions are imposed, we have Ui = I. There must be at least one free
+% parameter left for the ith equation.
+% nvar: number of endogeous variables
+% n0: nvar-by-1, ith element represents the number of free A0 parameters in ith equation
+%----------------
+% A0: nvar-by-nvar, contempareous matrix (columns correspond to equations)
+%
+% Tao Zha, February 2000. Revised, August 2000.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+b=b(:); n0=n0(:);
+A0 = zeros(nvar);
+n0cum = [0; cumsum(n0)];
+for kj = 1:nvar
+ A0(:,kj) = Ui{kj}*b(n0cum(kj)+1:n0cum(kj+1));
+end
diff --git a/MatlabFiles/fn_tran_f2g.m b/MatlabFiles/fn_tran_f2g.m
index 6bc2a6b3647cdb31d44fe008c8f79c345e099b03..a2c455596d0e487f8c717bbe789e26818ea93ae0 100644
--- a/MatlabFiles/fn_tran_f2g.m
+++ b/MatlabFiles/fn_tran_f2g.m
@@ -1,42 +1,42 @@
-function g = fn_tran_f2g(F,Vi,nvar,np)
-% g = fn_tran_f2g(F,Vi,nvar,np)
-% Transform F (A+) to free parameters g's. Note: columns correspond to equations
-% See Waggoner and Zha's ``A Gibbs sampler for structural VARs''
-%
-% F: ncoef-by-nvar matrix of original lagged parameters A+. Column corresponding to equation.
-% Note that ncoef is the number of original lagged variables per equation
-% Vi: nvar-by-1 cell. In each cell, k-by-ri orthonormal basis for the null of the ith
-% equation lagged restriction matrix where k is a total of exogenous variables and
-% ri is the number of free parameters. With this transformation, we have fi = Vi*gi
-% or Vi'*fi = gi where fi is a vector of total original parameters and gi is a
-% vector of free parameters. There must be at least one free parameter left for
-% the ith equation.
-% nvar: number of endogeous variables
-% np: nvar-element vector, ith element represents the number of free A+ parameters in ith equation
-%---------------
-% g: sum(np)-by-1 stacked vector of all free lagged parameters A+.
-%
-% August 2000, Tao Zha
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-np=np(:);
-npcum = [0;cumsum(np)];
-g = zeros(npcum(end),1);
-for kj=1:nvar
- g(npcum(kj)+1:npcum(kj+1)) = Vi{kj}'*F(:,kj);
-end
+function g = fn_tran_f2g(F,Vi,nvar,np)
+% g = fn_tran_f2g(F,Vi,nvar,np)
+% Transform F (A+) to free parameters g's. Note: columns correspond to equations
+% See Waggoner and Zha's ``A Gibbs sampler for structural VARs''
+%
+% F: ncoef-by-nvar matrix of original lagged parameters A+. Column corresponding to equation.
+% Note that ncoef is the number of original lagged variables per equation
+% Vi: nvar-by-1 cell. In each cell, k-by-ri orthonormal basis for the null of the ith
+% equation lagged restriction matrix where k is a total of exogenous variables and
+% ri is the number of free parameters. With this transformation, we have fi = Vi*gi
+% or Vi'*fi = gi where fi is a vector of total original parameters and gi is a
+% vector of free parameters. There must be at least one free parameter left for
+% the ith equation.
+% nvar: number of endogeous variables
+% np: nvar-element vector, ith element represents the number of free A+ parameters in ith equation
+%---------------
+% g: sum(np)-by-1 stacked vector of all free lagged parameters A+.
+%
+% August 2000, Tao Zha
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+np=np(:);
+npcum = [0;cumsum(np)];
+g = zeros(npcum(end),1);
+for kj=1:nvar
+ g(npcum(kj)+1:npcum(kj+1)) = Vi{kj}'*F(:,kj);
+end
diff --git a/MatlabFiles/fn_tran_g2f.m b/MatlabFiles/fn_tran_g2f.m
index 2e6a2cd28f9851b9fbc4fab9ab74c547268e341e..6bfd51f0da94b0da925d67c8db869e1e391c8eb5 100644
--- a/MatlabFiles/fn_tran_g2f.m
+++ b/MatlabFiles/fn_tran_g2f.m
@@ -1,41 +1,41 @@
-function F = fn_tran_g2f(g,Vi,nvar,ncoef,np)
-% Transform free parameters g's to F (A+). Note: columns correspond to equations
-% See Waggoner and Zha's ``A Gibbs sampler for structural VARs''
-%
-% g: sum(np)-by-1 stacked vector of all free lagged parameters A+.
-% Vi: nvar-by-1 cell. In each cell, k-by-ri orthonormal basis for the null of the ith
-% equation lagged restriction matrix where k is a total of exogenous variables and
-% ri is the number of free parameters. With this transformation, we have fi = Vi*gi
-% or Vi'*fi = gi where fi is a vector of total original parameters and gi is a
-% vector of free parameters. There must be at least one free parameter left for
-% the ith equation.
-% nvar: number of endogeous variables
-% ncoef: number of original lagged variables per equation
-% np: nvar-element vector, ith element represents the number of free A+ parameters in ith equation
-%---------------
-% F: ncoef-by-nvar matrix of original lagged parameters A+. Column corresponding to equation.
-%
-% August 2000, Tao Zha.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-g=g(:); np=np(:);
-npcum = [0;cumsum(np)];
-F = zeros(ncoef,nvar); % ncoef: maximum original lagged parameters per equation
-for kj=1:nvar
- F(:,kj) = Vi{kj}*g(npcum(kj)+1:npcum(kj+1));
-end
+function F = fn_tran_g2f(g,Vi,nvar,ncoef,np)
+% Transform free parameters g's to F (A+). Note: columns correspond to equations
+% See Waggoner and Zha's ``A Gibbs sampler for structural VARs''
+%
+% g: sum(np)-by-1 stacked vector of all free lagged parameters A+.
+% Vi: nvar-by-1 cell. In each cell, k-by-ri orthonormal basis for the null of the ith
+% equation lagged restriction matrix where k is a total of exogenous variables and
+% ri is the number of free parameters. With this transformation, we have fi = Vi*gi
+% or Vi'*fi = gi where fi is a vector of total original parameters and gi is a
+% vector of free parameters. There must be at least one free parameter left for
+% the ith equation.
+% nvar: number of endogeous variables
+% ncoef: number of original lagged variables per equation
+% np: nvar-element vector, ith element represents the number of free A+ parameters in ith equation
+%---------------
+% F: ncoef-by-nvar matrix of original lagged parameters A+. Column corresponding to equation.
+%
+% August 2000, Tao Zha.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+g=g(:); np=np(:);
+npcum = [0;cumsum(np)];
+F = zeros(ncoef,nvar); % ncoef: maximum original lagged parameters per equation
+for kj=1:nvar
+ F(:,kj) = Vi{kj}*g(npcum(kj)+1:npcum(kj+1));
+end
diff --git a/MatlabFiles/fn_uncondfcst_var1.m b/MatlabFiles/fn_uncondfcst_var1.m
index 7aab8e0d5a68569b9a310ffc0a0bc1095821b745..9fb5e5e46836b2f654b58d749cc8cb01aeb33b23 100644
--- a/MatlabFiles/fn_uncondfcst_var1.m
+++ b/MatlabFiles/fn_uncondfcst_var1.m
@@ -1,44 +1,44 @@
-function y = fn_uncondfcst_var1(G1, y0, nsteps);
-%y = fn_irf_var1(G1,y0,nsteps);
-% Inputs:
-% G1: n-by-n;
-% y0: n-by-1, initial condition;
-% nsteps: number of forecasts steps.
-%---
-% Outputs:
-% y: nsteps-by-n unconditional forecasts.
-%
-% See fn_vds.m, fn_irf_var1.m.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-n = length(y0);
-
-[n1,n2] = size(G1);
-if (n1 ~= n2) || (n1 ~= n)
- error('fn_uncondfcst_var1.m: make sure that (1) G1 is square and (2) size(G1,1) = length(y0)');
-end
-
-y = zeros(nsteps,n);
-
-
-%---- Forecast at the first step.
-y(1,:) = (G1*y0)';
-
-for ti = 2:nsteps
- y(ti,:) = (G1*y(ti-1,:)')';
-end
+function y = fn_uncondfcst_var1(G1, y0, nsteps);
+%y = fn_irf_var1(G1,y0,nsteps);
+% Inputs:
+% G1: n-by-n;
+% y0: n-by-1, initial condition;
+% nsteps: number of forecasts steps.
+%---
+% Outputs:
+% y: nsteps-by-n unconditional forecasts.
+%
+% See fn_vds.m, fn_irf_var1.m.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+n = length(y0);
+
+[n1,n2] = size(G1);
+if (n1 ~= n2) || (n1 ~= n)
+ error('fn_uncondfcst_var1.m: make sure that (1) G1 is square and (2) size(G1,1) = length(y0)');
+end
+
+y = zeros(nsteps,n);
+
+
+%---- Forecast at the first step.
+y(1,:) = (G1*y0)';
+
+for ti = 2:nsteps
+ y(ti,:) = (G1*y(ti-1,:)')';
+end
diff --git a/MatlabFiles/fn_varoots.m b/MatlabFiles/fn_varoots.m
index 2c342bc37b5d58be4705637108e076fc47144302..9fdf8df86626a1092c0b67806860e6abeeb0b90f 100644
--- a/MatlabFiles/fn_varoots.m
+++ b/MatlabFiles/fn_varoots.m
@@ -1,46 +1,46 @@
-function rootsinv = fn_varoots(Bhat,nvar,lags)
-%
-% Using eigenvalues to find the inverse of all roots associated with the VAR proceess:
-% y_t' = C + y_{t-1}'*B_1 + ... + Y_{t-p}'*B_p + u_t'.
-% where columns correspond to equations. See also Judge (1), pp.753-755 where rows correspond to equations.
-% Bhat: ncoef-by-nvar where ncoef=nvar*lags+nexo and nvar is the number of endogenous variables.
-% Columns corresponds to equations with
-% ncoef=[nvar for 1st lag, ..., nvar for last lag, other exogenous terms, const term]
-% ..., nvar coef in the last lag, and nexo coefficients.
-% Note that entries in the rows of Bhat that > nvar*lags are irrelevant.
-% nvar: number of endogenous variables.
-% lags: number of lags.
-%-------
-% rootsinv: a vector of nvar*lags inverse roots. When > 1, explosive. When all < 1, stationary.
-%
-% Tao Zha, September 2000
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-if size(Bhat,1)<nvar*lags
- disp(' ')
- warning('Make sure that Bhat has at least nvar*lags rows')
- return
-end
-
-%--------- Strack the VAR(p) to the VAR(1) with z_t = Az_{t-1}.
-%
-A1 = diag(ones(nvar*(lags-1),1));
-A2 = [A1 zeros(nvar*(lags-1),nvar)];
-A = [Bhat(1:nvar*lags,:)'; A2];
-rootsinv=eig(A);
+function rootsinv = fn_varoots(Bhat,nvar,lags)
+%
+% Using eigenvalues to find the inverse of all roots associated with the VAR proceess:
+% y_t' = C + y_{t-1}'*B_1 + ... + Y_{t-p}'*B_p + u_t'.
+% where columns correspond to equations. See also Judge (1), pp.753-755 where rows correspond to equations.
+% Bhat: ncoef-by-nvar where ncoef=nvar*lags+nexo and nvar is the number of endogenous variables.
+% Columns corresponds to equations with
+% ncoef=[nvar for 1st lag, ..., nvar for last lag, other exogenous terms, const term]
+% ..., nvar coef in the last lag, and nexo coefficients.
+% Note that entries in the rows of Bhat that > nvar*lags are irrelevant.
+% nvar: number of endogenous variables.
+% lags: number of lags.
+%-------
+% rootsinv: a vector of nvar*lags inverse roots. When > 1, explosive. When all < 1, stationary.
+%
+% Tao Zha, September 2000
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+if size(Bhat,1)<nvar*lags
+ disp(' ')
+ warning('Make sure that Bhat has at least nvar*lags rows')
+ return
+end
+
+%--------- Strack the VAR(p) to the VAR(1) with z_t = Az_{t-1}.
+%
+A1 = diag(ones(nvar*(lags-1),1));
+A2 = [A1 zeros(nvar*(lags-1),nvar)];
+A = [Bhat(1:nvar*lags,:)'; A2];
+rootsinv=eig(A);
diff --git a/MatlabFiles/fn_vds.m b/MatlabFiles/fn_vds.m
index cf5b64ea80a8d86e10d528da59c6ab0b4f89e61a..7128eb3c95da89a5d42f6aff28388194657ea895 100644
--- a/MatlabFiles/fn_vds.m
+++ b/MatlabFiles/fn_vds.m
@@ -1,33 +1,33 @@
-function vds = fn_vds(irfs);
-%vds = fn_vds(irfs)
-%
-% Inputs:
-% irfs: nsteps-by-n-by-r. For each shock of r shocks, impulse responses are nsteps-by-n.
-%---
-% Outputs:
-% vds: nsteps-by-n-by-r. For each shock of r shocks, variance decompositions (%) are nsteps-by-n.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-[nsteps, n,r] = size(irfs);
-
-vds = zeros(nsteps,n,r);
-
-vds_square_cum = cumsum(irfs.^2,1);
-vds_square_sum = repmat(sum(vds_square_cum,3),[1 1 r]);
-vds = (vds_square_cum ./ vds_square_sum) .* 100;
-
+function vds = fn_vds(irfs);
+%vds = fn_vds(irfs)
+%
+% Inputs:
+% irfs: nsteps-by-n-by-r. For each shock of r shocks, impulse responses are nsteps-by-n.
+%---
+% Outputs:
+% vds: nsteps-by-n-by-r. For each shock of r shocks, variance decompositions (%) are nsteps-by-n.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+[nsteps, n,r] = size(irfs);
+
+vds = zeros(nsteps,n,r);
+
+vds_square_cum = cumsum(irfs.^2,1);
+vds_square_sum = repmat(sum(vds_square_cum,3),[1 1 r]);
+vds = (vds_square_cum ./ vds_square_sum) .* 100;
+
diff --git a/MatlabFiles/fn_vds_abs.m b/MatlabFiles/fn_vds_abs.m
index d6ede7c5b7c398a3b0388d908359f8ef1d917634..49e36e7b9412c925abeaba6e0529de9cf534409c 100644
--- a/MatlabFiles/fn_vds_abs.m
+++ b/MatlabFiles/fn_vds_abs.m
@@ -1,33 +1,33 @@
-function vds = fn_vds_abs(irfs);
-%vds = fn_vds(irfs)
-%
-% Inputs:
-% irfs: nsteps-by-n-by-r. For each shock of r shocks, impulse responses are nsteps-by-n.
-%---
-% Outputs:
-% vds: nsteps-by-n-by-r. For each shock of r shocks, variance decompositions (%) are nsteps-by-n.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-[nsteps, n,r] = size(irfs);
-
-vds = zeros(nsteps,n,r);
-
-vds_square_cum = cumsum(abs(irfs),1);
-vds_square_sum = repmat(sum(vds_square_cum,3),[1 1 r]);
-vds = (vds_square_cum ./ vds_square_sum) .* 100;
-
+function vds = fn_vds_abs(irfs);
+%vds = fn_vds(irfs)
+%
+% Inputs:
+% irfs: nsteps-by-n-by-r. For each shock of r shocks, impulse responses are nsteps-by-n.
+%---
+% Outputs:
+% vds: nsteps-by-n-by-r. For each shock of r shocks, variance decompositions (%) are nsteps-by-n.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+[nsteps, n,r] = size(irfs);
+
+vds = zeros(nsteps,n,r);
+
+vds_square_cum = cumsum(abs(irfs),1);
+vds_square_sum = repmat(sum(vds_square_cum,3),[1 1 r]);
+vds = (vds_square_cum ./ vds_square_sum) .* 100;
+
diff --git a/MatlabFiles/fore_cal.m b/MatlabFiles/fore_cal.m
index 0dc5b147a3f970c65844cfdc288f8647168029ab..0b877b66f93d2fd4e4b6a90fb0c2244adf142633 100644
--- a/MatlabFiles/fore_cal.m
+++ b/MatlabFiles/fore_cal.m
@@ -1,267 +1,267 @@
-function [yforelml,yforemgml,yforeqgml,yforeCalygml] = fore_cal(yfore,xdata,nvar,...
- nSample,nSampleCal,forep,forepq,forepy,q_m,qmEnd,vlist,vlistlog,...
- vlistper,lmqyIndx)
-% Converting oringinal forecast "yfore" to series by calendar years and series
-% with growth rates (annualized for monthly and quarterly series).
-%
-%function [yforelml,yforemgml,yforeqgml,yforeCalygml] = fore_cal(yfore,xdata,nvar,...
-% nSample,nSampleCal,forep,forepq,forepy,q_m,qmEnd,vlist,vlistlog,...
-% vlistper,mqyIndx)
-%
-% yfore: oringal forecast series, all logged except R, U, etc.
-% xdata: oringal data set beyond the sample into forecast horizon
-% until yrFin:qmFin, all logged except R, U, etc.
-% nvar: number of variables
-% nSample: sample size (including lags or initial periods)
-% nSampleCal: sample size in terms of calendar years
-% forep: forecast periods (monthly)
-% forepq: forecast periods (quarterly)
-% forepy: forecast periods (yearly)
-% q_m: quarterly or monthly for the underlying model
-% qmEnd: last q_m before out-of-sample forecasting
-% vlist: a list of variables
-% vlistlog: sub list of variables that are in log
-% vlistper: sub list of variables that are in percent
-% lmyqIndx: 4-by-1 1 or 0's. Index for level, mg, qg, and yg; 1: yes; 0: no
-%----------------
-% yforelml: monthly level forecast (in percent for R, U, etc.) with the same size as "yfore" in log
-% yforemgml: monthly growth (at annual rates), in percent
-% yforeqgml: ML forecast: quarterly growth (at annual rates), in percent
-% yforeCalygml: ML forecast: annual growth (by calendar years), in percent
-% forepy-by-nvar
-%
-% Copyright (c) March 1998 by Tao Zha
-% Revision, October 1998. Added lmyqIndx so previous programs may not be compatible.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-%=================================================
-% Making everything presentable at FOMC
-%=================================================
-%
-%%%%
-%$$$ Monthly, prior quarter, and year-over-year change
-%$$$ Out-of-sample forecasts
-%%%%
-
-
-if length(lmqyIndx)~=4
- warning('lmqyIndx must be a 4-by-1 vector containing 1 or 0')
- return
-end
-
-
-
-%---------------------
-% Actual data
-%---------------------
-yact = xdata(nSample-q_m+1:nSample,:); % the latest (not calender) year
-yactCal = xdata(nSampleCal-q_m+1:nSampleCal,:); % the lastest calendar year
-
-
-
-%-----------------------------------------
-% Converted to monthly level
-%-----------------------------------------
-if lmqyIndx(1)
- yforelml=yfore;
- yforelml(:,vlistlog)=exp(yfore(:,vlistlog)); % mode, all levels
- yforelml(:,vlistper)=100*yfore(:,vlistper); % mode, all levels
-else
- yforelml = NaN;
-end
-
-
-
-%-----------------------------------------
-% Converted to monthly growth
-%-----------------------------------------
-if lmqyIndx(2)
- yactm = zeros(1,length(vlist)); % the latest month prior to forecasting
- yforem = zeros(1+forep,length(vlist)); % including the 1 month prior to forecasting
- %
- yactm = yact(length(yact(:,1)),:); % last month prior to forecasting
- yforem(1,:) = yactm; % last month prior to forecasting
- yforem(2:forep+1,:) = yfore(1:forep,:); % monthly forecasts, all logged.
- %
- %@@ monthly growth rate (annualized)
- yforemg = yforem(2:forep+1,:);
- yforemg(:,vlistlog) = ...
- ( yforem(2:forep+1,vlistlog) - yforem(1:forep,vlistlog) ) .* q_m;
- % monthly change (annualized), 12*log(1+growth rate)
- yforemgml=yforemg;
- yforemgml(:,vlistlog) = 100*(exp(yforemg(:,vlistlog))-1);
- % monthly growth rate (annualized)
- yforemgml(:,vlistper) = 100*yforemg(:,vlistper); % monthly growth rate (annualized)
-else
- yforemgml=NaN;
-end
-
-
-
-%-----------------------------------------
-% Converted to quarterly
-%-----------------------------------------
-if lmqyIndx(3)
- yactQ = xdata(nSample-mod(qmEnd,3)-q_m+1:nSample-mod(qmEnd,3),:);
- % the latest actual quarter
- yactq = zeros(4,length(vlist)); % the latest 4 quarters prior to forecasting
- yforeq = zeros(4+forepq,length(vlist));
- % including the 4 quarters prior to forecasting
- %
- qT1 = length(yactQ(:,1))/3;
- if qT1 ~= 4
- error('Must hae 4 actual quarters to compute quarterly change for forecasting!')
- end
- for i = 1:qT1
- i1 = 1+3*(i-1);
- i2 = 3*i;
- yactq(i,:) = sum(yact(i1:i2,:)) ./ 3;
- end
- yforeq(1:4,:) = yactq;
- %
- yforeq_1 = sum(xdata( nSample-mod(qmEnd,3)+1:nSample,:),1);
- yforeq(5,:) = ( yforeq_1 + sum(yfore(1:3-mod(qmEnd,3),:),1) ) ./ 3;
- % 1st quarterly forecast which may contain actual data within quarter
- % note, dimension "1" in sum(ytem,1) is necessary because when
- % qmEnd=1, sum(ytem,1) will give us a nomber, not a vector. But this
- % is not what we want.
- for i = 2:forepq
- i1 = 3-mod(qmEnd,3) + 1+3*(i-2);
- i2 = 3-mod(qmEnd,3) + 3*(i-1);
- yforeq(4+i,:) = sum(yfore(i1:i2,:)) ./ 3;
- end
- %
- %@@ prior quarter growth rate (annualized)
- yforeqg = yforeq(5:forepq+4,:);
- %yforeqg(:,vlistlog) = (100*(yforeq(5:qT2+4,vlistlog)-yforeq(1:qT2,vlistlog))) ...
- % ./ yforeq(1:qT2,vlistlog); % year-over-year
- %yforeqg(:,vlistlog) = 100*((yforeq(5:qT2+4,vlistlog)./yforeq(4:qT2+3,vlistlog)).^4 - 1 );
- % prior quarter
- yforeqg(:,vlistlog) = ...
- ( yforeq(5:forepq+4,vlistlog) - yforeq(4:forepq+3,vlistlog) ) .* 4;
- % prior quarter, 4*log(1+growth rate)
- yforeqgml=yforeqg;
- yforeqgml(:,vlistlog) = 100*(exp(yforeqg(:,vlistlog))-1);
- % quarterly growth rate (annualized)
- yforeqgml(:,vlistper) = 100*yforeqg(:,vlistper);
- % quarterly growth rate (annualized)
-else
- yforeqgml = NaN;
-end
-
-
-
-%-----------------------------------------
-% Converted to calendar years
-%-----------------------------------------
-if lmqyIndx(4)
- yactCaly = zeros(1,length(vlist)); % the latest calendar year
- yforeCaly = zeros(1+forepy,length(vlist));
- % including the calendar year prior to forecasting
- %
- yT1 = length(yactCal(:,1))/q_m;
- if yT1 ~= 1
- error('yT1 Beginings or ends of monthly and calendar series are not the same!')
- end
- for i = 1:yT1
- i1 = 1+q_m*(i-1);
- i2 = q_m*i;
- yactCaly(i,:) = sum(yactCal(i1:i2,:)) ./ q_m;
- end
- yforeCaly(1,:) = yactCaly;
- %
- %@@ initial monthly actual data for calendar years
- if qmEnd == q_m
- yforeCaly_1 = 0;
- else
- ytem = xdata(nSampleCal+1:nSampleCal+qmEnd,:);
- %ytem(:,vlistlog) = exp(ytem(:,vlistlog));
- yforeCaly_1 = sum(ytem,1);
- % note, dimension "1" in sum(ytem,1) is necessary because when
- % qmEnd=1, sum(ytem,1) will give us a nomber, not a vector. But this
- % is not what we want.
- end
- %
- if qmEnd == q_m
- for i = 1:forepy
- i1 = 1+q_m*(i-1);
- i2 = q_m*i;
- yforeCaly(1+i,:) = sum(yfore(i1:i2,:)) ./ q_m;
- end
- else
- yforeCaly(2,:) = (yforeCaly_1+sum(yfore(1:q_m-qmEnd,:),1)) ./ q_m;
- % note, dimension "1" in sum(yfore,1) is necessary because when
- % q_m-qmEnd=1, sum(yfore) will give us a number, not a vector. But this
- % is not what we want.
- for i = 2:forepy
- i1 = q_m-qmEnd+1+q_m*(i-2);
- i2 = q_m-qmEnd+q_m*(i-1);
- yforeCaly(1+i,:) = sum(yfore(i1:i2,:)) ./ q_m;
- end
- end
- %
- %@@ year-over-year growth rate
- yforeCalyg = yforeCaly(2:forepy+1,:);
- yforeCalyg(:,vlistlog) = ...
- yforeCaly(2:forepy+1,vlistlog) - yforeCaly(1:forepy,vlistlog);
- % year-over-year, log(1+growth rate)
- yforeCalygml=yforeCalyg;
- yforeCalygml(:,vlistlog) = 100*(exp(yforeCalyg(:,vlistlog))-1);
- % annaul growth rate
- yforeCalygml(:,vlistper) = 100*yforeCalyg(:,vlistper); % annaul growth rate
-else
- yforeCalygml = NaN;
-end
-
-
-
-%ndraws
-%
-% posterior mean of out-of-sample forecasts
-%yfore1 = (1.0/ndraws)*yfore1; % mean
-%yforeqg1 = (1.0/ndraws)*yforeqg1; % mean
-%yforeCalyg1 = (1.0/ndraws)*yforeCalyg1; % mean
-%
-%@@@ correlation of inflation and U
-%IUcov = zeros(forepq,1); % forepq quarters
-%IUbeta = zeros(forepq,2); % forepq quarters
-%for i = 1:forepq
-% indI = 4*forepq+i;
-% indU = 5*forepq+i;
-% %junk = corrcoef(yforeqgw(:,indI),yforeqgw(:,indU));
-% %IUcov(i,1) = junk(1,2);
-% xjnk = [100*yforeqgw(:,indU) ones(ndraws,1)];
-% yjnk = yforeqgw(:,indI);
-% junk = (xjnk'*xjnk)\(xjnk'*yjnk);
-% IUbeta(i,:) = junk';
-%end
-
-
-%%
-% *** .68 and .90 probability bands of out-of-sample forecasts
-%yforel = zeros(forep*nvar,1); % preallocating
-%yforeh = zeros(forep*nvar,1); % preallocating
-%yforeqgl = zeros(forepq*nvar,1); % preallocating
-%yforeqgh = zeros(forepq*nvar,1); % preallocating
-
-%clear yfores yforepgs yforeCalygs
-%*** write out final results
-%yforeml = reshape(yforeml,forep,nvar);
-%yforeqgml = reshape(yforeqgml,forepq,nvar);
-%yforeCalygml = reshape(yforeCalygml,forepy,nvar);
\ No newline at end of file
+function [yforelml,yforemgml,yforeqgml,yforeCalygml] = fore_cal(yfore,xdata,nvar,...
+ nSample,nSampleCal,forep,forepq,forepy,q_m,qmEnd,vlist,vlistlog,...
+ vlistper,lmqyIndx)
+% Converting oringinal forecast "yfore" to series by calendar years and series
+% with growth rates (annualized for monthly and quarterly series).
+%
+%function [yforelml,yforemgml,yforeqgml,yforeCalygml] = fore_cal(yfore,xdata,nvar,...
+% nSample,nSampleCal,forep,forepq,forepy,q_m,qmEnd,vlist,vlistlog,...
+% vlistper,mqyIndx)
+%
+% yfore: oringal forecast series, all logged except R, U, etc.
+% xdata: oringal data set beyond the sample into forecast horizon
+% until yrFin:qmFin, all logged except R, U, etc.
+% nvar: number of variables
+% nSample: sample size (including lags or initial periods)
+% nSampleCal: sample size in terms of calendar years
+% forep: forecast periods (monthly)
+% forepq: forecast periods (quarterly)
+% forepy: forecast periods (yearly)
+% q_m: quarterly or monthly for the underlying model
+% qmEnd: last q_m before out-of-sample forecasting
+% vlist: a list of variables
+% vlistlog: sub list of variables that are in log
+% vlistper: sub list of variables that are in percent
+% lmyqIndx: 4-by-1 1 or 0's. Index for level, mg, qg, and yg; 1: yes; 0: no
+%----------------
+% yforelml: monthly level forecast (in percent for R, U, etc.) with the same size as "yfore" in log
+% yforemgml: monthly growth (at annual rates), in percent
+% yforeqgml: ML forecast: quarterly growth (at annual rates), in percent
+% yforeCalygml: ML forecast: annual growth (by calendar years), in percent
+% forepy-by-nvar
+%
+% Written by Tao Zha March 1998
+% Revision, October 1998. Added lmyqIndx so previous programs may not be compatible.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+%=================================================
+% Making everything presentable at FOMC
+%=================================================
+%
+%%%%
+%$$$ Monthly, prior quarter, and year-over-year change
+%$$$ Out-of-sample forecasts
+%%%%
+
+
+if length(lmqyIndx)~=4
+ warning('lmqyIndx must be a 4-by-1 vector containing 1 or 0')
+ return
+end
+
+
+
+%---------------------
+% Actual data
+%---------------------
+yact = xdata(nSample-q_m+1:nSample,:); % the latest (not calender) year
+yactCal = xdata(nSampleCal-q_m+1:nSampleCal,:); % the lastest calendar year
+
+
+
+%-----------------------------------------
+% Converted to monthly level
+%-----------------------------------------
+if lmqyIndx(1)
+ yforelml=yfore;
+ yforelml(:,vlistlog)=exp(yfore(:,vlistlog)); % mode, all levels
+ yforelml(:,vlistper)=100*yfore(:,vlistper); % mode, all levels
+else
+ yforelml = NaN;
+end
+
+
+
+%-----------------------------------------
+% Converted to monthly growth
+%-----------------------------------------
+if lmqyIndx(2)
+ yactm = zeros(1,length(vlist)); % the latest month prior to forecasting
+ yforem = zeros(1+forep,length(vlist)); % including the 1 month prior to forecasting
+ %
+ yactm = yact(length(yact(:,1)),:); % last month prior to forecasting
+ yforem(1,:) = yactm; % last month prior to forecasting
+ yforem(2:forep+1,:) = yfore(1:forep,:); % monthly forecasts, all logged.
+ %
+ %@@ monthly growth rate (annualized)
+ yforemg = yforem(2:forep+1,:);
+ yforemg(:,vlistlog) = ...
+ ( yforem(2:forep+1,vlistlog) - yforem(1:forep,vlistlog) ) .* q_m;
+ % monthly change (annualized), 12*log(1+growth rate)
+ yforemgml=yforemg;
+ yforemgml(:,vlistlog) = 100*(exp(yforemg(:,vlistlog))-1);
+ % monthly growth rate (annualized)
+ yforemgml(:,vlistper) = 100*yforemg(:,vlistper); % monthly growth rate (annualized)
+else
+ yforemgml=NaN;
+end
+
+
+
+%-----------------------------------------
+% Converted to quarterly
+%-----------------------------------------
+if lmqyIndx(3)
+ yactQ = xdata(nSample-mod(qmEnd,3)-q_m+1:nSample-mod(qmEnd,3),:);
+ % the latest actual quarter
+ yactq = zeros(4,length(vlist)); % the latest 4 quarters prior to forecasting
+ yforeq = zeros(4+forepq,length(vlist));
+ % including the 4 quarters prior to forecasting
+ %
+ qT1 = length(yactQ(:,1))/3;
+ if qT1 ~= 4
+ error('Must hae 4 actual quarters to compute quarterly change for forecasting!')
+ end
+ for i = 1:qT1
+ i1 = 1+3*(i-1);
+ i2 = 3*i;
+ yactq(i,:) = sum(yact(i1:i2,:)) ./ 3;
+ end
+ yforeq(1:4,:) = yactq;
+ %
+ yforeq_1 = sum(xdata( nSample-mod(qmEnd,3)+1:nSample,:),1);
+ yforeq(5,:) = ( yforeq_1 + sum(yfore(1:3-mod(qmEnd,3),:),1) ) ./ 3;
+ % 1st quarterly forecast which may contain actual data within quarter
+ % note, dimension "1" in sum(ytem,1) is necessary because when
+ % qmEnd=1, sum(ytem,1) will give us a nomber, not a vector. But this
+ % is not what we want.
+ for i = 2:forepq
+ i1 = 3-mod(qmEnd,3) + 1+3*(i-2);
+ i2 = 3-mod(qmEnd,3) + 3*(i-1);
+ yforeq(4+i,:) = sum(yfore(i1:i2,:)) ./ 3;
+ end
+ %
+ %@@ prior quarter growth rate (annualized)
+ yforeqg = yforeq(5:forepq+4,:);
+ %yforeqg(:,vlistlog) = (100*(yforeq(5:qT2+4,vlistlog)-yforeq(1:qT2,vlistlog))) ...
+ % ./ yforeq(1:qT2,vlistlog); % year-over-year
+ %yforeqg(:,vlistlog) = 100*((yforeq(5:qT2+4,vlistlog)./yforeq(4:qT2+3,vlistlog)).^4 - 1 );
+ % prior quarter
+ yforeqg(:,vlistlog) = ...
+ ( yforeq(5:forepq+4,vlistlog) - yforeq(4:forepq+3,vlistlog) ) .* 4;
+ % prior quarter, 4*log(1+growth rate)
+ yforeqgml=yforeqg;
+ yforeqgml(:,vlistlog) = 100*(exp(yforeqg(:,vlistlog))-1);
+ % quarterly growth rate (annualized)
+ yforeqgml(:,vlistper) = 100*yforeqg(:,vlistper);
+ % quarterly growth rate (annualized)
+else
+ yforeqgml = NaN;
+end
+
+
+
+%-----------------------------------------
+% Converted to calendar years
+%-----------------------------------------
+if lmqyIndx(4)
+ yactCaly = zeros(1,length(vlist)); % the latest calendar year
+ yforeCaly = zeros(1+forepy,length(vlist));
+ % including the calendar year prior to forecasting
+ %
+ yT1 = length(yactCal(:,1))/q_m;
+ if yT1 ~= 1
+ error('yT1 Beginings or ends of monthly and calendar series are not the same!')
+ end
+ for i = 1:yT1
+ i1 = 1+q_m*(i-1);
+ i2 = q_m*i;
+ yactCaly(i,:) = sum(yactCal(i1:i2,:)) ./ q_m;
+ end
+ yforeCaly(1,:) = yactCaly;
+ %
+ %@@ initial monthly actual data for calendar years
+ if qmEnd == q_m
+ yforeCaly_1 = 0;
+ else
+ ytem = xdata(nSampleCal+1:nSampleCal+qmEnd,:);
+ %ytem(:,vlistlog) = exp(ytem(:,vlistlog));
+ yforeCaly_1 = sum(ytem,1);
+ % note, dimension "1" in sum(ytem,1) is necessary because when
+ % qmEnd=1, sum(ytem,1) will give us a nomber, not a vector. But this
+ % is not what we want.
+ end
+ %
+ if qmEnd == q_m
+ for i = 1:forepy
+ i1 = 1+q_m*(i-1);
+ i2 = q_m*i;
+ yforeCaly(1+i,:) = sum(yfore(i1:i2,:)) ./ q_m;
+ end
+ else
+ yforeCaly(2,:) = (yforeCaly_1+sum(yfore(1:q_m-qmEnd,:),1)) ./ q_m;
+ % note, dimension "1" in sum(yfore,1) is necessary because when
+ % q_m-qmEnd=1, sum(yfore) will give us a number, not a vector. But this
+ % is not what we want.
+ for i = 2:forepy
+ i1 = q_m-qmEnd+1+q_m*(i-2);
+ i2 = q_m-qmEnd+q_m*(i-1);
+ yforeCaly(1+i,:) = sum(yfore(i1:i2,:)) ./ q_m;
+ end
+ end
+ %
+ %@@ year-over-year growth rate
+ yforeCalyg = yforeCaly(2:forepy+1,:);
+ yforeCalyg(:,vlistlog) = ...
+ yforeCaly(2:forepy+1,vlistlog) - yforeCaly(1:forepy,vlistlog);
+ % year-over-year, log(1+growth rate)
+ yforeCalygml=yforeCalyg;
+ yforeCalygml(:,vlistlog) = 100*(exp(yforeCalyg(:,vlistlog))-1);
+ % annaul growth rate
+ yforeCalygml(:,vlistper) = 100*yforeCalyg(:,vlistper); % annaul growth rate
+else
+ yforeCalygml = NaN;
+end
+
+
+
+%ndraws
+%
+% posterior mean of out-of-sample forecasts
+%yfore1 = (1.0/ndraws)*yfore1; % mean
+%yforeqg1 = (1.0/ndraws)*yforeqg1; % mean
+%yforeCalyg1 = (1.0/ndraws)*yforeCalyg1; % mean
+%
+%@@@ correlation of inflation and U
+%IUcov = zeros(forepq,1); % forepq quarters
+%IUbeta = zeros(forepq,2); % forepq quarters
+%for i = 1:forepq
+% indI = 4*forepq+i;
+% indU = 5*forepq+i;
+% %junk = corrcoef(yforeqgw(:,indI),yforeqgw(:,indU));
+% %IUcov(i,1) = junk(1,2);
+% xjnk = [100*yforeqgw(:,indU) ones(ndraws,1)];
+% yjnk = yforeqgw(:,indI);
+% junk = (xjnk'*xjnk)\(xjnk'*yjnk);
+% IUbeta(i,:) = junk';
+%end
+
+
+%%
+% *** .68 and .90 probability bands of out-of-sample forecasts
+%yforel = zeros(forep*nvar,1); % preallocating
+%yforeh = zeros(forep*nvar,1); % preallocating
+%yforeqgl = zeros(forepq*nvar,1); % preallocating
+%yforeqgh = zeros(forepq*nvar,1); % preallocating
+
+%clear yfores yforepgs yforeCalygs
+%*** write out final results
+%yforeml = reshape(yforeml,forep,nvar);
+%yforeqgml = reshape(yforeqgml,forepq,nvar);
+%yforeCalygml = reshape(yforeCalygml,forepy,nvar);
diff --git a/MatlabFiles/fore_gh.m b/MatlabFiles/fore_gh.m
index daec61bdeb6669efba0f57d27cd382d2d6d47b5c..4d7268cde0ca7019d3e960cbc94daba7ebed2734 100644
--- a/MatlabFiles/fore_gh.m
+++ b/MatlabFiles/fore_gh.m
@@ -1,257 +1,256 @@
-function [yactCalyg,yforeCalygml,yAg,yFg,yactqg,yforeqgml,qAg,qFg,...
- yactmg,yforemgml,mAg,mFg,yact,yactCal] = fore_gh(xinput)
-% Plot graphics
-% [yactCalyg,yforeCalygml,yAg,yFg,yactqg,yforeqgml,qAg,qFg,...
-% yactmg,yforemgml,mAg,mFg,yact,yactCal] = fore_gh(xinput)
-%
-% xdata=xinput{1}: data, all logged except R, U, etc.
-% nvar=xinput{2}: number of variables
-% nSample=xinput{3}: sample size (including lags or initial periods)
-% nSampleCal=xinput{4}: converting nSample of calendar years;
-% yforelml=xinput{5}: monthly ML forecast, all in level, in percent
-% yforemgml=xinput{6}: monthly growth -- annualized rate, in percent
-% yforeqgml=xinput{7}: prior-quarter growth -- annualized rate, in percent
-% yforeCalygml=xinput{8}: annual rate, expressed in percentage point, in percent
-% actup=xinput{9}: periods for actual data (monthly)
-% actupq=xinput{10}: periods for actual data (quarterly)
-% actupy=xinput{11}: periods for actual data (calendar yearly)
-% vlist=xinput{12}: list of variables
-% vlistlog=xinput{13}: sub list of variables that are in log
-% vlistper=xinput{14}: sub list of variables that are in percent
-% q_m=xinput{15}: month or quarter in the model
-% forep=xinput{16}: forecast periods (monthly)
-% ylab=xinput{17}: labels for the y-axis
-% forepq=xinput{18}: quarters in the forecast period
-% forepy=xinput{19}: calendar years in the forecast period
-% Psuedo=xinput{20}: 1: Psuedo out-of-sample; 0: real time out-of-sample
-% Graphfore=xinput{21}: 1: graphics in this file; 0: no graphics in the execution.
-% yearsCal=xinput{22}: calendar years matching output "yactCalyg"
-% qmEnd=xinput{23}: last month (or quarter) of the sample
-%-------------
-% yactCalyg: annual growth for actual data
-% yforeCalygml: annaul growth for ML forecasts, expressed in percent
-% yAg: length of yactCalyg (actual data)
-% yFg: lenght of yforeCalyg (forecasts)
-%
-% yactqg: prior-quarter annualized growth for actual data
-% yforeqgml: prior-quarter annaulized growth for ML forecasts, expressed in percent
-% qAg: length of yactqg (actual data)
-% qFg: lenght of yforeqg (forecasts)
-%
-% yactmg: month-to-month annualized growth for actual data
-% yforemgml: month-to-month annaulized growth for ML forecasts, expressed in percent
-% mAg: length of yactqg (actual data)
-% mFg: lenght of yforeqg (forecasts)
-%
-% yact: log(y) except R, ect. (monthly). If Psuedo, yact includes "forep" months.
-% i.e., (actup+forep)-by-nvar
-% yactCal: same as yact but ends at the end of the last calendar year. If Psuedo,
-% (actup+forep)-by-nvar, which include some actual data in the 1st calendar year.
-%
-% Copyright (c) March 1998 by Tao Zha
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-xdata=xinput{1}; nvar=xinput{2}; nSample=xinput{3}; nSampleCal=xinput{4};
-yforelml=xinput{5}; yforemgml=xinput{6}; yforeqgml=xinput{7}; yforeCalygml=xinput{8};
-actup=xinput{9}; actupq=xinput{10}; actupy=xinput{11}; vlist=xinput{12};
-vlistlog=xinput{13}; vlistper=xinput{14}; q_m=xinput{15}; forep=xinput{16};
-ylab=xinput{17}; forepq=xinput{18}; forepy=xinput{19}; Psuedo=xinput{20};
-Graphfore=xinput{21}; yearsCal=xinput{22}; qmEnd=xinput{23};
-
-% ========= plot the graphics with actural vs forecast vs Full Bayesian =========
-%
-%*** actual data
-if Psuedo
- yact = xdata(nSample-actup+1:nSample+forep,:); % actup (not calendar) months + forep
- yactCal = xdata(nSampleCal-actupy*q_m+1:nSampleCal+forepy*q_m,:); % calendar years
-else
- yact = xdata(nSample-actup+1:nSample,:); % actup (not calendar) months
- yactCal = xdata(nSampleCal-actupy*q_m+1:nSampleCal,:); % caledar years
-end
-%
-
-
-
-%%%%---------------------------------------------------------------
-%$$$ Actual monthly growth rate, Prior quarter, and year-over-year change
-%%%%---------------------------------------------------------------
-
-%
-%@@@ Monthly change (annaluized rate)
-%
-yactm = yact;
-mT1 = length(yact(:,1));
-%
-yactmg = yactm(2:mT1,:); % start at second month to get growth rate
-yactmg(:,vlistlog) = (yactm(2:mT1,vlistlog) - yactm(1:mT1-1,vlistlog)) .* 12;
- % monthly, 12*log(1+growth rate), annualized growth rate
-yactmg(:,vlistlog) = 100*(exp(yactmg(:,vlistlog))-1);
-yactmg(:,vlistper) = 100*yactmg(:,vlistper);
-mAg = length(yactmg(:,1));
-
-
-%
-%@@@ Prior Quarter change (annaluized rate)
-%
-if Psuedo
- yactQ = xdata(nSample-mod(qmEnd,3)-actupq*3+1:nSample-mod(qmEnd,3)+forepq*3,:);
- yactq = zeros(actupq+forepq,length(vlist));
-else
- yactQ = xdata(nSample-mod(qmEnd,3)-actupq*3+1:nSample-mod(qmEnd,3),:);
- yactq = zeros(actupq,length(vlist));
-end
-%
-qT1 = length(yactQ(:,1))/3;
-qT1a = length(yactq(:,1));
-if qT1 ~= qT1a
- warning('line #')
- error('qT1: Beginings or ends of monthly and quarterly series do not match!')
-end
-%
-for i = 1:qT1
- i1 = 1+3*(i-1);
- i2 = 3*i;
- yactq(i,:) = sum(yactQ(i1:i2,:)) ./ 3;
-end
-%
-yactqg = yactq(5:qT1,:); % 4+1=5 where 4 means 4 quarters
-yactqg(:,vlistlog) = (yactq(5:qT1,vlistlog) - yactq(4:qT1-1,vlistlog)) .* 4;
- % quarterly, 4*log(1+growth rate), annualized growth rate
-yactqg(:,vlistlog) = 100*(exp(yactqg(:,vlistlog))-1);
-yactqg(:,vlistper) = 100*yactqg(:,vlistper);
-qAg = length(yactqg(:,1));
-
-
-%
-%@@@ Calendar year-over-year change
-%
-if Psuedo
- yactCaly = zeros(actupy+forepy,length(vlist)); % past "actupy" calendar years + forepy
-else
- yactCaly = zeros(actupy,length(vlist)); % past "actupy" calendar years
-end
-yT1 = length(yactCal(:,1))/q_m;
-yT1a = length(yactCaly(:,1));
-if yT1 ~= yT1a
- error('yT1: Beginings or ends of monthly and quarterly series are not the same!')
-end
-%
-for i = 1:yT1
- i1 = 1+q_m*(i-1);
- i2 = q_m*i;
- yactCaly(i,:) = sum(yactCal(i1:i2,:)) ./ q_m;
-end
-%
-yactCalyg = yactCaly(2:yT1,:); % 1+1=2 where 1 means 1 year
-yactCalyg(:,vlistlog) = (yactCaly(2:yT1,vlistlog) - yactCaly(1:yT1-1,vlistlog));
- % annual rate: log(1+growth rate)
-yactCalyg(:,vlistlog) = 100*(exp(yactCalyg(:,vlistlog))-1);
-yactCalyg(:,vlistper) = 100*yactCalyg(:,vlistper);
-yAg = length(yactCalyg(:,1));
-
-
-%----------------------------------------------------------------------
-%================= Graphics of actual vs forecast ===================
-%----------------------------------------------------------------------
-yactl(:,vlistlog)=exp(yact(:,vlistlog));
-yactl(:,vlistper)=100*yact(:,vlistper); % convert to levels, in percent
-%
-mFg = length(yforemgml(:,1));
-qFg = length(yforeqgml(:,1));
-yFg = length(yforeCalygml(:,1));
-
-if Psuedo
- t1=1:actup+forep;
-else
- t1=1:actup;
-end
-t2=actup+1:actup+forep;
-%
-if Graphfore
- figure % The graphics below are expressed in level (monthly)
- for i = 1:nvar
- subplot(nvar/2,2,i)
- plot(t1,yactl(:,i),t2,yforelml(:,i),'--')
- %title('solid-actual, dotted-forecast');
- %title(eval(['forelabel']));
- %ylabel(eval(['x' int2str(i)]));
- ylabel(char(ylab(i)))
- end
-end
-
-t1 = 1:mAg;
-if Psuedo
- t2 = mAg-mFg+1:mAg;
-else
- t2 = mAg+1:mAg+mFg;
-end
-%
-if Graphfore
- figure % The graphics below are monthly growth rates except R and U, at annualized rates
- for i = 1:nvar
- subplot(nvar/2,2,i)
- plot(t1,yactmg(:,i),t2,yforemgml(:,i),'--')
- %title('solid-actual, dotted-forecast');
- %xlabel(eval(['forelabel']));
- %ylabel(eval(['x' int2str(i)]));
- ylabel(char(ylab(i)))
- end
-end
-
-t1 = 1:qAg;
-if Psuedo
- t2 = qAg-qFg+1:qAg;
-else
- t2 = qAg+1:qAg+qFg;
-end
-%
-if Graphfore
- figure % The graphics below are prior quarter growth rate except R and U, at annualized rates
- for i = 1:nvar
- subplot(nvar/2,2,i)
- plot(t1,yactqg(:,i),t2,yforeqgml(:,i),'--')
- %title('quarterly growth, unconditional');
- %xlabel(eval(['forelabel']));
- %ylabel(eval(['x' int2str(i)]));
- ylabel(char(ylab(i)))
- end
-end
-
-
-%t1 = 1:yAg;
-t1 = yearsCal;
-yAgCal=yearsCal(length(yearsCal));
-if Psuedo
- t2 = yAgCal-yFg+1:yAgCal;
-else
- t2 = yAgCal+1:yAgCal+yFg;
-end
-%
-if Graphfore
- figure
- % The graphics below are over-the-year growth rate except R and U, annually
- for i = 1:nvar
- subplot(nvar/2,2,i)
- plot(t1,yactCalyg(:,i),t2,yforeCalygml(:,i),'--')
- %title('solid-actual, dotted-forecast');
- %xlabel(eval(['forelabel']));
- %ylabel(eval(['x' int2str(i)]));
- ylabel(char(ylab(i)))
- end
-end
\ No newline at end of file
+function [yactCalyg,yforeCalygml,yAg,yFg,yactqg,yforeqgml,qAg,qFg,...
+ yactmg,yforemgml,mAg,mFg,yact,yactCal] = fore_gh(xinput)
+% Plot graphics
+% [yactCalyg,yforeCalygml,yAg,yFg,yactqg,yforeqgml,qAg,qFg,...
+% yactmg,yforemgml,mAg,mFg,yact,yactCal] = fore_gh(xinput)
+%
+% xdata=xinput{1}: data, all logged except R, U, etc.
+% nvar=xinput{2}: number of variables
+% nSample=xinput{3}: sample size (including lags or initial periods)
+% nSampleCal=xinput{4}: converting nSample of calendar years;
+% yforelml=xinput{5}: monthly ML forecast, all in level, in percent
+% yforemgml=xinput{6}: monthly growth -- annualized rate, in percent
+% yforeqgml=xinput{7}: prior-quarter growth -- annualized rate, in percent
+% yforeCalygml=xinput{8}: annual rate, expressed in percentage point, in percent
+% actup=xinput{9}: periods for actual data (monthly)
+% actupq=xinput{10}: periods for actual data (quarterly)
+% actupy=xinput{11}: periods for actual data (calendar yearly)
+% vlist=xinput{12}: list of variables
+% vlistlog=xinput{13}: sub list of variables that are in log
+% vlistper=xinput{14}: sub list of variables that are in percent
+% q_m=xinput{15}: month or quarter in the model
+% forep=xinput{16}: forecast periods (monthly)
+% ylab=xinput{17}: labels for the y-axis
+% forepq=xinput{18}: quarters in the forecast period
+% forepy=xinput{19}: calendar years in the forecast period
+% Psuedo=xinput{20}: 1: Psuedo out-of-sample; 0: real time out-of-sample
+% Graphfore=xinput{21}: 1: graphics in this file; 0: no graphics in the execution.
+% yearsCal=xinput{22}: calendar years matching output "yactCalyg"
+% qmEnd=xinput{23}: last month (or quarter) of the sample
+%-------------
+% yactCalyg: annual growth for actual data
+% yforeCalygml: annaul growth for ML forecasts, expressed in percent
+% yAg: length of yactCalyg (actual data)
+% yFg: lenght of yforeCalyg (forecasts)
+%
+% yactqg: prior-quarter annualized growth for actual data
+% yforeqgml: prior-quarter annaulized growth for ML forecasts, expressed in percent
+% qAg: length of yactqg (actual data)
+% qFg: lenght of yforeqg (forecasts)
+%
+% yactmg: month-to-month annualized growth for actual data
+% yforemgml: month-to-month annaulized growth for ML forecasts, expressed in percent
+% mAg: length of yactqg (actual data)
+% mFg: lenght of yforeqg (forecasts)
+%
+% yact: log(y) except R, ect. (monthly). If Psuedo, yact includes "forep" months.
+% i.e., (actup+forep)-by-nvar
+% yactCal: same as yact but ends at the end of the last calendar year. If Psuedo,
+% (actup+forep)-by-nvar, which include some actual data in the 1st calendar year.
+%
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+xdata=xinput{1}; nvar=xinput{2}; nSample=xinput{3}; nSampleCal=xinput{4};
+yforelml=xinput{5}; yforemgml=xinput{6}; yforeqgml=xinput{7}; yforeCalygml=xinput{8};
+actup=xinput{9}; actupq=xinput{10}; actupy=xinput{11}; vlist=xinput{12};
+vlistlog=xinput{13}; vlistper=xinput{14}; q_m=xinput{15}; forep=xinput{16};
+ylab=xinput{17}; forepq=xinput{18}; forepy=xinput{19}; Psuedo=xinput{20};
+Graphfore=xinput{21}; yearsCal=xinput{22}; qmEnd=xinput{23};
+
+% ========= plot the graphics with actural vs forecast vs Full Bayesian =========
+%
+%*** actual data
+if Psuedo
+ yact = xdata(nSample-actup+1:nSample+forep,:); % actup (not calendar) months + forep
+ yactCal = xdata(nSampleCal-actupy*q_m+1:nSampleCal+forepy*q_m,:); % calendar years
+else
+ yact = xdata(nSample-actup+1:nSample,:); % actup (not calendar) months
+ yactCal = xdata(nSampleCal-actupy*q_m+1:nSampleCal,:); % caledar years
+end
+%
+
+
+
+%%%%---------------------------------------------------------------
+%$$$ Actual monthly growth rate, Prior quarter, and year-over-year change
+%%%%---------------------------------------------------------------
+
+%
+%@@@ Monthly change (annaluized rate)
+%
+yactm = yact;
+mT1 = length(yact(:,1));
+%
+yactmg = yactm(2:mT1,:); % start at second month to get growth rate
+yactmg(:,vlistlog) = (yactm(2:mT1,vlistlog) - yactm(1:mT1-1,vlistlog)) .* 12;
+ % monthly, 12*log(1+growth rate), annualized growth rate
+yactmg(:,vlistlog) = 100*(exp(yactmg(:,vlistlog))-1);
+yactmg(:,vlistper) = 100*yactmg(:,vlistper);
+mAg = length(yactmg(:,1));
+
+
+%
+%@@@ Prior Quarter change (annaluized rate)
+%
+if Psuedo
+ yactQ = xdata(nSample-mod(qmEnd,3)-actupq*3+1:nSample-mod(qmEnd,3)+forepq*3,:);
+ yactq = zeros(actupq+forepq,length(vlist));
+else
+ yactQ = xdata(nSample-mod(qmEnd,3)-actupq*3+1:nSample-mod(qmEnd,3),:);
+ yactq = zeros(actupq,length(vlist));
+end
+%
+qT1 = length(yactQ(:,1))/3;
+qT1a = length(yactq(:,1));
+if qT1 ~= qT1a
+ warning('line #')
+ error('qT1: Beginings or ends of monthly and quarterly series do not match!')
+end
+%
+for i = 1:qT1
+ i1 = 1+3*(i-1);
+ i2 = 3*i;
+ yactq(i,:) = sum(yactQ(i1:i2,:)) ./ 3;
+end
+%
+yactqg = yactq(5:qT1,:); % 4+1=5 where 4 means 4 quarters
+yactqg(:,vlistlog) = (yactq(5:qT1,vlistlog) - yactq(4:qT1-1,vlistlog)) .* 4;
+ % quarterly, 4*log(1+growth rate), annualized growth rate
+yactqg(:,vlistlog) = 100*(exp(yactqg(:,vlistlog))-1);
+yactqg(:,vlistper) = 100*yactqg(:,vlistper);
+qAg = length(yactqg(:,1));
+
+
+%
+%@@@ Calendar year-over-year change
+%
+if Psuedo
+ yactCaly = zeros(actupy+forepy,length(vlist)); % past "actupy" calendar years + forepy
+else
+ yactCaly = zeros(actupy,length(vlist)); % past "actupy" calendar years
+end
+yT1 = length(yactCal(:,1))/q_m;
+yT1a = length(yactCaly(:,1));
+if yT1 ~= yT1a
+ error('yT1: Beginings or ends of monthly and quarterly series are not the same!')
+end
+%
+for i = 1:yT1
+ i1 = 1+q_m*(i-1);
+ i2 = q_m*i;
+ yactCaly(i,:) = sum(yactCal(i1:i2,:)) ./ q_m;
+end
+%
+yactCalyg = yactCaly(2:yT1,:); % 1+1=2 where 1 means 1 year
+yactCalyg(:,vlistlog) = (yactCaly(2:yT1,vlistlog) - yactCaly(1:yT1-1,vlistlog));
+ % annual rate: log(1+growth rate)
+yactCalyg(:,vlistlog) = 100*(exp(yactCalyg(:,vlistlog))-1);
+yactCalyg(:,vlistper) = 100*yactCalyg(:,vlistper);
+yAg = length(yactCalyg(:,1));
+
+
+%----------------------------------------------------------------------
+%================= Graphics of actual vs forecast ===================
+%----------------------------------------------------------------------
+yactl(:,vlistlog)=exp(yact(:,vlistlog));
+yactl(:,vlistper)=100*yact(:,vlistper); % convert to levels, in percent
+%
+mFg = length(yforemgml(:,1));
+qFg = length(yforeqgml(:,1));
+yFg = length(yforeCalygml(:,1));
+
+if Psuedo
+ t1=1:actup+forep;
+else
+ t1=1:actup;
+end
+t2=actup+1:actup+forep;
+%
+if Graphfore
+ figure % The graphics below are expressed in level (monthly)
+ for i = 1:nvar
+ subplot(nvar/2,2,i)
+ plot(t1,yactl(:,i),t2,yforelml(:,i),'--')
+ %title('solid-actual, dotted-forecast');
+ %title(eval(['forelabel']));
+ %ylabel(eval(['x' int2str(i)]));
+ ylabel(char(ylab(i)))
+ end
+end
+
+t1 = 1:mAg;
+if Psuedo
+ t2 = mAg-mFg+1:mAg;
+else
+ t2 = mAg+1:mAg+mFg;
+end
+%
+if Graphfore
+ figure % The graphics below are monthly growth rates except R and U, at annualized rates
+ for i = 1:nvar
+ subplot(nvar/2,2,i)
+ plot(t1,yactmg(:,i),t2,yforemgml(:,i),'--')
+ %title('solid-actual, dotted-forecast');
+ %xlabel(eval(['forelabel']));
+ %ylabel(eval(['x' int2str(i)]));
+ ylabel(char(ylab(i)))
+ end
+end
+
+t1 = 1:qAg;
+if Psuedo
+ t2 = qAg-qFg+1:qAg;
+else
+ t2 = qAg+1:qAg+qFg;
+end
+%
+if Graphfore
+ figure % The graphics below are prior quarter growth rate except R and U, at annualized rates
+ for i = 1:nvar
+ subplot(nvar/2,2,i)
+ plot(t1,yactqg(:,i),t2,yforeqgml(:,i),'--')
+ %title('quarterly growth, unconditional');
+ %xlabel(eval(['forelabel']));
+ %ylabel(eval(['x' int2str(i)]));
+ ylabel(char(ylab(i)))
+ end
+end
+
+
+%t1 = 1:yAg;
+t1 = yearsCal;
+yAgCal=yearsCal(length(yearsCal));
+if Psuedo
+ t2 = yAgCal-yFg+1:yAgCal;
+else
+ t2 = yAgCal+1:yAgCal+yFg;
+end
+%
+if Graphfore
+ figure
+ % The graphics below are over-the-year growth rate except R and U, annually
+ for i = 1:nvar
+ subplot(nvar/2,2,i)
+ plot(t1,yactCalyg(:,i),t2,yforeCalygml(:,i),'--')
+ %title('solid-actual, dotted-forecast');
+ %xlabel(eval(['forelabel']));
+ %ylabel(eval(['x' int2str(i)]));
+ ylabel(char(ylab(i)))
+ end
+end
diff --git a/MatlabFiles/fore_mqy.m b/MatlabFiles/fore_mqy.m
index 6759e1be280d636ee5932ef8276593bd8a8eff06..3bb4394999bb1390a2d052579435ca3bfec3f014 100644
--- a/MatlabFiles/fore_mqy.m
+++ b/MatlabFiles/fore_mqy.m
@@ -1,338 +1,338 @@
-function [yforelml,yforemgml,yforeqgml,yforeCalygml,yactl,yactmg,yactqg,yactCalyg,yactlog] = ...
- fore_mqy(yfore,xdata,nvar,nSample,nSampleCal,...
- forep,forepq,forepy,q_m,qmEnd,vlist,vlistlog,vlistper,...
- lmqyIndx,indxCal)
-% [yforelml,yforemgml,yforeqgml,yforeCalygml,yactl,yactmg,yactqg,yactCalyg,yactlog] = ...
-% fore_mqy(yfore,xdata,nvar,nSample,nSampleCal,...
-% forep,forepq,forepy,q_m,qmEnd,vlist,vlistlog,vlistper,...
-% lmqyIndx,indxCal)
-%
-% Converting oringinal forecast "yfore" to series by calendar years and series
-% with growth rates (annualized for monthly and quarterly series) and
-% with corresponding actual data (ONLY valid when we have actual data.
-% Later should add an index to allow an option out 3/23/99).
-% 3/23/99 I guess that this program is more general than fore_cal because it allows
-% for non-Calendar annual growth rate calculation if nSample before "forep" and
-% and q_m before "vlist" are used.
-%
-% yfore: oringal forecast series, all logged except R, U, etc.
-% xdata: oringal data set, up to the period before (peudo-) out-of-sample forecasting,
-% all logged except R, U, etc.
-% nvar: number of variables
-% nSample: sample size (including lags or initial periods)
-% nSampleCal: sample size in terms of calendar years
-% forep: forecast periods (monthly)
-% forepq: forecast periods (quarterly)
-% forepy: forecast periods (yearly)
-% q_m: quarterly or monthly for the underlying model
-% qmEnd: last q_m before out-of-sample forecasting
-% vlist: a list of variables
-% vlistlog: sub list of variables that are in log
-% vlistper: sub list of variables that are in percent
-% lmyqIndx: 4-by-1 1 or 0's. Index for level, mg, qg, and yg; 1: yes; 0: no
-% indxCal: 1: calendar years; 0: (NOT calendar) years
-%----------------
-% yforelml: forep-by-nvar, ymonthly ML forecast, all at level, in percent
-% yforemgml: ML forecasts, monthly growth (at annual rates), in percent
-% yforeqgml: ML forecast: quarterly growth (at annual rates), in percent
-% yforeCalygml: forepq-by-nvar ML forecast: annual growth, in percent
-% yactl: forep-by-nvar, actual, all at level, in percent
-% yactmg:
-% yactqg:
-% yactCalyg: forepq-by-nvar, actual, annual growth, in percent
-% yactlog: forep-by-nvar, actual, all log except R and U.
-%
-% Copyright (c) March 1998 by Tao Zha
-% Revised, 3/23/99. Added "lmqyIndx" so that previous programs may not be compatible.
-% Revised, 4/27/99. Added "indxCal" so that previous programs may not be compatible.
-
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-%=================================================
-% Making everything presentable at FOMC
-%=================================================
-%
-%%%%
-%$$$ Monthly, prior quarter, and year-over-year change
-%$$$ Out-of-sample forecasts
-%%%%
-%
-
-if length(lmqyIndx)~=4
- warning('lmqyIndx must be a 4-by-1 vector containing 1 or 0')
- return
-end
-
-
-
-%---------------------
-% Actual data
-%---------------------
-yact = xdata(nSample-q_m+1:nSample,:); % the latest (not calender) year
-yactCal = xdata(nSampleCal-q_m+1:nSampleCal,:);
- % the lastest calendar year (always earlier than the lastest (not calendar) year
-
-
-
-%-----------------------------------------
-% Converted to monthly level
-%-----------------------------------------
-if lmqyIndx(1)
- yforelml=yfore;
- yforelml(:,vlistlog)=exp(yfore(:,vlistlog)); % mode, all levels
- yforelml(:,vlistper)=100*yfore(:,vlistper); % mode, all levels
-else
- yforelml = NaN;
-end
-
-
-%-----------------------------------------
-% Converted to monthly growth
-%-----------------------------------------
-if lmqyIndx(2)
- yactm = zeros(1,length(vlist)); % the latest month prior to forecasting
- yforem = zeros(1+forep,length(vlist)); % including the 1 month prior to forecasting
- %
- yactm = yact(length(yact(:,1)),:); % last month prior to forecasting
- yforem(1,:) = yactm; % last month prior to forecasting
- yforem(2:forep+1,:) = yfore(1:forep,:); % monthly forecasts, all logged.
- %
- %@@ monthly growth rate (annualized)
- yforemg = yforem(2:forep+1,:);
- yforemg(:,vlistlog) = ( yforem(2:forep+1,vlistlog) - yforem(1:forep,vlistlog) ) .* q_m;
- % monthly change (annualized), 12*log(1+growth rate)
- yforemgml=yforemg;
- yforemgml(:,vlistlog) = 100*(exp(yforemg(:,vlistlog))-1); % monthly growth rate (annualized)
- yforemgml(:,vlistper) = 100*yforemg(:,vlistper); % monthly growth rate (annualized)
-else
- yforemgml=NaN;
-end
-
-
-
-%-----------------------------------------
-% Converted to quarterly
-%-----------------------------------------
-if lmqyIndx(3)
- yactQ = xdata(nSample-mod(qmEnd,3)-q_m+1:nSample-mod(qmEnd,3),:); % the latest actual quarter
- yactq = zeros(4,length(vlist)); % the latest 4 quarters prior to forecasting
- yforeq = zeros(4+forepq,length(vlist)); % including the 4 quarters prior to forecasting
- %
- qT1 = length(yactQ(:,1))/3;
- if qT1 ~= 4
- error('Must hae 4 actual quarters to compute quarterly change for forecasting!')
- end
- for i = 1:qT1
- i1 = 1+3*(i-1);
- i2 = 3*i;
- yactq(i,:) = sum(yact(i1:i2,:)) ./ 3;
- end
- yforeq(1:4,:) = yactq;
- %
- yforeq_1 = sum(xdata( nSample-mod(qmEnd,3)+1:nSample,:),1);
- yforeq(5,:) = ( yforeq_1 + sum(yfore(1:3-mod(qmEnd,3),:),1) ) ./ 3;
- % 1st quarterly forecast which may contain actual data within quarter
- % note, dimension "1" in sum(ytem,1) is necessary because when
- % qmEnd=1, sum(ytem,1) will give us a nomber, not a vector. But this
- % is not what we want.
- for i = 2:forepq
- i1 = 3-mod(qmEnd,3) + 1+3*(i-2);
- i2 = 3-mod(qmEnd,3) + 3*(i-1);
- yforeq(4+i,:) = sum(yfore(i1:i2,:)) ./ 3;
- end
- %
- %@@ prior quarter growth rate (annualized)
- yforeqg = yforeq(5:forepq+4,:);
- %yforeqg(:,vlistlog) = (100*(yforeq(5:qT2+4,vlistlog)-yforeq(1:qT2,vlistlog))) ...
- % ./ yforeq(1:qT2,vlistlog); % year-over-year
- %yforeqg(:,vlistlog) = 100*((yforeq(5:qT2+4,vlistlog)./yforeq(4:qT2+3,vlistlog)).^4 - 1 );
- % prior quarter
- yforeqg(:,vlistlog) = ( yforeq(5:forepq+4,vlistlog) - yforeq(4:forepq+3,vlistlog) ) .* 4;
- % prior quarter, 4*log(1+growth rate)
- yforeqgml=yforeqg;
- yforeqgml(:,vlistlog) = 100*(exp(yforeqg(:,vlistlog))-1); % quarterly growth rate (annualized)
- yforeqgml(:,vlistper) = 100*yforeqg(:,vlistper); % quarterly growth rate (annualized)
-else
- yforeqgml = NaN;
-end
-
-
-%-----------------------------------------
-% Converted to annual years (may not be calendar)
-%-----------------------------------------
-if lmqyIndx(4)
- yactCaly = zeros(1,length(vlist)); % the latest calendar year
- yforeCaly = zeros(1+forepy,length(vlist)); % including the calendar year prior to forecasting
- %
- yT1 = length(yactCal(:,1))/q_m;
- if yT1 ~= 1
- error('yT1 Beginings or ends of monthly and calendar series are not the same!')
- end
- for i = 1:yT1
- i1 = 1+q_m*(i-1);
- i2 = q_m*i;
- yactCaly(i,:) = sum(yactCal(i1:i2,:)) ./ q_m;
- end
- yforeCaly(1,:) = yactCaly;
- %
- %@@ initial monthly actual data for calendar years
- if qmEnd == q_m
- yforeCaly_1 = 0;
- else
- ytem = xdata(nSampleCal+1:nSampleCal+qmEnd,:);
- %ytem(:,vlistlog) = exp(ytem(:,vlistlog));
- yforeCaly_1 = sum(ytem,1);
- % note, dimension "1" in sum(ytem,1) is necessary because when
- % qmEnd=1, sum(ytem,1) will give us a nomber, not a vector. But this
- % is not what we want.
- end
- %
- if qmEnd == q_m
- for i = 1:forepy
- i1 = 1+q_m*(i-1);
- i2 = q_m*i;
- yforeCaly(1+i,:) = sum(yfore(i1:i2,:)) ./ q_m;
- end
- else
- yforeCaly(2,:) = (yforeCaly_1+sum(yfore(1:q_m-qmEnd,:),1)) ./ q_m;
- % note, dimension "1" in sum(yfore,1) is necessary because when
- % q_m-qmEnd=1, sum(yfore) will give us a number, not a vector. But this
- % is not what we want.
- for i = 2:forepy
- i1 = q_m-qmEnd+1+q_m*(i-2);
- i2 = q_m-qmEnd+q_m*(i-1);
- yforeCaly(1+i,:) = sum(yfore(i1:i2,:)) ./ q_m;
- end
- end
- %
- %@@ year-over-year growth rate
- yforeCalyg = yforeCaly(2:forepy+1,:);
- yforeCalyg(:,vlistlog) = yforeCaly(2:forepy+1,vlistlog) - yforeCaly(1:forepy,vlistlog);
- % year-over-year, log(1+growth rate)
- yforeCalygml=yforeCalyg;
- yforeCalygml(:,vlistlog) = 100*(exp(yforeCalyg(:,vlistlog))-1); % annaul growth rate
- yforeCalygml(:,vlistper) = 100*yforeCalyg(:,vlistper); % annaul growth rate
-else
- yforeCalygml = NaN;
-end
-
-
-
-%----------------------------------------------------------------------
-% --------------------- Actual -----------------------------------------
-%----------------------------------------------------------------------
-if indxCal
- Rem_qm = q_m-qmEnd; % remaining months within the first calendar year
- Rem_y = floor((forep-Rem_qm)/q_m); % remaining calendar years after the first one
- yact = xdata(nSample-2*q_m+1:nSample+Rem_qm+Rem_y*q_m,:);
- % 2*q_m (not calendar) months + months ended at last calendar year
-else
- yact = xdata(nSample-q_m+1:nSample+forep,:); % q_m (not calendar) months + forep
-end
-mT1 = length(yact(:,1));
-yactlog = yact(q_m+1:mT1,:);
-
-
-%-----------------------------------------
-% Converted to monthly level
-%-----------------------------------------
-if lmqyIndx(1)
- yactl=yactlog;
- yactl(:,vlistlog)=exp(yactlog(:,vlistlog)); % mode, all levels
- yactl(:,vlistper)=100*yactlog(:,vlistper); % mode, all levels
-else
- yactl=NaN;
-end
-
-%-----------------------------------------
-% Converted to monthly growth
-%-----------------------------------------
-if lmqyIndx(2)
- yactmg = yactl;
- yactmg(:,vlistlog) = (yact(q_m+1:mT1,vlistlog) - yact(q_m:mT1-1,vlistlog)) .* 12;
- % monthly, 12*log(1+growth rate), annualized growth rate
- yactmg(:,vlistlog) = 100*(exp(yactmg(:,vlistlog))-1);
- yactmg(:,vlistper) = 100*yactmg(:,vlistper);
-else
- yactmg=NaN;
-end
-
-%-----------------------------------------
-% Converted to quarterly
-%-----------------------------------------
-if lmqyIndx(3)
- warning(' ')
- disp('Not worked out yet. 4/27/99')
- disp('Press ctrl-c to abort!')
- pause
-
- yactQ = yact(10:mT1,:); % October: beginning of the 4th quarter
- yactq = zeros(1+forepq,length(vlist));
- qT1 = 1+forepq;
- %
- for i = 1:qT1
- i1 = 1+3*(i-1);
- i2 = 3*i;
- yactq(i,:) = sum(yactQ(i1:i2,:)) ./ 3;
- end
- %
- yactqg = yactq(2:qT1,:); % 4+1=5 where 4 means 4 quarters
- yactqg(:,vlistlog) = (yactq(2:qT1,vlistlog) - yactq(1:qT1-1,vlistlog)) .* 4;
- % quarterly, 4*log(1+growth rate), annualized growth rate
- yactqg(:,vlistlog) = 100*(exp(yactqg(:,vlistlog))-1);
- yactqg(:,vlistper) = 100*yactqg(:,vlistper);
-else
- yactqg=NaN;
-end
-
-
-%-----------------------------------------
-% Converted to annual years (may not be calendar)
-%-----------------------------------------
-if lmqyIndx(4)
- if indxCal
- yT1 = 1+forepy;
- yactCaly = zeros(yT1,length(vlist)); % the latest 1+forepy calendar years
- for i = 1:yT1
- i1 = ( Rem_qm+q_m*(qmEnd==q_m) ) + 1+q_m*(i-1);
- i2 = ( Rem_qm+q_m*(qmEnd==q_m) ) + q_m*i;
- yactCaly(i,:) = sum(yact(i1:i2,:)) ./ q_m;
- end
- %
- yactCalyg = yactCaly(2:yT1,:); % 1+1=2 where 1 means 1 year
- yactCalyg(:,vlistlog) = (yactCaly(2:yT1,vlistlog) - yactCaly(1:yT1-1,vlistlog));
- % annual rate: log(1+growth rate)
- yactCalyg(:,vlistlog) = 100*(exp(yactCalyg(:,vlistlog))-1);
- yactCalyg(:,vlistper) = 100*yactCalyg(:,vlistper);
- else
- yT1 = 1+forepy;
- for i = 1:yT1
- i1 = 1+q_m*(i-1);
- i2 = q_m*i;
- yactCaly(i,:) = sum(yact(i1:i2,:)) ./ q_m;
- end
- %
- yactCalyg = yactCaly(2:yT1,:); % 1+1=2 where 1 means 1 year
- yactCalyg(:,vlistlog) = (yactCaly(2:yT1,vlistlog) - yactCaly(1:yT1-1,vlistlog));
- % annual rate: log(1+growth rate)
- yactCalyg(:,vlistlog) = 100*(exp(yactCalyg(:,vlistlog))-1);
- yactCalyg(:,vlistper) = 100*yactCalyg(:,vlistper);
- end
-else
- yactCalyg=NaN;
-end
\ No newline at end of file
+function [yforelml,yforemgml,yforeqgml,yforeCalygml,yactl,yactmg,yactqg,yactCalyg,yactlog] = ...
+ fore_mqy(yfore,xdata,nvar,nSample,nSampleCal,...
+ forep,forepq,forepy,q_m,qmEnd,vlist,vlistlog,vlistper,...
+ lmqyIndx,indxCal)
+% [yforelml,yforemgml,yforeqgml,yforeCalygml,yactl,yactmg,yactqg,yactCalyg,yactlog] = ...
+% fore_mqy(yfore,xdata,nvar,nSample,nSampleCal,...
+% forep,forepq,forepy,q_m,qmEnd,vlist,vlistlog,vlistper,...
+% lmqyIndx,indxCal)
+%
+% Converting oringinal forecast "yfore" to series by calendar years and series
+% with growth rates (annualized for monthly and quarterly series) and
+% with corresponding actual data (ONLY valid when we have actual data.
+% Later should add an index to allow an option out 3/23/99).
+% 3/23/99 I guess that this program is more general than fore_cal because it allows
+% for non-Calendar annual growth rate calculation if nSample before "forep" and
+% and q_m before "vlist" are used.
+%
+% yfore: oringal forecast series, all logged except R, U, etc.
+% xdata: oringal data set, up to the period before (peudo-) out-of-sample forecasting,
+% all logged except R, U, etc.
+% nvar: number of variables
+% nSample: sample size (including lags or initial periods)
+% nSampleCal: sample size in terms of calendar years
+% forep: forecast periods (monthly)
+% forepq: forecast periods (quarterly)
+% forepy: forecast periods (yearly)
+% q_m: quarterly or monthly for the underlying model
+% qmEnd: last q_m before out-of-sample forecasting
+% vlist: a list of variables
+% vlistlog: sub list of variables that are in log
+% vlistper: sub list of variables that are in percent
+% lmyqIndx: 4-by-1 1 or 0's. Index for level, mg, qg, and yg; 1: yes; 0: no
+% indxCal: 1: calendar years; 0: (NOT calendar) years
+%----------------
+% yforelml: forep-by-nvar, ymonthly ML forecast, all at level, in percent
+% yforemgml: ML forecasts, monthly growth (at annual rates), in percent
+% yforeqgml: ML forecast: quarterly growth (at annual rates), in percent
+% yforeCalygml: forepq-by-nvar ML forecast: annual growth, in percent
+% yactl: forep-by-nvar, actual, all at level, in percent
+% yactmg:
+% yactqg:
+% yactCalyg: forepq-by-nvar, actual, annual growth, in percent
+% yactlog: forep-by-nvar, actual, all log except R and U.
+%
+% Written by Tao Zha March 1998
+% Revised, 3/23/99. Added "lmqyIndx" so that previous programs may not be compatible.
+% Revised, 4/27/99. Added "indxCal" so that previous programs may not be compatible.
+
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+%=================================================
+% Making everything presentable at FOMC
+%=================================================
+%
+%%%%
+%$$$ Monthly, prior quarter, and year-over-year change
+%$$$ Out-of-sample forecasts
+%%%%
+%
+
+if length(lmqyIndx)~=4
+ warning('lmqyIndx must be a 4-by-1 vector containing 1 or 0')
+ return
+end
+
+
+
+%---------------------
+% Actual data
+%---------------------
+yact = xdata(nSample-q_m+1:nSample,:); % the latest (not calender) year
+yactCal = xdata(nSampleCal-q_m+1:nSampleCal,:);
+ % the lastest calendar year (always earlier than the lastest (not calendar) year
+
+
+
+%-----------------------------------------
+% Converted to monthly level
+%-----------------------------------------
+if lmqyIndx(1)
+ yforelml=yfore;
+ yforelml(:,vlistlog)=exp(yfore(:,vlistlog)); % mode, all levels
+ yforelml(:,vlistper)=100*yfore(:,vlistper); % mode, all levels
+else
+ yforelml = NaN;
+end
+
+
+%-----------------------------------------
+% Converted to monthly growth
+%-----------------------------------------
+if lmqyIndx(2)
+ yactm = zeros(1,length(vlist)); % the latest month prior to forecasting
+ yforem = zeros(1+forep,length(vlist)); % including the 1 month prior to forecasting
+ %
+ yactm = yact(length(yact(:,1)),:); % last month prior to forecasting
+ yforem(1,:) = yactm; % last month prior to forecasting
+ yforem(2:forep+1,:) = yfore(1:forep,:); % monthly forecasts, all logged.
+ %
+ %@@ monthly growth rate (annualized)
+ yforemg = yforem(2:forep+1,:);
+ yforemg(:,vlistlog) = ( yforem(2:forep+1,vlistlog) - yforem(1:forep,vlistlog) ) .* q_m;
+ % monthly change (annualized), 12*log(1+growth rate)
+ yforemgml=yforemg;
+ yforemgml(:,vlistlog) = 100*(exp(yforemg(:,vlistlog))-1); % monthly growth rate (annualized)
+ yforemgml(:,vlistper) = 100*yforemg(:,vlistper); % monthly growth rate (annualized)
+else
+ yforemgml=NaN;
+end
+
+
+
+%-----------------------------------------
+% Converted to quarterly
+%-----------------------------------------
+if lmqyIndx(3)
+ yactQ = xdata(nSample-mod(qmEnd,3)-q_m+1:nSample-mod(qmEnd,3),:); % the latest actual quarter
+ yactq = zeros(4,length(vlist)); % the latest 4 quarters prior to forecasting
+ yforeq = zeros(4+forepq,length(vlist)); % including the 4 quarters prior to forecasting
+ %
+ qT1 = length(yactQ(:,1))/3;
+ if qT1 ~= 4
+ error('Must hae 4 actual quarters to compute quarterly change for forecasting!')
+ end
+ for i = 1:qT1
+ i1 = 1+3*(i-1);
+ i2 = 3*i;
+ yactq(i,:) = sum(yact(i1:i2,:)) ./ 3;
+ end
+ yforeq(1:4,:) = yactq;
+ %
+ yforeq_1 = sum(xdata( nSample-mod(qmEnd,3)+1:nSample,:),1);
+ yforeq(5,:) = ( yforeq_1 + sum(yfore(1:3-mod(qmEnd,3),:),1) ) ./ 3;
+ % 1st quarterly forecast which may contain actual data within quarter
+ % note, dimension "1" in sum(ytem,1) is necessary because when
+ % qmEnd=1, sum(ytem,1) will give us a nomber, not a vector. But this
+ % is not what we want.
+ for i = 2:forepq
+ i1 = 3-mod(qmEnd,3) + 1+3*(i-2);
+ i2 = 3-mod(qmEnd,3) + 3*(i-1);
+ yforeq(4+i,:) = sum(yfore(i1:i2,:)) ./ 3;
+ end
+ %
+ %@@ prior quarter growth rate (annualized)
+ yforeqg = yforeq(5:forepq+4,:);
+ %yforeqg(:,vlistlog) = (100*(yforeq(5:qT2+4,vlistlog)-yforeq(1:qT2,vlistlog))) ...
+ % ./ yforeq(1:qT2,vlistlog); % year-over-year
+ %yforeqg(:,vlistlog) = 100*((yforeq(5:qT2+4,vlistlog)./yforeq(4:qT2+3,vlistlog)).^4 - 1 );
+ % prior quarter
+ yforeqg(:,vlistlog) = ( yforeq(5:forepq+4,vlistlog) - yforeq(4:forepq+3,vlistlog) ) .* 4;
+ % prior quarter, 4*log(1+growth rate)
+ yforeqgml=yforeqg;
+ yforeqgml(:,vlistlog) = 100*(exp(yforeqg(:,vlistlog))-1); % quarterly growth rate (annualized)
+ yforeqgml(:,vlistper) = 100*yforeqg(:,vlistper); % quarterly growth rate (annualized)
+else
+ yforeqgml = NaN;
+end
+
+
+%-----------------------------------------
+% Converted to annual years (may not be calendar)
+%-----------------------------------------
+if lmqyIndx(4)
+ yactCaly = zeros(1,length(vlist)); % the latest calendar year
+ yforeCaly = zeros(1+forepy,length(vlist)); % including the calendar year prior to forecasting
+ %
+ yT1 = length(yactCal(:,1))/q_m;
+ if yT1 ~= 1
+ error('yT1 Beginings or ends of monthly and calendar series are not the same!')
+ end
+ for i = 1:yT1
+ i1 = 1+q_m*(i-1);
+ i2 = q_m*i;
+ yactCaly(i,:) = sum(yactCal(i1:i2,:)) ./ q_m;
+ end
+ yforeCaly(1,:) = yactCaly;
+ %
+ %@@ initial monthly actual data for calendar years
+ if qmEnd == q_m
+ yforeCaly_1 = 0;
+ else
+ ytem = xdata(nSampleCal+1:nSampleCal+qmEnd,:);
+ %ytem(:,vlistlog) = exp(ytem(:,vlistlog));
+ yforeCaly_1 = sum(ytem,1);
+ % note, dimension "1" in sum(ytem,1) is necessary because when
+ % qmEnd=1, sum(ytem,1) will give us a nomber, not a vector. But this
+ % is not what we want.
+ end
+ %
+ if qmEnd == q_m
+ for i = 1:forepy
+ i1 = 1+q_m*(i-1);
+ i2 = q_m*i;
+ yforeCaly(1+i,:) = sum(yfore(i1:i2,:)) ./ q_m;
+ end
+ else
+ yforeCaly(2,:) = (yforeCaly_1+sum(yfore(1:q_m-qmEnd,:),1)) ./ q_m;
+ % note, dimension "1" in sum(yfore,1) is necessary because when
+ % q_m-qmEnd=1, sum(yfore) will give us a number, not a vector. But this
+ % is not what we want.
+ for i = 2:forepy
+ i1 = q_m-qmEnd+1+q_m*(i-2);
+ i2 = q_m-qmEnd+q_m*(i-1);
+ yforeCaly(1+i,:) = sum(yfore(i1:i2,:)) ./ q_m;
+ end
+ end
+ %
+ %@@ year-over-year growth rate
+ yforeCalyg = yforeCaly(2:forepy+1,:);
+ yforeCalyg(:,vlistlog) = yforeCaly(2:forepy+1,vlistlog) - yforeCaly(1:forepy,vlistlog);
+ % year-over-year, log(1+growth rate)
+ yforeCalygml=yforeCalyg;
+ yforeCalygml(:,vlistlog) = 100*(exp(yforeCalyg(:,vlistlog))-1); % annaul growth rate
+ yforeCalygml(:,vlistper) = 100*yforeCalyg(:,vlistper); % annaul growth rate
+else
+ yforeCalygml = NaN;
+end
+
+
+
+%----------------------------------------------------------------------
+% --------------------- Actual -----------------------------------------
+%----------------------------------------------------------------------
+if indxCal
+ Rem_qm = q_m-qmEnd; % remaining months within the first calendar year
+ Rem_y = floor((forep-Rem_qm)/q_m); % remaining calendar years after the first one
+ yact = xdata(nSample-2*q_m+1:nSample+Rem_qm+Rem_y*q_m,:);
+ % 2*q_m (not calendar) months + months ended at last calendar year
+else
+ yact = xdata(nSample-q_m+1:nSample+forep,:); % q_m (not calendar) months + forep
+end
+mT1 = length(yact(:,1));
+yactlog = yact(q_m+1:mT1,:);
+
+
+%-----------------------------------------
+% Converted to monthly level
+%-----------------------------------------
+if lmqyIndx(1)
+ yactl=yactlog;
+ yactl(:,vlistlog)=exp(yactlog(:,vlistlog)); % mode, all levels
+ yactl(:,vlistper)=100*yactlog(:,vlistper); % mode, all levels
+else
+ yactl=NaN;
+end
+
+%-----------------------------------------
+% Converted to monthly growth
+%-----------------------------------------
+if lmqyIndx(2)
+ yactmg = yactl;
+ yactmg(:,vlistlog) = (yact(q_m+1:mT1,vlistlog) - yact(q_m:mT1-1,vlistlog)) .* 12;
+ % monthly, 12*log(1+growth rate), annualized growth rate
+ yactmg(:,vlistlog) = 100*(exp(yactmg(:,vlistlog))-1);
+ yactmg(:,vlistper) = 100*yactmg(:,vlistper);
+else
+ yactmg=NaN;
+end
+
+%-----------------------------------------
+% Converted to quarterly
+%-----------------------------------------
+if lmqyIndx(3)
+ warning(' ')
+ disp('Not worked out yet. 4/27/99')
+ disp('Press ctrl-c to abort!')
+ pause
+
+ yactQ = yact(10:mT1,:); % October: beginning of the 4th quarter
+ yactq = zeros(1+forepq,length(vlist));
+ qT1 = 1+forepq;
+ %
+ for i = 1:qT1
+ i1 = 1+3*(i-1);
+ i2 = 3*i;
+ yactq(i,:) = sum(yactQ(i1:i2,:)) ./ 3;
+ end
+ %
+ yactqg = yactq(2:qT1,:); % 4+1=5 where 4 means 4 quarters
+ yactqg(:,vlistlog) = (yactq(2:qT1,vlistlog) - yactq(1:qT1-1,vlistlog)) .* 4;
+ % quarterly, 4*log(1+growth rate), annualized growth rate
+ yactqg(:,vlistlog) = 100*(exp(yactqg(:,vlistlog))-1);
+ yactqg(:,vlistper) = 100*yactqg(:,vlistper);
+else
+ yactqg=NaN;
+end
+
+
+%-----------------------------------------
+% Converted to annual years (may not be calendar)
+%-----------------------------------------
+if lmqyIndx(4)
+ if indxCal
+ yT1 = 1+forepy;
+ yactCaly = zeros(yT1,length(vlist)); % the latest 1+forepy calendar years
+ for i = 1:yT1
+ i1 = ( Rem_qm+q_m*(qmEnd==q_m) ) + 1+q_m*(i-1);
+ i2 = ( Rem_qm+q_m*(qmEnd==q_m) ) + q_m*i;
+ yactCaly(i,:) = sum(yact(i1:i2,:)) ./ q_m;
+ end
+ %
+ yactCalyg = yactCaly(2:yT1,:); % 1+1=2 where 1 means 1 year
+ yactCalyg(:,vlistlog) = (yactCaly(2:yT1,vlistlog) - yactCaly(1:yT1-1,vlistlog));
+ % annual rate: log(1+growth rate)
+ yactCalyg(:,vlistlog) = 100*(exp(yactCalyg(:,vlistlog))-1);
+ yactCalyg(:,vlistper) = 100*yactCalyg(:,vlistper);
+ else
+ yT1 = 1+forepy;
+ for i = 1:yT1
+ i1 = 1+q_m*(i-1);
+ i2 = q_m*i;
+ yactCaly(i,:) = sum(yact(i1:i2,:)) ./ q_m;
+ end
+ %
+ yactCalyg = yactCaly(2:yT1,:); % 1+1=2 where 1 means 1 year
+ yactCalyg(:,vlistlog) = (yactCaly(2:yT1,vlistlog) - yactCaly(1:yT1-1,vlistlog));
+ % annual rate: log(1+growth rate)
+ yactCalyg(:,vlistlog) = 100*(exp(yactCalyg(:,vlistlog))-1);
+ yactCalyg(:,vlistper) = 100*yactCalyg(:,vlistper);
+ end
+else
+ yactCalyg=NaN;
+end
diff --git a/MatlabFiles/forecast.m b/MatlabFiles/forecast.m
index e39d8e43a6c5e683423bcd2907cc8ef0fe1e69fc..54d7a93eb1c18bb1796d0b043d2d66db43ced614 100644
--- a/MatlabFiles/forecast.m
+++ b/MatlabFiles/forecast.m
@@ -1,41 +1,41 @@
-function yhat = forecast(Bh,phi,nn)
-% yhat = forecast(Bh,phi,nn)
-%
-% Forecast: unconditional forecating: yhat = forecast(Bh,phi,nn)
-% y_hat(t+h) = c + x_hat(t+h-1)*Bh, X: 1*k; Bh: k*nvar; y_hat: 1*nvar
-% where Bh: the (posterior) estimate of B;
-% phi: the 1-by-(nvar*lags+1) data matrix where k=nvar*lags+1
-% (last period plus lags before the beginning of forecast);
-% nn: [nvar,lags,forep], forep: forecast periods;
-% yhat: forep*nvar.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-% ** setup
-nvar = nn(1);
-lags = nn(2);
-forep = nn(3);
-tcwc = nvar*lags; % total coefficients without constant
-
-% ** reconstruct x(t) for y(t+h) = x(t+h-1)*B
-% ** where phi = x(t+h-1) with last column being constant
-yhat = zeros(forep,nvar);
-for k=1:forep
- yhat(k,:) = phi*Bh;
- phi(1,nvar+1:tcwc) = phi(1,1:tcwc-nvar);
- phi(1,1:nvar) = yhat(k,:);
-end
+function yhat = forecast(Bh,phi,nn)
+% yhat = forecast(Bh,phi,nn)
+%
+% Forecast: unconditional forecating: yhat = forecast(Bh,phi,nn)
+% y_hat(t+h) = c + x_hat(t+h-1)*Bh, X: 1*k; Bh: k*nvar; y_hat: 1*nvar
+% where Bh: the (posterior) estimate of B;
+% phi: the 1-by-(nvar*lags+1) data matrix where k=nvar*lags+1
+% (last period plus lags before the beginning of forecast);
+% nn: [nvar,lags,forep], forep: forecast periods;
+% yhat: forep*nvar.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+% ** setup
+nvar = nn(1);
+lags = nn(2);
+forep = nn(3);
+tcwc = nvar*lags; % total coefficients without constant
+
+% ** reconstruct x(t) for y(t+h) = x(t+h-1)*B
+% ** where phi = x(t+h-1) with last column being constant
+yhat = zeros(forep,nvar);
+for k=1:forep
+ yhat(k,:) = phi*Bh;
+ phi(1,nvar+1:tcwc) = phi(1,1:tcwc-nvar);
+ phi(1,1:nvar) = yhat(k,:);
+end
diff --git a/MatlabFiles/forecasterr.m b/MatlabFiles/forecasterr.m
index 7ee0e2b6198e4b8c65f7d1fc0fd5a997d7d64ef7..fc2c980453d2137c4c7b96c36895b15eca7eaf82 100644
--- a/MatlabFiles/forecasterr.m
+++ b/MatlabFiles/forecasterr.m
@@ -1,44 +1,44 @@
-function yhat = forecasterr(A0hin,Bh,phi,nn)
-% forecast: unconditional forecating: yhat = forecast(Bh,phi,nn)
-% y_hat(t+h) = x_hat(t+h-1)*Bh, X: 1*k; Bh: k*nvar; y_hat: 1*nvar
-% where A0hin: inv(A0h) where columns correspond to equations
-% Bh: the (posterior) estimate of B;
-% phi: the 1-by-(nvar*lags+1) data matrix where k=nvar*lags+1
-% (last period plus lags before the beginning of forecast);
-% nn: [nvar,lags,forep], forep: forecast periods;
-% yhat: forep*nvar.
-%
-% See fsim.m when A0h (rather than A0hin) is used.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-% ** setup
-nvar = nn(1);
-lags = nn(2);
-forep = nn(3);
-tcwc = nvar*lags; % total coefficients without constant
-
-% ** reconstruct x(t) for y(t+h) = x(t+h-1)*B
-% ** where phi = x(t+h-1) with last column being constant
-Estr = randn(forep,nvar);
-Ures = Estr*A0hin;
-yhat = zeros(forep,nvar);
-for k=1:forep
- yhat(k,:) = phi*Bh + Ures(k,:);
- phi(1,nvar+1:tcwc) = phi(1,1:tcwc-nvar);
- phi(1,1:nvar) = yhat(k,:);
-end
+function yhat = forecasterr(A0hin,Bh,phi,nn)
+% forecast: unconditional forecating: yhat = forecast(Bh,phi,nn)
+% y_hat(t+h) = x_hat(t+h-1)*Bh, X: 1*k; Bh: k*nvar; y_hat: 1*nvar
+% where A0hin: inv(A0h) where columns correspond to equations
+% Bh: the (posterior) estimate of B;
+% phi: the 1-by-(nvar*lags+1) data matrix where k=nvar*lags+1
+% (last period plus lags before the beginning of forecast);
+% nn: [nvar,lags,forep], forep: forecast periods;
+% yhat: forep*nvar.
+%
+% See fsim.m when A0h (rather than A0hin) is used.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+% ** setup
+nvar = nn(1);
+lags = nn(2);
+forep = nn(3);
+tcwc = nvar*lags; % total coefficients without constant
+
+% ** reconstruct x(t) for y(t+h) = x(t+h-1)*B
+% ** where phi = x(t+h-1) with last column being constant
+Estr = randn(forep,nvar);
+Ures = Estr*A0hin;
+yhat = zeros(forep,nvar);
+for k=1:forep
+ yhat(k,:) = phi*Bh + Ures(k,:);
+ phi(1,nvar+1:tcwc) = phi(1,1:tcwc-nvar);
+ phi(1,1:nvar) = yhat(k,:);
+end
diff --git a/MatlabFiles/forefixe.m b/MatlabFiles/forefixe.m
index 60834432d9014e8d74e1f20959573e3ce98147f1..3ef25bb66b71556332197b431fadabbfb464d0e7 100644
--- a/MatlabFiles/forefixe.m
+++ b/MatlabFiles/forefixe.m
@@ -1,46 +1,46 @@
-function yhat = forefixe(A0h,Bh,phi,nn,Estr)
-% yhat = forefixe(A0h,Bh,phi,nn,Estr)
-% Forecat conditional on particular structural shocks
-%
-% where A0h: column means equation
-% Bh: the (posterior) estimate of B;
-% phi: the 1-by-(nvar*lags+1) data matrix where k=nvar*lags+1
-% (last period plus lags before the beginning of forecast);
-% nn: [nvar,lags,forep], forep: forecast periods;
-% Estr: forep-by-nvar, each column corresponds to shocks from a particular
-% source such as MS;
-% yhat: forep*nvar;
-%
-% For reduced-form shocks, see fshock.m
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-% ** setup
-nvar = nn(1);
-lags = nn(2);
-forep = nn(3);
-tcwc = nvar*lags; % total coefficients without constant
-
-% ** reconstruct x(t) for y(t+h) = x(t+h-1)*B
-% ** where phi = x(t+h-1) with last column being constant
-Ures = Estr/A0h;
-yhat = zeros(forep,nvar);
-for k=1:forep
- yhat(k,:) = phi*Bh + Ures(k,:);
- phi(1,nvar+1:tcwc) = phi(1,1:tcwc-nvar);
- phi(1,1:nvar) = yhat(k,:);
-end
+function yhat = forefixe(A0h,Bh,phi,nn,Estr)
+% yhat = forefixe(A0h,Bh,phi,nn,Estr)
+% Forecat conditional on particular structural shocks
+%
+% where A0h: column means equation
+% Bh: the (posterior) estimate of B;
+% phi: the 1-by-(nvar*lags+1) data matrix where k=nvar*lags+1
+% (last period plus lags before the beginning of forecast);
+% nn: [nvar,lags,forep], forep: forecast periods;
+% Estr: forep-by-nvar, each column corresponds to shocks from a particular
+% source such as MS;
+% yhat: forep*nvar;
+%
+% For reduced-form shocks, see fshock.m
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+% ** setup
+nvar = nn(1);
+lags = nn(2);
+forep = nn(3);
+tcwc = nvar*lags; % total coefficients without constant
+
+% ** reconstruct x(t) for y(t+h) = x(t+h-1)*B
+% ** where phi = x(t+h-1) with last column being constant
+Ures = Estr/A0h;
+yhat = zeros(forep,nvar);
+for k=1:forep
+ yhat(k,:) = phi*Bh + Ures(k,:);
+ phi(1,nvar+1:tcwc) = phi(1,1:tcwc-nvar);
+ phi(1,1:nvar) = yhat(k,:);
+end
diff --git a/MatlabFiles/fshock.m b/MatlabFiles/fshock.m
index a5799576862a1b5307e41e0bd42fe56bd2ac3645..715cf5fb3710266320c3352895aef55c2b871101 100644
--- a/MatlabFiles/fshock.m
+++ b/MatlabFiles/fshock.m
@@ -1,44 +1,44 @@
-function yhat = fshock(Bh,phi,shock,nn)
-% fshock: unconditionally forecasts supplied with reduced-form shocks
-% yhat = fshock(Bh,phi,shock,nn)
-%
-% y_hat(t+h) = c + x_hat(t+h-1)*Bh, X: 1*k; Bh: k*nvar; y_hat: 1*nvar
-% where Bh: the (posterior) estimate of B;
-% phi: the 1-by-(nvar*lags+1) initial data matrix where k=nvar*lags+1
-% (last period plus lags before the beginning of forecast);
-% shock: forep-by-nvar reduced-form residuals, where forep: forecast periods;
-% nn: [nvar,lags,forep];
-% yhat: forep-by-nvar.
-%
-% For structural shocks, see forefixe.m
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-% ** setup
-nvar = nn(1);
-lags = nn(2);
-forep = nn(3);
-tcwc = nvar*lags; % total coefficients without constant
-
-% ** reconstruct x(t) for y(t+h) = x(t+h-1)*B
-% ** where phi = x(t+h-1) with last column being constant
-yhat = zeros(forep,nvar);
-for k=1:forep
- yhat(k,:) = phi*Bh + shock(k,:);
- phi(1,nvar+1:tcwc) = phi(1,1:tcwc-nvar);
- phi(1,1:nvar) = yhat(k,:);
-end
+function yhat = fshock(Bh,phi,shock,nn)
+% fshock: unconditionally forecasts supplied with reduced-form shocks
+% yhat = fshock(Bh,phi,shock,nn)
+%
+% y_hat(t+h) = c + x_hat(t+h-1)*Bh, X: 1*k; Bh: k*nvar; y_hat: 1*nvar
+% where Bh: the (posterior) estimate of B;
+% phi: the 1-by-(nvar*lags+1) initial data matrix where k=nvar*lags+1
+% (last period plus lags before the beginning of forecast);
+% shock: forep-by-nvar reduced-form residuals, where forep: forecast periods;
+% nn: [nvar,lags,forep];
+% yhat: forep-by-nvar.
+%
+% For structural shocks, see forefixe.m
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+% ** setup
+nvar = nn(1);
+lags = nn(2);
+forep = nn(3);
+tcwc = nvar*lags; % total coefficients without constant
+
+% ** reconstruct x(t) for y(t+h) = x(t+h-1)*B
+% ** where phi = x(t+h-1) with last column being constant
+yhat = zeros(forep,nvar);
+for k=1:forep
+ yhat(k,:) = phi*Bh + shock(k,:);
+ phi(1,nvar+1:tcwc) = phi(1,1:tcwc-nvar);
+ phi(1,1:nvar) = yhat(k,:);
+end
diff --git a/MatlabFiles/fsim.m b/MatlabFiles/fsim.m
index e01da891c1df8693522654ac5bd3f2b12cbe06cf..14a9fdc3ca0ce32b85b1c48695c006e88c376735 100644
--- a/MatlabFiles/fsim.m
+++ b/MatlabFiles/fsim.m
@@ -1,49 +1,49 @@
-function yhat = fsim(Bh,A0h,phi,nn)
-% yhat = fsim(Bh,A0h,phi,nn)
-% Unconditional forecasts with simulated shocks (which do not depend on reduced-form or structural shocks).
-% y_hat(t+h) = c + x_hat(t+h-1)*Bh, X: 1*k; Bh: k*nvar; y_hat: 1*nvar
-% where Bh: the (posterior) estimate of B;
-% A0h: has columns correpond to equations
-% phi: the 1-by-(nvar*lags+1) data matrix where k=nvar*lags+1
-% (last period plus lags before the beginning of forecast);
-% nn: [nvar,lags,forep], forep: forecast periods;
-% yhat: forep*nvar.
-%
-% 3/22/99. Revised so that A0h has columns corr. to equations. Previous
-% may use A0h' (papers before Sims and Zha IER paper). So please double
-% check.
-%
-% See forecasterr.m when A0h_in (instead of A0h) is used.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-% ** setup
-nvar = nn(1);
-lags = nn(2);
-forep = nn(3);
-tcwc = nvar*lags; % total coefficients without constant
-
-Ures = randn(forep,nvar)/A0h; % Unconditional forecast
- % Now, forep-by-nvar -- ready for forecasts
- % Ures: reduced-form residuals. Row--steps; Column--n shocks
-
-yhat = zeros(forep,nvar);
-for k=1:forep
- yhat(k,:) = phi*Bh + Ures(k,:);
- phi(1,nvar+1:tcwc) = phi(1,1:tcwc-nvar);
- phi(1,1:nvar) = yhat(k,:);
-end
+function yhat = fsim(Bh,A0h,phi,nn)
+% yhat = fsim(Bh,A0h,phi,nn)
+% Unconditional forecasts with simulated shocks (which do not depend on reduced-form or structural shocks).
+% y_hat(t+h) = c + x_hat(t+h-1)*Bh, X: 1*k; Bh: k*nvar; y_hat: 1*nvar
+% where Bh: the (posterior) estimate of B;
+% A0h: has columns correpond to equations
+% phi: the 1-by-(nvar*lags+1) data matrix where k=nvar*lags+1
+% (last period plus lags before the beginning of forecast);
+% nn: [nvar,lags,forep], forep: forecast periods;
+% yhat: forep*nvar.
+%
+% 3/22/99. Revised so that A0h has columns corr. to equations. Previous
+% may use A0h' (papers before Sims and Zha IER paper). So please double
+% check.
+%
+% See forecasterr.m when A0h_in (instead of A0h) is used.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+% ** setup
+nvar = nn(1);
+lags = nn(2);
+forep = nn(3);
+tcwc = nvar*lags; % total coefficients without constant
+
+Ures = randn(forep,nvar)/A0h; % Unconditional forecast
+ % Now, forep-by-nvar -- ready for forecasts
+ % Ures: reduced-form residuals. Row--steps; Column--n shocks
+
+yhat = zeros(forep,nvar);
+for k=1:forep
+ yhat(k,:) = phi*Bh + Ures(k,:);
+ phi(1,nvar+1:tcwc) = phi(1,1:tcwc-nvar);
+ phi(1,1:nvar) = yhat(k,:);
+end
diff --git a/MatlabFiles/gactual.m b/MatlabFiles/gactual.m
index 0765333594b5fcda8a5f79279bee6119a920498c..8d5b65a313af2829b1017b33d98891f4ebd986af 100644
--- a/MatlabFiles/gactual.m
+++ b/MatlabFiles/gactual.m
@@ -1,197 +1,197 @@
-function actual = gactual(begy,begm,begq,finy,finm,finq,ylab,mqyIndx,idfile)
-%
-% actual = gactual(begy,begm,begq,finy,finm,finq,ylab,mqyIndx,idfile)
-% Actual data according to mqyIndx; graph if no output argument is specified.
-%
-% begy: the beginning year for the graph
-% begm: the begiining month for the graph
-% begq: the begiining quarter for the graph
-% finy: the end year for the graph
-% finm: the end month for the graph
-% finq: the end quarter for the graph
-% ylab: label for each variable
-% mqyIndx: 1-by-3 index of monthly log, qg, and calendar yg. 1: yes; 0: no.
-% Only one at a time
-% idfile: import xinample.mat in the LZ paper under \condz
-%--------------
-% actual: T-by-nvar+1; 1st column is the dates; 2nd-7th columns: actual data of
-% of nvar variables with mlog, qg, or calendar yg, one at a time,
-% depending on mqyIndx
-% graph if no output argument is specified.
-%
-%
-% When insampleg.m is run, xinsample.mat is loaded for a long
-% history (e.g.,1960-1998)
-% When mspoint.m is run, outxshock.mat from msstart is loaded, which varies
-% with parac.m.
-%
-% October 1998 by Tao A. Zha
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-eval(['load ' idfile '.mat']);
-%
-
-if length(find(mqyIndx))>1
- warning('To get the number (not graph) out, only one at a time')
- disp('Make sure that mqyIndx has only 1 in a vector of 1 and 0s')
- disp(' ')
- disp('Press Enter to continue or Ctrl to abort')
- pause
-end
-
-if mqyIndx(1)==1 % monthly log
- ibegy = find(myears==begy);
- ifiny = find(myears==finy);
-
- ibegy1 = find(myears==begy+1);
- %
- if isempty(ibegy) & isempty(ibegy1)
- warning('Either ibegy or begm is out of the range of myears in xinsample.mat')
- disp('Print myears to see the range')
- disp('Or change actuap to make the range longer and save new results to Xshock.mat')
- disp('Press ctrl-c to abort now')
- pause
- elseif isempty(ibegy) & (begm<myears(1))
- warning('Either ibegy or begm is out of the range of myears in xinample.mat')
- disp('Print myears to see the range')
- disp('Or change actuap to make the range longer and save new results to Xshock.mat')
- disp('Press ctrl-c to abort now')
- pause
- elseif isempty(ibegy)
- ibegy = -myears(1)+2; % +2 is needed for the following -1 in
- % bar(Estrexa(ibegy+begm-1:ifiny+finm-1,i))
- %so that it 2-1=1 so that +1 is what we want
- end
- %
- if isempty(ifiny)
- warning('Either ifiny or finm is out of the range of myears')
- disp('Print myears to see the range')
- disp('Or change actuap to make the range longer and save new results to Xshock.mat')
- return
- elseif (ifiny+finm-1>size(myears,1))
- warning('Either ifiny or finm is out of the range of myears')
- disp('Print myears to see the range')
- disp('Or change actuap to make the range longer and save new results to Xshock.mat')
- return
- end
-
-
- if nargout==0
- t=length(yact(ibegy+begm-1:ifiny+finm-1,1));
- for i=1:size(yact,2)
- figure
- plot(1:t, yact(ibegy+begm-1:ifiny+finm-1,i),'*:');
- %bar(yact(ibegy+begm-1:ifiny+finm-1,i))
- title('Monthly Log')
- ylabel(char(ylab(i)))
- xlabel([ 'From ' num2str(begy) ':' num2str(begm) ' to ' ...
- num2str(finy) ':' num2str(finm) ])
- grid
- end
- end
-
- actual = [myears(ibegy+begm-1:ifiny+finm-1) yact(ibegy+begm-1:ifiny+finm-1,:)];
-end
-
-
-if mqyIndx(2)==1 % quarterly
- ibegy = find(qyears==begy);
- ifiny = find(qyears==finy);
-
- ibegy1 = find(qyears==begy+1);
- %
- if isempty(ibegy) & isempty(ibegy1)
- warning('Either ibegy or begq is out of the range of qyears')
- disp('Print qyears to see the range')
- disp('Or change actuap to make the range longer and save new results to Xshock.mat')
- return
- elseif isempty(ibegy) & (begq<qyears(1))
- warning('Either ibegy or begq is out of the range of qyears')
- disp('Print qyears to see the range')
- disp('Or change actuap to make the range longer and save new results to Xshock.mat')
- return
- elseif isempty(ibegy)
- ibegy = -qyears(1)+2; % +2 is needed for the following -1 in
- % bar(Estrexa(ibegy+begq-1:ifiny+finm-1,i))
- %so that it 2-1=1 so that +1 is what we want
- end
- %
- if isempty(ifiny)
- warning('Either ifiny or finq is out of the range of qyears')
- disp('Print qyears to see the range')
- disp('Or change actuap to make the range longer and save new results to Xshock.mat')
- return
- elseif (ifiny+finq-1>size(qyears,1))
- warning('Either ifiny or finq is out of the range of qyears')
- disp('Print qyears to see the range')
- disp('Or change actuap to make the range longer and save new results to Xshock.mat')
- return
- end
-
-
- if nargout==0
- t=length(yactqg(ibegy+begq-1:ifiny+finq-1,1));
- for i=1:size(yactqg,2)
- figure
- plot(1:t, yactqg(ibegy+begq-1:ifiny+finq-1,i),'*:');
- title('Quarter-to-quarter Growth Rate')
- ylabel(char(ylab(i)))
- xlabel([ 'From ' num2str(begy) ':' num2str(begq) ' to ' ...
- num2str(finy) ':' num2str(finq) ])
- grid
- end
- end
-
- actual = [qyears(ibegy+begq-1:ifiny+finq-1) yactqg(ibegy+begq-1:ifiny+finq-1,:)];
-end
-
-
-if mqyIndx(3)==1 % calendar year
- ibegy = find(yearsCal==begy);
- ifiny = find(yearsCal==finy);
- %
- if isempty(ibegy)
- warning('Either ibegy or begq is out of the range of yearsCal')
- disp('Print yearsCal to see the range')
- disp('Or change actuap to make the range longer and save new results to Xshock.mat')
- return
- end
- %
- if isempty(ifiny)
- warning('Either ifiny or finq is out of the range of yearsCal')
- disp('Print yearsCal to see the range')
- disp('Or change actuap to make the range longer and save new results to Xshock.mat')
- return
- end
-
- if nargout==0
- t=length(yactCalyg(ibegy:ifiny,1));
- for i=1:size(yactCalyg,2)
- figure
- plot(1:t, yactCalyg(ibegy:ifiny,i),'*:');
- title('Calendar Annual Average Growth Rate')
- ylabel(char(ylab(i)))
- xlabel([ 'From 19' num2str(begy) ' to 19' ...
- num2str(finy) ])
- grid
- end
- end
-
- actual = [yearsCal(ibegy:ifiny) yactCalyg(ibegy:ifiny,:)];
-end
+function actual = gactual(begy,begm,begq,finy,finm,finq,ylab,mqyIndx,idfile)
+%
+% actual = gactual(begy,begm,begq,finy,finm,finq,ylab,mqyIndx,idfile)
+% Actual data according to mqyIndx; graph if no output argument is specified.
+%
+% begy: the beginning year for the graph
+% begm: the begiining month for the graph
+% begq: the begiining quarter for the graph
+% finy: the end year for the graph
+% finm: the end month for the graph
+% finq: the end quarter for the graph
+% ylab: label for each variable
+% mqyIndx: 1-by-3 index of monthly log, qg, and calendar yg. 1: yes; 0: no.
+% Only one at a time
+% idfile: import xinample.mat in the LZ paper under \condz
+%--------------
+% actual: T-by-nvar+1; 1st column is the dates; 2nd-7th columns: actual data of
+% of nvar variables with mlog, qg, or calendar yg, one at a time,
+% depending on mqyIndx
+% graph if no output argument is specified.
+%
+%
+% When insampleg.m is run, xinsample.mat is loaded for a long
+% history (e.g.,1960-1998)
+% When mspoint.m is run, outxshock.mat from msstart is loaded, which varies
+% with parac.m.
+%
+% October 1998 by Tao A. Zha
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+eval(['load ' idfile '.mat']);
+%
+
+if length(find(mqyIndx))>1
+ warning('To get the number (not graph) out, only one at a time')
+ disp('Make sure that mqyIndx has only 1 in a vector of 1 and 0s')
+ disp(' ')
+ disp('Press Enter to continue or Ctrl to abort')
+ pause
+end
+
+if mqyIndx(1)==1 % monthly log
+ ibegy = find(myears==begy);
+ ifiny = find(myears==finy);
+
+ ibegy1 = find(myears==begy+1);
+ %
+ if isempty(ibegy) & isempty(ibegy1)
+ warning('Either ibegy or begm is out of the range of myears in xinsample.mat')
+ disp('Print myears to see the range')
+ disp('Or change actuap to make the range longer and save new results to Xshock.mat')
+ disp('Press ctrl-c to abort now')
+ pause
+ elseif isempty(ibegy) & (begm<myears(1))
+ warning('Either ibegy or begm is out of the range of myears in xinample.mat')
+ disp('Print myears to see the range')
+ disp('Or change actuap to make the range longer and save new results to Xshock.mat')
+ disp('Press ctrl-c to abort now')
+ pause
+ elseif isempty(ibegy)
+ ibegy = -myears(1)+2; % +2 is needed for the following -1 in
+ % bar(Estrexa(ibegy+begm-1:ifiny+finm-1,i))
+ %so that it 2-1=1 so that +1 is what we want
+ end
+ %
+ if isempty(ifiny)
+ warning('Either ifiny or finm is out of the range of myears')
+ disp('Print myears to see the range')
+ disp('Or change actuap to make the range longer and save new results to Xshock.mat')
+ return
+ elseif (ifiny+finm-1>size(myears,1))
+ warning('Either ifiny or finm is out of the range of myears')
+ disp('Print myears to see the range')
+ disp('Or change actuap to make the range longer and save new results to Xshock.mat')
+ return
+ end
+
+
+ if nargout==0
+ t=length(yact(ibegy+begm-1:ifiny+finm-1,1));
+ for i=1:size(yact,2)
+ figure
+ plot(1:t, yact(ibegy+begm-1:ifiny+finm-1,i),'*:');
+ %bar(yact(ibegy+begm-1:ifiny+finm-1,i))
+ title('Monthly Log')
+ ylabel(char(ylab(i)))
+ xlabel([ 'From ' num2str(begy) ':' num2str(begm) ' to ' ...
+ num2str(finy) ':' num2str(finm) ])
+ grid
+ end
+ end
+
+ actual = [myears(ibegy+begm-1:ifiny+finm-1) yact(ibegy+begm-1:ifiny+finm-1,:)];
+end
+
+
+if mqyIndx(2)==1 % quarterly
+ ibegy = find(qyears==begy);
+ ifiny = find(qyears==finy);
+
+ ibegy1 = find(qyears==begy+1);
+ %
+ if isempty(ibegy) & isempty(ibegy1)
+ warning('Either ibegy or begq is out of the range of qyears')
+ disp('Print qyears to see the range')
+ disp('Or change actuap to make the range longer and save new results to Xshock.mat')
+ return
+ elseif isempty(ibegy) & (begq<qyears(1))
+ warning('Either ibegy or begq is out of the range of qyears')
+ disp('Print qyears to see the range')
+ disp('Or change actuap to make the range longer and save new results to Xshock.mat')
+ return
+ elseif isempty(ibegy)
+ ibegy = -qyears(1)+2; % +2 is needed for the following -1 in
+ % bar(Estrexa(ibegy+begq-1:ifiny+finm-1,i))
+ %so that it 2-1=1 so that +1 is what we want
+ end
+ %
+ if isempty(ifiny)
+ warning('Either ifiny or finq is out of the range of qyears')
+ disp('Print qyears to see the range')
+ disp('Or change actuap to make the range longer and save new results to Xshock.mat')
+ return
+ elseif (ifiny+finq-1>size(qyears,1))
+ warning('Either ifiny or finq is out of the range of qyears')
+ disp('Print qyears to see the range')
+ disp('Or change actuap to make the range longer and save new results to Xshock.mat')
+ return
+ end
+
+
+ if nargout==0
+ t=length(yactqg(ibegy+begq-1:ifiny+finq-1,1));
+ for i=1:size(yactqg,2)
+ figure
+ plot(1:t, yactqg(ibegy+begq-1:ifiny+finq-1,i),'*:');
+ title('Quarter-to-quarter Growth Rate')
+ ylabel(char(ylab(i)))
+ xlabel([ 'From ' num2str(begy) ':' num2str(begq) ' to ' ...
+ num2str(finy) ':' num2str(finq) ])
+ grid
+ end
+ end
+
+ actual = [qyears(ibegy+begq-1:ifiny+finq-1) yactqg(ibegy+begq-1:ifiny+finq-1,:)];
+end
+
+
+if mqyIndx(3)==1 % calendar year
+ ibegy = find(yearsCal==begy);
+ ifiny = find(yearsCal==finy);
+ %
+ if isempty(ibegy)
+ warning('Either ibegy or begq is out of the range of yearsCal')
+ disp('Print yearsCal to see the range')
+ disp('Or change actuap to make the range longer and save new results to Xshock.mat')
+ return
+ end
+ %
+ if isempty(ifiny)
+ warning('Either ifiny or finq is out of the range of yearsCal')
+ disp('Print yearsCal to see the range')
+ disp('Or change actuap to make the range longer and save new results to Xshock.mat')
+ return
+ end
+
+ if nargout==0
+ t=length(yactCalyg(ibegy:ifiny,1));
+ for i=1:size(yactCalyg,2)
+ figure
+ plot(1:t, yactCalyg(ibegy:ifiny,i),'*:');
+ title('Calendar Annual Average Growth Rate')
+ ylabel(char(ylab(i)))
+ xlabel([ 'From 19' num2str(begy) ' to 19' ...
+ num2str(finy) ])
+ grid
+ end
+ end
+
+ actual = [yearsCal(ibegy:ifiny) yactCalyg(ibegy:ifiny,:)];
+end
diff --git a/MatlabFiles/gaf.m b/MatlabFiles/gaf.m
index 83cdc906d0dc6f7704cc859049afb364bb262d2d..b4be1e0f4f285eafba2c5d280217d0fd890654a3 100644
--- a/MatlabFiles/gaf.m
+++ b/MatlabFiles/gaf.m
@@ -1,104 +1,104 @@
-function gaf(yrEnd,qmEnd,q_m,yearsCal,yactCalyg,forepy,...
- yhatCalygml,xlab,ylab,forelabel,conlab,keyindx,rnum,cnum)
-% gaf(yrEnd,qmEnd,q_m,yearsCal,yactCalyg,forepy,...
-% yhatCalygml,xlab,ylab,forelabel,conlab,keyindx,rnum,cnum)
-% Graph (calendar year) forecasts vs actual (actual from "msstart.m")
-% Make sure that actup in "msstart.m" is set at the length for visual graphs
-%
-% yrEnd: the end year for the sample
-% qmEnd: last q_m before out-of-sample forecasting
-% q_m: quarterly or monthly for the underlying model
-% yearsCal: calendar years including actual and forecast data
-% yactCalyg: actual calendar annual growth data
-% yhatCalygml: conditional (calendar annual) forecasts on particular shocks
-% forepy: forecast periods (yearly)
-% xlab: label for structural equations
-% ylab: label for the variables
-% forelabel: title label for as of time of forecast
-% conlab: label for what conditions imposed; e.g., conlab = 'All bar MS shocks inspl'
-% keyindx: index for the variables to be graphed
-% rnum: number of rows in subplot
-% cnum: number of columns in subplot
-%-------------
-% No output argument for this graph file
-%
-% October 1998 Tao Zha; revised, 03/20/99
-
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-
-%*** Begining forecast (actual) period
-fbegm = qmEnd+1; % +1 because the first month out of sample
-if fbegm > q_m % the first forecast month into the next year
- fbegm = 1;
- fbegy = yrEnd+1; % the first forecast year for the graph
-else
- fbegy = yrEnd; % the first forecast year for the graph
-end
-abegy = min(yearsCal); % the actual beginning year for the graph, NOT the same as
- % the first forecast year
-%abegm = 1;
-ifbegy = find(yearsCal==fbegy-1); % index for the actual year in "yactCalyg"
- % before the first forecast year
-
-%*** Final forecast (and actual) period
-ffiny = fbegy+forepy-1; % the forecast final year
-afiny = max(yearsCal); % the actual final year for the graph, coinciding with
-
-
-
-figure
-
-nvar = size(yhatCalygml,2); % Pcm,M2,FFR,GdP, CPI, U
-%keyindx = 1:nvar; % Pcm,M2,FFR,GdP, CPI, U
-%keyindx = [4:nvar 3 2]; % GdP, CPI, U, FFR, M2
-
-hornum = cell(length(abegy:ffiny),1); % horizontal number
-count=0;
-for k=abegy:ffiny
- count=count+1;
- jnk=num2str(k);
- hornum{count}=jnk(3:4); % e.g., with '1990', we have '90'
-end
-
-count=0;
-cnum = 2;
-%
-for i = keyindx
- count = count+1;
- subplot(rnum,cnum,count)
- %
- if abegy>fbegy-1
- plot(abegy:afiny,yactCalyg(:,i),fbegy:ffiny,yhatCalygml(:,i),'-.')
- set(gca,'XTick',abegy:ffiny)
- else
- plot(abegy:afiny,yactCalyg(:,i),...
- fbegy-1:ffiny,[yactCalyg(ifbegy,i);yhatCalygml(:,i)],'-.')
- set(gca,'XTick',abegy:ffiny)
- end
- set(gca,'XTickLabel',char(hornum))
- if i<=cnum
- title(forelabel)
- elseif i>=nvar-1
- xlabel(conlab)
- end
- %
- grid
- ylabel(char(ylab(i)))
-end
\ No newline at end of file
+function gaf(yrEnd,qmEnd,q_m,yearsCal,yactCalyg,forepy,...
+ yhatCalygml,xlab,ylab,forelabel,conlab,keyindx,rnum,cnum)
+% gaf(yrEnd,qmEnd,q_m,yearsCal,yactCalyg,forepy,...
+% yhatCalygml,xlab,ylab,forelabel,conlab,keyindx,rnum,cnum)
+% Graph (calendar year) forecasts vs actual (actual from "msstart.m")
+% Make sure that actup in "msstart.m" is set at the length for visual graphs
+%
+% yrEnd: the end year for the sample
+% qmEnd: last q_m before out-of-sample forecasting
+% q_m: quarterly or monthly for the underlying model
+% yearsCal: calendar years including actual and forecast data
+% yactCalyg: actual calendar annual growth data
+% yhatCalygml: conditional (calendar annual) forecasts on particular shocks
+% forepy: forecast periods (yearly)
+% xlab: label for structural equations
+% ylab: label for the variables
+% forelabel: title label for as of time of forecast
+% conlab: label for what conditions imposed; e.g., conlab = 'All bar MS shocks inspl'
+% keyindx: index for the variables to be graphed
+% rnum: number of rows in subplot
+% cnum: number of columns in subplot
+%-------------
+% No output argument for this graph file
+%
+% October 1998 Tao Zha; revised, 03/20/99
+
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+
+%*** Begining forecast (actual) period
+fbegm = qmEnd+1; % +1 because the first month out of sample
+if fbegm > q_m % the first forecast month into the next year
+ fbegm = 1;
+ fbegy = yrEnd+1; % the first forecast year for the graph
+else
+ fbegy = yrEnd; % the first forecast year for the graph
+end
+abegy = min(yearsCal); % the actual beginning year for the graph, NOT the same as
+ % the first forecast year
+%abegm = 1;
+ifbegy = find(yearsCal==fbegy-1); % index for the actual year in "yactCalyg"
+ % before the first forecast year
+
+%*** Final forecast (and actual) period
+ffiny = fbegy+forepy-1; % the forecast final year
+afiny = max(yearsCal); % the actual final year for the graph, coinciding with
+
+
+
+figure
+
+nvar = size(yhatCalygml,2); % Pcm,M2,FFR,GdP, CPI, U
+%keyindx = 1:nvar; % Pcm,M2,FFR,GdP, CPI, U
+%keyindx = [4:nvar 3 2]; % GdP, CPI, U, FFR, M2
+
+hornum = cell(length(abegy:ffiny),1); % horizontal number
+count=0;
+for k=abegy:ffiny
+ count=count+1;
+ jnk=num2str(k);
+ hornum{count}=jnk(3:4); % e.g., with '1990', we have '90'
+end
+
+count=0;
+cnum = 2;
+%
+for i = keyindx
+ count = count+1;
+ subplot(rnum,cnum,count)
+ %
+ if abegy>fbegy-1
+ plot(abegy:afiny,yactCalyg(:,i),fbegy:ffiny,yhatCalygml(:,i),'-.')
+ set(gca,'XTick',abegy:ffiny)
+ else
+ plot(abegy:afiny,yactCalyg(:,i),...
+ fbegy-1:ffiny,[yactCalyg(ifbegy,i);yhatCalygml(:,i)],'-.')
+ set(gca,'XTick',abegy:ffiny)
+ end
+ set(gca,'XTickLabel',char(hornum))
+ if i<=cnum
+ title(forelabel)
+ elseif i>=nvar-1
+ xlabel(conlab)
+ end
+ %
+ grid
+ ylabel(char(ylab(i)))
+end
diff --git a/MatlabFiles/gaferr1.m b/MatlabFiles/gaferr1.m
index d9730c1ea148630e8e70f6310c1f9e8c77978f6e..ec919ebbb0b2214b6c761131189fd32b60eeb4f2 100644
--- a/MatlabFiles/gaferr1.m
+++ b/MatlabFiles/gaferr1.m
@@ -1,106 +1,106 @@
-function gaferr1(yrEnd,qmEnd,q_m,yearsCal,yactCalyg,forepy,yhatCalygml,yhatCalygl1,...
- yhatCalygh1,xlab,ylab,forelabel,conlab,keyindx,rnum,cnum)
-
-%
-% One (.68) error band: (calendar year) forecasts vs actual (actual from "msstart.m")
-% Make sure that actup in "msstart.m" is set at the length for visual graphs
-%
-% yrEnd: the end year for the sample
-% qmEnd: last q_m before out-of-sample forecasting
-% q_m: quarterly or monthly for the underlying model
-% yearsCal: calendar years including actual and forecast data
-% yactCalyg: actual calendar annual growth data
-% yhatCalygml: conditional (calendar annual) forecasts on particular shocks
-% yhatCalygl1: 0.68 lower band
-% yhatCalygh1: 0.68 upper band
-% forepy: forecast periods (yearly)
-% xlab: label for structural equations
-% ylab: label for the variables
-% forelabel: title label for as of time of forecast
-% conlab: label for what conditions imposed; e.g., conlab = 'All bar MS shocks inspl'
-% keyindx: index for the variables to be graphed
-% rnum: number of rows in subplot
-% cnum: number of columns in subplot
-%-------------
-% No output argument for this graph file
-%
-% 03/19/99
-
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-%*** Begining period
-fbegm = qmEnd+1; % +1 because the first month out of sample
-if fbegm > q_m % the first forecast month into the next year
- fbegm = 1;
- fbegy = yrEnd+1; % the first forecast year for the graph
-else
- fbegy = yrEnd; % the first forecast year for the graph
-end
-abegy = min(yearsCal); % the actual beginning year for the graph
-abegm = 1;
-ifbegy = find(yearsCal==fbegy-1); % index for the year before the first
- % forecast first year in actual data "yactCalyg"
-
-%*** Final period
-ffiny = fbegy+forepy-1; % the forecast final year
-afiny = max(yearsCal); % the actual final year
-
-
-
-figure
-
-nvar = size(yhatCalygml,2); % Pcm,M2,FFR,GdP, CPI, U
-%keyindx = 1:nvar; % Pcm,M2,FFR,GdP, CPI, U
-%keyindx = [4:nvar 3 2]; % GdP, CPI, U, FFR, M2
-
-hornum = cell(length(abegy:ffiny),1); % horizontal number
-count=0;
-for k=abegy:ffiny
- count=count+1;
- jnk=num2str(k);
- hornum{count}=jnk(3:4); % e.g., with '1990', we have '90'
-end
-
-count=0;
-%
-for i = keyindx
- count = count+1;
- subplot(rnum,cnum,count)
- %
- if abegy>fbegy-1
- plot(abegy:afiny,yactCalyg(:,i),fbegy:ffiny,yhatCalygml(:,i),'-.',...
- fbegy:ffiny,yhatCalygl1,'--',fbegy:ffiny,yhatCalygh1,'--' )
- set(gca,'XTick',abegy:ffiny)
- else
- plot(abegy:afiny,yactCalyg(:,i),...
- fbegy-1:ffiny,[yactCalyg(ifbegy,i);yhatCalygml(:,i)],'-.',...
- fbegy-1:ffiny,[yactCalyg(ifbegy,i);yhatCalygl1(:,i)],'--',...
- fbegy-1:ffiny,[yactCalyg(ifbegy,i);yhatCalygh1(:,i)],'--')
- set(gca,'XTick',abegy:ffiny)
- end
- set(gca,'XTickLabel',char(hornum))
- if i<=cnum
- title(forelabel)
- elseif i>=nvar-1
- xlabel(conlab)
- end
- %
- grid
- ylabel(char(ylab(i)))
-end
+function gaferr1(yrEnd,qmEnd,q_m,yearsCal,yactCalyg,forepy,yhatCalygml,yhatCalygl1,...
+ yhatCalygh1,xlab,ylab,forelabel,conlab,keyindx,rnum,cnum)
+
+%
+% One (.68) error band: (calendar year) forecasts vs actual (actual from "msstart.m")
+% Make sure that actup in "msstart.m" is set at the length for visual graphs
+%
+% yrEnd: the end year for the sample
+% qmEnd: last q_m before out-of-sample forecasting
+% q_m: quarterly or monthly for the underlying model
+% yearsCal: calendar years including actual and forecast data
+% yactCalyg: actual calendar annual growth data
+% yhatCalygml: conditional (calendar annual) forecasts on particular shocks
+% yhatCalygl1: 0.68 lower band
+% yhatCalygh1: 0.68 upper band
+% forepy: forecast periods (yearly)
+% xlab: label for structural equations
+% ylab: label for the variables
+% forelabel: title label for as of time of forecast
+% conlab: label for what conditions imposed; e.g., conlab = 'All bar MS shocks inspl'
+% keyindx: index for the variables to be graphed
+% rnum: number of rows in subplot
+% cnum: number of columns in subplot
+%-------------
+% No output argument for this graph file
+%
+% 03/19/99
+
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+%*** Begining period
+fbegm = qmEnd+1; % +1 because the first month out of sample
+if fbegm > q_m % the first forecast month into the next year
+ fbegm = 1;
+ fbegy = yrEnd+1; % the first forecast year for the graph
+else
+ fbegy = yrEnd; % the first forecast year for the graph
+end
+abegy = min(yearsCal); % the actual beginning year for the graph
+abegm = 1;
+ifbegy = find(yearsCal==fbegy-1); % index for the year before the first
+ % forecast first year in actual data "yactCalyg"
+
+%*** Final period
+ffiny = fbegy+forepy-1; % the forecast final year
+afiny = max(yearsCal); % the actual final year
+
+
+
+figure
+
+nvar = size(yhatCalygml,2); % Pcm,M2,FFR,GdP, CPI, U
+%keyindx = 1:nvar; % Pcm,M2,FFR,GdP, CPI, U
+%keyindx = [4:nvar 3 2]; % GdP, CPI, U, FFR, M2
+
+hornum = cell(length(abegy:ffiny),1); % horizontal number
+count=0;
+for k=abegy:ffiny
+ count=count+1;
+ jnk=num2str(k);
+ hornum{count}=jnk(3:4); % e.g., with '1990', we have '90'
+end
+
+count=0;
+%
+for i = keyindx
+ count = count+1;
+ subplot(rnum,cnum,count)
+ %
+ if abegy>fbegy-1
+ plot(abegy:afiny,yactCalyg(:,i),fbegy:ffiny,yhatCalygml(:,i),'-.',...
+ fbegy:ffiny,yhatCalygl1,'--',fbegy:ffiny,yhatCalygh1,'--' )
+ set(gca,'XTick',abegy:ffiny)
+ else
+ plot(abegy:afiny,yactCalyg(:,i),...
+ fbegy-1:ffiny,[yactCalyg(ifbegy,i);yhatCalygml(:,i)],'-.',...
+ fbegy-1:ffiny,[yactCalyg(ifbegy,i);yhatCalygl1(:,i)],'--',...
+ fbegy-1:ffiny,[yactCalyg(ifbegy,i);yhatCalygh1(:,i)],'--')
+ set(gca,'XTick',abegy:ffiny)
+ end
+ set(gca,'XTickLabel',char(hornum))
+ if i<=cnum
+ title(forelabel)
+ elseif i>=nvar-1
+ xlabel(conlab)
+ end
+ %
+ grid
+ ylabel(char(ylab(i)))
+end
diff --git a/MatlabFiles/gampar.m b/MatlabFiles/gampar.m
index e80d80b8e081ec4842f4db80c71c3bc49e46dc84..e103b059279c02b8addcf68b3da8f14e52af327b 100644
--- a/MatlabFiles/gampar.m
+++ b/MatlabFiles/gampar.m
@@ -1,31 +1,31 @@
-function f = gampar(ab, XLO, XUP, PLO, PUP);
-
-% The function takes as inputs the parameters ab=[a, b] of the gamma
-% distribution, the bounds of the support [XLO, XUP], the the corresponding
-% probability of the bounds [PLO, PUP] and returns the residual value f.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-a = abs(ab(1)); b = abs(ab(2)); % abs() is used to allow continuous search for the fsolve function find_gampar.m.
-f1 = PLO - gamcdf(XLO, a, 1/b); %Note that in Matlab, it is 1/b, NOT b.
-f2 = PUP - gamcdf(XUP, a, 1/b); %Note that in Matlab, it is 1/b, NOT b.
-% Equivalently, one can use the gaminv function to define the zero
-% conditions:
-% f1 = XLO - gaminv(PLO, a, b);
-% f2 = XUP - gaminv(PUP, a, b);
-
-f = [f1, f2];
+function f = gampar(ab, XLO, XUP, PLO, PUP);
+
+% The function takes as inputs the parameters ab=[a, b] of the gamma
+% distribution, the bounds of the support [XLO, XUP], the the corresponding
+% probability of the bounds [PLO, PUP] and returns the residual value f.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+a = abs(ab(1)); b = abs(ab(2)); % abs() is used to allow continuous search for the fsolve function find_gampar.m.
+f1 = PLO - gamcdf(XLO, a, 1/b); %Note that in Matlab, it is 1/b, NOT b.
+f2 = PUP - gamcdf(XUP, a, 1/b); %Note that in Matlab, it is 1/b, NOT b.
+% Equivalently, one can use the gaminv function to define the zero
+% conditions:
+% f1 = XLO - gaminv(PLO, a, b);
+% f2 = XUP - gaminv(PUP, a, b);
+
+f = [f1, f2];
diff --git a/MatlabFiles/gensys.m b/MatlabFiles/gensys.m
index 2914a55d42967a35a5b423e6610d630ad9fc273f..671b228b24f2751bed248235e4daf45d7d92e2a9 100644
--- a/MatlabFiles/gensys.m
+++ b/MatlabFiles/gensys.m
@@ -11,22 +11,22 @@ function [G1,C,impact,fmat,fwt,ywt,gev,eu]=gensys(g0,g1,c,psi,pi,div)
% existence only with not-s.c. z; eu=[-2,-2] for coincident zeros.
% By Christopher A. Sims
% Corrected 10/28/96 by CAS
-% Copyright (C) 1996-2012 Christopher A. Sims
%
-% This file is part of Dynare.
+% Copyright (C) 1996-2012 Christopher A. Sims
%
-% Dynare is free software: you can redistribute it and/or modify
+% This free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
-% Dynare is distributed in the hope that it will be useful,
+% It is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
eu=[0;0];
realsmall=1e-6;
diff --git a/MatlabFiles/gensys_z2.m b/MatlabFiles/gensys_z2.m
index 7a47757d209435df05ade14f06ca14439a752fb1..95b5b4530005b1cbe260797151eb80a53fd02973 100644
--- a/MatlabFiles/gensys_z2.m
+++ b/MatlabFiles/gensys_z2.m
@@ -14,22 +14,22 @@ function [z2,eu2]=gensys_z2(g0,g1,c,psi,gpi,div)
% eu2 = [-2,-2] for coincident zeros.
% By Christopher A. Sims
% Corrected 10/28/96 by CAS
-% Copyright (C) 1997-2012 Christopher A. Sims
%
-% This file is part of Dynare.
+% Copyright (C) 1997-2012 Christopher A. Sims
%
-% Dynare is free software: you can redistribute it and/or modify
+% This free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
-% Dynare is distributed in the hope that it will be useful,
+% It is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
eu2=NaN*ones(2,1);
realsmall=1e-6;
diff --git a/MatlabFiles/gensys_z2new.m b/MatlabFiles/gensys_z2new.m
index 599b3ade6121435454fc119d09e226dd9685bd8d..ccb2fcfab699385c76b47fce14349f748b78c515 100644
--- a/MatlabFiles/gensys_z2new.m
+++ b/MatlabFiles/gensys_z2new.m
@@ -14,22 +14,22 @@ function [z2,eu2,div]=gensys_z2new(g0,g1,c,psi,gpi)
% eu2 = [-2,-2] for coincident zeros.
% By Christopher A. Sims
% Corrected 10/28/96 by CAS
-% Copyright (C) 1997-2012 Christopher A. Sims
%
-% This file is part of Dynare.
+% Copyright (C) 1997-2012 Christopher A. Sims
%
-% Dynare is free software: you can redistribute it and/or modify
+% This free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
-% Dynare is distributed in the hope that it will be useful,
+% It is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
eu2=NaN*ones(2,1);
n2 = size(gpi,2); %number of expectational errors.
diff --git a/MatlabFiles/gensysct.m b/MatlabFiles/gensysct.m
index 13c053c15d45694e06a7fdfe0ac62ece83a685dc..079a79b2deb096825ca695d0c11663346ea11dac 100644
--- a/MatlabFiles/gensysct.m
+++ b/MatlabFiles/gensysct.m
@@ -1,197 +1,197 @@
-function [G1,C,impact,q,a,b,z,eu]=gensysct(g0,g1,c,psi,pi,div)
-%function [G1,C,impact,q,a,b,z,eu]=gensysct(g0,g1,c,psi,pi,div)
-%System given as
-% g0*Dy(t)=g1*y(t)+c+psi*epsilon(t)+pi*eta(t),
-%with epsilon an exogenous variable process and eta being endogenously determined
-%white noise expectational errors. Returned system is
-% Dy(t)=G1*y(t)+C+impact*epsilon(t).
-% epsilon(t) is assumed to be white noise.
-% If div is omitted from argument list, a div>0 is calculated.
-% Also returned is the qz decomposition, q'az'=g0, q'bz'=g1, with a and b
-% upper triangular and the system ordered so that all zeros on the diagonal of b are in
-% the lower right corner, all cases where the real part of bii/aii is greater than or
-% equal to div appear in the next block above the zeros, and the remaining bii/aii's
-% all have bii/aii<div . These elements can be used to construct the full backward and
-% forward solution. See the paper "Solving Linear Rational Expectations Models",
-% http://eco-072399b.princeton.edu/yftp/gensys . Note that if one simply wants the backward
-% and forward projection of y on epsilon, ignoring existence and uniqueness questions, the
-% projection can be computed by Fourier methods.
-% eu(1)=(existence); eu(2)=(uniqueness). Else eu=[-2,2] => indeterminacy via singularity
-% in the equation system. Else eu(1)=-1 => existence only for white noise epsilon.
-% realsmall=sqrt(eps)*10;
-% Copyright (C) 1997-2012 C. A. Sims
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-realsmall=sqrt(eps)*10;
-%realsmall=1e-3;
-eu=[0;0];
-fixdiv=(nargin==6)
-n=size(g0,1);
-[a b q z v]=qz(g0,g1);
-if ~fixdiv, div=.001; end
-nunstab=0;
-nzero=0;
-zxz=0;
-for i=1:n
- %------------------div calc------------
- if ~fixdiv
- if abs(a(i,i)) > realsmall
- divhat=real(b(i,i)/a(i,i));
- if realsmall<divhat & divhat<div
- div=.5*divhat;
- end
- end
- end
- %----------------------------------------
- if abs(a(i,i))<realsmall
- nzero=nzero+1;
- nunstab=nunstab+1;
- if abs(b(i,i))<realsmall
- zxz=1;
- end
- else
- nunstab=nunstab+ (real(b(i,i)/a(i,i))>div);
- end
-end
-div
-nunstab
-nzero
-% Note that qzdivct first puts all singularities in a in lower right, then puts unstable
-% roots on top of those.
-[a b q z]=qzdivct(div,a,b,q,z);
-gev=[diag(a) diag(b)];
-if zxz
- %disp('Coincident zeros. Indeterminacy and/or nonexistence.')
- eu=[-2;-2];
- return
-end
-q1=q(1:n-nunstab,:);
-q2=q(n-nunstab+1:n,:);
-z1=z(:,1:n-nunstab)';
-z2=z(:,n-nunstab+1:n)';
-a2=a(n-nunstab+1:n,n-nunstab+1:n);
-b2=b(n-nunstab+1:n,n-nunstab+1:n);
-etawt=q2*pi;
-zwt=q2*psi;
-[ueta,deta,veta]=svd(etawt);
-md=min(size(deta));
-bigev=find(diag(deta(1:md,1:md))>realsmall);
-ueta=ueta(:,bigev);
-veta=veta(:,bigev);
-deta=deta(bigev,bigev);
-[uz,dz,vz]=svd(zwt);
-md=min(size(dz));
-bigev=find(diag(dz(1:md,1:md))>realsmall);
-uz=uz(:,bigev);
-vz=vz(:,bigev);
-dz=dz(bigev,bigev);
-if isempty(bigev)
- exist=1;
-else
- exist=norm(uz-ueta*ueta'*uz,'fro') < realsmall*n;
-end
-if ~isempty(bigev)
- zwtx0=b2\zwt;
- zwtx=zwtx0;
- M=b2\a2;
- M=M/norm(M);
- for i=2:nunstab
- zwtx=[M*zwtx zwtx0];
- end
- zwtx=b2*zwtx;
- [ux,dx,vx]=svd(zwtx);
- md=min(size(dx));
- bigev=find(diag(dx(1:md,1:md))>realsmall);
- ux=ux(:,bigev);
- vx=vx(:,bigev);
- dx=dx(bigev,bigev);
- existx=norm(ux-ueta*ueta'*ux,'fro') < realsmall*n;
-else
- existx=1;
-end
-%----------------------------------------------------
-% Note that existence and uniqueness are not just matters of comparing
-% numbers of roots and numbers of endogenous errors. These counts are
-% reported below because usually they point to the source of the problem.
-%------------------------------------------------------
-[ueta1,deta1,veta1]=svd(q1*pi);
-md=min(size(deta1));
-bigev=find(diag(deta1(1:md,1:md))>realsmall);
-ueta1=ueta1(:,bigev);
-veta1=veta1(:,bigev);
-deta1=deta1(bigev,bigev);
-if existx | nunstab==0
- %disp('solution exists');
- eu(1)=1;
-else
- if exist
- %disp('solution exists for unforecastable z only');
- eu(1)=-1;
- %else
- %fprintf(1,'No solution. %d unstable roots. %d endog errors.\n',nunstab,size(ueta1,2));
- end
- %disp('Generalized eigenvalues')
- %disp(gev);
- %md=abs(diag(a))>realsmall;
- %ev=diag(md.*diag(a)+(1-md).*diag(b))\ev;
- %disp(ev)
-% return;
-end
-%disp('Generalized eigenvalues')
-%disp(gev);
-%md=abs(diag(a))>realsmall;
-%ev=diag(md.*diag(a)+(1-md).*diag(b))\ev;
-%disp(ev)
-% return;
-if isempty(veta1)
- unique=1;
-else
- unique=norm(veta1-veta*veta'*veta1,'fro')<realsmall*n;
-end
-if unique
- %disp('solution unique');
- eu(2)=1;
-%else
- %fprintf(1,'Indeterminacy. %d loose endog errors.\n',size(veta1,2)-size(veta,2));
- %disp('Generalized eigenvalues')
- %disp(gev);
- %disp(ev)
-% return;
-end
-tmat = [eye(n-nunstab) -ueta1*deta1*veta1'*veta*(deta\ueta')];
-G0= [tmat*a; zeros(nunstab,n-nunstab) eye(nunstab)];
-G1= [tmat*b; zeros(nunstab,n)];
-%----------------------
-% G0 is always non-singular because by construction there are no zeros on
-% the diagonal of a(1:n-nunstab,1:n-nunstab), which forms G0's ul corner.
-%-----------------------
-G0I=inv(G0);
-G1=G0I*G1;
-usix=n-nunstab+1:n;
-C=G0I*[tmat*q*c;(a(usix,usix)-b(usix,usix))\q2*c];
-impact=G0I*[tmat*q*psi;zeros(nunstab,size(psi,2))];
-%-------------------- above are output for system in terms of z'y -------
-G1=z*G1*z';
-if max(max(abs(imag(G1))))>100*realsmall
- disp('Inaccuracy in G1:')
- s1=svd(G1);
- s2=svd(real(G1));
- disp(max((s1-s2)./(1/12+s1))) % this is reasonable scaling for monthly time unit
-end
-G1=real(G1);
-C=real(z*C);
-impact=real(z*impact);
+function [G1,C,impact,q,a,b,z,eu]=gensysct(g0,g1,c,psi,pi,div)
+%function [G1,C,impact,q,a,b,z,eu]=gensysct(g0,g1,c,psi,pi,div)
+%System given as
+% g0*Dy(t)=g1*y(t)+c+psi*epsilon(t)+pi*eta(t),
+%with epsilon an exogenous variable process and eta being endogenously determined
+%white noise expectational errors. Returned system is
+% Dy(t)=G1*y(t)+C+impact*epsilon(t).
+% epsilon(t) is assumed to be white noise.
+% If div is omitted from argument list, a div>0 is calculated.
+% Also returned is the qz decomposition, q'az'=g0, q'bz'=g1, with a and b
+% upper triangular and the system ordered so that all zeros on the diagonal of b are in
+% the lower right corner, all cases where the real part of bii/aii is greater than or
+% equal to div appear in the next block above the zeros, and the remaining bii/aii's
+% all have bii/aii<div . These elements can be used to construct the full backward and
+% forward solution. See the paper "Solving Linear Rational Expectations Models",
+% http://eco-072399b.princeton.edu/yftp/gensys . Note that if one simply wants the backward
+% and forward projection of y on epsilon, ignoring existence and uniqueness questions, the
+% projection can be computed by Fourier methods.
+% eu(1)=(existence); eu(2)=(uniqueness). Else eu=[-2,2] => indeterminacy via singularity
+% in the equation system. Else eu(1)=-1 => existence only for white noise epsilon.
+% realsmall=sqrt(eps)*10;
+%
+% Copyright (C) 1997-2012 C. A. Sims
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+realsmall=sqrt(eps)*10;
+%realsmall=1e-3;
+eu=[0;0];
+fixdiv=(nargin==6)
+n=size(g0,1);
+[a b q z v]=qz(g0,g1);
+if ~fixdiv, div=.001; end
+nunstab=0;
+nzero=0;
+zxz=0;
+for i=1:n
+ %------------------div calc------------
+ if ~fixdiv
+ if abs(a(i,i)) > realsmall
+ divhat=real(b(i,i)/a(i,i));
+ if realsmall<divhat & divhat<div
+ div=.5*divhat;
+ end
+ end
+ end
+ %----------------------------------------
+ if abs(a(i,i))<realsmall
+ nzero=nzero+1;
+ nunstab=nunstab+1;
+ if abs(b(i,i))<realsmall
+ zxz=1;
+ end
+ else
+ nunstab=nunstab+ (real(b(i,i)/a(i,i))>div);
+ end
+end
+div
+nunstab
+nzero
+% Note that qzdivct first puts all singularities in a in lower right, then puts unstable
+% roots on top of those.
+[a b q z]=qzdivct(div,a,b,q,z);
+gev=[diag(a) diag(b)];
+if zxz
+ %disp('Coincident zeros. Indeterminacy and/or nonexistence.')
+ eu=[-2;-2];
+ return
+end
+q1=q(1:n-nunstab,:);
+q2=q(n-nunstab+1:n,:);
+z1=z(:,1:n-nunstab)';
+z2=z(:,n-nunstab+1:n)';
+a2=a(n-nunstab+1:n,n-nunstab+1:n);
+b2=b(n-nunstab+1:n,n-nunstab+1:n);
+etawt=q2*pi;
+zwt=q2*psi;
+[ueta,deta,veta]=svd(etawt);
+md=min(size(deta));
+bigev=find(diag(deta(1:md,1:md))>realsmall);
+ueta=ueta(:,bigev);
+veta=veta(:,bigev);
+deta=deta(bigev,bigev);
+[uz,dz,vz]=svd(zwt);
+md=min(size(dz));
+bigev=find(diag(dz(1:md,1:md))>realsmall);
+uz=uz(:,bigev);
+vz=vz(:,bigev);
+dz=dz(bigev,bigev);
+if isempty(bigev)
+ exist=1;
+else
+ exist=norm(uz-ueta*ueta'*uz,'fro') < realsmall*n;
+end
+if ~isempty(bigev)
+ zwtx0=b2\zwt;
+ zwtx=zwtx0;
+ M=b2\a2;
+ M=M/norm(M);
+ for i=2:nunstab
+ zwtx=[M*zwtx zwtx0];
+ end
+ zwtx=b2*zwtx;
+ [ux,dx,vx]=svd(zwtx);
+ md=min(size(dx));
+ bigev=find(diag(dx(1:md,1:md))>realsmall);
+ ux=ux(:,bigev);
+ vx=vx(:,bigev);
+ dx=dx(bigev,bigev);
+ existx=norm(ux-ueta*ueta'*ux,'fro') < realsmall*n;
+else
+ existx=1;
+end
+%----------------------------------------------------
+% Note that existence and uniqueness are not just matters of comparing
+% numbers of roots and numbers of endogenous errors. These counts are
+% reported below because usually they point to the source of the problem.
+%------------------------------------------------------
+[ueta1,deta1,veta1]=svd(q1*pi);
+md=min(size(deta1));
+bigev=find(diag(deta1(1:md,1:md))>realsmall);
+ueta1=ueta1(:,bigev);
+veta1=veta1(:,bigev);
+deta1=deta1(bigev,bigev);
+if existx | nunstab==0
+ %disp('solution exists');
+ eu(1)=1;
+else
+ if exist
+ %disp('solution exists for unforecastable z only');
+ eu(1)=-1;
+ %else
+ %fprintf(1,'No solution. %d unstable roots. %d endog errors.\n',nunstab,size(ueta1,2));
+ end
+ %disp('Generalized eigenvalues')
+ %disp(gev);
+ %md=abs(diag(a))>realsmall;
+ %ev=diag(md.*diag(a)+(1-md).*diag(b))\ev;
+ %disp(ev)
+% return;
+end
+%disp('Generalized eigenvalues')
+%disp(gev);
+%md=abs(diag(a))>realsmall;
+%ev=diag(md.*diag(a)+(1-md).*diag(b))\ev;
+%disp(ev)
+% return;
+if isempty(veta1)
+ unique=1;
+else
+ unique=norm(veta1-veta*veta'*veta1,'fro')<realsmall*n;
+end
+if unique
+ %disp('solution unique');
+ eu(2)=1;
+%else
+ %fprintf(1,'Indeterminacy. %d loose endog errors.\n',size(veta1,2)-size(veta,2));
+ %disp('Generalized eigenvalues')
+ %disp(gev);
+ %disp(ev)
+% return;
+end
+tmat = [eye(n-nunstab) -ueta1*deta1*veta1'*veta*(deta\ueta')];
+G0= [tmat*a; zeros(nunstab,n-nunstab) eye(nunstab)];
+G1= [tmat*b; zeros(nunstab,n)];
+%----------------------
+% G0 is always non-singular because by construction there are no zeros on
+% the diagonal of a(1:n-nunstab,1:n-nunstab), which forms G0's ul corner.
+%-----------------------
+G0I=inv(G0);
+G1=G0I*G1;
+usix=n-nunstab+1:n;
+C=G0I*[tmat*q*c;(a(usix,usix)-b(usix,usix))\q2*c];
+impact=G0I*[tmat*q*psi;zeros(nunstab,size(psi,2))];
+%-------------------- above are output for system in terms of z'y -------
+G1=z*G1*z';
+if max(max(abs(imag(G1))))>100*realsmall
+ disp('Inaccuracy in G1:')
+ s1=svd(G1);
+ s2=svd(real(G1));
+ disp(max((s1-s2)./(1/12+s1))) % this is reasonable scaling for monthly time unit
+end
+G1=real(G1);
+C=real(z*C);
+impact=real(z*impact);
diff --git a/MatlabFiles/gensysoldversion.m b/MatlabFiles/gensysoldversion.m
index 66cdc49907d0436df6101a8ca30e971d52d9f753..f723571db88fc380d981c0ab6d64e4abff0a4d19 100644
--- a/MatlabFiles/gensysoldversion.m
+++ b/MatlabFiles/gensysoldversion.m
@@ -1,168 +1,168 @@
-function [G1,C,impact,fmat,fwt,ywt,gev,eu]=gensys(g0,g1,c,psi,pi,div)
-%function [G1,C,impact,fmat,fwt,ywt,gev,eu]=gensys(g0,g1,c,psi,pi,div)
-%System given as
-% g0*y(t)=g1*y(t-1)+c+psi*z(t)+pi*eta(t),
-%with z an exogenous variable process and eta being endogenously determined
-%one-step-ahead expectational errors. Returned system is
-% y(t)=G1*y(t-1)+C+impact*z(t)+ywt*inv(I-fmat*inv(L))*fwt*z(t+1) .
-% If z(t) is i.i.d., the last term drops out.
-% If div is omitted from argument list, a div>1 is calculated.
-% eu(1)=1 for existence, eu(2)=1 for uniqueness. eu(1)=-1 for
-% existence only with not-s.c. z; eu=[-2,-2] for coincident zeros.
-% By Christopher A. Sims
-% Corrected 10/28/96 by CAS
-% Copyright (C) 1997-2012 Christopher A. Sims
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-eu=[0;0];
-realsmall=1e-6;
-fixdiv=(nargin==6);
-n=size(g0,1);
-[a b q z]=qz(g0,g1);
-if ~fixdiv, div=1.01; end
-nunstab=0;
-zxz=0;
-for i=1:n
-%------------------div calc------------
- if ~fixdiv
- if abs(a(i,i)) > 0
- divhat=abs(b(i,i))/abs(a(i,i));
- if 1+realsmall<divhat & divhat<div
- div=.5*(1+divhat);
- end
- end
- end
-%----------------------------------------
- nunstab=nunstab+(abs(b(i,i))>div*abs(a(i,i)));
- if abs(a(i,i))<realsmall & abs(b(i,i))<realsmall
- zxz=1;
- end
-end
-if ~zxz
- [a b q z]=qzdiv(div,a,b,q,z);
-end
-save d:\mex\gensysmkl\abqz.mat a b q z div
-gev=[diag(a) diag(b)];
-if zxz
- %disp('Coincident zeros. Indeterminacy and/or nonexistence.')
- eu=[-2;-2];
- return
-end
-q1=q(1:n-nunstab,:);
-q2=q(n-nunstab+1:n,:);
-a2=a(n-nunstab+1:n,n-nunstab+1:n);
-b2=b(n-nunstab+1:n,n-nunstab+1:n);
-etawt=q2*pi;
-zwt=q2*psi;
-[ueta,deta,veta]=svd(etawt);
-md=min(size(deta));
-bigev=find(diag(deta(1:md,1:md))>realsmall);
-ueta=ueta(:,bigev);
-veta=veta(:,bigev);
-deta=deta(bigev,bigev);
-[uz,dz,vz]=svd(zwt);
-md=min(size(dz));
-bigev=find(diag(dz(1:md,1:md))>realsmall);
-uz=uz(:,bigev);
-vz=vz(:,bigev);
-dz=dz(bigev,bigev);
-if isempty(bigev)
- exist=1;
- existx=1;
-else
- exist=norm(uz-ueta*ueta'*uz) < realsmall*n;
- zwtx0=b2\zwt;
- zwtx=zwtx0;
- M=b2\a2;
- for i=2:nunstab
- zwtx=[M*zwtx zwtx0];
- end
- zwtx=b2*zwtx;
- [ux,dx,vx]=svd(zwtx);
- md=min(size(dx));
- bigev=find(diag(dx(1:md,1:md))>realsmall);
- ux=ux(:,bigev);
- vx=vx(:,bigev);
- dx=dx(bigev,bigev);
- existx=norm(ux-ueta*ueta'*ux) < realsmall*n;
-end
-%----------------------------------------------------
-% Note that existence and uniqueness are not just matters of comparing
-% numbers of roots and numbers of endogenous errors. These counts are
-% reported below because usually they point to the source of the problem.
-%------------------------------------------------------
-[ueta1,deta1,veta1]=svd(q1*pi);
-md=min(size(deta1));
-bigev=find(diag(deta1(1:md,1:md))>realsmall);
-ueta1=ueta1(:,bigev);
-veta1=veta1(:,bigev);
-deta1=deta1(bigev,bigev);
-if existx | nunstab==0
- %disp('solution exists');
- eu(1)=1;
-else
- if exist
- %disp('solution exists for unforecastable z only');
- eu(1)=-1;
- %else
- %fprintf(1,'No solution. %d unstable roots. %d endog errors.\n',nunstab,size(ueta1,2));
- end
- %disp('Generalized eigenvalues')
- %disp(gev);
- %md=abs(diag(a))>realsmall;
- %ev=diag(md.*diag(a)+(1-md).*diag(b))\ev;
- %disp(ev)
-% return;
-end
-if isempty(veta1)
- unique=1;
-else
- unique=norm(veta1-veta*veta'*veta1)<realsmall*n;
-end
-if unique
- %disp('solution unique');
- eu(2)=1;
-else
- fprintf(1,'Indeterminacy. %d loose endog errors.\n',size(veta1,2)-size(veta,2));
- %disp('Generalized eigenvalues')
- %disp(gev);
- %md=abs(diag(a))>realsmall;
- %ev=diag(md.*diag(a)+(1-md).*diag(b))\ev;
- %disp(ev)
-% return;
-end
-tmat = [eye(n-nunstab) -(ueta*(deta\veta')*veta1*deta1*ueta1')'];
-G0= [tmat*a; zeros(nunstab,n-nunstab) eye(nunstab)];
-G1= [tmat*b; zeros(nunstab,n)];
-%----------------------
-% G0 is always non-singular because by construction there are no zeros on
-% the diagonal of a(1:n-nunstab,1:n-nunstab), which forms G0's ul corner.
-%-----------------------
-G0I=inv(G0);
-G1=G0I*G1;
-usix=n-nunstab+1:n;
-C=G0I*[tmat*q*c;(a(usix,usix)-b(usix,usix))\q2*c];
-impact=G0I*[tmat*q*psi;zeros(nunstab,size(psi,2))];
-fmat=b(usix,usix)\a(usix,usix);
-fwt=-b(usix,usix)\q2*psi;
-ywt=G0I(:,usix);
-%-------------------- above are output for system in terms of z'y -------
-G1=real(z*G1*z');
-C=real(z*C);
-impact=real(z*impact);
-% Correction 10/28/96: formerly line below had real(z*ywt) on rhs, an error.
-ywt=z*ywt;
\ No newline at end of file
+function [G1,C,impact,fmat,fwt,ywt,gev,eu]=gensys(g0,g1,c,psi,pi,div)
+%function [G1,C,impact,fmat,fwt,ywt,gev,eu]=gensys(g0,g1,c,psi,pi,div)
+%System given as
+% g0*y(t)=g1*y(t-1)+c+psi*z(t)+pi*eta(t),
+%with z an exogenous variable process and eta being endogenously determined
+%one-step-ahead expectational errors. Returned system is
+% y(t)=G1*y(t-1)+C+impact*z(t)+ywt*inv(I-fmat*inv(L))*fwt*z(t+1) .
+% If z(t) is i.i.d., the last term drops out.
+% If div is omitted from argument list, a div>1 is calculated.
+% eu(1)=1 for existence, eu(2)=1 for uniqueness. eu(1)=-1 for
+% existence only with not-s.c. z; eu=[-2,-2] for coincident zeros.
+% By Christopher A. Sims
+% Corrected 10/28/96 by CAS
+%
+% Copyright (C) 1997-2012 Christopher A. Sims
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+eu=[0;0];
+realsmall=1e-6;
+fixdiv=(nargin==6);
+n=size(g0,1);
+[a b q z]=qz(g0,g1);
+if ~fixdiv, div=1.01; end
+nunstab=0;
+zxz=0;
+for i=1:n
+%------------------div calc------------
+ if ~fixdiv
+ if abs(a(i,i)) > 0
+ divhat=abs(b(i,i))/abs(a(i,i));
+ if 1+realsmall<divhat & divhat<div
+ div=.5*(1+divhat);
+ end
+ end
+ end
+%----------------------------------------
+ nunstab=nunstab+(abs(b(i,i))>div*abs(a(i,i)));
+ if abs(a(i,i))<realsmall & abs(b(i,i))<realsmall
+ zxz=1;
+ end
+end
+if ~zxz
+ [a b q z]=qzdiv(div,a,b,q,z);
+end
+save d:\mex\gensysmkl\abqz.mat a b q z div
+gev=[diag(a) diag(b)];
+if zxz
+ %disp('Coincident zeros. Indeterminacy and/or nonexistence.')
+ eu=[-2;-2];
+ return
+end
+q1=q(1:n-nunstab,:);
+q2=q(n-nunstab+1:n,:);
+a2=a(n-nunstab+1:n,n-nunstab+1:n);
+b2=b(n-nunstab+1:n,n-nunstab+1:n);
+etawt=q2*pi;
+zwt=q2*psi;
+[ueta,deta,veta]=svd(etawt);
+md=min(size(deta));
+bigev=find(diag(deta(1:md,1:md))>realsmall);
+ueta=ueta(:,bigev);
+veta=veta(:,bigev);
+deta=deta(bigev,bigev);
+[uz,dz,vz]=svd(zwt);
+md=min(size(dz));
+bigev=find(diag(dz(1:md,1:md))>realsmall);
+uz=uz(:,bigev);
+vz=vz(:,bigev);
+dz=dz(bigev,bigev);
+if isempty(bigev)
+ exist=1;
+ existx=1;
+else
+ exist=norm(uz-ueta*ueta'*uz) < realsmall*n;
+ zwtx0=b2\zwt;
+ zwtx=zwtx0;
+ M=b2\a2;
+ for i=2:nunstab
+ zwtx=[M*zwtx zwtx0];
+ end
+ zwtx=b2*zwtx;
+ [ux,dx,vx]=svd(zwtx);
+ md=min(size(dx));
+ bigev=find(diag(dx(1:md,1:md))>realsmall);
+ ux=ux(:,bigev);
+ vx=vx(:,bigev);
+ dx=dx(bigev,bigev);
+ existx=norm(ux-ueta*ueta'*ux) < realsmall*n;
+end
+%----------------------------------------------------
+% Note that existence and uniqueness are not just matters of comparing
+% numbers of roots and numbers of endogenous errors. These counts are
+% reported below because usually they point to the source of the problem.
+%------------------------------------------------------
+[ueta1,deta1,veta1]=svd(q1*pi);
+md=min(size(deta1));
+bigev=find(diag(deta1(1:md,1:md))>realsmall);
+ueta1=ueta1(:,bigev);
+veta1=veta1(:,bigev);
+deta1=deta1(bigev,bigev);
+if existx | nunstab==0
+ %disp('solution exists');
+ eu(1)=1;
+else
+ if exist
+ %disp('solution exists for unforecastable z only');
+ eu(1)=-1;
+ %else
+ %fprintf(1,'No solution. %d unstable roots. %d endog errors.\n',nunstab,size(ueta1,2));
+ end
+ %disp('Generalized eigenvalues')
+ %disp(gev);
+ %md=abs(diag(a))>realsmall;
+ %ev=diag(md.*diag(a)+(1-md).*diag(b))\ev;
+ %disp(ev)
+% return;
+end
+if isempty(veta1)
+ unique=1;
+else
+ unique=norm(veta1-veta*veta'*veta1)<realsmall*n;
+end
+if unique
+ %disp('solution unique');
+ eu(2)=1;
+else
+ fprintf(1,'Indeterminacy. %d loose endog errors.\n',size(veta1,2)-size(veta,2));
+ %disp('Generalized eigenvalues')
+ %disp(gev);
+ %md=abs(diag(a))>realsmall;
+ %ev=diag(md.*diag(a)+(1-md).*diag(b))\ev;
+ %disp(ev)
+% return;
+end
+tmat = [eye(n-nunstab) -(ueta*(deta\veta')*veta1*deta1*ueta1')'];
+G0= [tmat*a; zeros(nunstab,n-nunstab) eye(nunstab)];
+G1= [tmat*b; zeros(nunstab,n)];
+%----------------------
+% G0 is always non-singular because by construction there are no zeros on
+% the diagonal of a(1:n-nunstab,1:n-nunstab), which forms G0's ul corner.
+%-----------------------
+G0I=inv(G0);
+G1=G0I*G1;
+usix=n-nunstab+1:n;
+C=G0I*[tmat*q*c;(a(usix,usix)-b(usix,usix))\q2*c];
+impact=G0I*[tmat*q*psi;zeros(nunstab,size(psi,2))];
+fmat=b(usix,usix)\a(usix,usix);
+fwt=-b(usix,usix)\q2*psi;
+ywt=G0I(:,usix);
+%-------------------- above are output for system in terms of z'y -------
+G1=real(z*G1*z');
+C=real(z*C);
+impact=real(z*impact);
+% Correction 10/28/96: formerly line below had real(z*ywt) on rhs, an error.
+ywt=z*ywt;
diff --git a/MatlabFiles/gfmean.m b/MatlabFiles/gfmean.m
index 360bb29ad82cf38da1a8500f7faf8aa0324f2909..5a77b71b9c8d4bd716c858a67d6a59e0e7e25618 100644
--- a/MatlabFiles/gfmean.m
+++ b/MatlabFiles/gfmean.m
@@ -1,66 +1,66 @@
-function [Fmat,gvec] = gfmean(b,P,Vi,nvar,ncoef,n0,np)
-% [Fmat,gvec] = gfmean(b,P,Vi,nvar,ncoef,n0,np)
-%
-% Mean of free lagged parameters g and original lagged parameters F, conditional on comtemporaneous b's
-% See Waggoner and Zha's Gibbs sampling
-%
-% b: sum(n0)-by-1 vector of A0 free parameters
-% P: cell(nvar,1). In each cell, posterior linear transformation for random walk prior for the ith equation % tld: tilda
-% Vi: nvar-by-1 cell. In each cell, k-by-ri orthonormal basis for the null of the ith
-% equation lagged restriction matrix where k is a total of exogenous variables and
-% ri is the number of free parameters. With this transformation, we have fi = Vi*gi
-% or Vi'*fi = gi where fi is a vector of total original parameters and gi is a
-% vector of free parameters. There must be at least one free parameter left for
-% the ith equation.
-% nvar: number of endogeous variables
-% ncoef: number of original lagged variables per equation
-% n0: nvar-by-1, ith element represents the number of free A0 parameters in ith equation
-% np: nvar-by-1, ith element represents the number of free A+ parameters in ith equation
-%---------------
-% Fmat: 0, the value that is used in csminwel.m
-% gvec: sum(n0)-by-1 analytical gradient for a0freefun.m
-%
-% Tao Zha, February 2000
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-b=b(:);
-
-
-n0cum = cumsum(n0);
-npcum = cumsum(np);
-gvec = zeros(npcum(end),1);
-Fmat = zeros(ncoef,nvar); % ncoef: maximum original lagged parameters per equation
-
-if ~(length(b)==n0cum(end))
- error('Make inputs n0 and length(b) match exactly')
-end
-
-for kj=1:nvar
- if kj==1
- bj = b(1:n0(kj));
- gj = P{kj}*bj;
- gvec(1:np(kj)) = gj;
- Fmat(:,kj) = Vi{kj}*gj;
- else
- bj = b(n0cum(kj-1)+1:n0cum(kj));
- gj = P{kj}*bj;
- gvec(npcum(kj-1)+1:npcum(kj)) = gj;
- Fmat(:,kj) = Vi{kj}*gj;
- end
-end
+function [Fmat,gvec] = gfmean(b,P,Vi,nvar,ncoef,n0,np)
+% [Fmat,gvec] = gfmean(b,P,Vi,nvar,ncoef,n0,np)
+%
+% Mean of free lagged parameters g and original lagged parameters F, conditional on comtemporaneous b's
+% See Waggoner and Zha's Gibbs sampling
+%
+% b: sum(n0)-by-1 vector of A0 free parameters
+% P: cell(nvar,1). In each cell, posterior linear transformation for random walk prior for the ith equation % tld: tilda
+% Vi: nvar-by-1 cell. In each cell, k-by-ri orthonormal basis for the null of the ith
+% equation lagged restriction matrix where k is a total of exogenous variables and
+% ri is the number of free parameters. With this transformation, we have fi = Vi*gi
+% or Vi'*fi = gi where fi is a vector of total original parameters and gi is a
+% vector of free parameters. There must be at least one free parameter left for
+% the ith equation.
+% nvar: number of endogeous variables
+% ncoef: number of original lagged variables per equation
+% n0: nvar-by-1, ith element represents the number of free A0 parameters in ith equation
+% np: nvar-by-1, ith element represents the number of free A+ parameters in ith equation
+%---------------
+% Fmat: 0, the value that is used in csminwel.m
+% gvec: sum(n0)-by-1 analytical gradient for a0freefun.m
+%
+% Tao Zha, February 2000
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+b=b(:);
+
+
+n0cum = cumsum(n0);
+npcum = cumsum(np);
+gvec = zeros(npcum(end),1);
+Fmat = zeros(ncoef,nvar); % ncoef: maximum original lagged parameters per equation
+
+if ~(length(b)==n0cum(end))
+ error('Make inputs n0 and length(b) match exactly')
+end
+
+for kj=1:nvar
+ if kj==1
+ bj = b(1:n0(kj));
+ gj = P{kj}*bj;
+ gvec(1:np(kj)) = gj;
+ Fmat(:,kj) = Vi{kj}*gj;
+ else
+ bj = b(n0cum(kj-1)+1:n0cum(kj));
+ gj = P{kj}*bj;
+ gvec(npcum(kj-1)+1:npcum(kj)) = gj;
+ Fmat(:,kj) = Vi{kj}*gj;
+ end
+end
diff --git a/MatlabFiles/gfore.m b/MatlabFiles/gfore.m
index 298b5e505ec264f1346feccf18440b4f5ce8cf8f..98baba6da65996443656f8d62d4362af6964bd20 100644
--- a/MatlabFiles/gfore.m
+++ b/MatlabFiles/gfore.m
@@ -1,102 +1,102 @@
-function gfore(yrEnd,qmEnd,q_m,yearsCal,yactCalyg,forepy,...
- yhatCalygml,xlab,ylab,forelabel,conlab,keyindx,rnum,cnum)
-% gfore(yrEnd,qmEnd,q_m,yearsCal,yactCalyg,forepy,...
-% yhatCalygml,xlab,ylab,forelabel,conlab,keyindx,rnum,cnum)
-% Graph (calendar year) forecasts vs actual (actual from "msstart.m")
-% Make sure that actup in "msstart.m" is set at the length for visual graphs
-%
-% yrEnd: the end year for the sample
-% qmEnd: last q_m before out-of-sample forecasting
-% q_m: quarterly or monthly for the underlying model
-% yearsCal: calendar years including actual and forecast data
-% yactCalyg: actual calendar annual growth data
-% yhatCalygml: conditional (calendar annual) forecasts on particular shocks
-% forepy: forecast periods (yearly)
-% xlab: label for structural equations
-% ylab: label for the variables
-% forelabel: title label for as of time of forecast
-% conlab: label for what conditions imposed; e.g., conlab = 'All bar MS shocks inspl'
-% keyindx: index for the variables to be graphed
-% rnum: number of rows in subplot
-% cnum: number of columns in subplot
-%-------------
-% No output argument for this graph file
-%
-% October 1998 Tao Zha; revised, 03/17/99 -- May not be compatible with previous programs
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-
-%*** Begining period
-fbegm = qmEnd+1; % +1 because the first month out of sample
-if fbegm > q_m % the first forecast month into the next year
- fbegm = 1;
- fbegy = yrEnd+1; % the first forecast year for the graph
-else
- fbegy = yrEnd; % the first forecast year for the graph
-end
-abegy = min(yearsCal); % the actual beginning year for the graph
-abegm = 1;
-ifbegy = find(yearsCal==fbegy-1); % index for the year before the first
- % forecast first year in actual data "yactCalyg"
-
-%*** Final period
-ffiny = fbegy+forepy-1; % the forecast final year
-afiny = max(yearsCal); % the actual final year
-
-
-
-figure
-
-nvar = size(yhatCalygml,2); % Pcm,M2,FFR,GdP, CPI, U
-%keyindx = 1:nvar; % Pcm,M2,FFR,GdP, CPI, U
-%keyindx = [4:nvar 3 2]; % GdP, CPI, U, FFR, M2
-
-hornum = cell(length(abegy:ffiny),1); % horizontal number
-count=0;
-for k=abegy:ffiny
- count=count+1;
- jnk=num2str(k);
- hornum{count}=jnk(3:4); % e.g., with '1990', we have '90'
-end
-
-count=0;
-cnum = 2;
-%
-for i = keyindx
- count = count+1;
- subplot(rnum,cnum,count)
- %
- if abegy>fbegy-1
- plot(abegy:afiny,yactCalyg(:,i),fbegy:ffiny,yhatCalygml(:,i),'-.')
- set(gca,'XTick',abegy:ffiny)
- else
- plot(abegy:afiny,yactCalyg(:,i),...
- fbegy-1:ffiny,[yactCalyg(ifbegy,i);yhatCalygml(:,i)],'-.')
- set(gca,'XTick',abegy:ffiny)
- end
- set(gca,'XTickLabel',char(hornum))
- if i<=cnum
- title(forelabel)
- elseif i>=nvar-1
- xlabel(conlab)
- end
- %
- grid
- ylabel(char(ylab(i)))
-end
+function gfore(yrEnd,qmEnd,q_m,yearsCal,yactCalyg,forepy,...
+ yhatCalygml,xlab,ylab,forelabel,conlab,keyindx,rnum,cnum)
+% gfore(yrEnd,qmEnd,q_m,yearsCal,yactCalyg,forepy,...
+% yhatCalygml,xlab,ylab,forelabel,conlab,keyindx,rnum,cnum)
+% Graph (calendar year) forecasts vs actual (actual from "msstart.m")
+% Make sure that actup in "msstart.m" is set at the length for visual graphs
+%
+% yrEnd: the end year for the sample
+% qmEnd: last q_m before out-of-sample forecasting
+% q_m: quarterly or monthly for the underlying model
+% yearsCal: calendar years including actual and forecast data
+% yactCalyg: actual calendar annual growth data
+% yhatCalygml: conditional (calendar annual) forecasts on particular shocks
+% forepy: forecast periods (yearly)
+% xlab: label for structural equations
+% ylab: label for the variables
+% forelabel: title label for as of time of forecast
+% conlab: label for what conditions imposed; e.g., conlab = 'All bar MS shocks inspl'
+% keyindx: index for the variables to be graphed
+% rnum: number of rows in subplot
+% cnum: number of columns in subplot
+%-------------
+% No output argument for this graph file
+%
+% October 1998 Tao Zha; revised, 03/17/99 -- May not be compatible with previous programs
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+
+%*** Begining period
+fbegm = qmEnd+1; % +1 because the first month out of sample
+if fbegm > q_m % the first forecast month into the next year
+ fbegm = 1;
+ fbegy = yrEnd+1; % the first forecast year for the graph
+else
+ fbegy = yrEnd; % the first forecast year for the graph
+end
+abegy = min(yearsCal); % the actual beginning year for the graph
+abegm = 1;
+ifbegy = find(yearsCal==fbegy-1); % index for the year before the first
+ % forecast first year in actual data "yactCalyg"
+
+%*** Final period
+ffiny = fbegy+forepy-1; % the forecast final year
+afiny = max(yearsCal); % the actual final year
+
+
+
+figure
+
+nvar = size(yhatCalygml,2); % Pcm,M2,FFR,GdP, CPI, U
+%keyindx = 1:nvar; % Pcm,M2,FFR,GdP, CPI, U
+%keyindx = [4:nvar 3 2]; % GdP, CPI, U, FFR, M2
+
+hornum = cell(length(abegy:ffiny),1); % horizontal number
+count=0;
+for k=abegy:ffiny
+ count=count+1;
+ jnk=num2str(k);
+ hornum{count}=jnk(3:4); % e.g., with '1990', we have '90'
+end
+
+count=0;
+cnum = 2;
+%
+for i = keyindx
+ count = count+1;
+ subplot(rnum,cnum,count)
+ %
+ if abegy>fbegy-1
+ plot(abegy:afiny,yactCalyg(:,i),fbegy:ffiny,yhatCalygml(:,i),'-.')
+ set(gca,'XTick',abegy:ffiny)
+ else
+ plot(abegy:afiny,yactCalyg(:,i),...
+ fbegy-1:ffiny,[yactCalyg(ifbegy,i);yhatCalygml(:,i)],'-.')
+ set(gca,'XTick',abegy:ffiny)
+ end
+ set(gca,'XTickLabel',char(hornum))
+ if i<=cnum
+ title(forelabel)
+ elseif i>=nvar-1
+ xlabel(conlab)
+ end
+ %
+ grid
+ ylabel(char(ylab(i)))
+end
diff --git a/MatlabFiles/gforerr1.m b/MatlabFiles/gforerr1.m
index db05b11d110e6586f3533ec7d9859fa16beac274..4122d36f3320323135453eb95b6b911a782d01ba 100644
--- a/MatlabFiles/gforerr1.m
+++ b/MatlabFiles/gforerr1.m
@@ -1,98 +1,98 @@
-function gforerr1(yrEnd,qmEnd,q_m,forepy,yhatCalygml,yhatCalygl1,...
- yhatCalygh1,xlab,ylab,forelabel,conlab,keyindx,rnum,cnum)
-
-%
-% One (.68) error band: (calendar year) forecasts only (no actual)
-%
-% yrEnd: the end year for the sample
-% qmEnd: last q_m before out-of-sample forecasting
-% q_m: quarterly or monthly for the underlying model
-% yearsCal: calendar years including actual and forecast data
-% yactCalyg: actual calendar annual growth data
-% yhatCalygml: conditional (calendar annual) forecasts on particular shocks
-% yhatCalygl1: 0.68 lower band
-% yhatCalygh1: 0.68 upper band
-% forepy: forecast periods (yearly)
-% xlab: label for structural equations
-% ylab: label for the variables
-% forelabel: title label for as of time of forecast
-% conlab: label for what conditions imposed; e.g., conlab = 'All bar MS shocks inspl'
-% keyindx: index for the variables to be graphed
-% rnum: number of rows in subplot
-% cnum: number of columns in subplot
-%-------------
-% No output argument for this graph file
-%
-% 03/19/99
-
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-%*** Begining period
-fbegm = qmEnd+1; % +1 because the first month out of sample
-if fbegm > q_m % the first forecast month into the next year
- fbegm = 1;
- fbegy = yrEnd+1; % the first forecast year for the graph
-else
- fbegy = yrEnd; % the first forecast year for the graph
-end
-%
-% abegy = min(yearsCal); % the actual beginning year for the graph
-% abegm = 1;
-% ifbegy = find(yearsCal==fbegy-1); % index for the year before the first
-% % forecast first year in actual data "yactCalyg"
-
-%*** Final period
-ffiny = fbegy+forepy-1; % the forecast final year
-%afiny = max(yearsCal); % the actual final year
-
-
-
-figure
-
-nvar = size(yhatCalygml,2); % Pcm,M2,FFR,GdP, CPI, U
-%keyindx = 1:nvar; % Pcm,M2,FFR,GdP, CPI, U
-%keyindx = [4:nvar 3 2]; % GdP, CPI, U, FFR, M2
-
-hornum = cell(length(fbegy:ffiny),1); % horizontal number
-count=0;
-for k=fbegy:ffiny
- count=count+1;
- jnk=num2str(k);
- hornum{count}=jnk(3:4); % e.g., with '1990', we have '90'
-end
-
-count=0;
-%
-for i = keyindx
- count = count+1;
- subplot(rnum,cnum,count)
- %
- plot(fbegy:ffiny,yhatCalygml(:,i),'-',...
- fbegy:ffiny,yhatCalygl1(:,i),'--',fbegy:ffiny,yhatCalygh1(:,i),'--' )
- set(gca,'XTick',fbegy:ffiny)
- set(gca,'XTickLabel',char(hornum))
- if i<=cnum
- title(forelabel)
- elseif i>=nvar-1
- xlabel(conlab)
- end
- %
- grid
- ylabel(char(ylab(i)))
-end
\ No newline at end of file
+function gforerr1(yrEnd,qmEnd,q_m,forepy,yhatCalygml,yhatCalygl1,...
+ yhatCalygh1,xlab,ylab,forelabel,conlab,keyindx,rnum,cnum)
+
+%
+% One (.68) error band: (calendar year) forecasts only (no actual)
+%
+% yrEnd: the end year for the sample
+% qmEnd: last q_m before out-of-sample forecasting
+% q_m: quarterly or monthly for the underlying model
+% yearsCal: calendar years including actual and forecast data
+% yactCalyg: actual calendar annual growth data
+% yhatCalygml: conditional (calendar annual) forecasts on particular shocks
+% yhatCalygl1: 0.68 lower band
+% yhatCalygh1: 0.68 upper band
+% forepy: forecast periods (yearly)
+% xlab: label for structural equations
+% ylab: label for the variables
+% forelabel: title label for as of time of forecast
+% conlab: label for what conditions imposed; e.g., conlab = 'All bar MS shocks inspl'
+% keyindx: index for the variables to be graphed
+% rnum: number of rows in subplot
+% cnum: number of columns in subplot
+%-------------
+% No output argument for this graph file
+%
+% 03/19/99
+
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+%*** Begining period
+fbegm = qmEnd+1; % +1 because the first month out of sample
+if fbegm > q_m % the first forecast month into the next year
+ fbegm = 1;
+ fbegy = yrEnd+1; % the first forecast year for the graph
+else
+ fbegy = yrEnd; % the first forecast year for the graph
+end
+%
+% abegy = min(yearsCal); % the actual beginning year for the graph
+% abegm = 1;
+% ifbegy = find(yearsCal==fbegy-1); % index for the year before the first
+% % forecast first year in actual data "yactCalyg"
+
+%*** Final period
+ffiny = fbegy+forepy-1; % the forecast final year
+%afiny = max(yearsCal); % the actual final year
+
+
+
+figure
+
+nvar = size(yhatCalygml,2); % Pcm,M2,FFR,GdP, CPI, U
+%keyindx = 1:nvar; % Pcm,M2,FFR,GdP, CPI, U
+%keyindx = [4:nvar 3 2]; % GdP, CPI, U, FFR, M2
+
+hornum = cell(length(fbegy:ffiny),1); % horizontal number
+count=0;
+for k=fbegy:ffiny
+ count=count+1;
+ jnk=num2str(k);
+ hornum{count}=jnk(3:4); % e.g., with '1990', we have '90'
+end
+
+count=0;
+%
+for i = keyindx
+ count = count+1;
+ subplot(rnum,cnum,count)
+ %
+ plot(fbegy:ffiny,yhatCalygml(:,i),'-',...
+ fbegy:ffiny,yhatCalygl1(:,i),'--',fbegy:ffiny,yhatCalygh1(:,i),'--' )
+ set(gca,'XTick',fbegy:ffiny)
+ set(gca,'XTickLabel',char(hornum))
+ if i<=cnum
+ title(forelabel)
+ elseif i>=nvar-1
+ xlabel(conlab)
+ end
+ %
+ grid
+ ylabel(char(ylab(i)))
+end
diff --git a/MatlabFiles/ghistd.m b/MatlabFiles/ghistd.m
index d5b0f68e5b3e7223fbe36eb061ce3ba4924b2758..0d062675ecca68e1ae4da6ed827a38c12f9f07af 100644
--- a/MatlabFiles/ghistd.m
+++ b/MatlabFiles/ghistd.m
@@ -1,93 +1,93 @@
-function noargout = ghistd(qmEnd,yrEnd,forepy,q_m,HDv,lendlab,titlab,ylab,timelab,rnum,cnum)
-%
-% Plot historical decompositions (annually at this time), no argument out
-%
-% qmEnd: the end month before the forecast
-% yrEnd: the end year before the forecast
-% forepy: the number of forecast calendar years
-% q_m: 12 or 4 (monthly or quarterly)
-% HDv: nHD-by-1 cell where nHD is number of decompositions;
-% each cell is forepy-by-nvar
-% lendlab: label for the lengend
-% titlab: label for the title
-% ylab: label for the variables
-% timelab: label for the time of forecast and indication of insample or out-of-sample
-% rnum: row number for the subplot (e.g., 3)
-% cnum: column number for the subplot (e.g., 2)
-%---------------
-% no argument out
-%
-% October 1998 Tao Zha
-%% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-% To be done:
-% lengend does not work well
-% colormap should be replaced by some non-color shading
-% title: need to have only one.
-
-fbegm = qmEnd+1; % +1 because the first month out of sample
-if fbegm > q_m % the first forecast month into the next year
- fbegm = 1; % reset
- fbegy = yrEnd+1; % the first forecast year for the graph
-else
- fbegy = yrEnd; % the first forecast year for the graph
-end
-ffiny = fbegy+forepy-1; % the forecast final year
-
-figure
-%cnum = 2;
-nvar = size(HDv{1},2);
-rnum = nvar/cnum;
-if ~(rnum==fix(rnum))
- warning('Make that subplot is divided properly')
- disp('Check rnum as well as cnum')
- return
-end
-
-hornum = cell(length(fbegy:ffiny),1); % horizontal number
-count=0;
-for k=fbegy:ffiny
- count=count+1;
- jnk=num2str(k);
- hornum{count}=jnk(3:4); % e.g., with '1990', we have '90'
-end
-
-
-for k=1:nvar
- bardata = [];
- for n=1:size(HDv,1)
- bardata = [bardata HDv{n}(:,k)];
- end
-
- subplot(rnum,cnum,k)
- bar(fbegy:ffiny,bardata,0.75,'group')
- set(gca,'XTickLabel',char(hornum))
- if k==1
- legend(char(lendlab),1)
- end
- colormatrix = [0.2 0.2 0.2;0.8 0.8 0.8];
- colormap(colormatrix)
- if k<=cnum
- title(char(titlab))
- end
- if k>((rnum-1)*cnum)
- xlabel(timelab)
- end
- grid
- ylabel(char(ylab{k}))
-end
\ No newline at end of file
+function noargout = ghistd(qmEnd,yrEnd,forepy,q_m,HDv,lendlab,titlab,ylab,timelab,rnum,cnum)
+%
+% Plot historical decompositions (annually at this time), no argument out
+%
+% qmEnd: the end month before the forecast
+% yrEnd: the end year before the forecast
+% forepy: the number of forecast calendar years
+% q_m: 12 or 4 (monthly or quarterly)
+% HDv: nHD-by-1 cell where nHD is number of decompositions;
+% each cell is forepy-by-nvar
+% lendlab: label for the lengend
+% titlab: label for the title
+% ylab: label for the variables
+% timelab: label for the time of forecast and indication of insample or out-of-sample
+% rnum: row number for the subplot (e.g., 3)
+% cnum: column number for the subplot (e.g., 2)
+%---------------
+% no argument out
+%
+% October 1998 Tao Zha
+%
+%% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+% To be done:
+% lengend does not work well
+% colormap should be replaced by some non-color shading
+% title: need to have only one.
+
+fbegm = qmEnd+1; % +1 because the first month out of sample
+if fbegm > q_m % the first forecast month into the next year
+ fbegm = 1; % reset
+ fbegy = yrEnd+1; % the first forecast year for the graph
+else
+ fbegy = yrEnd; % the first forecast year for the graph
+end
+ffiny = fbegy+forepy-1; % the forecast final year
+
+figure
+%cnum = 2;
+nvar = size(HDv{1},2);
+rnum = nvar/cnum;
+if ~(rnum==fix(rnum))
+ warning('Make that subplot is divided properly')
+ disp('Check rnum as well as cnum')
+ return
+end
+
+hornum = cell(length(fbegy:ffiny),1); % horizontal number
+count=0;
+for k=fbegy:ffiny
+ count=count+1;
+ jnk=num2str(k);
+ hornum{count}=jnk(3:4); % e.g., with '1990', we have '90'
+end
+
+
+for k=1:nvar
+ bardata = [];
+ for n=1:size(HDv,1)
+ bardata = [bardata HDv{n}(:,k)];
+ end
+
+ subplot(rnum,cnum,k)
+ bar(fbegy:ffiny,bardata,0.75,'group')
+ set(gca,'XTickLabel',char(hornum))
+ if k==1
+ legend(char(lendlab),1)
+ end
+ colormatrix = [0.2 0.2 0.2;0.8 0.8 0.8];
+ colormap(colormatrix)
+ if k<=cnum
+ title(char(titlab))
+ end
+ if k>((rnum-1)*cnum)
+ xlabel(timelab)
+ end
+ grid
+ ylabel(char(ylab{k}))
+end
diff --git a/MatlabFiles/gibbsglb.m b/MatlabFiles/gibbsglb.m
index 88f0a86954dada0a4f3edb200d747c34d2d520ac..25325bdfa39804c6fc6b05088ec541a15a9a08c5 100644
--- a/MatlabFiles/gibbsglb.m
+++ b/MatlabFiles/gibbsglb.m
@@ -1,73 +1,74 @@
-function [cT,vR,kdf] = gibbsglb(Sbd,idmat0,nvar,fss)
-% [cT,vR,kdf] = gibbsglb(Sbd,idmat0,nvar,fss)
-% Global setup outside the Gibbs loop -- c.f. gibbsvar
-% Ref.: D.F. Waggoner and T.A. Zha: "Does Normalization Matter for Inference?"
-% See Note Forecast (2) pp. 44-51
-%
-% Sbd: cell(nvar,1). Sbd=diag(S(1), ..., S(m)). Already divided by 'fss' for the
-% posterior of a0 or A0(:) in Waggoner and Zha when one has asymmetric prior.
-% Note,"bd" stands for block diagonal.
-% idmat0: identification matrix for A0 with asymmetric prior; column -- equation.
-% nvar: rank of A0 or # of variables
-% fss: effective sample size (in the exponential term) --
-% # of observations + # of dummy observations
-% (or nSample - lags + # of dummy observations)
-%-------------
-% cT{i}: nvar-by-nvar where T'*T=Sbd{i} which is kind of covariance martrix
-% divided by fss already
-% vR{i}: nvar-by-q{i} -- orthonormral basis for T*R, which is obtained through
-% single value decomposition of Q*inv(T)
-% kdf: the polynomial power in the Gamma or 1d Wishart distribution, used in
-% "gibbsvar.m"
-%
-% Written by Tao Zha; Copyright (c) 1999 by Waggoner and Zha
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-kdf = fss; %2; %fss;
-
-cT = cell(nvar,1);
-for k=1:nvar
- cT{k} = chol(Sbd{k}); % upper triangular but lower triangular Choleski
-end
-
-Q = cell(nvar,1);
- % Q{i}: nvar-by-nvar with rank nvar-q(i) with ith equation a and
- % q(i) which is # of non-zero elements in a. Note: Q*a=0.
-
-vR = cell(nvar,1);
-for k=1:nvar
- Q{k} = diag(idmat0(:,k)==0); % constructing Q{k}
- %
- [jnk1,d1,v1] = svd(Q{k}/cT{k});
- d1max=max(diag(d1));
- if d1max==0
- Idxk=1:nvar;
- else
- Idxk = find(diag(d1)<eps*d1max);
- end
- lenk = length(find(idmat0(:,k)));
- if ( length(Idxk)<lenk )
- warning('Dimension of non-zero A0(:,k) is different from svd(Q*inv(T))')
- disp('Press ctrl-c to abort')
- pause
- else
- jnk1 = v1(:,Idxk);
- vR{k} = jnk1(:,1:lenk); % orthonormal basis for T*R
- end
-end
+function [cT,vR,kdf] = gibbsglb(Sbd,idmat0,nvar,fss)
+% [cT,vR,kdf] = gibbsglb(Sbd,idmat0,nvar,fss)
+% Global setup outside the Gibbs loop -- c.f. gibbsvar
+% Ref.: D.F. Waggoner and T.A. Zha: "Does Normalization Matter for Inference?"
+% See Note Forecast (2) pp. 44-51
+%
+% Sbd: cell(nvar,1). Sbd=diag(S(1), ..., S(m)). Already divided by 'fss' for the
+% posterior of a0 or A0(:) in Waggoner and Zha when one has asymmetric prior.
+% Note,"bd" stands for block diagonal.
+% idmat0: identification matrix for A0 with asymmetric prior; column -- equation.
+% nvar: rank of A0 or # of variables
+% fss: effective sample size (in the exponential term) --
+% # of observations + # of dummy observations
+% (or nSample - lags + # of dummy observations)
+%-------------
+% cT{i}: nvar-by-nvar where T'*T=Sbd{i} which is kind of covariance martrix
+% divided by fss already
+% vR{i}: nvar-by-q{i} -- orthonormral basis for T*R, which is obtained through
+% single value decomposition of Q*inv(T)
+% kdf: the polynomial power in the Gamma or 1d Wishart distribution, used in
+% "gibbsvar.m"
+%
+% Written by Tao Zha;
+%
+%
+% Copyright (C) 1997-2012 Daniel Waggoner and Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+kdf = fss; %2; %fss;
+
+cT = cell(nvar,1);
+for k=1:nvar
+ cT{k} = chol(Sbd{k}); % upper triangular but lower triangular Choleski
+end
+
+Q = cell(nvar,1);
+ % Q{i}: nvar-by-nvar with rank nvar-q(i) with ith equation a and
+ % q(i) which is # of non-zero elements in a. Note: Q*a=0.
+
+vR = cell(nvar,1);
+for k=1:nvar
+ Q{k} = diag(idmat0(:,k)==0); % constructing Q{k}
+ %
+ [jnk1,d1,v1] = svd(Q{k}/cT{k});
+ d1max=max(diag(d1));
+ if d1max==0
+ Idxk=1:nvar;
+ else
+ Idxk = find(diag(d1)<eps*d1max);
+ end
+ lenk = length(find(idmat0(:,k)));
+ if ( length(Idxk)<lenk )
+ warning('Dimension of non-zero A0(:,k) is different from svd(Q*inv(T))')
+ disp('Press ctrl-c to abort')
+ pause
+ else
+ jnk1 = v1(:,Idxk);
+ vR{k} = jnk1(:,1:lenk); % orthonormal basis for T*R
+ end
+end
diff --git a/MatlabFiles/gibbsvar.m b/MatlabFiles/gibbsvar.m
index 5d12d432c174e66fc648f42d6ac824ef9e5dcdd8..4f3e3619085023785c8fd7e37695b480cb3bf398 100644
--- a/MatlabFiles/gibbsvar.m
+++ b/MatlabFiles/gibbsvar.m
@@ -1,93 +1,93 @@
-function A0gbs = gibbsvar(A0gbs,cT,vR,nvar,fss,kdf,Indxcol)
-% A0gbs = gibbsvar(A0gbs,cT,vR,nvar,fss,kdf)
-% One-step Gibbs sampler for structural VARs -- simultaneous equations approach
-% Ref.: D.F. Waggoner and T. Zha: "Does Normalization Matter for Inference?"
-% See Note Forecast (2) pp. 44-51
-%
-% A0gbs: the last draw of A0 matrix
-% cT{i}: nvar-by-nvar where T'*T=Sbd{i} which is kind of covariance martrix
-% divided by fss already
-% vR{i}: nvar-by-q{i} -- orthonormral basis for T*R, which is obtained through
-% single value decomposition of Q*inv(T). See gibbsglb.m
-% nvar: rank of A0 or # of variables
-% fss: effective sample size == nSample (T)-lags+# of dummy observations
-% kdf: polynomial power in the Gamma or 1d Wishart distribution
-% Indxcol: a vector of random columns this Gibbs draws.
-% When this input is not supplied, the Gibbs draws all columns
-%------------------
-% A0bgs: new draw of A0 matrix in this Gibbs step
-%
-% Written by Tao Zha; Copyright (c) 1999 by Waggoner and Zha
-% NOTE: Added Indxcol on 2/13/00 so that previous programs may not be compatible.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-if (nargin==6), Indxcol=[1:nvar]; end
-
-%---------------- Local loop for Gibbs given last A0gbs ----------
-%* uR{i}: nvar-by-q{i} -- orthonormal with first q(i)-1 vectors lies in the
-% span(T*a(j)|j~=i)
-%*** Constructing u(1),...,u(q{i}) at each Gibbs step
-%
-sw0 = zeros(nvar,1);
-for k=Indxcol % given last A0gbs and general new A0bgs
- X = cT{k}*A0gbs; % given the latest updated A0gbs
- X(:,k) = 0; % want to find non-zero sw s.t., X'*sw=0
- [jL,Ux] = lu(X');
- jIx0 = min(find(abs(diag(Ux))<eps)); % if isempty(jIx0), then something is wrong here
- %
- sw0(jIx0+1:end) = 0;
- sw0(jIx0) = 1;
- jA = Ux(1:jIx0-1,1:jIx0-1);
- jb = Ux(1:jIx0-1,jIx0);
- jy = -jA\jb;
- sw0(1:jIx0-1) = jy;
- sw = sw0/sqrt(sum(sw0.^2));
- %
- lenk = length(vR{k}(1,:));
- gkb = zeros(lenk,1); % greek beta's
- uR = zeros(nvar,lenk);
- sx = zeros(nvar,lenk);
- sx(:,1) = vR{k}(:,1);
- for ki = 1:lenk-1
- wxv = [sw'*sx(:,ki);sw'*vR{k}(:,ki+1)]; % w'*x and w'*v(+1)
- dwxv = sqrt(sum(wxv.^2));
- if (dwxv<eps)
- uR(:,ki)=sx(:,ki); sx(:,ki+1)=vR{k}(:,ki+1);
- else
- wxv = wxv/dwxv;
- uR(:,ki) = wxv(1)*vR{k}(:,ki+1) - wxv(2)*sx(:,ki);
- sx(:,ki+1) = wxv(2)*vR{k}(:,ki+1) + wxv(1)*sx(:,ki);
- end
- end
- uR(:,lenk) = sx(:,lenk); % uR now constructed
- %
- %--------- Gibbs loop ----------
- %*** draw independently beta's that combine uR to form a's (columns of A0)
- jcon = sqrt(1/fss);
- gkb(1:lenk-1) = jcon*randn(lenk-1,1);
- %* gamma or 1-d Wishart draw
- jnk = jcon*randn(kdf+1,1);
- if rand(1)<0.5
- gkb(lenk) = sqrt(jnk'*jnk);
- else
- gkb(lenk) = -sqrt(jnk'*jnk);
- end
- %
- %*** form new a(i) - ith column of A0
- A0gbs(:,k) = (cT{k}\uR)*gkb;
-end
+function A0gbs = gibbsvar(A0gbs,cT,vR,nvar,fss,kdf,Indxcol)
+% A0gbs = gibbsvar(A0gbs,cT,vR,nvar,fss,kdf)
+% One-step Gibbs sampler for structural VARs -- simultaneous equations approach
+% Ref.: D.F. Waggoner and T. Zha: "Does Normalization Matter for Inference?"
+% See Note Forecast (2) pp. 44-51
+%
+% A0gbs: the last draw of A0 matrix
+% cT{i}: nvar-by-nvar where T'*T=Sbd{i} which is kind of covariance martrix
+% divided by fss already
+% vR{i}: nvar-by-q{i} -- orthonormral basis for T*R, which is obtained through
+% single value decomposition of Q*inv(T). See gibbsglb.m
+% nvar: rank of A0 or # of variables
+% fss: effective sample size == nSample (T)-lags+# of dummy observations
+% kdf: polynomial power in the Gamma or 1d Wishart distribution
+% Indxcol: a vector of random columns this Gibbs draws.
+% When this input is not supplied, the Gibbs draws all columns
+%------------------
+% A0bgs: new draw of A0 matrix in this Gibbs step
+%
+% NOTE: Added Indxcol on 2/13/00 so that previous programs may not be compatible.
+%
+%
+% Copyright (C) 1997-2012 Daniel Waggoner and Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+if (nargin==6), Indxcol=[1:nvar]; end
+
+%---------------- Local loop for Gibbs given last A0gbs ----------
+%* uR{i}: nvar-by-q{i} -- orthonormal with first q(i)-1 vectors lies in the
+% span(T*a(j)|j~=i)
+%*** Constructing u(1),...,u(q{i}) at each Gibbs step
+%
+sw0 = zeros(nvar,1);
+for k=Indxcol % given last A0gbs and general new A0bgs
+ X = cT{k}*A0gbs; % given the latest updated A0gbs
+ X(:,k) = 0; % want to find non-zero sw s.t., X'*sw=0
+ [jL,Ux] = lu(X');
+ jIx0 = min(find(abs(diag(Ux))<eps)); % if isempty(jIx0), then something is wrong here
+ %
+ sw0(jIx0+1:end) = 0;
+ sw0(jIx0) = 1;
+ jA = Ux(1:jIx0-1,1:jIx0-1);
+ jb = Ux(1:jIx0-1,jIx0);
+ jy = -jA\jb;
+ sw0(1:jIx0-1) = jy;
+ sw = sw0/sqrt(sum(sw0.^2));
+ %
+ lenk = length(vR{k}(1,:));
+ gkb = zeros(lenk,1); % greek beta's
+ uR = zeros(nvar,lenk);
+ sx = zeros(nvar,lenk);
+ sx(:,1) = vR{k}(:,1);
+ for ki = 1:lenk-1
+ wxv = [sw'*sx(:,ki);sw'*vR{k}(:,ki+1)]; % w'*x and w'*v(+1)
+ dwxv = sqrt(sum(wxv.^2));
+ if (dwxv<eps)
+ uR(:,ki)=sx(:,ki); sx(:,ki+1)=vR{k}(:,ki+1);
+ else
+ wxv = wxv/dwxv;
+ uR(:,ki) = wxv(1)*vR{k}(:,ki+1) - wxv(2)*sx(:,ki);
+ sx(:,ki+1) = wxv(2)*vR{k}(:,ki+1) + wxv(1)*sx(:,ki);
+ end
+ end
+ uR(:,lenk) = sx(:,lenk); % uR now constructed
+ %
+ %--------- Gibbs loop ----------
+ %*** draw independently beta's that combine uR to form a's (columns of A0)
+ jcon = sqrt(1/fss);
+ gkb(1:lenk-1) = jcon*randn(lenk-1,1);
+ %* gamma or 1-d Wishart draw
+ jnk = jcon*randn(kdf+1,1);
+ if rand(1)<0.5
+ gkb(lenk) = sqrt(jnk'*jnk);
+ else
+ gkb(lenk) = -sqrt(jnk'*jnk);
+ end
+ %
+ %*** form new a(i) - ith column of A0
+ A0gbs(:,k) = (cT{k}\uR)*gkb;
+end
diff --git a/MatlabFiles/gradcd.m b/MatlabFiles/gradcd.m
index 1655d01ed0b89811a8665f152b072b3371a80fda..73da507d7fd9e2d07b393f58f60d0fef4d0b7829 100644
--- a/MatlabFiles/gradcd.m
+++ b/MatlabFiles/gradcd.m
@@ -1,83 +1,83 @@
-function grdd = gradcd(fcn,x0,grdh,P1,P2,P3,P4,P5,P6,P7,P8,P9,...
- P10,P11,P12,P13,P14,P15,P16,P17,P18,P19,P20)
-%function grdd = gradcd(fcn,x0,grdh,P1,P2,P3,P4,P5,P6,P7,P8,P9,...
-% P10,P11,P12,P13,P14,P15,P16,P17,P18,P19,P20)
-% computes numerical gradient of a single-valued function or Jacobian
-% matrix of a vector-valued function using a central difference with
-% function grdd = gradcd(fcn,x0,grdh)
-%
-% fcn: a string naming a vector-valued function (f:n*1 -> k*1).
-% x0: a column vector n*1, at which point the hessian is evaluated.
-% grdh: step size, n*1. Set as follows
-% step = eps^(1/3);
-% %step = 1e-04;
-% grdh = step * (max([abs(x) ones(length(x),1)]'))' .* (abs(x) ./ x);
-% grdd: Jacobian matrix, k*n.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-stps = eps^(1/3);
-% eps: floating point relative accuracy or machine precision: 2.22e-16
-% stps: step size recommended by Dennis and Schnabel: 6.006e-6
-
-x0 = x0(:);
-tailstr = ')';
-for i=nargin-3:-1:1
- tailstr=[ ',P' num2str(i) tailstr];
-end
-f0 = eval([fcn '(x0' tailstr]);
-
-% ** initializations
-n = length(x0);
-k = length(f0); % dimension of "fcn"
-
-% ** Computation of stepsize (dh)
-if all(grdh)
- dh = grdh;
-else
- ax0 = abs(x0);
- if all(x0)
- dax0 = x0 ./ ax0;
- else
- dax0 = 1;
- end
- dh = stps * (max([ax0 ones(n,1)]'))' .* dax0;
-end
-
-xdh = x0 + dh;
-dh = xdh - x0; % This increases precision slightly
-%
-argplus = x0(:,ones(n,1));
-argminus = argplus;
-dnum = 1:n+1:n^2; % positions of diagonals in vec(argplus).
-argplus(dnum) = xdh; % replace the diagonals of "argplus" by "xdh".
-argminus(dnum) = x0-dh; % replace the diagonals of "argplus" by "xdh".
-
-grdd = zeros(k,n); % preallocate to speed the loop.
-i = 0;
-while i ~= n
- i = i+1;
- fp = eval([fcn '(argplus(:,i)' tailstr]);
- fm = eval([fcn '(argminus(:,i)' tailstr]);
- grdd(:,i) = fp - fm;
-end
-dhm = dh(:,ones(k,1));
-dhm = dhm'; % k*n
-grdd = grdd ./ (2*dhm);
-
-
+function grdd = gradcd(fcn,x0,grdh,P1,P2,P3,P4,P5,P6,P7,P8,P9,...
+ P10,P11,P12,P13,P14,P15,P16,P17,P18,P19,P20)
+%function grdd = gradcd(fcn,x0,grdh,P1,P2,P3,P4,P5,P6,P7,P8,P9,...
+% P10,P11,P12,P13,P14,P15,P16,P17,P18,P19,P20)
+% computes numerical gradient of a single-valued function or Jacobian
+% matrix of a vector-valued function using a central difference with
+% function grdd = gradcd(fcn,x0,grdh)
+%
+% fcn: a string naming a vector-valued function (f:n*1 -> k*1).
+% x0: a column vector n*1, at which point the hessian is evaluated.
+% grdh: step size, n*1. Set as follows
+% step = eps^(1/3);
+% %step = 1e-04;
+% grdh = step * (max([abs(x) ones(length(x),1)]'))' .* (abs(x) ./ x);
+% grdd: Jacobian matrix, k*n.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+stps = eps^(1/3);
+% eps: floating point relative accuracy or machine precision: 2.22e-16
+% stps: step size recommended by Dennis and Schnabel: 6.006e-6
+
+x0 = x0(:);
+tailstr = ')';
+for i=nargin-3:-1:1
+ tailstr=[ ',P' num2str(i) tailstr];
+end
+f0 = eval([fcn '(x0' tailstr]);
+
+% ** initializations
+n = length(x0);
+k = length(f0); % dimension of "fcn"
+
+% ** Computation of stepsize (dh)
+if all(grdh)
+ dh = grdh;
+else
+ ax0 = abs(x0);
+ if all(x0)
+ dax0 = x0 ./ ax0;
+ else
+ dax0 = 1;
+ end
+ dh = stps * (max([ax0 ones(n,1)]'))' .* dax0;
+end
+
+xdh = x0 + dh;
+dh = xdh - x0; % This increases precision slightly
+%
+argplus = x0(:,ones(n,1));
+argminus = argplus;
+dnum = 1:n+1:n^2; % positions of diagonals in vec(argplus).
+argplus(dnum) = xdh; % replace the diagonals of "argplus" by "xdh".
+argminus(dnum) = x0-dh; % replace the diagonals of "argplus" by "xdh".
+
+grdd = zeros(k,n); % preallocate to speed the loop.
+i = 0;
+while i ~= n
+ i = i+1;
+ fp = eval([fcn '(argplus(:,i)' tailstr]);
+ fm = eval([fcn '(argminus(:,i)' tailstr]);
+ grdd(:,i) = fp - fm;
+end
+dhm = dh(:,ones(k,1));
+dhm = dhm'; % k*n
+grdd = grdd ./ (2*dhm);
+
+
diff --git a/MatlabFiles/gshock.m b/MatlabFiles/gshock.m
index 492188355e740279fecc808d073dc31624f7688e..33f614cee56e064563c7e3986bba452a0e129626 100644
--- a/MatlabFiles/gshock.m
+++ b/MatlabFiles/gshock.m
@@ -1,87 +1,87 @@
-function ExactSmyear = gshock(begy,begm,finy,finm,xlab,idfile,tlinput)
-% ExactSmyear = gshock(begy,begm,finy,finm,xlab)
-% Plot the structural shocks for the range given by the inputs
-%
-% begy: the beginning year for the graph
-% begm: the begiining month for the graph
-% finy: the end year for the graph
-% finm: the end month for the graph
-% xlab: label for each structural equation
-%-----------
-% ExactSmyear: time (myears) and all structural shocks
-% if no nargout, plot graphics.
-%
-% October 1998 by Tao A. Zha
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-
-eval(['load ' idfile '.mat']);
-
-%
-ibegy = find(myears==begy);
-ifiny = find(myears==finy);
-
-ibegy1 = find(myears==begy+1);
-
-%
-if isempty(ibegy) & isempty(ibegy1)
- warning('Either ibegy or begm is out of the range of myears')
- disp('Print myears to see the range')
- disp('Or change actuap to make the range longer and save new results to Xshock.mat')
- pause
-elseif isempty(ibegy) & (begm<myears(1))
- warning('Either ibegy or begm is out of the range of myears')
- disp('Print myears to see the range')
- disp('Or change actuap to make the range longer and save new results to Xshock.mat')
- pause
-elseif isempty(ibegy)
- ibegy = -myears(1)+2; % +2 is needed for the following -1 in
- % bar(Estrexa(ibegy+begm-1:ifiny+finm-1,i))
- %so that it 2-1=1 so that +1 is what we want
-end
-
-%
-if isempty(ifiny)
- warning('Either ifiny or finm is out of the range of myears')
- disp('Print myears to see the range')
- disp('Or change actuap to make the range longer and save new results to Xshock.mat')
- pause
-elseif (ifiny+finm-1>size(myears,1))
- warning('Either ifiny or finm is out of the range of myears')
- disp('Print myears to see the range')
- disp('Or change actuap to make the range longer and save new results to Xshock.mat')
- pause
-end
-
-
-if nargout==0
- t=length(Estrexa(ibegy+begm-1:ifiny+finm-1,1));
- for i=1:size(Estrexa,2)
- figure
- %plot(1:t, Estrexa(ibegy+begm-1:ifiny+finm-1,i));
- bar(Estrexa(ibegy+begm-1:ifiny+finm-1,i))
- title([ tlinput ' Monthly Structural Shocks' ])
- ylabel(char(xlab(i)))
- xlabel([ 'From ' num2str(begy) ':' num2str(begm) ' to ' ...
- num2str(finy) ':' num2str(finm) ])
- grid
- end
-else
- ExactSmyear = [myears(ibegy+begm-1:ifiny+finm-1) Estrexa(ibegy+begm-1:ifiny+finm-1,:)];
-end
+function ExactSmyear = gshock(begy,begm,finy,finm,xlab,idfile,tlinput)
+% ExactSmyear = gshock(begy,begm,finy,finm,xlab)
+% Plot the structural shocks for the range given by the inputs
+%
+% begy: the beginning year for the graph
+% begm: the begiining month for the graph
+% finy: the end year for the graph
+% finm: the end month for the graph
+% xlab: label for each structural equation
+%-----------
+% ExactSmyear: time (myears) and all structural shocks
+% if no nargout, plot graphics.
+%
+% October 1998 by Tao A. Zha
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+
+eval(['load ' idfile '.mat']);
+
+%
+ibegy = find(myears==begy);
+ifiny = find(myears==finy);
+
+ibegy1 = find(myears==begy+1);
+
+%
+if isempty(ibegy) & isempty(ibegy1)
+ warning('Either ibegy or begm is out of the range of myears')
+ disp('Print myears to see the range')
+ disp('Or change actuap to make the range longer and save new results to Xshock.mat')
+ pause
+elseif isempty(ibegy) & (begm<myears(1))
+ warning('Either ibegy or begm is out of the range of myears')
+ disp('Print myears to see the range')
+ disp('Or change actuap to make the range longer and save new results to Xshock.mat')
+ pause
+elseif isempty(ibegy)
+ ibegy = -myears(1)+2; % +2 is needed for the following -1 in
+ % bar(Estrexa(ibegy+begm-1:ifiny+finm-1,i))
+ %so that it 2-1=1 so that +1 is what we want
+end
+
+%
+if isempty(ifiny)
+ warning('Either ifiny or finm is out of the range of myears')
+ disp('Print myears to see the range')
+ disp('Or change actuap to make the range longer and save new results to Xshock.mat')
+ pause
+elseif (ifiny+finm-1>size(myears,1))
+ warning('Either ifiny or finm is out of the range of myears')
+ disp('Print myears to see the range')
+ disp('Or change actuap to make the range longer and save new results to Xshock.mat')
+ pause
+end
+
+
+if nargout==0
+ t=length(Estrexa(ibegy+begm-1:ifiny+finm-1,1));
+ for i=1:size(Estrexa,2)
+ figure
+ %plot(1:t, Estrexa(ibegy+begm-1:ifiny+finm-1,i));
+ bar(Estrexa(ibegy+begm-1:ifiny+finm-1,i))
+ title([ tlinput ' Monthly Structural Shocks' ])
+ ylabel(char(xlab(i)))
+ xlabel([ 'From ' num2str(begy) ':' num2str(begm) ' to ' ...
+ num2str(finy) ':' num2str(finm) ])
+ grid
+ end
+else
+ ExactSmyear = [myears(ibegy+begm-1:ifiny+finm-1) Estrexa(ibegy+begm-1:ifiny+finm-1,:)];
+end
diff --git a/MatlabFiles/gstate.m b/MatlabFiles/gstate.m
index b076e5d017797062a720f5013e538ff2e3552806..c693576439dd9a7a9b59c178e2f2a953a8ea6916 100644
--- a/MatlabFiles/gstate.m
+++ b/MatlabFiles/gstate.m
@@ -1,114 +1,114 @@
-function [GS,pickn,ok,uis,uiu,vs]=gstate(G1,impact,pick)
-%function [GS,pickn,ok,uis,vs]=gstate(G1,impact,pick)
-% G1: the coefficient on lagged y from the output of gensys.m.
-% impact:the coefficient on exogenous shocks from the output of gensys.m
-% pick: an optional guess at a matrix of coefficients that extracts
-% the state vector from y
-% ok: 0 if pick matrix is just no good.
-% 1 if pick matrix is usable forward, but loses initial date information
-% 2 if pick matrix is not usable forward, is part of a correct state vector, but is not complete
-% 3 if pick matrix is usable forward, is part of a correct state vector, but is not complete
-% 4 if pick matrix is not usable forward, summarizes past, but is redundant
-% 5 if pick matrix is usable forward, summarizes past, but is redundant
-% 6 if pick matrix summarizes past and is not redundant, but is not usable forward
-% 7 if pick matrix is perfect, both forward and as history summary
-% pickn: a matrix of coefficients that extracts the state vector from y. Equal
-% to pick if pick is supplied and ok=1; otherwise, an ok-maximizing pick matrix that
-% is somewhat similar to pick.
-% GS: the matrix of coefficients on the lagged state variable
-% uis,vs: If uis'*vs is square and full rank, ok=7 is possible, otherwise not. If ok<2, an ok>2 can be found by trying
-% a pick in the row space of vs'. Any pick with pick*uis full column rank will provide a foward state
-% (i.e. ok an odd number).
-% uiu: uiu'y(t)=0, and this is interpretable as the decision rule setting controls as functions of the states.
-% The solution was in the form y(t)=G1*y(t-1)+impact*z(t). Now it's in the form y(t)=GS*pickn*y(t-1)+impact*z(t).
-% In general, pickn is mxn with m<n.
-
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-REALSMALL=1e-9;
-[nr,nc]=size(G1);
-%if nr<nc
-% G1=[G1;zeros(nc-nr,nc)];
-%end
-[u d v]=svd(G1);
-top=find(diag(d)>REALSMALL);
-nd=top(end);
-us=u(:,top);
-uu=u(:,top+1:end);
-d=d(top,top);
-vs=v(:,top);
-if nargin<=2
- pick=vs';
- vp=v;
- vps=vs;
- dp=eye(nd);
- ups=eye(nr,nd);
- ndp=nd;
-else
- [up dp vp]=svd(pick);
- topp=find(diag(dp)>REALSMALL);
- ndp=topp(end);
- vps=vp(:,topp);
- dp=dp(topp,topp);
- ups=up(:,topp);
-end
- % If we were worried about efficiency, we'd skip some of this when pick=vs.
- %Does pick summarize history? (pick in v' row space, v in vp' row space).
- pinv=all(all((pick'-vs*vs'*pick').^2<REALSMALL));
- vinp=all(all((vs-vps*vps'*vs).^2<REALSMALL));
- okpast=pinv+vinp;
- % Does pick summarize all current info? (impact in us column space, pick*uu full rank)
- [ui,di,vi]=svd([impact us]);
- topi=find(diag(di)>REALSMALL);
- ndi=length(topi);
- uis=ui(:,topi);
- uiu=ui(:,ndi+1:size(ui,2));
- if ndi<size(G1,1)
- if(size(pick,1)<size(uis,2))
- oknow=0;
- else
- [ut,dt,vt]=svd(pick*uis);
- toppu=find(diag(dt)>REALSMALL);
- if length(toppu)<size(us,2)
- oknow=0;
- else
- oknow=1;
- end
- end
- else
- oknow=0;
- end
- if vinp
- GS=G1/pick;
- pickn=pick;
- elseif pinv
- r=vs-vps*vps'*vs;
- [ur,dr,vr]=svd(r);
- topr=find(dr>REALSMALL);
- p2=ur(:,topr);
- pickn=[pick;p2'];
- GS=G1/pickn;
- elseif oknow
- GS=G1/[pick;uiu'];
- GS=GS(:,1:size(pick,1));
- pickn=pick;
- else
- pickn=vs';
- GS=us*d;
- end
-ok=oknow+2*pinv+4*vinp;
+function [GS,pickn,ok,uis,uiu,vs]=gstate(G1,impact,pick)
+%function [GS,pickn,ok,uis,vs]=gstate(G1,impact,pick)
+% G1: the coefficient on lagged y from the output of gensys.m.
+% impact:the coefficient on exogenous shocks from the output of gensys.m
+% pick: an optional guess at a matrix of coefficients that extracts
+% the state vector from y
+% ok: 0 if pick matrix is just no good.
+% 1 if pick matrix is usable forward, but loses initial date information
+% 2 if pick matrix is not usable forward, is part of a correct state vector, but is not complete
+% 3 if pick matrix is usable forward, is part of a correct state vector, but is not complete
+% 4 if pick matrix is not usable forward, summarizes past, but is redundant
+% 5 if pick matrix is usable forward, summarizes past, but is redundant
+% 6 if pick matrix summarizes past and is not redundant, but is not usable forward
+% 7 if pick matrix is perfect, both forward and as history summary
+% pickn: a matrix of coefficients that extracts the state vector from y. Equal
+% to pick if pick is supplied and ok=1; otherwise, an ok-maximizing pick matrix that
+% is somewhat similar to pick.
+% GS: the matrix of coefficients on the lagged state variable
+% uis,vs: If uis'*vs is square and full rank, ok=7 is possible, otherwise not. If ok<2, an ok>2 can be found by trying
+% a pick in the row space of vs'. Any pick with pick*uis full column rank will provide a foward state
+% (i.e. ok an odd number).
+% uiu: uiu'y(t)=0, and this is interpretable as the decision rule setting controls as functions of the states.
+% The solution was in the form y(t)=G1*y(t-1)+impact*z(t). Now it's in the form y(t)=GS*pickn*y(t-1)+impact*z(t).
+% In general, pickn is mxn with m<n.
+
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+REALSMALL=1e-9;
+[nr,nc]=size(G1);
+%if nr<nc
+% G1=[G1;zeros(nc-nr,nc)];
+%end
+[u d v]=svd(G1);
+top=find(diag(d)>REALSMALL);
+nd=top(end);
+us=u(:,top);
+uu=u(:,top+1:end);
+d=d(top,top);
+vs=v(:,top);
+if nargin<=2
+ pick=vs';
+ vp=v;
+ vps=vs;
+ dp=eye(nd);
+ ups=eye(nr,nd);
+ ndp=nd;
+else
+ [up dp vp]=svd(pick);
+ topp=find(diag(dp)>REALSMALL);
+ ndp=topp(end);
+ vps=vp(:,topp);
+ dp=dp(topp,topp);
+ ups=up(:,topp);
+end
+ % If we were worried about efficiency, we'd skip some of this when pick=vs.
+ %Does pick summarize history? (pick in v' row space, v in vp' row space).
+ pinv=all(all((pick'-vs*vs'*pick').^2<REALSMALL));
+ vinp=all(all((vs-vps*vps'*vs).^2<REALSMALL));
+ okpast=pinv+vinp;
+ % Does pick summarize all current info? (impact in us column space, pick*uu full rank)
+ [ui,di,vi]=svd([impact us]);
+ topi=find(diag(di)>REALSMALL);
+ ndi=length(topi);
+ uis=ui(:,topi);
+ uiu=ui(:,ndi+1:size(ui,2));
+ if ndi<size(G1,1)
+ if(size(pick,1)<size(uis,2))
+ oknow=0;
+ else
+ [ut,dt,vt]=svd(pick*uis);
+ toppu=find(diag(dt)>REALSMALL);
+ if length(toppu)<size(us,2)
+ oknow=0;
+ else
+ oknow=1;
+ end
+ end
+ else
+ oknow=0;
+ end
+ if vinp
+ GS=G1/pick;
+ pickn=pick;
+ elseif pinv
+ r=vs-vps*vps'*vs;
+ [ur,dr,vr]=svd(r);
+ topr=find(dr>REALSMALL);
+ p2=ur(:,topr);
+ pickn=[pick;p2'];
+ GS=G1/pickn;
+ elseif oknow
+ GS=G1/[pick;uiu'];
+ GS=GS(:,1:size(pick,1));
+ pickn=pick;
+ else
+ pickn=vs';
+ GS=us*d;
+ end
+ok=oknow+2*pinv+4*vinp;
diff --git a/MatlabFiles/gyrfore.m b/MatlabFiles/gyrfore.m
index 7bee2d62b5737a8622c4b8101358c1ee69ed6be9..2ab6c7b1a283e3169326fe28fd4cc2062f9dc392 100644
--- a/MatlabFiles/gyrfore.m
+++ b/MatlabFiles/gyrfore.m
@@ -1,60 +1,60 @@
-function gyrfore(yfore,yacte,keyindx,rnum,cnum,ylab,forelabel,conlab)
-%
-% Graph annual (or calendar year) forecasts vs actual (all data from "msstart.m")
-%
-% yfore: actual and forecast annual growth data with dates.
-% yacte: actual annual growth data with dates.
-% keyindx: index for the variables to be graphed
-% rnum: number of rows in subplot
-% cnum: number of columns in subplot
-% ylab: label for the variables
-% forelabel: title label for as of time of forecast
-% conlab: label for what conditions imposed; e.g., conlab = 'All bar MS shocks inspl'
-%-------------
-% No output argument for this graph file
-%
-% Tao Zha, March 2000
-
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-vyrs = yfore(:,1);
-hornum = cell(length(vyrs),1); % horizontal year (number)
-count=0;
-for k=vyrs'
- count=count+1;
- jnk=num2str(k);
- hornum{count}=jnk(3:4); % e.g., with '1990', we have '90'
-end
-
-count=0;
-for i = keyindx
- count = count+1;
- subplot(rnum,cnum,count)
- %
- plot(yacte(:,1),yacte(:,2+i),vyrs,yfore(:,2+i),'--')
- set(gca,'XTick',vyrs)
- set(gca,'XTickLabel',char(hornum))
- if i==keyindx(1)
- title(forelabel)
- elseif i>=length(keyindx)-1
- xlabel(conlab)
- end
- %
- grid
- ylabel(char(ylab(i)))
-end
+function gyrfore(yfore,yacte,keyindx,rnum,cnum,ylab,forelabel,conlab)
+%
+% Graph annual (or calendar year) forecasts vs actual (all data from "msstart.m")
+%
+% yfore: actual and forecast annual growth data with dates.
+% yacte: actual annual growth data with dates.
+% keyindx: index for the variables to be graphed
+% rnum: number of rows in subplot
+% cnum: number of columns in subplot
+% ylab: label for the variables
+% forelabel: title label for as of time of forecast
+% conlab: label for what conditions imposed; e.g., conlab = 'All bar MS shocks inspl'
+%-------------
+% No output argument for this graph file
+%
+% Tao Zha, March 2000
+
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+vyrs = yfore(:,1);
+hornum = cell(length(vyrs),1); % horizontal year (number)
+count=0;
+for k=vyrs'
+ count=count+1;
+ jnk=num2str(k);
+ hornum{count}=jnk(3:4); % e.g., with '1990', we have '90'
+end
+
+count=0;
+for i = keyindx
+ count = count+1;
+ subplot(rnum,cnum,count)
+ %
+ plot(yacte(:,1),yacte(:,2+i),vyrs,yfore(:,2+i),'--')
+ set(gca,'XTick',vyrs)
+ set(gca,'XTickLabel',char(hornum))
+ if i==keyindx(1)
+ title(forelabel)
+ elseif i>=length(keyindx)-1
+ xlabel(conlab)
+ end
+ %
+ grid
+ ylabel(char(ylab(i)))
+end
diff --git a/MatlabFiles/hesscd.m b/MatlabFiles/hesscd.m
index 99ee783c73ee01f25e5cd4e6e0df4c29b7de1073..a52a388f3b776420028e5990f9b5a5efbfaa94d7 100644
--- a/MatlabFiles/hesscd.m
+++ b/MatlabFiles/hesscd.m
@@ -1,79 +1,79 @@
-function grdd = hesscd(fcn,x0,grdh,P1,P2,P3,P4,P5,P6,P7,P8,P9,...
- P10,P11,P12,P13,P14,P15,P16,P17,P18,P19,P20)
-% computing numerical hessian using a central difference with
-% function grdd = hesscd(fcn,x0,grdh,Passed variables1)
-%
-% fcn: a string naming the objective function.
-% x0: a column vector n*1, at which point the hessian is evaluated.
-% grdh: step size.
-% grdd: hessian matrix (second derivative), n*n.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-stps = eps^(1/3);
-% eps: floating point relative accuracy or machine precision: 2.22e-16
-% stps: step size recommended by Dennis and Schnabel: 6.006e-6
-
-x0 = x0(:);
-tailstr = ')';
-for i=nargin-3:-1:1
- tailstr=[ ',P' num2str(i) tailstr];
-end
-f0 = eval([fcn '(x0' tailstr]);
-
-% ** initializations
-k = length(x0);
-grdd = zeros(k);
-
-% ** Computation of stepsize (dh)
-if all(grdh)
- dh = grdh;
-else
- ax0 = abs(x0);
- if all(x0)
- dax0 = x0 ./ ax0;
- else
- dax0 = 1;
- end
- dh = stps * (max([ax0 (1e-2)*ones(k,1)]'))' .* dax0;
-end
-
-xdh = x0 + dh;
-dh = xdh - x0; % This increases precision slightly
-dhm = dh(:,ones(k,1));
-ee = eye(k) .* dhm;
-
-i = 1;
-while i <= k
- j = i;
- while j <= k
-
- fune1 = eval([fcn '(x0 + ee(:,i) + ee(:,j)' tailstr]);
- fune2 = eval([fcn '(x0 - ee(:,i) + ee(:,j)' tailstr]);
- fune3 = eval([fcn '(x0 + ee(:,i) - ee(:,j)' tailstr]);
- fune4 = eval([fcn '(x0 - ee(:,i) - ee(:,j)' tailstr]);
- grdd(i,j) = (fune1 - fune2 - fune3 + fune4) / (4 * dh(i) * dh(j));
-
- if i ~= j
- grdd(j,i) = grdd(i,j);
- end
-
- j = j+1;
- end
- i = i+1;
-end
-
+function grdd = hesscd(fcn,x0,grdh,P1,P2,P3,P4,P5,P6,P7,P8,P9,...
+ P10,P11,P12,P13,P14,P15,P16,P17,P18,P19,P20)
+% computing numerical hessian using a central difference with
+% function grdd = hesscd(fcn,x0,grdh,Passed variables1)
+%
+% fcn: a string naming the objective function.
+% x0: a column vector n*1, at which point the hessian is evaluated.
+% grdh: step size.
+% grdd: hessian matrix (second derivative), n*n.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+stps = eps^(1/3);
+% eps: floating point relative accuracy or machine precision: 2.22e-16
+% stps: step size recommended by Dennis and Schnabel: 6.006e-6
+
+x0 = x0(:);
+tailstr = ')';
+for i=nargin-3:-1:1
+ tailstr=[ ',P' num2str(i) tailstr];
+end
+f0 = eval([fcn '(x0' tailstr]);
+
+% ** initializations
+k = length(x0);
+grdd = zeros(k);
+
+% ** Computation of stepsize (dh)
+if all(grdh)
+ dh = grdh;
+else
+ ax0 = abs(x0);
+ if all(x0)
+ dax0 = x0 ./ ax0;
+ else
+ dax0 = 1;
+ end
+ dh = stps * (max([ax0 (1e-2)*ones(k,1)]'))' .* dax0;
+end
+
+xdh = x0 + dh;
+dh = xdh - x0; % This increases precision slightly
+dhm = dh(:,ones(k,1));
+ee = eye(k) .* dhm;
+
+i = 1;
+while i <= k
+ j = i;
+ while j <= k
+
+ fune1 = eval([fcn '(x0 + ee(:,i) + ee(:,j)' tailstr]);
+ fune2 = eval([fcn '(x0 - ee(:,i) + ee(:,j)' tailstr]);
+ fune3 = eval([fcn '(x0 + ee(:,i) - ee(:,j)' tailstr]);
+ fune4 = eval([fcn '(x0 - ee(:,i) - ee(:,j)' tailstr]);
+ grdd(i,j) = (fune1 - fune2 - fune3 + fune4) / (4 * dh(i) * dh(j));
+
+ if i ~= j
+ grdd(j,i) = grdd(i,j);
+ end
+
+ j = j+1;
+ end
+ i = i+1;
+end
+
diff --git a/MatlabFiles/hist2.m b/MatlabFiles/hist2.m
deleted file mode 100644
index 37bea11bc62ea965ac05ed723ceb489f840cd438..0000000000000000000000000000000000000000
--- a/MatlabFiles/hist2.m
+++ /dev/null
@@ -1,85 +0,0 @@
-function [no,xo,binwidth] = hist2(y,x)
-%HIST2 Histogram.
-% N = HIST2(Y) bins the elements of Y into 10 equally spaced containers
-% and returns the number of elements in each container. If Y is a
-% matrix, HIST2 works down the columns.
-%
-% N = HIST2(Y,M), where M is a scalar, uses M bins.
-%
-% N = HIST2(Y,X), where X is a vector, returns the distribution of Y
-% among bins with centers specified by X.
-%
-% [N,X] = HIST2(...) also returns the position of the bin centers in X.
-%
-% [N,X,BW] = HIST2(...) also returns the position of the bin centers in X and the width of the bin.
-%
-% HIST2(...) without output arguments produces a histogram bar plot of
-% the results.
-
-% J.N. Little 2-06-86
-% Revised 10-29-87, 12-29-88 LS
-% Revised 8-13-91 by cmt, 2-3-92 by ls.
-% Copyright (c) 1984-97 by The MathWorks, Inc.
-% $Revision: 5.10 $ $Date: 1997/04/08 05:22:50 $
-
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-if nargin == 0
- error('Requires one or two input arguments.')
-end
-if nargin == 1
- x = 10;
-end
-if min(size(y))==1, y = y(:); end
-if isstr(x) | isstr(y)
- error('Input arguments must be numeric.')
-end
-[m,n] = size(y);
-if length(x) == 1
- miny = min(min(y));
- maxy = max(max(y));
- binwidth = (maxy - miny) ./ x;
- xx = miny + binwidth*(0:x);
- xx(length(xx)) = maxy;
- x = xx(1:length(xx)-1) + binwidth/2;
-else
- xx = x(:)';
- miny = min(min(y));
- maxy = max(max(y));
- binwidth = [diff(xx) 0];
- xx = [xx(1)-binwidth(1)/2 xx+binwidth/2];
- xx(1) = miny;
- xx(length(xx)) = maxy;
-end
-nbin = length(xx);
-nn = zeros(nbin,n);
-for i=2:nbin
- nn(i,:) = sum(y <= xx(i));
-end
-nn = nn(2:nbin,:) - nn(1:nbin-1,:);
-if nargout == 0
- bar(x,nn,'hist');
-else
- if min(size(y))==1, % Return row vectors if possible.
- no = nn';
- xo = x;
- else
- no = nn;
- xo = x';
- end
-end
\ No newline at end of file
diff --git a/MatlabFiles/history.m b/MatlabFiles/history.m
index 4f224f32ad23ec922840cdeac9d05ed63c8e4a1a..b1f14487e3f6b034f6001000bcce8cdf8cfdaaed 100644
--- a/MatlabFiles/history.m
+++ b/MatlabFiles/history.m
@@ -1,124 +1,124 @@
-function [hd,ehat] = history(uhat,Bh,A0,nn)
-% Computing historical decompositions with
-% [hd,ehat] = history(uhat,Bh,A0,nn)
-% where hd: historical decompositions, dstp-by-nvar^2 matrix.
-% Column: 1st variable decompositions attributed to nvar
-% (cumulative) shocks, 2nd variable decompositions
-% attributed to nvar (cumulative) shocks, and so on.
-% Row: steps of decompositions "dstp".
-% ehat: structural one-step forecast errors
-% of each variable, dstp-by-nvar.
-% uhat: dstp-by-nvar reduced form one-step forecast errors
-% begining with the decomposition period.
-% Bh: the estimated reduced form coefficient in the form
-% Y(T*nvar) = XB + U, X: T*k, B: k*nvar. The matrix
-% form or dimension is the same as "Bh" from the function "sye".
-% A0: comtemporaneous A in the structural model A(L)y(t) = e(t).
-% nn: # of inputs -- [nvar,lags,dstp (steps of decompositions)].
-% ===== Note: this function can be easily modified to get variance
-% ===== decompositions and impulse responses.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-nvar = nn(1);
-lags = nn(2);
-dstp = nn(3); % number of steps for decompositions
-imstep = dstp; % steps of impulse responses
-tcwc = nvar*lags; % total coefficients without constant
-swish = inv(A0);
-
-
-Ah = Bh';
-% Row: nvar equations
-% Column: 1st lag (with nvar variables) to
-% lags (with nvar variables) + const = k.
-ehat = uhat*A0';
-% dstp-by-nvar matrix: structural one-step forecast errors.
-
-hd = zeros(dstp,nvar*nvar);
-% Column: 1st varaible to nvar shocks, 2nd variables to nvar shock, and so on.
-% Row: steps of decompositions.
-imf = zeros(dstp,nvar*nvar); % impulse responses
-% different format from "hd", i.e., nvar variables to 1st shock,
-% nvar variables to 2nd shock, etc. To make the format the same
-% as "hd", just change Mtem to Mtem' right before stacking "imf".
-% See also "impulseo.m" in \toolbox\cstz
-M = zeros(nvar*(lags+1),nvar); % stacked matrices for impulse responses
-% Stack M0;M1;M2;...;Mlags
-MH = zeros(nvar*dstp,nvar);
-% same as M, but stacked in a different fashion after t > lags so that
-% all the cumulations are recorded for historical decompositions.
-M(1:nvar,:) = swish;
-Mtem = M(1:nvar,:); % temporary M -- impulse responses.
-%
-Hdc = M(1:nvar,:)*diag(ehat(1,:));
-% first period historical decompositions.
-% * put in the form of "hd", as well as "imf"
-Hdc = Hdc';
-hd(1,:) = Hdc(:)';
-imf(1,:) = Mtem(:)';
-
-
-%
-% ** beginning with the second period. Note, 1st period is t=0
-%
-t = 1;
-ims1 = min([dstp-1 lags]);
-while t <= ims1
- % ** impulse response functions
- Mtem = zeros(nvar,nvar);
- for k = 1:t
- Mtem = Ah(:,nvar*(k-1)+1:nvar*k)*M(nvar*(t-k)+1:nvar*(t-k+1),:) + Mtem;
- % Row: nvar equations, each for the nvar variables at tth lag
- end
- M(nvar*t+1:nvar*(t+1),:) = Mtem;
- imf(t+1,:) = Mtem(:)';
- % stack imf with each step, Row: 6 var to 1st shock, 6 var to 2nd shock, etc.
- % ** historical decompositions
- Hdc = M(1:nvar,:)*diag(ehat(t+1,:));
- for k = 1:t
- Hdc = Hdc + M(nvar*k+1:nvar*(k+1),:)*diag(ehat(t+1-k,:));
- % Row: nvar variables; Column: nvar shocks
- end
- Hdc = Hdc';
- hd(t+1,:) = Hdc(:)';
- t= t+1;
-end
-
-MH(1:nvar*(lags+1),:) = M;
-for t = lags+1:imstep-1
- % ** impulse response functions
- M(1:nvar*lags,:) = M(nvar+1:nvar*(lags+1),:);
- Mtem = zeros(nvar,nvar);
- for k = 1:lags
- Mtem = Ah(:,nvar*(k-1)+1:nvar*k)*M(nvar*(lags-k)+1:nvar*(lags-k+1),:) + Mtem;
- % Row: nvar equations, each for the nvar variables at tth lag
- end
- M(nvar*lags+1:nvar*(lags+1),:) = Mtem;
- imf(t+1,:) = Mtem(:)';
- % stack imf with each step, Row: 6 var to 1st shock, 6 var to 2nd shock, etc.
- % ** historical decompositions
- MH(nvar*t+1:nvar*(t+1),:) = Mtem;
- Hdc = MH(1:nvar,:)*diag(ehat(t+1,:));
- for k = 1:t
- Hdc = Hdc + MH(nvar*k+1:nvar*(k+1),:)*diag(ehat(t+1-k,:));
- % Row: nvar variables; Column: nvar shocks
- end
- Hdc = Hdc';
- hd(t+1,:) = Hdc(:)';
-end
-
\ No newline at end of file
+function [hd,ehat] = history(uhat,Bh,A0,nn)
+% Computing historical decompositions with
+% [hd,ehat] = history(uhat,Bh,A0,nn)
+% where hd: historical decompositions, dstp-by-nvar^2 matrix.
+% Column: 1st variable decompositions attributed to nvar
+% (cumulative) shocks, 2nd variable decompositions
+% attributed to nvar (cumulative) shocks, and so on.
+% Row: steps of decompositions "dstp".
+% ehat: structural one-step forecast errors
+% of each variable, dstp-by-nvar.
+% uhat: dstp-by-nvar reduced form one-step forecast errors
+% begining with the decomposition period.
+% Bh: the estimated reduced form coefficient in the form
+% Y(T*nvar) = XB + U, X: T*k, B: k*nvar. The matrix
+% form or dimension is the same as "Bh" from the function "sye".
+% A0: comtemporaneous A in the structural model A(L)y(t) = e(t).
+% nn: # of inputs -- [nvar,lags,dstp (steps of decompositions)].
+% ===== Note: this function can be easily modified to get variance
+% ===== decompositions and impulse responses.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+nvar = nn(1);
+lags = nn(2);
+dstp = nn(3); % number of steps for decompositions
+imstep = dstp; % steps of impulse responses
+tcwc = nvar*lags; % total coefficients without constant
+swish = inv(A0);
+
+
+Ah = Bh';
+% Row: nvar equations
+% Column: 1st lag (with nvar variables) to
+% lags (with nvar variables) + const = k.
+ehat = uhat*A0';
+% dstp-by-nvar matrix: structural one-step forecast errors.
+
+hd = zeros(dstp,nvar*nvar);
+% Column: 1st varaible to nvar shocks, 2nd variables to nvar shock, and so on.
+% Row: steps of decompositions.
+imf = zeros(dstp,nvar*nvar); % impulse responses
+% different format from "hd", i.e., nvar variables to 1st shock,
+% nvar variables to 2nd shock, etc. To make the format the same
+% as "hd", just change Mtem to Mtem' right before stacking "imf".
+% See also "impulseo.m" in \toolbox\cstz
+M = zeros(nvar*(lags+1),nvar); % stacked matrices for impulse responses
+% Stack M0;M1;M2;...;Mlags
+MH = zeros(nvar*dstp,nvar);
+% same as M, but stacked in a different fashion after t > lags so that
+% all the cumulations are recorded for historical decompositions.
+M(1:nvar,:) = swish;
+Mtem = M(1:nvar,:); % temporary M -- impulse responses.
+%
+Hdc = M(1:nvar,:)*diag(ehat(1,:));
+% first period historical decompositions.
+% * put in the form of "hd", as well as "imf"
+Hdc = Hdc';
+hd(1,:) = Hdc(:)';
+imf(1,:) = Mtem(:)';
+
+
+%
+% ** beginning with the second period. Note, 1st period is t=0
+%
+t = 1;
+ims1 = min([dstp-1 lags]);
+while t <= ims1
+ % ** impulse response functions
+ Mtem = zeros(nvar,nvar);
+ for k = 1:t
+ Mtem = Ah(:,nvar*(k-1)+1:nvar*k)*M(nvar*(t-k)+1:nvar*(t-k+1),:) + Mtem;
+ % Row: nvar equations, each for the nvar variables at tth lag
+ end
+ M(nvar*t+1:nvar*(t+1),:) = Mtem;
+ imf(t+1,:) = Mtem(:)';
+ % stack imf with each step, Row: 6 var to 1st shock, 6 var to 2nd shock, etc.
+ % ** historical decompositions
+ Hdc = M(1:nvar,:)*diag(ehat(t+1,:));
+ for k = 1:t
+ Hdc = Hdc + M(nvar*k+1:nvar*(k+1),:)*diag(ehat(t+1-k,:));
+ % Row: nvar variables; Column: nvar shocks
+ end
+ Hdc = Hdc';
+ hd(t+1,:) = Hdc(:)';
+ t= t+1;
+end
+
+MH(1:nvar*(lags+1),:) = M;
+for t = lags+1:imstep-1
+ % ** impulse response functions
+ M(1:nvar*lags,:) = M(nvar+1:nvar*(lags+1),:);
+ Mtem = zeros(nvar,nvar);
+ for k = 1:lags
+ Mtem = Ah(:,nvar*(k-1)+1:nvar*k)*M(nvar*(lags-k)+1:nvar*(lags-k+1),:) + Mtem;
+ % Row: nvar equations, each for the nvar variables at tth lag
+ end
+ M(nvar*lags+1:nvar*(lags+1),:) = Mtem;
+ imf(t+1,:) = Mtem(:)';
+ % stack imf with each step, Row: 6 var to 1st shock, 6 var to 2nd shock, etc.
+ % ** historical decompositions
+ MH(nvar*t+1:nvar*(t+1),:) = Mtem;
+ Hdc = MH(1:nvar,:)*diag(ehat(t+1,:));
+ for k = 1:t
+ Hdc = Hdc + MH(nvar*k+1:nvar*(k+1),:)*diag(ehat(t+1-k,:));
+ % Row: nvar variables; Column: nvar shocks
+ end
+ Hdc = Hdc';
+ hd(t+1,:) = Hdc(:)';
+end
+
diff --git a/MatlabFiles/history2.m b/MatlabFiles/history2.m
index 07a7287f8c600dc58ad368d78cf53994a3572896..d2ceaf6e27d0d0f8a41c9c33df8772592d91de6c 100644
--- a/MatlabFiles/history2.m
+++ b/MatlabFiles/history2.m
@@ -1,160 +1,160 @@
-function [HDratio,HDvalue,yhatn,yforen] = history2(HDindx,A0,Bhml,phil,nn,...
- actup,Estrexa,xdata,nvar,nSample,nSampleCal,forep,forepq,forepy,q_m,...
- qmEnd,vlist,vlistlog,vlistper,lmqyIndx)
-% [HDratio,HDvalue,yhatn,yforen] = history2(HDindx,A0,Bhml,phil,nn,actup,...
-% xdata,nvar,nSample,nSampleCal,forep,forepq,forepy,q_m,...
-% qmEnd,vlist,vlistlog,vlistper,lmqyIndx)
-%
-% (Out-of-sample) historical decompostions: alternative approach to history.m in 3 aspects
-% (1) compute the percentage (not the value itself)
-% (2) NOT cumulative HD (THIS is NOT correct, TAZ, 03/17/99)
-% (3) conditional on time t (not time 0).
-% (4) condtional and uncondtional forecasts
-%
-% HDindx: k-by-1 cell where k groups of shocks; index number for particular shocks
-% A0h: nvar-by-nvar for y(t)A0 = X*A+ + E, where column means equation
-% Bh: the (posterior) estimate of B for y(t) = X*Bh + E*inv(A0)
-% phil: the 1-by-(nvar*lags+1) data matrix where k=nvar*lags+1
-% (last period plus lags before the beginning of forecast)
-% nn: [nvar,lags,forep], forep: forecast periods (monthly)
-% actup: actual data periods (monthly) before the beginning of forecasts
-% xdata: oringal data set, up to the period before (peudo-) out-of-sample forecasting,
-% all logged except R, U, etc.
-% Estrexa: forep-by-nvar -- backed out structural shocks for out-of-sample forecast
-% nvar: number of variables
-% nSample: sample size (including lags or initial periods)
-% nSampleCal: sample size in terms of calendar years
-% forep: forecast periods (monthly)
-% forepq: forecast periods (quarterly)
-% forepy: forecast periods (yearly)
-% q_m: quarterly or monthly for the underlying model
-% qmEnd: last q_m before out-of-sample forecasting
-% vlist: a list of variables
-% vlistlog: sub list of variables that are in log
-% vlistper: sub list of variables that are in percent
-% lmyqIndx: 4-by-1 1 or 0's. Index for m log, m level, qg, and yg; 1: yes; 0: no;
-% if lmyqIndx(1)==1, both monthly log and monthly level are returned
-%--------------
-% HDratio: 5-by-k cells, where k groups (see HDindx) of shocks associated with decomposition;
-% 5: monthly log, monthly level, mg, qg, and calendar yg in this order;
-% each cell is forep(q)(y)-by-nvar
-% HDvalue: same dimension as HDratio but with the values (differences between
-% conditional and unconditional forecasts
-% yhatn: same dimension as HDratio but with conditional forecasts
-% yforen: 5-by-1 cells and each cell is forep(q)(y)-by-nvar unconditional forecasts
-%
-% October 1998 by Tao Zha.
-% Last change 3/19/99 on the dimension of "Estrexa" so that previous programs may not be
-% compatible.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-nyout = 1+nargout('fore_cal');
- % +1 because we want the original logged variables as well.
-[nHD,jnk] = size(HDindx);
-yhat=cell(nHD,1);
-yhatn = cell(nyout,nHD);
- % row: output such as l, mg, qg, or yg; column: how many decomps
-
-
-
-%--------------------------------------------------------------------------
-% Unconditional point forecasts for log, monthly levle, mg, qg, and yg.
-%--------------------------------------------------------------------------
-yforeml = forecast(Bhml,phil,nn);
-yforen = cell(nyout,1);
-yforen{1} = yforeml;
-oputstr = '['; % output string
-for n=2:nyout
- if n<nyout
- oputstr = [oputstr 'yforen{' num2str(n) '},'];
- else
- oputstr = [oputstr 'yforen{' num2str(n) '}'];
- end
-end
-oputstr =[oputstr ']'];
-%
-eval([oputstr ' = fore_cal(yforeml,xdata,nvar,nSample,nSampleCal,forep,'...
- 'forepq,forepy,q_m,qmEnd,vlist,vlistlog,vlistper,lmqyIndx);']);
-
-
-
-%--------------------------------------------------------------------------
-% Conditional forecasts on specified paths of shocks
-% for log, monthly levle, mg, qg, and yg.
-%--------------------------------------------------------------------------
-for k=1:nHD
- Estr = zeros(forep,nvar); % out of sample
- Estr(:,HDindx{k}) = Estrexa(:,HDindx{k}); % out of sample, MS
- yhat{k} = forefixe(A0,Bhml,phil,nn,Estr);
- yhatn{1,k} = yhat{k}; % original logged variables
- %
- oputstr = '['; % output string
- for n=2:nyout
- if n<nyout
- oputstr = [oputstr 'yhatn{' num2str(n) ',k},'];
- else
- oputstr = [oputstr 'yhatn{' num2str(n) ',k}'];
- end
- end
- oputstr =[oputstr ']'];
- %
- eval([oputstr ' = fore_cal(yhat{k},xdata,nvar,nSample,nSampleCal,forep,'...
- 'forepq,forepy,q_m,qmEnd,vlist,vlistlog,vlistper,lmqyIndx);']);
-end
-
-
-
-%--------------------------------------------------------------------------
-% Historical decompositions: both values and raitos (%)
-%--------------------------------------------------------------------------
-HDratio=cell(nyout,nHD);
-HDvalue=HDratio;
-for n=2:nyout
- if lmqyIndx(n-1)
- if (n-1==1)
- yhatotal = zeros(size(yhatn{1,k}));
- for k=1:nHD
- HDvalue{1,k} = yhatn{1,k}-yforen{1};
- yhatotal = yhatotal + abs(HDvalue{1,k});
- % a sum of absolute values. One can also use square
- end
- %
- for k=1:nHD
- HDratio{1,k} = abs(HDvalue{1,k})*100 ./ yhatotal;
- end
- end
- %
- yhatotal = zeros(size(yhatn{n,k}));
- for k=1:nHD
- HDvalue{n,k} = yhatn{n,k}-yforen{n};
- yhatotal = yhatotal + abs(HDvalue{n,k});
- % a sum of absolute values. One can also use square
- end
- %
- for k=1:nHD
- HDratio{n,k} = abs(HDvalue{n,k})*100 ./ yhatotal;
- end
- end
-end
-
-
-%** check. This first may not be zeros, but the second (monthly log) must be zeros.
-%HDvalue{5,1}+HDvalue{5,2}-...
-% (yactCalyg(size(yactCalyg,1)-3:size(yactCalyg,1),:)-yforeCalygml)
-%HDvalue{1,1}+HDvalue{1,2}-...
-% (yact(size(yact,1)-forep+1:size(yact,1),:)-yforen{1,1})
\ No newline at end of file
+function [HDratio,HDvalue,yhatn,yforen] = history2(HDindx,A0,Bhml,phil,nn,...
+ actup,Estrexa,xdata,nvar,nSample,nSampleCal,forep,forepq,forepy,q_m,...
+ qmEnd,vlist,vlistlog,vlistper,lmqyIndx)
+% [HDratio,HDvalue,yhatn,yforen] = history2(HDindx,A0,Bhml,phil,nn,actup,...
+% xdata,nvar,nSample,nSampleCal,forep,forepq,forepy,q_m,...
+% qmEnd,vlist,vlistlog,vlistper,lmqyIndx)
+%
+% (Out-of-sample) historical decompostions: alternative approach to history.m in 3 aspects
+% (1) compute the percentage (not the value itself)
+% (2) NOT cumulative HD (THIS is NOT correct, TAZ, 03/17/99)
+% (3) conditional on time t (not time 0).
+% (4) condtional and uncondtional forecasts
+%
+% HDindx: k-by-1 cell where k groups of shocks; index number for particular shocks
+% A0h: nvar-by-nvar for y(t)A0 = X*A+ + E, where column means equation
+% Bh: the (posterior) estimate of B for y(t) = X*Bh + E*inv(A0)
+% phil: the 1-by-(nvar*lags+1) data matrix where k=nvar*lags+1
+% (last period plus lags before the beginning of forecast)
+% nn: [nvar,lags,forep], forep: forecast periods (monthly)
+% actup: actual data periods (monthly) before the beginning of forecasts
+% xdata: oringal data set, up to the period before (peudo-) out-of-sample forecasting,
+% all logged except R, U, etc.
+% Estrexa: forep-by-nvar -- backed out structural shocks for out-of-sample forecast
+% nvar: number of variables
+% nSample: sample size (including lags or initial periods)
+% nSampleCal: sample size in terms of calendar years
+% forep: forecast periods (monthly)
+% forepq: forecast periods (quarterly)
+% forepy: forecast periods (yearly)
+% q_m: quarterly or monthly for the underlying model
+% qmEnd: last q_m before out-of-sample forecasting
+% vlist: a list of variables
+% vlistlog: sub list of variables that are in log
+% vlistper: sub list of variables that are in percent
+% lmyqIndx: 4-by-1 1 or 0's. Index for m log, m level, qg, and yg; 1: yes; 0: no;
+% if lmyqIndx(1)==1, both monthly log and monthly level are returned
+%--------------
+% HDratio: 5-by-k cells, where k groups (see HDindx) of shocks associated with decomposition;
+% 5: monthly log, monthly level, mg, qg, and calendar yg in this order;
+% each cell is forep(q)(y)-by-nvar
+% HDvalue: same dimension as HDratio but with the values (differences between
+% conditional and unconditional forecasts
+% yhatn: same dimension as HDratio but with conditional forecasts
+% yforen: 5-by-1 cells and each cell is forep(q)(y)-by-nvar unconditional forecasts
+%
+% October 1998 by Tao Zha.
+% Last change 3/19/99 on the dimension of "Estrexa" so that previous programs may not be
+% compatible.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+nyout = 1+nargout('fore_cal');
+ % +1 because we want the original logged variables as well.
+[nHD,jnk] = size(HDindx);
+yhat=cell(nHD,1);
+yhatn = cell(nyout,nHD);
+ % row: output such as l, mg, qg, or yg; column: how many decomps
+
+
+
+%--------------------------------------------------------------------------
+% Unconditional point forecasts for log, monthly levle, mg, qg, and yg.
+%--------------------------------------------------------------------------
+yforeml = forecast(Bhml,phil,nn);
+yforen = cell(nyout,1);
+yforen{1} = yforeml;
+oputstr = '['; % output string
+for n=2:nyout
+ if n<nyout
+ oputstr = [oputstr 'yforen{' num2str(n) '},'];
+ else
+ oputstr = [oputstr 'yforen{' num2str(n) '}'];
+ end
+end
+oputstr =[oputstr ']'];
+%
+eval([oputstr ' = fore_cal(yforeml,xdata,nvar,nSample,nSampleCal,forep,'...
+ 'forepq,forepy,q_m,qmEnd,vlist,vlistlog,vlistper,lmqyIndx);']);
+
+
+
+%--------------------------------------------------------------------------
+% Conditional forecasts on specified paths of shocks
+% for log, monthly levle, mg, qg, and yg.
+%--------------------------------------------------------------------------
+for k=1:nHD
+ Estr = zeros(forep,nvar); % out of sample
+ Estr(:,HDindx{k}) = Estrexa(:,HDindx{k}); % out of sample, MS
+ yhat{k} = forefixe(A0,Bhml,phil,nn,Estr);
+ yhatn{1,k} = yhat{k}; % original logged variables
+ %
+ oputstr = '['; % output string
+ for n=2:nyout
+ if n<nyout
+ oputstr = [oputstr 'yhatn{' num2str(n) ',k},'];
+ else
+ oputstr = [oputstr 'yhatn{' num2str(n) ',k}'];
+ end
+ end
+ oputstr =[oputstr ']'];
+ %
+ eval([oputstr ' = fore_cal(yhat{k},xdata,nvar,nSample,nSampleCal,forep,'...
+ 'forepq,forepy,q_m,qmEnd,vlist,vlistlog,vlistper,lmqyIndx);']);
+end
+
+
+
+%--------------------------------------------------------------------------
+% Historical decompositions: both values and raitos (%)
+%--------------------------------------------------------------------------
+HDratio=cell(nyout,nHD);
+HDvalue=HDratio;
+for n=2:nyout
+ if lmqyIndx(n-1)
+ if (n-1==1)
+ yhatotal = zeros(size(yhatn{1,k}));
+ for k=1:nHD
+ HDvalue{1,k} = yhatn{1,k}-yforen{1};
+ yhatotal = yhatotal + abs(HDvalue{1,k});
+ % a sum of absolute values. One can also use square
+ end
+ %
+ for k=1:nHD
+ HDratio{1,k} = abs(HDvalue{1,k})*100 ./ yhatotal;
+ end
+ end
+ %
+ yhatotal = zeros(size(yhatn{n,k}));
+ for k=1:nHD
+ HDvalue{n,k} = yhatn{n,k}-yforen{n};
+ yhatotal = yhatotal + abs(HDvalue{n,k});
+ % a sum of absolute values. One can also use square
+ end
+ %
+ for k=1:nHD
+ HDratio{n,k} = abs(HDvalue{n,k})*100 ./ yhatotal;
+ end
+ end
+end
+
+
+%** check. This first may not be zeros, but the second (monthly log) must be zeros.
+%HDvalue{5,1}+HDvalue{5,2}-...
+% (yactCalyg(size(yactCalyg,1)-3:size(yactCalyg,1),:)-yforeCalygml)
+%HDvalue{1,1}+HDvalue{1,2}-...
+% (yact(size(yact,1)-forep+1:size(yact,1),:)-yforen{1,1})
diff --git a/MatlabFiles/histpdfcnt.m b/MatlabFiles/histpdfcnt.m
index cffb0567556223e61b385a986e93bb47577259d8..6e50618a67acb9d3b9b73f9cb75f9c2fbd643fbf 100644
--- a/MatlabFiles/histpdfcnt.m
+++ b/MatlabFiles/histpdfcnt.m
@@ -1,134 +1,134 @@
-function [imfpdf,imfpo,imfprob] = histpdfcnt(imfcnt,ndrawscnt,forep,shockp,nvar,ninv,invc,Am,...
- ixdeng,findex,sindex,vindex,idxuniscl,lval,hval,ncom,gIndx)
-% [imfpdf,imfpo,imfprob] = histpdfcnt(imfcnt,ndrawscnt,forep,shockp,nvar,ninv,invc,Am,...
-% ixdeng,findex,sindex,vindex,idxuniscl,lval,hval,ncom,gIndx)
-% From already ordered and binned (cnt: count) series (not pdf yet).
-% Produce (1) the dataset for generating p.d.f., (2) the dataset for
-% probability (NOT density) at each bin, (3) with option ixdeng=1,
-% graphs of density functions
-%
-% imfcnt: 2+ninv-by-forep*shockp*nvar. Counted logcnt, yhatqgcnt, or yhatCalygcnt.
-% In the case of impulse responses, forep=imstp and shockp=nvar.
-% In case of of A0(Avhx), forep=1, shockp=1, nvar=nfp
-% In case of forecasts, shockp=1
-% ndrawscnt: a total number of draws used in imfcnt
-% forep: forecast periods -- 1st dim (must be compatible with findex)
-% shockp: number of shocks -- 2nd dim (must be compatible with sindex)
-% nvar: number of responses (variables) -- 3rd dim (must be compatible with vindex)
-% ninv: the number of small interior intervals for sorting.
-% invc: 1-by-forep*shockp*nvar. Whole inverval lenghth from min to max for one of
-% (yhat, yhatqg, or yhatCalyg)
-% Am: 1-by-forep*shockp*nvar. Range5{i}(:,:,1) is lowest range for for one of
-% (yhat, yhatqg, or yhatCalyg)
-% ixdeng: index for density graphs. 1: enable; 0: disenable
-% findex: index for selected forecast periods, 1st dim (c.f., forep)
-% sindex: index for selected shocks, 2nd dim (c.f., shockp)
-% vindex: variable index, 3rd dim (c.f., nvar)
-% idxuniscl: 1: scale on each graph by using lval and hval; 0: disenable this
-% lval: (length(findex),length(sindex),length(vindex)); lowest point on the axis;
-% The number matches a total of findex, sindex, and vindex
-% hval: (length(findex),length(sindex),length(vindex)); highest point on the axis;
-% The number matches a total of findex, sindex, and vindex
-% ncom: number of combined intervals. Large ncom implies large size of the bin.
-% Must set ncom so that ninv can be divided
-% gIndx: 1: plot graphs of pdf's; 0: no plot
-%-----------------
-% imfpdf: 2+ninv-by-forep*shockp*nvar. Density
-% imfpo: 2+ninv-by-forep*shockp*nvar. Bin position (x-axis) in relation to imfs3
-% imfprob: 2+ninv-by-forep*shockp*nvar. Probability (NOT density) at each bin
-%
-% 27 August 1998 Tao Zha
-% Revised, October 1998, March 1999
-% 3/24/99, added gIndx so that the previous programs may not be compatible.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-invlength = invc ./ ninv;
-invlengthM = repmat(invlength,[2+ninv,1]);
-invlengthM([1 2+ninv],:) = 1;
- % first (-inf, low bound) and last (high bound, +inf), the interval is set
- % to be 1. Of course, theoretically, the interval length should be set
- % to infinity, but 1 is large enough compared with invlength.
-imfprob = imfcnt ./ ndrawscnt; % probability (NOT density)
-imfpdf = imfprob ./ invlengthM; % density
-
-imfpo = [1:2+ninv]'; % positions for each forecast
-imfpo = imfpo - 2; % the first step to put back to original form
-imfpo = repmat(imfpo,[1,forep*shockp*nvar]);
-invcM = repmat(invc,[2+ninv,1]);
-AmM = repmat(Am,[2+ninv,1]);
-imfpo = ((imfpo .* invcM) ./ ninv) + AmM; % 2+ninv-by-forep*shockp*nvar
- % the final step to put back to original form of impulse responses
-
-
-if mod(ninv,ncom)
- warning('Set ncom so that ninv can be divided')
- return
-end
-%
-ninv2=ninv/ncom;
-imfpdfn=zeros(2+ninv2,forep*shockp*nvar); %<<>>
-imfpon=imfpdfn;
-%
-for ik=1:ninv2 % from 2 to 1+ninv2 for imfpdfn and imfpon
- imfpdfn(1+ik,:) = sum(imfpdf(1+(ik-1)*ncom+1:1+ik*ncom,:))/ncom;
- imfpon(1+ik,:) = mean(imfpo(1+(ik-1)*ncom+1:1+ik*ncom,:));
-end
-
-imfpdf4 = reshape(imfpdfn,2+ninv2,forep,nvar,shockp);
-% imfpdf3: row--bin numbers, column--steps, 3rd dim--variables(responses), 4rd dime--shocks
-imfpdf4s = permute(imfpdf4,[1 2 4 3]);
- % imf3pdfs: permuted so that
- % row--bin numbers, column--steps, 3rd dim--shocks, 4rd dime--variables (responses)
-imfpo4 = reshape(imfpon,2+ninv2,forep,nvar,shockp);
-imfpo4s = permute(imfpo4,[1 2 4 3]);
-
-
-if gIndx
- ck1=0;
- for k1=findex
- ck1=ck1+1;
- ck2=0;
- for k2=sindex
- ck2=ck2+1;
- ck3=0;
- for k3=vindex
- ck3=ck3+1;
- %figure
- if idxuniscl
- lpos = min(find(imfpo4s(2:1+ninv2,k1,k2,k3)>lval(ck1,ck2,ck3))); % low position
- hpos = max(find(imfpo4s(2:1+ninv2,k1,k2,k3)<hval(ck1,ck2,ck3))); % high position
- plot(imfpo4s(lpos+1:hpos+1,k1,k2,k3),imfpdf4s(lpos+1:hpos+1,k1,k2,k3)) %
- % push everything forward by 1 because lpos and hpos start at 2
- set(gca,'XLim',[lval(ck1,ck2,ck3) hval(ck1,ck2,ck3)])
- grid
- else
- plot(imfpo4s(2:1+ninv2,k1,k2,k3),imfpdf4s(2:1+ninv2,k1,k2,k3)) %
- grid
- end
- end
- end
- end
-end
-
-%xlabel('The 1984 U Forecast') %The 1982 GDP Growth Forecast')
-%set(gca,'XLim',[lval hval])
-%xact = [7.508 7.508]; %[-2.13 -2.13];
-%yact = [0 0.5]; %[0 0.5];
-%set(line(xact,yact),'Linestyle','-')
-%title('Figure 8')
-%line([-2 -2],[0 400])
\ No newline at end of file
+function [imfpdf,imfpo,imfprob] = histpdfcnt(imfcnt,ndrawscnt,forep,shockp,nvar,ninv,invc,Am,...
+ ixdeng,findex,sindex,vindex,idxuniscl,lval,hval,ncom,gIndx)
+% [imfpdf,imfpo,imfprob] = histpdfcnt(imfcnt,ndrawscnt,forep,shockp,nvar,ninv,invc,Am,...
+% ixdeng,findex,sindex,vindex,idxuniscl,lval,hval,ncom,gIndx)
+% From already ordered and binned (cnt: count) series (not pdf yet).
+% Produce (1) the dataset for generating p.d.f., (2) the dataset for
+% probability (NOT density) at each bin, (3) with option ixdeng=1,
+% graphs of density functions
+%
+% imfcnt: 2+ninv-by-forep*shockp*nvar. Counted logcnt, yhatqgcnt, or yhatCalygcnt.
+% In the case of impulse responses, forep=imstp and shockp=nvar.
+% In case of of A0(Avhx), forep=1, shockp=1, nvar=nfp
+% In case of forecasts, shockp=1
+% ndrawscnt: a total number of draws used in imfcnt
+% forep: forecast periods -- 1st dim (must be compatible with findex)
+% shockp: number of shocks -- 2nd dim (must be compatible with sindex)
+% nvar: number of responses (variables) -- 3rd dim (must be compatible with vindex)
+% ninv: the number of small interior intervals for sorting.
+% invc: 1-by-forep*shockp*nvar. Whole inverval lenghth from min to max for one of
+% (yhat, yhatqg, or yhatCalyg)
+% Am: 1-by-forep*shockp*nvar. Range5{i}(:,:,1) is lowest range for for one of
+% (yhat, yhatqg, or yhatCalyg)
+% ixdeng: index for density graphs. 1: enable; 0: disenable
+% findex: index for selected forecast periods, 1st dim (c.f., forep)
+% sindex: index for selected shocks, 2nd dim (c.f., shockp)
+% vindex: variable index, 3rd dim (c.f., nvar)
+% idxuniscl: 1: scale on each graph by using lval and hval; 0: disenable this
+% lval: (length(findex),length(sindex),length(vindex)); lowest point on the axis;
+% The number matches a total of findex, sindex, and vindex
+% hval: (length(findex),length(sindex),length(vindex)); highest point on the axis;
+% The number matches a total of findex, sindex, and vindex
+% ncom: number of combined intervals. Large ncom implies large size of the bin.
+% Must set ncom so that ninv can be divided
+% gIndx: 1: plot graphs of pdf's; 0: no plot
+%-----------------
+% imfpdf: 2+ninv-by-forep*shockp*nvar. Density
+% imfpo: 2+ninv-by-forep*shockp*nvar. Bin position (x-axis) in relation to imfs3
+% imfprob: 2+ninv-by-forep*shockp*nvar. Probability (NOT density) at each bin
+%
+% 27 August 1998 Tao Zha
+% Revised, October 1998, March 1999
+% 3/24/99, added gIndx so that the previous programs may not be compatible.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+invlength = invc ./ ninv;
+invlengthM = repmat(invlength,[2+ninv,1]);
+invlengthM([1 2+ninv],:) = 1;
+ % first (-inf, low bound) and last (high bound, +inf), the interval is set
+ % to be 1. Of course, theoretically, the interval length should be set
+ % to infinity, but 1 is large enough compared with invlength.
+imfprob = imfcnt ./ ndrawscnt; % probability (NOT density)
+imfpdf = imfprob ./ invlengthM; % density
+
+imfpo = [1:2+ninv]'; % positions for each forecast
+imfpo = imfpo - 2; % the first step to put back to original form
+imfpo = repmat(imfpo,[1,forep*shockp*nvar]);
+invcM = repmat(invc,[2+ninv,1]);
+AmM = repmat(Am,[2+ninv,1]);
+imfpo = ((imfpo .* invcM) ./ ninv) + AmM; % 2+ninv-by-forep*shockp*nvar
+ % the final step to put back to original form of impulse responses
+
+
+if mod(ninv,ncom)
+ warning('Set ncom so that ninv can be divided')
+ return
+end
+%
+ninv2=ninv/ncom;
+imfpdfn=zeros(2+ninv2,forep*shockp*nvar); %<<>>
+imfpon=imfpdfn;
+%
+for ik=1:ninv2 % from 2 to 1+ninv2 for imfpdfn and imfpon
+ imfpdfn(1+ik,:) = sum(imfpdf(1+(ik-1)*ncom+1:1+ik*ncom,:))/ncom;
+ imfpon(1+ik,:) = mean(imfpo(1+(ik-1)*ncom+1:1+ik*ncom,:));
+end
+
+imfpdf4 = reshape(imfpdfn,2+ninv2,forep,nvar,shockp);
+% imfpdf3: row--bin numbers, column--steps, 3rd dim--variables(responses), 4rd dime--shocks
+imfpdf4s = permute(imfpdf4,[1 2 4 3]);
+ % imf3pdfs: permuted so that
+ % row--bin numbers, column--steps, 3rd dim--shocks, 4rd dime--variables (responses)
+imfpo4 = reshape(imfpon,2+ninv2,forep,nvar,shockp);
+imfpo4s = permute(imfpo4,[1 2 4 3]);
+
+
+if gIndx
+ ck1=0;
+ for k1=findex
+ ck1=ck1+1;
+ ck2=0;
+ for k2=sindex
+ ck2=ck2+1;
+ ck3=0;
+ for k3=vindex
+ ck3=ck3+1;
+ %figure
+ if idxuniscl
+ lpos = min(find(imfpo4s(2:1+ninv2,k1,k2,k3)>lval(ck1,ck2,ck3))); % low position
+ hpos = max(find(imfpo4s(2:1+ninv2,k1,k2,k3)<hval(ck1,ck2,ck3))); % high position
+ plot(imfpo4s(lpos+1:hpos+1,k1,k2,k3),imfpdf4s(lpos+1:hpos+1,k1,k2,k3)) %
+ % push everything forward by 1 because lpos and hpos start at 2
+ set(gca,'XLim',[lval(ck1,ck2,ck3) hval(ck1,ck2,ck3)])
+ grid
+ else
+ plot(imfpo4s(2:1+ninv2,k1,k2,k3),imfpdf4s(2:1+ninv2,k1,k2,k3)) %
+ grid
+ end
+ end
+ end
+ end
+end
+
+%xlabel('The 1984 U Forecast') %The 1982 GDP Growth Forecast')
+%set(gca,'XLim',[lval hval])
+%xact = [7.508 7.508]; %[-2.13 -2.13];
+%yact = [0 0.5]; %[0 0.5];
+%set(line(xact,yact),'Linestyle','-')
+%title('Figure 8')
+%line([-2 -2],[0 400])
diff --git a/MatlabFiles/histpdfg.m b/MatlabFiles/histpdfg.m
index 8fd378c4686613e181d97d1b3038a91bf5061078..b7859e69c69975c0fcdee5a0c656731239f8cf86 100644
--- a/MatlabFiles/histpdfg.m
+++ b/MatlabFiles/histpdfg.m
@@ -1,48 +1,48 @@
-function [n,z] = histpdfg(seqdraws,binnum,titstr,xlabstr,ylabstr)
-% [n,z] = histpdfg(seqdraws,binnum,titstr,xlabstr,ylabstr)
-% Plot (if no nargout) and export (if nargout) pdf by scaling histogram
-% of an unsorted sequence of draws
-%
-% seqdraws: an unordered sequence of draws
-% binnum: the total number of bins in histogram, with 10 being a starting value
-% titstr: title string; if [], no title
-% xlabstr: xlabel string; if [], no xlab
-% ylabstr: ylabel string; if [], no ylab
-%--------
-% n: a vector of the number of elements in each bin or container
-% z: a vector of the position of the center of the bin
-% plot p.d.f. graph if no nargout
-%
-% October 1998 Tao Zha
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-if (nargin==2), titstr=[]; xlabstr=[]; ylabstr=[]; end
-
-[n,z,bw]=hist2(seqdraws,binnum);
-n=(n/length(seqdraws))/bw; % make it p.d.f.
-%bar(z,n)
-if nargout
- z=z';
- n=n';
-else
- %figure
- plot(z,n)
- title(titstr)
- xlabel(xlabstr)
- ylabel(ylabstr)
-end
+function [n,z] = histpdfg(seqdraws,binnum,titstr,xlabstr,ylabstr)
+% [n,z] = histpdfg(seqdraws,binnum,titstr,xlabstr,ylabstr)
+% Plot (if no nargout) and export (if nargout) pdf by scaling histogram
+% of an unsorted sequence of draws
+%
+% seqdraws: an unordered sequence of draws
+% binnum: the total number of bins in histogram, with 10 being a starting value
+% titstr: title string; if [], no title
+% xlabstr: xlabel string; if [], no xlab
+% ylabstr: ylabel string; if [], no ylab
+%--------
+% n: a vector of the number of elements in each bin or container
+% z: a vector of the position of the center of the bin
+% plot p.d.f. graph if no nargout
+%
+% October 1998 Tao Zha
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+if (nargin==2), titstr=[]; xlabstr=[]; ylabstr=[]; end
+
+[n,z,bw]=hist2(seqdraws,binnum);
+n=(n/length(seqdraws))/bw; % make it p.d.f.
+%bar(z,n)
+if nargout
+ z=z';
+ n=n';
+else
+ %figure
+ plot(z,n)
+ title(titstr)
+ xlabel(xlabstr)
+ ylabel(ylabstr)
+end
diff --git a/MatlabFiles/histpdfg2d.m b/MatlabFiles/histpdfg2d.m
index ed24a8e5277c4bf374e926398d64e41699832f82..4386708222135b077cae1294546e4f70c28eb81c 100644
--- a/MatlabFiles/histpdfg2d.m
+++ b/MatlabFiles/histpdfg2d.m
@@ -1,124 +1,124 @@
-function [xc,yc,z,zn] = histpdfg2D(w,n,ditp,xlabstr,ylabstr)
-% histpdfg2D(w,n,ditp,xlabstr,ylabstr)
-% Given 2D draws, generate 2D pdf by scaling 2D histogram
-%
-% w: ndraws-by-2 matrix where ndraws is # of draws and 2 variables
-% n: 1-by-2 vector -- # of bins (integers in general) for both x-axis and y-axis
-% ditp: number -- degrees of bicubic interpolation
-% xlabstr: xlabel string; if [], no xlab
-% ylabstr: ylabel string; if [], no ylab
-%---------------------------------
-% xc: the position of centered bin on x-axis, from left to right. All rows are identical
-% yc: the position of centered bin on y-axis, from top to bottom. All columns are identical
-% z: size(xc,2)-by-size(yc,1) -- the pdf value in each rectangular cell
-% zn: size(xc,2)-by-size(yc,1) -- the probability value in each rectangular cell
-% if nargout==0, plot 2D p.d.f. graphics
-%
-% January 1999 by Tao Zha
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-if (size(w,2)~=2)
- error('The size of 1st argument must be *-by-2 (i.e., 2 columns)')
-end
-
-if (nargin==3), xlabstr=[]; ylabstr=[]; end
-
-if (length(n(:))~=2)
- error('2nd argument must have exactly 2 elements')
-end
-
-if (min(n)<3)
- error('2nd argument -- bin size -- must have at least 3 for each axis')
-end
-
-ndraws=size(w,1);
-
-%*** x-axis
-minx = min(min(w(:,1)));
-maxx = max(max(w(:,1)));
-h1 = (maxx-minx)/n(1); % binwidth for x-axis
-x = minx + h1*(0:n(1));
-xlen = length(x); % n(1)+1
-x(xlen) = maxx; % in case n(1) is not an integer
-xc = x(1:xlen-1) + h1/2; % the position of the center of the x-bin
- % 1-by-n(1) row vector: from left to right
-xc = repmat(xc,[n(2) 1]);
-
-%*** y-axis
-miny = min(min(w(:,2)));
-maxy = max(max(w(:,2)));
-h2 = (maxy-miny)/n(2); % binwidth for y-axis
-y = miny + h2*(0:n(2));
-ylen = length(y);
-y(ylen) = maxy; % in case n(2) is not an integer
-yc = y(1:ylen-1) + h2/2; % the position of the center of the y-bin
-yc = yc(:); % n(2)-by-1 column vector: from top to bottom
-yc = repmat(yc,[1 n(1)]);
-
-
-zn = zeros(n(2),n(1)); % the reverse of x and y is designed for mesh.
- % see meshgrid to understand this.
-tic
-for draws=1:ndraws
- k1 = floor((w(draws,1)-minx)/h1)+1;
- k2 = floor((w(draws,2)-miny)/h2)+1;
- %
- % if k1==0
- % k1=1;
- % end
- % if k2==0;
- % k2=1;
- % end
- %
- if k1>n(1)
- k1=n(1);
- end
- if k2>n(2)
- k2=n(2)
- end
- zn(k2,k1) = zn(k2,k1)+1;
-end
-timeloop=toc;
-disp(['Loop time -- minutes ' num2str(timeloop/60)])
-
-zn = zn/ndraws; % probability in each rectangular cell
-
-z=zn/(h1*h2); % converted to the height of the p.d.f.
-
-
-%*** scaled 2D histogram or p.d.f.
-if (nargout==0)
- %figure(1)
- colormap(zeros(1,3)); % set color to black
- mesh(xc,yc,z)
- title('Scaled histogram or p.d.f.')
- xlabel(xlabstr)
- ylabel(ylabstr)
-end
-
-%*** interpolation
-if (ditp & nargout==0)
- [xi,yi]=meshgrid(minx:h1/ditp:maxx,miny:h2/ditp:maxy);
- zi = interp2(xc,yc,z,xi,yi,'bicubic');
- figure(30)
- colormap(zeros(1,3)); % set color to black
- mesh(xi,yi,zi)
- title('Interpolated p.d.f.')
- xlabel(xlabstr)
- ylabel(ylabstr)
-end
\ No newline at end of file
+function [xc,yc,z,zn] = histpdfg2D(w,n,ditp,xlabstr,ylabstr)
+% histpdfg2D(w,n,ditp,xlabstr,ylabstr)
+% Given 2D draws, generate 2D pdf by scaling 2D histogram
+%
+% w: ndraws-by-2 matrix where ndraws is # of draws and 2 variables
+% n: 1-by-2 vector -- # of bins (integers in general) for both x-axis and y-axis
+% ditp: number -- degrees of bicubic interpolation
+% xlabstr: xlabel string; if [], no xlab
+% ylabstr: ylabel string; if [], no ylab
+%---------------------------------
+% xc: the position of centered bin on x-axis, from left to right. All rows are identical
+% yc: the position of centered bin on y-axis, from top to bottom. All columns are identical
+% z: size(xc,2)-by-size(yc,1) -- the pdf value in each rectangular cell
+% zn: size(xc,2)-by-size(yc,1) -- the probability value in each rectangular cell
+% if nargout==0, plot 2D p.d.f. graphics
+%
+% January 1999 by Tao Zha
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+if (size(w,2)~=2)
+ error('The size of 1st argument must be *-by-2 (i.e., 2 columns)')
+end
+
+if (nargin==3), xlabstr=[]; ylabstr=[]; end
+
+if (length(n(:))~=2)
+ error('2nd argument must have exactly 2 elements')
+end
+
+if (min(n)<3)
+ error('2nd argument -- bin size -- must have at least 3 for each axis')
+end
+
+ndraws=size(w,1);
+
+%*** x-axis
+minx = min(min(w(:,1)));
+maxx = max(max(w(:,1)));
+h1 = (maxx-minx)/n(1); % binwidth for x-axis
+x = minx + h1*(0:n(1));
+xlen = length(x); % n(1)+1
+x(xlen) = maxx; % in case n(1) is not an integer
+xc = x(1:xlen-1) + h1/2; % the position of the center of the x-bin
+ % 1-by-n(1) row vector: from left to right
+xc = repmat(xc,[n(2) 1]);
+
+%*** y-axis
+miny = min(min(w(:,2)));
+maxy = max(max(w(:,2)));
+h2 = (maxy-miny)/n(2); % binwidth for y-axis
+y = miny + h2*(0:n(2));
+ylen = length(y);
+y(ylen) = maxy; % in case n(2) is not an integer
+yc = y(1:ylen-1) + h2/2; % the position of the center of the y-bin
+yc = yc(:); % n(2)-by-1 column vector: from top to bottom
+yc = repmat(yc,[1 n(1)]);
+
+
+zn = zeros(n(2),n(1)); % the reverse of x and y is designed for mesh.
+ % see meshgrid to understand this.
+tic
+for draws=1:ndraws
+ k1 = floor((w(draws,1)-minx)/h1)+1;
+ k2 = floor((w(draws,2)-miny)/h2)+1;
+ %
+ % if k1==0
+ % k1=1;
+ % end
+ % if k2==0;
+ % k2=1;
+ % end
+ %
+ if k1>n(1)
+ k1=n(1);
+ end
+ if k2>n(2)
+ k2=n(2)
+ end
+ zn(k2,k1) = zn(k2,k1)+1;
+end
+timeloop=toc;
+disp(['Loop time -- minutes ' num2str(timeloop/60)])
+
+zn = zn/ndraws; % probability in each rectangular cell
+
+z=zn/(h1*h2); % converted to the height of the p.d.f.
+
+
+%*** scaled 2D histogram or p.d.f.
+if (nargout==0)
+ %figure(1)
+ colormap(zeros(1,3)); % set color to black
+ mesh(xc,yc,z)
+ title('Scaled histogram or p.d.f.')
+ xlabel(xlabstr)
+ ylabel(ylabstr)
+end
+
+%*** interpolation
+if (ditp & nargout==0)
+ [xi,yi]=meshgrid(minx:h1/ditp:maxx,miny:h2/ditp:maxy);
+ zi = interp2(xc,yc,z,xi,yi,'bicubic');
+ figure(30)
+ colormap(zeros(1,3)); % set color to black
+ mesh(xi,yi,zi)
+ title('Interpolated p.d.f.')
+ xlabel(xlabstr)
+ ylabel(ylabstr)
+end
diff --git a/MatlabFiles/histwpdfg.m b/MatlabFiles/histwpdfg.m
index 1daa8e555fbc5bf54d2a8a2a54c8dc10a9abe60c..8cb8a2f3e005419ecb4b7729aaa2545dbf2b8a56 100644
--- a/MatlabFiles/histwpdfg.m
+++ b/MatlabFiles/histwpdfg.m
@@ -1,78 +1,78 @@
-function [xp,xpd,xc,w,bw] = histwpdfg(s,nbin,gIdx,w,xlabstr,ylabstr,titstr)
-% [xp,xpd,xc,w,bw] = histwpdfg(s,nbin,gIdx,w,xlabstr,ylabstr,titstr)
-% Generate probabilities and plot scaled densitys, given the unsorted draws and weights
-%
-% s: ndraws-by-n draws where ndraws is # of draws and n is # of series
-% nbin: the number of bins (the maximum is ndraws)
-% gIdx: 1 if plotting the pdf; 0 if no graph
-% w (optional): ndraws-by-n (unscaled) weights where ndraws is # of draws and n is # of series
-% xlabstr (optional): xlabel string
-% ylabstr (optional): ylabel string
-% titstr (optional): title string
-%-------------
-% xp: nbin-by-n: probability (not density) in the centered bin on x-axis.
-% NOTE: sum(xp) must be 1 for each column
-% xpd: nbin-by-n: density (not probability) in the centered bin on x-axis.
-% xc: nbin-by-n: the position of centered bin on x-axis, from top to bottom.
-% All columns are identical
-% w: ndraws-by-n scaled weights so that sum(w)=1 for each column
-% bw: bandwidth
-%
-% August 1999 by Tao Zha
-% July 18 2000. Place w after gIdx so that previous programs need modifications accordingly.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-if (nargin<=4), titstr=[]; xlabstr=[]; ylabstr=[]; end
-[m,n] = size(s);
-if (nargin<=3)
- w=ones(m,n)/m;
-else
- %** normalized to 1 for the probability
- wsum = repmat(sum(w), [m 1]);
- w = w ./ wsum;
-end
-
-%if min(size(s))==1, s=s(:); w=w(:); end % making sure they are column vectors
-
-%*** the position of the center of the bin on the x-axis
-mins = min(min(s));
-maxs = max(max(s));
-bw = (maxs-mins)/nbin; % binwidth for x-axis
-x = mins + bw*(0:nbin);
-x(end) = maxs; % in case nbin is not an integer
-xc = x(1:end-1) + bw/2; % the position of the center of the x-bin
-xc = xc'; % nbin-by-1 row vector: from top to bottom
-xc = repmat(xc,[1 n]); % nbin-by-n, same for each column
-
-
-%*** the probability at each bin on the x-axis
-nbin = nbin+1; % + 1 to get the difference for getting probability xp
-nn = zeros(nbin,n);
-for i=2:nbin
- for k=1:n
- xidx = find(s(:,k) <= x(i)); % index for the positions
- nn(i,k) = sum(w(xidx,k));
- end
-end
-xp = nn(2:nbin,:) - nn(1:nbin-1,:);
-xpd = xp/bw; % the density, NOT the probability as xp
-
-if gIdx
- plot(xc,xpd)
- title(titstr), xlabel(xlabstr), ylabel(ylabstr);
-end
+function [xp,xpd,xc,w,bw] = histwpdfg(s,nbin,gIdx,w,xlabstr,ylabstr,titstr)
+% [xp,xpd,xc,w,bw] = histwpdfg(s,nbin,gIdx,w,xlabstr,ylabstr,titstr)
+% Generate probabilities and plot scaled densitys, given the unsorted draws and weights
+%
+% s: ndraws-by-n draws where ndraws is # of draws and n is # of series
+% nbin: the number of bins (the maximum is ndraws)
+% gIdx: 1 if plotting the pdf; 0 if no graph
+% w (optional): ndraws-by-n (unscaled) weights where ndraws is # of draws and n is # of series
+% xlabstr (optional): xlabel string
+% ylabstr (optional): ylabel string
+% titstr (optional): title string
+%-------------
+% xp: nbin-by-n: probability (not density) in the centered bin on x-axis.
+% NOTE: sum(xp) must be 1 for each column
+% xpd: nbin-by-n: density (not probability) in the centered bin on x-axis.
+% xc: nbin-by-n: the position of centered bin on x-axis, from top to bottom.
+% All columns are identical
+% w: ndraws-by-n scaled weights so that sum(w)=1 for each column
+% bw: bandwidth
+%
+% August 1999 by Tao Zha
+% July 18 2000. Place w after gIdx so that previous programs need modifications accordingly.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+if (nargin<=4), titstr=[]; xlabstr=[]; ylabstr=[]; end
+[m,n] = size(s);
+if (nargin<=3)
+ w=ones(m,n)/m;
+else
+ %** normalized to 1 for the probability
+ wsum = repmat(sum(w), [m 1]);
+ w = w ./ wsum;
+end
+
+%if min(size(s))==1, s=s(:); w=w(:); end % making sure they are column vectors
+
+%*** the position of the center of the bin on the x-axis
+mins = min(min(s));
+maxs = max(max(s));
+bw = (maxs-mins)/nbin; % binwidth for x-axis
+x = mins + bw*(0:nbin);
+x(end) = maxs; % in case nbin is not an integer
+xc = x(1:end-1) + bw/2; % the position of the center of the x-bin
+xc = xc'; % nbin-by-1 row vector: from top to bottom
+xc = repmat(xc,[1 n]); % nbin-by-n, same for each column
+
+
+%*** the probability at each bin on the x-axis
+nbin = nbin+1; % + 1 to get the difference for getting probability xp
+nn = zeros(nbin,n);
+for i=2:nbin
+ for k=1:n
+ xidx = find(s(:,k) <= x(i)); % index for the positions
+ nn(i,k) = sum(w(xidx,k));
+ end
+end
+xp = nn(2:nbin,:) - nn(1:nbin-1,:);
+xpd = xp/bw; % the density, NOT the probability as xp
+
+if gIdx
+ plot(xc,xpd)
+ title(titstr), xlabel(xlabstr), ylabel(ylabstr);
+end
diff --git a/MatlabFiles/histwpdfg2d.m b/MatlabFiles/histwpdfg2d.m
index 8362c7d196324350c7b75f39d47948860f1c617b..3ad73c7ba86dc03ba214f140315c0babc5d47380 100644
--- a/MatlabFiles/histwpdfg2d.m
+++ b/MatlabFiles/histwpdfg2d.m
@@ -1,127 +1,127 @@
-function [xc,yc,z,zn] = histwpdfg2D(s,w,n,ditp,xlabstr,ylabstr)
-% [xc,yc,z,zn] = histwpdfg2D(s,w,n,ditp,xlabstr,ylabstr)
-% Given uneven weights and 2D draws, generate 2D pdf and probabilites by scaling 2D histogram
-%
-% s: ndraws-by-2 matrix where ndraws is # of draws and 2 variables
-% w: ndraws-by-1 vector of (uneven) weights
-% n: 1-by-2 vector -- # of bins (integers in general) for both x-axis and y-axis
-% ditp: number -- degrees of bicubic interpolation
-% xlabstr: xlabel string; if [], no xlab
-% ylabstr: ylabel string; if [], no ylab
-%---------------------------------
-% xc: the position of centered bin on x-axis, from left to right. All rows are identical
-% yc: the position of centered bin on y-axis, from top to bottom. All columns are identical
-% z: size(xc,2)-by-size(yc,1) -- the pdf value in each rectangular cell
-% zn: size(xc,2)-by-size(yc,1) -- the probability value in each rectangular cell
-% if nargout==0, plot 2D p.d.f. graphics
-%
-% January 1999 by Tao Zha
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-if (size(s,2)~=2)
- error('The size of 1st argument must be *-by-2 (i.e., 2 columns)')
-end
-
-if (nargin==3), xlabstr=[]; ylabstr=[]; end
-
-if (length(n(:))~=2)
- error('2nd argument must have exactly 2 elements')
-end
-
-if (min(n)<3)
- error('2nd argument -- bin size -- must have at least 3 for each axis')
-end
-
-ndraws=size(s,1);
-
-%** normalized to 1 for the probability
-w=w(:); % make sure it's a column vector
-w = w/sum(w);
-
-%*** x-axis
-minx = min(min(s(:,1)));
-maxx = max(max(s(:,1)));
-h1 = (maxx-minx)/n(1); % binwidth for x-axis
-x = minx + h1*(0:n(1));
-xlen = length(x); % n(1)+1
-x(xlen) = maxx; % in case n(1) is not an integer
-xc = x(1:xlen-1) + h1/2; % the position of the center of the x-bin
- % 1-by-n(1) row vector: from left to right
-xc = repmat(xc,[n(2) 1]);
-
-%*** y-axis
-miny = min(min(s(:,2)));
-maxy = max(max(s(:,2)));
-h2 = (maxy-miny)/n(2); % binwidth for y-axis
-y = miny + h2*(0:n(2));
-ylen = length(y);
-y(ylen) = maxy; % in case n(2) is not an integer
-yc = y(1:ylen-1) + h2/2; % the position of the center of the y-bin
-yc = yc(:); % n(2)-by-1 column vector: from top to bottom
-yc = repmat(yc,[1 n(1)]);
-
-
-zn = zeros(n(2),n(1)); % the reverse of x and y is designed for mesh.
- % see meshgrid to understand this.
-tic
-for draws=1:ndraws
- k1 = floor((s(draws,1)-minx)/h1)+1;
- k2 = floor((s(draws,2)-miny)/h2)+1;
- %
- % if k1==0
- % k1=1;
- % end
- % if k2==0;
- % k2=1;
- % end
- %
- if k1>n(1)
- k1=n(1);
- end
- if k2>n(2)
- k2=n(2)
- end
- zn(k2,k1) = zn(k2,k1)+w(draws); % probability in each rectangular cell
-end
-timeloop=toc;
-disp(['Loop time -- minutes ' num2str(timeloop/60)])
-
-z=zn/(h1*h2); % converted to the height of the p.d.f.
-
-
-%*** scaled 2D histogram or p.d.f.
-if (nargout==0)
- %figure(1)
- colormap(zeros(1,3)); % set color to black
- mesh(xc,yc,z)
- title('Scaled histogram or p.d.f.')
- xlabel(xlabstr)
- ylabel(ylabstr)
-end
-
-%*** interpolation
-if (ditp & nargout==0)
- [xi,yi]=meshgrid(minx:h1/ditp:maxx,miny:h2/ditp:maxy);
- zi = interp2(xc,yc,z,xi,yi,'bicubic');
- figure(30)
- colormap(zeros(1,3)); % set color to black
- mesh(xi,yi,zi) % or mesh
- title('Interpolated p.d.f.')
- xlabel(xlabstr)
- ylabel(ylabstr)
-end
\ No newline at end of file
+function [xc,yc,z,zn] = histwpdfg2D(s,w,n,ditp,xlabstr,ylabstr)
+% [xc,yc,z,zn] = histwpdfg2D(s,w,n,ditp,xlabstr,ylabstr)
+% Given uneven weights and 2D draws, generate 2D pdf and probabilites by scaling 2D histogram
+%
+% s: ndraws-by-2 matrix where ndraws is # of draws and 2 variables
+% w: ndraws-by-1 vector of (uneven) weights
+% n: 1-by-2 vector -- # of bins (integers in general) for both x-axis and y-axis
+% ditp: number -- degrees of bicubic interpolation
+% xlabstr: xlabel string; if [], no xlab
+% ylabstr: ylabel string; if [], no ylab
+%---------------------------------
+% xc: the position of centered bin on x-axis, from left to right. All rows are identical
+% yc: the position of centered bin on y-axis, from top to bottom. All columns are identical
+% z: size(xc,2)-by-size(yc,1) -- the pdf value in each rectangular cell
+% zn: size(xc,2)-by-size(yc,1) -- the probability value in each rectangular cell
+% if nargout==0, plot 2D p.d.f. graphics
+%
+% January 1999 by Tao Zha
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+if (size(s,2)~=2)
+ error('The size of 1st argument must be *-by-2 (i.e., 2 columns)')
+end
+
+if (nargin==3), xlabstr=[]; ylabstr=[]; end
+
+if (length(n(:))~=2)
+ error('2nd argument must have exactly 2 elements')
+end
+
+if (min(n)<3)
+ error('2nd argument -- bin size -- must have at least 3 for each axis')
+end
+
+ndraws=size(s,1);
+
+%** normalized to 1 for the probability
+w=w(:); % make sure it's a column vector
+w = w/sum(w);
+
+%*** x-axis
+minx = min(min(s(:,1)));
+maxx = max(max(s(:,1)));
+h1 = (maxx-minx)/n(1); % binwidth for x-axis
+x = minx + h1*(0:n(1));
+xlen = length(x); % n(1)+1
+x(xlen) = maxx; % in case n(1) is not an integer
+xc = x(1:xlen-1) + h1/2; % the position of the center of the x-bin
+ % 1-by-n(1) row vector: from left to right
+xc = repmat(xc,[n(2) 1]);
+
+%*** y-axis
+miny = min(min(s(:,2)));
+maxy = max(max(s(:,2)));
+h2 = (maxy-miny)/n(2); % binwidth for y-axis
+y = miny + h2*(0:n(2));
+ylen = length(y);
+y(ylen) = maxy; % in case n(2) is not an integer
+yc = y(1:ylen-1) + h2/2; % the position of the center of the y-bin
+yc = yc(:); % n(2)-by-1 column vector: from top to bottom
+yc = repmat(yc,[1 n(1)]);
+
+
+zn = zeros(n(2),n(1)); % the reverse of x and y is designed for mesh.
+ % see meshgrid to understand this.
+tic
+for draws=1:ndraws
+ k1 = floor((s(draws,1)-minx)/h1)+1;
+ k2 = floor((s(draws,2)-miny)/h2)+1;
+ %
+ % if k1==0
+ % k1=1;
+ % end
+ % if k2==0;
+ % k2=1;
+ % end
+ %
+ if k1>n(1)
+ k1=n(1);
+ end
+ if k2>n(2)
+ k2=n(2)
+ end
+ zn(k2,k1) = zn(k2,k1)+w(draws); % probability in each rectangular cell
+end
+timeloop=toc;
+disp(['Loop time -- minutes ' num2str(timeloop/60)])
+
+z=zn/(h1*h2); % converted to the height of the p.d.f.
+
+
+%*** scaled 2D histogram or p.d.f.
+if (nargout==0)
+ %figure(1)
+ colormap(zeros(1,3)); % set color to black
+ mesh(xc,yc,z)
+ title('Scaled histogram or p.d.f.')
+ xlabel(xlabstr)
+ ylabel(ylabstr)
+end
+
+%*** interpolation
+if (ditp & nargout==0)
+ [xi,yi]=meshgrid(minx:h1/ditp:maxx,miny:h2/ditp:maxy);
+ zi = interp2(xc,yc,z,xi,yi,'bicubic');
+ figure(30)
+ colormap(zeros(1,3)); % set color to black
+ mesh(xi,yi,zi) % or mesh
+ title('Interpolated p.d.f.')
+ xlabel(xlabstr)
+ ylabel(ylabstr)
+end
diff --git a/MatlabFiles/imc2errgraph.m b/MatlabFiles/imc2errgraph.m
index 82b19c11944731c18d06bfbb6105a4b8de509f4b..c87fc0b350d37954a41b70ea23a2f7003f4e5b54 100644
--- a/MatlabFiles/imc2errgraph.m
+++ b/MatlabFiles/imc2errgraph.m
@@ -1,120 +1,120 @@
-function scaleout = imc2errgraph(imf,firstl1,firsth1,...
- firstl,firsth,nvar,imstp,xlab,ylab,XTick)
-% scaleout = imc2errgraph(imf,firstl1,firsth1,...
-% firstl,firsth,nvar,imstp,xlab,ylab,XTick)
-% imc2errgraph: impulse, c (column: shock 1 to N), 2 error bands, graph
-% imf: impulse responses, column (responses to 1st shock, responses to 2nd shock
-% etc), row (impusle steps),
-% firstl1: lower band, .68
-% highth1: high band, .68
-% firstl: lower band, .90
-% highth: high band, .90
-% nvar: number of variables
-% imstp: step of impulse responses
-% xlab,ylab: labels
-% Xtick: tick on x-axis; e.g., [1 12 36]; if [], no ticks
-%-----------------------
-% scaleout: gives out max and min for each row of graphs
-%
-% See imrgraph, imcerrgraph, imrerrgraph
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-t = 1:imstp;
-temp1=zeros(nvar,1);
-temp2=zeros(nvar,1);
-maxval=zeros(nvar,1);
-minval=zeros(nvar,1);
-for i = 1:nvar
- for j = 1:nvar
- jnk1=max(firsth(:,(j-1)*nvar+i));
- jnk2=max(firstl(:,(j-1)*nvar+i));
- jnk3=max(firsth1(:,(j-1)*nvar+i));
- jnk4=max(firstl1(:,(j-1)*nvar+i));
- jnk5=max(imf(:,(j-1)*nvar+i));
-
- temp1(j)=max([jnk1 jnk2 jnk3 jnk4 jnk5]);
- %
- jnk1=min(firstl(:,(j-1)*nvar+i));
- jnk2=min(firsth(:,(j-1)*nvar+i));
- jnk3=min(firstl1(:,(j-1)*nvar+i));
- jnk4=min(firsth1(:,(j-1)*nvar+i));
- jnk5=min(imf(:,(j-1)*nvar+i));
-
- temp2(j)=min([jnk1 jnk2 jnk3 jnk4 jnk5]);
- end
- maxval(i)=max(temp1);
- minval(i)=min(temp2);
-end
-
-scaleout = [maxval(:) minval(:)];
-
-%--------------
-% Column j: Shock 1 to N; Row i: Responses to
-%-------------
-%figure
-rowlabel = 1;
-for i = 1:nvar
- columnlabel = 1;
-
- if minval(i)<0
- if maxval(i)<=0
- yt=[minval(i) 0];
- else
- yt=[minval(i) 0 maxval(i)];
- end
- else % (minval(i) >=0)
- if maxval(i) > 0
- yt=[0 maxval(i)];
- else % (identically zero responses)
- yt=[-1 0 1];
- end
- end
- scale=[1 imstp minval(i) maxval(i)];
- for j = 1:nvar
- k1=(i-1)*nvar+j;
- k2=(j-1)*nvar+i;
- subplot(nvar,nvar,k1)
- plot(t,imf(:,k2),t,[firstl1(:,k2) firsth1(:,k2)],'--',...
- t,[firstl(:,k2) firsth(:,k2)],'-.',t,zeros(length(imf(:,k2)),1),'-');
- grid;
- axis(scale);
- %set(gca,'YLim',[minval(i) maxval(i)])
- %
- set(gca,'XTick',XTick)
- set(gca,'YTick',yt)
- if i<nvar
- set(gca,'XTickLabelMode','manual','XTickLabel',[])
- end
- %set(gca,'XTickLabel',' ');
- if j>1
- set(gca,'YTickLabel',' ');
- end
- if rowlabel == 1
- %title(['x' num2str(j)])
- %title(eval(['x' num2str(j)]))
- title(char(xlab(j)))
- end
- if columnlabel == 1
- %ylabel(['x' num2str(i)])
- %ylabel(eval(['x' num2str(i)]))
- ylabel(char(ylab(i)))
- end
- columnlabel = 0;
- end
- rowlabel = 0;
-end
\ No newline at end of file
+function scaleout = imc2errgraph(imf,firstl1,firsth1,...
+ firstl,firsth,nvar,imstp,xlab,ylab,XTick)
+% scaleout = imc2errgraph(imf,firstl1,firsth1,...
+% firstl,firsth,nvar,imstp,xlab,ylab,XTick)
+% imc2errgraph: impulse, c (column: shock 1 to N), 2 error bands, graph
+% imf: impulse responses, column (responses to 1st shock, responses to 2nd shock
+% etc), row (impusle steps),
+% firstl1: lower band, .68
+% highth1: high band, .68
+% firstl: lower band, .90
+% highth: high band, .90
+% nvar: number of variables
+% imstp: step of impulse responses
+% xlab,ylab: labels
+% Xtick: tick on x-axis; e.g., [1 12 36]; if [], no ticks
+%-----------------------
+% scaleout: gives out max and min for each row of graphs
+%
+% See imrgraph, imcerrgraph, imrerrgraph
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+t = 1:imstp;
+temp1=zeros(nvar,1);
+temp2=zeros(nvar,1);
+maxval=zeros(nvar,1);
+minval=zeros(nvar,1);
+for i = 1:nvar
+ for j = 1:nvar
+ jnk1=max(firsth(:,(j-1)*nvar+i));
+ jnk2=max(firstl(:,(j-1)*nvar+i));
+ jnk3=max(firsth1(:,(j-1)*nvar+i));
+ jnk4=max(firstl1(:,(j-1)*nvar+i));
+ jnk5=max(imf(:,(j-1)*nvar+i));
+
+ temp1(j)=max([jnk1 jnk2 jnk3 jnk4 jnk5]);
+ %
+ jnk1=min(firstl(:,(j-1)*nvar+i));
+ jnk2=min(firsth(:,(j-1)*nvar+i));
+ jnk3=min(firstl1(:,(j-1)*nvar+i));
+ jnk4=min(firsth1(:,(j-1)*nvar+i));
+ jnk5=min(imf(:,(j-1)*nvar+i));
+
+ temp2(j)=min([jnk1 jnk2 jnk3 jnk4 jnk5]);
+ end
+ maxval(i)=max(temp1);
+ minval(i)=min(temp2);
+end
+
+scaleout = [maxval(:) minval(:)];
+
+%--------------
+% Column j: Shock 1 to N; Row i: Responses to
+%-------------
+%figure
+rowlabel = 1;
+for i = 1:nvar
+ columnlabel = 1;
+
+ if minval(i)<0
+ if maxval(i)<=0
+ yt=[minval(i) 0];
+ else
+ yt=[minval(i) 0 maxval(i)];
+ end
+ else % (minval(i) >=0)
+ if maxval(i) > 0
+ yt=[0 maxval(i)];
+ else % (identically zero responses)
+ yt=[-1 0 1];
+ end
+ end
+ scale=[1 imstp minval(i) maxval(i)];
+ for j = 1:nvar
+ k1=(i-1)*nvar+j;
+ k2=(j-1)*nvar+i;
+ subplot(nvar,nvar,k1)
+ plot(t,imf(:,k2),t,[firstl1(:,k2) firsth1(:,k2)],'--',...
+ t,[firstl(:,k2) firsth(:,k2)],'-.',t,zeros(length(imf(:,k2)),1),'-');
+ grid;
+ axis(scale);
+ %set(gca,'YLim',[minval(i) maxval(i)])
+ %
+ set(gca,'XTick',XTick)
+ set(gca,'YTick',yt)
+ if i<nvar
+ set(gca,'XTickLabelMode','manual','XTickLabel',[])
+ end
+ %set(gca,'XTickLabel',' ');
+ if j>1
+ set(gca,'YTickLabel',' ');
+ end
+ if rowlabel == 1
+ %title(['x' num2str(j)])
+ %title(eval(['x' num2str(j)]))
+ title(char(xlab(j)))
+ end
+ if columnlabel == 1
+ %ylabel(['x' num2str(i)])
+ %ylabel(eval(['x' num2str(i)]))
+ ylabel(char(ylab(i)))
+ end
+ columnlabel = 0;
+ end
+ rowlabel = 0;
+end
diff --git a/MatlabFiles/imcerrgraph.m b/MatlabFiles/imcerrgraph.m
index 2e3b620d35e5b1ca665af38f7572b58dfdbe9dc1..607f50ad18ad9581d0f9b587dd2c328d17cb24b8 100644
--- a/MatlabFiles/imcerrgraph.m
+++ b/MatlabFiles/imcerrgraph.m
@@ -1,75 +1,75 @@
-function scaleout = imcerrgraph(imf,firstl,firsth,nvar,imstp,xlab,ylab)
-% function scaleout = imcerrgraph(imf,firstl,firsth,nvar,imstp,xlab,ylab)
-% imcerrgraph: impulse, c (column: shock 1 to N), error bands, graph
-% imf: impulse responses, column (responses to 1st shock, responses to 2nd shock
-% etc), row (impusle steps),
-% firstl: lower band
-% highth: high band
-% nvar: number of variables
-% imstp: step of impulse responses
-% xlab,ylab: labels
-%
-% See imrgraph, imcerrgraph, imrerrgraph
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-t = 1:imstp;
-temp1=zeros(nvar,1);
-temp2=zeros(nvar,1);
-maxval=zeros(nvar,1);
-minval=zeros(nvar,1);
-for i = 1:nvar
- for j = 1:nvar
- temp1(j)=max(firsth(:,(j-1)*nvar+i));
- temp2(j)=min(firstl(:,(j-1)*nvar+i));
- end
- maxval(i)=max(temp1);
- minval(i)=min(temp2);
-end
-
-scaleout = [maxval(:) minval(:)];
-
-%--------------
-% Column j: Shock 1 to N; Row i: Responses to
-%-------------
-figure
-rowlabel = 1;
-for i = 1:nvar
- columnlabel = 1;
- for j = 1:nvar
- k1=(i-1)*nvar+j;
- k2=(j-1)*nvar+i;
- subplot(nvar,nvar,k1)
- plot(t,imf(:,k2),t,[firstl(:,k2) firsth(:,k2)],':',...
- t,zeros(length(imf(:,k2)),1),'-');
- set(gca,'YLim',[minval(i) maxval(i)])
- set(gca,'XTickLabel',' ');
- set(gca,'YTickLabel',' ');
- if rowlabel == 1
- %title(['x' num2str(j)])
- %title(eval(['x' num2str(j)]))
- title(char(xlab(j)))
- end
- if columnlabel == 1
- %ylabel(['x' num2str(i)])
- %ylabel(eval(['x' num2str(i)]))
- ylabel(char(ylab(i)))
- end
- columnlabel = 0;
- end
- rowlabel = 0;
-end
\ No newline at end of file
+function scaleout = imcerrgraph(imf,firstl,firsth,nvar,imstp,xlab,ylab)
+% function scaleout = imcerrgraph(imf,firstl,firsth,nvar,imstp,xlab,ylab)
+% imcerrgraph: impulse, c (column: shock 1 to N), error bands, graph
+% imf: impulse responses, column (responses to 1st shock, responses to 2nd shock
+% etc), row (impusle steps),
+% firstl: lower band
+% highth: high band
+% nvar: number of variables
+% imstp: step of impulse responses
+% xlab,ylab: labels
+%
+% See imrgraph, imcerrgraph, imrerrgraph
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+t = 1:imstp;
+temp1=zeros(nvar,1);
+temp2=zeros(nvar,1);
+maxval=zeros(nvar,1);
+minval=zeros(nvar,1);
+for i = 1:nvar
+ for j = 1:nvar
+ temp1(j)=max(firsth(:,(j-1)*nvar+i));
+ temp2(j)=min(firstl(:,(j-1)*nvar+i));
+ end
+ maxval(i)=max(temp1);
+ minval(i)=min(temp2);
+end
+
+scaleout = [maxval(:) minval(:)];
+
+%--------------
+% Column j: Shock 1 to N; Row i: Responses to
+%-------------
+figure
+rowlabel = 1;
+for i = 1:nvar
+ columnlabel = 1;
+ for j = 1:nvar
+ k1=(i-1)*nvar+j;
+ k2=(j-1)*nvar+i;
+ subplot(nvar,nvar,k1)
+ plot(t,imf(:,k2),t,[firstl(:,k2) firsth(:,k2)],':',...
+ t,zeros(length(imf(:,k2)),1),'-');
+ set(gca,'YLim',[minval(i) maxval(i)])
+ set(gca,'XTickLabel',' ');
+ set(gca,'YTickLabel',' ');
+ if rowlabel == 1
+ %title(['x' num2str(j)])
+ %title(eval(['x' num2str(j)]))
+ title(char(xlab(j)))
+ end
+ if columnlabel == 1
+ %ylabel(['x' num2str(i)])
+ %ylabel(eval(['x' num2str(i)]))
+ ylabel(char(ylab(i)))
+ end
+ columnlabel = 0;
+ end
+ rowlabel = 0;
+end
diff --git a/MatlabFiles/imcgraph.m b/MatlabFiles/imcgraph.m
index 568daae11a27943e54dc67a38e604ab42a9e7dbd..3e3dfb8f93868d8664d8bbb4b2cd529f5439def2 100644
--- a/MatlabFiles/imcgraph.m
+++ b/MatlabFiles/imcgraph.m
@@ -1,80 +1,80 @@
-function scaleout = imcgraph(imf,nvar,imstp,xlab,ylab,indxGimfml)
-% scaleout = imcgraph(imf,nvar,imstp,xlab,ylab,indxGimfml)
-% imcgraph: impulse, c (column: shock 1 to N), graph
-% Graph the ML point impulse response
-%
-% imf: impulse responses, column (responses to 1st shock, responses to 2nd shock
-% etc), row (impusle steps),
-% nvar: number of variables
-% imstp: step of impulse responses
-% xlab,ylab: labels
-% indxGimfml: 1, graph; 0, no graph
-%
-% NOTE: I added "indxGimfml" so this function may not be compatible with programs
-% older than 03/06/99, TZ
-%
-% See imrgraph, imcerrgraph, imrerrgraph
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-t = 1:imstp;
-temp1=zeros(nvar,1);
-temp2=zeros(nvar,1);
-maxval=zeros(nvar,1);
-minval=zeros(nvar,1);
-for i = 1:nvar
- for j = 1:nvar
- temp1(j)=max(imf(:,(j-1)*nvar+i));
- temp2(j)=min(imf(:,(j-1)*nvar+i));
- end
- maxval(i)=max(temp1);
- minval(i)=min(temp2);
-end
-
-scaleout = [maxval(:) minval(:)];
-
-%--------------
-% Column j: Shock 1 to N; Row i: Responses to
-%-------------
-if indxGimfml
- figure(1)
- rowlabel = 1;
- for i = 1:nvar
- columnlabel = 1;
- for j = 1:nvar
- k1=(i-1)*nvar+j;
- k2=(j-1)*nvar+i;
- subplot(nvar,nvar,k1)
- plot(t,imf(:,k2),t,zeros(length(imf(:,k2)),1),':');
- set(gca,'YLim',[minval(i) maxval(i)])
- set(gca,'XTickLabel',' ');
- set(gca,'YTickLabel',' ');
- if rowlabel == 1
- %title(['x' num2str(j)])
- %title(eval(['x' num2str(j)]))
- title(char(xlab(j)))
- end
- if columnlabel == 1
- %ylabel(['x' num2str(i)])
- %ylabel(eval(['x' num2str(i)]))
- ylabel(char(ylab(i)))
- end
- columnlabel = 0;
- end
- rowlabel = 0;
- end
-end
\ No newline at end of file
+function scaleout = imcgraph(imf,nvar,imstp,xlab,ylab,indxGimfml)
+% scaleout = imcgraph(imf,nvar,imstp,xlab,ylab,indxGimfml)
+% imcgraph: impulse, c (column: shock 1 to N), graph
+% Graph the ML point impulse response
+%
+% imf: impulse responses, column (responses to 1st shock, responses to 2nd shock
+% etc), row (impusle steps),
+% nvar: number of variables
+% imstp: step of impulse responses
+% xlab,ylab: labels
+% indxGimfml: 1, graph; 0, no graph
+%
+% NOTE: I added "indxGimfml" so this function may not be compatible with programs
+% older than 03/06/99, TZ
+%
+% See imrgraph, imcerrgraph, imrerrgraph
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+t = 1:imstp;
+temp1=zeros(nvar,1);
+temp2=zeros(nvar,1);
+maxval=zeros(nvar,1);
+minval=zeros(nvar,1);
+for i = 1:nvar
+ for j = 1:nvar
+ temp1(j)=max(imf(:,(j-1)*nvar+i));
+ temp2(j)=min(imf(:,(j-1)*nvar+i));
+ end
+ maxval(i)=max(temp1);
+ minval(i)=min(temp2);
+end
+
+scaleout = [maxval(:) minval(:)];
+
+%--------------
+% Column j: Shock 1 to N; Row i: Responses to
+%-------------
+if indxGimfml
+ figure(1)
+ rowlabel = 1;
+ for i = 1:nvar
+ columnlabel = 1;
+ for j = 1:nvar
+ k1=(i-1)*nvar+j;
+ k2=(j-1)*nvar+i;
+ subplot(nvar,nvar,k1)
+ plot(t,imf(:,k2),t,zeros(length(imf(:,k2)),1),':');
+ set(gca,'YLim',[minval(i) maxval(i)])
+ set(gca,'XTickLabel',' ');
+ set(gca,'YTickLabel',' ');
+ if rowlabel == 1
+ %title(['x' num2str(j)])
+ %title(eval(['x' num2str(j)]))
+ title(char(xlab(j)))
+ end
+ if columnlabel == 1
+ %ylabel(['x' num2str(i)])
+ %ylabel(eval(['x' num2str(i)]))
+ ylabel(char(ylab(i)))
+ end
+ columnlabel = 0;
+ end
+ rowlabel = 0;
+ end
+end
diff --git a/MatlabFiles/imfsim.m b/MatlabFiles/imfsim.m
index 600cfc073fef56a9a1aa6196358abd6a04a4d5a1..e0ceea08ca458b74c78ddba9001e8cbb6a96425f 100644
--- a/MatlabFiles/imfsim.m
+++ b/MatlabFiles/imfsim.m
@@ -1,346 +1,346 @@
-function imfsim(xinput)
-% imfsim(xinput)
-% Save the simulated pdfs of impulse responses;
-% Print and save Gelman's measures of B and W for A0's,
-% only when nstarts (# of starting points) >1.
-% Ref: Waggoner and Zha "Does Normalization Matter for Inference"
-% See note Forecast (2)
-%
-% xinput{1}: nfp -- total number of free parameters
-% xinput{2}: nvar -- number of variables
-% xinput{3}: xhat -- ML estimate of free parameters in A0
-% xinput{4}: hess -- Hessian of -logLH
-% xinput{5}:Indxv -- index for selected variables of interest; normall first 2 are of our interest
-% to select variables, always check idmat0 to make sure
-% it plots: (1) pdf of 1st v for every buffer, (2) scattered plot of 1st and 2nd for every buffer,
-% (2) pdf of 1st v for all sequences; (4) scattered plot of 3rd and 4th for all sequences
-% (5) scattered plot of 1st and 2nd for al sequences.
-% xinput{6}: IndxGraph - 1: plot graphs; 0: no graphs
-% xinput{7}: idmat0 -- Index for non-zero elements in A0 with column to equation
-% xinput{8}: nstarts -- # of starting points in Gibbs sampling
-% xinput{9}: ndraws1 -- # of 1st loop to be discarded
-% xinput{10}: ndraws2 -- # of 2nd loop for saving A0 draws
-% xinput{11}: imndraws=nstarts*ndraws2
-% xinput{12}: a0indx -- index number for non-zero elements in A0
-% xinput{13}: tdf -- degrees of freedom for t-distribution
-% xinput{14}: nbuffer -- interval for printing, plotting, and saving
-% xinput{15}: Sbd -- nvar-by-nvar S{1}, ..., S{n} -- kind of covariance matrix for each simultaneous equation
-% Already divided by "fss."
-% xinput{16}: nSample -- the original sample size including lags
-% xinput{17}: IndxNmlr -- index for which normalization rule to choose
-% xinput{18}: IndxGibbs -- index for WZ Gibbs; 1; Gibbs; 0: Metropolis
-% xinput{19}: scf -- reduction scale factor for Metropolis jumping kernel
-% xinput{20}: H_sr -- square root of the inverse of the covariance matrix
-% for free elements in A0 (nfp-by-nfp)
-% xinput{21}: fss -- effective sample size == nSample-lags+# of dummy observations
-% xinput{22}: idfile1 -- calls "iden6std." Save stds. of both data and impulse responses in idfile1
-% xinput{23}: xxhpc -- chol(X'X+inv(H_p_tilde)): upper triangular but its transpose
-% is lower triagular Choleski
-% xinput{24}: ImfErr -- if 1, impulse response simulation; if 0, disable this simulation
-% xinput{25}: ninv -- number of bins pre-specified to put each draw of impulse response
-% into a proper bin (or small interval)
-% xinput{26}: imstp -- # of steps for impulse responses
-% xinput{27}: forep -- forecast periods (# of steps)
-% xinput{28}: yact -- actual data (in log except R, U, etc.)
-% xinput{29}: yactqg -- quarterly annualized growth in actual data
-% xinput{30}: yactCalyg -- calendar annual growth in actual data
-% xinput{31}: imfml -- imstp-by-nvar^2 ML impulse responses
-% xinput{32}: forepq -- forecast periods for quarterly growth
-% xinput{33}: forepy -- forecast periods for annual growth
-% xinput{34}: ncoef -- k: # of coeffients per equation
-% xinput{35}: Bhml -- ML reduced form parameter B (nvar-by-k)
-% xinput{36}: lags -- # of lags
-%------------------
-% imfcntmulti: (ninv+2,imstp*nvar^2,nstarts) matrix
-% cnt: count; for impulse responses; multi (nstarts) sequences
-% All output is saved in outB_W, including Range5, invc, ninv, and imfcntmulti
-%
-% Written by T. Zha 1999
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-nfp=xinput{1}; nvar=xinput{2}; xhat=xinput{3}; hess=xinput{4}; Indxv=xinput{5};
-IndxGraph=xinput{6}; idmat0=xinput{7}; nstarts=xinput{8}; ndraws1=xinput{9}; ndraws2=xinput{10};
-imndraws=xinput{11}; a0indx=xinput{12}; tdf=xinput{13}; nbuffer=xinput{14}; Sbd=xinput{15};
-nSample = xinput{16}; IndxNmlr=xinput{17}; IndxGibbs=xinput{18}; scf=xinput{19}; H_sr=xinput{20};
-fss=xinput{21}; idfile1=xinput{22}; xxhpc=xinput{23}; ImfErr=xinput{24}; ninv=xinput{25};
-imstp=xinput{26}; forep=xinput{27}; yact=xinput{28}; yactqg=xinput{29}; yactCalyg=xinput{30};
-imfml=xinput{31}; forepq=xinput{32}; forepy=xinput{33}; ncoef=xinput{34}; Bhml=xinput{35};
-lags=xinput{36};
-
-Avhx_bs = zeros(nfp,1);
-Avhx_bm = zeros(nfp,1);
-Avhx_bj = zeros(nfp,1);
-Avhx_cj = zeros(nfp,1);
-Avhx_cm = zeros(nfp,1);
-A0_h = zeros(nvar);
-A0gbs = A0_h; % drawn A0 in Gibbs sampling
-Avhxm = zeros(nfp,1);
-Avhxs = Avhxm;
-A0xhat = zeros(nvar);
-A0xhat(a0indx) = xhat;
-% A0hatw = zeros(nvar^2,nbuffer);
-
-countJump = zeros(nstarts,1);
-
-imfmean = zeros(imstp,nvar^2);
-imfcntmulti = zeros(ninv+2,imstp*nvar^2,nstarts);
- % cnt: count; for impulse responses; multi (nstarts) sequences
-
-
-%---------------------------------------------------
-% Specify the range for counting the empirical distribution
-%
-%** load the standard deviations of 6 variables, one for log(y), one for gq, and
-%** the third one for yg
-eval(['load ' idfile1 '.prn -ascii']);
-eval(['ABstd=' idfile1 ';']);
-Range5 = cell(4,1); % 4: log, qg, yg, and imf
-
-%@@@ Tony's trick to expand the matrix
-%
-%** In order of log(y), qg, and yg for Range5{i} for i=1:3
-Range5{1} =zeros(forep,nvar,2); % 2: min and max
-Range5{1}(:,:,1) = repmat(yact(length(yact(:,1)),:)-10*ABstd(1,:),[forep 1]); % min, 30 std.
-Range5{1}(:,:,2) = repmat(yact(length(yact(:,1)),:)+10*ABstd(1,:),[forep 1]); % max, 30 std.
-%
-Range5{2} =zeros(forepq,nvar,2); % 2: min and max
-Range5{2}(:,:,1) = repmat(yactqg(length(yactqg(:,1)),:)-10*ABstd(2,:),[forepq 1]); % min, 30 std.
-Range5{2}(:,:,2) = repmat(yactqg(length(yactqg(:,1)),:)+10*ABstd(2,:),[forepq 1]); % max, 30 std.
-%
-Range5{3} =zeros(forepy,nvar,2); % 2: min and max
-Range5{3}(:,:,1) = repmat(yactCalyg(length(yactCalyg(:,1)),:)-10*ABstd(3,:),[forepy 1]); % min, 30 std.
-Range5{3}(:,:,2) = repmat(yactCalyg(length(yactCalyg(:,1)),:)+10*ABstd(3,:),[forepy 1]); % max, 30 std.
-%
-Range5{4} =zeros(imstp,nvar^2,2); % 2: min and max
-imfscale = repmat(ABstd(4,:),[1 nvar]); % because nvar variables to 1, ..., nvar shocks
-Range5{4}(:,:,1) = repmat(imfml(1,:)-5*imfscale,[imstp 1]); % min, 5 std.
-Range5{4}(:,:,2) = repmat(imfml(1,:)+5*imfscale,[imstp 1]); % max, 5 std.
- % Range5(4)(:,:,1): imstp-by-nvar^2. Column: nvar responses to 1st shock,
- % nvar responses to 2nd shock, ...
-%**
-invc = cell(4,1); % interval length (used for counting later). 1st 3 cells have each
- % forep-by-nvar, and 4th has imstp-by-nvar^2.
-for i=1:4
- invc{i} = Range5{i}(:,:,2) - Range5{i}(:,:,1);
-end
-hbin = invc{4} ./ ninv; % bin size for each point of impulse responses kkdf
-imfloor = Range5{4}(:,:,1);
-
-
-
-%===================================
-% Here begins with the big loop
-%===================================
-H1 = chol(hess); % upper triangular so that H1' is a lower triangular decomp
-baseW = H_sr; %inv(H1); %H_sr; % covariance matrix without scaling
-nswitch=0; %<<>> total number of sign switches
-A0inxhat = inv(A0xhat); % inverse of ML estimate
-a0indx0 = find(idmat0==0); % index for all zero's in A0;
-nn=[nvar lags imstp];
-
-[cT,vR,kdf] = gibbsglb(Sbd,idmat0,nvar,fss);
-
-tic
-for starts = 1:nstarts
- starts
- if starts == 1
- A0gbs(a0indx) = xhat; % from "load ..."
- if ~IndxGibbs % Metropolist
- Avhx = xhat;
- hAvhx = a0asfun(Avhx,Sbd,fss,nvar,a0indx);
- hAvhx = -hAvhx; % converted to logLH
- end
- else
- Avhx = baseW*randn(nfp,1); %H_sr*randn(nfp,1); % D: discarded sequence
- csq=randn(tdf,1);
- csq=sum(csq .* csq);
- Avhx = xhat+Avhx/sqrt(csq/tdf);
- %** Normalization by the choice of IndxNmlr
- A0gbs(a0indx) = Avhx;
- if ~IndxNmlr(5)
- [A0gbs,jnk] = nmlzvar(A0gbs,A0xhat,A0inxhat,IndxNmlr,nswitch,[]);
- else
- A0ingbs = inv(A0gbs);
- [A0gbs,jnk,jnk1] = nmlzvar(A0gbs,A0xhat,A0inxhat,IndxNmlr,nswitch,A0ingbs);
- end
- %
- if ~IndxGibbs % Metropolist
- Avhx = A0gbs(a0indx);
- hAvhx = a0asfun(Avhx,Sbd,fss,nvar,a0indx);
- hAvhx = -hAvhx; % converted to logLH
- end
- end
- %
- Avhxmm = zeros(nfp,1);
- Avhxss = zeros(nfp,1);
- cJump = 0;
- imfcnt = zeros(ninv+2,imstp*nvar^2); % cnt: count; for impulse responses
-
- for draws = 1:ndraws1
- if IndxGibbs
- A0gbs = gibbsvar(A0gbs,cT,vR,nvar,fss,kdf);
- else % Metropolis
- [Avhx,hAvhx,cJump] = smtplis(Avhx,hAvhx,tdf,cJump,scf,...
- baseW,nfp,Sbd,fss,nvar,a0indx);
- end
- end
-
- wdraws=(starts-1)*ndraws2+0;
- for draws = 1:ndraws2
- drawsc = (starts-1)*ndraws2+draws;
- if IndxGibbs
- A0gbs = gibbsvar(A0gbs,cT,vR,nvar,fss,kdf);
- A0gbs(a0indx0) = 0; % set all zeros in A0gbs clean to avoid possible cumulative round-off errors
- else % Metropolis
- [Avhx,hAvhx,cJump] = smtplis(Avhx,hAvhx,tdf,cJump,scf,...
- baseW,nfp,Sbd,fss,nvar,a0indx);
- A0gbs(a0indx) = Avhx;
- end
-
- %*** call normalization so that A0_h is normalized
- if ~IndxNmlr(5)
- [A0_h,nswitch] = nmlzvar(A0gbs,A0xhat,A0inxhat,IndxNmlr,nswitch,[]);
- A0_hin = inv(A0_h);
- else
- A0ingbs = inv(A0gbs);
- [A0_h,nswitch,A0_hin] = nmlzvar(A0gbs,A0xhat,A0inxhat,IndxNmlr,nswitch,A0ingbs);
- end
- Avhx_norm = A0_h(a0indx);
-
- %
- % *** normal draws for posterior Aplus conditional on A0h
- %
- %** the mean is Aplushm, and the covariance is inv(xxhp)=Lxxhpc*Lxxhpc'
- Apindm = randn(ncoef,nvar);
- %
- if ~all(all(finite(Bhml)))
- Aplushm=zeros(ncoef,nvar);
- for i=1:nvar
- Aplushm(:,i)=Gb{i}*A0_h(:,i); % see Zha's forecast (1) p.9
- % Here, Gb is used to compute A+ where A+(i) = Gb(i)*a0(i)
- end
- Bh_h = (Aplushm + xxhpc\Apindm)*A0_hin;
- else
- Bh_h = Bhml + (xxhpc\Apindm)*A0_hin;
- end
-
- if ImfErr
- swish_h = A0_hin'; % Switching back to the form A0*y(t) = e(t)
- imf_h = zimpulse(Bh_h,swish_h,nn); % in the form that is congenial to RATS
- % imf3_h=reshape(imf_h,size(imf_h,1),nvar,nvar);
- % % imf3: row--steps, column--nvar responses, 3rd dimension--nvar shocks
- imfmean = imfmean + imf_h; % posterior mean
- imfcnt = empdfsort(imfcnt,imf_h,imfloor,hbin,ninv);
- % sorted counts (prob.) in bins
- end
-
- Avhxm = Avhxm + Avhx_norm; % 1st step to overall mean of parameter
- Avhxs = Avhxs + Avhx_norm.^2; % 1st step to overall 2nd moment of parameter
-
- %* compute the mean and 2nd moment
- %** Getting average of variances W and variance of means B/n -- B_n
- %* see Gelman, p.331, my Shock(0), 12-13, and my Forecast (2), 28-31
- if (nstarts>1)
- Avhxmm = Avhxmm + Avhx_norm; % n*(phi_.j)
- Avhxss = Avhxss + Avhx_norm.^2; % 1st step to (phi_ij) for fixed j
- end
-
- % A0hatw(:,drawsc-wdraws) = A0_h(:);
- if ~mod(draws,nbuffer)
- starts
- draws
- wdraws=drawsc
- % fwriteid = fopen('outA0.bin','a');
- % count = fwrite(fwriteid,A0hatw,'double');
- % status = fclose('all');
- end
- end
- %
- imfcntmulti(:,:,starts) = imfcnt;
-
- if ~IndxGibbs
- countJump(starts,1) = cJump;
- end
- %
- %** Getting average of variances W and variance of means B/n -- B_n
- %** see Gelman, p.331, my Shock(0), pp.12-13, and my Forecast (2), pp.28-31
- if (nstarts>1)
- Avhx_aj = Avhxmm/ndraws2; % (phi_.j)
- Avhx_bs = Avhx_bs + Avhx_aj.^2; % 1st step to 2nd moment of (phi_.j)
- Avhx_bm = Avhx_bm + Avhx_aj; % 1st step to (phi_..)
- %
- Avhx_bj = Avhxss/ndraws2; % 2nd moment of (phi_ij) for fixed j
- Avhx_cj = Avhx_bj - Avhx_aj.^2; % ((n-1)/n)*S^2_j
- Avhx_cm = Avhx_cm + Avhx_cj; % sum of ((n-1)/n)*S^2_j across j
- end
-end
-timend = toc
-timeminutes=timend/60
-
-if ~IndxGibbs
- countJump = countJump/ndraws2
-end
-
-Avhxm = Avhxm/(imndraws);
-Avhxs = Avhxs/(imndraws);
-Avhxv = Avhxs - Avhxm.^2;
-Avhxs = sqrt(Avhxv); % stardard deviation
-A0hm = zeros(nvar);
-A0hm(a0indx) = Avhxm % mean
-A0hv = zeros(nvar);
-A0hv(a0indx) = Avhxv; % varaince matrix
-A0hs = zeros(nvar);
-A0hs(a0indx) = Avhxs; % standar deviation
-
-imfmean = imfmean/(imndraws);
-
-%**** Getting Within-Sequence W and Between-Sequence B_n
-% see Gelman, p.331, my Shock(0), pp.12-13, and my Forecast (2), pp.28-31
-if (nstarts>1)
- AvhxW = (ndraws2/(ndraws2-1))*Avhx_cm/nstarts;
- % W: average of j within-sequence variances
- AvhxB_n = (nstarts/(nstarts-1)) * ( Avhx_bs/nstarts - (Avhx_bm/nstarts).^2 );
- % B/n: variance of J within-sequence means
- AvhxB = ndraws2*AvhxB_n; % B
- %
- B_W1 = AvhxB ./ AvhxW;
- B1 = AvhxB;
- W1 = AvhxW;
- GR1 = sqrt((ndraws2-1)/ndraws2 + B_W1/ndraws2);
- % measure of Gelman reduction; need not be 1 to be accurate,
- % contrary to what Gelman claims
- save outB_W B_W1 B1 W1 GR1 nstarts ndraws2 imndraws timeminutes Avhxs ...
- A0xhat A0hm A0hs A0hv IndxGibbs countJump
- if ImfErr
- save outB_W nswitch imfml imfmean imfcntmulti Range5 ninv invc imstp nvar -append
- end
-
- titstr = ['J ' num2str(nstarts) ' n1 ' num2str(ndraws1) ...
- ' n2 ' num2str(ndraws2) ' timend minutes ' num2str(timend/60)];
- disp(' ')
- disp(titstr)
- disp('B/W sqrt(B) sqrt(W) Std(A0) GR')
- format short g
- [B_W1 sqrt(B1) sqrt(W1) Avhxs GR1]
-else
- save outB_W nstarts ndraws1 ndraws2 imndraws timeminutes Avhxs ...
- A0xhat A0hm A0hs A0hv IndxGibbs countJump
- if ImfErr
- save outB_W nswitch imfml imfmean imfcntmulti Range5 ninv invc imstp nvar -append
- end
-end
+function imfsim(xinput)
+% imfsim(xinput)
+% Save the simulated pdfs of impulse responses;
+% Print and save Gelman's measures of B and W for A0's,
+% only when nstarts (# of starting points) >1.
+% Ref: Waggoner and Zha "Does Normalization Matter for Inference"
+% See note Forecast (2)
+%
+% xinput{1}: nfp -- total number of free parameters
+% xinput{2}: nvar -- number of variables
+% xinput{3}: xhat -- ML estimate of free parameters in A0
+% xinput{4}: hess -- Hessian of -logLH
+% xinput{5}:Indxv -- index for selected variables of interest; normall first 2 are of our interest
+% to select variables, always check idmat0 to make sure
+% it plots: (1) pdf of 1st v for every buffer, (2) scattered plot of 1st and 2nd for every buffer,
+% (2) pdf of 1st v for all sequences; (4) scattered plot of 3rd and 4th for all sequences
+% (5) scattered plot of 1st and 2nd for al sequences.
+% xinput{6}: IndxGraph - 1: plot graphs; 0: no graphs
+% xinput{7}: idmat0 -- Index for non-zero elements in A0 with column to equation
+% xinput{8}: nstarts -- # of starting points in Gibbs sampling
+% xinput{9}: ndraws1 -- # of 1st loop to be discarded
+% xinput{10}: ndraws2 -- # of 2nd loop for saving A0 draws
+% xinput{11}: imndraws=nstarts*ndraws2
+% xinput{12}: a0indx -- index number for non-zero elements in A0
+% xinput{13}: tdf -- degrees of freedom for t-distribution
+% xinput{14}: nbuffer -- interval for printing, plotting, and saving
+% xinput{15}: Sbd -- nvar-by-nvar S{1}, ..., S{n} -- kind of covariance matrix for each simultaneous equation
+% Already divided by "fss."
+% xinput{16}: nSample -- the original sample size including lags
+% xinput{17}: IndxNmlr -- index for which normalization rule to choose
+% xinput{18}: IndxGibbs -- index for WZ Gibbs; 1; Gibbs; 0: Metropolis
+% xinput{19}: scf -- reduction scale factor for Metropolis jumping kernel
+% xinput{20}: H_sr -- square root of the inverse of the covariance matrix
+% for free elements in A0 (nfp-by-nfp)
+% xinput{21}: fss -- effective sample size == nSample-lags+# of dummy observations
+% xinput{22}: idfile1 -- calls "iden6std." Save stds. of both data and impulse responses in idfile1
+% xinput{23}: xxhpc -- chol(X'X+inv(H_p_tilde)): upper triangular but its transpose
+% is lower triagular Choleski
+% xinput{24}: ImfErr -- if 1, impulse response simulation; if 0, disable this simulation
+% xinput{25}: ninv -- number of bins pre-specified to put each draw of impulse response
+% into a proper bin (or small interval)
+% xinput{26}: imstp -- # of steps for impulse responses
+% xinput{27}: forep -- forecast periods (# of steps)
+% xinput{28}: yact -- actual data (in log except R, U, etc.)
+% xinput{29}: yactqg -- quarterly annualized growth in actual data
+% xinput{30}: yactCalyg -- calendar annual growth in actual data
+% xinput{31}: imfml -- imstp-by-nvar^2 ML impulse responses
+% xinput{32}: forepq -- forecast periods for quarterly growth
+% xinput{33}: forepy -- forecast periods for annual growth
+% xinput{34}: ncoef -- k: # of coeffients per equation
+% xinput{35}: Bhml -- ML reduced form parameter B (nvar-by-k)
+% xinput{36}: lags -- # of lags
+%------------------
+% imfcntmulti: (ninv+2,imstp*nvar^2,nstarts) matrix
+% cnt: count; for impulse responses; multi (nstarts) sequences
+% All output is saved in outB_W, including Range5, invc, ninv, and imfcntmulti
+%
+% Written by T. Zha 1999
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+nfp=xinput{1}; nvar=xinput{2}; xhat=xinput{3}; hess=xinput{4}; Indxv=xinput{5};
+IndxGraph=xinput{6}; idmat0=xinput{7}; nstarts=xinput{8}; ndraws1=xinput{9}; ndraws2=xinput{10};
+imndraws=xinput{11}; a0indx=xinput{12}; tdf=xinput{13}; nbuffer=xinput{14}; Sbd=xinput{15};
+nSample = xinput{16}; IndxNmlr=xinput{17}; IndxGibbs=xinput{18}; scf=xinput{19}; H_sr=xinput{20};
+fss=xinput{21}; idfile1=xinput{22}; xxhpc=xinput{23}; ImfErr=xinput{24}; ninv=xinput{25};
+imstp=xinput{26}; forep=xinput{27}; yact=xinput{28}; yactqg=xinput{29}; yactCalyg=xinput{30};
+imfml=xinput{31}; forepq=xinput{32}; forepy=xinput{33}; ncoef=xinput{34}; Bhml=xinput{35};
+lags=xinput{36};
+
+Avhx_bs = zeros(nfp,1);
+Avhx_bm = zeros(nfp,1);
+Avhx_bj = zeros(nfp,1);
+Avhx_cj = zeros(nfp,1);
+Avhx_cm = zeros(nfp,1);
+A0_h = zeros(nvar);
+A0gbs = A0_h; % drawn A0 in Gibbs sampling
+Avhxm = zeros(nfp,1);
+Avhxs = Avhxm;
+A0xhat = zeros(nvar);
+A0xhat(a0indx) = xhat;
+% A0hatw = zeros(nvar^2,nbuffer);
+
+countJump = zeros(nstarts,1);
+
+imfmean = zeros(imstp,nvar^2);
+imfcntmulti = zeros(ninv+2,imstp*nvar^2,nstarts);
+ % cnt: count; for impulse responses; multi (nstarts) sequences
+
+
+%---------------------------------------------------
+% Specify the range for counting the empirical distribution
+%
+%** load the standard deviations of 6 variables, one for log(y), one for gq, and
+%** the third one for yg
+eval(['load ' idfile1 '.prn -ascii']);
+eval(['ABstd=' idfile1 ';']);
+Range5 = cell(4,1); % 4: log, qg, yg, and imf
+
+%@@@ Tony's trick to expand the matrix
+%
+%** In order of log(y), qg, and yg for Range5{i} for i=1:3
+Range5{1} =zeros(forep,nvar,2); % 2: min and max
+Range5{1}(:,:,1) = repmat(yact(length(yact(:,1)),:)-10*ABstd(1,:),[forep 1]); % min, 30 std.
+Range5{1}(:,:,2) = repmat(yact(length(yact(:,1)),:)+10*ABstd(1,:),[forep 1]); % max, 30 std.
+%
+Range5{2} =zeros(forepq,nvar,2); % 2: min and max
+Range5{2}(:,:,1) = repmat(yactqg(length(yactqg(:,1)),:)-10*ABstd(2,:),[forepq 1]); % min, 30 std.
+Range5{2}(:,:,2) = repmat(yactqg(length(yactqg(:,1)),:)+10*ABstd(2,:),[forepq 1]); % max, 30 std.
+%
+Range5{3} =zeros(forepy,nvar,2); % 2: min and max
+Range5{3}(:,:,1) = repmat(yactCalyg(length(yactCalyg(:,1)),:)-10*ABstd(3,:),[forepy 1]); % min, 30 std.
+Range5{3}(:,:,2) = repmat(yactCalyg(length(yactCalyg(:,1)),:)+10*ABstd(3,:),[forepy 1]); % max, 30 std.
+%
+Range5{4} =zeros(imstp,nvar^2,2); % 2: min and max
+imfscale = repmat(ABstd(4,:),[1 nvar]); % because nvar variables to 1, ..., nvar shocks
+Range5{4}(:,:,1) = repmat(imfml(1,:)-5*imfscale,[imstp 1]); % min, 5 std.
+Range5{4}(:,:,2) = repmat(imfml(1,:)+5*imfscale,[imstp 1]); % max, 5 std.
+ % Range5(4)(:,:,1): imstp-by-nvar^2. Column: nvar responses to 1st shock,
+ % nvar responses to 2nd shock, ...
+%**
+invc = cell(4,1); % interval length (used for counting later). 1st 3 cells have each
+ % forep-by-nvar, and 4th has imstp-by-nvar^2.
+for i=1:4
+ invc{i} = Range5{i}(:,:,2) - Range5{i}(:,:,1);
+end
+hbin = invc{4} ./ ninv; % bin size for each point of impulse responses kkdf
+imfloor = Range5{4}(:,:,1);
+
+
+
+%===================================
+% Here begins with the big loop
+%===================================
+H1 = chol(hess); % upper triangular so that H1' is a lower triangular decomp
+baseW = H_sr; %inv(H1); %H_sr; % covariance matrix without scaling
+nswitch=0; %<<>> total number of sign switches
+A0inxhat = inv(A0xhat); % inverse of ML estimate
+a0indx0 = find(idmat0==0); % index for all zero's in A0;
+nn=[nvar lags imstp];
+
+[cT,vR,kdf] = gibbsglb(Sbd,idmat0,nvar,fss);
+
+tic
+for starts = 1:nstarts
+ starts
+ if starts == 1
+ A0gbs(a0indx) = xhat; % from "load ..."
+ if ~IndxGibbs % Metropolist
+ Avhx = xhat;
+ hAvhx = a0asfun(Avhx,Sbd,fss,nvar,a0indx);
+ hAvhx = -hAvhx; % converted to logLH
+ end
+ else
+ Avhx = baseW*randn(nfp,1); %H_sr*randn(nfp,1); % D: discarded sequence
+ csq=randn(tdf,1);
+ csq=sum(csq .* csq);
+ Avhx = xhat+Avhx/sqrt(csq/tdf);
+ %** Normalization by the choice of IndxNmlr
+ A0gbs(a0indx) = Avhx;
+ if ~IndxNmlr(5)
+ [A0gbs,jnk] = nmlzvar(A0gbs,A0xhat,A0inxhat,IndxNmlr,nswitch,[]);
+ else
+ A0ingbs = inv(A0gbs);
+ [A0gbs,jnk,jnk1] = nmlzvar(A0gbs,A0xhat,A0inxhat,IndxNmlr,nswitch,A0ingbs);
+ end
+ %
+ if ~IndxGibbs % Metropolist
+ Avhx = A0gbs(a0indx);
+ hAvhx = a0asfun(Avhx,Sbd,fss,nvar,a0indx);
+ hAvhx = -hAvhx; % converted to logLH
+ end
+ end
+ %
+ Avhxmm = zeros(nfp,1);
+ Avhxss = zeros(nfp,1);
+ cJump = 0;
+ imfcnt = zeros(ninv+2,imstp*nvar^2); % cnt: count; for impulse responses
+
+ for draws = 1:ndraws1
+ if IndxGibbs
+ A0gbs = gibbsvar(A0gbs,cT,vR,nvar,fss,kdf);
+ else % Metropolis
+ [Avhx,hAvhx,cJump] = smtplis(Avhx,hAvhx,tdf,cJump,scf,...
+ baseW,nfp,Sbd,fss,nvar,a0indx);
+ end
+ end
+
+ wdraws=(starts-1)*ndraws2+0;
+ for draws = 1:ndraws2
+ drawsc = (starts-1)*ndraws2+draws;
+ if IndxGibbs
+ A0gbs = gibbsvar(A0gbs,cT,vR,nvar,fss,kdf);
+ A0gbs(a0indx0) = 0; % set all zeros in A0gbs clean to avoid possible cumulative round-off errors
+ else % Metropolis
+ [Avhx,hAvhx,cJump] = smtplis(Avhx,hAvhx,tdf,cJump,scf,...
+ baseW,nfp,Sbd,fss,nvar,a0indx);
+ A0gbs(a0indx) = Avhx;
+ end
+
+ %*** call normalization so that A0_h is normalized
+ if ~IndxNmlr(5)
+ [A0_h,nswitch] = nmlzvar(A0gbs,A0xhat,A0inxhat,IndxNmlr,nswitch,[]);
+ A0_hin = inv(A0_h);
+ else
+ A0ingbs = inv(A0gbs);
+ [A0_h,nswitch,A0_hin] = nmlzvar(A0gbs,A0xhat,A0inxhat,IndxNmlr,nswitch,A0ingbs);
+ end
+ Avhx_norm = A0_h(a0indx);
+
+ %
+ % *** normal draws for posterior Aplus conditional on A0h
+ %
+ %** the mean is Aplushm, and the covariance is inv(xxhp)=Lxxhpc*Lxxhpc'
+ Apindm = randn(ncoef,nvar);
+ %
+ if ~all(all(finite(Bhml)))
+ Aplushm=zeros(ncoef,nvar);
+ for i=1:nvar
+ Aplushm(:,i)=Gb{i}*A0_h(:,i); % see Zha's forecast (1) p.9
+ % Here, Gb is used to compute A+ where A+(i) = Gb(i)*a0(i)
+ end
+ Bh_h = (Aplushm + xxhpc\Apindm)*A0_hin;
+ else
+ Bh_h = Bhml + (xxhpc\Apindm)*A0_hin;
+ end
+
+ if ImfErr
+ swish_h = A0_hin'; % Switching back to the form A0*y(t) = e(t)
+ imf_h = zimpulse(Bh_h,swish_h,nn); % in the form that is congenial to RATS
+ % imf3_h=reshape(imf_h,size(imf_h,1),nvar,nvar);
+ % % imf3: row--steps, column--nvar responses, 3rd dimension--nvar shocks
+ imfmean = imfmean + imf_h; % posterior mean
+ imfcnt = empdfsort(imfcnt,imf_h,imfloor,hbin,ninv);
+ % sorted counts (prob.) in bins
+ end
+
+ Avhxm = Avhxm + Avhx_norm; % 1st step to overall mean of parameter
+ Avhxs = Avhxs + Avhx_norm.^2; % 1st step to overall 2nd moment of parameter
+
+ %* compute the mean and 2nd moment
+ %** Getting average of variances W and variance of means B/n -- B_n
+ %* see Gelman, p.331, my Shock(0), 12-13, and my Forecast (2), 28-31
+ if (nstarts>1)
+ Avhxmm = Avhxmm + Avhx_norm; % n*(phi_.j)
+ Avhxss = Avhxss + Avhx_norm.^2; % 1st step to (phi_ij) for fixed j
+ end
+
+ % A0hatw(:,drawsc-wdraws) = A0_h(:);
+ if ~mod(draws,nbuffer)
+ starts
+ draws
+ wdraws=drawsc
+ % fwriteid = fopen('outA0.bin','a');
+ % count = fwrite(fwriteid,A0hatw,'double');
+ % status = fclose('all');
+ end
+ end
+ %
+ imfcntmulti(:,:,starts) = imfcnt;
+
+ if ~IndxGibbs
+ countJump(starts,1) = cJump;
+ end
+ %
+ %** Getting average of variances W and variance of means B/n -- B_n
+ %** see Gelman, p.331, my Shock(0), pp.12-13, and my Forecast (2), pp.28-31
+ if (nstarts>1)
+ Avhx_aj = Avhxmm/ndraws2; % (phi_.j)
+ Avhx_bs = Avhx_bs + Avhx_aj.^2; % 1st step to 2nd moment of (phi_.j)
+ Avhx_bm = Avhx_bm + Avhx_aj; % 1st step to (phi_..)
+ %
+ Avhx_bj = Avhxss/ndraws2; % 2nd moment of (phi_ij) for fixed j
+ Avhx_cj = Avhx_bj - Avhx_aj.^2; % ((n-1)/n)*S^2_j
+ Avhx_cm = Avhx_cm + Avhx_cj; % sum of ((n-1)/n)*S^2_j across j
+ end
+end
+timend = toc
+timeminutes=timend/60
+
+if ~IndxGibbs
+ countJump = countJump/ndraws2
+end
+
+Avhxm = Avhxm/(imndraws);
+Avhxs = Avhxs/(imndraws);
+Avhxv = Avhxs - Avhxm.^2;
+Avhxs = sqrt(Avhxv); % stardard deviation
+A0hm = zeros(nvar);
+A0hm(a0indx) = Avhxm % mean
+A0hv = zeros(nvar);
+A0hv(a0indx) = Avhxv; % varaince matrix
+A0hs = zeros(nvar);
+A0hs(a0indx) = Avhxs; % standar deviation
+
+imfmean = imfmean/(imndraws);
+
+%**** Getting Within-Sequence W and Between-Sequence B_n
+% see Gelman, p.331, my Shock(0), pp.12-13, and my Forecast (2), pp.28-31
+if (nstarts>1)
+ AvhxW = (ndraws2/(ndraws2-1))*Avhx_cm/nstarts;
+ % W: average of j within-sequence variances
+ AvhxB_n = (nstarts/(nstarts-1)) * ( Avhx_bs/nstarts - (Avhx_bm/nstarts).^2 );
+ % B/n: variance of J within-sequence means
+ AvhxB = ndraws2*AvhxB_n; % B
+ %
+ B_W1 = AvhxB ./ AvhxW;
+ B1 = AvhxB;
+ W1 = AvhxW;
+ GR1 = sqrt((ndraws2-1)/ndraws2 + B_W1/ndraws2);
+ % measure of Gelman reduction; need not be 1 to be accurate,
+ % contrary to what Gelman claims
+ save outB_W B_W1 B1 W1 GR1 nstarts ndraws2 imndraws timeminutes Avhxs ...
+ A0xhat A0hm A0hs A0hv IndxGibbs countJump
+ if ImfErr
+ save outB_W nswitch imfml imfmean imfcntmulti Range5 ninv invc imstp nvar -append
+ end
+
+ titstr = ['J ' num2str(nstarts) ' n1 ' num2str(ndraws1) ...
+ ' n2 ' num2str(ndraws2) ' timend minutes ' num2str(timend/60)];
+ disp(' ')
+ disp(titstr)
+ disp('B/W sqrt(B) sqrt(W) Std(A0) GR')
+ format short g
+ [B_W1 sqrt(B1) sqrt(W1) Avhxs GR1]
+else
+ save outB_W nstarts ndraws1 ndraws2 imndraws timeminutes Avhxs ...
+ A0xhat A0hm A0hs A0hv IndxGibbs countJump
+ if ImfErr
+ save outB_W nswitch imfml imfmean imfcntmulti Range5 ninv invc imstp nvar -append
+ end
+end
diff --git a/MatlabFiles/imfvdscksim.m b/MatlabFiles/imfvdscksim.m
index 1d9d0df52aa4c7616d8f28a9da41e71708c3d71c..2e49e4aafdeccc7b7ac549c36897a109b9e947db 100644
--- a/MatlabFiles/imfvdscksim.m
+++ b/MatlabFiles/imfvdscksim.m
@@ -1,553 +1,553 @@
-function imfvdscksim(xinput,sa0indx,simfindx)
-% imfvdscksim(xinput,sa0indx,simfindx)
-% Save the simulated pdfs of impulse responses, vd, shocks, and A0's;
-% Ref: Waggoner and Zha "Does Normalization Matter for Inference"
-% See note Forecast (2)
-%
-% xinput{1}: nfp -- total number of free parameters
-% xinput{2}: nvar -- number of variables
-% xinput{3}: xhat -- ML estimate of free parameters in A0
-% xinput{4}: hess -- Hessian of -logLH
-% xinput{5}:Indxv -- index for selected variables of interest; normall first 2 are of our interest
-% to select variables, always check idmat0 to make sure
-% it plots: (1) pdf of 1st v for every buffer, (2) scattered plot of 1st and 2nd for every buffer,
-% (2) pdf of 1st v for all sequences; (4) scattered plot of 3rd and 4th for all sequences
-% (5) scattered plot of 1st and 2nd for al sequences.
-% xinput{6}: IndxGraph - 1: plot graphs; 0: no graphs
-% xinput{7}: idmat0 -- Index for non-zero elements in A0 with column to equation
-% xinput{8}: nstarts -- # of starting points in Gibbs sampling
-% xinput{9}: ndraws1 -- # of 1st loop to be discarded
-% xinput{10}: ndraws2 -- # of 2nd loop for saving A0 draws
-% xinput{11}: imndraws=nstarts*ndraws2
-% xinput{12}: a0indx -- index number for non-zero elements in A0
-% xinput{13}: tdf -- degrees of freedom for t-distribution
-% xinput{14}: nbuffer -- interval for printing, plotting, and saving
-% xinput{15}: Sbd -- nvar-by-nvar S{1}, ..., S{n} -- kind of covariance matrix for each simultaneous equation
-% xinput{16}: nSample -- the original sample size including lags
-% xinput{17}: IndxNmlr -- index for which normalization rule to choose
-% xinput{18}: IndxGibbs -- index for WZ Gibbs; 1; Gibbs; 0: Metropolis
-% xinput{19}: scf -- reduction scale factor for Metropolis jumping kernel
-% xinput{20}: H_sr -- covariance matrix for free elements in A0 (nfp-by-nfp)
-% xinput{21}: fss -- effective sample size == nSample-lags+# of dummy observations
-% xinput{22}: idfile1 -- calls "iden6std." Save stds. of both data and impulse responses in idfile1
-% xinput{23}: xxhpc -- chol(X'X+inv(H_p_tilde)): upper triangular but its transpose
-% is lower triagular Choleski
-% xinput{24}: ImfErr -- if 1, impulse response simulation; if 0, disable this simulation
-% xinput{25}: ninv -- number of bins pre-specified to put each draw of impulse response
-% into a proper bin (or small interval)
-% xinput{26}: imstp -- # of steps for impulse responses
-% xinput{27}: forep -- forecast periods (# of steps)
-% xinput{28}: yact -- actual data (in log except R, U, etc.)
-% xinput{29}: yactqg -- quarterly annualized growth in actual data
-% xinput{30}: yactCalyg -- calendar annual growth in actual data
-% xinput{31}: imfml -- imstp-by-nvar^2 ML impulse responses
-% xinput{32}: forepq -- forecast periods for quarterly growth
-% xinput{33}: forepy -- forecast periods for annual growth
-% xinput{34}: ncoef -- k: # of coeffients per equation
-% xinput{35}: Bhml -- ML reduced form parameter B (nvar-by-k)
-% xinput{36}: lags -- # of lags
-% xinput{37}: Psuedo -- 1: for Pseudo out-of-sample; 0: for in-sample (or real-time out-of-sample)
-% xinput{38}: q_m = 4 or 12 -- quarterly (4) or monthly (12)
-% xinput{39}: imf3ml -- ML impulse responses with row--steps, column--nvar responses,
-% 3rd dimension--nvar shocks
-% xinput{40}: vlistlog -- sub index for log in vlist
-% xinput{41}: vlistper -- sub index for percent in vlist
-% xinput{42}: phi -- X in the form of y = X*B+U. Row: nSmaple-lags+ndobs. Column: ncoef
-% xinput{43}: actup -- acutal periods for backing out structural shocks
-% xinput{44}: A0ml -- ML A0; column-equation
-% xinput{45}: Bhml -- ML Bh (k-by-nvar)
-% xinput{46}: yrEnd -- the end year for the estimation period
-% xinput{47}: qmEnd -- the end month or quarter for the estimation period
-% xinput{48}: yrStart -- the start year for the estimation period
-% xinput{49}: qmStart -- the start month or quarter for the estimation period
-% sa0indx: special a0 index. 1: use this; 0: disenable
-% simfindx: special impulse response index. 1: use it; 0: disenable
-%------------------
-% E.G.: imfcntmulti: (ninv+2,imstp*nvar^2,nstarts) matrix
-% cnt: count; for impulse responses; multi (nstarts) sequences
-% All output is saved in outB_W, including Range5, invc, ninv, imfcntmulti,
-% sckcorcntmulti, Avhxcntmulti, lzvdcntmulti
-%
-% Written by TAZ 1999
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-nfp=xinput{1}; nvar=xinput{2}; xhat=xinput{3}; hess=xinput{4}; Indxv=xinput{5};
-IndxGraph=xinput{6}; idmat0=xinput{7}; nstarts=xinput{8}; ndraws1=xinput{9}; ndraws2=xinput{10};
-imndraws=xinput{11}; a0indx=xinput{12}; tdf=xinput{13}; nbuffer=xinput{14}; Sbd=xinput{15};
-nSample = xinput{16}; IndxNmlr=xinput{17}; IndxGibbs=xinput{18}; scf=xinput{19}; H_sr=xinput{20};
-fss=xinput{21}; idfile1=xinput{22}; xxhpc=xinput{23}; ImfErr=xinput{24}; ninv=xinput{25};
-imstp=xinput{26}; forep=xinput{27}; yact=xinput{28}; yactqg=xinput{29}; yactCalyg=xinput{30};
-imfml=xinput{31}; forepq=xinput{32}; forepy=xinput{33}; ncoef=xinput{34}; Bhml=xinput{35};
-lags=xinput{36}; Psuedo=xinput{37}; q_m=xinput{38}; imf3ml=xinput{39}; vlistlog=xinput{40};
-vlistper=xinput{41}; phi=xinput{42}; actup=xinput{43}; A0ml=xinput{44}; Bhml=xinput{45};
-yrEnd=xinput{46}; qmEnd=xinput{47}; yrStart=xinput{48}; qmStart=xinput{49};
-
-
-if Psuedo
- disp('Make sure (1) Psuedo=0 in msstart.m and (2) "actuap=nSample-lags" for strucutral shocks')
- disp('Press ctrl-c to abort now')
-end
-
-A0_h = zeros(nvar);
-A0gbs = A0_h; % drawn A0 in Gibbs sampling
-Avhxml = xhat; % ML estimate
-Avhxmean = zeros(nfp,1);
-Avhxs = Avhxmean;
-
-A0xhat = zeros(nvar);
-A0xhat(a0indx) = xhat;
-% A0hatw = zeros(nvar^2,nbuffer);
-
-countJump = zeros(nstarts,1);
-imstpyer = floor(imstp/q_m); % yearly
-
-imfmean = zeros(imstp,nvar^2);
-imfs = imfmean;
-imfyermean = zeros(imstpyer,nvar^2);
-imfyers = imfyermean;
-imfyer3ml = zeros(imstpyer,nvar,nvar);
-imfyer3_h = zeros(imstpyer,nvar,nvar);
-
-
-imfcntmulti = zeros(ninv+2,imstp*nvar^2,nstarts);
- % cnt: count; for impulse responses; multi (nstarts) sequences
-Avhxcntmulti = zeros(ninv+2,nfp,nstarts);
- % cnt: count; for A0; multi (nstarts) sequences
-imfyercntmulti = zeros(ninv+2,imstpyer*nvar^2,nstarts);
- % cnt: count; for impulse responses; multi (nstarts) sequences
-lzvdcntmulti = zeros(ninv+2,imstp*nvar^2,nstarts);
- % cnt: count; for lz vd; multi (nstarts) sequences
-lzvdyercntmulti = zeros(ninv+2,imstpyer*nvar^2,nstarts);
- % cnt: count; for lz vd; multi (nstarts) sequences
-sckcorcntmulti = zeros(ninv+2,nvar^2,nstarts);
-
-%---------------------------------------------------
-% Specify the range for counting the empirical distribution
-%
-%** load the standard deviations of 6 variables, one for log(y), one for gq, and
-%** the third one for yg
-eval(['load ' idfile1 '.prn -ascii']);
-eval(['ABstd=' idfile1 ';']);
-Range5 = cell(9,1); % 8: log, qg, yg, imf, Avhx, imfyer (yearly), lzvd,
- % lzvdyer, sckcorv,
-
-%@@@ Tony's trick to expand the matrix
-%
-%** In order of log(y), qg, and yg for Range5{i} for i=1:3
-Range5{1} =zeros(forep,nvar,2); % 2: min and max
-Range5{1}(:,:,1) = repmat(yact(length(yact(:,1)),:)-10*ABstd(1,:),[forep 1]); % min, 30 std.
-Range5{1}(:,:,2) = repmat(yact(length(yact(:,1)),:)+10*ABstd(1,:),[forep 1]); % max, 30 std.
-%
-Range5{2} =zeros(forepq,nvar,2); % 2: min and max
-Range5{2}(:,:,1) = repmat(yactqg(length(yactqg(:,1)),:)-10*ABstd(2,:),[forepq 1]); % min, 30 std.
-Range5{2}(:,:,2) = repmat(yactqg(length(yactqg(:,1)),:)+10*ABstd(2,:),[forepq 1]); % max, 30 std.
-%
-Range5{3} =zeros(forepy,nvar,2); % 2: min and max
-Range5{3}(:,:,1) = repmat(yactCalyg(length(yactCalyg(:,1)),:)-10*ABstd(3,:),[forepy 1]); % min, 30 std.
-Range5{3}(:,:,2) = repmat(yactCalyg(length(yactCalyg(:,1)),:)+10*ABstd(3,:),[forepy 1]); % max, 30 std.
-%
-Range5{4} =zeros(imstp,nvar^2,2); % 2: min and max
-imfscale = repmat(ABstd(4,:),[1 nvar]); % because nvar variables to 1, ..., nvar shocks
-Range5{4}(:,:,1) = repmat(imfml(1,:)-5*imfscale,[imstp 1]); % min, 5 std.
-Range5{4}(:,:,2) = repmat(imfml(1,:)+5*imfscale,[imstp 1]); % max, 5 std.
- % Range5(4)(:,:,1): imstp-by-nvar^2. Column: nvar responses to 1st shock,
- % nvar responses to 2nd shock, ...
-%
-%*** for parameters A0's
-Range5{5} =zeros(nfp,2); % 2: min and max
-Avhxscale = abs(Avhxml);
-Range5{5}(:,1) = Avhxml-5*Avhxscale; % min, 5 std.
-Range5{5}(:,2) = Avhxml+5*Avhxscale; % max, 5 std.
-
-%
-%*** for yearly impulse responses
-yer3 = zeros(1+imstpyer,nvar,nvar);
-for k=1:imstpyer
- yer3(k+1,:,:) = mean(imf3ml(1+q_m*(k-1):q_m*k,:,:),1); % annual average
- % yer3: row--steps, column--nvar responses, 3rd dimension--nvar shocks
-end
-imfyer3ml(:,vlistlog,:) = ( exp(yer3(2:1+imstpyer,vlistlog,:)-yer3(1:imstpyer,vlistlog,:)) - 1 ).*100;
-imfyer3ml(:,vlistper,:) = yer3(2:1+imstpyer,vlistper,:) .* 100;
- % imfyer3ml: row--steps, column--nvar responses, 3rd dimension--nvar shocks
-tmp = max(squeeze(max(imfyer3ml,[],1)),[],2); % nvar-by-1
-tmp = tmp'; % 1-by-nvar (variables)
-imfyerml = reshape(imfyer3ml,imstpyer,nvar^2);
-imfyermlmax = max(abs(imfyerml)); % for error bands (imfyercnt) later
-%*** for annual impulse responses
-Range5{6} =zeros(imstpyer,nvar^2,2); % 2: min and max
-tmp = repmat(tmp,[1 nvar]); % because nvar variables to 1, ..., nvar shocks
-imfyerscl = repmat(tmp,[imstpyer,1]); % imstpyer-by-nvar^2
-Range5{6}(:,:,1) = imfyerml-5*imfyerscl; % min, 5 std.
-Range5{6}(:,:,2) = imfyerml+5*imfyerscl; % max, 5 std.
- % Range5(6)(:,:,1): imstpyer-by-nvar^2. Column: nvar responses to 1st shock,
- % nvar responses to 2nd shock, ...
-
-%*** lz variance decomposition (nunlike the traditional, non-cumulative).
-tmp0=abs(imf3ml);
-tmp1 = repmat(sum(tmp0,3),[1 1 nvar]); % imstp-by-nvar^2
- % imf3: row--steps, column--nvar responses, 3rd dimension--nvar shocks
-lzvd3ml = 100*(tmp0./tmp1);
-lzvdml = reshape(lzvd3ml,imstp,nvar^2);
-%** for lz vd (non-cumulative)
-Range5{7} =zeros(imstp,nvar^2,2); % 2: min and max
-Range5{7}(:,:,1) = zeros(imstp,nvar^2); % min, 5 std.
-Range5{7}(:,:,2) = 100*ones(imstp,nvar^2); % max, 5 std.
- % Range5(7)(:,:,1): imstp-by-nvar^2. Column: nvar responses to 1st shock,
- % nvar responses to 2nd shock, ...
-
-%*** lz annual variance decomposition (nunlike the traditional, non-cumulative).
-tmp0=abs(imfyer3ml);
-tmp1 = repmat(sum(tmp0,3),[1 1 nvar]); % imstp-by-nvar^2
- % imf3: row--steps, column--nvar responses, 3rd dimension--nvar shocks
-lzvdyer3ml = 100*(tmp0./tmp1);
-lzvdyerml = reshape(lzvdyer3ml,imstpyer,nvar^2);
-%** for lz annual vd
-Range5{8} =zeros(imstpyer,nvar^2,2); % 2: min and max
-Range5{8}(:,:,1) = zeros(imstpyer,nvar^2); % min, 5 std.
-Range5{8}(:,:,2) = 100*ones(imstpyer,nvar^2); % max, 5 std.
- % Range5(8)(:,:,1): imstpyer-by-nvar^2. Column: nvar responses to 1st shock,
- % nvar responses to 2nd shock, ...
-
-%**** Correlations among structural shocks
-philr = phi(size(phi,1)-actup+1,:); % +1 is absolutely needed
-yexa = yact(1:actup,:);
-Estrexaml = fidcndexa(yexa,philr,A0ml,Bhml,nvar,lags,actup); % actup-by-nvar
-sckvarml = (Estrexaml'*Estrexaml)/actup;
-sckcorml = corr(sckvarml);
-%** for shock correlation sckcor
-Range5{9} =zeros(nvar,nvar,2); % 2: min and max
-Range5{9}(:,:,1) = (-1)*ones(nvar,nvar); % min, 5 std.
-Range5{9}(:,:,2) = ones(nvar,nvar); % max, 5 std.
- % Range5(9)(:,:,1): nvar^2. Correlation among structural shocks
-
-
-
-
-%**
-invc = cell(9,1); % interval length (used for counting later).
-for i=[1:4 6:9]
- invc{i} = Range5{i}(:,:,2) - Range5{i}(:,:,1);
-end
-invc{5} = Range5{5}(:,2) - Range5{5}(:,1);
-%
-imfhbin = invc{4} ./ ninv; % bin size for each point of impulse responses
-imfloor = Range5{4}(:,:,1); % lowest point next to -infinity
-Avhxhbin = invc{5} ./ ninv; % bin size for each point of A0
-Avhxfloor = Range5{5}(:,1); % lowest point next to -infinity
-imfyerhbin = invc{6} ./ ninv; % bin size for each point of annual impulse responses
-imfyerfloor = Range5{6}(:,:,1); % lowest point next to -infinity
-lzvdhbin = invc{7} ./ ninv; % bin size for each point of variance decompositions
-lzvdfloor = Range5{7}(:,:,1); % lowest point next to -infinity
-lzvdyerhbin = invc{8} ./ ninv; % bin size for each point of annul variance decompositions
-lzvdyerfloor = Range5{8}(:,:,1); % lowest point next to -infinity
-sckcorhbin = invc{9} ./ ninv; % bin size for each point of shock correlations
-sckcorfloor = Range5{9}(:,:,1); % lowest point next to -infinity
-
-
-
-
-%*** <<>> Specific requests
-%*** compute prob(parameter>0) or joint prob. of sign matches in MP and MD
-if sa0indx
- csix1a0 = [-1 -1 1 1 1 -1 1]'; % for 7 parameters in MP and MD
- % from 7th to 13th in Avhx_norm
- nspeca0 = 7+3; % number of specific requests
- % additional 3: 1 for all MP parameters; 2 for all MD parameters;
- % 3 for all paramters in MP and MD
- cspeca0 = zeros(nspeca0,1);
-end
-%
-if simfindx
- csix1imf = [-1 -1 1 -1 -1 1]'; % for 6 variables to MP shock
- % maybe at different horizons (esp. for inflation)
- csix1pimf = [1 1 1 24 36 24]'; % the periods for the 6 varialbes to MP shock
- % Pcm, M2, FFR, y, P, U.
- nspecimf = nvar+2; % number of specific requests
- % additional 1: 1 for opposite signs of M2 and R
- cspecimf = zeros(nspecimf,1);
-end
-
-
-
-
-%===================================
-% Here begins with the big loop
-%===================================
-H1 = chol(hess); % upper triangular so that H1' is a lower triangular decomp
-baseW = H_sr; %inv(H1); %H_sr; % covariance matrix without scaling
-nswitch=0; %<<>> total number of sign switches
-A0inxhat = inv(A0xhat); % inverse of ML estimate
-a0indx0 = find(idmat0==0); % index for all zero's in A0;
-nn=[nvar lags imstp];
-
-[cT,vR,kdf] = gibbsglb(Sbd,idmat0,nvar,fss);
-
-tic
-for starts = 1:nstarts
- starts
- if starts == 1
- A0gbs(a0indx) = xhat; % from "load ..."
- if ~IndxGibbs % Metropolist
- Avhx = xhat;
- hAvhx = a0asfun(Avhx,Sbd,fss,nvar,a0indx);
- hAvhx = -hAvhx; % converted to logLH
- end
- else
- Avhx = baseW*randn(nfp,1); %H_sr*randn(nfp,1); % D: discarded sequence
- csq=randn(tdf,1);
- csq=sum(csq .* csq);
- Avhx = xhat+Avhx/sqrt(csq/tdf);
- %** Normalization by the choice of IndxNmlr
- A0gbs(a0indx) = Avhx;
- if ~IndxNmlr(5)
- [A0gbs,jnk] = nmlzvar(A0gbs,A0xhat,A0inxhat,IndxNmlr,nswitch,[]);
- else
- A0ingbs = inv(A0gbs);
- [A0gbs,jnk,jnk1] = nmlzvar(A0gbs,A0xhat,A0inxhat,IndxNmlr,nswitch,A0ingbs);
- end
- %
- if ~IndxGibbs % Metropolist
- Avhx = A0gbs(a0indx);
- hAvhx = a0asfun(Avhx,Sbd,fss,nvar,a0indx);
- hAvhx = -hAvhx; % converted to logLH
- end
- end
- %
- cJump = 0;
-
- imfcnt = zeros(ninv+2,imstp*nvar^2); % cnt: count; for impulse responses
- Avhxcnt = zeros(ninv+2,nfp); % cnt: count; for A0's
- imfyercnt = zeros(ninv+2,imstpyer*nvar^2); % cnt: count; for impulse responses
- lzvdcnt = zeros(ninv+2,imstp*nvar^2); % cnt: count; for lz vd (non-cumulative)
- lzvdyercnt = zeros(ninv+2,imstpyer*nvar^2); % cnt: count; for lz annual vd (non-cumulative)
- sckcorcnt = zeros(ninv+2,nvar^2); % cnt: count; for shock correlations
-
- for draws = 1:ndraws1
- if IndxGibbs
- A0gbs = gibbsvar(A0gbs,cT,vR,nvar,fss,kdf);
- else % Metropolis
- [Avhx,hAvhx,cJump] = smtplis(Avhx,hAvhx,tdf,cJump,scf,...
- baseW,nfp,Sbd,fss,nvar,a0indx);
- end
- end
-
- wdraws=(starts-1)*ndraws2+0;
- for draws = 1:ndraws2
- drawsc = (starts-1)*ndraws2+draws;
- if IndxGibbs
- A0gbs = gibbsvar(A0gbs,cT,vR,nvar,fss,kdf);
- A0gbs(a0indx0) = 0; % set all zeros in A0gbs clean to avoid possible cumulative round-off errors
- else % Metropolis
- [Avhx,hAvhx,cJump] = smtplis(Avhx,hAvhx,tdf,cJump,scf,...
- baseW,nfp,Sbd,fss,nvar,a0indx);
- A0gbs(a0indx) = Avhx;
- end
-
- %*** call normalization so that A0_h is normalized
- if ~IndxNmlr(5)
- [A0_h,nswitch] = nmlzvar(A0gbs,A0xhat,A0inxhat,IndxNmlr,nswitch,[]);
- A0_hin = inv(A0_h);
- else
- A0ingbs = inv(A0gbs);
- [A0_h,nswitch,A0_hin] = nmlzvar(A0gbs,A0xhat,A0inxhat,IndxNmlr,nswitch,A0ingbs);
- end
- Avhx_norm = A0_h(a0indx);
-
- if sa0indx
- for k=1:7
- cspeca0(k) = cspeca0(k) + ((csix1a0(k)*Avhx_norm(6+k))>0);
- end
- %*** Joint tests
- j1=csix1a0;
- j2=Avhx_norm;
- %** MP equation
- mpall = ((j1(2)*j2(8))/(j1(3)*j2(9)))<0; % & ((j1(1)*j2(7))/(j1(3)*j2(9)))<0;
-
- cspeca0(8) = cspeca0(8) + mpall;
- %** MD equation
- mdall = ((j1(4)*j2(10))/(j1(5)*j2(11)))<0; %& ((j1(4)*j2(10))/(j1(6)*j2(12)))<0;
- cspeca0(9) = cspeca0(9) + mdall;
- mpdall = mpall & mdall;
- cspeca0(10) = cspeca0(10) + mpdall;
- end
-
- %
- % *** normal draws for posterior Aplus conditional on A0h
- %
- %** the mean is Aplushm, and the covariance is inv(xxhp)=Lxxhpc*Lxxhpc'
- Apindm = randn(ncoef,nvar);
- %
- if ~all(all(finite(Bhml)))
- Aplushm=zeros(ncoef,nvar);
- for i=1:nvar
- Aplushm(:,i)=Gb{i}*A0_h(:,i); % see Zha's forecast (1) p.9
- % Here, Gb is used to compute A+ where A+(i) = Gb(i)*a0(i)
- end
- Bh_h = (Aplushm + xxhpc\Apindm)*A0_hin;
- else
- Bh_h = Bhml + (xxhpc\Apindm)*A0_hin;
- end
-
- if ImfErr
- swish_h = A0_hin'; % Switching back to the form A0*y(t) = e(t)
- imf_h = zimpulse(Bh_h,swish_h,nn); % in the form that is congenial to RATS
- imf3_h=reshape(imf_h,size(imf_h,1),nvar,nvar);
- % imf3: row--steps, column--nvar responses, 3rd dimension--nvar shocks
- imfmean = imfmean + imf_h; % posterior mean
- imfs = imfs + imf_h.^2; % posterior 2nd moment
- imfcnt = empdfsort(imfcnt,imf_h,imfloor,imfhbin,ninv);
- % sorted counts (prob.) in bins
-
- %**** annula impulse responses
- for k=1:imstpyer
- yer3(k+1,:,:) = mean(imf3_h(1+q_m*(k-1):q_m*k,:,:),1); % annual average
- % yer3: initialized earlier already
- % yer3: row--steps, column--nvar responses, 3rd dimension--nvar shocks
- end
- imfyer3_h(:,vlistlog,:) = ( exp(yer3(2:1+imstpyer,vlistlog,:)-yer3(1:imstpyer,vlistlog,:)) - 1 ).*100;
- imfyer3_h(:,vlistper,:) = yer3(2:1+imstpyer,vlistper,:) .* 100;
- % imfyer3_h: row--steps, column--nvar responses, 3rd dimension--nvar shocks
- imfyer_h = reshape(imfyer3_h,imstpyer,nvar^2);
- imfyermean = imfyermean + imfyer_h; % posterior mean
- imfyers = imfyers + imfyer_h.^2; % posterior 2nd moment
- imfyercnt = empdfsort(imfyercnt,imfyer_h,imfyerfloor,imfyerhbin,ninv);
- % sorted counts (prob.) in bins
-
- %**** Leeper-Zha variance decomposition (non-cumulative)
- tmp0=abs(imf3_h);
- tmp = repmat(sum(tmp0,3),[1 1 nvar]); % imstp-by-nvar^2
- % imf3: row--steps, column--nvar responses, 3rd dimension--nvar shocks
- lzvd3_h = 100*(tmp0./tmp);
- lzvd_h = reshape(lzvd3_h,imstp,nvar^2);
- lzvdcnt = empdfsort(lzvdcnt,lzvd_h,lzvdfloor,lzvdhbin,ninv);
- % sorted counts (prob.) in bins
-
- %**** Leeper-Zha annual variance decomposition (non-cumulative)
- tmp0=abs(imfyer3_h);
- tmp = repmat(sum(tmp0,3),[1 1 nvar]); % imstp-by-nvar^2
- % imf3: row--steps, column--nvar responses, 3rd dimension--nvar shocks
- lzvdyer3_h = 100*(tmp0./tmp);
- lzvdyer_h = reshape(lzvdyer3_h,imstpyer,nvar^2);
- lzvdyercnt = empdfsort(lzvdyercnt,lzvdyer_h,lzvdyerfloor,lzvdyerhbin,ninv);
- % sorted counts (prob.) in bins
-
- %**** Correlations among structural shocks
- Estrexa_h = fidcndexa(yexa,philr,A0_h,Bh_h,nvar,lags,actup); % actup-by-nvar
- sckvar_h = (Estrexa_h'*Estrexa_h)/actup;
- sckcor_h = corr(sckvar_h);
- sckcorcnt = empdfsort(sckcorcnt,sckcor_h,sckcorfloor,sckcorhbin,ninv);
-
- if simfindx
- for k=1:6
- cspecimf(k) = cspecimf(k) + ((csix1a0(k)*imf_h(1,nvar+k))>0);
- % 1st dim in imf_h: periods
- end
- %*** Joint tests
- j1=csix1imf;
- j2=imf_h;
- j3=csix1pimf;
- %** M2 and R in MP equation
- mpall = ((j1(2)*j2(j3(2),nvar+2))/(j1(3)*j2(j3(3),nvar+3)))>0; % & ((j1(1)*j2(7))/(j1(3)*j2(9)))<0;
- % 1st dim in j2: periods
- cspecimf(nvar+1) = cspecimf(nvar+1) + mpall;
- %** R and P in MP equation
- mpall = ((j1(3)*j2(j3(3),nvar+3))/(j1(5)*j2(j3(5),nvar+5)))>0; %
- % 1st dim in j2: periods
- cspecimf(nvar+2) = cspecimf(nvar+2) + mpall;
- %
- % %** MD equation
- % mdall = ((j1(4)*j2(10))/(j1(5)*j2(11)))<0; %& ((j1(4)*j2(10))/(j1(6)*j2(12)))<0;
- % cspeca0(9) = cspeca0(9) + mdall;
- % mpdall = mpall & mdall;
- % cspeca0(10) = cspeca0(10) + mpdall;
- end
- end
-
- Avhxmean = Avhxmean + Avhx_norm; % 1st step to overall mean of parameter
- Avhxs = Avhxs + Avhx_norm.^2; % 1st step to overall 2nd moment of parameter
- Avhxcnt = empdfsort(Avhxcnt,Avhx_norm,Avhxfloor,Avhxhbin,ninv);
-
-
- % A0hatw(:,drawsc-wdraws) = A0_h(:);
- if ~mod(draws,nbuffer)
- starts
- draws
- wdraws=drawsc
- % fwriteid = fopen('outA0.bin','a');
- % count = fwrite(fwriteid,A0hatw,'double');
- % status = fclose('all');
- end
- end
- %
- imfcntmulti(:,:,starts) = imfcnt;
- Avhxcntmulti(:,:,starts) = Avhxcnt;
- imfyercntmulti(:,:,starts) = imfyercnt;
- lzvdcntmulti(:,:,starts) = lzvdcnt;
- lzvdyercntmulti(:,:,starts) = lzvdyercnt;
- sckcorcntmulti(:,:,starts) = sckcorcnt;
-
- if ~IndxGibbs
- countJump(starts,1) = cJump;
- end
-end
-timend = toc
-timeminutes=timend/60
-
-if ~IndxGibbs
- countJump = countJump/ndraws2
-end
-
-Avhxmean = Avhxmean/(imndraws);
-Avhxs = Avhxs/(imndraws);
-Avhxs = sqrt(Avhxs - Avhxmean.^2); % stardard deviation
-A0hm = zeros(nvar);
-A0hm(a0indx) = Avhxmean % mean
-A0hs = zeros(nvar);
-A0hs(a0indx) = Avhxs; % standar deviation
-
-imfmean = imfmean/(imndraws);
-imfs = imfs/imndraws;
-imfs = sqrt(imfs - imfmean.^2); % standard deviation
-
-imfyermean = imfyermean/(imndraws);
-imfyers = imfyers/imndraws;
-imfyers = sqrt(imfyers - imfyermean.^2); % standard deviation
-
-
-save outB_W nstarts ndraws1 ndraws2 imndraws timeminutes Avhxml Avhxmean Avhxs ...
- Avhxcntmulti A0xhat A0hm A0hs IndxGibbs countJump nvar Range5 ...
- ninv invc nfp a0indx nswitch actup nSample lags yrEnd qmEnd ...
- yrStart qmStart q_m sckcorml sckcorcntmulti
-
-if ImfErr
- if simfindx
- cspecimf = cspecimf/imndraws;
- save outB_W cspecimf -append
- end
- save outB_W imfml imfmean imfs imfcntmulti imstp cspecimf ...
- imfyerml imfyermean imfyers imfyercntmulti imstpyer ...
- lzvdml lzvdcntmulti lzvdyerml lzvdyercntmulti -append
-end
-%
-if sa0indx
- cspeca0 = cspeca0/imndraws;
- save outB_W cspeca0 -append
-end
-
-
+function imfvdscksim(xinput,sa0indx,simfindx)
+% imfvdscksim(xinput,sa0indx,simfindx)
+% Save the simulated pdfs of impulse responses, vd, shocks, and A0's;
+% Ref: Waggoner and Zha "Does Normalization Matter for Inference"
+% See note Forecast (2)
+%
+% xinput{1}: nfp -- total number of free parameters
+% xinput{2}: nvar -- number of variables
+% xinput{3}: xhat -- ML estimate of free parameters in A0
+% xinput{4}: hess -- Hessian of -logLH
+% xinput{5}:Indxv -- index for selected variables of interest; normall first 2 are of our interest
+% to select variables, always check idmat0 to make sure
+% it plots: (1) pdf of 1st v for every buffer, (2) scattered plot of 1st and 2nd for every buffer,
+% (2) pdf of 1st v for all sequences; (4) scattered plot of 3rd and 4th for all sequences
+% (5) scattered plot of 1st and 2nd for al sequences.
+% xinput{6}: IndxGraph - 1: plot graphs; 0: no graphs
+% xinput{7}: idmat0 -- Index for non-zero elements in A0 with column to equation
+% xinput{8}: nstarts -- # of starting points in Gibbs sampling
+% xinput{9}: ndraws1 -- # of 1st loop to be discarded
+% xinput{10}: ndraws2 -- # of 2nd loop for saving A0 draws
+% xinput{11}: imndraws=nstarts*ndraws2
+% xinput{12}: a0indx -- index number for non-zero elements in A0
+% xinput{13}: tdf -- degrees of freedom for t-distribution
+% xinput{14}: nbuffer -- interval for printing, plotting, and saving
+% xinput{15}: Sbd -- nvar-by-nvar S{1}, ..., S{n} -- kind of covariance matrix for each simultaneous equation
+% xinput{16}: nSample -- the original sample size including lags
+% xinput{17}: IndxNmlr -- index for which normalization rule to choose
+% xinput{18}: IndxGibbs -- index for WZ Gibbs; 1; Gibbs; 0: Metropolis
+% xinput{19}: scf -- reduction scale factor for Metropolis jumping kernel
+% xinput{20}: H_sr -- covariance matrix for free elements in A0 (nfp-by-nfp)
+% xinput{21}: fss -- effective sample size == nSample-lags+# of dummy observations
+% xinput{22}: idfile1 -- calls "iden6std." Save stds. of both data and impulse responses in idfile1
+% xinput{23}: xxhpc -- chol(X'X+inv(H_p_tilde)): upper triangular but its transpose
+% is lower triagular Choleski
+% xinput{24}: ImfErr -- if 1, impulse response simulation; if 0, disable this simulation
+% xinput{25}: ninv -- number of bins pre-specified to put each draw of impulse response
+% into a proper bin (or small interval)
+% xinput{26}: imstp -- # of steps for impulse responses
+% xinput{27}: forep -- forecast periods (# of steps)
+% xinput{28}: yact -- actual data (in log except R, U, etc.)
+% xinput{29}: yactqg -- quarterly annualized growth in actual data
+% xinput{30}: yactCalyg -- calendar annual growth in actual data
+% xinput{31}: imfml -- imstp-by-nvar^2 ML impulse responses
+% xinput{32}: forepq -- forecast periods for quarterly growth
+% xinput{33}: forepy -- forecast periods for annual growth
+% xinput{34}: ncoef -- k: # of coeffients per equation
+% xinput{35}: Bhml -- ML reduced form parameter B (nvar-by-k)
+% xinput{36}: lags -- # of lags
+% xinput{37}: Psuedo -- 1: for Pseudo out-of-sample; 0: for in-sample (or real-time out-of-sample)
+% xinput{38}: q_m = 4 or 12 -- quarterly (4) or monthly (12)
+% xinput{39}: imf3ml -- ML impulse responses with row--steps, column--nvar responses,
+% 3rd dimension--nvar shocks
+% xinput{40}: vlistlog -- sub index for log in vlist
+% xinput{41}: vlistper -- sub index for percent in vlist
+% xinput{42}: phi -- X in the form of y = X*B+U. Row: nSmaple-lags+ndobs. Column: ncoef
+% xinput{43}: actup -- acutal periods for backing out structural shocks
+% xinput{44}: A0ml -- ML A0; column-equation
+% xinput{45}: Bhml -- ML Bh (k-by-nvar)
+% xinput{46}: yrEnd -- the end year for the estimation period
+% xinput{47}: qmEnd -- the end month or quarter for the estimation period
+% xinput{48}: yrStart -- the start year for the estimation period
+% xinput{49}: qmStart -- the start month or quarter for the estimation period
+% sa0indx: special a0 index. 1: use this; 0: disenable
+% simfindx: special impulse response index. 1: use it; 0: disenable
+%------------------
+% E.G.: imfcntmulti: (ninv+2,imstp*nvar^2,nstarts) matrix
+% cnt: count; for impulse responses; multi (nstarts) sequences
+% All output is saved in outB_W, including Range5, invc, ninv, imfcntmulti,
+% sckcorcntmulti, Avhxcntmulti, lzvdcntmulti
+%
+% Written by TAZ 1999
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+nfp=xinput{1}; nvar=xinput{2}; xhat=xinput{3}; hess=xinput{4}; Indxv=xinput{5};
+IndxGraph=xinput{6}; idmat0=xinput{7}; nstarts=xinput{8}; ndraws1=xinput{9}; ndraws2=xinput{10};
+imndraws=xinput{11}; a0indx=xinput{12}; tdf=xinput{13}; nbuffer=xinput{14}; Sbd=xinput{15};
+nSample = xinput{16}; IndxNmlr=xinput{17}; IndxGibbs=xinput{18}; scf=xinput{19}; H_sr=xinput{20};
+fss=xinput{21}; idfile1=xinput{22}; xxhpc=xinput{23}; ImfErr=xinput{24}; ninv=xinput{25};
+imstp=xinput{26}; forep=xinput{27}; yact=xinput{28}; yactqg=xinput{29}; yactCalyg=xinput{30};
+imfml=xinput{31}; forepq=xinput{32}; forepy=xinput{33}; ncoef=xinput{34}; Bhml=xinput{35};
+lags=xinput{36}; Psuedo=xinput{37}; q_m=xinput{38}; imf3ml=xinput{39}; vlistlog=xinput{40};
+vlistper=xinput{41}; phi=xinput{42}; actup=xinput{43}; A0ml=xinput{44}; Bhml=xinput{45};
+yrEnd=xinput{46}; qmEnd=xinput{47}; yrStart=xinput{48}; qmStart=xinput{49};
+
+
+if Psuedo
+ disp('Make sure (1) Psuedo=0 in msstart.m and (2) "actuap=nSample-lags" for strucutral shocks')
+ disp('Press ctrl-c to abort now')
+end
+
+A0_h = zeros(nvar);
+A0gbs = A0_h; % drawn A0 in Gibbs sampling
+Avhxml = xhat; % ML estimate
+Avhxmean = zeros(nfp,1);
+Avhxs = Avhxmean;
+
+A0xhat = zeros(nvar);
+A0xhat(a0indx) = xhat;
+% A0hatw = zeros(nvar^2,nbuffer);
+
+countJump = zeros(nstarts,1);
+imstpyer = floor(imstp/q_m); % yearly
+
+imfmean = zeros(imstp,nvar^2);
+imfs = imfmean;
+imfyermean = zeros(imstpyer,nvar^2);
+imfyers = imfyermean;
+imfyer3ml = zeros(imstpyer,nvar,nvar);
+imfyer3_h = zeros(imstpyer,nvar,nvar);
+
+
+imfcntmulti = zeros(ninv+2,imstp*nvar^2,nstarts);
+ % cnt: count; for impulse responses; multi (nstarts) sequences
+Avhxcntmulti = zeros(ninv+2,nfp,nstarts);
+ % cnt: count; for A0; multi (nstarts) sequences
+imfyercntmulti = zeros(ninv+2,imstpyer*nvar^2,nstarts);
+ % cnt: count; for impulse responses; multi (nstarts) sequences
+lzvdcntmulti = zeros(ninv+2,imstp*nvar^2,nstarts);
+ % cnt: count; for lz vd; multi (nstarts) sequences
+lzvdyercntmulti = zeros(ninv+2,imstpyer*nvar^2,nstarts);
+ % cnt: count; for lz vd; multi (nstarts) sequences
+sckcorcntmulti = zeros(ninv+2,nvar^2,nstarts);
+
+%---------------------------------------------------
+% Specify the range for counting the empirical distribution
+%
+%** load the standard deviations of 6 variables, one for log(y), one for gq, and
+%** the third one for yg
+eval(['load ' idfile1 '.prn -ascii']);
+eval(['ABstd=' idfile1 ';']);
+Range5 = cell(9,1); % 8: log, qg, yg, imf, Avhx, imfyer (yearly), lzvd,
+ % lzvdyer, sckcorv,
+
+%@@@ Tony's trick to expand the matrix
+%
+%** In order of log(y), qg, and yg for Range5{i} for i=1:3
+Range5{1} =zeros(forep,nvar,2); % 2: min and max
+Range5{1}(:,:,1) = repmat(yact(length(yact(:,1)),:)-10*ABstd(1,:),[forep 1]); % min, 30 std.
+Range5{1}(:,:,2) = repmat(yact(length(yact(:,1)),:)+10*ABstd(1,:),[forep 1]); % max, 30 std.
+%
+Range5{2} =zeros(forepq,nvar,2); % 2: min and max
+Range5{2}(:,:,1) = repmat(yactqg(length(yactqg(:,1)),:)-10*ABstd(2,:),[forepq 1]); % min, 30 std.
+Range5{2}(:,:,2) = repmat(yactqg(length(yactqg(:,1)),:)+10*ABstd(2,:),[forepq 1]); % max, 30 std.
+%
+Range5{3} =zeros(forepy,nvar,2); % 2: min and max
+Range5{3}(:,:,1) = repmat(yactCalyg(length(yactCalyg(:,1)),:)-10*ABstd(3,:),[forepy 1]); % min, 30 std.
+Range5{3}(:,:,2) = repmat(yactCalyg(length(yactCalyg(:,1)),:)+10*ABstd(3,:),[forepy 1]); % max, 30 std.
+%
+Range5{4} =zeros(imstp,nvar^2,2); % 2: min and max
+imfscale = repmat(ABstd(4,:),[1 nvar]); % because nvar variables to 1, ..., nvar shocks
+Range5{4}(:,:,1) = repmat(imfml(1,:)-5*imfscale,[imstp 1]); % min, 5 std.
+Range5{4}(:,:,2) = repmat(imfml(1,:)+5*imfscale,[imstp 1]); % max, 5 std.
+ % Range5(4)(:,:,1): imstp-by-nvar^2. Column: nvar responses to 1st shock,
+ % nvar responses to 2nd shock, ...
+%
+%*** for parameters A0's
+Range5{5} =zeros(nfp,2); % 2: min and max
+Avhxscale = abs(Avhxml);
+Range5{5}(:,1) = Avhxml-5*Avhxscale; % min, 5 std.
+Range5{5}(:,2) = Avhxml+5*Avhxscale; % max, 5 std.
+
+%
+%*** for yearly impulse responses
+yer3 = zeros(1+imstpyer,nvar,nvar);
+for k=1:imstpyer
+ yer3(k+1,:,:) = mean(imf3ml(1+q_m*(k-1):q_m*k,:,:),1); % annual average
+ % yer3: row--steps, column--nvar responses, 3rd dimension--nvar shocks
+end
+imfyer3ml(:,vlistlog,:) = ( exp(yer3(2:1+imstpyer,vlistlog,:)-yer3(1:imstpyer,vlistlog,:)) - 1 ).*100;
+imfyer3ml(:,vlistper,:) = yer3(2:1+imstpyer,vlistper,:) .* 100;
+ % imfyer3ml: row--steps, column--nvar responses, 3rd dimension--nvar shocks
+tmp = max(squeeze(max(imfyer3ml,[],1)),[],2); % nvar-by-1
+tmp = tmp'; % 1-by-nvar (variables)
+imfyerml = reshape(imfyer3ml,imstpyer,nvar^2);
+imfyermlmax = max(abs(imfyerml)); % for error bands (imfyercnt) later
+%*** for annual impulse responses
+Range5{6} =zeros(imstpyer,nvar^2,2); % 2: min and max
+tmp = repmat(tmp,[1 nvar]); % because nvar variables to 1, ..., nvar shocks
+imfyerscl = repmat(tmp,[imstpyer,1]); % imstpyer-by-nvar^2
+Range5{6}(:,:,1) = imfyerml-5*imfyerscl; % min, 5 std.
+Range5{6}(:,:,2) = imfyerml+5*imfyerscl; % max, 5 std.
+ % Range5(6)(:,:,1): imstpyer-by-nvar^2. Column: nvar responses to 1st shock,
+ % nvar responses to 2nd shock, ...
+
+%*** lz variance decomposition (nunlike the traditional, non-cumulative).
+tmp0=abs(imf3ml);
+tmp1 = repmat(sum(tmp0,3),[1 1 nvar]); % imstp-by-nvar^2
+ % imf3: row--steps, column--nvar responses, 3rd dimension--nvar shocks
+lzvd3ml = 100*(tmp0./tmp1);
+lzvdml = reshape(lzvd3ml,imstp,nvar^2);
+%** for lz vd (non-cumulative)
+Range5{7} =zeros(imstp,nvar^2,2); % 2: min and max
+Range5{7}(:,:,1) = zeros(imstp,nvar^2); % min, 5 std.
+Range5{7}(:,:,2) = 100*ones(imstp,nvar^2); % max, 5 std.
+ % Range5(7)(:,:,1): imstp-by-nvar^2. Column: nvar responses to 1st shock,
+ % nvar responses to 2nd shock, ...
+
+%*** lz annual variance decomposition (nunlike the traditional, non-cumulative).
+tmp0=abs(imfyer3ml);
+tmp1 = repmat(sum(tmp0,3),[1 1 nvar]); % imstp-by-nvar^2
+ % imf3: row--steps, column--nvar responses, 3rd dimension--nvar shocks
+lzvdyer3ml = 100*(tmp0./tmp1);
+lzvdyerml = reshape(lzvdyer3ml,imstpyer,nvar^2);
+%** for lz annual vd
+Range5{8} =zeros(imstpyer,nvar^2,2); % 2: min and max
+Range5{8}(:,:,1) = zeros(imstpyer,nvar^2); % min, 5 std.
+Range5{8}(:,:,2) = 100*ones(imstpyer,nvar^2); % max, 5 std.
+ % Range5(8)(:,:,1): imstpyer-by-nvar^2. Column: nvar responses to 1st shock,
+ % nvar responses to 2nd shock, ...
+
+%**** Correlations among structural shocks
+philr = phi(size(phi,1)-actup+1,:); % +1 is absolutely needed
+yexa = yact(1:actup,:);
+Estrexaml = fidcndexa(yexa,philr,A0ml,Bhml,nvar,lags,actup); % actup-by-nvar
+sckvarml = (Estrexaml'*Estrexaml)/actup;
+sckcorml = corr(sckvarml);
+%** for shock correlation sckcor
+Range5{9} =zeros(nvar,nvar,2); % 2: min and max
+Range5{9}(:,:,1) = (-1)*ones(nvar,nvar); % min, 5 std.
+Range5{9}(:,:,2) = ones(nvar,nvar); % max, 5 std.
+ % Range5(9)(:,:,1): nvar^2. Correlation among structural shocks
+
+
+
+
+%**
+invc = cell(9,1); % interval length (used for counting later).
+for i=[1:4 6:9]
+ invc{i} = Range5{i}(:,:,2) - Range5{i}(:,:,1);
+end
+invc{5} = Range5{5}(:,2) - Range5{5}(:,1);
+%
+imfhbin = invc{4} ./ ninv; % bin size for each point of impulse responses
+imfloor = Range5{4}(:,:,1); % lowest point next to -infinity
+Avhxhbin = invc{5} ./ ninv; % bin size for each point of A0
+Avhxfloor = Range5{5}(:,1); % lowest point next to -infinity
+imfyerhbin = invc{6} ./ ninv; % bin size for each point of annual impulse responses
+imfyerfloor = Range5{6}(:,:,1); % lowest point next to -infinity
+lzvdhbin = invc{7} ./ ninv; % bin size for each point of variance decompositions
+lzvdfloor = Range5{7}(:,:,1); % lowest point next to -infinity
+lzvdyerhbin = invc{8} ./ ninv; % bin size for each point of annul variance decompositions
+lzvdyerfloor = Range5{8}(:,:,1); % lowest point next to -infinity
+sckcorhbin = invc{9} ./ ninv; % bin size for each point of shock correlations
+sckcorfloor = Range5{9}(:,:,1); % lowest point next to -infinity
+
+
+
+
+%*** <<>> Specific requests
+%*** compute prob(parameter>0) or joint prob. of sign matches in MP and MD
+if sa0indx
+ csix1a0 = [-1 -1 1 1 1 -1 1]'; % for 7 parameters in MP and MD
+ % from 7th to 13th in Avhx_norm
+ nspeca0 = 7+3; % number of specific requests
+ % additional 3: 1 for all MP parameters; 2 for all MD parameters;
+ % 3 for all paramters in MP and MD
+ cspeca0 = zeros(nspeca0,1);
+end
+%
+if simfindx
+ csix1imf = [-1 -1 1 -1 -1 1]'; % for 6 variables to MP shock
+ % maybe at different horizons (esp. for inflation)
+ csix1pimf = [1 1 1 24 36 24]'; % the periods for the 6 varialbes to MP shock
+ % Pcm, M2, FFR, y, P, U.
+ nspecimf = nvar+2; % number of specific requests
+ % additional 1: 1 for opposite signs of M2 and R
+ cspecimf = zeros(nspecimf,1);
+end
+
+
+
+
+%===================================
+% Here begins with the big loop
+%===================================
+H1 = chol(hess); % upper triangular so that H1' is a lower triangular decomp
+baseW = H_sr; %inv(H1); %H_sr; % covariance matrix without scaling
+nswitch=0; %<<>> total number of sign switches
+A0inxhat = inv(A0xhat); % inverse of ML estimate
+a0indx0 = find(idmat0==0); % index for all zero's in A0;
+nn=[nvar lags imstp];
+
+[cT,vR,kdf] = gibbsglb(Sbd,idmat0,nvar,fss);
+
+tic
+for starts = 1:nstarts
+ starts
+ if starts == 1
+ A0gbs(a0indx) = xhat; % from "load ..."
+ if ~IndxGibbs % Metropolist
+ Avhx = xhat;
+ hAvhx = a0asfun(Avhx,Sbd,fss,nvar,a0indx);
+ hAvhx = -hAvhx; % converted to logLH
+ end
+ else
+ Avhx = baseW*randn(nfp,1); %H_sr*randn(nfp,1); % D: discarded sequence
+ csq=randn(tdf,1);
+ csq=sum(csq .* csq);
+ Avhx = xhat+Avhx/sqrt(csq/tdf);
+ %** Normalization by the choice of IndxNmlr
+ A0gbs(a0indx) = Avhx;
+ if ~IndxNmlr(5)
+ [A0gbs,jnk] = nmlzvar(A0gbs,A0xhat,A0inxhat,IndxNmlr,nswitch,[]);
+ else
+ A0ingbs = inv(A0gbs);
+ [A0gbs,jnk,jnk1] = nmlzvar(A0gbs,A0xhat,A0inxhat,IndxNmlr,nswitch,A0ingbs);
+ end
+ %
+ if ~IndxGibbs % Metropolist
+ Avhx = A0gbs(a0indx);
+ hAvhx = a0asfun(Avhx,Sbd,fss,nvar,a0indx);
+ hAvhx = -hAvhx; % converted to logLH
+ end
+ end
+ %
+ cJump = 0;
+
+ imfcnt = zeros(ninv+2,imstp*nvar^2); % cnt: count; for impulse responses
+ Avhxcnt = zeros(ninv+2,nfp); % cnt: count; for A0's
+ imfyercnt = zeros(ninv+2,imstpyer*nvar^2); % cnt: count; for impulse responses
+ lzvdcnt = zeros(ninv+2,imstp*nvar^2); % cnt: count; for lz vd (non-cumulative)
+ lzvdyercnt = zeros(ninv+2,imstpyer*nvar^2); % cnt: count; for lz annual vd (non-cumulative)
+ sckcorcnt = zeros(ninv+2,nvar^2); % cnt: count; for shock correlations
+
+ for draws = 1:ndraws1
+ if IndxGibbs
+ A0gbs = gibbsvar(A0gbs,cT,vR,nvar,fss,kdf);
+ else % Metropolis
+ [Avhx,hAvhx,cJump] = smtplis(Avhx,hAvhx,tdf,cJump,scf,...
+ baseW,nfp,Sbd,fss,nvar,a0indx);
+ end
+ end
+
+ wdraws=(starts-1)*ndraws2+0;
+ for draws = 1:ndraws2
+ drawsc = (starts-1)*ndraws2+draws;
+ if IndxGibbs
+ A0gbs = gibbsvar(A0gbs,cT,vR,nvar,fss,kdf);
+ A0gbs(a0indx0) = 0; % set all zeros in A0gbs clean to avoid possible cumulative round-off errors
+ else % Metropolis
+ [Avhx,hAvhx,cJump] = smtplis(Avhx,hAvhx,tdf,cJump,scf,...
+ baseW,nfp,Sbd,fss,nvar,a0indx);
+ A0gbs(a0indx) = Avhx;
+ end
+
+ %*** call normalization so that A0_h is normalized
+ if ~IndxNmlr(5)
+ [A0_h,nswitch] = nmlzvar(A0gbs,A0xhat,A0inxhat,IndxNmlr,nswitch,[]);
+ A0_hin = inv(A0_h);
+ else
+ A0ingbs = inv(A0gbs);
+ [A0_h,nswitch,A0_hin] = nmlzvar(A0gbs,A0xhat,A0inxhat,IndxNmlr,nswitch,A0ingbs);
+ end
+ Avhx_norm = A0_h(a0indx);
+
+ if sa0indx
+ for k=1:7
+ cspeca0(k) = cspeca0(k) + ((csix1a0(k)*Avhx_norm(6+k))>0);
+ end
+ %*** Joint tests
+ j1=csix1a0;
+ j2=Avhx_norm;
+ %** MP equation
+ mpall = ((j1(2)*j2(8))/(j1(3)*j2(9)))<0; % & ((j1(1)*j2(7))/(j1(3)*j2(9)))<0;
+
+ cspeca0(8) = cspeca0(8) + mpall;
+ %** MD equation
+ mdall = ((j1(4)*j2(10))/(j1(5)*j2(11)))<0; %& ((j1(4)*j2(10))/(j1(6)*j2(12)))<0;
+ cspeca0(9) = cspeca0(9) + mdall;
+ mpdall = mpall & mdall;
+ cspeca0(10) = cspeca0(10) + mpdall;
+ end
+
+ %
+ % *** normal draws for posterior Aplus conditional on A0h
+ %
+ %** the mean is Aplushm, and the covariance is inv(xxhp)=Lxxhpc*Lxxhpc'
+ Apindm = randn(ncoef,nvar);
+ %
+ if ~all(all(finite(Bhml)))
+ Aplushm=zeros(ncoef,nvar);
+ for i=1:nvar
+ Aplushm(:,i)=Gb{i}*A0_h(:,i); % see Zha's forecast (1) p.9
+ % Here, Gb is used to compute A+ where A+(i) = Gb(i)*a0(i)
+ end
+ Bh_h = (Aplushm + xxhpc\Apindm)*A0_hin;
+ else
+ Bh_h = Bhml + (xxhpc\Apindm)*A0_hin;
+ end
+
+ if ImfErr
+ swish_h = A0_hin'; % Switching back to the form A0*y(t) = e(t)
+ imf_h = zimpulse(Bh_h,swish_h,nn); % in the form that is congenial to RATS
+ imf3_h=reshape(imf_h,size(imf_h,1),nvar,nvar);
+ % imf3: row--steps, column--nvar responses, 3rd dimension--nvar shocks
+ imfmean = imfmean + imf_h; % posterior mean
+ imfs = imfs + imf_h.^2; % posterior 2nd moment
+ imfcnt = empdfsort(imfcnt,imf_h,imfloor,imfhbin,ninv);
+ % sorted counts (prob.) in bins
+
+ %**** annula impulse responses
+ for k=1:imstpyer
+ yer3(k+1,:,:) = mean(imf3_h(1+q_m*(k-1):q_m*k,:,:),1); % annual average
+ % yer3: initialized earlier already
+ % yer3: row--steps, column--nvar responses, 3rd dimension--nvar shocks
+ end
+ imfyer3_h(:,vlistlog,:) = ( exp(yer3(2:1+imstpyer,vlistlog,:)-yer3(1:imstpyer,vlistlog,:)) - 1 ).*100;
+ imfyer3_h(:,vlistper,:) = yer3(2:1+imstpyer,vlistper,:) .* 100;
+ % imfyer3_h: row--steps, column--nvar responses, 3rd dimension--nvar shocks
+ imfyer_h = reshape(imfyer3_h,imstpyer,nvar^2);
+ imfyermean = imfyermean + imfyer_h; % posterior mean
+ imfyers = imfyers + imfyer_h.^2; % posterior 2nd moment
+ imfyercnt = empdfsort(imfyercnt,imfyer_h,imfyerfloor,imfyerhbin,ninv);
+ % sorted counts (prob.) in bins
+
+ %**** Leeper-Zha variance decomposition (non-cumulative)
+ tmp0=abs(imf3_h);
+ tmp = repmat(sum(tmp0,3),[1 1 nvar]); % imstp-by-nvar^2
+ % imf3: row--steps, column--nvar responses, 3rd dimension--nvar shocks
+ lzvd3_h = 100*(tmp0./tmp);
+ lzvd_h = reshape(lzvd3_h,imstp,nvar^2);
+ lzvdcnt = empdfsort(lzvdcnt,lzvd_h,lzvdfloor,lzvdhbin,ninv);
+ % sorted counts (prob.) in bins
+
+ %**** Leeper-Zha annual variance decomposition (non-cumulative)
+ tmp0=abs(imfyer3_h);
+ tmp = repmat(sum(tmp0,3),[1 1 nvar]); % imstp-by-nvar^2
+ % imf3: row--steps, column--nvar responses, 3rd dimension--nvar shocks
+ lzvdyer3_h = 100*(tmp0./tmp);
+ lzvdyer_h = reshape(lzvdyer3_h,imstpyer,nvar^2);
+ lzvdyercnt = empdfsort(lzvdyercnt,lzvdyer_h,lzvdyerfloor,lzvdyerhbin,ninv);
+ % sorted counts (prob.) in bins
+
+ %**** Correlations among structural shocks
+ Estrexa_h = fidcndexa(yexa,philr,A0_h,Bh_h,nvar,lags,actup); % actup-by-nvar
+ sckvar_h = (Estrexa_h'*Estrexa_h)/actup;
+ sckcor_h = corr(sckvar_h);
+ sckcorcnt = empdfsort(sckcorcnt,sckcor_h,sckcorfloor,sckcorhbin,ninv);
+
+ if simfindx
+ for k=1:6
+ cspecimf(k) = cspecimf(k) + ((csix1a0(k)*imf_h(1,nvar+k))>0);
+ % 1st dim in imf_h: periods
+ end
+ %*** Joint tests
+ j1=csix1imf;
+ j2=imf_h;
+ j3=csix1pimf;
+ %** M2 and R in MP equation
+ mpall = ((j1(2)*j2(j3(2),nvar+2))/(j1(3)*j2(j3(3),nvar+3)))>0; % & ((j1(1)*j2(7))/(j1(3)*j2(9)))<0;
+ % 1st dim in j2: periods
+ cspecimf(nvar+1) = cspecimf(nvar+1) + mpall;
+ %** R and P in MP equation
+ mpall = ((j1(3)*j2(j3(3),nvar+3))/(j1(5)*j2(j3(5),nvar+5)))>0; %
+ % 1st dim in j2: periods
+ cspecimf(nvar+2) = cspecimf(nvar+2) + mpall;
+ %
+ % %** MD equation
+ % mdall = ((j1(4)*j2(10))/(j1(5)*j2(11)))<0; %& ((j1(4)*j2(10))/(j1(6)*j2(12)))<0;
+ % cspeca0(9) = cspeca0(9) + mdall;
+ % mpdall = mpall & mdall;
+ % cspeca0(10) = cspeca0(10) + mpdall;
+ end
+ end
+
+ Avhxmean = Avhxmean + Avhx_norm; % 1st step to overall mean of parameter
+ Avhxs = Avhxs + Avhx_norm.^2; % 1st step to overall 2nd moment of parameter
+ Avhxcnt = empdfsort(Avhxcnt,Avhx_norm,Avhxfloor,Avhxhbin,ninv);
+
+
+ % A0hatw(:,drawsc-wdraws) = A0_h(:);
+ if ~mod(draws,nbuffer)
+ starts
+ draws
+ wdraws=drawsc
+ % fwriteid = fopen('outA0.bin','a');
+ % count = fwrite(fwriteid,A0hatw,'double');
+ % status = fclose('all');
+ end
+ end
+ %
+ imfcntmulti(:,:,starts) = imfcnt;
+ Avhxcntmulti(:,:,starts) = Avhxcnt;
+ imfyercntmulti(:,:,starts) = imfyercnt;
+ lzvdcntmulti(:,:,starts) = lzvdcnt;
+ lzvdyercntmulti(:,:,starts) = lzvdyercnt;
+ sckcorcntmulti(:,:,starts) = sckcorcnt;
+
+ if ~IndxGibbs
+ countJump(starts,1) = cJump;
+ end
+end
+timend = toc
+timeminutes=timend/60
+
+if ~IndxGibbs
+ countJump = countJump/ndraws2
+end
+
+Avhxmean = Avhxmean/(imndraws);
+Avhxs = Avhxs/(imndraws);
+Avhxs = sqrt(Avhxs - Avhxmean.^2); % stardard deviation
+A0hm = zeros(nvar);
+A0hm(a0indx) = Avhxmean % mean
+A0hs = zeros(nvar);
+A0hs(a0indx) = Avhxs; % standar deviation
+
+imfmean = imfmean/(imndraws);
+imfs = imfs/imndraws;
+imfs = sqrt(imfs - imfmean.^2); % standard deviation
+
+imfyermean = imfyermean/(imndraws);
+imfyers = imfyers/imndraws;
+imfyers = sqrt(imfyers - imfyermean.^2); % standard deviation
+
+
+save outB_W nstarts ndraws1 ndraws2 imndraws timeminutes Avhxml Avhxmean Avhxs ...
+ Avhxcntmulti A0xhat A0hm A0hs IndxGibbs countJump nvar Range5 ...
+ ninv invc nfp a0indx nswitch actup nSample lags yrEnd qmEnd ...
+ yrStart qmStart q_m sckcorml sckcorcntmulti
+
+if ImfErr
+ if simfindx
+ cspecimf = cspecimf/imndraws;
+ save outB_W cspecimf -append
+ end
+ save outB_W imfml imfmean imfs imfcntmulti imstp cspecimf ...
+ imfyerml imfyermean imfyers imfyercntmulti imstpyer ...
+ lzvdml lzvdcntmulti lzvdyerml lzvdyercntmulti -append
+end
+%
+if sa0indx
+ cspeca0 = cspeca0/imndraws;
+ save outB_W cspeca0 -append
+end
+
+
diff --git a/MatlabFiles/impgraphs.m b/MatlabFiles/impgraphs.m
index e7eeadd8a2eca74a3966843b9ebee94f3cf0bfc2..62ae192cefd0716b4cd922bf86ed679ddb41e095 100644
--- a/MatlabFiles/impgraphs.m
+++ b/MatlabFiles/impgraphs.m
@@ -1,73 +1,73 @@
-function impgraphs(response,collabel,rowlabel)
-%function impgraphs(response,collabel,rowlabel)
-% collabel and rowlabel are cell arrays with string contents
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-XTick=[12 24 36 48]; %modify this line for other time units.
-%XTickLabel=[] % Use this to suppress labeling.
-[nrow,ncol,nt]=size(response);
-for irow=1:nrow
- smin=min(min(response(irow,:,:)));
- smax=max(max(response(irow,:,:)));
- if smin<0
- if smax<=0
- yt=[smin 0];
- else
- yt=[smin 0 smax];
- end
- else % (smin >=0)
- if smax > 0
- yt=[0 smax];
- else % (identically zero responses)
- yt=[-1 0 1];
- end
- end
- for i=1:length(yt)
- if yt(i)==0.0
- if length(yt)==3 & -yt(1)<.2*yt(3)
- ytc(i)={''};
- else
- ytc(i)={' 0'};
- end
- else
- ytc(i)={sprintf('%2.4f',yt(i))};
- end
- end
- scale=[1 nt smin smax];
- for icol=1:ncol
- subplot(nrow,ncol,(irow-1)*ncol+icol);
- plot(squeeze(response(irow,icol,:)));grid
- axis(scale);
- if irow==1
- title(collabel{icol});
- end
- if icol==1
- ylabel(rowlabel{irow});
- end
- set(gca,'XTick',XTick)
- set(gca,'YTick',yt)
- if irow<nrow
- set(gca,'XTickLabelMode','manual','XTickLabel',[])
- end
- if icol>1
- set(gca,'YTickLabelMode','manual','YTickLabel',[])
- else
- set(gca,'YTickLabelMode','manual','YTickLabel',char(ytc))
- end
- end
-end
\ No newline at end of file
+function impgraphs(response,collabel,rowlabel)
+%function impgraphs(response,collabel,rowlabel)
+% collabel and rowlabel are cell arrays with string contents
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+XTick=[12 24 36 48]; %modify this line for other time units.
+%XTickLabel=[] % Use this to suppress labeling.
+[nrow,ncol,nt]=size(response);
+for irow=1:nrow
+ smin=min(min(response(irow,:,:)));
+ smax=max(max(response(irow,:,:)));
+ if smin<0
+ if smax<=0
+ yt=[smin 0];
+ else
+ yt=[smin 0 smax];
+ end
+ else % (smin >=0)
+ if smax > 0
+ yt=[0 smax];
+ else % (identically zero responses)
+ yt=[-1 0 1];
+ end
+ end
+ for i=1:length(yt)
+ if yt(i)==0.0
+ if length(yt)==3 & -yt(1)<.2*yt(3)
+ ytc(i)={''};
+ else
+ ytc(i)={' 0'};
+ end
+ else
+ ytc(i)={sprintf('%2.4f',yt(i))};
+ end
+ end
+ scale=[1 nt smin smax];
+ for icol=1:ncol
+ subplot(nrow,ncol,(irow-1)*ncol+icol);
+ plot(squeeze(response(irow,icol,:)));grid
+ axis(scale);
+ if irow==1
+ title(collabel{icol});
+ end
+ if icol==1
+ ylabel(rowlabel{irow});
+ end
+ set(gca,'XTick',XTick)
+ set(gca,'YTick',yt)
+ if irow<nrow
+ set(gca,'XTickLabelMode','manual','XTickLabel',[])
+ end
+ if icol>1
+ set(gca,'YTickLabelMode','manual','YTickLabel',[])
+ else
+ set(gca,'YTickLabelMode','manual','YTickLabel',char(ytc))
+ end
+ end
+end
diff --git a/MatlabFiles/impulseo.m b/MatlabFiles/impulseo.m
index f72badda2f86ae90ca4fc1dc87a10d9bcdc4e91d..0497c11f50f590cc42093982167bc37fb330b744 100644
--- a/MatlabFiles/impulseo.m
+++ b/MatlabFiles/impulseo.m
@@ -1,77 +1,77 @@
-function imf = impulseo(Bh,swish,nn)
-% impulse: computing impulse functions with
-% imf = impulseo(Bh,swish)
-% where imf is in a format that is conveninet to generate the RATS graphics;
-% Bh is the estimated reduced form coefficient in the form
-% Y(T*nvar) = XB + U, X: T*k, B: k*nvar. The matrix
-% form or dimension is the same as "Bh" from the function "sye";
-% swish is the inv(A0) in the structural model A(L)y(t) = e(t).
-% nn is the numbers of inputs [nvar,lags,# of impulse responses].
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-nvar = nn(1);
-lags = nn(2);
-imstep = nn(3); % number of steps for impulse responses
-
-Ah = Bh';
-% Row: nvar equations
-% Column: 1st lag (with nvar variables) to lags (with nvar variables) + const = k.
-
-imf = zeros(imstep,nvar*nvar);
-% Column: 1st variable response to nvar shocks, 2nd variable response to
-% nvar shocks, and so on.
-% Row: steps of impulse responses.
-M = zeros(nvar*(lags+1),nvar);
-% Stack M0;M1;M2:...;Mlags
-M(1:nvar,:) = swish;
-Mtem = M(1:nvar,:); % temporary M.
-% first (initial) responses to 1 standard deviation shock. Row: responses; Column: shocks
-% * put in the form of "imf"
-Mtem = Mtem';
-imf(1,:) = Mtem(:)';
-
-t = 1;
-ims1 = min([imstep-1 lags]);
-while t <= ims1
- Mtem = zeros(nvar,nvar);
- for k = 1:t
- Mtem = Ah(:,nvar*(k-1)+1:nvar*k)*M(nvar*(t-k)+1:nvar*(t-k+1),:) + Mtem;
- % Row: nvar equations, each for the nvar variables at tth lag
- end
- M(nvar*t+1:nvar*(t+1),:) = Mtem;
- Mtem = Mtem';
- imf(t+1,:) = Mtem(:)';
- % stack imf with each step, Row: 1st variable to nvar shocks,
- % 2nd variable to nvar shocks, etc.
- t= t+1;
-end
-
-for t = lags+1:imstep-1
- M(1:nvar*lags,:) = M(nvar+1:nvar*(lags+1),:);
- Mtem = zeros(nvar,nvar);
- for k = 1:lags
- Mtem = Ah(:,nvar*(k-1)+1:nvar*k)*M(nvar*(lags-k)+1:nvar*(lags-k+1),:) + Mtem;
- % Row: nvar equations, each for the nvar variables at tth lag
- end
- M(nvar*lags+1:nvar*(lags+1),:) = Mtem;
- Mtem = Mtem';
- imf(t+1,:) = Mtem(:)';
- % stack imf with each step, Row: 1st variable to nvar shocks,
- % 2nd variable to nvar shocks, etc.
-end
-
\ No newline at end of file
+function imf = impulseo(Bh,swish,nn)
+% impulse: computing impulse functions with
+% imf = impulseo(Bh,swish)
+% where imf is in a format that is conveninet to generate the RATS graphics;
+% Bh is the estimated reduced form coefficient in the form
+% Y(T*nvar) = XB + U, X: T*k, B: k*nvar. The matrix
+% form or dimension is the same as "Bh" from the function "sye";
+% swish is the inv(A0) in the structural model A(L)y(t) = e(t).
+% nn is the numbers of inputs [nvar,lags,# of impulse responses].
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+nvar = nn(1);
+lags = nn(2);
+imstep = nn(3); % number of steps for impulse responses
+
+Ah = Bh';
+% Row: nvar equations
+% Column: 1st lag (with nvar variables) to lags (with nvar variables) + const = k.
+
+imf = zeros(imstep,nvar*nvar);
+% Column: 1st variable response to nvar shocks, 2nd variable response to
+% nvar shocks, and so on.
+% Row: steps of impulse responses.
+M = zeros(nvar*(lags+1),nvar);
+% Stack M0;M1;M2:...;Mlags
+M(1:nvar,:) = swish;
+Mtem = M(1:nvar,:); % temporary M.
+% first (initial) responses to 1 standard deviation shock. Row: responses; Column: shocks
+% * put in the form of "imf"
+Mtem = Mtem';
+imf(1,:) = Mtem(:)';
+
+t = 1;
+ims1 = min([imstep-1 lags]);
+while t <= ims1
+ Mtem = zeros(nvar,nvar);
+ for k = 1:t
+ Mtem = Ah(:,nvar*(k-1)+1:nvar*k)*M(nvar*(t-k)+1:nvar*(t-k+1),:) + Mtem;
+ % Row: nvar equations, each for the nvar variables at tth lag
+ end
+ M(nvar*t+1:nvar*(t+1),:) = Mtem;
+ Mtem = Mtem';
+ imf(t+1,:) = Mtem(:)';
+ % stack imf with each step, Row: 1st variable to nvar shocks,
+ % 2nd variable to nvar shocks, etc.
+ t= t+1;
+end
+
+for t = lags+1:imstep-1
+ M(1:nvar*lags,:) = M(nvar+1:nvar*(lags+1),:);
+ Mtem = zeros(nvar,nvar);
+ for k = 1:lags
+ Mtem = Ah(:,nvar*(k-1)+1:nvar*k)*M(nvar*(lags-k)+1:nvar*(lags-k+1),:) + Mtem;
+ % Row: nvar equations, each for the nvar variables at tth lag
+ end
+ M(nvar*lags+1:nvar*(lags+1),:) = Mtem;
+ Mtem = Mtem';
+ imf(t+1,:) = Mtem(:)';
+ % stack imf with each step, Row: 1st variable to nvar shocks,
+ % 2nd variable to nvar shocks, etc.
+end
+
diff --git a/MatlabFiles/imrgraph.m b/MatlabFiles/imrgraph.m
index b05eab206034a9256d2be9b743fae0d04d55eacf..45373932d3b832839f00412372b832f1130622ed 100644
--- a/MatlabFiles/imrgraph.m
+++ b/MatlabFiles/imrgraph.m
@@ -1,71 +1,71 @@
-function scaleout = imrgraph(imf,nvar,imstp,xlab,ylab)
-% function scaleout = imrgraph(imf,nvar,imstp,xlab,ylab)
-% imcgraph: impulse, r (row: shock 1 to N), graph
-% imf: impulse responses, column (responses to 1st shock, responses to 2nd shock
-% etc), row (impusle steps),
-% nvar: number of variables
-% imstp: step of impulse responses
-% xlab,ylab: labels
-%
-% See imcgraph, imcerrgraph, imrerrgraph
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-t = 1:imstp;
-temp1=zeros(nvar,1);
-temp2=zeros(nvar,1);
-maxval=zeros(nvar,1);
-minval=zeros(nvar,1);
-for i = 1:nvar
- for j = 1:nvar
- temp1(j)=max(imf(:,(j-1)*nvar+i));
- temp2(j)=min(imf(:,(j-1)*nvar+i));
- end
- maxval(i)=max(temp1);
- minval(i)=min(temp2);
-end
-
-scaleout = [maxval(:) minval(:)];
-
-%--------------
-% Column j: Reponses; Row i: Shock 1 to N
-%-------------
-figure
-rowlabel = 1;
-for i = 1:nvar
- columnlabel = 1;
- for j = 1:nvar
- k=(i-1)*nvar+j;
- subplot(nvar,nvar,k)
- plot(t,imf(:,k),t,zeros(length(imf(:,k)),1),':' );
- set(gca,'YLim',[minval(j) maxval(j)])
- set(gca,'XTickLabel',' ');
- set(gca,'YTickLabel',' ');
- if rowlabel == 1
- %title(['X' num2str(j)])
- %title(eval(['x' num2str(j)]))
- title(char(xlab(j)))
- end
- if columnlabel == 1
- %ylabel(['X' num2str(i)])
- %ylabel(eval(['x' num2str(i)]))
- ylabel(char(ylab(i)))
- end
- columnlabel = 0;
- end
- rowlabel = 0;
-end
\ No newline at end of file
+function scaleout = imrgraph(imf,nvar,imstp,xlab,ylab)
+% function scaleout = imrgraph(imf,nvar,imstp,xlab,ylab)
+% imcgraph: impulse, r (row: shock 1 to N), graph
+% imf: impulse responses, column (responses to 1st shock, responses to 2nd shock
+% etc), row (impusle steps),
+% nvar: number of variables
+% imstp: step of impulse responses
+% xlab,ylab: labels
+%
+% See imcgraph, imcerrgraph, imrerrgraph
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+t = 1:imstp;
+temp1=zeros(nvar,1);
+temp2=zeros(nvar,1);
+maxval=zeros(nvar,1);
+minval=zeros(nvar,1);
+for i = 1:nvar
+ for j = 1:nvar
+ temp1(j)=max(imf(:,(j-1)*nvar+i));
+ temp2(j)=min(imf(:,(j-1)*nvar+i));
+ end
+ maxval(i)=max(temp1);
+ minval(i)=min(temp2);
+end
+
+scaleout = [maxval(:) minval(:)];
+
+%--------------
+% Column j: Reponses; Row i: Shock 1 to N
+%-------------
+figure
+rowlabel = 1;
+for i = 1:nvar
+ columnlabel = 1;
+ for j = 1:nvar
+ k=(i-1)*nvar+j;
+ subplot(nvar,nvar,k)
+ plot(t,imf(:,k),t,zeros(length(imf(:,k)),1),':' );
+ set(gca,'YLim',[minval(j) maxval(j)])
+ set(gca,'XTickLabel',' ');
+ set(gca,'YTickLabel',' ');
+ if rowlabel == 1
+ %title(['X' num2str(j)])
+ %title(eval(['x' num2str(j)]))
+ title(char(xlab(j)))
+ end
+ if columnlabel == 1
+ %ylabel(['X' num2str(i)])
+ %ylabel(eval(['x' num2str(i)]))
+ ylabel(char(ylab(i)))
+ end
+ columnlabel = 0;
+ end
+ rowlabel = 0;
+end
diff --git a/MatlabFiles/invgamcdf.m b/MatlabFiles/invgamcdf.m
index 8058d644f607ce0528041e3f837ca5a7d346655d..366ee28e28aa742f83cd6ceb43269e980dea7031 100644
--- a/MatlabFiles/invgamcdf.m
+++ b/MatlabFiles/invgamcdf.m
@@ -1,44 +1,44 @@
-function P = invgamcdf(X, a, b);
-
-% This function computes the cumulative density for the inverse gamma
-% distribution with shape parameter a and scale parameter b. It uses the
-% following closed-form solution for the cdf of the inverse gamma
-% distribution:
-% ---------Cumulative density function for Inverse-Gamma distribution-------
-% --- P(x; a, b) = G(b/x, a)/G(a), where G(b/x, a) is the upper incomplete
-% gamma function and G(a) is the gamma function defined as
-% G(b/x, a) = int_{b/x}^{\infty} t^{a-1} e^{-t} dt.
-% The Matlab definition is different, which is
-% gammainc(b/x, a) = (1.0/Gamma(a)) int_{0}^{b/x} t^{a-1} e^{-t} dt.
-% Thus, we have
-% G(b/x, a)/G(a) = (1-gammainc(b./X, a)).
-
-% First, check for input errors
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-if X<=0;
- error('the support for the inverse gamma function needs to be positive')
-end;
-if a<= 0 || b <=0;
- a = abs(a);
- b = abs(b);
- % abs() is used to allow continuous search for the fsolve function find_invgampar.m.
- %error('parameter requirements: a>0, b>0');
-end;
-
-P = (1-gammainc(b./X, a));
+function P = invgamcdf(X, a, b);
+
+% This function computes the cumulative density for the inverse gamma
+% distribution with shape parameter a and scale parameter b. It uses the
+% following closed-form solution for the cdf of the inverse gamma
+% distribution:
+% ---------Cumulative density function for Inverse-Gamma distribution-------
+% --- P(x; a, b) = G(b/x, a)/G(a), where G(b/x, a) is the upper incomplete
+% gamma function and G(a) is the gamma function defined as
+% G(b/x, a) = int_{b/x}^{\infty} t^{a-1} e^{-t} dt.
+% The Matlab definition is different, which is
+% gammainc(b/x, a) = (1.0/Gamma(a)) int_{0}^{b/x} t^{a-1} e^{-t} dt.
+% Thus, we have
+% G(b/x, a)/G(a) = (1-gammainc(b./X, a)).
+
+% First, check for input errors
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+if X<=0;
+ error('the support for the inverse gamma function needs to be positive')
+end;
+if a<= 0 || b <=0;
+ a = abs(a);
+ b = abs(b);
+ % abs() is used to allow continuous search for the fsolve function find_invgampar.m.
+ %error('parameter requirements: a>0, b>0');
+end;
+
+P = (1-gammainc(b./X, a));
diff --git a/MatlabFiles/invgampar.m b/MatlabFiles/invgampar.m
index 97b6004909d8e192f2d12cdbe23335849fbf551e..28437282ed8167f574de94bcda6a6d857ca2020a 100644
--- a/MatlabFiles/invgampar.m
+++ b/MatlabFiles/invgampar.m
@@ -1,28 +1,28 @@
-function f = invgampar(ab, XLO, XUP, PLO, PUP);
-
-% The function takes as inputs the parameters ab=[a, b] of the Inverse gamma
-% distribution, the bounds of the support [XLO, XUP], the the corresponding
-% probability of the bounds [PLO, PUP] and returns the residual value f.
-
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-a = ab(1); b = ab(2);
-f1 = PLO - invgamcdf(XLO, a, b);
-f2 = PUP - invgamcdf(XUP, a, b);
-
-f = [f1, f2];
+function f = invgampar(ab, XLO, XUP, PLO, PUP);
+
+% The function takes as inputs the parameters ab=[a, b] of the Inverse gamma
+% distribution, the bounds of the support [XLO, XUP], the the corresponding
+% probability of the bounds [PLO, PUP] and returns the residual value f.
+
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+a = ab(1); b = ab(2);
+f1 = PLO - invgamcdf(XLO, a, b);
+f2 = PUP - invgamcdf(XUP, a, b);
+
+f = [f1, f2];
diff --git a/MatlabFiles/lcnmean.m b/MatlabFiles/lcnmean.m
index b516e1d9f6264244f05222f05f3b8da73ccbeed3..553ead1f40bc5febf238fc9db4e4209cc2302863 100644
--- a/MatlabFiles/lcnmean.m
+++ b/MatlabFiles/lcnmean.m
@@ -1,35 +1,35 @@
-function cutmean = lcnmean(x,pnIndx)
-%
-% cutmean = lcnmean(x,pnIndx)
-%
-% The mean of the truncated normal with the lower bound x if pnIndx>0
-% or the upper bound x if pnIndx<0. Zha Forecast (2) p.27
-%
-% x: the lower bound x if pnIndx > 0; the upper bound x if pnIndx < 0
-% pnIndx: 1: truncated for posivie mean (tight); 0 truncated for negative mean (loose)
-%------------
-% cutmean: the backed-out mean for the truncated normal
-%
-% October 1998 Tao Zha
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-if pnIndx
- cutmean = exp(-x^2/2)/(sqrt(2*pi)*(1-cdf('norm',x,0,1)));
-else
- cutmean = -exp(-x^2/2)/(sqrt(2*pi)*cdf('norm',x,0,1));
-end
+function cutmean = lcnmean(x,pnIndx)
+%
+% cutmean = lcnmean(x,pnIndx)
+%
+% The mean of the truncated normal with the lower bound x if pnIndx>0
+% or the upper bound x if pnIndx<0. Zha Forecast (2) p.27
+%
+% x: the lower bound x if pnIndx > 0; the upper bound x if pnIndx < 0
+% pnIndx: 1: truncated for posivie mean (tight); 0 truncated for negative mean (loose)
+%------------
+% cutmean: the backed-out mean for the truncated normal
+%
+% October 1998 Tao Zha
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+if pnIndx
+ cutmean = exp(-x^2/2)/(sqrt(2*pi)*(1-cdf('norm',x,0,1)));
+else
+ cutmean = -exp(-x^2/2)/(sqrt(2*pi)*cdf('norm',x,0,1));
+end
diff --git a/MatlabFiles/mformd.m b/MatlabFiles/mformd.m
index 5f869dc97b08f5c62e23da565e8fada1c8b60f79..20adcfd963d334fce4683d52e5c5b8f3c5e2e17a 100644
--- a/MatlabFiles/mformd.m
+++ b/MatlabFiles/mformd.m
@@ -1,49 +1,49 @@
-function [phi,YtY] = mformd(z,nn)
-% mformd: arrange matrix form data: [Y,YtY] = mformd(z,nn) as structural form
-% YA = E, Y: T*(nvar*lags+nvar+1)
-%
-% where z is the (T+lags)-by-(nvar+1) raw data matrix (nvar of variables + constant);
-% nn is the numbers of inputs [nvar,lags,sample period (total)];
-% phi: Y as in the structural form YA = E, Y: T*(nvar*lags+nvar+1)
-% YtY: Y'Y: (nvar*lags+nvar+1)*(nvar*lags+nvar+1)
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-% ** setup of orders and lengths **
-nvar = nn(1);
-lags = nn(2);
-sp = nn(3); % sample period
-
-ess = sp-lags; % effective sample size
-sb = lags+1; % sample beginning
-sl = sp; % sample last period
-ncoe = nvar*lags + nvar + 1; % with constant and contemporaneous data
-
-% ** construct Y as in YA = E where phi = Y **
-x = z(:,1:nvar);
-C = z(:,nvar+1);
-phi = zeros(ess,ncoe);
-phi(:,1:nvar) = x(sb:sl,:);
-phi(:,ncoe) = C(1:ess);
-for k=1:lags, phi(:,nvar*k+1:nvar*(k+1)) = x(sb-k:sl-k,:); end
-% column: T; row: [nvar for 0th lag, nvar for 1st lag,
-% ..., nvar for last lag, const]
-% Thus, # of columns is nvar*lags+nvar+1 = ncoef.
-% ** YtY, residuals **
-[u d v]=svd(phi,0); %trial
-vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
-YtY=vd*vd'; % YtY = phi'*phi; % X'X, k*k (ncoe*ncoe)
\ No newline at end of file
+function [phi,YtY] = mformd(z,nn)
+% mformd: arrange matrix form data: [Y,YtY] = mformd(z,nn) as structural form
+% YA = E, Y: T*(nvar*lags+nvar+1)
+%
+% where z is the (T+lags)-by-(nvar+1) raw data matrix (nvar of variables + constant);
+% nn is the numbers of inputs [nvar,lags,sample period (total)];
+% phi: Y as in the structural form YA = E, Y: T*(nvar*lags+nvar+1)
+% YtY: Y'Y: (nvar*lags+nvar+1)*(nvar*lags+nvar+1)
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+% ** setup of orders and lengths **
+nvar = nn(1);
+lags = nn(2);
+sp = nn(3); % sample period
+
+ess = sp-lags; % effective sample size
+sb = lags+1; % sample beginning
+sl = sp; % sample last period
+ncoe = nvar*lags + nvar + 1; % with constant and contemporaneous data
+
+% ** construct Y as in YA = E where phi = Y **
+x = z(:,1:nvar);
+C = z(:,nvar+1);
+phi = zeros(ess,ncoe);
+phi(:,1:nvar) = x(sb:sl,:);
+phi(:,ncoe) = C(1:ess);
+for k=1:lags, phi(:,nvar*k+1:nvar*(k+1)) = x(sb-k:sl-k,:); end
+% column: T; row: [nvar for 0th lag, nvar for 1st lag,
+% ..., nvar for last lag, const]
+% Thus, # of columns is nvar*lags+nvar+1 = ncoef.
+% ** YtY, residuals **
+[u d v]=svd(phi,0); %trial
+vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
+YtY=vd*vd'; % YtY = phi'*phi; % X'X, k*k (ncoe*ncoe)
diff --git a/MatlabFiles/mformd1.m b/MatlabFiles/mformd1.m
index 3f5f4afc4fa691c5a6b30f151268235a16c7418d..56bf1a908fd69eeba7476002bf100d1d5a8e47d2 100644
--- a/MatlabFiles/mformd1.m
+++ b/MatlabFiles/mformd1.m
@@ -1,70 +1,70 @@
-function [phi,y,ncoe] = mformd1(z,lags)
-%
-% [phi,y,ncoe] = mformd1(z,lags) where size(z,1)=T+lags.
-% This file is a subset of sye.m, but only designed to arrange matrix form data as
-% y(T*nvar) = XB + u, X--phi: T*k, B: k*nvar; where T=sp-lags, k=ncoe.
-% Note T>=0. If T=0, y is empty, but phi (X) is still well defined.
-%
-% z: (T+lags)-by-(nvar+1) raw data matrix (nvar of variables + constant).
-% e.g., C = ones(nSample,1); z=[xdget C];
-% lags: number of lags
-% phi: (T-lags)-by-k X; where k=ncoe=[nvar for 1st lag, ..., nvar for last lag, const]
-% y: Y: (T-lags)-by-nvar
-% ncoe: number of coeffcients per equation: k=nvar*lags + 1
-%
-% See also sye.m
-%
-% October 1998 Tao Zha. Revised, 03/13/99
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-% ** setup of orders and lengths **
-[sp,nvar] = size(z); % sp: sample period T include lags
-nvar = nvar-1; % -1: takes out the counting of constant
-
-ess = sp-lags; % effective sample size
-sb = lags+1; % sample beginning
-sl = sp; % sample last period
-ncoe = nvar*lags + 1; % with constant
-
-% ** construct X for Y = X*B + U where phi = X **
-if ess<0
- warning('The length of the data must be >= lags')
- disp('Check yrEnd and qmEnd in parac.m to make sure')
- disp('Press ctrl-c to abort')
- disp(' ')
- pause
-elseif ess==0
- x = z(:,1:nvar);
- C = z(:,nvar+1);
- phi = zeros(1,ncoe);
- phi(1,ncoe) = C(1);
- for k=1:lags, phi(1,nvar*(k-1)+1:nvar*k) = x(sb-k:sl-k+1,:); end
- % row: T-lags; column: [nvar for 1st lag, ..., nvar for last lag, const]
- % Thus, # of columns is nvar*lags+1 = ncoef.
- y = x(sb:sl,:);
-else
- x = z(:,1:nvar);
- C = z(:,nvar+1);
- phi = zeros(ess,ncoe);
- phi(:,ncoe) = C(1:ess);
- for k=1:lags, phi(:,nvar*(k-1)+1:nvar*k) = x(sb-k:sl-k,:); end
- % row: T-lags; column: [nvar for 1st lag, ..., nvar for last lag, const]
- % Thus, # of columns is nvar*lags+1 = ncoef.
- y = x(sb:sl,:);
-end
\ No newline at end of file
+function [phi,y,ncoe] = mformd1(z,lags)
+%
+% [phi,y,ncoe] = mformd1(z,lags) where size(z,1)=T+lags.
+% This file is a subset of sye.m, but only designed to arrange matrix form data as
+% y(T*nvar) = XB + u, X--phi: T*k, B: k*nvar; where T=sp-lags, k=ncoe.
+% Note T>=0. If T=0, y is empty, but phi (X) is still well defined.
+%
+% z: (T+lags)-by-(nvar+1) raw data matrix (nvar of variables + constant).
+% e.g., C = ones(nSample,1); z=[xdget C];
+% lags: number of lags
+% phi: (T-lags)-by-k X; where k=ncoe=[nvar for 1st lag, ..., nvar for last lag, const]
+% y: Y: (T-lags)-by-nvar
+% ncoe: number of coeffcients per equation: k=nvar*lags + 1
+%
+% See also sye.m
+%
+% October 1998 Tao Zha. Revised, 03/13/99
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+% ** setup of orders and lengths **
+[sp,nvar] = size(z); % sp: sample period T include lags
+nvar = nvar-1; % -1: takes out the counting of constant
+
+ess = sp-lags; % effective sample size
+sb = lags+1; % sample beginning
+sl = sp; % sample last period
+ncoe = nvar*lags + 1; % with constant
+
+% ** construct X for Y = X*B + U where phi = X **
+if ess<0
+ warning('The length of the data must be >= lags')
+ disp('Check yrEnd and qmEnd in parac.m to make sure')
+ disp('Press ctrl-c to abort')
+ disp(' ')
+ pause
+elseif ess==0
+ x = z(:,1:nvar);
+ C = z(:,nvar+1);
+ phi = zeros(1,ncoe);
+ phi(1,ncoe) = C(1);
+ for k=1:lags, phi(1,nvar*(k-1)+1:nvar*k) = x(sb-k:sl-k+1,:); end
+ % row: T-lags; column: [nvar for 1st lag, ..., nvar for last lag, const]
+ % Thus, # of columns is nvar*lags+1 = ncoef.
+ y = x(sb:sl,:);
+else
+ x = z(:,1:nvar);
+ C = z(:,nvar+1);
+ phi = zeros(ess,ncoe);
+ phi(:,ncoe) = C(1:ess);
+ for k=1:lags, phi(:,nvar*(k-1)+1:nvar*k) = x(sb-k:sl-k,:); end
+ % row: T-lags; column: [nvar for 1st lag, ..., nvar for last lag, const]
+ % Thus, # of columns is nvar*lags+1 = ncoef.
+ y = x(sb:sl,:);
+end
diff --git a/MatlabFiles/mnpdf.m b/MatlabFiles/mnpdf.m
index 6302ca1bfb962d4491119a0410a95a162d28727e..8fee71fb052bdd18a975b3ef0b50e6839e1d0ec1 100644
--- a/MatlabFiles/mnpdf.m
+++ b/MatlabFiles/mnpdf.m
@@ -1,44 +1,44 @@
-function y = mnpdf(x,xm,C,constIx)
-% y = mnpdf(x,xm,C,constIx)
-% The pdf value for multivariate normal distribution
-%
-% x: p-by-draws matrix of values evaluated at where p=size(x,1) is # of variables
-% xm: p-by-draws matrix of the mean of x
-% C: p-by-p Choleski square root of PDS S -- the covariance matrix so that S = C*C'
-% constIx: index for the constant. 1: constant (normalized); 0: no constant (unnormalized)
-%----------
-% y: p-by-draws matrix of pdf's for multivariate normal distribution
-%
-% Christian P. Robert, "The Bayesian Choice," Springer-Verlag, New York, 1994,
-% p. 381.
-%
-% November 1998 by Tao Zha
-% rewritten by CAS 12/98 to take matrix x, return vector y
-% Copyright (C) 1997-2012 Christopher A. Sims and Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-[p,nx]=size(x);
-z = C\(x-xm);
-
-if constIx
- dSh=sum(log(diag(C))); % (detSigma)^(1/2)
- y = exp(-dSh-sum(z.*z,1)/2) / ((2*pi)^(p/2));
- y = y';
-else
- y = exp(-sum(z.*z,1)/2);
- y = y';
-end
+function y = mnpdf(x,xm,C,constIx)
+% y = mnpdf(x,xm,C,constIx)
+% The pdf value for multivariate normal distribution
+%
+% x: p-by-draws matrix of values evaluated at where p=size(x,1) is # of variables
+% xm: p-by-draws matrix of the mean of x
+% C: p-by-p Choleski square root of PDS S -- the covariance matrix so that S = C*C'
+% constIx: index for the constant. 1: constant (normalized); 0: no constant (unnormalized)
+%----------
+% y: p-by-draws matrix of pdf's for multivariate normal distribution
+%
+% Christian P. Robert, "The Bayesian Choice," Springer-Verlag, New York, 1994,
+% p. 381.
+%
+% November 1998 by Tao Zha
+% rewritten by CAS 12/98 to take matrix x, return vector y
+%
+% Copyright (C) 1997-2012 Christopher A. Sims and Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+[p,nx]=size(x);
+z = C\(x-xm);
+
+if constIx
+ dSh=sum(log(diag(C))); % (detSigma)^(1/2)
+ y = exp(-dSh-sum(z.*z,1)/2) / ((2*pi)^(p/2));
+ y = y';
+else
+ y = exp(-sum(z.*z,1)/2);
+ y = y';
+end
diff --git a/MatlabFiles/mtpdf.m b/MatlabFiles/mtpdf.m
index 517f6ab0918b4c57178a9bb64307bde8f43af893..e83b4607302219fe3aea44de940d4789d10dc585 100644
--- a/MatlabFiles/mtpdf.m
+++ b/MatlabFiles/mtpdf.m
@@ -1,48 +1,48 @@
-function y = mtpdf(x,xm,C,v,constIx)
-% y = mtpdf(x,xm,C,v)
-% The pdf value for multivariate Student t distribution
-%
-% x: p-by-draws matrix of values evaluated at where p=size(x,1) is # of variables
-% xm: p-by-draws matrix of the mean of x
-% C: p-by-p Choleski square root of PDS S, which is the covariance matrix
-% in the normal case so that S = C*C'
-% v (>0): scaler -- the degrees of freedom
-% constIx: index for the constant. 1: constant (normalized); 0: no constant (unnormalized)
-%----------
-% y: p-by-draws matrix of pdf's for multivariate Student t distribution with v degrees of freedom
-%
-% Christian P. Robert, "The Bayesian Choice," Springer-Verlag, New York, 1994,
-% p. 382.
-%
-% December 1998 by Tao Zha
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-[p,nx]=size(x);
-z = C\(x-xm);
-
-
-if constIx
- dSh=sum(log(diag(C))); % (detSigma)^(1/2)
- %* Use gammaln function to avoid overflows.
- term = exp(gammaln((v + p) / 2) - gammaln(v/2) - dSh);
- y = (term*(v*pi)^(-p/2)) * (1 + sum(z.*z,1)/v).^(-(v + p)/2);
- y = y';
-else
- y = (1 + sum(z.*z,1)/v).^(-(v + p)/2);
- y = y';
-end
+function y = mtpdf(x,xm,C,v,constIx)
+% y = mtpdf(x,xm,C,v)
+% The pdf value for multivariate Student t distribution
+%
+% x: p-by-draws matrix of values evaluated at where p=size(x,1) is # of variables
+% xm: p-by-draws matrix of the mean of x
+% C: p-by-p Choleski square root of PDS S, which is the covariance matrix
+% in the normal case so that S = C*C'
+% v (>0): scaler -- the degrees of freedom
+% constIx: index for the constant. 1: constant (normalized); 0: no constant (unnormalized)
+%----------
+% y: p-by-draws matrix of pdf's for multivariate Student t distribution with v degrees of freedom
+%
+% Christian P. Robert, "The Bayesian Choice," Springer-Verlag, New York, 1994,
+% p. 382.
+%
+% December 1998 by Tao Zha
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+[p,nx]=size(x);
+z = C\(x-xm);
+
+
+if constIx
+ dSh=sum(log(diag(C))); % (detSigma)^(1/2)
+ %* Use gammaln function to avoid overflows.
+ term = exp(gammaln((v + p) / 2) - gammaln(v/2) - dSh);
+ y = (term*(v*pi)^(-p/2)) * (1 + sum(z.*z,1)/v).^(-(v + p)/2);
+ y = y';
+else
+ y = (1 + sum(z.*z,1)/v).^(-(v + p)/2);
+ y = y';
+end
diff --git a/MatlabFiles/nmlzvar.m b/MatlabFiles/nmlzvar.m
index 8a896d08bb490121dfe90928611b7b9377a1a107..a94cbd78b7603b4257d52ea65e1c496af7bea7db 100644
--- a/MatlabFiles/nmlzvar.m
+++ b/MatlabFiles/nmlzvar.m
@@ -1,121 +1,121 @@
-function [A0n,nswitch,A0inn] = nmlzvar(A0u,A0xhat,A0inxhat,IndxNmlr,nswitch,A0inu)
-% [A0n,nswitch,A0inn] = nmlzvar(A0u,A0xhat,A0inxhat,IndxNmlr,nswitch,A0inu)
-% Export normalized new draw A0 (and inv(A0) only if IndxNmlr(5)) and the number of sign switches
-% Ref.: D.F. Waggoner and T.A. Zha: "Does Normalization Matter for Inference?"
-% See Note Forecast (2) pp. 52-53
-%
-% A0u: unnormalized A0; column--equation
-% A0xhat: ML estimate or posterior mode of A0
-% A0inxhat: inv(A0xhat)
-% IndxNmlr: index for which normalization rule to choose
-% Only one of the elments in IndxNmlr can be non-zero
-% IndxNmlr(1): ML A distance rule (supposed to be the best)
-% IndxNmlr(2): ML Ahat distance rule (to approximate IndxNmlr(1))
-% IndxNmlr(3): ML Euclidean distance rule (not invariant to scale)
-% IndxNmlr(4): Positive diagonal rule
-% IndxNmlr(5): Positive inv(A) diagonal rule (if ~IndxNmlr(5), no need for A0inu,
-% so we set A0inn=[])
-% Or Euclidean distance rule for A0inv (not invariant to scale)
-% IndxNmlr(6): Assigned postive rule (such as off-diagonal elements). Added 1/3/00
-% nswitch: # of sign switches
-% A0inu: unnormalized inv(A0); used only if IndxNmlr(5)
-%-----------------
-% A0n: normalized new A0; column--equation
-% nswitch: updated # of sign switches
-% A0inn: normalized inv(A0); used only if IndxNmlr(5)
-%
-% Written by Tao Zha
-% 1/3/00: added IndxNmlr(6) so that previous programs may not be compatible.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-
-A0inn = []; % no output for normalized A0in unless IndxNmlr(5)
-
-if (length(find(IndxNmlr))>1)
- warning('You cannot choose more than one normalization rule at a time')
- disp('Press ctrl-c to abort')
- pause
-elseif isempty(find(IndxNmlr)) % no normalization
- A0n=A0u; nswitch=0;
-elseif IndxNmlr(1)
- a0dpindx = find(diag(A0u\A0xhat)<0);
- A0n = A0u;
- if ~isempty(a0dpindx)
- A0n(:,a0dpindx) = -A0u(:,a0dpindx); % normalized new draws
- nswitch = nswitch + 1; %<<>> # of sign switches
- end
-elseif IndxNmlr(2)
- a0dpindx = find(diag(A0inxhat*A0u)<0);
- A0n = A0u;
- if ~isempty(a0dpindx)
- A0n(:,a0dpindx) = -A0u(:,a0dpindx); % normalized new draws
- nswitch = nswitch + 1; %<<>> # of sign switches
- end
-elseif IndxNmlr(3)
- Adiff = (A0u - A0xhat).^2; % distance that may be far from axhat or A0xhat
- Adiffn = (-A0u - A0xhat).^2; % distance by chaning the sign of Atem
- cAdiff = sum(Adiff); % each column summed up
- cAdiffn = sum(Adiffn); % each column summed up
- cAindx = find(cAdiffn<cAdiff); % index for shorter distance
- A0n = A0u;
- if ~isempty(cAindx)
- A0n(:,cAindx) = -A0u(:,cAindx); % find the shortest or nearest distance
- nswitch = nswitch + 1; %<<>> # of sign switches
- end
-elseif IndxNmlr(4)
- a0dpindx = find(diag(A0u)<0);
- A0n = A0u;
- if ~isempty(a0dpindx)
- A0n(:,a0dpindx) = -A0u(:,a0dpindx); % normalized new draws
- nswitch = nswitch + 1; %<<>> # of sign switches
- end
-elseif IndxNmlr(5)
- if (0)
- a0dpindx = find(diag(A0inu)<0);
- A0n = A0u;
- A0inn = A0inu;
- if ~isempty(a0dpindx)
- A0n(:,a0dpindx) = -A0u(:,a0dpindx);
- A0inn(a0dpindx,:) = -A0inu(a0dpindx,:);
- nswitch = nswitch + 1; %<<>> # of sign switches
- end
- else
- A0n = [];
- Aindiff = (A0inu - A0inxhat).^2; % distance that may be far from A0inxhat
- Aindiffn = (-A0inu - A0inxhat).^2; % distance by chaning the sign of A0inu
- cAindiff = sum(Aindiff,2); % each row summed up
- cAindiffn = sum(Aindiffn, 2); % each row summed up
- cAinindx = find(cAindiffn<cAindiff); % index for shorter distance
- A0inn = A0inu;
- if ~isempty(cAinindx)
- A0inn(:,cAinindx) = -A0inu(:,cAinindx); % find the shortest or nearest distance
- nswitch = nswitch + 1; %<<>> # of sign switches
- end
- end
-elseif IndxNmlr(6) %*** This one has to be MANUALLY handled
- [jnk,nvar]=size(A0u);
- A0dummy=A0u;
- A0dummy(:,1:2)=-A0u(:,1:2); % keep it to a sign that coincide with Brooking paper
- a0dpindx = find(A0dummy(nvar,:)<0); % the last row
- A0n = A0u;
- if ~isempty(a0dpindx)
- A0n(:,a0dpindx) = -A0u(:,a0dpindx); % normalized new draws
- nswitch = nswitch + 1; %<<>> # of sign switches
- end
-end
+function [A0n,nswitch,A0inn] = nmlzvar(A0u,A0xhat,A0inxhat,IndxNmlr,nswitch,A0inu)
+% [A0n,nswitch,A0inn] = nmlzvar(A0u,A0xhat,A0inxhat,IndxNmlr,nswitch,A0inu)
+% Export normalized new draw A0 (and inv(A0) only if IndxNmlr(5)) and the number of sign switches
+% Ref.: D.F. Waggoner and T.A. Zha: "Does Normalization Matter for Inference?"
+% See Note Forecast (2) pp. 52-53
+%
+% A0u: unnormalized A0; column--equation
+% A0xhat: ML estimate or posterior mode of A0
+% A0inxhat: inv(A0xhat)
+% IndxNmlr: index for which normalization rule to choose
+% Only one of the elments in IndxNmlr can be non-zero
+% IndxNmlr(1): ML A distance rule (supposed to be the best)
+% IndxNmlr(2): ML Ahat distance rule (to approximate IndxNmlr(1))
+% IndxNmlr(3): ML Euclidean distance rule (not invariant to scale)
+% IndxNmlr(4): Positive diagonal rule
+% IndxNmlr(5): Positive inv(A) diagonal rule (if ~IndxNmlr(5), no need for A0inu,
+% so we set A0inn=[])
+% Or Euclidean distance rule for A0inv (not invariant to scale)
+% IndxNmlr(6): Assigned postive rule (such as off-diagonal elements). Added 1/3/00
+% nswitch: # of sign switches
+% A0inu: unnormalized inv(A0); used only if IndxNmlr(5)
+%-----------------
+% A0n: normalized new A0; column--equation
+% nswitch: updated # of sign switches
+% A0inn: normalized inv(A0); used only if IndxNmlr(5)
+%
+% Written by Tao Zha
+% 1/3/00: added IndxNmlr(6) so that previous programs may not be compatible.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+
+A0inn = []; % no output for normalized A0in unless IndxNmlr(5)
+
+if (length(find(IndxNmlr))>1)
+ warning('You cannot choose more than one normalization rule at a time')
+ disp('Press ctrl-c to abort')
+ pause
+elseif isempty(find(IndxNmlr)) % no normalization
+ A0n=A0u; nswitch=0;
+elseif IndxNmlr(1)
+ a0dpindx = find(diag(A0u\A0xhat)<0);
+ A0n = A0u;
+ if ~isempty(a0dpindx)
+ A0n(:,a0dpindx) = -A0u(:,a0dpindx); % normalized new draws
+ nswitch = nswitch + 1; %<<>> # of sign switches
+ end
+elseif IndxNmlr(2)
+ a0dpindx = find(diag(A0inxhat*A0u)<0);
+ A0n = A0u;
+ if ~isempty(a0dpindx)
+ A0n(:,a0dpindx) = -A0u(:,a0dpindx); % normalized new draws
+ nswitch = nswitch + 1; %<<>> # of sign switches
+ end
+elseif IndxNmlr(3)
+ Adiff = (A0u - A0xhat).^2; % distance that may be far from axhat or A0xhat
+ Adiffn = (-A0u - A0xhat).^2; % distance by chaning the sign of Atem
+ cAdiff = sum(Adiff); % each column summed up
+ cAdiffn = sum(Adiffn); % each column summed up
+ cAindx = find(cAdiffn<cAdiff); % index for shorter distance
+ A0n = A0u;
+ if ~isempty(cAindx)
+ A0n(:,cAindx) = -A0u(:,cAindx); % find the shortest or nearest distance
+ nswitch = nswitch + 1; %<<>> # of sign switches
+ end
+elseif IndxNmlr(4)
+ a0dpindx = find(diag(A0u)<0);
+ A0n = A0u;
+ if ~isempty(a0dpindx)
+ A0n(:,a0dpindx) = -A0u(:,a0dpindx); % normalized new draws
+ nswitch = nswitch + 1; %<<>> # of sign switches
+ end
+elseif IndxNmlr(5)
+ if (0)
+ a0dpindx = find(diag(A0inu)<0);
+ A0n = A0u;
+ A0inn = A0inu;
+ if ~isempty(a0dpindx)
+ A0n(:,a0dpindx) = -A0u(:,a0dpindx);
+ A0inn(a0dpindx,:) = -A0inu(a0dpindx,:);
+ nswitch = nswitch + 1; %<<>> # of sign switches
+ end
+ else
+ A0n = [];
+ Aindiff = (A0inu - A0inxhat).^2; % distance that may be far from A0inxhat
+ Aindiffn = (-A0inu - A0inxhat).^2; % distance by chaning the sign of A0inu
+ cAindiff = sum(Aindiff,2); % each row summed up
+ cAindiffn = sum(Aindiffn, 2); % each row summed up
+ cAinindx = find(cAindiffn<cAindiff); % index for shorter distance
+ A0inn = A0inu;
+ if ~isempty(cAinindx)
+ A0inn(:,cAinindx) = -A0inu(:,cAinindx); % find the shortest or nearest distance
+ nswitch = nswitch + 1; %<<>> # of sign switches
+ end
+ end
+elseif IndxNmlr(6) %*** This one has to be MANUALLY handled
+ [jnk,nvar]=size(A0u);
+ A0dummy=A0u;
+ A0dummy(:,1:2)=-A0u(:,1:2); % keep it to a sign that coincide with Brooking paper
+ a0dpindx = find(A0dummy(nvar,:)<0); % the last row
+ A0n = A0u;
+ if ~isempty(a0dpindx)
+ A0n(:,a0dpindx) = -A0u(:,a0dpindx); % normalized new draws
+ nswitch = nswitch + 1; %<<>> # of sign switches
+ end
+end
diff --git a/MatlabFiles/normpar.m b/MatlabFiles/normpar.m
index d8ee4958a0cad0e5647f2c3345b79064d6bc9218..8d7e3ba238ad30536d9f4c99f30869d8de26aa59 100644
--- a/MatlabFiles/normpar.m
+++ b/MatlabFiles/normpar.m
@@ -1,29 +1,29 @@
-function f = normpar(ab, XLO, XUP, PLO, PUP);
-
-% The function takes as inputs the parameters ab=[a, b] of the Normal
-% distribution (a = mean, b = standard deviation), the bounds of the
-% support [XLO, XUP], the the corresponding probabilities of the bounds
-% [PLO, PUP] and returns the residual value f.
-
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-a = ab(1); b = abs(ab(2));
-f1 = PLO - normcdf(XLO, a, b);
-f2 = PUP - normcdf(XUP, a, b);
-
-f = [f1, f2];
+function f = normpar(ab, XLO, XUP, PLO, PUP);
+
+% The function takes as inputs the parameters ab=[a, b] of the Normal
+% distribution (a = mean, b = standard deviation), the bounds of the
+% support [XLO, XUP], the the corresponding probabilities of the bounds
+% [PLO, PUP] and returns the residual value f.
+
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+a = ab(1); b = abs(ab(2));
+f1 = PLO - normcdf(XLO, a, b);
+f2 = PUP - normcdf(XUP, a, b);
+
+f = [f1, f2];
diff --git a/MatlabFiles/nqzdiv.m b/MatlabFiles/nqzdiv.m
index 4c3f6699f68bb6658e19fe5c959d4bf5a8c2086e..fa8a7f7288a711dc3d2a1ca5ef99672e51b1c704 100644
--- a/MatlabFiles/nqzdiv.m
+++ b/MatlabFiles/nqzdiv.m
@@ -1,61 +1,61 @@
-function [A,B,P,R] = nqzdiv(stake,A,B,V,P)
-%function [A,B,Q,Z] = nqzdiv(stake,A,B,V,P)
-%
-% Takes U.T. matrices A, B, V, and P and rearranges them
-% so that all cases of abs(B(i,i)/A(i,i))>stake are in lower right
-% corner, while the output A and B are diagonal, and P*A*R=G0, P*B*R=G1.
-% Note: the input A and B are U.T.; the output A and B are diagonal.
-%
-% Modified by T.Zha, 5/26/97
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-[n jnk] = size(A);
-
-ald = diag(diag(A)); % d: diagoanl
-bed = diag(diag(B));
-R = inv(V);
-
-root = abs([diag(ald) diag(bed)]);
-root(:,1) = root(:,1)-(root(:,1)<1.e-13).*(root(:,1)+root(:,2));
-root(:,2) = root(:,2)./root(:,1);
-for i = n:-1:1
- m=0;
- for j=i:-1:1
- if (root(j,2) > stake | root(j,2) < -.1)
- m=j;
- break
- end
- end
- if (m==0)
- return
- elseif (m<i-1)
- pindx = 1:n; % T.Z., 5/26/97
- pindx(m)=i-1;
- pindx(i-1)=m;
- %*** permutation begins
- ald = ald(pindx,pindx);
- bed = bed(pindx,pindx);
- P = P(:,pindx); % column permutation, T.Z., 5/26/97
- R = R(pindx,:); % row permutation
- end
-end
-
-%*** return the new Q, Z, A, B
-A = diag(1./diag(bed));
-B = diag(1./diag(ald));
\ No newline at end of file
+function [A,B,P,R] = nqzdiv(stake,A,B,V,P)
+%function [A,B,Q,Z] = nqzdiv(stake,A,B,V,P)
+%
+% Takes U.T. matrices A, B, V, and P and rearranges them
+% so that all cases of abs(B(i,i)/A(i,i))>stake are in lower right
+% corner, while the output A and B are diagonal, and P*A*R=G0, P*B*R=G1.
+% Note: the input A and B are U.T.; the output A and B are diagonal.
+%
+% Modified by T.Zha, 5/26/97
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+[n jnk] = size(A);
+
+ald = diag(diag(A)); % d: diagoanl
+bed = diag(diag(B));
+R = inv(V);
+
+root = abs([diag(ald) diag(bed)]);
+root(:,1) = root(:,1)-(root(:,1)<1.e-13).*(root(:,1)+root(:,2));
+root(:,2) = root(:,2)./root(:,1);
+for i = n:-1:1
+ m=0;
+ for j=i:-1:1
+ if (root(j,2) > stake | root(j,2) < -.1)
+ m=j;
+ break
+ end
+ end
+ if (m==0)
+ return
+ elseif (m<i-1)
+ pindx = 1:n; % T.Z., 5/26/97
+ pindx(m)=i-1;
+ pindx(i-1)=m;
+ %*** permutation begins
+ ald = ald(pindx,pindx);
+ bed = bed(pindx,pindx);
+ P = P(:,pindx); % column permutation, T.Z., 5/26/97
+ R = R(pindx,:); % row permutation
+ end
+end
+
+%*** return the new Q, Z, A, B
+A = diag(1./diag(bed));
+B = diag(1./diag(ald));
diff --git a/MatlabFiles/numgrad.m b/MatlabFiles/numgrad.m
index 89eb38e36e6a4055982ce2ab7b86c056d7829dca..6385b4a352ac000652f3fc7ef2d2692435529177 100644
--- a/MatlabFiles/numgrad.m
+++ b/MatlabFiles/numgrad.m
@@ -1,102 +1,102 @@
-function [grdd, badg] = numgrad(fcn,x0,varargin)
-%function grdd = fn_gradcd(fcn,x0,varargin)
-% computes numerical gradient of a single-valued function or Jacobian
-% matrix of a vector-valued function using a central difference with
-% function grdd = gradcd(fcn,x0,grdh)
-%
-% fcn: a string naming a vector-valued function (f:n-by-1 -> k-by-1).
-% x0: a column vector n-by-1, at which point the hessian is evaluated.
-% grdh: step size, n*1. Set as follows
-% step = eps^(1/3);
-% %step = 1e-04;
-% grdh = step * (max([abs(x) ones(length(x),1)]'))' .* (abs(x) ./ x);
-%--------------------
-% grdd: n-by-k Jacobian matrix (gradients).
-%
-% Written by Tao Zha, 2002.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-stps = eps^(1/3);
-% eps: floating point relative accuracy or machine precision: 2.22e-16
-% stps: step size recommended by Dennis and Schnabel: 6.006e-6
-
-x0 = x0(:);
-%tailstr = ')';
-%for i=nargin-3:-1:1
-% tailstr=[ ',P' num2str(i) tailstr];
-%end
-f0 = eval([fcn '(x0,varargin{:})']);
-%f0 = eval([fcn '(x0' tailstr]);
-
-% ** initializations
-n = length(x0);
-k = length(f0); % dimension of "fcn"
-
-% ** Computation of stepsize (dh)
-if (0)
- dh = 1.0e-06; %grdh;
-else
- ax0 = abs(x0);
- if all(x0)
- dax0 = x0 ./ ax0;
- else
- dax0 = 1;
- end
- dh = stps * (max([ax0 ones(n,1)]'))' .* dax0;
-end
-
-xdh = x0 + dh;
-dh = xdh - x0; % This increases precision slightly
-%
-argplus = x0(:,ones(n,1));
-argminus = argplus;
-dnum = 1:n+1:n^2; % positions of diagonals in vec(argplus).
-argplus(dnum) = xdh; % replace the diagonals of "argplus" by "xdh".
-argminus(dnum) = x0-dh; % replace the diagonals of "argplus" by "xdh".
-
-grdd = zeros(k,n); % preallocate to speed the loop.
-badg=0;
-i = 0;
-while i ~= n
- i = i+1;
- fp = eval([fcn '(argplus(:,i),varargin{:})']);
- fm = eval([fcn '(argminus(:,i),varargin{:})']);
-% fp = eval([fcn '(argplus(:,i)' tailstr]);
-% fm = eval([fcn '(argminus(:,i)' tailstr]);
- g0 = fp - fm;
-
- if abs(g0)< 1e15
- grdd(:,i)=g0;
- % disp('good gradient')
- else
- disp('bad gradient ------------------------')
- % fprintf('Gradient w.r.t. %3d: %10g\n',i,g0) %see above
- grdd(:,i)=0;
- badg=1;
- % return
- % can return here to save time if the gradient will never be
- % used when badg returns as true.
- end
-
-end
-dhm = dh(:,ones(k,1));
-dhm = dhm'; % k*n
-grdd = grdd ./ (2*dhm); % k-by-n.
-grdd = grdd'; % n-by-k.
-
+function [grdd, badg] = numgrad(fcn,x0,varargin)
+%function grdd = fn_gradcd(fcn,x0,varargin)
+% computes numerical gradient of a single-valued function or Jacobian
+% matrix of a vector-valued function using a central difference with
+% function grdd = gradcd(fcn,x0,grdh)
+%
+% fcn: a string naming a vector-valued function (f:n-by-1 -> k-by-1).
+% x0: a column vector n-by-1, at which point the hessian is evaluated.
+% grdh: step size, n*1. Set as follows
+% step = eps^(1/3);
+% %step = 1e-04;
+% grdh = step * (max([abs(x) ones(length(x),1)]'))' .* (abs(x) ./ x);
+%--------------------
+% grdd: n-by-k Jacobian matrix (gradients).
+%
+% Written by Tao Zha, 2002.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+stps = eps^(1/3);
+% eps: floating point relative accuracy or machine precision: 2.22e-16
+% stps: step size recommended by Dennis and Schnabel: 6.006e-6
+
+x0 = x0(:);
+%tailstr = ')';
+%for i=nargin-3:-1:1
+% tailstr=[ ',P' num2str(i) tailstr];
+%end
+f0 = eval([fcn '(x0,varargin{:})']);
+%f0 = eval([fcn '(x0' tailstr]);
+
+% ** initializations
+n = length(x0);
+k = length(f0); % dimension of "fcn"
+
+% ** Computation of stepsize (dh)
+if (0)
+ dh = 1.0e-06; %grdh;
+else
+ ax0 = abs(x0);
+ if all(x0)
+ dax0 = x0 ./ ax0;
+ else
+ dax0 = 1;
+ end
+ dh = stps * (max([ax0 ones(n,1)]'))' .* dax0;
+end
+
+xdh = x0 + dh;
+dh = xdh - x0; % This increases precision slightly
+%
+argplus = x0(:,ones(n,1));
+argminus = argplus;
+dnum = 1:n+1:n^2; % positions of diagonals in vec(argplus).
+argplus(dnum) = xdh; % replace the diagonals of "argplus" by "xdh".
+argminus(dnum) = x0-dh; % replace the diagonals of "argplus" by "xdh".
+
+grdd = zeros(k,n); % preallocate to speed the loop.
+badg=0;
+i = 0;
+while i ~= n
+ i = i+1;
+ fp = eval([fcn '(argplus(:,i),varargin{:})']);
+ fm = eval([fcn '(argminus(:,i),varargin{:})']);
+% fp = eval([fcn '(argplus(:,i)' tailstr]);
+% fm = eval([fcn '(argminus(:,i)' tailstr]);
+ g0 = fp - fm;
+
+ if abs(g0)< 1e15
+ grdd(:,i)=g0;
+ % disp('good gradient')
+ else
+ disp('bad gradient ------------------------')
+ % fprintf('Gradient w.r.t. %3d: %10g\n',i,g0) %see above
+ grdd(:,i)=0;
+ badg=1;
+ % return
+ % can return here to save time if the gradient will never be
+ % used when badg returns as true.
+ end
+
+end
+dhm = dh(:,ones(k,1));
+dhm = dhm'; % k*n
+grdd = grdd ./ (2*dhm); % k-by-n.
+grdd = grdd'; % n-by-k.
+
diff --git a/MatlabFiles/numgradcd.m b/MatlabFiles/numgradcd.m
index bf09580387ed4bea42a868046ef2a39239ecab02..c125fee99a66f5cb8d1b1983bdcda02d95267392 100644
--- a/MatlabFiles/numgradcd.m
+++ b/MatlabFiles/numgradcd.m
@@ -1,114 +1,114 @@
-function [grdd, badg] = numgradcd(fcn,x0,varargin)
-%function grdd = fn_gradcd(fcn,x0,varargin)
-% computes numerical gradient of a single-valued function or Jacobian
-% matrix of a vector-valued function using a central difference with
-% function grdd = gradcd(fcn,x0,grdh)
-%
-% fcn: a string naming a vector-valued function (f:n-by-1 -> k-by-1).
-% x0: a column vector n-by-1, at which point the hessian is evaluated.
-% grdh: step size, n*1. Set as follows
-% step = eps^(1/3);
-% %step = 1e-04;
-% grdh = step * (max([abs(x) ones(length(x),1)]'))' .* (abs(x) ./ x);
-%--------------------
-% grdd: n-by-k Jacobian matrix (gradients).
-%
-% Written by Tao Zha, 2002.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-stps = eps^(1/3);
-%stps = 1.0e-02;
-% eps: floating point relative accuracy or machine precision: 2.22e-16
-% stps: step size recommended by Dennis and Schnabel: 6.006e-6
-
-x0 = x0(:);
-%tailstr = ')';
-%for i=nargin-3:-1:1
-% tailstr=[ ',P' num2str(i) tailstr];
-%end
-if ischar(fcn)
- f0 = eval([fcn '(x0,varargin{:})']);
-else
- f0 = fcn(x0,varargin{:});
-end
-%f0 = eval([fcn '(x0' tailstr]);
-
-% ** initializations
-n = length(x0);
-k = length(f0); % dimension of "fcn"
-
-% ** Computation of stepsize (dh)
-if (0)
- dh = 1.0e-06; %grdh;
-else
- ax0 = abs(x0);
- if all(x0)
- dax0 = x0 ./ ax0;
- else
- dax0 = 1;
- end
- dh = stps * (max([ax0 ones(n,1)]'))' .* dax0;
-end
-
-xdh = x0 + dh;
-dh = xdh - x0; % This increases precision slightly
-%
-argplus = x0(:,ones(n,1));
-argminus = argplus;
-dnum = 1:n+1:n^2; % positions of diagonals in vec(argplus).
-argplus(dnum) = xdh; % replace the diagonals of "argplus" by "xdh".
-argminus(dnum) = x0-dh; % replace the diagonals of "argplus" by "xdh".
-
-grdd = zeros(k,n); % preallocate to speed the loop.
-badg=0;
-i = 0;
-while i ~= n
- i = i+1;
- if ischar(fcn)
-
- fp = eval([fcn '(argplus(:,i),varargin{:})']);
- fm = eval([fcn '(argminus(:,i),varargin{:})']);
- else
-
- fp = fcn(argplus(:,i),varargin{:});
- fm = fcn(argminus(:,i),varargin{:});
- end
-% fp = eval([fcn '(argplus(:,i)' tailstr]);
-% fm = eval([fcn '(argminus(:,i)' tailstr]);
- g0 = fp - fm;
-
- if abs(g0)< 1e15
- grdd(:,i)=g0;
- % disp('good gradient')
- else
- disp('bad gradient ------------------------')
- % fprintf('Gradient w.r.t. %3d: %10g\n',i,g0) %see above
- grdd(:,i)=0;
- badg=1;
- % return
- % can return here to save time if the gradient will never be
- % used when badg returns as true.
- end
-
-end
-dhm = dh(:,ones(k,1));
-dhm = dhm'; % k*n
-grdd = grdd ./ (2*dhm); % k-by-n.
-grdd = grdd'; % n-by-k.
-
+function [grdd, badg] = numgradcd(fcn,x0,varargin)
+%function grdd = fn_gradcd(fcn,x0,varargin)
+% computes numerical gradient of a single-valued function or Jacobian
+% matrix of a vector-valued function using a central difference with
+% function grdd = gradcd(fcn,x0,grdh)
+%
+% fcn: a string naming a vector-valued function (f:n-by-1 -> k-by-1).
+% x0: a column vector n-by-1, at which point the hessian is evaluated.
+% grdh: step size, n*1. Set as follows
+% step = eps^(1/3);
+% %step = 1e-04;
+% grdh = step * (max([abs(x) ones(length(x),1)]'))' .* (abs(x) ./ x);
+%--------------------
+% grdd: n-by-k Jacobian matrix (gradients).
+%
+% Written by Tao Zha, 2002.
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+stps = eps^(1/3);
+%stps = 1.0e-02;
+% eps: floating point relative accuracy or machine precision: 2.22e-16
+% stps: step size recommended by Dennis and Schnabel: 6.006e-6
+
+x0 = x0(:);
+%tailstr = ')';
+%for i=nargin-3:-1:1
+% tailstr=[ ',P' num2str(i) tailstr];
+%end
+if ischar(fcn)
+ f0 = eval([fcn '(x0,varargin{:})']);
+else
+ f0 = fcn(x0,varargin{:});
+end
+%f0 = eval([fcn '(x0' tailstr]);
+
+% ** initializations
+n = length(x0);
+k = length(f0); % dimension of "fcn"
+
+% ** Computation of stepsize (dh)
+if (0)
+ dh = 1.0e-06; %grdh;
+else
+ ax0 = abs(x0);
+ if all(x0)
+ dax0 = x0 ./ ax0;
+ else
+ dax0 = 1;
+ end
+ dh = stps * (max([ax0 ones(n,1)]'))' .* dax0;
+end
+
+xdh = x0 + dh;
+dh = xdh - x0; % This increases precision slightly
+%
+argplus = x0(:,ones(n,1));
+argminus = argplus;
+dnum = 1:n+1:n^2; % positions of diagonals in vec(argplus).
+argplus(dnum) = xdh; % replace the diagonals of "argplus" by "xdh".
+argminus(dnum) = x0-dh; % replace the diagonals of "argplus" by "xdh".
+
+grdd = zeros(k,n); % preallocate to speed the loop.
+badg=0;
+i = 0;
+while i ~= n
+ i = i+1;
+ if ischar(fcn)
+
+ fp = eval([fcn '(argplus(:,i),varargin{:})']);
+ fm = eval([fcn '(argminus(:,i),varargin{:})']);
+ else
+
+ fp = fcn(argplus(:,i),varargin{:});
+ fm = fcn(argminus(:,i),varargin{:});
+ end
+% fp = eval([fcn '(argplus(:,i)' tailstr]);
+% fm = eval([fcn '(argminus(:,i)' tailstr]);
+ g0 = fp - fm;
+
+ if abs(g0)< 1e15
+ grdd(:,i)=g0;
+ % disp('good gradient')
+ else
+ disp('bad gradient ------------------------')
+ % fprintf('Gradient w.r.t. %3d: %10g\n',i,g0) %see above
+ grdd(:,i)=0;
+ badg=1;
+ % return
+ % can return here to save time if the gradient will never be
+ % used when badg returns as true.
+ end
+
+end
+dhm = dh(:,ones(k,1));
+dhm = dhm'; % k*n
+grdd = grdd ./ (2*dhm); % k-by-n.
+grdd = grdd'; % n-by-k.
+
diff --git a/MatlabFiles/numgradorig.m b/MatlabFiles/numgradorig.m
index 9cf92a02ef6fd9cc0843df96e603aaf701760f1d..2aebc4d4d69e42e4aa714d75bae04c1fcde82e5a 100644
--- a/MatlabFiles/numgradorig.m
+++ b/MatlabFiles/numgradorig.m
@@ -1,116 +1,116 @@
-function [g, badg] = numgrad(fcn,x,varargin)
-% function [g badg] = numgrad(fcn,xvarargin)
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-%
-delta = 1e-6; % works for a general case
-%delta = 1e-2; % bigger step -- works for a flat peak
-%delta=1e-04;
-
-n=length(x);
-tvec=delta*eye(n);
-g=zeros(n,1);
-%--------------------old way to deal with variable # of P's--------------
-%tailstr = ')';
-%stailstr = [];
-%for i=nargin-2:-1:1
-% tailstr=[ ',P' num2str(i) tailstr];
-% stailstr=[' P' num2str(i) stailstr];
-%end
-%f0 = eval([fcn '(x' tailstr]); % Is there a way not to do this?
-%---------------------------------------------------------------^yes
-f0 = eval([fcn '(x,varargin{:})']);
-% disp(' first fcn in numgrad.m ------------------')
-%home
-% disp('numgrad.m is working. ----') % Jiinil on 9/5/95
-% sizex=size(x),sizetvec=size(tvec),x, % Jinill on 9/6/95
-badg=0;
-for i=1:n
- scale=1; % originally 1
- % i,tveci=tvec(:,i)% ,plus=x+scale*tvec(:,i) % Jinill Kim on 9/6/95
- if size(x,1)>size(x,2)
- tvecv=tvec(i,:);
- else
- tvecv=tvec(:,i);
- end
- g0 = (eval([fcn '(x+scale*tvecv'', varargin{:})']) - f0) ...
- /(scale*delta);
- % disp(' fcn in the i=1:n loop of numgrad.m ------------------')% Jinill 9/6/95
- % disp(' and i is') % Jinill
- % i % Jinill
- % fprintf('Gradient w.r.t. %3d: %10g\n',i,g0) %see below Jinill 9/6/95
-% -------------------------- special code to essentially quit here
- % absg0=abs(g0) % Jinill on 9/6/95
- if abs(g0)< 1e15
- g(i)=g0;
- % disp('good gradient') % Jinill Kim
- else
- disp('bad gradient ------------------------') % Jinill Kim
- % fprintf('Gradient w.r.t. %3d: %10g\n',i,g0) %see above
- g(i)=0;
- badg=1;
- % return
- % can return here to save time if the gradient will never be
- % used when badg returns as true.
- end
-end
-%-------------------------------------------------------------
-% if g0 > 0
-% sided=2;
-% g1 = -(eval([fcn '(x-scale*tvec(:,i)''' tailstr]) - f0) ...
-% /(scale*delta);
-% if g1<0
-% scale = scale/10;
-% else
-% break
-% end
-% else
-% sided=1;
-% break
-% end
-% end
-% if sided==1
-% g(i)=g0;
-% else
-% if (g0<1e20)
-% if (g1>-1e20)
-% g(i)=(g0+g1)/2;
-% else
-% g(i)=0;
-% badg=1;
-% disp( ['Banging against wall, parameter ' int2str(i)] );
-% end
-% else
-% if g1>-1e20
-% if g1<0
-% g(i)=0;
-% badg=1;
-% disp( ['Banging against wall, parameter ' int2str(i)] );
-% else
-% g(i)=g1;
-% end
-% else
-% g(i)=0;
-% badg=1;
-% disp(['Valley around parameter ' int2str(i)])
-% end
-% end
-% end
-%end
-%save g.dat g x f0
-%eval(['save g g x f0 ' stailstr]);
+function [g, badg] = numgrad(fcn,x,varargin)
+% function [g badg] = numgrad(fcn,xvarargin)
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+%
+delta = 1e-6; % works for a general case
+%delta = 1e-2; % bigger step -- works for a flat peak
+%delta=1e-04;
+
+n=length(x);
+tvec=delta*eye(n);
+g=zeros(n,1);
+%--------------------old way to deal with variable # of P's--------------
+%tailstr = ')';
+%stailstr = [];
+%for i=nargin-2:-1:1
+% tailstr=[ ',P' num2str(i) tailstr];
+% stailstr=[' P' num2str(i) stailstr];
+%end
+%f0 = eval([fcn '(x' tailstr]); % Is there a way not to do this?
+%---------------------------------------------------------------^yes
+f0 = eval([fcn '(x,varargin{:})']);
+% disp(' first fcn in numgrad.m ------------------')
+%home
+% disp('numgrad.m is working. ----') % Jiinil on 9/5/95
+% sizex=size(x),sizetvec=size(tvec),x, % Jinill on 9/6/95
+badg=0;
+for i=1:n
+ scale=1; % originally 1
+ % i,tveci=tvec(:,i)% ,plus=x+scale*tvec(:,i) % Jinill Kim on 9/6/95
+ if size(x,1)>size(x,2)
+ tvecv=tvec(i,:);
+ else
+ tvecv=tvec(:,i);
+ end
+ g0 = (eval([fcn '(x+scale*tvecv'', varargin{:})']) - f0) ...
+ /(scale*delta);
+ % disp(' fcn in the i=1:n loop of numgrad.m ------------------')% Jinill 9/6/95
+ % disp(' and i is') % Jinill
+ % i % Jinill
+ % fprintf('Gradient w.r.t. %3d: %10g\n',i,g0) %see below Jinill 9/6/95
+% -------------------------- special code to essentially quit here
+ % absg0=abs(g0) % Jinill on 9/6/95
+ if abs(g0)< 1e15
+ g(i)=g0;
+ % disp('good gradient') % Jinill Kim
+ else
+ disp('bad gradient ------------------------') % Jinill Kim
+ % fprintf('Gradient w.r.t. %3d: %10g\n',i,g0) %see above
+ g(i)=0;
+ badg=1;
+ % return
+ % can return here to save time if the gradient will never be
+ % used when badg returns as true.
+ end
+end
+%-------------------------------------------------------------
+% if g0 > 0
+% sided=2;
+% g1 = -(eval([fcn '(x-scale*tvec(:,i)''' tailstr]) - f0) ...
+% /(scale*delta);
+% if g1<0
+% scale = scale/10;
+% else
+% break
+% end
+% else
+% sided=1;
+% break
+% end
+% end
+% if sided==1
+% g(i)=g0;
+% else
+% if (g0<1e20)
+% if (g1>-1e20)
+% g(i)=(g0+g1)/2;
+% else
+% g(i)=0;
+% badg=1;
+% disp( ['Banging against wall, parameter ' int2str(i)] );
+% end
+% else
+% if g1>-1e20
+% if g1<0
+% g(i)=0;
+% badg=1;
+% disp( ['Banging against wall, parameter ' int2str(i)] );
+% else
+% g(i)=g1;
+% end
+% else
+% g(i)=0;
+% badg=1;
+% disp(['Valley around parameter ' int2str(i)])
+% end
+% end
+% end
+%end
+%save g.dat g x f0
+%eval(['save g g x f0 ' stailstr]);
diff --git a/MatlabFiles/ols.m b/MatlabFiles/ols.m
index e4fcb29fa030da961cb04d16707f08209cd71b54..20d7920accadb3d04a0a349b1ad0e7a7518785ec 100644
--- a/MatlabFiles/ols.m
+++ b/MatlabFiles/ols.m
@@ -1,44 +1,44 @@
-function [Bh,e,xtx,xty] = ols(y,phi)
-% ols: estimate a system of equations: [Bh,e,xtx,xty] = ols(y,phi)
-% Y(T*nvar) = XB + u, X: T*k, B: k*nvar.
-% where Bh: the estimated B; column: nvar; row: number of r.h.s. variables.
-% e: estimated residual e = y -xBh, T*nvar
-% xtx: X'X: k-by-k
-% xty: X'Y: k-by-nvar
-% phi: X; T-by-k; column: number of r.h.s. variables (including
-% deterministic terms)
-% y: Y: T-by-nvar
-%
-% See also sye.m and syed.m
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-% ** setup of orders and lengths **
-[u d v]=svd(phi,0); %trial
-%xtx = phi'*phi; % X'X, k*k (ncoe*ncoe)
-vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
-dinv = 1./diag(d); % inv(diag(d))
-vdinv=v.*(ones(size(v,2),1)*dinv'); %trial
-xtx=vd*vd';
-xtxinv = vdinv*vdinv';
-%xty = phi'*y; % X'Y
-uy = u'*y; %trial
-xty = vd*uy; %trial
-%Bh = xtx\xty; %inv(X'X)*(X'Y), k*m (ncoe*nvar).
-Bh = xtxinv*xty;
-%e = y - phi*Bh; % from Y = XB + U, e: (T-lags)*nvar
-e = y - u*uy;
\ No newline at end of file
+function [Bh,e,xtx,xty] = ols(y,phi)
+% ols: estimate a system of equations: [Bh,e,xtx,xty] = ols(y,phi)
+% Y(T*nvar) = XB + u, X: T*k, B: k*nvar.
+% where Bh: the estimated B; column: nvar; row: number of r.h.s. variables.
+% e: estimated residual e = y -xBh, T*nvar
+% xtx: X'X: k-by-k
+% xty: X'Y: k-by-nvar
+% phi: X; T-by-k; column: number of r.h.s. variables (including
+% deterministic terms)
+% y: Y: T-by-nvar
+%
+% See also sye.m and syed.m
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+% ** setup of orders and lengths **
+[u d v]=svd(phi,0); %trial
+%xtx = phi'*phi; % X'X, k*k (ncoe*ncoe)
+vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
+dinv = 1./diag(d); % inv(diag(d))
+vdinv=v.*(ones(size(v,2),1)*dinv'); %trial
+xtx=vd*vd';
+xtxinv = vdinv*vdinv';
+%xty = phi'*y; % X'Y
+uy = u'*y; %trial
+xty = vd*uy; %trial
+%Bh = xtx\xty; %inv(X'X)*(X'Y), k*m (ncoe*nvar).
+Bh = xtxinv*xty;
+%e = y - phi*Bh; % from Y = XB + U, e: (T-lags)*nvar
+e = y - u*uy;
diff --git a/MatlabFiles/pdfforg.m b/MatlabFiles/pdfforg.m
index c41c494ecc5d48fa2fb974ab30a6a066cd8d3a45..86c3e22c0b6d40a01fb6a8b29bd4206ed0e5381a 100644
--- a/MatlabFiles/pdfforg.m
+++ b/MatlabFiles/pdfforg.m
@@ -1,53 +1,53 @@
-function [imfpdf,imfpo,imfprob] = pdfforg(imfcnt,imndraws,forep,nvar,ninv,invc,Am)
-% [imfpdf,imfpo,imfprob] = pdfforg(imfcnt,imndraws,forep,nvar,ninv,invc,Am)
-%
-% Produce the dataset for generating p.d.f. and export the dataset for
-% probability (NOT density) at each bin
-%
-% imfcnt: 2+ninv-by-forep*nvar. Counted logcnt, yhatqgcnt, or yhatCalygcnt.
-% In the case of impulse responses, forep=imstp and nvar=nvar^2
-% ninv: the number of small interior intervals for sorting.
-% invc: 1-by-forep*nvar. Whole inverval lenghth from min to max for one of
-% (yhat, yhatqg, or yhatCalyg)
-% Am: 1-by-forep*nvar. Range5{i}(:,:,1) is lowest range for for one of
-% (yhat, yhatqg, or yhatCalyg)
-%-----------------
-% imfpdf: 2+ninv-by-forep*nvar. Density
-% imfpo: 2+ninv-by-forep*nvar. Bin position (x-axis) in relation to imfs3
-% imfprob: 2+ninv-by-forep*nvar. Probability (NOT density) at each bin
-%
-% 27 August 1998 Tao Zha
-% Revised, October 1998
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-invlength = invc ./ ninv;
-invlengthM = repmat(invlength,[2+ninv,1]);
-invlengthM([1 2+ninv],:) = 1;
- % first (-inf, low bound) and last (high bound, +inf), the interval is set
- % to be 1. Of course, theoretically, the interval length should be set
- % to infinity, but 1 is large enough compared with invlength.
-imfprob = imfcnt ./ imndraws; % probability (NOT density)
-imfpdf = imfprob ./ invlengthM; % density
-
-imfpo = [1:2+ninv]'; % positions for each forecast
-imfpo = imfpo - 2; % the first step to put back to original form
-imfpo = repmat(imfpo,[1,forep*nvar]);
-invcM = repmat(invc,[2+ninv,1]);
-AmM = repmat(Am,[2+ninv,1]);
-imfpo = ((imfpo .* invcM) ./ ninv) + AmM; % 2+ninv-by-forep*nvar
- % the final step to put back to original form of impulse responses
\ No newline at end of file
+function [imfpdf,imfpo,imfprob] = pdfforg(imfcnt,imndraws,forep,nvar,ninv,invc,Am)
+% [imfpdf,imfpo,imfprob] = pdfforg(imfcnt,imndraws,forep,nvar,ninv,invc,Am)
+%
+% Produce the dataset for generating p.d.f. and export the dataset for
+% probability (NOT density) at each bin
+%
+% imfcnt: 2+ninv-by-forep*nvar. Counted logcnt, yhatqgcnt, or yhatCalygcnt.
+% In the case of impulse responses, forep=imstp and nvar=nvar^2
+% ninv: the number of small interior intervals for sorting.
+% invc: 1-by-forep*nvar. Whole inverval lenghth from min to max for one of
+% (yhat, yhatqg, or yhatCalyg)
+% Am: 1-by-forep*nvar. Range5{i}(:,:,1) is lowest range for for one of
+% (yhat, yhatqg, or yhatCalyg)
+%-----------------
+% imfpdf: 2+ninv-by-forep*nvar. Density
+% imfpo: 2+ninv-by-forep*nvar. Bin position (x-axis) in relation to imfs3
+% imfprob: 2+ninv-by-forep*nvar. Probability (NOT density) at each bin
+%
+% 27 August 1998 Tao Zha
+% Revised, October 1998
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+invlength = invc ./ ninv;
+invlengthM = repmat(invlength,[2+ninv,1]);
+invlengthM([1 2+ninv],:) = 1;
+ % first (-inf, low bound) and last (high bound, +inf), the interval is set
+ % to be 1. Of course, theoretically, the interval length should be set
+ % to infinity, but 1 is large enough compared with invlength.
+imfprob = imfcnt ./ imndraws; % probability (NOT density)
+imfpdf = imfprob ./ invlengthM; % density
+
+imfpo = [1:2+ninv]'; % positions for each forecast
+imfpo = imfpo - 2; % the first step to put back to original form
+imfpo = repmat(imfpo,[1,forep*nvar]);
+invcM = repmat(invc,[2+ninv,1]);
+AmM = repmat(Am,[2+ninv,1]);
+imfpo = ((imfpo .* invcM) ./ ninv) + AmM; % 2+ninv-by-forep*nvar
+ % the final step to put back to original form of impulse responses
diff --git a/MatlabFiles/perr1graph.m b/MatlabFiles/perr1graph.m
index 4efeb2b5c25dab8f0ffaf8d57aa13c73edd54de6..9c07cc8c60caa1e90a39cb274a1f1645d32e5e1a 100644
--- a/MatlabFiles/perr1graph.m
+++ b/MatlabFiles/perr1graph.m
@@ -1,133 +1,133 @@
-function scaleout = perr1graph(imf,firstl1,firsth1,...
- nrow,ncol,nstp,xlab,ylab,XTick,YTickIndx,scaleIndx)
-% imf: row: "nstp" time horizon (in the graphics), column: "nrow "variables (row in
-% the graphics), 3rd D: across "ncol" different situations (column in
-% the graphics)
-% nrow: # of rows for the graphics
-% ncol: # of columns for the graphics
-% YTickIndx: 1: enable YTick; 0: disable
-% scaleIndx: 1: enable scale along Y-axis; 0: disable
-%function scaleout = imc2errgraph(imf,firstl1,firsth1,...
-% firstl,firsth,nvar,imstp,xlab,ylab)
-% imc2errgraph: impulse, c (column: shock 1 to N), 2 error bands, graph
-% imf: impulse responses, column (responses to 1st shock, responses to 2nd shock
-% etc), row (impusle steps),
-% firstl1: lower band, .68
-% highth1: high band, .68
-% firstl: lower band, .90
-% highth: high band, .90
-% nvar: number of variables
-% imstp: step of impulse responses
-% xlab,ylab: labels
-%
-% See imrgraph, imcerrgraph, imrerrgraph
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-t = 1:nstp;
-nrow
-ncol
-
-temp1=zeros(ncol,1);
-temp2=zeros(ncol,1);
-maxval=zeros(nrow,1);
-minval=zeros(nrow,1);
-for i = 1:nrow
- for j = 1:ncol
- %jnk1=max(firsth(:,i,j));
- %jnk2=max(firstl(:,i,j));
- jnk3=max(firsth1(:,i,j));
- jnk4=max(firstl1(:,i,j));
- jnk5=max(imf(:,i,j));
-
- temp1(j)=max([jnk3 jnk4 jnk5]);
- %
- %jnk1=min(firstl(:,i,j));
- %jnk2=min(firsth(:,i,j));
- jnk3=min(firstl1(:,i,j));
- jnk4=min(firsth1(:,i,j));
- jnk5=min(imf(:,i,j));
-
- temp2(j)=min([jnk3 jnk4 jnk5]);
- end
- maxval(i)=max(temp1);
- minval(i)=min(temp2);
-end
-
-scaleout = [maxval(:) minval(:)];
-
-%--------------
-% Column j: Shock 1 to N; Row i: Responses to
-%-------------
-figure
-rowlabel = 1;
-for i = 1:nrow % column: from top to bottom
- columnlabel = 1;
-
- if minval(i)<0
- if maxval(i)<=0
- yt=[minval(i) 0];
- else
- yt=[minval(i) 0 maxval(i)];
- end
- else % (minval(i) >=0)
- if maxval(i) > 0
- yt=[0 maxval(i)];
- else % (identically zero responses)
- yt=[-1 0 1];
- end
- end
-
-
- scale=[1 nstp minval(i) maxval(i)];
- for j = 1:ncol % row: from left to right
- k1=(i-1)*ncol+j;
- subplot(nrow,ncol,k1)
- plot(t,imf(:,i,j),t,firstl1(:,i,j),'--',t,firsth1(:,i,j),'--')
- %t,[firstl(:,i,j) firsth(:,i,j)],':');
- grid;
- if scaleIndx
- axis(scale);
- end
- %set(gca,'YLim',[minval(i) maxval(i)])
- %
- set(gca,'XTick',XTick)
- if YTickIndx
- set(gca,'YTick',yt)
- end
-
- if i<nrow
- set(gca,'XTickLabelMode','manual','XTickLabel',[])
- end
- %set(gca,'XTickLabel',' ');
- if j>1
- set(gca,'YTickLabel',' ');
- end
- if rowlabel == 1
- %title(['x' num2str(j)])
- %title(eval(['x' num2str(j)]))
- title(char(xlab(j)))
- end
- if columnlabel == 1
- %ylabel(['x' num2str(i)])
- %ylabel(eval(['x' num2str(i)]))
- ylabel(char(ylab(i)))
- end
- columnlabel = 0;
- end
- rowlabel = 0;
-end
\ No newline at end of file
+function scaleout = perr1graph(imf,firstl1,firsth1,...
+ nrow,ncol,nstp,xlab,ylab,XTick,YTickIndx,scaleIndx)
+% imf: row: "nstp" time horizon (in the graphics), column: "nrow "variables (row in
+% the graphics), 3rd D: across "ncol" different situations (column in
+% the graphics)
+% nrow: # of rows for the graphics
+% ncol: # of columns for the graphics
+% YTickIndx: 1: enable YTick; 0: disable
+% scaleIndx: 1: enable scale along Y-axis; 0: disable
+%function scaleout = imc2errgraph(imf,firstl1,firsth1,...
+% firstl,firsth,nvar,imstp,xlab,ylab)
+% imc2errgraph: impulse, c (column: shock 1 to N), 2 error bands, graph
+% imf: impulse responses, column (responses to 1st shock, responses to 2nd shock
+% etc), row (impusle steps),
+% firstl1: lower band, .68
+% highth1: high band, .68
+% firstl: lower band, .90
+% highth: high band, .90
+% nvar: number of variables
+% imstp: step of impulse responses
+% xlab,ylab: labels
+%
+% See imrgraph, imcerrgraph, imrerrgraph
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+t = 1:nstp;
+nrow
+ncol
+
+temp1=zeros(ncol,1);
+temp2=zeros(ncol,1);
+maxval=zeros(nrow,1);
+minval=zeros(nrow,1);
+for i = 1:nrow
+ for j = 1:ncol
+ %jnk1=max(firsth(:,i,j));
+ %jnk2=max(firstl(:,i,j));
+ jnk3=max(firsth1(:,i,j));
+ jnk4=max(firstl1(:,i,j));
+ jnk5=max(imf(:,i,j));
+
+ temp1(j)=max([jnk3 jnk4 jnk5]);
+ %
+ %jnk1=min(firstl(:,i,j));
+ %jnk2=min(firsth(:,i,j));
+ jnk3=min(firstl1(:,i,j));
+ jnk4=min(firsth1(:,i,j));
+ jnk5=min(imf(:,i,j));
+
+ temp2(j)=min([jnk3 jnk4 jnk5]);
+ end
+ maxval(i)=max(temp1);
+ minval(i)=min(temp2);
+end
+
+scaleout = [maxval(:) minval(:)];
+
+%--------------
+% Column j: Shock 1 to N; Row i: Responses to
+%-------------
+figure
+rowlabel = 1;
+for i = 1:nrow % column: from top to bottom
+ columnlabel = 1;
+
+ if minval(i)<0
+ if maxval(i)<=0
+ yt=[minval(i) 0];
+ else
+ yt=[minval(i) 0 maxval(i)];
+ end
+ else % (minval(i) >=0)
+ if maxval(i) > 0
+ yt=[0 maxval(i)];
+ else % (identically zero responses)
+ yt=[-1 0 1];
+ end
+ end
+
+
+ scale=[1 nstp minval(i) maxval(i)];
+ for j = 1:ncol % row: from left to right
+ k1=(i-1)*ncol+j;
+ subplot(nrow,ncol,k1)
+ plot(t,imf(:,i,j),t,firstl1(:,i,j),'--',t,firsth1(:,i,j),'--')
+ %t,[firstl(:,i,j) firsth(:,i,j)],':');
+ grid;
+ if scaleIndx
+ axis(scale);
+ end
+ %set(gca,'YLim',[minval(i) maxval(i)])
+ %
+ set(gca,'XTick',XTick)
+ if YTickIndx
+ set(gca,'YTick',yt)
+ end
+
+ if i<nrow
+ set(gca,'XTickLabelMode','manual','XTickLabel',[])
+ end
+ %set(gca,'XTickLabel',' ');
+ if j>1
+ set(gca,'YTickLabel',' ');
+ end
+ if rowlabel == 1
+ %title(['x' num2str(j)])
+ %title(eval(['x' num2str(j)]))
+ title(char(xlab(j)))
+ end
+ if columnlabel == 1
+ %ylabel(['x' num2str(i)])
+ %ylabel(eval(['x' num2str(i)]))
+ ylabel(char(ylab(i)))
+ end
+ columnlabel = 0;
+ end
+ rowlabel = 0;
+end
diff --git a/MatlabFiles/perr2graph.m b/MatlabFiles/perr2graph.m
index ba33cd00a566223f13c368aa1eb2d8efde67a097..c9af1b41acc7338a291f03fd3fe9cb584a49e60d 100644
--- a/MatlabFiles/perr2graph.m
+++ b/MatlabFiles/perr2graph.m
@@ -1,128 +1,128 @@
-function scaleout = perr2graph(imf,firstl1,firsth1,...
- firstl,firsth,nrow,ncol,nstp,xlab,ylab,XTick,YTickIndx,scaleIndx)
-% scaleout = perr2graph(imf,firstl1,firsth1,...
-% firstl,firsth,nrow,ncol,nstp,xlab,ylab,XTick,YTickIndx,scaleIndx)
-%
-% imf: nstp-by-nrow-by-ncol
-% nrow: # of rows for the graphics (normally variables)
-% ncol: # of columns for the graphics (normally situations such as shocks)
-% firstl1: lower band, .68, nstp-by-nrow-by-ncol
-% highth1: high band, .68, nstp-by-nrow-by-ncol
-% firstl: lower band, .90, nstp-by-nrow-by-ncol
-% highth: high band, .90, nstp-by-nrow-by-ncol
-% YTickIndx: 1: enable YTick; 0: disable
-% scaleIndx: 1: enable scale along Y-axis; 0: disable
-% xlab,ylab: labels
-%
-% See imrgraph, imcerrgraph, imrerrgraph
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-t = 1:nstp;
-nrow
-ncol
-
-temp1=zeros(ncol,1);
-temp2=zeros(ncol,1);
-maxval=zeros(nrow,1);
-minval=zeros(nrow,1);
-for i = 1:nrow
- for j = 1:ncol
- jnk1=max(firsth(:,i,j));
- jnk2=max(firstl(:,i,j));
- jnk3=max(firsth1(:,i,j));
- jnk4=max(firstl1(:,i,j));
- jnk5=max(imf(:,i,j));
-
- temp1(j)=max([jnk1 jnk2 jnk3 jnk4 jnk5]);
- %
- jnk1=min(firstl(:,i,j));
- jnk2=min(firsth(:,i,j));
- jnk3=min(firstl1(:,i,j));
- jnk4=min(firsth1(:,i,j));
- jnk5=min(imf(:,i,j));
-
- temp2(j)=min([jnk1 jnk2 jnk3 jnk4 jnk5]);
- end
- maxval(i)=max(temp1);
- minval(i)=min(temp2);
-end
-
-scaleout = [maxval(:) minval(:)];
-
-%--------------
-% Column j: Shock 1 to N; Row i: Responses to
-%-------------
-figure
-rowlabel = 1;
-for i = 1:nrow % column: from top to bottom
- columnlabel = 1;
-
- if minval(i)<0
- if maxval(i)<=0
- yt=[minval(i) 0];
- else
- yt=[minval(i) 0 maxval(i)];
- end
- else % (minval(i) >=0)
- if maxval(i) > 0
- yt=[0 maxval(i)];
- else % (identically zero responses)
- yt=[-1 0 1];
- end
- end
-
-
- scale=[1 nstp minval(i) maxval(i)];
- for j = 1:ncol % row: from left to right
- k1=(i-1)*ncol+j;
- subplot(nrow,ncol,k1)
- plot(t,imf(:,i,j),t,[firstl1(:,i,j) firsth1(:,i,j)],'k--',...
- t,[firstl(:,i,j) firsth(:,i,j)],'k-.',t,zeros(length(imf(:,i,j)),1),'k-');
- grid;
- if scaleIndx
- axis(scale);
- end
- %set(gca,'YLim',[minval(i) maxval(i)])
- %
- set(gca,'XTick',XTick)
- if YTickIndx
- set(gca,'YTick',yt)
- end
-
- if i<nrow
- set(gca,'XTickLabelMode','manual','XTickLabel',[])
- end
- %set(gca,'XTickLabel',' ');
- if j>1
- set(gca,'YTickLabel',' ');
- end
- if rowlabel == 1
- %title(['x' num2str(j)])
- %title(eval(['x' num2str(j)]))
- title(char(xlab(j)))
- end
- if columnlabel == 1
- %ylabel(['x' num2str(i)])
- %ylabel(eval(['x' num2str(i)]))
- ylabel(char(ylab(i)))
- end
- columnlabel = 0;
- end
- rowlabel = 0;
-end
\ No newline at end of file
+function scaleout = perr2graph(imf,firstl1,firsth1,...
+ firstl,firsth,nrow,ncol,nstp,xlab,ylab,XTick,YTickIndx,scaleIndx)
+% scaleout = perr2graph(imf,firstl1,firsth1,...
+% firstl,firsth,nrow,ncol,nstp,xlab,ylab,XTick,YTickIndx,scaleIndx)
+%
+% imf: nstp-by-nrow-by-ncol
+% nrow: # of rows for the graphics (normally variables)
+% ncol: # of columns for the graphics (normally situations such as shocks)
+% firstl1: lower band, .68, nstp-by-nrow-by-ncol
+% highth1: high band, .68, nstp-by-nrow-by-ncol
+% firstl: lower band, .90, nstp-by-nrow-by-ncol
+% highth: high band, .90, nstp-by-nrow-by-ncol
+% YTickIndx: 1: enable YTick; 0: disable
+% scaleIndx: 1: enable scale along Y-axis; 0: disable
+% xlab,ylab: labels
+%
+% See imrgraph, imcerrgraph, imrerrgraph
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+t = 1:nstp;
+nrow
+ncol
+
+temp1=zeros(ncol,1);
+temp2=zeros(ncol,1);
+maxval=zeros(nrow,1);
+minval=zeros(nrow,1);
+for i = 1:nrow
+ for j = 1:ncol
+ jnk1=max(firsth(:,i,j));
+ jnk2=max(firstl(:,i,j));
+ jnk3=max(firsth1(:,i,j));
+ jnk4=max(firstl1(:,i,j));
+ jnk5=max(imf(:,i,j));
+
+ temp1(j)=max([jnk1 jnk2 jnk3 jnk4 jnk5]);
+ %
+ jnk1=min(firstl(:,i,j));
+ jnk2=min(firsth(:,i,j));
+ jnk3=min(firstl1(:,i,j));
+ jnk4=min(firsth1(:,i,j));
+ jnk5=min(imf(:,i,j));
+
+ temp2(j)=min([jnk1 jnk2 jnk3 jnk4 jnk5]);
+ end
+ maxval(i)=max(temp1);
+ minval(i)=min(temp2);
+end
+
+scaleout = [maxval(:) minval(:)];
+
+%--------------
+% Column j: Shock 1 to N; Row i: Responses to
+%-------------
+figure
+rowlabel = 1;
+for i = 1:nrow % column: from top to bottom
+ columnlabel = 1;
+
+ if minval(i)<0
+ if maxval(i)<=0
+ yt=[minval(i) 0];
+ else
+ yt=[minval(i) 0 maxval(i)];
+ end
+ else % (minval(i) >=0)
+ if maxval(i) > 0
+ yt=[0 maxval(i)];
+ else % (identically zero responses)
+ yt=[-1 0 1];
+ end
+ end
+
+
+ scale=[1 nstp minval(i) maxval(i)];
+ for j = 1:ncol % row: from left to right
+ k1=(i-1)*ncol+j;
+ subplot(nrow,ncol,k1)
+ plot(t,imf(:,i,j),t,[firstl1(:,i,j) firsth1(:,i,j)],'k--',...
+ t,[firstl(:,i,j) firsth(:,i,j)],'k-.',t,zeros(length(imf(:,i,j)),1),'k-');
+ grid;
+ if scaleIndx
+ axis(scale);
+ end
+ %set(gca,'YLim',[minval(i) maxval(i)])
+ %
+ set(gca,'XTick',XTick)
+ if YTickIndx
+ set(gca,'YTick',yt)
+ end
+
+ if i<nrow
+ set(gca,'XTickLabelMode','manual','XTickLabel',[])
+ end
+ %set(gca,'XTickLabel',' ');
+ if j>1
+ set(gca,'YTickLabel',' ');
+ end
+ if rowlabel == 1
+ %title(['x' num2str(j)])
+ %title(eval(['x' num2str(j)]))
+ title(char(xlab(j)))
+ end
+ if columnlabel == 1
+ %ylabel(['x' num2str(i)])
+ %ylabel(eval(['x' num2str(i)]))
+ ylabel(char(ylab(i)))
+ end
+ columnlabel = 0;
+ end
+ rowlabel = 0;
+end
diff --git a/MatlabFiles/phg233.m b/MatlabFiles/phg233.m
index 0e23d5b646b8804bf2f8bd7b2ce7e673b403adce..cbcee066a469d4e653e5596f75b0347ff93b4f65 100644
--- a/MatlabFiles/phg233.m
+++ b/MatlabFiles/phg233.m
@@ -1,272 +1,272 @@
-function g = phg233(x,idmat0,idmat1,fss,nvar,ncoef,phi, ...
- y,A0b,sg0bid,sgpbid)
-%function of = pmddf232(x,idmat,fss,nvar,ncoef,phi, ...
-% y,A0b,sg0bid,sgpbid)
-% Leeper-Sims-Zha BVAR setup
-% general program to setup A0 matrix and compute the likelihood
-% requires
-% x (parameter vector)
-% a0indx (matrix indicating the free parameters in A0)
-% fss (forecast sample size)
-% nvar (number of variables)
-% ncoef (number of coefficients in a single equation in A+)
-% phi (r.h.s. observations in the system for A+)
-% y (l.h.s. observations for A0)
-% A0b (initial prior mean on A0)
-% sg0bid (diagonal of Sigma0_bar, unweighted prior vcv on the parameters in i-th equation in A0)
-% sgpbid (diagonal of Sigma+_bar, unweighted prior vcv on the parameters in i-th equation in A+)
-%
-% Revisions by CAS 9/27/96: If we transmit idmat, rather than a0indx, we can scale the elements of
-% idmat so that it carries information about relative prior variances.
-% Copyright (C) 1997-2012 Christopher A. Sims and Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-global vaya0 vaha0 vaxah vahad vbiga GlbAph FRESHFUNCTION
-% FRESHFUNCTION added 8/20/90 by CAS. It signals the pmddg23 whether the global variables
-% it uses have been refreshed by a new pmddf231 call since the last gradient call.
-a0indx=find(idmat0);
-% pre-allocate GlbAph to be zeros(nvar,lags*nvar+1=ncoef)
-
-nhp = 0; % <<>> 4 hyperparameters
-na0p = length(a0indx); % <<>> number of A0 parameters
-nfp = na0p+nhp;
-
-% *** initializing
-vaya0 = zeros(na0p,1);
-vaha0 = zeros(na0p,1);
-vaxah = zeros(na0p,1);
-vahad = zeros(na0p,1);
-vbiga = zeros(na0p,1);
-
-
-A0h = zeros(nvar,nvar);
-A0h(a0indx) = x(1:na0p);
-%A0h = reshape(a0,nvar,nvar); CAS 9/24/96. The reshape is not necessary if a0 starts as nvar x nvar
- % restrictions on A0
-
-[A0hl,A0hu] = lu(A0h);
-
-% ** hyperparameters
-%%mu = x(na0p+1:nfp);
-mu = ones(5,1);
-mu(1) = 1;
-%mu(1) = 2;
-mu(2) = 0.2;
-mu(3) = 1;
-%mu(4)=1;
-mu(4)=1;
-mu(5) =1;
-%mu(5) = 40;
-% results from ...\dummy1\foreh\pmdd6.out
-%
-% mu(1): overall tightness and also for A0;
-% mu(2): relative tightness for A+;
-% mu(3): relative tightness for the constant term;
-% mu(4): weight on single dummy initial observation including constant;
-% mu(5): weight on nvar sums of coeffs dummy observations.
-
-% ** weight prior dummy observations
-%phi(1:nvar,:) = (mu(5)^2/mu(1)^2)*phi(1:nvar,:);
-%y(1:nvar,:) = (mu(5)^2/mu(1)^2)*y(1:nvar,:);
-%phi(ndobs,:) = mu
-% modified by CAS 8/6/96. The dummy obs weights are naturally in std units, not var units.
-phi(1:nvar,:) = mu(5)*phi(1:nvar,:);
-y(1:nvar,:) = mu(5)*y(1:nvar,:);
-phi(nvar+1,:) = mu(4)*phi(nvar+1,:);
-y(nvar+1,:) = mu(4)*y(nvar+1,:);
-
-% ** set up the conditional prior variance sg0bi and sgpbi.
-sg0bida = mu(1)^2*sg0bid;
-sgpbida = mu(1)^2*mu(2)^2*sgpbid;
-sgpbida(ncoef) = mu(1)^2*mu(3)^2;
-sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in a SZ paper
-sgppb = diag(sgppbd);
-
-%
-% ** some common terms
-[u d v]=svd(phi,0); %trial
-% xtx = phi'*phi; %trial
-vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
-xtx=vd*vd';
-% M = eye(fss) - phi*(xtx\phi'); % not used except in line below %trial
-yu = y'*u; %trial
-cxyxx=yu*yu'; %trial
-ymy=y'*y-cxyxx; %trial
-%ymy = y'*M*y;
-%cxy = phi'*y; %trial
-cxy = vd*yu'; %trial
-%cxpy = xtx\cxy; %not used further except in line below
-%cxyxx = cxy'*cxpy;
-
-% ** estimate A+_hat conditional on A0, ncoef*nvar, but not using full prior
-%GlbAph = cxpy*A0h;
-
-
-%%%%%%%
-%%% build the analytical gradient
-%%%%%%%
-%
-disp(sprintf('Starting loop (of): %g',toc))
-
-stril = 0;
-Hp0p = zeros(ncoef);
-% initializing Htd_+0*inv(Htd_00)*Htd_0+: conditional prior covariance
-for i = 1:nvar
- stri = find(idmat0(:,i)); %CAS change 9/24/96, to avoid slim chance of an exact zero in x vector
- %at some point during an optimization.
- strm = length(stri);
-
- A0hb = A0h(stri,i)-A0b(stri,i);
- Hp0 = zeros(ncoef,strm);
- % initializing Htd_+0*inv(Htd_00)*Htd_0
-
- % ** set up the conditional prior variance sg0bi and sgpbi.
- % sg0bd = zeros(stri,1); Commented out by CAS, 9/24/96. Looks like this meant to have
- % strm where it has stri, and in any case it is "overwritten" below.
- %------------------------------
- % Introduce prior information on which variables "belong" in various equations.
- % In this first trial, we just introduce this information here, in a model-specific way.
- % Eventually this info has to be passed parametricly. In our first shot, we just damp down
- % all coefficients except those on the diagonal.
- factor0=idmat0(:,i);
- sg0bd = sg0bida(stri).*factor0(stri);
- sg0b = diag(sg0bd);
- %
- factor1=idmat1(:,i);
- sg1bd = sgpbida(1:nvar).*factor1;
- sg1b = diag(sg1bd);
-
- % ** set up the unconditional prior variance on A0i and A+i
- XX = zeros(nvar,strm);
- XX(stri,:) = eye(strm); % XX in Gelman, etel, on p479
- %
- Hb = zeros(strm+ncoef); % including A0i and A+i
- Hb(1:strm,1:strm) = sg0b;
- sg0bxx = sg0b*XX';
- Hb(1:strm,strm+1:strm+nvar) = sg0bxx;
- %Hb(strm+1:strm+nvar,1:strm) = XX*sg0b; CAS 9/24/96. Saves a little multiplication by using line below.
- Hb(strm+1:strm+nvar,1:strm) = sg0bxx';
- Hb(strm+1:strm+nvar,strm+1:strm+nvar) = XX*sg0bxx+sg1b;
- Hb(strm+nvar+1:strm+ncoef,strm+nvar+1:strm+ncoef) = sgppb;
-
- % ** set up the final prior variance on A0i and A+i, i =1,..,6 separately.
- % ** using dummy initial observations
- %zs1 = [ys1(:,stri) -xd0]; % z*1 for the 1st equation
-
- % * final unconditional prior variance on A0i and A+i
- %Hbzs1=Hb*zs1';
- %Htd = Hb - Hbzs1*((zs1*Hbzs1+mu(1)^2*mu(4)^2*eye(nvar))\Hbzs1');
- % CAS mod 8/7/96: Hope is that dropping above two lines in favor of the one below
- % removes double-counting of dummy observations and a possible scale sensitivity.
- Htd = Hb;
- % H_~: td: tilde for ~
-
- % * final conditional prior variance on A+i give that on A0i, and
- % * unconditional variance on A0+
- H0td = Htd(1:strm,1:strm); % unconditional
- % ** inverse and chol decomp
- H0tdi = inv(H0td);
-
- %
- Hptd10 = Htd(strm+1:strm+nvar,1:strm);
- % top matrix of nvar in Htd_+0
- Hptd100 = Hptd10*H0tdi;
- Hp0(1:nvar,:) = Hptd100;
- Hptd101 = Hptd100*Hptd10';
- Hp0p(1:nvar,1:nvar) = Hptd101;
- Hptd = Htd(strm+1:strm+ncoef,strm+1:strm+ncoef) - Hp0p;
- % condtional on A0i, H_plus_tilde
-
- % ** inverse
- Hptdi = inv(Hptd);
- chhh = Hptdi*Hp0; % <<>> the term not used for "of"
-
- % common term
- xxhp = xtx+Hptdi;
-
- % ** final conditional prior mean on A+i
- alptd = zeros(ncoef,1);
- alptd(1:nvar) = XX*A0b(stri,i);
- %alptd = alptd + Hptdf*(H0tdi*A0hb); CAS efficiency improvement 8/8/96
- alptd = alptd + Hp0*A0hb;
- % conditional on A0i, alpha-plus-tilde
-
- % * alpha_plus_* for the ith equation, ncoef*1
- % *alps = xxhp\(phi'*y*A0h(:,i)+HptdDinalptd);
- HptdDinalptd=Hptdi*alptd;
- xaha = cxy*A0h(:,i)+HptdDinalptd; %CAS 8/7/96. Replaced phi'*y with cxy
- alpst = xaha'/xxhp;
- GlbAph(i,:)=alpst;
- % alpst': transpose of alps.
-
- A0hy = A0h(:,i)'*ymy;
- A0hbH = A0hb'*H0tdi;
- A0hcxyxx = A0h(:,i)'*cxyxx;
-
- %%%%
- % *** passing the gradient to "pmdg6.m" (analytical gradient)
- %%%%
- % ** d1aya0 = d(alpha0'*Y'MY*alpha0)/d(a0??)
- daya0 = 2.0*A0hy;
- daya0 = daya0(:);
- vaya0(stril+1:stril+strm) = daya0(stri);
-
- % ** daha0 = d((alpha0-alpha0_tilde)'*inv(H_0_tilde)*
- % ** (alpha0-alpha0_tilde))/d(a0??)
- daha0 = 2.0*A0hbH;
- vaha0(stril+1:stril+strm) = daha0(:);
-
- % ** daxah = d(alpha_+^'*X'X*alpha_+^)/d(a0??)
- % ** daxah = d(alpha_+^'*X'X*alpha_+^)/d(a0??)
- %% daxah = 2*(Aph(:,i)'*xtx) * cxpy(:,stri);
- %% vaxah(stril+1:stril+strm) = daxah';
- daxah = 2*A0hcxyxx;
- daxah = daxah(:);
- vaxah(stril+1:stril+strm) = daxah(stri);
-
-
- % ** dahad = d(alpha_+~'*inv(H_+~)*alpha_+~)/d(a0??)
- dahad = 2*HptdDinalptd' * Hp0;
- % <<>> multiplications not used in "of"
- vahad(stril+1:stril+strm) = dahad';
-
- % ** dbiga = d((X'X*alpha_+^+inv(H_+~)*alpha_+*)'*a_+_*)/d(a0??)
- dbiga = 2*alpst*(cxy(:,stri)+chhh);
- % <<>> multiplications not used in "of"
- vbiga(stril+1:stril+strm) = dbiga';
-
- stril = stril + strm;
-end
-
-%disp(sprintf('Loop %d end: %g', i, toc))
-disp(sprintf('Loop end (of): %g', toc))
-
-g = zeros(nfp,1);
-
-%%%%%%%
-%%% analytical gradient for A0 below
-%%%%%%%
-%
-% ** dla0 = dlog|A0|/d(a0??)
-vdla0 = zeros(na0p,1);
-dla0 = inv(A0h)';
-dla0 = dla0(:);
-vdla0 = dla0(a0indx);
-
-g = -fss*vdla0 + 0.5*vaya0 + 0.5*vaha0 + 0.5*vaxah + 0.5*vahad - 0.5*vbiga;
-
\ No newline at end of file
+function g = phg233(x,idmat0,idmat1,fss,nvar,ncoef,phi, ...
+ y,A0b,sg0bid,sgpbid)
+%function of = pmddf232(x,idmat,fss,nvar,ncoef,phi, ...
+% y,A0b,sg0bid,sgpbid)
+% Leeper-Sims-Zha BVAR setup
+% general program to setup A0 matrix and compute the likelihood
+% requires
+% x (parameter vector)
+% a0indx (matrix indicating the free parameters in A0)
+% fss (forecast sample size)
+% nvar (number of variables)
+% ncoef (number of coefficients in a single equation in A+)
+% phi (r.h.s. observations in the system for A+)
+% y (l.h.s. observations for A0)
+% A0b (initial prior mean on A0)
+% sg0bid (diagonal of Sigma0_bar, unweighted prior vcv on the parameters in i-th equation in A0)
+% sgpbid (diagonal of Sigma+_bar, unweighted prior vcv on the parameters in i-th equation in A+)
+%
+% Revisions by CAS 9/27/96: If we transmit idmat, rather than a0indx, we can scale the elements of
+% idmat so that it carries information about relative prior variances.
+%
+% Copyright (C) 1997-2012 Christopher A. Sims and Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+global vaya0 vaha0 vaxah vahad vbiga GlbAph FRESHFUNCTION
+% FRESHFUNCTION added 8/20/90 by CAS. It signals the pmddg23 whether the global variables
+% it uses have been refreshed by a new pmddf231 call since the last gradient call.
+a0indx=find(idmat0);
+% pre-allocate GlbAph to be zeros(nvar,lags*nvar+1=ncoef)
+
+nhp = 0; % <<>> 4 hyperparameters
+na0p = length(a0indx); % <<>> number of A0 parameters
+nfp = na0p+nhp;
+
+% *** initializing
+vaya0 = zeros(na0p,1);
+vaha0 = zeros(na0p,1);
+vaxah = zeros(na0p,1);
+vahad = zeros(na0p,1);
+vbiga = zeros(na0p,1);
+
+
+A0h = zeros(nvar,nvar);
+A0h(a0indx) = x(1:na0p);
+%A0h = reshape(a0,nvar,nvar); CAS 9/24/96. The reshape is not necessary if a0 starts as nvar x nvar
+ % restrictions on A0
+
+[A0hl,A0hu] = lu(A0h);
+
+% ** hyperparameters
+%%mu = x(na0p+1:nfp);
+mu = ones(5,1);
+mu(1) = 1;
+%mu(1) = 2;
+mu(2) = 0.2;
+mu(3) = 1;
+%mu(4)=1;
+mu(4)=1;
+mu(5) =1;
+%mu(5) = 40;
+% results from ...\dummy1\foreh\pmdd6.out
+%
+% mu(1): overall tightness and also for A0;
+% mu(2): relative tightness for A+;
+% mu(3): relative tightness for the constant term;
+% mu(4): weight on single dummy initial observation including constant;
+% mu(5): weight on nvar sums of coeffs dummy observations.
+
+% ** weight prior dummy observations
+%phi(1:nvar,:) = (mu(5)^2/mu(1)^2)*phi(1:nvar,:);
+%y(1:nvar,:) = (mu(5)^2/mu(1)^2)*y(1:nvar,:);
+%phi(ndobs,:) = mu
+% modified by CAS 8/6/96. The dummy obs weights are naturally in std units, not var units.
+phi(1:nvar,:) = mu(5)*phi(1:nvar,:);
+y(1:nvar,:) = mu(5)*y(1:nvar,:);
+phi(nvar+1,:) = mu(4)*phi(nvar+1,:);
+y(nvar+1,:) = mu(4)*y(nvar+1,:);
+
+% ** set up the conditional prior variance sg0bi and sgpbi.
+sg0bida = mu(1)^2*sg0bid;
+sgpbida = mu(1)^2*mu(2)^2*sgpbid;
+sgpbida(ncoef) = mu(1)^2*mu(3)^2;
+sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in a SZ paper
+sgppb = diag(sgppbd);
+
+%
+% ** some common terms
+[u d v]=svd(phi,0); %trial
+% xtx = phi'*phi; %trial
+vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
+xtx=vd*vd';
+% M = eye(fss) - phi*(xtx\phi'); % not used except in line below %trial
+yu = y'*u; %trial
+cxyxx=yu*yu'; %trial
+ymy=y'*y-cxyxx; %trial
+%ymy = y'*M*y;
+%cxy = phi'*y; %trial
+cxy = vd*yu'; %trial
+%cxpy = xtx\cxy; %not used further except in line below
+%cxyxx = cxy'*cxpy;
+
+% ** estimate A+_hat conditional on A0, ncoef*nvar, but not using full prior
+%GlbAph = cxpy*A0h;
+
+
+%%%%%%%
+%%% build the analytical gradient
+%%%%%%%
+%
+disp(sprintf('Starting loop (of): %g',toc))
+
+stril = 0;
+Hp0p = zeros(ncoef);
+% initializing Htd_+0*inv(Htd_00)*Htd_0+: conditional prior covariance
+for i = 1:nvar
+ stri = find(idmat0(:,i)); %CAS change 9/24/96, to avoid slim chance of an exact zero in x vector
+ %at some point during an optimization.
+ strm = length(stri);
+
+ A0hb = A0h(stri,i)-A0b(stri,i);
+ Hp0 = zeros(ncoef,strm);
+ % initializing Htd_+0*inv(Htd_00)*Htd_0
+
+ % ** set up the conditional prior variance sg0bi and sgpbi.
+ % sg0bd = zeros(stri,1); Commented out by CAS, 9/24/96. Looks like this meant to have
+ % strm where it has stri, and in any case it is "overwritten" below.
+ %------------------------------
+ % Introduce prior information on which variables "belong" in various equations.
+ % In this first trial, we just introduce this information here, in a model-specific way.
+ % Eventually this info has to be passed parametricly. In our first shot, we just damp down
+ % all coefficients except those on the diagonal.
+ factor0=idmat0(:,i);
+ sg0bd = sg0bida(stri).*factor0(stri);
+ sg0b = diag(sg0bd);
+ %
+ factor1=idmat1(:,i);
+ sg1bd = sgpbida(1:nvar).*factor1;
+ sg1b = diag(sg1bd);
+
+ % ** set up the unconditional prior variance on A0i and A+i
+ XX = zeros(nvar,strm);
+ XX(stri,:) = eye(strm); % XX in Gelman, etel, on p479
+ %
+ Hb = zeros(strm+ncoef); % including A0i and A+i
+ Hb(1:strm,1:strm) = sg0b;
+ sg0bxx = sg0b*XX';
+ Hb(1:strm,strm+1:strm+nvar) = sg0bxx;
+ %Hb(strm+1:strm+nvar,1:strm) = XX*sg0b; CAS 9/24/96. Saves a little multiplication by using line below.
+ Hb(strm+1:strm+nvar,1:strm) = sg0bxx';
+ Hb(strm+1:strm+nvar,strm+1:strm+nvar) = XX*sg0bxx+sg1b;
+ Hb(strm+nvar+1:strm+ncoef,strm+nvar+1:strm+ncoef) = sgppb;
+
+ % ** set up the final prior variance on A0i and A+i, i =1,..,6 separately.
+ % ** using dummy initial observations
+ %zs1 = [ys1(:,stri) -xd0]; % z*1 for the 1st equation
+
+ % * final unconditional prior variance on A0i and A+i
+ %Hbzs1=Hb*zs1';
+ %Htd = Hb - Hbzs1*((zs1*Hbzs1+mu(1)^2*mu(4)^2*eye(nvar))\Hbzs1');
+ % CAS mod 8/7/96: Hope is that dropping above two lines in favor of the one below
+ % removes double-counting of dummy observations and a possible scale sensitivity.
+ Htd = Hb;
+ % H_~: td: tilde for ~
+
+ % * final conditional prior variance on A+i give that on A0i, and
+ % * unconditional variance on A0+
+ H0td = Htd(1:strm,1:strm); % unconditional
+ % ** inverse and chol decomp
+ H0tdi = inv(H0td);
+
+ %
+ Hptd10 = Htd(strm+1:strm+nvar,1:strm);
+ % top matrix of nvar in Htd_+0
+ Hptd100 = Hptd10*H0tdi;
+ Hp0(1:nvar,:) = Hptd100;
+ Hptd101 = Hptd100*Hptd10';
+ Hp0p(1:nvar,1:nvar) = Hptd101;
+ Hptd = Htd(strm+1:strm+ncoef,strm+1:strm+ncoef) - Hp0p;
+ % condtional on A0i, H_plus_tilde
+
+ % ** inverse
+ Hptdi = inv(Hptd);
+ chhh = Hptdi*Hp0; % <<>> the term not used for "of"
+
+ % common term
+ xxhp = xtx+Hptdi;
+
+ % ** final conditional prior mean on A+i
+ alptd = zeros(ncoef,1);
+ alptd(1:nvar) = XX*A0b(stri,i);
+ %alptd = alptd + Hptdf*(H0tdi*A0hb); CAS efficiency improvement 8/8/96
+ alptd = alptd + Hp0*A0hb;
+ % conditional on A0i, alpha-plus-tilde
+
+ % * alpha_plus_* for the ith equation, ncoef*1
+ % *alps = xxhp\(phi'*y*A0h(:,i)+HptdDinalptd);
+ HptdDinalptd=Hptdi*alptd;
+ xaha = cxy*A0h(:,i)+HptdDinalptd; %CAS 8/7/96. Replaced phi'*y with cxy
+ alpst = xaha'/xxhp;
+ GlbAph(i,:)=alpst;
+ % alpst': transpose of alps.
+
+ A0hy = A0h(:,i)'*ymy;
+ A0hbH = A0hb'*H0tdi;
+ A0hcxyxx = A0h(:,i)'*cxyxx;
+
+ %%%%
+ % *** passing the gradient to "pmdg6.m" (analytical gradient)
+ %%%%
+ % ** d1aya0 = d(alpha0'*Y'MY*alpha0)/d(a0??)
+ daya0 = 2.0*A0hy;
+ daya0 = daya0(:);
+ vaya0(stril+1:stril+strm) = daya0(stri);
+
+ % ** daha0 = d((alpha0-alpha0_tilde)'*inv(H_0_tilde)*
+ % ** (alpha0-alpha0_tilde))/d(a0??)
+ daha0 = 2.0*A0hbH;
+ vaha0(stril+1:stril+strm) = daha0(:);
+
+ % ** daxah = d(alpha_+^'*X'X*alpha_+^)/d(a0??)
+ % ** daxah = d(alpha_+^'*X'X*alpha_+^)/d(a0??)
+ %% daxah = 2*(Aph(:,i)'*xtx) * cxpy(:,stri);
+ %% vaxah(stril+1:stril+strm) = daxah';
+ daxah = 2*A0hcxyxx;
+ daxah = daxah(:);
+ vaxah(stril+1:stril+strm) = daxah(stri);
+
+
+ % ** dahad = d(alpha_+~'*inv(H_+~)*alpha_+~)/d(a0??)
+ dahad = 2*HptdDinalptd' * Hp0;
+ % <<>> multiplications not used in "of"
+ vahad(stril+1:stril+strm) = dahad';
+
+ % ** dbiga = d((X'X*alpha_+^+inv(H_+~)*alpha_+*)'*a_+_*)/d(a0??)
+ dbiga = 2*alpst*(cxy(:,stri)+chhh);
+ % <<>> multiplications not used in "of"
+ vbiga(stril+1:stril+strm) = dbiga';
+
+ stril = stril + strm;
+end
+
+%disp(sprintf('Loop %d end: %g', i, toc))
+disp(sprintf('Loop end (of): %g', toc))
+
+g = zeros(nfp,1);
+
+%%%%%%%
+%%% analytical gradient for A0 below
+%%%%%%%
+%
+% ** dla0 = dlog|A0|/d(a0??)
+vdla0 = zeros(na0p,1);
+dla0 = inv(A0h)';
+dla0 = dla0(:);
+vdla0 = dla0(a0indx);
+
+g = -fss*vdla0 + 0.5*vaya0 + 0.5*vaha0 + 0.5*vaxah + 0.5*vahad - 0.5*vbiga;
+
diff --git a/MatlabFiles/phg234.m b/MatlabFiles/phg234.m
index 36e6f9c79493fb8953426a74d45901755182bb1d..21ddcaf97e69be30e74eb9286bb97df39555261c 100644
--- a/MatlabFiles/phg234.m
+++ b/MatlabFiles/phg234.m
@@ -1,259 +1,259 @@
-function g = phg234(x,idmat0,idmat1,fss,nvar,ncoef,phi, ...
- y,A0b,sg0bid,sgpbid)
-%function of = pmddf232(x,idmat,fss,nvar,ncoef,phi, ...
-% y,A0b,sg0bid,sgpbid)
-% Leeper-Sims-Zha BVAR setup
-% general program to setup A0 matrix and compute the likelihood
-% requires
-% x (parameter vector)
-% a0indx (matrix indicating the free parameters in A0)
-% fss (forecast sample size)
-% nvar (number of variables)
-% ncoef (number of coefficients in a single equation in A+)
-% phi (r.h.s. observations in the system for A+)
-% y (l.h.s. observations for A0)
-% A0b (initial prior mean on A0)
-% sg0bid (diagonal of Sigma0_bar, unweighted prior vcv on the parameters in i-th equation in A0)
-% sgpbid (diagonal of Sigma+_bar, unweighted prior vcv on the parameters in i-th equation in A+)
-%
-% Revisions by CAS 9/27/96: If we transmit idmat, rather than a0indx, we can scale the elements of
-% idmat so that it carries information about relative prior variances.
-%
-% Revsions by TZ, 10/12/96: efficiency improvement by streamlining the previous code
-% according to the general setup in Sims and Zha "Bayesian Methods for ...".
-% Copyright (C) 1997-2012 Christopher A. Sims and Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-global vaya0 vaha0 vaxah vahad vbiga GlbAph FRESHFUNCTION
-% FRESHFUNCTION added 8/20/90 by CAS. It signals the pmddg23 whether the global variables
-% it uses have been refreshed by a new pmddf231 call since the last gradient call.
-a0indx=find(idmat0);
-% pre-allocate GlbAph to be zeros(nvar,lags*nvar+1=ncoef)
-
-nhp = 0; % <<>> 4 hyperparameters
-na0p = length(a0indx); % <<>> number of A0 parameters
-nfp = na0p+nhp;
-
-% *** initializing
-vaya0 = zeros(na0p,1);
-vaha0 = zeros(na0p,1);
-vaxah = zeros(na0p,1);
-vahad = zeros(na0p,1);
-vbiga = zeros(na0p,1);
-
-
-A0h = zeros(nvar,nvar);
-A0h(a0indx) = x(1:na0p);
-%A0h = reshape(a0,nvar,nvar); CAS 9/24/96. The reshape is not necessary if a0 starts as nvar x nvar
- % restrictions on A0
-
-[A0hl,A0hu] = lu(A0h);
-
-% ** hyperparameters
-%%mu = x(na0p+1:nfp);
-mu = ones(5,1);
-mu(1) = 1;
-%mu(1) = 2;
-mu(2) = 0.3;
-mu(3) = 25;
-%mu(4)=1;
-mu(4)=1;
-mu(5) =1;
-%mu(5) = 40;
-% results from ...\dummy1\foreh\pmdd6.out
-%
-% mu(1): overall tightness and also for A0;
-% mu(2): relative tightness for A+;
-% mu(3): relative tightness for the constant term;
-% mu(4): weight on single dummy initial observation including constant;
-% mu(5): weight on nvar sums of coeffs dummy observations.
-
-% ** weight prior dummy observations
-%phi(1:nvar,:) = (mu(5)^2/mu(1)^2)*phi(1:nvar,:);
-%y(1:nvar,:) = (mu(5)^2/mu(1)^2)*y(1:nvar,:);
-%phi(ndobs,:) = mu
-% modified by CAS 8/6/96. The dummy obs weights are naturally in std units, not var units.
-%%phi(1:nvar,:) = mu(5)*phi(1:nvar,:);
-%%y(1:nvar,:) = mu(5)*y(1:nvar,:);
-%%phi(nvar+1,:) = mu(4)*phi(nvar+1,:);
-%%y(nvar+1,:) = mu(4)*y(nvar+1,:);
-
-% ** set up the conditional prior variance sg0bi and sgpbi.
-%%sg0bida = mu(1)^2*sg0bid;
-sgpbida = mu(1)^2*mu(2)^2*sgpbid;
-sgpbida(ncoef) = mu(1)^2*mu(3)^2;
-sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in an SZ paper
-sgppb = diag(sgppbd);
-
-%
-% ** some common terms
-[u d v]=svd(phi,0); %trial
-% xtx = phi'*phi; %trial
-vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
-xtx=vd*vd';
-% M = eye(fss) - phi*(xtx\phi'); % not used except in line below %trial
-yu = y'*u; %trial
-cxyxx=yu*yu'; %trial
-ymy=y'*y-cxyxx; %trial
-%ymy = y'*M*y;
-%cxy = phi'*y; %trial
-cxy = vd*yu'; %trial
-%cxpy = xtx\cxy; %not used further except in line below
-%cxyxx = cxy'*cxpy;
-
-% ** estimate A+_hat conditional on A0, ncoef*nvar, but not using full prior
-%GlbAph = cxpy*A0h;
-
-
-%%%%%%%
-%%% build the objective function below
-%%%%%%%
-%
-disp(sprintf('Starting loop (of): %g',toc))
-
-stril = 0;
-Hp0p = zeros(ncoef);
-% initializing Htd_+0*inv(Htd_00)*Htd_0+: conditional prior covariance
-for i = 1:nvar
- stri = find(idmat0(:,i)); %CAS change 9/24/96, to avoid slim chance of an exact zero in x vector
- %at some point during an optimization.
- strm = length(stri);
-
- %%A0hb = A0h(stri,i)-A0b(stri,i);
-
- % ** set up the conditional prior variance sg0bi and sgpbi.
- % sg0bd = zeros(stri,1); Commented out by CAS, 9/24/96. Looks like this meant to have
- % strm where it has stri, and in any case it is "overwritten" below.
- %------------------------------
- % Introduce prior information on which variables "belong" in various equations.
- % In this first trial, we just introduce this information here, in a model-specific way.
- % Eventually this info has to be passed parametricly. In our first shot, we just damp down
- % all coefficients except those on the diagonal.
- %%factor0=idmat0(:,i);
- %%sg0bd = sg0bida(stri).*factor0(stri);
- %%sg0b = diag(sg0bd);
- %
- factor1=idmat1(:,i);
- sg1bd = sgpbida(1:nvar).*factor1;
- sg1b = diag(sg1bd);
-
- % ** set up the unconditional prior variance on A0i and A+i
- XX = zeros(nvar,strm);
- XX(stri,:) = eye(strm); % XX in Gelman, etel, on p479
- %
- % * final conditional prior variance on A+i give that on A0i, and
- % * unconditional variance on A0+
- %%H0td = sg0b; % unconditional
- % H_~: td: tilde for ~
- % ** inverse and chol decomp
- %%H0tdi = inv(H0td);
-
- %
- Hptd = zeros(ncoef);
- Hptd(1:nvar,1:nvar)=sg1b;
- Hptd(nvar+1:ncoef,nvar+1:ncoef)=sgppb;
- % condtional on A0i, H_plus_tilde
-
- % ** inverse
- Hptdi = inv(Hptd);
- %%chhh=Hptdi*Hp0;
- chhh = zeros(ncoef,strm); % <<>> the term not used for "of"\
- chhh(1:nvar,:) = Hptdi(1:nvar,1:nvar)*XX;
- chhh1 = XX'*chhh(1:nvar,:);
-
- % common term
- xxhp = xtx+Hptdi;
-
- % ** final conditional prior mean on A+i
- alptd = zeros(ncoef,1);
- alptd(1:nvar) = XX*A0h(stri,i);
- % conditional on A0i, alpha-plus-tilde
-
- % * alpha_plus_* for the ith equation, ncoef*1
- % *alps = xxhp\(phi'*y*A0h(:,i)+HptdDinalptd);
- %%HptdDinalptd=Hptdi*alptd;
- HptdDinalptd=zeros(ncoef,1);
- HptdDinalptd(1:nvar) = Hptdi(1:nvar,1:nvar)*alptd(1:nvar);
- xaha = cxy*A0h(:,i)+HptdDinalptd; %CAS 8/7/96. Replaced phi'*y with cxy
- alpst = xaha'/xxhp;
- GlbAph(i,:)=alpst;
- % alpst': transpose of alps.
-
- % ** 3rd bid term in i-th equation,
- % ** with all other 3rd big terms prior to i-th
- A0hy = A0h(:,i)'*ymy;
- % ** 4th bid term in i-th equation,
- % ** with all other 4th big terms prior to i-th
- A0hcxyxx = A0h(:,i)'*cxyxx;
- %%atdHatd = alptd'*HptdDinalptd; % efficiency improvement
- atdHatd = alptd(1:nvar)'*HptdDinalptd(1:nvar);
-
- %%%%
- % *** passing the gradient to "pmdg6.m" (analytical gradient)
- %%%%
- % ** d1aya0 = d(alpha0'*Y'MY*alpha0)/d(a0??)
- daya0 = 2.0*A0hy;
- daya0 = daya0(:);
- vaya0(stril+1:stril+strm) = daya0(stri);
-
- % ** daha0 = d((alpha0-alpha0_tilde)'*inv(H_0_tilde)*
- % ** (alpha0-alpha0_tilde))/d(a0??)
- %%daha0 = 2.0*A0hbH;
- %%vaha0(stril+1:stril+strm) = daha0(:);
-
- % ** daxah = d(alpha_+^'*X'X*alpha_+^)/d(a0??)
- % ** daxah = d(alpha_+^'*X'X*alpha_+^)/d(a0??)
- %% daxah = 2*(Aph(:,i)'*xtx) * cxpy(:,stri);
- %% vaxah(stril+1:stril+strm) = daxah';
- daxah = 2*A0hcxyxx;
- daxah = daxah(:);
- vaxah(stril+1:stril+strm) = daxah(stri);
-
-
- % ** dahad = d(alpha_+~'*inv(H_+~)*alpha_+~)/d(a0??)
- dahad = 2*A0h(stri,i)'*chhh1;
- % <<>> multiplications not used in "of"
- vahad(stril+1:stril+strm) = dahad(:);
-
-
- % ** dbiga = d((X'X*alpha_+^+inv(H_+~)*alpha_+*)'*a_+_*)/d(a0??)
- dbiga = 2*alpst*(cxy(:,stri)+chhh);
- % <<>> multiplications not used in "of"
- vbiga(stril+1:stril+strm) = dbiga';
-
- stril = stril + strm;
-end
-
-%disp(sprintf('Loop %d end: %g', i, toc))
-disp(sprintf('Loop end (of): %g', toc))
-
-%e_tfat = toc
-
-g = zeros(nfp,1);
-
-%%%%%%%
-%%% analytical gradient for A0 below
-%%%%%%%
-%
-% ** dla0 = dlog|A0|/d(a0??)
-vdla0 = zeros(na0p,1);
-dla0 = inv(A0h)';
-dla0 = dla0(:);
-vdla0 = dla0(a0indx);
-
-% without the prior |A0|^k
-g = -fss*vdla0 + 0.5*vaya0 + 0.5*vaha0 + 0.5*vaxah + 0.5*vahad - 0.5*vbiga;
\ No newline at end of file
+function g = phg234(x,idmat0,idmat1,fss,nvar,ncoef,phi, ...
+ y,A0b,sg0bid,sgpbid)
+%function of = pmddf232(x,idmat,fss,nvar,ncoef,phi, ...
+% y,A0b,sg0bid,sgpbid)
+% Leeper-Sims-Zha BVAR setup
+% general program to setup A0 matrix and compute the likelihood
+% requires
+% x (parameter vector)
+% a0indx (matrix indicating the free parameters in A0)
+% fss (forecast sample size)
+% nvar (number of variables)
+% ncoef (number of coefficients in a single equation in A+)
+% phi (r.h.s. observations in the system for A+)
+% y (l.h.s. observations for A0)
+% A0b (initial prior mean on A0)
+% sg0bid (diagonal of Sigma0_bar, unweighted prior vcv on the parameters in i-th equation in A0)
+% sgpbid (diagonal of Sigma+_bar, unweighted prior vcv on the parameters in i-th equation in A+)
+%
+% Revisions by CAS 9/27/96: If we transmit idmat, rather than a0indx, we can scale the elements of
+% idmat so that it carries information about relative prior variances.
+%
+% Revsions by TZ, 10/12/96: efficiency improvement by streamlining the previous code
+% according to the general setup in Sims and Zha "Bayesian Methods for ...".
+%
+% Copyright (C) 1997-2012 Christopher A. Sims and Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+global vaya0 vaha0 vaxah vahad vbiga GlbAph FRESHFUNCTION
+% FRESHFUNCTION added 8/20/90 by CAS. It signals the pmddg23 whether the global variables
+% it uses have been refreshed by a new pmddf231 call since the last gradient call.
+a0indx=find(idmat0);
+% pre-allocate GlbAph to be zeros(nvar,lags*nvar+1=ncoef)
+
+nhp = 0; % <<>> 4 hyperparameters
+na0p = length(a0indx); % <<>> number of A0 parameters
+nfp = na0p+nhp;
+
+% *** initializing
+vaya0 = zeros(na0p,1);
+vaha0 = zeros(na0p,1);
+vaxah = zeros(na0p,1);
+vahad = zeros(na0p,1);
+vbiga = zeros(na0p,1);
+
+
+A0h = zeros(nvar,nvar);
+A0h(a0indx) = x(1:na0p);
+%A0h = reshape(a0,nvar,nvar); CAS 9/24/96. The reshape is not necessary if a0 starts as nvar x nvar
+ % restrictions on A0
+
+[A0hl,A0hu] = lu(A0h);
+
+% ** hyperparameters
+%%mu = x(na0p+1:nfp);
+mu = ones(5,1);
+mu(1) = 1;
+%mu(1) = 2;
+mu(2) = 0.3;
+mu(3) = 25;
+%mu(4)=1;
+mu(4)=1;
+mu(5) =1;
+%mu(5) = 40;
+% results from ...\dummy1\foreh\pmdd6.out
+%
+% mu(1): overall tightness and also for A0;
+% mu(2): relative tightness for A+;
+% mu(3): relative tightness for the constant term;
+% mu(4): weight on single dummy initial observation including constant;
+% mu(5): weight on nvar sums of coeffs dummy observations.
+
+% ** weight prior dummy observations
+%phi(1:nvar,:) = (mu(5)^2/mu(1)^2)*phi(1:nvar,:);
+%y(1:nvar,:) = (mu(5)^2/mu(1)^2)*y(1:nvar,:);
+%phi(ndobs,:) = mu
+% modified by CAS 8/6/96. The dummy obs weights are naturally in std units, not var units.
+%%phi(1:nvar,:) = mu(5)*phi(1:nvar,:);
+%%y(1:nvar,:) = mu(5)*y(1:nvar,:);
+%%phi(nvar+1,:) = mu(4)*phi(nvar+1,:);
+%%y(nvar+1,:) = mu(4)*y(nvar+1,:);
+
+% ** set up the conditional prior variance sg0bi and sgpbi.
+%%sg0bida = mu(1)^2*sg0bid;
+sgpbida = mu(1)^2*mu(2)^2*sgpbid;
+sgpbida(ncoef) = mu(1)^2*mu(3)^2;
+sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in an SZ paper
+sgppb = diag(sgppbd);
+
+%
+% ** some common terms
+[u d v]=svd(phi,0); %trial
+% xtx = phi'*phi; %trial
+vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
+xtx=vd*vd';
+% M = eye(fss) - phi*(xtx\phi'); % not used except in line below %trial
+yu = y'*u; %trial
+cxyxx=yu*yu'; %trial
+ymy=y'*y-cxyxx; %trial
+%ymy = y'*M*y;
+%cxy = phi'*y; %trial
+cxy = vd*yu'; %trial
+%cxpy = xtx\cxy; %not used further except in line below
+%cxyxx = cxy'*cxpy;
+
+% ** estimate A+_hat conditional on A0, ncoef*nvar, but not using full prior
+%GlbAph = cxpy*A0h;
+
+
+%%%%%%%
+%%% build the objective function below
+%%%%%%%
+%
+disp(sprintf('Starting loop (of): %g',toc))
+
+stril = 0;
+Hp0p = zeros(ncoef);
+% initializing Htd_+0*inv(Htd_00)*Htd_0+: conditional prior covariance
+for i = 1:nvar
+ stri = find(idmat0(:,i)); %CAS change 9/24/96, to avoid slim chance of an exact zero in x vector
+ %at some point during an optimization.
+ strm = length(stri);
+
+ %%A0hb = A0h(stri,i)-A0b(stri,i);
+
+ % ** set up the conditional prior variance sg0bi and sgpbi.
+ % sg0bd = zeros(stri,1); Commented out by CAS, 9/24/96. Looks like this meant to have
+ % strm where it has stri, and in any case it is "overwritten" below.
+ %------------------------------
+ % Introduce prior information on which variables "belong" in various equations.
+ % In this first trial, we just introduce this information here, in a model-specific way.
+ % Eventually this info has to be passed parametricly. In our first shot, we just damp down
+ % all coefficients except those on the diagonal.
+ %%factor0=idmat0(:,i);
+ %%sg0bd = sg0bida(stri).*factor0(stri);
+ %%sg0b = diag(sg0bd);
+ %
+ factor1=idmat1(:,i);
+ sg1bd = sgpbida(1:nvar).*factor1;
+ sg1b = diag(sg1bd);
+
+ % ** set up the unconditional prior variance on A0i and A+i
+ XX = zeros(nvar,strm);
+ XX(stri,:) = eye(strm); % XX in Gelman, etel, on p479
+ %
+ % * final conditional prior variance on A+i give that on A0i, and
+ % * unconditional variance on A0+
+ %%H0td = sg0b; % unconditional
+ % H_~: td: tilde for ~
+ % ** inverse and chol decomp
+ %%H0tdi = inv(H0td);
+
+ %
+ Hptd = zeros(ncoef);
+ Hptd(1:nvar,1:nvar)=sg1b;
+ Hptd(nvar+1:ncoef,nvar+1:ncoef)=sgppb;
+ % condtional on A0i, H_plus_tilde
+
+ % ** inverse
+ Hptdi = inv(Hptd);
+ %%chhh=Hptdi*Hp0;
+ chhh = zeros(ncoef,strm); % <<>> the term not used for "of"\
+ chhh(1:nvar,:) = Hptdi(1:nvar,1:nvar)*XX;
+ chhh1 = XX'*chhh(1:nvar,:);
+
+ % common term
+ xxhp = xtx+Hptdi;
+
+ % ** final conditional prior mean on A+i
+ alptd = zeros(ncoef,1);
+ alptd(1:nvar) = XX*A0h(stri,i);
+ % conditional on A0i, alpha-plus-tilde
+
+ % * alpha_plus_* for the ith equation, ncoef*1
+ % *alps = xxhp\(phi'*y*A0h(:,i)+HptdDinalptd);
+ %%HptdDinalptd=Hptdi*alptd;
+ HptdDinalptd=zeros(ncoef,1);
+ HptdDinalptd(1:nvar) = Hptdi(1:nvar,1:nvar)*alptd(1:nvar);
+ xaha = cxy*A0h(:,i)+HptdDinalptd; %CAS 8/7/96. Replaced phi'*y with cxy
+ alpst = xaha'/xxhp;
+ GlbAph(i,:)=alpst;
+ % alpst': transpose of alps.
+
+ % ** 3rd bid term in i-th equation,
+ % ** with all other 3rd big terms prior to i-th
+ A0hy = A0h(:,i)'*ymy;
+ % ** 4th bid term in i-th equation,
+ % ** with all other 4th big terms prior to i-th
+ A0hcxyxx = A0h(:,i)'*cxyxx;
+ %%atdHatd = alptd'*HptdDinalptd; % efficiency improvement
+ atdHatd = alptd(1:nvar)'*HptdDinalptd(1:nvar);
+
+ %%%%
+ % *** passing the gradient to "pmdg6.m" (analytical gradient)
+ %%%%
+ % ** d1aya0 = d(alpha0'*Y'MY*alpha0)/d(a0??)
+ daya0 = 2.0*A0hy;
+ daya0 = daya0(:);
+ vaya0(stril+1:stril+strm) = daya0(stri);
+
+ % ** daha0 = d((alpha0-alpha0_tilde)'*inv(H_0_tilde)*
+ % ** (alpha0-alpha0_tilde))/d(a0??)
+ %%daha0 = 2.0*A0hbH;
+ %%vaha0(stril+1:stril+strm) = daha0(:);
+
+ % ** daxah = d(alpha_+^'*X'X*alpha_+^)/d(a0??)
+ % ** daxah = d(alpha_+^'*X'X*alpha_+^)/d(a0??)
+ %% daxah = 2*(Aph(:,i)'*xtx) * cxpy(:,stri);
+ %% vaxah(stril+1:stril+strm) = daxah';
+ daxah = 2*A0hcxyxx;
+ daxah = daxah(:);
+ vaxah(stril+1:stril+strm) = daxah(stri);
+
+
+ % ** dahad = d(alpha_+~'*inv(H_+~)*alpha_+~)/d(a0??)
+ dahad = 2*A0h(stri,i)'*chhh1;
+ % <<>> multiplications not used in "of"
+ vahad(stril+1:stril+strm) = dahad(:);
+
+
+ % ** dbiga = d((X'X*alpha_+^+inv(H_+~)*alpha_+*)'*a_+_*)/d(a0??)
+ dbiga = 2*alpst*(cxy(:,stri)+chhh);
+ % <<>> multiplications not used in "of"
+ vbiga(stril+1:stril+strm) = dbiga';
+
+ stril = stril + strm;
+end
+
+%disp(sprintf('Loop %d end: %g', i, toc))
+disp(sprintf('Loop end (of): %g', toc))
+
+%e_tfat = toc
+
+g = zeros(nfp,1);
+
+%%%%%%%
+%%% analytical gradient for A0 below
+%%%%%%%
+%
+% ** dla0 = dlog|A0|/d(a0??)
+vdla0 = zeros(na0p,1);
+dla0 = inv(A0h)';
+dla0 = dla0(:);
+vdla0 = dla0(a0indx);
+
+% without the prior |A0|^k
+g = -fss*vdla0 + 0.5*vaya0 + 0.5*vaha0 + 0.5*vaxah + 0.5*vahad - 0.5*vbiga;
diff --git a/MatlabFiles/phg235.m b/MatlabFiles/phg235.m
index 936ebfd90920a420fc221f5e8fbbfa37ef13b32c..37e61787b150e409666f4c8c604017ca86ac1092 100644
--- a/MatlabFiles/phg235.m
+++ b/MatlabFiles/phg235.m
@@ -1,211 +1,211 @@
-function g = phg235(x,idmat0,idmat1,fss,nvar,ncoef,phi, ...
- y,A0b,sg0bid,sgpbid)
-%function of = pmddf232(x,idmat,fss,nvar,ncoef,phi, ...
-% y,A0b,sg0bid,sgpbid)
-% Leeper-Sims-Zha BVAR setup
-% general program to setup A0 matrix and compute the likelihood
-% requires
-% x (parameter vector)
-% a0indx (matrix indicating the free parameters in A0)
-% fss (forecast sample size)
-% nvar (number of variables)
-% ncoef (number of coefficients in a single equation in A+)
-% phi (r.h.s. observations in the system for A+)
-% y (l.h.s. observations for A0)
-% A0b (initial prior mean on A0)
-% sg0bid (diagonal of Sigma0_bar, unweighted prior vcv on the parameters in i-th equation in A0)
-% sgpbid (diagonal of Sigma+_bar, unweighted prior vcv on the parameters in i-th equation in A+)
-%
-% Revisions by CAS 9/27/96: If we transmit idmat, rather than a0indx, we can scale the elements of
-% idmat so that it carries information about relative prior variances.
-%
-% Revsions by TZ, 10/13/96: efficiency improvement by streamlining the previous code
-% according to the general setup in Sims and Zha "Bayesian Methods for ...".
-% Copyright (C) 1997-2012 Christopher A. Sims and Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-global vaha0 vaMMa0 GlbAph FRESHFUNCTION
-% FRESHFUNCTION added 8/20/90 by CAS. It signals the pmddg23 whether the global variables
-% it uses have been refreshed by a new pmddf231 call since the last gradient call.
-a0indx=find(idmat0);
-% pre-allocate GlbAph to be zeros(nvar,lags*nvar+1=ncoef)
-
-nhp = 0; % <<>> 4 hyperparameters
-na0p = length(a0indx); % <<>> number of A0 parameters
-nfp = na0p+nhp;
-
-% *** initializing
-vaha0 = zeros(na0p,1);
-vaMMa0 = zeros(na0p,1);
-
-%
-A0h = zeros(nvar,nvar);
-A0h(a0indx) = x(1:na0p);
-%A0h = reshape(a0,nvar,nvar); CAS 9/24/96. The reshape is not necessary if a0 starts as nvar x nvar
- % restrictions on A0
-
-[A0hl,A0hu] = lu(A0h);
-
-% ** hyperparameters
-%%mu = x(na0p+1:nfp);
-mu = ones(5,1);
-mu(1) = 1;
-%mu(1) = 2;
-mu(2) = 0.2;
-mu(3) = 1;
-%mu(4)=1;
-mu(4)=10;
-mu(5) =1;
-%mu(5) = 40;
-% results from ...\dummy1\foreh\pmdd6.out
-%
-% mu(1): overall tightness and also for A0;
-% mu(2): relative tightness for A+;
-% mu(3): relative tightness for the constant term;
-% mu(4): weight on single dummy initial observation including constant;
-% mu(5): weight on nvar sums of coeffs dummy observations.
-
-% ** weight prior dummy observations
-%phi(1:nvar,:) = (mu(5)^2/mu(1)^2)*phi(1:nvar,:);
-%y(1:nvar,:) = (mu(5)^2/mu(1)^2)*y(1:nvar,:);
-%phi(ndobs,:) = mu
-% modified by CAS 8/6/96. The dummy obs weights are naturally in std units, not var units.
-phi(1:nvar,:) = mu(5)*phi(1:nvar,:);
-y(1:nvar,:) = mu(5)*y(1:nvar,:);
-phi(nvar+1,:) = mu(4)*phi(nvar+1,:);
-y(nvar+1,:) = mu(4)*y(nvar+1,:);
-
-% ** set up the conditional prior variance sg0bi and sgpbi.
-sg0bida = mu(1)^2*sg0bid;
-sgpbida = mu(1)^2*mu(2)^2*sgpbid;
-sgpbida(ncoef) = mu(1)^2*mu(3)^2;
-sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in an SZ paper
-sgppbdi = 1./sgppbd;
-%sgppb = diag(sgppbd);
-
-%
-% ** some common terms
-[u d v]=svd(phi,0); %trial
-% xtx = phi'*phi; %trial
-vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
-xtx=vd*vd';
-% M = eye(fss) - phi*(xtx\phi'); % not used except in line below %trial
-yu = y'*u; %trial
-cxyxx=yu*yu'; %trial
-yty=y'*y;
-ymy=yty-cxyxx; %trial
-%ymy = y'*M*y;
-%cxy = phi'*y; %trial
-cxy = vd*yu'; %trial
-cyx = cxy';
-%cxpy = xtx\cxy; %not used further except in line below
-%cxyxx = cxy'*cxpy;
-
-% ** estimate A+_hat conditional on A0, ncoef*nvar, but not using full prior
-%GlbAph = cxpy*A0h;
-
-
-%%%%%%%
-%%% Analytical gradient for A0 below
-%%%%%%%
-%
-% ** dla0 = dlog|A0|/d(a0??)
-vdla0 = zeros(na0p,1);
-dla0 = inv(A0h)';
-dla0 = dla0(:);
-vdla0 = dla0(a0indx);
-
-
-stril = 0;
-Hptd = zeros(ncoef);
-Hptdi=Hptd;
-Hptd(nvar+1:ncoef,nvar+1:ncoef)=diag(sgppbd);
-Hptdi(nvar+1:ncoef,nvar+1:ncoef)=diag(sgppbdi);
- % condtional on A0i, H_plus_tilde
-for i = 1:nvar
- stri = find(idmat0(:,i)); %CAS change 9/24/96, to avoid slim chance of an exact zero in x vector
- %at some point during an optimization.
- strm = length(stri);
-
- A0hb = A0h(stri,i)-A0b(stri,i);
-
- % ** set up the conditional prior variance sg0bi and sgpbi.
- % sg0bd = zeros(stri,1); Commented out by CAS, 9/24/96. Looks like this meant to have
- % strm where it has stri, and in any case it is "overwritten" below.
- %------------------------------
- % Introduce prior information on which variables "belong" in various equations.
- % In this first trial, we just introduce this information here, in a model-specific way.
- % Eventually this info has to be passed parametricly. In our first shot, we just damp down
- % all coefficients except those on the diagonal.
- factor0=idmat0(:,i);
- sg0bd = sg0bida(stri).*factor0(stri);
- sg0bdi = 1./sg0bd;
- %sg0b = diag(sg0bd);
- %
- factor1=idmat1(:,i);
- sg1bd = sgpbida(1:nvar).*factor1;
- sg1bdi = 1./sg1bd;
- %sg1b = diag(sg1bd);
-
- % ** set up the unconditional prior variance on A0i and A+i
- XX = zeros(nvar,strm);
- XX(stri,:) = eye(strm); % XX in Gelman, etel, on p479
- %
- % * final conditional prior variance on A+i give that on A0i, and
- % * unconditional variance on A0+
- H0td = diag(sg0bd); % unconditional
- % H_~: td: tilde for ~
- % ** inverse and chol decomp
- H0tdi = diag(sg0bdi);
-
- %
- Hptd(1:nvar,1:nvar)=diag(sg1bd);
- Hptdi(1:nvar,1:nvar)=diag(sg1bdi);
- % condtional on A0i, H_plus_tilde
-
- % common terms
- xxhp = xtx+Hptdi;
- A0hbH = A0hb'*H0tdi;
- H1p_1 = zeros(ncoef,nvar);
- H1p_1(1:nvar,:) = Hptdi(1:nvar,1:nvar);
- %%Hm = (cyx+H1p_1')*(xxhp\(cxy+H1p_1));
- Hm1 = (cyx+H1p_1')/xxhp;
- Hm2 = cxy+H1p_1;
- GlbAph(i,:) = A0h(stri,i)'*XX'*Hm1; % alpha_plus*
- %%alpMpyh = A0h(stri,i)'*XX'*(yty+Hptdi(1:nvar,1:nvar)-Hm)*XX;
- alpMpyh = A0h(stri,i)'*XX'*(yty+Hptdi(1:nvar,1:nvar))*XX - GlbAph(i,:)*Hm2*XX;
-
- %%%%
- % *** passing the gradient to "pmdg6.m" (analytical gradient)
- %%%%
- %
- % ** daha0 = d((alpha0-alpha0_tilde)'*inv(H_0_tilde)*
- % ** (alpha0-alpha0_tilde))/d(a0??)
- daha0 = 2.0*A0hbH;
- vaha0(stril+1:stril+strm) = daha0(:);
- %
- % ** daMMa0 = d(alpha0'*P'*(Y'Y+H_1^(-1)+H_m)*P*apha0) / d(a0?)
- daMMa0 = 2.0*alpMpyh;
- vaMMa0(stril+1:stril+strm) = daMMa0(:);
-
- stril = stril + strm;
-end
-
-% without the prior |A0|^k
-g = -fss*vdla0 + 0.5*vaha0 + 0.5*vaMMa0;
-
-%disp(sprintf('Loop end (gradient): %g', toc))
\ No newline at end of file
+function g = phg235(x,idmat0,idmat1,fss,nvar,ncoef,phi, ...
+ y,A0b,sg0bid,sgpbid)
+%function of = pmddf232(x,idmat,fss,nvar,ncoef,phi, ...
+% y,A0b,sg0bid,sgpbid)
+% Leeper-Sims-Zha BVAR setup
+% general program to setup A0 matrix and compute the likelihood
+% requires
+% x (parameter vector)
+% a0indx (matrix indicating the free parameters in A0)
+% fss (forecast sample size)
+% nvar (number of variables)
+% ncoef (number of coefficients in a single equation in A+)
+% phi (r.h.s. observations in the system for A+)
+% y (l.h.s. observations for A0)
+% A0b (initial prior mean on A0)
+% sg0bid (diagonal of Sigma0_bar, unweighted prior vcv on the parameters in i-th equation in A0)
+% sgpbid (diagonal of Sigma+_bar, unweighted prior vcv on the parameters in i-th equation in A+)
+%
+% Revisions by CAS 9/27/96: If we transmit idmat, rather than a0indx, we can scale the elements of
+% idmat so that it carries information about relative prior variances.
+%
+% Revsions by TZ, 10/13/96: efficiency improvement by streamlining the previous code
+% according to the general setup in Sims and Zha "Bayesian Methods for ...".
+%
+% Copyright (C) 1997-2012 Christopher A. Sims and Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+global vaha0 vaMMa0 GlbAph FRESHFUNCTION
+% FRESHFUNCTION added 8/20/90 by CAS. It signals the pmddg23 whether the global variables
+% it uses have been refreshed by a new pmddf231 call since the last gradient call.
+a0indx=find(idmat0);
+% pre-allocate GlbAph to be zeros(nvar,lags*nvar+1=ncoef)
+
+nhp = 0; % <<>> 4 hyperparameters
+na0p = length(a0indx); % <<>> number of A0 parameters
+nfp = na0p+nhp;
+
+% *** initializing
+vaha0 = zeros(na0p,1);
+vaMMa0 = zeros(na0p,1);
+
+%
+A0h = zeros(nvar,nvar);
+A0h(a0indx) = x(1:na0p);
+%A0h = reshape(a0,nvar,nvar); CAS 9/24/96. The reshape is not necessary if a0 starts as nvar x nvar
+ % restrictions on A0
+
+[A0hl,A0hu] = lu(A0h);
+
+% ** hyperparameters
+%%mu = x(na0p+1:nfp);
+mu = ones(5,1);
+mu(1) = 1;
+%mu(1) = 2;
+mu(2) = 0.2;
+mu(3) = 1;
+%mu(4)=1;
+mu(4)=10;
+mu(5) =1;
+%mu(5) = 40;
+% results from ...\dummy1\foreh\pmdd6.out
+%
+% mu(1): overall tightness and also for A0;
+% mu(2): relative tightness for A+;
+% mu(3): relative tightness for the constant term;
+% mu(4): weight on single dummy initial observation including constant;
+% mu(5): weight on nvar sums of coeffs dummy observations.
+
+% ** weight prior dummy observations
+%phi(1:nvar,:) = (mu(5)^2/mu(1)^2)*phi(1:nvar,:);
+%y(1:nvar,:) = (mu(5)^2/mu(1)^2)*y(1:nvar,:);
+%phi(ndobs,:) = mu
+% modified by CAS 8/6/96. The dummy obs weights are naturally in std units, not var units.
+phi(1:nvar,:) = mu(5)*phi(1:nvar,:);
+y(1:nvar,:) = mu(5)*y(1:nvar,:);
+phi(nvar+1,:) = mu(4)*phi(nvar+1,:);
+y(nvar+1,:) = mu(4)*y(nvar+1,:);
+
+% ** set up the conditional prior variance sg0bi and sgpbi.
+sg0bida = mu(1)^2*sg0bid;
+sgpbida = mu(1)^2*mu(2)^2*sgpbid;
+sgpbida(ncoef) = mu(1)^2*mu(3)^2;
+sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in an SZ paper
+sgppbdi = 1./sgppbd;
+%sgppb = diag(sgppbd);
+
+%
+% ** some common terms
+[u d v]=svd(phi,0); %trial
+% xtx = phi'*phi; %trial
+vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
+xtx=vd*vd';
+% M = eye(fss) - phi*(xtx\phi'); % not used except in line below %trial
+yu = y'*u; %trial
+cxyxx=yu*yu'; %trial
+yty=y'*y;
+ymy=yty-cxyxx; %trial
+%ymy = y'*M*y;
+%cxy = phi'*y; %trial
+cxy = vd*yu'; %trial
+cyx = cxy';
+%cxpy = xtx\cxy; %not used further except in line below
+%cxyxx = cxy'*cxpy;
+
+% ** estimate A+_hat conditional on A0, ncoef*nvar, but not using full prior
+%GlbAph = cxpy*A0h;
+
+
+%%%%%%%
+%%% Analytical gradient for A0 below
+%%%%%%%
+%
+% ** dla0 = dlog|A0|/d(a0??)
+vdla0 = zeros(na0p,1);
+dla0 = inv(A0h)';
+dla0 = dla0(:);
+vdla0 = dla0(a0indx);
+
+
+stril = 0;
+Hptd = zeros(ncoef);
+Hptdi=Hptd;
+Hptd(nvar+1:ncoef,nvar+1:ncoef)=diag(sgppbd);
+Hptdi(nvar+1:ncoef,nvar+1:ncoef)=diag(sgppbdi);
+ % condtional on A0i, H_plus_tilde
+for i = 1:nvar
+ stri = find(idmat0(:,i)); %CAS change 9/24/96, to avoid slim chance of an exact zero in x vector
+ %at some point during an optimization.
+ strm = length(stri);
+
+ A0hb = A0h(stri,i)-A0b(stri,i);
+
+ % ** set up the conditional prior variance sg0bi and sgpbi.
+ % sg0bd = zeros(stri,1); Commented out by CAS, 9/24/96. Looks like this meant to have
+ % strm where it has stri, and in any case it is "overwritten" below.
+ %------------------------------
+ % Introduce prior information on which variables "belong" in various equations.
+ % In this first trial, we just introduce this information here, in a model-specific way.
+ % Eventually this info has to be passed parametricly. In our first shot, we just damp down
+ % all coefficients except those on the diagonal.
+ factor0=idmat0(:,i);
+ sg0bd = sg0bida(stri).*factor0(stri);
+ sg0bdi = 1./sg0bd;
+ %sg0b = diag(sg0bd);
+ %
+ factor1=idmat1(:,i);
+ sg1bd = sgpbida(1:nvar).*factor1;
+ sg1bdi = 1./sg1bd;
+ %sg1b = diag(sg1bd);
+
+ % ** set up the unconditional prior variance on A0i and A+i
+ XX = zeros(nvar,strm);
+ XX(stri,:) = eye(strm); % XX in Gelman, etel, on p479
+ %
+ % * final conditional prior variance on A+i give that on A0i, and
+ % * unconditional variance on A0+
+ H0td = diag(sg0bd); % unconditional
+ % H_~: td: tilde for ~
+ % ** inverse and chol decomp
+ H0tdi = diag(sg0bdi);
+
+ %
+ Hptd(1:nvar,1:nvar)=diag(sg1bd);
+ Hptdi(1:nvar,1:nvar)=diag(sg1bdi);
+ % condtional on A0i, H_plus_tilde
+
+ % common terms
+ xxhp = xtx+Hptdi;
+ A0hbH = A0hb'*H0tdi;
+ H1p_1 = zeros(ncoef,nvar);
+ H1p_1(1:nvar,:) = Hptdi(1:nvar,1:nvar);
+ %%Hm = (cyx+H1p_1')*(xxhp\(cxy+H1p_1));
+ Hm1 = (cyx+H1p_1')/xxhp;
+ Hm2 = cxy+H1p_1;
+ GlbAph(i,:) = A0h(stri,i)'*XX'*Hm1; % alpha_plus*
+ %%alpMpyh = A0h(stri,i)'*XX'*(yty+Hptdi(1:nvar,1:nvar)-Hm)*XX;
+ alpMpyh = A0h(stri,i)'*XX'*(yty+Hptdi(1:nvar,1:nvar))*XX - GlbAph(i,:)*Hm2*XX;
+
+ %%%%
+ % *** passing the gradient to "pmdg6.m" (analytical gradient)
+ %%%%
+ %
+ % ** daha0 = d((alpha0-alpha0_tilde)'*inv(H_0_tilde)*
+ % ** (alpha0-alpha0_tilde))/d(a0??)
+ daha0 = 2.0*A0hbH;
+ vaha0(stril+1:stril+strm) = daha0(:);
+ %
+ % ** daMMa0 = d(alpha0'*P'*(Y'Y+H_1^(-1)+H_m)*P*apha0) / d(a0?)
+ daMMa0 = 2.0*alpMpyh;
+ vaMMa0(stril+1:stril+strm) = daMMa0(:);
+
+ stril = stril + strm;
+end
+
+% without the prior |A0|^k
+g = -fss*vdla0 + 0.5*vaha0 + 0.5*vaMMa0;
+
+%disp(sprintf('Loop end (gradient): %g', toc))
diff --git a/MatlabFiles/pmddf233.m b/MatlabFiles/pmddf233.m
index 67ce8a1287a90425d4584fc35bd5cee615d3ac59..d6f7ebf00b8e4b45a1ce4a56fd65b074896e4e73 100644
--- a/MatlabFiles/pmddf233.m
+++ b/MatlabFiles/pmddf233.m
@@ -1,278 +1,278 @@
-function of = pmddf233(x,idmat0,idmat1,fss,nvar,ncoef,phi, ...
- y,A0b,sg0bid,sgpbid)
-%function of = pmddf232(x,idmat,fss,nvar,ncoef,phi, ...
-% y,A0b,sg0bid,sgpbid)
-% Leeper-Sims-Zha BVAR setup
-% general program to setup A0 matrix and compute the likelihood
-% requires
-% x (parameter vector)
-% a0indx (matrix indicating the free parameters in A0)
-% fss (forecast sample size)
-% nvar (number of variables)
-% ncoef (number of coefficients in a single equation in A+)
-% phi (r.h.s. observations in the system for A+)
-% y (l.h.s. observations for A0)
-% A0b (initial prior mean on A0)
-% sg0bid (diagonal of Sigma0_bar, unweighted prior vcv on the parameters in i-th equation in A0)
-% sgpbid (diagonal of Sigma+_bar, unweighted prior vcv on the parameters in i-th equation in A+)
-%
-% Revisions by CAS 9/27/96: If we transmit idmat, rather than a0indx, we can scale the elements of
-% idmat so that it carries information about relative prior variances.
-
-% Copyright (C) 1997-2012 Christopher A. Sims
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-global vaya0 vaha0 vaxah vahad vbiga GlbAph FRESHFUNCTION
-% FRESHFUNCTION added 8/20/90 by CAS. It signals the pmddg23 whether the global variables
-% it uses have been refreshed by a new pmddf231 call since the last gradient call.
-a0indx=find(idmat0);
-% pre-allocate GlbAph to be zeros(nvar,lags*nvar+1=ncoef)
-
-nhp = 0; % <<>> 4 hyperparameters
-na0p = length(a0indx); % <<>> number of A0 parameters
-nfp = na0p+nhp;
-
-% *** initializing
-vaya0 = zeros(na0p,1);
-vaha0 = zeros(na0p,1);
-vaxah = zeros(na0p,1);
-vahad = zeros(na0p,1);
-vbiga = zeros(na0p,1);
-
-
-A0h = zeros(nvar,nvar);
-A0h(a0indx) = x(1:na0p);
-%A0h = reshape(a0,nvar,nvar); CAS 9/24/96. The reshape is not necessary if a0 starts as nvar x nvar
- % restrictions on A0
-
-[A0hl,A0hu] = lu(A0h);
-
-% ** hyperparameters
-%%mu = x(na0p+1:nfp);
-mu = ones(5,1);
-mu(1) = 1;
-%mu(1) = 2;
-mu(2) = 0.2;
-mu(3) = 1;
-%mu(4)=1;
-mu(4)=1;
-mu(5) =1;
-%mu(5) = 40;
-% results from ...\dummy1\foreh\pmdd6.out
-%
-% mu(1): overall tightness and also for A0;
-% mu(2): relative tightness for A+;
-% mu(3): relative tightness for the constant term;
-% mu(4): weight on single dummy initial observation including constant;
-% mu(5): weight on nvar sums of coeffs dummy observations.
-
-% ** weight prior dummy observations
-%phi(1:nvar,:) = (mu(5)^2/mu(1)^2)*phi(1:nvar,:);
-%y(1:nvar,:) = (mu(5)^2/mu(1)^2)*y(1:nvar,:);
-%phi(ndobs,:) = mu
-% modified by CAS 8/6/96. The dummy obs weights are naturally in std units, not var units.
-phi(1:nvar,:) = mu(5)*phi(1:nvar,:);
-y(1:nvar,:) = mu(5)*y(1:nvar,:);
-phi(nvar+1,:) = mu(4)*phi(nvar+1,:);
-y(nvar+1,:) = mu(4)*y(nvar+1,:);
-
-% ** set up the conditional prior variance sg0bi and sgpbi.
-sg0bida = mu(1)^2*sg0bid;
-sgpbida = mu(1)^2*mu(2)^2*sgpbid;
-sgpbida(ncoef) = mu(1)^2*mu(3)^2;
-sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in a SZ paper
-sgppb = diag(sgppbd);
-
-%
-% ** some common terms
-[u d v]=svd(phi,0); %trial
-% xtx = phi'*phi; %trial
-vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
-xtx=vd*vd';
-% M = eye(fss) - phi*(xtx\phi'); % not used except in line below %trial
-yu = y'*u; %trial
-cxyxx=yu*yu'; %trial
-ymy=y'*y-cxyxx; %trial
-%ymy = y'*M*y;
-%cxy = phi'*y; %trial
-cxy = vd*yu'; %trial
-%cxpy = xtx\cxy; %not used further except in line below
-%cxyxx = cxy'*cxpy;
-
-% ** estimate A+_hat conditional on A0, ncoef*nvar, but not using full prior
-%GlbAph = cxpy*A0h;
-
-
-%%%%%%%
-%%% build the objective function below
-%%%%%%%
-%
-t1f = diag(abs(A0hu));
-t1f = log(t1f);
-tt1 = (-1.0)*fss*sum(t1f);
-% Commented out by TZ, 10/3/96, to allow the prior |A0|^k
-%%tt1 = (-1.0)*(fss+ncoef)*sum(t1f);
-
-tt3 = 0;
-tt4 = 0;
-
-disp(sprintf('Starting loop (of): %g',toc))
-
-stril = 0;
-Hp0p = zeros(ncoef);
-% initializing Htd_+0*inv(Htd_00)*Htd_0+: conditional prior covariance
-for i = 1:nvar
- stri = find(idmat0(:,i)); %CAS change 9/24/96, to avoid slim chance of an exact zero in x vector
- %at some point during an optimization.
- strm = length(stri);
-
- A0hb = A0h(stri,i)-A0b(stri,i);
- Hp0 = zeros(ncoef,strm);
- % initializing Htd_+0*inv(Htd_00)*Htd_0
-
- % ** set up the conditional prior variance sg0bi and sgpbi.
- % sg0bd = zeros(stri,1); Commented out by CAS, 9/24/96. Looks like this meant to have
- % strm where it has stri, and in any case it is "overwritten" below.
- %------------------------------
- % Introduce prior information on which variables "belong" in various equations.
- % In this first trial, we just introduce this information here, in a model-specific way.
- % Eventually this info has to be passed parametricly. In our first shot, we just damp down
- % all coefficients except those on the diagonal.
- factor0=idmat0(:,i);
- sg0bd = sg0bida(stri).*factor0(stri);
- sg0b = diag(sg0bd);
- %
- factor1=idmat1(:,i);
- sg1bd = sgpbida(1:nvar).*factor1;
- sg1b = diag(sg1bd);
-
- % ** set up the unconditional prior variance on A0i and A+i
- XX = zeros(nvar,strm);
- XX(stri,:) = eye(strm); % XX in Gelman, etel, on p479
- %
- Hb = zeros(strm+ncoef); % including A0i and A+i
- Hb(1:strm,1:strm) = sg0b;
- sg0bxx = sg0b*XX';
- Hb(1:strm,strm+1:strm+nvar) = sg0bxx;
- %Hb(strm+1:strm+nvar,1:strm) = XX*sg0b; CAS 9/24/96. Saves a little multiplication by using line below.
- Hb(strm+1:strm+nvar,1:strm) = sg0bxx';
- Hb(strm+1:strm+nvar,strm+1:strm+nvar) = XX*sg0bxx+sg1b;
- Hb(strm+nvar+1:strm+ncoef,strm+nvar+1:strm+ncoef) = sgppb;
-
- % ** set up the final prior variance on A0i and A+i, i =1,..,6 separately.
- % ** using dummy initial observations
- %zs1 = [ys1(:,stri) -xd0]; % z*1 for the 1st equation
-
- % * final unconditional prior variance on A0i and A+i
- %Hbzs1=Hb*zs1';
- %Htd = Hb - Hbzs1*((zs1*Hbzs1+mu(1)^2*mu(4)^2*eye(nvar))\Hbzs1');
- % CAS mod 8/7/96: Hope is that dropping above two lines in favor of the one below
- % removes double-counting of dummy observations and a possible scale sensitivity.
- Htd = Hb;
- % H_~: td: tilde for ~
-
- % * final conditional prior variance on A+i give that on A0i, and
- % * unconditional variance on A0+
- H0td = Htd(1:strm,1:strm); % unconditional
- % ** inverse and chol decomp
- H0tdi = inv(H0td);
-
- %
- Hptd10 = Htd(strm+1:strm+nvar,1:strm);
- % top matrix of nvar in Htd_+0
- Hptd100 = Hptd10*H0tdi;
- Hp0(1:nvar,:) = Hptd100;
- Hptd101 = Hptd100*Hptd10';
- Hp0p(1:nvar,1:nvar) = Hptd101;
- Hptd = Htd(strm+1:strm+ncoef,strm+1:strm+ncoef) - Hp0p;
- % condtional on A0i, H_plus_tilde
-
- % ** inverse
- Hptdi = inv(Hptd);
- chhh = Hptdi*Hp0; % <<>> the term not used for "of"
-
- % common term
- xxhp = xtx+Hptdi;
-
- % ** final conditional prior mean on A+i
- alptd = zeros(ncoef,1);
- alptd(1:nvar) = XX*A0b(stri,i);
- %alptd = alptd + Hptdf*(H0tdi*A0hb); CAS efficiency improvement 8/8/96
- alptd = alptd + Hp0*A0hb;
- % conditional on A0i, alpha-plus-tilde
-
- % * alpha_plus_* for the ith equation, ncoef*1
- % *alps = xxhp\(phi'*y*A0h(:,i)+HptdDinalptd);
- HptdDinalptd=Hptdi*alptd;
- xaha = cxy*A0h(:,i)+HptdDinalptd; %CAS 8/7/96. Replaced phi'*y with cxy
- alpst = xaha'/xxhp;
- GlbAph(i,:)=alpst;
- % alpst': transpose of alps.
-
- % ** 3rd bid term in i-th equation,
- % ** with all other 3rd big terms prior to i-th
- A0hy = A0h(:,i)'*ymy;
- A0hbH = A0hb'*H0tdi;
- tt3 = tt3 + A0hy*A0h(:,i) + A0hbH*A0hb;
- % ** 4th bid term in i-th equation,
- % ** with all other 4th big terms prior to i-th
- A0hcxyxx = A0h(:,i)'*cxyxx;
- atdHatd = alptd'*HptdDinalptd;
- tt4 = tt4 + A0hcxyxx*A0h(:,i)+atdHatd-alpst*xaha;
-
- %%%%
- % *** passing the gradient to "pmdg6.m" (analytical gradient)
- %%%%
- % ** d1aya0 = d(alpha0'*Y'MY*alpha0)/d(a0??)
- daya0 = 2.0*A0hy;
- daya0 = daya0(:);
- vaya0(stril+1:stril+strm) = daya0(stri);
-
- % ** daha0 = d((alpha0-alpha0_tilde)'*inv(H_0_tilde)*
- % ** (alpha0-alpha0_tilde))/d(a0??)
- daha0 = 2.0*A0hbH;
- vaha0(stril+1:stril+strm) = daha0(:);
-
- % ** daxah = d(alpha_+^'*X'X*alpha_+^)/d(a0??)
- % ** daxah = d(alpha_+^'*X'X*alpha_+^)/d(a0??)
- %% daxah = 2*(Aph(:,i)'*xtx) * cxpy(:,stri);
- %% vaxah(stril+1:stril+strm) = daxah';
- daxah = 2*A0hcxyxx;
- daxah = daxah(:);
- vaxah(stril+1:stril+strm) = daxah(stri);
-
-
- % ** dahad = d(alpha_+~'*inv(H_+~)*alpha_+~)/d(a0??)
- dahad = 2*HptdDinalptd' * Hp0;
- % <<>> multiplications not used in "of"
- vahad(stril+1:stril+strm) = dahad';
-
- % ** dbiga = d((X'X*alpha_+^+inv(H_+~)*alpha_+*)'*a_+_*)/d(a0??)
- dbiga = 2*alpst*(cxy(:,stri)+chhh);
- % <<>> multiplications not used in "of"
- vbiga(stril+1:stril+strm) = dbiga';
-
- stril = stril + strm;
-end
-
-%disp(sprintf('Loop %d end: %g', i, toc))
-disp(sprintf('Loop end (of): %g', toc))
-
-%e_tfat = toc
-
-of = tt1 + 0.5*tt3 + 0.5*tt4;
-FRESHFUNCTION=1;
+function of = pmddf233(x,idmat0,idmat1,fss,nvar,ncoef,phi, ...
+ y,A0b,sg0bid,sgpbid)
+%function of = pmddf232(x,idmat,fss,nvar,ncoef,phi, ...
+% y,A0b,sg0bid,sgpbid)
+% Leeper-Sims-Zha BVAR setup
+% general program to setup A0 matrix and compute the likelihood
+% requires
+% x (parameter vector)
+% a0indx (matrix indicating the free parameters in A0)
+% fss (forecast sample size)
+% nvar (number of variables)
+% ncoef (number of coefficients in a single equation in A+)
+% phi (r.h.s. observations in the system for A+)
+% y (l.h.s. observations for A0)
+% A0b (initial prior mean on A0)
+% sg0bid (diagonal of Sigma0_bar, unweighted prior vcv on the parameters in i-th equation in A0)
+% sgpbid (diagonal of Sigma+_bar, unweighted prior vcv on the parameters in i-th equation in A+)
+%
+% Revisions by CAS 9/27/96: If we transmit idmat, rather than a0indx, we can scale the elements of
+% idmat so that it carries information about relative prior variances.
+
+%
+% Copyright (C) 1997-2012 Christopher A. Sims
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+global vaya0 vaha0 vaxah vahad vbiga GlbAph FRESHFUNCTION
+% FRESHFUNCTION added 8/20/90 by CAS. It signals the pmddg23 whether the global variables
+% it uses have been refreshed by a new pmddf231 call since the last gradient call.
+a0indx=find(idmat0);
+% pre-allocate GlbAph to be zeros(nvar,lags*nvar+1=ncoef)
+
+nhp = 0; % <<>> 4 hyperparameters
+na0p = length(a0indx); % <<>> number of A0 parameters
+nfp = na0p+nhp;
+
+% *** initializing
+vaya0 = zeros(na0p,1);
+vaha0 = zeros(na0p,1);
+vaxah = zeros(na0p,1);
+vahad = zeros(na0p,1);
+vbiga = zeros(na0p,1);
+
+
+A0h = zeros(nvar,nvar);
+A0h(a0indx) = x(1:na0p);
+%A0h = reshape(a0,nvar,nvar); CAS 9/24/96. The reshape is not necessary if a0 starts as nvar x nvar
+ % restrictions on A0
+
+[A0hl,A0hu] = lu(A0h);
+
+% ** hyperparameters
+%%mu = x(na0p+1:nfp);
+mu = ones(5,1);
+mu(1) = 1;
+%mu(1) = 2;
+mu(2) = 0.2;
+mu(3) = 1;
+%mu(4)=1;
+mu(4)=1;
+mu(5) =1;
+%mu(5) = 40;
+% results from ...\dummy1\foreh\pmdd6.out
+%
+% mu(1): overall tightness and also for A0;
+% mu(2): relative tightness for A+;
+% mu(3): relative tightness for the constant term;
+% mu(4): weight on single dummy initial observation including constant;
+% mu(5): weight on nvar sums of coeffs dummy observations.
+
+% ** weight prior dummy observations
+%phi(1:nvar,:) = (mu(5)^2/mu(1)^2)*phi(1:nvar,:);
+%y(1:nvar,:) = (mu(5)^2/mu(1)^2)*y(1:nvar,:);
+%phi(ndobs,:) = mu
+% modified by CAS 8/6/96. The dummy obs weights are naturally in std units, not var units.
+phi(1:nvar,:) = mu(5)*phi(1:nvar,:);
+y(1:nvar,:) = mu(5)*y(1:nvar,:);
+phi(nvar+1,:) = mu(4)*phi(nvar+1,:);
+y(nvar+1,:) = mu(4)*y(nvar+1,:);
+
+% ** set up the conditional prior variance sg0bi and sgpbi.
+sg0bida = mu(1)^2*sg0bid;
+sgpbida = mu(1)^2*mu(2)^2*sgpbid;
+sgpbida(ncoef) = mu(1)^2*mu(3)^2;
+sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in a SZ paper
+sgppb = diag(sgppbd);
+
+%
+% ** some common terms
+[u d v]=svd(phi,0); %trial
+% xtx = phi'*phi; %trial
+vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
+xtx=vd*vd';
+% M = eye(fss) - phi*(xtx\phi'); % not used except in line below %trial
+yu = y'*u; %trial
+cxyxx=yu*yu'; %trial
+ymy=y'*y-cxyxx; %trial
+%ymy = y'*M*y;
+%cxy = phi'*y; %trial
+cxy = vd*yu'; %trial
+%cxpy = xtx\cxy; %not used further except in line below
+%cxyxx = cxy'*cxpy;
+
+% ** estimate A+_hat conditional on A0, ncoef*nvar, but not using full prior
+%GlbAph = cxpy*A0h;
+
+
+%%%%%%%
+%%% build the objective function below
+%%%%%%%
+%
+t1f = diag(abs(A0hu));
+t1f = log(t1f);
+tt1 = (-1.0)*fss*sum(t1f);
+% Commented out by TZ, 10/3/96, to allow the prior |A0|^k
+%%tt1 = (-1.0)*(fss+ncoef)*sum(t1f);
+
+tt3 = 0;
+tt4 = 0;
+
+disp(sprintf('Starting loop (of): %g',toc))
+
+stril = 0;
+Hp0p = zeros(ncoef);
+% initializing Htd_+0*inv(Htd_00)*Htd_0+: conditional prior covariance
+for i = 1:nvar
+ stri = find(idmat0(:,i)); %CAS change 9/24/96, to avoid slim chance of an exact zero in x vector
+ %at some point during an optimization.
+ strm = length(stri);
+
+ A0hb = A0h(stri,i)-A0b(stri,i);
+ Hp0 = zeros(ncoef,strm);
+ % initializing Htd_+0*inv(Htd_00)*Htd_0
+
+ % ** set up the conditional prior variance sg0bi and sgpbi.
+ % sg0bd = zeros(stri,1); Commented out by CAS, 9/24/96. Looks like this meant to have
+ % strm where it has stri, and in any case it is "overwritten" below.
+ %------------------------------
+ % Introduce prior information on which variables "belong" in various equations.
+ % In this first trial, we just introduce this information here, in a model-specific way.
+ % Eventually this info has to be passed parametricly. In our first shot, we just damp down
+ % all coefficients except those on the diagonal.
+ factor0=idmat0(:,i);
+ sg0bd = sg0bida(stri).*factor0(stri);
+ sg0b = diag(sg0bd);
+ %
+ factor1=idmat1(:,i);
+ sg1bd = sgpbida(1:nvar).*factor1;
+ sg1b = diag(sg1bd);
+
+ % ** set up the unconditional prior variance on A0i and A+i
+ XX = zeros(nvar,strm);
+ XX(stri,:) = eye(strm); % XX in Gelman, etel, on p479
+ %
+ Hb = zeros(strm+ncoef); % including A0i and A+i
+ Hb(1:strm,1:strm) = sg0b;
+ sg0bxx = sg0b*XX';
+ Hb(1:strm,strm+1:strm+nvar) = sg0bxx;
+ %Hb(strm+1:strm+nvar,1:strm) = XX*sg0b; CAS 9/24/96. Saves a little multiplication by using line below.
+ Hb(strm+1:strm+nvar,1:strm) = sg0bxx';
+ Hb(strm+1:strm+nvar,strm+1:strm+nvar) = XX*sg0bxx+sg1b;
+ Hb(strm+nvar+1:strm+ncoef,strm+nvar+1:strm+ncoef) = sgppb;
+
+ % ** set up the final prior variance on A0i and A+i, i =1,..,6 separately.
+ % ** using dummy initial observations
+ %zs1 = [ys1(:,stri) -xd0]; % z*1 for the 1st equation
+
+ % * final unconditional prior variance on A0i and A+i
+ %Hbzs1=Hb*zs1';
+ %Htd = Hb - Hbzs1*((zs1*Hbzs1+mu(1)^2*mu(4)^2*eye(nvar))\Hbzs1');
+ % CAS mod 8/7/96: Hope is that dropping above two lines in favor of the one below
+ % removes double-counting of dummy observations and a possible scale sensitivity.
+ Htd = Hb;
+ % H_~: td: tilde for ~
+
+ % * final conditional prior variance on A+i give that on A0i, and
+ % * unconditional variance on A0+
+ H0td = Htd(1:strm,1:strm); % unconditional
+ % ** inverse and chol decomp
+ H0tdi = inv(H0td);
+
+ %
+ Hptd10 = Htd(strm+1:strm+nvar,1:strm);
+ % top matrix of nvar in Htd_+0
+ Hptd100 = Hptd10*H0tdi;
+ Hp0(1:nvar,:) = Hptd100;
+ Hptd101 = Hptd100*Hptd10';
+ Hp0p(1:nvar,1:nvar) = Hptd101;
+ Hptd = Htd(strm+1:strm+ncoef,strm+1:strm+ncoef) - Hp0p;
+ % condtional on A0i, H_plus_tilde
+
+ % ** inverse
+ Hptdi = inv(Hptd);
+ chhh = Hptdi*Hp0; % <<>> the term not used for "of"
+
+ % common term
+ xxhp = xtx+Hptdi;
+
+ % ** final conditional prior mean on A+i
+ alptd = zeros(ncoef,1);
+ alptd(1:nvar) = XX*A0b(stri,i);
+ %alptd = alptd + Hptdf*(H0tdi*A0hb); CAS efficiency improvement 8/8/96
+ alptd = alptd + Hp0*A0hb;
+ % conditional on A0i, alpha-plus-tilde
+
+ % * alpha_plus_* for the ith equation, ncoef*1
+ % *alps = xxhp\(phi'*y*A0h(:,i)+HptdDinalptd);
+ HptdDinalptd=Hptdi*alptd;
+ xaha = cxy*A0h(:,i)+HptdDinalptd; %CAS 8/7/96. Replaced phi'*y with cxy
+ alpst = xaha'/xxhp;
+ GlbAph(i,:)=alpst;
+ % alpst': transpose of alps.
+
+ % ** 3rd bid term in i-th equation,
+ % ** with all other 3rd big terms prior to i-th
+ A0hy = A0h(:,i)'*ymy;
+ A0hbH = A0hb'*H0tdi;
+ tt3 = tt3 + A0hy*A0h(:,i) + A0hbH*A0hb;
+ % ** 4th bid term in i-th equation,
+ % ** with all other 4th big terms prior to i-th
+ A0hcxyxx = A0h(:,i)'*cxyxx;
+ atdHatd = alptd'*HptdDinalptd;
+ tt4 = tt4 + A0hcxyxx*A0h(:,i)+atdHatd-alpst*xaha;
+
+ %%%%
+ % *** passing the gradient to "pmdg6.m" (analytical gradient)
+ %%%%
+ % ** d1aya0 = d(alpha0'*Y'MY*alpha0)/d(a0??)
+ daya0 = 2.0*A0hy;
+ daya0 = daya0(:);
+ vaya0(stril+1:stril+strm) = daya0(stri);
+
+ % ** daha0 = d((alpha0-alpha0_tilde)'*inv(H_0_tilde)*
+ % ** (alpha0-alpha0_tilde))/d(a0??)
+ daha0 = 2.0*A0hbH;
+ vaha0(stril+1:stril+strm) = daha0(:);
+
+ % ** daxah = d(alpha_+^'*X'X*alpha_+^)/d(a0??)
+ % ** daxah = d(alpha_+^'*X'X*alpha_+^)/d(a0??)
+ %% daxah = 2*(Aph(:,i)'*xtx) * cxpy(:,stri);
+ %% vaxah(stril+1:stril+strm) = daxah';
+ daxah = 2*A0hcxyxx;
+ daxah = daxah(:);
+ vaxah(stril+1:stril+strm) = daxah(stri);
+
+
+ % ** dahad = d(alpha_+~'*inv(H_+~)*alpha_+~)/d(a0??)
+ dahad = 2*HptdDinalptd' * Hp0;
+ % <<>> multiplications not used in "of"
+ vahad(stril+1:stril+strm) = dahad';
+
+ % ** dbiga = d((X'X*alpha_+^+inv(H_+~)*alpha_+*)'*a_+_*)/d(a0??)
+ dbiga = 2*alpst*(cxy(:,stri)+chhh);
+ % <<>> multiplications not used in "of"
+ vbiga(stril+1:stril+strm) = dbiga';
+
+ stril = stril + strm;
+end
+
+%disp(sprintf('Loop %d end: %g', i, toc))
+disp(sprintf('Loop end (of): %g', toc))
+
+%e_tfat = toc
+
+of = tt1 + 0.5*tt3 + 0.5*tt4;
+FRESHFUNCTION=1;
diff --git a/MatlabFiles/pmddf234.m b/MatlabFiles/pmddf234.m
index 6bad4ee13c7646e30f84ea8f6566cda8a1e3bdcd..9a1c7d413b728e56d51f29d005aa6b15112ad38d 100644
--- a/MatlabFiles/pmddf234.m
+++ b/MatlabFiles/pmddf234.m
@@ -1,259 +1,259 @@
-function of = pmddf234(x,idmat0,idmat1,fss,nvar,ncoef,phi, ...
- y,A0b,sg0bid,sgpbid)
-%function of = pmddf232(x,idmat,fss,nvar,ncoef,phi, ...
-% y,A0b,sg0bid,sgpbid)
-% Leeper-Sims-Zha BVAR setup
-% general program to setup A0 matrix and compute the likelihood
-% requires
-% x (parameter vector)
-% a0indx (matrix indicating the free parameters in A0)
-% fss (forecast sample size)
-% nvar (number of variables)
-% ncoef (number of coefficients in a single equation in A+)
-% phi (r.h.s. observations in the system for A+)
-% y (l.h.s. observations for A0)
-% A0b (initial prior mean on A0)
-% sg0bid (diagonal of Sigma0_bar, unweighted prior vcv on the parameters in i-th equation in A0)
-% sgpbid (diagonal of Sigma+_bar, unweighted prior vcv on the parameters in i-th equation in A+)
-%
-% Revisions by CAS 9/27/96: If we transmit idmat, rather than a0indx, we can scale the elements of
-% idmat so that it carries information about relative prior variances.
-%
-% Revsions by TZ, 10/12/96: efficiency improvement by streamlining the previous code
-% according to the general setup in Sims and Zha "Bayesian Methods for ...".
-% Copyright (C) 1997-2012 Christopher A. Sims and Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-global vaya0 vaha0 vaxah vahad vbiga GlbAph FRESHFUNCTION
-% FRESHFUNCTION added 8/20/90 by CAS. It signals the pmddg23 whether the global variables
-% it uses have been refreshed by a new pmddf231 call since the last gradient call.
-a0indx=find(idmat0);
-% pre-allocate GlbAph to be zeros(nvar,lags*nvar+1=ncoef)
-
-nhp = 0; % <<>> 4 hyperparameters
-na0p = length(a0indx); % <<>> number of A0 parameters
-nfp = na0p+nhp;
-
-% *** initializing
-vaya0 = zeros(na0p,1);
-vaha0 = zeros(na0p,1);
-vaxah = zeros(na0p,1);
-vahad = zeros(na0p,1);
-vbiga = zeros(na0p,1);
-
-
-A0h = zeros(nvar,nvar);
-A0h(a0indx) = x(1:na0p);
-%A0h = reshape(a0,nvar,nvar); CAS 9/24/96. The reshape is not necessary if a0 starts as nvar x nvar
- % restrictions on A0
-
-[A0hl,A0hu] = lu(A0h);
-
-% ** hyperparameters
-%%mu = x(na0p+1:nfp);
-mu = ones(5,1);
-mu(1) = 1;
-%mu(1) = 2;
-mu(2) = 0.3;
-mu(3) = 25;
-%mu(4)=1;
-mu(4)=1;
-mu(5) =1;
-%mu(5) = 40;
-% results from ...\dummy1\foreh\pmdd6.out
-%
-% mu(1): overall tightness and also for A0;
-% mu(2): relative tightness for A+;
-% mu(3): relative tightness for the constant term;
-% mu(4): weight on single dummy initial observation including constant;
-% mu(5): weight on nvar sums of coeffs dummy observations.
-
-% ** weight prior dummy observations
-%phi(1:nvar,:) = (mu(5)^2/mu(1)^2)*phi(1:nvar,:);
-%y(1:nvar,:) = (mu(5)^2/mu(1)^2)*y(1:nvar,:);
-%phi(ndobs,:) = mu
-% modified by CAS 8/6/96. The dummy obs weights are naturally in std units, not var units.
-%%phi(1:nvar,:) = mu(5)*phi(1:nvar,:);
-%%y(1:nvar,:) = mu(5)*y(1:nvar,:);
-%%phi(nvar+1,:) = mu(4)*phi(nvar+1,:);
-%%y(nvar+1,:) = mu(4)*y(nvar+1,:);
-
-% ** set up the conditional prior variance sg0bi and sgpbi.
-%%sg0bida = mu(1)^2*sg0bid;
-sgpbida = mu(1)^2*mu(2)^2*sgpbid;
-sgpbida(ncoef) = mu(1)^2*mu(3)^2;
-sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in an SZ paper
-sgppb = diag(sgppbd);
-
-%
-% ** some common terms
-[u d v]=svd(phi,0); %trial
-% xtx = phi'*phi; %trial
-vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
-xtx=vd*vd';
-% M = eye(fss) - phi*(xtx\phi'); % not used except in line below %trial
-yu = y'*u; %trial
-cxyxx=yu*yu'; %trial
-ymy=y'*y-cxyxx; %trial
-%ymy = y'*M*y;
-%cxy = phi'*y; %trial
-cxy = vd*yu'; %trial
-%cxpy = xtx\cxy; %not used further except in line below
-%cxyxx = cxy'*cxpy;
-
-% ** estimate A+_hat conditional on A0, ncoef*nvar, but not using full prior
-%GlbAph = cxpy*A0h;
-
-
-%%%%%%%
-%%% build the objective function below
-%%%%%%%
-%
-t1f = diag(abs(A0hu));
-t1f = log(t1f);
-% * without the prior |A0|^k
-tt1 = (-1.0)*fss*sum(t1f);
-
-tt3 = 0;
-tt4 = 0;
-
-disp(sprintf('Starting loop (of): %g',toc))
-
-stril = 0;
-Hp0p = zeros(ncoef);
-% initializing Htd_+0*inv(Htd_00)*Htd_0+: conditional prior covariance
-for i = 1:nvar
- stri = find(idmat0(:,i)); %CAS change 9/24/96, to avoid slim chance of an exact zero in x vector
- %at some point during an optimization.
- strm = length(stri);
-
- %%A0hb = A0h(stri,i)-A0b(stri,i);
-
- % ** set up the conditional prior variance sg0bi and sgpbi.
- % sg0bd = zeros(stri,1); Commented out by CAS, 9/24/96. Looks like this meant to have
- % strm where it has stri, and in any case it is "overwritten" below.
- %------------------------------
- % Introduce prior information on which variables "belong" in various equations.
- % In this first trial, we just introduce this information here, in a model-specific way.
- % Eventually this info has to be passed parametricly. In our first shot, we just damp down
- % all coefficients except those on the diagonal.
- %%factor0=idmat0(:,i);
- %%sg0bd = sg0bida(stri).*factor0(stri);
- %%sg0b = diag(sg0bd);
- %
- factor1=idmat1(:,i);
- sg1bd = sgpbida(1:nvar).*factor1;
- sg1b = diag(sg1bd);
-
- % ** set up the unconditional prior variance on A0i and A+i
- XX = zeros(nvar,strm);
- XX(stri,:) = eye(strm); % XX in Gelman, etel, on p479
- %
- % * final conditional prior variance on A+i give that on A0i, and
- % * unconditional variance on A0+
- %%H0td = sg0b; % unconditional
- % H_~: td: tilde for ~
- % ** inverse and chol decomp
- %%H0tdi = inv(H0td);
-
- %
- Hptd = zeros(ncoef);
- Hptd(1:nvar,1:nvar)=sg1b;
- Hptd(nvar+1:ncoef,nvar+1:ncoef)=sgppb;
- % condtional on A0i, H_plus_tilde
-
- % ** inverse
- Hptdi = inv(Hptd);
- %%chhh=Hptdi*Hp0;
- chhh = zeros(ncoef,strm); % <<>> the term not used for "of"\
- chhh(1:nvar,:) = Hptdi(1:nvar,1:nvar)*XX;
- chhh1 = XX'*chhh(1:nvar,:);
-
- % common term
- xxhp = xtx+Hptdi;
-
- % ** final conditional prior mean on A+i
- alptd = zeros(ncoef,1);
- alptd(1:nvar) = XX*A0h(stri,i);
- % conditional on A0i, alpha-plus-tilde
-
- % * alpha_plus_* for the ith equation, ncoef*1
- % *alps = xxhp\(phi'*y*A0h(:,i)+HptdDinalptd);
- %%HptdDinalptd=Hptdi*alptd;
- HptdDinalptd=zeros(ncoef,1);
- HptdDinalptd(1:nvar) = Hptdi(1:nvar,1:nvar)*alptd(1:nvar);
- xaha = cxy*A0h(:,i)+HptdDinalptd; %CAS 8/7/96. Replaced phi'*y with cxy
- alpst = xaha'/xxhp;
- GlbAph(i,:)=alpst;
- % alpst': transpose of alps.
-
- % ** 3rd bid term in i-th equation,
- % ** with all other 3rd big terms prior to i-th
- A0hy = A0h(:,i)'*ymy;
- %%A0hbH = A0hb'*H0tdi;
- %%tt3 = tt3 + A0hy*A0h(:,i) + A0hbH*A0hb;
- tt3 = tt3 + A0hy*A0h(:,i);
- % ** 4th bid term in i-th equation,
- % ** with all other 4th big terms prior to i-th
- A0hcxyxx = A0h(:,i)'*cxyxx;
- %%atdHatd = alptd'*HptdDinalptd; % efficiency improvement
- atdHatd = alptd(1:nvar)'*HptdDinalptd(1:nvar);
- tt4 = tt4 + A0hcxyxx*A0h(:,i)+atdHatd-alpst*xaha;
-
- %%%%
- % *** passing the gradient to "pmdg6.m" (analytical gradient)
- %%%%
- % ** d1aya0 = d(alpha0'*Y'MY*alpha0)/d(a0??)
- daya0 = 2.0*A0hy;
- daya0 = daya0(:);
- vaya0(stril+1:stril+strm) = daya0(stri);
-
- % ** daha0 = d((alpha0-alpha0_tilde)'*inv(H_0_tilde)*
- % ** (alpha0-alpha0_tilde))/d(a0??)
- %%daha0 = 2.0*A0hbH;
- %%vaha0(stril+1:stril+strm) = daha0(:);
-
- % ** daxah = d(alpha_+^'*X'X*alpha_+^)/d(a0??)
- % ** daxah = d(alpha_+^'*X'X*alpha_+^)/d(a0??)
- %% daxah = 2*(Aph(:,i)'*xtx) * cxpy(:,stri);
- %% vaxah(stril+1:stril+strm) = daxah';
- daxah = 2*A0hcxyxx;
- daxah = daxah(:);
- vaxah(stril+1:stril+strm) = daxah(stri);
-
-
- % ** dahad = d(alpha_+~'*inv(H_+~)*alpha_+~)/d(a0??)
- dahad = 2*A0h(stri,i)'*chhh1;
- % <<>> multiplications not used in "of"
- vahad(stril+1:stril+strm) = dahad(:);
-
-
- % ** dbiga = d((X'X*alpha_+^+inv(H_+~)*alpha_+*)'*a_+_*)/d(a0??)
- dbiga = 2*alpst*(cxy(:,stri)+chhh);
- % <<>> multiplications not used in "of"
- vbiga(stril+1:stril+strm) = dbiga';
-
- stril = stril + strm;
-end
-
-%disp(sprintf('Loop %d end: %g', i, toc))
-disp(sprintf('Loop end (of): %g', toc))
-
-%e_tfat = toc
-
-of = tt1 + 0.5*tt3 + 0.5*tt4;
-FRESHFUNCTION=1;
\ No newline at end of file
+function of = pmddf234(x,idmat0,idmat1,fss,nvar,ncoef,phi, ...
+ y,A0b,sg0bid,sgpbid)
+%function of = pmddf232(x,idmat,fss,nvar,ncoef,phi, ...
+% y,A0b,sg0bid,sgpbid)
+% Leeper-Sims-Zha BVAR setup
+% general program to setup A0 matrix and compute the likelihood
+% requires
+% x (parameter vector)
+% a0indx (matrix indicating the free parameters in A0)
+% fss (forecast sample size)
+% nvar (number of variables)
+% ncoef (number of coefficients in a single equation in A+)
+% phi (r.h.s. observations in the system for A+)
+% y (l.h.s. observations for A0)
+% A0b (initial prior mean on A0)
+% sg0bid (diagonal of Sigma0_bar, unweighted prior vcv on the parameters in i-th equation in A0)
+% sgpbid (diagonal of Sigma+_bar, unweighted prior vcv on the parameters in i-th equation in A+)
+%
+% Revisions by CAS 9/27/96: If we transmit idmat, rather than a0indx, we can scale the elements of
+% idmat so that it carries information about relative prior variances.
+%
+% Revsions by TZ, 10/12/96: efficiency improvement by streamlining the previous code
+% according to the general setup in Sims and Zha "Bayesian Methods for ...".
+%
+% Copyright (C) 1997-2012 Christopher A. Sims and Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+global vaya0 vaha0 vaxah vahad vbiga GlbAph FRESHFUNCTION
+% FRESHFUNCTION added 8/20/90 by CAS. It signals the pmddg23 whether the global variables
+% it uses have been refreshed by a new pmddf231 call since the last gradient call.
+a0indx=find(idmat0);
+% pre-allocate GlbAph to be zeros(nvar,lags*nvar+1=ncoef)
+
+nhp = 0; % <<>> 4 hyperparameters
+na0p = length(a0indx); % <<>> number of A0 parameters
+nfp = na0p+nhp;
+
+% *** initializing
+vaya0 = zeros(na0p,1);
+vaha0 = zeros(na0p,1);
+vaxah = zeros(na0p,1);
+vahad = zeros(na0p,1);
+vbiga = zeros(na0p,1);
+
+
+A0h = zeros(nvar,nvar);
+A0h(a0indx) = x(1:na0p);
+%A0h = reshape(a0,nvar,nvar); CAS 9/24/96. The reshape is not necessary if a0 starts as nvar x nvar
+ % restrictions on A0
+
+[A0hl,A0hu] = lu(A0h);
+
+% ** hyperparameters
+%%mu = x(na0p+1:nfp);
+mu = ones(5,1);
+mu(1) = 1;
+%mu(1) = 2;
+mu(2) = 0.3;
+mu(3) = 25;
+%mu(4)=1;
+mu(4)=1;
+mu(5) =1;
+%mu(5) = 40;
+% results from ...\dummy1\foreh\pmdd6.out
+%
+% mu(1): overall tightness and also for A0;
+% mu(2): relative tightness for A+;
+% mu(3): relative tightness for the constant term;
+% mu(4): weight on single dummy initial observation including constant;
+% mu(5): weight on nvar sums of coeffs dummy observations.
+
+% ** weight prior dummy observations
+%phi(1:nvar,:) = (mu(5)^2/mu(1)^2)*phi(1:nvar,:);
+%y(1:nvar,:) = (mu(5)^2/mu(1)^2)*y(1:nvar,:);
+%phi(ndobs,:) = mu
+% modified by CAS 8/6/96. The dummy obs weights are naturally in std units, not var units.
+%%phi(1:nvar,:) = mu(5)*phi(1:nvar,:);
+%%y(1:nvar,:) = mu(5)*y(1:nvar,:);
+%%phi(nvar+1,:) = mu(4)*phi(nvar+1,:);
+%%y(nvar+1,:) = mu(4)*y(nvar+1,:);
+
+% ** set up the conditional prior variance sg0bi and sgpbi.
+%%sg0bida = mu(1)^2*sg0bid;
+sgpbida = mu(1)^2*mu(2)^2*sgpbid;
+sgpbida(ncoef) = mu(1)^2*mu(3)^2;
+sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in an SZ paper
+sgppb = diag(sgppbd);
+
+%
+% ** some common terms
+[u d v]=svd(phi,0); %trial
+% xtx = phi'*phi; %trial
+vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
+xtx=vd*vd';
+% M = eye(fss) - phi*(xtx\phi'); % not used except in line below %trial
+yu = y'*u; %trial
+cxyxx=yu*yu'; %trial
+ymy=y'*y-cxyxx; %trial
+%ymy = y'*M*y;
+%cxy = phi'*y; %trial
+cxy = vd*yu'; %trial
+%cxpy = xtx\cxy; %not used further except in line below
+%cxyxx = cxy'*cxpy;
+
+% ** estimate A+_hat conditional on A0, ncoef*nvar, but not using full prior
+%GlbAph = cxpy*A0h;
+
+
+%%%%%%%
+%%% build the objective function below
+%%%%%%%
+%
+t1f = diag(abs(A0hu));
+t1f = log(t1f);
+% * without the prior |A0|^k
+tt1 = (-1.0)*fss*sum(t1f);
+
+tt3 = 0;
+tt4 = 0;
+
+disp(sprintf('Starting loop (of): %g',toc))
+
+stril = 0;
+Hp0p = zeros(ncoef);
+% initializing Htd_+0*inv(Htd_00)*Htd_0+: conditional prior covariance
+for i = 1:nvar
+ stri = find(idmat0(:,i)); %CAS change 9/24/96, to avoid slim chance of an exact zero in x vector
+ %at some point during an optimization.
+ strm = length(stri);
+
+ %%A0hb = A0h(stri,i)-A0b(stri,i);
+
+ % ** set up the conditional prior variance sg0bi and sgpbi.
+ % sg0bd = zeros(stri,1); Commented out by CAS, 9/24/96. Looks like this meant to have
+ % strm where it has stri, and in any case it is "overwritten" below.
+ %------------------------------
+ % Introduce prior information on which variables "belong" in various equations.
+ % In this first trial, we just introduce this information here, in a model-specific way.
+ % Eventually this info has to be passed parametricly. In our first shot, we just damp down
+ % all coefficients except those on the diagonal.
+ %%factor0=idmat0(:,i);
+ %%sg0bd = sg0bida(stri).*factor0(stri);
+ %%sg0b = diag(sg0bd);
+ %
+ factor1=idmat1(:,i);
+ sg1bd = sgpbida(1:nvar).*factor1;
+ sg1b = diag(sg1bd);
+
+ % ** set up the unconditional prior variance on A0i and A+i
+ XX = zeros(nvar,strm);
+ XX(stri,:) = eye(strm); % XX in Gelman, etel, on p479
+ %
+ % * final conditional prior variance on A+i give that on A0i, and
+ % * unconditional variance on A0+
+ %%H0td = sg0b; % unconditional
+ % H_~: td: tilde for ~
+ % ** inverse and chol decomp
+ %%H0tdi = inv(H0td);
+
+ %
+ Hptd = zeros(ncoef);
+ Hptd(1:nvar,1:nvar)=sg1b;
+ Hptd(nvar+1:ncoef,nvar+1:ncoef)=sgppb;
+ % condtional on A0i, H_plus_tilde
+
+ % ** inverse
+ Hptdi = inv(Hptd);
+ %%chhh=Hptdi*Hp0;
+ chhh = zeros(ncoef,strm); % <<>> the term not used for "of"\
+ chhh(1:nvar,:) = Hptdi(1:nvar,1:nvar)*XX;
+ chhh1 = XX'*chhh(1:nvar,:);
+
+ % common term
+ xxhp = xtx+Hptdi;
+
+ % ** final conditional prior mean on A+i
+ alptd = zeros(ncoef,1);
+ alptd(1:nvar) = XX*A0h(stri,i);
+ % conditional on A0i, alpha-plus-tilde
+
+ % * alpha_plus_* for the ith equation, ncoef*1
+ % *alps = xxhp\(phi'*y*A0h(:,i)+HptdDinalptd);
+ %%HptdDinalptd=Hptdi*alptd;
+ HptdDinalptd=zeros(ncoef,1);
+ HptdDinalptd(1:nvar) = Hptdi(1:nvar,1:nvar)*alptd(1:nvar);
+ xaha = cxy*A0h(:,i)+HptdDinalptd; %CAS 8/7/96. Replaced phi'*y with cxy
+ alpst = xaha'/xxhp;
+ GlbAph(i,:)=alpst;
+ % alpst': transpose of alps.
+
+ % ** 3rd bid term in i-th equation,
+ % ** with all other 3rd big terms prior to i-th
+ A0hy = A0h(:,i)'*ymy;
+ %%A0hbH = A0hb'*H0tdi;
+ %%tt3 = tt3 + A0hy*A0h(:,i) + A0hbH*A0hb;
+ tt3 = tt3 + A0hy*A0h(:,i);
+ % ** 4th bid term in i-th equation,
+ % ** with all other 4th big terms prior to i-th
+ A0hcxyxx = A0h(:,i)'*cxyxx;
+ %%atdHatd = alptd'*HptdDinalptd; % efficiency improvement
+ atdHatd = alptd(1:nvar)'*HptdDinalptd(1:nvar);
+ tt4 = tt4 + A0hcxyxx*A0h(:,i)+atdHatd-alpst*xaha;
+
+ %%%%
+ % *** passing the gradient to "pmdg6.m" (analytical gradient)
+ %%%%
+ % ** d1aya0 = d(alpha0'*Y'MY*alpha0)/d(a0??)
+ daya0 = 2.0*A0hy;
+ daya0 = daya0(:);
+ vaya0(stril+1:stril+strm) = daya0(stri);
+
+ % ** daha0 = d((alpha0-alpha0_tilde)'*inv(H_0_tilde)*
+ % ** (alpha0-alpha0_tilde))/d(a0??)
+ %%daha0 = 2.0*A0hbH;
+ %%vaha0(stril+1:stril+strm) = daha0(:);
+
+ % ** daxah = d(alpha_+^'*X'X*alpha_+^)/d(a0??)
+ % ** daxah = d(alpha_+^'*X'X*alpha_+^)/d(a0??)
+ %% daxah = 2*(Aph(:,i)'*xtx) * cxpy(:,stri);
+ %% vaxah(stril+1:stril+strm) = daxah';
+ daxah = 2*A0hcxyxx;
+ daxah = daxah(:);
+ vaxah(stril+1:stril+strm) = daxah(stri);
+
+
+ % ** dahad = d(alpha_+~'*inv(H_+~)*alpha_+~)/d(a0??)
+ dahad = 2*A0h(stri,i)'*chhh1;
+ % <<>> multiplications not used in "of"
+ vahad(stril+1:stril+strm) = dahad(:);
+
+
+ % ** dbiga = d((X'X*alpha_+^+inv(H_+~)*alpha_+*)'*a_+_*)/d(a0??)
+ dbiga = 2*alpst*(cxy(:,stri)+chhh);
+ % <<>> multiplications not used in "of"
+ vbiga(stril+1:stril+strm) = dbiga';
+
+ stril = stril + strm;
+end
+
+%disp(sprintf('Loop %d end: %g', i, toc))
+disp(sprintf('Loop end (of): %g', toc))
+
+%e_tfat = toc
+
+of = tt1 + 0.5*tt3 + 0.5*tt4;
+FRESHFUNCTION=1;
diff --git a/MatlabFiles/pmddf235.m b/MatlabFiles/pmddf235.m
index ca4d78235f8d470488890b21a629fe7e6aa3c35d..72abb4a0a498e7f1e233f149ec9025b1300d3d20 100644
--- a/MatlabFiles/pmddf235.m
+++ b/MatlabFiles/pmddf235.m
@@ -1,220 +1,220 @@
-function of = pmddf235(x,idmat0,idmat1,fss,nvar,ncoef,phi, ...
- y,A0b,sg0bid,sgpbid)
-%function of = pmddf232(x,idmat,fss,nvar,ncoef,phi, ...
-% y,A0b,sg0bid,sgpbid)
-% Leeper-Sims-Zha BVAR setup
-% general program to setup A0 matrix and compute the likelihood
-% requires
-% x (parameter vector)
-% a0indx (matrix indicating the free parameters in A0)
-% fss (forecast sample size)
-% nvar (number of variables)
-% ncoef (number of coefficients in a single equation in A+)
-% phi (r.h.s. observations in the system for A+)
-% y (l.h.s. observations for A0)
-% A0b (initial prior mean on A0)
-% sg0bid (diagonal of Sigma0_bar, unweighted prior vcv on the parameters in i-th equation in A0)
-% sgpbid (diagonal of Sigma+_bar, unweighted prior vcv on the parameters in i-th equation in A+)
-%
-% Revisions by CAS 9/27/96: If we transmit idmat, rather than a0indx, we can scale the elements of
-% idmat so that it carries information about relative prior variances.
-%
-% Revsions by TZ, 10/13/96: efficiency improvement by streamlining the previous code
-% according to the general setup in Sims and Zha "Bayesian Methods for ...".
-% Copyright (C) 1997-2012 Christopher A. Sims and Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-global vaha0 vaMMa0 GlbAph FRESHFUNCTION
-% FRESHFUNCTION added 8/20/90 by CAS. It signals the pmddg23 whether the global variables
-% it uses have been refreshed by a new pmddf231 call since the last gradient call.
-a0indx=find(idmat0);
-% pre-allocate GlbAph to be zeros(nvar,lags*nvar+1=ncoef)
-
-nhp = 0; % <<>> 4 hyperparameters
-na0p = length(a0indx); % <<>> number of A0 parameters
-nfp = na0p+nhp;
-
-% *** initializing
-vaha0 = zeros(na0p,1);
-vaMMa0 = zeros(na0p,1);
-
-%
-A0h = zeros(nvar,nvar);
-A0h(a0indx) = x(1:na0p);
-%A0h = reshape(a0,nvar,nvar); CAS 9/24/96. The reshape is not necessary if a0 starts as nvar x nvar
- % restrictions on A0
-
-[A0hl,A0hu] = lu(A0h);
-
-% ** hyperparameters
-%%mu = x(na0p+1:nfp);
-mu = ones(5,1);
-mu(1) = 1;
-%mu(1) = 2;
-mu(2) = 0.2;
-mu(3) = 1;
-%mu(4)=1;
-mu(4)=10;
-mu(5) =1;
-%mu(5) = 40;
-% results from ...\dummy1\foreh\pmdd6.out
-%
-% mu(1): overall tightness and also for A0;
-% mu(2): relative tightness for A+;
-% mu(3): relative tightness for the constant term;
-% mu(4): weight on single dummy initial observation including constant;
-% mu(5): weight on nvar sums of coeffs dummy observations.
-
-% ** weight prior dummy observations
-%phi(1:nvar,:) = (mu(5)^2/mu(1)^2)*phi(1:nvar,:);
-%y(1:nvar,:) = (mu(5)^2/mu(1)^2)*y(1:nvar,:);
-%phi(ndobs,:) = mu
-% modified by CAS 8/6/96. The dummy obs weights are naturally in std units, not var units.
-phi(1:nvar,:) = mu(5)*phi(1:nvar,:);
-y(1:nvar,:) = mu(5)*y(1:nvar,:);
-phi(nvar+1,:) = mu(4)*phi(nvar+1,:);
-y(nvar+1,:) = mu(4)*y(nvar+1,:);
-
-% ** set up the conditional prior variance sg0bi and sgpbi.
-sg0bida = mu(1)^2*sg0bid;
-sgpbida = mu(1)^2*mu(2)^2*sgpbid;
-sgpbida(ncoef) = mu(1)^2*mu(3)^2;
-sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in an SZ paper
-sgppbdi = 1./sgppbd;
-%sgppb = diag(sgppbd);
-
-%
-% ** some common terms
-[u d v]=svd(phi,0); %trial
-% xtx = phi'*phi; %trial
-vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
-xtx=vd*vd';
-% M = eye(fss) - phi*(xtx\phi'); % not used except in line below %trial
-yu = y'*u; %trial
-cxyxx=yu*yu'; %trial
-yty=y'*y;
-ymy=yty-cxyxx; %trial
-%ymy = y'*M*y;
-%cxy = phi'*y; %trial
-cxy = vd*yu'; %trial
-cyx = cxy';
-%cxpy = xtx\cxy; %not used further except in line below
-%cxyxx = cxy'*cxpy;
-
-% ** estimate A+_hat conditional on A0, ncoef*nvar, but not using full prior
-%GlbAph = cxpy*A0h;
-
-
-%%%%%%%
-%%% build the objective function below
-%%%%%%%
-%
-t1f = diag(abs(A0hu));
-t1f = log(t1f);
-% * without the prior |A0|^k
-tt1 = (-1.0)*fss*sum(t1f);
-
-tt3 = 0;
-
-disp(sprintf('Starting loop (of): %g',toc))
-
-stril = 0;
-Hptd = zeros(ncoef);
-Hptdi=Hptd;
-Hptd(nvar+1:ncoef,nvar+1:ncoef)=diag(sgppbd);
-Hptdi(nvar+1:ncoef,nvar+1:ncoef)=diag(sgppbdi);
- % condtional on A0i, H_plus_tilde
-for i = 1:nvar
- stri = find(idmat0(:,i)); %CAS change 9/24/96, to avoid slim chance of an exact zero in x vector
- %at some point during an optimization.
- strm = length(stri);
-
- A0hb = A0h(stri,i)-A0b(stri,i);
-
- % ** set up the conditional prior variance sg0bi and sgpbi.
- % sg0bd = zeros(stri,1); Commented out by CAS, 9/24/96. Looks like this meant to have
- % strm where it has stri, and in any case it is "overwritten" below.
- %------------------------------
- % Introduce prior information on which variables "belong" in various equations.
- % In this first trial, we just introduce this information here, in a model-specific way.
- % Eventually this info has to be passed parametricly. In our first shot, we just damp down
- % all coefficients except those on the diagonal.
- factor0=idmat0(:,i);
- sg0bd = sg0bida(stri).*factor0(stri);
- sg0bdi = 1./sg0bd;
- %sg0b = diag(sg0bd);
- %
- factor1=idmat1(:,i);
- sg1bd = sgpbida(1:nvar).*factor1;
- sg1bdi = 1./sg1bd;
- %sg1b = diag(sg1bd);
-
- % ** set up the unconditional prior variance on A0i and A+i
- XX = zeros(nvar,strm);
- XX(stri,:) = eye(strm); % XX in Gelman, etel, on p479
- %
- % * final conditional prior variance on A+i give that on A0i, and
- % * unconditional variance on A0+
- H0td = diag(sg0bd); % unconditional
- % H_~: td: tilde for ~
- % ** inverse and chol decomp
- H0tdi = diag(sg0bdi);
-
- %
- Hptd(1:nvar,1:nvar)=diag(sg1bd);
- Hptdi(1:nvar,1:nvar)=diag(sg1bdi);
- % condtional on A0i, H_plus_tilde
-
- % common terms
- xxhp = xtx+Hptdi;
- A0hbH = A0hb'*H0tdi;
- H1p_1 = zeros(ncoef,nvar);
- H1p_1(1:nvar,:) = Hptdi(1:nvar,1:nvar);
- %%Hm = (cyx+H1p_1')*(xxhp\(cxy+H1p_1));
- Hm1 = (cyx+H1p_1')/xxhp;
- Hm2 = cxy+H1p_1;
- GlbAph(i,:) = A0h(stri,i)'*XX'*Hm1; % alpha_plus_*_transpose
- %%alpMpyh = A0h(stri,i)'*XX'*(yty+Hptdi(1:nvar,1:nvar)-Hm)*XX;
- alpMpyh = A0h(stri,i)'*XX'*(yty+Hptdi(1:nvar,1:nvar))*XX - GlbAph(i,:)*Hm2*XX;
-
- % ** 3rd bid term in i-th equation,
- % ** with all other 3rd big terms before the i-th equation
- tt3 = tt3 + A0hbH*A0hb + alpMpyh*A0h(stri,i);
-
- %%%%
- % *** passing the gradient to "pmdg6.m" (analytical gradient)
- %%%%
- %
- % ** daha0 = d((alpha0-alpha0_tilde)'*inv(H_0_tilde)*
- % ** (alpha0-alpha0_tilde))/d(a0??)
- daha0 = 2.0*A0hbH;
- vaha0(stril+1:stril+strm) = daha0(:);
- %
- % ** daMMa0 = d(alpha0'*P'*(Y'Y+H_1^(-1)+H_m)*P*apha0) / d(a0?)
- daMMa0 = 2.0*alpMpyh;
- vaMMa0(stril+1:stril+strm) = daMMa0(:);
-
- stril = stril + strm;
-end
-
-%disp(sprintf('Loop %d end: %g', i, toc))
-disp(sprintf('Loop end (of): %g', toc))
-
-%e_tfat = toc
-
-of = tt1 + 0.5*tt3;
-FRESHFUNCTION=1;
\ No newline at end of file
+function of = pmddf235(x,idmat0,idmat1,fss,nvar,ncoef,phi, ...
+ y,A0b,sg0bid,sgpbid)
+%function of = pmddf232(x,idmat,fss,nvar,ncoef,phi, ...
+% y,A0b,sg0bid,sgpbid)
+% Leeper-Sims-Zha BVAR setup
+% general program to setup A0 matrix and compute the likelihood
+% requires
+% x (parameter vector)
+% a0indx (matrix indicating the free parameters in A0)
+% fss (forecast sample size)
+% nvar (number of variables)
+% ncoef (number of coefficients in a single equation in A+)
+% phi (r.h.s. observations in the system for A+)
+% y (l.h.s. observations for A0)
+% A0b (initial prior mean on A0)
+% sg0bid (diagonal of Sigma0_bar, unweighted prior vcv on the parameters in i-th equation in A0)
+% sgpbid (diagonal of Sigma+_bar, unweighted prior vcv on the parameters in i-th equation in A+)
+%
+% Revisions by CAS 9/27/96: If we transmit idmat, rather than a0indx, we can scale the elements of
+% idmat so that it carries information about relative prior variances.
+%
+% Revsions by TZ, 10/13/96: efficiency improvement by streamlining the previous code
+% according to the general setup in Sims and Zha "Bayesian Methods for ...".
+%
+% Copyright (C) 1997-2012 Christopher A. Sims and Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+global vaha0 vaMMa0 GlbAph FRESHFUNCTION
+% FRESHFUNCTION added 8/20/90 by CAS. It signals the pmddg23 whether the global variables
+% it uses have been refreshed by a new pmddf231 call since the last gradient call.
+a0indx=find(idmat0);
+% pre-allocate GlbAph to be zeros(nvar,lags*nvar+1=ncoef)
+
+nhp = 0; % <<>> 4 hyperparameters
+na0p = length(a0indx); % <<>> number of A0 parameters
+nfp = na0p+nhp;
+
+% *** initializing
+vaha0 = zeros(na0p,1);
+vaMMa0 = zeros(na0p,1);
+
+%
+A0h = zeros(nvar,nvar);
+A0h(a0indx) = x(1:na0p);
+%A0h = reshape(a0,nvar,nvar); CAS 9/24/96. The reshape is not necessary if a0 starts as nvar x nvar
+ % restrictions on A0
+
+[A0hl,A0hu] = lu(A0h);
+
+% ** hyperparameters
+%%mu = x(na0p+1:nfp);
+mu = ones(5,1);
+mu(1) = 1;
+%mu(1) = 2;
+mu(2) = 0.2;
+mu(3) = 1;
+%mu(4)=1;
+mu(4)=10;
+mu(5) =1;
+%mu(5) = 40;
+% results from ...\dummy1\foreh\pmdd6.out
+%
+% mu(1): overall tightness and also for A0;
+% mu(2): relative tightness for A+;
+% mu(3): relative tightness for the constant term;
+% mu(4): weight on single dummy initial observation including constant;
+% mu(5): weight on nvar sums of coeffs dummy observations.
+
+% ** weight prior dummy observations
+%phi(1:nvar,:) = (mu(5)^2/mu(1)^2)*phi(1:nvar,:);
+%y(1:nvar,:) = (mu(5)^2/mu(1)^2)*y(1:nvar,:);
+%phi(ndobs,:) = mu
+% modified by CAS 8/6/96. The dummy obs weights are naturally in std units, not var units.
+phi(1:nvar,:) = mu(5)*phi(1:nvar,:);
+y(1:nvar,:) = mu(5)*y(1:nvar,:);
+phi(nvar+1,:) = mu(4)*phi(nvar+1,:);
+y(nvar+1,:) = mu(4)*y(nvar+1,:);
+
+% ** set up the conditional prior variance sg0bi and sgpbi.
+sg0bida = mu(1)^2*sg0bid;
+sgpbida = mu(1)^2*mu(2)^2*sgpbid;
+sgpbida(ncoef) = mu(1)^2*mu(3)^2;
+sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in an SZ paper
+sgppbdi = 1./sgppbd;
+%sgppb = diag(sgppbd);
+
+%
+% ** some common terms
+[u d v]=svd(phi,0); %trial
+% xtx = phi'*phi; %trial
+vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
+xtx=vd*vd';
+% M = eye(fss) - phi*(xtx\phi'); % not used except in line below %trial
+yu = y'*u; %trial
+cxyxx=yu*yu'; %trial
+yty=y'*y;
+ymy=yty-cxyxx; %trial
+%ymy = y'*M*y;
+%cxy = phi'*y; %trial
+cxy = vd*yu'; %trial
+cyx = cxy';
+%cxpy = xtx\cxy; %not used further except in line below
+%cxyxx = cxy'*cxpy;
+
+% ** estimate A+_hat conditional on A0, ncoef*nvar, but not using full prior
+%GlbAph = cxpy*A0h;
+
+
+%%%%%%%
+%%% build the objective function below
+%%%%%%%
+%
+t1f = diag(abs(A0hu));
+t1f = log(t1f);
+% * without the prior |A0|^k
+tt1 = (-1.0)*fss*sum(t1f);
+
+tt3 = 0;
+
+disp(sprintf('Starting loop (of): %g',toc))
+
+stril = 0;
+Hptd = zeros(ncoef);
+Hptdi=Hptd;
+Hptd(nvar+1:ncoef,nvar+1:ncoef)=diag(sgppbd);
+Hptdi(nvar+1:ncoef,nvar+1:ncoef)=diag(sgppbdi);
+ % condtional on A0i, H_plus_tilde
+for i = 1:nvar
+ stri = find(idmat0(:,i)); %CAS change 9/24/96, to avoid slim chance of an exact zero in x vector
+ %at some point during an optimization.
+ strm = length(stri);
+
+ A0hb = A0h(stri,i)-A0b(stri,i);
+
+ % ** set up the conditional prior variance sg0bi and sgpbi.
+ % sg0bd = zeros(stri,1); Commented out by CAS, 9/24/96. Looks like this meant to have
+ % strm where it has stri, and in any case it is "overwritten" below.
+ %------------------------------
+ % Introduce prior information on which variables "belong" in various equations.
+ % In this first trial, we just introduce this information here, in a model-specific way.
+ % Eventually this info has to be passed parametricly. In our first shot, we just damp down
+ % all coefficients except those on the diagonal.
+ factor0=idmat0(:,i);
+ sg0bd = sg0bida(stri).*factor0(stri);
+ sg0bdi = 1./sg0bd;
+ %sg0b = diag(sg0bd);
+ %
+ factor1=idmat1(:,i);
+ sg1bd = sgpbida(1:nvar).*factor1;
+ sg1bdi = 1./sg1bd;
+ %sg1b = diag(sg1bd);
+
+ % ** set up the unconditional prior variance on A0i and A+i
+ XX = zeros(nvar,strm);
+ XX(stri,:) = eye(strm); % XX in Gelman, etel, on p479
+ %
+ % * final conditional prior variance on A+i give that on A0i, and
+ % * unconditional variance on A0+
+ H0td = diag(sg0bd); % unconditional
+ % H_~: td: tilde for ~
+ % ** inverse and chol decomp
+ H0tdi = diag(sg0bdi);
+
+ %
+ Hptd(1:nvar,1:nvar)=diag(sg1bd);
+ Hptdi(1:nvar,1:nvar)=diag(sg1bdi);
+ % condtional on A0i, H_plus_tilde
+
+ % common terms
+ xxhp = xtx+Hptdi;
+ A0hbH = A0hb'*H0tdi;
+ H1p_1 = zeros(ncoef,nvar);
+ H1p_1(1:nvar,:) = Hptdi(1:nvar,1:nvar);
+ %%Hm = (cyx+H1p_1')*(xxhp\(cxy+H1p_1));
+ Hm1 = (cyx+H1p_1')/xxhp;
+ Hm2 = cxy+H1p_1;
+ GlbAph(i,:) = A0h(stri,i)'*XX'*Hm1; % alpha_plus_*_transpose
+ %%alpMpyh = A0h(stri,i)'*XX'*(yty+Hptdi(1:nvar,1:nvar)-Hm)*XX;
+ alpMpyh = A0h(stri,i)'*XX'*(yty+Hptdi(1:nvar,1:nvar))*XX - GlbAph(i,:)*Hm2*XX;
+
+ % ** 3rd bid term in i-th equation,
+ % ** with all other 3rd big terms before the i-th equation
+ tt3 = tt3 + A0hbH*A0hb + alpMpyh*A0h(stri,i);
+
+ %%%%
+ % *** passing the gradient to "pmdg6.m" (analytical gradient)
+ %%%%
+ %
+ % ** daha0 = d((alpha0-alpha0_tilde)'*inv(H_0_tilde)*
+ % ** (alpha0-alpha0_tilde))/d(a0??)
+ daha0 = 2.0*A0hbH;
+ vaha0(stril+1:stril+strm) = daha0(:);
+ %
+ % ** daMMa0 = d(alpha0'*P'*(Y'Y+H_1^(-1)+H_m)*P*apha0) / d(a0?)
+ daMMa0 = 2.0*alpMpyh;
+ vaMMa0(stril+1:stril+strm) = daMMa0(:);
+
+ stril = stril + strm;
+end
+
+%disp(sprintf('Loop %d end: %g', i, toc))
+disp(sprintf('Loop end (of): %g', toc))
+
+%e_tfat = toc
+
+of = tt1 + 0.5*tt3;
+FRESHFUNCTION=1;
diff --git a/MatlabFiles/pmddf236.m b/MatlabFiles/pmddf236.m
index a8d33d0c07af530d0acdd59794a76731bddbc029..7759785194c7970074cca0b6d7196b2fcd72d98f 100644
--- a/MatlabFiles/pmddf236.m
+++ b/MatlabFiles/pmddf236.m
@@ -1,229 +1,229 @@
-function of = pmddf236(x,idmat0,idmatp,fss,nvar,ncoef,phi, ...
- y,A0b,sg0bid,sgpbid)
-%function of = pmddf232(x,idmat,fss,nvar,ncoef,phi, ...
-% y,A0b,sg0bid,sgpbid)
-% Leeper-Sims-Zha BVAR setup
-% general program to setup A0 matrix and compute the likelihood
-% requires
-% x (parameter vector)
-% a0indx (matrix indicating the free parameters in A0)
-% fss (forecast sample size)
-% nvar (number of variables)
-% ncoef (number of coefficients in a single equation in A+)
-% phi (r.h.s. observations in the system for A+)
-% y (l.h.s. observations for A0)
-% A0b (initial prior mean on A0)
-% sg0bid (diagonal of Sigma0_bar, unweighted prior vcv on the parameters in i-th equation in A0)
-% sgpbid (diagonal of Sigma+_bar, unweighted prior vcv on the parameters in i-th equation in A+)
-%
-% Revisions by CAS 9/27/96: If we transmit idmat, rather than a0indx, we can scale the elements of
-% idmat so that it carries information about relative prior variances.
-%
-% Revsions by TZ, 10/13/96: efficiency improvement by streamlining the previous code
-% according to the general setup in Sims and Zha "Bayesian Methods for ...".
-% Copyright (C) 1997-2012 Christopher A. Sims and Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-global vaha0 vaMMa0 GlbAph FRESHFUNCTION
-% FRESHFUNCTION added 8/20/90 by CAS. It signals the pmddg23 whether the global variables
-% it uses have been refreshed by a new pmddf231 call since the last gradient call.
-a0indx=find(idmat0);
-% pre-allocate GlbAph to be zeros(nvar,lags*nvar+1=ncoef)
-
-nhp = 0; % <<>> 4 hyperparameters
-na0p = length(a0indx); % <<>> number of A0 parameters
-nfp = na0p+nhp;
-
-% *** initializing
-vaha0 = zeros(na0p,1);
-vaMMa0 = zeros(na0p,1);
-
-%
-A0h = zeros(nvar,nvar);
-A0h(a0indx) = x(1:na0p);
-%A0h = reshape(a0,nvar,nvar); CAS 9/24/96. The reshape is not necessary if a0 starts as nvar x nvar
- % restrictions on A0
-
-[A0hl,A0hu] = lu(A0h);
-
-% ** hyperparameters
-%%mu = x(na0p+1:nfp);
-mu = ones(5,1);
-mu(1) = 1;
-%mu(1) = 2;
-mu(2) = 0.2;
-mu(3) = 1;
-%mu(4)=1;
-mu(4)=10;
-mu(5) =1;
-%mu(5) = 40;
-% results from ...\dummy1\foreh\pmdd6.out
-%
-% mu(1): overall tightness and also for A0;
-% mu(2): relative tightness for A+;
-% mu(3): relative tightness for the constant term;
-% mu(4): weight on single dummy initial observation including constant;
-% mu(5): weight on nvar sums of coeffs dummy observations.
-
-% ** weight prior dummy observations
-%phi(1:nvar,:) = (mu(5)^2/mu(1)^2)*phi(1:nvar,:);
-%y(1:nvar,:) = (mu(5)^2/mu(1)^2)*y(1:nvar,:);
-%phi(ndobs,:) = mu
-% modified by CAS 8/6/96. The dummy obs weights are naturally in std units, not var units.
-phi(1:nvar,:) = mu(5)*phi(1:nvar,:);
-y(1:nvar,:) = mu(5)*y(1:nvar,:);
-phi(nvar+1,:) = mu(4)*phi(nvar+1,:);
-y(nvar+1,:) = mu(4)*y(nvar+1,:);
-
-% ** set up the conditional prior variance sg0bi and sgpbi.
-sg0bida = mu(1)^2*sg0bid;
-sgpbida = mu(1)^2*mu(2)^2*sgpbid;
-sgpbida(ncoef) = mu(1)^2*mu(3)^2;
-sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in an SZ paper
-%%sgppbdi = 1./sgppbd;
-%sgppb = diag(sgppbd);
-
-%
-% ** some common terms
-[u d v]=svd(phi,0); %trial
-% xtx = phi'*phi; %trial
-vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
-xtx=vd*vd';
-% M = eye(fss) - phi*(xtx\phi'); % not used except in line below %trial
-yu = y'*u; %trial
-cxyxx=yu*yu'; %trial
-yty=y'*y;
-ymy=yty-cxyxx; %trial
-%ymy = y'*M*y;
-%cxy = phi'*y; %trial
-cxy = vd*yu'; %trial
-cyx = cxy';
-%cxpy = xtx\cxy; %not used further except in line below
-%cxyxx = cxy'*cxpy;
-
-% ** estimate A+_hat conditional on A0, ncoef*nvar, but not using full prior
-%GlbAph = cxpy*A0h;
-
-
-%%%%%%%
-%%% build the objective function below
-%%%%%%%
-%
-t1f = diag(abs(A0hu));
-t1f = log(t1f);
-% * without the prior |A0|^k
-tt1 = (-1.0)*fss*sum(t1f);
-
-tt3 = 0;
-
-disp(sprintf('Starting loop (of): %g',toc))
-
-stril = 0;
-Hptd = zeros(ncoef);
-Hptdi=Hptd;
-%%Hptd(nvar+1:ncoef,nvar+1:ncoef)=diag(sgppbd);
-%%Hptdi(nvar+1:ncoef,nvar+1:ncoef)=diag(sgppbdi);
-Hptd(ncoef,ncoef)=sgppbd(ncoef-nvar);
-Hptdi(ncoef,ncoef)=1/sgppbd(ncoef-nvar);
- % condtional on A0i, H_plus_tilde
-for i = 1:nvar
- stri = find(idmat0(:,i)); %CAS change 9/24/96, to avoid slim chance of an exact zero in x vector
- %at some point during an optimization.
- strm = length(stri);
-
- A0hb = A0h(stri,i)-A0b(stri,i);
-
- % ** set up the conditional prior variance sg0bi and sgpbi.
- % sg0bd = zeros(stri,1); Commented out by CAS, 9/24/96. Looks like this meant to have
- % strm where it has stri, and in any case it is "overwritten" below.
- %------------------------------
- % Introduce prior information on which variables "belong" in various equations.
- % In this first trial, we just introduce this information here, in a model-specific way.
- % Eventually this info has to be passed parametricly. In our first shot, we just damp down
- % all coefficients except those on the diagonal.
- factor0=idmat0(:,i);
- sg0bd = sg0bida(stri).*factor0(stri);
- sg0bdi = 1./sg0bd;
- %sg0b = diag(sg0bd);
- %
- factor1=idmatp(1:nvar,i);
- sg1bd = sgpbida(1:nvar).*factor1;
- sg1bdi = 1./sg1bd;
- %sg1b = diag(sg1bd);
- %
- factorpp=idmatp(nvar+1:ncoef-1,i);
- sgpp_cbd = sgppbd(1:ncoef-nvar-1) .* factorpp;
- sgpp_cbdi = 1./sgpp_cbd;
-
-
- % ** set up the unconditional prior variance on A0i and A+i
- XX = zeros(nvar,strm);
- XX(stri,:) = eye(strm); % XX in Gelman, etel, on p479
- %
- % * final conditional prior variance on A+i give that on A0i, and
- % * unconditional variance on A0+
- H0td = diag(sg0bd); % unconditional
- % H_~: td: tilde for ~
- % ** inverse and chol decomp
- H0tdi = diag(sg0bdi);
-
- %
- Hptd(1:nvar,1:nvar)=diag(sg1bd);
- Hptdi(1:nvar,1:nvar)=diag(sg1bdi);
- Hptd(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbd);
- Hptdi(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbdi);
- % condtional on A0i, H_plus_tilde
-
- % common terms
- xxhp = xtx+Hptdi;
- A0hbH = A0hb'*H0tdi;
- H1p_1 = zeros(ncoef,nvar);
- H1p_1(1:nvar,:) = Hptdi(1:nvar,1:nvar);
- %%Hm = (cyx+H1p_1')*(xxhp\(cxy+H1p_1));
- Hm1 = (cyx+H1p_1')/xxhp;
- Hm2 = cxy+H1p_1;
- GlbAph(i,:) = A0h(stri,i)'*XX'*Hm1; % alpha_plus_*_transpose
- %%alpMpyh = A0h(stri,i)'*XX'*(yty+Hptdi(1:nvar,1:nvar)-Hm)*XX;
- alpMpyh = A0h(stri,i)'*XX'*(yty+Hptdi(1:nvar,1:nvar))*XX - GlbAph(i,:)*Hm2*XX;
-
- % ** 3rd bid term in i-th equation,
- % ** with all other 3rd big terms before the i-th equation
- tt3 = tt3 + A0hbH*A0hb + alpMpyh*A0h(stri,i);
-
- %%%%
- % *** passing the gradient to "pmdg6.m" (analytical gradient)
- %%%%
- %
- % ** daha0 = d((alpha0-alpha0_tilde)'*inv(H_0_tilde)*
- % ** (alpha0-alpha0_tilde))/d(a0??)
- daha0 = 2.0*A0hbH;
- vaha0(stril+1:stril+strm) = daha0(:);
- %
- % ** daMMa0 = d(alpha0'*P'*(Y'Y+H_1^(-1)+H_m)*P*apha0) / d(a0?)
- daMMa0 = 2.0*alpMpyh;
- vaMMa0(stril+1:stril+strm) = daMMa0(:);
-
- stril = stril + strm;
-end
-
-%disp(sprintf('Loop %d end: %g', i, toc))
-disp(sprintf('Loop end (of): %g', toc))
-
-%e_tfat = toc
-
-of = tt1 + 0.5*tt3;
-FRESHFUNCTION=1;
\ No newline at end of file
+function of = pmddf236(x,idmat0,idmatp,fss,nvar,ncoef,phi, ...
+ y,A0b,sg0bid,sgpbid)
+%function of = pmddf232(x,idmat,fss,nvar,ncoef,phi, ...
+% y,A0b,sg0bid,sgpbid)
+% Leeper-Sims-Zha BVAR setup
+% general program to setup A0 matrix and compute the likelihood
+% requires
+% x (parameter vector)
+% a0indx (matrix indicating the free parameters in A0)
+% fss (forecast sample size)
+% nvar (number of variables)
+% ncoef (number of coefficients in a single equation in A+)
+% phi (r.h.s. observations in the system for A+)
+% y (l.h.s. observations for A0)
+% A0b (initial prior mean on A0)
+% sg0bid (diagonal of Sigma0_bar, unweighted prior vcv on the parameters in i-th equation in A0)
+% sgpbid (diagonal of Sigma+_bar, unweighted prior vcv on the parameters in i-th equation in A+)
+%
+% Revisions by CAS 9/27/96: If we transmit idmat, rather than a0indx, we can scale the elements of
+% idmat so that it carries information about relative prior variances.
+%
+% Revsions by TZ, 10/13/96: efficiency improvement by streamlining the previous code
+% according to the general setup in Sims and Zha "Bayesian Methods for ...".
+%
+% Copyright (C) 1997-2012 Christopher A. Sims and Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+global vaha0 vaMMa0 GlbAph FRESHFUNCTION
+% FRESHFUNCTION added 8/20/90 by CAS. It signals the pmddg23 whether the global variables
+% it uses have been refreshed by a new pmddf231 call since the last gradient call.
+a0indx=find(idmat0);
+% pre-allocate GlbAph to be zeros(nvar,lags*nvar+1=ncoef)
+
+nhp = 0; % <<>> 4 hyperparameters
+na0p = length(a0indx); % <<>> number of A0 parameters
+nfp = na0p+nhp;
+
+% *** initializing
+vaha0 = zeros(na0p,1);
+vaMMa0 = zeros(na0p,1);
+
+%
+A0h = zeros(nvar,nvar);
+A0h(a0indx) = x(1:na0p);
+%A0h = reshape(a0,nvar,nvar); CAS 9/24/96. The reshape is not necessary if a0 starts as nvar x nvar
+ % restrictions on A0
+
+[A0hl,A0hu] = lu(A0h);
+
+% ** hyperparameters
+%%mu = x(na0p+1:nfp);
+mu = ones(5,1);
+mu(1) = 1;
+%mu(1) = 2;
+mu(2) = 0.2;
+mu(3) = 1;
+%mu(4)=1;
+mu(4)=10;
+mu(5) =1;
+%mu(5) = 40;
+% results from ...\dummy1\foreh\pmdd6.out
+%
+% mu(1): overall tightness and also for A0;
+% mu(2): relative tightness for A+;
+% mu(3): relative tightness for the constant term;
+% mu(4): weight on single dummy initial observation including constant;
+% mu(5): weight on nvar sums of coeffs dummy observations.
+
+% ** weight prior dummy observations
+%phi(1:nvar,:) = (mu(5)^2/mu(1)^2)*phi(1:nvar,:);
+%y(1:nvar,:) = (mu(5)^2/mu(1)^2)*y(1:nvar,:);
+%phi(ndobs,:) = mu
+% modified by CAS 8/6/96. The dummy obs weights are naturally in std units, not var units.
+phi(1:nvar,:) = mu(5)*phi(1:nvar,:);
+y(1:nvar,:) = mu(5)*y(1:nvar,:);
+phi(nvar+1,:) = mu(4)*phi(nvar+1,:);
+y(nvar+1,:) = mu(4)*y(nvar+1,:);
+
+% ** set up the conditional prior variance sg0bi and sgpbi.
+sg0bida = mu(1)^2*sg0bid;
+sgpbida = mu(1)^2*mu(2)^2*sgpbid;
+sgpbida(ncoef) = mu(1)^2*mu(3)^2;
+sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in an SZ paper
+%%sgppbdi = 1./sgppbd;
+%sgppb = diag(sgppbd);
+
+%
+% ** some common terms
+[u d v]=svd(phi,0); %trial
+% xtx = phi'*phi; %trial
+vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
+xtx=vd*vd';
+% M = eye(fss) - phi*(xtx\phi'); % not used except in line below %trial
+yu = y'*u; %trial
+cxyxx=yu*yu'; %trial
+yty=y'*y;
+ymy=yty-cxyxx; %trial
+%ymy = y'*M*y;
+%cxy = phi'*y; %trial
+cxy = vd*yu'; %trial
+cyx = cxy';
+%cxpy = xtx\cxy; %not used further except in line below
+%cxyxx = cxy'*cxpy;
+
+% ** estimate A+_hat conditional on A0, ncoef*nvar, but not using full prior
+%GlbAph = cxpy*A0h;
+
+
+%%%%%%%
+%%% build the objective function below
+%%%%%%%
+%
+t1f = diag(abs(A0hu));
+t1f = log(t1f);
+% * without the prior |A0|^k
+tt1 = (-1.0)*fss*sum(t1f);
+
+tt3 = 0;
+
+disp(sprintf('Starting loop (of): %g',toc))
+
+stril = 0;
+Hptd = zeros(ncoef);
+Hptdi=Hptd;
+%%Hptd(nvar+1:ncoef,nvar+1:ncoef)=diag(sgppbd);
+%%Hptdi(nvar+1:ncoef,nvar+1:ncoef)=diag(sgppbdi);
+Hptd(ncoef,ncoef)=sgppbd(ncoef-nvar);
+Hptdi(ncoef,ncoef)=1/sgppbd(ncoef-nvar);
+ % condtional on A0i, H_plus_tilde
+for i = 1:nvar
+ stri = find(idmat0(:,i)); %CAS change 9/24/96, to avoid slim chance of an exact zero in x vector
+ %at some point during an optimization.
+ strm = length(stri);
+
+ A0hb = A0h(stri,i)-A0b(stri,i);
+
+ % ** set up the conditional prior variance sg0bi and sgpbi.
+ % sg0bd = zeros(stri,1); Commented out by CAS, 9/24/96. Looks like this meant to have
+ % strm where it has stri, and in any case it is "overwritten" below.
+ %------------------------------
+ % Introduce prior information on which variables "belong" in various equations.
+ % In this first trial, we just introduce this information here, in a model-specific way.
+ % Eventually this info has to be passed parametricly. In our first shot, we just damp down
+ % all coefficients except those on the diagonal.
+ factor0=idmat0(:,i);
+ sg0bd = sg0bida(stri).*factor0(stri);
+ sg0bdi = 1./sg0bd;
+ %sg0b = diag(sg0bd);
+ %
+ factor1=idmatp(1:nvar,i);
+ sg1bd = sgpbida(1:nvar).*factor1;
+ sg1bdi = 1./sg1bd;
+ %sg1b = diag(sg1bd);
+ %
+ factorpp=idmatp(nvar+1:ncoef-1,i);
+ sgpp_cbd = sgppbd(1:ncoef-nvar-1) .* factorpp;
+ sgpp_cbdi = 1./sgpp_cbd;
+
+
+ % ** set up the unconditional prior variance on A0i and A+i
+ XX = zeros(nvar,strm);
+ XX(stri,:) = eye(strm); % XX in Gelman, etel, on p479
+ %
+ % * final conditional prior variance on A+i give that on A0i, and
+ % * unconditional variance on A0+
+ H0td = diag(sg0bd); % unconditional
+ % H_~: td: tilde for ~
+ % ** inverse and chol decomp
+ H0tdi = diag(sg0bdi);
+
+ %
+ Hptd(1:nvar,1:nvar)=diag(sg1bd);
+ Hptdi(1:nvar,1:nvar)=diag(sg1bdi);
+ Hptd(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbd);
+ Hptdi(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbdi);
+ % condtional on A0i, H_plus_tilde
+
+ % common terms
+ xxhp = xtx+Hptdi;
+ A0hbH = A0hb'*H0tdi;
+ H1p_1 = zeros(ncoef,nvar);
+ H1p_1(1:nvar,:) = Hptdi(1:nvar,1:nvar);
+ %%Hm = (cyx+H1p_1')*(xxhp\(cxy+H1p_1));
+ Hm1 = (cyx+H1p_1')/xxhp;
+ Hm2 = cxy+H1p_1;
+ GlbAph(i,:) = A0h(stri,i)'*XX'*Hm1; % alpha_plus_*_transpose
+ %%alpMpyh = A0h(stri,i)'*XX'*(yty+Hptdi(1:nvar,1:nvar)-Hm)*XX;
+ alpMpyh = A0h(stri,i)'*XX'*(yty+Hptdi(1:nvar,1:nvar))*XX - GlbAph(i,:)*Hm2*XX;
+
+ % ** 3rd bid term in i-th equation,
+ % ** with all other 3rd big terms before the i-th equation
+ tt3 = tt3 + A0hbH*A0hb + alpMpyh*A0h(stri,i);
+
+ %%%%
+ % *** passing the gradient to "pmdg6.m" (analytical gradient)
+ %%%%
+ %
+ % ** daha0 = d((alpha0-alpha0_tilde)'*inv(H_0_tilde)*
+ % ** (alpha0-alpha0_tilde))/d(a0??)
+ daha0 = 2.0*A0hbH;
+ vaha0(stril+1:stril+strm) = daha0(:);
+ %
+ % ** daMMa0 = d(alpha0'*P'*(Y'Y+H_1^(-1)+H_m)*P*apha0) / d(a0?)
+ daMMa0 = 2.0*alpMpyh;
+ vaMMa0(stril+1:stril+strm) = daMMa0(:);
+
+ stril = stril + strm;
+end
+
+%disp(sprintf('Loop %d end: %g', i, toc))
+disp(sprintf('Loop end (of): %g', toc))
+
+%e_tfat = toc
+
+of = tt1 + 0.5*tt3;
+FRESHFUNCTION=1;
diff --git a/MatlabFiles/pmddg233.m b/MatlabFiles/pmddg233.m
index 0091ed6f04af459b34af668eac42f20663742403..300f1865eb0d27beadeccb6ed0587279f00a4e4b 100644
--- a/MatlabFiles/pmddg233.m
+++ b/MatlabFiles/pmddg233.m
@@ -1,66 +1,66 @@
-function [g,badg] = pmddg233(x,idmat0,idmat1,fss,nvar, ...
- ncoef,phi,y,A0b,sg0bid,sgpbid);
-% Leeper-Sims-Zha BVAR setup, analytical gradient for "csminwel.m"
-% general program to setup A0 matrix and compute the likelihood
-% requires x (parameter vector), a0indx (matrix indicating the free
-% parameters in A0), fss (forecast sample size), nvar (number of variables),
-% ncoef (number of coefficients in a single equation in A+), phi (r.h.s.
-% observations in the system for A+), y (l.h.s. observations for A0),
-% ymy (Y'*M*Y), xd0 (X(0) for dummy observations y*(1)), ys1 (dummy initial
-% observations y*(1)), xtx (X'X or phi'*phi), A0b (initial prior on A0),
-% sg0bid (diagonal of Sigma0_bar on the parameters in i-th equation in A0,
-% initial prior covariace matrix), sgpbid (diagonal of Sigma+_bar on the
-% parameters in i-th equation in A0, initial prior covariance matrix).
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-global vaya0 vaha0 vaxah vahad vbiga FRESHFUNCTION
-% CAS added FRESHFUNCTION 8/20/96 to allow use of pmddg23 in a numerical Hessian calculation, where
-% it is invoked repeatedly without new function evaluations.
-if ~FRESHFUNCTION
- fjnk = pmddf233(x,idmat0,idmat1,fss,nvar,ncoef,phi,y,A0b,sg0bid,sgpbid);
-end
-FRESHFUNCTION=0;
-badg = 0;
-%
-a0indx=find(idmat0);
-nhp = 0; % <<>> 4 hyperparameters
-na0p = length(a0indx); % <<>> number of A0 parameters
-nfp = na0p+nhp;
-g = zeros(nfp,1);
-A0h= zeros(nvar);
-A0h(a0indx) = x(1:na0p);
- % restrictions on A0
-
-%disp(sprintf('Starting loop (gradient): %g',toc))
-
-
-%%%%%%%
-%%% analytical gradient for A0 below
-%%%%%%%
-%
-% ** dla0 = dlog|A0|/d(a0??)
-vdla0 = zeros(na0p,1);
-dla0 = inv(A0h)';
-dla0 = dla0(:);
-vdla0 = dla0(a0indx);
-
-g = -fss*vdla0 + 0.5*vaya0 + 0.5*vaha0 + 0.5*vaxah + 0.5*vahad - 0.5*vbiga;
-% Commented out by TZ, 10/3/96, to allow the prior |A0|^k
-%%g = -(fss+ncoef)*vdla0 + 0.5*vaya0 + 0.5*vaha0 + 0.5*vaxah + 0.5*vahad - 0.5*vbiga;
-
-%disp(sprintf('Loop end (gradient): %g', toc))
\ No newline at end of file
+function [g,badg] = pmddg233(x,idmat0,idmat1,fss,nvar, ...
+ ncoef,phi,y,A0b,sg0bid,sgpbid);
+% Leeper-Sims-Zha BVAR setup, analytical gradient for "csminwel.m"
+% general program to setup A0 matrix and compute the likelihood
+% requires x (parameter vector), a0indx (matrix indicating the free
+% parameters in A0), fss (forecast sample size), nvar (number of variables),
+% ncoef (number of coefficients in a single equation in A+), phi (r.h.s.
+% observations in the system for A+), y (l.h.s. observations for A0),
+% ymy (Y'*M*Y), xd0 (X(0) for dummy observations y*(1)), ys1 (dummy initial
+% observations y*(1)), xtx (X'X or phi'*phi), A0b (initial prior on A0),
+% sg0bid (diagonal of Sigma0_bar on the parameters in i-th equation in A0,
+% initial prior covariace matrix), sgpbid (diagonal of Sigma+_bar on the
+% parameters in i-th equation in A0, initial prior covariance matrix).
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+global vaya0 vaha0 vaxah vahad vbiga FRESHFUNCTION
+% CAS added FRESHFUNCTION 8/20/96 to allow use of pmddg23 in a numerical Hessian calculation, where
+% it is invoked repeatedly without new function evaluations.
+if ~FRESHFUNCTION
+ fjnk = pmddf233(x,idmat0,idmat1,fss,nvar,ncoef,phi,y,A0b,sg0bid,sgpbid);
+end
+FRESHFUNCTION=0;
+badg = 0;
+%
+a0indx=find(idmat0);
+nhp = 0; % <<>> 4 hyperparameters
+na0p = length(a0indx); % <<>> number of A0 parameters
+nfp = na0p+nhp;
+g = zeros(nfp,1);
+A0h= zeros(nvar);
+A0h(a0indx) = x(1:na0p);
+ % restrictions on A0
+
+%disp(sprintf('Starting loop (gradient): %g',toc))
+
+
+%%%%%%%
+%%% analytical gradient for A0 below
+%%%%%%%
+%
+% ** dla0 = dlog|A0|/d(a0??)
+vdla0 = zeros(na0p,1);
+dla0 = inv(A0h)';
+dla0 = dla0(:);
+vdla0 = dla0(a0indx);
+
+g = -fss*vdla0 + 0.5*vaya0 + 0.5*vaha0 + 0.5*vaxah + 0.5*vahad - 0.5*vbiga;
+% Commented out by TZ, 10/3/96, to allow the prior |A0|^k
+%%g = -(fss+ncoef)*vdla0 + 0.5*vaya0 + 0.5*vaha0 + 0.5*vaxah + 0.5*vahad - 0.5*vbiga;
+
+%disp(sprintf('Loop end (gradient): %g', toc))
diff --git a/MatlabFiles/pmddg234.m b/MatlabFiles/pmddg234.m
index 4b4e95aad8089ab0fa3ea0d944fe3f30fbe01b27..d0e6fbe3db23cfe942147802d192cb77c7dcef5b 100644
--- a/MatlabFiles/pmddg234.m
+++ b/MatlabFiles/pmddg234.m
@@ -1,65 +1,65 @@
-function [g,badg] = pmddg234(x,idmat0,idmat1,fss,nvar, ...
- ncoef,phi,y,A0b,sg0bid,sgpbid);
-% Leeper-Sims-Zha BVAR setup, analytical gradient for "csminwel.m"
-% general program to setup A0 matrix and compute the likelihood
-% requires x (parameter vector), a0indx (matrix indicating the free
-% parameters in A0), fss (forecast sample size), nvar (number of variables),
-% ncoef (number of coefficients in a single equation in A+), phi (r.h.s.
-% observations in the system for A+), y (l.h.s. observations for A0),
-% ymy (Y'*M*Y), xd0 (X(0) for dummy observations y*(1)), ys1 (dummy initial
-% observations y*(1)), xtx (X'X or phi'*phi), A0b (initial prior on A0),
-% sg0bid (diagonal of Sigma0_bar on the parameters in i-th equation in A0,
-% initial prior covariace matrix), sgpbid (diagonal of Sigma+_bar on the
-% parameters in i-th equation in A0, initial prior covariance matrix).
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-global vaya0 vaha0 vaxah vahad vbiga FRESHFUNCTION
-% CAS added FRESHFUNCTION 8/20/96 to allow use of pmddg23 in a numerical Hessian calculation, where
-% it is invoked repeatedly without new function evaluations.
-if ~FRESHFUNCTION
- fjnk = pmddf234(x,idmat0,idmat1,fss,nvar,ncoef,phi,y,A0b,sg0bid,sgpbid);
-end
-FRESHFUNCTION=0;
-badg = 0;
-%
-a0indx=find(idmat0);
-nhp = 0; % <<>> 4 hyperparameters
-na0p = length(a0indx); % <<>> number of A0 parameters
-nfp = na0p+nhp;
-g = zeros(nfp,1);
-A0h= zeros(nvar);
-A0h(a0indx) = x(1:na0p);
- % restrictions on A0
-
-%disp(sprintf('Starting loop (gradient): %g',toc))
-
-
-%%%%%%%
-%%% analytical gradient for A0 below
-%%%%%%%
-%
-% ** dla0 = dlog|A0|/d(a0??)
-vdla0 = zeros(na0p,1);
-dla0 = inv(A0h)';
-dla0 = dla0(:);
-vdla0 = dla0(a0indx);
-
-% without the prior |A0|^k
-g = -fss*vdla0 + 0.5*vaya0 + 0.5*vaha0 + 0.5*vaxah + 0.5*vahad - 0.5*vbiga;
-
-%disp(sprintf('Loop end (gradient): %g', toc))
\ No newline at end of file
+function [g,badg] = pmddg234(x,idmat0,idmat1,fss,nvar, ...
+ ncoef,phi,y,A0b,sg0bid,sgpbid);
+% Leeper-Sims-Zha BVAR setup, analytical gradient for "csminwel.m"
+% general program to setup A0 matrix and compute the likelihood
+% requires x (parameter vector), a0indx (matrix indicating the free
+% parameters in A0), fss (forecast sample size), nvar (number of variables),
+% ncoef (number of coefficients in a single equation in A+), phi (r.h.s.
+% observations in the system for A+), y (l.h.s. observations for A0),
+% ymy (Y'*M*Y), xd0 (X(0) for dummy observations y*(1)), ys1 (dummy initial
+% observations y*(1)), xtx (X'X or phi'*phi), A0b (initial prior on A0),
+% sg0bid (diagonal of Sigma0_bar on the parameters in i-th equation in A0,
+% initial prior covariace matrix), sgpbid (diagonal of Sigma+_bar on the
+% parameters in i-th equation in A0, initial prior covariance matrix).
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+global vaya0 vaha0 vaxah vahad vbiga FRESHFUNCTION
+% CAS added FRESHFUNCTION 8/20/96 to allow use of pmddg23 in a numerical Hessian calculation, where
+% it is invoked repeatedly without new function evaluations.
+if ~FRESHFUNCTION
+ fjnk = pmddf234(x,idmat0,idmat1,fss,nvar,ncoef,phi,y,A0b,sg0bid,sgpbid);
+end
+FRESHFUNCTION=0;
+badg = 0;
+%
+a0indx=find(idmat0);
+nhp = 0; % <<>> 4 hyperparameters
+na0p = length(a0indx); % <<>> number of A0 parameters
+nfp = na0p+nhp;
+g = zeros(nfp,1);
+A0h= zeros(nvar);
+A0h(a0indx) = x(1:na0p);
+ % restrictions on A0
+
+%disp(sprintf('Starting loop (gradient): %g',toc))
+
+
+%%%%%%%
+%%% analytical gradient for A0 below
+%%%%%%%
+%
+% ** dla0 = dlog|A0|/d(a0??)
+vdla0 = zeros(na0p,1);
+dla0 = inv(A0h)';
+dla0 = dla0(:);
+vdla0 = dla0(a0indx);
+
+% without the prior |A0|^k
+g = -fss*vdla0 + 0.5*vaya0 + 0.5*vaha0 + 0.5*vaxah + 0.5*vahad - 0.5*vbiga;
+
+%disp(sprintf('Loop end (gradient): %g', toc))
diff --git a/MatlabFiles/pmddg235.m b/MatlabFiles/pmddg235.m
index 187999952fe8ac0d05e05f891db8e96af5d85604..7993d9208df456b1f15b788110b8cbc76b73c585 100644
--- a/MatlabFiles/pmddg235.m
+++ b/MatlabFiles/pmddg235.m
@@ -1,65 +1,65 @@
-function [g,badg] = pmddg235(x,idmat0,idmat1,fss,nvar, ...
- ncoef,phi,y,A0b,sg0bid,sgpbid);
-% Leeper-Sims-Zha BVAR setup, analytical gradient for "csminwel.m"
-% general program to setup A0 matrix and compute the likelihood
-% requires x (parameter vector), a0indx (matrix indicating the free
-% parameters in A0), fss (forecast sample size), nvar (number of variables),
-% ncoef (number of coefficients in a single equation in A+), phi (r.h.s.
-% observations in the system for A+), y (l.h.s. observations for A0),
-% ymy (Y'*M*Y), xd0 (X(0) for dummy observations y*(1)), ys1 (dummy initial
-% observations y*(1)), xtx (X'X or phi'*phi), A0b (initial prior on A0),
-% sg0bid (diagonal of Sigma0_bar on the parameters in i-th equation in A0,
-% initial prior covariace matrix), sgpbid (diagonal of Sigma+_bar on the
-% parameters in i-th equation in A0, initial prior covariance matrix).
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-global vaha0 vaMMa0 FRESHFUNCTION
-% CAS added FRESHFUNCTION 8/20/96 to allow use of pmddg23 in a numerical Hessian calculation, where
-% it is invoked repeatedly without new function evaluations.
-if ~FRESHFUNCTION
- fjnk = pmddf235(x,idmat0,idmat1,fss,nvar,ncoef,phi,y,A0b,sg0bid,sgpbid);
-end
-FRESHFUNCTION=0;
-badg = 0;
-%
-a0indx=find(idmat0);
-nhp = 0; % <<>> 4 hyperparameters
-na0p = length(a0indx); % <<>> number of A0 parameters
-nfp = na0p+nhp;
-g = zeros(nfp,1);
-A0h= zeros(nvar);
-A0h(a0indx) = x(1:na0p);
- % restrictions on A0
-
-%disp(sprintf('Starting loop (gradient): %g',toc))
-
-
-%%%%%%%
-%%% analytical gradient for A0 below
-%%%%%%%
-%
-% ** dla0 = dlog|A0|/d(a0??)
-vdla0 = zeros(na0p,1);
-dla0 = inv(A0h)';
-dla0 = dla0(:);
-vdla0 = dla0(a0indx);
-
-% without the prior |A0|^k
-g = -fss*vdla0 + 0.5*vaha0 + 0.5*vaMMa0;
-
-%disp(sprintf('Loop end (gradient): %g', toc))
\ No newline at end of file
+function [g,badg] = pmddg235(x,idmat0,idmat1,fss,nvar, ...
+ ncoef,phi,y,A0b,sg0bid,sgpbid);
+% Leeper-Sims-Zha BVAR setup, analytical gradient for "csminwel.m"
+% general program to setup A0 matrix and compute the likelihood
+% requires x (parameter vector), a0indx (matrix indicating the free
+% parameters in A0), fss (forecast sample size), nvar (number of variables),
+% ncoef (number of coefficients in a single equation in A+), phi (r.h.s.
+% observations in the system for A+), y (l.h.s. observations for A0),
+% ymy (Y'*M*Y), xd0 (X(0) for dummy observations y*(1)), ys1 (dummy initial
+% observations y*(1)), xtx (X'X or phi'*phi), A0b (initial prior on A0),
+% sg0bid (diagonal of Sigma0_bar on the parameters in i-th equation in A0,
+% initial prior covariace matrix), sgpbid (diagonal of Sigma+_bar on the
+% parameters in i-th equation in A0, initial prior covariance matrix).
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+global vaha0 vaMMa0 FRESHFUNCTION
+% CAS added FRESHFUNCTION 8/20/96 to allow use of pmddg23 in a numerical Hessian calculation, where
+% it is invoked repeatedly without new function evaluations.
+if ~FRESHFUNCTION
+ fjnk = pmddf235(x,idmat0,idmat1,fss,nvar,ncoef,phi,y,A0b,sg0bid,sgpbid);
+end
+FRESHFUNCTION=0;
+badg = 0;
+%
+a0indx=find(idmat0);
+nhp = 0; % <<>> 4 hyperparameters
+na0p = length(a0indx); % <<>> number of A0 parameters
+nfp = na0p+nhp;
+g = zeros(nfp,1);
+A0h= zeros(nvar);
+A0h(a0indx) = x(1:na0p);
+ % restrictions on A0
+
+%disp(sprintf('Starting loop (gradient): %g',toc))
+
+
+%%%%%%%
+%%% analytical gradient for A0 below
+%%%%%%%
+%
+% ** dla0 = dlog|A0|/d(a0??)
+vdla0 = zeros(na0p,1);
+dla0 = inv(A0h)';
+dla0 = dla0(:);
+vdla0 = dla0(a0indx);
+
+% without the prior |A0|^k
+g = -fss*vdla0 + 0.5*vaha0 + 0.5*vaMMa0;
+
+%disp(sprintf('Loop end (gradient): %g', toc))
diff --git a/MatlabFiles/pmddg236.m b/MatlabFiles/pmddg236.m
index bb7596b024dae92a161d744a91b2a1dbadac0059..9b478f1109470637c28a55d748e8cc2973310406 100644
--- a/MatlabFiles/pmddg236.m
+++ b/MatlabFiles/pmddg236.m
@@ -1,66 +1,66 @@
-function [g,badg] = pmddg236(x,idmat0,idmatp,fss,nvar, ...
- ncoef,phi,y,A0b,sg0bid,sgpbid);
-%
-% Leeper-Sims-Zha BVAR setup, analytical gradient for "csminwel.m"
-% general program to setup A0 matrix and compute the likelihood
-% requires x (parameter vector), a0indx (matrix indicating the free
-% parameters in A0), fss (forecast sample size), nvar (number of variables),
-% ncoef (number of coefficients in a single equation in A+), phi (r.h.s.
-% observations in the system for A+), y (l.h.s. observations for A0),
-% ymy (Y'*M*Y), xd0 (X(0) for dummy observations y*(1)), ys1 (dummy initial
-% observations y*(1)), xtx (X'X or phi'*phi), A0b (initial prior on A0),
-% sg0bid (diagonal of Sigma0_bar on the parameters in i-th equation in A0,
-% initial prior covariace matrix), sgpbid (diagonal of Sigma+_bar on the
-% parameters in i-th equation in A0, initial prior covariance matrix).
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-global vaha0 vaMMa0 FRESHFUNCTION
-% CAS added FRESHFUNCTION 8/20/96 to allow use of pmddg23 in a numerical Hessian calculation, where
-% it is invoked repeatedly without new function evaluations.
-if ~FRESHFUNCTION
- fjnk = pmddf236(x,idmat0,idmatp,fss,nvar,ncoef,phi,y,A0b,sg0bid,sgpbid);
-end
-FRESHFUNCTION=0;
-badg = 0;
-%
-a0indx=find(idmat0);
-nhp = 0; % <<>> 4 hyperparameters
-na0p = length(a0indx); % <<>> number of A0 parameters
-nfp = na0p+nhp;
-g = zeros(nfp,1);
-A0h= zeros(nvar);
-A0h(a0indx) = x(1:na0p);
- % restrictions on A0
-
-%disp(sprintf('Starting loop (gradient): %g',toc))
-
-
-%%%%%%%
-%%% analytical gradient for A0 below
-%%%%%%%
-%
-% ** dla0 = dlog|A0|/d(a0??)
-vdla0 = zeros(na0p,1);
-dla0 = inv(A0h)';
-dla0 = dla0(:);
-vdla0 = dla0(a0indx);
-
-% without the prior |A0|^k
-g = -fss*vdla0 + 0.5*vaha0 + 0.5*vaMMa0;
-
-%disp(sprintf('Loop end (gradient): %g', toc))
\ No newline at end of file
+function [g,badg] = pmddg236(x,idmat0,idmatp,fss,nvar, ...
+ ncoef,phi,y,A0b,sg0bid,sgpbid);
+%
+% Leeper-Sims-Zha BVAR setup, analytical gradient for "csminwel.m"
+% general program to setup A0 matrix and compute the likelihood
+% requires x (parameter vector), a0indx (matrix indicating the free
+% parameters in A0), fss (forecast sample size), nvar (number of variables),
+% ncoef (number of coefficients in a single equation in A+), phi (r.h.s.
+% observations in the system for A+), y (l.h.s. observations for A0),
+% ymy (Y'*M*Y), xd0 (X(0) for dummy observations y*(1)), ys1 (dummy initial
+% observations y*(1)), xtx (X'X or phi'*phi), A0b (initial prior on A0),
+% sg0bid (diagonal of Sigma0_bar on the parameters in i-th equation in A0,
+% initial prior covariace matrix), sgpbid (diagonal of Sigma+_bar on the
+% parameters in i-th equation in A0, initial prior covariance matrix).
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+global vaha0 vaMMa0 FRESHFUNCTION
+% CAS added FRESHFUNCTION 8/20/96 to allow use of pmddg23 in a numerical Hessian calculation, where
+% it is invoked repeatedly without new function evaluations.
+if ~FRESHFUNCTION
+ fjnk = pmddf236(x,idmat0,idmatp,fss,nvar,ncoef,phi,y,A0b,sg0bid,sgpbid);
+end
+FRESHFUNCTION=0;
+badg = 0;
+%
+a0indx=find(idmat0);
+nhp = 0; % <<>> 4 hyperparameters
+na0p = length(a0indx); % <<>> number of A0 parameters
+nfp = na0p+nhp;
+g = zeros(nfp,1);
+A0h= zeros(nvar);
+A0h(a0indx) = x(1:na0p);
+ % restrictions on A0
+
+%disp(sprintf('Starting loop (gradient): %g',toc))
+
+
+%%%%%%%
+%%% analytical gradient for A0 below
+%%%%%%%
+%
+% ** dla0 = dlog|A0|/d(a0??)
+vdla0 = zeros(na0p,1);
+dla0 = inv(A0h)';
+dla0 = dla0(:);
+vdla0 = dla0(a0indx);
+
+% without the prior |A0|^k
+g = -fss*vdla0 + 0.5*vaha0 + 0.5*vaMMa0;
+
+%disp(sprintf('Loop end (gradient): %g', toc))
diff --git a/MatlabFiles/pmddwf23.m b/MatlabFiles/pmddwf23.m
index dcf99fcc8fdf730ad7ec9a6f6c36bbf65ecd0bf6..428ed0ca843ec1392df6fd283245874d03dbd086 100644
--- a/MatlabFiles/pmddwf23.m
+++ b/MatlabFiles/pmddwf23.m
@@ -1,213 +1,213 @@
-function [of,GlbAphh] = pmddwf23(x,a0indx,fss,nvar,ncoef,phi, ...
- y,xd0,ys1,A0b,sg0bid,sgpbid)
-%function of = pmddf23(x,a0indx,fss,nvar,ncoef,phi, ...
-% y,xd0,ys1,A0b,sg0bid,sgpbid)
-% Leeper-Sims-Zha BVAR setup
-% general program to setup A0 matrix and compute the likelihood
-% requires
-% x (parameter vector)
-% a0indx (matrix indicating the free parameters in A0)
-% fss (forecast sample size)
-% nvar (number of variables)
-% ncoef (number of coefficients in a single equation in A+)
-% phi (r.h.s. observations in the system for A+)
-% y (l.h.s. observations for A0)
-% xd0 (X(0) for dummy observations y*(1))
-% ys1 (dummy initial observations y*(1))
-% A0b (initial prior mean on A0)
-% sg0bid (diagonal of Sigma0_bar, unweighted prior vcv on the parameters in i-th equation in A0)
-% sgpbid (diagonal of Sigma+_bar, unweighted prior vcv on the parameters in i-th equation in A+)
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-global vaya0 vaha0 vaxah vahad vbiga GlbAph
-% pre-allocate GlbAph to be zeros(nvar,lags*nvar+1=ncoef)
-
-GlbAphh=zeros(ncoef,nvar);
-
-
-nhp = 0; % <<>> 4 hyperparameters
-na0p = length(a0indx); % <<>> number of A0 parameters
-nfp = na0p+nhp;
-
-a0 = zeros(nvar^2,1);
-a0(a0indx) = x(1:na0p);
-A0h = reshape(a0,nvar,nvar);
- % restrictions on A0
-
-[A0hl,A0hu] = lu(A0h);
-
-% ** hyperparameters
-%%mu = x(na0p+1:nfp);
-mu = ones(5,1);
-mu(1) = 1;
-mu(2) = 0.2;
-mu(3) = 20;
-mu(5) = 1;
-% results from ...\dummy1\foreh\pmdd6.out
-%
-% mu(1): overall tightness and also for A0;
-% mu(2): relative tightness for A+;
-% mu(3): relative tightness for the constant term;
-% mu(4): relative tightness for dummy initial observations;
-% mu(5): relative tightness for prior dummy observations.
-
-% ** weight prior dummy observations
-%phi(1:nvar,:) = (mu(5)^2/mu(1)^2)*phi(1:nvar,:);
-%y(1:nvar,:) = (mu(5)^2/mu(1)^2)*y(1:nvar,:);
-% modified by CAS 8/6/96. The dummy obs weights are naturally in std units, not var units.
-phi(1:nvar,:) = mu(5)*phi(1:nvar,:);
-y(1:nvar,:) = mu(5)*y(1:nvar,:);
-
-% ** set up the conditional prior variance sg0bi and sgpbi.
-sg0bida = mu(1)^2*sg0bid;
-sgpbida = mu(1)^2*mu(2)^2*sgpbid;
-sgpbida(ncoef) = mu(1)^2*mu(3)^2;
-sg0bi = diag(sg0bida);
-sgpbi = diag(sgpbida);
-
-%
-% ** some common terms
-[u d v]=svd(phi,0); %trial
-% xtx = phi'*phi; %trial
-vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
-xtx=vd*vd';
-% M = eye(fss) - phi*(xtx\phi'); % not used except in line below %trial
-yu = y'*u; %trial
-cxyxx=yu*yu'; %trial
-ymy=y'*y-cxyxx; %trial
-%ymy = y'*M*y;
-%cxy = phi'*y; %trial
-cxy = vd*yu'; %trial
-%cxpy = xtx\cxy; %not used further except in line below
-%cxyxx = cxy'*cxpy;
-
-% ** estimate A+_hat conditional on A0, ncoef*nvar, but not using full prior
-%GlbAph = cxpy*A0h;
-
-
-%%%%%%%
-%%% build the objective function below
-%%%%%%%
-%
-t1f = diag(abs(A0hu));
-t1f = log(t1f);
-tt1 = (-1.0)*fss*sum(t1f);
-
-tt3 = 0;
-tt4 = 0;
-
-stril = 0;
-Hp0p = zeros(ncoef);
-% initializing Htd_+0*inv(Htd_00)*Htd_0+: conditional prior covariance
-for i = 1:nvar
- stri = find(A0h(:,i));
- strm = length(stri);
-
- A0hb = A0h(stri,i)-A0b(stri,i);
- Hp0 = zeros(ncoef,strm);
- % initializing Htd_+0*inv(Htd_00)*Htd_0
-
- % ** set up the conditional prior variance sg0bi and sgpbi.
- sg0bd = zeros(stri,1);
- sg0bd = sg0bida(stri);
- sg0b = diag(sg0bd);
-
- % ** set up the unconditional prior variance on A0i and A+i
- XX = zeros(nvar,strm);
- XX(stri,:) = eye(strm); % XX in Gelman, etel, on p479
- %
- Hb = zeros(strm+ncoef); % including A0i and A+i
- Hb(1:strm,1:strm) = sg0b;
- Hb(1:strm,strm+1:strm+nvar) = sg0b*XX';
- Hb(strm+1:strm+nvar,1:strm) = XX*sg0b;
- Hb(strm+1:strm+nvar,strm+1:strm+nvar) = XX*sg0b*XX'+sgpbi(1:nvar,1:nvar);
- Hb(strm+nvar+1:strm+ncoef,strm+nvar+1:strm+ncoef) = ...
- sgpbi(nvar+1:ncoef,nvar+1:ncoef);
-
- % ** set up the final prior variance on A0i and A+i, i =1,..,6 separately.
- % ** using dummy initial observations
- %zs1 = [ys1(:,stri) -xd0]; % z*1 for the 1st equation
-
- % * final unconditional prior variance on A0i and A+i
- %Hbzs1=Hb*zs1';
- %Htd = Hb - Hbzs1*((zs1*Hbzs1+mu(1)^2*mu(4)^2*eye(nvar))\Hbzs1');
- % CAS mod 8/7/96: Hope is that dropping above two lines in favor of the one below
- % removes double-counting of dummy observations and a possible scale sensitivity.
- Htd = Hb;
- % H_~: td: tilde for ~
-
- % * final conditional prior variance on A+i give that on A0i, and
- % * unconditional variance on A0+
- H0td = Htd(1:strm,1:strm); % unconditional
- % ** inverse and chol decomp
- H0tdi = inv(H0td);
-
- %
- Hptd10 = Htd(strm+1:strm+nvar,1:strm);
- % top matrix of nvar in Htd_+0
- Hptd100 = Hptd10*H0tdi;
- Hp0(1:nvar,:) = Hptd100;
- Hptd101 = Hptd100*Hptd10';
- Hp0p(1:nvar,1:nvar) = Hptd101;
- Hptd = Htd(strm+1:strm+ncoef,strm+1:strm+ncoef) - Hp0p;
- % condtional on A0i, H_plus_tilde
-
- % ** inverse
- Hptdi = inv(Hptd);
-
- % common term
- xxhp = xtx+Hptdi;
- Vxxhp = chol(xxhp);
- % inverse of Vxxhp is the upper triangular decomp
- % of inv(Vxxhp) (covariance of alpst)
-
- % ** final conditional prior mean on A+i
- alptd = zeros(ncoef,1);
- alptd(1:nvar) = XX*A0b(stri,i);
- %alptd = alptd + Hptdf*(H0tdi*A0hb); CAS efficiency improvement 8/8/96
- alptd = alptd + Hp0*A0hb;
- % conditional on A0i, alpha-plus-tilde
-
- % * alpha_plus_* for the ith equation, ncoef*1
- % *alps = xxhp\(phi'*y*A0h(:,i)+HptdDinalptd);
- HptdDinalptd=Hptdi*alptd;
- xaha = cxy*A0h(:,i)+HptdDinalptd; %CAS 8/7/96. Replaced phi'*y with cxy
- alpst = xaha'/xxhp;
- GlbAph(i,:)=alpst;
- % alpst': transpose of alps.
- GlbAphh(:,i)=alpst' + Vxxhp\randn(length(alpst),1);
- % posterior alpha_plus_*
-
-
- % ** 3rd bid term in i-th equation,
- % ** with all other 3rd big terms prior to i-th
- A0hy = A0h(:,i)'*ymy;
- A0hbH = A0hb'*H0tdi;
- tt3 = tt3 + A0hy*A0h(:,i) + A0hbH*A0hb;
-
- % ** 4th bid term in i-th equation,
- % ** with all other 4th big terms prior to i-th
- A0hcxyxx = A0h(:,i)'*cxyxx;
- atdHatd = alptd'*HptdDinalptd;
- tt4 = tt4 + A0hcxyxx*A0h(:,i)+atdHatd-alpst*xaha;
-
- stril = stril + strm;
-end
-
-of = tt1 + 0.5*tt3 + 0.5*tt4;
-
\ No newline at end of file
+function [of,GlbAphh] = pmddwf23(x,a0indx,fss,nvar,ncoef,phi, ...
+ y,xd0,ys1,A0b,sg0bid,sgpbid)
+%function of = pmddf23(x,a0indx,fss,nvar,ncoef,phi, ...
+% y,xd0,ys1,A0b,sg0bid,sgpbid)
+% Leeper-Sims-Zha BVAR setup
+% general program to setup A0 matrix and compute the likelihood
+% requires
+% x (parameter vector)
+% a0indx (matrix indicating the free parameters in A0)
+% fss (forecast sample size)
+% nvar (number of variables)
+% ncoef (number of coefficients in a single equation in A+)
+% phi (r.h.s. observations in the system for A+)
+% y (l.h.s. observations for A0)
+% xd0 (X(0) for dummy observations y*(1))
+% ys1 (dummy initial observations y*(1))
+% A0b (initial prior mean on A0)
+% sg0bid (diagonal of Sigma0_bar, unweighted prior vcv on the parameters in i-th equation in A0)
+% sgpbid (diagonal of Sigma+_bar, unweighted prior vcv on the parameters in i-th equation in A+)
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+global vaya0 vaha0 vaxah vahad vbiga GlbAph
+% pre-allocate GlbAph to be zeros(nvar,lags*nvar+1=ncoef)
+
+GlbAphh=zeros(ncoef,nvar);
+
+
+nhp = 0; % <<>> 4 hyperparameters
+na0p = length(a0indx); % <<>> number of A0 parameters
+nfp = na0p+nhp;
+
+a0 = zeros(nvar^2,1);
+a0(a0indx) = x(1:na0p);
+A0h = reshape(a0,nvar,nvar);
+ % restrictions on A0
+
+[A0hl,A0hu] = lu(A0h);
+
+% ** hyperparameters
+%%mu = x(na0p+1:nfp);
+mu = ones(5,1);
+mu(1) = 1;
+mu(2) = 0.2;
+mu(3) = 20;
+mu(5) = 1;
+% results from ...\dummy1\foreh\pmdd6.out
+%
+% mu(1): overall tightness and also for A0;
+% mu(2): relative tightness for A+;
+% mu(3): relative tightness for the constant term;
+% mu(4): relative tightness for dummy initial observations;
+% mu(5): relative tightness for prior dummy observations.
+
+% ** weight prior dummy observations
+%phi(1:nvar,:) = (mu(5)^2/mu(1)^2)*phi(1:nvar,:);
+%y(1:nvar,:) = (mu(5)^2/mu(1)^2)*y(1:nvar,:);
+% modified by CAS 8/6/96. The dummy obs weights are naturally in std units, not var units.
+phi(1:nvar,:) = mu(5)*phi(1:nvar,:);
+y(1:nvar,:) = mu(5)*y(1:nvar,:);
+
+% ** set up the conditional prior variance sg0bi and sgpbi.
+sg0bida = mu(1)^2*sg0bid;
+sgpbida = mu(1)^2*mu(2)^2*sgpbid;
+sgpbida(ncoef) = mu(1)^2*mu(3)^2;
+sg0bi = diag(sg0bida);
+sgpbi = diag(sgpbida);
+
+%
+% ** some common terms
+[u d v]=svd(phi,0); %trial
+% xtx = phi'*phi; %trial
+vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
+xtx=vd*vd';
+% M = eye(fss) - phi*(xtx\phi'); % not used except in line below %trial
+yu = y'*u; %trial
+cxyxx=yu*yu'; %trial
+ymy=y'*y-cxyxx; %trial
+%ymy = y'*M*y;
+%cxy = phi'*y; %trial
+cxy = vd*yu'; %trial
+%cxpy = xtx\cxy; %not used further except in line below
+%cxyxx = cxy'*cxpy;
+
+% ** estimate A+_hat conditional on A0, ncoef*nvar, but not using full prior
+%GlbAph = cxpy*A0h;
+
+
+%%%%%%%
+%%% build the objective function below
+%%%%%%%
+%
+t1f = diag(abs(A0hu));
+t1f = log(t1f);
+tt1 = (-1.0)*fss*sum(t1f);
+
+tt3 = 0;
+tt4 = 0;
+
+stril = 0;
+Hp0p = zeros(ncoef);
+% initializing Htd_+0*inv(Htd_00)*Htd_0+: conditional prior covariance
+for i = 1:nvar
+ stri = find(A0h(:,i));
+ strm = length(stri);
+
+ A0hb = A0h(stri,i)-A0b(stri,i);
+ Hp0 = zeros(ncoef,strm);
+ % initializing Htd_+0*inv(Htd_00)*Htd_0
+
+ % ** set up the conditional prior variance sg0bi and sgpbi.
+ sg0bd = zeros(stri,1);
+ sg0bd = sg0bida(stri);
+ sg0b = diag(sg0bd);
+
+ % ** set up the unconditional prior variance on A0i and A+i
+ XX = zeros(nvar,strm);
+ XX(stri,:) = eye(strm); % XX in Gelman, etel, on p479
+ %
+ Hb = zeros(strm+ncoef); % including A0i and A+i
+ Hb(1:strm,1:strm) = sg0b;
+ Hb(1:strm,strm+1:strm+nvar) = sg0b*XX';
+ Hb(strm+1:strm+nvar,1:strm) = XX*sg0b;
+ Hb(strm+1:strm+nvar,strm+1:strm+nvar) = XX*sg0b*XX'+sgpbi(1:nvar,1:nvar);
+ Hb(strm+nvar+1:strm+ncoef,strm+nvar+1:strm+ncoef) = ...
+ sgpbi(nvar+1:ncoef,nvar+1:ncoef);
+
+ % ** set up the final prior variance on A0i and A+i, i =1,..,6 separately.
+ % ** using dummy initial observations
+ %zs1 = [ys1(:,stri) -xd0]; % z*1 for the 1st equation
+
+ % * final unconditional prior variance on A0i and A+i
+ %Hbzs1=Hb*zs1';
+ %Htd = Hb - Hbzs1*((zs1*Hbzs1+mu(1)^2*mu(4)^2*eye(nvar))\Hbzs1');
+ % CAS mod 8/7/96: Hope is that dropping above two lines in favor of the one below
+ % removes double-counting of dummy observations and a possible scale sensitivity.
+ Htd = Hb;
+ % H_~: td: tilde for ~
+
+ % * final conditional prior variance on A+i give that on A0i, and
+ % * unconditional variance on A0+
+ H0td = Htd(1:strm,1:strm); % unconditional
+ % ** inverse and chol decomp
+ H0tdi = inv(H0td);
+
+ %
+ Hptd10 = Htd(strm+1:strm+nvar,1:strm);
+ % top matrix of nvar in Htd_+0
+ Hptd100 = Hptd10*H0tdi;
+ Hp0(1:nvar,:) = Hptd100;
+ Hptd101 = Hptd100*Hptd10';
+ Hp0p(1:nvar,1:nvar) = Hptd101;
+ Hptd = Htd(strm+1:strm+ncoef,strm+1:strm+ncoef) - Hp0p;
+ % condtional on A0i, H_plus_tilde
+
+ % ** inverse
+ Hptdi = inv(Hptd);
+
+ % common term
+ xxhp = xtx+Hptdi;
+ Vxxhp = chol(xxhp);
+ % inverse of Vxxhp is the upper triangular decomp
+ % of inv(Vxxhp) (covariance of alpst)
+
+ % ** final conditional prior mean on A+i
+ alptd = zeros(ncoef,1);
+ alptd(1:nvar) = XX*A0b(stri,i);
+ %alptd = alptd + Hptdf*(H0tdi*A0hb); CAS efficiency improvement 8/8/96
+ alptd = alptd + Hp0*A0hb;
+ % conditional on A0i, alpha-plus-tilde
+
+ % * alpha_plus_* for the ith equation, ncoef*1
+ % *alps = xxhp\(phi'*y*A0h(:,i)+HptdDinalptd);
+ HptdDinalptd=Hptdi*alptd;
+ xaha = cxy*A0h(:,i)+HptdDinalptd; %CAS 8/7/96. Replaced phi'*y with cxy
+ alpst = xaha'/xxhp;
+ GlbAph(i,:)=alpst;
+ % alpst': transpose of alps.
+ GlbAphh(:,i)=alpst' + Vxxhp\randn(length(alpst),1);
+ % posterior alpha_plus_*
+
+
+ % ** 3rd bid term in i-th equation,
+ % ** with all other 3rd big terms prior to i-th
+ A0hy = A0h(:,i)'*ymy;
+ A0hbH = A0hb'*H0tdi;
+ tt3 = tt3 + A0hy*A0h(:,i) + A0hbH*A0hb;
+
+ % ** 4th bid term in i-th equation,
+ % ** with all other 4th big terms prior to i-th
+ A0hcxyxx = A0h(:,i)'*cxyxx;
+ atdHatd = alptd'*HptdDinalptd;
+ tt4 = tt4 + A0hcxyxx*A0h(:,i)+atdHatd-alpst*xaha;
+
+ stril = stril + strm;
+end
+
+of = tt1 + 0.5*tt3 + 0.5*tt4;
+
diff --git a/MatlabFiles/probvalsec.m b/MatlabFiles/probvalsec.m
index aabcd4eb08bc1f5b14931b6494790c7a489a8a80..30a1f8bdacb6a0ef9fe33e5559e7b3b16fce6c97 100644
--- a/MatlabFiles/probvalsec.m
+++ b/MatlabFiles/probvalsec.m
@@ -1,90 +1,90 @@
-function [probelow,valow,yhatmean] = probvalsec(yhatprob,yhatpo,ninv,forep,nvar,findex,...
- sindex,valix,prolix)
-% [probelow,valow,yhatmean] = probvalsec(yhatprob,yhatpo,ninv,forep,nvar,findex,...
-% sindex,valix,prolix)
-%
-% Probabilites (below) and values for selected variables and levels.
-% This program takes outputs from "histpdfcnt.m" which must be run first.
-% Compute (1) the probability of the x-value (such as FFR) that is below a level
-% prespecified by valix (e.g., probability of R below 5%); (2) the x-value
-% (such as median or .60 lower-tail value) below which the probability is at a level
-% prespecified by "prolix" (e.g., median when 0.50 is prespecified); (3) the mean
-% of the x-values (e.g., the mean of Pcm, M2, FFR, etc.).
-%
-% yhatprob: 2+ninv-by-forep*shockp(=1 here)*nvar. Probability (NOT density) at each bin
-% yhatpo: 2+ninv-by-forep*shockp(=1 here)*nvar. Bin position (x-axis) in relation to yhat
-% ninv: the number of bins which are small interior intervals on the x-axis
-% forep: forecast periods -- 1st dim (must be compatible with findex)
-% nvar: number of shocks or variables -- 2nd dim (must be compatible with sindex)
-% findex: index for sected forecast periods 1st dim (c.f., forep)
-% sindex: index for selected shocks or variables, 2nd dim (c.f., nvar)
-% valix: length(findex)-by-length(sindex). Selected values on the x-axis
-% prolix: length(findex)-by-length(sindex). Selected (below) probabilites
-%-----------
-% probelow: length(findex)-by-length(sindex). The probability of the x-value
-% (such as FFR) that is below a level prespecified by valix (e.g.,
-% probability of R below 5%).
-% valow: length(findex)-by-length(sindex). The x-value (such as median or .60
-% lower-tail value) below which the probability is at a level
-% prespecified by "prolix" (e.g., median when 0.50 is prespecified);
-% yhatmean: forep-by-nvar. The mean of forecasts of Pcm, M2, FFR, etc.
-%
-% 3/25/99 Tao A. Zha
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-yhatprob2=reshape(yhatprob,2+ninv,forep,nvar);
-yhatpo2=reshape(yhatpo,2+ninv,forep,nvar);
-%
-%** Mean
-yhatmean0 = sum(yhatprob.*yhatpo,1);
-yhatmean = reshape(yhatmean0,forep,nvar);
-%
-probelow=zeros(length(findex),length(sindex));
- % probablity of the x-value that is below a specified level
- % E.g., probability of R below 5%.
-valow=probelow;
- % x-value for the probability that is below a specified level
- % E.g., median when 0.50 is specified.
-cumprob=cumsum(yhatprob2,1); % cumulative probabilities
-%
-count1=0;
-for k1=sindex % forecast variables (or shocks)
- count1=count1+1;
- count2=0;
- for k2=findex % forecast periods
- count2=count2+1;
- posix = max(find(yhatpo2(:,k2,k1)<valix(count2,count1)));
- % index for the position corr. to the x-value respeicied.
- if isempty(posix)
- probelow(count2,count1) = cumprob(1,k2,k1);
- else
- probelow(count2,count1) = cumprob(posix,k2,k1);
- end
- posix1 = max(find(cumprob(:,k2,k1)<prolix(count2,count1)));
- % index for the position corr. probability (below) specified.
- if isempty(posix1)
- valow(count2,count1) = yhatpo2(1,k2,k1);
- else
- valow(count2,count1) = yhatpo2(posix1,k2,k1);
- end
- end
-end
-
-
-
+function [probelow,valow,yhatmean] = probvalsec(yhatprob,yhatpo,ninv,forep,nvar,findex,...
+ sindex,valix,prolix)
+% [probelow,valow,yhatmean] = probvalsec(yhatprob,yhatpo,ninv,forep,nvar,findex,...
+% sindex,valix,prolix)
+%
+% Probabilites (below) and values for selected variables and levels.
+% This program takes outputs from "histpdfcnt.m" which must be run first.
+% Compute (1) the probability of the x-value (such as FFR) that is below a level
+% prespecified by valix (e.g., probability of R below 5%); (2) the x-value
+% (such as median or .60 lower-tail value) below which the probability is at a level
+% prespecified by "prolix" (e.g., median when 0.50 is prespecified); (3) the mean
+% of the x-values (e.g., the mean of Pcm, M2, FFR, etc.).
+%
+% yhatprob: 2+ninv-by-forep*shockp(=1 here)*nvar. Probability (NOT density) at each bin
+% yhatpo: 2+ninv-by-forep*shockp(=1 here)*nvar. Bin position (x-axis) in relation to yhat
+% ninv: the number of bins which are small interior intervals on the x-axis
+% forep: forecast periods -- 1st dim (must be compatible with findex)
+% nvar: number of shocks or variables -- 2nd dim (must be compatible with sindex)
+% findex: index for sected forecast periods 1st dim (c.f., forep)
+% sindex: index for selected shocks or variables, 2nd dim (c.f., nvar)
+% valix: length(findex)-by-length(sindex). Selected values on the x-axis
+% prolix: length(findex)-by-length(sindex). Selected (below) probabilites
+%-----------
+% probelow: length(findex)-by-length(sindex). The probability of the x-value
+% (such as FFR) that is below a level prespecified by valix (e.g.,
+% probability of R below 5%).
+% valow: length(findex)-by-length(sindex). The x-value (such as median or .60
+% lower-tail value) below which the probability is at a level
+% prespecified by "prolix" (e.g., median when 0.50 is prespecified);
+% yhatmean: forep-by-nvar. The mean of forecasts of Pcm, M2, FFR, etc.
+%
+% 3/25/99 Tao A. Zha
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+yhatprob2=reshape(yhatprob,2+ninv,forep,nvar);
+yhatpo2=reshape(yhatpo,2+ninv,forep,nvar);
+%
+%** Mean
+yhatmean0 = sum(yhatprob.*yhatpo,1);
+yhatmean = reshape(yhatmean0,forep,nvar);
+%
+probelow=zeros(length(findex),length(sindex));
+ % probablity of the x-value that is below a specified level
+ % E.g., probability of R below 5%.
+valow=probelow;
+ % x-value for the probability that is below a specified level
+ % E.g., median when 0.50 is specified.
+cumprob=cumsum(yhatprob2,1); % cumulative probabilities
+%
+count1=0;
+for k1=sindex % forecast variables (or shocks)
+ count1=count1+1;
+ count2=0;
+ for k2=findex % forecast periods
+ count2=count2+1;
+ posix = max(find(yhatpo2(:,k2,k1)<valix(count2,count1)));
+ % index for the position corr. to the x-value respeicied.
+ if isempty(posix)
+ probelow(count2,count1) = cumprob(1,k2,k1);
+ else
+ probelow(count2,count1) = cumprob(posix,k2,k1);
+ end
+ posix1 = max(find(cumprob(:,k2,k1)<prolix(count2,count1)));
+ % index for the position corr. probability (below) specified.
+ if isempty(posix1)
+ valow(count2,count1) = yhatpo2(1,k2,k1);
+ else
+ valow(count2,count1) = yhatpo2(posix1,k2,k1);
+ end
+ end
+end
+
+
+
diff --git a/MatlabFiles/pwf233.m b/MatlabFiles/pwf233.m
index c3096cdca0fabd660143c0e1d73ce8a49ea997e0..d727d1c4b536dc71d5b81141b10433bf5c9c997b 100644
--- a/MatlabFiles/pwf233.m
+++ b/MatlabFiles/pwf233.m
@@ -1,242 +1,242 @@
-function of = pwf233(x,idmat0,idmat1,fss,nvar,ncoef,phi, ...
- y,A0b,sg0bid,sgpbid)
-%function of = pmddf232(x,idmat,fss,nvar,ncoef,phi, ...
-% y,A0b,sg0bid,sgpbid)
-% Leeper-Sims-Zha BVAR setup
-% general program to setup A0 matrix and compute the likelihood
-% requires
-% x (parameter vector)
-% a0indx (matrix indicating the free parameters in A0)
-% fss (forecast sample size)
-% nvar (number of variables)
-% ncoef (number of coefficients in a single equation in A+)
-% phi (r.h.s. observations in the system for A+)
-% y (l.h.s. observations for A0)
-% A0b (initial prior mean on A0)
-% sg0bid (diagonal of Sigma0_bar, unweighted prior vcv on the parameters in i-th equation in A0)
-% sgpbid (diagonal of Sigma+_bar, unweighted prior vcv on the parameters in i-th equation in A+)
-%
-% Revisions by CAS 9/27/96: If we transmit idmat, rather than a0indx, we can scale the elements of
-% idmat so that it carries information about relative prior variances.
-% Copyright (C) 1997-2012 Christopher A. Sims
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-global vaya0 vaha0 vaxah vahad vbiga GlbAph FRESHFUNCTION
-% FRESHFUNCTION added 8/20/90 by CAS. It signals the pmddg23 whether the global variables
-% it uses have been refreshed by a new pmddf231 call since the last gradient call.
-a0indx=find(idmat0);
-% pre-allocate GlbAph to be zeros(nvar,lags*nvar+1=ncoef)
-
-nhp = 0; % <<>> 4 hyperparameters
-na0p = length(a0indx); % <<>> number of A0 parameters
-nfp = na0p+nhp;
-
-% *** initializing
-vaya0 = zeros(na0p,1);
-vaha0 = zeros(na0p,1);
-vaxah = zeros(na0p,1);
-vahad = zeros(na0p,1);
-vbiga = zeros(na0p,1);
-
-
-A0h = zeros(nvar,nvar);
-A0h(a0indx) = x(1:na0p);
-%A0h = reshape(a0,nvar,nvar); CAS 9/24/96. The reshape is not necessary if a0 starts as nvar x nvar
- % restrictions on A0
-
-[A0hl,A0hu] = lu(A0h);
-
-% ** hyperparameters
-%%mu = x(na0p+1:nfp);
-mu = ones(5,1);
-mu(1) = 1;
-%mu(1) = 2;
-mu(2) = 0.2;
-mu(3) = 1;
-%mu(4)=1;
-mu(4)=1;
-mu(5) =1;
-%mu(5) = 40;
-% results from ...\dummy1\foreh\pmdd6.out
-%
-% mu(1): overall tightness and also for A0;
-% mu(2): relative tightness for A+;
-% mu(3): relative tightness for the constant term;
-% mu(4): weight on single dummy initial observation including constant;
-% mu(5): weight on nvar sums of coeffs dummy observations.
-
-% ** weight prior dummy observations
-%phi(1:nvar,:) = (mu(5)^2/mu(1)^2)*phi(1:nvar,:);
-%y(1:nvar,:) = (mu(5)^2/mu(1)^2)*y(1:nvar,:);
-%phi(ndobs,:) = mu
-% modified by CAS 8/6/96. The dummy obs weights are naturally in std units, not var units.
-phi(1:nvar,:) = mu(5)*phi(1:nvar,:);
-y(1:nvar,:) = mu(5)*y(1:nvar,:);
-phi(nvar+1,:) = mu(4)*phi(nvar+1,:);
-y(nvar+1,:) = mu(4)*y(nvar+1,:);
-
-% ** set up the conditional prior variance sg0bi and sgpbi.
-sg0bida = mu(1)^2*sg0bid;
-sgpbida = mu(1)^2*mu(2)^2*sgpbid;
-sgpbida(ncoef) = mu(1)^2*mu(3)^2;
-sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in a SZ paper
-sgppb = diag(sgppbd);
-
-%
-% ** some common terms
-[u d v]=svd(phi,0); %trial
-% xtx = phi'*phi; %trial
-vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
-xtx=vd*vd';
-% M = eye(fss) - phi*(xtx\phi'); % not used except in line below %trial
-yu = y'*u; %trial
-cxyxx=yu*yu'; %trial
-ymy=y'*y-cxyxx; %trial
-%ymy = y'*M*y;
-%cxy = phi'*y; %trial
-cxy = vd*yu'; %trial
-%cxpy = xtx\cxy; %not used further except in line below
-%cxyxx = cxy'*cxpy;
-
-% ** estimate A+_hat conditional on A0, ncoef*nvar, but not using full prior
-%GlbAph = cxpy*A0h;
-
-
-%%%%%%%
-%%% build the objective function below
-%%%%%%%
-%
-t1f = diag(abs(A0hu));
-t1f = log(t1f);
-tt1 = (-1.0)*fss*sum(t1f);
-
-tt3 = 0;
-tt4 = 0;
-
-stril = 0;
-Hp0p = zeros(ncoef);
-% initializing Htd_+0*inv(Htd_00)*Htd_0+: conditional prior covariance
-for i = 1:nvar
- stri = find(idmat0(:,i)); %CAS change 9/24/96, to avoid slim chance of an exact zero in x vector
- %at some point during an optimization.
- strm = length(stri);
-
- A0hb = A0h(stri,i)-A0b(stri,i);
- Hp0 = zeros(ncoef,strm);
- % initializing Htd_+0*inv(Htd_00)*Htd_0
-
- % ** set up the conditional prior variance sg0bi and sgpbi.
- % sg0bd = zeros(stri,1); Commented out by CAS, 9/24/96. Looks like this meant to have
- % strm where it has stri, and in any case it is "overwritten" below.
- %------------------------------
- % Introduce prior information on which variables "belong" in various equations.
- % In this first trial, we just introduce this information here, in a model-specific way.
- % Eventually this info has to be passed parametricly. In our first shot, we just damp down
- % all coefficients except those on the diagonal.
- factor0=idmat0(:,i);
- sg0bd = sg0bida(stri).*factor0(stri);
- sg0b = diag(sg0bd);
- %
- factor1=idmat1(:,i);
- sg1bd = sgpbida(1:nvar).*factor1;
- sg1b = diag(sg1bd);
-
- % ** set up the unconditional prior variance on A0i and A+i
- XX = zeros(nvar,strm);
- XX(stri,:) = eye(strm); % XX in Gelman, etel, on p479
- %
- Hb = zeros(strm+ncoef); % including A0i and A+i
- Hb(1:strm,1:strm) = sg0b;
- sg0bxx = sg0b*XX';
- Hb(1:strm,strm+1:strm+nvar) = sg0bxx;
- %Hb(strm+1:strm+nvar,1:strm) = XX*sg0b; CAS 9/24/96. Saves a little multiplication by using line below.
- Hb(strm+1:strm+nvar,1:strm) = sg0bxx';
- Hb(strm+1:strm+nvar,strm+1:strm+nvar) = XX*sg0bxx+sg1b;
- Hb(strm+nvar+1:strm+ncoef,strm+nvar+1:strm+ncoef) = sgppb;
-
- % ** set up the final prior variance on A0i and A+i, i =1,..,6 separately.
- % ** using dummy initial observations
- %zs1 = [ys1(:,stri) -xd0]; % z*1 for the 1st equation
-
- % * final unconditional prior variance on A0i and A+i
- %Hbzs1=Hb*zs1';
- %Htd = Hb - Hbzs1*((zs1*Hbzs1+mu(1)^2*mu(4)^2*eye(nvar))\Hbzs1');
- % CAS mod 8/7/96: Hope is that dropping above two lines in favor of the one below
- % removes double-counting of dummy observations and a possible scale sensitivity.
- Htd = Hb;
- % H_~: td: tilde for ~
-
- % * final conditional prior variance on A+i give that on A0i, and
- % * unconditional variance on A0+
- H0td = Htd(1:strm,1:strm); % unconditional
- % ** inverse and chol decomp
- H0tdi = inv(H0td);
-
- %
- Hptd10 = Htd(strm+1:strm+nvar,1:strm);
- % top matrix of nvar in Htd_+0
- Hptd100 = Hptd10*H0tdi;
- Hp0(1:nvar,:) = Hptd100;
- Hptd101 = Hptd100*Hptd10';
- Hp0p(1:nvar,1:nvar) = Hptd101;
- Hptd = Htd(strm+1:strm+ncoef,strm+1:strm+ncoef) - Hp0p;
- % condtional on A0i, H_plus_tilde
-
- % ** inverse
- Hptdi = inv(Hptd);
- chhh = Hptdi*Hp0; % <<>> the term not used for "of"
-
- % common term
- xxhp = xtx+Hptdi;
-
- % ** final conditional prior mean on A+i
- alptd = zeros(ncoef,1);
- alptd(1:nvar) = XX*A0b(stri,i);
- %alptd = alptd + Hptdf*(H0tdi*A0hb); CAS efficiency improvement 8/8/96
- alptd = alptd + Hp0*A0hb;
- % conditional on A0i, alpha-plus-tilde
-
- % * alpha_plus_* for the ith equation, ncoef*1
- % *alps = xxhp\(phi'*y*A0h(:,i)+HptdDinalptd);
- HptdDinalptd=Hptdi*alptd;
- xaha = cxy*A0h(:,i)+HptdDinalptd; %CAS 8/7/96. Replaced phi'*y with cxy
- alpst = xaha'/xxhp;
- GlbAph(i,:)=alpst;
- % alpst': transpose of alps.
-
- % ** 3rd bid term in i-th equation,
- % ** with all other 3rd big terms prior to i-th
- A0hy = A0h(:,i)'*ymy;
- A0hbH = A0hb'*H0tdi;
- tt3 = tt3 + A0hy*A0h(:,i) + A0hbH*A0hb;
- % ** 4th bid term in i-th equation,
- % ** with all other 4th big terms prior to i-th
- A0hcxyxx = A0h(:,i)'*cxyxx;
- atdHatd = alptd'*HptdDinalptd;
- tt4 = tt4 + A0hcxyxx*A0h(:,i)+atdHatd-alpst*xaha;
-
-
- stril = stril + strm;
-end
-
-%disp(sprintf('Loop %d end: %g', i, toc))
-%e_tfat = toc
-
-of = tt1 + 0.5*tt3 + 0.5*tt4;
-FRESHFUNCTION=1;
-
\ No newline at end of file
+function of = pwf233(x,idmat0,idmat1,fss,nvar,ncoef,phi, ...
+ y,A0b,sg0bid,sgpbid)
+%function of = pmddf232(x,idmat,fss,nvar,ncoef,phi, ...
+% y,A0b,sg0bid,sgpbid)
+% Leeper-Sims-Zha BVAR setup
+% general program to setup A0 matrix and compute the likelihood
+% requires
+% x (parameter vector)
+% a0indx (matrix indicating the free parameters in A0)
+% fss (forecast sample size)
+% nvar (number of variables)
+% ncoef (number of coefficients in a single equation in A+)
+% phi (r.h.s. observations in the system for A+)
+% y (l.h.s. observations for A0)
+% A0b (initial prior mean on A0)
+% sg0bid (diagonal of Sigma0_bar, unweighted prior vcv on the parameters in i-th equation in A0)
+% sgpbid (diagonal of Sigma+_bar, unweighted prior vcv on the parameters in i-th equation in A+)
+%
+% Revisions by CAS 9/27/96: If we transmit idmat, rather than a0indx, we can scale the elements of
+% idmat so that it carries information about relative prior variances.
+%
+% Copyright (C) 1997-2012 Christopher A. Sims
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+global vaya0 vaha0 vaxah vahad vbiga GlbAph FRESHFUNCTION
+% FRESHFUNCTION added 8/20/90 by CAS. It signals the pmddg23 whether the global variables
+% it uses have been refreshed by a new pmddf231 call since the last gradient call.
+a0indx=find(idmat0);
+% pre-allocate GlbAph to be zeros(nvar,lags*nvar+1=ncoef)
+
+nhp = 0; % <<>> 4 hyperparameters
+na0p = length(a0indx); % <<>> number of A0 parameters
+nfp = na0p+nhp;
+
+% *** initializing
+vaya0 = zeros(na0p,1);
+vaha0 = zeros(na0p,1);
+vaxah = zeros(na0p,1);
+vahad = zeros(na0p,1);
+vbiga = zeros(na0p,1);
+
+
+A0h = zeros(nvar,nvar);
+A0h(a0indx) = x(1:na0p);
+%A0h = reshape(a0,nvar,nvar); CAS 9/24/96. The reshape is not necessary if a0 starts as nvar x nvar
+ % restrictions on A0
+
+[A0hl,A0hu] = lu(A0h);
+
+% ** hyperparameters
+%%mu = x(na0p+1:nfp);
+mu = ones(5,1);
+mu(1) = 1;
+%mu(1) = 2;
+mu(2) = 0.2;
+mu(3) = 1;
+%mu(4)=1;
+mu(4)=1;
+mu(5) =1;
+%mu(5) = 40;
+% results from ...\dummy1\foreh\pmdd6.out
+%
+% mu(1): overall tightness and also for A0;
+% mu(2): relative tightness for A+;
+% mu(3): relative tightness for the constant term;
+% mu(4): weight on single dummy initial observation including constant;
+% mu(5): weight on nvar sums of coeffs dummy observations.
+
+% ** weight prior dummy observations
+%phi(1:nvar,:) = (mu(5)^2/mu(1)^2)*phi(1:nvar,:);
+%y(1:nvar,:) = (mu(5)^2/mu(1)^2)*y(1:nvar,:);
+%phi(ndobs,:) = mu
+% modified by CAS 8/6/96. The dummy obs weights are naturally in std units, not var units.
+phi(1:nvar,:) = mu(5)*phi(1:nvar,:);
+y(1:nvar,:) = mu(5)*y(1:nvar,:);
+phi(nvar+1,:) = mu(4)*phi(nvar+1,:);
+y(nvar+1,:) = mu(4)*y(nvar+1,:);
+
+% ** set up the conditional prior variance sg0bi and sgpbi.
+sg0bida = mu(1)^2*sg0bid;
+sgpbida = mu(1)^2*mu(2)^2*sgpbid;
+sgpbida(ncoef) = mu(1)^2*mu(3)^2;
+sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in a SZ paper
+sgppb = diag(sgppbd);
+
+%
+% ** some common terms
+[u d v]=svd(phi,0); %trial
+% xtx = phi'*phi; %trial
+vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
+xtx=vd*vd';
+% M = eye(fss) - phi*(xtx\phi'); % not used except in line below %trial
+yu = y'*u; %trial
+cxyxx=yu*yu'; %trial
+ymy=y'*y-cxyxx; %trial
+%ymy = y'*M*y;
+%cxy = phi'*y; %trial
+cxy = vd*yu'; %trial
+%cxpy = xtx\cxy; %not used further except in line below
+%cxyxx = cxy'*cxpy;
+
+% ** estimate A+_hat conditional on A0, ncoef*nvar, but not using full prior
+%GlbAph = cxpy*A0h;
+
+
+%%%%%%%
+%%% build the objective function below
+%%%%%%%
+%
+t1f = diag(abs(A0hu));
+t1f = log(t1f);
+tt1 = (-1.0)*fss*sum(t1f);
+
+tt3 = 0;
+tt4 = 0;
+
+stril = 0;
+Hp0p = zeros(ncoef);
+% initializing Htd_+0*inv(Htd_00)*Htd_0+: conditional prior covariance
+for i = 1:nvar
+ stri = find(idmat0(:,i)); %CAS change 9/24/96, to avoid slim chance of an exact zero in x vector
+ %at some point during an optimization.
+ strm = length(stri);
+
+ A0hb = A0h(stri,i)-A0b(stri,i);
+ Hp0 = zeros(ncoef,strm);
+ % initializing Htd_+0*inv(Htd_00)*Htd_0
+
+ % ** set up the conditional prior variance sg0bi and sgpbi.
+ % sg0bd = zeros(stri,1); Commented out by CAS, 9/24/96. Looks like this meant to have
+ % strm where it has stri, and in any case it is "overwritten" below.
+ %------------------------------
+ % Introduce prior information on which variables "belong" in various equations.
+ % In this first trial, we just introduce this information here, in a model-specific way.
+ % Eventually this info has to be passed parametricly. In our first shot, we just damp down
+ % all coefficients except those on the diagonal.
+ factor0=idmat0(:,i);
+ sg0bd = sg0bida(stri).*factor0(stri);
+ sg0b = diag(sg0bd);
+ %
+ factor1=idmat1(:,i);
+ sg1bd = sgpbida(1:nvar).*factor1;
+ sg1b = diag(sg1bd);
+
+ % ** set up the unconditional prior variance on A0i and A+i
+ XX = zeros(nvar,strm);
+ XX(stri,:) = eye(strm); % XX in Gelman, etel, on p479
+ %
+ Hb = zeros(strm+ncoef); % including A0i and A+i
+ Hb(1:strm,1:strm) = sg0b;
+ sg0bxx = sg0b*XX';
+ Hb(1:strm,strm+1:strm+nvar) = sg0bxx;
+ %Hb(strm+1:strm+nvar,1:strm) = XX*sg0b; CAS 9/24/96. Saves a little multiplication by using line below.
+ Hb(strm+1:strm+nvar,1:strm) = sg0bxx';
+ Hb(strm+1:strm+nvar,strm+1:strm+nvar) = XX*sg0bxx+sg1b;
+ Hb(strm+nvar+1:strm+ncoef,strm+nvar+1:strm+ncoef) = sgppb;
+
+ % ** set up the final prior variance on A0i and A+i, i =1,..,6 separately.
+ % ** using dummy initial observations
+ %zs1 = [ys1(:,stri) -xd0]; % z*1 for the 1st equation
+
+ % * final unconditional prior variance on A0i and A+i
+ %Hbzs1=Hb*zs1';
+ %Htd = Hb - Hbzs1*((zs1*Hbzs1+mu(1)^2*mu(4)^2*eye(nvar))\Hbzs1');
+ % CAS mod 8/7/96: Hope is that dropping above two lines in favor of the one below
+ % removes double-counting of dummy observations and a possible scale sensitivity.
+ Htd = Hb;
+ % H_~: td: tilde for ~
+
+ % * final conditional prior variance on A+i give that on A0i, and
+ % * unconditional variance on A0+
+ H0td = Htd(1:strm,1:strm); % unconditional
+ % ** inverse and chol decomp
+ H0tdi = inv(H0td);
+
+ %
+ Hptd10 = Htd(strm+1:strm+nvar,1:strm);
+ % top matrix of nvar in Htd_+0
+ Hptd100 = Hptd10*H0tdi;
+ Hp0(1:nvar,:) = Hptd100;
+ Hptd101 = Hptd100*Hptd10';
+ Hp0p(1:nvar,1:nvar) = Hptd101;
+ Hptd = Htd(strm+1:strm+ncoef,strm+1:strm+ncoef) - Hp0p;
+ % condtional on A0i, H_plus_tilde
+
+ % ** inverse
+ Hptdi = inv(Hptd);
+ chhh = Hptdi*Hp0; % <<>> the term not used for "of"
+
+ % common term
+ xxhp = xtx+Hptdi;
+
+ % ** final conditional prior mean on A+i
+ alptd = zeros(ncoef,1);
+ alptd(1:nvar) = XX*A0b(stri,i);
+ %alptd = alptd + Hptdf*(H0tdi*A0hb); CAS efficiency improvement 8/8/96
+ alptd = alptd + Hp0*A0hb;
+ % conditional on A0i, alpha-plus-tilde
+
+ % * alpha_plus_* for the ith equation, ncoef*1
+ % *alps = xxhp\(phi'*y*A0h(:,i)+HptdDinalptd);
+ HptdDinalptd=Hptdi*alptd;
+ xaha = cxy*A0h(:,i)+HptdDinalptd; %CAS 8/7/96. Replaced phi'*y with cxy
+ alpst = xaha'/xxhp;
+ GlbAph(i,:)=alpst;
+ % alpst': transpose of alps.
+
+ % ** 3rd bid term in i-th equation,
+ % ** with all other 3rd big terms prior to i-th
+ A0hy = A0h(:,i)'*ymy;
+ A0hbH = A0hb'*H0tdi;
+ tt3 = tt3 + A0hy*A0h(:,i) + A0hbH*A0hb;
+ % ** 4th bid term in i-th equation,
+ % ** with all other 4th big terms prior to i-th
+ A0hcxyxx = A0h(:,i)'*cxyxx;
+ atdHatd = alptd'*HptdDinalptd;
+ tt4 = tt4 + A0hcxyxx*A0h(:,i)+atdHatd-alpst*xaha;
+
+
+ stril = stril + strm;
+end
+
+%disp(sprintf('Loop %d end: %g', i, toc))
+%e_tfat = toc
+
+of = tt1 + 0.5*tt3 + 0.5*tt4;
+FRESHFUNCTION=1;
+
diff --git a/MatlabFiles/pwf234.m b/MatlabFiles/pwf234.m
index 8ccd987057661c6c58a77aeb6c166e85c1e7f1ca..323f849df80293fa89f07e0a13095e7d5cbb6dd8 100644
--- a/MatlabFiles/pwf234.m
+++ b/MatlabFiles/pwf234.m
@@ -1,224 +1,224 @@
-function of = pwf234(x,idmat0,idmat1,fss,nvar,ncoef,phi, ...
- y,A0b,sg0bid,sgpbid)
-%function of = pmddf232(x,idmat,fss,nvar,ncoef,phi, ...
-% y,A0b,sg0bid,sgpbid)
-% Leeper-Sims-Zha BVAR setup
-% general program to setup A0 matrix and compute the likelihood
-% requires
-% x (parameter vector)
-% a0indx (matrix indicating the free parameters in A0)
-% fss (forecast sample size)
-% nvar (number of variables)
-% ncoef (number of coefficients in a single equation in A+)
-% phi (r.h.s. observations in the system for A+)
-% y (l.h.s. observations for A0)
-% A0b (initial prior mean on A0)
-% sg0bid (diagonal of Sigma0_bar, unweighted prior vcv on the parameters in i-th equation in A0)
-% sgpbid (diagonal of Sigma+_bar, unweighted prior vcv on the parameters in i-th equation in A+)
-%
-% Revisions by CAS 9/27/96: If we transmit idmat, rather than a0indx, we can scale the elements of
-% idmat so that it carries information about relative prior variances.
-%
-% Revsions by TZ, 10/12/96: efficiency improvement by streamlining the previous code
-% according to the general setup in Sims and Zha "Bayesian Methods for ...".
-% Copyright (C) 1997-2012 Christopher A. Sims and Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-global vaya0 vaha0 vaxah vahad vbiga GlbAph FRESHFUNCTION
-% FRESHFUNCTION added 8/20/90 by CAS. It signals the pmddg23 whether the global variables
-% it uses have been refreshed by a new pmddf231 call since the last gradient call.
-a0indx=find(idmat0);
-% pre-allocate GlbAph to be zeros(nvar,lags*nvar+1=ncoef)
-
-nhp = 0; % <<>> 4 hyperparameters
-na0p = length(a0indx); % <<>> number of A0 parameters
-nfp = na0p+nhp;
-
-% *** initializing
-vaya0 = zeros(na0p,1);
-vaha0 = zeros(na0p,1);
-vaxah = zeros(na0p,1);
-vahad = zeros(na0p,1);
-vbiga = zeros(na0p,1);
-
-
-A0h = zeros(nvar,nvar);
-A0h(a0indx) = x(1:na0p);
-%A0h = reshape(a0,nvar,nvar); CAS 9/24/96. The reshape is not necessary if a0 starts as nvar x nvar
- % restrictions on A0
-
-[A0hl,A0hu] = lu(A0h);
-
-% ** hyperparameters
-%%mu = x(na0p+1:nfp);
-mu = ones(5,1);
-mu(1) = 1;
-%mu(1) = 2;
-mu(2) = 0.3;
-mu(3) = 25;
-%mu(4)=1;
-mu(4)=1;
-mu(5) =1;
-%mu(5) = 40;
-% results from ...\dummy1\foreh\pmdd6.out
-%
-% mu(1): overall tightness and also for A0;
-% mu(2): relative tightness for A+;
-% mu(3): relative tightness for the constant term;
-% mu(4): weight on single dummy initial observation including constant;
-% mu(5): weight on nvar sums of coeffs dummy observations.
-
-% ** weight prior dummy observations
-%phi(1:nvar,:) = (mu(5)^2/mu(1)^2)*phi(1:nvar,:);
-%y(1:nvar,:) = (mu(5)^2/mu(1)^2)*y(1:nvar,:);
-%phi(ndobs,:) = mu
-% modified by CAS 8/6/96. The dummy obs weights are naturally in std units, not var units.
-%%phi(1:nvar,:) = mu(5)*phi(1:nvar,:);
-%%y(1:nvar,:) = mu(5)*y(1:nvar,:);
-%%phi(nvar+1,:) = mu(4)*phi(nvar+1,:);
-%%y(nvar+1,:) = mu(4)*y(nvar+1,:);
-
-% ** set up the conditional prior variance sg0bi and sgpbi.
-%%sg0bida = mu(1)^2*sg0bid;
-sgpbida = mu(1)^2*mu(2)^2*sgpbid;
-sgpbida(ncoef) = mu(1)^2*mu(3)^2;
-sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in an SZ paper
-sgppb = diag(sgppbd);
-
-%
-% ** some common terms
-[u d v]=svd(phi,0); %trial
-% xtx = phi'*phi; %trial
-vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
-xtx=vd*vd';
-% M = eye(fss) - phi*(xtx\phi'); % not used except in line below %trial
-yu = y'*u; %trial
-cxyxx=yu*yu'; %trial
-ymy=y'*y-cxyxx; %trial
-%ymy = y'*M*y;
-%cxy = phi'*y; %trial
-cxy = vd*yu'; %trial
-%cxpy = xtx\cxy; %not used further except in line below
-%cxyxx = cxy'*cxpy;
-
-% ** estimate A+_hat conditional on A0, ncoef*nvar, but not using full prior
-%GlbAph = cxpy*A0h;
-
-
-%%%%%%%
-%%% build the objective function below
-%%%%%%%
-%
-t1f = diag(abs(A0hu));
-t1f = log(t1f);
-% * without the prior |A0|^k
-tt1 = (-1.0)*fss*sum(t1f);
-
-tt3 = 0;
-tt4 = 0;
-
-
-stril = 0;
-Hp0p = zeros(ncoef);
-% initializing Htd_+0*inv(Htd_00)*Htd_0+: conditional prior covariance
-for i = 1:nvar
- stri = find(idmat0(:,i)); %CAS change 9/24/96, to avoid slim chance of an exact zero in x vector
- %at some point during an optimization.
- strm = length(stri);
-
- %%A0hb = A0h(stri,i)-A0b(stri,i);
-
- % ** set up the conditional prior variance sg0bi and sgpbi.
- % sg0bd = zeros(stri,1); Commented out by CAS, 9/24/96. Looks like this meant to have
- % strm where it has stri, and in any case it is "overwritten" below.
- %------------------------------
- % Introduce prior information on which variables "belong" in various equations.
- % In this first trial, we just introduce this information here, in a model-specific way.
- % Eventually this info has to be passed parametricly. In our first shot, we just damp down
- % all coefficients except those on the diagonal.
- %%factor0=idmat0(:,i);
- %%sg0bd = sg0bida(stri).*factor0(stri);
- %%sg0b = diag(sg0bd);
- %
- factor1=idmat1(:,i);
- sg1bd = sgpbida(1:nvar).*factor1;
- sg1b = diag(sg1bd);
-
- % ** set up the unconditional prior variance on A0i and A+i
- XX = zeros(nvar,strm);
- XX(stri,:) = eye(strm); % XX in Gelman, etel, on p479
- %
- % * final conditional prior variance on A+i give that on A0i, and
- % * unconditional variance on A0+
- %%H0td = sg0b; % unconditional
- % H_~: td: tilde for ~
- % ** inverse and chol decomp
- %%H0tdi = inv(H0td);
-
- %
- Hptd = zeros(ncoef);
- Hptd(1:nvar,1:nvar)=sg1b;
- Hptd(nvar+1:ncoef,nvar+1:ncoef)=sgppb;
- % condtional on A0i, H_plus_tilde
-
- % ** inverse
- Hptdi = inv(Hptd);
- %%chhh=Hptdi*Hp0;
- chhh = zeros(ncoef,strm); % <<>> the term not used for "of"\
- chhh(1:nvar,:) = Hptdi(1:nvar,1:nvar)*XX;
- chhh1 = XX'*chhh(1:nvar,:);
-
- % common term
- xxhp = xtx+Hptdi;
-
- % ** final conditional prior mean on A+i
- alptd = zeros(ncoef,1);
- alptd(1:nvar) = XX*A0h(stri,i);
- % conditional on A0i, alpha-plus-tilde
-
- % * alpha_plus_* for the ith equation, ncoef*1
- % *alps = xxhp\(phi'*y*A0h(:,i)+HptdDinalptd);
- %%HptdDinalptd=Hptdi*alptd;
- HptdDinalptd=zeros(ncoef,1);
- HptdDinalptd(1:nvar) = Hptdi(1:nvar,1:nvar)*alptd(1:nvar);
- xaha = cxy*A0h(:,i)+HptdDinalptd; %CAS 8/7/96. Replaced phi'*y with cxy
- alpst = xaha'/xxhp;
- GlbAph(i,:)=alpst;
- % alpst': transpose of alps.
-
- % ** 3rd bid term in i-th equation,
- % ** with all other 3rd big terms prior to i-th
- A0hy = A0h(:,i)'*ymy;
- %%A0hbH = A0hb'*H0tdi;
- %%tt3 = tt3 + A0hy*A0h(:,i) + A0hbH*A0hb;
- tt3 = tt3 + A0hy*A0h(:,i);
- % ** 4th bid term in i-th equation,
- % ** with all other 4th big terms prior to i-th
- A0hcxyxx = A0h(:,i)'*cxyxx;
- %%atdHatd = alptd'*HptdDinalptd; % efficiency improvement
- atdHatd = alptd(1:nvar)'*HptdDinalptd(1:nvar);
- tt4 = tt4 + A0hcxyxx*A0h(:,i)+atdHatd-alpst*xaha;
-
- stril = stril + strm;
-end
-
-%disp(sprintf('Loop %d end: %g', i, toc))
-
-%e_tfat = toc
-
-of = tt1 + 0.5*tt3 + 0.5*tt4;
-FRESHFUNCTION=1;
\ No newline at end of file
+function of = pwf234(x,idmat0,idmat1,fss,nvar,ncoef,phi, ...
+ y,A0b,sg0bid,sgpbid)
+%function of = pmddf232(x,idmat,fss,nvar,ncoef,phi, ...
+% y,A0b,sg0bid,sgpbid)
+% Leeper-Sims-Zha BVAR setup
+% general program to setup A0 matrix and compute the likelihood
+% requires
+% x (parameter vector)
+% a0indx (matrix indicating the free parameters in A0)
+% fss (forecast sample size)
+% nvar (number of variables)
+% ncoef (number of coefficients in a single equation in A+)
+% phi (r.h.s. observations in the system for A+)
+% y (l.h.s. observations for A0)
+% A0b (initial prior mean on A0)
+% sg0bid (diagonal of Sigma0_bar, unweighted prior vcv on the parameters in i-th equation in A0)
+% sgpbid (diagonal of Sigma+_bar, unweighted prior vcv on the parameters in i-th equation in A+)
+%
+% Revisions by CAS 9/27/96: If we transmit idmat, rather than a0indx, we can scale the elements of
+% idmat so that it carries information about relative prior variances.
+%
+% Revsions by TZ, 10/12/96: efficiency improvement by streamlining the previous code
+% according to the general setup in Sims and Zha "Bayesian Methods for ...".
+%
+% Copyright (C) 1997-2012 Christopher A. Sims and Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+global vaya0 vaha0 vaxah vahad vbiga GlbAph FRESHFUNCTION
+% FRESHFUNCTION added 8/20/90 by CAS. It signals the pmddg23 whether the global variables
+% it uses have been refreshed by a new pmddf231 call since the last gradient call.
+a0indx=find(idmat0);
+% pre-allocate GlbAph to be zeros(nvar,lags*nvar+1=ncoef)
+
+nhp = 0; % <<>> 4 hyperparameters
+na0p = length(a0indx); % <<>> number of A0 parameters
+nfp = na0p+nhp;
+
+% *** initializing
+vaya0 = zeros(na0p,1);
+vaha0 = zeros(na0p,1);
+vaxah = zeros(na0p,1);
+vahad = zeros(na0p,1);
+vbiga = zeros(na0p,1);
+
+
+A0h = zeros(nvar,nvar);
+A0h(a0indx) = x(1:na0p);
+%A0h = reshape(a0,nvar,nvar); CAS 9/24/96. The reshape is not necessary if a0 starts as nvar x nvar
+ % restrictions on A0
+
+[A0hl,A0hu] = lu(A0h);
+
+% ** hyperparameters
+%%mu = x(na0p+1:nfp);
+mu = ones(5,1);
+mu(1) = 1;
+%mu(1) = 2;
+mu(2) = 0.3;
+mu(3) = 25;
+%mu(4)=1;
+mu(4)=1;
+mu(5) =1;
+%mu(5) = 40;
+% results from ...\dummy1\foreh\pmdd6.out
+%
+% mu(1): overall tightness and also for A0;
+% mu(2): relative tightness for A+;
+% mu(3): relative tightness for the constant term;
+% mu(4): weight on single dummy initial observation including constant;
+% mu(5): weight on nvar sums of coeffs dummy observations.
+
+% ** weight prior dummy observations
+%phi(1:nvar,:) = (mu(5)^2/mu(1)^2)*phi(1:nvar,:);
+%y(1:nvar,:) = (mu(5)^2/mu(1)^2)*y(1:nvar,:);
+%phi(ndobs,:) = mu
+% modified by CAS 8/6/96. The dummy obs weights are naturally in std units, not var units.
+%%phi(1:nvar,:) = mu(5)*phi(1:nvar,:);
+%%y(1:nvar,:) = mu(5)*y(1:nvar,:);
+%%phi(nvar+1,:) = mu(4)*phi(nvar+1,:);
+%%y(nvar+1,:) = mu(4)*y(nvar+1,:);
+
+% ** set up the conditional prior variance sg0bi and sgpbi.
+%%sg0bida = mu(1)^2*sg0bid;
+sgpbida = mu(1)^2*mu(2)^2*sgpbid;
+sgpbida(ncoef) = mu(1)^2*mu(3)^2;
+sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in an SZ paper
+sgppb = diag(sgppbd);
+
+%
+% ** some common terms
+[u d v]=svd(phi,0); %trial
+% xtx = phi'*phi; %trial
+vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
+xtx=vd*vd';
+% M = eye(fss) - phi*(xtx\phi'); % not used except in line below %trial
+yu = y'*u; %trial
+cxyxx=yu*yu'; %trial
+ymy=y'*y-cxyxx; %trial
+%ymy = y'*M*y;
+%cxy = phi'*y; %trial
+cxy = vd*yu'; %trial
+%cxpy = xtx\cxy; %not used further except in line below
+%cxyxx = cxy'*cxpy;
+
+% ** estimate A+_hat conditional on A0, ncoef*nvar, but not using full prior
+%GlbAph = cxpy*A0h;
+
+
+%%%%%%%
+%%% build the objective function below
+%%%%%%%
+%
+t1f = diag(abs(A0hu));
+t1f = log(t1f);
+% * without the prior |A0|^k
+tt1 = (-1.0)*fss*sum(t1f);
+
+tt3 = 0;
+tt4 = 0;
+
+
+stril = 0;
+Hp0p = zeros(ncoef);
+% initializing Htd_+0*inv(Htd_00)*Htd_0+: conditional prior covariance
+for i = 1:nvar
+ stri = find(idmat0(:,i)); %CAS change 9/24/96, to avoid slim chance of an exact zero in x vector
+ %at some point during an optimization.
+ strm = length(stri);
+
+ %%A0hb = A0h(stri,i)-A0b(stri,i);
+
+ % ** set up the conditional prior variance sg0bi and sgpbi.
+ % sg0bd = zeros(stri,1); Commented out by CAS, 9/24/96. Looks like this meant to have
+ % strm where it has stri, and in any case it is "overwritten" below.
+ %------------------------------
+ % Introduce prior information on which variables "belong" in various equations.
+ % In this first trial, we just introduce this information here, in a model-specific way.
+ % Eventually this info has to be passed parametricly. In our first shot, we just damp down
+ % all coefficients except those on the diagonal.
+ %%factor0=idmat0(:,i);
+ %%sg0bd = sg0bida(stri).*factor0(stri);
+ %%sg0b = diag(sg0bd);
+ %
+ factor1=idmat1(:,i);
+ sg1bd = sgpbida(1:nvar).*factor1;
+ sg1b = diag(sg1bd);
+
+ % ** set up the unconditional prior variance on A0i and A+i
+ XX = zeros(nvar,strm);
+ XX(stri,:) = eye(strm); % XX in Gelman, etel, on p479
+ %
+ % * final conditional prior variance on A+i give that on A0i, and
+ % * unconditional variance on A0+
+ %%H0td = sg0b; % unconditional
+ % H_~: td: tilde for ~
+ % ** inverse and chol decomp
+ %%H0tdi = inv(H0td);
+
+ %
+ Hptd = zeros(ncoef);
+ Hptd(1:nvar,1:nvar)=sg1b;
+ Hptd(nvar+1:ncoef,nvar+1:ncoef)=sgppb;
+ % condtional on A0i, H_plus_tilde
+
+ % ** inverse
+ Hptdi = inv(Hptd);
+ %%chhh=Hptdi*Hp0;
+ chhh = zeros(ncoef,strm); % <<>> the term not used for "of"\
+ chhh(1:nvar,:) = Hptdi(1:nvar,1:nvar)*XX;
+ chhh1 = XX'*chhh(1:nvar,:);
+
+ % common term
+ xxhp = xtx+Hptdi;
+
+ % ** final conditional prior mean on A+i
+ alptd = zeros(ncoef,1);
+ alptd(1:nvar) = XX*A0h(stri,i);
+ % conditional on A0i, alpha-plus-tilde
+
+ % * alpha_plus_* for the ith equation, ncoef*1
+ % *alps = xxhp\(phi'*y*A0h(:,i)+HptdDinalptd);
+ %%HptdDinalptd=Hptdi*alptd;
+ HptdDinalptd=zeros(ncoef,1);
+ HptdDinalptd(1:nvar) = Hptdi(1:nvar,1:nvar)*alptd(1:nvar);
+ xaha = cxy*A0h(:,i)+HptdDinalptd; %CAS 8/7/96. Replaced phi'*y with cxy
+ alpst = xaha'/xxhp;
+ GlbAph(i,:)=alpst;
+ % alpst': transpose of alps.
+
+ % ** 3rd bid term in i-th equation,
+ % ** with all other 3rd big terms prior to i-th
+ A0hy = A0h(:,i)'*ymy;
+ %%A0hbH = A0hb'*H0tdi;
+ %%tt3 = tt3 + A0hy*A0h(:,i) + A0hbH*A0hb;
+ tt3 = tt3 + A0hy*A0h(:,i);
+ % ** 4th bid term in i-th equation,
+ % ** with all other 4th big terms prior to i-th
+ A0hcxyxx = A0h(:,i)'*cxyxx;
+ %%atdHatd = alptd'*HptdDinalptd; % efficiency improvement
+ atdHatd = alptd(1:nvar)'*HptdDinalptd(1:nvar);
+ tt4 = tt4 + A0hcxyxx*A0h(:,i)+atdHatd-alpst*xaha;
+
+ stril = stril + strm;
+end
+
+%disp(sprintf('Loop %d end: %g', i, toc))
+
+%e_tfat = toc
+
+of = tt1 + 0.5*tt3 + 0.5*tt4;
+FRESHFUNCTION=1;
diff --git a/MatlabFiles/pwf235.m b/MatlabFiles/pwf235.m
index 2a6188b8be0c30b6e0edd745abd45777c39019a8..6de42b706b5acb0e1a42417cb5351b0c26282a79 100644
--- a/MatlabFiles/pwf235.m
+++ b/MatlabFiles/pwf235.m
@@ -1,206 +1,206 @@
-function [of,GlbAphh] = pwf235(x,idmat0,idmat1,fss,nvar,ncoef,phi, ...
- y,A0b,sg0bid,sgpbid)
-%function of = pmddf232(x,idmat,fss,nvar,ncoef,phi, ...
-% y,A0b,sg0bid,sgpbid)
-% Leeper-Sims-Zha BVAR setup
-% general program to setup A0 matrix and compute the likelihood
-% requires
-% x (parameter vector)
-% a0indx (matrix indicating the free parameters in A0)
-% fss (forecast sample size)
-% nvar (number of variables)
-% ncoef (number of coefficients in a single equation in A+)
-% phi (r.h.s. observations in the system for A+)
-% y (l.h.s. observations for A0)
-% A0b (initial prior mean on A0)
-% sg0bid (diagonal of Sigma0_bar, unweighted prior vcv on the parameters in i-th equation in A0)
-% sgpbid (diagonal of Sigma+_bar, unweighted prior vcv on the parameters in i-th equation in A+)
-%
-% Revisions by CAS 9/27/96: If we transmit idmat, rather than a0indx, we can scale the elements of
-% idmat so that it carries information about relative prior variances.
-%
-% Revsions by TZ, 10/13/96: efficiency improvement by streamlining the previous code
-% according to the general setup in Sims and Zha "Bayesian Methods for ...".
-% Copyright (C) 1997-2012 Christopher A. Sims and Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-global vaha0 vaMMa0 GlbAph FRESHFUNCTION
-% FRESHFUNCTION added 8/20/90 by CAS. It signals the pmddg23 whether the global variables
-% it uses have been refreshed by a new pmddf231 call since the last gradient call.
-a0indx=find(idmat0);
-GlbAphh=zeros(ncoef,nvar);
-% pre-allocate GlbAph to be zeros(nvar,lags*nvar+1=ncoef)
-
-nhp = 0; % <<>> 4 hyperparameters
-na0p = length(a0indx); % <<>> number of A0 parameters
-nfp = na0p+nhp;
-
-% *** initializing
-vaha0 = zeros(na0p,1);
-vaMMa0 = zeros(na0p,1);
-
-%
-A0h = zeros(nvar,nvar);
-A0h(a0indx) = x(1:na0p);
-%A0h = reshape(a0,nvar,nvar); CAS 9/24/96. The reshape is not necessary if a0 starts as nvar x nvar
- % restrictions on A0
-
-[A0hl,A0hu] = lu(A0h);
-
-% ** hyperparameters
-%%mu = x(na0p+1:nfp);
-mu = ones(5,1);
-mu(1) = 1;
-%mu(1) = 2;
-mu(2) = 0.2;
-mu(3) = 1;
-%mu(4)=1;
-mu(4)=10;
-mu(5) =1;
-%mu(5) = 40;
-% results from ...\dummy1\foreh\pmdd6.out
-%
-% mu(1): overall tightness and also for A0;
-% mu(2): relative tightness for A+;
-% mu(3): relative tightness for the constant term;
-% mu(4): weight on single dummy initial observation including constant;
-% mu(5): weight on nvar sums of coeffs dummy observations.
-
-% ** weight prior dummy observations
-%phi(1:nvar,:) = (mu(5)^2/mu(1)^2)*phi(1:nvar,:);
-%y(1:nvar,:) = (mu(5)^2/mu(1)^2)*y(1:nvar,:);
-%phi(ndobs,:) = mu
-% modified by CAS 8/6/96. The dummy obs weights are naturally in std units, not var units.
-phi(1:nvar,:) = mu(5)*phi(1:nvar,:);
-y(1:nvar,:) = mu(5)*y(1:nvar,:);
-phi(nvar+1,:) = mu(4)*phi(nvar+1,:);
-y(nvar+1,:) = mu(4)*y(nvar+1,:);
-
-% ** set up the conditional prior variance sg0bi and sgpbi.
-sg0bida = mu(1)^2*sg0bid;
-sgpbida = mu(1)^2*mu(2)^2*sgpbid;
-sgpbida(ncoef) = mu(1)^2*mu(3)^2;
-sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in an SZ paper
-sgppbdi = 1./sgppbd;
-%sgppb = diag(sgppbd);
-
-%
-% ** some common terms
-[u d v]=svd(phi,0); %trial
-% xtx = phi'*phi; %trial
-vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
-xtx=vd*vd';
-% M = eye(fss) - phi*(xtx\phi'); % not used except in line below %trial
-yu = y'*u; %trial
-cxyxx=yu*yu'; %trial
-yty=y'*y;
-ymy=yty-cxyxx; %trial
-%ymy = y'*M*y;
-%cxy = phi'*y; %trial
-cxy = vd*yu'; %trial
-cyx = cxy';
-%cxpy = xtx\cxy; %not used further except in line below
-%cxyxx = cxy'*cxpy;
-
-% ** estimate A+_hat conditional on A0, ncoef*nvar, but not using full prior
-%GlbAph = cxpy*A0h;
-
-
-%%%%%%%
-%%% build the objective function below
-%%%%%%%
-%
-t1f = diag(abs(A0hu));
-t1f = log(t1f);
-% * without the prior |A0|^k
-tt1 = (-1.0)*fss*sum(t1f);
-
-tt3 = 0;
-
-stril = 0;
-Hptd = zeros(ncoef);
-Hptdi=Hptd;
-Hptd(nvar+1:ncoef,nvar+1:ncoef)=diag(sgppbd);
-Hptdi(nvar+1:ncoef,nvar+1:ncoef)=diag(sgppbdi);
- % condtional on A0i, H_plus_tilde
-for i = 1:nvar
- stri = find(idmat0(:,i)); %CAS change 9/24/96, to avoid slim chance of an exact zero in x vector
- %at some point during an optimization.
- strm = length(stri);
-
- A0hb = A0h(stri,i)-A0b(stri,i);
-
- % ** set up the conditional prior variance sg0bi and sgpbi.
- % sg0bd = zeros(stri,1); Commented out by CAS, 9/24/96. Looks like this meant to have
- % strm where it has stri, and in any case it is "overwritten" below.
- %------------------------------
- % Introduce prior information on which variables "belong" in various equations.
- % In this first trial, we just introduce this information here, in a model-specific way.
- % Eventually this info has to be passed parametricly. In our first shot, we just damp down
- % all coefficients except those on the diagonal.
- factor0=idmat0(:,i);
- sg0bd = sg0bida(stri).*factor0(stri);
- sg0bdi = 1./sg0bd;
- %sg0b = diag(sg0bd);
- %
- factor1=idmat1(:,i);
- sg1bd = sgpbida(1:nvar).*factor1;
- sg1bdi = 1./sg1bd;
- %sg1b = diag(sg1bd);
-
- % ** set up the unconditional prior variance on A0i and A+i
- XX = zeros(nvar,strm);
- XX(stri,:) = eye(strm); % XX in Gelman, etel, on p479
- %
- % * final conditional prior variance on A+i give that on A0i, and
- % * unconditional variance on A0+
- H0td = diag(sg0bd); % unconditional
- % H_~: td: tilde for ~
- % ** inverse and chol decomp
- H0tdi = diag(sg0bdi);
-
- %
- Hptd(1:nvar,1:nvar)=diag(sg1bd);
- Hptdi(1:nvar,1:nvar)=diag(sg1bdi);
- % condtional on A0i, H_plus_tilde
-
- % common terms
- xxhp = xtx+Hptdi;
- Vxxhp = chol(xxhp);
- % inverse of Vxxhp is the upper triangular decomp
- % of inv(Vxxhp) (covariance of alpst)
- A0hbH = A0hb'*H0tdi;
- H1p_1 = zeros(ncoef,nvar);
- H1p_1(1:nvar,:) = Hptdi(1:nvar,1:nvar);
- %%Hm = (cyx+H1p_1')*(xxhp\(cxy+H1p_1));
- Hm1 = (cyx+H1p_1')/xxhp;
- Hm2 = cxy+H1p_1;
- GlbAph(i,:) = A0h(stri,i)'*XX'*Hm1; % alpha_plus_*_transpose
- GlbAphh(:,i)=GlbAph(i,:)' + Vxxhp\randn(length(GlbAph(i,:)),1);
- % posterior alpha_plus_*
-
- %%alpMpyh = A0h(stri,i)'*XX'*(yty+Hptdi(1:nvar,1:nvar)-Hm)*XX;
- alpMpyh = A0h(stri,i)'*XX'*(yty+Hptdi(1:nvar,1:nvar))*XX - GlbAph(i,:)*Hm2*XX;
-
- % ** 3rd bid term in i-th equation,
- % ** with all other 3rd big terms before the i-th equation
- tt3 = tt3 + A0hbH*A0hb + alpMpyh*A0h(stri,i);
-
- stril = stril + strm;
-end
-
-of = tt1 + 0.5*tt3;
\ No newline at end of file
+function [of,GlbAphh] = pwf235(x,idmat0,idmat1,fss,nvar,ncoef,phi, ...
+ y,A0b,sg0bid,sgpbid)
+%function of = pmddf232(x,idmat,fss,nvar,ncoef,phi, ...
+% y,A0b,sg0bid,sgpbid)
+% Leeper-Sims-Zha BVAR setup
+% general program to setup A0 matrix and compute the likelihood
+% requires
+% x (parameter vector)
+% a0indx (matrix indicating the free parameters in A0)
+% fss (forecast sample size)
+% nvar (number of variables)
+% ncoef (number of coefficients in a single equation in A+)
+% phi (r.h.s. observations in the system for A+)
+% y (l.h.s. observations for A0)
+% A0b (initial prior mean on A0)
+% sg0bid (diagonal of Sigma0_bar, unweighted prior vcv on the parameters in i-th equation in A0)
+% sgpbid (diagonal of Sigma+_bar, unweighted prior vcv on the parameters in i-th equation in A+)
+%
+% Revisions by CAS 9/27/96: If we transmit idmat, rather than a0indx, we can scale the elements of
+% idmat so that it carries information about relative prior variances.
+%
+% Revsions by TZ, 10/13/96: efficiency improvement by streamlining the previous code
+% according to the general setup in Sims and Zha "Bayesian Methods for ...".
+%
+% Copyright (C) 1997-2012 Christopher A. Sims and Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+global vaha0 vaMMa0 GlbAph FRESHFUNCTION
+% FRESHFUNCTION added 8/20/90 by CAS. It signals the pmddg23 whether the global variables
+% it uses have been refreshed by a new pmddf231 call since the last gradient call.
+a0indx=find(idmat0);
+GlbAphh=zeros(ncoef,nvar);
+% pre-allocate GlbAph to be zeros(nvar,lags*nvar+1=ncoef)
+
+nhp = 0; % <<>> 4 hyperparameters
+na0p = length(a0indx); % <<>> number of A0 parameters
+nfp = na0p+nhp;
+
+% *** initializing
+vaha0 = zeros(na0p,1);
+vaMMa0 = zeros(na0p,1);
+
+%
+A0h = zeros(nvar,nvar);
+A0h(a0indx) = x(1:na0p);
+%A0h = reshape(a0,nvar,nvar); CAS 9/24/96. The reshape is not necessary if a0 starts as nvar x nvar
+ % restrictions on A0
+
+[A0hl,A0hu] = lu(A0h);
+
+% ** hyperparameters
+%%mu = x(na0p+1:nfp);
+mu = ones(5,1);
+mu(1) = 1;
+%mu(1) = 2;
+mu(2) = 0.2;
+mu(3) = 1;
+%mu(4)=1;
+mu(4)=10;
+mu(5) =1;
+%mu(5) = 40;
+% results from ...\dummy1\foreh\pmdd6.out
+%
+% mu(1): overall tightness and also for A0;
+% mu(2): relative tightness for A+;
+% mu(3): relative tightness for the constant term;
+% mu(4): weight on single dummy initial observation including constant;
+% mu(5): weight on nvar sums of coeffs dummy observations.
+
+% ** weight prior dummy observations
+%phi(1:nvar,:) = (mu(5)^2/mu(1)^2)*phi(1:nvar,:);
+%y(1:nvar,:) = (mu(5)^2/mu(1)^2)*y(1:nvar,:);
+%phi(ndobs,:) = mu
+% modified by CAS 8/6/96. The dummy obs weights are naturally in std units, not var units.
+phi(1:nvar,:) = mu(5)*phi(1:nvar,:);
+y(1:nvar,:) = mu(5)*y(1:nvar,:);
+phi(nvar+1,:) = mu(4)*phi(nvar+1,:);
+y(nvar+1,:) = mu(4)*y(nvar+1,:);
+
+% ** set up the conditional prior variance sg0bi and sgpbi.
+sg0bida = mu(1)^2*sg0bid;
+sgpbida = mu(1)^2*mu(2)^2*sgpbid;
+sgpbida(ncoef) = mu(1)^2*mu(3)^2;
+sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in an SZ paper
+sgppbdi = 1./sgppbd;
+%sgppb = diag(sgppbd);
+
+%
+% ** some common terms
+[u d v]=svd(phi,0); %trial
+% xtx = phi'*phi; %trial
+vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
+xtx=vd*vd';
+% M = eye(fss) - phi*(xtx\phi'); % not used except in line below %trial
+yu = y'*u; %trial
+cxyxx=yu*yu'; %trial
+yty=y'*y;
+ymy=yty-cxyxx; %trial
+%ymy = y'*M*y;
+%cxy = phi'*y; %trial
+cxy = vd*yu'; %trial
+cyx = cxy';
+%cxpy = xtx\cxy; %not used further except in line below
+%cxyxx = cxy'*cxpy;
+
+% ** estimate A+_hat conditional on A0, ncoef*nvar, but not using full prior
+%GlbAph = cxpy*A0h;
+
+
+%%%%%%%
+%%% build the objective function below
+%%%%%%%
+%
+t1f = diag(abs(A0hu));
+t1f = log(t1f);
+% * without the prior |A0|^k
+tt1 = (-1.0)*fss*sum(t1f);
+
+tt3 = 0;
+
+stril = 0;
+Hptd = zeros(ncoef);
+Hptdi=Hptd;
+Hptd(nvar+1:ncoef,nvar+1:ncoef)=diag(sgppbd);
+Hptdi(nvar+1:ncoef,nvar+1:ncoef)=diag(sgppbdi);
+ % condtional on A0i, H_plus_tilde
+for i = 1:nvar
+ stri = find(idmat0(:,i)); %CAS change 9/24/96, to avoid slim chance of an exact zero in x vector
+ %at some point during an optimization.
+ strm = length(stri);
+
+ A0hb = A0h(stri,i)-A0b(stri,i);
+
+ % ** set up the conditional prior variance sg0bi and sgpbi.
+ % sg0bd = zeros(stri,1); Commented out by CAS, 9/24/96. Looks like this meant to have
+ % strm where it has stri, and in any case it is "overwritten" below.
+ %------------------------------
+ % Introduce prior information on which variables "belong" in various equations.
+ % In this first trial, we just introduce this information here, in a model-specific way.
+ % Eventually this info has to be passed parametricly. In our first shot, we just damp down
+ % all coefficients except those on the diagonal.
+ factor0=idmat0(:,i);
+ sg0bd = sg0bida(stri).*factor0(stri);
+ sg0bdi = 1./sg0bd;
+ %sg0b = diag(sg0bd);
+ %
+ factor1=idmat1(:,i);
+ sg1bd = sgpbida(1:nvar).*factor1;
+ sg1bdi = 1./sg1bd;
+ %sg1b = diag(sg1bd);
+
+ % ** set up the unconditional prior variance on A0i and A+i
+ XX = zeros(nvar,strm);
+ XX(stri,:) = eye(strm); % XX in Gelman, etel, on p479
+ %
+ % * final conditional prior variance on A+i give that on A0i, and
+ % * unconditional variance on A0+
+ H0td = diag(sg0bd); % unconditional
+ % H_~: td: tilde for ~
+ % ** inverse and chol decomp
+ H0tdi = diag(sg0bdi);
+
+ %
+ Hptd(1:nvar,1:nvar)=diag(sg1bd);
+ Hptdi(1:nvar,1:nvar)=diag(sg1bdi);
+ % condtional on A0i, H_plus_tilde
+
+ % common terms
+ xxhp = xtx+Hptdi;
+ Vxxhp = chol(xxhp);
+ % inverse of Vxxhp is the upper triangular decomp
+ % of inv(Vxxhp) (covariance of alpst)
+ A0hbH = A0hb'*H0tdi;
+ H1p_1 = zeros(ncoef,nvar);
+ H1p_1(1:nvar,:) = Hptdi(1:nvar,1:nvar);
+ %%Hm = (cyx+H1p_1')*(xxhp\(cxy+H1p_1));
+ Hm1 = (cyx+H1p_1')/xxhp;
+ Hm2 = cxy+H1p_1;
+ GlbAph(i,:) = A0h(stri,i)'*XX'*Hm1; % alpha_plus_*_transpose
+ GlbAphh(:,i)=GlbAph(i,:)' + Vxxhp\randn(length(GlbAph(i,:)),1);
+ % posterior alpha_plus_*
+
+ %%alpMpyh = A0h(stri,i)'*XX'*(yty+Hptdi(1:nvar,1:nvar)-Hm)*XX;
+ alpMpyh = A0h(stri,i)'*XX'*(yty+Hptdi(1:nvar,1:nvar))*XX - GlbAph(i,:)*Hm2*XX;
+
+ % ** 3rd bid term in i-th equation,
+ % ** with all other 3rd big terms before the i-th equation
+ tt3 = tt3 + A0hbH*A0hb + alpMpyh*A0h(stri,i);
+
+ stril = stril + strm;
+end
+
+of = tt1 + 0.5*tt3;
diff --git a/MatlabFiles/qplot2.m b/MatlabFiles/qplot2.m
index 5152157cf05424c09e370d6e0716164b20f1b048..feee21cad22c948f1c27cc683bd9cfafb42c92a0 100644
--- a/MatlabFiles/qplot2.m
+++ b/MatlabFiles/qplot2.m
@@ -1,81 +1,81 @@
-function dummyout = qplot2(datavector,inityear,initquart,quarter_label)
-%QPLOT Quarterly data plot
-%QPLOT(DATA,YEAR,QUARTER,LABEL) plots quarterly data against years
-%and quarters begining with YEAR:QUARTER. Quarters are not labeled if
-%LABEL=0.
-%
-%e.g. if GDP is a vector containing quarterly observations
-%on gdp beginning in the third quarter of 1985, the command
-%QPLOT(GDP,85,3) plots the observations against years and quarters.
-%You may specify the year as 85 or 1985 but using 85 looks better.
-%
-%Setting LABEL to the values 5 or 10 will cause only every fifth
-%or every tenth year to be labeled beginning with the first decade
-%after YEAR.
-
-% QPLOT was written by Clark A. Burdick of the research
-% department of the Federal Reserve Bank of Atlanta.
-% Original: June 18, 1997
-% Last Modified: July 23, 1997
-
-% Copyright (C) 1997-2012 Clark A. Burdick
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-% TO BE DONE:
-% 1. Add better bullet proofing
-% 2. Cleaning and streamlining
-% 3. Better string manipulation
-% 4. Options for XTick
-
-
-if nargin == 1
- warning('Proper usage is QPLOT(data,year,quarter,label')
- inityear = input('What is the initial year? ');
- initquart = input('What is the initial quarter? ');
- quarter_label = input('Enter 1 for labeled quarters, 0 for unlableled quarters: ');
-end
-
-if nargin == 3
- quarter_label = 1;
-end
-
-nquart = length(datavector);
-nyear = ceil(nquart/4);
-
-years = inityear + [0:nyear];
-if quarter_label == 5
- years = years.*[round(years/5) == years/5];
-end
-if quarter_label == 10
- years = years.*[round(years/10) == years/10];
-end
-
-xtickvec = (kron(years,[1 0 0 0])+kron(ones(1,nyear+1),[0 2 3 4]))';
-
-if quarter_label ~= 1
- xtickvec = num2str(xtickvec);
- xtickvec = strrep(xtickvec,{' 0'},{' '});
- xtickvec = strrep(xtickvec,{' 2'},{' '});
- xtickvec = strrep(xtickvec,{' 3'},{' '});
- xtickvec = strrep(xtickvec,{' 4'},{' '});
-end
-
-
-plot(datavector);
-set(gca,'XTick',[1:nquart]);
-set(gca,'XTickLabel',xtickvec(initquart:initquart+(nquart-1)));
\ No newline at end of file
+function dummyout = qplot2(datavector,inityear,initquart,quarter_label)
+%QPLOT Quarterly data plot
+%QPLOT(DATA,YEAR,QUARTER,LABEL) plots quarterly data against years
+%and quarters begining with YEAR:QUARTER. Quarters are not labeled if
+%LABEL=0.
+%
+%e.g. if GDP is a vector containing quarterly observations
+%on gdp beginning in the third quarter of 1985, the command
+%QPLOT(GDP,85,3) plots the observations against years and quarters.
+%You may specify the year as 85 or 1985 but using 85 looks better.
+%
+%Setting LABEL to the values 5 or 10 will cause only every fifth
+%or every tenth year to be labeled beginning with the first decade
+%after YEAR.
+
+% QPLOT was written by Clark A. Burdick of the research
+% department of the Federal Reserve Bank of Atlanta.
+% Original: June 18, 1997
+% Last Modified: July 23, 1997
+
+%
+% Copyright (C) 1997-2012 Clark A. Burdick
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+% TO BE DONE:
+% 1. Add better bullet proofing
+% 2. Cleaning and streamlining
+% 3. Better string manipulation
+% 4. Options for XTick
+
+
+if nargin == 1
+ warning('Proper usage is QPLOT(data,year,quarter,label')
+ inityear = input('What is the initial year? ');
+ initquart = input('What is the initial quarter? ');
+ quarter_label = input('Enter 1 for labeled quarters, 0 for unlableled quarters: ');
+end
+
+if nargin == 3
+ quarter_label = 1;
+end
+
+nquart = length(datavector);
+nyear = ceil(nquart/4);
+
+years = inityear + [0:nyear];
+if quarter_label == 5
+ years = years.*[round(years/5) == years/5];
+end
+if quarter_label == 10
+ years = years.*[round(years/10) == years/10];
+end
+
+xtickvec = (kron(years,[1 0 0 0])+kron(ones(1,nyear+1),[0 2 3 4]))';
+
+if quarter_label ~= 1
+ xtickvec = num2str(xtickvec);
+ xtickvec = strrep(xtickvec,{' 0'},{' '});
+ xtickvec = strrep(xtickvec,{' 2'},{' '});
+ xtickvec = strrep(xtickvec,{' 3'},{' '});
+ xtickvec = strrep(xtickvec,{' 4'},{' '});
+end
+
+
+plot(datavector);
+set(gca,'XTick',[1:nquart]);
+set(gca,'XTickLabel',xtickvec(initquart:initquart+(nquart-1)));
diff --git a/MatlabFiles/qzdiv.m b/MatlabFiles/qzdiv.m
index 2713198a0ee431551066af9d550fd04cd3206b55..260017253ce63ca1a209284ffb05d14e89d99cac 100644
--- a/MatlabFiles/qzdiv.m
+++ b/MatlabFiles/qzdiv.m
@@ -1,58 +1,58 @@
-function [A,B,Q,Z,v] = qzdiv(stake,A,B,Q,Z,v)
-%function [A,B,Q,Z,v] = qzdiv(stake,A,B,Q,Z,v)
-%
-% Takes U.T. matrices A, B, orthonormal matrices Q,Z, rearranges them
-% so that all cases of abs(B(i,i)/A(i,i))>stake are in lower right
-% corner, while preserving U.T. and orthonormal properties and Q'AZ' and
-% Q'BZ'. The columns of v are sorted correspondingly.
-%
-% by Christopher A. Sims
-% modified (to add v to input and output) 7/27/00
-% Copyright (C) 1997-2012 Christopher A. Sims
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-vin = nargin==6;
-if ~vin
- v=[];
-end
-[n jnk] = size(A);
-root = abs([diag(A) diag(B)]);
-root(:,1) = root(:,1)-(root(:,1)<1.e-13).*(root(:,1)+root(:,2));
-root(:,2) = root(:,2)./root(:,1);
-for i = n:-1:1
- m=0;
- for j=i:-1:1
- if (root(j,2) > stake | root(j,2) < -.1)
- m=j;
- break
- end
- end
- if (m==0)
- return
- end
- for k=m:1:i-1
- [A B Q Z] = qzswitch(k,A,B,Q,Z);
- tmp = root(k,2);
- root(k,2) = root(k+1,2);
- root(k+1,2) = tmp;
- if vin
- tmp=v(:,k);
- v(:,k)=v(:,k+1);
- v(:,k+1)=tmp;
- end
- end
-end
+function [A,B,Q,Z,v] = qzdiv(stake,A,B,Q,Z,v)
+%function [A,B,Q,Z,v] = qzdiv(stake,A,B,Q,Z,v)
+%
+% Takes U.T. matrices A, B, orthonormal matrices Q,Z, rearranges them
+% so that all cases of abs(B(i,i)/A(i,i))>stake are in lower right
+% corner, while preserving U.T. and orthonormal properties and Q'AZ' and
+% Q'BZ'. The columns of v are sorted correspondingly.
+%
+% by Christopher A. Sims
+% modified (to add v to input and output) 7/27/00
+%
+% Copyright (C) 1997-2012 Christopher A. Sims
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+vin = nargin==6;
+if ~vin
+ v=[];
+end
+[n jnk] = size(A);
+root = abs([diag(A) diag(B)]);
+root(:,1) = root(:,1)-(root(:,1)<1.e-13).*(root(:,1)+root(:,2));
+root(:,2) = root(:,2)./root(:,1);
+for i = n:-1:1
+ m=0;
+ for j=i:-1:1
+ if (root(j,2) > stake | root(j,2) < -.1)
+ m=j;
+ break
+ end
+ end
+ if (m==0)
+ return
+ end
+ for k=m:1:i-1
+ [A B Q Z] = qzswitch(k,A,B,Q,Z);
+ tmp = root(k,2);
+ root(k,2) = root(k+1,2);
+ root(k+1,2) = tmp;
+ if vin
+ tmp=v(:,k);
+ v(:,k)=v(:,k+1);
+ v(:,k+1)=tmp;
+ end
+ end
+end
diff --git a/MatlabFiles/qzdivct.m b/MatlabFiles/qzdivct.m
index 128ae219e68f8627cfd373eedfe5349eaf1c763e..066a218d1a4deaaa6952a002ab31382ad6810c76 100644
--- a/MatlabFiles/qzdivct.m
+++ b/MatlabFiles/qzdivct.m
@@ -12,22 +12,22 @@ function [A,B,Q,Z] = qzdivct(stake,A,B,Q,Z)
% qzdiv in that it works on the real part's value, as is appropriate for
% continuous time models, instead of on the absolute value, as is
% appropriate for discrete time models.
-% Copyright (C) 1997-2012 Tao Zha
%
-% This file is part of Dynare.
+% Copyright (C) 1997-2012 Tao Zha
%
-% Dynare is free software: you can redistribute it and/or modify
+% This free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
-% Dynare is distributed in the hope that it will be useful,
+% It is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
realsmall=sqrt(eps)*10;
%realsmall=1e-3;
@@ -83,4 +83,4 @@ for i = n:-1:1
[gevOld gevOld(:,1).\gevOld(:,2);gev gev(:,1).\gev(:,2)]
end
end
-end
\ No newline at end of file
+end
diff --git a/MatlabFiles/qzswitch.m b/MatlabFiles/qzswitch.m
index ad86b0e021e38a8ab95f88bfccad3c6f607f2cad..a6f6cc40d8def60d05a7925605404e2748c6404c 100644
--- a/MatlabFiles/qzswitch.m
+++ b/MatlabFiles/qzswitch.m
@@ -9,22 +9,22 @@ function [A,B,Q,Z] = qzswitch(i,A,B,Q,Z)
% to drive all zeros on the diagonal of A to the lower right, but in this case
% the qz transformation is not unique and it is not possible simply to switch
% the positions of the diagonal elements of both A and B.
-% Copyright (C) 1997-2012 Tao Zha
%
-% This file is part of Dynare.
+% Copyright (C) 1997-2012 Tao Zha
%
-% Dynare is free software: you can redistribute it and/or modify
+% This free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
-% Dynare is distributed in the hope that it will be useful,
+% It is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
realsmall=sqrt(eps)*10;
%realsmall=1e-3;
@@ -74,4 +74,4 @@ B(i:i+1,:) = xy*B(i:i+1,:);
A(:,i:i+1) = A(:,i:i+1)*wz;
B(:,i:i+1) = B(:,i:i+1)*wz;
Z(:,i:i+1) = Z(:,i:i+1)*wz;
-Q(i:i+1,:) = xy*Q(i:i+1,:);
\ No newline at end of file
+Q(i:i+1,:) = xy*Q(i:i+1,:);
diff --git a/MatlabFiles/reset_ini_seed.m b/MatlabFiles/reset_ini_seed.m
index 4285e91c76284f96b1982bddafa2277fd5c1510f..78368618a5545b313e614a8ea93a73897c2f6092 100644
--- a/MatlabFiles/reset_ini_seed.m
+++ b/MatlabFiles/reset_ini_seed.m
@@ -1,27 +1,27 @@
-function reset_ini_seed(seednumber)
-%After Matlab starts, run this function to reset the seed number; otherwise, Matlab automatically resets to the same initial seed.
-% seednumber: 0 -- random state reset to the clock time;
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-if seednumber
- randn('state',seednumber);
- rand('state',seednumber);
-else
- randn('state',fix(100*sum(clock)));
- rand('state',fix(100*sum(clock)));
-end
+function reset_ini_seed(seednumber)
+%After Matlab starts, run this function to reset the seed number; otherwise, Matlab automatically resets to the same initial seed.
+% seednumber: 0 -- random state reset to the clock time;
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+if seednumber
+ randn('state',seednumber);
+ rand('state',seednumber);
+else
+ randn('state',fix(100*sum(clock)));
+ rand('state',fix(100*sum(clock)));
+end
diff --git a/MatlabFiles/rlrpostr.m b/MatlabFiles/rlrpostr.m
index abdfabf55d4476d2aab8c48a0d7b645ec256d94d..83a2997aa9f93aeb5149507f0e50629259e2e8d5 100644
--- a/MatlabFiles/rlrpostr.m
+++ b/MatlabFiles/rlrpostr.m
@@ -1,62 +1,62 @@
-function [P,H0inv,Hpinv] = rlrpostr(xtx,xty,yty,Ptld,H0invtld,Hpinvtld,Ui,Vi)
-% [P,H0inv,Hpinv] = rlrpostr(xtx,xty,yty,Ptld,H0tld,Hptld,Ui,Vi)
-%
-% Exporting random Bayesian posterior matrices with linear restrictions
-% See Waggoner and Zha's Gibbs sampling paper
-%
-% xtx: X'X: k-by-k where k=ncoef
-% xty: X'Y: k-by-nvar
-% yty: Y'Y: nvar-by-nvar
-% Ptld: cell(nvar,1), linear transformation for random walk prior for the ith equation
-% H0invtld: cell(nvar,1), transformed inv covaraince for free parameters in A0(:,i).
-% Hpinvtld: cell(nvar,1), transformed inv covaraince for free parameters in A+(:,i);
-% Ui: nvar-by-1 cell. In each cell, nvar-by-qi orthonormal basis for the null of the ith
-% equation contemporaneous restriction matrix where qi is the number of free parameters.
-% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
-% of total original parameters and bi is a vector of free parameters. When no
-% restrictions are imposed, we have Ui = I. There must be at least one free
-% parameter left for the ith equation. Imported from dnrprior.m.
-% Vi: nvar-by-1 cell. In each cell, k-by-ri orthonormal basis for the null of the ith
-% equation lagged restriction matrix where k (ncoef) is a total number of RHS variables and
-% ri is the number of free parameters. With this transformation, we have fi = Vi*gi
-% or Vi'*fi = gi where fi is a vector of total original parameters and gi is a
-% vector of free parameters. There must be at least one free parameter left for
-% the ith equation. Imported from dnrprior.m.
-%-----------------
-% P: cell(nvar,1). In each cell, posterior linear transformation for random walk prior for the ith equation % tld: tilda
-% H0inv: cell(nvar,1). In each cell, posterior inverse of covariance inv(H0) for the ith equation,
-% resembling old SpH in the exponent term in posterior of A0, but not divided by T yet.
-% Hpinv: cell(nvar,1). In each cell, posterior inv(Hp) for the ith equation.
-%
-% Tao Zha, February 2000
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-nvar = size(yty,1);
-
-P = cell(nvar,1); % tld: tilda
-H0inv = cell(nvar,1); % posterior inv(H0), resemble old SpH, but not divided by T yet.
-Hpinv = cell(nvar,1); % posterior inv(Hp).
-
-
-for n=1:nvar % one for each equation
- Hpinv{n} = Vi{n}'*xtx*Vi{n} + Hpinvtld{n};
- P1 = Vi{n}'*xty*Ui{n} + Hpinvtld{n}*Ptld{n};
- P{n} = Hpinv{n}\P1;
- H0inv{n} = Ui{n}'*yty*Ui{n} + H0invtld{n} + Ptld{n}'*Hpinvtld{n}*Ptld{n} ...
- - P1'*(Hpinv{n}\P1);
-end
+function [P,H0inv,Hpinv] = rlrpostr(xtx,xty,yty,Ptld,H0invtld,Hpinvtld,Ui,Vi)
+% [P,H0inv,Hpinv] = rlrpostr(xtx,xty,yty,Ptld,H0tld,Hptld,Ui,Vi)
+%
+% Exporting random Bayesian posterior matrices with linear restrictions
+% See Waggoner and Zha's Gibbs sampling paper
+%
+% xtx: X'X: k-by-k where k=ncoef
+% xty: X'Y: k-by-nvar
+% yty: Y'Y: nvar-by-nvar
+% Ptld: cell(nvar,1), linear transformation for random walk prior for the ith equation
+% H0invtld: cell(nvar,1), transformed inv covaraince for free parameters in A0(:,i).
+% Hpinvtld: cell(nvar,1), transformed inv covaraince for free parameters in A+(:,i);
+% Ui: nvar-by-1 cell. In each cell, nvar-by-qi orthonormal basis for the null of the ith
+% equation contemporaneous restriction matrix where qi is the number of free parameters.
+% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
+% of total original parameters and bi is a vector of free parameters. When no
+% restrictions are imposed, we have Ui = I. There must be at least one free
+% parameter left for the ith equation. Imported from dnrprior.m.
+% Vi: nvar-by-1 cell. In each cell, k-by-ri orthonormal basis for the null of the ith
+% equation lagged restriction matrix where k (ncoef) is a total number of RHS variables and
+% ri is the number of free parameters. With this transformation, we have fi = Vi*gi
+% or Vi'*fi = gi where fi is a vector of total original parameters and gi is a
+% vector of free parameters. There must be at least one free parameter left for
+% the ith equation. Imported from dnrprior.m.
+%-----------------
+% P: cell(nvar,1). In each cell, posterior linear transformation for random walk prior for the ith equation % tld: tilda
+% H0inv: cell(nvar,1). In each cell, posterior inverse of covariance inv(H0) for the ith equation,
+% resembling old SpH in the exponent term in posterior of A0, but not divided by T yet.
+% Hpinv: cell(nvar,1). In each cell, posterior inv(Hp) for the ith equation.
+%
+% Tao Zha, February 2000
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+nvar = size(yty,1);
+
+P = cell(nvar,1); % tld: tilda
+H0inv = cell(nvar,1); % posterior inv(H0), resemble old SpH, but not divided by T yet.
+Hpinv = cell(nvar,1); % posterior inv(Hp).
+
+
+for n=1:nvar % one for each equation
+ Hpinv{n} = Vi{n}'*xtx*Vi{n} + Hpinvtld{n};
+ P1 = Vi{n}'*xty*Ui{n} + Hpinvtld{n}*Ptld{n};
+ P{n} = Hpinv{n}\P1;
+ H0inv{n} = Ui{n}'*yty*Ui{n} + H0invtld{n} + Ptld{n}'*Hpinvtld{n}*Ptld{n} ...
+ - P1'*(Hpinv{n}\P1);
+end
diff --git a/MatlabFiles/rlrprior.m b/MatlabFiles/rlrprior.m
index 5d9156194b8c9016bca09abaae921dfc65021cb9..165193a56f3ac8b36e00d0d27dd72558cdd1e3b0 100644
--- a/MatlabFiles/rlrprior.m
+++ b/MatlabFiles/rlrprior.m
@@ -1,55 +1,55 @@
-function [Ptld,H0invtld,Hpinvtld] = rlrprior(Ui,Vi,Pi,H0multi,Hpmulti,nvar)
-% [Ptld,H0invtld,Hpinvtld] = rlrprior(Ui,Vi,Pi,H0multi,Hpmulti,nvar)
-%
-% Exporting random Bayesian prior with linear restrictions
-% See Waggoner and Zha's Gibbs sampling paper
-%
-% Ui: nvar-by-1 cell. In each cell, nvar-by-qi orthonormal basis for the null of the ith
-% equation contemporaneous restriction matrix where qi is the number of free parameters.
-% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
-% of total original parameters and bi is a vector of free parameters. When no
-% restrictions are imposed, we have Ui = I. There must be at least one free
-% parameter left for the ith equation. Imported from dnrprior.m.
-% Vi: nvar-by-1 cell. In each cell, k-by-ri orthonormal basis for the null of the ith
-% equation lagged restriction matrix where k (ncoef) is a total number of RHS variables and
-% ri is the number of free parameters. With this transformation, we have fi = Vi*gi
-% or Vi'*fi = gi where fi is a vector of total original parameters and gi is a
-% vector of free parameters. There must be at least one free parameter left for
-% the ith equation. Imported from dnrprior.m.
-% Pi: ncoef-by-nvar matrix for the ith equation under random walk. Same for all equations
-% H0multi: nvar-by-nvar-by-nvar; H0 for different equations under asymmetric prior
-% Hpmulti: ncoef-by-ncoef-by-nvar; H+ for different equations under asymmetric prior
-% nvar: number of endogenous variables
-% --------------------
-% Ptld: cell(nvar,1), linear transformation for random walk prior for the ith equation
-% H0invtld: cell(nvar,1), transformed inv covaraince for free parameters in A0(:,i).
-% Hpinvtld: cell(nvar,1), transformed inv covaraince for free parameters in A+(:,i);
-%
-% Tao Zha, February 2000
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-Ptld = cell(nvar,1); % tld: tilda
-H0invtld = cell(nvar,1); % H0 for different equations under linear restrictions
-Hpinvtld = cell(nvar,1); % H+ for different equations under linear restrictions
-
-for n=1:nvar % one for each equation
- Hpinvtld{n} = Vi{n}'*(Hpmulti(:,:,n)\Vi{n});
- Ptld{n} = (Hpinvtld{n}\Vi{n}')*(Hpmulti(:,:,n)\Pi)*Ui{n};
- H0invtld{n} = Ui{n}'*(H0multi(:,:,n)\Ui{n}) + Ui{n}'*Pi'*(Hpmulti(:,:,n)\Pi)*Ui{n} ...
- - Ptld{n}'*Hpinvtld{n}*Ptld{n};
-end
+function [Ptld,H0invtld,Hpinvtld] = rlrprior(Ui,Vi,Pi,H0multi,Hpmulti,nvar)
+% [Ptld,H0invtld,Hpinvtld] = rlrprior(Ui,Vi,Pi,H0multi,Hpmulti,nvar)
+%
+% Exporting random Bayesian prior with linear restrictions
+% See Waggoner and Zha's Gibbs sampling paper
+%
+% Ui: nvar-by-1 cell. In each cell, nvar-by-qi orthonormal basis for the null of the ith
+% equation contemporaneous restriction matrix where qi is the number of free parameters.
+% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
+% of total original parameters and bi is a vector of free parameters. When no
+% restrictions are imposed, we have Ui = I. There must be at least one free
+% parameter left for the ith equation. Imported from dnrprior.m.
+% Vi: nvar-by-1 cell. In each cell, k-by-ri orthonormal basis for the null of the ith
+% equation lagged restriction matrix where k (ncoef) is a total number of RHS variables and
+% ri is the number of free parameters. With this transformation, we have fi = Vi*gi
+% or Vi'*fi = gi where fi is a vector of total original parameters and gi is a
+% vector of free parameters. There must be at least one free parameter left for
+% the ith equation. Imported from dnrprior.m.
+% Pi: ncoef-by-nvar matrix for the ith equation under random walk. Same for all equations
+% H0multi: nvar-by-nvar-by-nvar; H0 for different equations under asymmetric prior
+% Hpmulti: ncoef-by-ncoef-by-nvar; H+ for different equations under asymmetric prior
+% nvar: number of endogenous variables
+% --------------------
+% Ptld: cell(nvar,1), linear transformation for random walk prior for the ith equation
+% H0invtld: cell(nvar,1), transformed inv covaraince for free parameters in A0(:,i).
+% Hpinvtld: cell(nvar,1), transformed inv covaraince for free parameters in A+(:,i);
+%
+% Tao Zha, February 2000
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+Ptld = cell(nvar,1); % tld: tilda
+H0invtld = cell(nvar,1); % H0 for different equations under linear restrictions
+Hpinvtld = cell(nvar,1); % H+ for different equations under linear restrictions
+
+for n=1:nvar % one for each equation
+ Hpinvtld{n} = Vi{n}'*(Hpmulti(:,:,n)\Vi{n});
+ Ptld{n} = (Hpinvtld{n}\Vi{n}')*(Hpmulti(:,:,n)\Pi)*Ui{n};
+ H0invtld{n} = Ui{n}'*(H0multi(:,:,n)\Ui{n}) + Ui{n}'*Pi'*(Hpmulti(:,:,n)\Pi)*Ui{n} ...
+ - Ptld{n}'*Hpinvtld{n}*Ptld{n};
+end
diff --git a/MatlabFiles/rnrprior.m b/MatlabFiles/rnrprior.m
index 76ee6ea69fbae58ecb39a07d879d672e6755a10c..b692e954896cd5a55205f354760e5303d1351246 100644
--- a/MatlabFiles/rnrprior.m
+++ b/MatlabFiles/rnrprior.m
@@ -1,231 +1,231 @@
-function [Pi,H0multi,Hpmulti,H0invmulti,Hpinvmulti,asym0,asymp] ...
- = rnrprior(nvar,q_m,lags,xdgel,mu,nexo)
-% [Pi,H0multi,Hpmulti,H0invmulti,Hpinvmulti,asym0,asymp] ...
-% = rnrprior(nvar,q_m,lags,xdgel,mu,nexo)
-%
-% Exporting random Bayesian prior of Sims and Zha with asymmetric rior (but no linear restrictions)
-% See Waggoner and Zha's Gibbs sampling paper
-%
-% nvar: number of endogenous variables
-% q_m: quarter or month
-% lags: the maximum length of lag
-% xdgel: the general matrix of the original data (no manipulation involved)
-% with sample size including lags. Used only to get variances of residuals for
-% the scaling purpose.
-% mu: 6-by-1 vector of hyperparameters (the following numbers for Atlanta Fed's forecast)
-% mu(1): overall tightness and also for A0; (0.57)
-% mu(2): relative tightness for A+; (0.13)
-% mu(3): relative tightness for the constant term; (0.1). NOTE: for other
-% exogenous terms, the variance of each exogenous term must be taken into
-% acount to eliminate the scaling factor.
-% mu(4): tightness on lag decay; (1)
-% mu(5): weight on nvar sums of coeffs dummy observations (unit roots); (5)
-% mu(6): weight on single dummy initial observation including constant
-% (cointegration, unit roots, and stationarity); (5)
-% NOTE: for this subdirectory, mu(5) and mu(6) are not used.
-% nexo: number of exogenous variables (if not specified, nexo=1 (constant) by default).
-% The constant term is always put to the last of all endogenous and exogenous variables.
-% --------------------
-% Pi: ncoef-by-nvar matrix for the ith equation under random walk. Same for all equations
-% H0multi: nvar-by-nvar-by-nvar; H0 for different equations under asymmetric prior
-% Hpmulti: ncoef-by-ncoef-by-nvar; H+ for different equations under asymmetric prior
-% H0invmulti: nvar-by-nvar-by-nvar; inv(H0) for different equations under asymmetric prior
-% Hpinvmulti: ncoef-by-ncoef-by-nvar; inv(H+) for different equations under asymmetric prior
-% asym0: nvar-by-nvar asymmetric prior on A0. Column -- equation
-% asymp: ncoef-1-by-nvar asymmetric prior on A+ bar constant. Column -- equation
-%
-% Tao Zha, February 2000
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-if nargin==5, nexo=1; end
-ncoef = nvar*lags+nexo; % number of coefficients in *each* equation, RHS coefficients only.
-
-H0multi=zeros(nvar,nvar,nvar); % H0 for different equations under asymmetric prior
-Hpmulti=zeros(ncoef,ncoef,nvar); % H+ for different equations under asymmetric prior
-H0invmulti=zeros(nvar,nvar,nvar); % inv(H0) for different equations under asymmetric prior
-Hpinvmulti=zeros(ncoef,ncoef,nvar); % inv(H+) for different equations under asymmetric prior
-
-%*** Constructing Pi for the ith equation under the random walk assumption
-Pi = zeros(ncoef,nvar); % same for all equations
-Pi(1:nvar,1:nvar) = eye(nvar); % random walk
-
-%
-%@@@ Prepared for Bayesian prior
-%
-%
-% ** monthly lag decay in order to match quarterly decay: a*exp(bl) where
-% ** l is the monthly lag. Suppose quarterly decay is 1/x where x=1,2,3,4.
-% ** Let the decay of l1 (a*exp(b*l1)) match that of x1 (say, beginning: 1/1)
-% ** and the decay of l2 (a*exp(b*l2)) match that of x2 (say, end: 1/5),
-% ** we can solve for a and b which are
-% ** b = (log_x1-log_x2)/(l1-l2), and a = x1*exp(-b*l1).
-if q_m==12
- l1 = 1; % 1st month == 1st quarter
- xx1 = 1; % 1st quarter
- l2 = lags; % last month
- xx2 = 1/((ceil(lags/3))^mu(4)); % last quarter
- %xx2 = 1/6; % last quarter
- % 3rd quarter: i.e., we intend to let decay of the 6th month match
- % that of the 3rd quarter, so that the 6th month decays a little
- % faster than the second quarter which is 1/2.
- if lags==1
- b = 0;
- else
- b = (log(xx1)-log(xx2))/(l1-l2);
- end
- a = xx1*exp(-b*l1);
-end
-
-
-
-%
-% *** specify the prior for each equation separately, SZ method,
-% ** get the residuals from univariate regressions.
-%
-sgh = zeros(nvar,1); % square root
-sgsh = sgh; % square
-nSample=size(xdgel,1); % sample size-lags
-yu = xdgel;
-C = ones(nSample,1);
-for k=1:nvar
- [Bk,ek,junk1,junk2,junk3,junk4] = sye([yu(:,k) C],lags);
- clear Bk junk1 junk2 junk3 junk4;
- sgsh(k) = ek'*ek/(nSample-lags);
- sgh(k) = sqrt(sgsh(k));
-end
-% ** prior variance for A0(:,1), same for all equations!!!
-sg0bid = zeros(nvar,1); % Sigma0_bar diagonal only for the ith equation
-for j=1:nvar
- sg0bid(j) = 1/sgsh(j); % sgsh = sigmai^2
-end
-% ** prior variance for lagged and exogeous variables, same for all equations
-sgpbid = zeros(ncoef,1); % Sigma_plus_bar, diagonal, for the ith equation
-for i = 1:lags
- if (q_m==12)
- lagdecay = a*exp(b*i*mu(4));
- end
- %
- for j = 1:nvar
- if (q_m==12)
- % exponential decay to match quarterly decay
- sgpbid((i-1)*nvar+j) = lagdecay^2/sgsh(j); % ith equation
- elseif (q_m==4)
- sgpbid((i-1)*nvar+j) = (1/i^mu(4))^2/sgsh(j); % ith equation
- else
- error('Incompatibility with lags, check the possible errors!!!')
- %warning('Incompatibility with lags, check the possible errors!!!')
- %return
- end
- end
-end
-%
-
-
-%=================================================
-% Computing the (prior) covariance matrix for the posterior of A0, no data yet
-%=================================================
-%
-%
-% ** set up the conditional prior variance sg0bi and sgpbi.
-sg0bida = mu(1)^2*sg0bid; % ith equation
-sgpbida = mu(1)^2*mu(2)^2*sgpbid;
-sgpbida(ncoef-nexo+1:ncoef) = mu(1)^2*mu(3)^2;
- %<<>> No scaling adjustment has been made for exogenous terms other than constant
-sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in a Sims-Zha paper
-
-Hptd = zeros(ncoef);
-Hptdi=Hptd;
-Hptd(ncoef,ncoef)=sgppbd(ncoef-nvar);
-Hptdinv(ncoef,ncoef)=1./sgppbd(ncoef-nvar);
- % condtional on A0i, H_plus_tilde
-
-%**** Asymmetric Information
-asym0 = ones(nvar,nvar); % if not ones, then we have relative (asymmetric) tightness
-asymp = ones(ncoef-1,nvar); % pp: plus without constant. Column -- equation
-%>>>>>> B: asymmetric prior variance for asymp <<<<<<<<
-%
-%for i = 1:lags
-% rowif = (i-1)*nvar+1;
-% rowil = i*nvar;
-% idmatw0 = 0.5; % weight assigned to idmat0 in the formation of asymp
-% if (i==1)
-% asymp(rowif:rowil,:)=(1-idmatw0)*ones(nvar)+idmatw0*idmat0; % first lag
-% % note: idmat1 is already transposed. Column -- equation
-% else
-% %asymp(rowif:rowil,1:nvar) = (1-idmatw0)*ones(nvar)+idmatw0*idmat0;
-% % <<<<<<< toggle +
-% % Note: already transposed, since idmat0 is transposed.
-% % Meaning: column implies equation
-% asymp(rowif:rowil,1:nvar) = ones(nvar);
-% % >>>>>>> toggle -
-% end
-%end
-%
-%>>>>>> E: asymmetric prior variance for asymp <<<<<<<<
-
-
-%=================================================
-% Computing the final covariance matrix (S1,...,Sm) for the prior of A0,
-% and final Gb=(G1,...,Gm) for A+ if asymmetric prior or for
-% B if symmetric prior for A+
-%=================================================
-%
-for i = 1:nvar
- %------------------------------
- % Introduce prior information on which variables "belong" in various equations.
- % In this first trial, we just introduce this information here, in a model-specific way.
- % Eventually this info has to be passed parametricly. In our first shot, we just damp down
- % all coefficients except those on the diagonal.
-
- %*** For A0
- factor0=asym0(:,i);
- sg0bd = sg0bida.*factor0; % Note, this only works for the prior variance Sg(i)
- % of a0(i) being diagonal. If the prior variance Sg(i) is not
- % diagonal, we have to work on inv(Sg(i)) or sg0bdinv directly.
- sg0bdinv = 1./sg0bd;
- % * unconditional variance on A0+
- H0td = diag(sg0bd); % unconditional
- H0tdinv = diag(sg0bdinv);
- %
- H0multi(:,:,i)=H0td;
- H0invmulti(:,:,i)=H0tdinv;
-
-
- %*** For A+
- if ~(lags==0) % For A1 to remain random walk properties
- factor1=asymp(1:nvar,i);
- sg1bd = sgpbida(1:nvar).*factor1;
- sg1bdinv = 1./sg1bd;
- %
- Hptd(1:nvar,1:nvar)=diag(sg1bd);
- Hptdinv(1:nvar,1:nvar)=diag(sg1bdinv);
- if lags>1
- factorpp=asymp(nvar+1:ncoef-1,i);
- sgpp_cbd = sgppbd(1:ncoef-nvar-1) .* factorpp;
- sgpp_cbdinv = 1./sgpp_cbd;
- Hptd(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbd);
- Hptdinv(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbdinv);
- % condtional on A0i, H_plus_tilde
- end
- end
- Hpmulti(:,:,i)=Hptd;
- Hpinvmulti(:,:,i)=Hptdinv;
-end
-
-
+function [Pi,H0multi,Hpmulti,H0invmulti,Hpinvmulti,asym0,asymp] ...
+ = rnrprior(nvar,q_m,lags,xdgel,mu,nexo)
+% [Pi,H0multi,Hpmulti,H0invmulti,Hpinvmulti,asym0,asymp] ...
+% = rnrprior(nvar,q_m,lags,xdgel,mu,nexo)
+%
+% Exporting random Bayesian prior of Sims and Zha with asymmetric rior (but no linear restrictions)
+% See Waggoner and Zha's Gibbs sampling paper
+%
+% nvar: number of endogenous variables
+% q_m: quarter or month
+% lags: the maximum length of lag
+% xdgel: the general matrix of the original data (no manipulation involved)
+% with sample size including lags. Used only to get variances of residuals for
+% the scaling purpose.
+% mu: 6-by-1 vector of hyperparameters (the following numbers for Atlanta Fed's forecast)
+% mu(1): overall tightness and also for A0; (0.57)
+% mu(2): relative tightness for A+; (0.13)
+% mu(3): relative tightness for the constant term; (0.1). NOTE: for other
+% exogenous terms, the variance of each exogenous term must be taken into
+% acount to eliminate the scaling factor.
+% mu(4): tightness on lag decay; (1)
+% mu(5): weight on nvar sums of coeffs dummy observations (unit roots); (5)
+% mu(6): weight on single dummy initial observation including constant
+% (cointegration, unit roots, and stationarity); (5)
+% NOTE: for this subdirectory, mu(5) and mu(6) are not used.
+% nexo: number of exogenous variables (if not specified, nexo=1 (constant) by default).
+% The constant term is always put to the last of all endogenous and exogenous variables.
+% --------------------
+% Pi: ncoef-by-nvar matrix for the ith equation under random walk. Same for all equations
+% H0multi: nvar-by-nvar-by-nvar; H0 for different equations under asymmetric prior
+% Hpmulti: ncoef-by-ncoef-by-nvar; H+ for different equations under asymmetric prior
+% H0invmulti: nvar-by-nvar-by-nvar; inv(H0) for different equations under asymmetric prior
+% Hpinvmulti: ncoef-by-ncoef-by-nvar; inv(H+) for different equations under asymmetric prior
+% asym0: nvar-by-nvar asymmetric prior on A0. Column -- equation
+% asymp: ncoef-1-by-nvar asymmetric prior on A+ bar constant. Column -- equation
+%
+% Tao Zha, February 2000
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+if nargin==5, nexo=1; end
+ncoef = nvar*lags+nexo; % number of coefficients in *each* equation, RHS coefficients only.
+
+H0multi=zeros(nvar,nvar,nvar); % H0 for different equations under asymmetric prior
+Hpmulti=zeros(ncoef,ncoef,nvar); % H+ for different equations under asymmetric prior
+H0invmulti=zeros(nvar,nvar,nvar); % inv(H0) for different equations under asymmetric prior
+Hpinvmulti=zeros(ncoef,ncoef,nvar); % inv(H+) for different equations under asymmetric prior
+
+%*** Constructing Pi for the ith equation under the random walk assumption
+Pi = zeros(ncoef,nvar); % same for all equations
+Pi(1:nvar,1:nvar) = eye(nvar); % random walk
+
+%
+%@@@ Prepared for Bayesian prior
+%
+%
+% ** monthly lag decay in order to match quarterly decay: a*exp(bl) where
+% ** l is the monthly lag. Suppose quarterly decay is 1/x where x=1,2,3,4.
+% ** Let the decay of l1 (a*exp(b*l1)) match that of x1 (say, beginning: 1/1)
+% ** and the decay of l2 (a*exp(b*l2)) match that of x2 (say, end: 1/5),
+% ** we can solve for a and b which are
+% ** b = (log_x1-log_x2)/(l1-l2), and a = x1*exp(-b*l1).
+if q_m==12
+ l1 = 1; % 1st month == 1st quarter
+ xx1 = 1; % 1st quarter
+ l2 = lags; % last month
+ xx2 = 1/((ceil(lags/3))^mu(4)); % last quarter
+ %xx2 = 1/6; % last quarter
+ % 3rd quarter: i.e., we intend to let decay of the 6th month match
+ % that of the 3rd quarter, so that the 6th month decays a little
+ % faster than the second quarter which is 1/2.
+ if lags==1
+ b = 0;
+ else
+ b = (log(xx1)-log(xx2))/(l1-l2);
+ end
+ a = xx1*exp(-b*l1);
+end
+
+
+
+%
+% *** specify the prior for each equation separately, SZ method,
+% ** get the residuals from univariate regressions.
+%
+sgh = zeros(nvar,1); % square root
+sgsh = sgh; % square
+nSample=size(xdgel,1); % sample size-lags
+yu = xdgel;
+C = ones(nSample,1);
+for k=1:nvar
+ [Bk,ek,junk1,junk2,junk3,junk4] = sye([yu(:,k) C],lags);
+ clear Bk junk1 junk2 junk3 junk4;
+ sgsh(k) = ek'*ek/(nSample-lags);
+ sgh(k) = sqrt(sgsh(k));
+end
+% ** prior variance for A0(:,1), same for all equations!!!
+sg0bid = zeros(nvar,1); % Sigma0_bar diagonal only for the ith equation
+for j=1:nvar
+ sg0bid(j) = 1/sgsh(j); % sgsh = sigmai^2
+end
+% ** prior variance for lagged and exogeous variables, same for all equations
+sgpbid = zeros(ncoef,1); % Sigma_plus_bar, diagonal, for the ith equation
+for i = 1:lags
+ if (q_m==12)
+ lagdecay = a*exp(b*i*mu(4));
+ end
+ %
+ for j = 1:nvar
+ if (q_m==12)
+ % exponential decay to match quarterly decay
+ sgpbid((i-1)*nvar+j) = lagdecay^2/sgsh(j); % ith equation
+ elseif (q_m==4)
+ sgpbid((i-1)*nvar+j) = (1/i^mu(4))^2/sgsh(j); % ith equation
+ else
+ error('Incompatibility with lags, check the possible errors!!!')
+ %warning('Incompatibility with lags, check the possible errors!!!')
+ %return
+ end
+ end
+end
+%
+
+
+%=================================================
+% Computing the (prior) covariance matrix for the posterior of A0, no data yet
+%=================================================
+%
+%
+% ** set up the conditional prior variance sg0bi and sgpbi.
+sg0bida = mu(1)^2*sg0bid; % ith equation
+sgpbida = mu(1)^2*mu(2)^2*sgpbid;
+sgpbida(ncoef-nexo+1:ncoef) = mu(1)^2*mu(3)^2;
+ %<<>> No scaling adjustment has been made for exogenous terms other than constant
+sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in a Sims-Zha paper
+
+Hptd = zeros(ncoef);
+Hptdi=Hptd;
+Hptd(ncoef,ncoef)=sgppbd(ncoef-nvar);
+Hptdinv(ncoef,ncoef)=1./sgppbd(ncoef-nvar);
+ % condtional on A0i, H_plus_tilde
+
+%**** Asymmetric Information
+asym0 = ones(nvar,nvar); % if not ones, then we have relative (asymmetric) tightness
+asymp = ones(ncoef-1,nvar); % pp: plus without constant. Column -- equation
+%>>>>>> B: asymmetric prior variance for asymp <<<<<<<<
+%
+%for i = 1:lags
+% rowif = (i-1)*nvar+1;
+% rowil = i*nvar;
+% idmatw0 = 0.5; % weight assigned to idmat0 in the formation of asymp
+% if (i==1)
+% asymp(rowif:rowil,:)=(1-idmatw0)*ones(nvar)+idmatw0*idmat0; % first lag
+% % note: idmat1 is already transposed. Column -- equation
+% else
+% %asymp(rowif:rowil,1:nvar) = (1-idmatw0)*ones(nvar)+idmatw0*idmat0;
+% % <<<<<<< toggle +
+% % Note: already transposed, since idmat0 is transposed.
+% % Meaning: column implies equation
+% asymp(rowif:rowil,1:nvar) = ones(nvar);
+% % >>>>>>> toggle -
+% end
+%end
+%
+%>>>>>> E: asymmetric prior variance for asymp <<<<<<<<
+
+
+%=================================================
+% Computing the final covariance matrix (S1,...,Sm) for the prior of A0,
+% and final Gb=(G1,...,Gm) for A+ if asymmetric prior or for
+% B if symmetric prior for A+
+%=================================================
+%
+for i = 1:nvar
+ %------------------------------
+ % Introduce prior information on which variables "belong" in various equations.
+ % In this first trial, we just introduce this information here, in a model-specific way.
+ % Eventually this info has to be passed parametricly. In our first shot, we just damp down
+ % all coefficients except those on the diagonal.
+
+ %*** For A0
+ factor0=asym0(:,i);
+ sg0bd = sg0bida.*factor0; % Note, this only works for the prior variance Sg(i)
+ % of a0(i) being diagonal. If the prior variance Sg(i) is not
+ % diagonal, we have to work on inv(Sg(i)) or sg0bdinv directly.
+ sg0bdinv = 1./sg0bd;
+ % * unconditional variance on A0+
+ H0td = diag(sg0bd); % unconditional
+ H0tdinv = diag(sg0bdinv);
+ %
+ H0multi(:,:,i)=H0td;
+ H0invmulti(:,:,i)=H0tdinv;
+
+
+ %*** For A+
+ if ~(lags==0) % For A1 to remain random walk properties
+ factor1=asymp(1:nvar,i);
+ sg1bd = sgpbida(1:nvar).*factor1;
+ sg1bdinv = 1./sg1bd;
+ %
+ Hptd(1:nvar,1:nvar)=diag(sg1bd);
+ Hptdinv(1:nvar,1:nvar)=diag(sg1bdinv);
+ if lags>1
+ factorpp=asymp(nvar+1:ncoef-1,i);
+ sgpp_cbd = sgppbd(1:ncoef-nvar-1) .* factorpp;
+ sgpp_cbdinv = 1./sgpp_cbd;
+ Hptd(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbd);
+ Hptdinv(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbdinv);
+ % condtional on A0i, H_plus_tilde
+ end
+ end
+ Hpmulti(:,:,i)=Hptd;
+ Hpinvmulti(:,:,i)=Hptdinv;
+end
+
+
diff --git a/MatlabFiles/run.m b/MatlabFiles/run.m
index c1e7a1e684afdbcb0ed6043ddfcdd7c9891dc375..f239023dca4c66648cd770a48146ef6fdba5dce6 100644
--- a/MatlabFiles/run.m
+++ b/MatlabFiles/run.m
@@ -1,20 +1,20 @@
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-intervalcl
-clear all
-clsort
\ No newline at end of file
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+intervalcl
+clear all
+clsort
diff --git a/MatlabFiles/sbcontest.m b/MatlabFiles/sbcontest.m
index 1fa6c64b2622f0377d482ef5c487577d612614c4..44cc6bdf11c2fd9da6a82f4cea6fc0d85e2c79e2 100644
--- a/MatlabFiles/sbcontest.m
+++ b/MatlabFiles/sbcontest.m
@@ -1,79 +1,79 @@
-function [pabove,pbelow]=sbcontest(xinput)
-% [pabove,pbelow]=sbcontest(xinput)
-% Small Sample Bayesian Contour Test (sbcontest) for overidentified models
-%
-% xinput{1}: idenmlh -- handle for the file idenml.mat
-% xinput{2}: SpHUouth -- handle for the file sphuout.mat
-% xinput{3}: fss -- effective sample size == nSample-lags+# of dummy observations
-% xinput{4}: imndraws=nstarts*ndraws2
-% xinput{5}: nvar -- # of endogenous variables
-% xinput{6}: nbuffer -- interval of whcih for printing, plotting, saving, etc.
-%--------------
-% pabove: probablity of the reduced-form LH value above the overidentified LH peak value
-% pbelow: probablity of the reduced-form LH value below the overidentified LH peak value
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-idenmlh = xinput{1}; SpHUouth = xinput{2}; fss = xinput{3}; imndraws = xinput{4}; nvar = xinput{5};
-nbuffer = xinput{6};
-
-neqn = nvar; %<<>> number of equations
-eval(['load ' idenmlh]);
-eval(['load ' SpHUouth]);
-
-pabove=0;
-pbelow=0;
-load idenml % fhat at ML for identified version
-fhat=-fhat;
- % this value is strictly smaller than that at the peak of reduced-form LH
-disp('If you havent run idenml with Rform=1, you must do so now')
-disp('Press ctrl-c to abort now or any other key to continue')
-disp(' ')
-pause
-load SpHUout % this is the output file by running "idenml" with Rform=1
- % with output SpHU
-SpHs = SpHU*fss; % for the Wishart draw
-SpHsc = chol(SpHs); % upper triangular where Sphsc' is
- % lower triangular Choleski, for Wishart draw
-tic
-for draws=1:imndraws
- if ~mod(draws,nbuffer)
- draws
- end
- ranw = randn(neqn,fss-neqn-1);
- ranw = SpHsc\ranw; % inv(SpHsc) is upper triagular Choleski of inv(SpHs)
- %ranw = SpHsic*ranw;
- % normal draws (T-nvar-1)*nvar, with variance inv(SpHs) (note, NOT inv(SpH))
- sinh = ranw*ranw'; % Wishart draws for A0h*A0h'
- A0_h = chol2(sinh); % upper triangular Choleski
- a0indx = find(A0_h);
-
- xhat_h = A0_h(a0indx);
- jnk = a0lhfun(xhat_h,SpHU,fss,nvar,a0indx);
- fhat_h = -jnk;
-
- if fhat_h > fhat
- pabove = pabove+1;
- else
- pbelow = pbelow+1;
- end
-end
-timend=toc;
-timeminutes=timend/60
-
-pabove = pabove/imndraws
-pbelow = pbelow/imndraws
+function [pabove,pbelow]=sbcontest(xinput)
+% [pabove,pbelow]=sbcontest(xinput)
+% Small Sample Bayesian Contour Test (sbcontest) for overidentified models
+%
+% xinput{1}: idenmlh -- handle for the file idenml.mat
+% xinput{2}: SpHUouth -- handle for the file sphuout.mat
+% xinput{3}: fss -- effective sample size == nSample-lags+# of dummy observations
+% xinput{4}: imndraws=nstarts*ndraws2
+% xinput{5}: nvar -- # of endogenous variables
+% xinput{6}: nbuffer -- interval of whcih for printing, plotting, saving, etc.
+%--------------
+% pabove: probablity of the reduced-form LH value above the overidentified LH peak value
+% pbelow: probablity of the reduced-form LH value below the overidentified LH peak value
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+idenmlh = xinput{1}; SpHUouth = xinput{2}; fss = xinput{3}; imndraws = xinput{4}; nvar = xinput{5};
+nbuffer = xinput{6};
+
+neqn = nvar; %<<>> number of equations
+eval(['load ' idenmlh]);
+eval(['load ' SpHUouth]);
+
+pabove=0;
+pbelow=0;
+load idenml % fhat at ML for identified version
+fhat=-fhat;
+ % this value is strictly smaller than that at the peak of reduced-form LH
+disp('If you havent run idenml with Rform=1, you must do so now')
+disp('Press ctrl-c to abort now or any other key to continue')
+disp(' ')
+pause
+load SpHUout % this is the output file by running "idenml" with Rform=1
+ % with output SpHU
+SpHs = SpHU*fss; % for the Wishart draw
+SpHsc = chol(SpHs); % upper triangular where Sphsc' is
+ % lower triangular Choleski, for Wishart draw
+tic
+for draws=1:imndraws
+ if ~mod(draws,nbuffer)
+ draws
+ end
+ ranw = randn(neqn,fss-neqn-1);
+ ranw = SpHsc\ranw; % inv(SpHsc) is upper triagular Choleski of inv(SpHs)
+ %ranw = SpHsic*ranw;
+ % normal draws (T-nvar-1)*nvar, with variance inv(SpHs) (note, NOT inv(SpH))
+ sinh = ranw*ranw'; % Wishart draws for A0h*A0h'
+ A0_h = chol2(sinh); % upper triangular Choleski
+ a0indx = find(A0_h);
+
+ xhat_h = A0_h(a0indx);
+ jnk = a0lhfun(xhat_h,SpHU,fss,nvar,a0indx);
+ fhat_h = -jnk;
+
+ if fhat_h > fhat
+ pabove = pabove+1;
+ else
+ pbelow = pbelow+1;
+ end
+end
+timend=toc;
+timeminutes=timend/60
+
+pabove = pabove/imndraws
+pbelow = pbelow/imndraws
diff --git a/MatlabFiles/simtanzphi.m b/MatlabFiles/simtanzphi.m
index f9ae33086ad6c8bb2bb2cc2f9eeed1a37d368b22..f36a7c5554339c913ffb2598384f5c2137d64e52 100644
--- a/MatlabFiles/simtanzphi.m
+++ b/MatlabFiles/simtanzphi.m
@@ -1,61 +1,60 @@
-function simtanzphi(X,k,nvar,U);
-% simtanzphi(X,k,nvar,U)
-% Recursive function with global output argument ZPHI
-% Export ZPHI: PHI_k--the span of proj(R(k))(w(a(i))|i~=k), whose rank determines
-% the degree of simultaneity
-%
-% X: nvar-by-nvar matrix so that the 1st k columns are orthonormal
-% k: <=nvar, orthonormal columns
-% nvar: number of variables
-% U: nvar-by-1 cell. Each cell contains a set of orthonormal bases for the ith
-% column of idmat0s (restriction or R(i))
-%-----------
-% ZPHI: global variable PHI_k--the span of proj(R(k))(w(a(i))|i~=k), whose rank determines
-% the degree of simultaneity
-%
-% Copyright (c) 1999 by D.F. Waggoner and T.A. Zha
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-global ZPHI
-
-if k<nvar
- V=[X(:,1:k) U{k+1}];
- [Q,R,eindx]=qr(V,0); % eindx: index vector
- Q1=Q;
- Q1(:,1:k) = Q(:,find(eindx<=k)); % keep the first k col's in V in Q1
- jnk = find(eindx>k);
- jnk1=length(Q(1,:))-k;
- Q1(:,k+1:length(Q(1,:))) = Q(:,jnk(1:jnk1));
- tmp = abs(diag(R)); % largest absoluate value in R
- jnk = find(tmp<max(tmp)*eps); % index for the zero's in R
- if isempty(jnk)
- Y = Q1(:,k+1:length(tmp));
- else
- Y = Q1(:,k+1:min(jnk)-1); % all meaningful orthonormal columns after Q(1:k)
- end
- %
- for ik=1:length(Y(1,:))
- X=[X(:,1:k) Y(:,ik)];
- simtanzphi(X,k+1,nvar,U);
- end
-else
- Xn=X(:,nvar);
- %* Projection of Xn into R{n}
- Xnproj=U{nvar}*(Xn'*U{nvar})';
- ZPHI=[ZPHI Xnproj];
-end
+function simtanzphi(X,k,nvar,U);
+% simtanzphi(X,k,nvar,U)
+% Recursive function with global output argument ZPHI
+% Export ZPHI: PHI_k--the span of proj(R(k))(w(a(i))|i~=k), whose rank determines
+% the degree of simultaneity
+%
+% X: nvar-by-nvar matrix so that the 1st k columns are orthonormal
+% k: <=nvar, orthonormal columns
+% nvar: number of variables
+% U: nvar-by-1 cell. Each cell contains a set of orthonormal bases for the ith
+% column of idmat0s (restriction or R(i))
+%-----------
+% ZPHI: global variable PHI_k--the span of proj(R(k))(w(a(i))|i~=k), whose rank determines
+% the degree of simultaneity
+%
+%
+% Copyright (C) 1997-2012 Daniel Waggoner and Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+global ZPHI
+
+if k<nvar
+ V=[X(:,1:k) U{k+1}];
+ [Q,R,eindx]=qr(V,0); % eindx: index vector
+ Q1=Q;
+ Q1(:,1:k) = Q(:,find(eindx<=k)); % keep the first k col's in V in Q1
+ jnk = find(eindx>k);
+ jnk1=length(Q(1,:))-k;
+ Q1(:,k+1:length(Q(1,:))) = Q(:,jnk(1:jnk1));
+ tmp = abs(diag(R)); % largest absoluate value in R
+ jnk = find(tmp<max(tmp)*eps); % index for the zero's in R
+ if isempty(jnk)
+ Y = Q1(:,k+1:length(tmp));
+ else
+ Y = Q1(:,k+1:min(jnk)-1); % all meaningful orthonormal columns after Q(1:k)
+ end
+ %
+ for ik=1:length(Y(1,:))
+ X=[X(:,1:k) Y(:,ik)];
+ simtanzphi(X,k+1,nvar,U);
+ end
+else
+ Xn=X(:,nvar);
+ %* Projection of Xn into R{n}
+ Xnproj=U{nvar}*(Xn'*U{nvar})';
+ ZPHI=[ZPHI Xnproj];
+end
diff --git a/MatlabFiles/smtplis.m b/MatlabFiles/smtplis.m
index e609f33e2f3fe000ad55bd5adbb24d3bf5804622..9f89a5ac9a2da148579bf2a013c3966f15a38722 100644
--- a/MatlabFiles/smtplis.m
+++ b/MatlabFiles/smtplis.m
@@ -1,61 +1,61 @@
-function [Avhx,hAvhx,cJump] = smtplis(Avhx,hAvhx,tdf,cJump,scf,...
- H_sr,nfp,Sbd,fss,nvar,a0indx)
-%
-% Straight Metropolis Algorithm for identified VARs
-%
-% Avhx: previous draw of parameter x in 1st (kept) sequence
-% hAvhx: lh value of previous draw in 1st (kept) sequence
-% tdf: degrees of freedom of the jump t-distribution
-% cJump: old count for times of jumping
-% scf: scale down factor for stand. dev. -- square root of covariance matrix H
-% H_sr: square root of covariance matrix H in approximate density
-% nfp: number of free parameters
-% Sbd: S in block diagonal covariance matrix in asymmetric prior case
-% fss: effective sample size, including # of dummy observations
-% nvar: number of variables
-% a0indx: index of locations of free elements in A0
-%--------------
-% Avhx: new draw of parameter x in 1st (kept) sequence
-% hAvhx: lh value of new draw in 1st (kept) sequence
-% cJump: new count for times of jumping
-%
-% December 1998 by Tao Zha
-
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-%** draw free elements Avh in A0 and hyperparameters from t-dist
-Avhz1 = scf*H_sr*randn(nfp,1); % normal draws
-csq=randn(tdf,1);
-csq=sum(csq .* csq);
-Avhz = Avhz1/sqrt(csq/tdf);
-
-Avhy = Avhx + Avhz; % random walk chain -- Metropolis
-% ** actual density, having taken log
-hAvhy = a0asfun(Avhy,Sbd,fss,nvar,a0indx);
-hAvhy = -hAvhy; % converted to logLH
-
-mphxy = exp(hAvhy-hAvhx);
-
-%** draw u from uniform(0,1)
-u = rand(1);
-Jump = min([mphxy 1]);
-if u <= Jump
- Avhx = Avhy;
- hAvhx = hAvhy;
- cJump = cJump+1;
-end
+function [Avhx,hAvhx,cJump] = smtplis(Avhx,hAvhx,tdf,cJump,scf,...
+ H_sr,nfp,Sbd,fss,nvar,a0indx)
+%
+% Straight Metropolis Algorithm for identified VARs
+%
+% Avhx: previous draw of parameter x in 1st (kept) sequence
+% hAvhx: lh value of previous draw in 1st (kept) sequence
+% tdf: degrees of freedom of the jump t-distribution
+% cJump: old count for times of jumping
+% scf: scale down factor for stand. dev. -- square root of covariance matrix H
+% H_sr: square root of covariance matrix H in approximate density
+% nfp: number of free parameters
+% Sbd: S in block diagonal covariance matrix in asymmetric prior case
+% fss: effective sample size, including # of dummy observations
+% nvar: number of variables
+% a0indx: index of locations of free elements in A0
+%--------------
+% Avhx: new draw of parameter x in 1st (kept) sequence
+% hAvhx: lh value of new draw in 1st (kept) sequence
+% cJump: new count for times of jumping
+%
+% December 1998 by Tao Zha
+
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+%** draw free elements Avh in A0 and hyperparameters from t-dist
+Avhz1 = scf*H_sr*randn(nfp,1); % normal draws
+csq=randn(tdf,1);
+csq=sum(csq .* csq);
+Avhz = Avhz1/sqrt(csq/tdf);
+
+Avhy = Avhx + Avhz; % random walk chain -- Metropolis
+% ** actual density, having taken log
+hAvhy = a0asfun(Avhy,Sbd,fss,nvar,a0indx);
+hAvhy = -hAvhy; % converted to logLH
+
+mphxy = exp(hAvhy-hAvhx);
+
+%** draw u from uniform(0,1)
+u = rand(1);
+Jump = min([mphxy 1]);
+if u <= Jump
+ Avhx = Avhy;
+ hAvhx = hAvhy;
+ cJump = cJump+1;
+end
diff --git a/MatlabFiles/smtplis2.m b/MatlabFiles/smtplis2.m
index 83fb0add90a10f068c22d1ca7ff9b88b93e546ac..ca1487b615ab2238da85c4f18f6705dd1a189ef2 100644
--- a/MatlabFiles/smtplis2.m
+++ b/MatlabFiles/smtplis2.m
@@ -1,115 +1,115 @@
-function [Avhx,AvhxD,hAvhx,hAvhxD,cJump,hAvhh] = smtplis2(Avhx,AvhxD,hAvhx,hAvhxD,...
- A0xhat,tdf,cJump,scf,H_sr,nfp,Sbd,fss,nvar,a0indx,hAvhh)
-% [Avhx,AvhxD,hAvhx,hAvhxD,cJump,hAvhh] = smtplis2(Avhx,AvhxD,hAvhx,hAvhxD,...
-% A0xhat,tdf,cJump,scf,H_sr,nfp,Sbd,fss,nvar,a0indx,hAvhh)
-% Straight Metropolis Algorithm for identified VARs with 2 parallel sequences
-%
-% Avhx: previous draw of parameter x in 1st (kept) sequence
-% AvhxD: previous draw of parameter x in 2nd (discarded) sequence
-% hAvhx: lh value of previous draw in 1st (kept) sequence
-% hAvhxD: lh vlaue of previous draw in 2nd (discarded) sequence
-% A0xhat: ML estimate of A0
-% tdf: degrees of freedom of the jump t-distribution
-% cJump: old count for times of jumping
-% scf: scale down factor for stand. dev. -- square root of covariance matrix H
-% H_sr: square root of covariance matrix H
-% nfp: number of free parameters
-% Sbd: S in block diagonal covariance matrix in asymmetric prior case
-% fss: effective sample size
-% nvar: number of variables
-% a0indx: index of locations of free elements in A0
-% hAvhh: highest point of lh
-%--------------
-% Avhx: new draw of parameter x in 1st (kept) sequence
-% AvhxD: new draw of parameter x in 2nd (discarded) sequence
-% hAvhx: lh value of new draw in 1st (kept) sequence
-% AvhxD: lh vlaue of new draw in 2nd (discarded) sequence
-% cJump: new count for times of jumping
-% hAvhh: highest point of lh
-%
-% November 1998 by Tao Zha
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-if ~(fix(tdf)-tdf==0)
- warning('tdf in msstart.m must be integer for drawing chi^2 from normal')
- disp('press ctrl-c to abort')
- pause
-end
-
-for k=1:2 % parallel sequences; 2nd to be discarded
- %** draw free elements Avh in A0 and hyperparameters from t-dist
- %gsg = gamrnd(ga,gb); % G-sigma from Gamma draw
- %Avhz = (1/sqrt(gsg))*(scf*H_sr*randn(nfp,1));
- % scf is used to control the acceptance ratio -- countJump
- Avhz1 = scf*H_sr*randn(nfp,1); % normal draws
- %Avhz1 = 2*randn(nfp,1); % normal draws
- csq=randn(tdf,1);
- csq=sum(csq .* csq);
- Avhz = Avhz1/sqrt(csq/tdf);
-
- if (k==1)
- Avhy = Avhx + Avhz; % random walk chain -- Metropolis
- else
- Avhy = AvhxD + Avhz; % random walk chain -- Metropolis
- end
-
- % ** actual density, having taken log
- hAvhy = a0asfun(Avhy,Sbd,fss,nvar,a0indx);
- hAvhy = -hAvhy; % converted to logLH
-
- if (k==1)
- mphxy = exp(hAvhy-hAvhx);
- else
- mphxy = exp(hAvhy-hAvhxD);
- end
-
- %** draw u from uniform(0,1)
- u = rand(1);
- Jump = min([mphxy 1]);
- if u <= Jump
- %** Normalization: get the point that is nearest to axhat or A0xhat
- Atem=zeros(nvar);
- Atem(a0indx) = Avhy;
- Adiff = (Atem - A0xhat).^2;
- % distance that may be far from axhat or A0xhat
- Adiffn = (-Atem - A0xhat).^2;
- % distance by chaning the sign of Atem
- cAdiff = sum(Adiff); % each column summed up
- cAdiffn = sum(Adiffn); % each column summed up
- cAindx = find(cAdiffn<cAdiff); % index for shorter distance
- Atemn = Atem;
- Atemn(:,cAindx) = -Atem(:,cAindx);
- % find the shortest or nearest distance
- %** get the value of logPoster or logLH
- Avhy = Atemn(a0indx);
- %
-
- if (k==1)
- Avhx = Avhy;
- hAvhx = hAvhy;
- if hAvhy > hAvhh
- hAvhh = hAvhy;
- Avhh = Avhy;
- end
- cJump = cJump+1;
- else
- AvhxD = Avhy;
- hAvhxD = hAvhy;
- end
- end
-end
+function [Avhx,AvhxD,hAvhx,hAvhxD,cJump,hAvhh] = smtplis2(Avhx,AvhxD,hAvhx,hAvhxD,...
+ A0xhat,tdf,cJump,scf,H_sr,nfp,Sbd,fss,nvar,a0indx,hAvhh)
+% [Avhx,AvhxD,hAvhx,hAvhxD,cJump,hAvhh] = smtplis2(Avhx,AvhxD,hAvhx,hAvhxD,...
+% A0xhat,tdf,cJump,scf,H_sr,nfp,Sbd,fss,nvar,a0indx,hAvhh)
+% Straight Metropolis Algorithm for identified VARs with 2 parallel sequences
+%
+% Avhx: previous draw of parameter x in 1st (kept) sequence
+% AvhxD: previous draw of parameter x in 2nd (discarded) sequence
+% hAvhx: lh value of previous draw in 1st (kept) sequence
+% hAvhxD: lh vlaue of previous draw in 2nd (discarded) sequence
+% A0xhat: ML estimate of A0
+% tdf: degrees of freedom of the jump t-distribution
+% cJump: old count for times of jumping
+% scf: scale down factor for stand. dev. -- square root of covariance matrix H
+% H_sr: square root of covariance matrix H
+% nfp: number of free parameters
+% Sbd: S in block diagonal covariance matrix in asymmetric prior case
+% fss: effective sample size
+% nvar: number of variables
+% a0indx: index of locations of free elements in A0
+% hAvhh: highest point of lh
+%--------------
+% Avhx: new draw of parameter x in 1st (kept) sequence
+% AvhxD: new draw of parameter x in 2nd (discarded) sequence
+% hAvhx: lh value of new draw in 1st (kept) sequence
+% AvhxD: lh vlaue of new draw in 2nd (discarded) sequence
+% cJump: new count for times of jumping
+% hAvhh: highest point of lh
+%
+% November 1998 by Tao Zha
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+if ~(fix(tdf)-tdf==0)
+ warning('tdf in msstart.m must be integer for drawing chi^2 from normal')
+ disp('press ctrl-c to abort')
+ pause
+end
+
+for k=1:2 % parallel sequences; 2nd to be discarded
+ %** draw free elements Avh in A0 and hyperparameters from t-dist
+ %gsg = gamrnd(ga,gb); % G-sigma from Gamma draw
+ %Avhz = (1/sqrt(gsg))*(scf*H_sr*randn(nfp,1));
+ % scf is used to control the acceptance ratio -- countJump
+ Avhz1 = scf*H_sr*randn(nfp,1); % normal draws
+ %Avhz1 = 2*randn(nfp,1); % normal draws
+ csq=randn(tdf,1);
+ csq=sum(csq .* csq);
+ Avhz = Avhz1/sqrt(csq/tdf);
+
+ if (k==1)
+ Avhy = Avhx + Avhz; % random walk chain -- Metropolis
+ else
+ Avhy = AvhxD + Avhz; % random walk chain -- Metropolis
+ end
+
+ % ** actual density, having taken log
+ hAvhy = a0asfun(Avhy,Sbd,fss,nvar,a0indx);
+ hAvhy = -hAvhy; % converted to logLH
+
+ if (k==1)
+ mphxy = exp(hAvhy-hAvhx);
+ else
+ mphxy = exp(hAvhy-hAvhxD);
+ end
+
+ %** draw u from uniform(0,1)
+ u = rand(1);
+ Jump = min([mphxy 1]);
+ if u <= Jump
+ %** Normalization: get the point that is nearest to axhat or A0xhat
+ Atem=zeros(nvar);
+ Atem(a0indx) = Avhy;
+ Adiff = (Atem - A0xhat).^2;
+ % distance that may be far from axhat or A0xhat
+ Adiffn = (-Atem - A0xhat).^2;
+ % distance by chaning the sign of Atem
+ cAdiff = sum(Adiff); % each column summed up
+ cAdiffn = sum(Adiffn); % each column summed up
+ cAindx = find(cAdiffn<cAdiff); % index for shorter distance
+ Atemn = Atem;
+ Atemn(:,cAindx) = -Atem(:,cAindx);
+ % find the shortest or nearest distance
+ %** get the value of logPoster or logLH
+ Avhy = Atemn(a0indx);
+ %
+
+ if (k==1)
+ Avhx = Avhy;
+ hAvhx = hAvhy;
+ if hAvhy > hAvhh
+ hAvhh = hAvhy;
+ Avhh = Avhy;
+ end
+ cJump = cJump+1;
+ else
+ AvhxD = Avhy;
+ hAvhxD = hAvhy;
+ end
+ end
+end
diff --git a/MatlabFiles/smtplis2seq.m b/MatlabFiles/smtplis2seq.m
index a66258c43a5c4a90ff2cb9465f7aef37d1399438..689a587d889f7818714c47945fdea1db365a9c3f 100644
--- a/MatlabFiles/smtplis2seq.m
+++ b/MatlabFiles/smtplis2seq.m
@@ -1,115 +1,115 @@
-function [Avhx,AvhxD,hAvhx,hAvhxD,cJump,hAvhh] = smtplis2seq(Avhx,AvhxD,hAvhx,hAvhxD,...
- A0xhat,tdf,cJump,scf,H_sr,nfp,Sbd,fss,nvar,a0indx,hAvhh)
-% [Avhx,AvhxD,hAvhx,hAvhxD,cJump,hAvhh] = smtplis2(Avhx,AvhxD,hAvhx,hAvhxD,...
-% A0xhat,tdf,cJump,scf,H_sr,nfp,Sbd,fss,nvar,a0indx,hAvhh)
-% Straight Metropolis Algorithm for identified VARs with 2 parallel sequences
-%
-% Avhx: previous draw of parameter x in 1st (kept) sequence
-% AvhxD: previous draw of parameter x in 2nd (discarded) sequence
-% hAvhx: lh value of previous draw in 1st (kept) sequence
-% hAvhxD: lh vlaue of previous draw in 2nd (discarded) sequence
-% A0xhat: ML estimate of A0
-% tdf: degrees of freedom of the jump t-distribution
-% cJump: old count for times of jumping
-% scf: scale down factor for stand. dev. -- square root of covariance matrix H
-% H_sr: square root of covariance matrix H
-% nfp: number of free parameters
-% Sbd: S in block diagonal covariance matrix in asymmetric prior case
-% fss: effective sample size
-% nvar: number of variables
-% a0indx: index of locations of free elements in A0
-% hAvhh: highest point of lh
-%--------------
-% Avhx: new draw of parameter x in 1st (kept) sequence
-% AvhxD: new draw of parameter x in 2nd (discarded) sequence
-% hAvhx: lh value of new draw in 1st (kept) sequence
-% AvhxD: lh vlaue of new draw in 2nd (discarded) sequence
-% cJump: new count for times of jumping
-% hAvhh: highest point of lh
-%
-% November 1998 by Tao Zha
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-if ~(fix(tdf)-tdf==0)
- warning('tdf in msstart.m must be integer for drawing chi^2 from normal')
- disp('press ctrl-c to abort')
- pause
-end
-
-for k=1:2 % parallel sequences; 2nd to be discarded
- %** draw free elements Avh in A0 and hyperparameters from t-dist
- %gsg = gamrnd(ga,gb); % G-sigma from Gamma draw
- %Avhz = (1/sqrt(gsg))*(scf*H_sr*randn(nfp,1));
- % scf is used to control the acceptance ratio -- countJump
- Avhz1 = scf*H_sr*randn(nfp,1); % normal draws
- %Avhz1 = 2*randn(nfp,1); % normal draws
- csq=randn(tdf,1);
- csq=sum(csq .* csq);
- Avhz = Avhz1/sqrt(csq/tdf);
-
- if (k==1)
- Avhy = Avhx + Avhz; % random walk chain -- Metropolis
- else
- Avhy = AvhxD + Avhz; % random walk chain -- Metropolis
- end
-
- % ** actual density, having taken log
- hAvhy = a0asfun(Avhy,Sbd,fss,nvar,a0indx);
- hAvhy = -hAvhy; % converted to logLH
-
- if (k==1)
- mphxy = exp(hAvhy-hAvhx);
- else
- mphxy = exp(hAvhy-hAvhxD);
- end
-
- %** draw u from uniform(0,1)
- u = rand(1);
- Jump = min([mphxy 1]);
- if u <= Jump
- %** Normalization: get the point that is nearest to axhat or A0xhat
- Atem=zeros(nvar);
- Atem(a0indx) = Avhy;
- Adiff = (Atem - A0xhat).^2;
- % distance that may be far from axhat or A0xhat
- Adiffn = (-Atem - A0xhat).^2;
- % distance by chaning the sign of Atem
- cAdiff = sum(Adiff); % each column summed up
- cAdiffn = sum(Adiffn); % each column summed up
- cAindx = find(cAdiffn<cAdiff); % index for shorter distance
- Atemn = Atem;
- Atemn(:,cAindx) = -Atem(:,cAindx);
- % find the shortest or nearest distance
- %** get the value of logPoster or logLH
- Avhy = Atemn(a0indx);
- %
-
- if (k==1)
- Avhx = Avhy;
- hAvhx = hAvhy;
- if hAvhy > hAvhh
- hAvhh = hAvhy;
- Avhh = Avhy;
- end
- cJump = cJump+1;
- else
- AvhxD = Avhy;
- hAvhxD = hAvhy;
- end
- end
-end
\ No newline at end of file
+function [Avhx,AvhxD,hAvhx,hAvhxD,cJump,hAvhh] = smtplis2seq(Avhx,AvhxD,hAvhx,hAvhxD,...
+ A0xhat,tdf,cJump,scf,H_sr,nfp,Sbd,fss,nvar,a0indx,hAvhh)
+% [Avhx,AvhxD,hAvhx,hAvhxD,cJump,hAvhh] = smtplis2(Avhx,AvhxD,hAvhx,hAvhxD,...
+% A0xhat,tdf,cJump,scf,H_sr,nfp,Sbd,fss,nvar,a0indx,hAvhh)
+% Straight Metropolis Algorithm for identified VARs with 2 parallel sequences
+%
+% Avhx: previous draw of parameter x in 1st (kept) sequence
+% AvhxD: previous draw of parameter x in 2nd (discarded) sequence
+% hAvhx: lh value of previous draw in 1st (kept) sequence
+% hAvhxD: lh vlaue of previous draw in 2nd (discarded) sequence
+% A0xhat: ML estimate of A0
+% tdf: degrees of freedom of the jump t-distribution
+% cJump: old count for times of jumping
+% scf: scale down factor for stand. dev. -- square root of covariance matrix H
+% H_sr: square root of covariance matrix H
+% nfp: number of free parameters
+% Sbd: S in block diagonal covariance matrix in asymmetric prior case
+% fss: effective sample size
+% nvar: number of variables
+% a0indx: index of locations of free elements in A0
+% hAvhh: highest point of lh
+%--------------
+% Avhx: new draw of parameter x in 1st (kept) sequence
+% AvhxD: new draw of parameter x in 2nd (discarded) sequence
+% hAvhx: lh value of new draw in 1st (kept) sequence
+% AvhxD: lh vlaue of new draw in 2nd (discarded) sequence
+% cJump: new count for times of jumping
+% hAvhh: highest point of lh
+%
+% November 1998 by Tao Zha
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+if ~(fix(tdf)-tdf==0)
+ warning('tdf in msstart.m must be integer for drawing chi^2 from normal')
+ disp('press ctrl-c to abort')
+ pause
+end
+
+for k=1:2 % parallel sequences; 2nd to be discarded
+ %** draw free elements Avh in A0 and hyperparameters from t-dist
+ %gsg = gamrnd(ga,gb); % G-sigma from Gamma draw
+ %Avhz = (1/sqrt(gsg))*(scf*H_sr*randn(nfp,1));
+ % scf is used to control the acceptance ratio -- countJump
+ Avhz1 = scf*H_sr*randn(nfp,1); % normal draws
+ %Avhz1 = 2*randn(nfp,1); % normal draws
+ csq=randn(tdf,1);
+ csq=sum(csq .* csq);
+ Avhz = Avhz1/sqrt(csq/tdf);
+
+ if (k==1)
+ Avhy = Avhx + Avhz; % random walk chain -- Metropolis
+ else
+ Avhy = AvhxD + Avhz; % random walk chain -- Metropolis
+ end
+
+ % ** actual density, having taken log
+ hAvhy = a0asfun(Avhy,Sbd,fss,nvar,a0indx);
+ hAvhy = -hAvhy; % converted to logLH
+
+ if (k==1)
+ mphxy = exp(hAvhy-hAvhx);
+ else
+ mphxy = exp(hAvhy-hAvhxD);
+ end
+
+ %** draw u from uniform(0,1)
+ u = rand(1);
+ Jump = min([mphxy 1]);
+ if u <= Jump
+ %** Normalization: get the point that is nearest to axhat or A0xhat
+ Atem=zeros(nvar);
+ Atem(a0indx) = Avhy;
+ Adiff = (Atem - A0xhat).^2;
+ % distance that may be far from axhat or A0xhat
+ Adiffn = (-Atem - A0xhat).^2;
+ % distance by chaning the sign of Atem
+ cAdiff = sum(Adiff); % each column summed up
+ cAdiffn = sum(Adiffn); % each column summed up
+ cAindx = find(cAdiffn<cAdiff); % index for shorter distance
+ Atemn = Atem;
+ Atemn(:,cAindx) = -Atem(:,cAindx);
+ % find the shortest or nearest distance
+ %** get the value of logPoster or logLH
+ Avhy = Atemn(a0indx);
+ %
+
+ if (k==1)
+ Avhx = Avhy;
+ hAvhx = hAvhy;
+ if hAvhy > hAvhh
+ hAvhh = hAvhy;
+ Avhh = Avhy;
+ end
+ cJump = cJump+1;
+ else
+ AvhxD = Avhy;
+ hAvhxD = hAvhy;
+ end
+ end
+end
diff --git a/MatlabFiles/srestrictrwzalg.m b/MatlabFiles/srestrictrwzalg.m
index cdf1bdc6126f1ea112a033d9b8da1256ee006e83..edbf3c82d154d998ddd8b787ca2b14d0788bd4f9 100644
--- a/MatlabFiles/srestrictrwzalg.m
+++ b/MatlabFiles/srestrictrwzalg.m
@@ -1,161 +1,160 @@
-function Q = SRestrictRWZalg(A0hatinv,Bhat,nvar,lags,irs)
-% Rubio-Waggoner-Zha (RWZ) method of sign restrictions. For related methods, see Canova, Faust, and Uhlig.
-% The detailed theoretical foundation of this algorithm can be found in Theorem 3 of Rubio, Waggoner, and Zha (RWZ)'s article "Regime Changes in the Euro Area."
-% Other M functions called by this function can be downloaded by clicking on Archived Matlab Library ZhaZippedCode on http://home.earthlink.net/~tzha02/programCode.html
-% Strcutural VAR form: Y*A0hat = X*Aphat + E, X where Y is T-by-nvar, A0hat is nvar-by-nvar, X is T-by-k (including the constant term and all other exogenous terms),
-% Aphat is k-by-nvar, and E is T-by-nvar. Rows are in the order of 1st lag (with nvar variables) to lags (with nvar variables) plus the exogenous terms.
-% Note that columns of A0hat or Aphat correspond to equations.
-% Inputs:
-% A0hatinv = inv(A0hat).
-% Bhat = Aphat*inv(A0hat).
-% nvar = number of endogenous variables.
-% lags = lag length.
-% irs = maximum number of periods in which sign restrictions are imposed.
-% Outputs:
-% Q: orthogonal rotation matrix so that Q*A0hatinv or A0hat*Q' gives impulse responses that will satisfy
-% sign restrictions of Canova, Faust, and Uhlig.
-%
-% Modified Nov 2004 by T. Zha to
-% (1) correct the existing bugs;
-% (2) make the signs explicit to avoid the normalization problem when computing error bands;
-% (3) construct efficient way (i.e., earlier exit) to make all restrictions satisfied;
-% (4) construct efficient way to normalize.
-% In this example, we have
-% Variables are in the following order: 1: y; 2: P; 3: R; 4: M3; 5: Exec (per $).
-% Shocks are in the following order: 1: AS; 2: AD; 3: MP; 4: MD; 5: Exec.
-%
-% Copyright (c) Rubio, Waggoner, and Zha, 2005.
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-nn = [nvar lags irs];
-control=0;
-
-
-Q=eye(nvar);
-
-while control==0
- newmatrix=normrnd(0,1,nvar,nvar);
- [Q,R]=qr(newmatrix);
- for i=1:nvar;
- if R(i,i)<0
- Q(:,i)=-Q(:,i);
- end
- end
- imfhat = fn_impulse(Bhat,Q*A0hatinv,nn);
- %In the form that is congenial to RATS
- imf3hat=reshape(imfhat,size(imfhat,1),nvar,nvar);
- %imf3hat: row--steps, column--nvar responses, 3rd dimension--nvar shocks
-
- %=== Responses to a moneaty policy (MP) shock.
- % R>0, M<0, y<0, and P<0 for the irs periods.
- a = (imf3hat(1:irs,3,3) > 0) .* (imf3hat(1:irs,4,3) < 0) .* (imf3hat(1:irs,1,3) < 0) .* (imf3hat(1:irs,2,3) < 0);
- if (max(a)==0)
- %--- Swiching the sign of the shock.
- am = (imf3hat(1:irs,3,3) < 0) .* (imf3hat(1:irs,4,3) > 0) .* (imf3hat(1:irs,1,3) > 0) .* (imf3hat(1:irs,2,3) > 0);
- if (min(am)==0)
- continue; %The restrictions are not satisfied. Go the beginning to redraw.
- else
- %--- Normalizing according to the switched sign.
- Q(3,:) = -Q(3,:);
- end
- elseif (min(a)==0)
- continue; %The restrictions are not satisfied. Go the beginning to redraw.
- end
- %--- R>0 and M<0 for the irs periods.
- % a = (imf3hat(1:irs,3,3) > 0) .* (imf3hat(1:irs,4,3) < 0);
- % if (max(a)==0)
- % %--- Swiching the sign of the shock.
- % am = (imf3hat(1:irs,3,3) < 0) .* (imf3hat(1:irs,4,3) > 0);
- % if (min(am)==0)
- % continue; %The restrictions are not satisfied. Go the beginning to redraw.
- % else
- % %--- Normalizing according to the switched sign.
- % Q(3,:) = -Q(3,:);
- % end
- % elseif (min(a)==0)
- % continue; %The restrictions are not satisfied. Go the beginning to redraw.
- % end
-
- %=== Responses to an money demand (MD) shock.
- % R>0 and M>0 for the irs periods.
- a = (imf3hat(1:irs,3,4) > 0) .* (imf3hat(1:irs,4,4) > 0);
- if (max(a)==0)
- %--- Swiching the sign of the shock and normalize.
- am = (imf3hat(1:irs,3,4) < 0) .* (imf3hat(1:irs,4,4) < 0);
- if (min(am)==0)
- continue; %The restrictions are not satisfied. Go the beginning to redraw.
- else
- %--- Normalizing according to the switched sign.
- Q(4,:) = -Q(4,:);
- end
- elseif (min(a)==0)
- continue; %The restrictions are not satisfied. Go the beginning to redraw.
- end
-
- %=== Responses to an aggregate demand (AD) shock.
- % P>0 and y>0 for the irs periods.
- a = (imf3hat(1:irs,1,2) > 0) .* (imf3hat(1:irs,2,2) > 0);
- if (max(a)==0)
- %--- Swiching the sign of the shock and normalize.
- am = (imf3hat(1:irs,1,2) < 0) .* (imf3hat(1:irs,2,2) < 0);
- if (min(am)==0)
- continue; %The restrictions are not satisfied. Go the beginning to redraw.
- else
- %--- Normalizing according to the switched sign.
- Q(2,:) = -Q(2,:);
- end
- elseif (min(a)==0)
- continue; %The restrictions are not satisfied. Go the beginning to redraw.
- end
-
- %=== Responses to an aggregate supply (AS) shock.
- % P>0 and y<0 for the irs periods.
- a = (imf3hat(1:irs,1,1) < 0) .* (imf3hat(1:irs,2,1) > 0);
- if (max(a)==0)
- %--- Swiching the sign of the shock and normalize.
- am = (imf3hat(1:irs,1,1) > 0) .* (imf3hat(1:irs,2,1) < 0);
- if (min(am)==0)
- continue; %The restrictions are not satisfied. Go the beginning to redraw.
- else
- %--- Normalizing according to the switched sign.
- Q(1,:) = -Q(1,:);
- end
- elseif (min(a)==0)
- continue; %The restrictions are not satisfied. Go the beginning to redraw.
- end
-
- %=== Responses to an exchange-rate shock (depreciation ==> exports >0 ==> y>0);
- % Ex>0 and y>0 for the irs periods.
- a= (imf3hat(1:irs,1,5) > 0) .* (imf3hat(1:irs,5,5) > 0);
- if (max(a)==0)
- %--- Swiching the sign of the shock and normalize.
- am = (imf3hat(1:irs,1,5) < 0) .* (imf3hat(1:irs,5,5) < 0);
- if (min(am)==0)
- continue; %The restrictions are not satisfied. Go the beginning to redraw.
- else
- %--- Normalizing according to the switched sign.
- Q(5,:) = -Q(5,:);
- end
- elseif (min(a)==0)
- continue; %The restrictions are not satisfied. Go the beginning to redraw.
- end
-
- %--- Terminating condition: all restrictions are satisfied.
- control=1;
-end
+function Q = SRestrictRWZalg(A0hatinv,Bhat,nvar,lags,irs)
+% Rubio-Waggoner-Zha (RWZ) method of sign restrictions. For related methods, see Canova, Faust, and Uhlig.
+% The detailed theoretical foundation of this algorithm can be found in Theorem 3 of Rubio, Waggoner, and Zha (RWZ)'s article "Regime Changes in the Euro Area."
+% Other M functions called by this function can be downloaded by clicking on Archived Matlab Library ZhaZippedCode on http://home.earthlink.net/~tzha02/programCode.html
+% Strcutural VAR form: Y*A0hat = X*Aphat + E, X where Y is T-by-nvar, A0hat is nvar-by-nvar, X is T-by-k (including the constant term and all other exogenous terms),
+% Aphat is k-by-nvar, and E is T-by-nvar. Rows are in the order of 1st lag (with nvar variables) to lags (with nvar variables) plus the exogenous terms.
+% Note that columns of A0hat or Aphat correspond to equations.
+% Inputs:
+% A0hatinv = inv(A0hat).
+% Bhat = Aphat*inv(A0hat).
+% nvar = number of endogenous variables.
+% lags = lag length.
+% irs = maximum number of periods in which sign restrictions are imposed.
+% Outputs:
+% Q: orthogonal rotation matrix so that Q*A0hatinv or A0hat*Q' gives impulse responses that will satisfy
+% sign restrictions of Canova, Faust, and Uhlig.
+%
+% Modified Nov 2004 by T. Zha to
+% (1) correct the existing bugs;
+% (2) make the signs explicit to avoid the normalization problem when computing error bands;
+% (3) construct efficient way (i.e., earlier exit) to make all restrictions satisfied;
+% (4) construct efficient way to normalize.
+% In this example, we have
+% Variables are in the following order: 1: y; 2: P; 3: R; 4: M3; 5: Exec (per $).
+% Shocks are in the following order: 1: AS; 2: AD; 3: MP; 4: MD; 5: Exec.
+%
+%
+% Copyright (C) 1997-2012 Juan Rubio-Ramirez, Daniel Waggoner and Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+nn = [nvar lags irs];
+control=0;
+
+
+Q=eye(nvar);
+
+while control==0
+ newmatrix=normrnd(0,1,nvar,nvar);
+ [Q,R]=qr(newmatrix);
+ for i=1:nvar;
+ if R(i,i)<0
+ Q(:,i)=-Q(:,i);
+ end
+ end
+ imfhat = fn_impulse(Bhat,Q*A0hatinv,nn);
+ %In the form that is congenial to RATS
+ imf3hat=reshape(imfhat,size(imfhat,1),nvar,nvar);
+ %imf3hat: row--steps, column--nvar responses, 3rd dimension--nvar shocks
+
+ %=== Responses to a moneaty policy (MP) shock.
+ % R>0, M<0, y<0, and P<0 for the irs periods.
+ a = (imf3hat(1:irs,3,3) > 0) .* (imf3hat(1:irs,4,3) < 0) .* (imf3hat(1:irs,1,3) < 0) .* (imf3hat(1:irs,2,3) < 0);
+ if (max(a)==0)
+ %--- Swiching the sign of the shock.
+ am = (imf3hat(1:irs,3,3) < 0) .* (imf3hat(1:irs,4,3) > 0) .* (imf3hat(1:irs,1,3) > 0) .* (imf3hat(1:irs,2,3) > 0);
+ if (min(am)==0)
+ continue; %The restrictions are not satisfied. Go the beginning to redraw.
+ else
+ %--- Normalizing according to the switched sign.
+ Q(3,:) = -Q(3,:);
+ end
+ elseif (min(a)==0)
+ continue; %The restrictions are not satisfied. Go the beginning to redraw.
+ end
+ %--- R>0 and M<0 for the irs periods.
+ % a = (imf3hat(1:irs,3,3) > 0) .* (imf3hat(1:irs,4,3) < 0);
+ % if (max(a)==0)
+ % %--- Swiching the sign of the shock.
+ % am = (imf3hat(1:irs,3,3) < 0) .* (imf3hat(1:irs,4,3) > 0);
+ % if (min(am)==0)
+ % continue; %The restrictions are not satisfied. Go the beginning to redraw.
+ % else
+ % %--- Normalizing according to the switched sign.
+ % Q(3,:) = -Q(3,:);
+ % end
+ % elseif (min(a)==0)
+ % continue; %The restrictions are not satisfied. Go the beginning to redraw.
+ % end
+
+ %=== Responses to an money demand (MD) shock.
+ % R>0 and M>0 for the irs periods.
+ a = (imf3hat(1:irs,3,4) > 0) .* (imf3hat(1:irs,4,4) > 0);
+ if (max(a)==0)
+ %--- Swiching the sign of the shock and normalize.
+ am = (imf3hat(1:irs,3,4) < 0) .* (imf3hat(1:irs,4,4) < 0);
+ if (min(am)==0)
+ continue; %The restrictions are not satisfied. Go the beginning to redraw.
+ else
+ %--- Normalizing according to the switched sign.
+ Q(4,:) = -Q(4,:);
+ end
+ elseif (min(a)==0)
+ continue; %The restrictions are not satisfied. Go the beginning to redraw.
+ end
+
+ %=== Responses to an aggregate demand (AD) shock.
+ % P>0 and y>0 for the irs periods.
+ a = (imf3hat(1:irs,1,2) > 0) .* (imf3hat(1:irs,2,2) > 0);
+ if (max(a)==0)
+ %--- Swiching the sign of the shock and normalize.
+ am = (imf3hat(1:irs,1,2) < 0) .* (imf3hat(1:irs,2,2) < 0);
+ if (min(am)==0)
+ continue; %The restrictions are not satisfied. Go the beginning to redraw.
+ else
+ %--- Normalizing according to the switched sign.
+ Q(2,:) = -Q(2,:);
+ end
+ elseif (min(a)==0)
+ continue; %The restrictions are not satisfied. Go the beginning to redraw.
+ end
+
+ %=== Responses to an aggregate supply (AS) shock.
+ % P>0 and y<0 for the irs periods.
+ a = (imf3hat(1:irs,1,1) < 0) .* (imf3hat(1:irs,2,1) > 0);
+ if (max(a)==0)
+ %--- Swiching the sign of the shock and normalize.
+ am = (imf3hat(1:irs,1,1) > 0) .* (imf3hat(1:irs,2,1) < 0);
+ if (min(am)==0)
+ continue; %The restrictions are not satisfied. Go the beginning to redraw.
+ else
+ %--- Normalizing according to the switched sign.
+ Q(1,:) = -Q(1,:);
+ end
+ elseif (min(a)==0)
+ continue; %The restrictions are not satisfied. Go the beginning to redraw.
+ end
+
+ %=== Responses to an exchange-rate shock (depreciation ==> exports >0 ==> y>0);
+ % Ex>0 and y>0 for the irs periods.
+ a= (imf3hat(1:irs,1,5) > 0) .* (imf3hat(1:irs,5,5) > 0);
+ if (max(a)==0)
+ %--- Swiching the sign of the shock and normalize.
+ am = (imf3hat(1:irs,1,5) < 0) .* (imf3hat(1:irs,5,5) < 0);
+ if (min(am)==0)
+ continue; %The restrictions are not satisfied. Go the beginning to redraw.
+ else
+ %--- Normalizing according to the switched sign.
+ Q(5,:) = -Q(5,:);
+ end
+ elseif (min(a)==0)
+ continue; %The restrictions are not satisfied. Go the beginning to redraw.
+ end
+
+ %--- Terminating condition: all restrictions are satisfied.
+ control=1;
+end
diff --git a/MatlabFiles/startd.m b/MatlabFiles/startd.m
index c9a373bdd1b738a43d0b3124347a60fc93d72467..873a2a9539f1d8f4afea8354d873a82f163d6081 100644
--- a/MatlabFiles/startd.m
+++ b/MatlabFiles/startd.m
@@ -1,24 +1,24 @@
-function startd(sd)
-%function startd(sd)
-% to set the directory started in the next time matlab is invoked,
-% use this function. E.g.
-% sd=cd %to set sd to the current directory
-% startd(sd)
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-save '/Users/tzha/ZhaData/Git/TZcode/MatlabFiles/startdir0' sd
+function startd(sd)
+%function startd(sd)
+% to set the directory started in the next time matlab is invoked,
+% use this function. E.g.
+% sd=cd %to set sd to the current directory
+% startd(sd)
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+save '/Users/tzha/ZhaData/Git/TZcode/MatlabFiles/startdir0' sd
diff --git a/MatlabFiles/startup.m b/MatlabFiles/startup.m
index 05332cf3dc7865f938f6ff831eb6986a8be7f8cd..3df008ad8f2d975641489e6199f3ee744a7de5d1 100644
--- a/MatlabFiles/startup.m
+++ b/MatlabFiles/startup.m
@@ -1,43 +1,43 @@
-function startup()
-% Remembers a previously set startup directory to set the directory, invoke startd. E.g.,
-% sd=cd % Set sd to the current directory
-% startd(sd)
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-%----------------------------------
-
-%path(path,'d:\Program Files\MATLAB\R2007a\work\cstz')
-%path(path,'c:\softwdisk\matlabr12\toolbox\cstz\cmexfiles\csminwelfinal\csminwelmex')
-%path(path,'C:\ZhaData\TZCcode')
-%path(path,'C:\Program Files\Intel\MKL\ia32\lib')
-%path(path,'C:\Program Files\Intel\MKL\include')
-%path(path,'d:\matlabr12\toolbox\cstz\lzpaper2')
-%path(path,'d:\matlabr12\toolbox\cstz\rvarcode')
-%path(path,'C:\Program Files\MATLAB\R2006b\work\cstz')
-path(path,'/Users/tzha/ZhaData/Git/TZcode/MatlabFiles')
-path(path,'/Users/tzha/ZhaData/Git/TZcode/MatlabFiles/MSV')
-if exist('startdir0.mat')==2
- load /Users/tzha/ZhaData/Git/TZcode/MatlabFiles/startdir0
- cd(sd)
-end
-format compact
-
-fn_reset_ini_seed(0); %Reset the random seed to clockcycle. Can be reoverwritten by fn_reset_ini_seed(number);
-
-
-
+function startup()
+% Remembers a previously set startup directory to set the directory, invoke startd. E.g.,
+% sd=cd % Set sd to the current directory
+% startd(sd)
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+%----------------------------------
+
+%path(path,'d:\Program Files\MATLAB\R2007a\work\cstz')
+%path(path,'c:\softwdisk\matlabr12\toolbox\cstz\cmexfiles\csminwelfinal\csminwelmex')
+%path(path,'C:\ZhaData\TZCcode')
+%path(path,'C:\Program Files\Intel\MKL\ia32\lib')
+%path(path,'C:\Program Files\Intel\MKL\include')
+%path(path,'d:\matlabr12\toolbox\cstz\lzpaper2')
+%path(path,'d:\matlabr12\toolbox\cstz\rvarcode')
+%path(path,'C:\Program Files\MATLAB\R2006b\work\cstz')
+path(path,'/Users/tzha/ZhaData/Git/TZcode/MatlabFiles')
+path(path,'/Users/tzha/ZhaData/Git/TZcode/MatlabFiles/MSV')
+if exist('startdir0.mat')==2
+ load /Users/tzha/ZhaData/Git/TZcode/MatlabFiles/startdir0
+ cd(sd)
+end
+format compact
+
+fn_reset_ini_seed(0); %Reset the random seed to clockcycle. Can be reoverwritten by fn_reset_ini_seed(number);
+
+
+
diff --git a/MatlabFiles/subtitle.m b/MatlabFiles/subtitle.m
index ec184e9e2a5a3049c5c0eb51c977607695e60f18..cccfd0fd8eafa5ac1e9bceb8273e5e4f872f18b5 100644
--- a/MatlabFiles/subtitle.m
+++ b/MatlabFiles/subtitle.m
@@ -1,37 +1,37 @@
- function [ax,h]=subtitle(text)
- %
- %Centers a title over a group of subplots.
- %Returns a handle to the title and the handle to an axis.
- % [ax,h]=subtitle(text)
- % returns handles to both the axis and the title.
- % ax=subtitle(text)
- % returns a handle to the axis only.
- % Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-% ax=axes('Units','Normal','Position',[.05 .05 .9 .9],'Visible','off','FontSize',14,'FontWeight','Bold');
- ax=axes('Units','Normal','Position',[.05 .05 .9 .9],'Visible','off','FontSize',10,'FontWeight','Bold','FontName','Times New Roman');
-% ax=axes('Units','Normal','Position',[.075 .075 .85 .85],'Visible','off');
- set(get(ax,'Title'),'Visible','on')
- title(text);
- if (nargout < 2)
- return
- end
- h=get(ax,'Title');
-
- %%%END CODE%%%
+ function [ax,h]=subtitle(text)
+ %
+ %Centers a title over a group of subplots.
+ %Returns a handle to the title and the handle to an axis.
+ % [ax,h]=subtitle(text)
+ % returns handles to both the axis and the title.
+ % ax=subtitle(text)
+ % returns a handle to the axis only.
+%
+ % Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+% ax=axes('Units','Normal','Position',[.05 .05 .9 .9],'Visible','off','FontSize',14,'FontWeight','Bold');
+ ax=axes('Units','Normal','Position',[.05 .05 .9 .9],'Visible','off','FontSize',10,'FontWeight','Bold','FontName','Times New Roman');
+% ax=axes('Units','Normal','Position',[.075 .075 .85 .85],'Visible','off');
+ set(get(ax,'Title'),'Visible','on')
+ title(text);
+ if (nargout < 2)
+ return
+ end
+ h=get(ax,'Title');
+
+ %%%END CODE%%%
diff --git a/MatlabFiles/suptitle.m b/MatlabFiles/suptitle.m
index a8f90f4d5550a964a46753bf06223f5ca25d8d48..e36429d54eaf003e46e2df3dc37f753630e2937f 100644
--- a/MatlabFiles/suptitle.m
+++ b/MatlabFiles/suptitle.m
@@ -5,22 +5,22 @@ function hout=suptitle(str)
% after all subplot commands.
% Drea Thomas 6/15/95 drea@mathworks.com
-% Copyright (C) 1997-2012 Drea Thomas
%
-% This file is part of Dynare.
+% Copyright (C) 1997-2012 Drea Thomas
%
-% Dynare is free software: you can redistribute it and/or modify
+% This free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
-% Dynare is distributed in the hope that it will be useful,
+% It is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
% Warning: If the figure or axis units are non-default, this
diff --git a/MatlabFiles/sye.m b/MatlabFiles/sye.m
index d85235145f2e07966116d125c0cfa2ba1c9ade90..516f8a6eb23d89253e9dc04e38fe2d257fd28d84 100644
--- a/MatlabFiles/sye.m
+++ b/MatlabFiles/sye.m
@@ -1,100 +1,100 @@
-function [Bh,e,xtx,xty,phi,y,ncoe,xr] = sye(z,lags)
-% Now [Bh,e,xtx,xty,phi,y,ncoe,xr] = sye(z,lags)
-% Old: [Bh,e,xtx,xty,phi,y,ncoe,Sigu,xtxinv] = sye(z,lags)
-%
-% Estimate a system of equations in the form of y(T*nvar) = XB + u,
-% X--phi: T*k, B: k*nvar; where T=sp-lags, k=ncoe,
-%
-% z: (T+lags)-by-(nvar+1) raw data matrix (nvar of variables + constant).
-% lags: number of lags
-%--------------------
-% Bh: k-by-nvar estimated reduced-form parameter; column: nvar;
-% row: k=ncoe=[nvar for 1st lag, ..., nvar for last lag, const]
-% e: estimated residual e = y -xBh, T-by-nvar
-% xtx: X'X: k-by-k
-% xty: X'Y: k-by-nvar
-% phi: X; T-by-k; column: [nvar for 1st lag, ..., nvar for last lag, const]
-% y: Y: T-by-nvar
-% ncoe: number of coeffcients per equation: nvar*lags + 1
-% xr: the economy size (k-by-k) in qr(phi) so that xr=chol(X'*X)
-% Sigu: e'*e: nvar-by-nvar. Note, not divided (undivided) by "nobs"
-% xtxinv: inv(X'X): k-by-k
-%
-% See also syed.m (allowing for more predetermined terms) which has not
-% been yet updated as "sye.m".
-%
-% Note, "lags" is something I changed recently, so it may not be compatible
-% with old programs, 10/15/98 by TAZ.
-%
-% Revised, 5/2/99. Replaced outputs Sigu and xtxinv with xr so that previous
-% programs may be incompatible.
-
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-% ** setup of orders and lengths **
-[sp,nvar] = size(z); % sp: sample period T include lags
-nvar = nvar-1; % -1: takes out the counting of constant
-
-ess = sp-lags; % effective sample size
-sb = lags+1; % sample beginning
-sl = sp; % sample last period
-ncoe = nvar*lags + 1; % with constant
-
-% ** construct X for Y = X*B + U where phi = X **
-x = z(:,1:nvar);
-C = z(:,nvar+1);
-phi = zeros(ess,ncoe);
-phi(:,ncoe) = C(1:ess);
-for k=1:lags, phi(:,nvar*(k-1)+1:nvar*k) = x(sb-k:sl-k,:); end
-% row: T-lags; column: [nvar for 1st lag, ..., nvar for last lag, const]
-% Thus, # of columns is nvar*lags+1 = ncoef.
-% ** estimate: B, XTX, residuals **
-y = x(sb:sl,:);
-%
-%**** The following, though stable, is too slow *****
-% [u d v]=svd(phi,0); %trial
-% %xtx = phi'*phi; % X'X, k*k (ncoe*ncoe)
-% vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
-% dinv = 1./diag(d); % inv(diag(d))
-% vdinv=v.*(ones(size(v,2),1)*dinv'); %trial
-% xtx=vd*vd';
-% xtxinv = vdinv*vdinv';
-% %xty = phi'*y; % X'Y
-% uy = u'*y; %trial
-% xty = vd*uy; %trial
-% %Bh = xtx\xty; %inv(X'X)*(X'Y), k*m (ncoe*nvar).
-% Bh = xtxinv*xty;
-% %e = y - phi*Bh; % from Y = XB + U, e: (T-lags)*nvar
-% e = y - u*uy;
-%**** The following, though stable, is too slow *****
-
-%===== (Fast but perhaps less accurate) alternative to the above =========
-[xq,xr]=qr(phi,0);
-xtx=xr'*xr;
-xty=phi'*y;
-Bh = xr\(xr'\xty);
-e=y-phi*Bh;
-%===== (Fast but perhaps less accurate) alternative to the above =========
-
-
-%* not numerically stable way of computing e'*e
-%Sigu = y'*y-xty'*Bh;
-%Sigu = y'*(eye(ess)-phi*xtxinv*phi')*y; % probablly better way, commented out
- % by TZ, 2/28/98. See following
-%Sigu = y'*(eye(ess)-u*u')*y; % I think this is the best, TZ, 2/28/98
- % Note, u*u'=x*inv(x'x)*x.
+function [Bh,e,xtx,xty,phi,y,ncoe,xr] = sye(z,lags)
+% Now [Bh,e,xtx,xty,phi,y,ncoe,xr] = sye(z,lags)
+% Old: [Bh,e,xtx,xty,phi,y,ncoe,Sigu,xtxinv] = sye(z,lags)
+%
+% Estimate a system of equations in the form of y(T*nvar) = XB + u,
+% X--phi: T*k, B: k*nvar; where T=sp-lags, k=ncoe,
+%
+% z: (T+lags)-by-(nvar+1) raw data matrix (nvar of variables + constant).
+% lags: number of lags
+%--------------------
+% Bh: k-by-nvar estimated reduced-form parameter; column: nvar;
+% row: k=ncoe=[nvar for 1st lag, ..., nvar for last lag, const]
+% e: estimated residual e = y -xBh, T-by-nvar
+% xtx: X'X: k-by-k
+% xty: X'Y: k-by-nvar
+% phi: X; T-by-k; column: [nvar for 1st lag, ..., nvar for last lag, const]
+% y: Y: T-by-nvar
+% ncoe: number of coeffcients per equation: nvar*lags + 1
+% xr: the economy size (k-by-k) in qr(phi) so that xr=chol(X'*X)
+% Sigu: e'*e: nvar-by-nvar. Note, not divided (undivided) by "nobs"
+% xtxinv: inv(X'X): k-by-k
+%
+% See also syed.m (allowing for more predetermined terms) which has not
+% been yet updated as "sye.m".
+%
+% Note, "lags" is something I changed recently, so it may not be compatible
+% with old programs, 10/15/98 by TAZ.
+%
+% Revised, 5/2/99. Replaced outputs Sigu and xtxinv with xr so that previous
+% programs may be incompatible.
+
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+% ** setup of orders and lengths **
+[sp,nvar] = size(z); % sp: sample period T include lags
+nvar = nvar-1; % -1: takes out the counting of constant
+
+ess = sp-lags; % effective sample size
+sb = lags+1; % sample beginning
+sl = sp; % sample last period
+ncoe = nvar*lags + 1; % with constant
+
+% ** construct X for Y = X*B + U where phi = X **
+x = z(:,1:nvar);
+C = z(:,nvar+1);
+phi = zeros(ess,ncoe);
+phi(:,ncoe) = C(1:ess);
+for k=1:lags, phi(:,nvar*(k-1)+1:nvar*k) = x(sb-k:sl-k,:); end
+% row: T-lags; column: [nvar for 1st lag, ..., nvar for last lag, const]
+% Thus, # of columns is nvar*lags+1 = ncoef.
+% ** estimate: B, XTX, residuals **
+y = x(sb:sl,:);
+%
+%**** The following, though stable, is too slow *****
+% [u d v]=svd(phi,0); %trial
+% %xtx = phi'*phi; % X'X, k*k (ncoe*ncoe)
+% vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
+% dinv = 1./diag(d); % inv(diag(d))
+% vdinv=v.*(ones(size(v,2),1)*dinv'); %trial
+% xtx=vd*vd';
+% xtxinv = vdinv*vdinv';
+% %xty = phi'*y; % X'Y
+% uy = u'*y; %trial
+% xty = vd*uy; %trial
+% %Bh = xtx\xty; %inv(X'X)*(X'Y), k*m (ncoe*nvar).
+% Bh = xtxinv*xty;
+% %e = y - phi*Bh; % from Y = XB + U, e: (T-lags)*nvar
+% e = y - u*uy;
+%**** The following, though stable, is too slow *****
+
+%===== (Fast but perhaps less accurate) alternative to the above =========
+[xq,xr]=qr(phi,0);
+xtx=xr'*xr;
+xty=phi'*y;
+Bh = xr\(xr'\xty);
+e=y-phi*Bh;
+%===== (Fast but perhaps less accurate) alternative to the above =========
+
+
+%* not numerically stable way of computing e'*e
+%Sigu = y'*y-xty'*Bh;
+%Sigu = y'*(eye(ess)-phi*xtxinv*phi')*y; % probablly better way, commented out
+ % by TZ, 2/28/98. See following
+%Sigu = y'*(eye(ess)-u*u')*y; % I think this is the best, TZ, 2/28/98
+ % Note, u*u'=x*inv(x'x)*x.
diff --git a/MatlabFiles/syed.m b/MatlabFiles/syed.m
index e9df9cee59076a1665824f467733ec4616094285..7e75d498394ddb314f01eced090dd0e4f21b9908 100644
--- a/MatlabFiles/syed.m
+++ b/MatlabFiles/syed.m
@@ -1,84 +1,84 @@
-function [Bh,e,xtx,phi,y] = syed(z,nn)
-% syed: estimate a system of equations: [Bh,e,xtx,phi,y] = syed(z,nn)
-% Y((T-lags)*nvar) = XB + u, X: (T-lags)*k, B: k*nvar.
-% where z is the T*(nvar+ndt) raw data matrix (nvar of variables +
-% ndt -- number of deterministic terms);
-% nn is 5 inputs [auindx, ndt, nvar,lags,sample period (total)];
-% auindx = 0 (no autoregressive) and 1 (autoregressive);
-% total -- including lags, etc.
-% Bh: the estimated B; column: nvar; row: [nvar for 1st lag, ...,
-% nvar for last lag, deterministic terms (ndt)]
-% e: estimated residual e = y -xBh, (T-lags)*nvar
-% xtx: X'X
-% phi: X; column: [nvar for 1st lag, ...,
-% nvar for last lag, deterministic terms (ndt)]
-% y: Y
-%
-% See also "sye.m".
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-% ** setup of orders and lengths **
-auindx = nn(1);
-ndt = nn(2); % # of deterministic terms including constant
-nvar = nn(3); % # of endogenous variables
-lags = nn(4);
-sp = nn(5); % sample period
-
-ess = sp-lags; % effective sample size
-sb = lags+1; % sample beginning
-sl = sp; % sample last period
-
-% ** construct X for Y = X*B + U where phi = X **
-x = z(:,1:nvar);
-C = z(:,nvar+1:nvar+ndt); % C = [] when ndt=0
-%
-if auindx == 0
- ncoe = ndt; % with deterministic terms
- phi = zeros(sp,ncoe); % preallocating
- y = x;
-else
- y = x(sb:sl,:);
- ncoe = nvar*lags + ndt; % with deterministic terms
- phi = zeros(ess,ncoe); % preallocating
- for k=1:lags, phi(:,nvar*(k-1)+1:nvar*k) = x(sb-k:sl-k,:); end
-end
-%
-if length(C) == 0
- phi(:,ncoe-ndt+1:ncoe) = C;
-else
- phi(:,ncoe-ndt+1:ncoe) = C(1:sp,:); % perhaps, it should have been be C(sb:sp,:). 2/24/00
-end
-%
-% row: T-lags; column: [nvar for 1st lag, ..., nvar for last lag,
-% deterministic terms (ndt)]
-% Thus, # of columns is nvar*lags+ndt = ncoe.
-% ** estimate: B, XTX, residuals **
-[u d v]=svd(phi,0); %trial
-%xtx = phi'*phi; % X'X, k*k (ncoe*ncoe)
-vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
-dinv = 1./diag(d); % inv(diag(d))
-vdinv=v.*(ones(size(v,2),1)*dinv'); %trial
-xtx=vd*vd';
-xtxinv = vdinv*vdinv';
-%xty = phi'*y; % X'Y
-uy = u'*y; %trial
-xty = vd*uy; %trial
-%Bh = xtx\xty; %inv(X'X)*(X'Y), k*m (ncoe*nvar).
-Bh = xtxinv*xty;
-%e = y - phi*Bh; % from Y = XB + U, e: (T-lags)*nvar
-e = y - u*uy;
+function [Bh,e,xtx,phi,y] = syed(z,nn)
+% syed: estimate a system of equations: [Bh,e,xtx,phi,y] = syed(z,nn)
+% Y((T-lags)*nvar) = XB + u, X: (T-lags)*k, B: k*nvar.
+% where z is the T*(nvar+ndt) raw data matrix (nvar of variables +
+% ndt -- number of deterministic terms);
+% nn is 5 inputs [auindx, ndt, nvar,lags,sample period (total)];
+% auindx = 0 (no autoregressive) and 1 (autoregressive);
+% total -- including lags, etc.
+% Bh: the estimated B; column: nvar; row: [nvar for 1st lag, ...,
+% nvar for last lag, deterministic terms (ndt)]
+% e: estimated residual e = y -xBh, (T-lags)*nvar
+% xtx: X'X
+% phi: X; column: [nvar for 1st lag, ...,
+% nvar for last lag, deterministic terms (ndt)]
+% y: Y
+%
+% See also "sye.m".
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+% ** setup of orders and lengths **
+auindx = nn(1);
+ndt = nn(2); % # of deterministic terms including constant
+nvar = nn(3); % # of endogenous variables
+lags = nn(4);
+sp = nn(5); % sample period
+
+ess = sp-lags; % effective sample size
+sb = lags+1; % sample beginning
+sl = sp; % sample last period
+
+% ** construct X for Y = X*B + U where phi = X **
+x = z(:,1:nvar);
+C = z(:,nvar+1:nvar+ndt); % C = [] when ndt=0
+%
+if auindx == 0
+ ncoe = ndt; % with deterministic terms
+ phi = zeros(sp,ncoe); % preallocating
+ y = x;
+else
+ y = x(sb:sl,:);
+ ncoe = nvar*lags + ndt; % with deterministic terms
+ phi = zeros(ess,ncoe); % preallocating
+ for k=1:lags, phi(:,nvar*(k-1)+1:nvar*k) = x(sb-k:sl-k,:); end
+end
+%
+if length(C) == 0
+ phi(:,ncoe-ndt+1:ncoe) = C;
+else
+ phi(:,ncoe-ndt+1:ncoe) = C(1:sp,:); % perhaps, it should have been be C(sb:sp,:). 2/24/00
+end
+%
+% row: T-lags; column: [nvar for 1st lag, ..., nvar for last lag,
+% deterministic terms (ndt)]
+% Thus, # of columns is nvar*lags+ndt = ncoe.
+% ** estimate: B, XTX, residuals **
+[u d v]=svd(phi,0); %trial
+%xtx = phi'*phi; % X'X, k*k (ncoe*ncoe)
+vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
+dinv = 1./diag(d); % inv(diag(d))
+vdinv=v.*(ones(size(v,2),1)*dinv'); %trial
+xtx=vd*vd';
+xtxinv = vdinv*vdinv';
+%xty = phi'*y; % X'Y
+uy = u'*y; %trial
+xty = vd*uy; %trial
+%Bh = xtx\xty; %inv(X'X)*(X'Y), k*m (ncoe*nvar).
+Bh = xtxinv*xty;
+%e = y - phi*Bh; % from Y = XB + U, e: (T-lags)*nvar
+e = y - u*uy;
diff --git a/MatlabFiles/szasbvar.m b/MatlabFiles/szasbvar.m
index a2b13463a67d28efb351f040ae0347d113d56b7e..7166b4ef9c21c75c3779451b4dc6806c2662ef6c 100644
--- a/MatlabFiles/szasbvar.m
+++ b/MatlabFiles/szasbvar.m
@@ -1,502 +1,500 @@
-function [Gb,Sbd,Bh,SpH,fss,ndobs,phi,y,nvar,ncoef,xxhpc,a0indx,na0p,...
- idmat0,idmatpp,H0invmulti,Hpinvmulti,xxhp] = szasbvar(idfile,q_m,lags,xdgel,mu)
-% [Gb,Sbd,Bh,SpH,fss,ndobs,phi,y,nvar,ncoef,xxhpc,a0indx,na0p,...
-% idmat0,idmatpp,H0invmulti,Hpinvmulti] = szasbvar(idfile,q_m,lags,xdgel,mu)
-%
-% Estimating the Bayesian VAR of Sims and Zha with asymmetric prior (as)
-%
-% idfile: Identification filename with rows corresponding to equations.
-% But all the output will be transposed to columns
-% corresponding to equations.
-% q_m: quarter or month
-% lags: the maximum length of lag
-% nhp: number of haperparameters
-% xdgel: the general matrix of the original data (no manipulation involved)
-% with sample size including lags
-% mu: 6-by-1 vector of hyperparameters (the following numbers for Atlanta Fed's forecast)
-% mu(1): overall tightness and also for A0; (0.57)
-% mu(2): relative tightness for A+; (0.13)
-% mu(3): relative tightness for the constant term; (0.1)
-% mu(4): tightness on lag decay; (1)
-% mu(5): weight on nvar sums of coeffs dummy observations (unit roots); (5)
-% mu(6): weight on single dummy initial observation including constant
-% (cointegration, unit roots, and stationarity); (5)
-% --------------------
-% --------------------
-% Gb: cell(nvar,1). Each cell, when postmultiplied by A0(:,i), is used to compute A+(:,i)
-% where A+ is k-by-m, where k--ncnoef, m--nvar, if asymmetric prior.
-% In particular, Aplus(:,i)=Gb{i}*A0(:,i), so Bh = Aplus/A0
-% Sbd: cell(nvar,1). Sbd=diag(S(1), ..., S(m)). Already divided by 'fss' for the
-% posterior of a0 or A0(:) in Waggoner and Zha when one has asymmetric prior.
-% Note,"bd" stands for block diagonal.
-% Bh: reduced form B, k(mp+1)-by-m, column--equation. Bh=NaN if asymmetric prior.
-% In the form of y=X*B+U where B=[B1|B2| ...|Bp|C]
-% where Bi: m-by-m (i=1,..,p--lag length), C: 1-by-ml, constant.
-% SpH: divided by T, the final S in the Waggoner-Zha exponential part of p(A0|y)
-% SpH=NaN if asymmetric prior
-% fss: in-sample-size (for forecasting). Including dummy observations
-% ndobs: number of dummy observations, ndobs=nvar+1
-% phi: X in the form of y = X*B+U. Row: nSmaple-lags+ndobs. Column: ncoef
-% y: y in the form y = X*B+U. Including the dummy observations too,
-% T-lags+ndobs-by-nvar.
-% nvar: number of variables
-% ncoef: number of coefficients in *each* equation. RHS coefficients only, nvar*lags+1
-% xxhpc: chol(X'X+inv(H_p_tilde)): upper triangular but its transpose
-% is lower triagular Choleski
-% a0indx: the index number for free parameters in A0. Column meaning equation
-% na0p: number of free parameters in A0
-% idmat0: identification matrix for A0 with asymmetric prior; column -- equation.
-% idmatpp: asymmetric prior variance for A+ without constant; column -- equation.
-% H0invmulti: nvar-by-nvar-by-nvar; inv(H0) for different equations under asymmetric prior
-% Hpinvmulti: ncoef-by-ncoef-by-nvar; inv(H+) for different equations under asymmetric prior
-% xxhp: ncoef-by-nocef, X'X+inv(H_p_tilde)
-%
-% Revisions by CAS 9/27/96: If we transmit idmat, rather than a0indx, we can scale
-% the elements of idmat0 or idmatp so that it carries information about relative
-% prior variances.
-% Revsions by TZ, 10/13/96: Efficiency improvement by streamlining the previous
-% code according to the general setup in Sims and Zha's IER and according
-% to Zha's notes Forecast (0) (p.3) and Forecast (1) (p.9).
-% Quick Revisions: May 2003. See H1p_1 on lines 406-409.
-%
-% Copyright (c) December 1997 by C.A. Sims and T. Zha,
-%
-% NOTE1: "nSample" is something I deleted as an input recently, so it may not
-% be compatible with old programs, 10/15/98 by TZ.
-% NOTE2: I put "mu" as an input arg and delete "nhp" as an input arg, so it may
-% not be compatible with old programs, 03/06/99 by TZ
-% NOTE3: added three output arguments: H0invmulti and Hpinvmulti and xxhp. 9/27/99
-
-% Copyright (C) 1997-2012 Christopher A. Sims and Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-%
-%@@@ Prepared for Bayesian VAR of Sims and Zha
-%
-%* Load identification and obtain idmat0
-eval(['load ' idfile '.prn -ascii']);
-eval(['idmat0=' idfile ';']);
-idmat0=idmat0'; % so that each column corresponds to an equation
-a0indx=find(idmat0); % column meaning equation
-na0p = length(a0indx); % number of free parameters in A0
-nhp = length(mu); % total number of hyperparameters
-nfp = na0p + nhp; % total number of free parameters (including hyperparameters)
-[nvar,neqn]=size(idmat0);
-%
-idmatpp = ones(nvar*lags,nvar); % pp: plus without constant. Column -- equation
-%>>>>>> B: asymmetric prior variance for idmatpp <<<<<<<<
-%
-%for i = 1:lags
-% rowif = (i-1)*nvar+1;
-% rowil = i*nvar;
-% idmatw0 = 0.5; % weight assigned to idmat0 in the formation of idmatpp
-% if (i==1)
-% idmatpp(rowif:rowil,:)=(1-idmatw0)*ones(nvar)+idmatw0*idmat0; % first lag
-% % note: idmat1 is already transposed. Column -- equation
-% else
-% %idmatpp(rowif:rowil,1:nvar) = (1-idmatw0)*ones(nvar)+idmatw0*idmat0;
-% % <<<<<<< toggle +
-% % Note: already transposed, since idmat0 is transposed.
-% % Meaning: column implies equation
-% idmatpp(rowif:rowil,1:nvar) = ones(nvar);
-% % >>>>>>> toggle -
-% end
-%end
-%
-%>>>>>> E: asymmetric prior variance for idmatpp <<<<<<<<
-
-
-%$$$ available computations
-%
-%@@@ original
-% total number of the sample under study, including lags, etc. -- original sample size
-nSample = size(xdgel,1); % the sample size (including lags, of course)
-sb = lags+1; % original beginning without dummies
-sl = nSample; % original last period without dummies
-ssp = nSample - lags; % original number of observations
-%
-ndobs=nvar+1; % number of dummy observations
-ncoef = nvar*lags+1; % number of coefficients in *each* equation, RHS coefficients only.
-%*** initializing for global variables
-%%GlbAph=zeros(nvar,ncoef);
-%Aplus=zeros(ncoef,nvar);
-Gb = cell(nvar,1); % Storing potential reduced-form Bh (k-by-m) or prepared
- % for computing Aplus (k-by-m), where k--ncnoef, m--nvar
-Sbd = cell(nvar,1); % Storing for diag(S(1), ..., S(m)) for the posterior of a0
- % or vec(A0) when one has asymmetric prior. Note,
- % "bd" stands for block diagonal.
-SpH = ones(nvar,nvar);
-%* flp: forecast last period (with dummies);
-%* fbp: forecast beginning period (with dummies).
-flp = sl+ndobs;
-%fbp = ndobs+lags+1; % <<>> true begining by skipping dummies
-fss = flp-lags; % forecast sample size (with dummies).
-
-% ** hyperparameters
-% mu = zeros(nhp,1);
-% mu(1) = 0.57;
-% mu(2) = 0.13;
-% mu(3) = 0.1;
-% mu(4) = 1;
-% mu(5) = 5; %10;
-% mu(6) = 5; %10;
-%
-% mu(1): overall tightness and also for A0;
-% mu(2): relative tightness for A+;
-% mu(3): relative tightness for the constant term;
-% mu(4): tightness on lag decay;
-% mu(5): weight on nvar sums of coeffs dummy observations (unit roots);
-% mu(6): weight on single dummy initial observation including constant (cointegration, unit
-% roots, and stationarity).
-
-
-% ** monthly lag decay in order to match quarterly decay: a*exp(bl) where
-% ** l is the monthly lag. Suppose quarterly decay is 1/x where x=1,2,3,4.
-% ** Let the decay of l1 (a*exp(b*l1)) match that of x1 (say, beginning: 1/1)
-% ** and the decay of l2 (a*exp(b*l2)) match that of x2 (say, end: 1/5),
-% ** we can solve for a and b which are
-% ** b = (log_x1-log_x2)/(l1-l2), and a = x1*exp(-b*l1).
-if q_m==12
- l1 = 1; % 1st month == 1st quarter
- xx1 = 1; % 1st quarter
- l2 = lags; % last month
- xx2 = 1/((ceil(lags/3))^mu(4)); % last quarter
- %xx2 = 1/6; % last quarter
- % 3rd quarter: i.e., we intend to let decay of the 6th month match
- % that of the 3rd quarter, so that the 6th month decays a little
- % faster than the second quarter which is 1/2.
- if lags==1
- b = 0;
- else
- b = (log(xx1)-log(xx2))/(l1-l2);
- end
- a = xx1*exp(-b*l1);
-end
-
-%
-% ** now run the VAR with
-%
-% **** nvar prior dummy observations with the sum of coefficients
-% ** construct X for Y = X*B + U where phi = X: (T-lags)*k, Y: (T-lags)*nvar
-% ** columns: k = # of [nvar for 1st lag, ..., nvar for last lag, const]
-% ** Now, T=T+ndobs -- added with "ndobs" dummy observations
-%
-phi = zeros(fss,ncoef);
-const = ones(fss,1);
-const(1:nvar) = zeros(nvar,1);
-phi(:,ncoef) = const; % the first nvar periods: no or zero constant!
-%
-xdgelint = mean(xdgel(1:lags,:),1); % mean of the first lags initial conditions
-%* Dummies
-for k=1:nvar
- for m=1:lags
- phi(ndobs,nvar*(m-1)+k) = xdgelint(k);
- phi(k,nvar*(m-1)+k) = xdgelint(k);
- % <<>> multiply hyperparameter later
- end
-end
-%* True data
-for k=1:lags
- phi(ndobs+1:fss,nvar*(k-1)+1:nvar*k) = xdgel(sb-k:sl-k,:);
- % row: T-lags; column: [nvar for 1st lag, ..., nvar for last lag, const]
- % Thus, # of columns is nvar*lags+1 = ncoef.
-end
-%
-% ** Y with "ndobs" dummies added
-y = zeros(fss,nvar);
-%* Dummies
-for k=1:nvar
- y(ndobs,k) = xdgelint(k);
- y(k,k) = xdgelint(k);
- % multiply hyperparameter later
-end
-%* True data
-y(ndobs+1:fss,:) = xdgel(sb:sl,:);
-
-
-
-%
-% *** specify the prior for each equation separately, SZ method,
-% ***
-% *** obtaining the residuals for the univariate processes:
-% *** a total of "nvar" equations
-% *** obtain the posterior peak of A0 which is Sims iden1 (Sims 1986)
-% ** get the residuals from univariate regressions.
-%
-sgh = zeros(nvar,1); % square root
-sgsh = sgh; % square
-yu = xdgel;
-C = ones(nSample,1);
-for k=1:nvar
- [Bk,ek,junk1,junk2,junk3,junk4] = sye([yu(:,k) C],lags);
- clear Bk junk1 junk2 junk3 junk4;
- sgsh(k) = ek'*ek/ssp;
- %% sgh(k) = ek'*ek/(fss-7); to match "sqrt(ess)" in RATS univariate regression
- sgh(k) = sqrt(sgsh(k));
-end
-% ** prior variance for alpha0, same for all equations!!!
-al0b = zeros(nvar*neqn,1); % prior mean for all A0
-sg0bid = zeros(nvar,1); % Sigma0_bar diagonal only
-for j=1:nvar
- sg0bid(j) = 1/sgsh(j); % sgsh = sigmai^2
-end
-% ** prior variance for alpha_plus, same for all equations
-sgpbid = zeros(ncoef,1); % Sigma_plus_bar, diagonal
-for i = 1:lags
- if (q_m==12)
- lagdecay = a*exp(b*i);
- end
- %
- for j = 1:nvar
- if (q_m==12)
- % exponential decay to match quarterly decay
- sgpbid((i-1)*nvar+j) = lagdecay^2/sgsh(j);
- %%sgpbid((i-1)*nvar+j) = (1/i)^2/sgsh(j);
- elseif (q_m==4)
- sgpbid((i-1)*nvar+j) = (1/i^mu(4))^2/sgsh(j);
- else
- error('Incompatibility with lags, check the possible errors!!!')
- %warning('Incompatibility with lags, check the possible errors!!!')
- %return
- end
- end
-end
-%%A0bd = sqrt(sg0bid);
-% commented out by T.A. Zha, 10/3/96, to avoid double-counting of scaling and the problem
-% of potential multiple peaks.
-A0bd = zeros(nvar,1);
-A0b = sparse(1:nvar,1:nvar,A0bd,nvar,nvar);
-A0b = A0b'; % making a column in A0b correspond to an equation
-
-%
-
-
-%=================================================
-% Computing the (prior) covariance matrix for the posterior of A0, no data yet
-%=================================================
-%
-%
-% ** weight prior dummy observations
-%phi(1:nvar,:) = (mu(5)^2/mu(1)^2)*phi(1:nvar,:);
-%y(1:nvar,:) = (mu(5)^2/mu(1)^2)*y(1:nvar,:);
-%phi(ndobs,:) = mu
-% modified by CAS 8/6/96. The dummy obs weights are naturally in std units, not var units.
-%
-phi(1:nvar,:) = 1*mu(5)*phi(1:nvar,:); % standard Sims and Zha prior
-y(1:nvar,:) = mu(5)*y(1:nvar,:); % standard Sims and Zha prior
-%----- The following prior designed for GLZ
-%phi(1,:) = 1.00*mu(5)*phi(1,:);
-%phi(2:nvar,:) = 1.02*mu(7)*phi(2:nvar,:);
-%y(1,:) = mu(5)*y(1,:);
-%y(2:nvar,:) = mu(7)*y(2:nvar,:);
-%----------------------------
-%
-phi(nvar+1,:) = mu(6)*phi(nvar+1,:);
-y(nvar+1,:) = mu(6)*y(nvar+1,:);
-
-
-% ** set up the conditional prior variance sg0bi and sgpbi.
-sg0bida = mu(1)^2*sg0bid;
-sgpbida = mu(1)^2*mu(2)^2*sgpbid;
-sgpbida(ncoef) = mu(1)^2*mu(3)^2;
-sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in a Sims-Zha paper
-%%sgppbdi = 1./sgppbd;
-%sgppb = diag(sgppbd);
-
-Hptd = zeros(ncoef);
-Hptdi=Hptd;
-%%Hptd(nvar+1:ncoef,nvar+1:ncoef)=diag(sgppbd);
-%%Hptdi(nvar+1:ncoef,nvar+1:ncoef)=diag(sgppbdi);
-Hptd(ncoef,ncoef)=sgppbd(ncoef-nvar);
-Hptdi(ncoef,ncoef)=1/sgppbd(ncoef-nvar);
- % condtional on A0i, H_plus_tilde
-
-% ** some common terms
-[u d v]=svd(phi,0); %trial
-% xtx = phi'*phi; %trial
-vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
-xtx=vd*vd';
-% M = eye(fss) - phi*(xtx\phi'); % not used except in line below %trial
-yu = y'*u; %trial
-cxyxx=yu*yu'; %trial
-yty=y'*y;
-ymy=yty-cxyxx; %trial
-%ymy = y'*M*y;
-%cxy = phi'*y; %trial
-cxy = vd*yu'; %trial
-cyx = cxy';
-%cxpy = xtx\cxy; %not used further except in line below
-%cxyxx = cxy'*cxpy;
-
-% ** estimate A+_hat conditional on A0, ncoef*nvar, but not using full prior
-%GlbAph = cxpy*A0h;
-
-
-%=================================================
-% Computing the final covariance matrix (S1,...,Sm) for the posterior of A0,
-% with the data, and final Gb=(G1,...,Gm) for A+ if asymmetric prior or for
-% B if symmetric prior for A+
-%=================================================
-%
-H0invmulti=zeros(nvar,nvar,nvar); % inv(H0) for different equations under asymmetric prior
-Hpinvmulti=zeros(ncoef,ncoef,nvar); % inv(H+) for different equations under asymmetric prior
-for i = 1:nvar
- stri = find(idmat0(:,i));
- % CAS change 9/24/96, to avoid slim chance of an exact zero in x vector
- % at some point during an optimization.
- %strm = length(stri); % TZ, 11/27/97, no use any more
-
- %A0hb = A0h(stri,i)-A0b(stri,i);
-
- % ** set up the conditional prior variance sg0bi and sgpbi.
- % sg0bd = zeros(stri,1); Commented out by CAS, 9/24/96. Looks like this
- % meant to have strm where it has stri, and in any case it is "overwritten" below.
-
- %------------------------------
- % Introduce prior information on which variables "belong" in various equations.
- % In this first trial, we just introduce this information here, in a model-specific way.
- % Eventually this info has to be passed parametricly. In our first shot, we just damp down
- % all coefficients except those on the diagonal.
- factor0=idmat0(:,i);
- sg0bd = sg0bida; % added by TZ, 2/26/98. I think at each equation, sg0bd must
- % be refreshed. Note, this only works for the prior variance Sg(i)
- % of a0(i) being diagonal. If the prior variance Sg(i) is not
- % diagonal, we have to work on inv(Sg(i)) or sg0bdi directly.
- sg0bd(stri) = sg0bida(stri).*factor0(stri);
- sg0bdi = 1./sg0bd;
- %
- factor1=idmatpp(1:nvar,i);
- sg1bd = sgpbida(1:nvar).*factor1;
- sg1bdi = 1./sg1bd;
- %
- if lags>1
- factorpp=idmatpp(nvar+1:ncoef-1,i);
- sgpp_cbd = sgppbd(1:ncoef-nvar-1) .* factorpp;
- sgpp_cbdi = 1./sgpp_cbd;
- end
-
- % ** set up the unconditional prior variance on A0i and A+i
- %XX = zeros(nvar,strm);
- %XX(stri,:) = eye(strm); % XX in Gelman, etel, on p479
- % Commented out by TZ, 11/27/97. Streamline. No need for these any more.
-
-
- % * final conditional prior variance on A+i give that on A0i, and
- % * unconditional variance on A0+
- H0td = diag(sg0bd); % unconditional
- % H_~: td: tilde for ~
- % ** inverse and chol decomp
- H0tdi = diag(sg0bdi);
- H0invmulti(:,:,i)=H0tdi;
-
- %
- Hptd(1:nvar,1:nvar)=diag(sg1bd);
- Hptdi(1:nvar,1:nvar)=diag(sg1bdi);
- if lags>1
- Hptd(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbd);
- Hptdi(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbdi);
- % condtional on A0i, H_plus_tilde
- end
- Hpinvmulti(:,:,i)=Hptdi;
-
- % common terms
- xxhp = xtx+Hptdi;
- %Lxxhpc = chol(inv(xxhp))'; % lower triangular
- xxhpc = chol(xxhp); % upper triangular but its transpose is lower triagular Choleski
- %A0hbH = A0hb'*H0tdi;
- %====== The following two lines seem incorrect. The third line is supposed to correc the mistake. May 2003.
- % H1p_1 = zeros(ncoef,nvar);
- % H1p_1(1:nvar,:) = Hptdi(1:nvar,1:nvar);
- H1p_1 = Hptdi(:,1:nvar);
- %%Hm = (cyx+H1p_1')*(xxhp\(cxy+H1p_1));
- Hm1 = (cyx+H1p_1')/xxhp; % if symmetric prior, Bh=Hm1' -- reduced form k-by-m.
- Gb{i} = Hm1'; % k-by-m, where k--ncoef, m--nvar.
- % If asymmetric prior, Gb is used to compute A+ where A+(i) = Gb(i)*a0(i)
- % where a0(i) is m-by-1 for the ith column of A0 or the ith equation.
- %Hm2 = cxy+H1p_1;
- Hm = Hm1*(cxy+H1p_1);
- %GlbAph(i,:) = A0h(stri,i)'*XX'*Hm1; % alpha_plus_*_transpose
- %%alpMpyh = A0h(stri,i)'*XX'*(yty+Hptdi(1:nvar,1:nvar)-Hm)*XX;
- %Hss = XX'*(yty+Hptdi(1:nvar,1:nvar)-Hm)*XX;
- Hss = yty+Hptdi(1:nvar,1:nvar)-Hm;
- Sbd{i} = (H0tdi+Hss)./fss; % m-by-m, where m--nvar. If asymmetric prior, Scd is
- % usde to form diag(Sbd{1}, ..., Sbd{m}) for a0 or vec(A0) in the
- % posterior of A0. Note, "bd" stands for block diagonal and divided by
- % "fss" already to make it compatible with SpH used in "a0lhfun" and make it
- % it easier according to the notations by Waggoner and Zha
-end
-
-
-%%=========================================
-%% check if there is asymmetric prior on both A+ and A0
-%%=========================================
-%
-spindx=1; % A+ index for asymmetric prior
-s0indx=1; % A0 index for asymmetric prior
-for i=2:nvar
- diffGb = Gb{i}-Gb{i-1};
- diffSbd = Sbd{i}-Sbd{i-1};
- if ~any(any(diffGb))
- spindx=spindx+1;
- end
- if ~any(any(diffSbd))
- s0indx=s0indx+1;
- end
-end
-%
-if (spindx==nvar)
- Bh = Gb{1}; % reduced-form parameter. Note: does not depend on A0
-else
- Bh = NaN;
-end
-%
-if (s0indx==nvar)
- SpH = (H0tdi+Hss)/fss; % the final S in the exponential part of p(A0|y)
- % divided by nobs in order to make Choleski decomp without optimization
- % or in the form conformable to Waggoner and Zha
-else
- SpH=NaN;
-end
-
-
-
-
-
-%----------------------------------------------
-%
-
-% ***
-% *** Form the inverse of covarinace to draw from t- or Gaussian distribution
-% ***
-%SpHs = SpH*fss; % for the Wishart draw
-%SpHsc = chol(SpHs); % upper triangular where Sphsc' is
-% % lower triangular Choleski, for Wishart draw
-%SpHsic = chol(inv(SpHs))'; % inverse Choleski decomposition -- lower triangular
-%A0hin = chol(SpH); % upper triangular, inverse of A0h, each column
- % corresponds to an equation.
-
-
-%@@@ The following can be used for other purposes such as forecasting
-%
-%swish = A0hin'; % each row corresponds to an equation
-%A0h = inv(A0hin); % each column corresponds to an equation
-%xa0 = A0h(a0indx);
-
-%%*** form Bh (ncoef*nvar)
-%Aplus = Hm1t*A0h; % estimate of Aplus -- the same as Astrar
-%Bhp = Hm1t;
+function [Gb,Sbd,Bh,SpH,fss,ndobs,phi,y,nvar,ncoef,xxhpc,a0indx,na0p,...
+ idmat0,idmatpp,H0invmulti,Hpinvmulti,xxhp] = szasbvar(idfile,q_m,lags,xdgel,mu)
+% [Gb,Sbd,Bh,SpH,fss,ndobs,phi,y,nvar,ncoef,xxhpc,a0indx,na0p,...
+% idmat0,idmatpp,H0invmulti,Hpinvmulti] = szasbvar(idfile,q_m,lags,xdgel,mu)
+%
+% Estimating the Bayesian VAR of Sims and Zha with asymmetric prior (as)
+%
+% idfile: Identification filename with rows corresponding to equations.
+% But all the output will be transposed to columns
+% corresponding to equations.
+% q_m: quarter or month
+% lags: the maximum length of lag
+% nhp: number of haperparameters
+% xdgel: the general matrix of the original data (no manipulation involved)
+% with sample size including lags
+% mu: 6-by-1 vector of hyperparameters (the following numbers for Atlanta Fed's forecast)
+% mu(1): overall tightness and also for A0; (0.57)
+% mu(2): relative tightness for A+; (0.13)
+% mu(3): relative tightness for the constant term; (0.1)
+% mu(4): tightness on lag decay; (1)
+% mu(5): weight on nvar sums of coeffs dummy observations (unit roots); (5)
+% mu(6): weight on single dummy initial observation including constant
+% (cointegration, unit roots, and stationarity); (5)
+% --------------------
+% --------------------
+% Gb: cell(nvar,1). Each cell, when postmultiplied by A0(:,i), is used to compute A+(:,i)
+% where A+ is k-by-m, where k--ncnoef, m--nvar, if asymmetric prior.
+% In particular, Aplus(:,i)=Gb{i}*A0(:,i), so Bh = Aplus/A0
+% Sbd: cell(nvar,1). Sbd=diag(S(1), ..., S(m)). Already divided by 'fss' for the
+% posterior of a0 or A0(:) in Waggoner and Zha when one has asymmetric prior.
+% Note,"bd" stands for block diagonal.
+% Bh: reduced form B, k(mp+1)-by-m, column--equation. Bh=NaN if asymmetric prior.
+% In the form of y=X*B+U where B=[B1|B2| ...|Bp|C]
+% where Bi: m-by-m (i=1,..,p--lag length), C: 1-by-ml, constant.
+% SpH: divided by T, the final S in the Waggoner-Zha exponential part of p(A0|y)
+% SpH=NaN if asymmetric prior
+% fss: in-sample-size (for forecasting). Including dummy observations
+% ndobs: number of dummy observations, ndobs=nvar+1
+% phi: X in the form of y = X*B+U. Row: nSmaple-lags+ndobs. Column: ncoef
+% y: y in the form y = X*B+U. Including the dummy observations too,
+% T-lags+ndobs-by-nvar.
+% nvar: number of variables
+% ncoef: number of coefficients in *each* equation. RHS coefficients only, nvar*lags+1
+% xxhpc: chol(X'X+inv(H_p_tilde)): upper triangular but its transpose
+% is lower triagular Choleski
+% a0indx: the index number for free parameters in A0. Column meaning equation
+% na0p: number of free parameters in A0
+% idmat0: identification matrix for A0 with asymmetric prior; column -- equation.
+% idmatpp: asymmetric prior variance for A+ without constant; column -- equation.
+% H0invmulti: nvar-by-nvar-by-nvar; inv(H0) for different equations under asymmetric prior
+% Hpinvmulti: ncoef-by-ncoef-by-nvar; inv(H+) for different equations under asymmetric prior
+% xxhp: ncoef-by-nocef, X'X+inv(H_p_tilde)
+%
+% Revisions by CAS 9/27/96: If we transmit idmat, rather than a0indx, we can scale
+% the elements of idmat0 or idmatp so that it carries information about relative
+% prior variances.
+% Revsions by TZ, 10/13/96: Efficiency improvement by streamlining the previous
+% code according to the general setup in Sims and Zha's IER and according
+% to Zha's notes Forecast (0) (p.3) and Forecast (1) (p.9).
+% Quick Revisions: May 2003. See H1p_1 on lines 406-409.
+%
+% NOTE1: "nSample" is something I deleted as an input recently, so it may not
+% be compatible with old programs, 10/15/98 by TZ.
+% NOTE2: I put "mu" as an input arg and delete "nhp" as an input arg, so it may
+% not be compatible with old programs, 03/06/99 by TZ
+% NOTE3: added three output arguments: H0invmulti and Hpinvmulti and xxhp. 9/27/99
+
+%
+% Copyright (C) 1997-2012 Christopher A. Sims and Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+%
+%@@@ Prepared for Bayesian VAR of Sims and Zha
+%
+%* Load identification and obtain idmat0
+eval(['load ' idfile '.prn -ascii']);
+eval(['idmat0=' idfile ';']);
+idmat0=idmat0'; % so that each column corresponds to an equation
+a0indx=find(idmat0); % column meaning equation
+na0p = length(a0indx); % number of free parameters in A0
+nhp = length(mu); % total number of hyperparameters
+nfp = na0p + nhp; % total number of free parameters (including hyperparameters)
+[nvar,neqn]=size(idmat0);
+%
+idmatpp = ones(nvar*lags,nvar); % pp: plus without constant. Column -- equation
+%>>>>>> B: asymmetric prior variance for idmatpp <<<<<<<<
+%
+%for i = 1:lags
+% rowif = (i-1)*nvar+1;
+% rowil = i*nvar;
+% idmatw0 = 0.5; % weight assigned to idmat0 in the formation of idmatpp
+% if (i==1)
+% idmatpp(rowif:rowil,:)=(1-idmatw0)*ones(nvar)+idmatw0*idmat0; % first lag
+% % note: idmat1 is already transposed. Column -- equation
+% else
+% %idmatpp(rowif:rowil,1:nvar) = (1-idmatw0)*ones(nvar)+idmatw0*idmat0;
+% % <<<<<<< toggle +
+% % Note: already transposed, since idmat0 is transposed.
+% % Meaning: column implies equation
+% idmatpp(rowif:rowil,1:nvar) = ones(nvar);
+% % >>>>>>> toggle -
+% end
+%end
+%
+%>>>>>> E: asymmetric prior variance for idmatpp <<<<<<<<
+
+
+%$$$ available computations
+%
+%@@@ original
+% total number of the sample under study, including lags, etc. -- original sample size
+nSample = size(xdgel,1); % the sample size (including lags, of course)
+sb = lags+1; % original beginning without dummies
+sl = nSample; % original last period without dummies
+ssp = nSample - lags; % original number of observations
+%
+ndobs=nvar+1; % number of dummy observations
+ncoef = nvar*lags+1; % number of coefficients in *each* equation, RHS coefficients only.
+%*** initializing for global variables
+%%GlbAph=zeros(nvar,ncoef);
+%Aplus=zeros(ncoef,nvar);
+Gb = cell(nvar,1); % Storing potential reduced-form Bh (k-by-m) or prepared
+ % for computing Aplus (k-by-m), where k--ncnoef, m--nvar
+Sbd = cell(nvar,1); % Storing for diag(S(1), ..., S(m)) for the posterior of a0
+ % or vec(A0) when one has asymmetric prior. Note,
+ % "bd" stands for block diagonal.
+SpH = ones(nvar,nvar);
+%* flp: forecast last period (with dummies);
+%* fbp: forecast beginning period (with dummies).
+flp = sl+ndobs;
+%fbp = ndobs+lags+1; % <<>> true begining by skipping dummies
+fss = flp-lags; % forecast sample size (with dummies).
+
+% ** hyperparameters
+% mu = zeros(nhp,1);
+% mu(1) = 0.57;
+% mu(2) = 0.13;
+% mu(3) = 0.1;
+% mu(4) = 1;
+% mu(5) = 5; %10;
+% mu(6) = 5; %10;
+%
+% mu(1): overall tightness and also for A0;
+% mu(2): relative tightness for A+;
+% mu(3): relative tightness for the constant term;
+% mu(4): tightness on lag decay;
+% mu(5): weight on nvar sums of coeffs dummy observations (unit roots);
+% mu(6): weight on single dummy initial observation including constant (cointegration, unit
+% roots, and stationarity).
+
+
+% ** monthly lag decay in order to match quarterly decay: a*exp(bl) where
+% ** l is the monthly lag. Suppose quarterly decay is 1/x where x=1,2,3,4.
+% ** Let the decay of l1 (a*exp(b*l1)) match that of x1 (say, beginning: 1/1)
+% ** and the decay of l2 (a*exp(b*l2)) match that of x2 (say, end: 1/5),
+% ** we can solve for a and b which are
+% ** b = (log_x1-log_x2)/(l1-l2), and a = x1*exp(-b*l1).
+if q_m==12
+ l1 = 1; % 1st month == 1st quarter
+ xx1 = 1; % 1st quarter
+ l2 = lags; % last month
+ xx2 = 1/((ceil(lags/3))^mu(4)); % last quarter
+ %xx2 = 1/6; % last quarter
+ % 3rd quarter: i.e., we intend to let decay of the 6th month match
+ % that of the 3rd quarter, so that the 6th month decays a little
+ % faster than the second quarter which is 1/2.
+ if lags==1
+ b = 0;
+ else
+ b = (log(xx1)-log(xx2))/(l1-l2);
+ end
+ a = xx1*exp(-b*l1);
+end
+
+%
+% ** now run the VAR with
+%
+% **** nvar prior dummy observations with the sum of coefficients
+% ** construct X for Y = X*B + U where phi = X: (T-lags)*k, Y: (T-lags)*nvar
+% ** columns: k = # of [nvar for 1st lag, ..., nvar for last lag, const]
+% ** Now, T=T+ndobs -- added with "ndobs" dummy observations
+%
+phi = zeros(fss,ncoef);
+const = ones(fss,1);
+const(1:nvar) = zeros(nvar,1);
+phi(:,ncoef) = const; % the first nvar periods: no or zero constant!
+%
+xdgelint = mean(xdgel(1:lags,:),1); % mean of the first lags initial conditions
+%* Dummies
+for k=1:nvar
+ for m=1:lags
+ phi(ndobs,nvar*(m-1)+k) = xdgelint(k);
+ phi(k,nvar*(m-1)+k) = xdgelint(k);
+ % <<>> multiply hyperparameter later
+ end
+end
+%* True data
+for k=1:lags
+ phi(ndobs+1:fss,nvar*(k-1)+1:nvar*k) = xdgel(sb-k:sl-k,:);
+ % row: T-lags; column: [nvar for 1st lag, ..., nvar for last lag, const]
+ % Thus, # of columns is nvar*lags+1 = ncoef.
+end
+%
+% ** Y with "ndobs" dummies added
+y = zeros(fss,nvar);
+%* Dummies
+for k=1:nvar
+ y(ndobs,k) = xdgelint(k);
+ y(k,k) = xdgelint(k);
+ % multiply hyperparameter later
+end
+%* True data
+y(ndobs+1:fss,:) = xdgel(sb:sl,:);
+
+
+
+%
+% *** specify the prior for each equation separately, SZ method,
+% ***
+% *** obtaining the residuals for the univariate processes:
+% *** a total of "nvar" equations
+% *** obtain the posterior peak of A0 which is Sims iden1 (Sims 1986)
+% ** get the residuals from univariate regressions.
+%
+sgh = zeros(nvar,1); % square root
+sgsh = sgh; % square
+yu = xdgel;
+C = ones(nSample,1);
+for k=1:nvar
+ [Bk,ek,junk1,junk2,junk3,junk4] = sye([yu(:,k) C],lags);
+ clear Bk junk1 junk2 junk3 junk4;
+ sgsh(k) = ek'*ek/ssp;
+ %% sgh(k) = ek'*ek/(fss-7); to match "sqrt(ess)" in RATS univariate regression
+ sgh(k) = sqrt(sgsh(k));
+end
+% ** prior variance for alpha0, same for all equations!!!
+al0b = zeros(nvar*neqn,1); % prior mean for all A0
+sg0bid = zeros(nvar,1); % Sigma0_bar diagonal only
+for j=1:nvar
+ sg0bid(j) = 1/sgsh(j); % sgsh = sigmai^2
+end
+% ** prior variance for alpha_plus, same for all equations
+sgpbid = zeros(ncoef,1); % Sigma_plus_bar, diagonal
+for i = 1:lags
+ if (q_m==12)
+ lagdecay = a*exp(b*i);
+ end
+ %
+ for j = 1:nvar
+ if (q_m==12)
+ % exponential decay to match quarterly decay
+ sgpbid((i-1)*nvar+j) = lagdecay^2/sgsh(j);
+ %%sgpbid((i-1)*nvar+j) = (1/i)^2/sgsh(j);
+ elseif (q_m==4)
+ sgpbid((i-1)*nvar+j) = (1/i^mu(4))^2/sgsh(j);
+ else
+ error('Incompatibility with lags, check the possible errors!!!')
+ %warning('Incompatibility with lags, check the possible errors!!!')
+ %return
+ end
+ end
+end
+%%A0bd = sqrt(sg0bid);
+% commented out by T.A. Zha, 10/3/96, to avoid double-counting of scaling and the problem
+% of potential multiple peaks.
+A0bd = zeros(nvar,1);
+A0b = sparse(1:nvar,1:nvar,A0bd,nvar,nvar);
+A0b = A0b'; % making a column in A0b correspond to an equation
+
+%
+
+
+%=================================================
+% Computing the (prior) covariance matrix for the posterior of A0, no data yet
+%=================================================
+%
+%
+% ** weight prior dummy observations
+%phi(1:nvar,:) = (mu(5)^2/mu(1)^2)*phi(1:nvar,:);
+%y(1:nvar,:) = (mu(5)^2/mu(1)^2)*y(1:nvar,:);
+%phi(ndobs,:) = mu
+% modified by CAS 8/6/96. The dummy obs weights are naturally in std units, not var units.
+%
+phi(1:nvar,:) = 1*mu(5)*phi(1:nvar,:); % standard Sims and Zha prior
+y(1:nvar,:) = mu(5)*y(1:nvar,:); % standard Sims and Zha prior
+%----- The following prior designed for GLZ
+%phi(1,:) = 1.00*mu(5)*phi(1,:);
+%phi(2:nvar,:) = 1.02*mu(7)*phi(2:nvar,:);
+%y(1,:) = mu(5)*y(1,:);
+%y(2:nvar,:) = mu(7)*y(2:nvar,:);
+%----------------------------
+%
+phi(nvar+1,:) = mu(6)*phi(nvar+1,:);
+y(nvar+1,:) = mu(6)*y(nvar+1,:);
+
+
+% ** set up the conditional prior variance sg0bi and sgpbi.
+sg0bida = mu(1)^2*sg0bid;
+sgpbida = mu(1)^2*mu(2)^2*sgpbid;
+sgpbida(ncoef) = mu(1)^2*mu(3)^2;
+sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in a Sims-Zha paper
+%%sgppbdi = 1./sgppbd;
+%sgppb = diag(sgppbd);
+
+Hptd = zeros(ncoef);
+Hptdi=Hptd;
+%%Hptd(nvar+1:ncoef,nvar+1:ncoef)=diag(sgppbd);
+%%Hptdi(nvar+1:ncoef,nvar+1:ncoef)=diag(sgppbdi);
+Hptd(ncoef,ncoef)=sgppbd(ncoef-nvar);
+Hptdi(ncoef,ncoef)=1/sgppbd(ncoef-nvar);
+ % condtional on A0i, H_plus_tilde
+
+% ** some common terms
+[u d v]=svd(phi,0); %trial
+% xtx = phi'*phi; %trial
+vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
+xtx=vd*vd';
+% M = eye(fss) - phi*(xtx\phi'); % not used except in line below %trial
+yu = y'*u; %trial
+cxyxx=yu*yu'; %trial
+yty=y'*y;
+ymy=yty-cxyxx; %trial
+%ymy = y'*M*y;
+%cxy = phi'*y; %trial
+cxy = vd*yu'; %trial
+cyx = cxy';
+%cxpy = xtx\cxy; %not used further except in line below
+%cxyxx = cxy'*cxpy;
+
+% ** estimate A+_hat conditional on A0, ncoef*nvar, but not using full prior
+%GlbAph = cxpy*A0h;
+
+
+%=================================================
+% Computing the final covariance matrix (S1,...,Sm) for the posterior of A0,
+% with the data, and final Gb=(G1,...,Gm) for A+ if asymmetric prior or for
+% B if symmetric prior for A+
+%=================================================
+%
+H0invmulti=zeros(nvar,nvar,nvar); % inv(H0) for different equations under asymmetric prior
+Hpinvmulti=zeros(ncoef,ncoef,nvar); % inv(H+) for different equations under asymmetric prior
+for i = 1:nvar
+ stri = find(idmat0(:,i));
+ % CAS change 9/24/96, to avoid slim chance of an exact zero in x vector
+ % at some point during an optimization.
+ %strm = length(stri); % TZ, 11/27/97, no use any more
+
+ %A0hb = A0h(stri,i)-A0b(stri,i);
+
+ % ** set up the conditional prior variance sg0bi and sgpbi.
+ % sg0bd = zeros(stri,1); Commented out by CAS, 9/24/96. Looks like this
+ % meant to have strm where it has stri, and in any case it is "overwritten" below.
+
+ %------------------------------
+ % Introduce prior information on which variables "belong" in various equations.
+ % In this first trial, we just introduce this information here, in a model-specific way.
+ % Eventually this info has to be passed parametricly. In our first shot, we just damp down
+ % all coefficients except those on the diagonal.
+ factor0=idmat0(:,i);
+ sg0bd = sg0bida; % added by TZ, 2/26/98. I think at each equation, sg0bd must
+ % be refreshed. Note, this only works for the prior variance Sg(i)
+ % of a0(i) being diagonal. If the prior variance Sg(i) is not
+ % diagonal, we have to work on inv(Sg(i)) or sg0bdi directly.
+ sg0bd(stri) = sg0bida(stri).*factor0(stri);
+ sg0bdi = 1./sg0bd;
+ %
+ factor1=idmatpp(1:nvar,i);
+ sg1bd = sgpbida(1:nvar).*factor1;
+ sg1bdi = 1./sg1bd;
+ %
+ if lags>1
+ factorpp=idmatpp(nvar+1:ncoef-1,i);
+ sgpp_cbd = sgppbd(1:ncoef-nvar-1) .* factorpp;
+ sgpp_cbdi = 1./sgpp_cbd;
+ end
+
+ % ** set up the unconditional prior variance on A0i and A+i
+ %XX = zeros(nvar,strm);
+ %XX(stri,:) = eye(strm); % XX in Gelman, etel, on p479
+ % Commented out by TZ, 11/27/97. Streamline. No need for these any more.
+
+
+ % * final conditional prior variance on A+i give that on A0i, and
+ % * unconditional variance on A0+
+ H0td = diag(sg0bd); % unconditional
+ % H_~: td: tilde for ~
+ % ** inverse and chol decomp
+ H0tdi = diag(sg0bdi);
+ H0invmulti(:,:,i)=H0tdi;
+
+ %
+ Hptd(1:nvar,1:nvar)=diag(sg1bd);
+ Hptdi(1:nvar,1:nvar)=diag(sg1bdi);
+ if lags>1
+ Hptd(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbd);
+ Hptdi(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbdi);
+ % condtional on A0i, H_plus_tilde
+ end
+ Hpinvmulti(:,:,i)=Hptdi;
+
+ % common terms
+ xxhp = xtx+Hptdi;
+ %Lxxhpc = chol(inv(xxhp))'; % lower triangular
+ xxhpc = chol(xxhp); % upper triangular but its transpose is lower triagular Choleski
+ %A0hbH = A0hb'*H0tdi;
+ %====== The following two lines seem incorrect. The third line is supposed to correc the mistake. May 2003.
+ % H1p_1 = zeros(ncoef,nvar);
+ % H1p_1(1:nvar,:) = Hptdi(1:nvar,1:nvar);
+ H1p_1 = Hptdi(:,1:nvar);
+ %%Hm = (cyx+H1p_1')*(xxhp\(cxy+H1p_1));
+ Hm1 = (cyx+H1p_1')/xxhp; % if symmetric prior, Bh=Hm1' -- reduced form k-by-m.
+ Gb{i} = Hm1'; % k-by-m, where k--ncoef, m--nvar.
+ % If asymmetric prior, Gb is used to compute A+ where A+(i) = Gb(i)*a0(i)
+ % where a0(i) is m-by-1 for the ith column of A0 or the ith equation.
+ %Hm2 = cxy+H1p_1;
+ Hm = Hm1*(cxy+H1p_1);
+ %GlbAph(i,:) = A0h(stri,i)'*XX'*Hm1; % alpha_plus_*_transpose
+ %%alpMpyh = A0h(stri,i)'*XX'*(yty+Hptdi(1:nvar,1:nvar)-Hm)*XX;
+ %Hss = XX'*(yty+Hptdi(1:nvar,1:nvar)-Hm)*XX;
+ Hss = yty+Hptdi(1:nvar,1:nvar)-Hm;
+ Sbd{i} = (H0tdi+Hss)./fss; % m-by-m, where m--nvar. If asymmetric prior, Scd is
+ % usde to form diag(Sbd{1}, ..., Sbd{m}) for a0 or vec(A0) in the
+ % posterior of A0. Note, "bd" stands for block diagonal and divided by
+ % "fss" already to make it compatible with SpH used in "a0lhfun" and make it
+ % it easier according to the notations by Waggoner and Zha
+end
+
+
+%%=========================================
+%% check if there is asymmetric prior on both A+ and A0
+%%=========================================
+%
+spindx=1; % A+ index for asymmetric prior
+s0indx=1; % A0 index for asymmetric prior
+for i=2:nvar
+ diffGb = Gb{i}-Gb{i-1};
+ diffSbd = Sbd{i}-Sbd{i-1};
+ if ~any(any(diffGb))
+ spindx=spindx+1;
+ end
+ if ~any(any(diffSbd))
+ s0indx=s0indx+1;
+ end
+end
+%
+if (spindx==nvar)
+ Bh = Gb{1}; % reduced-form parameter. Note: does not depend on A0
+else
+ Bh = NaN;
+end
+%
+if (s0indx==nvar)
+ SpH = (H0tdi+Hss)/fss; % the final S in the exponential part of p(A0|y)
+ % divided by nobs in order to make Choleski decomp without optimization
+ % or in the form conformable to Waggoner and Zha
+else
+ SpH=NaN;
+end
+
+
+
+
+
+%----------------------------------------------
+%
+
+% ***
+% *** Form the inverse of covarinace to draw from t- or Gaussian distribution
+% ***
+%SpHs = SpH*fss; % for the Wishart draw
+%SpHsc = chol(SpHs); % upper triangular where Sphsc' is
+% % lower triangular Choleski, for Wishart draw
+%SpHsic = chol(inv(SpHs))'; % inverse Choleski decomposition -- lower triangular
+%A0hin = chol(SpH); % upper triangular, inverse of A0h, each column
+ % corresponds to an equation.
+
+
+%@@@ The following can be used for other purposes such as forecasting
+%
+%swish = A0hin'; % each row corresponds to an equation
+%A0h = inv(A0hin); % each column corresponds to an equation
+%xa0 = A0h(a0indx);
+
+%%*** form Bh (ncoef*nvar)
+%Aplus = Hm1t*A0h; % estimate of Aplus -- the same as Astrar
+%Bhp = Hm1t;
diff --git a/MatlabFiles/szbvar.m b/MatlabFiles/szbvar.m
index 46344b45ff754b9ed08f158a0f370e07f88e21cf..058762d352e1dd7fea7593e787676212952d720d 100644
--- a/MatlabFiles/szbvar.m
+++ b/MatlabFiles/szbvar.m
@@ -1,353 +1,352 @@
-function [A0hin,Hm1t,fss,ndobs,phi,y,nvar,ncoef,SpH,SpHsc,xxhpc,a0indx,na0p,...
- idmat0] = szbvar(idfile,q_m,lags,nSample,nhp,xdgel)
-% Estimating the Bayesian VAR of Sims and Zha:
-% function [A0hin,Hm1t,fss,ndobs,phi,y,nvar,ncoef,SpH,SpHsc,xxhpc,a0indx,na0p,...
-% idmat0] = szbvar(idfile,q_m,lags,nSample,nhp,xdgel)
-%
-% idfile: Identification filename such as "iden4" (with the extension ".prn").
-% q_m: quarter or month
-% lags: the maximum length of lag
-% nSample: the sample size (including lags, of course)
-% nhp: number of haperparameters
-% imstp: number of impusle response steps
-% xdgel: the general matrix of the original data (no manipulation involved)
-% ------
-% A0hin: chol(SpH) -- inverse of A0h, upper triangular, each column corresponds to an equation
-% Hm1t: reduced form B
-% fss: in-sample-size (for forecasting). Including dummy observations
-% ndobs: number of dummy observations, ndobs=nvar+1
-% phi: X in the form of y = X*B+U. Row: nSmaple-lags+ndobs. Column: ncoef
-% y: y in the form y = X*B+U. Including the dummy observations too,
-% T-lags+ndobs-by-nvar.
-% nvar: number of variables
-% ncoef: number of coefficients in *each* equation. RHS coefficients only, nvar*lags+1
-% SpH: divided by T, the final S in the Waggoner-Zha exponential part of p(A0|y)
-% SpHsc: upper triagular in the lower triangular Choleski of SpH*fss, for Wishart
-% xxhpc: chol(X'X+inv(H_p_tilde)): upper triangular but its transpose
-% is lower triagular Choleski
-% a0indx: the index number for free parameters in A0. Column meaning equation
-% na0p: number of free parameters in A0
-% idmat0: identification matrix for A0; only 1's and 0's. Column meaning equation.
-%
-% Copyright (c) July 1997 by C.A. Sims and T. Zha
-%
-% Quick Revisions: May 2003. See H1p_1 on lines 301-304.
-% Copyright (C) 1997-2012 Christopher A. Sims and Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-%
-%@@@ Prepared for Bayesian VAR of Sims and Zha
-%
-%* Load identification and obtain idmat0
-eval(['load ' idfile '.prn -ascii']);
-eval(['idmat0=' idfile ';']);
-idmat0=idmat0'; % so that each column corresponds to an equation
-a0indx=find(idmat0); % column meaning equation
-na0p = length(a0indx); % number of free parameters in A0
-nfp = na0p + nhp; % total number of free parameter
-[nvar,neqn]=size(idmat0);
-
-%$$$ available computations
-%
-%@@@ original
-% total number of the sample under study, including lags, etc. -- original sample size
-sb = lags+1; % original beginning without dummies
-sl = nSample; % original last period without dummies
-ssp = nSample - lags; % original number of observations
-%
-ndobs=nvar+1; % number of dummy observations
-ncoef = nvar*lags+1; % number of coefficients in *each* equation, RHS coefficients only.
-%*** initializing for global variables
-%%GlbAph=zeros(nvar,ncoef);
-Aplus=zeros(ncoef,nvar);
-SpH = ones(nvar,nvar);
-%* flp: forecast last period (with dummies);
-%* fbp: forecast beginning period (with dummies).
-flp = sl+ndobs;
-fbp = ndobs+lags+1; % <<>> true begining by skipping dummies
-fss = flp-lags; % forecast sample size (with dummies).
-
-% ** hyperparameters
-mu = zeros(nhp,1);
-mu(1) = 0.57;
-mu(2) = 0.13;
-mu(3) = 0.1;
-mu(4) = 1;
-mu(5) = 5;
-mu(6) = 5;
-%
-% mu(1): overall tightness and also for A0;
-% mu(2): relative tightness for A+;
-% mu(3): relative tightness for the constant term;
-% mu(4): tightness on lag decay;
-% mu(5): weight on nvar sums of coeffs dummy observations (unit roots);
-% mu(6): weight on single dummy initial observation including constant (cointegration, unit
-% roots, and stationarity).
-
-
-% ** monthly lag decay in order to match quarterly decay: a*exp(bl) where
-% ** l is the monthly lag. Suppose quarterly decay is 1/x where x=1,2,3,4.
-% ** Let the decay of l1 match that of x1 (say, beginning) and the decay
-% ** of l2 match that of x2 (say, end), we can solve for a and b which are
-% ** b = (log_x1-log_x2)/(l1-l2), and a = x1*exp(-b*l1).
-if q_m==12
- l1 = 1; % 1st month == 1st quarter
- xx1 = 1; % 1st quarter
- l2 = lags; % last month
- xx2 = 1/((ceil(lags/3))^mu(4)); % last quarter
- %xx2 = 1/6; % last quarter
- % 3rd quarter: i.e., we intend to let decay of the 6th month match
- % that of the 3rd quarter, so that the 6th month decays a little
- % faster than the second quarter which is 1/2.
- if lags==1
- b = 0;
- else
- b = (log(xx1)-log(xx2))/(l1-l2);
- end
- a = xx1*exp(-b*l1);
-end
-
-%
-% ** now run the VAR with
-%
-% **** nvar prior dummy observations with the sum of coefficients
-% ** construct X for Y = X*B + U where phi = X: (T-lags)*k, Y: (T-lags)*nvar
-% ** columns: k = # of [nvar for 1st lag, ..., nvar for last lag, const]
-% ** Now, T=T+ndobs -- added with "ndobs" dummy observations
-%
-phi = zeros(fss,ncoef);
-const = ones(fss,1);
-const(1:nvar) = zeros(nvar,1);
-phi(:,ncoef) = const; % the first nvar periods: no or zero constant!
-%
-xdgelint = mean(xdgel(1:lags,:),1);
-% mean of the first lags initial conditions
-for k=1:nvar
- for m=1:lags
- phi(ndobs,nvar*(m-1)+k) = xdgelint(k);
- phi(k,nvar*(m-1)+k) = xdgelint(k);
- % <<>> multiply hyperparameter later
- end
-end
-for k=1:lags
- phi(ndobs+1:fss,nvar*(k-1)+1:nvar*k) = xdgel(sb-k:sl-k,:);
- % row: T-lags; column: [nvar for 1st lag, ..., nvar for last lag, const]
- % Thus, # of columns is nvar*lags+1 = ncoef.
-end
-%
-% ** Y with "ndobs" dummies added
-y = zeros(fss,nvar);
-for k=1:nvar
- y(ndobs,k) = xdgelint(k);
- y(k,k) = xdgelint(k);
- % <<>> place hyperparameter later
-end
-y(ndobs+1:fss,:) = xdgel(sb:sl,:);
-
-%
-% *** specify the prior for each equation separately, SZ method,
-% ***
-% *** obtaining the residuals for the univariate processes:
-% *** a total of "nvar" equations
-% *** obtain the posterior peak of A0 which is Sims iden1 (Sims 1986)
-% ** get the residuals from univariate regressions.
-%
-sgh = zeros(nvar,1); % square root
-sgsh = sgh; % square
-nn = [1 lags nSample];
-yu = xdgel;
-C = ones(nSample,1);
-for k=1:nvar
- [Bk,ek,junk1,junk2,junk3,junk4] = sye([yu(:,k) C],nn);
- clear Bk junk1 junk2 junk3 junk4;
- sgsh(k) = ek'*ek/ssp;
- %% sgh(k) = ek'*ek/(fss-7); to match "sqrt(ess)" in RATS univariate regression
- sgh(k) = sqrt(sgsh(k));
-end
-% ** prior variance for alpha0, same for all equations!!!
-al0b = zeros(nvar*neqn,0); % prior mean for all A0
-sg0bid = zeros(nvar,1); % Sigma0_bar diagonal only
-for j=1:nvar
- sg0bid(j) = 1/sgsh(j); % sgsh = sigmai^2
-end
-% ** prior variance for alpha_plus, same for all equations
-sgpbid = zeros(ncoef,1); % Sigma_plus_bar, diagonal
-for i = 1:lags
- if (q_m==12)
- lagdecay = a*exp(b*i);
- end
- %
- for j = 1:nvar
- if (q_m==12)
- % exponential decay to match quarterly decay
- sgpbid((i-1)*nvar+j) = lagdecay^2/sgsh(j);
- %%sgpbid((i-1)*nvar+j) = (1/i)^2/sgsh(j);
- elseif (q_m==4)
- sgpbid((i-1)*nvar+j) = (1/i^mu(4))^2/sgsh(j);
- else
- warning('Incompatibility with lags, check the possible errors!!!')
- return
- end
- end
-end
-%%A0bd = sqrt(sg0bid);
-% commented out by T.A. Zha, 10/3/96, to avoid double-counting of scaling and the problem
-% of potential multiple peaks.
-A0bd = zeros(nvar,1);
-A0b = sparse(1:nvar,1:nvar,A0bd,nvar,nvar);
-A0b = A0b'; % making a column in A0b correspond to an equation
-
-%
-
-
-%=================================================
-% Computing the (prior) covariance matrix for the posterior of A0, no data yet
-%=================================================
-%
-%
-% ** weight prior dummy observations
-%phi(1:nvar,:) = (mu(5)^2/mu(1)^2)*phi(1:nvar,:);
-%y(1:nvar,:) = (mu(5)^2/mu(1)^2)*y(1:nvar,:);
-%phi(ndobs,:) = mu
-% modified by CAS 8/6/96. The dummy obs weights are naturally in std units, not var units.
-%
-phi(1:nvar,:) = mu(5)*phi(1:nvar,:); % standard Sims and Zha prior
-y(1:nvar,:) = mu(5)*y(1:nvar,:); % standard Sims and Zha prior
-%----- The following prior designed for GLZ
-%phi(1,:) = 1.00*mu(5)*phi(1,:);
-%phi(2:nvar,:) = 1.02*mu(7)*phi(2:nvar,:);
-%y(1,:) = mu(5)*y(1,:);
-%y(2:nvar,:) = mu(7)*y(2:nvar,:);
-%----------------------------
-%
-phi(nvar+1,:) = mu(6)*phi(nvar+1,:);
-y(nvar+1,:) = mu(6)*y(nvar+1,:);
-
-
-% ** set up the conditional prior variance sg0bi and sgpbi.
-sg0bida = mu(1)^2*sg0bid;
-sgpbida = mu(1)^2*mu(2)^2*sgpbid;
-sgpbida(ncoef) = mu(1)^2*mu(3)^2;
-sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in a Sims-Zha paper
-%%sgppbdi = 1./sgppbd;
-%sgppb = diag(sgppbd);
-
-Hptd = zeros(ncoef);
-Hptdi=Hptd;
-%%Hptd(nvar+1:ncoef,nvar+1:ncoef)=diag(sgppbd);
-%%Hptdi(nvar+1:ncoef,nvar+1:ncoef)=diag(sgppbdi);
-Hptd(ncoef,ncoef)=sgppbd(ncoef-nvar);
-Hptdi(ncoef,ncoef)=1/sgppbd(ncoef-nvar);
- % condtional on A0i, H_plus_tilde
-sg0bd = sg0bida;
-sg0bdi = 1./sg0bd;
-sg1bd = sgpbida(1:nvar);
-sg1bdi = 1./sg1bd;
-if lags>1
- sgpp_cbd = sgppbd(1:ncoef-nvar-1);
- sgpp_cbdi = 1./sgpp_cbd; % _cbd: c -- constant, b -- barred (without), d -- diagonal
-end
-
-
-% ** set up the unconditional prior variance on A0i and A+i
-%%XX = zeros(nvar,strm);
-%%XX(stri,:) = eye(strm); % XX in Gelman, etel, on p479
-%
-% * final conditional prior variance on A+i give that on A0i, and
-% * unconditional variance on A0+
-H0td = diag(sg0bd); % unconditional
-% H_~: td: tilde for ~
-% ** inverse and chol decomp
-H0tdi = diag(sg0bdi);
-
-%
-Hptd(1:nvar,1:nvar)=diag(sg1bd);
-Hptdi(1:nvar,1:nvar)=diag(sg1bdi);
-if lags>1
- Hptd(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbd);
- Hptdi(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbdi);
- % condtional on A0i, H_plus_tilde
-end
-
-
-%=================================================
-% Computing the final covariance matrix for the posterior of A0, with the data
-%=================================================
-%
-% ** some common terms
-[u d v]=svd(phi,0); %trial
-% xtx = phi'*phi; %trial
-vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
-xtx=vd*vd';
-% M = eye(fss) - phi*(xtx\phi'); % not used except in line below %trial
-yu = y'*u; %trial
-cxyxx=yu*yu'; %trial
-yty=y'*y;
-ymy=yty-cxyxx; %trial
-%ymy = y'*M*y;
-%cxy = phi'*y; %trial
-cxy = vd*yu'; %trial
-cyx = cxy';
-%cxpy = xtx\cxy; %not used further except in line below
-%cxyxx = cxy'*cxpy;
-
-% ** estimate A+_hat conditional on A0, ncoef*nvar, but not using full prior
-%GlbAph = cxpy*A0h;
-
-
-% common terms
-xxhp = xtx+Hptdi;
-%Lxxhpc = chol(inv(xxhp))'; % lower triangular
-xxhpc = chol(xxhp); % upper triangular but its transpose is lower triagular Choleski
-%A0hbH = A0hb'*H0tdi;
-%====== The following two lines seem incorrect. The third line is supposed to correc the mistake. May 2003.
-% H1p_1 = zeros(ncoef,nvar);
-% H1p_1(1:nvar,:) = Hptdi(1:nvar,1:nvar);
-H1p_1 = Hptdi(:,1:nvar);
-%%Hm = (cyx+H1p_1')*(xxhp\(cxy+H1p_1));
-Hm1 = (cyx+H1p_1')/xxhp;
-Hm1t = Hm1';
-%Hm2 = cxy+H1p_1;
-Hm = Hm1*(cxy+H1p_1);
-%%alpMpyh = A0h(stri,i)'*XX'*(yty+Hptdi(1:nvar,1:nvar)-Hm)*XX;
-Hss = yty+Hptdi(1:nvar,1:nvar)-Hm;
-SpH = (H0tdi+Hss)/fss; % the final S in the exponential part of p(A0|y)
-% divided by nobs in order to make Choleski decomp without optimization
-% or in the form conformable to Waggoner and Zha
-
-% ***
-% *** Form the inverse of covarinace to draw from t- or Gaussian distribution
-% ***
-SpHs = SpH*fss; % for the Wishart draw
-SpHsc = chol(SpHs); % upper triangular where Sphsc' is lower triangular Choleski
-%SpHsic = chol(inv(SpHs))'; % inverse Choleski decomposition -- lower triangular
-
-A0hin = chol(SpH); % upper triangular, inverse of A0h, each column
- % corresponds to an equation.
-
-
-%----------------------------------------------
-%
-%@@@ The following can be used for other purposes such as forecasting
-%
-%swish = A0hin'; % each row corresponds to an equation
-%A0h = inv(A0hin); % each column corresponds to an equation
-%xa0 = A0h(a0indx);
-
-%%*** form Bh (ncoef*nvar)
-%Aplus = Hm1t*A0h; % estimate of Aplus -- the same as Astrar
-%Bhp = Hm1t;
+function [A0hin,Hm1t,fss,ndobs,phi,y,nvar,ncoef,SpH,SpHsc,xxhpc,a0indx,na0p,...
+ idmat0] = szbvar(idfile,q_m,lags,nSample,nhp,xdgel)
+% Estimating the Bayesian VAR of Sims and Zha:
+% function [A0hin,Hm1t,fss,ndobs,phi,y,nvar,ncoef,SpH,SpHsc,xxhpc,a0indx,na0p,...
+% idmat0] = szbvar(idfile,q_m,lags,nSample,nhp,xdgel)
+%
+% idfile: Identification filename such as "iden4" (with the extension ".prn").
+% q_m: quarter or month
+% lags: the maximum length of lag
+% nSample: the sample size (including lags, of course)
+% nhp: number of haperparameters
+% imstp: number of impusle response steps
+% xdgel: the general matrix of the original data (no manipulation involved)
+% ------
+% A0hin: chol(SpH) -- inverse of A0h, upper triangular, each column corresponds to an equation
+% Hm1t: reduced form B
+% fss: in-sample-size (for forecasting). Including dummy observations
+% ndobs: number of dummy observations, ndobs=nvar+1
+% phi: X in the form of y = X*B+U. Row: nSmaple-lags+ndobs. Column: ncoef
+% y: y in the form y = X*B+U. Including the dummy observations too,
+% T-lags+ndobs-by-nvar.
+% nvar: number of variables
+% ncoef: number of coefficients in *each* equation. RHS coefficients only, nvar*lags+1
+% SpH: divided by T, the final S in the Waggoner-Zha exponential part of p(A0|y)
+% SpHsc: upper triagular in the lower triangular Choleski of SpH*fss, for Wishart
+% xxhpc: chol(X'X+inv(H_p_tilde)): upper triangular but its transpose
+% is lower triagular Choleski
+% a0indx: the index number for free parameters in A0. Column meaning equation
+% na0p: number of free parameters in A0
+% idmat0: identification matrix for A0; only 1's and 0's. Column meaning equation.
+%
+% Quick Revisions: May 2003. See H1p_1 on lines 301-304.
+%
+%
+% Copyright (C) 1997-2012 Christopher A. Sims and Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+%
+%@@@ Prepared for Bayesian VAR of Sims and Zha
+%
+%* Load identification and obtain idmat0
+eval(['load ' idfile '.prn -ascii']);
+eval(['idmat0=' idfile ';']);
+idmat0=idmat0'; % so that each column corresponds to an equation
+a0indx=find(idmat0); % column meaning equation
+na0p = length(a0indx); % number of free parameters in A0
+nfp = na0p + nhp; % total number of free parameter
+[nvar,neqn]=size(idmat0);
+
+%$$$ available computations
+%
+%@@@ original
+% total number of the sample under study, including lags, etc. -- original sample size
+sb = lags+1; % original beginning without dummies
+sl = nSample; % original last period without dummies
+ssp = nSample - lags; % original number of observations
+%
+ndobs=nvar+1; % number of dummy observations
+ncoef = nvar*lags+1; % number of coefficients in *each* equation, RHS coefficients only.
+%*** initializing for global variables
+%%GlbAph=zeros(nvar,ncoef);
+Aplus=zeros(ncoef,nvar);
+SpH = ones(nvar,nvar);
+%* flp: forecast last period (with dummies);
+%* fbp: forecast beginning period (with dummies).
+flp = sl+ndobs;
+fbp = ndobs+lags+1; % <<>> true begining by skipping dummies
+fss = flp-lags; % forecast sample size (with dummies).
+
+% ** hyperparameters
+mu = zeros(nhp,1);
+mu(1) = 0.57;
+mu(2) = 0.13;
+mu(3) = 0.1;
+mu(4) = 1;
+mu(5) = 5;
+mu(6) = 5;
+%
+% mu(1): overall tightness and also for A0;
+% mu(2): relative tightness for A+;
+% mu(3): relative tightness for the constant term;
+% mu(4): tightness on lag decay;
+% mu(5): weight on nvar sums of coeffs dummy observations (unit roots);
+% mu(6): weight on single dummy initial observation including constant (cointegration, unit
+% roots, and stationarity).
+
+
+% ** monthly lag decay in order to match quarterly decay: a*exp(bl) where
+% ** l is the monthly lag. Suppose quarterly decay is 1/x where x=1,2,3,4.
+% ** Let the decay of l1 match that of x1 (say, beginning) and the decay
+% ** of l2 match that of x2 (say, end), we can solve for a and b which are
+% ** b = (log_x1-log_x2)/(l1-l2), and a = x1*exp(-b*l1).
+if q_m==12
+ l1 = 1; % 1st month == 1st quarter
+ xx1 = 1; % 1st quarter
+ l2 = lags; % last month
+ xx2 = 1/((ceil(lags/3))^mu(4)); % last quarter
+ %xx2 = 1/6; % last quarter
+ % 3rd quarter: i.e., we intend to let decay of the 6th month match
+ % that of the 3rd quarter, so that the 6th month decays a little
+ % faster than the second quarter which is 1/2.
+ if lags==1
+ b = 0;
+ else
+ b = (log(xx1)-log(xx2))/(l1-l2);
+ end
+ a = xx1*exp(-b*l1);
+end
+
+%
+% ** now run the VAR with
+%
+% **** nvar prior dummy observations with the sum of coefficients
+% ** construct X for Y = X*B + U where phi = X: (T-lags)*k, Y: (T-lags)*nvar
+% ** columns: k = # of [nvar for 1st lag, ..., nvar for last lag, const]
+% ** Now, T=T+ndobs -- added with "ndobs" dummy observations
+%
+phi = zeros(fss,ncoef);
+const = ones(fss,1);
+const(1:nvar) = zeros(nvar,1);
+phi(:,ncoef) = const; % the first nvar periods: no or zero constant!
+%
+xdgelint = mean(xdgel(1:lags,:),1);
+% mean of the first lags initial conditions
+for k=1:nvar
+ for m=1:lags
+ phi(ndobs,nvar*(m-1)+k) = xdgelint(k);
+ phi(k,nvar*(m-1)+k) = xdgelint(k);
+ % <<>> multiply hyperparameter later
+ end
+end
+for k=1:lags
+ phi(ndobs+1:fss,nvar*(k-1)+1:nvar*k) = xdgel(sb-k:sl-k,:);
+ % row: T-lags; column: [nvar for 1st lag, ..., nvar for last lag, const]
+ % Thus, # of columns is nvar*lags+1 = ncoef.
+end
+%
+% ** Y with "ndobs" dummies added
+y = zeros(fss,nvar);
+for k=1:nvar
+ y(ndobs,k) = xdgelint(k);
+ y(k,k) = xdgelint(k);
+ % <<>> place hyperparameter later
+end
+y(ndobs+1:fss,:) = xdgel(sb:sl,:);
+
+%
+% *** specify the prior for each equation separately, SZ method,
+% ***
+% *** obtaining the residuals for the univariate processes:
+% *** a total of "nvar" equations
+% *** obtain the posterior peak of A0 which is Sims iden1 (Sims 1986)
+% ** get the residuals from univariate regressions.
+%
+sgh = zeros(nvar,1); % square root
+sgsh = sgh; % square
+nn = [1 lags nSample];
+yu = xdgel;
+C = ones(nSample,1);
+for k=1:nvar
+ [Bk,ek,junk1,junk2,junk3,junk4] = sye([yu(:,k) C],nn);
+ clear Bk junk1 junk2 junk3 junk4;
+ sgsh(k) = ek'*ek/ssp;
+ %% sgh(k) = ek'*ek/(fss-7); to match "sqrt(ess)" in RATS univariate regression
+ sgh(k) = sqrt(sgsh(k));
+end
+% ** prior variance for alpha0, same for all equations!!!
+al0b = zeros(nvar*neqn,0); % prior mean for all A0
+sg0bid = zeros(nvar,1); % Sigma0_bar diagonal only
+for j=1:nvar
+ sg0bid(j) = 1/sgsh(j); % sgsh = sigmai^2
+end
+% ** prior variance for alpha_plus, same for all equations
+sgpbid = zeros(ncoef,1); % Sigma_plus_bar, diagonal
+for i = 1:lags
+ if (q_m==12)
+ lagdecay = a*exp(b*i);
+ end
+ %
+ for j = 1:nvar
+ if (q_m==12)
+ % exponential decay to match quarterly decay
+ sgpbid((i-1)*nvar+j) = lagdecay^2/sgsh(j);
+ %%sgpbid((i-1)*nvar+j) = (1/i)^2/sgsh(j);
+ elseif (q_m==4)
+ sgpbid((i-1)*nvar+j) = (1/i^mu(4))^2/sgsh(j);
+ else
+ warning('Incompatibility with lags, check the possible errors!!!')
+ return
+ end
+ end
+end
+%%A0bd = sqrt(sg0bid);
+% commented out by T.A. Zha, 10/3/96, to avoid double-counting of scaling and the problem
+% of potential multiple peaks.
+A0bd = zeros(nvar,1);
+A0b = sparse(1:nvar,1:nvar,A0bd,nvar,nvar);
+A0b = A0b'; % making a column in A0b correspond to an equation
+
+%
+
+
+%=================================================
+% Computing the (prior) covariance matrix for the posterior of A0, no data yet
+%=================================================
+%
+%
+% ** weight prior dummy observations
+%phi(1:nvar,:) = (mu(5)^2/mu(1)^2)*phi(1:nvar,:);
+%y(1:nvar,:) = (mu(5)^2/mu(1)^2)*y(1:nvar,:);
+%phi(ndobs,:) = mu
+% modified by CAS 8/6/96. The dummy obs weights are naturally in std units, not var units.
+%
+phi(1:nvar,:) = mu(5)*phi(1:nvar,:); % standard Sims and Zha prior
+y(1:nvar,:) = mu(5)*y(1:nvar,:); % standard Sims and Zha prior
+%----- The following prior designed for GLZ
+%phi(1,:) = 1.00*mu(5)*phi(1,:);
+%phi(2:nvar,:) = 1.02*mu(7)*phi(2:nvar,:);
+%y(1,:) = mu(5)*y(1,:);
+%y(2:nvar,:) = mu(7)*y(2:nvar,:);
+%----------------------------
+%
+phi(nvar+1,:) = mu(6)*phi(nvar+1,:);
+y(nvar+1,:) = mu(6)*y(nvar+1,:);
+
+
+% ** set up the conditional prior variance sg0bi and sgpbi.
+sg0bida = mu(1)^2*sg0bid;
+sgpbida = mu(1)^2*mu(2)^2*sgpbid;
+sgpbida(ncoef) = mu(1)^2*mu(3)^2;
+sgppbd = sgpbida(nvar+1:ncoef); % corresponding to A++, in a Sims-Zha paper
+%%sgppbdi = 1./sgppbd;
+%sgppb = diag(sgppbd);
+
+Hptd = zeros(ncoef);
+Hptdi=Hptd;
+%%Hptd(nvar+1:ncoef,nvar+1:ncoef)=diag(sgppbd);
+%%Hptdi(nvar+1:ncoef,nvar+1:ncoef)=diag(sgppbdi);
+Hptd(ncoef,ncoef)=sgppbd(ncoef-nvar);
+Hptdi(ncoef,ncoef)=1/sgppbd(ncoef-nvar);
+ % condtional on A0i, H_plus_tilde
+sg0bd = sg0bida;
+sg0bdi = 1./sg0bd;
+sg1bd = sgpbida(1:nvar);
+sg1bdi = 1./sg1bd;
+if lags>1
+ sgpp_cbd = sgppbd(1:ncoef-nvar-1);
+ sgpp_cbdi = 1./sgpp_cbd; % _cbd: c -- constant, b -- barred (without), d -- diagonal
+end
+
+
+% ** set up the unconditional prior variance on A0i and A+i
+%%XX = zeros(nvar,strm);
+%%XX(stri,:) = eye(strm); % XX in Gelman, etel, on p479
+%
+% * final conditional prior variance on A+i give that on A0i, and
+% * unconditional variance on A0+
+H0td = diag(sg0bd); % unconditional
+% H_~: td: tilde for ~
+% ** inverse and chol decomp
+H0tdi = diag(sg0bdi);
+
+%
+Hptd(1:nvar,1:nvar)=diag(sg1bd);
+Hptdi(1:nvar,1:nvar)=diag(sg1bdi);
+if lags>1
+ Hptd(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbd);
+ Hptdi(nvar+1:ncoef-1,nvar+1:ncoef-1)=diag(sgpp_cbdi);
+ % condtional on A0i, H_plus_tilde
+end
+
+
+%=================================================
+% Computing the final covariance matrix for the posterior of A0, with the data
+%=================================================
+%
+% ** some common terms
+[u d v]=svd(phi,0); %trial
+% xtx = phi'*phi; %trial
+vd=v.*(ones(size(v,2),1)*diag(d)'); %trial
+xtx=vd*vd';
+% M = eye(fss) - phi*(xtx\phi'); % not used except in line below %trial
+yu = y'*u; %trial
+cxyxx=yu*yu'; %trial
+yty=y'*y;
+ymy=yty-cxyxx; %trial
+%ymy = y'*M*y;
+%cxy = phi'*y; %trial
+cxy = vd*yu'; %trial
+cyx = cxy';
+%cxpy = xtx\cxy; %not used further except in line below
+%cxyxx = cxy'*cxpy;
+
+% ** estimate A+_hat conditional on A0, ncoef*nvar, but not using full prior
+%GlbAph = cxpy*A0h;
+
+
+% common terms
+xxhp = xtx+Hptdi;
+%Lxxhpc = chol(inv(xxhp))'; % lower triangular
+xxhpc = chol(xxhp); % upper triangular but its transpose is lower triagular Choleski
+%A0hbH = A0hb'*H0tdi;
+%====== The following two lines seem incorrect. The third line is supposed to correc the mistake. May 2003.
+% H1p_1 = zeros(ncoef,nvar);
+% H1p_1(1:nvar,:) = Hptdi(1:nvar,1:nvar);
+H1p_1 = Hptdi(:,1:nvar);
+%%Hm = (cyx+H1p_1')*(xxhp\(cxy+H1p_1));
+Hm1 = (cyx+H1p_1')/xxhp;
+Hm1t = Hm1';
+%Hm2 = cxy+H1p_1;
+Hm = Hm1*(cxy+H1p_1);
+%%alpMpyh = A0h(stri,i)'*XX'*(yty+Hptdi(1:nvar,1:nvar)-Hm)*XX;
+Hss = yty+Hptdi(1:nvar,1:nvar)-Hm;
+SpH = (H0tdi+Hss)/fss; % the final S in the exponential part of p(A0|y)
+% divided by nobs in order to make Choleski decomp without optimization
+% or in the form conformable to Waggoner and Zha
+
+% ***
+% *** Form the inverse of covarinace to draw from t- or Gaussian distribution
+% ***
+SpHs = SpH*fss; % for the Wishart draw
+SpHsc = chol(SpHs); % upper triangular where Sphsc' is lower triangular Choleski
+%SpHsic = chol(inv(SpHs))'; % inverse Choleski decomposition -- lower triangular
+
+A0hin = chol(SpH); % upper triangular, inverse of A0h, each column
+ % corresponds to an equation.
+
+
+%----------------------------------------------
+%
+%@@@ The following can be used for other purposes such as forecasting
+%
+%swish = A0hin'; % each row corresponds to an equation
+%A0h = inv(A0hin); % each column corresponds to an equation
+%xa0 = A0h(a0indx);
+
+%%*** form Bh (ncoef*nvar)
+%Aplus = Hm1t*A0h; % estimate of Aplus -- the same as Astrar
+%Bhp = Hm1t;
diff --git a/MatlabFiles/tran_a2b.m b/MatlabFiles/tran_a2b.m
index 7abce26e1cec2690853d973a2e13aa473fb41b7a..318f112ac750a2429097a56f143eab5dc069f36c 100644
--- a/MatlabFiles/tran_a2b.m
+++ b/MatlabFiles/tran_a2b.m
@@ -1,45 +1,45 @@
-function b = tran_a2b(A0,Ui,nvar,n0)
-% b = tran_a2b(A0,Ui,nvar,n0)
-%
-% Transform A0 to free parameters b's. Note: columns correspond to equations
-%
-% A0: nvar-by-nvar, contempareous matrix (columns correspond to equations)
-% Ui: nvar-by-1 cell. In each cell, nvar-by-qi orthonormal basis for the null of the ith
-% equation contemporaneous restriction matrix where qi is the number of free parameters.
-% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
-% of total original parameters and bi is a vector of free parameters. When no
-% restrictions are imposed, we have Ui = I. There must be at least one free
-% parameter left for the ith equation.
-% nvar: number of endogeous variables
-% n0: nvar-by-1, ith element represents the number of free A0 parameters in ith equation
-%----------------
-% b: sum(n0)-by-1 vector of A0 free parameters
-%
-% Tao Zha, February 2000
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-n0cum = cumsum(n0);
-b=zeros(n0cum(end),1);
-for kj = 1:nvar
- if kj==1
- b(1:n0(kj))=Ui{kj}'*A0(:,kj);
- else
- b(n0cum(kj-1)+1:n0cum(kj))=Ui{kj}'*A0(:,kj);
- end
-end
+function b = tran_a2b(A0,Ui,nvar,n0)
+% b = tran_a2b(A0,Ui,nvar,n0)
+%
+% Transform A0 to free parameters b's. Note: columns correspond to equations
+%
+% A0: nvar-by-nvar, contempareous matrix (columns correspond to equations)
+% Ui: nvar-by-1 cell. In each cell, nvar-by-qi orthonormal basis for the null of the ith
+% equation contemporaneous restriction matrix where qi is the number of free parameters.
+% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
+% of total original parameters and bi is a vector of free parameters. When no
+% restrictions are imposed, we have Ui = I. There must be at least one free
+% parameter left for the ith equation.
+% nvar: number of endogeous variables
+% n0: nvar-by-1, ith element represents the number of free A0 parameters in ith equation
+%----------------
+% b: sum(n0)-by-1 vector of A0 free parameters
+%
+% Tao Zha, February 2000
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+n0cum = cumsum(n0);
+b=zeros(n0cum(end),1);
+for kj = 1:nvar
+ if kj==1
+ b(1:n0(kj))=Ui{kj}'*A0(:,kj);
+ else
+ b(n0cum(kj-1)+1:n0cum(kj))=Ui{kj}'*A0(:,kj);
+ end
+end
diff --git a/MatlabFiles/tran_b2a.m b/MatlabFiles/tran_b2a.m
index 22b13961048a0172461d5e75c8c26b0b7debd206..c83ff1aa47a24543456c46f83c39a261ff5cbd8b 100644
--- a/MatlabFiles/tran_b2a.m
+++ b/MatlabFiles/tran_b2a.m
@@ -1,45 +1,45 @@
-function A0 = tran_b2a(b,Ui,nvar,n0)
-% A0 = tran_b2a(b,Ui,nvar,n0)
-%
-% Transform free parameters b's to A0. Note: columns correspond to equations
-%
-% b: sum(n0)-by-1 vector of A0 free parameters
-% Ui: nvar-by-1 cell. In each cell, nvar-by-qi orthonormal basis for the null of the ith
-% equation contemporaneous restriction matrix where qi is the number of free parameters.
-% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
-% of total original parameters and bi is a vector of free parameters. When no
-% restrictions are imposed, we have Ui = I. There must be at least one free
-% parameter left for the ith equation.
-% nvar: number of endogeous variables
-% n0: nvar-by-1, ith element represents the number of free A0 parameters in ith equation
-%----------------
-% A0: nvar-by-nvar, contempareous matrix (columns correspond to equations)
-%
-% Tao Zha, February 2000
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-b=b(:);
-A0 = zeros(nvar);
-n0cum = cumsum(n0);
-for kj = 1:nvar
- if kj==1
- A0(:,kj) = Ui{kj}*b(1:n0(kj));
- else
- A0(:,kj) = Ui{kj}*b(n0cum(kj-1)+1:n0cum(kj));
- end
-end
+function A0 = tran_b2a(b,Ui,nvar,n0)
+% A0 = tran_b2a(b,Ui,nvar,n0)
+%
+% Transform free parameters b's to A0. Note: columns correspond to equations
+%
+% b: sum(n0)-by-1 vector of A0 free parameters
+% Ui: nvar-by-1 cell. In each cell, nvar-by-qi orthonormal basis for the null of the ith
+% equation contemporaneous restriction matrix where qi is the number of free parameters.
+% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
+% of total original parameters and bi is a vector of free parameters. When no
+% restrictions are imposed, we have Ui = I. There must be at least one free
+% parameter left for the ith equation.
+% nvar: number of endogeous variables
+% n0: nvar-by-1, ith element represents the number of free A0 parameters in ith equation
+%----------------
+% A0: nvar-by-nvar, contempareous matrix (columns correspond to equations)
+%
+% Tao Zha, February 2000
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+b=b(:);
+A0 = zeros(nvar);
+n0cum = cumsum(n0);
+for kj = 1:nvar
+ if kj==1
+ A0(:,kj) = Ui{kj}*b(1:n0(kj));
+ else
+ A0(:,kj) = Ui{kj}*b(n0cum(kj-1)+1:n0cum(kj));
+ end
+end
diff --git a/MatlabFiles/xydata.m b/MatlabFiles/xydata.m
index 59bb08118b3d9c61238e105519a8eb8d6aef194c..11da63ffda2af0be2420c047b821a59ec2a32e5d 100644
--- a/MatlabFiles/xydata.m
+++ b/MatlabFiles/xydata.m
@@ -1,190 +1,188 @@
-function [phi,y,fss,xtx,xty,yty,Bols] = xydata(xdgel,lags,mu)
-% [phi,y,xtx,xty,yty,Bols,fss] = xydata(xdgel,lags,mu)
-% Construct matrices X, Y, X'X, etc. so that y = X*B + U, where y is T-by-1, X T-by-ncoef,
-% and B is ncoef-by-1.
-%
-% xdgel: the general matrix of the original data (no manipulation involved)
-% with sample size including lags
-% lags: the lag length in the AR(p) process
-% mu: 2-by-1 vector of hyperparameters for dummy observations (FRA's parameter settings)
-% mu(1): weight on nvar sums of coeffs dummy observations (unit roots); (5)
-% mu(2): weight on single dummy initial observation including constant
-% (cointegration, unit roots, and stationarity); (5)
-%---------------
-% phi: X: T-by-ncoef where ncoef=nvar*lags + constant and T=fss
-% y: Y: T-by-nvar where T=fss
-% fss: effective sample size (including dummies if mu is provided) exclusing all lags
-% xtx: X'X (Data)
-% xty: X'Y (Data)
-% yty: Y'Y (Data)
-% Bols: ncoef-by-1: OLS estimate of B
-% Copyright (c) September 1999 Tao Zha
-%
-% Copyright Kilian and Zha
-%function [Gb,Sbd,Bh,SpH,fss,ndobs,phi,y,nvar,ncoef,xxhpc,a0indx,na0p,...
-% idmat0,idmatpp] = szasbvar(idfile,q_m,lags,xdgel,mu)
-% Now version: [Gb,Sbd,Bh,SpH,fss,ndobs,phi,y,nvar,ncoef,xxhpc,a0indx,na0p,...
-% idmat0,idmatpp] = szasbvar(idfile,q_m,lags,xdgel,mu)
-%
-% Estimating the Bayesian VAR of Sims and Zha with asymmetric prior (as)
-%
-% idfile: Identification filename with rows corresponding to equations.
-% But all the output will be transposed to columns
-% corresponding to equations.
-% q_m: quarter or month
-% lags: the maximum length of lag
-% nhp: number of haperparameters
-% xdgel: the general matrix of the original data (no manipulation involved)
-% with sample size including lags
-% mu: 6-by-1 vector of hyperparameters (the following numbers for Atlanta Fed's forecast)
-% mu(1): overall tightness and also for A0; (0.57)
-% mu(2): relative tightness for A+; (0.13)
-% mu(3): relative tightness for the constant term; (0.1)
-% mu(4): tightness on lag decay; (1)
-% mu(5): weight on nvar sums of coeffs dummy observations (unit roots); (5)
-% mu(6): weight on single dummy initial observation including constant
-% (cointegration, unit roots, and stationarity); (5)
-% --------------------
-% --------------------
-% Gb: cell(nvar,1). Each cell, postmultiplied by A0(:,i), is used to compute A+(:,i)
-% where A+ is k-by-m, where k--ncnoef, m--nvar, if asymmetric prior
-% Sbd: cell(nvar,1). Sbd=diag(S(1), ..., S(m)). Already divided by 'fss' for the
-% posterior of a0 or A0(:) in Waggoner and Zha when one has asymmetric prior.
-% Note,"bd" stands for block diagonal.
-% Bh: reduced form B, k(mp+1)-by-m, column--equation. Bh=NaN if asymmetric prior.
-% In the form of y=X*B+U where B=[B1|B2| ...|Bp|C]
-% where Bi: m-by-m (i=1,..,p--lag length), C: 1-by-ml, constant.
-% SpH: divided by T, the final S in the Waggoner-Zha exponential part of p(A0|y)
-% SpH=NaN if asymmetric prior
-% fss: in-sample-size (for forecasting). Including dummy observations
-% ndobs: number of dummy observations, ndobs=nvar+1
-% phi: X in the form of y = X*B+U. Row: nSmaple-lags+ndobs. Column: ncoef
-% y: y in the form y = X*B+U. Including the dummy observations too,
-% T-lags+ndobs-by-nvar.
-% nvar: number of variables
-% ncoef: number of coefficients in *each* equation. RHS coefficients only, nvar*lags+1
-% xxhpc: chol(X'X+inv(H_p_tilde)): upper triangular but its transpose
-% is lower triagular Choleski
-% a0indx: the index number for free parameters in A0. Column meaning equation
-% na0p: number of free parameters in A0
-% idmat0: identification matrix for A0 with asymmetric prior; column -- equation.
-% idmatpp: asymmetric prior variance for A+ without constant; column -- equation.
-%
-% Revisions by CAS 9/27/96: If we transmit idmat, rather than a0indx, we can scale
-% the elements of idmat0 or idmatp so that it carries information about relative
-% prior variances.
-% Revsions by TZ, 10/13/96: Efficiency improvement by streamlining the previous
-% code according to the general setup in Sims and Zha's IER and according
-% to Zha's notes Forecast (0) (p.3) and Forecast (1) (p.9).
-%
-% Copyright (c) September 1999 by Tao Zha
-%
-% NOTE: "nSample" is something I deleted as an input recently, so it may not
-% be compatible with old programs, 10/15/98 by TAZ.
-%
-% NOTE2: I put "mu" as an input arg and delete "nhp" as an input arg, so it may
-% not be compatible with old programs, 03/06/99 by TAZ
-% Copyright (C) 1997-2012 Christopher A. Sims and Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-[nSample,nvar]=size(xdgel); % sample size including lags and number of variables
-ncoef=nvar*lags+1; % number of coefficients in *each* equation, RHS coefficients only,
- % with only constant
-ndobs=nvar+1; % number of dummy observations
-sb = lags+1; % the beginning of original sample without dummies
-
-if nargin==3 % activate the option of dummy observations
- fss = nSample+ndobs-lags;
- % Effective sample size including dummies but exclusing all lags
-
- %
- % **** nvar prior dummy observations with the sum of coefficients
- % ** construct X for Y = X*B + U where phi = X: (T-lags)*k, Y: (T-lags)*nvar
- % ** columns: k = # of [nvar for 1st lag, ..., nvar for last lag, const]
- % ** Now, T=T+ndobs -- added with "ndobs" dummy observations
- %
- phi = zeros(fss,ncoef);
- const = ones(fss,1);
- const(1:nvar) = zeros(nvar,1);
- % the first nvar periods: no or zero constant (designed for dummies)!
- phi(:,ncoef) = const;
- %
- xdgelint = mean(xdgel(1:lags,:),1); % mean of the first lags initial conditions
- %**** Dummies for phi
- for k=1:nvar
- for m=1:lags
- phi(ndobs,nvar*(m-1)+k) = xdgelint(k);
- phi(k,nvar*(m-1)+k) = xdgelint(k);
- % <<>> multiply hyperparameter later
- end
- end
- %* True data for phi
- for k=1:lags
- phi(ndobs+1:fss,nvar*(k-1)+1:nvar*k) = xdgel(sb-k:nSample-k,:);
- % row: T-lags; column: [nvar for 1st lag, ..., nvar for last lag, const]
- % Thus, # of columns is nvar*lags+1 = ncoef.
- end
- %
- % ** Y with "ndobs" dummies added
- y = zeros(fss,nvar);
- %* Dummies for y
- for k=1:nvar
- y(ndobs,k) = xdgelint(k);
- y(k,k) = xdgelint(k);
- % multiply hyperparameter later
- end
- %* True data for y
- y(ndobs+1:fss,:) = xdgel(sb:nSample,:);
- %
- %*** Dummies once again (finfal version)
- phi(1:nvar,:) = 1*mu(1)*phi(1:nvar,:); % standard Sims and Zha prior
- y(1:nvar,:) = mu(1)*y(1:nvar,:); % standard Sims and Zha prior
- %
- phi(nvar+1,:) = mu(2)*phi(nvar+1,:);
- y(nvar+1,:) = mu(2)*y(nvar+1,:);
-else % dummy observations
- fss = nSample-lags;
- % Effective sample size with no dummies but exclusing all lags
-
- %
- % **** nvar prior dummy observations with the sum of coefficients
- % ** construct X for Y = X*B + U where phi = X: (T-lags)*k, Y: (T-lags)*nvar
- % ** columns: k = # of [nvar for 1st lag, ..., nvar for last lag, const]
- % ** Now, T=T+ndobs -- added with "ndobs" dummy observations
- %
- phi = zeros(fss,ncoef);
- const = ones(fss,1);
- phi(:,ncoef) = const;
- %* True data for phi
- for k=1:lags
- phi(1:fss,nvar*(k-1)+1:nvar*k) = xdgel(sb-k:nSample-k,:);
- % row: T-lags; column: [nvar for 1st lag, ..., nvar for last lag, const]
- % Thus, # of columns is nvar*lags+1 = ncoef.
- end
- %
- %* True data for y
- y(1:fss,:) = xdgel(sb:nSample,:);
-end
-%*** data input for the posterior density
-[xq,xr]=qr(phi,0);
-xtx=xr'*xr; % X'X
-xty=phi'*y; % X'Y
-yty = y'*y; % Y'Y
-
-Bols = xr\(xr'\xty); % inv(X'X)*X'Y
-
+function [phi,y,fss,xtx,xty,yty,Bols] = xydata(xdgel,lags,mu)
+% [phi,y,xtx,xty,yty,Bols,fss] = xydata(xdgel,lags,mu)
+% Construct matrices X, Y, X'X, etc. so that y = X*B + U, where y is T-by-1, X T-by-ncoef,
+% and B is ncoef-by-1.
+%
+% xdgel: the general matrix of the original data (no manipulation involved)
+% with sample size including lags
+% lags: the lag length in the AR(p) process
+% mu: 2-by-1 vector of hyperparameters for dummy observations (FRA's parameter settings)
+% mu(1): weight on nvar sums of coeffs dummy observations (unit roots); (5)
+% mu(2): weight on single dummy initial observation including constant
+% (cointegration, unit roots, and stationarity); (5)
+%---------------
+% phi: X: T-by-ncoef where ncoef=nvar*lags + constant and T=fss
+% y: Y: T-by-nvar where T=fss
+% fss: effective sample size (including dummies if mu is provided) exclusing all lags
+% xtx: X'X (Data)
+% xty: X'Y (Data)
+% yty: Y'Y (Data)
+% Bols: ncoef-by-1: OLS estimate of B
+%
+%function [Gb,Sbd,Bh,SpH,fss,ndobs,phi,y,nvar,ncoef,xxhpc,a0indx,na0p,...
+% idmat0,idmatpp] = szasbvar(idfile,q_m,lags,xdgel,mu)
+% Now version: [Gb,Sbd,Bh,SpH,fss,ndobs,phi,y,nvar,ncoef,xxhpc,a0indx,na0p,...
+% idmat0,idmatpp] = szasbvar(idfile,q_m,lags,xdgel,mu)
+%
+% Estimating the Bayesian VAR of Sims and Zha with asymmetric prior (as)
+%
+% idfile: Identification filename with rows corresponding to equations.
+% But all the output will be transposed to columns
+% corresponding to equations.
+% q_m: quarter or month
+% lags: the maximum length of lag
+% nhp: number of haperparameters
+% xdgel: the general matrix of the original data (no manipulation involved)
+% with sample size including lags
+% mu: 6-by-1 vector of hyperparameters (the following numbers for Atlanta Fed's forecast)
+% mu(1): overall tightness and also for A0; (0.57)
+% mu(2): relative tightness for A+; (0.13)
+% mu(3): relative tightness for the constant term; (0.1)
+% mu(4): tightness on lag decay; (1)
+% mu(5): weight on nvar sums of coeffs dummy observations (unit roots); (5)
+% mu(6): weight on single dummy initial observation including constant
+% (cointegration, unit roots, and stationarity); (5)
+% --------------------
+% --------------------
+% Gb: cell(nvar,1). Each cell, postmultiplied by A0(:,i), is used to compute A+(:,i)
+% where A+ is k-by-m, where k--ncnoef, m--nvar, if asymmetric prior
+% Sbd: cell(nvar,1). Sbd=diag(S(1), ..., S(m)). Already divided by 'fss' for the
+% posterior of a0 or A0(:) in Waggoner and Zha when one has asymmetric prior.
+% Note,"bd" stands for block diagonal.
+% Bh: reduced form B, k(mp+1)-by-m, column--equation. Bh=NaN if asymmetric prior.
+% In the form of y=X*B+U where B=[B1|B2| ...|Bp|C]
+% where Bi: m-by-m (i=1,..,p--lag length), C: 1-by-ml, constant.
+% SpH: divided by T, the final S in the Waggoner-Zha exponential part of p(A0|y)
+% SpH=NaN if asymmetric prior
+% fss: in-sample-size (for forecasting). Including dummy observations
+% ndobs: number of dummy observations, ndobs=nvar+1
+% phi: X in the form of y = X*B+U. Row: nSmaple-lags+ndobs. Column: ncoef
+% y: y in the form y = X*B+U. Including the dummy observations too,
+% T-lags+ndobs-by-nvar.
+% nvar: number of variables
+% ncoef: number of coefficients in *each* equation. RHS coefficients only, nvar*lags+1
+% xxhpc: chol(X'X+inv(H_p_tilde)): upper triangular but its transpose
+% is lower triagular Choleski
+% a0indx: the index number for free parameters in A0. Column meaning equation
+% na0p: number of free parameters in A0
+% idmat0: identification matrix for A0 with asymmetric prior; column -- equation.
+% idmatpp: asymmetric prior variance for A+ without constant; column -- equation.
+%
+% Revisions by CAS 9/27/96: If we transmit idmat, rather than a0indx, we can scale
+% the elements of idmat0 or idmatp so that it carries information about relative
+% prior variances.
+% Revsions by TZ, 10/13/96: Efficiency improvement by streamlining the previous
+% code according to the general setup in Sims and Zha's IER and according
+% to Zha's notes Forecast (0) (p.3) and Forecast (1) (p.9).
+%
+%
+% NOTE: "nSample" is something I deleted as an input recently, so it may not
+% be compatible with old programs, 10/15/98 by TAZ.
+%
+% NOTE2: I put "mu" as an input arg and delete "nhp" as an input arg, so it may
+% not be compatible with old programs, 03/06/99 by TAZ
+%
+%
+% Copyright (C) 1997-2012 Lutz Kilian and Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+[nSample,nvar]=size(xdgel); % sample size including lags and number of variables
+ncoef=nvar*lags+1; % number of coefficients in *each* equation, RHS coefficients only,
+ % with only constant
+ndobs=nvar+1; % number of dummy observations
+sb = lags+1; % the beginning of original sample without dummies
+
+if nargin==3 % activate the option of dummy observations
+ fss = nSample+ndobs-lags;
+ % Effective sample size including dummies but exclusing all lags
+
+ %
+ % **** nvar prior dummy observations with the sum of coefficients
+ % ** construct X for Y = X*B + U where phi = X: (T-lags)*k, Y: (T-lags)*nvar
+ % ** columns: k = # of [nvar for 1st lag, ..., nvar for last lag, const]
+ % ** Now, T=T+ndobs -- added with "ndobs" dummy observations
+ %
+ phi = zeros(fss,ncoef);
+ const = ones(fss,1);
+ const(1:nvar) = zeros(nvar,1);
+ % the first nvar periods: no or zero constant (designed for dummies)!
+ phi(:,ncoef) = const;
+ %
+ xdgelint = mean(xdgel(1:lags,:),1); % mean of the first lags initial conditions
+ %**** Dummies for phi
+ for k=1:nvar
+ for m=1:lags
+ phi(ndobs,nvar*(m-1)+k) = xdgelint(k);
+ phi(k,nvar*(m-1)+k) = xdgelint(k);
+ % <<>> multiply hyperparameter later
+ end
+ end
+ %* True data for phi
+ for k=1:lags
+ phi(ndobs+1:fss,nvar*(k-1)+1:nvar*k) = xdgel(sb-k:nSample-k,:);
+ % row: T-lags; column: [nvar for 1st lag, ..., nvar for last lag, const]
+ % Thus, # of columns is nvar*lags+1 = ncoef.
+ end
+ %
+ % ** Y with "ndobs" dummies added
+ y = zeros(fss,nvar);
+ %* Dummies for y
+ for k=1:nvar
+ y(ndobs,k) = xdgelint(k);
+ y(k,k) = xdgelint(k);
+ % multiply hyperparameter later
+ end
+ %* True data for y
+ y(ndobs+1:fss,:) = xdgel(sb:nSample,:);
+ %
+ %*** Dummies once again (finfal version)
+ phi(1:nvar,:) = 1*mu(1)*phi(1:nvar,:); % standard Sims and Zha prior
+ y(1:nvar,:) = mu(1)*y(1:nvar,:); % standard Sims and Zha prior
+ %
+ phi(nvar+1,:) = mu(2)*phi(nvar+1,:);
+ y(nvar+1,:) = mu(2)*y(nvar+1,:);
+else % dummy observations
+ fss = nSample-lags;
+ % Effective sample size with no dummies but exclusing all lags
+
+ %
+ % **** nvar prior dummy observations with the sum of coefficients
+ % ** construct X for Y = X*B + U where phi = X: (T-lags)*k, Y: (T-lags)*nvar
+ % ** columns: k = # of [nvar for 1st lag, ..., nvar for last lag, const]
+ % ** Now, T=T+ndobs -- added with "ndobs" dummy observations
+ %
+ phi = zeros(fss,ncoef);
+ const = ones(fss,1);
+ phi(:,ncoef) = const;
+ %* True data for phi
+ for k=1:lags
+ phi(1:fss,nvar*(k-1)+1:nvar*k) = xdgel(sb-k:nSample-k,:);
+ % row: T-lags; column: [nvar for 1st lag, ..., nvar for last lag, const]
+ % Thus, # of columns is nvar*lags+1 = ncoef.
+ end
+ %
+ %* True data for y
+ y(1:fss,:) = xdgel(sb:nSample,:);
+end
+%*** data input for the posterior density
+[xq,xr]=qr(phi,0);
+xtx=xr'*xr; % X'X
+xty=phi'*y; % X'Y
+yty = y'*y; % Y'Y
+
+Bols = xr\(xr'\xty); % inv(X'X)*X'Y
+
diff --git a/MatlabFiles/zimpulse.m b/MatlabFiles/zimpulse.m
index 70db3814dffd6270ea72cea7d2791708fe1b5ce5..1829a17861c5d6fceb1025877bb7997be1474b21 100644
--- a/MatlabFiles/zimpulse.m
+++ b/MatlabFiles/zimpulse.m
@@ -1,72 +1,71 @@
-function imf = zimpulse(Bh,swish,nn)
-% Computing impulse functions with
-% imf = zimpulse(Bh,swish,nn)
-% where imf is in a format that is the SAME as in RATS.
-% Column: nvar responses to 1st shock,
-% nvar responses to 2nd shock, and so on.
-% Row: steps of impulse responses.
-% Bh is the estimated reduced form coefficient in the form
-% Y(T*nvar) = XB + U, X: T*k, B: k*nvar. The matrix
-% form or dimension is the same as "Bh" from the function "sye";
-% swish is the inv(A0) in the structural model A(L)y(t) = e(t).
-% nn is the numbers of inputs [nvar,lags,# of impulse responses].
-% Written by Tao Zha
-%
-% Copyright (c) 1994 by Tao Zha
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-nvar = nn(1);
-lags = nn(2);
-imstep = nn(3); % number of steps for impulse responses
-
-Ah = Bh';
-% Row: nvar equations
-% Column: 1st lag (with nvar variables) to lags (with nvar variables) + const = k.
-
-imf = zeros(imstep,nvar*nvar);
-% Column: nvar responses to 1st shock, nvar responses to 2nd shock, and so on.
-% Row: steps of impulse responses.
-M = zeros(nvar*(lags+1),nvar);
-% Stack lags M's in the order of, e.g., [Mlags, ..., M2,M1;M0]
-M(1:nvar,:) = swish;
-Mtem = M(1:nvar,:); % temporary M.
-% first (initial) responses to 1 standard deviation shock. Row: responses; Column: shocks
-% * put in the form of "imf"
-imf(1,:) = Mtem(:)';
-
-t = 1;
-ims1 = min([imstep-1 lags]);
-while t <= ims1
- Mtem = Ah(:,1:nvar*t)*M(1:nvar*t,:);
- % Row: nvar equations, each for the nvar variables at tth lag
- M(nvar+1:nvar*(t+1),:)=M(1:nvar*t,:);
- M(1:nvar,:) = Mtem;
- imf(t+1,:) = Mtem(:)';
- % stack imf with each step, Row: 6 var to 1st shock, 6 var to 2nd shock, etc.
- t= t+1;
-end
-
-for t = lags+1:imstep-1
- Mtem = Ah(:,1:nvar*lags)*M(1:nvar*lags,:);
- % Row: nvar equations, each for the nvar variables at tth lag
- M(nvar+1:nvar*(t+1),:) = M(1:nvar*t,:);
- M(1:nvar,:)=Mtem;
- imf(t+1,:) = Mtem(:)';
- % stack imf with each step, Row: 6 var to 1st shock, 6 var to 2nd shock, etc.
-end
\ No newline at end of file
+function imf = zimpulse(Bh,swish,nn)
+% Computing impulse functions with
+% imf = zimpulse(Bh,swish,nn)
+% where imf is in a format that is the SAME as in RATS.
+% Column: nvar responses to 1st shock,
+% nvar responses to 2nd shock, and so on.
+% Row: steps of impulse responses.
+% Bh is the estimated reduced form coefficient in the form
+% Y(T*nvar) = XB + U, X: T*k, B: k*nvar. The matrix
+% form or dimension is the same as "Bh" from the function "sye";
+% swish is the inv(A0) in the structural model A(L)y(t) = e(t).
+% nn is the numbers of inputs [nvar,lags,# of impulse responses].
+% Written by Tao Zha
+%
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+nvar = nn(1);
+lags = nn(2);
+imstep = nn(3); % number of steps for impulse responses
+
+Ah = Bh';
+% Row: nvar equations
+% Column: 1st lag (with nvar variables) to lags (with nvar variables) + const = k.
+
+imf = zeros(imstep,nvar*nvar);
+% Column: nvar responses to 1st shock, nvar responses to 2nd shock, and so on.
+% Row: steps of impulse responses.
+M = zeros(nvar*(lags+1),nvar);
+% Stack lags M's in the order of, e.g., [Mlags, ..., M2,M1;M0]
+M(1:nvar,:) = swish;
+Mtem = M(1:nvar,:); % temporary M.
+% first (initial) responses to 1 standard deviation shock. Row: responses; Column: shocks
+% * put in the form of "imf"
+imf(1,:) = Mtem(:)';
+
+t = 1;
+ims1 = min([imstep-1 lags]);
+while t <= ims1
+ Mtem = Ah(:,1:nvar*t)*M(1:nvar*t,:);
+ % Row: nvar equations, each for the nvar variables at tth lag
+ M(nvar+1:nvar*(t+1),:)=M(1:nvar*t,:);
+ M(1:nvar,:) = Mtem;
+ imf(t+1,:) = Mtem(:)';
+ % stack imf with each step, Row: 6 var to 1st shock, 6 var to 2nd shock, etc.
+ t= t+1;
+end
+
+for t = lags+1:imstep-1
+ Mtem = Ah(:,1:nvar*lags)*M(1:nvar*lags,:);
+ % Row: nvar equations, each for the nvar variables at tth lag
+ M(nvar+1:nvar*(t+1),:) = M(1:nvar*t,:);
+ M(1:nvar,:)=Mtem;
+ imf(t+1,:) = Mtem(:)';
+ % stack imf with each step, Row: 6 var to 1st shock, 6 var to 2nd shock, etc.
+end
diff --git a/MatlabFiles/zroot.m b/MatlabFiles/zroot.m
index 033590ba213d609b52941e4870ba0d0f4d9a3fd1..01f464ea26946600b411f010ced14ca498ca4799 100644
--- a/MatlabFiles/zroot.m
+++ b/MatlabFiles/zroot.m
@@ -1,46 +1,46 @@
-function zstar = zroot(B)
-% zroot(B); find roots of a matrix polynomial
-% B: the 3 dimensional array B, e.g. B(:,:,1) is B_1, B(:,:,2) is B_2 and
-% B(:,:,p) is B_p. Note: rows correpond to equations. See Judge (1), pp. 763-764.
-% For the system of equations:
-% y_t = B_1*y_{t-1} + ... + B_p*Y_{t-p} + u_t.
-% The corresponding matrix polynomial is:
-% det(eye(m)-B_1*z-...-B_p*z^p) = 0
-% zroot(B) solves for the roots of this above polynomial. The matrices B_1, B_2,....B_p
-% are passed through B. The degree of the matrix polynomial is determined
-% by the size of the third dimension of B.
-% Each of the B(:,:,i) must be an m by m matrix.
-
-% ZROOT was written by Clark A. Burdick of the research
-% department of the Federal Reserve Bank of Atlanta.
-% Original: July 31, 1997
-% Last Modified: July 31, 1997
-
-% TO BE DONE:
-% Better bullet proofing
-% Copyright (C) 1997-2012 Tao Zha
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-[a b c] = size(B);
-syms z;
-thepoly = eye(size(B(:,:,1)))-B(:,:,1)*z;
-for i=2:c
- thepoly = thepoly-B(:,:,i)*z^i;
-end
-zstar=roots(sym2poly(det(thepoly)));
-
+function zstar = zroot(B)
+% zroot(B); find roots of a matrix polynomial
+% B: the 3 dimensional array B, e.g. B(:,:,1) is B_1, B(:,:,2) is B_2 and
+% B(:,:,p) is B_p. Note: rows correpond to equations. See Judge (1), pp. 763-764.
+% For the system of equations:
+% y_t = B_1*y_{t-1} + ... + B_p*Y_{t-p} + u_t.
+% The corresponding matrix polynomial is:
+% det(eye(m)-B_1*z-...-B_p*z^p) = 0
+% zroot(B) solves for the roots of this above polynomial. The matrices B_1, B_2,....B_p
+% are passed through B. The degree of the matrix polynomial is determined
+% by the size of the third dimension of B.
+% Each of the B(:,:,i) must be an m by m matrix.
+
+% ZROOT was written by Clark A. Burdick of the research
+% department of the Federal Reserve Bank of Atlanta.
+% Original: July 31, 1997
+% Last Modified: July 31, 1997
+
+% TO BE DONE:
+% Better bullet proofing
+%
+% Copyright (C) 1997-2012 Tao Zha
+%
+% This free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% It is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% If you did not received a copy of the GNU General Public License
+% with this software, see <http://www.gnu.org/licenses/>.
+%
+
+
+[a b c] = size(B);
+syms z;
+thepoly = eye(size(B(:,:,1)))-B(:,:,1)*z;
+for i=2:c
+ thepoly = thepoly-B(:,:,i)*z^i;
+end
+zstar=roots(sym2poly(det(thepoly)));
+