diff --git a/matlab/AHessian.m b/matlab/AHessian.m
index 2a632e7fd7561033139903e34123732999b61b08..f9d9db6e0ca667eb13c3da412dd412207f9aeb55 100644
--- a/matlab/AHessian.m
+++ b/matlab/AHessian.m
@@ -26,107 +26,107 @@ function [AHess, DLIK, LIK] = AHessian(T,R,Q,H,P,Y,DT,DYss,DOm,DH,DP,start,mf,ka
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- k = size(DT,3); % number of structural parameters
- smpl = size(Y,2); % Sample size.
- pp = size(Y,1); % Maximum number of observed variables.
- mm = size(T,2); % Number of state variables.
- a = zeros(mm,1); % State vector.
- Om = R*Q*transpose(R); % Variance of R times the vector of structural innovations.
- t = 0; % Initialization of the time index.
- oldK = 0;
- notsteady = 1; % Steady state flag.
- F_singular = 1;
+k = size(DT,3); % number of structural parameters
+smpl = size(Y,2); % Sample size.
+pp = size(Y,1); % Maximum number of observed variables.
+mm = size(T,2); % Number of state variables.
+a = zeros(mm,1); % State vector.
+Om = R*Q*transpose(R); % Variance of R times the vector of structural innovations.
+t = 0; % Initialization of the time index.
+oldK = 0;
+notsteady = 1; % Steady state flag.
+F_singular = 1;
lik = zeros(smpl,1); % Initialization of the vector gathering the densities.
LIK = Inf; % Default value of the log likelihood.
if nargout > 1
DLIK = zeros(k,1); % Initialization of the score.
end
- AHess = zeros(k,k); % Initialization of the Hessian
- Da = zeros(mm,k); % State vector.
- Dv = zeros(length(mf),k);
-
+AHess = zeros(k,k); % Initialization of the Hessian
+Da = zeros(mm,k); % State vector.
+Dv = zeros(length(mf),k);
+
% for ii = 1:k
-% DOm = DR(:,:,ii)*Q*transpose(R) + R*DQ(:,:,ii)*transpose(R) + R*Q*transpose(DR(:,:,ii));
+% DOm = DR(:,:,ii)*Q*transpose(R) + R*DQ(:,:,ii)*transpose(R) + R*Q*transpose(DR(:,:,ii));
% end
-
- while notsteady && t<smpl
- t = t+1;
- v = Y(:,t)-a(mf);
- F = P(mf,mf) + H;
- if rcond(F) < kalman_tol
- if ~all(abs(F(:))<kalman_tol)
- return
- else
- a = T*a;
- P = T*P*transpose(T)+Om;
- end
+
+while notsteady && t<smpl
+ t = t+1;
+ v = Y(:,t)-a(mf);
+ F = P(mf,mf) + H;
+ if rcond(F) < kalman_tol
+ if ~all(abs(F(:))<kalman_tol)
+ return
else
- F_singular = 0;
- iF = inv(F);
- K = P(:,mf)*iF;
- lik(t) = log(det(F))+transpose(v)*iF*v;
-
- [DK,DF,DP1] = computeDKalman(T,DT,DOm,P,DP,DH,mf,iF,K);
-
- for ii = 1:k
- Dv(:,ii) = -Da(mf,ii) - DYss(mf,ii);
- Da(:,ii) = DT(:,:,ii)*(a+K*v) + T*(Da(:,ii)+DK(:,:,ii)*v + K*Dv(:,ii));
- if t>=start && nargout > 1
- DLIK(ii,1) = DLIK(ii,1) + trace( iF*DF(:,:,ii) ) + 2*Dv(:,ii)'*iF*v - v'*(iF*DF(:,:,ii)*iF)*v;
- end
- end
- vecDPmf = reshape(DP(mf,mf,:),[],k);
-% iPmf = inv(P(mf,mf));
- if t>=start
- AHess = AHess + Dv'*iF*Dv + .5*(vecDPmf' * kron(iF,iF) * vecDPmf);
- end
- a = T*(a+K*v);
- P = T*(P-K*P(mf,:))*transpose(T)+Om;
- DP = DP1;
+ a = T*a;
+ P = T*P*transpose(T)+Om;
end
- notsteady = max(max(abs(K-oldK))) > riccati_tol;
- oldK = K;
- end
+ else
+ F_singular = 0;
+ iF = inv(F);
+ K = P(:,mf)*iF;
+ lik(t) = log(det(F))+transpose(v)*iF*v;
- if F_singular
- error('The variance of the forecast error remains singular until the end of the sample')
- end
+ [DK,DF,DP1] = computeDKalman(T,DT,DOm,P,DP,DH,mf,iF,K);
-
- if t < smpl
- t0 = t+1;
- while t < smpl
- t = t+1;
- v = Y(:,t)-a(mf);
- for ii = 1:k
- Dv(:,ii) = -Da(mf,ii)-DYss(mf,ii);
- Da(:,ii) = DT(:,:,ii)*(a+K*v) + T*(Da(:,ii)+DK(:,:,ii)*v + K*Dv(:,ii));
- if t>=start && nargout >1
- DLIK(ii,1) = DLIK(ii,1) + trace( iF*DF(:,:,ii) ) + 2*Dv(:,ii)'*iF*v - v'*(iF*DF(:,:,ii)*iF)*v;
- end
- end
- if t>=start
- AHess = AHess + Dv'*iF*Dv;
- end
- a = T*(a+K*v);
- lik(t) = transpose(v)*iF*v;
+ for ii = 1:k
+ Dv(:,ii) = -Da(mf,ii) - DYss(mf,ii);
+ Da(:,ii) = DT(:,:,ii)*(a+K*v) + T*(Da(:,ii)+DK(:,:,ii)*v + K*Dv(:,ii));
+ if t>=start && nargout > 1
+ DLIK(ii,1) = DLIK(ii,1) + trace( iF*DF(:,:,ii) ) + 2*Dv(:,ii)'*iF*v - v'*(iF*DF(:,:,ii)*iF)*v;
+ end
+ end
+ vecDPmf = reshape(DP(mf,mf,:),[],k);
+ % iPmf = inv(P(mf,mf));
+ if t>=start
+ AHess = AHess + Dv'*iF*Dv + .5*(vecDPmf' * kron(iF,iF) * vecDPmf);
end
- AHess = AHess + .5*(smpl-t0+1)*(vecDPmf' * kron(iF,iF) * vecDPmf);
- if nargout > 1
+ a = T*(a+K*v);
+ P = T*(P-K*P(mf,:))*transpose(T)+Om;
+ DP = DP1;
+ end
+ notsteady = max(max(abs(K-oldK))) > riccati_tol;
+ oldK = K;
+end
+
+if F_singular
+ error('The variance of the forecast error remains singular until the end of the sample')
+end
+
+
+if t < smpl
+ t0 = t+1;
+ while t < smpl
+ t = t+1;
+ v = Y(:,t)-a(mf);
for ii = 1:k
-% DLIK(ii,1) = DLIK(ii,1) + (smpl-t0+1)*trace( iF*DF(:,:,ii) );
+ Dv(:,ii) = -Da(mf,ii)-DYss(mf,ii);
+ Da(:,ii) = DT(:,:,ii)*(a+K*v) + T*(Da(:,ii)+DK(:,:,ii)*v + K*Dv(:,ii));
+ if t>=start && nargout >1
+ DLIK(ii,1) = DLIK(ii,1) + trace( iF*DF(:,:,ii) ) + 2*Dv(:,ii)'*iF*v - v'*(iF*DF(:,:,ii)*iF)*v;
+ end
+ end
+ if t>=start
+ AHess = AHess + Dv'*iF*Dv;
end
+ a = T*(a+K*v);
+ lik(t) = transpose(v)*iF*v;
+ end
+ AHess = AHess + .5*(smpl-t0+1)*(vecDPmf' * kron(iF,iF) * vecDPmf);
+ if nargout > 1
+ for ii = 1:k
+ % DLIK(ii,1) = DLIK(ii,1) + (smpl-t0+1)*trace( iF*DF(:,:,ii) );
end
- lik(t0:smpl) = lik(t0:smpl) + log(det(F));
-% for ii = 1:k;
-% for jj = 1:ii
-% H(ii,jj) = trace(iPmf*(.5*DP(mf,mf,ii)*iPmf*DP(mf,mf,jj) + Dv(:,ii)*Dv(:,jj)'));
-% end
-% end
- end
-
-AHess = -AHess;
+ end
+ lik(t0:smpl) = lik(t0:smpl) + log(det(F));
+ % for ii = 1:k;
+ % for jj = 1:ii
+ % H(ii,jj) = trace(iPmf*(.5*DP(mf,mf,ii)*iPmf*DP(mf,mf,jj) + Dv(:,ii)*Dv(:,jj)'));
+ % end
+ % end
+end
+
+AHess = -AHess;
if nargout > 1
DLIK = DLIK/2;
end
@@ -134,15 +134,15 @@ end
lik = (lik + pp*log(2*pi))/2;
LIK = sum(lik(start:end)); % Minus the log-likelihood.
-% end of main function
-
+ % end of main function
+
function [DK,DF,DP1] = computeDKalman(T,DT,DOm,P,DP,DH,mf,iF,K)
- k = size(DT,3);
- tmp = P-K*P(mf,:);
+k = size(DT,3);
+tmp = P-K*P(mf,:);
for ii = 1:k
- DF(:,:,ii) = DP(mf,mf,ii) + DH(:,:,ii);
+ DF(:,:,ii) = DP(mf,mf,ii) + DH(:,:,ii);
DiF(:,:,ii) = -iF*DF(:,:,ii)*iF;
DK(:,:,ii) = DP(:,mf,ii)*iF + P(:,mf)*DiF(:,:,ii);
Dtmp = DP(:,:,ii) - DK(:,:,ii)*P(mf,:) - K*DP(mf,:,ii);
@@ -150,6 +150,3 @@ for ii = 1:k
end
% end of computeDKalman
-
-
-
\ No newline at end of file
diff --git a/matlab/AIM/SPAimerr.m b/matlab/AIM/SPAimerr.m
index 9bffb54aa9f0646275b898fd270e3b34994af25e..6467479287eaab62df1caf12537c31d9ba4af85f 100644
--- a/matlab/AIM/SPAimerr.m
+++ b/matlab/AIM/SPAimerr.m
@@ -29,7 +29,7 @@ function e = SPAimerr(c)
% Journal of Economic Dynamics and Control, 2010, vol. 34, issue 3,
% pages 472-489
- if(c==1) e='Aim: unique solution.';
+if(c==1) e='Aim: unique solution.';
elseif(c==2) e='Aim: roots not correctly computed by real_schur.';
elseif(c==3) e='Aim: too many big roots.';
elseif(c==35) e='Aim: too many big roots, and q(:,right) is singular.';
diff --git a/matlab/AIM/SPAmalg.m b/matlab/AIM/SPAmalg.m
index 6da8ac52428e0e4fbb9351050bcdcdea0b6723fb..c371a91b0f1b2cf11eb5f6541959fa7dcbc4b810 100644
--- a/matlab/AIM/SPAmalg.m
+++ b/matlab/AIM/SPAmalg.m
@@ -1,5 +1,5 @@
function [b,rts,ia,nexact,nnumeric,lgroots,aimcode] = ...
- SPAmalg(h,neq,nlag,nlead,condn,uprbnd)
+ SPAmalg(h,neq,nlag,nlead,condn,uprbnd)
% [b,rts,ia,nexact,nnumeric,lgroots,aimcode] = ...
% SPAmalg(h,neq,nlag,nlead,condn,uprbnd)
%
@@ -8,9 +8,9 @@ function [b,rts,ia,nexact,nnumeric,lgroots,aimcode] = ...
% roots. This procedure will fail if the companion matrix is
% defective and does not have a linearly independent set of
% eigenvectors associated with the big roots.
-%
+%
% Input arguments:
-%
+%
% h Structural coefficient matrix (neq,neq*(nlag+1+nlead)).
% neq Number of equations.
% nlag Number of lags.
@@ -19,9 +19,9 @@ function [b,rts,ia,nexact,nnumeric,lgroots,aimcode] = ...
% by numeric_shift and reduced_form.
% uprbnd Inclusive upper bound for the modulus of roots
% allowed in the reduced form.
-%
+%
% Output arguments:
-%
+%
% b Reduced form coefficient matrix (neq,neq*nlag).
% rts Roots returned by eig.
% ia Dimension of companion matrix (number of non-trivial
@@ -57,7 +57,7 @@ function [b,rts,ia,nexact,nnumeric,lgroots,aimcode] = ...
% pages 472-489
b=[];rts=[];ia=[];nexact=[];nnumeric=[];lgroots=[];aimcode=[];
-if(nlag<1 || nlead<1)
+if(nlag<1 || nlead<1)
error('Aim_eig: model must have at least one lag and one lead');
end
% Initialization.
@@ -75,17 +75,17 @@ if (iq>qrows)
end
[a,ia,js] = SPBuild_a(h,qcols,neq);
if (ia ~= 0)
- if any(any(isnan(a))) || any(any(isinf(a)))
+ if any(any(isnan(a))) || any(any(isinf(a)))
disp('A is NAN or INF')
- aimcode=63;
- return
- end
+ aimcode=63;
+ return
+ end
[w,rts,lgroots,flag_trouble]=SPEigensystem(a,uprbnd,min(length(js),qrows-iq+1));
if flag_trouble==1
- disp('Problem in SPEIG');
+ disp('Problem in SPEIG');
aimcode=64;
return
- end
+ end
q = SPCopy_w(q,w,js,iq,qrows);
end
test=nexact+nnumeric+lgroots;
diff --git a/matlab/AIM/SPBuild_a.m b/matlab/AIM/SPBuild_a.m
index eb12473a8afef4a70b8088a7fc40233508253ed1..5701d3bf118856cadee3522ab5122fe910ceac3a 100644
--- a/matlab/AIM/SPBuild_a.m
+++ b/matlab/AIM/SPBuild_a.m
@@ -41,9 +41,9 @@ hs(:,left) = -hs(:,right)\hs(:,left);
a = zeros(qcols,qcols);
if(qcols > neq)
- eyerows = 1:qcols-neq;
- eyecols = neq+1:qcols;
- a(eyerows,eyecols) = eye(qcols-neq);
+ eyerows = 1:qcols-neq;
+ eyecols = neq+1:qcols;
+ a(eyerows,eyecols) = eye(qcols-neq);
end
hrows = qcols-neq+1:qcols;
a(hrows,:) = hs(:,left);
@@ -51,14 +51,14 @@ a(hrows,:) = hs(:,left);
% Delete inessential lags and build index array js. js indexes the
% columns in the big transition matrix that correspond to the
% essential lags in the model. They are the columns of q that will
-% get the unstable left eigenvectors.
+% get the unstable left eigenvectors.
js = 1:qcols;
zerocols = sum(abs(a)) == 0;
while( any(zerocols) )
- a(:,zerocols) = [];
- a(zerocols,:) = [];
- js(zerocols) = [];
- zerocols = sum(abs(a)) == 0;
+ a(:,zerocols) = [];
+ a(zerocols,:) = [];
+ js(zerocols) = [];
+ zerocols = sum(abs(a)) == 0;
end
ia = length(js);
diff --git a/matlab/AIM/SPCopy_w.m b/matlab/AIM/SPCopy_w.m
index 714a1e27466116e2d35df8838c535cbd8e519009..82dafe40ab50e044249b9db1fb00eb347103b704 100644
--- a/matlab/AIM/SPCopy_w.m
+++ b/matlab/AIM/SPCopy_w.m
@@ -2,7 +2,7 @@ function q = SPCopy_w(q,w,js,iq,qrows)
% q = SPCopy_w(q,w,js,iq,qrows)
%
% Copy the eigenvectors corresponding to the largest roots into the
-% remaining empty rows and columns js of q
+% remaining empty rows and columns js of q
% Author: Gary Anderson
% Original file downloaded from:
@@ -30,7 +30,7 @@ function q = SPCopy_w(q,w,js,iq,qrows)
if(iq < qrows)
- lastrows = iq+1:qrows;
- wrows = 1:length(lastrows);
- q(lastrows,js) = w(:,wrows)';
+ lastrows = iq+1:qrows;
+ wrows = 1:length(lastrows);
+ q(lastrows,js) = w(:,wrows)';
end
diff --git a/matlab/AIM/SPEigensystem.m b/matlab/AIM/SPEigensystem.m
index 16855401a521704a22778f62c1dce3edcaa39f99..61dd4606907159c34bb91442f9d9b7feb001aa5a 100644
--- a/matlab/AIM/SPEigensystem.m
+++ b/matlab/AIM/SPEigensystem.m
@@ -30,7 +30,7 @@ function [w,rts,lgroots,flag_trouble] = SPEigensystem(a,uprbnd,rowsLeft)
% Journal of Economic Dynamics and Control, 2010, vol. 34, issue 3,
% pages 472-489
-opts.disp=0;
+opts.disp=0;
% next block is commented out because eigs() intermitently returns different rts
%try
% [w,d] = eigs(a',rowsLeft,'LM',opts);
@@ -39,24 +39,24 @@ opts.disp=0;
% [mag,k] = sort(-mag);
% rts = rts(k);
%catch
- %disp('Catch in SPE');
- %pause(0.5);
- %aStr=datestr(clock);
- %eval(['save ' regexprep(aStr,' ','') ' a']);
- try
- [w,d]=eig(a');
- catch
- lasterr
- w=[];rts=[];lgroots=[];
- flag_trouble=1;
- return
- end
- rts = diag(d);
- mag = abs(rts);
- [mag,k] = sort(-mag);
- rts = rts(k);
+%disp('Catch in SPE');
+%pause(0.5);
+%aStr=datestr(clock);
+%eval(['save ' regexprep(aStr,' ','') ' a']);
+try
+ [w,d]=eig(a');
+catch
+ lasterr
+ w=[];rts=[];lgroots=[];
+ flag_trouble=1;
+ return
+end
+rts = diag(d);
+mag = abs(rts);
+[mag,k] = sort(-mag);
+rts = rts(k);
%end
-flag_trouble=0;
+flag_trouble=0;
%ws=SPSparse(w);
ws=sparse(w);
@@ -65,7 +65,7 @@ ws = ws(:,k);
% Given a complex conjugate pair of vectors W = [w1,w2], there is a
% nonsingular matrix D such that W*D = real(W) + imag(W). That is to
% say, W and real(W)+imag(W) span the same subspace, which is all
-% that aim cares about.
+% that aim cares about.
ws = real(ws) + imag(ws);
diff --git a/matlab/AIM/SPExact_shift.m b/matlab/AIM/SPExact_shift.m
index c9034e319bd3d309b4e49ebb53d6fa28a1e261d1..5ba49aa11430450905c99ff4d91cb73a1ffcf4e3 100644
--- a/matlab/AIM/SPExact_shift.m
+++ b/matlab/AIM/SPExact_shift.m
@@ -36,12 +36,11 @@ right = qcols+1:qcols+neq;
zerorows = find( sum(abs( hs(:,right)' ))==0 );
while( any(zerorows) && iq <= qrows )
- nz = length(zerorows);
- q(iq+1:iq+nz,:) = hs(zerorows,left);
- hs(zerorows,:) = SPShiftright(hs(zerorows,:),neq);
- iq = iq + nz;
- nexact = nexact + nz;
- zerorows = find( sum(abs( hs(:,right)' ))==0 );
+ nz = length(zerorows);
+ q(iq+1:iq+nz,:) = hs(zerorows,left);
+ hs(zerorows,:) = SPShiftright(hs(zerorows,:),neq);
+ iq = iq + nz;
+ nexact = nexact + nz;
+ zerorows = find( sum(abs( hs(:,right)' ))==0 );
end
h=full(hs);
-
diff --git a/matlab/AIM/SPNumeric_shift.m b/matlab/AIM/SPNumeric_shift.m
index cc1e1825f7588a1481d6fabe986c86b8d1b60616..27049ca0cc41a629fe0a2d6c54fbe7e1044705a8 100644
--- a/matlab/AIM/SPNumeric_shift.m
+++ b/matlab/AIM/SPNumeric_shift.m
@@ -37,14 +37,14 @@ right = qcols+1:qcols+neq;
zerorows = find( abs(diag(R)) <= condn );
while( any(zerorows) && iq <= qrows )
- h=sparse(h);
- Q=sparse(Q);
- h = Q'*h;
- nz = length(zerorows);
- q(iq+1:iq+nz,:) = h(zerorows,left);
- h(zerorows,:) = SPShiftright( h(zerorows,:), neq );
- iq = iq + nz;
- nnumeric = nnumeric + nz;
- [Q,R,E] = qr( full(h(:,right)) );
- zerorows = find( abs(diag(R)) <= condn );
+ h=sparse(h);
+ Q=sparse(Q);
+ h = Q'*h;
+ nz = length(zerorows);
+ q(iq+1:iq+nz,:) = h(zerorows,left);
+ h(zerorows,:) = SPShiftright( h(zerorows,:), neq );
+ iq = iq + nz;
+ nnumeric = nnumeric + nz;
+ [Q,R,E] = qr( full(h(:,right)) );
+ zerorows = find( abs(diag(R)) <= condn );
end
diff --git a/matlab/AIM/SPObstruct.m b/matlab/AIM/SPObstruct.m
index 8620ca1b9b562ae859f342dfa093bc4337b29a68..9d9206501fd3cdea1401bff9e821be703a9e08fa 100644
--- a/matlab/AIM/SPObstruct.m
+++ b/matlab/AIM/SPObstruct.m
@@ -2,7 +2,7 @@ function scof = SPObstruct(cof,cofb,neq,nlag,nlead)
% scof = SPObstruct(cof,cofb,neq,nlag,nlead)
%
% Construct the coefficients in the observable structure.
-%
+%
% Input arguments:
%
% cof structural coefficients
@@ -51,17 +51,17 @@ qs=sparse(q);
qs(1:rc,1:cc) = sparse(cofb);
qcols = neq*(nlag+nlead);
-if( nlead > 1 )
- for i = 1:nlead-1
- rows = i*neq + (1:neq);
- qs(rows,:) = SPShiftright( qs((rows-neq),:), neq );
- end
+if( nlead > 1 )
+ for i = 1:nlead-1
+ rows = i*neq + (1:neq);
+ qs(rows,:) = SPShiftright( qs((rows-neq),:), neq );
+ end
end
l = (1: neq*nlag);
r = (neq*nlag+1: neq*(nlag+nlead));
- qs(:,l) = - qs(:,r) \ qs(:,l);
+qs(:,l) = - qs(:,r) \ qs(:,l);
minus = 1: neq*(nlag+1);
plus = neq*(nlag+1)+1: neq*(nlag+1+nlead);
diff --git a/matlab/AIM/SPReduced_form.m b/matlab/AIM/SPReduced_form.m
index 79ab3711db83529af8b3fd36f9676bee1ebbaa4e..e7d60a1801c031dddc864cdeda845a1d435951dc 100644
--- a/matlab/AIM/SPReduced_form.m
+++ b/matlab/AIM/SPReduced_form.m
@@ -38,7 +38,7 @@ if(nonsing)
b = qs(1:neq,1:bcols);
b = full(b);
else %rescale by dividing row by maximal qr element
- %'inverse condition number small, rescaling '
+ %'inverse condition number small, rescaling '
themax=max(abs(qs(:,right)),[],2);
oneover = diag(1 ./ themax);
nonsing = rcond(full(oneover *qs(:,right))) > condn;
@@ -48,4 +48,3 @@ else %rescale by dividing row by maximal qr element
b = full(b);
end
end
-
diff --git a/matlab/AIM/SPShiftright.m b/matlab/AIM/SPShiftright.m
index 8299ce516b302d9b591e34eb1ed2afb33bb91363..70490848435230595ed56bfa00e24a81c7092a8c 100644
--- a/matlab/AIM/SPShiftright.m
+++ b/matlab/AIM/SPShiftright.m
@@ -3,7 +3,7 @@ function [y] = SPShiftright(x,n)
% [y] = shiftright(x,n)
%
% Shift the rows of x to the right by n columns, leaving zeros in the
-% first n columns.
+% first n columns.
% Original author: Gary Anderson
% Original file downloaded from:
diff --git a/matlab/AIM/dynAIMsolver1.m b/matlab/AIM/dynAIMsolver1.m
index 2e23e0222004d70c4341fd48fbf81147f7f0719f..3589153951901ea5ef9ebaaf5520dc0b2743dea1 100644
--- a/matlab/AIM/dynAIMsolver1.m
+++ b/matlab/AIM/dynAIMsolver1.m
@@ -1,20 +1,20 @@
function [dr,aimcode,rts]=dynAIMsolver1(jacobia_,M_,dr)
% function [dr,aimcode]=dynAIMsolver1(jacobia_,M_,dr)
-% Maps Dynare jacobia to AIM 1st order model solver designed and developed by Gary ANderson
+% Maps Dynare jacobia to AIM 1st order model solver designed and developed by Gary ANderson
% and derives the solution for gy=dr.hgx and gu=dr.hgu from the AIM outputs
-% AIM System is given as a sum:
-% i.e. for i=-$...+& SUM(Hi*xt+i)= �*zt, t = 0, . . . ,?
+% AIM System is given as a sum:
+% i.e. for i=-$...+& SUM(Hi*xt+i)= £*zt, t = 0, . . . ,?
% and its input as single array of matrices: [H-$... Hi ... H+&]
-% and its solution as xt=SUM( Bi*xt+i) + @*�*zt for i=-$...-1
-% with the output in form bb=[B-$... Bi ... B-1] and @=inv(Ho+H1*B-1)
-% Dynare jacobian = [fy'-$... fy'i ... fy'+& fu']
-% where [fy'-$... fy'i ... fy'+&]=[H-$... Hi ... H+&] and fu'= �
+% and its solution as xt=SUM( Bi*xt+i) + @*£*zt for i=-$...-1
+% with the output in form bb=[B-$... Bi ... B-1] and @=inv(Ho+H1*B-1)
+% Dynare jacobian = [fy'-$... fy'i ... fy'+& fu']
+% where [fy'-$... fy'i ... fy'+&]=[H-$... Hi ... H+&] and fu'= £
%
% INPUTS
-% jacobia_ [matrix] 1st order derivative of the model
+% jacobia_ [matrix] 1st order derivative of the model
% dr [matlab structure] Decision rules for stochastic simulations.
-% M_ [matlab structure] Definition of the model.
-%
+% M_ [matlab structure] Definition of the model.
+%
% OUTPUTS
% dr [matlab structure] Decision rules for stochastic simulations.
% aimcode [integer] 1: the model defines variables uniquely
@@ -31,20 +31,20 @@ function [dr,aimcode,rts]=dynAIMsolver1(jacobia_,M_,dr)
% (c==63) e='Aim: A is NAN or INF.';
% (c==64) e='Aim: Problem in SPEIG.';
% else e='Aimerr: return code not properly specified';
-%
+%
% SPECIAL REQUIREMENTS
-% Dynare use:
+% Dynare use:
% 1) the lognormal block in DR1 is being invoked for some models and changing
% values of ghx and ghy. We need to return the AIM output
% values before that block and run the block with the current returned values
-% of gy (i.e. dr.ghx) and gu (dr.ghu) if it is needed even when the AIM is used
+% of gy (i.e. dr.ghx) and gu (dr.ghu) if it is needed even when the AIM is used
% (it does not depend on mjdgges output).
-%
-% 2) passing in aa={Q'|1}*jacobia_ can produce ~ one order closer
-% results to the Dynare solutiion then when if plain jacobia_ is passed,
-% i.e. diff < e-14 for aa and diff < *e-13 for jacobia_ if Q' is used.
%
-% GP July 2008
+% 2) passing in aa={Q'|1}*jacobia_ can produce ~ one order closer
+% results to the Dynare solutiion then when if plain jacobia_ is passed,
+% i.e. diff < e-14 for aa and diff < *e-13 for jacobia_ if Q' is used.
+%
+% GP July 2008
% Copyright (C) 2008-2012 Dynare Team
%
@@ -69,8 +69,8 @@ lags=M_.maximum_endo_lag; % no of lags and leads
leads=M_.maximum_endo_lead;
klen=(leads+lags+1); % total lenght
theAIM_H=zeros(neq, neq*klen); % alloc space
-lli=M_.lead_lag_incidence';
-% "sparse" the compact jacobia into AIM H aray of matrices
+lli=M_.lead_lag_incidence';
+% "sparse" the compact jacobia into AIM H aray of matrices
% without exogenous shoc
theAIM_H(:,find(lli(:)))=jacobia_(:,nonzeros(lli(:)));
condn = 1.e-10;%SPAmalg uses this in zero tests
@@ -102,7 +102,7 @@ if aimcode==1 %if OK
col_order=[((i-1)*neq)+dr.order_var' col_order];
end
bb_ord= bb(dr.order_var,col_order); % derive ordered gy
-
+
% variables are present in the state space at the lag at which they
% appear and at all smaller lags. The are ordered from smaller to
% higher lag (reversed order of M_.lead_lag_incidence rows for lagged
@@ -117,7 +117,7 @@ if aimcode==1 %if OK
%theH0= theAIM_H (:,lags*neq+1: (lags+1)*neq);
% theHP= theAIM_H (:,(M_.maximum_endo_lag+1)*neq+1: (M_.maximum_endo_lag+2)*neq);
%theHP= theAIM_H (:,(lags+1)*neq+1: (lags+2)*neq);
- theAIM_Psi= - jacobia_(:, size(nonzeros(lli(:)))+1:end);%
+ theAIM_Psi= - jacobia_(:, size(nonzeros(lli(:)))+1:end);%
%? = inv(H0 + H1B1)
%phi= (theH0+theHP*sparse(bb(:,(lags-1)*neq+1:end)))\eye( neq);
%AIM_ghu=phi*theAIM_Psi;
@@ -137,8 +137,8 @@ else
if aimcode < 1 || aimcode > 5 % too big exception, use mjdgges
error('Error in AIM: aimcode=%d ; %s', aimcode, err);
end
- % if aimcode > 5
+ % if aimcode > 5
% disp(['Error in AIM: aimcode=' sprintf('%d : %s',aimcode, err)]);
% aimcode=5;
- % end
+ % end
end
diff --git a/matlab/AIM_first_order_solver.m b/matlab/AIM_first_order_solver.m
index eabe791738f73f68667d1eb62a7e1d429947b8b7..893deefb0c43ce611bb9553fbec8851b7f628edc 100644
--- a/matlab/AIM_first_order_solver.m
+++ b/matlab/AIM_first_order_solver.m
@@ -67,35 +67,34 @@ function [dr,info]=AIM_first_order_solver(jacobia,M,dr,qz_criterium)
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
- info = 0;
-
- [dr,aimcode]=dynAIMsolver1(jacobia,M,dr);
- if aimcode ~=1
- info(1) = convertAimCodeToInfo(aimCode); %convert to be in the 100 range
- info(2) = 1.0e+8;
- return
- end
- A = kalman_transition_matrix(dr,M.nstatic+(1:M.nspred), 1:M.nspred,...
- M.exo_nbr);
- dr.eigval = eig(A);
- disp(dr.eigval)
- nd = size(dr.kstate,1);
- nba = nd-sum( abs(dr.eigval) < qz_criterium );
+info = 0;
- nsfwrd = M.nsfwrd;
+[dr,aimcode]=dynAIMsolver1(jacobia,M,dr);
- if nba ~= nsfwrd
- temp = sort(abs(dr.eigval));
- if nba > nsfwrd
- temp = temp(nd-nba+1:nd-nsfwrd)-1-qz_criterium;
- info(1) = 3;
- elseif nba < nsfwrd
- temp = temp(nd-nsfwrd+1:nd-nba)-1-qz_criterium;
- info(1) = 4;
- end
- info(2) = temp'*temp;
- return
- end
+if aimcode ~=1
+ info(1) = convertAimCodeToInfo(aimCode); %convert to be in the 100 range
+ info(2) = 1.0e+8;
+ return
+end
+A = kalman_transition_matrix(dr,M.nstatic+(1:M.nspred), 1:M.nspred,...
+ M.exo_nbr);
+dr.eigval = eig(A);
+disp(dr.eigval)
+nd = size(dr.kstate,1);
+nba = nd-sum( abs(dr.eigval) < qz_criterium );
+nsfwrd = M.nsfwrd;
+
+if nba ~= nsfwrd
+ temp = sort(abs(dr.eigval));
+ if nba > nsfwrd
+ temp = temp(nd-nba+1:nd-nsfwrd)-1-qz_criterium;
+ info(1) = 3;
+ elseif nba < nsfwrd
+ temp = temp(nd-nsfwrd+1:nd-nba)-1-qz_criterium;
+ info(1) = 4;
+ end
+ info(2) = temp'*temp;
+ return
+end
diff --git a/matlab/CheckPath.m b/matlab/CheckPath.m
index c8300551307d7887bc118e9657bf2de7cde86ec8..bd770e0eb0e495b6915e37ab6fa40f1e91f4ce90 100644
--- a/matlab/CheckPath.m
+++ b/matlab/CheckPath.m
@@ -2,7 +2,7 @@ function [DirectoryName, info] = CheckPath(type,dname)
% Creates the subfolder "./M_.dname/type" if it does not exist yet.
%
% INPUTS
-% type [string] Name of the subfolder.
+% type [string] Name of the subfolder.
% dname [string] Name of the directory
%
% OUTPUTS
diff --git a/matlab/CutSample.m b/matlab/CutSample.m
index 5ec0881a8677eda180b302911d4f6a6a35c701aa..697e56071007514796c3568dcbc4f8548064d7ed 100644
--- a/matlab/CutSample.m
+++ b/matlab/CutSample.m
@@ -66,7 +66,7 @@ if (TotalNumberOfMhFiles-1)-(FirstMhFile+1)+1 > 0
elseif TotalNumberOfMhFiles == 1
record.KeepedDraws.Distribution = [];
elseif TotalNumberOfMhFiles == 2 && FirstMhFile > 1
- record.KeepedDraws.Distribution = [MAX_nruns-FirstLine+1 ; record.MhDraws(end,3)];
+ record.KeepedDraws.Distribution = [MAX_nruns-FirstLine+1 ; record.MhDraws(end,3)];
end
% Save updated mh-history file.
diff --git a/matlab/DsgeSmoother.m b/matlab/DsgeSmoother.m
index b25dcf1ef2d2b96713c73ade228d518590ef6c95..997904afcf14acf81b3612aebb3fc18ee2c9950e 100644
--- a/matlab/DsgeSmoother.m
+++ b/matlab/DsgeSmoother.m
@@ -1,8 +1,8 @@
function [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK,T,R,P,PK,decomp,trend_addition,state_uncertainty,M_,oo_,options_,bayestopt_] = DsgeSmoother(xparam1,gend,Y,data_index,missing_value,M_,oo_,options_,bayestopt_,estim_params_)
-% Estimation of the smoothed variables and innovations.
-%
-% INPUTS
-% o xparam1 [double] (p*1) vector of (estimated) parameters.
+% Estimation of the smoothed variables and innovations.
+%
+% INPUTS
+% o xparam1 [double] (p*1) vector of (estimated) parameters.
% o gend [integer] scalar specifying the number of observations ==> varargin{1}.
% o data [double] (n*T) matrix of data.
% o data_index [cell] 1*smpl cell of column vectors of indices.
@@ -12,7 +12,7 @@ function [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK,T,R,P,PK,de
% o options_ [structure] describing the options
% o bayestopt_ [structure] describing the priors
% o estim_params_ [structure] characterizing parameters to be estimated
-%
+%
% OUTPUTS
% o alphahat [double] (m*T) matrix, smoothed endogenous variables (a_{t|T}) (decision-rule order)
% o etahat [double] (r*T) matrix, smoothed structural shocks (r>=n is the number of shocks).
@@ -29,31 +29,31 @@ function [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK,T,R,P,PK,de
% matrices (meaningless for periods 1:d) (decision-rule order)
% o decomp (K*m*r*(T+K)) 4D array of shock decomposition of k-step ahead
% filtered variables (decision-rule order)
-% o trend_addition [double] (n*T) pure trend component; stored in options_.varobs order
+% o trend_addition [double] (n*T) pure trend component; stored in options_.varobs order
% o state_uncertainty [double] (K,K,T) array, storing the uncertainty
% about the smoothed state (decision-rule order)
% o M_ [structure] decribing the model
% o oo_ [structure] storing the results
% o options_ [structure] describing the options
% o bayestopt_ [structure] describing the priors
-%
+%
% Notes:
% m: number of endogenous variables (M_.endo_nbr)
% T: number of Time periods (options_.nobs)
% r: number of strucural shocks (M_.exo_nbr)
% n: number of observables (length(options_.varobs))
% K: maximum forecast horizon (max(options_.nk))
-%
+%
% To get variables that are stored in decision rule order in order of declaration
% as in M_.endo_names, ones needs code along the lines of:
% variables_declaration_order(dr.order_var,:) = alphahat
-%
-% Defines bayestopt_.mf = bayestopt_.smoother_mf (positions of observed variables
-% and requested smoothed variables in decision rules (decision rule order)) and
+%
+% Defines bayestopt_.mf = bayestopt_.smoother_mf (positions of observed variables
+% and requested smoothed variables in decision rules (decision rule order)) and
% passes it back via global variable
-%
-% ALGORITHM
-% Diffuse Kalman filter (Durbin and Koopman)
+%
+% ALGORITHM
+% Diffuse Kalman filter (Durbin and Koopman)
%
% SPECIAL REQUIREMENTS
% None
@@ -97,7 +97,7 @@ end
%------------------------------------------------------------------------------
% 2. call model setup & reduction program
%------------------------------------------------------------------------------
-oldoo.restrict_var_list = oo_.dr.restrict_var_list;
+oldoo.restrict_var_list = oo_.dr.restrict_var_list;
oldoo.restrict_columns = oo_.dr.restrict_columns;
oo_.dr.restrict_var_list = bayestopt_.smoother_var_list;
oo_.dr.restrict_columns = bayestopt_.smoother_restrict_columns;
@@ -133,8 +133,8 @@ mf = bayestopt_.mf;
% ------------------------------------------------------------------------------
% 3. Initial condition of the Kalman filter
% ------------------------------------------------------------------------------
-%
-% Here, Pinf and Pstar are determined. If the model is stationary, determine
+%
+% Here, Pinf and Pstar are determined. If the model is stationary, determine
% Pstar as the solution of the Lyapunov equation and set Pinf=[] (Notation follows
% Koopman/Durbin (2003), Journal of Time Series Analysis 24(1))
%
@@ -169,7 +169,7 @@ elseif options_.lik_init == 3 % Diffuse Kalman filter
kalman_algo = 3;
else
if ~all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
- %Augment state vector (follows Section 6.4.3 of DK (2012))
+ %Augment state vector (follows Section 6.4.3 of DK (2012))
expanded_state_vector_for_univariate_filter=1;
T = blkdiag(T,zeros(vobs));
np = size(T,1);
@@ -240,32 +240,32 @@ if kalman_algo == 1 || kalman_algo == 3
end
if kalman_algo == 2 || kalman_algo == 4
- if ~all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
- if ~expanded_state_vector_for_univariate_filter
- %Augment state vector (follows Section 6.4.3 of DK (2012))
- expanded_state_vector_for_univariate_filter=1;
- Z = [Z, eye(vobs)];
- ST = blkdiag(ST,zeros(vobs));
- np = size(ST,1);
- Q = blkdiag(Q,H);
- R1 = blkdiag(R,eye(vobs));
- if kalman_algo == 4
- %recompute Schur state space transformation with
- %expanded state space
- [Pstar,Pinf] = compute_Pinf_Pstar(mf,ST,R1,Q,options_.qz_criterium);
- else
- Pstar = blkdiag(Pstar,H);
- if ~isempty(Pinf)
- Pinf = blkdiag(Pinf,zeros(vobs));
- end
- end
- %now reset H to 0
- H = zeros(vobs,vobs);
+ if ~all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
+ if ~expanded_state_vector_for_univariate_filter
+ %Augment state vector (follows Section 6.4.3 of DK (2012))
+ expanded_state_vector_for_univariate_filter=1;
+ Z = [Z, eye(vobs)];
+ ST = blkdiag(ST,zeros(vobs));
+ np = size(ST,1);
+ Q = blkdiag(Q,H);
+ R1 = blkdiag(R,eye(vobs));
+ if kalman_algo == 4
+ %recompute Schur state space transformation with
+ %expanded state space
+ [Pstar,Pinf] = compute_Pinf_Pstar(mf,ST,R1,Q,options_.qz_criterium);
else
- %do nothing, state vector was already expanded
+ Pstar = blkdiag(Pstar,H);
+ if ~isempty(Pinf)
+ Pinf = blkdiag(Pinf,zeros(vobs));
+ end
end
+ %now reset H to 0
+ H = zeros(vobs,vobs);
+ else
+ %do nothing, state vector was already expanded
end
-
+ end
+
[alphahat,epsilonhat,etahat,ahat,P,aK,PK,decomp,state_uncertainty] = missing_DiffuseKalmanSmootherH3_Z(ST, ...
Z,R1,Q,diag(H), ...
Pinf,Pstar,data1,vobs,np,smpl,data_index, ...
@@ -282,7 +282,7 @@ if expanded_state_vector_for_univariate_filter && (kalman_algo == 2 || kalman_al
ahat = ahat(k,:);
aK = aK(:,k,:,:);
epsilonhat=etahat(end-vobs+1:end,:);
- etahat=etahat(1:end-vobs,:);
+ etahat=etahat(1:end-vobs,:);
if ~isempty(PK)
PK = PK(:,k,k,:);
end
diff --git a/matlab/GetAllPosteriorDraws.m b/matlab/GetAllPosteriorDraws.m
index 88e80f9182121cfe739d460349c2fe93b3151738..d1019fb66cc296ee3cba5bc90c1956d4755a874d 100644
--- a/matlab/GetAllPosteriorDraws.m
+++ b/matlab/GetAllPosteriorDraws.m
@@ -5,14 +5,14 @@ function Draws = GetAllPosteriorDraws(column, FirstMhFile, FirstLine, TotalNumbe
%
% INPUTS
% column: column
-% FirstMhFile: first mh file
+% FirstMhFile: first mh file
% FirstLine: first line
-% TotalNumberOfMhFile: total number of mh file
+% TotalNumberOfMhFile: total number of mh file
% NumberOfDraws: number of draws
% OUTPUTS
% Draws: draws from posterior distribution
-%
+%
% SPECIAL REQUIREMENTS
% none
@@ -55,7 +55,7 @@ if nblck>1 && nargin<6
iline = 1;
end
end
- else
+ else
for blck = 1:nblck
iline=iline0;
for file = FirstMhFile:TotalNumberOfMhFile
diff --git a/matlab/GetOneDraw.m b/matlab/GetOneDraw.m
index 82d4c088fa04f0b195c98f5aa89c389a096b7d13..a3e0f9f4b2779884df996f68f2cabcd358ecba02 100644
--- a/matlab/GetOneDraw.m
+++ b/matlab/GetOneDraw.m
@@ -5,11 +5,11 @@ function [xparams, logpost] = GetOneDraw(type)
% INPUTS
% type: [string] 'posterior': draw from MCMC draws
% 'prior': draw from prior
-%
+%
% OUTPUTS
% xparams: vector of estimated parameters (drawn from posterior or prior distribution)
% logpost: log of the posterior density of this parameter vector
-%
+%
% SPECIAL REQUIREMENTS
% none
diff --git a/matlab/GetPosteriorMeanVariance.m b/matlab/GetPosteriorMeanVariance.m
index d1e2935873b0e5a969446e4c2572c31756469140..285b9448023e727a5ad558f2312ddb7720c2e2ab 100644
--- a/matlab/GetPosteriorMeanVariance.m
+++ b/matlab/GetPosteriorMeanVariance.m
@@ -16,38 +16,38 @@ function [mean,variance] = GetPosteriorMeanVariance(M,drop)
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
- MetropolisFolder = CheckPath('metropolis',M.dname);
- FileName = M.fname;
- BaseName = [MetropolisFolder filesep FileName];
- load_last_mh_history_file(MetropolisFolder, FileName);
- NbrDraws = sum(record.MhDraws(:,1));
- NbrFiles = sum(record.MhDraws(:,2));
- NbrBlocks = record.Nblck;
- mean = 0;
- variance = 0;
-
- NbrKeptDraws = 0;
- for i=1:NbrBlocks
- NbrDrawsCurrentBlock = 0;
- for j=1:NbrFiles
- o = load([BaseName '_mh' int2str(j) '_blck' int2str(i),'.mat']);
- NbrDrawsCurrentFile = size(o.x2,1);
- if NbrDrawsCurrentBlock + NbrDrawsCurrentFile <= drop*NbrDraws
- NbrDrawsCurrentBlock = NbrDrawsCurrentBlock + NbrDrawsCurrentFile;
- continue
- elseif NbrDrawsCurrentBlock < drop*NbrDraws
- FirstDraw = ceil(drop*NbrDraws - NbrDrawsCurrentBlock + 1);
- x2 = o.x2(FirstDraw:end,:);
- else
- x2 = o.x2;
- end
- NbrKeptDrawsCurrentFile = size(x2,1);
- %recursively compute mean and variance
- mean = (NbrKeptDraws*mean + sum(x2)')/(NbrKeptDraws+NbrKeptDrawsCurrentFile);
- x2Demeaned = bsxfun(@minus,x2,mean');
- variance = (NbrKeptDraws*variance + x2Demeaned'*x2Demeaned)/(NbrKeptDraws+NbrKeptDrawsCurrentFile);
+
+MetropolisFolder = CheckPath('metropolis',M.dname);
+FileName = M.fname;
+BaseName = [MetropolisFolder filesep FileName];
+load_last_mh_history_file(MetropolisFolder, FileName);
+NbrDraws = sum(record.MhDraws(:,1));
+NbrFiles = sum(record.MhDraws(:,2));
+NbrBlocks = record.Nblck;
+mean = 0;
+variance = 0;
+
+NbrKeptDraws = 0;
+for i=1:NbrBlocks
+ NbrDrawsCurrentBlock = 0;
+ for j=1:NbrFiles
+ o = load([BaseName '_mh' int2str(j) '_blck' int2str(i),'.mat']);
+ NbrDrawsCurrentFile = size(o.x2,1);
+ if NbrDrawsCurrentBlock + NbrDrawsCurrentFile <= drop*NbrDraws
NbrDrawsCurrentBlock = NbrDrawsCurrentBlock + NbrDrawsCurrentFile;
- NbrKeptDraws = NbrKeptDraws + NbrKeptDrawsCurrentFile;
+ continue
+ elseif NbrDrawsCurrentBlock < drop*NbrDraws
+ FirstDraw = ceil(drop*NbrDraws - NbrDrawsCurrentBlock + 1);
+ x2 = o.x2(FirstDraw:end,:);
+ else
+ x2 = o.x2;
end
+ NbrKeptDrawsCurrentFile = size(x2,1);
+ %recursively compute mean and variance
+ mean = (NbrKeptDraws*mean + sum(x2)')/(NbrKeptDraws+NbrKeptDrawsCurrentFile);
+ x2Demeaned = bsxfun(@minus,x2,mean');
+ variance = (NbrKeptDraws*variance + x2Demeaned'*x2Demeaned)/(NbrKeptDraws+NbrKeptDrawsCurrentFile);
+ NbrDrawsCurrentBlock = NbrDrawsCurrentBlock + NbrDrawsCurrentFile;
+ NbrKeptDraws = NbrKeptDraws + NbrKeptDrawsCurrentFile;
end
+end
diff --git a/matlab/GetPosteriorParametersStatistics.m b/matlab/GetPosteriorParametersStatistics.m
index 1ad0bd0271914121cc17b256a1b71cfe02b39be4..6bea8d66a11b72c82eca2172f504e0435015fb79 100644
--- a/matlab/GetPosteriorParametersStatistics.m
+++ b/matlab/GetPosteriorParametersStatistics.m
@@ -1,17 +1,17 @@
function oo_ = GetPosteriorParametersStatistics(estim_params_, M_, options_, bayestopt_, oo_, pnames)
% This function prints and saves posterior estimates after the mcmc
-% (+updates of oo_ & TeX output).
-%
-% INPUTS
-% estim_params_ [structure]
+% (+updates of oo_ & TeX output).
+%
+% INPUTS
+% estim_params_ [structure]
% M_ [structure]
% options_ [structure]
% bayestopt_ [structure]
% oo_ [structure]
% pnames [char] Array of char, names of the prior shapes available
-%
-% OUTPUTS
-% oo_ [structure]
+%
+% OUTPUTS
+% oo_ [structure]
%
% SPECIAL REQUIREMENTS
% None.
@@ -34,7 +34,7 @@ function oo_ = GetPosteriorParametersStatistics(estim_params_, M_, options_, bay
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
%if ~options_.mh_replic && options_.load_mh_file
-% load([M_.fname '_results.mat'],'oo_');
+% load([M_.fname '_results.mat'],'oo_');
%end
TeX = options_.TeX;
@@ -48,7 +48,7 @@ nx = nvx+nvn+ncx+ncn+np;
MetropolisFolder = CheckPath('metropolis',M_.dname);
OutputFolder = CheckPath('Output',M_.dname);
-FileName = M_.fname;
+FileName = M_.fname;
load_last_mh_history_file(MetropolisFolder,FileName);
@@ -108,14 +108,14 @@ if np
[post_mean, post_median, post_var, hpd_interval, post_deciles, ...
density] = posterior_moments(Draws,1,options_.mh_conf_sig);
name = bayestopt_.name{ip};
- oo_ = Filloo(oo_,name,type,post_mean,hpd_interval,post_median,post_var,post_deciles,density);
+ oo_ = Filloo(oo_,name,type,post_mean,hpd_interval,post_median,post_var,post_deciles,density);
end
end
disp(sprintf(pformat,header_width,name,bayestopt_.p1(ip),...
post_mean, ...
hpd_interval, ...
pnames(bayestopt_.pshape(ip)+1,:), ...
- bayestopt_.p2(ip)));
+ bayestopt_.p2(ip)));
if TeX
k = estim_params_.param_vals(i,1);
name = deblank(M_.param_names_tex(k,:));
@@ -171,7 +171,7 @@ if nvx
ip = ip+1;
end
if TeX
- TeXEnd(fid,2,'standard deviation of structural shocks');
+ TeXEnd(fid,2,'standard deviation of structural shocks');
end
end
if nvn
@@ -213,7 +213,7 @@ if nvn
ip = ip+1;
end
if TeX
- TeXEnd(fid,3,'standard deviation of measurement errors');
+ TeXEnd(fid,3,'standard deviation of measurement errors');
end
end
if ncx
@@ -314,12 +314,12 @@ if ncn
if TeX
name = ['(',deblank(M_.endo_names_tex(k1,:)) ',' deblank(M_.endo_names_tex(k2,:)),')'];
TeXCore(fid,name,deblank(pnames(bayestopt_.pshape(ip)+1,:)),bayestopt_.p1(ip),...
- bayestopt_.p2(ip),post_mean,sqrt(post_var),hpd_interval);
+ bayestopt_.p2(ip),post_mean,sqrt(post_var),hpd_interval);
end
ip = ip+1;
end
if TeX
- TeXEnd(fid,5,'correlation of measurement errors');
+ TeXEnd(fid,5,'correlation of measurement errors');
end
end
@@ -359,7 +359,7 @@ fid = fidTeX;
function TeXCore(fid,name,shape,priormean,priorstd,postmean,poststd,hpd)
-fprintf(fid,['$%s$ & %s & %7.3f & %6.4f & %7.3f& %6.4f & %7.4f & %7.4f \\\\ \n'],...
+fprintf(fid,['$%s$ & %s & %7.3f & %6.4f & %7.3f& %6.4f & %7.4f & %7.4f \\\\ \n'],...
name,...
shape,...
priormean,...
@@ -371,7 +371,7 @@ fprintf(fid,['$%s$ & %s & %7.3f & %6.4f & %7.3f& %6.4f & %7.4f & %7.4f \\\\ \n']
function TeXEnd(fid,fnum,title)
-fprintf(fid,'\\end{longtable}\n ');
+fprintf(fid,'\\end{longtable}\n ');
fprintf(fid,'\\end{center}\n');
fprintf(fid,'%% End of TeX file.\n');
fclose(fid);
diff --git a/matlab/MakeAllFigures.m b/matlab/MakeAllFigures.m
index d24f4fadac8e1193dba959a368ad2fec6af7cffa..74e94e4cbe4f6b1847d838e889dc55a26be19f80 100644
--- a/matlab/MakeAllFigures.m
+++ b/matlab/MakeAllFigures.m
@@ -19,11 +19,11 @@ function MakeAllFigures(NumberOfPlots,Caption,FigureProperties,Info)
global M_ options_
-FigHandle = figure('Name',FigureProperties.Name);
+FigHandle = figure('Name',FigureProperties.Name);
NAMES = cell(NumberOfPlots,1);
if options_.TeX
- TeXNAMES = cell(NumberOfPlots,1);
+ TeXNAMES = cell(NumberOfPlots,1);
end
if NumberOfPlots == 9
@@ -53,7 +53,7 @@ elseif NumberOfPlots == 2
elseif NumberOfPlots == 1
nr = 1;
nc = 1;
-end
+end
for plt = 1:NumberOfPlots
eval(['NumberOfCurves = Info.Box' int2str(plt) '.Number;'])
@@ -138,7 +138,7 @@ for plt = 1:NumberOfPlots
set(hh,'Color','r','LineStyle','-','LineWidth',2)
%
%
- end
+ end
end
axis([xmin xmax ymin ymax])
title(NAMES{plt})
@@ -150,14 +150,14 @@ if Info.SaveFormat.Eps
if isempty(Info.SaveFormat.Name)
eval(['print -depsc2 ' M_.fname Info.SaveFormat.GenericName int2str(Info.SaveFormat.Number) '.eps']);
else
- eval(['print -depsc2 ' M_.fname Info.SaveFormat.GenericName Info.SaveFormat.Name '.eps']);
+ eval(['print -depsc2 ' M_.fname Info.SaveFormat.GenericName Info.SaveFormat.Name '.eps']);
end
end
if Info.SaveFormat.Pdf && ~isoctave
if isempty(Info.SaveFormat.Name)
eval(['print -dpdf ' M_.fname Info.SaveFormat.GenericName int2str(Info.SaveFormat.Number)]);
else
- eval(['print -dpdf ' M_.fname Info.SaveFormat.GenericName Info.SaveFormat.Name]);
+ eval(['print -dpdf ' M_.fname Info.SaveFormat.GenericName Info.SaveFormat.Name]);
end
end
if Info.SaveFormat.Fig && ~isoctave
diff --git a/matlab/PlotPosteriorDistributions.m b/matlab/PlotPosteriorDistributions.m
index 4358b07e4a3c682dca39a342d7b75c5e2fdc56e9..84ff775ea3612180655b6da05cd4b8c662c5563a 100644
--- a/matlab/PlotPosteriorDistributions.m
+++ b/matlab/PlotPosteriorDistributions.m
@@ -4,15 +4,15 @@ function oo_ = PlotPosteriorDistributions(estim_params_, M_, options_, bayestopt
% plots posterior distributions
%
% INPUTS
-% estim_params_ [structure]
+% estim_params_ [structure]
% M_ [structure]
-% options_ [structure]
+% options_ [structure]
% bayestopt_ [structure]
% oo_ [structure]
-%
+%
% OUTPUTS
-% oo_ [structure]
-%
+% oo_ [structure]
+%
% SPECIAL REQUIREMENTS
% none
@@ -49,7 +49,7 @@ nn = sqrt(MaxNumberOfPlotPerFigure);
figurename = 'Priors and posteriors';
-if TeX && any(strcmp('eps',cellstr(options_.graph_format)))
+if TeX && any(strcmp('eps',cellstr(options_.graph_format)))
fidTeX = fopen([OutputDirectoryName '/' M_.fname '_PriorsAndPosteriors.tex'],'w');
fprintf(fidTeX,'%% TeX eps-loader file generated by PlotPosteriorDistributions.m (Dynare).\n');
fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
@@ -78,9 +78,9 @@ for i=1:npar
end
end
[x2,f2,abscissa,dens,binf2,bsup2] = draw_prior_density(i,bayestopt_);
- top2 = max(f2);
+ top2 = max(f2);
if i <= nvx
- name = deblank(M_.exo_names(estim_params_.var_exo(i,1),:));
+ name = deblank(M_.exo_names(estim_params_.var_exo(i,1),:));
x1 = oo_.posterior_density.shocks_std.(name)(:,1);
f1 = oo_.posterior_density.shocks_std.(name)(:,2);
oo_.prior_density.shocks_std.(name)(:,1) = x2;
@@ -96,18 +96,18 @@ for i=1:npar
oo_.prior_density.measurement_errors_std.(name)(:,2) = f2;
if ~options_.mh_posterior_mode_estimation
pmod = oo_.posterior_mode.measurement_errors_std.(name);
- end
+ end
elseif i <= nvx+nvn+ncx
j = i - (nvx+nvn);
k1 = estim_params_.corrx(j,1);
k2 = estim_params_.corrx(j,2);
- name = [deblank(M_.exo_names(k1,:)) '_' deblank(M_.exo_names(k2,:))];
+ name = [deblank(M_.exo_names(k1,:)) '_' deblank(M_.exo_names(k2,:))];
x1 = oo_.posterior_density.shocks_corr.(name)(:,1);
f1 = oo_.posterior_density.shocks_corr.(name)(:,2);
oo_.prior_density.shocks_corr.(name)(:,1) = x2;
oo_.prior_density.shocks_corr.(name)(:,2) = f2;
if ~options_.mh_posterior_mode_estimation
- pmod = oo_.posterior_mode.shocks_corr.(name);
+ pmod = oo_.posterior_mode.shocks_corr.(name);
end
elseif i <= nvx+nvn+ncx+ncn
j = i - (nvx+nvn+ncx);
@@ -157,7 +157,7 @@ for i=1:npar
fprintf(fidTeX,'\\begin{figure}[H]\n');
for j = 1:size(NAMES,1)
fprintf(fidTeX,'\\psfrag{%s}[1][][0.5][0]{%s}\n',deblank(NAMES(j,:)),deblank(TeXNAMES(j,:)));
- end
+ end
fprintf(fidTeX,'\\centering\n');
fprintf(fidTeX,'\\includegraphics[width=%2.2f\\textwidth]{%s/%s_PriorsAndPosteriors%s}\n',options_.figures.textwidth*min(subplotnum/nn,1),OutputDirectoryName,M_.fname,int2str(figunumber));
fprintf(fidTeX,'\\caption{Priors and posteriors.}');
diff --git a/matlab/PosteriorIRF.m b/matlab/PosteriorIRF.m
index 1aeba91cf72be34b41907291049c9da7119368ba..157a5e3a13a22be663ff72f1db6d11716bf05562 100644
--- a/matlab/PosteriorIRF.m
+++ b/matlab/PosteriorIRF.m
@@ -368,101 +368,101 @@ end
% PosteriorIRF_core2.m function.
if ~options_.nograph && ~options_.no_graph.posterior
-% Save the local variables.
-localVars=[];
+ % Save the local variables.
+ localVars=[];
-Check=options_.TeX;
-if (Check)
- localVars.varlist_TeX=varlist_TeX;
-end
+ Check=options_.TeX;
+ if (Check)
+ localVars.varlist_TeX=varlist_TeX;
+ end
-localVars.nvar=nvar;
-localVars.MeanIRF=MeanIRF;
-localVars.tit=tit;
-localVars.nn=nn;
-localVars.MAX_nirfs_dsgevar=MAX_nirfs_dsgevar;
-localVars.HPDIRF=HPDIRF;
-localVars.varlist=varlist;
-localVars.MaxNumberOfPlotPerFigure=MaxNumberOfPlotPerFigure;
-if options_.dsge_var
- localVars.HPDIRFdsgevar=HPDIRFdsgevar;
- localVars.MeanIRFdsgevar = MeanIRFdsgevar;
-end
+ localVars.nvar=nvar;
+ localVars.MeanIRF=MeanIRF;
+ localVars.tit=tit;
+ localVars.nn=nn;
+ localVars.MAX_nirfs_dsgevar=MAX_nirfs_dsgevar;
+ localVars.HPDIRF=HPDIRF;
+ localVars.varlist=varlist;
+ localVars.MaxNumberOfPlotPerFigure=MaxNumberOfPlotPerFigure;
+ if options_.dsge_var
+ localVars.HPDIRFdsgevar=HPDIRFdsgevar;
+ localVars.MeanIRFdsgevar = MeanIRFdsgevar;
+ end
-% The files .TeX are genereted in sequential way always!
-
-% The files .TeX are generated in sequential way always!
-subplotnum = 0;
-tit_TeX(M_.exo_names_orig_ord,:) = M_.exo_names_tex;
-if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
- fidTeX = fopen([DirectoryName filesep M_.fname '_BayesianIRF.tex'],'w');
- fprintf(fidTeX,'%% TeX eps-loader file generated by PosteriorIRF.m (Dynare).\n');
- fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
- fprintf(fidTeX,' \n');
- titTeX(M_.exo_names_orig_ord,:) = M_.exo_names_tex;
-
- for ii=irf_shocks_indx
- figunumber = 0;
-
- for jj=1:nvar
- if max(abs(MeanIRF(:,jj,ii))) >= options_.impulse_responses.plot_threshold
- subplotnum = subplotnum+1;
-
- if subplotnum == 1
- fprintf(fidTeX,'\\begin{figure}[H]\n');
+ % The files .TeX are genereted in sequential way always!
+
+ % The files .TeX are generated in sequential way always!
+ subplotnum = 0;
+ tit_TeX(M_.exo_names_orig_ord,:) = M_.exo_names_tex;
+ if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
+ fidTeX = fopen([DirectoryName filesep M_.fname '_BayesianIRF.tex'],'w');
+ fprintf(fidTeX,'%% TeX eps-loader file generated by PosteriorIRF.m (Dynare).\n');
+ fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
+ fprintf(fidTeX,' \n');
+ titTeX(M_.exo_names_orig_ord,:) = M_.exo_names_tex;
+
+ for ii=irf_shocks_indx
+ figunumber = 0;
+
+ for jj=1:nvar
+ if max(abs(MeanIRF(:,jj,ii))) >= options_.impulse_responses.plot_threshold
+ subplotnum = subplotnum+1;
+
+ if subplotnum == 1
+ fprintf(fidTeX,'\\begin{figure}[H]\n');
+ end
+ name = deblank(varlist(jj,:));
+ texname = deblank(varlist_TeX(jj,:));
+ fprintf(fidTeX,['\\psfrag{%s}[1][][0.5][0]{%s}\n'],name,['$' texname '$']);
end
- name = deblank(varlist(jj,:));
- texname = deblank(varlist_TeX(jj,:));
- fprintf(fidTeX,['\\psfrag{%s}[1][][0.5][0]{%s}\n'],name,['$' texname '$']);
- end
- if subplotnum == MaxNumberOfPlotPerFigure || (jj == nvar && subplotnum> 0)
- figunumber = figunumber+1;
-
- fprintf(fidTeX,'\\centering \n');
- fprintf(fidTeX,'\\includegraphics[width=%2.2f\\textwidth]{%s/%s_Bayesian_IRF_%s_%d}\n',options_.figures.textwidth*min(subplotnum/nn,1),DirectoryName,M_.fname,deblank(tit(ii,:)),figunumber);
- if options_.relative_irf
- fprintf(fidTeX,['\\caption{Bayesian relative IRF.}']);
- else
- fprintf(fidTeX,'\\caption{Bayesian IRF: Orthogonalized shock to $%s$.}\n',deblank(tit_TeX(ii,:)));
+ if subplotnum == MaxNumberOfPlotPerFigure || (jj == nvar && subplotnum> 0)
+ figunumber = figunumber+1;
+
+ fprintf(fidTeX,'\\centering \n');
+ fprintf(fidTeX,'\\includegraphics[width=%2.2f\\textwidth]{%s/%s_Bayesian_IRF_%s_%d}\n',options_.figures.textwidth*min(subplotnum/nn,1),DirectoryName,M_.fname,deblank(tit(ii,:)),figunumber);
+ if options_.relative_irf
+ fprintf(fidTeX,['\\caption{Bayesian relative IRF.}']);
+ else
+ fprintf(fidTeX,'\\caption{Bayesian IRF: Orthogonalized shock to $%s$.}\n',deblank(tit_TeX(ii,:)));
+ end
+ fprintf(fidTeX,'\\label{Fig:BayesianIRF:%s:%d}\n',deblank(tit(ii,:)),figunumber);
+ fprintf(fidTeX,'\\end{figure}\n');
+ fprintf(fidTeX,' \n');
+
+ subplotnum = 0;
end
- fprintf(fidTeX,'\\label{Fig:BayesianIRF:%s:%d}\n',deblank(tit(ii,:)),figunumber);
- fprintf(fidTeX,'\\end{figure}\n');
- fprintf(fidTeX,' \n');
-
- subplotnum = 0;
- end
+ end
end
+ fprintf(fidTeX,'%% End of TeX file.\n');
+ fclose(fidTeX);
end
- fprintf(fidTeX,'%% End of TeX file.\n');
- fclose(fidTeX);
-end
-% The others file format are generated in parallel by PosteriorIRF_core2!
+ % The others file format are generated in parallel by PosteriorIRF_core2!
-% Comment for testing!
-if ~isoctave
- if isnumeric(options_.parallel) || (M_.exo_nbr*ceil(size(varlist,1)/MaxNumberOfPlotPerFigure))<8
- [fout] = PosteriorIRF_core2(localVars,1,M_.exo_nbr,0);
- else
- isRemoteOctave = 0;
- for indPC=1:length(options_.parallel)
- isRemoteOctave = isRemoteOctave + (findstr(options_.parallel(indPC).MatlabOctavePath, 'octave'));
- end
- if isRemoteOctave
+ % Comment for testing!
+ if ~isoctave
+ if isnumeric(options_.parallel) || (M_.exo_nbr*ceil(size(varlist,1)/MaxNumberOfPlotPerFigure))<8
[fout] = PosteriorIRF_core2(localVars,1,M_.exo_nbr,0);
else
- globalVars = struct('M_',M_, ...
- 'options_', options_);
+ isRemoteOctave = 0;
+ for indPC=1:length(options_.parallel)
+ isRemoteOctave = isRemoteOctave + (findstr(options_.parallel(indPC).MatlabOctavePath, 'octave'));
+ end
+ if isRemoteOctave
+ [fout] = PosteriorIRF_core2(localVars,1,M_.exo_nbr,0);
+ else
+ globalVars = struct('M_',M_, ...
+ 'options_', options_);
- [fout] = masterParallel(options_.parallel, 1, M_.exo_nbr,NamFileInput,'PosteriorIRF_core2', localVars, globalVars, options_.parallel_info);
+ [fout] = masterParallel(options_.parallel, 1, M_.exo_nbr,NamFileInput,'PosteriorIRF_core2', localVars, globalVars, options_.parallel_info);
+ end
end
+ else
+ [fout] = PosteriorIRF_core2(localVars,1,M_.exo_nbr,0);
end
-else
- [fout] = PosteriorIRF_core2(localVars,1,M_.exo_nbr,0);
-end
-% END parallel code!
+ % END parallel code!
end
diff --git a/matlab/PosteriorIRF_core2.m b/matlab/PosteriorIRF_core2.m
index 9f922a409c771398a82cebea3174e81f963f98c5..09e003c430cabca00cf8cae7edb9c7ec222baea2 100644
--- a/matlab/PosteriorIRF_core2.m
+++ b/matlab/PosteriorIRF_core2.m
@@ -2,10 +2,10 @@ function myoutput=PosteriorIRF_core2(myinputs,fpar,npar,whoiam,ThisMatlab)
% function myoutput=PosteriorIRF_core2(myinputs,fpar,npar,whoiam, ThisMatlab)
% Generates the Posterior IRFs plot from the IRFs generated in
% PosteriorIRF_core1
-%
+%
% PARALLEL CONTEXT
% Performs in parallel execution a portion of the PosteriorIRF.m code.
-% For more information, see the comment in posterior_sampler_core.m
+% For more information, see the comment in posterior_sampler_core.m
% function.
%
% INPUTS
@@ -135,7 +135,7 @@ for i=fpar:npar
plot(1:options_.irf,HPDIRFdsgevar(:,1,j,i),'--k','linewidth',1)
plot(1:options_.irf,HPDIRFdsgevar(:,2,j,i),'--k','linewidth',1)
end
- % plot([1 options_.irf],[0 0],'-r','linewidth',0.5);
+ % plot([1 options_.irf],[0 0],'-r','linewidth',0.5);
box on
axis tight
xlim([1 options_.irf]);
@@ -162,7 +162,7 @@ for i=fpar:npar
if whoiam
fprintf('Done! \n');
waitbarString = [ 'Exog. shocks ' int2str(i) '/' int2str(npar) ' done.'];
-% fMessageStatus((i-fpar+1)/(npar-fpar+1),whoiam,waitbarString, waitbarTitle, Parallel(ThisMatlab));
+ % fMessageStatus((i-fpar+1)/(npar-fpar+1),whoiam,waitbarString, waitbarTitle, Parallel(ThisMatlab));
dyn_waitbar((i-fpar+1)/(npar-fpar+1),[],waitbarString);
end
end% loop over exo_var
diff --git a/matlab/ReshapeMatFiles.m b/matlab/ReshapeMatFiles.m
index 81054379f39d1139ebc4a1660f764c0aed3c0c6a..1d127a67bacdc9c2a20611e7d701f0b5b64c488c 100644
--- a/matlab/ReshapeMatFiles.m
+++ b/matlab/ReshapeMatFiles.m
@@ -18,9 +18,9 @@ function ReshapeMatFiles(type, type2)
% posterior
% gsa
% prior
-%
+%
% OUTPUTS:
-% none
+% none
%
% SPECIAL REQUIREMENTS
% none
@@ -61,17 +61,17 @@ else
end
else
MhDirectoryName = [CheckPath('prior',M_.dname) filesep ];
- end
+ end
end
switch type
case 'irf_dsge'
CAPtype = 'IRF_DSGE';
TYPEsize = [ options_.irf , size(options_.varlist,1) , M_.exo_nbr ];
- TYPEarray = 4;
+ TYPEarray = 4;
case 'irf_bvardsge'
CAPtype = 'IRF_BVARDSGE';
TYPEsize = [ options_.irf , length(options_.varobs) , M_.exo_nbr ];
- TYPEarray = 4;
+ TYPEarray = 4;
case 'smooth'
CAPtype = 'SMOOTH';
TYPEsize = [ M_.endo_nbr , options_.nobs ];
@@ -134,7 +134,7 @@ switch TYPEarray
eval(['idx = idx + size(stock_' type ',4);'])
end
%eval(['STOCK_' CAPtype ' = sort(STOCK_' CAPtype ',4);'])
- save([MhDirectoryName M_.fname '_' CAPtype 's' int2str(NumberOfTYPEfiles-foffset+1) '.mat'],['STOCK_' CAPtype]);
+ save([MhDirectoryName M_.fname '_' CAPtype 's' int2str(NumberOfTYPEfiles-foffset+1) '.mat'],['STOCK_' CAPtype]);
end
else
load([MhDirectoryName M_.fname '_' type '1.mat']);
@@ -176,7 +176,7 @@ switch TYPEarray
load([MhDirectoryName M_.fname '_' type '1.mat']);
%eval(['STOCK_' CAPtype ' = sort(stock_' type ',3);'])
eval(['STOCK_' CAPtype ' = stock_' type ';'])
- save([MhDirectoryName M_.fname '_' CAPtype 's' int2str(1) '.mat'],['STOCK_' CAPtype ]);
+ save([MhDirectoryName M_.fname '_' CAPtype 's' int2str(1) '.mat'],['STOCK_' CAPtype ]);
end
% Original file format may be useful in some cases...
% for file = 1:NumberOfTYPEfiles
diff --git a/matlab/TaRB_optimizer_wrapper.m b/matlab/TaRB_optimizer_wrapper.m
index 4d29add34a3c4777b98097124e03bfa62d2ea32a..fe62c57333b1a181f1f477f5404fcc4c12166ec1 100644
--- a/matlab/TaRB_optimizer_wrapper.m
+++ b/matlab/TaRB_optimizer_wrapper.m
@@ -3,15 +3,15 @@ function [fval,info,exit_flag,DLIK,Hess,SteadyState,trend_coeff] = TaRB_optimiz
% Wrapper function for target function used in TaRB algorithm; reassembles
% full parameter vector before calling target function
%
-% INPUTS
-% o optpar [double] (p_opt*1) vector of subset of parameters to be considered
-% o par_vector [double] (p*1) full vector of parameters
+% INPUTS
+% o optpar [double] (p_opt*1) vector of subset of parameters to be considered
+% o par_vector [double] (p*1) full vector of parameters
% o parameterindices [double] (p_opt*1) index of optpar entries in
% par_vector
% o TargetFun [char] string specifying the name of the objective
% function (posterior kernel).
% o varargin [structure] other inputs of target function
-%
+%
% OUTPUTS
% o fval [scalar] value of (minus) the likelihood.
% o info [double] (p*2) error code vector
@@ -20,7 +20,7 @@ function [fval,info,exit_flag,DLIK,Hess,SteadyState,trend_coeff] = TaRB_optimiz
% o Hess [double] (p*p) asymptotic Hessian matrix.
% o SteadyState [double] Vector of doubles, steady state level for the endogenous variables.
% o trend_coeff [double] Matrix of doubles, coefficients of the deterministic trend in the measurement equation
-%
+%
% Copyright (C) 2015-16 Dynare Team
%
% This file is part of Dynare.
@@ -40,4 +40,3 @@ function [fval,info,exit_flag,DLIK,Hess,SteadyState,trend_coeff] = TaRB_optimiz
par_vector(parameterindices,:)=optpar; %reassemble parameter
[fval,info,exit_flag,DLIK,Hess,SteadyState,trend_coeff] = feval(TargetFun,par_vector,varargin{:}); %call target function
-
diff --git a/matlab/Tracing.m b/matlab/Tracing.m
index 7d0e947c9772fe09098cd1d1fc3ce86a224ae7d5..4d5348ad0d14e416e6e4315b0692ff76950f0008 100644
--- a/matlab/Tracing.m
+++ b/matlab/Tracing.m
@@ -2,15 +2,15 @@ function [] = Tracing()
% DESCRIPTION
% This function is used to test the correct execution of a matlab section
% on remote machine.
-%
+%
% If no error happen the function simply create a file.
%
% INPUTS
% ...
-%
+%
% OUTPUTS
% ...
-%
+%
% SPECIAL REQUIREMENTS
% none
diff --git a/matlab/UnivariateSpectralDensity.m b/matlab/UnivariateSpectralDensity.m
index d5a37f24f8416bbd197fff2060d0467e20c4580e..41b67c4031c87a4197e8c7d21c6ff52f193d6385 100644
--- a/matlab/UnivariateSpectralDensity.m
+++ b/matlab/UnivariateSpectralDensity.m
@@ -1,15 +1,15 @@
function [oo_] = UnivariateSpectralDensity(M_,oo_,options_,var_list)
% This function computes the theoretical spectral density of each
-% endogenous variable declared in var_list. Results are stored in
-% oo_.SpectralDensity and may be plotted. Plots are saved into the
-% graphs-folder.
-%
+% endogenous variable declared in var_list. Results are stored in
+% oo_.SpectralDensity and may be plotted. Plots are saved into the
+% graphs-folder.
+%
% INPUTS
% M_ [structure] Dynare's model structure
% oo_ [structure] Dynare's results structure
% options_ [structure] Dynare's options structure
% var_list [integer] Vector of indices for a subset of variables.
-%
+%
% OUTPUTS
% oo_ [structure] Dynare's results structure,
% containing the subfield
@@ -17,7 +17,7 @@ function [oo_] = UnivariateSpectralDensity(M_,oo_,options_,var_list)
% and density, which are of size nvar*ngrid.
%
-% Adapted from th_autocovariances.m.
+% Adapted from th_autocovariances.m.
% Copyright (C) 2006-2017 Dynare Team
%
@@ -38,7 +38,7 @@ function [oo_] = UnivariateSpectralDensity(M_,oo_,options_,var_list)
if options_.order > 1
- disp('UnivariateSpectralDensity :: I Cannot compute the theoretical spectral density')
+ disp('UnivariateSpectralDensity :: I Cannot compute the theoretical spectral density')
disp('with a second order approximation of the DSGE model!')
disp('Please set order = 1. I abort')
return
@@ -102,7 +102,7 @@ if ~isempty(u)
ivar = oo_.dr.order_var(iky);
end
-iky = iv(ivar);
+iky = iv(ivar);
aa = ghx(iky,:);
bb = ghu(iky,:);
ngrid = options_.hp_ngrid; %number of grid points
@@ -112,7 +112,7 @@ tneg = exp(-sqrt(-1)*freqs); %negative frequencies
if options_.one_sided_hp_filter
error('UnivariateSpectralDensity:: spectral density estimate not available with one-sided HP filter')
elseif options_.hp_filter == 0 && ~options_.bandpass.indicator %do not filter
- filter_gain=ones(ngrid,1);
+ filter_gain=ones(ngrid,1);
elseif ~(options_.hp_filter == 0 && ~options_.bandpass.indicator) && options_.bandpass.indicator %filter with bandpass
filter_gain = zeros(1,ngrid);
lowest_periodicity=options_.bandpass.passband(2);
@@ -122,7 +122,7 @@ elseif ~(options_.hp_filter == 0 && ~options_.bandpass.indicator) && options_.ba
filter_gain(freqs<=-2*pi/lowest_periodicity+2*pi & freqs>=-2*pi/highest_periodicity+2*pi)=1;
elseif ~(options_.hp_filter == 0 && ~options_.bandpass.indicator) && ~options_.bandpass.indicator %filter with HP-filter
lambda = options_.hp_filter;
- filter_gain = 4*lambda*(1 - cos(freqs)).^2 ./ (1 + 4*lambda*(1 - cos(freqs)).^2);
+ filter_gain = 4*lambda*(1 - cos(freqs)).^2 ./ (1 + 4*lambda*(1 - cos(freqs)).^2);
end
mathp_col = NaN(ngrid,length(ivar)^2);
@@ -139,7 +139,7 @@ end
f = zeros(nvar,ngrid);
for i=1:nvar
f(i,:) = real(mathp_col(:,(i-1)*nvar+i)); %read out spectral density
-end
+end
oo_.SpectralDensity.freqs=freqs;
oo_.SpectralDensity.density=f;
@@ -164,7 +164,7 @@ if options_.nograph == 0
xlabel('0 \leq \omega \leq \pi')
ylabel('f(\omega)')
box on
- axis tight
+ axis tight
dyn_saveas(hh,[M_.fname ,filesep,'graphs', filesep, 'SpectralDensity_' deblank(M_.endo_names(ivar(i),:))],options_.nodisplay,options_.graph_format)
end
end
diff --git a/matlab/WriteShockDecomp2Excel.m b/matlab/WriteShockDecomp2Excel.m
index 618a6b064bc0326afe4108898879da5ddc329c09..5417e5250b0a42001b54d77c277d4a5b65965fa3 100644
--- a/matlab/WriteShockDecomp2Excel.m
+++ b/matlab/WriteShockDecomp2Excel.m
@@ -1,7 +1,7 @@
function WriteShockDecomp2Excel(z,shock_names,endo_names,i_var,initial_date,DynareModel,DynareOptions,opts_decomp)
%function WriteShockDecomp2Excel(z,shock_names,endo_names,i_var,initial_date,DynareModel,DynareOptions)
% Saves the results from the shock_decomposition command to xls
-%
+%
% Inputs
% z [n_var*(nshock+2)*nperiods] shock decomposition array, see shock_decomposition.m for details
% shock_names [endo_nbr*string length] shock names from M_.exo_names
@@ -48,8 +48,8 @@ if nargin==8
end
if isfield(opts_decomp,'fig_mode') && ~isempty(opts_decomp.fig_mode)
fig_mode = opts_decomp.fig_mode;
- fig_mode1 = ['_' fig_mode];
- fig_mode = [fig_mode '_'];
+ fig_mode1 = ['_' fig_mode];
+ fig_mode = [fig_mode '_'];
end
if isfield(opts_decomp,'screen_shocks')
if use_shock_groups
@@ -60,15 +60,15 @@ if nargin==8
end
if isfield(opts_decomp,'fig_name')
fig_name = opts_decomp.fig_name;
-% fig_name = ['_' fig_name];
- fig_name1 = [fig_name];
- fig_name = [fig_name '_'];
+ % fig_name = ['_' fig_name];
+ fig_name1 = [fig_name];
+ fig_name = [fig_name '_'];
end
if screen_shocks
- fig_name1 = [fig_name1 '_screen'];
- fig_name = [fig_name 'screen_'];
+ fig_name1 = [fig_name1 '_screen'];
+ fig_name = [fig_name 'screen_'];
end
-end
+end
gend = size(z,3);
@@ -99,7 +99,7 @@ for j=1:nvar
z1 = [z1(isort(1:16),:); zres; z1(comp_nbr0:end,:)];
comp_nbr=18;
end
-
+
d0(1,:)=[{'Decomposition'} cellstr(labels(1:comp_nbr,:))' {'Smoot Var'}];
d0=[d0; num2cell([x' z1'])];
LastRow=size(d0,1);
@@ -112,7 +112,7 @@ for j=1:nvar
d0(LastRow+2+ic,2:1+length(group_members))=group_members;
end
end
-
+
warning off
if ~ismac
[STATUS,MESSAGE] = xlswrite([DynareModel.fname,'_shock_decomposition',fig_mode,fig_name1],d0,deblank(endo_names(i_var(j),:)));
@@ -122,6 +122,5 @@ for j=1:nvar
warning on
clear d0
-
-end
+end
diff --git a/matlab/add_filter_subtitle.m b/matlab/add_filter_subtitle.m
index 65258751793cc66945e27c822291c9589dd16e05..689b8a5acf447c779af5d792372e7e7ab0b24c29 100644
--- a/matlab/add_filter_subtitle.m
+++ b/matlab/add_filter_subtitle.m
@@ -1,7 +1,7 @@
function title=add_filter_subtitle(title,options_)
if ~options_.hp_filter && ~options_.one_sided_hp_filter && ~options_.bandpass.indicator %do not filter
- %nothing to add here
+ %nothing to add here
elseif ~options_.hp_filter && ~options_.one_sided_hp_filter && options_.bandpass.indicator
title = [title ' (Bandpass filter, (' ...
num2str(options_.bandpass.passband(1)),' ',num2str(options_.bandpass.passband(2)), '))'];
@@ -10,6 +10,6 @@ elseif options_.hp_filter && ~options_.one_sided_hp_filter && ~options_.bandpas
num2str(options_.hp_filter) ')'];
elseif ~options_.hp_filter && options_.one_sided_hp_filter && ~options_.bandpass.indicator
title = [title ' (One-sided HP filter, lambda = ' ...
- num2str(options_.one_sided_hp_filter) ')'];
+ num2str(options_.one_sided_hp_filter) ')'];
end
end
\ No newline at end of file
diff --git a/matlab/add_path_to_mex_files.m b/matlab/add_path_to_mex_files.m
index e35d29a6b9a3d959b49b5030a7660cfd420ee942..13140bba5b03243ea71ca3e4f5fd9044665c4c34 100644
--- a/matlab/add_path_to_mex_files.m
+++ b/matlab/add_path_to_mex_files.m
@@ -1,5 +1,5 @@
function mexpath = add_path_to_mex_files(dynareroot, modifypath)
-
+
% Copyright (C) 2015-2017 Dynare Team
%
% This file is part of Dynare.
@@ -20,7 +20,7 @@ function mexpath = add_path_to_mex_files(dynareroot, modifypath)
if nargin<2
modifypath = true;
end
-
+
if exist('OCTAVE_VERSION')
if ispc() && strcmpi(computer(), 'i686-w64-mingw32')
mexpath = {[dynareroot '../mex/octave32/']};
diff --git a/matlab/annualized_shock_decomposition.m b/matlab/annualized_shock_decomposition.m
index f5ee9c8e62121f41d827fdbef59c434312aa7a00..4972edb3078a218f64b8117fd1387174d0fe51d6 100644
--- a/matlab/annualized_shock_decomposition.m
+++ b/matlab/annualized_shock_decomposition.m
@@ -1,28 +1,28 @@
function [z, endo_names, endo_names_tex, steady_state, i_var, oo_] = annualized_shock_decomposition(oo_, M_, options_, i_var, t0, t1, realtime_, vintage_, steady_state, q2a, cumfix)
% function oo_ = annualized_shock_decomposition(oo_,t0,options_.nobs);
% Computes annualized shocks contribution to a simulated trajectory. The fields set are
-% oo_.annualized_shock_decomposition, oo_.annualized_realtime_shock_decomposition,
-% oo_.annualized_realtime_conditional_shock_decomposition and oo_.annualized_realtime_forecast_shock_decomposition.
+% oo_.annualized_shock_decomposition, oo_.annualized_realtime_shock_decomposition,
+% oo_.annualized_realtime_conditional_shock_decomposition and oo_.annualized_realtime_forecast_shock_decomposition.
% Subfields are arrays n_var by nshock+2 by nperiods. The
% first nshock columns store the respective shock contributions, column n+1
% stores the role of the initial conditions, while column n+2 stores the
-% value of the smoothed variables. Both the variables and shocks are stored
+% value of the smoothed variables. Both the variables and shocks are stored
% in the order of endo_names and M_.exo_names, respectively.
%
% INPUTS
% oo_: [structure] Storage of results
% M_: [structure] Storage of model
-% opts: [structure] options for shock decomp
+% opts: [structure] options for shock decomp
% i_var: [array] index of vars
% t0: [integer] first period
% t1: [integer] last period
-% realtime_: [integer]
+% realtime_: [integer]
% vintage_: [integer]
% steady_state: [array] steady state value of quarterly (log-) level vars
% q2a: [structure] info on q2a
%
% OUTPUTS
-% z: [matrix] shock decomp to plot
+% z: [matrix] shock decomp to plot
% endo_names: [char] updated var names
% endo_names_tex: [char] updated TeX var names
% steady_state: [array] updated stady state of vars
@@ -57,13 +57,13 @@ islog = q2a.islog;
aux = q2a.aux;
aux0 = aux;
cumfix = q2a.cumfix;
-% usual shock decomp
+% usual shock decomp
if isstruct(oo_)
-% z = oo_.shock_decomposition;
- myopts=options_;
- myopts.plot_shock_decomp.type='qoq';
- myopts.plot_shock_decomp.realtime=0;
- [z, junk] = plot_shock_decomposition(M_,oo_,myopts,[]);
+ % z = oo_.shock_decomposition;
+ myopts=options_;
+ myopts.plot_shock_decomp.type='qoq';
+ myopts.plot_shock_decomp.realtime=0;
+ [z, junk] = plot_shock_decomposition(M_,oo_,myopts,[]);
else
z = oo_;
end
@@ -177,147 +177,147 @@ end
% realtime
if realtime_ && isstruct(oo_) && isfield(oo_, 'realtime_shock_decomposition')
-init=1;
-for i=t0:4:t1
- yr=floor(i/4);
- za=[];
- gza=[];
+ init=1;
+ for i=t0:4:t1
+ yr=floor(i/4);
+ za=[];
+ gza=[];
myopts=options_;
myopts.plot_shock_decomp.type='qoq';
myopts.plot_shock_decomp.realtime=1;
myopts.plot_shock_decomp.vintage=i;
[z, steady_state_aux] = plot_shock_decomposition(M_,oo_,myopts,[]);
z = z(i_var,:,:);
-if isstruct(aux)
- if ischar(aux0.y)
- [y_aux, steady_state_aux] = plot_shock_decomposition(M_,oo_,myopts,aux0.y);
- aux.y=y_aux;
- aux.yss=steady_state_aux;
- end
- yaux=aux.y;
-end
+ if isstruct(aux)
+ if ischar(aux0.y)
+ [y_aux, steady_state_aux] = plot_shock_decomposition(M_,oo_,myopts,aux0.y);
+ aux.y=y_aux;
+ aux.yss=steady_state_aux;
+ end
+ yaux=aux.y;
+ end
nterms = size(z,2);
-
-% z = oo_.realtime_shock_decomposition.(['time_' int2str(i)]);
-% z = z(i_var,:,:);
-
- for j=1:nvar
- for k =nterms:-1:1
-% if k<nterms
-% ztmp = squeeze(sum(z(j,[1:k-1,k+1:end-1],t0-4:end)));
-% else
+
+ % z = oo_.realtime_shock_decomposition.(['time_' int2str(i)]);
+ % z = z(i_var,:,:);
+
+ for j=1:nvar
+ for k =nterms:-1:1
+ % if k<nterms
+ % ztmp = squeeze(sum(z(j,[1:k-1,k+1:end-1],t0-4:end)));
+ % else
ztmp = squeeze(z(j,k,min((t0-3):-4:1):end));
-% end
- if isstruct(aux)
- aux.y = squeeze(yaux(j,k,min((t0-3):-4:1):end));
+ % end
+ if isstruct(aux)
+ aux.y = squeeze(yaux(j,k,min((t0-3):-4:1):end));
+ end
+ [za(j,k,:), steady_state_a(j,1), gza(j,k,:), steady_state_ga(j,1)] = ...
+ quarterly2annual(ztmp,steady_state(j),GYTREND0,var_type,islog,aux);
+ % if k<nterms
+ % za(j,k,:) = za(j,end,:) - za(j,k,:);
+ % gza(j,k,:) = gza(j,end,:) - gza(j,k,:);
+ % end
+
end
- [za(j,k,:), steady_state_a(j,1), gza(j,k,:), steady_state_ga(j,1)] = ...
- quarterly2annual(ztmp,steady_state(j),GYTREND0,var_type,islog,aux);
-% if k<nterms
-% za(j,k,:) = za(j,end,:) - za(j,k,:);
-% gza(j,k,:) = gza(j,end,:) - gza(j,k,:);
-% end
-
+
+ ztmp=squeeze(za(j,:,:));
+
+ if cumfix==0
+ zscale = sum(ztmp(1:end-1,:))./ztmp(end,:);
+ ztmp(1:end-1,:) = ztmp(1:end-1,:)./repmat(zscale,[nterms-1,1]);
+ else
+ zres = ztmp(end,:)-sum(ztmp(1:end-1,:));
+ ztmp(end-1,:) = ztmp(end-1,:) + zres;
+ end
+
+ gztmp=squeeze(gza(j,:,:));
+ if cumfix==0
+ gscale = sum(gztmp(1:end-1,:))./ gztmp(end,:);
+ gztmp(1:end-1,:) = gztmp(1:end-1,:)./repmat(gscale,[nterms-1,1]);
+ else
+ gres = gztmp(end,:) - sum(gztmp(1:end-1,:));
+ gztmp(end-1,:) = gztmp(end-1,:)+gres;
+ end
+
+ za(j,:,:) = ztmp;
+ gza(j,:,:) = gztmp;
end
-
- ztmp=squeeze(za(j,:,:));
- if cumfix==0
- zscale = sum(ztmp(1:end-1,:))./ztmp(end,:);
- ztmp(1:end-1,:) = ztmp(1:end-1,:)./repmat(zscale,[nterms-1,1]);
+ if q2a.plot ==1
+ z=gza;
+ elseif q2a.plot == 2
+ z=za;
else
- zres = ztmp(end,:)-sum(ztmp(1:end-1,:));
- ztmp(end-1,:) = ztmp(end-1,:) + zres;
+ z=cat(1,za,gza);
end
-
- gztmp=squeeze(gza(j,:,:));
- if cumfix==0
- gscale = sum(gztmp(1:end-1,:))./ gztmp(end,:);
- gztmp(1:end-1,:) = gztmp(1:end-1,:)./repmat(gscale,[nterms-1,1]);
+
+ if init==1
+ oo_.annualized_realtime_shock_decomposition.pool = z;
else
- gres = gztmp(end,:) - sum(gztmp(1:end-1,:));
- gztmp(end-1,:) = gztmp(end-1,:)+gres;
+ oo_.annualized_realtime_shock_decomposition.pool(:,:,yr) = z(:,:,end-nfrcst);
end
-
- za(j,:,:) = ztmp;
- gza(j,:,:) = gztmp;
- end
-
- if q2a.plot ==1
- z=gza;
- elseif q2a.plot == 2
- z=za;
- else
- z=cat(1,za,gza);
- end
-
- if init==1
- oo_.annualized_realtime_shock_decomposition.pool = z;
- else
- oo_.annualized_realtime_shock_decomposition.pool(:,:,yr) = z(:,:,end-nfrcst);
- end
- oo_.annualized_realtime_shock_decomposition.(['yr_' int2str(yr)]) = z;
-
- if opts.forecast
- oo_.annualized_realtime_forecast_shock_decomposition.(['yr_' int2str(yr)]) = z(:,:,end-nfrcst:end);
- if init>nfrcst
- oo_.annualized_realtime_conditional_shock_decomposition.(['yr_' int2str(yr-nfrcst)]) = ...
- oo_.annualized_realtime_shock_decomposition.pool(:,:,yr-nfrcst:end) - ...
- oo_.annualized_realtime_forecast_shock_decomposition.(['yr_' int2str(yr-nfrcst)]);
- % fix others
- oo_.annualized_realtime_conditional_shock_decomposition.(['yr_' int2str(yr-nfrcst)])(:,end-1,:) = ...
- oo_.annualized_realtime_conditional_shock_decomposition.(['yr_' int2str(yr-nfrcst)])(:,end-1,:) + ...
- oo_.annualized_realtime_forecast_shock_decomposition.(['yr_' int2str(yr-nfrcst)])(:,end,:);
- % fix total
- oo_.annualized_realtime_conditional_shock_decomposition.(['yr_' int2str(yr-nfrcst)])(:,end,:) = ...
- oo_.annualized_realtime_shock_decomposition.pool(:,end,yr-nfrcst:end);
- if i==t1
- for my_forecast_=(nfrcst-1):-1:1
- oo_.annualized_realtime_conditional_shock_decomposition.(['yr_' int2str(yr-my_forecast_)]) = ...
- oo_.annualized_realtime_shock_decomposition.pool(:,:,yr-my_forecast_:yr) - ...
- oo_.annualized_realtime_forecast_shock_decomposition.(['yr_' int2str(yr-my_forecast_)])(:,:,1:my_forecast_+1);
- oo_.annualized_realtime_conditional_shock_decomposition.(['yr_' int2str(yr-my_forecast_)])(:,end-1,:) = ...
- oo_.annualized_realtime_forecast_shock_decomposition.(['yr_' int2str(yr-my_forecast_)])(:,end,1:my_forecast_+1);
- oo_.annualized_realtime_conditional_shock_decomposition.(['yr_' int2str(yr-my_forecast_)])(:,end,:) = ...
- oo_.annualized_realtime_shock_decomposition.pool(:,end,yr-my_forecast_:yr);
+ oo_.annualized_realtime_shock_decomposition.(['yr_' int2str(yr)]) = z;
+
+ if opts.forecast
+ oo_.annualized_realtime_forecast_shock_decomposition.(['yr_' int2str(yr)]) = z(:,:,end-nfrcst:end);
+ if init>nfrcst
+ oo_.annualized_realtime_conditional_shock_decomposition.(['yr_' int2str(yr-nfrcst)]) = ...
+ oo_.annualized_realtime_shock_decomposition.pool(:,:,yr-nfrcst:end) - ...
+ oo_.annualized_realtime_forecast_shock_decomposition.(['yr_' int2str(yr-nfrcst)]);
+ % fix others
+ oo_.annualized_realtime_conditional_shock_decomposition.(['yr_' int2str(yr-nfrcst)])(:,end-1,:) = ...
+ oo_.annualized_realtime_conditional_shock_decomposition.(['yr_' int2str(yr-nfrcst)])(:,end-1,:) + ...
+ oo_.annualized_realtime_forecast_shock_decomposition.(['yr_' int2str(yr-nfrcst)])(:,end,:);
+ % fix total
+ oo_.annualized_realtime_conditional_shock_decomposition.(['yr_' int2str(yr-nfrcst)])(:,end,:) = ...
+ oo_.annualized_realtime_shock_decomposition.pool(:,end,yr-nfrcst:end);
+ if i==t1
+ for my_forecast_=(nfrcst-1):-1:1
+ oo_.annualized_realtime_conditional_shock_decomposition.(['yr_' int2str(yr-my_forecast_)]) = ...
+ oo_.annualized_realtime_shock_decomposition.pool(:,:,yr-my_forecast_:yr) - ...
+ oo_.annualized_realtime_forecast_shock_decomposition.(['yr_' int2str(yr-my_forecast_)])(:,:,1:my_forecast_+1);
+ oo_.annualized_realtime_conditional_shock_decomposition.(['yr_' int2str(yr-my_forecast_)])(:,end-1,:) = ...
+ oo_.annualized_realtime_forecast_shock_decomposition.(['yr_' int2str(yr-my_forecast_)])(:,end,1:my_forecast_+1);
+ oo_.annualized_realtime_conditional_shock_decomposition.(['yr_' int2str(yr-my_forecast_)])(:,end,:) = ...
+ oo_.annualized_realtime_shock_decomposition.pool(:,end,yr-my_forecast_:yr);
+ end
end
end
end
- end
-% ztmp=oo_.realtime_shock_decomposition.pool(:,:,21:29)-oo_.realtime_forecast_shock_decomposition.time_21;
+ % ztmp=oo_.realtime_shock_decomposition.pool(:,:,21:29)-oo_.realtime_forecast_shock_decomposition.time_21;
- init=init+1;
-end
+ init=init+1;
+ end
+
+ switch realtime_
-switch realtime_
-
- case 0
+ case 0
z = oo_.annualized_shock_decomposition;
-
- case 1 % realtime
+
+ case 1 % realtime
if vintage_
z = oo_.annualized_realtime_shock_decomposition.(['yr_' int2str(floor(vintage_/4))]);
else
z = oo_.annualized_realtime_shock_decomposition.pool;
end
-
- case 2 % conditional
+
+ case 2 % conditional
if vintage_
z = oo_.annualized_realtime_conditional_shock_decomposition.(['yr_' int2str(floor(vintage_/4))]);
else
error();
end
-
- case 3 % forecast
+
+ case 3 % forecast
if vintage_
z = oo_.annualized_realtime_forecast_shock_decomposition.(['yr_' int2str(floor(vintage_/4))]);
else
error()
end
-end
+ end
end
if q2a.plot ==0
@@ -331,4 +331,3 @@ else
steady_state = steady_state_a;
end
end
-
diff --git a/matlab/autoregressive_process_specification.m b/matlab/autoregressive_process_specification.m
index 7cc34db27db46895e6780c6e2a54d927953a6fe4..3c909a9b42bed35f26429033b5ddb6752a333266 100644
--- a/matlab/autoregressive_process_specification.m
+++ b/matlab/autoregressive_process_specification.m
@@ -2,43 +2,43 @@ function [InnovationVariance,AutoregressiveParameters] = autoregressive_process_
% This function computes the parameters of an AR(p) process from the variance and the autocorrelation function
% (the first p terms) of this process.
%
-% INPUTS
+% INPUTS
% [1] Variance [double] scalar, variance of the variable.
% [2] Rho [double] p*1 vector, the autocorelation function: \rho(1), \rho(2), ..., \rho(p).
% [3] p [double] scalar, the number of lags in the AR process.
%
-% OUTPUTS
+% OUTPUTS
% [1] InnovationVariance [double] scalar, the variance of the innovation.
% [2] AutoregressiveParameters [double] p*1 vector of autoregressive parameters.
%
-% NOTES
+% NOTES
%
% The AR(p) model for {y_t} is:
-%
-% y_t = \phi_1 * y_{t-1} + \phi_2 * y_{t-2} + ... + \phi_p * y_{t-p} + e_t
+%
+% y_t = \phi_1 * y_{t-1} + \phi_2 * y_{t-2} + ... + \phi_p * y_{t-p} + e_t
%
% Let \gamma(0) and \rho(1), ..., \rho(2) be the variance and the autocorrelation function of {y_t}. This function
-% compute the variance of {e_t} and the \phi_i (i=1,...,p) from the variance and the autocorrelation function of {y_t}.
+% compute the variance of {e_t} and the \phi_i (i=1,...,p) from the variance and the autocorrelation function of {y_t}.
% We know that:
-%
+%
% \gamma(0) = \phi_1 \gamma(1) + ... + \phi_p \gamma(p) + \sigma^2
%
% where \sigma^2 is the variance of {e_t}. Equivalently we have:
%
-% \sigma^2 = \gamma(0) (1-\rho(1)\phi_1 - ... - \rho(p)\phi_p)
+% \sigma^2 = \gamma(0) (1-\rho(1)\phi_1 - ... - \rho(p)\phi_p)
%
% We also have for any integer h>0:
-%
+%
% \rho(h) = \phi_1 \rho(h-1) + ... + \phi_p \rho(h-p)
%
% We can write the equations for \rho(1), ..., \rho(p) using matrices. Let R be the p*p autocorelation
-% matrix and v be the p*1 vector gathering the first p terms of the autocorrelation function. We have:
+% matrix and v be the p*1 vector gathering the first p terms of the autocorrelation function. We have:
%
% v = R*PHI
-%
+%
% where PHI is a p*1 vector with the autoregressive parameters of the AR(p) process. We can recover the autoregressive
% parameters by inverting the autocorrelation matrix: PHI = inv(R)*v.
-%
+%
% This function first computes the vector PHI by inverting R and computes the variance of the innovation by evaluating
%
% \sigma^2 = \gamma(0)*(1-PHI'*v)
diff --git a/matlab/backward_model_inversion.m b/matlab/backward_model_inversion.m
index c94b8403b29ed35a1dd94e15ad5877951a547fa3..5e305ae559e826a6802def9db0b3df6e56e3cb00 100644
--- a/matlab/backward_model_inversion.m
+++ b/matlab/backward_model_inversion.m
@@ -1,6 +1,6 @@
function [endogenousvariables, exogenousvariables] = backward_model_inversion(constraints, exogenousvariables, initialconditions, endo_names, exo_names, freeinnovations, DynareModel, DynareOptions, DynareOutput)
-% INPUTS
+% INPUTS
% - constraints [dseries] with N constrained endogenous variables from t1 to t2.
% - exogenousvariables [dseries] with Q exogenous variables.
% - initialconditions [dseries] with M endogenous variables starting before t1 (M initialcond must contain at least the state variables).
@@ -8,11 +8,11 @@ function [endogenousvariables, exogenousvariables] = backward_model_inversion(co
% - exo_names [cell] list of exogenous variable names.
% - freeinstruments [cell] list of exogenous variable names used to control the constrained endogenous variables.
%
-% OUTPUTS
+% OUTPUTS
% - endogenous [dseries]
% - exogenous [dseries]
%
-% REMARKS
+% REMARKS
% Copyright (C) 2017 Dynare Team
%
diff --git a/matlab/basic_plan.m b/matlab/basic_plan.m
index 390b2076e9b9eb5a1f6ece93317843eb1f31a0cb..ae7d6bcbf6a5f2dab454d187c825993e83d18ec5 100644
--- a/matlab/basic_plan.m
+++ b/matlab/basic_plan.m
@@ -31,60 +31,60 @@ function plan = basic_plan(plan, exogenous, expectation_type, date, value)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- if ~ischar(expectation_type) || size(expectation_type,1) ~= 1
- error(['in basic_plan the third argument should be a string containing the simulation type (''perfect_foresight'' or ''surprise'')']);
- end
- exogenous = strtrim(exogenous);
- ix = find(strcmp(exogenous, plan.exo_names));
- if isempty(ix)
- error(['in basic_plan the second argument ' exogenous ' is not an exogenous variable']);
- end
- sdate = length(date);
- if sdate > 1
- if date(1) < plan.date(1) || date(end) > plan.date(end)
- error(['in basic_plan the fourth argument (date=' date ') must lay inside the plan.date ' plan.date]);
- end
- else
- if date < plan.date(1) || date > plan.date(end)
- error(['in basic_plan the fourth argument (date=' date ') must lay iside the plan.date ' plan.date]);
- end
- end
- if length(date) ~= length(value)
- error(['in basic_plan the number of dates (' int2str(length(date)) ') is not equal to the numbers of shock (' int2str(length(value)) ') for exogenous variable ' exogenous]);
- end
- if ~isempty(plan.options_cond_fcst_.controlled_varexo)
- common_var = find(ix == plan.options_cond_fcst_.controlled_varexo);
- if ~isempty(common_var)
- common_date = intersect(date, plan.constrained_date_{common_var});
- if ~isempty(common_date)
- [date_, i_date] = setdiff(date, common_date);
- value = value(i_date);
- if common_date.length > 1
- the_dates = [cell2mat(strings(common_date(1))) ':' cell2mat(strings(common_date(end)))];
- else
- the_dates = cell2mat(strings(common_date));
- end
- warning(['Impossible case: ' plan.exo_names{plan.options_cond_fcst_.controlled_varexo(common_var)} ' is used both as a shock and as an endogenous variable to control the path of ' plan.endo_names{plan.constrained_vars_(common_var)} ' at the dates ' the_dates]);
- warning('This shock will not be considered');
- end
- end
- end
- if isempty(plan.shock_vars_)
- plan.shock_vars_ = ix;
- if strcmp(expectation_type, 'perfect_foresight')
- plan.shock_perfect_foresight_ = 1;
- else
- plan.shock_perfect_foresight_ = 0;
- end
- else
- plan.shock_vars_ = [plan.shock_vars_ ; ix];
- if strcmp(expectation_type, 'perfect_foresight')
- plan.shock_perfect_foresight_ = [plan.shock_perfect_foresight_ ; 1];
- else
- plan.shock_perfect_foresight_ = [plan.shock_perfect_foresight_ ; 0];
- end
- end
- plan.shock_date_{length(plan.shock_date_) + 1} = date;
- plan.shock_str_date_{length(plan.shock_str_date_) + 1} = strings(date);
- plan.shock_int_date_{length(plan.shock_int_date_) + 1} = date - plan.date(1) + 1;
- plan.shock_paths_{length(plan.shock_paths_) + 1} = value;
+if ~ischar(expectation_type) || size(expectation_type,1) ~= 1
+ error(['in basic_plan the third argument should be a string containing the simulation type (''perfect_foresight'' or ''surprise'')']);
+end
+exogenous = strtrim(exogenous);
+ix = find(strcmp(exogenous, plan.exo_names));
+if isempty(ix)
+ error(['in basic_plan the second argument ' exogenous ' is not an exogenous variable']);
+end
+sdate = length(date);
+if sdate > 1
+ if date(1) < plan.date(1) || date(end) > plan.date(end)
+ error(['in basic_plan the fourth argument (date=' date ') must lay inside the plan.date ' plan.date]);
+ end
+else
+ if date < plan.date(1) || date > plan.date(end)
+ error(['in basic_plan the fourth argument (date=' date ') must lay iside the plan.date ' plan.date]);
+ end
+end
+if length(date) ~= length(value)
+ error(['in basic_plan the number of dates (' int2str(length(date)) ') is not equal to the numbers of shock (' int2str(length(value)) ') for exogenous variable ' exogenous]);
+end
+if ~isempty(plan.options_cond_fcst_.controlled_varexo)
+ common_var = find(ix == plan.options_cond_fcst_.controlled_varexo);
+ if ~isempty(common_var)
+ common_date = intersect(date, plan.constrained_date_{common_var});
+ if ~isempty(common_date)
+ [date_, i_date] = setdiff(date, common_date);
+ value = value(i_date);
+ if common_date.length > 1
+ the_dates = [cell2mat(strings(common_date(1))) ':' cell2mat(strings(common_date(end)))];
+ else
+ the_dates = cell2mat(strings(common_date));
+ end
+ warning(['Impossible case: ' plan.exo_names{plan.options_cond_fcst_.controlled_varexo(common_var)} ' is used both as a shock and as an endogenous variable to control the path of ' plan.endo_names{plan.constrained_vars_(common_var)} ' at the dates ' the_dates]);
+ warning('This shock will not be considered');
+ end
+ end
+end
+if isempty(plan.shock_vars_)
+ plan.shock_vars_ = ix;
+ if strcmp(expectation_type, 'perfect_foresight')
+ plan.shock_perfect_foresight_ = 1;
+ else
+ plan.shock_perfect_foresight_ = 0;
+ end
+else
+ plan.shock_vars_ = [plan.shock_vars_ ; ix];
+ if strcmp(expectation_type, 'perfect_foresight')
+ plan.shock_perfect_foresight_ = [plan.shock_perfect_foresight_ ; 1];
+ else
+ plan.shock_perfect_foresight_ = [plan.shock_perfect_foresight_ ; 0];
+ end
+end
+plan.shock_date_{length(plan.shock_date_) + 1} = date;
+plan.shock_str_date_{length(plan.shock_str_date_) + 1} = strings(date);
+plan.shock_int_date_{length(plan.shock_int_date_) + 1} = date - plan.date(1) + 1;
+plan.shock_paths_{length(plan.shock_paths_) + 1} = value;
diff --git a/matlab/bksup0.m b/matlab/bksup0.m
index c304b42a48b1013b45b5d69a3a51a31bd582aa8b..82dfaab94429f99a96326637d707cc2ca2151515 100644
--- a/matlab/bksup0.m
+++ b/matlab/bksup0.m
@@ -4,7 +4,7 @@ function d = bksup0(c,ny,jcf,iyf,icf,periods)
% INPUTS
% ny: number of endogenous variables
% jcf: variables index forward
-%
+%
% OUTPUTS
% d: vector of backsubstitution results
%
diff --git a/matlab/bksup1.m b/matlab/bksup1.m
index a6bcb3b9f3070afc3ea780925e3611417f75ea66..4a7340976bd15820e0a3b4f497a28c60087dfb9d 100644
--- a/matlab/bksup1.m
+++ b/matlab/bksup1.m
@@ -5,7 +5,7 @@ function d = bksup1(c,ny,jcf,iyf,periods)
% INPUTS
% ny: number of endogenous variables
% jcf: variables index forward
-%
+%
% OUTPUTS
% d: vector of backsubstitution results
%
@@ -40,4 +40,3 @@ for i = 2:periods
end
d = c(:,jcf) ;
-
diff --git a/matlab/bksupk.m b/matlab/bksupk.m
index 33f9c18b0008cfc1254f9b8a7847ec61a3807e4c..c33f0a551d8623261822912c5f61749bab5912be 100644
--- a/matlab/bksupk.m
+++ b/matlab/bksupk.m
@@ -68,7 +68,7 @@ while i <= options_.periods
c = fread(fid,[jcf,ny],'float64')' ;
d1(ir) = c(:,jcf)-c(:,icf)*d1(irf) ;
- ir = ir-ny ;
+ ir = ir-ny ;
irf = irf-ny ;
i = i+1;
end
diff --git a/matlab/bseastr.m b/matlab/bseastr.m
index 2ab688e0b81c0560c6659f3ab8e9f5c03cf47008..7222e2a7bc3051a7dc074ef4c17fa9e4ecfd5b70 100644
--- a/matlab/bseastr.m
+++ b/matlab/bseastr.m
@@ -33,16 +33,15 @@ for im = 1:m
for i = 1:min(length(key),length(temp))
if temp(i) > key(i)
h = mid - 1 ;
- break
+ break
else
l = mid + 1 ;
- break
+ break
end
end
else
x(im) = mid ;
- break
+ break
end
end
end
-
diff --git a/matlab/bvar_density.m b/matlab/bvar_density.m
index b147834ed6b37701508de8d416289ddeb53b1318..53eebebd1d8d761ce2e61c8bab2c37223c9c5bd6 100644
--- a/matlab/bvar_density.m
+++ b/matlab/bvar_density.m
@@ -37,16 +37,16 @@ for nlags = 1:maxnlags
[ny, nx, posterior, prior] = bvar_toolbox(nlags);
oo_.bvar.posterior{nlags}=posterior;
oo_.bvar.prior{nlags}=prior;
-
+
posterior_int = matrictint(posterior.S, posterior.df, posterior.XXi);
prior_int = matrictint(prior.S, prior.df, prior.XXi);
-
+
lik_nobs = posterior.df - prior.df;
-
+
log_dnsty = posterior_int - prior_int - 0.5*ny*lik_nobs*log(2*pi);
-
+
oo_.bvar.log_marginal_data_density(nlags)=log_dnsty;
-
+
skipline()
fprintf('The marginal log density of the BVAR(%g) model is equal to %10.4f\n', ...
nlags, log_dnsty);
@@ -61,7 +61,7 @@ function w = matrictint(S, df, XXi)
% S: parameter of inverse-Wishart distribution
% df: number of degrees of freedom of inverse-Wishart distribution
% XXi: first component of VCV matrix of matrix-normal distribution
-%
+%
% Computes the integral over (Phi, Sigma) of:
%
% det(Sigma)^(-k/2)*exp(-0.5*Tr((Phi-PhiHat)'*(XXi)^(-1)*(Phi-PhiHat)*Sigma^(-1)))*
diff --git a/matlab/bvar_forecast.m b/matlab/bvar_forecast.m
index d070884b4b3baade7aae42560d1fcb827c0b80cc..55fdf9ce9503b5985ab0438af82d6edebd0882b0 100644
--- a/matlab/bvar_forecast.m
+++ b/matlab/bvar_forecast.m
@@ -54,7 +54,7 @@ p = 0;
% Loop counter initialization
d = 0;
while d <= options_.bvar_replic
-
+
Sigma = rand_inverse_wishart(ny, posterior.df, S_inv_upper_chol);
% Option 'lower' of chol() not available in old versions of
@@ -62,15 +62,15 @@ while d <= options_.bvar_replic
Sigma_lower_chol = chol(Sigma)';
Phi = rand_matrix_normal(k, ny, posterior.PhiHat, Sigma_lower_chol, XXi_lower_chol);
-
+
% All the eigenvalues of the companion matrix have to be on or inside the unit circle
- Companion_matrix(1:ny,:) = Phi(1:ny*nlags,:)';
+ Companion_matrix(1:ny,:) = Phi(1:ny*nlags,:)';
test = (abs(eig(Companion_matrix)));
if any(test>1.0000000000001)
p = p+1;
end
d = d+1;
-
+
% Without shocks
lags_data = forecast_data.initval;
for t = 1:options_.forecast
@@ -80,7 +80,7 @@ while d <= options_.bvar_replic
lags_data(end,:) = y;
sims_no_shock(t, :, d) = y;
end
-
+
% With shocks
lags_data = forecast_data.initval;
for t = 1:options_.forecast
@@ -120,9 +120,9 @@ dyn_graph=dynare_graph_init(sprintf('BVAR forecasts (nlags = %d)', nlags), ny, {
for i = 1:ny
dyn_graph=dynare_graph(dyn_graph,[ sims_no_shock_median(:, i) ...
- sims_no_shock_up_conf(:, i) sims_no_shock_down_conf(:, i) ...
- sims_with_shocks_up_conf(:, i) sims_with_shocks_down_conf(:, i) ], ...
- options_.varobs{i});
+ sims_no_shock_up_conf(:, i) sims_no_shock_down_conf(:, i) ...
+ sims_with_shocks_up_conf(:, i) sims_with_shocks_down_conf(:, i) ], ...
+ options_.varobs{i});
end
dyn_saveas(dyn_graph.fh,[OutputDirectoryName '/' M_.fname '_BVAR_forecast_',num2str(nlags)],options_.nodisplay,options_.graph_format)
@@ -130,9 +130,9 @@ dyn_saveas(dyn_graph.fh,[OutputDirectoryName '/' M_.fname '_BVAR_forecast_',num2
% Compute RMSE
if ~isempty(forecast_data.realized_val)
-
+
sq_err_cumul = zeros(1, ny);
-
+
lags_data = forecast_data.initval;
for t = 1:size(forecast_data.realized_val, 1)
X = [ reshape(flipdim(lags_data, 1)', 1, ny*nlags) forecast_data.realized_xdata(t, :) ];
@@ -141,14 +141,14 @@ if ~isempty(forecast_data.realized_val)
lags_data(end,:) = y;
sq_err_cumul = sq_err_cumul + (y - forecast_data.realized_val(t, :)) .^ 2;
end
-
+
rmse = sqrt(sq_err_cumul / size(forecast_data.realized_val, 1));
-
+
fprintf('RMSE of BVAR(%d):\n', nlags);
-
+
for i = 1:length(options_.varobs)
fprintf('%s: %10.4f\n', options_.varobs{i}, rmse(i));
- end
+ end
end
% Store results
diff --git a/matlab/bvar_irf.m b/matlab/bvar_irf.m
index c3f6dfc7abfcd897988fb491c04bddc3840d6996..408eb55443d145b35ddbe4f027dcc24107dbc945 100644
--- a/matlab/bvar_irf.m
+++ b/matlab/bvar_irf.m
@@ -54,7 +54,7 @@ p = 0;
sampled_irfs = NaN(ny, ny, options_.irf, options_.bvar_replic);
for draw=1:options_.bvar_replic
-
+
% Get a covariance matrix from an inverted Wishart distribution.
Sigma = rand_inverse_wishart(ny, posterior.df, S_inv_upper_chol);
Sigma_upper_chol = chol(Sigma);
@@ -62,10 +62,10 @@ for draw=1:options_.bvar_replic
% Get the Autoregressive matrices from a matrix variate distribution.
Phi = rand_matrix_normal(k, ny, posterior.PhiHat, Sigma_lower_chol, XXi_lower_chol);
-
+
% Form the companion matrix.
- Companion_matrix(1:ny,:) = transpose(Phi(1:ny*nlags,:));
-
+ Companion_matrix(1:ny,:) = transpose(Phi(1:ny*nlags,:));
+
% All the eigenvalues of the companion matrix have to be on or
% inside the unit circle to rule out explosive time series.
test = (abs(eig(Companion_matrix)));
@@ -78,7 +78,7 @@ for draw=1:options_.bvar_replic
elseif strcmpi(identification,'SquareRoot')
StructuralMat = sqrtm(Sigma);
end
-
+
% Build the IRFs...
lags_data = zeros(ny,ny*nlags) ;
sampled_irfs(:,:,1,draw) = Sigma_lower_chol ;
@@ -88,7 +88,7 @@ for draw=1:options_.bvar_replic
lags_data(:,ny+1:end) = lags_data(:,1:end-ny) ;
lags_data(:,1:ny) = sampled_irfs(:,:,t,draw) ;
end
-
+
end
if p > 0
@@ -106,7 +106,7 @@ sort_idx = round((0.5 + [-options_.bvar.conf_sig, options_.bvar.conf_sig, .0]/2)
posterior_down_conf_irfs = sorted_irfs(:,:,:,sort_idx(1));
posterior_up_conf_irfs = sorted_irfs(:,:,:,sort_idx(2));
-posterior_median_irfs = sorted_irfs(:,:,:,sort_idx(3));
+posterior_median_irfs = sorted_irfs(:,:,:,sort_idx(3));
number_of_columns = fix(sqrt(ny));
number_of_rows = ceil(ny / number_of_columns) ;
diff --git a/matlab/bvar_toolbox.m b/matlab/bvar_toolbox.m
index 76c95ef9de6964105a31c5db690a849022fcac04..67d505f001ddf60e1caffb0b84d65133136208fb 100644
--- a/matlab/bvar_toolbox.m
+++ b/matlab/bvar_toolbox.m
@@ -29,7 +29,7 @@ function [ny, nx, posterior, prior, forecast_data] = bvar_toolbox(nlags)
% forecasting, of size options_.forecast*nx (actually only
% contains "1" values for the constant term if nx ~= 0)
% - realized_val: only non-empty if options_.nobs doesn't point
-% to the end of sample
+% to the end of sample
% In that case, contains values of endogenous variables after
% options_.nobs and up to the end of the sample
% - realized_xdata: contains values of exogenous variables after
@@ -67,7 +67,7 @@ options_ = set_default_option(options_, 'nobs', size(dataset,1)-options_.first_o
if (options_.first_obs+options_.nobs-1)> size(dataset,1)
fprintf('Incorrect or missing specification of the number of observations. nobs can be at most %4u\n',size(dataset,1)-options_.first_obs+1);
- error('Inconsistent number of observations.')
+ error('Inconsistent number of observations.')
end
% Parameters for prior
@@ -160,7 +160,7 @@ function [ydum,xdum,breaks]=varprior(nv,nx,lags,mnprior,vprior)
%function [ydum,xdum,breaks]=varprior(nv,nx,lags,mnprior,vprior)
% ydum, xdum: dummy observation data that implement the prior
% breaks: vector of points in the dummy data after which new dummy obs's start
-% Set breaks=T+[0;breaks], ydata=[ydata;ydum], xdum=[xdata;xdum], where
+% Set breaks=T+[0;breaks], ydata=[ydata;ydum], xdum=[xdata;xdum], where
% actual data matrix has T rows, in preparing input for rfvar3
% nv,nx,lags: VAR dimensions
% mnprior.tight:Overall tightness of Minnesota prior
@@ -175,8 +175,8 @@ function [ydum,xdum,breaks]=varprior(nv,nx,lags,mnprior,vprior)
% taken to include the sum-of-coefficients and co-persistence components
% that are implemented directly in rfvar3.m. The diagonal prior on v, combined
% with sum-of-coefficients and co-persistence components and with the unit own-first-lag
-% prior mean generates larger prior variances for own than for cross-effects even in
-% this formulation, but here there is no way to shrink toward a set of unconstrained
+% prior mean generates larger prior variances for own than for cross-effects even in
+% this formulation, but here there is no way to shrink toward a set of unconstrained
% univariate AR's.
% Original file downloaded from:
@@ -228,9 +228,9 @@ function var=rfvar3(ydata,lags,xdata,breaks,lambda,mu)
% discontinuities in the data (e.g. war years) and for the possibility of
% adding dummy observations to implement a prior. This must be a column vector.
% Note that a single dummy observation becomes lags+1 rows of the data matrix,
-% with a break separating it from the rest of the data. The function treats the
+% with a break separating it from the rest of the data. The function treats the
% first lags observations at the top and after each "break" in ydata and xdata as
-% initial conditions.
+% initial conditions.
% lambda: weight on "co-persistence" prior dummy observations. This expresses
% belief that when data on *all* y's are stable at their initial levels, they will
% tend to persist at that level. lambda=5 is a reasonable first try. With lambda<0,
@@ -243,7 +243,7 @@ function var=rfvar3(ydata,lags,xdata,breaks,lambda,mu)
% one of these for each variable. A reasonable first guess is mu=2.
% The program assumes that the first lags rows of ydata and xdata are real data, not dummies.
% Dummy observations should go at the end, if any. If pre-sample x's are not available,
-% repeating the initial xdata(lags+1,:) row or copying xdata(lags+1:2*lags,:) into
+% repeating the initial xdata(lags+1,:) row or copying xdata(lags+1:2*lags,:) into
% xdata(1:lags,:) are reasonable subsititutes. These values are used in forming the
% persistence priors.
@@ -280,7 +280,7 @@ for is = 1:length(smpl)
end
X = [X(:,:) xdata(smpl,:)];
y = ydata(smpl,:);
-% Everything now set up with input data for y=Xb+e
+% Everything now set up with input data for y=Xb+e
% Add persistence dummies
if lambda ~= 0 || mu > 0
diff --git a/matlab/cartesian_product_of_sets.m b/matlab/cartesian_product_of_sets.m
index 8901886ff40a59a2ebde75fd29cabcc10ad0c810..7362ba671f106203dfa8dd4111a37e3279d51669 100644
--- a/matlab/cartesian_product_of_sets.m
+++ b/matlab/cartesian_product_of_sets.m
@@ -5,7 +5,7 @@ function cprod = cartesian_product_of_sets(varargin)
%! @deftypefn {Function File} {@var{cprod} =} cartesian_product_of_sets (@var{a},@var{b}, ...)
%! @anchor{cartesian_product_of_sets}
%! @sp 1
-%! Computes A_1 * A_2 * .... * A_n with a generic set A_i = {e_1,e_2,e_3,...} where e_i is a string
+%! Computes A_1 * A_2 * .... * A_n with a generic set A_i = {e_1,e_2,e_3,...} where e_i is a string
%! or a number. It is assumed that each element e_i is unique in set A_i.
%! @sp 2
%! @strong{Inputs}
diff --git a/matlab/cellofchar2mfile.m b/matlab/cellofchar2mfile.m
index 6cfce7c5fc563f5e5543a6bcc9c7517526b4f705..206a6bbcea410c99f85acacad2b06dffc853ad61 100644
--- a/matlab/cellofchar2mfile.m
+++ b/matlab/cellofchar2mfile.m
@@ -2,11 +2,11 @@ function cellofchar2mfile(fname, c, cname)
% Write a cell of char in a matlab script.
%
-% INPUTS
+% INPUTS
% - fname [string] name of the file where c is to be saved.
% - c [cell] a two dimensional cell of char.
%
-% OUTPUTS
+% OUTPUTS
% None.
% Copyright (C) 2015 Dynare Team
diff --git a/matlab/check_for_calibrated_covariances.m b/matlab/check_for_calibrated_covariances.m
index b6d960c2b6f66532c848de620f35a6fec201f6fc..ecc5d85fe33f06ec557b7a1d19dd13eebcee3398 100644
--- a/matlab/check_for_calibrated_covariances.m
+++ b/matlab/check_for_calibrated_covariances.m
@@ -42,7 +42,7 @@ calibrated_covariance_pos=covariance_pos(~ismember(covariance_pos,correlation_po
if any(calibrated_covariance_pos)
[rows, columns]=ind2sub(size(M.Sigma_e),calibrated_covariance_pos); %find linear indices of lower triangular covariance entries
estim_params.calibrated_covariances.position=[calibrated_covariance_pos;sub2ind(size(M.Sigma_e),columns,rows)]; %get linear entries of upper triangular parts
- estim_params.calibrated_covariances.cov_value=Sigma_e_calibrated(estim_params.calibrated_covariances.position);
+ estim_params.calibrated_covariances.cov_value=Sigma_e_calibrated(estim_params.calibrated_covariances.position);
end
correlation_pos_ME=find(tril(M.Correlation_matrix_ME,-1)); %off-diagonal elements set by correlations after accounting for estimation
@@ -50,6 +50,5 @@ calibrated_covariance_pos_ME=covariance_pos_ME(~ismember(covariance_pos_ME,corre
if any(calibrated_covariance_pos_ME)
[rows, columns]=ind2sub(size(M.H),calibrated_covariance_pos_ME); %find linear indices of lower triangular covariance entries
estim_params.calibrated_covariances_ME.position=[calibrated_covariance_pos_ME;sub2ind(size(M.H),columns,rows)]; %get linear entries of upper triangular parts
- estim_params.calibrated_covariances_ME.cov_value=H_calibrated(estim_params.calibrated_covariances_ME.position);
+ estim_params.calibrated_covariances_ME.cov_value=H_calibrated(estim_params.calibrated_covariances_ME.position);
end
-
diff --git a/matlab/check_list_of_variables.m b/matlab/check_list_of_variables.m
index be821719f04b1de804b761f2fba9c965c489f29b..7bf05e4783fdaf6c6f3719bfba4b744fdeb7d1b3 100644
--- a/matlab/check_list_of_variables.m
+++ b/matlab/check_list_of_variables.m
@@ -1,17 +1,17 @@
function varlist = check_list_of_variables(options_, M_, varlist)
% This function defines, if necessary, the list of endogenous variables
-% for which the posterior statistics have to be computed.
-%
+% for which the posterior statistics have to be computed.
%
-% INPUTS
+%
+% INPUTS
%
% options_ [structure] Dynare structure.
% M_ [structure] Dynare structure (related to model definition).
% varlist [string] Array of strings with name of the endogenous variables.
-%
-% OUTPUTS
-% varlist [string]
-%
+%
+% OUTPUTS
+% varlist [string]
+%
% SPECIAL REQUIREMENTS
% Copyright (C) 2003-2017 Dynare Team
@@ -63,7 +63,7 @@ if ~isempty(varlist) && ~isempty(options_.endo_vars_for_moment_computations_in_e
error('You cannot use the consider_all_endogenous or consider_all_observed options when listing variables after the estimation command')
elseif isempty(varlist) && ~isempty(options_.endo_vars_for_moment_computations_in_estimation)
if strcmp(options_.endo_vars_for_moment_computations_in_estimation,'all_endogenous_variables')
- varlist = M_.endo_names(1:M_.orig_endo_nbr, :);
+ varlist = M_.endo_names(1:M_.orig_endo_nbr, :);
elseif strcmp(options_.endo_vars_for_moment_computations_in_estimation,'only_observed_variables')
varlist = char(options_.varobs');
else
@@ -106,7 +106,7 @@ elseif isempty(varlist) && isempty(options_.endo_vars_for_moment_computations_in
end
if ~isempty(cas)
string = [ cas , ' will be computed for the ' num2str(M_.endo_nbr) ' endogenous variables'];
- string = [ string ' of your model, this can be very long....'];
+ string = [ string ' of your model, this can be very long....'];
format_text(string, 10)
if options_.nointeractive
% Default behaviour is to consider all the endogenous variables.
diff --git a/matlab/check_matlab_path.m b/matlab/check_matlab_path.m
index 3817def6be90e02781b2a85254c74ca36fcfa03d..e98988da122cf4ebdc480f13ea21b1b063885c63 100644
--- a/matlab/check_matlab_path.m
+++ b/matlab/check_matlab_path.m
@@ -1,5 +1,5 @@
function check_matlab_path(change_path_flag)
-
+
% Copyright (C) 2015-2016 Dynare Team
%
% This file is part of Dynare.
@@ -82,7 +82,7 @@ else
warning(msg);
skipline()
rmpath(DYNARE_PATH)
- addpath(DYNARE_PATH)
+ addpath(DYNARE_PATH)
end
warning on backtrace
end
@@ -90,7 +90,7 @@ else
else
% Check that the user did not put all the subfolders in the path.
% => If DYNARE_PATH/qz is in the path while mjdgges dll is available
- % it most likely means that user wrongly put all subfolders in the
+ % it most likely means that user wrongly put all subfolders in the
% matlab's path!
mexpath = add_path_to_mex_files([DYNARE_PATH filesep], false);
MATLAB_PATH = path2cell(MATLAB_PATH);
@@ -107,40 +107,40 @@ else
end
function q = path2cell(p)
- % Converts the output of path() to a cell
- s = strfind(p,pathsep);
- n = length(s)+1;
- q = cell(n,1);
- q(1) = {p(1:s(1)-1)};
- q(n) = {p(s(end)+1:end)};
- for i=2:n-1
- q(i) = {p(s(i-1)+1:s(i)-1)};
- end
-
+% Converts the output of path() to a cell
+s = strfind(p,pathsep);
+n = length(s)+1;
+q = cell(n,1);
+q(1) = {p(1:s(1)-1)};
+q(n) = {p(s(end)+1:end)};
+for i=2:n-1
+ q(i) = {p(s(i-1)+1:s(i)-1)};
+end
+
function flist = getallroutinenames(p, excludedsubfolders)
- if nargin<2
- excludedsubfolders = {};
- end
- flist={};
- %get m-files in this directory
- dd = dir([p,filesep '*.m']);
- temp=struct2cell(dd);
- flist=[flist temp(1,:)];
- %deal with subdirectories
- dlist=getalldirectories(p,excludedsubfolders); %first call with excluded directories
- for ii=1:length(dlist)
- flist=[flist getallroutinenames([ p filesep dlist{ii}])]; %recursive calls without subfolders
- end
-
+if nargin<2
+ excludedsubfolders = {};
+end
+flist={};
+%get m-files in this directory
+dd = dir([p,filesep '*.m']);
+temp=struct2cell(dd);
+flist=[flist temp(1,:)];
+%deal with subdirectories
+dlist=getalldirectories(p,excludedsubfolders); %first call with excluded directories
+for ii=1:length(dlist)
+ flist=[flist getallroutinenames([ p filesep dlist{ii}])]; %recursive calls without subfolders
+end
+
function dlist = getalldirectories(p,excluded_directories)
- if nargin<2
- excluded_directories = {};
- end
- dd = dir(p);
- dir_result=struct2cell(dd);
- directory_indicator=cell2mat(dir_result(4,:));
- dlist = dir_result(1,directory_indicator==1 & ~strcmp('.',dir_result(1,:)) & ~strcmp('..',dir_result(1,:)) & ~ismember(dir_result(1,:),excluded_directories));
+if nargin<2
+ excluded_directories = {};
+end
+dd = dir(p);
+dir_result=struct2cell(dd);
+directory_indicator=cell2mat(dir_result(4,:));
+dlist = dir_result(1,directory_indicator==1 & ~strcmp('.',dir_result(1,:)) & ~strcmp('..',dir_result(1,:)) & ~ismember(dir_result(1,:),excluded_directories));
function n = position(i, currentpath)
- n = length(strfind(currentpath(1:i), pathsep));
\ No newline at end of file
+n = length(strfind(currentpath(1:i), pathsep));
\ No newline at end of file
diff --git a/matlab/check_posterior_analysis_data.m b/matlab/check_posterior_analysis_data.m
index b1d25b3d131dd2b35f0665098a2ddacaf4c92218..2e8ac01010571153ef671fae50ef3f3ebdfd325b 100644
--- a/matlab/check_posterior_analysis_data.m
+++ b/matlab/check_posterior_analysis_data.m
@@ -1,13 +1,13 @@
function [info,description] = check_posterior_analysis_data(type,M_)
% function [info,description] = check_posterior_analysis_data(type,M_)
-% Checks the status of posterior analysis and in particular if files need to be
+% Checks the status of posterior analysis and in particular if files need to be
% created or updated; called by posterior_analysis.m
-%
+%
% Inputs:
% type [string] name of the posterior moment considered
% M_ [structure] Dynare model structure
-%
-% Outputs:
+%
+% Outputs:
% info [scalar] return code
% info = 1; % select_posterior_draws has to be called first.
% info = 2; % _posterior_draws files have to be updated.
@@ -99,7 +99,7 @@ else
number_of_the_last_post_data_file = length(pdfinfo);
name_of_the_last_post_data_file = ...
[ pwd filesep MetropolisFolder filesep ...
- M_.fname '_' ...
+ M_.fname '_' ...
generic_post_data_file_name ...
int2str(number_of_the_last_post_data_file) ...
'.mat' ];
@@ -108,7 +108,7 @@ else
info = 5; % posterior data files have to be updated.
if nargout>1
description = 'posterior data files have to be updated.';
- end
+ end
else
info = 6; % Ok (nothing to do ;-)
if nargout>1
diff --git a/matlab/check_posterior_sampler_options.m b/matlab/check_posterior_sampler_options.m
index cb555ea1a17f6fda0893b77c48fcfc7e67592b38..4683639f3af1418b084e16d42ad58e0f3645093c 100644
--- a/matlab/check_posterior_sampler_options.m
+++ b/matlab/check_posterior_sampler_options.m
@@ -40,339 +40,339 @@ if init
% set default options and user defined options
posterior_sampler_options.posterior_sampling_method = options_.posterior_sampler_options.posterior_sampling_method;
posterior_sampler_options.bounds = bounds;
-
+
switch posterior_sampler_options.posterior_sampling_method
-
- case 'random_walk_metropolis_hastings'
- posterior_sampler_options.parallel_bar_refresh_rate=50;
- posterior_sampler_options.serial_bar_refresh_rate=3;
- posterior_sampler_options.parallel_bar_title='RWMH';
- posterior_sampler_options.serial_bar_title='RW Metropolis-Hastings';
-
- % default options
- posterior_sampler_options = add_fields_(posterior_sampler_options,options_.posterior_sampler_options.rwmh);
-
- % user defined options
- if ~isempty(options_.posterior_sampler_options.sampling_opt)
- options_list = read_key_value_string(options_.posterior_sampler_options.sampling_opt);
- for i=1:rows(options_list)
- switch options_list{i,1}
-
- case 'proposal_distribution'
- if ~(strcmpi(options_list{i,2}, 'rand_multivariate_student') || ...
- strcmpi(options_list{i,2}, 'rand_multivariate_normal'))
- error(['initial_estimation_checks:: the proposal_distribution option to estimation takes either ' ...
- 'rand_multivariate_student or rand_multivariate_normal as options']);
- else
- posterior_sampler_options.proposal_distribution=options_list{i,2};
- end
-
-
- case 'student_degrees_of_freedom'
- if options_list{i,2} <= 0
- error('initial_estimation_checks:: the student_degrees_of_freedom takes a positive integer argument');
- else
- posterior_sampler_options.student_degrees_of_freedom=options_list{i,2};
- end
-
- case 'use_mh_covariance_matrix'
- % indicates to use the covariance matrix from previous iterations to
- % define the covariance of the proposal distribution
- % default = 0
- posterior_sampler_options.use_mh_covariance_matrix = options_list{i,2};
- options_.use_mh_covariance_matrix = options_list{i,2};
- case 'scale_file'
- % load optimal_mh_scale parameter if previous run was with mode_compute=6
- % will overwrite jscale from set_prior.m
- if exist(options_list{i,2},'file') || exist([options_list{i,2},'.mat'],'file')
- tmp = load(options_list{i,2},'Scale');
- global bayestopt_
- bayestopt_.mh_jscale = tmp.Scale;
- options_.mh_jscale = tmp.Scale;
- bayestopt_.jscale = ones(size(bounds.lb,1),1)*tmp.Scale;
-% options_.mh_init_scale = 2*options_.mh_jscale;
- else
- error('initial_estimation_checks:: The specified mh_scale_file does not exist.')
- end
- case 'save_tmp_file'
- posterior_sampler_options.save_tmp_file = options_list{i,2};
- otherwise
- warning(['rwmh_sampler: Unknown option (' options_list{i,1} ')!'])
+
+ case 'random_walk_metropolis_hastings'
+ posterior_sampler_options.parallel_bar_refresh_rate=50;
+ posterior_sampler_options.serial_bar_refresh_rate=3;
+ posterior_sampler_options.parallel_bar_title='RWMH';
+ posterior_sampler_options.serial_bar_title='RW Metropolis-Hastings';
+
+ % default options
+ posterior_sampler_options = add_fields_(posterior_sampler_options,options_.posterior_sampler_options.rwmh);
+
+ % user defined options
+ if ~isempty(options_.posterior_sampler_options.sampling_opt)
+ options_list = read_key_value_string(options_.posterior_sampler_options.sampling_opt);
+ for i=1:rows(options_list)
+ switch options_list{i,1}
+
+ case 'proposal_distribution'
+ if ~(strcmpi(options_list{i,2}, 'rand_multivariate_student') || ...
+ strcmpi(options_list{i,2}, 'rand_multivariate_normal'))
+ error(['initial_estimation_checks:: the proposal_distribution option to estimation takes either ' ...
+ 'rand_multivariate_student or rand_multivariate_normal as options']);
+ else
+ posterior_sampler_options.proposal_distribution=options_list{i,2};
+ end
+
+
+ case 'student_degrees_of_freedom'
+ if options_list{i,2} <= 0
+ error('initial_estimation_checks:: the student_degrees_of_freedom takes a positive integer argument');
+ else
+ posterior_sampler_options.student_degrees_of_freedom=options_list{i,2};
+ end
+
+ case 'use_mh_covariance_matrix'
+ % indicates to use the covariance matrix from previous iterations to
+ % define the covariance of the proposal distribution
+ % default = 0
+ posterior_sampler_options.use_mh_covariance_matrix = options_list{i,2};
+ options_.use_mh_covariance_matrix = options_list{i,2};
+ case 'scale_file'
+ % load optimal_mh_scale parameter if previous run was with mode_compute=6
+ % will overwrite jscale from set_prior.m
+ if exist(options_list{i,2},'file') || exist([options_list{i,2},'.mat'],'file')
+ tmp = load(options_list{i,2},'Scale');
+ global bayestopt_
+ bayestopt_.mh_jscale = tmp.Scale;
+ options_.mh_jscale = tmp.Scale;
+ bayestopt_.jscale = ones(size(bounds.lb,1),1)*tmp.Scale;
+ % options_.mh_init_scale = 2*options_.mh_jscale;
+ else
+ error('initial_estimation_checks:: The specified mh_scale_file does not exist.')
end
+ case 'save_tmp_file'
+ posterior_sampler_options.save_tmp_file = options_list{i,2};
+ otherwise
+ warning(['rwmh_sampler: Unknown option (' options_list{i,1} ')!'])
end
end
-
- case 'tailored_random_block_metropolis_hastings'
- posterior_sampler_options.parallel_bar_refresh_rate=5;
- posterior_sampler_options.serial_bar_refresh_rate=1;
- posterior_sampler_options.parallel_bar_title='TaRB-MH';
- posterior_sampler_options.serial_bar_title='TaRB Metropolis-Hastings';
-
- % default options
- posterior_sampler_options = add_fields_(posterior_sampler_options,options_.posterior_sampler_options.tarb);
-
- % user defined options
- if ~isempty(options_.posterior_sampler_options.sampling_opt)
- options_list = read_key_value_string(options_.posterior_sampler_options.sampling_opt);
- for i=1:rows(options_list)
-
- switch options_list{i,1}
-
- case 'proposal_distribution'
- if ~(strcmpi(options_list{i,2}, 'rand_multivariate_student') || ...
- strcmpi(options_list{i,2}, 'rand_multivariate_normal'))
- error(['initial_estimation_checks:: the proposal_distribution option to estimation takes either ' ...
- 'rand_multivariate_student or rand_multivariate_normal as options']);
- else
- posterior_sampler_options.proposal_distribution=options_list{i,2};
- end
-
-
- case 'student_degrees_of_freedom'
- if options_list{i,2} <= 0
- error('initial_estimation_checks:: the student_degrees_of_freedom takes a positive integer argument');
- else
- posterior_sampler_options.student_degrees_of_freedom=options_list{i,2};
- end
-
- case 'mode_compute'
- posterior_sampler_options.mode_compute=options_list{i,2};
-
- case 'optim'
- posterior_sampler_options.optim_opt=options_list{i,2};
-
- case 'new_block_probability'
- if options_list{i,2}<0 || options_list{i,2}>1
- error('check_posterior_sampler_options:: The tarb new_block_probability must be between 0 and 1!')
- else
- posterior_sampler_options.new_block_probability=options_list{i,2};
- end
- case 'scale_file'
- % load optimal_mh_scale parameter if previous run was with mode_compute=6
- % will overwrite jscale from set_prior.m
- if exist(options_list{i,2},'file') || exist([options_list{i,2},'.mat'],'file')
- tmp = load(options_list{i,2},'Scale');
- global bayestopt_
- bayestopt_.mh_jscale = tmp.Scale;
- options_.mh_jscale = tmp.Scale;
- bayestopt_.jscale = ones(size(bounds.lb,1),1)*tmp.Scale;
-% options_.mh_init_scale = 2*options_.mh_jscale;
- else
- error('initial_estimation_checks:: The specified scale_file does not exist.')
- end
- case 'save_tmp_file'
- posterior_sampler_options.save_tmp_file = options_list{i,2};
-
- otherwise
- warning(['tarb_sampler: Unknown option (' options_list{i,1} ')!'])
-
+ end
+
+ case 'tailored_random_block_metropolis_hastings'
+ posterior_sampler_options.parallel_bar_refresh_rate=5;
+ posterior_sampler_options.serial_bar_refresh_rate=1;
+ posterior_sampler_options.parallel_bar_title='TaRB-MH';
+ posterior_sampler_options.serial_bar_title='TaRB Metropolis-Hastings';
+
+ % default options
+ posterior_sampler_options = add_fields_(posterior_sampler_options,options_.posterior_sampler_options.tarb);
+
+ % user defined options
+ if ~isempty(options_.posterior_sampler_options.sampling_opt)
+ options_list = read_key_value_string(options_.posterior_sampler_options.sampling_opt);
+ for i=1:rows(options_list)
+
+ switch options_list{i,1}
+
+ case 'proposal_distribution'
+ if ~(strcmpi(options_list{i,2}, 'rand_multivariate_student') || ...
+ strcmpi(options_list{i,2}, 'rand_multivariate_normal'))
+ error(['initial_estimation_checks:: the proposal_distribution option to estimation takes either ' ...
+ 'rand_multivariate_student or rand_multivariate_normal as options']);
+ else
+ posterior_sampler_options.proposal_distribution=options_list{i,2};
end
-
+
+
+ case 'student_degrees_of_freedom'
+ if options_list{i,2} <= 0
+ error('initial_estimation_checks:: the student_degrees_of_freedom takes a positive integer argument');
+ else
+ posterior_sampler_options.student_degrees_of_freedom=options_list{i,2};
+ end
+
+ case 'mode_compute'
+ posterior_sampler_options.mode_compute=options_list{i,2};
+
+ case 'optim'
+ posterior_sampler_options.optim_opt=options_list{i,2};
+
+ case 'new_block_probability'
+ if options_list{i,2}<0 || options_list{i,2}>1
+ error('check_posterior_sampler_options:: The tarb new_block_probability must be between 0 and 1!')
+ else
+ posterior_sampler_options.new_block_probability=options_list{i,2};
+ end
+ case 'scale_file'
+ % load optimal_mh_scale parameter if previous run was with mode_compute=6
+ % will overwrite jscale from set_prior.m
+ if exist(options_list{i,2},'file') || exist([options_list{i,2},'.mat'],'file')
+ tmp = load(options_list{i,2},'Scale');
+ global bayestopt_
+ bayestopt_.mh_jscale = tmp.Scale;
+ options_.mh_jscale = tmp.Scale;
+ bayestopt_.jscale = ones(size(bounds.lb,1),1)*tmp.Scale;
+ % options_.mh_init_scale = 2*options_.mh_jscale;
+ else
+ error('initial_estimation_checks:: The specified scale_file does not exist.')
+ end
+ case 'save_tmp_file'
+ posterior_sampler_options.save_tmp_file = options_list{i,2};
+
+ otherwise
+ warning(['tarb_sampler: Unknown option (' options_list{i,1} ')!'])
+
end
-
+
end
-
- case 'independent_metropolis_hastings'
- posterior_sampler_options.parallel_bar_refresh_rate=50;
- posterior_sampler_options.serial_bar_refresh_rate=3;
- posterior_sampler_options.parallel_bar_title='IMH';
- posterior_sampler_options.serial_bar_title='Ind. Metropolis-Hastings';
-
- % default options
- posterior_sampler_options = add_fields_(posterior_sampler_options,options_.posterior_sampler_options.imh);
-
- % user defined options
- if ~isempty(options_.posterior_sampler_options.sampling_opt)
- options_list = read_key_value_string(options_.posterior_sampler_options.sampling_opt);
- for i=1:rows(options_list)
- switch options_list{i,1}
-
- case 'proposal_distribution'
- if ~(strcmpi(options_list{i,2}, 'rand_multivariate_student') || ...
- strcmpi(options_list{i,2}, 'rand_multivariate_normal'))
- error(['initial_estimation_checks:: the proposal_distribution option to estimation takes either ' ...
- 'rand_multivariate_student or rand_multivariate_normal as options']);
- else
- posterior_sampler_options.proposal_distribution=options_list{i,2};
- end
-
-
- case 'student_degrees_of_freedom'
- if options_list{i,2} <= 0
- error('initial_estimation_checks:: the student_degrees_of_freedom takes a positive integer argument');
- else
- posterior_sampler_options.student_degrees_of_freedom=options_list{i,2};
- end
-
- case 'use_mh_covariance_matrix'
- % indicates to use the covariance matrix from previous iterations to
- % define the covariance of the proposal distribution
- % default = 0
- posterior_sampler_options.use_mh_covariance_matrix = options_list{i,2};
- options_.use_mh_covariance_matrix = options_list{i,2};
-
- case 'save_tmp_file'
- posterior_sampler_options.save_tmp_file = options_list{i,2};
-
- otherwise
- warning(['imh_sampler: Unknown option (' options_list{i,1} ')!'])
+
+ end
+
+ case 'independent_metropolis_hastings'
+ posterior_sampler_options.parallel_bar_refresh_rate=50;
+ posterior_sampler_options.serial_bar_refresh_rate=3;
+ posterior_sampler_options.parallel_bar_title='IMH';
+ posterior_sampler_options.serial_bar_title='Ind. Metropolis-Hastings';
+
+ % default options
+ posterior_sampler_options = add_fields_(posterior_sampler_options,options_.posterior_sampler_options.imh);
+
+ % user defined options
+ if ~isempty(options_.posterior_sampler_options.sampling_opt)
+ options_list = read_key_value_string(options_.posterior_sampler_options.sampling_opt);
+ for i=1:rows(options_list)
+ switch options_list{i,1}
+
+ case 'proposal_distribution'
+ if ~(strcmpi(options_list{i,2}, 'rand_multivariate_student') || ...
+ strcmpi(options_list{i,2}, 'rand_multivariate_normal'))
+ error(['initial_estimation_checks:: the proposal_distribution option to estimation takes either ' ...
+ 'rand_multivariate_student or rand_multivariate_normal as options']);
+ else
+ posterior_sampler_options.proposal_distribution=options_list{i,2};
end
+
+
+ case 'student_degrees_of_freedom'
+ if options_list{i,2} <= 0
+ error('initial_estimation_checks:: the student_degrees_of_freedom takes a positive integer argument');
+ else
+ posterior_sampler_options.student_degrees_of_freedom=options_list{i,2};
+ end
+
+ case 'use_mh_covariance_matrix'
+ % indicates to use the covariance matrix from previous iterations to
+ % define the covariance of the proposal distribution
+ % default = 0
+ posterior_sampler_options.use_mh_covariance_matrix = options_list{i,2};
+ options_.use_mh_covariance_matrix = options_list{i,2};
+
+ case 'save_tmp_file'
+ posterior_sampler_options.save_tmp_file = options_list{i,2};
+
+ otherwise
+ warning(['imh_sampler: Unknown option (' options_list{i,1} ')!'])
end
end
-
-
- case 'slice'
- posterior_sampler_options.parallel_bar_refresh_rate=1;
- posterior_sampler_options.serial_bar_refresh_rate=1;
- posterior_sampler_options.parallel_bar_title='SLICE';
- posterior_sampler_options.serial_bar_title='SLICE';
-
- % default options
- posterior_sampler_options = add_fields_(posterior_sampler_options,options_.posterior_sampler_options.slice);
-
- % user defined options
- if ~isempty(options_.posterior_sampler_options.sampling_opt)
- options_list = read_key_value_string(options_.posterior_sampler_options.sampling_opt);
- for i=1:rows(options_list)
- switch options_list{i,1}
- case 'rotated'
- % triggers rotated slice iterations using a covariance
- % matrix from initial burn-in iterations
- % must be associated with:
- % <use_mh_covariance_matrix> or <slice_initialize_with_mode>
- % default = 0
- posterior_sampler_options.rotated = options_list{i,2};
-
- case 'mode'
- % for multimodal posteriors, provide the list of modes as a
- % matrix, ordered by column, i.e. [x1 x2 x3] for three
- % modes x1 x2 x3
- % MR note: not sure this is possible with the
- % read_key_value_string ???
- % if this is not possible <mode_files> does to job in any case
- % This will automatically trigger <rotated>
- % default = []
- tmp_mode = options_list{i,2};
- for j=1:size(tmp_mode,2)
- posterior_sampler_options.mode(j).m = tmp_mode(:,j);
- end
-
- case 'mode_files'
- % for multimodal posteriors provide the name of
- % a file containing a variable array xparams = [nparam * nmodes]
- % one column per mode. With this info, the code will automatically
- % set the <mode> option.
- % This will automatically trigger <rotated>
- % default = []
- posterior_sampler_options.mode_files = options_list{i,2};
-
- case 'slice_initialize_with_mode'
- % the default for slice is to set mode_compute = 0 in the
- % preprocessor and start the chain(s) from a random location in the prior.
- % This option first runs the optimizer and then starts the
- % chain from the mode. Associated with optios <rotated>, it will
- % use invhess from the mode to perform rotated slice
- % iterations.
- % default = 0
- posterior_sampler_options.slice_initialize_with_mode = options_list{i,2};
-
- case 'initial_step_size'
- % sets the initial size of the interval in the STEPPING-OUT PROCEDURE
- % the initial_step_size must be a real number in [0, 1],
- % and it sets the size as a proportion of the prior bounds,
- % i.e. the size will be initial_step_size*(UB-LB)
- % slice sampler requires prior_truncation > 0!
- % default = 0.8
- if options_list{i,2}<=0 || options_list{i,2}>=1
- error('check_posterior_sampler_options:: slice initial_step_size must be between 0 and 1')
- else
- posterior_sampler_options.initial_step_size=options_list{i,2};
- end
- case 'use_mh_covariance_matrix'
- % in association with <rotated> indicates to use the
- % covariance matrix from previous iterations to define the
- % rotated slice
- % default = 0
- posterior_sampler_options.use_mh_covariance_matrix = options_list{i,2};
- options_.use_mh_covariance_matrix = options_list{i,2};
-
- case 'save_tmp_file'
- posterior_sampler_options.save_tmp_file = options_list{i,2};
-
- otherwise
- warning(['slice_sampler: Unknown option (' options_list{i,1} ')!'])
+ end
+
+
+ case 'slice'
+ posterior_sampler_options.parallel_bar_refresh_rate=1;
+ posterior_sampler_options.serial_bar_refresh_rate=1;
+ posterior_sampler_options.parallel_bar_title='SLICE';
+ posterior_sampler_options.serial_bar_title='SLICE';
+
+ % default options
+ posterior_sampler_options = add_fields_(posterior_sampler_options,options_.posterior_sampler_options.slice);
+
+ % user defined options
+ if ~isempty(options_.posterior_sampler_options.sampling_opt)
+ options_list = read_key_value_string(options_.posterior_sampler_options.sampling_opt);
+ for i=1:rows(options_list)
+ switch options_list{i,1}
+ case 'rotated'
+ % triggers rotated slice iterations using a covariance
+ % matrix from initial burn-in iterations
+ % must be associated with:
+ % <use_mh_covariance_matrix> or <slice_initialize_with_mode>
+ % default = 0
+ posterior_sampler_options.rotated = options_list{i,2};
+
+ case 'mode'
+ % for multimodal posteriors, provide the list of modes as a
+ % matrix, ordered by column, i.e. [x1 x2 x3] for three
+ % modes x1 x2 x3
+ % MR note: not sure this is possible with the
+ % read_key_value_string ???
+ % if this is not possible <mode_files> does to job in any case
+ % This will automatically trigger <rotated>
+ % default = []
+ tmp_mode = options_list{i,2};
+ for j=1:size(tmp_mode,2)
+ posterior_sampler_options.mode(j).m = tmp_mode(:,j);
+ end
+
+ case 'mode_files'
+ % for multimodal posteriors provide the name of
+ % a file containing a variable array xparams = [nparam * nmodes]
+ % one column per mode. With this info, the code will automatically
+ % set the <mode> option.
+ % This will automatically trigger <rotated>
+ % default = []
+ posterior_sampler_options.mode_files = options_list{i,2};
+
+ case 'slice_initialize_with_mode'
+ % the default for slice is to set mode_compute = 0 in the
+ % preprocessor and start the chain(s) from a random location in the prior.
+ % This option first runs the optimizer and then starts the
+ % chain from the mode. Associated with optios <rotated>, it will
+ % use invhess from the mode to perform rotated slice
+ % iterations.
+ % default = 0
+ posterior_sampler_options.slice_initialize_with_mode = options_list{i,2};
+
+ case 'initial_step_size'
+ % sets the initial size of the interval in the STEPPING-OUT PROCEDURE
+ % the initial_step_size must be a real number in [0, 1],
+ % and it sets the size as a proportion of the prior bounds,
+ % i.e. the size will be initial_step_size*(UB-LB)
+ % slice sampler requires prior_truncation > 0!
+ % default = 0.8
+ if options_list{i,2}<=0 || options_list{i,2}>=1
+ error('check_posterior_sampler_options:: slice initial_step_size must be between 0 and 1')
+ else
+ posterior_sampler_options.initial_step_size=options_list{i,2};
end
+ case 'use_mh_covariance_matrix'
+ % in association with <rotated> indicates to use the
+ % covariance matrix from previous iterations to define the
+ % rotated slice
+ % default = 0
+ posterior_sampler_options.use_mh_covariance_matrix = options_list{i,2};
+ options_.use_mh_covariance_matrix = options_list{i,2};
+
+ case 'save_tmp_file'
+ posterior_sampler_options.save_tmp_file = options_list{i,2};
+
+ otherwise
+ warning(['slice_sampler: Unknown option (' options_list{i,1} ')!'])
end
end
-
- % slice posterior sampler does not require mode or hessian to run
- % needs to be set to 1 to skip parts in dynare_estimation_1.m
- % requiring posterior maximization/calibrated smoother before MCMC
- options_.mh_posterior_mode_estimation=1;
-
- if ~ posterior_sampler_options.slice_initialize_with_mode
+ end
+
+ % slice posterior sampler does not require mode or hessian to run
+ % needs to be set to 1 to skip parts in dynare_estimation_1.m
+ % requiring posterior maximization/calibrated smoother before MCMC
+ options_.mh_posterior_mode_estimation=1;
+
+ if ~ posterior_sampler_options.slice_initialize_with_mode
% by default, slice sampler should trigger
% mode_compute=0 and
% mh_replic=100 (much smaller than the default mh_replic=20000 of RWMH)
- options_.mode_compute = 0;
- options_.cova_compute = 0;
- else
- if (isequal(options_.mode_compute,0) && isempty(options_.mode_file) )
- skipline()
- disp('check_posterior_sampler_options:: You have specified the option "slice_initialize_with_mode"')
- disp('check_posterior_sampler_options:: to initialize the slice sampler using mode information')
- disp('check_posterior_sampler_options:: but no mode file nor posterior maximization is selected,')
- error('check_posterior_sampler_options:: The option "slice_initialize_with_mode" is inconsistent with mode_compute=0 or empty mode_file.')
- else
- options_.mh_posterior_mode_estimation=0;
- end
- end
-
- if any(isinf(bounds.lb)) || any(isinf(bounds.ub))
+ options_.mode_compute = 0;
+ options_.cova_compute = 0;
+ else
+ if (isequal(options_.mode_compute,0) && isempty(options_.mode_file) )
skipline()
- disp('some priors are unbounded and prior_trunc is set to zero')
- error('The option "slice" is inconsistent with prior_trunc=0.')
+ disp('check_posterior_sampler_options:: You have specified the option "slice_initialize_with_mode"')
+ disp('check_posterior_sampler_options:: to initialize the slice sampler using mode information')
+ disp('check_posterior_sampler_options:: but no mode file nor posterior maximization is selected,')
+ error('check_posterior_sampler_options:: The option "slice_initialize_with_mode" is inconsistent with mode_compute=0 or empty mode_file.')
+ else
+ options_.mh_posterior_mode_estimation=0;
end
-
- % moreover slice must be associated to:
- % options_.mh_posterior_mode_estimation = 0;
- % this is done below, but perhaps preprocessing should do this?
-
- if ~isempty(posterior_sampler_options.mode)
- % multimodal case
- posterior_sampler_options.rotated = 1;
- posterior_sampler_options.WR=[];
+ end
+
+ if any(isinf(bounds.lb)) || any(isinf(bounds.ub))
+ skipline()
+ disp('some priors are unbounded and prior_trunc is set to zero')
+ error('The option "slice" is inconsistent with prior_trunc=0.')
+ end
+
+ % moreover slice must be associated to:
+ % options_.mh_posterior_mode_estimation = 0;
+ % this is done below, but perhaps preprocessing should do this?
+
+ if ~isempty(posterior_sampler_options.mode)
+ % multimodal case
+ posterior_sampler_options.rotated = 1;
+ posterior_sampler_options.WR=[];
+ end
+ % posterior_sampler_options = set_default_option(posterior_sampler_options,'mode_files',[]);
+
+
+ posterior_sampler_options.W1=posterior_sampler_options.initial_step_size*(bounds.ub-bounds.lb);
+ if options_.load_mh_file
+ posterior_sampler_options.slice_initialize_with_mode = 0;
+ else
+ if ~posterior_sampler_options.slice_initialize_with_mode
+ posterior_sampler_options.invhess=[];
end
- % posterior_sampler_options = set_default_option(posterior_sampler_options,'mode_files',[]);
-
-
- posterior_sampler_options.W1=posterior_sampler_options.initial_step_size*(bounds.ub-bounds.lb);
- if options_.load_mh_file
- posterior_sampler_options.slice_initialize_with_mode = 0;
- else
- if ~posterior_sampler_options.slice_initialize_with_mode
- posterior_sampler_options.invhess=[];
- end
+ end
+
+ if ~isempty(posterior_sampler_options.mode_files) % multimodal case
+ modes = posterior_sampler_options.mode_files; % these can be also mean files from previous parallel slice chains
+ load(modes, 'xparams')
+ if size(xparams,2)<2
+ error(['check_posterior_sampler_options:: Variable xparams loaded in file <' modes '> has size [' int2str(size(xparams,1)) 'x' int2str(size(xparams,2)) ']: it must contain at least two columns, to allow multi-modal sampling.'])
end
-
- if ~isempty(posterior_sampler_options.mode_files) % multimodal case
- modes = posterior_sampler_options.mode_files; % these can be also mean files from previous parallel slice chains
- load(modes, 'xparams')
- if size(xparams,2)<2
- error(['check_posterior_sampler_options:: Variable xparams loaded in file <' modes '> has size [' int2str(size(xparams,1)) 'x' int2str(size(xparams,2)) ']: it must contain at least two columns, to allow multi-modal sampling.'])
- end
- for j=1:size(xparams,2)
- mode(j).m=xparams(:,j);
- end
- posterior_sampler_options.mode = mode;
- posterior_sampler_options.rotated = 1;
- posterior_sampler_options.WR=[];
+ for j=1:size(xparams,2)
+ mode(j).m=xparams(:,j);
end
-
- otherwise
- error('check_posterior_sampler_options:: Unknown posterior_sampling_method option %s ',posterior_sampler_options.posterior_sampling_method);
+ posterior_sampler_options.mode = mode;
+ posterior_sampler_options.rotated = 1;
+ posterior_sampler_options.WR=[];
+ end
+
+ otherwise
+ error('check_posterior_sampler_options:: Unknown posterior_sampling_method option %s ',posterior_sampler_options.posterior_sampling_method);
end
-
+
return
end
@@ -434,6 +434,3 @@ fnam = fieldnames(sampler_options);
for j=1:length(fnam)
posterior_sampler_options.(fnam{j}) = sampler_options.(fnam{j});
end
-
-
-
diff --git a/matlab/check_prior_analysis_data.m b/matlab/check_prior_analysis_data.m
index 1ad6a252c35ec5767a39884fdea3e3e4b9651469..81d11c519e2981204c2a90fda03824483d309613 100644
--- a/matlab/check_prior_analysis_data.m
+++ b/matlab/check_prior_analysis_data.m
@@ -1,13 +1,13 @@
function [info,description] = check_prior_analysis_data(type,M_)
% function [info,description] = check_prior_analysis_data(type,M_)
-% Checks the status of prior analysis and in particular if files need to be
+% Checks the status of prior analysis and in particular if files need to be
% created or updated; called by prior_analysis.m
-%
+%
% Inputs:
% type [string] name of the posterior moment considered
% M_ [structure] Dynare model structure
-%
-% Outputs:
+%
+% Outputs:
% info [scalar] return code
% info = 1; % prior_sampler has to be called first.
% info = 2; % _prior_draws files have to be updated.
@@ -40,7 +40,7 @@ if nargout>1
description = '';
end
-%% Get informations about prior draws files.
+%% Get informations about prior draws files.
if ~exist([ M_.dname '/prior/draws'],'dir')
disp('check_prior_analysis_data:: Can''t find any prior draws file!')
return
@@ -65,7 +65,7 @@ else
end
return
else
- info = 3; % Nothing to do!
+ info = 3; % Nothing to do!
if nargout>1
description = 'prior draws files are up to date.';
end
@@ -98,9 +98,9 @@ else
name_of_the_last_prior_data_file = pdinfo(end).name;
pdfdate = pdinfo(end).datenum;
% /!\ REMARK /!\
- % The user can change the model or the value of a calibrated
- % parameter without changing the prior. In this case the (prior)
- % moments should be computed. But this case cannot be detected!!!
+ % The user can change the model or the value of a calibrated
+ % parameter without changing the prior. In this case the (prior)
+ % moments should be computed. But this case cannot be detected!!!
if pdfdate<date_of_the_last_prior_draw_file
info = 5; % prior data files have to be updated.
if nargout>1
diff --git a/matlab/check_prior_bounds.m b/matlab/check_prior_bounds.m
index b6bdc6ece58522e038cafdad30158401cd6e368c..41a16e790c7f966225947cbb1c776d9e1c7a4f32 100644
--- a/matlab/check_prior_bounds.m
+++ b/matlab/check_prior_bounds.m
@@ -5,7 +5,7 @@ function check_prior_bounds(xparam1,bounds,M_,estim_params_,options_,bayestopt_)
% -xparam1 [double] vector of parameters to be estimated (initial values)
% -bounds [vector] vector containing the lower and upper
% bounds
-% -M_ [structure] characterizing the model.
+% -M_ [structure] characterizing the model.
% -estim_params_ [structure] characterizing parameters to be estimated
% -options_ [structure] characterizing the options
% -bayestopt_ [structure] characterizing priors
diff --git a/matlab/chol_SE.m b/matlab/chol_SE.m
index ae02493f0fc40f9ad95ebcb56d2143993746c495..992d88a81d6a1bb47e186fc483f39e00f3d69417 100644
--- a/matlab/chol_SE.m
+++ b/matlab/chol_SE.m
@@ -1,17 +1,17 @@
function [R,indef, E, P]=chol_SE(A,pivoting)
% [R,indef, E, P]=chol_SE(A,pivoting)
-% Performs a (modified) Cholesky factorization of the form
-%
+% Performs a (modified) Cholesky factorization of the form
+%
% P'*A*P + E = R'*R
-%
+%
% As detailed in Schnabel/Eskow (1990), the factorization has 2 phases:
% Phase 1: While A can still be positive definite, pivot on the maximum diagonal element.
% Check that the standard Cholesky update would result in a positive diagonal
-% at the current iteration. If so, continue with the normal Cholesky update.
-% Otherwise switch to phase 2.
-% If A is safely positive definite, stage 1 is never left, resulting in
-% the standard Cholesky decomposition.
-%
+% at the current iteration. If so, continue with the normal Cholesky update.
+% Otherwise switch to phase 2.
+% If A is safely positive definite, stage 1 is never left, resulting in
+% the standard Cholesky decomposition.
+%
% Phase 2: Pivot on the minimum of the negatives of the lower Gershgorin bound
% estimates. To prevent negative diagonals, compute the amount to add to the
% pivot element and add this. Then, do the Cholesky update and update the estimates of the
@@ -21,18 +21,18 @@ function [R,indef, E, P]=chol_SE(A,pivoting)
% - During factorization, L=R' is stored in the lower triangle of the original matrix A,
% miminizing the memory requirements
% - Conforming with the original Schnabel/Eskow (1990) algorithm:
-% - at each iteration the updated Gershgorin bounds are estimated instead of recomputed,
+% - at each iteration the updated Gershgorin bounds are estimated instead of recomputed,
% reducing the computational requirements from o(n^3) to o (n^2)
% - For the last 2 by 2 submatrix, an eigenvalue-based decomposition is used
-% - While pivoting is not necessary, it improves the size of E, the add-on on to the diagonal. But this comes at
+% - While pivoting is not necessary, it improves the size of E, the add-on on to the diagonal. But this comes at
% the cost of introduding a permutation.
-%
%
-% Inputs
+%
+% Inputs
% A [n*n] Matrix to be factorized
% pivoting [scalar] dummy whether pivoting is used
-%
-% Outputs
+%
+% Outputs
% R [n*n] originally stored in lower triangular portion of A, including the main diagonal
%
% E [n*1] Elements added to the diagonal of A
@@ -40,18 +40,18 @@ function [R,indef, E, P]=chol_SE(A,pivoting)
% performing the decomposition
%
% REFERENCES:
-% This implementation is based on
+% This implementation is based on
%
% Robert B. Schnabel and Elizabeth Eskow. 1990. "A New Modified Cholesky
% Factorization," SIAM Journal of Scientific Statistical Computating,
% 11, 6: 1136-58.
-%
+%
% Elizabeth Eskow and Robert B. Schnabel 1991. "Algorithm 695 - Software for a New Modified Cholesky
% Factorization," ACM Transactions on Mathematical Software, Vol 17, No 3: 306-312
-%
-%
+%
+%
% Author: Johannes Pfeifer based on Eskow/Schnabel (1991)
-%
+%
% Copyright (C) 2015 Johannes Pfeifer
% Copyright (C) 2015 Dynare Team
% This file is part of Dynare.
@@ -148,7 +148,7 @@ for j = 1:n-1
% Swap elements of the permutation vector
itemp = P(j);
P(j) = P(imaxd);
- P(imaxd) = itemp;
+ P(imaxd) = itemp;
end
end
% check to see whether the normal Cholesky update for this
@@ -183,13 +183,13 @@ for j = 1:n-1
g=gersh_nested(A,j,n);
end
end
-
+
% PHASE 2
if ~phase1
if j ~= n-1
if pivoting
% Find the minimum negative Gershgorin bound
- [tmp,iming] = min(g(j:n));
+ [tmp,iming] = min(g(j:n));
iming=iming+j-1;
% Pivot to the top the row and column with the
% minimum negative Gershgorin bound
@@ -229,9 +229,9 @@ for j = 1:n-1
end
% Calculate delta and add to the diagonal. delta=max{0,-A(j,j) + max{normj,taugam},delta_previous}
% where normj=sum of |A(i,j)|,for i=1,n, delta_previous is the delta computed at the previous iter and taugam is tau1*gamma.
-
+
normj=sum(abs(A(j+1:n,j)));
-
+
delta = max([0;delta;-A(j,j)+normj;-A(j,j)+taugam]); % get adjustment based on formula on bottom of p. 309 of Eskow/Schnabel (1991)
E(j) = delta;
@@ -259,9 +259,9 @@ for j = 1:n-1
% Find eigenvalues of final 2 by 2 submatrix
% Find delta such that:
% 1. the l2 condition number of the final 2X2 submatrix + delta*I <= tau2
- % 2. delta >= previous delta,
+ % 2. delta >= previous delta,
% 3. min(eigvals) + delta >= tau2 * gamma, where min(eigvals) is the smallest eigenvalue of the final 2X2 submatrix
-
+
A(n-1,n)=A(n,n-1); %set value above diagonal for computation of eigenvalues
eigvals = eig(A(n-1:n,n-1:n));
delta = max([ 0 ; delta ; -min(eigvals)+tau2*max((max(eigvals)-min(eigvals))/(1-tau1),gammma) ]); %Formula 5.3.2 of Schnabel/Eskow (1990)
@@ -272,7 +272,7 @@ for j = 1:n-1
E(n-1) = delta;
E(n) = delta;
end
-
+
% Final update
A(n-1,n-1) = sqrt(A(n-1,n-1));
A(n,n-1) = A(n,n-1)/A(n-1,n-1);
@@ -306,4 +306,3 @@ for ii = j:n
g(ii) = sum_up_to_i + sum_after_i- A(ii,ii);
end
end
-
diff --git a/matlab/clear_persistent_variables.m b/matlab/clear_persistent_variables.m
index e2fe5666f6c2dc45f22a73a7b91cce2761319540..acc7cd56cbf4b0788102f10d39dcd76fd428b502 100644
--- a/matlab/clear_persistent_variables.m
+++ b/matlab/clear_persistent_variables.m
@@ -58,5 +58,5 @@ if writelistofroutinestobecleared
return
end
-list_of_functions_to_be_cleared;
+list_of_functions_to_be_cleared;
clear(list_of_functions{:});
\ No newline at end of file
diff --git a/matlab/cli/prior.m b/matlab/cli/prior.m
index 9e044506646b29224efcada72ef6969ce8af48c1..843d64689295e2ff422b997409738b9f58cf068e 100644
--- a/matlab/cli/prior.m
+++ b/matlab/cli/prior.m
@@ -42,7 +42,7 @@ if isempty(varargin) || ( isequal(length(varargin), 1) && isequal(varargin{1},'h
return
end
-global options_ M_ estim_params_ bayestopt_ oo_
+global options_ M_ estim_params_ bayestopt_ oo_
donesomething = false;
@@ -53,11 +53,11 @@ end
if (size(estim_params_.var_endo,1) || size(estim_params_.corrn,1))
% Prior over measurement errors are defined...
- if ((isfield(options_,'varobs') && isempty(options_.varobs)) || ~isfield(options_,'varobs'))
- % ... But the list of observed variabled is not yet defined.
- warning('Prior detected on measurement erros, but no list of observed variables (varobs is missing)!')
- return
- end
+ if ((isfield(options_,'varobs') && isempty(options_.varobs)) || ~isfield(options_,'varobs'))
+ % ... But the list of observed variabled is not yet defined.
+ warning('Prior detected on measurement erros, but no list of observed variables (varobs is missing)!')
+ return
+ end
end
% Fill or update bayestopt_ structure
@@ -114,7 +114,7 @@ if ismember('optimize', varargin) % Prior optimization.
end
if ismember('moments', varargin) % Prior simulations (2nd order moments).
- % Set estimated parameters to the prior mode...
+ % Set estimated parameters to the prior mode...
xparam1 = BayesOptions.p5;
% ... Except for uniform priors (use the prior mean)!
k = find(isnan(xparam1));
diff --git a/matlab/collect_latex_files.m b/matlab/collect_latex_files.m
index 5c4f2b7d643d419c558053edbcde65783be13ca7..e150baab507c4368db923f3f0c872cdd62abb744 100644
--- a/matlab/collect_latex_files.m
+++ b/matlab/collect_latex_files.m
@@ -1,10 +1,10 @@
function collect_latex_files
% function collect_LaTeX_Files;
% Creates TeX-File embedding all eps-loaders created for current mod-file
-%
+%
% Inputs: none
-%
-% Notes:
+%
+% Notes:
% - The packages loaded enable pdflatex to run
% - The _dynamic and _static TeX-model files are not included as they are standalone TeX-files
@@ -45,11 +45,11 @@ TeX_Files=dir([M_.fname,'*.tex']);
for ii=1:length(TeX_Files)
[pathstr,f_name,ext] = fileparts(TeX_Files(ii).name);
if ~strcmp(TeX_Files(ii).name,f_name_binder) && ...
- ~strcmp(TeX_Files(ii).name,[M_.fname,'_dynamic.tex']) && ...
- ~strcmp(TeX_Files(ii).name,[M_.fname,'_static.tex']) && ...
- ~strcmp(TeX_Files(ii).name,[M_.fname,'_original.tex']) && ...
- ~strcmp(TeX_Files(ii).name,[M_.fname,'_TeX_binder.tex'])
- fprintf(fid,'%s \n',['\include{',f_name,'}']);
+ ~strcmp(TeX_Files(ii).name,[M_.fname,'_dynamic.tex']) && ...
+ ~strcmp(TeX_Files(ii).name,[M_.fname,'_static.tex']) && ...
+ ~strcmp(TeX_Files(ii).name,[M_.fname,'_original.tex']) && ...
+ ~strcmp(TeX_Files(ii).name,[M_.fname,'_TeX_binder.tex'])
+ fprintf(fid,'%s \n',['\include{',f_name,'}']);
end
end
@@ -58,7 +58,7 @@ TeX_Files=dir([M_.dname filesep 'Output' filesep M_.fname '*.tex']);
for ii=1:length(TeX_Files)
[pathstr,f_name,ext] = fileparts(TeX_Files(ii).name);
if ~strcmp(TeX_Files(ii).name,f_name_binder)
- fprintf(fid,'%s \n',['\include{', M_.dname '/Output' '/',f_name,'}']);
+ fprintf(fid,'%s \n',['\include{', M_.dname '/Output' '/',f_name,'}']);
end
end
@@ -67,7 +67,7 @@ TeX_Files=dir([M_.dname filesep 'graphs' filesep M_.fname '*.tex']);
for ii=1:length(TeX_Files)
[pathstr,f_name,ext] = fileparts(TeX_Files(ii).name);
if ~strcmp(TeX_Files(ii).name,f_name_binder)
- fprintf(fid,'%s \n',['\include{', M_.dname '/graphs' '/',f_name,'}']);
+ fprintf(fid,'%s \n',['\include{', M_.dname '/graphs' '/',f_name,'}']);
end
end
@@ -76,7 +76,7 @@ TeX_Files=dir([M_.dname filesep 'identification' filesep M_.fname '*.tex']);
for ii=1:length(TeX_Files)
[pathstr,f_name,ext] = fileparts(TeX_Files(ii).name);
if ~strcmp(TeX_Files(ii).name,f_name_binder)
- fprintf(fid,'%s \n',['\include{', M_.dname '/identification' '/',f_name,'}']);
+ fprintf(fid,'%s \n',['\include{', M_.dname '/identification' '/',f_name,'}']);
end
end
@@ -86,7 +86,7 @@ TeX_Files=dir([M_.dname filesep 'identification' filesep 'Output' filesep M_.fna
for ii=1:length(TeX_Files)
[pathstr,f_name,ext] = fileparts(TeX_Files(ii).name);
if ~strcmp(TeX_Files(ii).name,f_name_binder)
- fprintf(fid,'%s \n',['\include{', M_.dname '/identification/Output' '/',f_name,'}']);
+ fprintf(fid,'%s \n',['\include{', M_.dname '/identification/Output' '/',f_name,'}']);
end
end
@@ -95,7 +95,7 @@ TeX_Files=dir([M_.dname filesep 'gsa' filesep M_.fname '*.tex']);
for ii=1:length(TeX_Files)
[pathstr,f_name,ext] = fileparts(TeX_Files(ii).name);
if ~strcmp(TeX_Files(ii).name,f_name_binder)
- fprintf(fid,'%s \n',['\include{', M_.dname '/gsa' '/',f_name,'}']);
+ fprintf(fid,'%s \n',['\include{', M_.dname '/gsa' '/',f_name,'}']);
end
end
@@ -104,7 +104,7 @@ TeX_Files=dir([M_.dname filesep 'gsa' filesep 'Output' filesep M_.fname '*.tex'
for ii=1:length(TeX_Files)
[pathstr,f_name,ext] = fileparts(TeX_Files(ii).name);
if ~strcmp(TeX_Files(ii).name,f_name_binder)
- fprintf(fid,'%s \n',['\include{', M_.dname '/gsa/Output' '/',f_name,'}']);
+ fprintf(fid,'%s \n',['\include{', M_.dname '/gsa/Output' '/',f_name,'}']);
end
end
diff --git a/matlab/compute_Pinf_Pstar.m b/matlab/compute_Pinf_Pstar.m
index 3ab4ace579053286275f2748350c9dfe135642f4..d847ffacc28b98ca806c817401085bedd758502b 100644
--- a/matlab/compute_Pinf_Pstar.m
+++ b/matlab/compute_Pinf_Pstar.m
@@ -1,30 +1,30 @@
function [Pstar,Pinf] = compute_Pinf_Pstar(mf,T,R,Q,qz_criterium, restrict_columns)
% function [Z,ST,QT,R1,Pstar,Pinf] = schur_statespace_transformation(mf,T,R,Q,qz_criterium, restrict_columns)
% Kitagawa transformation of state space system with a quasi-triangular
-% transition matrix with unit roots at the top, but excluding zero columns of the transition matrix.
+% transition matrix with unit roots at the top, but excluding zero columns of the transition matrix.
% Computation of Pstar and Pinf for Durbin and Koopman Diffuse filter
%
-% The transformed state space is
+% The transformed state space is
% y = [ss; z; x];
% s = static variables (zero columns of T)
% z = unit roots
% x = stable roots
% ss = s - z = stationarized static variables
-%
-% INPUTS
+%
+% INPUTS
% mf [integer] vector of indices of observed variables in
% state vector
% T [double] matrix of transition
% R [double] matrix of structural shock effects
% Q [double] matrix of covariance of structural shocks
-% qz_criterium [double] numerical criterium for unit roots
-%
-% OUTPUTS
+% qz_criterium [double] numerical criterium for unit roots
+%
+% OUTPUTS
% Pstar [double] matrix of covariance of stationary part
% Pinf [double] matrix of covariance initialization for
-% nonstationary part
-%
-% ALGORITHM
+% nonstationary part
+%
+% ALGORITHM
% Real Schur transformation of transition equation
%
% SPECIAL REQUIREMENTS
@@ -49,7 +49,7 @@ function [Pstar,Pinf] = compute_Pinf_Pstar(mf,T,R,Q,qz_criterium, restrict_colum
np = size(T,1);
-% perform Kitagawa transformation
+% perform Kitagawa transformation
[QT,ST] = schur(T);
e1 = abs(ordeig(ST)) > 2-qz_criterium;
[QT,ST] = ordschur(QT,ST,e1);
@@ -112,4 +112,3 @@ end
Pinf = QT*Pinf*QT';
Pstar = QT*Pstar*QT';
-
diff --git a/matlab/compute_mh_covariance_matrix.m b/matlab/compute_mh_covariance_matrix.m
index 28064d970c06a978148b1059b7195bf7dfa5f7fe..c13eb28bf1396c8201fa71e35a7d7edd4f84b7d5 100644
--- a/matlab/compute_mh_covariance_matrix.m
+++ b/matlab/compute_mh_covariance_matrix.m
@@ -1,15 +1,15 @@
function [posterior_mean,posterior_covariance,posterior_mode,posterior_kernel_at_the_mode] = compute_mh_covariance_matrix()
% Estimation of the posterior covariance matrix, posterior mean, posterior mode and evaluation of the posterior kernel at the
% estimated mode, using the draws from a metropolis-hastings. The estimated posterior mode and covariance matrix are saved in
-% a file <M_.fname>_mh_mode.mat.
-%
-% INPUTS
+% a file <M_.fname>_mh_mode.mat.
+%
+% INPUTS
% None.
-%
+%
% OUTPUTS
% o posterior_mean [double] (n*1) vector, posterior expectation of the parameters.
% o posterior_covariance [double] (n*n) matrix, posterior covariance of the parameters (computed from previous metropolis hastings).
-% o posterior_mode [double] (n*1) vector, posterior mode of the parameters.
+% o posterior_mode [double] (n*1) vector, posterior mode of the parameters.
% o posterior_kernel_at_the_mode [double] scalar.
%
% SPECIAL REQUIREMENTS
@@ -62,7 +62,7 @@ offset = 0;
for b=1:nblck
first_line = FirstLine;
for n = FirstMhFile:TotalNumberOfMhFiles
- load([ BaseName '_mh' int2str(n) '_blck' int2str(b) '.mat'],'x2','logpo2');
+ load([ BaseName '_mh' int2str(n) '_blck' int2str(b) '.mat'],'x2','logpo2');
[tmp,idx] = max(logpo2);
if tmp>posterior_kernel_at_the_mode
posterior_kernel_at_the_mode = tmp;
diff --git a/matlab/compute_model_moments.m b/matlab/compute_model_moments.m
index c3b51ad833a423df497f0030f719e096c119224f..64894e6ec360ed5aa9e244fb5a3c9f85c5edc9eb 100644
--- a/matlab/compute_model_moments.m
+++ b/matlab/compute_model_moments.m
@@ -4,10 +4,10 @@ function moments=compute_model_moments(dr,M_,options_)
% dr: structure describing model solution
% M_: structure of Dynare options
% options_
-%
+%
% OUTPUTS
% moments: a cell array containing requested moments
-%
+%
% SPECIAL REQUIREMENTS
% none
diff --git a/matlab/compute_moments_varendo.m b/matlab/compute_moments_varendo.m
index d8a5c868b199a8da96e333fe1eecd4669549e40f..a811a12a68ffdd2c11233e99ad2305cb85551026 100644
--- a/matlab/compute_moments_varendo.m
+++ b/matlab/compute_moments_varendo.m
@@ -2,14 +2,14 @@ function oo_ = compute_moments_varendo(type,options_,M_,oo_,var_list_)
% Computes the second order moments (autocorrelation function, covariance
% matrix and variance decomposition) distributions for all the endogenous variables selected in
% var_list_. The results are saved in oo_
-%
+%
% INPUTS:
% type [string] 'posterior' or 'prior'
% options_ [structure] Dynare structure.
% M_ [structure] Dynare structure (related to model definition).
% oo_ [structure] Dynare structure (results).
% var_list_ [string] Array of string with endogenous variable names.
-%
+%
% OUTPUTS
% oo_ [structure] Dynare structure (results).
%
@@ -31,7 +31,7 @@ function oo_ = compute_moments_varendo(type,options_,M_,oo_,var_list_)
% 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/>.
+% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
fprintf('Estimation::compute_moments_varendo: I''m computing endogenous moments (this may take a while)... ');
@@ -129,7 +129,7 @@ if M_.exo_nbr > 1
headers = char(' ',headers);
lh = size(deblank(var_list_),2)+2;
dyntable(options_,title,headers,deblank(var_list_),100* ...
- temp,lh,8,2);
+ temp,lh,8,2);
if options_.TeX
headers=M_.exo_names_tex;
headers = char(' ',headers);
@@ -166,7 +166,7 @@ if M_.exo_nbr > 1
headers = char(' ',headers);
lh = size(deblank(var_list_),2)+2;
dyntable(options_,title_print,headers,deblank(var_list_),100* ...
- temp(:,:,step_iter),lh,8,2);
+ temp(:,:,step_iter),lh,8,2);
if options_.TeX
headers=M_.exo_names_tex;
headers = char(' ',headers);
diff --git a/matlab/compute_overall_acceptance_ratio.m b/matlab/compute_overall_acceptance_ratio.m
index d3b323eb309659191033067caf984802170a5bf5..448fac02b99ed3e794a8e41d72f66eefa3c65888 100644
--- a/matlab/compute_overall_acceptance_ratio.m
+++ b/matlab/compute_overall_acceptance_ratio.m
@@ -16,7 +16,7 @@ function overallacceptanceratio = compute_overall_acceptance_ratio(MetropolisFol
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
+
BaseName = [MetropolisFolder filesep ModelName];
mh_history_files = dir([BaseName '_mh_history_*.mat']);
@@ -24,12 +24,12 @@ n = length(mh_history_files);
load([BaseName '_mh_history_' num2str(0)]);
TotalNumberOfDraws = record.MhDraws(end,1);
-TotalNumberOfAcceptedProposals = record.AcceptanceRatio*record.MhDraws(end,1);
+TotalNumberOfAcceptedProposals = record.AcceptanceRatio*record.MhDraws(end,1);
for i=2:n
load([BaseName '_mh_history_' num2str(i-1)]);
TotalNumberOfDraws = TotalNumberOfDraws + record.MhDraws(end,1);
- TotalNumberOfAcceptedProposals = TotalNumberOfAcceptedProposals + record.AcceptanceRatio*record.MhDraws(end,1);
+ TotalNumberOfAcceptedProposals = TotalNumberOfAcceptedProposals + record.AcceptanceRatio*record.MhDraws(end,1);
end
overallacceptanceratio = TotalNumberOfAcceptedProposals/TotalNumberOfDraws;
\ No newline at end of file
diff --git a/matlab/conditional_variance_decomposition.m b/matlab/conditional_variance_decomposition.m
index 3ecf7441039d3f1ef60b8c729d45f0b3b1f00d06..25b236b054ad766ffcff31e1f340fe4a0ab761df 100644
--- a/matlab/conditional_variance_decomposition.m
+++ b/matlab/conditional_variance_decomposition.m
@@ -1,14 +1,14 @@
function ConditionalVarianceDecomposition = conditional_variance_decomposition(StateSpaceModel, Steps, SubsetOfVariables,sigma_e_is_diagonal)
% This function computes the conditional variance decomposition of a given state space model
% for a subset of endogenous variables.
-%
-% INPUTS
+%
+% INPUTS
% StateSpaceModel [structure] Specification of the state space model.
% Steps [integer] 1*h vector of dates.
% SubsetOfVariables [integer] 1*q vector of indices.
-%
-% OUTPUTS
-% ConditionalVarianceDecomposition [double] [n h p] array, where
+%
+% OUTPUTS
+% ConditionalVarianceDecomposition [double] [n h p] array, where
% n is equal to length(SubsetOfVariables)
% h is the number of Steps
% p is the number of state innovations and
@@ -77,7 +77,7 @@ for h = 1:length(Steps)
SumOfVariances(:,h) = sum(ConditionalVariance(:,h,:),3);
end
-ConditionalVarianceDecomposition = zeros(NumberOfVariables,length(Steps),number_of_state_innovations);
+ConditionalVarianceDecomposition = zeros(NumberOfVariables,length(Steps),number_of_state_innovations);
for i=1:number_of_state_innovations
for h = 1:length(Steps)
ConditionalVarianceDecomposition(:,h,i) = squeeze(ConditionalVariance(:,h,i))./SumOfVariances(:,h);
diff --git a/matlab/conditional_variance_decomposition_mc_analysis.m b/matlab/conditional_variance_decomposition_mc_analysis.m
index 68fbeb6b7f16fc17bcd712d43f477f9ce7869e6c..7d41766c522da559ac4abb592bd340396f14d646 100644
--- a/matlab/conditional_variance_decomposition_mc_analysis.m
+++ b/matlab/conditional_variance_decomposition_mc_analysis.m
@@ -9,7 +9,7 @@ function oo_ = ...
% dname [string] directory name where to save
% fname [string] name of the mod-file
% Steps [integers] horizons at which to conduct decomposition
-% exonames [string] (n_exo*char_length) character array with names of exogenous variables
+% exonames [string] (n_exo*char_length) character array with names of exogenous variables
% exo [string] name of current exogenous
% variable
% var_list [string] (n_endo*char_length) character array with name
@@ -106,7 +106,7 @@ for i=1:length(Steps)
p_density(:,:,i) = pp_density;
else
[pp_mean, pp_median, pp_var, hpd_interval, pp_deciles] = ...
- posterior_moments(tmp(:,i),0,mh_conf_sig);
+ posterior_moments(tmp(:,i),0,mh_conf_sig);
end
p_mean(i) = pp_mean;
p_median(i) = pp_median;
diff --git a/matlab/convergence_diagnostics/McMCDiagnostics.m b/matlab/convergence_diagnostics/McMCDiagnostics.m
index 769b70d35f587e2cfbe1802e651f5c9fcb877259..a3fa70fd90b6b8b808b402511619d2fdbc2c8b8e 100644
--- a/matlab/convergence_diagnostics/McMCDiagnostics.m
+++ b/matlab/convergence_diagnostics/McMCDiagnostics.m
@@ -1,14 +1,14 @@
function oo_ = McMCDiagnostics(options_, estim_params_, M_, oo_)
% function McMCDiagnostics
-% Computes convergence tests
-%
-% INPUTS
+% Computes convergence tests
+%
+% INPUTS
% options_ [structure]
% estim_params_ [structure]
% M_ [structure]
%
-% OUTPUTS
-% oo_ [structure]
+% OUTPUTS
+% oo_ [structure]
%
% SPECIAL REQUIREMENTS
% none
@@ -82,12 +82,12 @@ param_name_tex=[];
for jj=1:npar
if options_.TeX
[par_name_temp,par_name_tex_temp]=get_the_name(jj,options_.TeX,M_,estim_params_,options_);
- param_name = strvcat(param_name,par_name_temp);
+ param_name = strvcat(param_name,par_name_temp);
par_name_tex_temp = strrep(par_name_tex_temp,'$','');
param_name_tex = strvcat(param_name_tex,par_name_tex_temp);
else
[par_name_temp]=get_the_name(jj,options_.TeX,M_,estim_params_,options_);
- param_name = strvcat(param_name,par_name_temp);
+ param_name = strvcat(param_name,par_name_temp);
end
Draws = GetAllPosteriorDraws(jj,FirstMhFile,FirstLine,TotalNumberOfMhFiles,NumberOfDraws);
Draws = reshape(Draws,[NumberOfDraws nblck]);
@@ -130,7 +130,7 @@ end
if nblck == 1 % Brooks and Gelman tests need more than one block
convergence_diagnostics_geweke=zeros(npar,4+2*length(options_.convergence.geweke.taper_steps));
if any(options_.convergence.geweke.geweke_interval<0) || any(options_.convergence.geweke.geweke_interval>1) || length(options_.convergence.geweke.geweke_interval)~=2 ...
- || (options_.convergence.geweke.geweke_interval(2)-options_.convergence.geweke.geweke_interval(1)<0)
+ || (options_.convergence.geweke.geweke_interval(2)-options_.convergence.geweke.geweke_interval(1)<0)
fprintf('\nCONVERGENCE DIAGNOSTICS: Invalid option for geweke_interval. Using the default of [0.2 0.5].\n')
options_.convergence.geweke.geweke_interval=[0.2 0.5];
end
@@ -139,9 +139,9 @@ if nblck == 1 % Brooks and Gelman tests need more than one block
first_obs_end_sample = first_obs_begin_sample+round(options_.convergence.geweke.geweke_interval(2)*NumberOfDraws*(1-options_.mh_drop));
param_name=[];
if options_.TeX
- param_name_tex=[];
+ param_name_tex=[];
end
- for jj=1:npar
+ for jj=1:npar
if options_.TeX
[param_name_temp, param_name_tex_temp]= get_the_name(jj,options_.TeX,M_,estim_params_,options_);
param_name_tex = strvcat(param_name_tex,strrep(param_name_tex_temp,'$',''));
@@ -152,7 +152,7 @@ if nblck == 1 % Brooks and Gelman tests need more than one block
end
end
fprintf('\nGeweke (1992) Convergence Tests, based on means of draws %d to %d vs %d to %d.\n',first_obs_begin_sample,last_obs_begin_sample,first_obs_end_sample,NumberOfDraws);
- fprintf('p-values are for Chi2-test for equality of means.\n');
+ fprintf('p-values are for Chi2-test for equality of means.\n');
Geweke_header=char('Parameter', 'Post. Mean', 'Post. Std', 'p-val No Taper');
for ii=1:length(options_.convergence.geweke.taper_steps)
Geweke_header=char(Geweke_header,['p-val ' num2str(options_.convergence.geweke.taper_steps(ii)),'% Taper']);
@@ -168,12 +168,12 @@ if nblck == 1 % Brooks and Gelman tests need more than one block
end
[results_vec, results_struct] = geweke_moments(param_draws,options_);
convergence_diagnostics_geweke(jj,:)=results_vec;
-
+
param_draws1 = param_draws(first_obs_begin_sample:last_obs_begin_sample,:);
param_draws2 = param_draws(first_obs_end_sample:end,:);
[results_vec1] = geweke_moments(param_draws1,options_);
[results_vec2] = geweke_moments(param_draws2,options_);
-
+
results_struct = geweke_chi2_test(results_vec1,results_vec2,results_struct,options_);
eval(['oo_.convergence.geweke.',param_name(jj,:),'=results_struct;'])
datamat(jj,:)=[results_struct.posteriormean,results_struct.posteriorstd,results_struct.prob_chi2_test];
@@ -183,29 +183,29 @@ if nblck == 1 % Brooks and Gelman tests need more than one block
if options_.TeX
Geweke_tex_header=char('Parameter', 'Mean', 'Std', 'No\ Taper');
additional_header={[' & \multicolumn{2}{c}{Posterior} & \multicolumn{',num2str(1+length(options_.convergence.geweke.taper_steps)),'}{c}{p-values} \\'],
- ['\cmidrule(r{.75em}){2-3} \cmidrule(r{.75em}){4-',num2str(4+length(options_.convergence.geweke.taper_steps)),'}']};
+ ['\cmidrule(r{.75em}){2-3} \cmidrule(r{.75em}){4-',num2str(4+length(options_.convergence.geweke.taper_steps)),'}']};
for ii=1:length(options_.convergence.geweke.taper_steps)
Geweke_tex_header=char(Geweke_tex_header,[num2str(options_.convergence.geweke.taper_steps(ii)),'\%%\ Taper']);
end
headers = char(Geweke_tex_header);
lh = size(param_name_tex,2)+2;
my_title=sprintf('Geweke (1992) Convergence Tests, based on means of draws %d to %d vs %d to %d. p-values are for $\\\\chi^2$-test for equality of means.',first_obs_begin_sample,last_obs_begin_sample,first_obs_end_sample,NumberOfDraws);
- dyn_latex_table(M_,options_,my_title,'geweke',headers,param_name_tex,datamat,lh,12,4,additional_header);
+ dyn_latex_table(M_,options_,my_title,'geweke',headers,param_name_tex,datamat,lh,12,4,additional_header);
end
skipline(2);
-
+
if options_.convergence.rafterylewis.indicator
if any(options_.convergence.rafterylewis.qrs<0) || any(options_.convergence.rafterylewis.qrs>1) || length(options_.convergence.rafterylewis.qrs)~=3 ...
- || (options_.convergence.rafterylewis.qrs(1)-options_.convergence.rafterylewis.qrs(2)<=0)
+ || (options_.convergence.rafterylewis.qrs(1)-options_.convergence.rafterylewis.qrs(2)<=0)
fprintf('\nCONVERGENCE DIAGNOSTICS: Invalid option for raftery_lewis_qrs. Using the default of [0.025 0.005 0.95].\n')
options_.convergence.rafterylewis.qrs=[0.025 0.005 0.95];
- end
+ end
Raftery_Lewis_q=options_.convergence.rafterylewis.qrs(1);
Raftery_Lewis_r=options_.convergence.rafterylewis.qrs(2);
Raftery_Lewis_s=options_.convergence.rafterylewis.qrs(3);
oo_.Raftery_Lewis = raftery_lewis(x2,Raftery_Lewis_q,Raftery_Lewis_r,Raftery_Lewis_s);
oo_.Raftery_Lewis.parameter_names=param_name;
- my_title=sprintf('Raftery/Lewis (1992) Convergence Diagnostics, based on quantile q=%4.3f with precision r=%4.3f with probability s=%4.3f.',Raftery_Lewis_q,Raftery_Lewis_r,Raftery_Lewis_s);
+ my_title=sprintf('Raftery/Lewis (1992) Convergence Diagnostics, based on quantile q=%4.3f with precision r=%4.3f with probability s=%4.3f.',Raftery_Lewis_q,Raftery_Lewis_r,Raftery_Lewis_s);
headers = char('Variables','M (burn-in)','N (req. draws)','N+M (total draws)','k (thinning)');
raftery_data_mat=[oo_.Raftery_Lewis.M_burn,oo_.Raftery_Lewis.N_prec,oo_.Raftery_Lewis.N_total,oo_.Raftery_Lewis.k_thin];
@@ -217,15 +217,15 @@ if nblck == 1 % Brooks and Gelman tests need more than one block
labels_Raftery_Lewis_tex=char(param_name_tex,'Maximum');
lh = size(labels_Raftery_Lewis_tex,2)+2;
dyn_latex_table(M_,options_,my_title,'raftery_lewis',headers,labels_Raftery_Lewis_tex,raftery_data_mat,lh,10,0);
- end
+ end
end
-
+
return
end
Origin = 1000;
-StepSize = ceil((NumberOfDraws-Origin)/100);% So that the computational time does not
-ALPHA = 0.2; % increase too much with the number of simulations.
+StepSize = ceil((NumberOfDraws-Origin)/100);% So that the computational time does not
+ALPHA = 0.2; % increase too much with the number of simulations.
time = 1:NumberOfDraws;
xx = Origin:StepSize:NumberOfDraws;
NumberOfLines = length(xx);
@@ -273,7 +273,7 @@ else
ModelName = [ModelName '_bvar'];
end
NamFileInput={[M_.dname '/metropolis/'],[ModelName '_mh*_blck*.mat']};
-
+
[fout, nBlockPerCPU, totCPU] = masterParallel(options_.parallel, 1, npar,NamFileInput,'McMCDiagnostics_core', localVars, [], options_.parallel_info);
UDIAG = fout(1).UDIAG;
for j=2:totCPU
@@ -283,9 +283,9 @@ end
UDIAG(:,[2 4 6],:) = UDIAG(:,[2 4 6],:)/nblck;
skipline()
-clear pmet temp moyenne CSUP CINF csup cinf n linea iter tmp;
+clear pmet temp moyenne CSUP CINF csup cinf n linea iter tmp;
pages = floor(npar/3);
-k = 0;
+k = 0;
for i = 1:pages
h=dyn_figure(options_.nodisplay,'Name','MCMC univariate convergence diagnostic (Brooks and Gelman,1998)');
boxplot = 1;
@@ -296,12 +296,12 @@ for i = 1:pages
if crit == 1
plt1 = UDIAG(:,1,k);
plt2 = UDIAG(:,2,k);
- namnam = [nam , ' (Interval)'];
+ namnam = [nam , ' (Interval)'];
elseif crit == 2
plt1 = UDIAG(:,3,k);
plt2 = UDIAG(:,4,k);
namnam = [nam , ' (m2)'];
- elseif crit == 3
+ elseif crit == 3
plt1 = UDIAG(:,5,k);
plt2 = UDIAG(:,6,k);
namnam = [nam , ' (m3)'];
@@ -330,7 +330,7 @@ for i = 1:pages
fprintf(fidTeX,'\\begin{figure}[H]\n');
for jj = 1:size(NAMES,1)
fprintf(fidTeX,'\\psfrag{%s}[1][][0.5][0]{%s}\n',deblank(NAMES(jj,:)),deblank(TEXNAMES(jj,:)));
- end
+ end
fprintf(fidTeX,'\\centering \n');
fprintf(fidTeX,'\\includegraphics[width=%2.2f\\textwidth]{%s_udiag%s}\n',options_.figures.textwidth*min((boxplot-1)/3,1),[OutputFolder '/' ModelName],int2str(i));
fprintf(fidTeX,'\\caption{Univariate convergence diagnostics for the Metropolis-Hastings.\n');
@@ -359,12 +359,12 @@ if reste
if crit == 1
plt1 = UDIAG(:,1,k);
plt2 = UDIAG(:,2,k);
- namnam = [nam , ' (Interval)'];
+ namnam = [nam , ' (Interval)'];
elseif crit == 2
plt1 = UDIAG(:,3,k);
plt2 = UDIAG(:,4,k);
namnam = [nam , ' (m2)'];
- elseif crit == 3
+ elseif crit == 3
plt1 = UDIAG(:,5,k);
plt2 = UDIAG(:,6,k);
namnam = [nam , ' (m3)'];
@@ -393,7 +393,7 @@ if reste
fprintf(fidTeX,'\\begin{figure}[H]\n');
for jj = 1:size(NAMES,1)
fprintf(fidTeX,'\\psfrag{%s}[1][][0.5][0]{%s}\n',deblank(NAMES(jj,:)),deblank(TEXNAMES(jj,:)));
- end
+ end
fprintf(fidTeX,'\\centering \n');
fprintf(fidTeX,'\\includegraphics[width=%2.2f\\textwidth]{%s_udiag%s}\n',options_.figures.textwidth*min((boxplot-1)/nc,1),[OutputFolder '/' ModelName],int2str(pages+1));
if reste == 2
@@ -435,7 +435,7 @@ for b = 1:nblck
end
clear logpo2;
tmp(:,2) = kron(transpose(1:nblck),ones(NumberOfDraws,1));
-tmp(:,3) = kron(ones(nblck,1),time');
+tmp(:,3) = kron(ones(nblck,1),time');
tmp = sortrows(tmp,1);
ligne = 0;
for iter = Origin:StepSize:NumberOfDraws
@@ -454,12 +454,12 @@ for iter = Origin:StepSize:NumberOfDraws
for i=1:nblck
pmet = temp(find(temp(:,2)==i));
MDIAG(ligne,2) = MDIAG(ligne,2) + pmet(csup,1)-pmet(cinf,1);
- moyenne = mean(pmet,1); %% Within mean.
+ moyenne = mean(pmet,1); %% Within mean.
MDIAG(ligne,4) = MDIAG(ligne,4) + sum((pmet(:,1)-moyenne).^2)/(n-1);
MDIAG(ligne,6) = MDIAG(ligne,6) + sum(abs(pmet(:,1)-moyenne).^3)/(n-1);
end
end
-MDIAG(:,[2 4 6],:) = MDIAG(:,[2 4 6],:)/nblck;
+MDIAG(:,[2 4 6],:) = MDIAG(:,[2 4 6],:)/nblck;
h = dyn_figure(options_.nodisplay,'Name','Multivariate convergence diagnostic');
boxplot = 1;
@@ -467,12 +467,12 @@ for crit = 1:3
if crit == 1
plt1 = MDIAG(:,1);
plt2 = MDIAG(:,2);
- namnam = 'Interval';
+ namnam = 'Interval';
elseif crit == 2
plt1 = MDIAG(:,3);
plt2 = MDIAG(:,4);
namnam = 'm2';
- elseif crit == 3
+ elseif crit == 3
plt1 = MDIAG(:,5);
plt2 = MDIAG(:,6);
namnam = 'm3';
@@ -499,7 +499,7 @@ if TeX && any(strcmp('eps',cellstr(options_.graph_format)))
fprintf(fidTeX,'\\begin{figure}[H]\n');
for jj = 1:3
fprintf(fidTeX,'\\psfrag{%s}[1][][0.5][0]{%s}\n',deblank(NAMES(jj,:)),' ');
- end
+ end
fprintf(fidTeX,'\\centering \n');
fprintf(fidTeX,'\\includegraphics[width=0.8\\textwidth]{%s_mdiag}\n',[OutputFolder '/' ModelName]);
fprintf(fidTeX,'\\caption{Multivariate convergence diagnostics for the Metropolis-Hastings.\n');
@@ -512,4 +512,3 @@ if TeX && any(strcmp('eps',cellstr(options_.graph_format)))
fprintf(fidTeX,'%% End Of TeX file.');
fclose(fidTeX);
end
-
diff --git a/matlab/convergence_diagnostics/McMCDiagnostics_core.m b/matlab/convergence_diagnostics/McMCDiagnostics_core.m
index c9eda9a3bca4352577c5e4762bbe06190bf7ef4f..283e6ada1079a3f86f86d11ad9c5338bc336fd43 100644
--- a/matlab/convergence_diagnostics/McMCDiagnostics_core.m
+++ b/matlab/convergence_diagnostics/McMCDiagnostics_core.m
@@ -2,12 +2,12 @@ function myoutput = McMCDiagnostics_core(myinputs,fpar,npar,whoiam, ThisMatlab)
% function myoutput = McMCDiagnostics_core(myinputs,fpar,npar,whoiam, ThisMatlab)
% Computes the Brooks/Gelman (1998) convergence diagnostics, both the
% parameteric and the non-parameteric versions
-%
+%
% PARALLEL CONTEXT
% Core functionality for MCMC Diagnostics, which can be parallelized.
% See also the comment in posterior_sampler_core.m funtion.
-%
-%
+%
+%
% INPUTS
% See See the comment in posterior_sampler_core.m funtion.
@@ -25,10 +25,10 @@ function myoutput = McMCDiagnostics_core(myinputs,fpar,npar,whoiam, ThisMatlab)
%
% ALGORITHM
% Computes part of the convergence diagnostics, the rest is computed in McMCDiagnostics.m .
-% The methodology and terminology is based on: Brooks/Gelman (1998): General
-% Methods for Monitoring Convergence of Iterative Simulations, Journal of Computational
+% The methodology and terminology is based on: Brooks/Gelman (1998): General
+% Methods for Monitoring Convergence of Iterative Simulations, Journal of Computational
% and Graphical Statistics, Volume 7, Number 4, Pages 434-455
-%
+%
%
% SPECIAL REQUIREMENTS.
% None.
diff --git a/matlab/convergence_diagnostics/geweke_chi2_test.m b/matlab/convergence_diagnostics/geweke_chi2_test.m
index 6f0860af28175ef011192afe43d59c40cc8d6c02..8acf673993479e8e99880ba71c0eeca12f48d076 100644
--- a/matlab/convergence_diagnostics/geweke_chi2_test.m
+++ b/matlab/convergence_diagnostics/geweke_chi2_test.m
@@ -2,7 +2,7 @@ function results_struct = geweke_chi2_test(results1,results2,results_struct,opti
% results_struct = geweke_chi2_test(results1,results2,results_struct,options)
% PURPOSE: computes Geweke's chi-squared test for two sets of MCMC sample draws
%
-% INPUTS
+% INPUTS
% results1 [1 by (4+n_taper*2) vector] vector with post. mean,
% std, NSE_iid, RNE_iid, and tapered NSE and RNE
% for chain part 1
@@ -12,7 +12,7 @@ function results_struct = geweke_chi2_test(results1,results2,results_struct,opti
% results_struct [structure] results structure generated by geweke_moments
% Dynareoptions [structure]
%
-% OUTPUTS
+% OUTPUTS
% results_struct [structure] containing the following fields:
% pooled_mean Pooled mean of the chain parts, weighted
% with precision
@@ -49,26 +49,25 @@ function results_struct = geweke_chi2_test(results1,results2,results_struct,opti
% J.M. Bernardo, A.P. Dawid, and A.F.M. Smith (eds.) Proceedings of
% the Fourth Valencia International Meeting on Bayesian Statistics,
% pp. 169-194, Oxford University Press
-% Geweke (1999): `Using simulation methods for Bayesian econometric models:
+% Geweke (1999): `Using simulation methods for Bayesian econometric models:
% Inference, development and communication', Econometric Reviews, 18(1),
% 1-73
-% written by: Johannes Pfeifer,
-% based on code by James P. LeSage, who in turn
-% drew on MATLAB programs written by Siddartha Chib
+% written by: Johannes Pfeifer,
+% based on code by James P. LeSage, who in turn
+% drew on MATLAB programs written by Siddartha Chib
for k=1:length(options.convergence.geweke.taper_steps)+1
- NSE=[results1(:,3+(k-1)*2) results2(:,3+(k-1)*2)];
- means=[results1(:,1) results2(:,1)];
- diff_Means=means(:,1)-means(:,2);
- sum_of_weights=sum(1./(NSE.^2),2);
- pooled_mean=sum(means./(NSE.^2),2)./sum_of_weights;
- pooled_NSE=1./sqrt(sum_of_weights);
+ NSE=[results1(:,3+(k-1)*2) results2(:,3+(k-1)*2)];
+ means=[results1(:,1) results2(:,1)];
+ diff_Means=means(:,1)-means(:,2);
+ sum_of_weights=sum(1./(NSE.^2),2);
+ pooled_mean=sum(means./(NSE.^2),2)./sum_of_weights;
+ pooled_NSE=1./sqrt(sum_of_weights);
- test_stat=diff_Means.^2./sum(NSE.^2,2);
- p = 1-chi2cdf(test_stat,1);
- results_struct.pooled_mean(:,k) = pooled_mean;
- results_struct.pooled_nse(:,k) = pooled_NSE;
- results_struct.prob_chi2_test(:,k) = p;
+ test_stat=diff_Means.^2./sum(NSE.^2,2);
+ p = 1-chi2cdf(test_stat,1);
+ results_struct.pooled_mean(:,k) = pooled_mean;
+ results_struct.pooled_nse(:,k) = pooled_NSE;
+ results_struct.prob_chi2_test(:,k) = p;
end
-
diff --git a/matlab/convergence_diagnostics/geweke_moments.m b/matlab/convergence_diagnostics/geweke_moments.m
index 27204dd09a209eb329a889969095e895eb9f9a33..61c22979fb074658503708bcef980f60051e0276 100644
--- a/matlab/convergence_diagnostics/geweke_moments.m
+++ b/matlab/convergence_diagnostics/geweke_moments.m
@@ -1,13 +1,13 @@
function [results_vec, results_struct] = geweke_moments(draws,Dynareoptions)
%[results_vec, results_struct] = geweke_moments(draws,Dynareoptions)
-% PURPOSE: computes Gewke's convergence diagnostics NSE and RNE
+% PURPOSE: computes Gewke's convergence diagnostics NSE and RNE
% (numerical std error and relative numerical efficiencies)
-% INPUTS
-% draws [ndraws by 1 vector]
+% INPUTS
+% draws [ndraws by 1 vector]
% Dynareoptions [structure]
-%
-% OUTPUTS
+%
+% OUTPUTS
% results_vec
% results_struct [structure] containing the following fields:
% posteriormean= posterior parameter mean
@@ -44,16 +44,16 @@ function [results_vec, results_struct] = geweke_moments(draws,Dynareoptions)
% J.M. Bernardo, A.P. Dawid, and A.F.M. Smith (eds.) Proceedings of
% the Fourth Valencia International Meeting on Bayesian Statistics,
% pp. 169-194, Oxford University Press
-% Geweke (1999): `Using simulation methods for Bayesian econometric models:
+% Geweke (1999): `Using simulation methods for Bayesian econometric models:
% Inference, development and communication', Econometric Reviews, 18(1),
% 1-73
% -----------------------------------------------------------------
-% written by: Johannes Pfeifer,
-% based on code by James P. LeSage, who in turn
-% drew on MATLAB programs written by Siddartha Chib
+% written by: Johannes Pfeifer,
+% based on code by James P. LeSage, who in turn
+% drew on MATLAB programs written by Siddartha Chib
+
-
ndraw = size(draws,1);
n_groups=100;
taper_steps=Dynareoptions.convergence.geweke.taper_steps;
@@ -66,7 +66,7 @@ window_means= zeros(n_groups,1);
window_uncentered_variances= zeros(n_groups,1);
for ig=1:n_groups
window_means(ig,1)=sum(draws((ig-1)*ns+1:ig*ns,1))/ns;
- window_uncentered_variances(ig,1)=sum(draws((ig-1)*ns+1:ig*ns,1).^2)/ns;
+ window_uncentered_variances(ig,1)=sum(draws((ig-1)*ns+1:ig*ns,1).^2)/ns;
end %for ig
total_mean=mean(window_means);
total_variance=mean(window_uncentered_variances)-total_mean^2;
@@ -106,4 +106,3 @@ for taper_index=1:length(taper_steps)
eval(['results_struct.nse_taper_',num2str(taper),'= NSE_taper;']);
eval(['results_struct.rne_taper_',num2str(taper),'= total_variance/(n_draws_used*NSE_taper^2);']);
end % end of for mm loop
-
diff --git a/matlab/convergence_diagnostics/mcmc_ifac.m b/matlab/convergence_diagnostics/mcmc_ifac.m
index 7537e62eb10065804fca52167ff609b5ea89f4a9..faf7d165980b0562de07bf305cfac4c1c1d9b8e4 100644
--- a/matlab/convergence_diagnostics/mcmc_ifac.m
+++ b/matlab/convergence_diagnostics/mcmc_ifac.m
@@ -1,71 +1,71 @@
-function Ifac = mcmc_ifac(X, Nc)
-% function Ifac = mcmc_ifac(X, Nc)
-% Compute inefficiency factor of a MCMC sample X based on a Parzen Window
-%
-% INPUTS
-% X: time series
-% Nc: # of lags
-%
-% OUTPUTS
-% Ifac: inefficiency factor of MCMC sample
-%
-% SPECIAL REQUIREMENTS
-% none
-% ALGORITHM:
-% Inefficiency factors are computed as
-% \[
-% Ifac = 1 + 2\sum\limits_{i=1}^{Nc} {\hat \rho(i)}
-% \]
-% where $\hat \rho(i)$ denotes the autocorrelation at lag i and the terms
-% of the sum are truncated using a Parzen window.
-%
-% For inefficiency factors, see Section 6.1 of Paolo Giordani, Michael Pitt, and Robert Kohn (2011):
-% "Bayesian Inference for Time Series State Space Models" in : John Geweke, Gary Koop,
-% Herman van Dijk (editors): "The Oxford Handbook of Bayesian
-% Econometrics", Oxford University Press
-%
-% The Parzen-Window is given by
-% \[
-% k(x) = \left\{ {\begin{array}{*{20}{c}}
-% {1 - 6{x^2} + 6|x|^3} \text{ for } 0 \leqslant |x| \leqslant \frac{1}{2}} \\
-% {2(1-|x|^3) \text{ for } \frac{1}{2} \leqslant |x| \leqslant 1} \\
-% {0 \text{ otherwise}}
-% \end{array}} \right.
-% \]
-% See Donald W.K Andrews (1991): "Heteroskedasticity and autocorrelation
-% consistent covariance matrix estimation", Econometrica, 59(3), p. 817-858
-
-
-% Copyright (C) 2015-16 Dynare Team
-%
-% 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/>.
-
-Nc = floor(min(Nc, length(X)/2));
-if mod(Nc,2)
- Nc=Nc-1;
-end
-AcorrXSIM = dyn_autocorr(X(:), Nc);
-%
-%Calculate the Parzen Weight
-Parzen=zeros(Nc+1,1);
-for i=1: Nc/2+1
- Parzen(i)=1 - 6*(i/Nc)^2+ 6*(i/Nc)^3;
-end
-for i=(Nc/2)+1: Nc+1
- Parzen(i)=2 * (1-(i/Nc))^3;
-end
-Parzen=Parzen';
-Ifac= 1+2*sum(Parzen(:).* AcorrXSIM);
+function Ifac = mcmc_ifac(X, Nc)
+% function Ifac = mcmc_ifac(X, Nc)
+% Compute inefficiency factor of a MCMC sample X based on a Parzen Window
+%
+% INPUTS
+% X: time series
+% Nc: # of lags
+%
+% OUTPUTS
+% Ifac: inefficiency factor of MCMC sample
+%
+% SPECIAL REQUIREMENTS
+% none
+% ALGORITHM:
+% Inefficiency factors are computed as
+% \[
+% Ifac = 1 + 2\sum\limits_{i=1}^{Nc} {\hat \rho(i)}
+% \]
+% where $\hat \rho(i)$ denotes the autocorrelation at lag i and the terms
+% of the sum are truncated using a Parzen window.
+%
+% For inefficiency factors, see Section 6.1 of Paolo Giordani, Michael Pitt, and Robert Kohn (2011):
+% "Bayesian Inference for Time Series State Space Models" in : John Geweke, Gary Koop,
+% Herman van Dijk (editors): "The Oxford Handbook of Bayesian
+% Econometrics", Oxford University Press
+%
+% The Parzen-Window is given by
+% \[
+% k(x) = \left\{ {\begin{array}{*{20}{c}}
+% {1 - 6{x^2} + 6|x|^3} \text{ for } 0 \leqslant |x| \leqslant \frac{1}{2}} \\
+% {2(1-|x|^3) \text{ for } \frac{1}{2} \leqslant |x| \leqslant 1} \\
+% {0 \text{ otherwise}}
+% \end{array}} \right.
+% \]
+% See Donald W.K Andrews (1991): "Heteroskedasticity and autocorrelation
+% consistent covariance matrix estimation", Econometrica, 59(3), p. 817-858
+
+
+% Copyright (C) 2015-16 Dynare Team
+%
+% 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/>.
+
+Nc = floor(min(Nc, length(X)/2));
+if mod(Nc,2)
+ Nc=Nc-1;
+end
+AcorrXSIM = dyn_autocorr(X(:), Nc);
+%
+%Calculate the Parzen Weight
+Parzen=zeros(Nc+1,1);
+for i=1: Nc/2+1
+ Parzen(i)=1 - 6*(i/Nc)^2+ 6*(i/Nc)^3;
+end
+for i=(Nc/2)+1: Nc+1
+ Parzen(i)=2 * (1-(i/Nc))^3;
+end
+Parzen=Parzen';
+Ifac= 1+2*sum(Parzen(:).* AcorrXSIM);
diff --git a/matlab/convergence_diagnostics/raftery_lewis.m b/matlab/convergence_diagnostics/raftery_lewis.m
index 0b61ca9baa99fa1a9c626e919fd3b20a864e526c..342e36ab5561573474aa2ed3aa5ab7479216f81f 100644
--- a/matlab/convergence_diagnostics/raftery_lewis.m
+++ b/matlab/convergence_diagnostics/raftery_lewis.m
@@ -1,15 +1,15 @@
function [raftery_lewis] = raftery_lewis(runs,q,r,s)
% function raftery_lewis = raftery_lewis(runs,q,r,s)
-% Computes the convergence diagnostics of Raftery and Lewis (1992), i.e. the
-% number of draws needed in MCMC to estimate the posterior cdf of the q-quantile
+% Computes the convergence diagnostics of Raftery and Lewis (1992), i.e. the
+% number of draws needed in MCMC to estimate the posterior cdf of the q-quantile
% within an accuracy r with probability s
-%
+%
% Inputs:
-% - draws [n_draws by n_var] double matrix of draws from the sampler
+% - draws [n_draws by n_var] double matrix of draws from the sampler
% - q [scalar] quantile of the quantity of interest
-% - r [scalar] level of desired precision
+% - r [scalar] level of desired precision
% - s [scalar] probability associated with r
-%
+%
% Output:
% raftery_lewis [structure] containing the fields:
% - M_burn [n_draws by 1] number of draws required for burn-in
@@ -21,7 +21,7 @@ function [raftery_lewis] = raftery_lewis(runs,q,r,s)
% iterations due to dependence in chain
% - N_min [scalar] # draws if the chain is white noise
% - N_total [n_draws by 1] nburn + nprec
-%
+%
% ---------------------------------------------------------------------
% NOTES: Example values of q, r, s:
@@ -30,18 +30,18 @@ function [raftery_lewis] = raftery_lewis(runs,q,r,s)
%
% - The result is quite sensitive to r, being proportional to the
% inverse of r^2.
-% - For epsilon (closeness of probabilities to equilibrium values),
+% - For epsilon (closeness of probabilities to equilibrium values),
% Raftery/Lewis use 0.001 and argue that the results
% are quite robust to changes in this value
%
% ---------------------------------------------------------------------
-% REFERENCES:
+% REFERENCES:
% Raftery, Adrien E./Lewis, Steven (1992a): "How many iterations in the Gibbs sampler?"
-% in: Bernardo/Berger/Dawid/Smith (eds.): Bayesian Statistics, Vol. 4, Clarendon Press: Oxford,
-% pp. 763-773.
-% Raftery, Adrien E./Lewis, Steven (1992b): "Comment: One long run with diagnostics:
-% Implementation strategies for Markov chain Monte Carlo." Statistical Science,
-% 7(4), pp. 493-497.
+% in: Bernardo/Berger/Dawid/Smith (eds.): Bayesian Statistics, Vol. 4, Clarendon Press: Oxford,
+% pp. 763-773.
+% Raftery, Adrien E./Lewis, Steven (1992b): "Comment: One long run with diagnostics:
+% Implementation strategies for Markov chain Monte Carlo." Statistical Science,
+% 7(4), pp. 493-497.
%
% ----------------------------------------------------
@@ -80,26 +80,26 @@ thinned_chain = zeros(n_runs,1);
Phi = norminv((s+1)/2); %note the missing ^{-1} at the Phi in equation top page 5, see RL (1995)
raftery_lewis.N_min = fix(Phi^2*(1-q)*q/r^2+1);
-for nv = 1:n_vars % big loop over variables
+for nv = 1:n_vars % big loop over variables
if q > 0 && q < 1
work = (runs(:,nv) <= quantile(runs(:,nv),q));
else
- error('Quantile must be between 0 and 1');
+ error('Quantile must be between 0 and 1');
end
-
- k_thin_current_var = 1;
- bic = 1;
+
+ k_thin_current_var = 1;
+ bic = 1;
epss = 0.001;
% Find thinning factor for which first-order Markov Chain is preferred to second-order one
while(bic > 0)
- thinned_chain=work(1:k_thin_current_var:n_runs,1);
+ thinned_chain=work(1:k_thin_current_var:n_runs,1);
[g2, bic] = first_vs_second_order_MC_test(thinned_chain);
k_thin_current_var = k_thin_current_var+1;
end
-
+
k_thin_current_var = k_thin_current_var-1; %undo last step
-
- %compute transition probabilities
+
+ %compute transition probabilities
transition_matrix = zeros(2,2);
for i1 = 2:size(thinned_chain,1)
transition_matrix(thinned_chain(i1-1)+1,thinned_chain(i1)+1) = transition_matrix(thinned_chain(i1-1)+1,thinned_chain(i1)+1)+1;
@@ -109,13 +109,13 @@ for nv = 1:n_vars % big loop over variables
kmind=k_thin_current_var;
[g2, bic]=independence_chain_test(thinned_chain);
-
+
while(bic > 0)
- thinned_chain=work(1:kmind:n_runs,1);
+ thinned_chain=work(1:kmind:n_runs,1);
[g2, bic] = independence_chain_test(thinned_chain);
kmind = kmind+1;
end
-
+
m_star = log((alpha + beta)*epss/max(alpha,beta))/log(abs(1 - alpha - beta)); %equation bottom page 4
raftery_lewis.M_burn(nv) = fix((m_star+1)*k_thin_current_var);
n_star = (2 - (alpha + beta))*alpha*beta*(Phi^2)/((alpha + beta)^3 * r^2); %equation top page 5
@@ -131,7 +131,7 @@ end
function [g2, bic] = first_vs_second_order_MC_test(d)
%conducts a test of first vs. second order Markov Chain via BIC criterion
n_obs=size(d,1);
-g2 = 0;
+g2 = 0;
tran=zeros(2,2,2);
for t_iter=3:n_obs % count state transitions
tran(d(t_iter-2,1)+1,d(t_iter-1,1)+1,d(t_iter,1)+1)=tran(d(t_iter-2,1)+1,d(t_iter-1,1)+1,d(t_iter,1)+1)+1;
@@ -142,17 +142,17 @@ for ind_1 = 1:2
for ind_3 = 1:2
if tran(ind_1,ind_2,ind_3) ~= 0
fitted = (tran(ind_1,ind_2,1) + tran(ind_1,ind_2,2))*(tran(1,ind_2,ind_3) + tran(2,ind_2,ind_3))/...
- (tran(1,ind_2,1) + tran(1,ind_2,2) + tran(2,ind_2,1) + tran(2,ind_2,2));
+ (tran(1,ind_2,1) + tran(1,ind_2,2) + tran(2,ind_2,1) + tran(2,ind_2,2));
focus = tran(ind_1,ind_2,ind_3);
g2 = g2 + log(focus/fitted)*focus;
end
end % end of for i3
end % end of for i2
end % end of for i1
-g2 = g2*2;
-bic = g2 - log(n_obs-2)*2;
+ g2 = g2*2;
+ bic = g2 - log(n_obs-2)*2;
-end
+ end
function [g2, bic] = independence_chain_test(d)
@@ -162,18 +162,18 @@ trans = zeros(2,2);
for ind_1 = 2:n_obs
trans(d(ind_1-1)+1,d(ind_1)+1)=trans(d(ind_1-1)+1,d(ind_1)+1)+1;
end
-dcm1 = n_obs - 1;
+dcm1 = n_obs - 1;
g2 = 0;
% Compute the log likelihood ratio statistic for second-order MC vs first-order MC. G2 statistic of Bishop, Fienberg and Holland (1975)
for ind_1 = 1:2
for ind_2 = 1:2
if trans(ind_1,ind_2) ~= 0
- fitted = ((trans(ind_1,1) + trans(ind_1,2))*(trans(1,ind_2) + trans(2,ind_2)))/dcm1;
+ fitted = ((trans(ind_1,1) + trans(ind_1,2))*(trans(1,ind_2) + trans(2,ind_2)))/dcm1;
focus = trans(ind_1,ind_2);
g2 = g2 + log(focus/fitted)*focus;
end
end
end
-g2 = g2*2;
+g2 = g2*2;
bic = g2 - log(dcm1);
end
diff --git a/matlab/convert_dyn_45_to_44.m b/matlab/convert_dyn_45_to_44.m
index ba3dbc671fc55d793993e98462b5b37ac448a13b..14859d678620bafb96a12cab809ba7139adb2828 100644
--- a/matlab/convert_dyn_45_to_44.m
+++ b/matlab/convert_dyn_45_to_44.m
@@ -40,7 +40,7 @@ if isfield(oo_,'PointForecast')
oo_.MeanForecast.(moment_names{moment_iter}).(var_names{var_iter})=...
[oo_.SmoothedVariables.(moment_names{moment_iter}).(var_names{var_iter})(:,end)*ones(M_.maximum_endo_lag,1) oo_.MeanForecast.(moment_names{moment_iter}).(var_names{var_iter})];
oo_.PointForecast.(moment_names{moment_iter}).(var_names{var_iter})=...
- [oo_.SmoothedVariables.(moment_names{moment_iter}).(var_names{var_iter})(:,end)*ones(M_.maximum_endo_lag,1) oo_.PointForecast.(moment_names{moment_iter}).(var_names{var_iter})];
+ [oo_.SmoothedVariables.(moment_names{moment_iter}).(var_names{var_iter})(:,end)*ones(M_.maximum_endo_lag,1) oo_.PointForecast.(moment_names{moment_iter}).(var_names{var_iter})];
else
oo_.MeanForecast.(moment_names{moment_iter}).(var_names{var_iter})=...
[oo_.SmoothedVariables.(moment_names{moment_iter}).(var_names{var_iter})(end)*ones(M_.maximum_endo_lag,1); oo_.MeanForecast.(moment_names{moment_iter}).(var_names{var_iter})];
@@ -106,7 +106,7 @@ if isfield(oo_,'UpdatedVariables')
for ii=1:length(names)
%make sure Bayesian fields are not affected
if ~strcmp(names{ii},'Mean') && ~strcmp(names{ii},'Median') && ~strcmp(names{ii},'deciles') ...
- && ~strcmp(names{ii},'Var') && ~strcmp(names{ii},'HPDinf') && ~strcmp(names{ii},'HPDsup')
+ && ~strcmp(names{ii},'Var') && ~strcmp(names{ii},'HPDinf') && ~strcmp(names{ii},'HPDsup')
current_var_index=find(strmatch(names{ii},deblank(M_.endo_names),'exact'));
if options_.loglinear == 1 %logged steady state must be used
constant_current_variable=log(oo_.dr.ys(current_var_index));
@@ -127,7 +127,7 @@ if isfield(oo_,'FilteredVariables')
for ii=1:length(names)
%make sure Bayesian fields are not affected
if ~strcmp(names{ii},'Mean') && ~strcmp(names{ii},'Median') && ~strcmp(names{ii},'deciles') ...
- && ~strcmp(names{ii},'Var') && ~strcmp(names{ii},'HPDinf') && ~strcmp(names{ii},'HPDsup')
+ && ~strcmp(names{ii},'Var') && ~strcmp(names{ii},'HPDinf') && ~strcmp(names{ii},'HPDsup')
current_var_index=find(strmatch(names{ii},deblank(M_.endo_names),'exact'));
if options_.loglinear == 1 %logged steady state must be used
constant_current_variable=log(oo_.dr.ys(current_var_index));
@@ -147,11 +147,11 @@ end
if ~isempty(options_.nk) && options_.nk ~= 0 && ~isempty(bayestopt_)
if ~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.pshape> 0) && options_.load_mh_file)) %no Bayesian estimation
positions_in_decision_order=oo_.dr.inv_order_var(bayestopt_.smoother_var_list(bayestopt_.smoother_saved_var_list));
- if options_.loglinear == 1 %logged steady state must be used
- constant_all_variables=log(oo_.dr.ys(bayestopt_.smoother_var_list(bayestopt_.smoother_saved_var_list)));
- elseif options_.loglinear == 0 %unlogged steady state must be used
- constant_all_variables=oo_.dr.ys(bayestopt_.smoother_var_list(bayestopt_.smoother_saved_var_list));
- end
+ if options_.loglinear == 1 %logged steady state must be used
+ constant_all_variables=log(oo_.dr.ys(bayestopt_.smoother_var_list(bayestopt_.smoother_saved_var_list)));
+ elseif options_.loglinear == 0 %unlogged steady state must be used
+ constant_all_variables=oo_.dr.ys(bayestopt_.smoother_var_list(bayestopt_.smoother_saved_var_list));
+ end
if ~(options_.selected_variables_only && ~(options_.forecast > 0)) %happens only when selected_variables_only is not used
oo_.FilteredVariablesKStepAhead(:,positions_in_decision_order,:)=oo_.FilteredVariablesKStepAhead-constant_all_variables;
if ~isempty(PK) %get K-step ahead variances
@@ -182,11 +182,11 @@ end
%Deal with OSR
if ~isempty(M_.osr.variable_weights)
- evalin('base','optim_weights_=M_.osr.variable_weights')
+ evalin('base','optim_weights_=M_.osr.variable_weights')
end
if ~isempty(M_.osr.variable_indices)
- evalin('base','obj_var_=M_.osr.variable_indices')
+ evalin('base','obj_var_=M_.osr.variable_indices')
end
if ~isempty(M_.osr.param_names)
- evalin('base','osr_params_=char(M_.osr.param_names)')
+ evalin('base','osr_params_=char(M_.osr.param_names)')
end
diff --git a/matlab/convert_oo_.m b/matlab/convert_oo_.m
index d7cdac1f88138f4e20a347b4c9fa81f59bef76cf..914cc0adbe36d916f8ecedfb3766df0d248a1dbb 100644
--- a/matlab/convert_oo_.m
+++ b/matlab/convert_oo_.m
@@ -81,5 +81,5 @@ else
end
eval(['oo_ = convert_dyn_' strrep(new_from_ver, '.', '') '_to_' ...
- strrep(to_ver, '.', '') '(M_, options_, oo_);']);
+ strrep(to_ver, '.', '') '(M_, options_, oo_);']);
end
diff --git a/matlab/correlation_mc_analysis.m b/matlab/correlation_mc_analysis.m
index 94388f2fdaf6a7ea684a2c769186ad32d60d4e4d..5ba0ca813ead3c2142486db8da0221b3adfd20cd 100644
--- a/matlab/correlation_mc_analysis.m
+++ b/matlab/correlation_mc_analysis.m
@@ -137,7 +137,7 @@ end
function oo_ = fill_output_structure(var1,var2,type,oo_,moment,lag,result)
switch moment
- case {'Mean','Median','Variance','HPDinf','HPDsup'}
+ case {'Mean','Median','Variance','HPDinf','HPDsup'}
oo_.([type, 'TheoreticalMoments']).dsge.correlation.(moment).(var1).(var2)(lag,1) = result;
case {'deciles','density'}
oo_.([type, 'TheoreticalMoments']).dsge.correlation.(moment).(var1).(var2)(lag,1) = {result};
diff --git a/matlab/cosn.m b/matlab/cosn.m
index 33bba8c4e86f4527dbc6def77a0aa5a19266cde5..b64245e7b8ee53ae494129b249d9b24b49f74fdd 100644
--- a/matlab/cosn.m
+++ b/matlab/cosn.m
@@ -37,6 +37,3 @@ if rank(yhat)
else
co=0;
end
-
-
-
diff --git a/matlab/covariance_mc_analysis.m b/matlab/covariance_mc_analysis.m
index 60753407b41dead7f5fad6daaa99137a9a713938..16b46eb9a68065acc8ead47951375c5ffba4f705 100644
--- a/matlab/covariance_mc_analysis.m
+++ b/matlab/covariance_mc_analysis.m
@@ -1,7 +1,7 @@
function oo_ = covariance_mc_analysis(NumberOfSimulations,type,dname,fname,vartan,nvar,var1,var2,mh_conf_sig,oo_,options_)
% This function analyses the (posterior or prior) distribution of the
% endogenous variables' covariance matrix.
-%
+%
% INPUTS
% NumberOfSimulations [integer] scalar, number of simulations.
% type [string] 'prior' or 'posterior'
@@ -14,7 +14,7 @@ function oo_ = covariance_mc_analysis(NumberOfSimulations,type,dname,fname,varta
% mh_conf_sig [double] 2 by 1 vector with upper
% and lower bound of HPD intervals
% oo_ [structure] Dynare structure where the results are saved.
-% options_ [structure] Dynare options structure
+% options_ [structure] Dynare options structure
%
% OUTPUTS
% oo_ [structure] Dynare structure where the results are saved.
@@ -64,7 +64,7 @@ var1=deblank(var1);
var2=deblank(var2);
if isfield(oo_,[ TYPE 'TheoreticalMoments'])
- temporary_structure = oo_.([TYPE, 'TheoreticalMoments']);
+ temporary_structure = oo_.([TYPE, 'TheoreticalMoments']);
if isfield(temporary_structure,'dsge')
temporary_structure = oo_.([TYPE, 'TheoreticalMoments']).dsge;
if isfield(temporary_structure,'covariance')
@@ -104,17 +104,17 @@ for file = 1:length(ListOfFiles)
temp=Covariance_matrix(:,cov_pos)./(sqrt(Covariance_matrix(:,var_pos_1)).*sqrt(Covariance_matrix(:,var_pos_2)));
temp(Covariance_matrix(:,cov_pos)==0)=0; %filter out 0 correlations that would result in 0/0
tmp_corr_mat(i1:i2)=temp;
- end
+ end
i1 = i2+1;
end
if options_.estimation.moments_posterior_density.indicator
[p_mean, p_median, p_var, hpd_interval, p_deciles, density] = ...
- posterior_moments(tmp,1,mh_conf_sig);
+ posterior_moments(tmp,1,mh_conf_sig);
oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.density.(var1).(var2) = density;
else
[p_mean, p_median, p_var, hpd_interval, p_deciles] = ...
- posterior_moments(tmp,0,mh_conf_sig);
+ posterior_moments(tmp,0,mh_conf_sig);
end
oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.Mean.(var1).(var2) = p_mean;
oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.Median.(var1).(var2) = p_median;
diff --git a/matlab/csolve.m b/matlab/csolve.m
index b4d79229befac5bd2126ff78fc749faf7f25f07b..a0db73369fa282964fec76e628ed87f61ecb7280 100644
--- a/matlab/csolve.m
+++ b/matlab/csolve.m
@@ -12,7 +12,7 @@ function [x,rc] = csolve(FUN,x,gradfun,crit,itmax,varargin)
% the equation is solved.
% itmax: the solver stops when this number of iterations is reached, with rc=4
% varargin: in this position the user can place any number of additional arguments, all
-% of which are passed on to FUN and gradfun (when it is non-empty) as a list of
+% of which are passed on to FUN and gradfun (when it is non-empty) as a list of
% arguments following x.
% rc: 0 means normal solution, 1 and 3 mean no solution despite extremely fine adjustments
% in step length (very likely a numerical problem, or a discontinuity). 4 means itmax
@@ -60,7 +60,7 @@ if isempty(varargin)
f0=feval(FUN,x);
else
f0=feval(FUN,x,varargin{:});
-end
+end
af0=sum(abs(f0));
af00=af0;
itct=0;
diff --git a/matlab/cubature_with_gaussian_weight.m b/matlab/cubature_with_gaussian_weight.m
index 55f7d2a23385d500b6dbfacc1492d330b2f3a2a0..8e31d1f842f5251f80f00e6c063e52fc9bb84974 100644
--- a/matlab/cubature_with_gaussian_weight.m
+++ b/matlab/cubature_with_gaussian_weight.m
@@ -29,8 +29,8 @@ function [nodes, weights] = cubature_with_gaussian_weight(d,n,method)
%! The routine returns nodes and associated weights to compute a multivariate integral of the form:
%!
%! \int_D f(x)*\exp(-<x,x>) dx
-%!
-%!
+%!
+%!
%! @end deftypefn
%@eod:
@@ -52,7 +52,7 @@ function [nodes, weights] = cubature_with_gaussian_weight(d,n,method)
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
% AUTHOR(S) stephane DOT adjemian AT univ DASH lemans DOT fr
-
+
% Set default.
if nargin<3 || isempty(method)
method = 'Stroud';
@@ -90,7 +90,7 @@ if strcmp(method,'Stroud') && isequal(n,5)
nodes = zeros(d,m);
weights = zeros(m,1);
% Set the weight for the first node (0)
- weights(1) = A;
+ weights(1) = A;
skip = 1;
% Set the remaining nodes and associated weights.
nodes(:,skip+(1:d)) = r*eye(d);
@@ -117,20 +117,20 @@ end
function v = e(n,i)
- v = zeros(n,1);
- v(i) = 1;
-
+v = zeros(n,1);
+v(i) = 1;
+
function m = ee(n,i,j)
- m = zeros(n,4);
- m(:,1) = e(n,i)+e(n,j);
- m(:,2) = e(n,i)-e(n,j);
- m(:,3) = -m(:,2);
- m(:,4) = -m(:,1);
-
+m = zeros(n,4);
+m(:,1) = e(n,i)+e(n,j);
+m(:,2) = e(n,i)-e(n,j);
+m(:,3) = -m(:,2);
+m(:,4) = -m(:,1);
+
%@test:1
%$ % Set problem
%$ d = 4;
-%$
+%$
%$ t = zeros(5,1);
%$
%$ % Call the tested routine
@@ -289,7 +289,7 @@ function m = ee(n,i,j)
%@test:5
%$ % Set problem
%$ d = 5;
-%$
+%$
%$ t = zeros(6,1);
%$
%$ % Call the tested routine
@@ -333,7 +333,7 @@ function m = ee(n,i,j)
%@test:6
%$ % Set problem
%$ d = 3;
-%$
+%$
%$ t = zeros(4,1);
%$
%$ % Call the tested routine
diff --git a/matlab/cycle_reduction.m b/matlab/cycle_reduction.m
index c1edc26c1b8e0ce1de954bf9386591b34f0b9529..be8fdb3e8fd78b063e77a84993d8aee75cd3c036 100644
--- a/matlab/cycle_reduction.m
+++ b/matlab/cycle_reduction.m
@@ -76,7 +76,7 @@ id0 = 1:n;
id2 = id0+n;
cont = 1;
-while cont
+while cont
tmp = ([A0; A2]/A1)*[A0 A2];
A1 = A1 - tmp(id0,id2) - tmp(id2,id0);
A0 = -tmp(id0,id0);
@@ -97,7 +97,7 @@ while cont
info(2) = log(norm(A1,1));
end
return
- end
+ end
it = it + 1;
end
diff --git a/matlab/datatomfile.m b/matlab/datatomfile.m
index 4f6a5ff49acd04aabdefa4194da65c2e7e77f9c2..76928c77fa82e3447ebbf9e3713b786453dc1b3d 100644
--- a/matlab/datatomfile.m
+++ b/matlab/datatomfile.m
@@ -3,16 +3,16 @@ function datatomfile (s, var_list, names)
% This command saves the simulation results in a text file. The name of each
% variable preceeds the corresponding results. This command must follow SIMUL
% or STOCH_SIMUL commands.
-%
-% INPUTS
+%
+% INPUTS
% - s: data file name
% - var_list: vector of selected endogenous variables
% - names: vector of strings (alternative names for the endogenous variables in the data file)
%
-% OUTPUTS
+% OUTPUTS
% none
%
-% REMARKS
+% REMARKS
% Only the first argument is mandatory. If only one input argument is
% provided, all the variables as defined in M_.endo_names will be saved in
% the generated m file.
@@ -36,7 +36,7 @@ function datatomfile (s, var_list, names)
global M_ oo_
-% Open the data file.
+% Open the data file.
sm=[s,'.m'];
fid=fopen(sm,'w') ;
@@ -65,8 +65,8 @@ for i=1:n
end
end
stack = dbstack;
- fprintf(fid,'%% Dataset generated by %s.\n',stack(2).file);
- fprintf(fid,['%% ' datestr(now,0) '\n']);
+fprintf(fid,'%% Dataset generated by %s.\n',stack(2).file);
+fprintf(fid,['%% ' datestr(now,0) '\n']);
% Save the selected data.
for i = 1:n
fprintf(fid,[strtrim(names(i,:)), ' = ['],'\n') ;
@@ -78,4 +78,3 @@ end
% Close the data file.
fclose(fid) ;
-
diff --git a/matlab/dcompare.m b/matlab/dcompare.m
index 6efc5ae2722987d8fa854ef82cf616cda8adb318..9a6ac2c7b9de24bd735127df63a525ed4306f348 100644
--- a/matlab/dcompare.m
+++ b/matlab/dcompare.m
@@ -52,5 +52,3 @@ z = mean(mean(abs(x(j,i)-y(j,i)))) ;
disp (['The mean absolute difference between set ' s1(1,:) 'and set ' s1(2,:)]) ;
disp (['is : ' num2str(z)]) ;
return ;
-
-
diff --git a/matlab/delete_mh_history_files.m b/matlab/delete_mh_history_files.m
index 64b720ec9ba9f7dd666f2f272b93de9df0240c61..635bb9c94d9b0211b889882eb847e846e72102f1 100644
--- a/matlab/delete_mh_history_files.m
+++ b/matlab/delete_mh_history_files.m
@@ -1,5 +1,5 @@
function info = delete_mh_history_files(MetropolisFolder, ModelName)
-
+
% Copyright (C) 2013 Dynare Team
%
% This file is part of Dynare.
diff --git a/matlab/discretionary_policy_1.m b/matlab/discretionary_policy_1.m
index 931d3b596f634a87d3814eca5c1bfc622cc91587..9657dfa023a25d3a2626a338b50b1051cd6e9f47 100644
--- a/matlab/discretionary_policy_1.m
+++ b/matlab/discretionary_policy_1.m
@@ -39,7 +39,7 @@ if isfield(M_,'orig_model')
M_.maximum_lag = orig_model.maximum_lag;
M_.maximum_endo_lag = orig_model.maximum_endo_lag;
else
- M_.orig_model = M_;
+ M_.orig_model = M_;
end
beta = get_optimal_policy_discount_factor(M_.params,M_.param_names);
@@ -53,10 +53,10 @@ if isfield(M_,'orig_model')
MaxLead = orig_model.maximum_lead;
MaxLag = orig_model.maximum_lag;
else
- endo_names = M_.endo_names;
- endo_nbr = M_.endo_nbr;
- MaxLag=M_.maximum_lag;
- MaxLead=M_.maximum_lead;
+ endo_names = M_.endo_names;
+ endo_nbr = M_.endo_nbr;
+ MaxLag=M_.maximum_lag;
+ MaxLead=M_.maximum_lead;
lead_lag_incidence = M_.lead_lag_incidence;
end
@@ -64,7 +64,7 @@ end
if options_.steadystate_flag
% explicit steady state file
[junk,M_.params,info] = evaluate_steady_state_file(oo_.steady_state,[oo_.exo_steady_state; oo_.exo_det_steady_state],M_, ...
- options_,0);
+ options_,0);
end
[U,Uy,W] = feval([M_.fname,'_objective_static'],zeros(endo_nbr,1),[], M_.params);
if any(any(Uy~=0))
@@ -96,32 +96,32 @@ eq_nbr= size(jacobia_,1);
instr_nbr=endo_nbr-eq_nbr;
if instr_nbr==0
- error('discretionary_policy:: There are no available instruments, because the model has as many equations as variables.')
+ error('discretionary_policy:: There are no available instruments, because the model has as many equations as variables.')
end
if size(Instruments,1)~= instr_nbr
- error('discretionary_policy:: There are more declared instruments than omitted equations.')
-end
+ error('discretionary_policy:: There are more declared instruments than omitted equations.')
+end
instr_id=nan(instr_nbr,1);
for j=1:instr_nbr
- vj=deblank(Instruments(j,:));
- vj_id=strmatch(vj,endo_names,'exact');
- if ~isempty(vj_id)
- instr_id(j)=vj_id;
- else
- error([mfilename,':: instrument ',vj,' not found'])
- end
+ vj=deblank(Instruments(j,:));
+ vj_id=strmatch(vj,endo_names,'exact');
+ if ~isempty(vj_id)
+ instr_id(j)=vj_id;
+ else
+ error([mfilename,':: instrument ',vj,' not found'])
+ end
end
Indices={'lag','0','lead'};
iter=1;
for j=1:numel(Indices)
eval(['A',Indices{j},'=zeros(eq_nbr,endo_nbr);'])
- if strcmp(Indices{j},'0')||(strcmp(Indices{j},'lag') && MaxLag)||(strcmp(Indices{j},'lead') && MaxLead)
- [junk,row,col]=find(lead_lag_incidence(iter,:));
- eval(['A',Indices{j},'(:,row)=jacobia_(:,col);'])
- iter=iter+1;
- end
+ if strcmp(Indices{j},'0')||(strcmp(Indices{j},'lag') && MaxLag)||(strcmp(Indices{j},'lead') && MaxLead)
+ [junk,row,col]=find(lead_lag_incidence(iter,:));
+ eval(['A',Indices{j},'(:,row)=jacobia_(:,col);'])
+ iter=iter+1;
+ end
end
B=jacobia_(:,nnz(iyv)+1:end);
@@ -131,17 +131,17 @@ solve_maxit = options_.dp.maxit;
discretion_tol = options_.discretionary_tol;
if ~isempty(Hold)
- [H,G,info]=discretionary_policy_engine(Alag,A0,Alead,B,W,instr_id,beta,solve_maxit,discretion_tol,qz_criterium,Hold);
+ [H,G,info]=discretionary_policy_engine(Alag,A0,Alead,B,W,instr_id,beta,solve_maxit,discretion_tol,qz_criterium,Hold);
else
- [H,G,info]=discretionary_policy_engine(Alag,A0,Alead,B,W,instr_id,beta,solve_maxit,discretion_tol,qz_criterium);
+ [H,G,info]=discretionary_policy_engine(Alag,A0,Alead,B,W,instr_id,beta,solve_maxit,discretion_tol,qz_criterium);
end
if info
dr=[];
return
else
- Hold=H; %save previous solution
- % Hold=[]; use this line if persistent command is not used.
+ Hold=H; %save previous solution
+ % Hold=[]; use this line if persistent command is not used.
end
% set the state
dr=oo_.dr;
@@ -159,7 +159,7 @@ dr.ys=ys; % <--- dr.ys =zeros(NewEndo_nbr,1);
function ys=NondistortionarySteadyState(M_)
if exist([M_.fname,'_steadystate.m'],'file')
- eval(['ys=',M_.fname,'_steadystate.m;'])
+ eval(['ys=',M_.fname,'_steadystate.m;'])
else
- ys=zeros(M_.endo_nbr,1);
+ ys=zeros(M_.endo_nbr,1);
end
diff --git a/matlab/discretionary_policy_engine.m b/matlab/discretionary_policy_engine.m
index f86276eecfcb8f2d555ab008a770d6d8a58a4fcc..05c781ae4abccd0cbabfcf55b979f45f8d829abc 100644
--- a/matlab/discretionary_policy_engine.m
+++ b/matlab/discretionary_policy_engine.m
@@ -1,23 +1,23 @@
function [H,G,retcode]=discretionary_policy_engine(AAlag,AA0,AAlead,BB,bigw,instr_id,beta,solve_maxit,discretion_tol,qz_criterium,H00,verbose)
% Solves the discretionary problem for a model of the form:
-%
+%
% Loss=E_0 sum_{t=0}^{\infty} beta^t [y_t'*W*y+x_t'*Q*x_t]
% subject to
% AAlag*yy_{t-1}+AA0*yy_t+AAlead*yy_{t+1}+BB*e=0
-%
+%
% with W the weight on the variables in vector y_t.
-%
+%
% The solution takes the form
% y_t=H*y_{t-1}+G*e_t
% where H=[H1;F1] and G=[H2;F2].
-%
+%
% We use the Dennis (2007, Macroeconomic Dynamics) algorithm and so we need
% to re-write the model in the form
% A0*y_t=A1*y_{t-1}+A2*y_{t+1}+A3*x_t+A4*x_{t+1}+A5*e_t, with W the
% weight on the y_t vector and Q the weight on the x_t vector of
% instruments.
-%
+%
% Inputs:
% AAlag [double] matrix of coefficients on lagged
% variables
@@ -28,7 +28,7 @@ function [H,G,retcode]=discretionary_policy_engine(AAlag,AA0,AAlead,BB,bigw,inst
% BB [double] matrix of coefficients on
% shocks
% bigw [double] matrix of coefficients on variables in
-% loss/objective function; stacks [W and Q]
+% loss/objective function; stacks [W and Q]
% instr_id [double] location vector of the instruments in the yy_t vector.
% beta [scalar] planner discount factor
% solve_maxit [scalar] maximum number of iterations
@@ -41,12 +41,12 @@ function [H,G,retcode]=discretionary_policy_engine(AAlag,AA0,AAlead,BB,bigw,inst
% H [double] (endo_nbr*endo_nbr) solution matrix for endogenous
% variables, stacks [H1 and H1]
% G [double] (endo_nbr*exo_nbr) solution matrix for shocks, stacks [H2 and F2]
-%
+%
% retcode [scalar] return code
%
% Algorithm:
% Dennis, Richard (2007): Optimal policy in rational expectations models: new solution algorithms,
-% Macroeconomic Dynamics, 11, 31�55.
+% Macroeconomic Dynamics, 11, 3155.
% Copyright (C) 2007-2017 Dynare Team
%
@@ -121,10 +121,10 @@ while 1
end
D=A0-A2*H1-A4*F1; %equation (20)
Dinv=inv(D);
- A3DPD=A3'*Dinv'*P*Dinv;
+ A3DPD=A3'*Dinv'*P*Dinv;
F1=-(Q+A3DPD*A3)\(A3DPD*A1); %component of (26)
H1=Dinv*(A1+A3*F1); %component of (27)
-
+
[rcode,NQ]=CheckConvergence([H1;F1]-[H10;F10],iter,solve_maxit,discretion_tol);
if rcode
break
@@ -174,7 +174,7 @@ else
H(instr_id,endo_augm_id)=F1;
G(endo_augm_id,:)=H2;
G(instr_id,:)=F2;
-
+
% Account for auxilliary variables
H(:,instr_id(aux))=H(:,end-(AuxiliaryVariables_nbr-1:-1:0));
H=H(1:endo_nbr,1:endo_nbr);
@@ -253,7 +253,7 @@ end
function v = SylvesterHessenbergSchur(d,g,h)
%
-% DSYLHS Solves a discrete time sylvester equation using the
+% DSYLHS Solves a discrete time sylvester equation using the
% Hessenberg-Schur algorithm
%
% v = DSYLHS(g,d,h) computes the matrix v that satisfies the
@@ -301,7 +301,7 @@ if i< n
i = i+1;
else
A = [w-g*h(i,i) (-g*h(i+1,i));...
- -g*h(i,i+1) w-g*h(i+1,i+1)];
+ -g*h(i,i+1) w-g*h(i+1,i+1)];
C = [d(:,i); d(:,i+1)];
X = A\C;
v(:,i) = X(1:m,:);
@@ -320,9 +320,9 @@ while i<n
i = i+1;
else
A = [w - g*h(i,i) (-g*h(i+1,i)); ...
- -g*h(i,i+1) w - g*h(i+1,i+1)];
+ -g*h(i,i+1) w - g*h(i+1,i+1)];
C = [d(:,i) + temp*h(1:b,i); ...
- d(:,i+1) + temp*h(1:b,i+1)];
+ d(:,i+1) + temp*h(1:b,i+1)];
X = A\C;
v(:,i) = X(1:m,:);
v(:,i+1) = X(m+1:2*m, :);
diff --git a/matlab/disp_dr.m b/matlab/disp_dr.m
index d16ba2423889cc282a7e3ab50138578dd504bdbb..f8f4e4b4cba4ae45830231964808dd90dbdd620c 100644
--- a/matlab/disp_dr.m
+++ b/matlab/disp_dr.m
@@ -7,7 +7,7 @@ function disp_dr(dr,order,var_list)
% order [int]: order of approximation
% var_list [char array]: list of endogenous variables for which the
% decision rules should be printed
-%
+%
% Copyright (C) 2001-2017 Dynare Team
%
% This file is part of Dynare.
@@ -80,7 +80,7 @@ var_name_width=max([max(size(deblank(M_.endo_names(k1(ivar),:)),2)),max(size(deb
%deal with covariances
if order > 1
- var_name_width=max(2*(var_name_width+aux_var_additional_characters)+2,20); %account for covariances, separated by comma
+ var_name_width=max(2*(var_name_width+aux_var_additional_characters)+2,20); %account for covariances, separated by comma
else
var_name_width=max(var_name_width+aux_var_additional_characters,20);
end
@@ -90,7 +90,7 @@ label_format = sprintf('%%-%ds',var_name_width);
%% start displayimg
disp('POLICY AND TRANSITION FUNCTIONS')
% variable names
-str = char(32*ones(1,var_name_width));
+str = char(32*ones(1,var_name_width));
for i=1:nvar
str = [str sprintf(header_label_format,deblank(M_.endo_names(k1(ivar(i)),:)))];
end
@@ -232,7 +232,7 @@ for i = 1:length(M_.aux_vars)
if M_.aux_vars(i).endo_index == aux_index
switch M_.aux_vars(i).type
case 0
- str = sprintf('%s(%d)',deblank(M_.endo_names(aux_index,:)),aux_lead_lag);
+ str = sprintf('%s(%d)',deblank(M_.endo_names(aux_index,:)),aux_lead_lag);
return
case 1
orig_name = deblank(M_.endo_names(M_.aux_vars(i).orig_index, :));
@@ -256,10 +256,10 @@ error(sprintf('Could not find aux var: %s', M_.endo_names(aux_index, :)))
end
function [str,flag]=get_print_string(str,x,value_format_zero,value_format_float,flag,options_)
- if abs(x) >= options_.dr_display_tol
- flag = 1;
- str = [str sprintf(value_format_float,x)];
- else
- str = [str sprintf(value_format_zero,0)];
- end
+if abs(x) >= options_.dr_display_tol
+ flag = 1;
+ str = [str sprintf(value_format_float,x)];
+else
+ str = [str sprintf(value_format_zero,0)];
+end
end
\ No newline at end of file
diff --git a/matlab/disp_identification.m b/matlab/disp_identification.m
index 2bc6d8db059d9f01c3440d88f4e3ada33044fd0f..b8a3421d57688352a53d2c9dd6195a7e69bceaa2 100644
--- a/matlab/disp_identification.m
+++ b/matlab/disp_identification.m
@@ -118,7 +118,7 @@ if any(idemodel.ino)
disp([' [',name{jx(1)},',',name{jy(1)},'] are PAIRWISE collinear (with tol = 1.e-10) !' ])
end
end
-
+
end
skipline()
for j=1:npar
@@ -133,23 +133,23 @@ if any(idemodel.ino)
end
end
end
-% if npar>(j+1),
-% [ipair, jpair] = find(squeeze(idemodel.Pco(j,j+1:end,:))'>(1-1.e-10));
-% else
-% [ipair, jpair] = find(squeeze(idemodel.Pco(j,j+1:end,:))>(1-1.e-10));
-% end
-% if ~isempty(jpair),
-% for jx=j+1:npar,
-% ixp = find(jx==(jpair+j));
-% if ~isempty(ixp)
-% if SampleSize > 1,
-% disp([' [',name{j},',',name{jx},'] are PAIRWISE collinear (with tol = 1.e-10) for ',num2str(length(ixp)/SampleSize*100),'% of MC runs!' ])
-% else
-% disp([' [',name{j},',',name{jx},'] are PAIRWISE collinear (with tol = 1.e-10)!' ])
-% end
-% end
-% end
-% end
+ % if npar>(j+1),
+ % [ipair, jpair] = find(squeeze(idemodel.Pco(j,j+1:end,:))'>(1-1.e-10));
+ % else
+ % [ipair, jpair] = find(squeeze(idemodel.Pco(j,j+1:end,:))>(1-1.e-10));
+ % end
+ % if ~isempty(jpair),
+ % for jx=j+1:npar,
+ % ixp = find(jx==(jpair+j));
+ % if ~isempty(ixp)
+ % if SampleSize > 1,
+ % disp([' [',name{j},',',name{jx},'] are PAIRWISE collinear (with tol = 1.e-10) for ',num2str(length(ixp)/SampleSize*100),'% of MC runs!' ])
+ % else
+ % disp([' [',name{j},',',name{jx},'] are PAIRWISE collinear (with tol = 1.e-10)!' ])
+ % end
+ % end
+ % end
+ % end
end
if ~any(idemodel.ino) && ~any(any(idemodel.ind0==0))
@@ -167,10 +167,10 @@ if any(idemoments.ino)
end
% disp('WARNING !!!')
% disp(['The rank of J (moments) is deficient for ', num2str(length(find(idemoments.ino))/SampleSize*100),'% of MC runs!' ]),
-% indno=[];
-% for j=1:SampleSize, indno=[indno;idemoments.indno{j}]; end
-% freqno = mean(indno)*100;
-% ifreq=find(freqno);
+ % indno=[];
+ % for j=1:SampleSize, indno=[indno;idemoments.indno{j}]; end
+ % freqno = mean(indno)*100;
+ % ifreq=find(freqno);
% disp('MOMENT RANK FAILURE DUE TO COLLINEARITY OF PARAMETERS:');
skipline()
for j=1:npar
@@ -199,7 +199,7 @@ if any(idemoments.ino)
disp([' [',name{jx(1)},',',name{jy(1)},'] are PAIRWISE collinear (with tol = 1.e-10) !' ])
end
end
-
+
end
skipline()
for j=1:npar
@@ -214,25 +214,25 @@ if any(idemoments.ino)
end
end
end
-% if npar>(j+1),
-% [ipair, jpair] = find(squeeze(idemoments.Pco(j,j+1:end,:))'>(1-1.e-10));
-% else
-% [ipair, jpair] = find(squeeze(idemoments.Pco(j,j+1:end,:))>(1-1.e-10));
-% end
-% if ~isempty(jpair),
-% for jx=j+1:npar,
-% ixp = find(jx==(jpair+j));
-% if ~isempty(ixp)
-% if SampleSize > 1
-% disp([' [',name{j},',',name{jx},'] are PAIRWISE collinear (with tol = 1.e-10) for ',num2str(length(ixp)/SampleSize*100),'% of MC runs!' ])
-% else
-% disp([' [',name{j},',',name{jx},'] are PAIRWISE collinear (with tol = 1.e-10) !' ])
-% end
-% end
-% end
-% end
-% end
-% end
+ % if npar>(j+1),
+ % [ipair, jpair] = find(squeeze(idemoments.Pco(j,j+1:end,:))'>(1-1.e-10));
+ % else
+ % [ipair, jpair] = find(squeeze(idemoments.Pco(j,j+1:end,:))>(1-1.e-10));
+ % end
+ % if ~isempty(jpair),
+ % for jx=j+1:npar,
+ % ixp = find(jx==(jpair+j));
+ % if ~isempty(ixp)
+ % if SampleSize > 1
+ % disp([' [',name{j},',',name{jx},'] are PAIRWISE collinear (with tol = 1.e-10) for ',num2str(length(ixp)/SampleSize*100),'% of MC runs!' ])
+ % else
+ % disp([' [',name{j},',',name{jx},'] are PAIRWISE collinear (with tol = 1.e-10) !' ])
+ % end
+ % end
+ % end
+ % end
+ % end
+ % end
end
if ~any(idemoments.ino) && ~any(any(idemoments.ind0==0))
skipline()
@@ -255,12 +255,12 @@ end
% if advanced && (~options_.noprint),
% for j=1:length(kokP),
% dyntable([name{kokP(j)},' pairwise correlations in the model'],char(' ','min','mean','max'), ...
-% char(name{jpM{j}}),[pminM{j}' pmeanM{j}' pmaxM{j}'],10,10,3);
+% char(name{jpM{j}}),[pminM{j}' pmeanM{j}' pmaxM{j}'],10,10,3);
% end
-%
+%
% for j=1:length(kokPJ),
% dyntable([name{kokPJ(j)},' pairwise correlations in J moments'],char(' ','min','mean','max'), ...
-% char(name{jpJ{j}}),[pminJ{j}' pmeanJ{j}' pmaxJ{j}'],10,10,3);
+% char(name{jpJ{j}}),[pminJ{j}' pmeanJ{j}' pmaxJ{j}'],10,10,3);
% end
% end
% disp(' ')
diff --git a/matlab/disp_model_summary.m b/matlab/disp_model_summary.m
index 394bc00d9dea1c385afd6e8958e086f7dad30034..563efad9ff4385a91ef242cc9499199c3289a1c4 100644
--- a/matlab/disp_model_summary.m
+++ b/matlab/disp_model_summary.m
@@ -4,7 +4,7 @@ function disp_model_summary(M,dr,options)
% displays the model summary
%
% INPUTS
-% M [matlab structure] Definition of the model.
+% M [matlab structure] Definition of the model.
% dr [matlab structure] Decision rules
% options [matlab structure] Options
%
@@ -40,4 +40,3 @@ labels = deblank(M.exo_names);
headers = char('Variables',labels);
lh = size(labels,2)+2;
dyntable(options,my_title,headers,labels,M.Sigma_e,lh,10,6);
-
diff --git a/matlab/disp_moments.m b/matlab/disp_moments.m
index 9fd634c133222991f46503ef270d9960f2e4816d..6e55bb571145d951e9d7704bdf20968d8552b75b 100644
--- a/matlab/disp_moments.m
+++ b/matlab/disp_moments.m
@@ -7,7 +7,7 @@ function oo_=disp_moments(y,var_list,M_,options_,oo_)
% M_ [structure] Dynare's model structure
% oo_ [structure] Dynare's results structure
% options_ [structure] Dynare's options structure
-%
+%
% OUTPUTS
% oo_ [structure] Dynare's results structure,
@@ -57,18 +57,18 @@ s2 = mean(y.*y);
s = sqrt(s2);
oo_.mean = transpose(m);
oo_.var = y'*y/size(y,1);
-oo_.skewness = (mean(y.^3)./s2.^1.5)';
+oo_.skewness = (mean(y.^3)./s2.^1.5)';
oo_.kurtosis = (mean(y.^4)./(s2.*s2)-3)';
labels = deblank(M_.endo_names(ivar,:));
labels_TeX = deblank(M_.endo_names_tex(ivar,:));
if options_.nomoments == 0
- z = [ m' s' s2' (mean(y.^3)./s2.^1.5)' (mean(y.^4)./(s2.*s2)-3)' ];
+ z = [ m' s' s2' (mean(y.^3)./s2.^1.5)' (mean(y.^4)./(s2.*s2)-3)' ];
title='MOMENTS OF SIMULATED VARIABLES';
-
+
title=add_filter_subtitle(title,options_);
-
+
headers=char('VARIABLE','MEAN','STD. DEV.','VARIANCE','SKEWNESS', ...
'KURTOSIS');
dyntable(options_,title,headers,labels,z,size(labels,2)+2,16,6);
@@ -79,7 +79,7 @@ end
if options_.nocorr == 0
corr = (y'*y/size(y,1))./(s'*s);
- if options_.contemporaneous_correlation
+ if options_.contemporaneous_correlation
oo_.contemporaneous_correlation = corr;
end
if options_.noprint == 0
@@ -98,7 +98,7 @@ if options_.nocorr == 0
end
if options_.noprint == 0 && length(options_.conditional_variance_decomposition)
- fprintf('\nSTOCH_SIMUL: conditional_variance_decomposition requires theoretical moments, i.e. periods=0.\n')
+ fprintf('\nSTOCH_SIMUL: conditional_variance_decomposition requires theoretical moments, i.e. periods=0.\n')
end
ar = options_.ar;
@@ -119,7 +119,7 @@ if ar > 0
dyn_latex_table(M_,options_,title,'sim_autocorr_matrix',headers,labels_TeX,autocorr,size(labels_TeX,2)+2,8,4);
end
end
-
+
end
@@ -143,7 +143,7 @@ if ~options_.nodecomposition
chol_S = chol(M_.Sigma_e(i_exo_var,i_exo_var)); %decompose rest
shock_mat=zeros(options_.periods,M_.exo_nbr); %initialize
shock_mat(:,i_exo_var)=oo_.exo_simul(:,i_exo_var)/chol_S; %invert construction of oo_.exo_simul from simult.m
-
+
for shock_iter=1:length(i_exo_var)
temp_shock_mat=zeros(size(shock_mat));
temp_shock_mat(:,i_exo_var(shock_iter))=shock_mat(:,i_exo_var(shock_iter));
@@ -156,9 +156,9 @@ if ~options_.nodecomposition
if ~options_.noprint %options_.nomoments == 0
skipline()
title='VARIANCE DECOMPOSITION SIMULATING ONE SHOCK AT A TIME (in percent)';
-
+
title=add_filter_subtitle(title,options_);
-
+
headers = M_.exo_names;
headers(M_.exo_names_orig_ord,:) = headers;
headers = char(' ',headers);
@@ -181,12 +181,12 @@ if ~options_.nodecomposition
end
end
-
+
warning(warning_old_state);
end
function y=get_filtered_time_series(y,m,options_)
-
+
if options_.hp_filter && ~options_.one_sided_hp_filter && ~options_.bandpass.indicator
[hptrend,y] = sample_hp_filter(y,options_.hp_filter);
elseif ~options_.hp_filter && options_.one_sided_hp_filter && ~options_.bandpass.indicator
@@ -197,8 +197,8 @@ elseif ~options_.hp_filter && ~options_.one_sided_hp_filter && options_.bandpass
y=data_temp.data;
elseif ~options_.hp_filter && ~options_.one_sided_hp_filter && ~options_.bandpass.indicator
y = bsxfun(@minus, y, m);
-else
+else
error('disp_moments:: You cannot use more than one filter at the same time')
end
-
+
end
\ No newline at end of file
diff --git a/matlab/disp_steady_state.m b/matlab/disp_steady_state.m
index 5424c6e3b2d191c6d402346c6d54d370264b62c8..81984ed685e31256016fe925e5298a924ad0a223 100644
--- a/matlab/disp_steady_state.m
+++ b/matlab/disp_steady_state.m
@@ -1,11 +1,11 @@
function disp_steady_state(M,oo)
% function disp_steady_state(M,oo)
% computes and prints the steady state calculations
-%
+%
% INPUTS
% M structure of parameters
% oo structure of results
-%
+%
% OUTPUTS
% none
%
diff --git a/matlab/disp_th_moments.m b/matlab/disp_th_moments.m
index 7218ee1feb6c72c68fe49a7a0b00661a798e7b4c..014102f5cf48271738156aea28b0eae235ddd9c1 100644
--- a/matlab/disp_th_moments.m
+++ b/matlab/disp_th_moments.m
@@ -76,7 +76,7 @@ if size(stationary_vars, 1) > 0
if M_.exo_nbr > 1 && ~nodecomposition
skipline()
if options_.order == 2
- title='APPROXIMATED VARIANCE DECOMPOSITION (in percent)';
+ title='APPROXIMATED VARIANCE DECOMPOSITION (in percent)';
else
title='VARIANCE DECOMPOSITION (in percent)';
end
@@ -86,7 +86,7 @@ if size(stationary_vars, 1) > 0
headers = char(' ',headers);
lh = size(deblank(M_.endo_names(ivar(stationary_vars),:)),2)+2;
dyntable(options_,title,headers,deblank(M_.endo_names(ivar(stationary_vars), ...
- :)),100* ...
+ :)),100* ...
oo_.gamma_y{options_.ar+2}(stationary_vars,:),lh,8,2);
if options_.TeX
headers=M_.exo_names_tex;
@@ -97,7 +97,7 @@ if size(stationary_vars, 1) > 0
end
end
end
-
+
conditional_variance_steps = options_.conditional_variance_decomposition;
if length(conditional_variance_steps)
StateSpaceModel.number_of_state_equations = M_.endo_nbr;
@@ -107,10 +107,10 @@ if size(stationary_vars, 1) > 0
StateSpaceModel.state_innovations_covariance_matrix = M_.Sigma_e;
StateSpaceModel.order_var = dr.order_var;
oo_.conditional_variance_decomposition = conditional_variance_decomposition(StateSpaceModel,conditional_variance_steps,ivar);
-
+
if options_.noprint == 0
display_conditional_variance_decomposition(oo_.conditional_variance_decomposition,conditional_variance_steps,...
- ivar,M_,options_);
+ ivar,M_,options_);
end
end
end
@@ -124,13 +124,13 @@ end
if options_.nocorr == 0 && size(stationary_vars, 1) > 0
corr = oo_.gamma_y{1}(i1,i1)./(sd(i1)*sd(i1)');
- if options_.contemporaneous_correlation
+ if options_.contemporaneous_correlation
oo_.contemporaneous_correlation = corr;
end
if ~options_.noprint
skipline()
if options_.order == 2
- title='APPROXIMATED MATRIX OF CORRELATIONS';
+ title='APPROXIMATED MATRIX OF CORRELATIONS';
else
title='MATRIX OF CORRELATIONS';
end
@@ -154,22 +154,22 @@ if options_.ar > 0 && size(stationary_vars, 1) > 0
z(:,i) = diag(oo_.gamma_y{i+1}(i1,i1));
end
if ~options_.noprint
- skipline()
+ skipline()
if options_.order == 2
- title='APPROXIMATED COEFFICIENTS OF AUTOCORRELATION';
+ title='APPROXIMATED COEFFICIENTS OF AUTOCORRELATION';
else
title='COEFFICIENTS OF AUTOCORRELATION';
end
title=add_filter_subtitle(title,options_);
- labels = deblank(M_.endo_names(ivar(i1),:));
+ labels = deblank(M_.endo_names(ivar(i1),:));
headers = char('Order ',int2str([1:options_.ar]'));
lh = size(labels,2)+2;
dyntable(options_,title,headers,labels,z,lh,8,4);
if options_.TeX
- labels = deblank(M_.endo_names_tex(ivar(i1),:));
+ labels = deblank(M_.endo_names_tex(ivar(i1),:));
headers=char('Order ',int2str([1:options_.ar]'));
lh = size(labels,2)+2;
dyn_latex_table(M_,options_,title,'th_autocorr_matrix',headers,labels,z,lh,8,4);
end
- end
+ end
end
diff --git a/matlab/display_conditional_variance_decomposition.m b/matlab/display_conditional_variance_decomposition.m
index 8aab292d00d32a369174a66fabae101308ece832..4f5836925ec9397921e44f6f7b62144b00cef77b 100644
--- a/matlab/display_conditional_variance_decomposition.m
+++ b/matlab/display_conditional_variance_decomposition.m
@@ -1,8 +1,8 @@
function display_conditional_variance_decomposition(conditional_decomposition_array,Steps,SubsetOfVariables,M_,options_)
% This function displays the conditional variance decomposition of a given state space model
% for a subset of endogenous variables.
-%
-% INPUTS
+%
+% INPUTS
% conditional_decomposition_array [matrix] Output matrix from compute_conditional_variance_decomposition
% Steps [integer] 1*h vector of dates.
% SubsetOfVariables [integer] 1*q vector of indices.
@@ -10,7 +10,7 @@ function display_conditional_variance_decomposition(conditional_decomposition_ar
% Model description
% options_ [structure] Dynare structure containing the
% options
-% OUTPUTS
+% OUTPUTS
% none
%
% Copyright (C) 2010-2016 Dynare Team
@@ -56,10 +56,10 @@ for i=1:length(Steps)
dyntable(options_,'',headers,...
deblank(M_.endo_names(SubsetOfVariables,:)),...
vardec_i,lh,8,2);
- if options_.TeX
- labels_TeX = deblank(M_.endo_names_tex(SubsetOfVariables,:));
- headers_TeX=char('',deblank(M_.exo_names_tex));
- lh = size(labels_TeX,2)+2;
- dyn_latex_table(M_,options_,[title,'; Period ' int2str(Steps(i))],['th_var_decomp_cond_h',int2str(Steps(i))],headers_TeX,labels_TeX,vardec_i,lh,8,2);
- end
+ if options_.TeX
+ labels_TeX = deblank(M_.endo_names_tex(SubsetOfVariables,:));
+ headers_TeX=char('',deblank(M_.exo_names_tex));
+ lh = size(labels_TeX,2)+2;
+ dyn_latex_table(M_,options_,[title,'; Period ' int2str(Steps(i))],['th_var_decomp_cond_h',int2str(Steps(i))],headers_TeX,labels_TeX,vardec_i,lh,8,2);
+ end
end
\ No newline at end of file
diff --git a/matlab/display_estimation_results_table.m b/matlab/display_estimation_results_table.m
index 152ea006b95605375357470ddb4f317498a0deb9..aaf23cb4feaf5418f1aa9367f2253468d6ef5252 100644
--- a/matlab/display_estimation_results_table.m
+++ b/matlab/display_estimation_results_table.m
@@ -1,20 +1,20 @@
function oo_=display_estimation_results_table(xparam1,stdh,M_,options_,estim_params_,bayestopt_,oo_,pnames,table_title,field_name)
%function oo_=display_results_table(xparam1,stdh,M_,estim_params_,bayestopt_,oo_,pnames,table_title,field_name)
% Display estimation results on screen and write them to TeX-file
-%
-% INPUTS
+%
+% INPUTS
% o xparam1 [double] (p*1) vector of estimate parameters.
% o stdh [double] (p*1) vector of estimate parameters.
-% o M_ Matlab's structure describing the Model (initialized by dynare, see @ref{M_}).
+% o M_ Matlab's structure describing the Model (initialized by dynare, see @ref{M_}).
% o estim_params_ Matlab's structure describing the estimated_parameters (initialized by dynare, see @ref{estim_params_}).
% o options_ Matlab's structure describing the options (initialized by dynare, see @ref{options_}).
% o bayestopt_ Matlab's structure describing the priors (initialized by dynare, see @ref{bayesopt_}).
% o oo_ Matlab's structure gathering the results (initialized by dynare, see @ref{oo_}).
-% o pnames [string] Character Array storing the names for prior distributions
-% o table_title [string] Title of the Table
+% o pnames [string] Character Array storing the names for prior distributions
+% o table_title [string] Title of the Table
% o field_name [string] String storing the name of the fields for oo_ where the parameters are stored
-%
-% OUTPUTS
+%
+% OUTPUTS
% o oo_ Matlab's structure gathering the results
%
% SPECIAL REQUIREMENTS
@@ -64,13 +64,13 @@ if np
name = bayestopt_.name{ip};
if strcmp(field_name,'posterior')
fprintf('%-*s %7.3f %8.4f %7.4f %4s %6.4f \n', ...
- header_width,name, ...
- bayestopt_.p1(ip),xparam1(ip),stdh(ip), ...
- pnames(bayestopt_.pshape(ip)+1,:), ...
- bayestopt_.p2(ip));
+ header_width,name, ...
+ bayestopt_.p1(ip),xparam1(ip),stdh(ip), ...
+ pnames(bayestopt_.pshape(ip)+1,:), ...
+ bayestopt_.p2(ip));
else
fprintf('%-*s %8.4f %7.4f %7.4f \n', ...
- header_width,name,xparam1(ip),stdh(ip),tstath(ip));
+ header_width,name,xparam1(ip),stdh(ip),tstath(ip));
end
eval(['oo_.' field_name '_mode.parameters.' name ' = xparam1(ip);']);
eval(['oo_.' field_name '_std_at_mode.parameters.' name ' = stdh(ip);']);
@@ -87,9 +87,9 @@ if nvx
name = deblank(M_.exo_names(k,:));
if strcmp(field_name,'posterior')
fprintf('%-*s %7.3f %8.4f %7.4f %4s %6.4f \n', ...
- header_width,name,bayestopt_.p1(ip),xparam1(ip), ...
- stdh(ip),pnames(bayestopt_.pshape(ip)+1,:), ...
- bayestopt_.p2(ip));
+ header_width,name,bayestopt_.p1(ip),xparam1(ip), ...
+ stdh(ip),pnames(bayestopt_.pshape(ip)+1,:), ...
+ bayestopt_.p2(ip));
else
fprintf('%-*s %8.4f %7.4f %7.4f \n',header_width,name,xparam1(ip),stdh(ip),tstath(ip));
end
@@ -99,28 +99,28 @@ if nvx
ip = ip+1;
end
skipline()
- end
- if nvn
+end
+if nvn
disp('standard deviation of measurement errors')
disp(tit1)
ip = nvx+1;
for i=1:nvn
name = options_.varobs{estim_params_.nvn_observable_correspondence(i,1)};
- if strcmp(field_name,'posterior')
+ if strcmp(field_name,'posterior')
fprintf('%-*s %7.3f %8.4f %7.4f %4s %6.4f \n', ...
- header_width,name,bayestopt_.p1(ip), ...
- xparam1(ip),stdh(ip), ...
- pnames(bayestopt_.pshape(ip)+1,:), ...
- bayestopt_.p2(ip));
+ header_width,name,bayestopt_.p1(ip), ...
+ xparam1(ip),stdh(ip), ...
+ pnames(bayestopt_.pshape(ip)+1,:), ...
+ bayestopt_.p2(ip));
else
- fprintf('%-*s %8.4f %7.4f %7.4f \n',header_width,name,xparam1(ip),stdh(ip),tstath(ip))
+ fprintf('%-*s %8.4f %7.4f %7.4f \n',header_width,name,xparam1(ip),stdh(ip),tstath(ip))
end
eval(['oo_.' field_name '_mode.measurement_errors_std.' name ' = xparam1(ip);']);
eval(['oo_.' field_name '_std_at_mode.measurement_errors_std.' name ' = stdh(ip);']);
ip = ip+1;
end
skipline()
- end
+end
if ncx
disp('correlation of shocks')
@@ -131,12 +131,12 @@ if ncx
k2 = estim_params_.corrx(i,2);
name = [deblank(M_.exo_names(k1,:)) ',' deblank(M_.exo_names(k2,:))];
NAME = [deblank(M_.exo_names(k1,:)) '_' deblank(M_.exo_names(k2,:))];
- if strcmp(field_name,'posterior')
+ if strcmp(field_name,'posterior')
fprintf('%-*s %7.3f %8.4f %7.4f %4s %6.4f \n', ...
- header_width,name,bayestopt_.p1(ip),xparam1(ip),stdh(ip), ...
- pnames(bayestopt_.pshape(ip)+1,:), bayestopt_.p2(ip));
+ header_width,name,bayestopt_.p1(ip),xparam1(ip),stdh(ip), ...
+ pnames(bayestopt_.pshape(ip)+1,:), bayestopt_.p2(ip));
else
- fprintf('%-*s %8.4f %7.4f %7.4f \n', header_width,name,xparam1(ip),stdh(ip),tstath(ip));
+ fprintf('%-*s %8.4f %7.4f %7.4f \n', header_width,name,xparam1(ip),stdh(ip),tstath(ip));
end
M_.Sigma_e(k1,k2) = xparam1(ip)*sqrt(M_.Sigma_e(k1,k1)*M_.Sigma_e(k2,k2));
M_.Sigma_e(k2,k1) = M_.Sigma_e(k1,k2);
@@ -156,12 +156,12 @@ if ncn
k2 = estim_params_.corrn(i,2);
name = [deblank(M_.endo_names(k1,:)) ',' deblank(M_.endo_names(k2,:))];
NAME = [deblank(M_.endo_names(k1,:)) '_' deblank(M_.endo_names(k2,:))];
- if strcmp(field_name,'posterior')
+ if strcmp(field_name,'posterior')
fprintf('%-*s %7.3f %8.4f %7.4f %4s %6.4f \n', ...
- header_width,name,bayestopt_.p1(ip),xparam1(ip),stdh(ip), ...
- pnames(bayestopt_.pshape(ip)+1,:), bayestopt_.p2(ip));
+ header_width,name,bayestopt_.p1(ip),xparam1(ip),stdh(ip), ...
+ pnames(bayestopt_.pshape(ip)+1,:), bayestopt_.p2(ip));
else
- fprintf('%-*s %8.4f %7.4f %7.4f \n',header_width,name,xparam1(ip),stdh(ip),tstath(ip));
+ fprintf('%-*s %8.4f %7.4f %7.4f \n',header_width,name,xparam1(ip),stdh(ip),tstath(ip));
end
eval(['oo_.' field_name '_mode.measurement_errors_corr.' NAME ' = xparam1(ip);']);
eval(['oo_.' field_name '_std_at_mode.measurement_errors_corr.' NAME ' = stdh(ip);']);
@@ -268,17 +268,17 @@ if any(bayestopt_.pshape > 0) && options_.TeX %% Bayesian estimation (posterior
TeXEnd(fidTeX)
end
elseif all(bayestopt_.pshape == 0) && options_.TeX %% MLE and GMM Latex output
- if np
+ if np
filename = [OutputDirectoryName '/' M_.fname '_' LaTeXtitle '_Mode_1.tex'];
fidTeX = fopen(filename,'w');
- TeXBegin_ML(fidTeX,1,'parameters',table_title,LaTeXtitle)
+ TeXBegin_ML(fidTeX,1,'parameters',table_title,LaTeXtitle)
ip = nvx+nvn+ncx+ncn+1;
for i=1:np
fprintf(fidTeX,'$%s$ & %8.4f & %7.4f & %7.4f\\\\ \n',...
M_.param_names_tex(estim_params_.param_vals(i,1),:),...
xparam1(ip),...
stdh(ip),...
- tstath(ip));
+ tstath(ip));
ip = ip + 1;
end
TeXEnd(fidTeX)
@@ -286,7 +286,7 @@ elseif all(bayestopt_.pshape == 0) && options_.TeX %% MLE and GMM Latex output
if nvx
filename = [OutputDirectoryName '/' M_.fname '_' LaTeXtitle '_Mode_2.tex'];
fidTeX = fopen(filename,'w');
- TeXBegin_ML(fidTeX,2,'standard deviation of structural shocks',table_title,LaTeXtitle)
+ TeXBegin_ML(fidTeX,2,'standard deviation of structural shocks',table_title,LaTeXtitle)
ip = 1;
for i=1:nvx
k = estim_params_.var_exo(i,1);
@@ -302,11 +302,11 @@ elseif all(bayestopt_.pshape == 0) && options_.TeX %% MLE and GMM Latex output
if nvn
filename = [OutputDirectoryName '/' M_.fname '_' LaTeXtitle '_Mode_3.tex'];
fidTeX = fopen(filename,'w');
- TeXBegin_ML(fidTeX,3,'standard deviation of measurement errors',table_title,LaTeXtitle)
+ TeXBegin_ML(fidTeX,3,'standard deviation of measurement errors',table_title,LaTeXtitle)
ip = nvx+1;
for i=1:nvn
- idx = strmatch(options_.varobs{estim_params_.nvn_observable_correspondence(i,1)},M_.endo_names);
- fprintf(fidTeX,'$%s$ & %8.4f & %7.4f & %7.4f \\\\ \n',...
+ idx = strmatch(options_.varobs{estim_params_.nvn_observable_correspondence(i,1)},M_.endo_names);
+ fprintf(fidTeX,'$%s$ & %8.4f & %7.4f & %7.4f \\\\ \n',...
deblank(M_.endo_names_tex(idx,:)), ...
xparam1(ip),...
stdh(ip),...
@@ -318,7 +318,7 @@ elseif all(bayestopt_.pshape == 0) && options_.TeX %% MLE and GMM Latex output
if ncx
filename = [OutputDirectoryName '/' M_.fname '_' LaTeXtitle '_Mode_4.tex'];
fidTeX = fopen(filename,'w');
- TeXBegin_ML(fidTeX,4,'correlation of structural shocks',table_title,LaTeXtitle)
+ TeXBegin_ML(fidTeX,4,'correlation of structural shocks',table_title,LaTeXtitle)
ip = nvx+nvn+1;
for i=1:ncx
k1 = estim_params_.corrx(i,1);
@@ -335,7 +335,7 @@ elseif all(bayestopt_.pshape == 0) && options_.TeX %% MLE and GMM Latex output
if ncn
filename = [OutputDirectoryName '/' M_.fname '_' LaTeXtitle '_Mode_5.tex'];
fidTeX = fopen(filename,'w');
- TeXBegin_ML(fidTeX,5,'correlation of measurement errors',table_title,LaTeXtitle)
+ TeXBegin_ML(fidTeX,5,'correlation of measurement errors',table_title,LaTeXtitle)
ip = nvx+nvn+ncx+1;
for i=1:ncn
k1 = estim_params_.corrn(i,1);
@@ -347,7 +347,7 @@ elseif all(bayestopt_.pshape == 0) && options_.TeX %% MLE and GMM Latex output
tstath(ip));
ip = ip+1;
end
- TeXEnd(fidTeX)
+ TeXEnd(fidTeX)
end
end
@@ -356,51 +356,51 @@ end
%% subfunctions:
%
function TeXBegin_Bayesian(fid,fnum,title)
- fprintf(fid,'%% TeX-table generated by dynare_estimation (Dynare).\n');
- fprintf(fid,['%% RESULTS FROM POSTERIOR MAXIMIZATION (' title ')\n']);
- fprintf(fid,['%% ' datestr(now,0)]);
- fprintf(fid,' \n');
- fprintf(fid,' \n');
- fprintf(fid,'\\begin{center}\n');
- fprintf(fid,'\\begin{longtable}{llcccc} \n');
- fprintf(fid,['\\caption{Results from posterior maximization (' title ')}\\\\\n ']);
- fprintf(fid,['\\label{Table:Posterior:' int2str(fnum) '}\\\\\n']);
- fprintf(fid,'\\toprule \n');
- fprintf(fid,' & \\multicolumn{3}{c}{Prior} & \\multicolumn{2}{c}{Posterior} \\\\\n');
- fprintf(fid,' \\cmidrule(r{.75em}){2-4} \\cmidrule(r{.75em}){5-6}\n');
- fprintf(fid,' & Dist. & Mean & Stdev & Mode & Stdev \\\\ \n');
- fprintf(fid,'\\midrule \\endfirsthead \n');
- fprintf(fid,'\\caption{(continued)}\\\\\n ');
- fprintf(fid,'\\bottomrule \n');
- fprintf(fid,' & \\multicolumn{3}{c}{Prior} & \\multicolumn{2}{c}{Posterior} \\\\\n');
- fprintf(fid,' \\cmidrule(r{.75em}){2-4} \\cmidrule(r{.75em}){5-6}\n');
- fprintf(fid,' & Dist. & Mean & Stdev & Mode & Stdev \\\\ \n');
- fprintf(fid,'\\midrule \\endhead \n');
- fprintf(fid,'\\bottomrule \\multicolumn{6}{r}{(Continued on next page)}\\endfoot \n');
- fprintf(fid,'\\bottomrule\\endlastfoot \n');
+fprintf(fid,'%% TeX-table generated by dynare_estimation (Dynare).\n');
+fprintf(fid,['%% RESULTS FROM POSTERIOR MAXIMIZATION (' title ')\n']);
+fprintf(fid,['%% ' datestr(now,0)]);
+fprintf(fid,' \n');
+fprintf(fid,' \n');
+fprintf(fid,'\\begin{center}\n');
+fprintf(fid,'\\begin{longtable}{llcccc} \n');
+fprintf(fid,['\\caption{Results from posterior maximization (' title ')}\\\\\n ']);
+fprintf(fid,['\\label{Table:Posterior:' int2str(fnum) '}\\\\\n']);
+fprintf(fid,'\\toprule \n');
+fprintf(fid,' & \\multicolumn{3}{c}{Prior} & \\multicolumn{2}{c}{Posterior} \\\\\n');
+fprintf(fid,' \\cmidrule(r{.75em}){2-4} \\cmidrule(r{.75em}){5-6}\n');
+fprintf(fid,' & Dist. & Mean & Stdev & Mode & Stdev \\\\ \n');
+fprintf(fid,'\\midrule \\endfirsthead \n');
+fprintf(fid,'\\caption{(continued)}\\\\\n ');
+fprintf(fid,'\\bottomrule \n');
+fprintf(fid,' & \\multicolumn{3}{c}{Prior} & \\multicolumn{2}{c}{Posterior} \\\\\n');
+fprintf(fid,' \\cmidrule(r{.75em}){2-4} \\cmidrule(r{.75em}){5-6}\n');
+fprintf(fid,' & Dist. & Mean & Stdev & Mode & Stdev \\\\ \n');
+fprintf(fid,'\\midrule \\endhead \n');
+fprintf(fid,'\\bottomrule \\multicolumn{6}{r}{(Continued on next page)}\\endfoot \n');
+fprintf(fid,'\\bottomrule\\endlastfoot \n');
+
+function TeXBegin_ML(fid,fnum,title,table_title,LaTeXtitle)
+fprintf(fid,'%% TeX-table generated by dynare_estimation (Dynare).\n');
+fprintf(fid,['%% RESULTS FROM ' table_title ' MAXIMIZATION (' title ')\n']);
+fprintf(fid,['%% ' datestr(now,0)]);
+fprintf(fid,' \n');
+fprintf(fid,' \n');
+fprintf(fid,'\\begin{center}\n');
+fprintf(fid,'\\begin{longtable}{llcc} \n');
+fprintf(fid,['\\caption{Results from ' table_title ' maximization (' title ')}\\\\\n ']);
+fprintf(fid,['\\label{Table:' LaTeXtitle ':' int2str(fnum) '}\\\\\n']);
+fprintf(fid,'\\toprule \n');
+fprintf(fid,' & Mode & s.d. & t-stat\\\\ \n');
+fprintf(fid,'\\midrule \\endfirsthead \n');
+fprintf(fid,'\\caption{(continued)}\\\\\n ');
+fprintf(fid,'\\toprule \n');
+fprintf(fid,' & Mode & s.d. & t-stat\\\\ \n');
+fprintf(fid,'\\midrule \\endhead \n');
+fprintf(fid,'\\bottomrule \\multicolumn{4}{r}{(Continued on next page)} \\endfoot \n');
+fprintf(fid,'\\bottomrule \\endlastfoot \n');
- function TeXBegin_ML(fid,fnum,title,table_title,LaTeXtitle)
- fprintf(fid,'%% TeX-table generated by dynare_estimation (Dynare).\n');
- fprintf(fid,['%% RESULTS FROM ' table_title ' MAXIMIZATION (' title ')\n']);
- fprintf(fid,['%% ' datestr(now,0)]);
- fprintf(fid,' \n');
- fprintf(fid,' \n');
- fprintf(fid,'\\begin{center}\n');
- fprintf(fid,'\\begin{longtable}{llcc} \n');
- fprintf(fid,['\\caption{Results from ' table_title ' maximization (' title ')}\\\\\n ']);
- fprintf(fid,['\\label{Table:' LaTeXtitle ':' int2str(fnum) '}\\\\\n']);
- fprintf(fid,'\\toprule \n');
- fprintf(fid,' & Mode & s.d. & t-stat\\\\ \n');
- fprintf(fid,'\\midrule \\endfirsthead \n');
- fprintf(fid,'\\caption{(continued)}\\\\\n ');
- fprintf(fid,'\\toprule \n');
- fprintf(fid,' & Mode & s.d. & t-stat\\\\ \n');
- fprintf(fid,'\\midrule \\endhead \n');
- fprintf(fid,'\\bottomrule \\multicolumn{4}{r}{(Continued on next page)} \\endfoot \n');
- fprintf(fid,'\\bottomrule \\endlastfoot \n');
-
function TeXEnd(fid)
-fprintf(fid,'\\end{longtable}\n ');
+fprintf(fid,'\\end{longtable}\n ');
fprintf(fid,'\\end{center}\n');
fprintf(fid,'%% End of TeX file.\n');
fclose(fid);
\ No newline at end of file
diff --git a/matlab/display_problematic_vars_Jacobian.m b/matlab/display_problematic_vars_Jacobian.m
index c302df21371bc9fe941d4aeb47be9b462f72c194..c1bb0429ee62e7ad2704d0b4fa42839181d551cf 100644
--- a/matlab/display_problematic_vars_Jacobian.m
+++ b/matlab/display_problematic_vars_Jacobian.m
@@ -1,20 +1,20 @@
function []=display_problematic_vars_Jacobian(problemrow,problemcol,M_,x,type,caller_string)
% []=display_problematic_vars_Jacobian(problemrow,problemcol,M_,ys,caller_string)
-% print the equation numbers and variables associated with problematic entries
-% of the Jacobian
+% print the equation numbers and variables associated with problematic entries
+% of the Jacobian
%
% INPUTS
% problemrow [vector] rows associated with problematic entries
% problemcol [vector] columns associated with problematic entries
-% M_ [matlab structure] Definition of the model.
+% M_ [matlab structure] Definition of the model.
% x [vector] point at which the Jacobian was evaluated
% type [string] 'static' or 'dynamic' depending on the type of
% Jacobian
-% caller_string [string] contains name of calling function for printing
-%
+% caller_string [string] contains name of calling function for printing
+%
% OUTPUTS
% none.
-%
+%
% Copyright (C) 2014-16 Dynare Team
%
@@ -62,25 +62,25 @@ if strcmp(type,'dynamic')
fprintf('Derivative of Equation %d with respect to %s Variable %s (initial value of %s: %g) \n',eq_nbr,type_string,deblank(M_.endo_names(var_index,:)),deblank(M_.endo_names(var_index,:)),x(var_index))
end
elseif problemcol(ii)<=max(max(M_.lead_lag_incidence)) && var_index>M_.orig_endo_nbr %auxiliary vars
- if M_.aux_vars(1,problemcol(ii)-M_.orig_endo_nbr).type ==6 %Ramsey Lagrange Multiplier
- if problemrow(ii)<=aux_eq_nbr
- eq_nbr=problemrow(ii);
- fprintf('Derivative of Auxiliary Equation %d with respect to %s of Langrange multiplier of equation %s (initial value: %g) \n',eq_nbr,type_string,M_.aux_vars(1,problemcol(ii)-M_.orig_endo_nbr).eq_nbr,x(problemcol(ii)))
- else
- eq_nbr=problemrow(ii)-aux_eq_nbr;
- fprintf('Derivative of Equation %d with respect to %s of Langrange multiplier of equation %s (initial value: %g) \n',eq_nbr,type_string,M_.aux_vars(1,problemcol(ii)-M_.orig_endo_nbr).eq_nbr,x(problemcol(ii)))
- end
+ if M_.aux_vars(1,problemcol(ii)-M_.orig_endo_nbr).type ==6 %Ramsey Lagrange Multiplier
+ if problemrow(ii)<=aux_eq_nbr
+ eq_nbr=problemrow(ii);
+ fprintf('Derivative of Auxiliary Equation %d with respect to %s of Langrange multiplier of equation %s (initial value: %g) \n',eq_nbr,type_string,M_.aux_vars(1,problemcol(ii)-M_.orig_endo_nbr).eq_nbr,x(problemcol(ii)))
else
- if problemrow(ii)<=aux_eq_nbr
- eq_nbr=problemrow(ii);
- orig_var_index=M_.aux_vars(1,var_index-M_.orig_endo_nbr).orig_index;
- fprintf('Derivative of Auxiliary Equation %d with respect to %s Variable %s (initial value of %s: %g) \n',eq_nbr,type_string,deblank(M_.endo_names(orig_var_index,:)),deblank(M_.endo_names(orig_var_index,:)),x(orig_var_index))
- else
- eq_nbr=problemrow(ii)-aux_eq_nbr;
- orig_var_index=M_.aux_vars(1,var_index-M_.orig_endo_nbr).orig_index;
- fprintf('Derivative of Equation %d with respect to %s Variable %s (initial value of %s: %g) \n',eq_nbr,type_string,deblank(M_.endo_names(orig_var_index,:)),deblank(M_.endo_names(orig_var_index,:)),x(orig_var_index))
- end
+ eq_nbr=problemrow(ii)-aux_eq_nbr;
+ fprintf('Derivative of Equation %d with respect to %s of Langrange multiplier of equation %s (initial value: %g) \n',eq_nbr,type_string,M_.aux_vars(1,problemcol(ii)-M_.orig_endo_nbr).eq_nbr,x(problemcol(ii)))
end
+ else
+ if problemrow(ii)<=aux_eq_nbr
+ eq_nbr=problemrow(ii);
+ orig_var_index=M_.aux_vars(1,var_index-M_.orig_endo_nbr).orig_index;
+ fprintf('Derivative of Auxiliary Equation %d with respect to %s Variable %s (initial value of %s: %g) \n',eq_nbr,type_string,deblank(M_.endo_names(orig_var_index,:)),deblank(M_.endo_names(orig_var_index,:)),x(orig_var_index))
+ else
+ eq_nbr=problemrow(ii)-aux_eq_nbr;
+ orig_var_index=M_.aux_vars(1,var_index-M_.orig_endo_nbr).orig_index;
+ fprintf('Derivative of Equation %d with respect to %s Variable %s (initial value of %s: %g) \n',eq_nbr,type_string,deblank(M_.endo_names(orig_var_index,:)),deblank(M_.endo_names(orig_var_index,:)),x(orig_var_index))
+ end
+ end
elseif problemcol(ii)>max(max(M_.lead_lag_incidence)) && var_index<=M_.exo_nbr
if problemrow(ii)<=aux_eq_nbr
eq_nbr=problemrow(ii);
@@ -88,7 +88,7 @@ if strcmp(type,'dynamic')
else
eq_nbr=problemrow(ii)-aux_eq_nbr;
fprintf('Derivative of Equation %d with respect to %s shock %s \n',eq_nbr,type_string,deblank(M_.exo_names(var_index,:)));
- end
+ end
else
error('display_problematic_vars_Jacobian:: The error should not happen. Please contact the developers')
end
@@ -108,24 +108,24 @@ elseif strcmp(type,'static')
fprintf('Derivative of Equation %d with respect to Variable %s (initial value of %s: %g) \n',eq_nbr,deblank(M_.endo_names(problemcol(ii),:)),deblank(M_.endo_names(problemcol(ii),:)),x(problemcol(ii)))
end
else %auxiliary vars
- if M_.aux_vars(1,problemcol(ii)-M_.orig_endo_nbr).type ==6 %Ramsey Lagrange Multiplier
+ if M_.aux_vars(1,problemcol(ii)-M_.orig_endo_nbr).type ==6 %Ramsey Lagrange Multiplier
if problemrow(ii)<=aux_eq_nbr
eq_nbr=problemrow(ii);
- fprintf('Derivative of Auxiliary Equation %d with respect to Lagrange multiplier of equation %d (initial value: %g) \n',eq_nbr,M_.aux_vars(1,problemcol(ii)-M_.orig_endo_nbr).eq_nbr,x(problemcol(ii)))
+ fprintf('Derivative of Auxiliary Equation %d with respect to Lagrange multiplier of equation %d (initial value: %g) \n',eq_nbr,M_.aux_vars(1,problemcol(ii)-M_.orig_endo_nbr).eq_nbr,x(problemcol(ii)))
else
eq_nbr=problemrow(ii)-aux_eq_nbr;
- fprintf('Derivative of Equation %d with respect to Lagrange multiplier of equation %d (initial value: %g) \n',eq_nbr,M_.aux_vars(1,problemcol(ii)-M_.orig_endo_nbr).eq_nbr,x(problemcol(ii)))
+ fprintf('Derivative of Equation %d with respect to Lagrange multiplier of equation %d (initial value: %g) \n',eq_nbr,M_.aux_vars(1,problemcol(ii)-M_.orig_endo_nbr).eq_nbr,x(problemcol(ii)))
end
else
- if problemrow(ii)<=aux_eq_nbr
- eq_nbr=problemrow(ii);
- orig_var_index=M_.aux_vars(1,problemcol(ii)-M_.orig_endo_nbr).orig_index;
- fprintf('Derivative of Auxiliary Equation %d with respect to Variable %s (initial value of %s: %g) \n',eq_nbr,deblank(M_.endo_names(orig_var_index,:)),deblank(M_.endo_names(orig_var_index,:)),x(problemcol(ii)))
- else
- eq_nbr=problemrow(ii)-aux_eq_nbr;
- orig_var_index=M_.aux_vars(1,problemcol(ii)-M_.orig_endo_nbr).orig_index;
- fprintf('Derivative of Equation %d with respect to Variable %s (initial value of %s: %g) \n',eq_nbr,deblank(M_.endo_names(orig_var_index,:)),deblank(M_.endo_names(orig_var_index,:)),x(problemcol(ii)))
- end
+ if problemrow(ii)<=aux_eq_nbr
+ eq_nbr=problemrow(ii);
+ orig_var_index=M_.aux_vars(1,problemcol(ii)-M_.orig_endo_nbr).orig_index;
+ fprintf('Derivative of Auxiliary Equation %d with respect to Variable %s (initial value of %s: %g) \n',eq_nbr,deblank(M_.endo_names(orig_var_index,:)),deblank(M_.endo_names(orig_var_index,:)),x(problemcol(ii)))
+ else
+ eq_nbr=problemrow(ii)-aux_eq_nbr;
+ orig_var_index=M_.aux_vars(1,problemcol(ii)-M_.orig_endo_nbr).orig_index;
+ fprintf('Derivative of Equation %d with respect to Variable %s (initial value of %s: %g) \n',eq_nbr,deblank(M_.endo_names(orig_var_index,:)),deblank(M_.endo_names(orig_var_index,:)),x(problemcol(ii)))
+ end
end
end
end
@@ -134,5 +134,5 @@ elseif strcmp(type,'static')
fprintf('%s and evaluating it at the steady state then results in a division by 0.\n',caller_string)
fprintf('%s If you are using model-local variables (# operator), check their values as well.\n',caller_string)
else
- error('Unknown Type')
+ error('Unknown Type')
end
\ No newline at end of file
diff --git a/matlab/distributions/beta_specification.m b/matlab/distributions/beta_specification.m
index 33a38085d36804066a0630228e4d8ef290b6b72a..9d84f04da8da2cab728109d5b5475bde5433b474 100644
--- a/matlab/distributions/beta_specification.m
+++ b/matlab/distributions/beta_specification.m
@@ -2,13 +2,13 @@ function [a, b] = beta_specification(mu, sigma2, lb, ub, name) % --*-- Unitary
% Returns the hyperparameters of the beta distribution given the expectation and variance.
%
-% INPUTS
+% INPUTS
% - mu [double] Expectation of the Gamma random variable.
% - sigma2 [double] Variance of the Gamma random variable.
% - lb [double] Lower bound of the domain (default is zero).
% - ub [double] Upper bound of the domain (default is one).
%
-% OUTPUTS
+% OUTPUTS
% - a [double] First hyperparameter of the Beta density.
% - b [double] Second hyperparameter of the Beta density.
@@ -63,7 +63,7 @@ if sigma2>(1-mu)*mu
error('Beta prior%s. Given the declared prior expectation, prior lower and upper bounds, the prior std. has to be smaller than %f.',name2,sqrt((1-mu)*mu))
end
-a = (1-mu)*mu*mu/sigma2-mu;
+a = (1-mu)*mu*mu/sigma2-mu;
b = a*(1/mu-1);
%@test:1
diff --git a/matlab/distributions/compute_prior_mode.m b/matlab/distributions/compute_prior_mode.m
index 3286c08eaba15e5ac2c7697766b283da4a3cfde4..57a0d103589e4cf16637fef47fa6718f4e52850e 100644
--- a/matlab/distributions/compute_prior_mode.m
+++ b/matlab/distributions/compute_prior_mode.m
@@ -1,8 +1,8 @@
function m = compute_prior_mode(hyperparameters,shape) % --*-- Unitary tests --*--
% This function computes the mode of the prior distribution given the (two, three or four) hyperparameters
% of the prior distribution.
-%
-% INPUTS
+%
+% INPUTS
% hyperparameters [double] 1*n vector of hyper parameters.
% shape [integer] scalar specifying the prior shape:
% shape=1 => Beta distribution,
@@ -12,14 +12,14 @@ function m = compute_prior_mode(hyperparameters,shape) % --*-- Unitary tests --*
% shape=5 => Uniform distribution,
% shape=6 => Inverse Gamma (type 2) distribution,
% shape=8 => Weibull distribution.
-%
-% OUTPUTS
+%
+% OUTPUTS
% m [double] scalar or 2*1 vector, the prior mode.
%
-% REMARKS
-% [1] The size of the vector of hyperparameters is 3 when the Gamma or Inverse Gamma is shifted and 4 when
-% the support of the Beta distribution is not [0,1].
-% [2] The hyperparameters of the uniform distribution are the lower and upper bounds.
+% REMARKS
+% [1] The size of the vector of hyperparameters is 3 when the Gamma or Inverse Gamma is shifted and 4 when
+% the support of the Beta distribution is not [0,1].
+% [2] The hyperparameters of the uniform distribution are the lower and upper bounds.
% [3] The uniform distribution has an infinity of modes. In this case the function returns the prior mean.
% [4] For the beta distribution we can have 1, 2 or an infinity of modes.
diff --git a/matlab/distributions/gamma_specification.m b/matlab/distributions/gamma_specification.m
index 0f7301193285af0311d889ed1f4d6026afe9084c..279c0702b8cd6b614bd12e7dc9c8a89014c67587 100644
--- a/matlab/distributions/gamma_specification.m
+++ b/matlab/distributions/gamma_specification.m
@@ -2,13 +2,13 @@ function [a, b] = gamma_specification(mu, sigma2, lb, name) % --*-- Unitary te
% Returns the hyperparameters of the gamma distribution given the expectation and variance.
%
-% INPUTS
+% INPUTS
% - mu [double] Expectation of the Gamma random variable.
% - sigma2 [double] Variance of the Gamma random variable.
% - lb [double] Lower bound of the domain (default is zero).
% - name [string] Name of the parameter (or random variable).
%
-% OUTPUTS
+% OUTPUTS
% - a [double] First hyperparameter of the Gamma density (shape).
% - b [double] Second hyperparameter of the Gamma density (scale).
diff --git a/matlab/distributions/inverse_gamma_specification.m b/matlab/distributions/inverse_gamma_specification.m
index 3018921c966218c3540661c5c3a22b1a35cddb9f..410d4e52b277f6b6409858f80feb75f8f49e22d7 100644
--- a/matlab/distributions/inverse_gamma_specification.m
+++ b/matlab/distributions/inverse_gamma_specification.m
@@ -2,7 +2,7 @@ function [s,nu] = inverse_gamma_specification(mu, sigma2, lb, type, use_fzero_fl
% Computes the inverse Gamma hyperparameters from the prior mean and standard deviation.
%
-% INPUTS
+% INPUTS
% - mu [double] scalar, prior mean.
% - sigma2 [double] positive scalar, prior variance.
% - type [integer] scalar equal to 1 or 2, type of the inverse gamma distribution
@@ -10,11 +10,11 @@ function [s,nu] = inverse_gamma_specification(mu, sigma2, lb, type, use_fzero_fl
% dynare's implementation of the secant method otherwise.
% - name [string] name of the parameter or random variable.
%
-% OUTPUS
+% OUTPUS
% - s [double] scalar, first hyperparameter.
% - nu [double] scalar, second hyperparameter.
%
-% REMARK
+% REMARK
% The call to the matlab's implementation of the secant method is here for testing purpose and should not be used. This routine fails
% more often in finding an interval for nu containing a signe change because it expands the interval on both sides and eventually
% violates the condition nu>2.
diff --git a/matlab/distributions/lpdfgweibull.m b/matlab/distributions/lpdfgweibull.m
index 7aa1d718af83b777a62135ffd3357e5243c6eb7e..708a5b4d440787a60cadd87065fc183ae796b38a 100644
--- a/matlab/distributions/lpdfgweibull.m
+++ b/matlab/distributions/lpdfgweibull.m
@@ -1,14 +1,14 @@
function [ldens,Dldens,D2ldens] = lpdfgweibull(x,a,b,c) % --*-- Unitary tests --*--
-% Evaluates the logged Weibull PDF at x.
+% Evaluates the logged Weibull PDF at x.
%
-% INPUTS
+% INPUTS
% - x [double] m*n matrix of points where the (logged) density will be evaluated,
% - a [double] m*n matrix of First Weibull distribution parameters (shape parameter, k),
% - b [double] m*n matrix of Second Weibull distribution parameters (scale parameter, λ),
% - c [double] m*n matrix of Third Weibull distribution parameters (location parameter, default is 0).
%
-% OUTPUTS
+% OUTPUTS
% - ldens [double] m*n matrix of logged (generalized) Weibull densities.
% - Dldens [double] m*n matrix (first order derivatives w.r.t. x)
% - D2ldens [double] m*n matrix (second order derivatives w.r.t. x)
@@ -54,11 +54,11 @@ end
if ~isscalar(x)
if isscalar(a)
a = repmat(a, size(x));
- end
+ end
if isscalar(b)
b = repmat(b, size(x));
end
- if isscalar(c)
+ if isscalar(c)
c = repmat(c, size(x));
end
end
@@ -77,7 +77,7 @@ if isempty(idx), return, end
jdx = find( abs(a-1)<1e-12 & x>=c & (x-c)<1e-12) ;
ldens(jdx) = 1.0;
-
+
if ~isempty(idx)
x0 = x(idx)-c(idx);
x1 = x0./b(idx);
@@ -136,7 +136,7 @@ end
%$ catch
%$ t(1) = false;
%$ end
-%$
+%$
%$ if t(1)
%$ t(2) = isinf(a);
%$ end
@@ -179,7 +179,7 @@ end
%$ scale = 1;
%$ shape = 2;
%$ mode = scale*((shape-1)/shape)^(1/shape);
-%$
+%$
%$ try
%$ [a, b, c] = lpdfgweibull(mode, shape, scale);
%$ p = rand(1000,1)*4;
@@ -203,7 +203,7 @@ end
%$ scale = 1;
%$ shape = 2;
%$ density = @(x) exp(lpdfgweibull(x,shape,scale));
-%$
+%$
%$ try
%$ if isoctave
%$ s = quadv(density, .0000000001, 100000,1e-10);
@@ -226,7 +226,7 @@ end
%$ scale = 1;
%$ shape = 1;
%$ density = @(x) exp(lpdfgweibull(x,shape,scale));
-%$
+%$
%$ try
%$ if isoctave
%$ s = quadv(density, .0000000001, 100000,1e-10);
@@ -249,7 +249,7 @@ end
%$ scale = 1;
%$ shape = .5;
%$ density = @(x) exp(lpdfgweibull(x,shape,scale));
-%$
+%$
%$ try
%$ if isoctave
%$ s = quadv(density, .0000000001, 100000,1e-10)
@@ -276,7 +276,7 @@ end
%$ scale = 1;
%$ shape = 2;
%$ xdens = @(x) x.*exp(lpdfgweibull(x,shape,scale));
-%$
+%$
%$ try
%$ if isoctave
%$ s = quadv(xdens, .0000000001, 20,1e-10)
@@ -299,7 +299,7 @@ end
%$ scale = 1;
%$ shape = 1;
%$ xdens = @(x) x.*exp(lpdfgweibull(x,shape,scale));
-%$
+%$
%$ try
%$ if isoctave
%$ s = quadv(xdens, .0000000001, 100000,1e-10)
@@ -322,7 +322,7 @@ end
%$ scale = 1;
%$ shape = .5;
%$ xdens = @(x) x.*exp(lpdfgweibull(x,shape,scale));
-%$
+%$
%$ try
%$ if isoctave
%$ s = quadv(xdens, .0000000001, 100000,1e-10)
diff --git a/matlab/distributions/mode_and_variance_to_mean.m b/matlab/distributions/mode_and_variance_to_mean.m
index a69f0cdbe2638710086a821a14213b38a4cd65e2..6035721fa0e62de91211e351eb0503f353c51c07 100644
--- a/matlab/distributions/mode_and_variance_to_mean.m
+++ b/matlab/distributions/mode_and_variance_to_mean.m
@@ -1,21 +1,21 @@
function [mu, parameters] = mode_and_variance_to_mean(m,s2,distribution,lower_bound,upper_bound)
% This function computes the mean of a distribution given the mode and variance of this distribution.
%
-% INPUTS
+% INPUTS
% m [double] scalar, mode of the distribution.
% s2 [double] scalar, variance of the distribution.
-% distribution [integer] scalar for the distribution shape
+% distribution [integer] scalar for the distribution shape
% 1 gamma
% 2 inv-gamma-2
% 3 inv-gamma-1
-% 4 beta
+% 4 beta
% lower_bound [double] scalar, lower bound of the random variable support (optional).
% upper_bound [double] scalar, upper bound of the random variable support (optional).
-%
-% OUTPUT
+%
+% OUTPUT
% mu [double] scalar, mean of the distribution.
% parameters [double] 2*1 vector, parameters of the distribution.
-%
+%
% Copyright (C) 2009 Dynare Team
%
@@ -34,7 +34,7 @@ function [mu, parameters] = mode_and_variance_to_mean(m,s2,distribution,lower_bo
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-% Check input aruments.
+% Check input aruments.
if ~(nargin==3 || nargin==5 || nargin==4 )
error('mode_and_variance_to mean:: 3 or 5 input arguments are needed!')
end
@@ -80,7 +80,7 @@ if (distribution==1)% Gamma distribution
end
if (m-lower_bound)<1e-12
error('The gamma distribution should be specified with the mean and variance.')
- end
+ end
m = m - lower_bound ;
beta = -.5*m*(1-sqrt(1+4*s2/(m*m))) ;
alpha = (m+beta)/beta ;
diff --git a/matlab/distributions/multivariate_normal_pdf.m b/matlab/distributions/multivariate_normal_pdf.m
index e65858404e6c3dae8ae5e48715324bd411abe682..55bae7283a3765857c4087056ccdaf7ad640b363 100644
--- a/matlab/distributions/multivariate_normal_pdf.m
+++ b/matlab/distributions/multivariate_normal_pdf.m
@@ -1,18 +1,18 @@
function density = multivariate_normal_pdf(X,Mean,Sigma_upper_chol,n)
% Evaluates the density of a multivariate gaussian, with expectation Mean
% and variance Sigma_upper_chol'*Sigma_upper_chol, at X.
-%
%
-% INPUTS
%
-% X [double] 1*n vector
+% INPUTS
+%
+% X [double] 1*n vector
% Mean [double] 1*n vector, expectation of the multivariate random variable.
% Sigma_upper_chol [double] n*n matrix, upper triangular Cholesky decomposition of Sigma (the covariance matrix).
% n [integer] dimension.
-%
-% OUTPUTS
-% density [double] density
-%
+%
+% OUTPUTS
+% density [double] density
+%
% SPECIAL REQUIREMENTS
% Copyright (C) 2003-2017 Dynare Team
@@ -31,6 +31,6 @@ function density = multivariate_normal_pdf(X,Mean,Sigma_upper_chol,n)
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-density = (2*pi)^(-.5*n) * ...
+density = (2*pi)^(-.5*n) * ...
prod(diag(Sigma_upper_chol))^(-1) * ...
exp(-.5*(X-Mean)*(Sigma_upper_chol\(transpose(Sigma_upper_chol)\transpose(X-Mean))));
\ No newline at end of file
diff --git a/matlab/distributions/multivariate_student_pdf.m b/matlab/distributions/multivariate_student_pdf.m
index bb3ee1ef2e709e7a6ad78b9a666dc26a4d2d6fff..1465d4402429ffce0ffa5daa00143e57f5735faf 100644
--- a/matlab/distributions/multivariate_student_pdf.m
+++ b/matlab/distributions/multivariate_student_pdf.m
@@ -2,16 +2,16 @@ function density = multivariate_student_pdf(X,Mean,Sigma_upper_chol,df)
% Evaluates the density of a multivariate student, with expectation Mean,
% variance Sigma_upper_chol'*Sigma_upper_chol and degrees of freedom df, at X.
%
-% INPUTS
+% INPUTS
%
-% X [double] 1*n vector
+% X [double] 1*n vector
% Mean [double] 1*n vector, expectation of the multivariate random variable.
% Sigma_upper_chol [double] n*n matrix, upper triangular Cholesky decomposition of Sigma (the "covariance matrix").
% df [integer] degrees of freedom.
-%
-% OUTPUTS
-% density [double] density.
-%
+%
+% OUTPUTS
+% density [double] density.
+%
% SPECIAL REQUIREMENTS
% Copyright (C) 2003-2017 Dynare Team
diff --git a/matlab/distributions/rand_inverse_wishart.m b/matlab/distributions/rand_inverse_wishart.m
index c561fb7f466acf57c57aa1ca7ce206054ae66a44..3c65c18670e9f763181cf2e67b82951b04626f49 100644
--- a/matlab/distributions/rand_inverse_wishart.m
+++ b/matlab/distributions/rand_inverse_wishart.m
@@ -17,12 +17,12 @@ function G = rand_inverse_wishart(m, v, H_inv_upper_chol)
% OUTPUTS:
% G: G ~ IW(m, v, H) where H = inv(H_inv_upper_chol'*H_inv_upper_chol)
% or, equivalently, using the correspondence between Wishart and
-% inverse-Wishart: inv(G) ~ W(m, v, S) where
+% inverse-Wishart: inv(G) ~ W(m, v, S) where
% S = H_inv_upper_chol'*H_inv_upper_chol = inv(H)
-%
+%
% SPECIAL REQUIREMENT
% none
-%
+%
% Copyright (C) 2003-2009 Dynare Team
%
@@ -41,7 +41,7 @@ function G = rand_inverse_wishart(m, v, H_inv_upper_chol)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-X = randn(v, m) * H_inv_upper_chol;
+X = randn(v, m) * H_inv_upper_chol;
% At this point, X'*X is Wishart distributed
diff --git a/matlab/distributions/rand_matrix_normal.m b/matlab/distributions/rand_matrix_normal.m
index ff595cca873938cd12b1a011604e8d361a388982..04c6273f4326b1d36bbb64620d02f74569d1cf3a 100644
--- a/matlab/distributions/rand_matrix_normal.m
+++ b/matlab/distributions/rand_matrix_normal.m
@@ -2,7 +2,7 @@ function B = rand_matrix_normal(n, p, M, Omega_lower_chol, Sigma_lower_chol)
% function B = rand_matrix_normal(n, p, M, Omega_lower_chol, Sigma_lower_chol)
% Pseudo random matrices drawn from a matrix-normal distribution
-% B ~ MN_n*p(M, Omega, Sigma)
+% B ~ MN_n*p(M, Omega, Sigma)
% Equivalent to vec(B) ~ N(vec(Mu), kron(Omega, Sigma))
%
% INPUTS
@@ -13,10 +13,10 @@ function B = rand_matrix_normal(n, p, M, Omega_lower_chol, Sigma_lower_chol)
% (Omega_lower_chol = chol(Omega, 'lower'))
% Sigma_lower_chol: (n*n), lower Cholesky decomposition of Sigma,
% (Sigma_lower_chol = chol(Sigma, 'lower'))
-%
+%
% OUTPUTS
% B: (n*p) matrix drawn from a Matrix-normal distribution
-%
+%
% SPECIAL REQUIREMENTS
% Same notations than: http://en.wikipedia.org/wiki/Matrix_normal_distribution
diff --git a/matlab/distributions/rand_multivariate_normal.m b/matlab/distributions/rand_multivariate_normal.m
index 174c3706cceedd5f71cfec03c1e9be39a0c3eedd..992f9c9e3d7642c49926ff3b01f2100910f2e919 100644
--- a/matlab/distributions/rand_multivariate_normal.m
+++ b/matlab/distributions/rand_multivariate_normal.m
@@ -2,16 +2,16 @@ function draw = rand_multivariate_normal(Mean,Sigma_upper_chol,n)
% Pseudo random draws from a multivariate normal distribution,
% \mathcal N_n(Mean,Sigma), with expectation Mean and variance Sigma.
%
-% INPUTS
+% INPUTS
%
% Mean [double] 1*n vector, expectation of the multivariate random variable.
% Sigma_upper_chol [double] n*n matrix, upper triangular Cholesky decomposition of Sigma (the covariance matrix).
% n [integer] dimension.
-%
-% OUTPUTS
+%
+% OUTPUTS
% draw [double] 1*n vector drawn from a multivariate normal distribution with expectation Mean and
-% covariance Sigma
-%
+% covariance Sigma
+%
% SPECIAL REQUIREMENTS
% Copyright (C) 2003-2009 Dynare Team
diff --git a/matlab/distributions/rand_multivariate_student.m b/matlab/distributions/rand_multivariate_student.m
index 37e2419f8dd77d66abf8b6a3eaa6fac778bd117f..cdf06b2f1262c1adb7f1c50ffc0c3525a48736c2 100644
--- a/matlab/distributions/rand_multivariate_student.m
+++ b/matlab/distributions/rand_multivariate_student.m
@@ -3,22 +3,22 @@ function draw = rand_multivariate_student(Mean,Sigma_upper_chol,df)
% Pseudo random draws from a multivariate student distribution,
% with expectation Mean, variance Sigma*df/(df-2) and degrees of freedom df>0.
%
-% INPUTS
+% INPUTS
%
% Mean [double] 1*n vector, expectation of the multivariate random variable.
-% Sigma_upper_chol [double] n*n matrix, upper triangular Cholesky decomposition of Sigma
+% Sigma_upper_chol [double] n*n matrix, upper triangular Cholesky decomposition of Sigma
% (the covariance matrix up to a factor df/(df-2)).
% df [integer] degrees of freedom.
-%
-% OUTPUTS
+%
+% OUTPUTS
% draw [double] 1*n vector drawn from a multivariate normal distribution with expectation Mean and
% covariance Sigma.
-%
%
-% NOTE See Zellner (appendix B.2, 1971) for a definition.
-% Computes the t-distributed random numbers from
+%
+% NOTE See Zellner (appendix B.2, 1971) for a definition.
+% Computes the t-distributed random numbers from
% X = \mu + Y\sqrt{\frac{\nu}{U}}
-% where
+% where
% Y~N(0,Sigma) with Sigma=Sigma_upper_chol'*Sigma_upper_chol
% U~\Chi^2_{\nu}
% The latter is constructed as the sum of \nu standard normals.
diff --git a/matlab/distributions/weibull_specification.m b/matlab/distributions/weibull_specification.m
index 8036dcb8dfd21912df52d2580dacbb99ef370c82..064924cf48ad8669cee0ba53bb02d490fd2a93b1 100644
--- a/matlab/distributions/weibull_specification.m
+++ b/matlab/distributions/weibull_specification.m
@@ -2,12 +2,12 @@ function [shape, scale] = weibull_specification(mu, sigma2, lb, name) % --*--
% Returns the hyperparameters of the Weibull distribution given the expectation and variance.
%
-% INPUTS
+% INPUTS
%
%
-% OUTPUTS
+% OUTPUTS
+%
%
-%
% Copyright (C) 2015-2016 Dynare Team
%
@@ -49,8 +49,8 @@ end
scale = NaN;
shape = NaN;
-mu = mu-lb;
-mu2 = mu*mu;
+mu = mu-lb;
+mu2 = mu*mu;
eqn = @(k) gammaln(1+2./k) - 2*gammaln(1+1./k) - log(1+sigma2/mu2);
eqn2 = @(k) eqn(k).*eqn(k);
@@ -140,4 +140,3 @@ scale = mu/gamma(1+1/shape);
%$ end
%$ T = all(t);
%@eof:1
-
diff --git a/matlab/do_parameter_initialization.m b/matlab/do_parameter_initialization.m
index 1f18c3f5663995224917fa4098bc4f6d2e03f813..161c14641f88e9a4946df241ad1279228c9cfe94 100644
--- a/matlab/do_parameter_initialization.m
+++ b/matlab/do_parameter_initialization.m
@@ -6,20 +6,20 @@ function [xparam1,estim_params_,xparam1_explicitly_initialized,xparam1_properly_
% o estim_params_ [structure] characterizing parameters to be estimated.
% o xparam1_calib [double] vector of parameters to be estimated, with parameters
% initialized from calibration using get_all_parameters
-%
+%
% o xparam1_NaN_set_to_prior_mean [double] vector of parameters to be estimated, with parameters
% initialized using dynare_estimation_init; not explicitly initialized
% parameters are at prior mean
% OUTPUTS
% o xparam1 [double] vector of initialized parameters; uses the hierarchy: 1) explicitly initialized parameters,
% 2) calibrated parameters, 3) prior mean
-% o estim_params_ [structure] characterizing parameters to be estimated; it is
+% o estim_params_ [structure] characterizing parameters to be estimated; it is
% updated here to reflect calibrated parameters
% o xparam1_explicitly_initialized [double] vector of parameters to be estimated that
-% were explicitly initialized
+% were explicitly initialized
% o xparam1_properly_calibrated [double] vector of parameters to be estimated that
-% were properly calibrated
-%
+% were properly calibrated
+%
% SPECIAL REQUIREMENTS
% None
@@ -59,7 +59,7 @@ offset=0;
if nvx
initialized_par_index=find(~isnan(estim_params_.var_exo(:,2)));
calibrated_par_index=find(isnan(estim_params_.var_exo(:,2)) & ~isnan(xparam1_calib(offset+1:offset+nvx,1)));
- uninitialized_par_index=find(isnan(estim_params_.var_exo(:,2)) & isnan(xparam1_calib(offset+1:offset+nvx,1)));
+ uninitialized_par_index=find(isnan(estim_params_.var_exo(:,2)) & isnan(xparam1_calib(offset+1:offset+nvx,1)));
xparam1_explicitly_initialized(offset+initialized_par_index,1) = estim_params_.var_exo(initialized_par_index,2);
%update estim_params_ with calibrated starting values
estim_params_.var_exo(calibrated_par_index,2)=xparam1_calib(offset+calibrated_par_index,1);
@@ -67,13 +67,13 @@ if nvx
xparam1_properly_calibrated(offset+calibrated_par_index,1) = xparam1_calib(offset+calibrated_par_index,1);
inv_gamma_violation=find(estim_params_.var_exo(calibrated_par_index,2)==0 & estim_params_.var_exo(calibrated_par_index,5)==4);
if inv_gamma_violation
- estim_params_.var_exo(calibrated_par_index(inv_gamma_violation),2)=NaN;
- xparam1_properly_calibrated(offset+calibrated_par_index(inv_gamma_violation),1)=NaN;
- fprintf('PARAMETER INITIALIZATION: Some standard deviations of shocks of the calibrated model are 0 and\n')
- fprintf('PARAMETER INITIALIZATION: violate the inverse gamma prior. They will instead be initialized with the prior mean.\n')
+ estim_params_.var_exo(calibrated_par_index(inv_gamma_violation),2)=NaN;
+ xparam1_properly_calibrated(offset+calibrated_par_index(inv_gamma_violation),1)=NaN;
+ fprintf('PARAMETER INITIALIZATION: Some standard deviations of shocks of the calibrated model are 0 and\n')
+ fprintf('PARAMETER INITIALIZATION: violate the inverse gamma prior. They will instead be initialized with the prior mean.\n')
end
if uninitialized_par_index
- fprintf('PARAMETER INITIALIZATION: Warning, some estimated standard deviations of shocks are not\n')
+ fprintf('PARAMETER INITIALIZATION: Warning, some estimated standard deviations of shocks are not\n')
fprintf('PARAMETER INITIALIZATION: initialized. They will be initialized with the prior mean.\n')
end
end
@@ -88,10 +88,10 @@ if nvn
xparam1_properly_calibrated(offset+calibrated_par_index,1) = xparam1_calib(offset+calibrated_par_index,1);
inv_gamma_violation=find(estim_params_.var_endo(calibrated_par_index,2)==0 & estim_params_.var_endo(calibrated_par_index,5)==4);
if inv_gamma_violation
- estim_params_.var_endo(calibrated_par_index(inv_gamma_violation),2)=NaN;
- xparam1_properly_calibrated(offset+calibrated_par_index(inv_gamma_violation),1)=NaN;
+ estim_params_.var_endo(calibrated_par_index(inv_gamma_violation),2)=NaN;
+ xparam1_properly_calibrated(offset+calibrated_par_index(inv_gamma_violation),1)=NaN;
fprintf('PARAMETER INITIALIZATION: Some measurement errors of the calibrated model are 0 and violate the\n')
- fprintf('PARAMETER INITIALIZATION: inverse gamma prior. They will instead be initialized with the prior mean.\n')
+ fprintf('PARAMETER INITIALIZATION: inverse gamma prior. They will instead be initialized with the prior mean.\n')
end
if uninitialized_par_index
fprintf('PARAMETER INITIALIZATION: Warning, some measurement errors are not initialized. They will be initialized\n')
@@ -140,4 +140,3 @@ end
xparam1=xparam1_explicitly_initialized;
xparam1(isnan(xparam1))=xparam1_properly_calibrated(isnan(xparam1)); %set not explicitly initialized parameters that do not obviously violate prior distribution to calibrated parameter values
xparam1(isnan(xparam1))=xparam1_NaN_set_to_prior_mean(isnan(xparam1)); %set not yet initialized parameters to prior mean coming from dynare_estimation_init
-
diff --git a/matlab/dr_block.m b/matlab/dr_block.m
index 67e9a4f4660fa609f40a8d505b1ea7a11d585d23..61826d390f467bdd1a4bad7d976831125d9e1ebb 100644
--- a/matlab/dr_block.m
+++ b/matlab/dr_block.m
@@ -1,38 +1,38 @@
function [dr,info,M_,options_,oo_] = dr_block(dr,task,M_,options_,oo_,varargin)
% function [dr,info,M_,options_,oo_] = dr_block(dr,task,M_,options_,oo_,varargin)
% computes the reduced form solution of a rational expectations model
-% (first order approximation of the stochastic model around the deterministic steady state).
+% (first order approximation of the stochastic model around the deterministic steady state).
%
% INPUTS
% dr [matlab structure] Decision rules for stochastic simulations.
% task [integer] if task = 0 then dr_block computes decision rules.
% if task = 1 then dr_block computes eigenvalues.
-% M_ [matlab structure] Definition of the model.
+% M_ [matlab structure] Definition of the model.
% options_ [matlab structure] Global options.
-% oo_ [matlab structure] Results
+% oo_ [matlab structure] Results
% oo_ [matlab cell] Other input arguments
-%
+%
% OUTPUTS
% dr [matlab structure] Decision rules for stochastic simulations.
% info [integer] info=1: the model doesn't define current variables uniquely
-% info=2: problem in mjdgges.dll info(2) contains error code.
+% info=2: problem in mjdgges.dll info(2) contains error code.
% info=3: BK order condition not satisfied info(2) contains "distance"
% absence of stable trajectory.
% info=4: BK order condition not satisfied info(2) contains "distance"
% indeterminacy.
% info=5: BK rank condition not satisfied.
-% info=6: The jacobian matrix evaluated at the steady state is complex.
-% M_ [matlab structure]
+% info=6: The jacobian matrix evaluated at the steady state is complex.
+% M_ [matlab structure]
% options_ [matlab structure]
% oo_ [matlab structure]
-%
+%
% ALGORITHM
% first order block relaxation method applied to the model
% E[A Yt-1 + B Yt + C Yt+1 + ut] = 0
-%
+%
% SPECIAL REQUIREMENTS
% none.
-%
+%
% Copyright (C) 2010-2017 Dynare Team
%
@@ -54,7 +54,7 @@ function [dr,info,M_,options_,oo_] = dr_block(dr,task,M_,options_,oo_,varargin)
info = 0;
verbose = 0;
if nargin > 5
- verbose = varargin{1};
+ verbose = varargin{1};
end
%verbose = options_.verbosity;
if options_.order > 1
@@ -122,19 +122,19 @@ for i = 1:Size
maximum_lag = data(i).maximum_endo_lag;
maximum_lead = data(i).maximum_endo_lead;
n = n_dynamic + n_static;
-
+
block_type = M_.block_structure.block(i).Simulation_Type;
if task ~= 1
- if block_type == 2 || block_type == 4 || block_type == 7
+ if block_type == 2 || block_type == 4 || block_type == 7
block_type = 8;
end
end
- if maximum_lag > 0 && (n_pred > 0 || n_both > 0) && block_type ~= 1
+ if maximum_lag > 0 && (n_pred > 0 || n_both > 0) && block_type ~= 1
indexi_0 = min(lead_lag_incidence(2,:));
end
switch block_type
case 1
- %% ------------------------------------------------------------------
+ %% ------------------------------------------------------------------
%Evaluate Forward
if maximum_lag > 0 && n_pred > 0
indx_r = find(M_.block_structure.block(i).lead_lag_incidence(1,:));
@@ -175,11 +175,11 @@ for i = 1:Size
l_x_sv = dr.ghx(dr.state_var, 1:n_sv);
selector_tm1 = M_.block_structure.block(i).tm1;
-
+
ghx_other = - B \ (fx_t * l_x + (fx_tp1 * l_x * l_x_sv) + fx_tm1 * selector_tm1);
dr.ghx(endo, :) = dr.ghx(endo, :) + ghx_other;
end
-
+
if exo_nbr
fu = data(i).g1_x;
exo = dr.exo_var;
@@ -198,17 +198,17 @@ for i = 1:Size
else
exo = dr.exo_var;
if other_endo_nbr > 0
- l_u_sv = dr.ghu(dr.state_var,:);
- l_x = dr.ghx(data(i).other_endogenous,:);
- l_u = dr.ghu(data(i).other_endogenous,:);
- ghu = -B \ (fx_tp1 * l_x * l_u_sv + (fx_t) * l_u );
+ l_u_sv = dr.ghu(dr.state_var,:);
+ l_x = dr.ghx(data(i).other_endogenous,:);
+ l_u = dr.ghu(data(i).other_endogenous,:);
+ ghu = -B \ (fx_tp1 * l_x * l_u_sv + (fx_t) * l_u );
else
ghu = [];
end
end
end
case 2
- %% ------------------------------------------------------------------
+ %% ------------------------------------------------------------------
%Evaluate Backward
if maximum_lead > 0 && n_fwrd > 0
indx_r = find(M_.block_structure.block(i).lead_lag_incidence(3,:));
@@ -233,7 +233,7 @@ for i = 1:Size
ghu = - inv(jacob(indx_r, indx_c)) * data(i).g1_x;
end
case 3
- %% ------------------------------------------------------------------
+ %% ------------------------------------------------------------------
%Solve Forward single equation
if maximum_lag > 0 && n_pred > 0
data(i).eigval = - jacob(1 , 1 : n_pred) / jacob(1 , n_pred + n_static + 1 : n_pred + n_static + n_pred + n_both);
@@ -246,9 +246,9 @@ for i = 1:Size
%First order approximation
if task ~= 1
if (maximum_lag > 0)
- ghx = - jacob(1 , 1 : n_pred) / jacob(1 , n_pred + n_static + 1 : n_pred + n_static + n_pred + n_both);
+ ghx = - jacob(1 , 1 : n_pred) / jacob(1 , n_pred + n_static + 1 : n_pred + n_static + n_pred + n_both);
else
- ghx = 0;
+ ghx = 0;
end
if other_endo_nbr
fx = data(i).g1_o;
@@ -269,7 +269,7 @@ for i = 1:Size
l_x = dr.ghx(data(i).other_endogenous,:);
l_x_sv = dr.ghx(dr.state_var, 1:n_sv);
-
+
selector_tm1 = M_.block_structure.block(i).tm1;
ghx_other = - (fx_t * l_x + (fx_tp1 * l_x * l_x_sv) + fx_tm1 * selector_tm1) / jacob(1 , n_pred + 1 : n_pred + n_static + n_pred + n_both);
dr.ghx(endo, :) = dr.ghx(endo, :) + ghx_other;
@@ -289,19 +289,19 @@ for i = 1:Size
ghu = - fu / jacob(1 , n_pred + 1 : n_pred + n_static + n_pred + n_both);
end
else
- if other_endo_nbr > 0
- l_u_sv = dr.ghu(dr.state_var,:);
- l_x = dr.ghx(data(i).other_endogenous,:);
- l_u = dr.ghu(data(i).other_endogenous,:);
- ghu = -(fx_tp1 * l_x * l_u_sv + (fx_t) * l_u ) / jacob(1 , n_pred + 1 : n_pred + n_static + n_pred + n_both);
- exo = dr.exo_var;
- else
- ghu = [];
- end
+ if other_endo_nbr > 0
+ l_u_sv = dr.ghu(dr.state_var,:);
+ l_x = dr.ghx(data(i).other_endogenous,:);
+ l_u = dr.ghu(data(i).other_endogenous,:);
+ ghu = -(fx_tp1 * l_x * l_u_sv + (fx_t) * l_u ) / jacob(1 , n_pred + 1 : n_pred + n_static + n_pred + n_both);
+ exo = dr.exo_var;
+ else
+ ghu = [];
+ end
end
end
case 4
- %% ------------------------------------------------------------------
+ %% ------------------------------------------------------------------
%Solve Backward single equation
if maximum_lead > 0 && n_fwrd > 0
data(i).eigval = - jacob(1 , n_pred + n - n_fwrd + 1 : n_pred + n) / jacob(1 , n_pred + n + 1 : n_pred + n + n_fwrd) ;
@@ -315,11 +315,11 @@ for i = 1:Size
dr.full_rank = dr.full_rank && full_rank;
dr.eigval = [dr.eigval ; data(i).eigval];
case 6
- %% ------------------------------------------------------------------
- %Solve Forward complete
+ %% ------------------------------------------------------------------
+ %Solve Forward complete
if (maximum_lag > 0)
ghx = - jacob(: , n_pred + 1 : n_pred + n_static ...
- + n_pred + n_both) \ jacob(: , 1 : n_pred);
+ + n_pred + n_both) \ jacob(: , 1 : n_pred);
else
ghx = 0;
end
@@ -354,7 +354,7 @@ for i = 1:Size
l_x = dr.ghx(data(i).other_endogenous,:);
l_x_sv = dr.ghx(dr.state_var, 1:n_sv);
-
+
selector_tm1 = M_.block_structure.block(i).tm1;
ghx_other = - (fx_t * l_x + (fx_tp1 * l_x * l_x_sv) + fx_tm1 * selector_tm1) / jacob(: , n_pred + 1 : n_pred + n_static + n_pred + n_both);
dr.ghx(endo, :) = dr.ghx(endo, :) + ghx_other;
@@ -373,19 +373,19 @@ for i = 1:Size
ghu = - fu / jacob(: , n_pred + 1 : n_pred + n_static + n_pred + n_both);
end
else
- if other_endo_nbr > 0
- l_u_sv = dr.ghu(dr.state_var,:);
- l_x = dr.ghx(data(i).other_endogenous,:);
- l_u = dr.ghu(data(i).other_endogenous,:);
- ghu = -(fx_tp1 * l_x * l_u_sv + (fx_t) * l_u ) / jacob(1 , n_pred + 1 : n_pred + n_static + n_pred + n_both);
- exo = dr.exo_var;
- else
- ghu = [];
- end
+ if other_endo_nbr > 0
+ l_u_sv = dr.ghu(dr.state_var,:);
+ l_x = dr.ghx(data(i).other_endogenous,:);
+ l_u = dr.ghu(data(i).other_endogenous,:);
+ ghu = -(fx_tp1 * l_x * l_u_sv + (fx_t) * l_u ) / jacob(1 , n_pred + 1 : n_pred + n_static + n_pred + n_both);
+ exo = dr.exo_var;
+ else
+ ghu = [];
+ end
end
end
case 7
- %% ------------------------------------------------------------------
+ %% ------------------------------------------------------------------
%Solve Backward complete
if maximum_lead > 0 && n_fwrd > 0
data(i).eigval = eig(- jacob(: , n_pred + n - n_fwrd + 1: n_pred + n))/ ...
@@ -401,10 +401,10 @@ for i = 1:Size
dr.full_rank = dr.full_rank && full_rank;
dr.eigval = [dr.eigval ; data(i).eigval];
case {5,8}
- %% ------------------------------------------------------------------
+ %% ------------------------------------------------------------------
%The lead_lag_incidence contains columns in the following order:
% static variables, backward variable, mixed variables and forward variables
- %
+ %
%Proceeds to a QR decomposition on the jacobian matrix in order to reduce the problem size
index_c = lead_lag_incidence(2,:); % Index of all endogenous variables present at time=t
index_s = lead_lag_incidence(2,1:n_static); % Index of all static endogenous variables present at time=t
@@ -435,7 +435,7 @@ for i = 1:Size
hx = ghx(1:n_pred+n_both,:);
gx = ghx(1+n_pred:end,:);
end
-
+
if (task ~= 1 && ((options_.dr_cycle_reduction == 1 && info ==1) || options_.dr_cycle_reduction == 0)) || task == 1
D = [[aa(row_indx,index_0m) zeros(n_dynamic,n_both) aa(row_indx,index_p)] ; [zeros(n_both, n_pred) eye(n_both) zeros(n_both, n_both + n_fwrd)]];
E = [-aa(row_indx,[index_m index_0p]) ; [zeros(n_both, n_both + n_pred) eye(n_both, n_both + n_fwrd) ] ];
@@ -483,7 +483,7 @@ for i = 1:Size
nba = nyf;
end
else
- sorted_roots = sort(abs(data(i).eigval));
+ sorted_roots = sort(abs(data(i).eigval));
if nba > nyf
temp = sorted_roots(nd-nba+1:nd-nyf)-1-options_.qz_criterium;
info(1) = 3;
@@ -497,8 +497,8 @@ for i = 1:Size
end
indx_stable_root = 1: (nd - nyf); %=> index of stable roots
indx_explosive_root = n_pred + n_both + 1:nd; %=> index of explosive roots
- % derivatives with respect to dynamic state variables
- % forward variables
+ % derivatives with respect to dynamic state variables
+ % forward variables
Z = w';
Z11t = Z(indx_stable_root, indx_stable_root)';
Z21 = Z(indx_explosive_root, indx_stable_root);
@@ -522,13 +522,13 @@ for i = 1:Size
ghx = [hx(k1,:); gx(k2(n_both+1:end),:)];
end
end
-
- if task~= 1
+
+ if task~= 1
%lead variables actually present in the model
j4 = n_static+n_pred+1:n_static+n_pred+n_both+n_fwrd; % Index on the forward and both variables
j3 = nonzeros(lead_lag_incidence(2,j4)) - n_static - 2 * n_pred - n_both; % Index on the non-zeros forward and both variables
- j4 = find(lead_lag_incidence(2,j4));
-
+ j4 = find(lead_lag_incidence(2,j4));
+
if n_static > 0
B_static = B(:,1:n_static); % submatrix containing the derivatives w.r. to static variables
else
@@ -550,12 +550,12 @@ for i = 1:Size
ghx = [temp; ghx];
temp = [];
end
-
+
A_ = real([B_static C(:,j3)*gx+B_pred B_fyd]); % The state_variable of the block are located at [B_pred B_both]
-
+
if other_endo_nbr
if n_static > 0
- fx = Q' * data(i).g1_o;
+ fx = Q' * data(i).g1_o;
else
fx = data(i).g1_o;
end
@@ -575,10 +575,10 @@ for i = 1:Size
fx_tp1(:,c) = fx(:,lead);
l_x = dr.ghx(data(i).other_endogenous,:);
-
+
l_x_sv = dr.ghx(dr.state_var, :);
-
- selector_tm1 = M_.block_structure.block(i).tm1;
+
+ selector_tm1 = M_.block_structure.block(i).tm1;
B_ = [zeros(size(B_static)) zeros(n,n_pred) C(:,j3) ];
C_ = l_x_sv;
@@ -590,14 +590,14 @@ for i = 1:Size
ghx_other = reshape(vghx_other, size(D_,1), size(D_,2));
elseif options_.sylvester_fp == 1
ghx_other = gensylv_fp(A_, B_, C_, D_, i, options_.sylvester_fixed_point_tol);
- else
+ else
[err, ghx_other] = gensylv(1, A_, B_, C_, -D_);
end
if options_.aim_solver ~= 1 && options_.use_qzdiv
- % Necessary when using Sims' routines for QZ
- ghx_other = real(ghx_other);
+ % Necessary when using Sims' routines for QZ
+ ghx_other = real(ghx_other);
end
-
+
dr.ghx(endo, :) = dr.ghx(endo, :) + ghx_other;
end
@@ -616,7 +616,7 @@ for i = 1:Size
fu_complet(:,data(i).exogenous) = fu;
% Solve the equation in ghu:
% A_ * ghu + (fu_complet + fx_tp1 * l_x * l_u_sv + (fx_t + B_ * ghx_other) * l_u ) = 0
-
+
ghu = -A_\ (fu_complet + fx_tp1 * l_x * l_u_sv + fx_t * l_u + B_ * ghx_other * l_u_sv );
exo = dr.exo_var;
else
@@ -637,16 +637,16 @@ for i = 1:Size
end
-
+
if options_.loglinear
error('The loglinear option is not yet supported in first order approximation for a block decomposed model');
-% k = find(dr.kstate(:,2) <= M_.maximum_endo_lag+1);
-% klag = dr.kstate(k,[1 2]);
-% k1 = dr.order_var;
-%
-% ghx = repmat(1./dr.ys(k1),1,size(ghx,2)).*ghx.* ...
-% repmat(dr.ys(k1(klag(:,1)))',size(ghx,1),1);
-% ghu = repmat(1./dr.ys(k1),1,size(ghu,2)).*ghu;
+ % k = find(dr.kstate(:,2) <= M_.maximum_endo_lag+1);
+ % klag = dr.kstate(k,[1 2]);
+ % k1 = dr.order_var;
+ %
+ % ghx = repmat(1./dr.ys(k1),1,size(ghx,2)).*ghx.* ...
+ % repmat(dr.ys(k1(klag(:,1)))',size(ghx,1),1);
+ % ghu = repmat(1./dr.ys(k1),1,size(ghu,2)).*ghu;
end
@@ -659,16 +659,16 @@ for i = 1:Size
%exogenous deterministic variables
if exo_det_nbr > 0
error('Deterministic exogenous variables are not yet implemented in first order approximation for a block decomposed model');
-% f1 = sparse(jacobia_(:,nonzeros(M_.lead_lag_incidence(M_.maximum_endo_lag+2:end,order_var))));
-% f0 = sparse(jacobia_(:,nonzeros(M_.lead_lag_incidence(M_.maximum_endo_lag+1,order_var))));
-% fudet = data(i).g1_xd;
-% M1 = inv(f0+[zeros(n,n_static) f1*gx zeros(n,nyf-n_both)]);
-% M2 = M1*f1;
-% dr.ghud = cell(M_.exo_det_length,1);
-% dr.ghud{1} = -M1*fudet;
-% for i = 2:M_.exo_det_length
-% dr.ghud{i} = -M2*dr.ghud{i-1}(end-nyf+1:end,:);
-% end
+ % f1 = sparse(jacobia_(:,nonzeros(M_.lead_lag_incidence(M_.maximum_endo_lag+2:end,order_var))));
+ % f0 = sparse(jacobia_(:,nonzeros(M_.lead_lag_incidence(M_.maximum_endo_lag+1,order_var))));
+ % fudet = data(i).g1_xd;
+ % M1 = inv(f0+[zeros(n,n_static) f1*gx zeros(n,nyf-n_both)]);
+ % M2 = M1*f1;
+ % dr.ghud = cell(M_.exo_det_length,1);
+ % dr.ghud{1} = -M1*fudet;
+ % for i = 2:M_.exo_det_length
+ % dr.ghud{i} = -M2*dr.ghud{i-1}(end-nyf+1:end,:);
+ % end
end
end
end
@@ -685,22 +685,22 @@ for i = 1:Size
dr.ghu(endo, exo) = ghu;
data(i).pol.i_ghu = exo;
end
-
- if (verbose)
+
+ if (verbose)
disp('dr.ghx');
dr.ghx
disp('dr.ghu');
dr.ghu
- end
-
+ end
+
end
M_.block_structure.block = data ;
if (verbose)
- disp('dr.ghx');
- disp(real(dr.ghx));
- disp('dr.ghu');
- disp(real(dr.ghu));
-end
+ disp('dr.ghx');
+ disp(real(dr.ghx));
+ disp('dr.ghu');
+ disp(real(dr.ghu));
+end
if (task == 1)
return
end
diff --git a/matlab/draw_prior_density.m b/matlab/draw_prior_density.m
index 22b43518ca930daca64848b929b9c6f25047388c..94f32ac8813c7a4822331dd06567086f35939867 100644
--- a/matlab/draw_prior_density.m
+++ b/matlab/draw_prior_density.m
@@ -4,11 +4,11 @@ function [x,f,abscissa,dens,binf,bsup] = draw_prior_density(indx,bayestopt_)
% INPUTS
% indx [integer] Parameter number.
% bayestopt_ [structure] Describes the prior beliefs.
-%
+%
% OUTPUTS
% x [double] Row vector, subset of 'abscissa' such as the density is less than 10
% f [double] Row vector, subset of 'dens' such as the density is less than 10
-% abscissa [double] Row vector, abscissa
+% abscissa [double] Row vector, abscissa
% dens [double] Row vector, density
% binf: [double] Scalar, first element of x
% bsup: [double] Scalar, last element of x
@@ -63,10 +63,10 @@ switch pshape(indx)
abscissa = linspace(infbound,supbound,steps);
dens = density(abscissa,p6(indx),p7(indx),p3(indx));
case 3% Gaussian prior
- infbound = norminv(truncprior,p6(indx),p7(indx));
+ infbound = norminv(truncprior,p6(indx),p7(indx));
supbound = norminv(1-truncprior,p6(indx),p7(indx));
abscissa = linspace(infbound,supbound,steps);
- dens = normpdf(abscissa,p6(indx),p7(indx));
+ dens = normpdf(abscissa,p6(indx),p7(indx));
case 4% Inverse-gamma of type 1 prior
try
infbound = 1/sqrt(gaminv(1-10*truncprior, p7(indx)/2, 2/p6(indx)))+p3(indx);
@@ -80,7 +80,7 @@ switch pshape(indx)
end
end
abscissa = linspace(infbound,supbound,steps);
- dens = exp(lpdfig1(abscissa-p3(indx),p6(indx),p7(indx)));
+ dens = exp(lpdfig1(abscissa-p3(indx),p6(indx),p7(indx)));
case 5% Uniform prior
infbound = p6(indx);
supbound = p7(indx);
@@ -108,9 +108,9 @@ switch pshape(indx)
dens = density(abscissa,p6(indx),p7(indx),p3(indx));
otherwise
error(sprintf('draw_prior_density: unknown distribution shape (index %d, type %d)', indx, pshape(indx)));
-end
+end
-if pshape(indx) ~= 5
+if pshape(indx) ~= 5
[junk,k1] = max(dens);
if k1 == 1 || k1 == length(dens)
k = find(dens > 10);
diff --git a/matlab/dsample.m b/matlab/dsample.m
index 2e7251ac1853683dca2ca987fa97bcb64ffe3a51..761f3290fbaf34d2a1ac996601612de7f8940ba3 100644
--- a/matlab/dsample.m
+++ b/matlab/dsample.m
@@ -1,17 +1,17 @@
function dsample(s1,s2)
% function dsample(s1,s2)
% This optional command permits to reduce the number of periods considered in following output commands.
-% If only one argument is provided, output is from period 1 to the period specified in the DSAMPLE command.
+% If only one argument is provided, output is from period 1 to the period specified in the DSAMPLE command.
% If two arguments are present output is done for the interval between the two periods.
% DSAMPLE without arguments reset the sample to the one specified by PERIODS
%
% INPUTS
% s1: first period
% s2: last period
-%
+%
% OUTPUTS
% none
-%
+%
% SPECIAL REQUIREMENTS
% none
diff --git a/matlab/dsge_likelihood.m b/matlab/dsge_likelihood.m
index 2e869d4c8ac9d4c20dc64347b742230d60ad13d1..0b62e516753e59b6fd4e6c8332f1d08d1e7416ee 100644
--- a/matlab/dsge_likelihood.m
+++ b/matlab/dsge_likelihood.m
@@ -149,7 +149,7 @@ xparam1 = xparam1(:);
if DynareOptions.estimation_dll
[fval,exit_flag,SteadyState,trend_coeff,info,params,H,Q] ...
= logposterior(xparam1,DynareDataset, DynareOptions,Model, ...
- EstimatedParameters,BayesInfo,DynareResults);
+ EstimatedParameters,BayesInfo,DynareResults);
mexErrCheck('logposterior', exit_flag);
Model.params = params;
if ~isequal(Model.H,0)
@@ -268,8 +268,8 @@ end
% Return, with endogenous penalty when possible, if dynare_resolve issues an error code (defined in resol).
if info(1)
if info(1) == 3 || info(1) == 4 || info(1) == 5 || info(1)==6 ||info(1) == 19 ||...
- info(1) == 20 || info(1) == 21 || info(1) == 23 || info(1) == 26 || ...
- info(1) == 81 || info(1) == 84 || info(1) == 85 || info(1) == 86
+ info(1) == 20 || info(1) == 21 || info(1) == 23 || info(1) == 26 || ...
+ info(1) == 81 || info(1) == 84 || info(1) == 85 || info(1) == 86
%meaningful second entry of output that can be used
fval = Inf;
info(4) = info(2);
@@ -320,8 +320,8 @@ if BayesInfo.with_trend
[trend_addition, trend_coeff]=compute_trend_coefficients(Model,DynareOptions,DynareDataset.vobs,DynareDataset.nobs);
trend = repmat(constant,1,DynareDataset.nobs)+trend_addition;
else
- trend_coeff = zeros(DynareDataset.vobs,1);
- trend = repmat(constant,1,DynareDataset.nobs);
+ trend_coeff = zeros(DynareDataset.vobs,1);
+ trend = repmat(constant,1,DynareDataset.nobs);
end
% Get needed informations for kalman filter routines.
@@ -369,8 +369,8 @@ switch DynareOptions.lik_init
if kalman_algo == 0
kalman_algo = 3;
elseif ~((kalman_algo == 3) || (kalman_algo == 4))
- error(['The model requires Diffuse filter, but you specified a different Kalman filter. You must set options_.kalman_algo ' ...
- 'to 0 (default), 3 or 4'])
+ error(['The model requires Diffuse filter, but you specified a different Kalman filter. You must set options_.kalman_algo ' ...
+ 'to 0 (default), 3 or 4'])
end
[Pstar,Pinf] = compute_Pinf_Pstar(Z,T,R,Q,DynareOptions.qz_criterium,[1:length(T)]);
Z =zeros(length(BayesInfo.mf),size(T,1));
@@ -384,9 +384,9 @@ switch DynareOptions.lik_init
Pstar0 = Pstar; % store Pstar
if no_missing_data_flag
[dLIK,dlik,a,Pstar] = kalman_filter_d(Y, 1, size(Y,2), ...
- zeros(mm,1), Pinf, Pstar, ...
- kalman_tol, diffuse_kalman_tol, riccati_tol, DynareOptions.presample, ...
- T,R,Q,H,Z,mm,pp,rr);
+ zeros(mm,1), Pinf, Pstar, ...
+ kalman_tol, diffuse_kalman_tol, riccati_tol, DynareOptions.presample, ...
+ T,R,Q,H,Z,mm,pp,rr);
else
[dLIK,dlik,a,Pstar] = missing_observations_kalman_filter_d(DatasetInfo.missing.aindex,DatasetInfo.missing.number_of_observations,DatasetInfo.missing.no_more_missing_observations, ...
Y, 1, size(Y,2), ...
@@ -430,17 +430,17 @@ switch DynareOptions.lik_init
Pinf = blkdiag(Pinf,zeros(pp));
H1 = zeros(pp,1);
mmm = mm+pp;
-
+
end
end
[dLIK,dlik,a,Pstar] = univariate_kalman_filter_d(DatasetInfo.missing.aindex,...
- DatasetInfo.missing.number_of_observations,...
- DatasetInfo.missing.no_more_missing_observations, ...
- Y, 1, size(Y,2), ...
- zeros(mmm,1), Pinf, Pstar, ...
- kalman_tol, diffuse_kalman_tol, riccati_tol, DynareOptions.presample, ...
- T,R,Q,H1,Z,mmm,pp,rr);
+ DatasetInfo.missing.number_of_observations,...
+ DatasetInfo.missing.no_more_missing_observations, ...
+ Y, 1, size(Y,2), ...
+ zeros(mmm,1), Pinf, Pstar, ...
+ kalman_tol, diffuse_kalman_tol, riccati_tol, DynareOptions.presample, ...
+ T,R,Q,H1,Z,mmm,pp,rr);
diffuse_periods = size(dlik,1);
end
if isnan(dLIK)
@@ -450,7 +450,7 @@ switch DynareOptions.lik_init
exit_flag = 0;
return
end
-
+
case 4% Start from the solution of the Riccati equation.
if kalman_algo ~= 2
kalman_algo = 1;
@@ -536,9 +536,9 @@ if analytic_derivation
full_Hess = derivatives_info.full_Hess;
end
if full_Hess
- D2T = derivatives_info.D2T;
- D2Om = derivatives_info.D2Om;
- D2Yss = derivatives_info.D2Yss;
+ D2T = derivatives_info.D2T;
+ D2Om = derivatives_info.D2Om;
+ D2Yss = derivatives_info.D2Yss;
end
if isfield(derivatives_info,'no_DLIK')
no_DLIK = derivatives_info.no_DLIK;
@@ -551,7 +551,7 @@ if analytic_derivation
DP=zeros([size(T),length(xparam1)]);
if full_Hess
for j=1:size(D2Yss,1)
- tmp(j,:,:) = blkdiag(zeros(offset,offset), squeeze(D2Yss(j,:,:)));
+ tmp(j,:,:) = blkdiag(zeros(offset,offset), squeeze(D2Yss(j,:,:)));
end
D2Yss = tmp;
D2H=sparse(size(D2Om,1),size(D2Om,2)); %zeros([size(H),length(xparam1),length(xparam1)]);
@@ -559,58 +559,58 @@ if analytic_derivation
jcount=0;
end
if DynareOptions.lik_init==1
- for i=1:EstimatedParameters.nvx
- k =EstimatedParameters.var_exo(i,1);
- DQ(k,k,i) = 2*sqrt(Q(k,k));
- dum = lyapunov_symm(T,DOm(:,:,i),DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold,[],DynareOptions.debug);
-% kk = find(abs(dum) < 1e-12);
-% dum(kk) = 0;
- DP(:,:,i)=dum;
- if full_Hess
- for j=1:i
- jcount=jcount+1;
- dum = lyapunov_symm(T,dyn_unvech(D2Om(:,jcount)),DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold,[],DynareOptions.debug);
-% kk = (abs(dum) < 1e-12);
-% dum(kk) = 0;
- D2P(:,jcount)=dyn_vech(dum);
-% D2P(:,:,j,i)=dum;
- end
+ for i=1:EstimatedParameters.nvx
+ k =EstimatedParameters.var_exo(i,1);
+ DQ(k,k,i) = 2*sqrt(Q(k,k));
+ dum = lyapunov_symm(T,DOm(:,:,i),DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold,[],DynareOptions.debug);
+ % kk = find(abs(dum) < 1e-12);
+ % dum(kk) = 0;
+ DP(:,:,i)=dum;
+ if full_Hess
+ for j=1:i
+ jcount=jcount+1;
+ dum = lyapunov_symm(T,dyn_unvech(D2Om(:,jcount)),DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold,[],DynareOptions.debug);
+ % kk = (abs(dum) < 1e-12);
+ % dum(kk) = 0;
+ D2P(:,jcount)=dyn_vech(dum);
+ % D2P(:,:,j,i)=dum;
+ end
+ end
end
end
- end
offset = EstimatedParameters.nvx;
for i=1:EstimatedParameters.nvn
k = EstimatedParameters.var_endo(i,1);
DH(k,k,i+offset) = 2*sqrt(H(k,k));
if full_Hess
- D2H(k,k,i+offset,i+offset) = 2;
+ D2H(k,k,i+offset,i+offset) = 2;
end
end
offset = offset + EstimatedParameters.nvn;
if DynareOptions.lik_init==1
- for j=1:EstimatedParameters.np
- dum = lyapunov_symm(T,DT(:,:,j+offset)*Pstar*T'+T*Pstar*DT(:,:,j+offset)'+DOm(:,:,j+offset),DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold,[],DynareOptions.debug);
-% kk = find(abs(dum) < 1e-12);
-% dum(kk) = 0;
- DP(:,:,j+offset)=dum;
- if full_Hess
- DTj = DT(:,:,j+offset);
- DPj = dum;
- for i=1:j+offset
- jcount=jcount+1;
- DTi = DT(:,:,i);
- DPi = DP(:,:,i);
- D2Tij = reshape(D2T(:,jcount),size(T));
- D2Omij = dyn_unvech(D2Om(:,jcount));
- tmp = D2Tij*Pstar*T' + T*Pstar*D2Tij' + DTi*DPj*T' + DTj*DPi*T' + T*DPj*DTi' + T*DPi*DTj' + DTi*Pstar*DTj' + DTj*Pstar*DTi' + D2Omij;
- dum = lyapunov_symm(T,tmp,DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold,[],DynareOptions.debug);
-% dum(abs(dum)<1.e-12) = 0;
- D2P(:,jcount) = dyn_vech(dum);
-% D2P(:,:,j+offset,i) = dum;
- end
+ for j=1:EstimatedParameters.np
+ dum = lyapunov_symm(T,DT(:,:,j+offset)*Pstar*T'+T*Pstar*DT(:,:,j+offset)'+DOm(:,:,j+offset),DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold,[],DynareOptions.debug);
+ % kk = find(abs(dum) < 1e-12);
+ % dum(kk) = 0;
+ DP(:,:,j+offset)=dum;
+ if full_Hess
+ DTj = DT(:,:,j+offset);
+ DPj = dum;
+ for i=1:j+offset
+ jcount=jcount+1;
+ DTi = DT(:,:,i);
+ DPi = DP(:,:,i);
+ D2Tij = reshape(D2T(:,jcount),size(T));
+ D2Omij = dyn_unvech(D2Om(:,jcount));
+ tmp = D2Tij*Pstar*T' + T*Pstar*D2Tij' + DTi*DPj*T' + DTj*DPi*T' + T*DPj*DTi' + T*DPi*DTj' + DTi*Pstar*DTj' + DTj*Pstar*DTi' + D2Omij;
+ dum = lyapunov_symm(T,tmp,DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold,[],DynareOptions.debug);
+ % dum(abs(dum)<1.e-12) = 0;
+ D2P(:,jcount) = dyn_vech(dum);
+ % D2P(:,:,j+offset,i) = dum;
+ end
+ end
end
end
- end
if analytic_derivation==1
analytic_deriv_info={analytic_derivation,DT,DYss,DOm,DH,DP,asy_Hess};
else
@@ -640,7 +640,7 @@ if ((kalman_algo==1) || (kalman_algo==3))% Multivariate Kalman Filter
fval = Inf;
info(1) = 55;
info(4) = 0.1;
- exit_flag = 0;
+ exit_flag = 0;
return
end
[LIK,lik] = kalman_filter_fast(Y,diffuse_periods+1,size(Y,2), ...
@@ -746,7 +746,7 @@ if (kalman_algo==2) || (kalman_algo==4)
if singularity_has_been_detected
a = zeros(mmm,1);
elseif ~expanded_state_vector_for_univariate_filter
- a = [a; zeros(pp,1)];
+ a = [a; zeros(pp,1)];
end
end
end
@@ -841,14 +841,14 @@ else
end
if DynareOptions.endogenous_prior==1
- if DynareOptions.lik_init==2 || DynareOptions.lik_init==3
- error('Endogenous prior not supported with non-stationary models')
- else
- [lnpriormom] = endogenous_prior(Y,Pstar,BayesInfo,H);
- fval = (likelihood-lnprior-lnpriormom);
- end
+ if DynareOptions.lik_init==2 || DynareOptions.lik_init==3
+ error('Endogenous prior not supported with non-stationary models')
+ else
+ [lnpriormom] = endogenous_prior(Y,Pstar,BayesInfo,H);
+ fval = (likelihood-lnprior-lnpriormom);
+ end
else
- fval = (likelihood-lnprior);
+ fval = (likelihood-lnprior);
end
if DynareOptions.prior_restrictions.status
diff --git a/matlab/dsge_simulated_theoretical_correlation.m b/matlab/dsge_simulated_theoretical_correlation.m
index b468defe30fb74edb98c6f7b081b2b84f8c1ec79..ae141905b3a06b45705d0a83788d37db8304d216 100644
--- a/matlab/dsge_simulated_theoretical_correlation.m
+++ b/matlab/dsge_simulated_theoretical_correlation.m
@@ -16,7 +16,7 @@ function [nvar,vartan,CorrFileNumber] = dsge_simulated_theoretical_correlation(S
% nvar [integer] nvar is the number of stationary variables.
% vartan [char] array of characters (with nvar rows).
% CorrFileNumber [integer] scalar, number of prior or posterior data files (for correlation).
-
+
% Copyright (C) 2007-2017 Dynare Team
%
% This file is part of Dynare.
diff --git a/matlab/dsge_simulated_theoretical_variance_decomposition.m b/matlab/dsge_simulated_theoretical_variance_decomposition.m
index a6ce7313fb6daa93bcaaa50a730984998e1110be..9eb9851741966edc28c80d5a5ed7e35ecd9967ec 100644
--- a/matlab/dsge_simulated_theoretical_variance_decomposition.m
+++ b/matlab/dsge_simulated_theoretical_variance_decomposition.m
@@ -119,17 +119,17 @@ for file = 1:NumberOfDrawsFiles
if file==1 && linee==1
[tmp, stationary_vars] = th_autocovariances(dr,ivar,M_,options_,nodecomposition);
if isempty(stationary_vars)
- fprintf('\ndsge_simulated_theoretical_variance_decomposition:: All requested endogenous variables have a unit root and thus infinite variance.\n')
- fprintf('dsge_simulated_theoretical_variance_decomposition:: No decomposition is performed.\n')
- only_non_stationary_vars=1;
+ fprintf('\ndsge_simulated_theoretical_variance_decomposition:: All requested endogenous variables have a unit root and thus infinite variance.\n')
+ fprintf('dsge_simulated_theoretical_variance_decomposition:: No decomposition is performed.\n')
+ only_non_stationary_vars=1;
end
end
if only_non_stationary_vars
- for i=1:nvar
+ for i=1:nvar
for j=1:nexo
Decomposition_array(linea,(i-1)*nexo+j) = NaN;
end
- end
+ end
else
tmp = th_autocovariances(dr,ivar,M_,options_,nodecomposition);
for i=1:nvar
diff --git a/matlab/dsge_var_likelihood.m b/matlab/dsge_var_likelihood.m
index d67dcfb84d9bb8700b424876754893e78fcc234f..9878d19c97718b0255ca136eb0ed0d2aeeab58c1 100644
--- a/matlab/dsge_var_likelihood.m
+++ b/matlab/dsge_var_likelihood.m
@@ -9,15 +9,15 @@ function [fval,info,exit_flag,grad,hess,SteadyState,trend_coeff,PHI_tilde,SIGMA_
% o fval [double] Value of the posterior kernel at xparam1.
% o info [integer] Vector of informations about the penalty.
% o exit_flag [integer] Zero if the function returns a penalty, one otherwise.
-% o grad [double] place holder for gradient of the likelihood
+% o grad [double] place holder for gradient of the likelihood
% currently not supported by dsge_var
-% o hess [double] place holder for hessian matrix of the likelihood
+% o hess [double] place holder for hessian matrix of the likelihood
% currently not supported by dsge_var
% o SteadyState [double] Steady state vector possibly recomputed
% by call to dynare_resolve()
% o trend_coeff [double] place holder for trend coefficients,
% currently not supported by dsge_var
-% o PHI_tilde [double] Stacked BVAR-DSGE autoregressive matrices (at the mode associated to xparam1);
+% o PHI_tilde [double] Stacked BVAR-DSGE autoregressive matrices (at the mode associated to xparam1);
% formula (28), DS (2004)
% o SIGMA_u_tilde [double] Covariance matrix of the BVAR-DSGE (at the mode associated to xparam1),
% formula (29), DS (2004)
@@ -32,8 +32,8 @@ function [fval,info,exit_flag,grad,hess,SteadyState,trend_coeff,PHI_tilde,SIGMA_
% ALGORITHMS
% Follows the computations outlined in Del Negro/Schorfheide (2004):
% Priors from general equilibrium models for VARs, International Economic
-% Review, 45(2), pp. 643-673
-%
+% Review, 45(2), pp. 643-673
+%
% SPECIAL REQUIREMENTS
% None.
@@ -158,8 +158,8 @@ end
% Return, with endogenous penalty when possible, if dynare_resolve issues an error code (defined in resol).
if info(1)
if info(1) == 3 || info(1) == 4 || info(1) == 5 || info(1)==6 ||info(1) == 19 ||...
- info(1) == 20 || info(1) == 21 || info(1) == 23 || info(1) == 26 || ...
- info(1) == 81 || info(1) == 84 || info(1) == 85
+ info(1) == 20 || info(1) == 21 || info(1) == 23 || info(1) == 26 || ...
+ info(1) == 81 || info(1) == 84 || info(1) == 85
%meaningful second entry of output that can be used
fval = Inf;
info(4) = info(2);
@@ -246,7 +246,7 @@ if ~isinf(dsge_prior_weight)% Evaluation of the likelihood of the dsge-var model
tmp0 = dsge_prior_weight*NumberOfObservations*TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1) + mYY ; %first term of square bracket in formula (29), DS (2004)
tmp1 = dsge_prior_weight*NumberOfObservations*GYX + mYX; %first element of second term of square bracket in formula (29), DS (2004)
tmp2 = inv(dsge_prior_weight*NumberOfObservations*GXX+mXX); %middle element of second term of square bracket in formula (29), DS (2004)
- SIGMA_u_tilde = tmp0 - tmp1*tmp2*tmp1'; %square bracket term in formula (29), DS (2004)
+ SIGMA_u_tilde = tmp0 - tmp1*tmp2*tmp1'; %square bracket term in formula (29), DS (2004)
clear('tmp0');
[SIGMAu_is_positive_definite, penalty] = ispd(SIGMA_u_tilde);
if ~SIGMAu_is_positive_definite
@@ -265,7 +265,7 @@ if ~isinf(dsge_prior_weight)% Evaluation of the likelihood of the dsge-var model
prodlng2 = sum(gammaln(.5*(dsge_prior_weight*NumberOfObservations- ...
NumberOfParameters ...
+1-(1:NumberOfObservedVariables)'))); %last term in denominator of third line of (A.2), DS (2004)
- %Compute minus log likelihood according to (A.2), DS (2004)
+ %Compute minus log likelihood according to (A.2), DS (2004)
lik = .5*NumberOfObservedVariables*log(det(dsge_prior_weight*NumberOfObservations*GXX+mXX)) ... %first term in numerator of second line of (A.2), DS (2004)
+ .5*((dsge_prior_weight+1)*NumberOfObservations-NumberOfParameters)*log(det((dsge_prior_weight+1)*NumberOfObservations*SIGMA_u_tilde)) ... %second term in numerator of second line of (A.2), DS (2004)
- .5*NumberOfObservedVariables*log(det(dsge_prior_weight*NumberOfObservations*GXX)) ... %first term in denominator of second line of (A.2), DS (2004)
@@ -278,7 +278,7 @@ else% Evaluation of the likelihood of the dsge-var model when the dsge prior wei
PHI_star = iGXX*transpose(GYX);
%Compute minus log likelihood according to (33), DS (2004) (where the last term in the trace operator has been multiplied out)
lik = NumberOfObservations * ( log(det(SIGMA_u_star)) + NumberOfObservedVariables*log(2*pi) + ...
- trace(inv(SIGMA_u_star)*(mYY - transpose(mYX*PHI_star) - mYX*PHI_star + transpose(PHI_star)*mXX*PHI_star)/NumberOfObservations));
+ trace(inv(SIGMA_u_star)*(mYY - transpose(mYX*PHI_star) - mYX*PHI_star + transpose(PHI_star)*mXX*PHI_star)/NumberOfObservations));
lik = .5*lik;% Minus likelihood
SIGMA_u_tilde=SIGMA_u_star;
PHI_tilde=PHI_star;
@@ -337,8 +337,8 @@ if (nargout >= 10)
end
if (nargout==11)
- prior.SIGMA_u_star = SIGMA_u_star;
- prior.PHI_star = PHI_star;
+ prior.SIGMA_u_star = SIGMA_u_star;
+ prior.PHI_star = PHI_star;
prior.ArtificialSampleSize = fix(dsge_prior_weight*NumberOfObservations);
prior.DF = prior.ArtificialSampleSize - NumberOfParameters - NumberOfObservedVariables;
prior.iGXX_star = iGXX;
diff --git a/matlab/dy_date.m b/matlab/dy_date.m
index 7ce72e836a1b729356a3d4a1eae7f0cabb8fde6b..980e838977d902b124e44fecdaeb3ef37e2592f0 100644
--- a/matlab/dy_date.m
+++ b/matlab/dy_date.m
@@ -20,4 +20,3 @@ function y=dy_date(year,period)
global M_
y = M_.freq*(year-M_.start_date(1))+period-M_.start_date(2)+1;
-
diff --git a/matlab/dyn2vec.m b/matlab/dyn2vec.m
index a944dc1883c25ccc5e080ade2defa8e6526ff08f..2690275542c66123678922329c3376fb9aea0203 100644
--- a/matlab/dyn2vec.m
+++ b/matlab/dyn2vec.m
@@ -12,7 +12,7 @@ function [z,zss]=dyn2vec(s1,s2)
%
% SPECIAL REQUIREMENTS
% none
-%
+%
% Copyright (C) 2001-2009 Dynare Team
%
@@ -50,7 +50,7 @@ if nargin == 0
end
return
else
- j = strmatch(s1,M_.endo_names,'exact');
+ j = strmatch(s1,M_.endo_names,'exact');
if ~ isempty(j)
z = oo_.endo_simul(j,k)';
else
@@ -83,6 +83,3 @@ end
% 08/24/01 MJ replaced globlize by internal assignin
% 08/24/01 MJ added 'exact' to strmatch (thanks to David Vavra)
% 01/31/03 MJ added provision for alternative name of variable
-
-
-
diff --git a/matlab/dyn_diag_vech.m b/matlab/dyn_diag_vech.m
index 5d5d9466486b19be9ae52eaec04e87c677b97bd2..4491038086a97f8dc6406ca18c2964e5ab911330 100644
--- a/matlab/dyn_diag_vech.m
+++ b/matlab/dyn_diag_vech.m
@@ -1,11 +1,11 @@
function d = dyn_diag_vech(Vector)
% This function returns the diagonal elements of a symmetric matrix
% stored in vech form
-%
-% INPUTS
+%
+% INPUTS
% Vector [double] a m*1 vector.
-%
-% OUTPUTS
+%
+% OUTPUTS
% d [double] a n*1 vector, where n solves n*(n+1)/2=m.
% Copyright (C) 2010 Dynare Team
diff --git a/matlab/dyn_first_order_solver.m b/matlab/dyn_first_order_solver.m
index 63d79cbe27e45d04da3caed9b59953e99f87db85..a09aa1645e93f64417984dbadd0e2432d7b59291 100644
--- a/matlab/dyn_first_order_solver.m
+++ b/matlab/dyn_first_order_solver.m
@@ -66,8 +66,8 @@ function [dr,info] = dyn_first_order_solver(jacobia,DynareModel,dr,DynareOptions
persistent reorder_jacobian_columns innovations_idx index_s index_m index_c
persistent index_p row_indx index_0m index_0p k1 k2 state_var
-persistent ndynamic nstatic nfwrd npred nboth nd nsfwrd n_current index_d
-persistent index_e index_d1 index_d2 index_e1 index_e2 row_indx_de_1
+persistent ndynamic nstatic nfwrd npred nboth nd nsfwrd n_current index_d
+persistent index_e index_d1 index_d2 index_e1 index_e2 row_indx_de_1
persistent row_indx_de_2 cols_b
@@ -82,7 +82,7 @@ end
exo_nbr = DynareModel.exo_nbr;
if isempty(reorder_jacobian_columns)
-
+
maximum_lag = DynareModel.maximum_endo_lag;
kstate = dr.kstate;
nfwrd = DynareModel.nfwrd;
@@ -149,7 +149,7 @@ if isempty(reorder_jacobian_columns)
index_e1 = [1:npred+nboth, npred+nboth+find(llx(maximum_lag+1,nstatic+npred+(1: ...
nsfwrd)))];
index_e2 = npred+nboth+(1:nboth);
-
+
[junk,cols_b] = find(lead_lag_incidence(maximum_lag+1, order_var));
reorder_jacobian_columns = [nonzeros(lead_lag_incidence(:,order_var)'); ...
@@ -179,12 +179,12 @@ if task ~= 1 && (DynareOptions.dr_cycle_reduction || DynareOptions.dr_logarithmi
if n_current < DynareModel.endo_nbr
if DynareOptions.dr_cycle_reduction
error(['The cycle reduction algorithme can''t be used when the ' ...
- 'coefficient matrix for current variables isn''t invertible'])
+ 'coefficient matrix for current variables isn''t invertible'])
elseif DynareOptions.dr_logarithmic_reduction
error(['The logarithmic reduction algorithme can''t be used when the ' ...
'coefficient matrix for current variables isn''t invertible'])
end
- end
+ end
if DynareOptions.gpu
gpuArray(A1);
gpuArray(B1);
@@ -212,7 +212,7 @@ else
D(row_indx_de_2,index_d2) = eye(nboth);
E(row_indx_de_1,index_e1) = -aa(row_indx,index_e);
E(row_indx_de_2,index_e2) = eye(nboth);
-
+
[err, ss, tt, w, sdim, dr.eigval, info1] = mjdgges(E, D, DynareOptions.qz_criterium, DynareOptions.qz_zero_threshold);
mexErrCheck('mjdgges', err);
@@ -235,7 +235,7 @@ else
dr.full_rank = 0;
else
dr.full_rank = 1;
- end
+ end
return
end
diff --git a/matlab/dyn_forecast.m b/matlab/dyn_forecast.m
index edb1e4ef13dd8acefc4d32ae469ed85681b48528..a58b126a187b23e3c123cea13cd2c8d862f376fd 100644
--- a/matlab/dyn_forecast.m
+++ b/matlab/dyn_forecast.m
@@ -1,7 +1,7 @@
function [forecast,info] = dyn_forecast(var_list,M,options,oo,task,dataset_info)
% function dyn_forecast(var_list,M,options,oo,task,dataset_info)
% computes mean forecast for a given value of the parameters
-% compues also confidence band for the forecast
+% compues also confidence band for the forecast
%
% INPUTS
% var_list: list of variables (character matrix)
@@ -43,7 +43,7 @@ if nargin<6 && options.prefilter
elseif nargin==6
mean_varobs=dataset_info.descriptive.mean';
end
-
+
info = 0;
oo=make_ex_(M,options,oo);
@@ -92,7 +92,7 @@ switch task
for i = 1:M.endo_nbr
v_name = deblank(M.endo_names(i,:));
y0(i,:) = y_smoothed.(v_name)(end-maximum_lag+1:end); %includes steady state or mean, but simult_ will subtract only steady state
- % 2. Subtract mean/steady state and add steady state; takes care of prefiltering
+ % 2. Subtract mean/steady state and add steady state; takes care of prefiltering
if isfield(oo.Smoother,'Constant') && isfield(oo.Smoother.Constant,v_name)
y0(i,:)=y0(i,:)-oo.Smoother.Constant.(v_name)(end-maximum_lag+1:end); %subtract mean or steady state
if options.loglinear
@@ -100,7 +100,7 @@ switch task
else
y0(i,:)=y0(i,:)+oo.dr.ys(strmatch(v_name,deblank(M.endo_names),'exact'));
end
- end
+ end
% 2. Subtract trend
if isfield(oo.Smoother,'Trend') && isfield(oo.Smoother.Trend,v_name)
y0(i,:)=y0(i,:)-oo.Smoother.Trend.(v_name)(end-maximum_lag+1:end); %subtract trend, which is not subtracted by simult_
@@ -121,8 +121,8 @@ switch task
trend_coeffs = [trend_coeffs; oo.Smoother.TrendCoeffs(trend_var_index)];
end
end
- if ~isempty(trend_coeffs)
- trend = trend_coeffs*(options.first_obs+gend-1+(1-M.maximum_lag:horizon));
+ if ~isempty(trend_coeffs)
+ trend = trend_coeffs*(options.first_obs+gend-1+(1-M.maximum_lag:horizon));
if options.prefilter
trend = trend - repmat(mean(trend_coeffs*[options.first_obs:options.first_obs+gend-1],2),1,horizon+1); %subtract mean trend
end
@@ -132,13 +132,13 @@ switch task
end
otherwise
error('Wrong flag value')
-end
+end
if M.exo_det_nbr == 0
if isequal(M.H,0)
[yf,int_width] = forcst(oo.dr,y0,horizon,var_list,M,oo,options);
else
- [yf,int_width,int_width_ME] = forcst(oo.dr,y0,horizon,var_list,M,oo,options);
+ [yf,int_width,int_width_ME] = forcst(oo.dr,y0,horizon,var_list,M,oo,options);
end
else
exo_det_length = size(oo.exo_det_simul,1)-M.maximum_lag;
@@ -146,17 +146,17 @@ else
ex = zeros(horizon,M.exo_nbr);
oo.exo_det_simul = [ oo.exo_det_simul;...
repmat(oo.exo_det_steady_state',...
- horizon- ...
+ horizon- ...
exo_det_length,1)];
- elseif horizon <= exo_det_length
- ex = zeros(exo_det_length,M.exo_nbr);
+ elseif horizon <= exo_det_length
+ ex = zeros(exo_det_length,M.exo_nbr);
end
if isequal(M.H,0)
[yf,int_width] = simultxdet(y0,ex,oo.exo_det_simul,...
- options.order,var_list,M,oo,options);
+ options.order,var_list,M,oo,options);
else
[yf,int_width,int_width_ME] = simultxdet(y0,ex,oo.exo_det_simul,...
- options.order,var_list,M,oo,options);
+ options.order,var_list,M,oo,options);
end
end
@@ -171,7 +171,7 @@ if options.loglinear == 1
else
if options.prefilter == 1 %subtract steady state and add mean for observables
yf(i_var_obs,:)=yf(i_var_obs,:)-repmat(oo.dr.ys(i_var_obs),1,horizon+M.maximum_lag)+ repmat(mean_varobs,1,horizon+M.maximum_lag);
- end
+ end
end
for i=1:n_var
diff --git a/matlab/dyn_latex_table.m b/matlab/dyn_latex_table.m
index 63fb6095f6d392da82b8f39a46d38bf80df9ee05..ca14e351f5f398ba2bc8d603fa2692d07b03c93b 100644
--- a/matlab/dyn_latex_table.m
+++ b/matlab/dyn_latex_table.m
@@ -27,7 +27,7 @@ OutputDirectoryName = CheckPath('Output',M_.dname);
%% get width of label column
if ~isempty(label_width)
label_width = max(size(deblank(char(headers(1,:),labels)),2)+2, ...
- label_width);
+ label_width);
else %use default length
label_width = max(size(deblank(char(headers(1,:),labels)),2))+2;
end
diff --git a/matlab/dyn_ramsey_static.m b/matlab/dyn_ramsey_static.m
index e87b59b4d8ff62191aabc27eb701be67ec2e72ad..25b5301e63ce35c43e0f05e0b1923e355629c01b 100644
--- a/matlab/dyn_ramsey_static.m
+++ b/matlab/dyn_ramsey_static.m
@@ -8,7 +8,7 @@ function [steady_state,params,check] = dyn_ramsey_static(ys_init,M,options_,oo)
% M: Dynare model structure
% options: Dynare options structure
% oo: Dynare results structure
-%
+%
% OUTPUTS
% steady_state: steady state value
% params: parameters at steady state, potentially updated by
@@ -39,7 +39,7 @@ function [steady_state,params,check] = dyn_ramsey_static(ys_init,M,options_,oo)
params = M.params;
check = 0;
options_.steadystate.nocheck = 1; %locally disable checking because Lagrange multipliers are not accounted for in evaluate_steady_state_file
-% dyn_ramsey_static_1 is a subfunction
+ % dyn_ramsey_static_1 is a subfunction
nl_func = @(x) dyn_ramsey_static_1(x,M,options_,oo);
% check_static_model is a subfunction
@@ -61,7 +61,7 @@ elseif options_.steadystate_flag
end
if info1==4
check=87;
- end
+ end
else
%solve for instrument, using multivariate solver, starting at
%initial value for instrument
@@ -102,7 +102,7 @@ endo_nbr = M.endo_nbr;
endo_names = M.endo_names;
orig_endo_nbr = M.orig_endo_nbr;
aux_vars_type = [M.aux_vars.type];
-orig_endo_aux_nbr = orig_endo_nbr + min(find(aux_vars_type == 6)) - 1;
+orig_endo_aux_nbr = orig_endo_nbr + min(find(aux_vars_type == 6)) - 1;
orig_eq_nbr = M.orig_eq_nbr;
inst_nbr = orig_endo_aux_nbr - orig_eq_nbr;
% indices of Lagrange multipliers
@@ -120,7 +120,7 @@ if options_.steadystate_flag
ys_init(k_inst) = x; %set instrument, the only value required for steady state computation, to current value
[x,params,check] = evaluate_steady_state_file(ys_init,... %returned x now has size endo_nbr as opposed to input size of n_instruments
[oo.exo_steady_state; ...
- oo.exo_det_steady_state], ...
+ oo.exo_det_steady_state], ...
M,options_,~options_.steadystate.nocheck);
if any(imag(x(1:M.orig_endo_nbr))) %return with penalty
resids=1+sum(abs(imag(x(1:M.orig_endo_nbr)))); %return with penalty
@@ -137,7 +137,7 @@ xx(1:M.orig_endo_nbr) = x(1:M.orig_endo_nbr); %set values of original endogenous
if any([M.aux_vars.type] ~= 6) %auxiliary variables other than multipliers
needs_set_auxiliary_variables = 1;
fh = str2func([M.fname '_set_auxiliary_variables']);
- s_a_v_func = @(z) fh(z,...
+ s_a_v_func = @(z) fh(z,...
[oo.exo_steady_state,...
oo.exo_det_steady_state],...
params);
@@ -154,12 +154,12 @@ Uyy = reshape(Uyy,endo_nbr,endo_nbr);
% set multipliers and auxiliary variables that
% depends on multipliers to 0 to compute residuals
if (options_.bytecode)
- [chck, res, junk] = bytecode('static',xx,[oo.exo_steady_state oo.exo_det_steady_state], ...
- params, 'evaluate');
- fJ = junk.g1;
+ [chck, res, junk] = bytecode('static',xx,[oo.exo_steady_state oo.exo_det_steady_state], ...
+ params, 'evaluate');
+ fJ = junk.g1;
else
- [res,fJ] = feval([fname '_static'],xx,[oo.exo_steady_state oo.exo_det_steady_state], ...
- params);
+ [res,fJ] = feval([fname '_static'],xx,[oo.exo_steady_state oo.exo_det_steady_state], ...
+ params);
end
% index of multipliers and corresponding equations
% the auxiliary variables before the Lagrange multipliers are treated
@@ -193,7 +193,7 @@ function result = check_static_model(ys,M,options_,oo)
result = false;
if (options_.bytecode)
[chck, res, junk] = bytecode('static',ys,[oo.exo_steady_state oo.exo_det_steady_state], ...
- M.params, 'evaluate');
+ M.params, 'evaluate');
else
res = feval([M.fname '_static'],ys,[oo.exo_steady_state oo.exo_det_steady_state], ...
M.params);
@@ -201,4 +201,3 @@ end
if norm(res) < options_.solve_tolf
result = true;
end
-
diff --git a/matlab/dyn_risky_steadystate_solver.m b/matlab/dyn_risky_steadystate_solver.m
index c198ef25ecda9cc1af1739e8fea0cbf9c4d86eff..3ea116df9e8feec5a87298e931801d95e1840192 100644
--- a/matlab/dyn_risky_steadystate_solver.m
+++ b/matlab/dyn_risky_steadystate_solver.m
@@ -1,550 +1,550 @@
function [dr,info] = dyn_risky_steadystate_solver(ys0,M, ...
dr,options,oo)
- %@info:
- %! @deftypefn {Function File} {[@var{dr},@var{info}] =} dyn_risky_steadystate_solver (@var{ys0},@var{M},@var{dr},@var{options},@var{oo})
- %! @anchor{dyn_risky_steadystate_solver}
- %! @sp 1
- %! Computes the second order risky steady state and first and second order reduced form of the DSGE model.
- %! @sp 2
- %! @strong{Inputs}
- %! @sp 1
- %! @table @ @var
- %! @item ys0
- %! Vector containing a guess value for the risky steady state
- %! @item M
- %! Matlab's structure describing the model (initialized by @code{dynare}).
- %! @item dr
- %! Matlab's structure describing the reduced form solution of the model.
- %! @item options
- %! Matlab's structure describing the options (initialized by @code{dynare}).
- %! @item oo
- %! Matlab's structure gathering the results (initialized by @code{dynare}).
- %! @end table
- %! @sp 2
- %! @strong{Outputs}
- %! @sp 1
- %! @table @ @var
- %! @item dr
- %! Matlab's structure describing the reduced form solution of the model.
- %! @item info
- %! Integer scalar, error code.
- %! @sp 1
- %! @table @ @code
- %! @item info==0
- %! No error.
- %! @item info==1
- %! The model doesn't determine the current variables uniquely.
- %! @item info==2
- %! MJDGGES returned an error code.
- %! @item info==3
- %! Blanchard & Kahn conditions are not satisfied: no stable equilibrium.
- %! @item info==4
- %! Blanchard & Kahn conditions are not satisfied: indeterminacy.
- %! @item info==5
- %! Blanchard & Kahn conditions are not satisfied: indeterminacy due to rank failure.
- %! @item info==6
- %! The jacobian evaluated at the deterministic steady state is complex.
- %! @item info==19
- %! The steadystate routine thrown an exception (inconsistent deep parameters).
- %! @item info==20
- %! Cannot find the steady state, info(2) contains the sum of square residuals (of the static equations).
- %! @item info==21
- %! The steady state is complex, info(2) contains the sum of square of imaginary parts of the steady state.
- %! @item info==22
- %! The steady has NaNs.
- %! @item info==23
- %! M_.params has been updated in the steadystate routine and has complex valued scalars.
- %! @item info==24
- %! M_.params has been updated in the steadystate routine and has some NaNs.
- %! @end table
- %! @end table
- %! @end deftypefn
- %@eod:
-
- % Copyright (C) 2001-2014 Dynare Team
- %
- % 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/>.
-
-
- info = 0;
- lead_lag_incidence = M.lead_lag_incidence;
- order_var = dr.order_var;
- endo_nbr = M.endo_nbr;
- exo_nbr = M.exo_nbr;
-
- M.var_order_endo_names = M.endo_names(dr.order_var,:);
-
- [junk,dr.i_fwrd_g,i_fwrd_f] = find(lead_lag_incidence(3,order_var));
- dr.i_fwrd_f = i_fwrd_f;
- nd = nnz(lead_lag_incidence) + M.exo_nbr;
- dr.nd = nd;
- kk = reshape(1:nd^2,nd,nd);
- kkk = reshape(1:nd^3,nd^2,nd);
- dr.i_fwrd2_f = kk(i_fwrd_f,i_fwrd_f);
- dr.i_fwrd2a_f = kk(i_fwrd_f,:);
- dr.i_fwrd3_f = kkk(dr.i_fwrd2_f,:);
- dr.i_uu = kk(end-exo_nbr+1:end,end-exo_nbr+1:end);
- if options.k_order_solver
- func = @risky_residuals_k_order;
- else
- func = @risky_residuals;
- end
-
- if isfield(options,'portfolio') && options.portfolio == 1
- pm = portfolio_model_structure(M,options);
-
- x0 = ys0(pm.v_p);
- n = length(x0);
- [x, info] = solve1(@risky_residuals_ds,x0,1:n,1:n,0,options.gstep, ...
- options.solve_tolf,options.solve_tolx, ...
- options.steady.maxit,options.debug,pm,M,dr, ...
- options,oo);
- if info
- error('DS approach can''t be computed')
- end
- %[x, info] = csolve(@risky_residuals_ds,x0,[],1e-10,100,M,dr,options,oo);
- % ys0(l_var) = x;
- [resids,dr1] = risky_residuals_ds(x,pm,M,dr,options,oo);
- ys1 = dr1.ys;
- else
- pm = model_structure(M,options);
- end
-
- [ys, info] = solve1(func,ys0,1:endo_nbr,1:endo_nbr,0,options.gstep, ...
- options.solve_tolf,options.solve_tolx, ...
- options.steady.maxit,options.debug,pm,M,dr,options,oo);
- % [ys, info] = csolve(func,ys0,[],1e-10,100,M,dr,options,oo);
- if info
- error('RSS approach can''t be computed')
- end
- dr.ys = ys;
+%@info:
+%! @deftypefn {Function File} {[@var{dr},@var{info}] =} dyn_risky_steadystate_solver (@var{ys0},@var{M},@var{dr},@var{options},@var{oo})
+%! @anchor{dyn_risky_steadystate_solver}
+%! @sp 1
+%! Computes the second order risky steady state and first and second order reduced form of the DSGE model.
+%! @sp 2
+%! @strong{Inputs}
+%! @sp 1
+%! @table @ @var
+%! @item ys0
+%! Vector containing a guess value for the risky steady state
+%! @item M
+%! Matlab's structure describing the model (initialized by @code{dynare}).
+%! @item dr
+%! Matlab's structure describing the reduced form solution of the model.
+%! @item options
+%! Matlab's structure describing the options (initialized by @code{dynare}).
+%! @item oo
+%! Matlab's structure gathering the results (initialized by @code{dynare}).
+%! @end table
+%! @sp 2
+%! @strong{Outputs}
+%! @sp 1
+%! @table @ @var
+%! @item dr
+%! Matlab's structure describing the reduced form solution of the model.
+%! @item info
+%! Integer scalar, error code.
+%! @sp 1
+%! @table @ @code
+%! @item info==0
+%! No error.
+%! @item info==1
+%! The model doesn't determine the current variables uniquely.
+%! @item info==2
+%! MJDGGES returned an error code.
+%! @item info==3
+%! Blanchard & Kahn conditions are not satisfied: no stable equilibrium.
+%! @item info==4
+%! Blanchard & Kahn conditions are not satisfied: indeterminacy.
+%! @item info==5
+%! Blanchard & Kahn conditions are not satisfied: indeterminacy due to rank failure.
+%! @item info==6
+%! The jacobian evaluated at the deterministic steady state is complex.
+%! @item info==19
+%! The steadystate routine thrown an exception (inconsistent deep parameters).
+%! @item info==20
+%! Cannot find the steady state, info(2) contains the sum of square residuals (of the static equations).
+%! @item info==21
+%! The steady state is complex, info(2) contains the sum of square of imaginary parts of the steady state.
+%! @item info==22
+%! The steady has NaNs.
+%! @item info==23
+%! M_.params has been updated in the steadystate routine and has complex valued scalars.
+%! @item info==24
+%! M_.params has been updated in the steadystate routine and has some NaNs.
+%! @end table
+%! @end table
+%! @end deftypefn
+%@eod:
+
+% Copyright (C) 2001-2014 Dynare Team
+%
+% 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/>.
+
+
+info = 0;
+lead_lag_incidence = M.lead_lag_incidence;
+order_var = dr.order_var;
+endo_nbr = M.endo_nbr;
+exo_nbr = M.exo_nbr;
+
+M.var_order_endo_names = M.endo_names(dr.order_var,:);
+
+[junk,dr.i_fwrd_g,i_fwrd_f] = find(lead_lag_incidence(3,order_var));
+dr.i_fwrd_f = i_fwrd_f;
+nd = nnz(lead_lag_incidence) + M.exo_nbr;
+dr.nd = nd;
+kk = reshape(1:nd^2,nd,nd);
+kkk = reshape(1:nd^3,nd^2,nd);
+dr.i_fwrd2_f = kk(i_fwrd_f,i_fwrd_f);
+dr.i_fwrd2a_f = kk(i_fwrd_f,:);
+dr.i_fwrd3_f = kkk(dr.i_fwrd2_f,:);
+dr.i_uu = kk(end-exo_nbr+1:end,end-exo_nbr+1:end);
+if options.k_order_solver
+ func = @risky_residuals_k_order;
+else
+ func = @risky_residuals;
+end
- [resid,dr] = func(ys,pm,M,dr,options,oo);
- dr.ghs2 = zeros(M.endo_nbr,1);
-
- for i=1:M.endo_nbr
- if isfield(options,'portfolio') && options.portfolio == 1
- disp(sprintf('%16s %12.6f %12.6f',M.endo_names(i,:),ys1(i), ...
- ys(i)))
- else
- disp(sprintf('%16s %12.6f %12.6f',M.endo_names(i,:),ys(i)))
- end
+if isfield(options,'portfolio') && options.portfolio == 1
+ pm = portfolio_model_structure(M,options);
+
+ x0 = ys0(pm.v_p);
+ n = length(x0);
+ [x, info] = solve1(@risky_residuals_ds,x0,1:n,1:n,0,options.gstep, ...
+ options.solve_tolf,options.solve_tolx, ...
+ options.steady.maxit,options.debug,pm,M,dr, ...
+ options,oo);
+ if info
+ error('DS approach can''t be computed')
end
+ %[x, info] = csolve(@risky_residuals_ds,x0,[],1e-10,100,M,dr,options,oo);
+ % ys0(l_var) = x;
+ [resids,dr1] = risky_residuals_ds(x,pm,M,dr,options,oo);
+ ys1 = dr1.ys;
+else
+ pm = model_structure(M,options);
+end
+[ys, info] = solve1(func,ys0,1:endo_nbr,1:endo_nbr,0,options.gstep, ...
+ options.solve_tolf,options.solve_tolx, ...
+ options.steady.maxit,options.debug,pm,M,dr,options,oo);
+% [ys, info] = csolve(func,ys0,[],1e-10,100,M,dr,options,oo);
+if info
+ error('RSS approach can''t be computed')
end
+dr.ys = ys;
-function [resid,dr] = risky_residuals(ys,pm,M,dr,options,oo)
-
- lead_lag_incidence = M.lead_lag_incidence;
- iyv = lead_lag_incidence';
- iyv = iyv(:);
- iyr0 = find(iyv) ;
-
- if M.exo_nbr == 0
- oo.exo_steady_state = [] ;
- end
-
- z = repmat(ys,1,3);
- z = z(iyr0) ;
- [resid1,d1,d2] = feval([M.fname '_dynamic'],z,...
- [oo.exo_simul ...
- oo.exo_det_simul], M.params, dr.ys, 2);
- if ~isreal(d1) || ~isreal(d2)
- pause
- end
-
- if options.use_dll
- % In USE_DLL mode, the hessian is in the 3-column sparse representation
- d2 = sparse(d2(:,1), d2(:,2), d2(:,3), ...
- size(d1, 1), size(d1, 2)*size(d1, 2));
- end
+[resid,dr] = func(ys,pm,M,dr,options,oo);
+dr.ghs2 = zeros(M.endo_nbr,1);
+for i=1:M.endo_nbr
if isfield(options,'portfolio') && options.portfolio == 1
- pm = portfolio_model_structure(M,options);
- x = ys(pm.v_p);
- dr = first_step_ds(x,pm,M,dr,options,oo);
- dr.ys = ys;
+ disp(sprintf('%16s %12.6f %12.6f',M.endo_names(i,:),ys1(i), ...
+ ys(i)))
else
- pm = model_structure(M,options);
- [dr,info] = dyn_first_order_solver(d1,M,dr,options,0);
- if info
- print_info(info,options.noprint,options);
- end
- dr = dyn_second_order_solver(d1,d2,dr,M,...
- options.threads.kronecker.A_times_B_kronecker_C,...
- options.threads.kronecker.sparse_hessian_times_B_kronecker_C);
+ disp(sprintf('%16s %12.6f %12.6f',M.endo_names(i,:),ys(i)))
end
-
- gu1 = dr.ghu(pm.i_fwrd_g,:);
+end
- resid = resid1+0.5*(d1(:,pm.i_fwrd_f1)*dr.ghuu(pm.i_fwrd_g,:)+ ...
- d2(:,pm.i_fwrd_f2)*kron(gu1,gu1))*vec(M.Sigma_e);
end
-function [resid,dr] = risky_residuals_ds(x,pm,M,dr,options,oo)
-
- v_p = pm.v_p;
- v_np = pm.v_np;
-
- % computing steady state of non-portfolio variables consistent with
- % assumed portfolio
- dr.ys(v_p) = x;
- ys0 = dr.ys(v_np);
- f_h =str2func([M.fname '_static']);
- [dr.ys(v_np),info] = csolve(@ds_static_model,ys0,[],1e-10,100,f_h,x,pm.eq_np,v_np,v_p, ...
- M.endo_nbr,M.exo_nbr,M.params);
+function [resid,dr] = risky_residuals(ys,pm,M,dr,options,oo)
+
+lead_lag_incidence = M.lead_lag_incidence;
+iyv = lead_lag_incidence';
+iyv = iyv(:);
+iyr0 = find(iyv) ;
+
+if M.exo_nbr == 0
+ oo.exo_steady_state = [] ;
+end
+
+z = repmat(ys,1,3);
+z = z(iyr0) ;
+[resid1,d1,d2] = feval([M.fname '_dynamic'],z,...
+ [oo.exo_simul ...
+ oo.exo_det_simul], M.params, dr.ys, 2);
+if ~isreal(d1) || ~isreal(d2)
+ pause
+end
+
+if options.use_dll
+ % In USE_DLL mode, the hessian is in the 3-column sparse representation
+ d2 = sparse(d2(:,1), d2(:,2), d2(:,3), ...
+ size(d1, 1), size(d1, 2)*size(d1, 2));
+end
+
+if isfield(options,'portfolio') && options.portfolio == 1
+ pm = portfolio_model_structure(M,options);
+ x = ys(pm.v_p);
+ dr = first_step_ds(x,pm,M,dr,options,oo);
+ dr.ys = ys;
+else
+ pm = model_structure(M,options);
+ [dr,info] = dyn_first_order_solver(d1,M,dr,options,0);
if info
- error('can''t compute non-portfolio steady state')
- end
-
- dr_np = first_step_ds(x,pm,M,dr,options,oo);
-
- lead_lag_incidence = M.lead_lag_incidence;
- iyv = lead_lag_incidence';
- iyv = iyv(:);
- iyr0 = find(iyv) ;
-
- z = repmat(dr.ys,1,3);
- z = z(iyr0) ;
- [resid1,d1,d2] = feval([M.fname '_dynamic'],z,...
- [oo.exo_simul ...
- oo.exo_det_simul], M.params, dr.ys, 2);
- if ~isreal(d1) || ~isreal(d2)
- pause
- end
-
- if options.use_dll
- % In USE_DLL mode, the hessian is in the 3-column sparse representation
- d2 = sparse(d2(:,1), d2(:,2), d2(:,3), ...
- size(d1, 1), size(d1, 2)*size(d1, 2));
+ print_info(info,options.noprint,options);
end
+ dr = dyn_second_order_solver(d1,d2,dr,M,...
+ options.threads.kronecker.A_times_B_kronecker_C,...
+ options.threads.kronecker.sparse_hessian_times_B_kronecker_C);
+end
+
+gu1 = dr.ghu(pm.i_fwrd_g,:);
+
+resid = resid1+0.5*(d1(:,pm.i_fwrd_f1)*dr.ghuu(pm.i_fwrd_g,:)+ ...
+ d2(:,pm.i_fwrd_f2)*kron(gu1,gu1))*vec(M.Sigma_e);
+end
+
+function [resid,dr] = risky_residuals_ds(x,pm,M,dr,options,oo)
+
+v_p = pm.v_p;
+v_np = pm.v_np;
+
+% computing steady state of non-portfolio variables consistent with
+% assumed portfolio
+dr.ys(v_p) = x;
+ys0 = dr.ys(v_np);
+f_h =str2func([M.fname '_static']);
+[dr.ys(v_np),info] = csolve(@ds_static_model,ys0,[],1e-10,100,f_h,x,pm.eq_np,v_np,v_p, ...
+ M.endo_nbr,M.exo_nbr,M.params);
+if info
+ error('can''t compute non-portfolio steady state')
+end
+
+dr_np = first_step_ds(x,pm,M,dr,options,oo);
+
+lead_lag_incidence = M.lead_lag_incidence;
+iyv = lead_lag_incidence';
+iyv = iyv(:);
+iyr0 = find(iyv) ;
+
+z = repmat(dr.ys,1,3);
+z = z(iyr0) ;
+[resid1,d1,d2] = feval([M.fname '_dynamic'],z,...
+ [oo.exo_simul ...
+ oo.exo_det_simul], M.params, dr.ys, 2);
+if ~isreal(d1) || ~isreal(d2)
+ pause
+end
+
+if options.use_dll
+ % In USE_DLL mode, the hessian is in the 3-column sparse representation
+ d2 = sparse(d2(:,1), d2(:,2), d2(:,3), ...
+ size(d1, 1), size(d1, 2)*size(d1, 2));
+end
+
-
- gu1 = dr_np.ghu(pm.i_fwrd_g,:);
+gu1 = dr_np.ghu(pm.i_fwrd_g,:);
- resid = resid1+0.5*(d2(:,pm.i_fwrd_f2)*kron(gu1,gu1))*vec(M.Sigma_e);
+resid = resid1+0.5*(d2(:,pm.i_fwrd_f2)*kron(gu1,gu1))*vec(M.Sigma_e);
- resid = resid(pm.eq_p)
+resid = resid(pm.eq_p)
end
function dr_np = first_step_ds(x,pm,M,dr,options,oo)
- lead_lag_incidence = M.lead_lag_incidence;
- iyv = lead_lag_incidence';
- iyv = iyv(:);
- iyr0 = find(iyv) ;
-
- ys = dr.ys;
- ys(pm.v_p) = x;
-
- z = repmat(ys,1,3);
- z = z(iyr0) ;
- [resid1,d1,d2] = feval([M.fname '_dynamic'],z,...
- [oo.exo_simul ...
- oo.exo_det_simul], M.params, dr.ys, 2);
- if ~isreal(d1) || ~isreal(d2)
- pause
- end
-
- if options.use_dll
- % In USE_DLL mode, the hessian is in the 3-column sparse representation
- d2 = sparse(d2(:,1), d2(:,2), d2(:,3), ...
- size(d1, 1), size(d1, 2)*size(d1, 2));
- end
+lead_lag_incidence = M.lead_lag_incidence;
+iyv = lead_lag_incidence';
+iyv = iyv(:);
+iyr0 = find(iyv) ;
+
+ys = dr.ys;
+ys(pm.v_p) = x;
+
+z = repmat(ys,1,3);
+z = z(iyr0) ;
+[resid1,d1,d2] = feval([M.fname '_dynamic'],z,...
+ [oo.exo_simul ...
+ oo.exo_det_simul], M.params, dr.ys, 2);
+if ~isreal(d1) || ~isreal(d2)
+ pause
+end
+
+if options.use_dll
+ % In USE_DLL mode, the hessian is in the 3-column sparse representation
+ d2 = sparse(d2(:,1), d2(:,2), d2(:,3), ...
+ size(d1, 1), size(d1, 2)*size(d1, 2));
+end
+
+d1_np = d1(pm.eq_np,pm.i_d1_np);
+d2_np = d2(pm.eq_np,pm.i_d2_np);
+
+[dr_np,info] = dyn_first_order_solver(d1_np,pm.M_np,pm.dr_np,options,0);
+if info
+ print_info(info, 0, options);
+ return
+end
+
+dr_np = dyn_second_order_solver(d1_np,d2_np,dr_np,pm.M_np,...
+ options.threads.kronecker.A_times_B_kronecker_C,...
+ options.threads.kronecker.sparse_hessian_times_B_kronecker_C);
+end
+
+function [resid,dr] = risky_residuals_k_order(ys,pm,M,dr,options,oo)
+exo_nbr = M.exo_nbr;
+endo_nbr = M.endo_nbr;
+
+iyv = M.lead_lag_incidence';
+iyv = iyv(:);
+iyr0 = find(iyv) ;
+
+if exo_nbr == 0
+ oo.exo_steady_state = [] ;
+end
+
+z = repmat(ys,1,3);
+z = z(iyr0) ;
+[resid1,d1,d2] = feval([M.fname '_dynamic'],z,...
+ [oo.exo_simul ...
+ oo.exo_det_simul], M.params, dr.ys, 2);
+
+if isfield(options,'portfolio') && options.portfolio == 1
+ eq_np = pm.eq_np;
+
+ d1_np = d1(eq_np,pm.i_d1_np);
+ d2_np = d2(eq_np,pm.i_d2_np);
+
+ M_np = pm.M_np;
+ dr_np = pm.dr_np;
- d1_np = d1(pm.eq_np,pm.i_d1_np);
- d2_np = d2(pm.eq_np,pm.i_d2_np);
-
[dr_np,info] = dyn_first_order_solver(d1_np,pm.M_np,pm.dr_np,options,0);
if info
print_info(info, 0, options);
return
end
-
+
dr_np = dyn_second_order_solver(d1_np,d2_np,dr_np,pm.M_np,...
options.threads.kronecker.A_times_B_kronecker_C,...
options.threads.kronecker.sparse_hessian_times_B_kronecker_C);
end
-function [resid,dr] = risky_residuals_k_order(ys,pm,M,dr,options,oo)
- exo_nbr = M.exo_nbr;
- endo_nbr = M.endo_nbr;
-
- iyv = M.lead_lag_incidence';
- iyv = iyv(:);
- iyr0 = find(iyv) ;
-
- if exo_nbr == 0
- oo.exo_steady_state = [] ;
- end
-
- z = repmat(ys,1,3);
- z = z(iyr0) ;
- [resid1,d1,d2] = feval([M.fname '_dynamic'],z,...
+i_fwrd_f1 = pm.i_fwrd_f1;
+i_fwrd_f2 = pm.i_fwrd_f2;
+i_fwrd_f3 = pm.i_fwrd_f3;
+i_fwrd_g = pm.i_fwrd_g;
+gu1 = dr_np.ghu(i_fwrd_g,:);
+ghuu = dr_np.ghuu;
+
+resid = resid1+0.5*(d1(:,i_fwrd_f1)*ghuu(i_fwrd_g,:)+d2(:,i_fwrd_f2)* ...
+ kron(gu1,gu1))*vec(M.Sigma_e);
+
+if nargout > 1
+ [resid1,d1,d2,d3] = feval([M.fname '_dynamic'],z,...
[oo.exo_simul ...
oo.exo_det_simul], M.params, dr.ys, 2);
-
- if isfield(options,'portfolio') && options.portfolio == 1
- eq_np = pm.eq_np;
-
- d1_np = d1(eq_np,pm.i_d1_np);
- d2_np = d2(eq_np,pm.i_d2_np);
-
- M_np = pm.M_np;
- dr_np = pm.dr_np;
-
- [dr_np,info] = dyn_first_order_solver(d1_np,pm.M_np,pm.dr_np,options,0);
- if info
- print_info(info, 0, options);
- return
- end
-
- dr_np = dyn_second_order_solver(d1_np,d2_np,dr_np,pm.M_np,...
- options.threads.kronecker.A_times_B_kronecker_C,...
- options.threads.kronecker.sparse_hessian_times_B_kronecker_C);
- end
-
- i_fwrd_f1 = pm.i_fwrd_f1;
- i_fwrd_f2 = pm.i_fwrd_f2;
- i_fwrd_f3 = pm.i_fwrd_f3;
- i_fwrd_g = pm.i_fwrd_g;
- gu1 = dr_np.ghu(i_fwrd_g,:);
- ghuu = dr_np.ghuu;
-
- resid = resid1+0.5*(d1(:,i_fwrd_f1)*ghuu(i_fwrd_g,:)+d2(:,i_fwrd_f2)* ...
- kron(gu1,gu1))*vec(M.Sigma_e);
-
- if nargout > 1
- [resid1,d1,d2,d3] = feval([M.fname '_dynamic'],z,...
- [oo.exo_simul ...
- oo.exo_det_simul], M.params, dr.ys, 2);
-
-
- [a,b,c] = find(d2(eq_np,pm.i_d2_np));
- d2_np = [a b c];
-
- [a,b,c] = find(d3(eq_np,pm.i_d3_np));
- d3_np = [a b c];
-
- options.order = 3;
- % space holder, unused by k_order_pertrubation
- dr_np.ys = dr.ys(pm.v_np);
- nu2 = exo_nbr*(exo_nbr+1)/2;
- nu3 = exo_nbr*(exo_nbr+1)*(exo_nbr+2)/3;
- M_np.NZZDerivatives = [nnz(d1_np); nnz(d2_np); nnz(d3_np)];
- [err,g_0, g_1, g_2, g_3] = k_order_perturbation(dr_np,M_np,options,d1_np,d2_np,d3_np);
- mexErrCheck('k_order_perturbation', err);
-
- gu1 = g_1(i_fwrd_g,end-exo_nbr+1:end);
- ghuu = unfold2(g_2(:,end-nu2+1:end),exo_nbr);
- ghsuu = get_ghsuu(g_3,size(g_1,2),exo_nbr);
-
- i_fwrd1_f2 = pm.i_fwrd1_f2;
- i_fwrd1_f3 = pm.i_fwrd1_f3;
- n = size(d1,2);
- d1b = d1 + 0.5*( ...
- d1(:,i_fwrd_f1)*...
- d2(:,i_fwrd1_f2)*kron(eye(n),dr_np.ghuu(i_fwrd_g,:)*vec(M.Sigma_e))...
- + 0.5*d3(:,i_fwrd1_f3)*kron(eye(n),kron(gu1,gu1)*vec(M.Sigma_e)));
- format short
- kk1 = [nonzeros(M.lead_lag_incidence(:,1:6)'); ...
- nnz(M.lead_lag_incidence)+[1; 2]]
- kk2 = [nonzeros(M.lead_lag_incidence(:,1:6)'); ...
- nnz(M.lead_lag_incidence)+[3; 4]]
- format short
- gu1
- kron(gu1,gu1)*vec(M.Sigma_e)
- disp(d1(:,:))
- disp(d1b(:,:))
- aa2=d2(:,i_fwrd1_f2)*kron(eye(n),dr_np.ghuu(i_fwrd_g,:)*vec(M.Sigma_e));
- aa3=d3(:,i_fwrd1_f3)*kron(eye(n),kron(gu1,gu1)*vec(M.Sigma_e));
- disp(d3(4,7+6*n+6*n*n))
- disp(d3(4,8+16*n+17*n*n)) %8,17,18
- disp(d3(4,8+17*n+16*n*n)) %8,17,18
- disp(d3(4,7*n+17+17*n*n)) %8,17,18
- disp(d3(4,7*n+18+16*n*n)) %8,17,18
- disp(d3(4,7*n*n+16*n+18)) %8,17,18
- disp(d3(4,7*n*n+17+17*n)) %8,17,18
- pause
- disp(aa2(:,kk1))
- disp(aa2(:,kk2))
- disp(aa3(:,kk1))
- disp(aa3(:,kk2))
- [dr,info] = dyn_first_order_solver(d1b,M,dr,options,0);
- if info
- print_info(info, 0, options);
- return
- end
-
- disp_dr(dr,dr.order_var,[]);
-
+
+
+ [a,b,c] = find(d2(eq_np,pm.i_d2_np));
+ d2_np = [a b c];
+
+ [a,b,c] = find(d3(eq_np,pm.i_d3_np));
+ d3_np = [a b c];
+
+ options.order = 3;
+ % space holder, unused by k_order_pertrubation
+ dr_np.ys = dr.ys(pm.v_np);
+ nu2 = exo_nbr*(exo_nbr+1)/2;
+ nu3 = exo_nbr*(exo_nbr+1)*(exo_nbr+2)/3;
+ M_np.NZZDerivatives = [nnz(d1_np); nnz(d2_np); nnz(d3_np)];
+ [err,g_0, g_1, g_2, g_3] = k_order_perturbation(dr_np,M_np,options,d1_np,d2_np,d3_np);
+ mexErrCheck('k_order_perturbation', err);
+
+ gu1 = g_1(i_fwrd_g,end-exo_nbr+1:end);
+ ghuu = unfold2(g_2(:,end-nu2+1:end),exo_nbr);
+ ghsuu = get_ghsuu(g_3,size(g_1,2),exo_nbr);
+
+ i_fwrd1_f2 = pm.i_fwrd1_f2;
+ i_fwrd1_f3 = pm.i_fwrd1_f3;
+ n = size(d1,2);
+ d1b = d1 + 0.5*( ...
+ d1(:,i_fwrd_f1)*...
+ d2(:,i_fwrd1_f2)*kron(eye(n),dr_np.ghuu(i_fwrd_g,:)*vec(M.Sigma_e))...
+ + 0.5*d3(:,i_fwrd1_f3)*kron(eye(n),kron(gu1,gu1)*vec(M.Sigma_e)));
+ format short
+ kk1 = [nonzeros(M.lead_lag_incidence(:,1:6)'); ...
+ nnz(M.lead_lag_incidence)+[1; 2]]
+ kk2 = [nonzeros(M.lead_lag_incidence(:,1:6)'); ...
+ nnz(M.lead_lag_incidence)+[3; 4]]
+ format short
+ gu1
+ kron(gu1,gu1)*vec(M.Sigma_e)
+ disp(d1(:,:))
+ disp(d1b(:,:))
+ aa2=d2(:,i_fwrd1_f2)*kron(eye(n),dr_np.ghuu(i_fwrd_g,:)*vec(M.Sigma_e));
+ aa3=d3(:,i_fwrd1_f3)*kron(eye(n),kron(gu1,gu1)*vec(M.Sigma_e));
+ disp(d3(4,7+6*n+6*n*n))
+ disp(d3(4,8+16*n+17*n*n)) %8,17,18
+ disp(d3(4,8+17*n+16*n*n)) %8,17,18
+ disp(d3(4,7*n+17+17*n*n)) %8,17,18
+ disp(d3(4,7*n+18+16*n*n)) %8,17,18
+ disp(d3(4,7*n*n+16*n+18)) %8,17,18
+ disp(d3(4,7*n*n+17+17*n)) %8,17,18
+ pause
+ disp(aa2(:,kk1))
+ disp(aa2(:,kk2))
+ disp(aa3(:,kk1))
+ disp(aa3(:,kk2))
+ [dr,info] = dyn_first_order_solver(d1b,M,dr,options,0);
+ if info
+ print_info(info, 0, options);
+ return
end
+
+ disp_dr(dr,dr.order_var,[]);
+
+end
end
function y=unfold2(x,n)
- y = zeros(size(x,1),n*n);
- k = 1;
- for i=1:n
- for j=i:n
- y(:,(i-1)*n+j) = x(:,k);
- if i ~= j
- y(:,(j-1)*n+i) = x(:,k);
- end
- k = k+1;
+y = zeros(size(x,1),n*n);
+k = 1;
+for i=1:n
+ for j=i:n
+ y(:,(i-1)*n+j) = x(:,k);
+ if i ~= j
+ y(:,(j-1)*n+i) = x(:,k);
end
+ k = k+1;
end
end
+end
function y=unfold3(x,n)
- y = zeros(size(x,1),n*n*n);
- k = 1;
- for i=1:n
- for j=i:n
- for m=j:n
- y(:,(i-1)*n*n+(j-1)*n+m) = x(:,k);
- y(:,(i-1)*n*n+(m-1)*n+j) = x(:,k);
- y(:,(j-1)*n*n+(i-1)*n+m) = x(:,k);
- y(:,(j-1)*n*n+(m-1)*n+i) = x(:,k);
- y(:,(m-1)*n*n+(i-1)*n+j) = x(:,k);
- y(:,(m-1)*n*n+(j-1)*n+i) = x(:,k);
-
- k = k+1;
- end
+y = zeros(size(x,1),n*n*n);
+k = 1;
+for i=1:n
+ for j=i:n
+ for m=j:n
+ y(:,(i-1)*n*n+(j-1)*n+m) = x(:,k);
+ y(:,(i-1)*n*n+(m-1)*n+j) = x(:,k);
+ y(:,(j-1)*n*n+(i-1)*n+m) = x(:,k);
+ y(:,(j-1)*n*n+(m-1)*n+i) = x(:,k);
+ y(:,(m-1)*n*n+(i-1)*n+j) = x(:,k);
+ y(:,(m-1)*n*n+(j-1)*n+i) = x(:,k);
+
+ k = k+1;
end
end
end
+end
function pm = model_structure(M,options)
- lead_index = M.maximum_endo_lag+2;
- lead_lag_incidence = M.lead_lag_incidence;
- dr = struct();
- dr = set_state_space(dr,M,options);
- pm.i_fwrd_g = find(lead_lag_incidence(lead_index,dr.order_var)');
-
- i_fwrd_f1 = nonzeros(lead_lag_incidence(lead_index,dr.order_var));
- pm.i_fwrd_f1 = i_fwrd_f1;
- n = nnz(lead_lag_incidence)+M.exo_nbr;
- ih = reshape(1:n*n,n,n);
- i_fwrd_f2 = ih(i_fwrd_f1,i_fwrd_f1);
- pm.i_fwrd_f2 = i_fwrd_f2(:);
- i_fwrd1_f2 = ih(i_fwrd_f1,:);
- pm.i_fwrd1_f2 = i_fwrd1_f2(:);
-
- ih = reshape(1:n*n*n,n,n,n);
- i_fwrd_f3 = ih(i_fwrd_f1,i_fwrd_f1,i_fwrd_f1);
- pm.i_fwrd_f3 = i_fwrd_f3(:);
- i_fwrd1_f3 = ih(i_fwrd_f1,i_fwrd_f1,:);
- pm.i_fwrd1_f3 = i_fwrd1_f3(:);
+lead_index = M.maximum_endo_lag+2;
+lead_lag_incidence = M.lead_lag_incidence;
+dr = struct();
+dr = set_state_space(dr,M,options);
+pm.i_fwrd_g = find(lead_lag_incidence(lead_index,dr.order_var)');
+
+i_fwrd_f1 = nonzeros(lead_lag_incidence(lead_index,dr.order_var));
+pm.i_fwrd_f1 = i_fwrd_f1;
+n = nnz(lead_lag_incidence)+M.exo_nbr;
+ih = reshape(1:n*n,n,n);
+i_fwrd_f2 = ih(i_fwrd_f1,i_fwrd_f1);
+pm.i_fwrd_f2 = i_fwrd_f2(:);
+i_fwrd1_f2 = ih(i_fwrd_f1,:);
+pm.i_fwrd1_f2 = i_fwrd1_f2(:);
+
+ih = reshape(1:n*n*n,n,n,n);
+i_fwrd_f3 = ih(i_fwrd_f1,i_fwrd_f1,i_fwrd_f1);
+pm.i_fwrd_f3 = i_fwrd_f3(:);
+i_fwrd1_f3 = ih(i_fwrd_f1,i_fwrd_f1,:);
+pm.i_fwrd1_f3 = i_fwrd1_f3(:);
end
function pm = portfolio_model_structure(M,options)
- i_d3_np = [];
- i_d3_p = [];
-
- lead_index = M.maximum_endo_lag+2;
- lead_lag_incidence = M.lead_lag_incidence;
- eq_tags = M.equations_tags;
- n_tags = size(eq_tags,1);
- eq_p = cell2mat(eq_tags(strcmp(eq_tags(:,2), ...
- 'portfolio'),1));
- pm.eq_p = eq_p;
- pm.eq_np = setdiff(1:M.endo_nbr,eq_p);
- v_p = zeros(n_tags,1);
- for i=1:n_tags
- v_p(i) = find(strncmp(eq_tags(i,3),M.endo_names, ...
- length(cell2mat(eq_tags(i,3)))));
- end
- if any(lead_lag_incidence(lead_index,v_p))
- error(['portfolio variables appear in the model as forward ' ...
- 'variable'])
- end
- pm.v_p = v_p;
- v_np = setdiff(1:M.endo_nbr,v_p);
- pm.v_np = v_np;
- lli_np = lead_lag_incidence(:,v_np)';
- k = find(lli_np);
- lead_lag_incidence_np = lli_np;
- lead_lag_incidence_np(k) = 1:nnz(lli_np);
- lead_lag_incidence_np = lead_lag_incidence_np';
- pm.lead_lag_incidence_np = lead_lag_incidence_np;
- i_d1_np = [nonzeros(lli_np); nnz(lead_lag_incidence)+(1:M.exo_nbr)'];
- pm.i_d1_np = i_d1_np;
-
- n = nnz(lead_lag_incidence)+M.exo_nbr;
- ih = reshape(1:n*n,n,n);
- i_d2_np = ih(i_d1_np,i_d1_np);
- pm.i_d2_np = i_d2_np(:);
-
- ih = reshape(1:n*n*n,n,n,n);
- i_d3_np = ih(i_d1_np,i_d1_np,i_d1_np);
- pm.i_d3_np = i_d3_np(:);
-
- M_np = M;
- M_np.lead_lag_incidence = lead_lag_incidence_np;
- M_np.lead_lag_incidence = lead_lag_incidence_np;
- M_np.endo_nbr = length(v_np);
- M_np.endo_names = M.endo_names(v_np,:);
- dr_np = struct();
- dr_np = set_state_space(dr_np,M_np,options);
- pm.dr_np = dr_np;
- M_np.var_order_endo_names = M_np.endo_names(dr_np.order_var,:);
- pm.M_np = M_np;
- pm.i_fwrd_g = find(lead_lag_incidence_np(lead_index,dr_np.order_var)');
-
- i_fwrd_f1 = nonzeros(lead_lag_incidence(lead_index,:));
- pm.i_fwrd_f1 = i_fwrd_f1;
- n = nnz(lead_lag_incidence)+M.exo_nbr;
- ih = reshape(1:n*n,n,n);
- i_fwrd_f2 = ih(i_fwrd_f1,i_fwrd_f1);
- pm.i_fwrd_f2 = i_fwrd_f2(:);
- i_fwrd1_f2 = ih(i_fwrd_f1,:);
- pm.i_fwrd1_f2 = i_fwrd1_f2(:);
-
- ih = reshape(1:n*n*n,n,n,n);
- i_fwrd_f3 = ih(i_fwrd_f1,i_fwrd_f1,i_fwrd_f1);
- pm.i_fwrd_f3 = i_fwrd_f3(:);
- i_fwrd1_f3 = ih(i_fwrd_f1,i_fwrd_f1,:);
- pm.i_fwrd1_f3 = i_fwrd1_f3(:);
+i_d3_np = [];
+i_d3_p = [];
+
+lead_index = M.maximum_endo_lag+2;
+lead_lag_incidence = M.lead_lag_incidence;
+eq_tags = M.equations_tags;
+n_tags = size(eq_tags,1);
+eq_p = cell2mat(eq_tags(strcmp(eq_tags(:,2), ...
+ 'portfolio'),1));
+pm.eq_p = eq_p;
+pm.eq_np = setdiff(1:M.endo_nbr,eq_p);
+v_p = zeros(n_tags,1);
+for i=1:n_tags
+ v_p(i) = find(strncmp(eq_tags(i,3),M.endo_names, ...
+ length(cell2mat(eq_tags(i,3)))));
+end
+if any(lead_lag_incidence(lead_index,v_p))
+ error(['portfolio variables appear in the model as forward ' ...
+ 'variable'])
+end
+pm.v_p = v_p;
+v_np = setdiff(1:M.endo_nbr,v_p);
+pm.v_np = v_np;
+lli_np = lead_lag_incidence(:,v_np)';
+k = find(lli_np);
+lead_lag_incidence_np = lli_np;
+lead_lag_incidence_np(k) = 1:nnz(lli_np);
+lead_lag_incidence_np = lead_lag_incidence_np';
+pm.lead_lag_incidence_np = lead_lag_incidence_np;
+i_d1_np = [nonzeros(lli_np); nnz(lead_lag_incidence)+(1:M.exo_nbr)'];
+pm.i_d1_np = i_d1_np;
+
+n = nnz(lead_lag_incidence)+M.exo_nbr;
+ih = reshape(1:n*n,n,n);
+i_d2_np = ih(i_d1_np,i_d1_np);
+pm.i_d2_np = i_d2_np(:);
+
+ih = reshape(1:n*n*n,n,n,n);
+i_d3_np = ih(i_d1_np,i_d1_np,i_d1_np);
+pm.i_d3_np = i_d3_np(:);
+
+M_np = M;
+M_np.lead_lag_incidence = lead_lag_incidence_np;
+M_np.lead_lag_incidence = lead_lag_incidence_np;
+M_np.endo_nbr = length(v_np);
+M_np.endo_names = M.endo_names(v_np,:);
+dr_np = struct();
+dr_np = set_state_space(dr_np,M_np,options);
+pm.dr_np = dr_np;
+M_np.var_order_endo_names = M_np.endo_names(dr_np.order_var,:);
+pm.M_np = M_np;
+pm.i_fwrd_g = find(lead_lag_incidence_np(lead_index,dr_np.order_var)');
+
+i_fwrd_f1 = nonzeros(lead_lag_incidence(lead_index,:));
+pm.i_fwrd_f1 = i_fwrd_f1;
+n = nnz(lead_lag_incidence)+M.exo_nbr;
+ih = reshape(1:n*n,n,n);
+i_fwrd_f2 = ih(i_fwrd_f1,i_fwrd_f1);
+pm.i_fwrd_f2 = i_fwrd_f2(:);
+i_fwrd1_f2 = ih(i_fwrd_f1,:);
+pm.i_fwrd1_f2 = i_fwrd1_f2(:);
+
+ih = reshape(1:n*n*n,n,n,n);
+i_fwrd_f3 = ih(i_fwrd_f1,i_fwrd_f1,i_fwrd_f1);
+pm.i_fwrd_f3 = i_fwrd_f3(:);
+i_fwrd1_f3 = ih(i_fwrd_f1,i_fwrd_f1,:);
+pm.i_fwrd1_f3 = i_fwrd1_f3(:);
end
function r=ds_static_model(y0,f_h,p0,eq_np,v_np,v_p,endo_nbr,exo_nbr,params)
- ys = zeros(endo_nbr,1);
- ys(v_p) = p0;
- ys(v_np) = y0;
- r = f_h(ys,zeros(exo_nbr,1),params);
- r = r(eq_np);
+ys = zeros(endo_nbr,1);
+ys(v_p) = p0;
+ys(v_np) = y0;
+r = f_h(ys,zeros(exo_nbr,1),params);
+r = r(eq_np);
end
function ghsuu = get_ghsuu(g,ns,nx)
- nxx = nx*(nx+1)/2;
- m1 = 0;
- m2 = ns*(ns+1)/2;
- kk = 1:(nx*nx);
- ghsuu = zeros(size(g,1),(ns*nx*nx));
-
- for i=1:n
- j = m1+(1:m2);
- k = j(end-nxx+1:end);
- ghsuu(:,kk) = unfold2(g(:,k),nx);
- m1 = m1+m2;
- m2 = m2 - (n-i+1);
- kk = kk + nx*nx;
- end
+nxx = nx*(nx+1)/2;
+m1 = 0;
+m2 = ns*(ns+1)/2;
+kk = 1:(nx*nx);
+ghsuu = zeros(size(g,1),(ns*nx*nx));
+
+for i=1:n
+ j = m1+(1:m2);
+ k = j(end-nxx+1:end);
+ ghsuu(:,kk) = unfold2(g(:,k),nx);
+ m1 = m1+m2;
+ m2 = m2 - (n-i+1);
+ kk = kk + nx*nx;
+end
end
\ No newline at end of file
diff --git a/matlab/dyn_saveas.m b/matlab/dyn_saveas.m
index a31c724b2e13e654e0e63e09fa9a557812b90c6b..009a49d5e8af99ede13d9cdefd99d3e80590f85f 100644
--- a/matlab/dyn_saveas.m
+++ b/matlab/dyn_saveas.m
@@ -39,13 +39,13 @@ if any(strcmp('eps',cellstr(graph_format)))
set(h, 'Visible','on');
end
end
- print(h,'-depsc2',[fname,'.eps'])
+ print(h,'-depsc2',[fname,'.eps'])
end
if any(strcmp('pdf',cellstr(graph_format)))
if isoctave
error('Octave cannot create pdf files!')
else
- print(h,'-dpdf',[fname,'.pdf'])
+ print(h,'-dpdf',[fname,'.pdf'])
end
end
if any(strcmp('fig',cellstr(graph_format)))
@@ -53,19 +53,19 @@ if any(strcmp('fig',cellstr(graph_format)))
error('Octave cannot create fig files!')
else
if nodisplay
-% THE FOLLOWING LINES COULD BE USED IF BUGS/PROBLEMS ARE REPORTED USING LINE 60
-% set(h,'Units','Normalized')
-% mypos=get(h,'Position');
-% set(h,'Position',[-1 -1 mypos(3:4)])
-% set(h, 'Visible','on');
+ % THE FOLLOWING LINES COULD BE USED IF BUGS/PROBLEMS ARE REPORTED USING LINE 60
+ % set(h,'Units','Normalized')
+ % mypos=get(h,'Position');
+ % set(h,'Position',[-1 -1 mypos(3:4)])
+ % set(h, 'Visible','on');
set(h,'CreateFcn','set(gcf, ''Visible'',''on'')') ;
end
saveas(h,[fname '.fig']);
end
end
if any(strcmp('none',cellstr(graph_format)))
-% don't save
-% check here as a reminder that none is an option to graph_format
+ % don't save
+ % check here as a reminder that none is an option to graph_format
end
if nodisplay
close(h);
diff --git a/matlab/dyn_second_order_solver.m b/matlab/dyn_second_order_solver.m
index 76fe1baa3d5a873c51f4e69fabd92687f8dbfb02..11de2659b779dcfc90896f97817e20674c96063f 100644
--- a/matlab/dyn_second_order_solver.m
+++ b/matlab/dyn_second_order_solver.m
@@ -48,135 +48,135 @@ function dr = dyn_second_order_solver(jacobia,hessian_mat,dr,M_,threads_ABC,thre
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
- dr.ghxx = [];
- dr.ghuu = [];
- dr.ghxu = [];
- dr.ghs2 = [];
- Gy = dr.Gy;
-
- kstate = dr.kstate;
- nstatic = M_.nstatic;
- nfwrd = M_.nfwrd;
- nspred = M_.nspred;
- nboth = M_.nboth;
- nsfwrd = M_.nsfwrd;
- order_var = dr.order_var;
- nd = size(kstate,1);
- lead_lag_incidence = M_.lead_lag_incidence;
-
- np = nd - nsfwrd;
-
- k1 = nonzeros(lead_lag_incidence(:,order_var)');
- kk = [k1; length(k1)+(1:M_.exo_nbr+M_.exo_det_nbr)'];
- nk = size(kk,1);
- kk1 = reshape([1:nk^2],nk,nk);
- kk1 = kk1(kk,kk);
- % reordering second order derivatives
- hessian_mat = hessian_mat(:,kk1(:));
-
- zx = zeros(np,np);
- zu=zeros(np,M_.exo_nbr);
- zx(1:np,:)=eye(np);
- k0 = [1:M_.endo_nbr];
- gx1 = dr.ghx;
- hu = dr.ghu(nstatic+[1:nspred],:);
- k0 = find(lead_lag_incidence(M_.maximum_endo_lag+1,order_var)');
- zx = [zx; gx1(k0,:)];
- zu = [zu; dr.ghu(k0,:)];
- k1 = find(lead_lag_incidence(M_.maximum_endo_lag+2,order_var)');
- zu = [zu; gx1(k1,:)*hu];
- zx = [zx; gx1(k1,:)*Gy];
- zx=[zx; zeros(M_.exo_nbr,np);zeros(M_.exo_det_nbr,np)];
- zu=[zu; eye(M_.exo_nbr);zeros(M_.exo_det_nbr,M_.exo_nbr)];
- [nrzx,nczx] = size(zx);
-
- [rhs, err] = sparse_hessian_times_B_kronecker_C(hessian_mat,zx,threads_BC);
- mexErrCheck('sparse_hessian_times_B_kronecker_C', err);
- rhs = -rhs;
-
- %lhs
- n = M_.endo_nbr+sum(kstate(:,2) > M_.maximum_endo_lag+1 & kstate(:,2) < M_.maximum_endo_lag+M_.maximum_endo_lead+1);
- A = zeros(M_.endo_nbr,M_.endo_nbr);
- B = zeros(M_.endo_nbr,M_.endo_nbr);
- A(:,k0) = jacobia(:,nonzeros(lead_lag_incidence(M_.maximum_endo_lag+1,order_var)));
- % variables with the highest lead
- k1 = find(kstate(:,2) == M_.maximum_endo_lag+2);
- % Jacobian with respect to the variables with the highest lead
- fyp = jacobia(:,kstate(k1,3)+nnz(M_.lead_lag_incidence(M_.maximum_endo_lag+1,:)));
- B(:,nstatic+M_.npred+1:end) = fyp;
- [junk,k1,k2] = find(M_.lead_lag_incidence(M_.maximum_endo_lag+M_.maximum_endo_lead+1,order_var));
- A(1:M_.endo_nbr,nstatic+1:nstatic+nspred)=...
- A(1:M_.endo_nbr,nstatic+[1:nspred])+fyp*gx1(k1,1:nspred);
- C = Gy;
- D = [rhs; zeros(n-M_.endo_nbr,size(rhs,2))];
-
-
- [err, dr.ghxx] = gensylv(2,A,B,C,D);
- mexErrCheck('gensylv', err);
-
- %ghxu
- %rhs
- hu = dr.ghu(nstatic+1:nstatic+nspred,:);
- [rhs, err] = sparse_hessian_times_B_kronecker_C(hessian_mat,zx,zu,threads_BC);
- mexErrCheck('sparse_hessian_times_B_kronecker_C', err);
-
- hu1 = [hu;zeros(np-nspred,M_.exo_nbr)];
- [nrhx,nchx] = size(Gy);
- [nrhu1,nchu1] = size(hu1);
-
- [abcOut,err] = A_times_B_kronecker_C(dr.ghxx,Gy,hu1,threads_ABC);
- mexErrCheck('A_times_B_kronecker_C', err);
- B1 = B*abcOut;
- rhs = -[rhs; zeros(n-M_.endo_nbr,size(rhs,2))]-B1;
-
-
- %lhs
- dr.ghxu = A\rhs;
-
- %ghuu
- %rhs
- [rhs, err] = sparse_hessian_times_B_kronecker_C(hessian_mat,zu,threads_BC);
- mexErrCheck('sparse_hessian_times_B_kronecker_C', err);
-
- [B1, err] = A_times_B_kronecker_C(B*dr.ghxx,hu1,threads_ABC);
- mexErrCheck('A_times_B_kronecker_C', err);
- rhs = -[rhs; zeros(n-M_.endo_nbr,size(rhs,2))]-B1;
-
- %lhs
- dr.ghuu = A\rhs;
-
- % dr.ghs2
- % derivatives of F with respect to forward variables
- % reordering predetermined variables in diminishing lag order
- O1 = zeros(M_.endo_nbr,nstatic);
- O2 = zeros(M_.endo_nbr,M_.endo_nbr-nstatic-nspred);
- LHS = zeros(M_.endo_nbr,M_.endo_nbr);
- LHS(:,k0) = jacobia(:,nonzeros(lead_lag_incidence(M_.maximum_endo_lag+1,order_var)));
- RHS = zeros(M_.endo_nbr,M_.exo_nbr^2);
- gu = dr.ghu;
- guu = dr.ghuu;
- E = eye(M_.endo_nbr);
- kh = reshape([1:nk^2],nk,nk);
- kp = sum(kstate(:,2) <= M_.maximum_endo_lag+1);
- E1 = [eye(nspred); zeros(kp-nspred,nspred)];
- H = E1;
- hxx = dr.ghxx(nstatic+[1:nspred],:);
- [junk,k2a,k2] = find(M_.lead_lag_incidence(M_.maximum_endo_lag+2,order_var));
- k3 = nnz(M_.lead_lag_incidence(1:M_.maximum_endo_lag+1,:))+(1:M_.nsfwrd)';
- [B1, err] = sparse_hessian_times_B_kronecker_C(hessian_mat(:,kh(k3,k3)),gu(k2a,:),threads_BC);
- mexErrCheck('sparse_hessian_times_B_kronecker_C', err);
- RHS = RHS + jacobia(:,k2)*guu(k2a,:)+B1;
-
- % LHS
- LHS = LHS + jacobia(:,k2)*(E(k2a,:)+[O1(k2a,:) dr.ghx(k2a,:)*H O2(k2a,:)]);
-
- RHS = RHS*M_.Sigma_e(:);
- dr.fuu = RHS;
- %RHS = -RHS-dr.fbias;
- RHS = -RHS;
- dr.ghs2 = LHS\RHS;
-
- % deterministic exogenous variables
- if M_.exo_det_nbr > 0
- end
+
+dr.ghxx = [];
+dr.ghuu = [];
+dr.ghxu = [];
+dr.ghs2 = [];
+Gy = dr.Gy;
+
+kstate = dr.kstate;
+nstatic = M_.nstatic;
+nfwrd = M_.nfwrd;
+nspred = M_.nspred;
+nboth = M_.nboth;
+nsfwrd = M_.nsfwrd;
+order_var = dr.order_var;
+nd = size(kstate,1);
+lead_lag_incidence = M_.lead_lag_incidence;
+
+np = nd - nsfwrd;
+
+k1 = nonzeros(lead_lag_incidence(:,order_var)');
+kk = [k1; length(k1)+(1:M_.exo_nbr+M_.exo_det_nbr)'];
+nk = size(kk,1);
+kk1 = reshape([1:nk^2],nk,nk);
+kk1 = kk1(kk,kk);
+% reordering second order derivatives
+hessian_mat = hessian_mat(:,kk1(:));
+
+zx = zeros(np,np);
+zu=zeros(np,M_.exo_nbr);
+zx(1:np,:)=eye(np);
+k0 = [1:M_.endo_nbr];
+gx1 = dr.ghx;
+hu = dr.ghu(nstatic+[1:nspred],:);
+k0 = find(lead_lag_incidence(M_.maximum_endo_lag+1,order_var)');
+zx = [zx; gx1(k0,:)];
+zu = [zu; dr.ghu(k0,:)];
+k1 = find(lead_lag_incidence(M_.maximum_endo_lag+2,order_var)');
+zu = [zu; gx1(k1,:)*hu];
+zx = [zx; gx1(k1,:)*Gy];
+zx=[zx; zeros(M_.exo_nbr,np);zeros(M_.exo_det_nbr,np)];
+zu=[zu; eye(M_.exo_nbr);zeros(M_.exo_det_nbr,M_.exo_nbr)];
+[nrzx,nczx] = size(zx);
+
+[rhs, err] = sparse_hessian_times_B_kronecker_C(hessian_mat,zx,threads_BC);
+mexErrCheck('sparse_hessian_times_B_kronecker_C', err);
+rhs = -rhs;
+
+%lhs
+n = M_.endo_nbr+sum(kstate(:,2) > M_.maximum_endo_lag+1 & kstate(:,2) < M_.maximum_endo_lag+M_.maximum_endo_lead+1);
+A = zeros(M_.endo_nbr,M_.endo_nbr);
+B = zeros(M_.endo_nbr,M_.endo_nbr);
+A(:,k0) = jacobia(:,nonzeros(lead_lag_incidence(M_.maximum_endo_lag+1,order_var)));
+% variables with the highest lead
+k1 = find(kstate(:,2) == M_.maximum_endo_lag+2);
+% Jacobian with respect to the variables with the highest lead
+fyp = jacobia(:,kstate(k1,3)+nnz(M_.lead_lag_incidence(M_.maximum_endo_lag+1,:)));
+B(:,nstatic+M_.npred+1:end) = fyp;
+[junk,k1,k2] = find(M_.lead_lag_incidence(M_.maximum_endo_lag+M_.maximum_endo_lead+1,order_var));
+A(1:M_.endo_nbr,nstatic+1:nstatic+nspred)=...
+ A(1:M_.endo_nbr,nstatic+[1:nspred])+fyp*gx1(k1,1:nspred);
+C = Gy;
+D = [rhs; zeros(n-M_.endo_nbr,size(rhs,2))];
+
+
+[err, dr.ghxx] = gensylv(2,A,B,C,D);
+mexErrCheck('gensylv', err);
+
+%ghxu
+%rhs
+hu = dr.ghu(nstatic+1:nstatic+nspred,:);
+[rhs, err] = sparse_hessian_times_B_kronecker_C(hessian_mat,zx,zu,threads_BC);
+mexErrCheck('sparse_hessian_times_B_kronecker_C', err);
+
+hu1 = [hu;zeros(np-nspred,M_.exo_nbr)];
+[nrhx,nchx] = size(Gy);
+[nrhu1,nchu1] = size(hu1);
+
+[abcOut,err] = A_times_B_kronecker_C(dr.ghxx,Gy,hu1,threads_ABC);
+mexErrCheck('A_times_B_kronecker_C', err);
+B1 = B*abcOut;
+rhs = -[rhs; zeros(n-M_.endo_nbr,size(rhs,2))]-B1;
+
+
+%lhs
+dr.ghxu = A\rhs;
+
+%ghuu
+%rhs
+[rhs, err] = sparse_hessian_times_B_kronecker_C(hessian_mat,zu,threads_BC);
+mexErrCheck('sparse_hessian_times_B_kronecker_C', err);
+
+[B1, err] = A_times_B_kronecker_C(B*dr.ghxx,hu1,threads_ABC);
+mexErrCheck('A_times_B_kronecker_C', err);
+rhs = -[rhs; zeros(n-M_.endo_nbr,size(rhs,2))]-B1;
+
+%lhs
+dr.ghuu = A\rhs;
+
+% dr.ghs2
+% derivatives of F with respect to forward variables
+% reordering predetermined variables in diminishing lag order
+O1 = zeros(M_.endo_nbr,nstatic);
+O2 = zeros(M_.endo_nbr,M_.endo_nbr-nstatic-nspred);
+LHS = zeros(M_.endo_nbr,M_.endo_nbr);
+LHS(:,k0) = jacobia(:,nonzeros(lead_lag_incidence(M_.maximum_endo_lag+1,order_var)));
+RHS = zeros(M_.endo_nbr,M_.exo_nbr^2);
+gu = dr.ghu;
+guu = dr.ghuu;
+E = eye(M_.endo_nbr);
+kh = reshape([1:nk^2],nk,nk);
+kp = sum(kstate(:,2) <= M_.maximum_endo_lag+1);
+E1 = [eye(nspred); zeros(kp-nspred,nspred)];
+H = E1;
+hxx = dr.ghxx(nstatic+[1:nspred],:);
+[junk,k2a,k2] = find(M_.lead_lag_incidence(M_.maximum_endo_lag+2,order_var));
+k3 = nnz(M_.lead_lag_incidence(1:M_.maximum_endo_lag+1,:))+(1:M_.nsfwrd)';
+[B1, err] = sparse_hessian_times_B_kronecker_C(hessian_mat(:,kh(k3,k3)),gu(k2a,:),threads_BC);
+mexErrCheck('sparse_hessian_times_B_kronecker_C', err);
+RHS = RHS + jacobia(:,k2)*guu(k2a,:)+B1;
+
+% LHS
+LHS = LHS + jacobia(:,k2)*(E(k2a,:)+[O1(k2a,:) dr.ghx(k2a,:)*H O2(k2a,:)]);
+
+RHS = RHS*M_.Sigma_e(:);
+dr.fuu = RHS;
+%RHS = -RHS-dr.fbias;
+RHS = -RHS;
+dr.ghs2 = LHS\RHS;
+
+% deterministic exogenous variables
+if M_.exo_det_nbr > 0
+end
diff --git a/matlab/dyn_unvech.m b/matlab/dyn_unvech.m
index f70670a986f44a3be3275a4ac5ea14942e4e7f49..6ed9c40f9adda1c335f793bc123a63acec707bcc 100644
--- a/matlab/dyn_unvech.m
+++ b/matlab/dyn_unvech.m
@@ -1,10 +1,10 @@
function Matrix = dyn_unvech(Vector)
% This function implements the unvech operator.
-%
-% INPUTS
+%
+% INPUTS
% Vector [double] a m*1 vector.
-%
-% OUTPUTS
+%
+% OUTPUTS
% Matrix [double] a n*n symetric matrix, where n solves n*(n+1)/2=m.
% Copyright (C) 2010 Dynare Team
diff --git a/matlab/dyn_vech.m b/matlab/dyn_vech.m
index 6e54c40502b7f70cc2ebbdf0fa4fd8c5c59ff314..fef8d134debfae4170e39300a80a58b953f12923 100644
--- a/matlab/dyn_vech.m
+++ b/matlab/dyn_vech.m
@@ -1,10 +1,10 @@
function Vector = dyn_vech(Matrix)
% This function implements the vech operator.
-%
-% INPUTS
+%
+% INPUTS
% Matrix [double] a squared n*n symetric matrix.
-%
-% OUTPUTS
+%
+% OUTPUTS
% Vector [double] a n*(n+1)/2 vector.
% Copyright (C) 2010 Dynare Team
diff --git a/matlab/dyn_waitbar.m b/matlab/dyn_waitbar.m
index f8bc2aa785ee3bf47600a9e3fe005f6bdcce9364..43567dcd34a9c57d546910c1556c10ab6db47b42 100644
--- a/matlab/dyn_waitbar.m
+++ b/matlab/dyn_waitbar.m
@@ -43,7 +43,7 @@ if nargout
end
if ~whoiam
- if isoctave || options_.console_mode
+ if isoctave || options_.console_mode
if init
diary off;
running_text = varargin{1};
@@ -58,7 +58,7 @@ if ~whoiam
s0=repmat('\b',1,length(newString));
newString=sprintf([running_text,' %3.f%% done'], prctdone*100);
fprintf([s0,'%s'],newString);
- end
+ end
else
if nargout
h = waitbar(prctdone,varargin{:});
@@ -79,4 +79,3 @@ else
end
fMessageStatus(prctdone,whoiam,running_text, waitbarTitle, Parallel);
end
-
diff --git a/matlab/dyn_waitbar_close.m b/matlab/dyn_waitbar_close.m
index da7d17bbf6b1f7cbbf678d910c9f1d35af6946b1..c2864a9b6ad8cf748af934e56e4bc70b93c9d757 100644
--- a/matlab/dyn_waitbar_close.m
+++ b/matlab/dyn_waitbar_close.m
@@ -1,6 +1,6 @@
function dyn_waitbar_close(h)
% h = dyn_waitbar_close(h)
-% adaptive close waitbar, compatible with
+% adaptive close waitbar, compatible with
% octave and when console_mode=1
%
@@ -31,5 +31,3 @@ else
end
clear dyn_waitbar;
-
-
diff --git a/matlab/dynare.m b/matlab/dynare.m
index a1fd7688851398c9b537c4e7c348fee2ee5f5d12..472888be9dbb93f6b895c6378fe4b911951d24a5 100644
--- a/matlab/dynare.m
+++ b/matlab/dynare.m
@@ -1,7 +1,7 @@
function dynare(fname, varargin)
% This command runs dynare with specified model file in argument
% Filename.
-% The name of model file begins with an alphabetic character,
+% The name of model file begins with an alphabetic character,
% and has a filename extension of .mod or .dyn.
% When extension is omitted, a model file with .mod extension
% is processed.
@@ -9,10 +9,10 @@ function dynare(fname, varargin)
% INPUTS
% fname: file name
% varargin: list of arguments following fname
-%
+%
% OUTPUTS
% none
-%
+%
% SPECIAL REQUIREMENTS
% none
@@ -64,7 +64,7 @@ dynareroot = dynare_config;
warning_config()
-if isoctave
+if isoctave
if octave_ver_less_than('3.6.0')
warning('This version of Dynare has only been tested on Octave 3.6.0 and above. Since your Octave version is older than that, Dynare may fail to run, or give unexpected results. Consider upgrading your Octave installation.');
end
@@ -111,7 +111,7 @@ if isempty(strfind(fname,'.'))
% Checking file extension
else
if dot_location~=length(fname)-3 ... %if the file name has fewer than 4 characters and there is a period
- || ~strcmp(upper(fname(size(fname,2)-3:size(fname,2))),'.MOD') ...
+ || ~strcmp(upper(fname(size(fname,2)-3:size(fname,2))),'.MOD') ...
&& ~strcmp(upper(fname(size(fname,2)-3:size(fname,2))),'.DYN')
error('DYNARE: argument must be a filename with .mod or .dyn extension and must not include any other periods')
end
@@ -176,11 +176,11 @@ else
end
if isempty(strfind(arch, '64'))
- arch_ext = '32';
- disp('Using 32-bit preprocessor');
+ arch_ext = '32';
+ disp('Using 32-bit preprocessor');
else
- arch_ext = '64';
- disp('Using 64-bit preprocessor');
+ arch_ext = '64';
+ disp('Using 64-bit preprocessor');
end
command = ['"' dynareroot 'preprocessor' arch_ext filesep 'dynare_m" ' fname] ;
diff --git a/matlab/dynare_config.m b/matlab/dynare_config.m
index baa8503084a3005bc5f28ef63011675745045ca1..65a363999e235b69de81e35746aa57611fc8f828 100644
--- a/matlab/dynare_config.m
+++ b/matlab/dynare_config.m
@@ -58,7 +58,7 @@ p = {'/distributions/' ; ...
'/particles/src' ; ...
'/gsa/' ; ...
'/ep/' ; ...
- '/convergence_diagnostics/' ; ...
+ '/convergence_diagnostics/' ; ...
'/cli/' ; ...
'/lmmcp/' ; ...
'/optimization/' ; ...
@@ -114,7 +114,7 @@ end
% nanmean is in Octave Forge Statistics package and in MATLAB Statistics
% toolbox
if (isoctave && ~user_has_octave_forge_package('statistics')) ...
- || (~isoctave && ~user_has_matlab_license('statistics_toolbox'))
+ || (~isoctave && ~user_has_matlab_license('statistics_toolbox'))
p{end+1} = '/missing/nanmean';
end
diff --git a/matlab/dynare_estimation.m b/matlab/dynare_estimation.m
index cca524d64ee12fc9cf108a07a40e30347f126627..98ad16c32d5fa7e335dd7377ee56dd25c26d4086 100644
--- a/matlab/dynare_estimation.m
+++ b/matlab/dynare_estimation.m
@@ -33,7 +33,7 @@ global options_ oo_ M_ dataset_ dataset_info
oo_recursive_={};
mode_file0 = options_.mode_file; % store mode_file set by the user
-% Test if the order of approximation is nonzero (the preprocessor tests if order is non negative).
+ % Test if the order of approximation is nonzero (the preprocessor tests if order is non negative).
if isequal(options_.order,0)
error('Estimation:: The order of the Taylor approximation cannot be 0!')
end
@@ -46,8 +46,8 @@ end
var_list = check_list_of_variables(options_, M_, var_list);
options_.varlist = var_list;
-nobs = sort(options_.nobs);
-first_obs = sort(options_.first_obs);
+nobs = sort(options_.nobs);
+first_obs = sort(options_.first_obs);
nnobs = length(nobs);
nfirstobs = length(first_obs);
@@ -55,7 +55,7 @@ nfirstobs = length(first_obs);
if nnobs~=1 && nfirstobs~=1
error('You cannot simultaneously do rolling window and recursive estimation')
end
-
+
horizon = options_.forecast;
if nargin<2 || ~exist('dname','var') || isempty(dname)
@@ -83,7 +83,7 @@ if nnobs>1 || nfirstobs > 1
options_.nobs = nobs(i);
M_.dname = [dname '_' int2str(nobs(i))];
elseif nfirstobs>1
- options_.first_obs=first_obs(i);
+ options_.first_obs=first_obs(i);
M_.dname = [dname '_' int2str(first_obs(i))];
end
dynare_estimation_1(var_list,M_.dname);
@@ -138,11 +138,11 @@ if nnobs > 1 && horizon > 0
IdObs(j,1) = iobs;
end
end
-
+
gend = dataset_.nobs;
time_offset=min(3,gend-1); %for observables, plot 3 previous periods unless data is shorter
k = time_offset+min(nobs(end)-nobs(1)+horizon, ...
- size(dataset_.data,1)-nobs(1));
+ size(dataset_.data,1)-nobs(1));
data2 = dataset_info.rawdata(end-k+1:end,:);
[nbplt,nr,nc,lr,lc,nstar] = pltorg(nvar);
m = 1;
@@ -167,23 +167,23 @@ if nnobs > 1 && horizon > 0
for j=1:nnobs
if mh_replic > 0
oo_.RecursiveForecast.Mean.(vname)(j,:) = ...
- oo_recursive_{nobs(j)}.MeanForecast.Mean.(vname);
+ oo_recursive_{nobs(j)}.MeanForecast.Mean.(vname);
oo_.RecursiveForecast.HPDinf.(vname)(j,:) = ...
- oo_recursive_{nobs(j)}.MeanForecast.HPDinf.(vname);
+ oo_recursive_{nobs(j)}.MeanForecast.HPDinf.(vname);
oo_.RecursiveForecast.HPDsup.(vname)(j,:) = ...
- oo_recursive_{nobs(j)}.MeanForecast.HPDsup.(vname);
+ oo_recursive_{nobs(j)}.MeanForecast.HPDsup.(vname);
oo_.RecursiveForecast.HPDTotalinf.(vname)(j,:) = ...
- oo_recursive_{nobs(j)}.PointForecast.HPDinf.(vname);
+ oo_recursive_{nobs(j)}.PointForecast.HPDinf.(vname);
oo_.RecursiveForecast.HPDTotalsup.(vname)(j,:) = ...
- oo_recursive_{nobs(j)}.PointForecast.HPDsup.(vname);
+ oo_recursive_{nobs(j)}.PointForecast.HPDsup.(vname);
else
oo_.RecursiveForecast.Mean.(vname)(j,:) =...
- oo_recursive_{nobs(j)}.forecast.Mean.(vname);
+ oo_recursive_{nobs(j)}.forecast.Mean.(vname);
oo_.RecursiveForecast.HPDinf.(vname)(j,:) =...
- oo_recursive_{nobs(j)}.forecast.HPDinf.(vname);
+ oo_recursive_{nobs(j)}.forecast.HPDinf.(vname);
oo_.RecursiveForecast.HPDsup.(vname)(j,:) =...
- oo_recursive_{nobs(j)}.forecast.HPDsup.(vname);
- end
+ oo_recursive_{nobs(j)}.forecast.HPDsup.(vname);
+ end
x = nobs(1)+nobs(j)-nobs(1)+(1:horizon);
y = oo_.RecursiveForecast.Mean.(vname)(j,:);
@@ -212,5 +212,5 @@ if nnobs > 1 && horizon > 0
end
end
end
-options_.mode_file = mode_file0;
+options_.mode_file = mode_file0;
%reset stored mode-file to user defined one (and in case it was only set by the recursive estimation)
diff --git a/matlab/dynare_estimation_1.m b/matlab/dynare_estimation_1.m
index cd0e28c9bd0e6b15750155c5ade7c879d20a015c..a4b4166145fae3a0184c98d5bf5d1c1d7bf32583 100644
--- a/matlab/dynare_estimation_1.m
+++ b/matlab/dynare_estimation_1.m
@@ -52,12 +52,12 @@ end
% Set particle filter flag.
if options_.order > 1
- if options_.particle.status && options_.order==2
+ if options_.particle.status && options_.order==2
skipline()
disp('Estimation using a non linear filter!')
skipline()
if ~options_.nointeractive && ismember(options_.mode_compute,[1,3,4]) && ~strcmpi(options_.particle.filter_algorithm,'gf')% Known gradient-based optimizers
- disp('You are using a gradient-based mode-finder. Particle filtering introduces discontinuities in the')
+ disp('You are using a gradient-based mode-finder. Particle filtering introduces discontinuities in the')
disp('objective function w.r.t the parameters. Thus, should use a non-gradient based optimizer.')
fprintf('\nPlease choose a mode-finder:\n')
fprintf('\t 0 - Continue using gradient-based method (it is most likely that you will no get any sensible result).\n')
@@ -170,7 +170,7 @@ catch % if check fails, provide info on using calibration if present
fprintf('ESTIMATION_CHECKS: There was an error in computing the likelihood for initial parameter values.\n')
fprintf('ESTIMATION_CHECKS: If this is not a problem with the setting of options (check the error message below),\n')
fprintf('ESTIMATION_CHECKS: you should try using the calibrated version of the model as starting values. To do\n')
- fprintf('ESTIMATION_CHECKS: this, add an empty estimated_params_init-block with use_calibration option immediately before the estimation\n')
+ fprintf('ESTIMATION_CHECKS: this, add an empty estimated_params_init-block with use_calibration option immediately before the estimation\n')
fprintf('ESTIMATION_CHECKS: command (and after the estimated_params-block so that it does not get overwritten):\n');
skipline(2);
end
@@ -210,30 +210,30 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
else %use default
newratflag=0; %exclude DYNARE numerical hessian
end
- elseif ~options_.analytic_derivation
- if isempty(newratflag)
- newratflag=options_.newrat.hess; %use default numerical dynare hessian
+ elseif ~options_.analytic_derivation
+ if isempty(newratflag)
+ newratflag=options_.newrat.hess; %use default numerical dynare hessian
end
end
end
-
+
[xparam1, fval, exitflag, hh, options_, Scale, new_rat_hess_info] = dynare_minimize_objective(objective_function,xparam1,options_.mode_compute,options_,[bounds.lb bounds.ub],bayestopt_.name,bayestopt_,hh,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
fprintf('\nFinal value of minus the log posterior (or likelihood):%f \n', fval);
if isnumeric(options_.mode_compute) && options_.mode_compute==5 && options_.analytic_derivation==-1 %reset options changed by newrat
- options_.analytic_derivation = options_analytic_derivation_old; %reset
+ options_.analytic_derivation = options_analytic_derivation_old; %reset
elseif isnumeric(options_.mode_compute) && options_.mode_compute==6 %save scaling factor
save([M_.fname '_optimal_mh_scale_parameter.mat'],'Scale');
options_.mh_jscale = Scale;
bayestopt_.jscale = ones(length(xparam1),1)*Scale;
- end
+ end
if ~isnumeric(options_.mode_compute) || ~isequal(options_.mode_compute,6) %always already computes covariance matrix
if options_.cova_compute == 1 %user did not request covariance not to be computed
if options_.analytic_derivation && strcmp(func2str(objective_function),'dsge_likelihood')
ana_deriv_old = options_.analytic_derivation;
options_.analytic_derivation = 2;
[junk1, junk2,junk3, junk4, hh] = feval(objective_function,xparam1, ...
- dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
+ dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
options_.analytic_derivation = ana_deriv_old;
elseif ~isnumeric(options_.mode_compute) || ~(isequal(options_.mode_compute,5) && newratflag~=1)
% with flag==0, we force to use the hessian from outer product gradient of optimizer 5
@@ -264,7 +264,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
options_.kalman_algo=4;
end
elseif newratflag==0 % hh already contains outer product gradient with univariate filter
- compute_hessian = 0;
+ compute_hessian = 0;
end
if compute_hessian
crit = options_.newrat.tolerance.f;
@@ -296,7 +296,7 @@ if ~options_.mh_posterior_mode_estimation && options_.cova_compute
params_at_bound=find(abs(xparam1-bounds.ub)<1.e-10 | abs(xparam1-bounds.lb)<1.e-10);
if ~isempty(params_at_bound)
for ii=1:length(params_at_bound)
- params_at_bound_name{ii,1}=get_the_name(params_at_bound(ii),0,M_,estim_params_,options_);
+ params_at_bound_name{ii,1}=get_the_name(params_at_bound(ii),0,M_,estim_params_,options_);
end
disp_string=[params_at_bound_name{1,:}];
for ii=2:size(params_at_bound_name,1)
@@ -385,36 +385,36 @@ if np > 0
end
switch options_.MCMC_jumping_covariance
- case 'hessian' %Baseline
- %do nothing and use hessian from mode_compute
- case 'prior_variance' %Use prior variance
- if any(isinf(bayestopt_.p2))
- error('Infinite prior variances detected. You cannot use the prior variances as the proposal density, if some variances are Inf.')
- else
- hh = diag(1./(bayestopt_.p2.^2));
- end
+ case 'hessian' %Baseline
+ %do nothing and use hessian from mode_compute
+ case 'prior_variance' %Use prior variance
+ if any(isinf(bayestopt_.p2))
+ error('Infinite prior variances detected. You cannot use the prior variances as the proposal density, if some variances are Inf.')
+ else
+ hh = diag(1./(bayestopt_.p2.^2));
+ end
+ hsd = sqrt(diag(hh));
+ invhess = inv(hh./(hsd*hsd'))./(hsd*hsd');
+ case 'identity_matrix' %Use identity
+ invhess = eye(nx);
+ otherwise %user specified matrix in file
+ try
+ load(options_.MCMC_jumping_covariance,'jumping_covariance')
+ hh=jumping_covariance;
+ catch
+ error(['No matrix named ''jumping_covariance'' could be found in ',options_.MCMC_jumping_covariance,'.mat'])
+ end
+ [nrow, ncol]=size(hh);
+ if ~isequal(nrow,ncol) && ~isequal(nrow,nx) %check if square and right size
+ error(['jumping_covariance matrix must be square and have ',num2str(nx),' rows and columns'])
+ end
+ try %check for positive definiteness
+ chol(hh);
hsd = sqrt(diag(hh));
invhess = inv(hh./(hsd*hsd'))./(hsd*hsd');
- case 'identity_matrix' %Use identity
- invhess = eye(nx);
- otherwise %user specified matrix in file
- try
- load(options_.MCMC_jumping_covariance,'jumping_covariance')
- hh=jumping_covariance;
- catch
- error(['No matrix named ''jumping_covariance'' could be found in ',options_.MCMC_jumping_covariance,'.mat'])
- end
- [nrow, ncol]=size(hh);
- if ~isequal(nrow,ncol) && ~isequal(nrow,nx) %check if square and right size
- error(['jumping_covariance matrix must be square and have ',num2str(nx),' rows and columns'])
- end
- try %check for positive definiteness
- chol(hh);
- hsd = sqrt(diag(hh));
- invhess = inv(hh./(hsd*hsd'))./(hsd*hsd');
- catch
- error(['Specified jumping_covariance is not positive definite'])
- end
+ catch
+ error(['Specified jumping_covariance is not positive definite'])
+ end
end
if (any(bayestopt_.pshape >0 ) && options_.mh_replic) || ...
@@ -457,9 +457,9 @@ if (any(bayestopt_.pshape >0 ) && options_.mh_replic) || ...
else
%get stored results if required
if options_.load_mh_file && options_.load_results_after_load_mh
- oo_load_mh=load([M_.fname '_results'],'oo_');
+ oo_load_mh=load([M_.fname '_results'],'oo_');
end
- if ~options_.nodiagnostic
+ if ~options_.nodiagnostic
if (options_.mh_replic>0 || (options_.load_mh_file && ~options_.load_results_after_load_mh))
oo_= McMCDiagnostics(options_, estim_params_, M_,oo_);
elseif options_.load_mh_file && options_.load_results_after_load_mh
@@ -481,20 +481,20 @@ if (any(bayestopt_.pshape >0 ) && options_.mh_replic) || ...
[oo_.posterior.metropolis.mean,oo_.posterior.metropolis.Variance] ...
= GetPosteriorMeanVariance(M_,options_.mh_drop);
elseif options_.load_mh_file && options_.load_results_after_load_mh
- %% load fields from previous MCMC run stored in results-file
+ %% load fields from previous MCMC run stored in results-file
field_names={'posterior_mode','posterior_std_at_mode',...% fields set by marginal_density
- 'posterior_mean','posterior_hpdinf','posterior_hpdsup','posterior_median','posterior_variance','posterior_std','posterior_deciles','posterior_density',...% fields set by GetPosteriorParametersStatistics
- 'prior_density',...%fields set by PlotPosteriorDistributions
- };
+ 'posterior_mean','posterior_hpdinf','posterior_hpdsup','posterior_median','posterior_variance','posterior_std','posterior_deciles','posterior_density',...% fields set by GetPosteriorParametersStatistics
+ 'prior_density',...%fields set by PlotPosteriorDistributions
+ };
for field_iter=1:size(field_names,2)
if isfield(oo_load_mh.oo_,field_names{1,field_iter})
oo_.(field_names{1,field_iter})=oo_load_mh.oo_.(field_names{1,field_iter});
end
- end
+ end
% field set by marginal_density
if isfield(oo_load_mh.oo_,'MarginalDensity') && isfield(oo_load_mh.oo_.MarginalDensity,'ModifiedHarmonicMean')
oo_.MarginalDensity.ModifiedHarmonicMean=oo_load_mh.oo_.MarginalDensity.ModifiedHarmonicMean;
- end
+ end
% field set by GetPosteriorMeanVariance
if isfield(oo_load_mh.oo_,'posterior') && isfield(oo_load_mh.oo_.posterior,'metropolis')
oo_.posterior.metropolis=oo_load_mh.oo_.posterior.metropolis;
@@ -691,76 +691,76 @@ if (~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.psha
%% Historical and smoothed variabes
%%
if ~options_.nograph
- [nbplt,nr,nc,lr,lc,nstar] = pltorg(n_varobs);
- if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
- fidTeX = fopen([M_.fname '_HistoricalAndSmoothedVariables.tex'],'w');
- fprintf(fidTeX,'%% TeX eps-loader file generated by dynare_estimation_1.m (Dynare).\n');
- fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
- fprintf(fidTeX,' \n');
- end
- for plt = 1:nbplt
- fh = dyn_figure(options_.nodisplay,'Name','Historical and smoothed variables');
- NAMES = [];
- if options_.TeX, TeXNAMES = []; end
- nstar0=min(nstar,n_varobs-(plt-1)*nstar);
- if gend==1
- marker_string{1,1}='-ro';
- marker_string{2,1}='--ko';
- else
- marker_string{1,1}='-r';
- marker_string{2,1}='--k';
+ [nbplt,nr,nc,lr,lc,nstar] = pltorg(n_varobs);
+ if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
+ fidTeX = fopen([M_.fname '_HistoricalAndSmoothedVariables.tex'],'w');
+ fprintf(fidTeX,'%% TeX eps-loader file generated by dynare_estimation_1.m (Dynare).\n');
+ fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
+ fprintf(fidTeX,' \n');
end
- for i=1:nstar0
- k = (plt-1)*nstar+i;
- subplot(nr,nc,i);
- plot(1:gend,yf(k,:),marker_string{1,1},'linewidth',1)
- hold on
- plot(1:gend,rawdata(:,k),marker_string{2,1},'linewidth',1)
- hold off
- name = options_.varobs{k};
- if isempty(NAMES)
- NAMES = name;
+ for plt = 1:nbplt
+ fh = dyn_figure(options_.nodisplay,'Name','Historical and smoothed variables');
+ NAMES = [];
+ if options_.TeX, TeXNAMES = []; end
+ nstar0=min(nstar,n_varobs-(plt-1)*nstar);
+ if gend==1
+ marker_string{1,1}='-ro';
+ marker_string{2,1}='--ko';
else
- NAMES = char(NAMES,name);
- end
- if ~isempty(options_.XTick)
- set(gca,'XTick',options_.XTick)
- set(gca,'XTickLabel',options_.XTickLabel)
- end
- if gend>1
- xlim([1 gend])
+ marker_string{1,1}='-r';
+ marker_string{2,1}='--k';
end
- if options_.TeX
- idx = strmatch(options_.varobs{k},M_.endo_names,'exact');
- texname = M_.endo_names_tex(idx,:);
- if isempty(TeXNAMES)
- TeXNAMES = ['$ ' deblank(texname) ' $'];
+ for i=1:nstar0
+ k = (plt-1)*nstar+i;
+ subplot(nr,nc,i);
+ plot(1:gend,yf(k,:),marker_string{1,1},'linewidth',1)
+ hold on
+ plot(1:gend,rawdata(:,k),marker_string{2,1},'linewidth',1)
+ hold off
+ name = options_.varobs{k};
+ if isempty(NAMES)
+ NAMES = name;
else
- TeXNAMES = char(TeXNAMES,['$ ' deblank(texname) ' $']);
+ NAMES = char(NAMES,name);
+ end
+ if ~isempty(options_.XTick)
+ set(gca,'XTick',options_.XTick)
+ set(gca,'XTickLabel',options_.XTickLabel)
+ end
+ if gend>1
+ xlim([1 gend])
+ end
+ if options_.TeX
+ idx = strmatch(options_.varobs{k},M_.endo_names,'exact');
+ texname = M_.endo_names_tex(idx,:);
+ if isempty(TeXNAMES)
+ TeXNAMES = ['$ ' deblank(texname) ' $'];
+ else
+ TeXNAMES = char(TeXNAMES,['$ ' deblank(texname) ' $']);
+ end
end
+ title(name,'Interpreter','none')
+ end
+ dyn_saveas(fh,[M_.fname '_HistoricalAndSmoothedVariables' int2str(plt)],options_.nodisplay,options_.graph_format);
+ if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
+ fprintf(fidTeX,'\\begin{figure}[H]\n');
+ for jj = 1:nstar0
+ fprintf(fidTeX,'\\psfrag{%s}[1][][0.5][0]{%s}\n',deblank(NAMES(jj,:)),deblank(TeXNAMES(jj,:)));
+ end
+ fprintf(fidTeX,'\\centering \n');
+ fprintf(fidTeX,'\\includegraphics[width=%2.2f\\textwidth]{%s_HistoricalAndSmoothedVariables%s}\n',options_.figures.textwidth*min(i/nc,1),M_.fname,int2str(plt));
+ fprintf(fidTeX,'\\caption{Historical and smoothed variables.}');
+ fprintf(fidTeX,'\\label{Fig:HistoricalAndSmoothedVariables:%s}\n',int2str(plt));
+ fprintf(fidTeX,'\\end{figure}\n');
+ fprintf(fidTeX,'\n');
end
- title(name,'Interpreter','none')
end
- dyn_saveas(fh,[M_.fname '_HistoricalAndSmoothedVariables' int2str(plt)],options_.nodisplay,options_.graph_format);
if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
- fprintf(fidTeX,'\\begin{figure}[H]\n');
- for jj = 1:nstar0
- fprintf(fidTeX,'\\psfrag{%s}[1][][0.5][0]{%s}\n',deblank(NAMES(jj,:)),deblank(TeXNAMES(jj,:)));
- end
- fprintf(fidTeX,'\\centering \n');
- fprintf(fidTeX,'\\includegraphics[width=%2.2f\\textwidth]{%s_HistoricalAndSmoothedVariables%s}\n',options_.figures.textwidth*min(i/nc,1),M_.fname,int2str(plt));
- fprintf(fidTeX,'\\caption{Historical and smoothed variables.}');
- fprintf(fidTeX,'\\label{Fig:HistoricalAndSmoothedVariables:%s}\n',int2str(plt));
- fprintf(fidTeX,'\\end{figure}\n');
fprintf(fidTeX,'\n');
+ fprintf(fidTeX,'%% End of TeX file.\n');
+ fclose(fidTeX);
end
end
- if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
- fprintf(fidTeX,'\n');
- fprintf(fidTeX,'%% End of TeX file.\n');
- fclose(fidTeX);
- end
- end
end
if options_.forecast > 0 && options_.mh_replic == 0 && ~options_.load_mh_file
diff --git a/matlab/dynare_estimation_init.m b/matlab/dynare_estimation_init.m
index d3e8f5b60029939b7d9d28ed81459931158187bd..5ee9a9fa717cd46a348c0d5f6ab2d94f64a9aa11 100644
--- a/matlab/dynare_estimation_init.m
+++ b/matlab/dynare_estimation_init.m
@@ -14,7 +14,7 @@ function [dataset_, dataset_info, xparam1, hh, M_, options_, oo_, estim_params_,
% estim_params_: structure storing information about estimated
% parameters
% bayestopt_: structure storing information about priors
-
+
% OUTPUTS
% dataset_: the dataset after required transformation
% dataset_info: Various informations about the dataset (descriptive statistics and missing observations).
@@ -28,7 +28,7 @@ function [dataset_, dataset_info, xparam1, hh, M_, options_, oo_, estim_params_,
% parameters
% bayestopt_: structure storing information about priors
% bounds: structure containing prior bounds
-%
+%
% SPECIAL REQUIREMENTS
% none
@@ -104,13 +104,13 @@ if options_.analytic_derivation && options_.fast_kalman_filter
end
% fast kalman filter is only available with kalman_algo == 0,1,3
-if options_.fast_kalman_filter
+if options_.fast_kalman_filter
if ~ismember(options_.kalman_algo, [0,1,3])
error(['estimation option conflict: fast_kalman_filter is only available ' ...
- 'with kalman_algo = 0, 1 or 3'])
+ 'with kalman_algo = 0, 1 or 3'])
elseif options_.block
error(['estimation option conflict: fast_kalman_filter is not available ' ...
- 'with block'])
+ 'with block'])
end
end
@@ -189,7 +189,7 @@ if ~isempty(estim_params_) && ~(isfield(estim_params_,'nvx') && sum(estim_params
% The posterior mode is not estimated.
error('Please change the mode_file option, the list of estimated parameters or set mode_compute>0.')
else
- % The posterior mode is estimated, the Hessian evaluated at the mode is not needed so we set values for the parameters missing in the mode file using the prior mean.
+ % The posterior mode is estimated, the Hessian evaluated at the mode is not needed so we set values for the parameters missing in the mode file using the prior mean.
if ~isempty(xd)
xparam1(xd) = mode_file.xparam1(md);
else
@@ -229,7 +229,7 @@ if ~isempty(estim_params_) && ~(isfield(estim_params_,'nvx') && sum(estim_params
error('Please change the mode_file option, the list of estimated parameters or set mode_compute>0.')
end
else
- % The posterior mode is estimated, the Hessian evaluated at the mode is not needed so we set values for the parameters missing in the mode file using the prior mean.
+ % The posterior mode is estimated, the Hessian evaluated at the mode is not needed so we set values for the parameters missing in the mode file using the prior mean.
if ~isempty(xd)
xparam1(xd) = mode_file.xparam1(md);
else
@@ -238,7 +238,7 @@ if ~isempty(estim_params_) && ~(isfield(estim_params_,'nvx') && sum(estim_params
end
end
else
- % The number of declared estimated parameters match the number of parameters in the mode file.
+ % The number of declared estimated parameters match the number of parameters in the mode file.
% Check that the parameters in the mode file and according to the current mod file are identical.
if ~isfield(mode_file,'parameter_names')
disp(['The posterior mode file ' options_.mode_file ' has been generated using an older version of Dynare. It cannot be verified if it matches the present model. Proceed at your own risk.'])
@@ -275,7 +275,7 @@ if ~isempty(estim_params_) && ~(isfield(estim_params_,'nvx') && sum(estim_params
error('Please change the mode_file option, the list of estimated parameters or set mode_compute>0.')
end
else
- % The posterior mode is estimated, the Hessian evaluated at the mode is not needed so we set values for the parameters missing in the mode file using the prior mean.
+ % The posterior mode is estimated, the Hessian evaluated at the mode is not needed so we set values for the parameters missing in the mode file using the prior mean.
if ~isempty(xd)
xparam1(xd) = mode_file.xparam1(md);
if isfield(mode_file,'hh')
@@ -322,8 +322,8 @@ if ~isempty(estim_params_) && ~(all(strcmp(fieldnames(estim_params_),'full_calib
bounds.lb = max(bounds.lb,lb);
bounds.ub = min(bounds.ub,ub);
else % estimated parameters but no declared priors
- % No priors are declared so Dynare will estimate the model by
- % maximum likelihood with inequality constraints for the parameters.
+ % No priors are declared so Dynare will estimate the model by
+ % maximum likelihood with inequality constraints for the parameters.
options_.mh_replic = 0;% No metropolis.
bounds.lb = lb;
bounds.ub = ub;
@@ -339,7 +339,7 @@ if ~isempty(estim_params_) && ~(all(strcmp(fieldnames(estim_params_),'full_calib
end
else
check_prior_bounds(xparam1,bounds,M_,estim_params_,options_,bayestopt_)
- end
+ end
end
if isempty(estim_params_) || all(strcmp(fieldnames(estim_params_),'full_calibration_detected')) || (isfield(estim_params_,'nvx') && sum(estim_params_.nvx+estim_params_.nvn+estim_params_.ncx+estim_params_.ncn+estim_params_.np)==0) % If estim_params_ is empty (e.g. when running the smoother on a calibrated model)
@@ -423,7 +423,7 @@ if options_.selected_variables_only
if options_.forecast > 0 && options_.mh_replic == 0 && ~options_.load_mh_file
fprintf('\nEstimation: The selected_variables_only option is incompatible with classical forecasts. It will be ignored.\n')
k3 = (1:M_.endo_nbr)';
- k3p = (1:M_.endo_nbr)';
+ k3p = (1:M_.endo_nbr)';
else
for i=1:size(var_list_,1)
k3 = [k3; strmatch(var_list_(i,:),M_.endo_names(dr.order_var,:), 'exact')];
@@ -551,7 +551,7 @@ ncx = estim_params_.ncx;
nvn = estim_params_.nvn;
ncn = estim_params_.ncn;
if estim_params_.np
- M.params(estim_params_.param_vals(:,1)) = xparam1(nvx+ncx+nvn+ncn+1:end);
+ M.params(estim_params_.param_vals(:,1)) = xparam1(nvx+ncx+nvn+ncn+1:end);
end
[oo_.steady_state, params,info] = evaluate_steady_state(oo_.steady_state,M,options_,oo_,steadystate_check_flag);
diff --git a/matlab/dynare_gradient.m b/matlab/dynare_gradient.m
index 91b4140c15bbd6b969518712854447f0b73188e8..fc15ba633bed7c13324927a38e608fa84b08e155 100644
--- a/matlab/dynare_gradient.m
+++ b/matlab/dynare_gradient.m
@@ -4,14 +4,14 @@ function [F,G] = dynare_gradient(fcn,x,epsilon,varargin)
% INPUTS:
% fcn [string] name of the matlab's function.
% x [double] m*1 vector (where the gradient is evaluated).
-% epsilon [double] scalar or m*1 vector of steps.
+% epsilon [double] scalar or m*1 vector of steps.
%
-% OUTPUTS:
+% OUTPUTS:
% F [double] n*1 vector, evaluation of the function at x.
% G [double] n*m matrix, evaluation of the gradient at x.
%
% OUTPUTS
-%
+%
% Copyright (C) 2010-2012 Dynare Team
%
% This file is part of Dynare.
@@ -51,7 +51,7 @@ for i=1:m
h = H(i,:);
else
h = H(:,i);
- end
+ end
[Fh,junk1,junk2,flag] = feval(fcn, x+transpose(h), varargin{:});
if flag
G(:,i) = (Fh-F)/epsilon;
diff --git a/matlab/dynare_graph.m b/matlab/dynare_graph.m
index 9400f624ee4b48568da340a1bff313a4b2936587..3717efa45f1b317078accec6217e2ba82bf6ad08 100644
--- a/matlab/dynare_graph.m
+++ b/matlab/dynare_graph.m
@@ -1,5 +1,5 @@
function dyn_graph=dynare_graph(dyn_graph,y,tit,x)
-% function dynare_graph(y,tit,x)
+% function dynare_graph(y,tit,x)
% graphs
%
% INPUT
@@ -32,7 +32,7 @@ function dyn_graph=dynare_graph(dyn_graph,y,tit,x)
if nargin < 4
x = (1:size(y,1))';
end
-nplot = dyn_graph.plot_nbr + 1;
+nplot = dyn_graph.plot_nbr + 1;
if nplot > dyn_graph.max_nplot
figure('Name',dyn_graph.figure_name);
nplot = 1;
@@ -46,7 +46,7 @@ for i=1:size(y,2)
if length(line_types) > 1
line_type = line_types{i};
end
-
+
plot(x,y(:,i),line_type);
hold on
end
diff --git a/matlab/dynare_graph_close.m b/matlab/dynare_graph_close.m
index ed4371034c6e55cc2dc6c2edee2bb580c8d7fc44..a91d9f63e9d070caf018ae72512e8564741055ae 100644
--- a/matlab/dynare_graph_close.m
+++ b/matlab/dynare_graph_close.m
@@ -1,5 +1,5 @@
function dynare_graph_close()
-% function dynare_graph_close()
+% function dynare_graph_close()
% close a figure
%
% INPUT
@@ -27,4 +27,3 @@ function dynare_graph_close()
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
diff --git a/matlab/dynare_graph_init.m b/matlab/dynare_graph_init.m
index 0783a4029add1e37533be7344686c8a424cda6d8..b97c2b5f84a4e38f1397d60902852ba604e4aeec 100644
--- a/matlab/dynare_graph_init.m
+++ b/matlab/dynare_graph_init.m
@@ -1,5 +1,5 @@
function dyn_graph=dynare_graph_init(figure_name,nplot,line_types,line_width)
-% function dynare_graph_init(figure_name,colors)
+% function dynare_graph_init(figure_name,colors)
% initializes set of graphs
%
% INPUTS:
diff --git a/matlab/dynare_identification.m b/matlab/dynare_identification.m
index e568909131a72f100dbda432a5d8c4396337d205..7b5c1e5569e803ecd1d8a1f952620e8896452288 100644
--- a/matlab/dynare_identification.m
+++ b/matlab/dynare_identification.m
@@ -3,8 +3,8 @@ function [pdraws, TAU, GAM, LRE, gp, H, JJ] = dynare_identification(options_iden
%
% INPUTS
% o options_ident [structure] identification options
-% o pdraws0 [matrix] optional: matrix of MC sample of model params.
-%
+% o pdraws0 [matrix] optional: matrix of MC sample of model params.
+%
% OUTPUTS
% o pdraws [matrix] matrix of MC sample of model params used
% o TAU, [matrix] MC sample of entries in the model solution (stacked vertically)
@@ -13,11 +13,11 @@ function [pdraws, TAU, GAM, LRE, gp, H, JJ] = dynare_identification(options_iden
% o gp, [matrix] derivatives of the Jacobian (LRE model)
% o H, [matrix] derivatives of the model solution
% o JJ [matrix] derivatives of the moments
-%
+%
% SPECIAL REQUIREMENTS
% None
-% main
+% main
%
% Copyright (C) 2010-2017 Dynare Team
%
@@ -67,7 +67,7 @@ else
if options_.diffuse_filter==1 %warning if estimation with diffuse filter was done, but not passed
warning('IDENTIFICATION:: Previously the diffuse_filter option was used, but it was not passed to the identification command. This may result in problems if your model contains unit roots.')
end
- if isfield(options_ident,'lik_init')
+ if isfield(options_ident,'lik_init')
options_.lik_init=options_ident.lik_init; %make options_ inherit lik_init
if options_ident.lik_init==3 %user specified diffuse filter using the lik_init option
options_ident.analytic_derivation=0; %diffuse filter not compatible with analytic derivation
@@ -195,7 +195,7 @@ if prior_exist
end
name=cell(nparam,1);
name_tex=cell(nparam,1);
- for jj=1:nparam
+ for jj=1:nparam
if options_.TeX
[param_name_temp, param_name_tex_temp]= get_the_name(jj,options_.TeX,M_,estim_params_,options_);
name_tex{jj,1} = strrep(param_name_tex_temp,'$','');
@@ -245,18 +245,18 @@ store_options_ident = options_ident;
if iload <=0
[I,J]=find(M_.lead_lag_incidence');
if prior_exist
-% if exist([fname_,'_mean.mat'],'file'),
-% % disp('Testing posterior mean')
-% load([fname_,'_mean'],'xparam1')
-% pmean = xparam1';
-% clear xparam1
-% end
-% if exist([fname_,'_mode.mat'],'file'),
-% % disp('Testing posterior mode')
-% load([fname_,'_mode'],'xparam1')
-% pmode = xparam1';
-% clear xparam1
-% end
+ % if exist([fname_,'_mean.mat'],'file'),
+ % % disp('Testing posterior mean')
+ % load([fname_,'_mean'],'xparam1')
+ % pmean = xparam1';
+ % clear xparam1
+ % end
+ % if exist([fname_,'_mode.mat'],'file'),
+ % % disp('Testing posterior mode')
+ % load([fname_,'_mode'],'xparam1')
+ % pmode = xparam1';
+ % clear xparam1
+ % end
params = set_prior(estim_params_,M_,options_)';
if all(bayestopt_.pshape == 0)
parameters = 'ML_Starting_value';
@@ -264,38 +264,38 @@ if iload <=0
disp('Testing ML Starting value')
else
switch parameters
- case 'calibration'
- parameters_TeX = 'Calibration';
- disp('Testing calibration')
- params(1,:) = get_all_parameters(estim_params_,M_);
- case 'posterior_mode'
- parameters_TeX = 'Posterior mode';
- disp('Testing posterior mode')
- params(1,:) = get_posterior_parameters('mode');
- case 'posterior_mean'
- parameters_TeX = 'Posterior mean';
- disp('Testing posterior mean')
- params(1,:) = get_posterior_parameters('mean');
- case 'posterior_median'
- parameters_TeX = 'Posterior median';
- disp('Testing posterior median')
- params(1,:) = get_posterior_parameters('median');
- case 'prior_mode'
- parameters_TeX = 'Prior mode';
- disp('Testing prior mode')
- params(1,:) = bayestopt_.p5(:);
- case 'prior_mean'
- parameters_TeX = 'Prior mean';
- disp('Testing prior mean')
- params(1,:) = bayestopt_.p1;
- otherwise
- disp('The option parameter_set has to be equal to:')
- disp(' ''posterior_mode'', ')
- disp(' ''posterior_mean'', ')
- disp(' ''posterior_median'', ')
- disp(' ''prior_mode'' or')
- disp(' ''prior_mean''.')
- error;
+ case 'calibration'
+ parameters_TeX = 'Calibration';
+ disp('Testing calibration')
+ params(1,:) = get_all_parameters(estim_params_,M_);
+ case 'posterior_mode'
+ parameters_TeX = 'Posterior mode';
+ disp('Testing posterior mode')
+ params(1,:) = get_posterior_parameters('mode');
+ case 'posterior_mean'
+ parameters_TeX = 'Posterior mean';
+ disp('Testing posterior mean')
+ params(1,:) = get_posterior_parameters('mean');
+ case 'posterior_median'
+ parameters_TeX = 'Posterior median';
+ disp('Testing posterior median')
+ params(1,:) = get_posterior_parameters('median');
+ case 'prior_mode'
+ parameters_TeX = 'Prior mode';
+ disp('Testing prior mode')
+ params(1,:) = bayestopt_.p5(:);
+ case 'prior_mean'
+ parameters_TeX = 'Prior mean';
+ disp('Testing prior mean')
+ params(1,:) = bayestopt_.p1;
+ otherwise
+ disp('The option parameter_set has to be equal to:')
+ disp(' ''posterior_mode'', ')
+ disp(' ''posterior_mean'', ')
+ disp(' ''posterior_median'', ')
+ disp(' ''prior_mode'' or')
+ disp(' ''prior_mean''.')
+ error;
end
end
else
@@ -313,31 +313,31 @@ if iload <=0
disp(['The model does not solve for ', parameters, ' with error code info = ', int2str(info(1))]),
skipline()
if info(1)==1
- disp('info==1 %! The model doesn''t determine the current variables uniquely.')
+ disp('info==1 %! The model doesn''t determine the current variables uniquely.')
elseif info(1)==2
- disp('info==2 %! MJDGGES returned an error code.')
+ disp('info==2 %! MJDGGES returned an error code.')
elseif info(1)==3
- disp('info==3 %! Blanchard & Kahn conditions are not satisfied: no stable equilibrium. ')
+ disp('info==3 %! Blanchard & Kahn conditions are not satisfied: no stable equilibrium. ')
elseif info(1)==4
- disp('info==4 %! Blanchard & Kahn conditions are not satisfied: indeterminacy. ')
+ disp('info==4 %! Blanchard & Kahn conditions are not satisfied: indeterminacy. ')
elseif info(1)==5
- disp('info==5 %! Blanchard & Kahn conditions are not satisfied: indeterminacy due to rank failure. ')
+ disp('info==5 %! Blanchard & Kahn conditions are not satisfied: indeterminacy due to rank failure. ')
elseif info(1)==6
- disp('info==6 %! The jacobian evaluated at the deterministic steady state is complex.')
+ disp('info==6 %! The jacobian evaluated at the deterministic steady state is complex.')
elseif info(1)==19
- disp('info==19 %! The steadystate routine thrown an exception (inconsistent deep parameters). ')
+ disp('info==19 %! The steadystate routine thrown an exception (inconsistent deep parameters). ')
elseif info(1)==20
- disp('info==20 %! Cannot find the steady state, info(2) contains the sum of square residuals (of the static equations). ')
+ disp('info==20 %! Cannot find the steady state, info(2) contains the sum of square residuals (of the static equations). ')
elseif info(1)==21
- disp('info==21 %! The steady state is complex, info(2) contains the sum of square of imaginary parts of the steady state.')
+ disp('info==21 %! The steady state is complex, info(2) contains the sum of square of imaginary parts of the steady state.')
elseif info(1)==22
- disp('info==22 %! The steady has NaNs. ')
+ disp('info==22 %! The steady has NaNs. ')
elseif info(1)==23
- disp('info==23 %! M_.params has been updated in the steadystate routine and has complex valued scalars. ')
+ disp('info==23 %! M_.params has been updated in the steadystate routine and has complex valued scalars. ')
elseif info(1)==24
- disp('info==24 %! M_.params has been updated in the steadystate routine and has some NaNs. ')
+ disp('info==24 %! M_.params has been updated in the steadystate routine and has some NaNs. ')
elseif info(1)==30
- disp('info==30 %! Ergodic variance can''t be computed. ')
+ disp('info==30 %! Ergodic variance can''t be computed. ')
end
disp('----------- ')
skipline()
@@ -367,12 +367,12 @@ if iload <=0
end
end
idehess_point.params=params;
-% siH = idemodel_point.siH;
-% siJ = idemoments_point.siJ;
-% siLRE = idelre_point.siLRE;
-% normH = max(abs(siH)')';
-% normJ = max(abs(siJ)')';
-% normLRE = max(abs(siLRE)')';
+ % siH = idemodel_point.siH;
+ % siJ = idemoments_point.siJ;
+ % siLRE = idelre_point.siLRE;
+ % normH = max(abs(siH)')';
+ % normJ = max(abs(siJ)')';
+ % normLRE = max(abs(siLRE)')';
save([IdentifDirectoryName '/' M_.fname '_identif.mat'], 'idehess_point', 'idemoments_point','idemodel_point', 'idelre_point','store_options_ident')
save([IdentifDirectoryName '/' M_.fname '_' parameters '_identif.mat'], 'idehess_point', 'idemoments_point','idemodel_point', 'idelre_point','store_options_ident')
disp_identification(params, idemodel_point, idemoments_point, name, advanced);
@@ -476,21 +476,21 @@ if iload <=0
stoH = zeros(size(stoH));
stoJJ = zeros(size(stoJJ));
stoLRE = zeros(size(stoLRE));
-
+
end
-
+
if SampleSize > 1
-% if isoctave || options_.console_mode,
-% console_waitbar(0,iteration/SampleSize);
-% else
- dyn_waitbar(iteration/SampleSize,h,['MC identification checks ',int2str(iteration),'/',int2str(SampleSize)])
-% end
+ % if isoctave || options_.console_mode,
+ % console_waitbar(0,iteration/SampleSize);
+ % else
+ dyn_waitbar(iteration/SampleSize,h,['MC identification checks ',int2str(iteration),'/',int2str(SampleSize)])
+ % end
end
end
-
+
end
-
-
+
+
if SampleSize > 1
if isoctave || options_.console_mode
fprintf('\n');
@@ -511,28 +511,28 @@ if iload <=0
siHnorm(iter,:) = vnorm(stoH(:,:,irun)./repmat(normTAU,1,nparam)).*normaliz1;
siLREnorm(iter,:) = vnorm(stoLRE(:,:,irun)./repmat(normLRE,1,nparam-offset)).*normaliz1(offset+1:end);
end
-
+
end
idemoments.siJnorm = siJnorm;
idemodel.siHnorm = siHnorm;
idelre.siLREnorm = siLREnorm;
save([IdentifDirectoryName '/' M_.fname '_identif.mat'], 'pdraws', 'idemodel', 'idemoments', 'idelre', ... %'indJJ', 'indH', 'indLRE', ...
- 'TAU', 'GAM', 'LRE','-append')
+ 'TAU', 'GAM', 'LRE','-append')
else
siJnorm = idemoments_point.siJnorm;
siHnorm = idemodel_point.siHnorm;
siLREnorm = idelre_point.siLREnorm;
end
-
+
else
load([IdentifDirectoryName '/' M_.fname '_identif'])
-% identFiles = dir([IdentifDirectoryName '/' M_.fname '_identif_*']);
+ % identFiles = dir([IdentifDirectoryName '/' M_.fname '_identif_*']);
parameters = store_options_ident.parameter_set;
options_ident.parameter_set = parameters;
options_ident.prior_mc=size(pdraws,1);
SampleSize = options_ident.prior_mc;
options_.options_ident = options_ident;
-end
+end
if nargout>3 && iload
filnam = dir([IdentifDirectoryName '/' M_.fname '_identif_*.mat']);
@@ -544,7 +544,7 @@ if nargout>3 && iload
H = cat(3,H, stoH(:,abs(iload),:));
JJ = cat(3,JJ, stoJJ(:,abs(iload),:));
gp = cat(3,gp, stoLRE(:,abs(iload),:));
-
+
end
end
diff --git a/matlab/dynare_sensitivity.m b/matlab/dynare_sensitivity.m
index cb1f332f1081d66068a48170c5c3bc71e1c586aa..acb0f12b543e436faebceebf95ac08b43990544b 100644
--- a/matlab/dynare_sensitivity.m
+++ b/matlab/dynare_sensitivity.m
@@ -24,7 +24,7 @@ function x0=dynare_sensitivity(options_gsa)
global M_ options_ oo_ bayestopt_ estim_params_
if options_.dsge_var
- error('Identification does not support DSGE-VARs at the current stage')
+ error('Identification does not support DSGE-VARs at the current stage')
end
fname_ = M_.fname;
@@ -43,7 +43,7 @@ end
if isfield(options_gsa,'morris') && options_gsa.morris==1
if isfield(options_gsa,'identification') && options_gsa.identification==0
-% options_gsa.redform=1;
+ % options_gsa.redform=1;
end
if isfield(options_gsa,'ppost') && options_gsa.ppost
error('sensitivity:: Morris is incompatible with posterior sampling')
@@ -144,7 +144,7 @@ if options_gsa.identification
if isfield(options_,'options_ident')
options_.options_ident.load_ident_files = options_gsa.load_ident_files;
options_.options_ident.useautocorr = options_gsa.useautocorr;
- options_.options_ident.ar = options_gsa.ar;
+ options_.options_ident.ar = options_gsa.ar;
options_ident=options_.options_ident;
else
options_ident=[];
@@ -213,7 +213,7 @@ if options_gsa.morris>2
disp('the option is reset at morris = 1 .')
options_gsa.morris=1;
end
-
+
if options_gsa.morris==1
if ~options_gsa.identification
options_gsa.redform=1;
@@ -231,12 +231,12 @@ if options_gsa.morris==1
options_gsa.alpha2_stab=1;
options_gsa.pvalue_ks=0;
options_gsa.pvalue_corr=0;
-% if options_gsa.morris==3,
-% options_gsa = set_default_option(options_gsa,'Nsam',256);
-% OutputDirectoryName = CheckPath('gsa/identif',M_.dname);
-% else
- OutputDirectoryName = CheckPath('gsa/screen',M_.dname);
-% end
+ % if options_gsa.morris==3,
+ % options_gsa = set_default_option(options_gsa,'Nsam',256);
+ % OutputDirectoryName = CheckPath('gsa/identif',M_.dname);
+ % else
+ OutputDirectoryName = CheckPath('gsa/screen',M_.dname);
+ % end
else
OutputDirectoryName = CheckPath('gsa',M_.dname);
end
@@ -252,8 +252,8 @@ if (options_gsa.load_stab || options_gsa.load_rmse || options_gsa.load_redform)
return
else
if isempty(strmatch('bkpprior',who('-file', filetoload),'exact'))
- disp('Warning! Missing prior info for saved sample') % trap for files previous
- disp('The saved files are generated with previous version of GSA package') % trap for files previous
+ disp('Warning! Missing prior info for saved sample') % trap for files previous
+ disp('The saved files are generated with previous version of GSA package') % trap for files previous
else
load(filetoload,'bkpprior')
if any(bayestopt_.pshape~=bkpprior.pshape) || ...
@@ -305,7 +305,7 @@ if options_gsa.redform && ~isempty(options_gsa.namendo)
nshock = nshock + estim_params_.nvn;
nshock = nshock + estim_params_.ncx;
nshock = nshock + estim_params_.ncn;
-
+
lpmat0=lpmat(:,1:nshock);
lpmat=lpmat(:,nshock+1:end);
istable=(1:size(lpmat,1));
@@ -314,7 +314,7 @@ if options_gsa.redform && ~isempty(options_gsa.namendo)
iindeterm=[];
save([OutputDirectoryName filesep M_.fname '_mc.mat'],'lpmat','lpmat0','istable','iunstable','iwrong','iindeterm')
options_gsa.load_stab=1;
-
+
x0 = stab_map_(OutputDirectoryName,options_gsa);
end
if strmatch(':',options_gsa.namendo,'exact')
@@ -326,7 +326,7 @@ if options_gsa.redform && ~isempty(options_gsa.namendo)
if strmatch(':',options_gsa.namlagendo,'exact')
options_gsa.namlagendo=M_.endo_names(1:M_.orig_endo_nbr,:);
end
-% options_.opt_gsa = options_gsa;
+ % options_.opt_gsa = options_gsa;
if options_gsa.morris==1
redform_screen(OutputDirectoryName,options_gsa);
else
@@ -365,54 +365,54 @@ if options_gsa.rmse
end
end
if isempty(a)
- if options_gsa.lik_only
- options_.smoother=0;
- options_.filter_step_ahead=[];
- options_.forecast=0;
- options_.filtered_vars=0;
- end
-% dynare_MC([],OutputDirectoryName,data,rawdata,data_info);
- if options_gsa.pprior
- TmpDirectoryName = ([M_.dname filesep 'gsa' filesep 'prior']);
- else
- TmpDirectoryName = ([M_.dname filesep 'gsa' filesep 'mc']);
- end
- if exist(TmpDirectoryName,'dir')
- mydelete([M_.fname '_filter_step_ahead*.mat'],[TmpDirectoryName filesep]);
- mydelete([M_.fname '_inno*.mat'],[TmpDirectoryName filesep]);
- mydelete([M_.fname '_smooth*.mat'],[TmpDirectoryName filesep]);
- mydelete([M_.fname '_update*.mat'],[TmpDirectoryName filesep]);
- filparam = dir([TmpDirectoryName filesep M_.fname '_param*.mat']);
- for j=1:length(filparam)
- if isempty(strmatch([M_.fname '_param_irf'],filparam(j).name))
- delete([TmpDirectoryName filesep filparam(j).name]);
+ if options_gsa.lik_only
+ options_.smoother=0;
+ options_.filter_step_ahead=[];
+ options_.forecast=0;
+ options_.filtered_vars=0;
+ end
+ % dynare_MC([],OutputDirectoryName,data,rawdata,data_info);
+ if options_gsa.pprior
+ TmpDirectoryName = ([M_.dname filesep 'gsa' filesep 'prior']);
+ else
+ TmpDirectoryName = ([M_.dname filesep 'gsa' filesep 'mc']);
+ end
+ if exist(TmpDirectoryName,'dir')
+ mydelete([M_.fname '_filter_step_ahead*.mat'],[TmpDirectoryName filesep]);
+ mydelete([M_.fname '_inno*.mat'],[TmpDirectoryName filesep]);
+ mydelete([M_.fname '_smooth*.mat'],[TmpDirectoryName filesep]);
+ mydelete([M_.fname '_update*.mat'],[TmpDirectoryName filesep]);
+ filparam = dir([TmpDirectoryName filesep M_.fname '_param*.mat']);
+ for j=1:length(filparam)
+ if isempty(strmatch([M_.fname '_param_irf'],filparam(j).name))
+ delete([TmpDirectoryName filesep filparam(j).name]);
+ end
end
+
end
-
- end
- prior_posterior_statistics('gsa',dataset_, dataset_info);
- if options_.bayesian_irf
- PosteriorIRF('gsa');
- end
- options_gsa.load_rmse=0;
- % else
- % if options_gsa.load_rmse==0,
- % disp('You already saved a MC filter/smoother analysis ')
- % disp('Do you want to overwrite ?')
- % pause;
- % if options_gsa.pprior
- % delete([OutputDirectoryName,'/',fname_,'_prior_*.mat'])
- % else
- % delete([OutputDirectoryName,'/',fname_,'_mc_*.mat'])
- % end
- % dynare_MC([],OutputDirectoryName);
- % options_gsa.load_rmse=0;
- % end
-
+ prior_posterior_statistics('gsa',dataset_, dataset_info);
+ if options_.bayesian_irf
+ PosteriorIRF('gsa');
+ end
+ options_gsa.load_rmse=0;
+ % else
+ % if options_gsa.load_rmse==0,
+ % disp('You already saved a MC filter/smoother analysis ')
+ % disp('Do you want to overwrite ?')
+ % pause;
+ % if options_gsa.pprior
+ % delete([OutputDirectoryName,'/',fname_,'_prior_*.mat'])
+ % else
+ % delete([OutputDirectoryName,'/',fname_,'_mc_*.mat'])
+ % end
+ % dynare_MC([],OutputDirectoryName);
+ % options_gsa.load_rmse=0;
+ % end
+
end
end
clear a;
-% filt_mc_(OutputDirectoryName,data_info);
+ % filt_mc_(OutputDirectoryName,data_info);
filt_mc_(OutputDirectoryName,options_gsa,dataset_,dataset_info);
end
options_.opt_gsa = options_gsa;
@@ -448,14 +448,14 @@ if options_gsa.glue
else
data = transpose(rawdata);
end
-
+
Obs.data = data;
Obs.time = [1:gend];
Obs.num = gend;
for j=1:length(options_.varobs)
Obs.name{j} = options_.varobs{j};
vj = options_.varobs{j};
-
+
jxj = strmatch(vj,lgy_(dr_.order_var,:),'exact');
js = strmatch(vj,lgy_,'exact');
if ~options_gsa.ppost
@@ -477,7 +477,7 @@ if options_gsa.glue
Lik(j).ini = 'yes';
Lik(j).isam = 1;
Lik(j).data = rmse_MC(:,j)';
-
+
if ~options_gsa.ppost
% y0 = squeeze(stock_smooth(:,jxj,:)) + ...
% kron(stock_ys(js,:),ones(size(stock_smooth,1),1));
@@ -490,14 +490,14 @@ if options_gsa.glue
Out1=Out;
end
ismoo(j)=jxj;
-
+
end
jsmoo = length(options_.varobs);
for j=1:M_.endo_nbr
if ~ismember(j,ismoo)
jsmoo=jsmoo+1;
vj=deblank(M_.endo_names(dr_.order_var(j),:));
- if ~options_gsa.ppost
+ if ~options_gsa.ppost
% y0 = squeeze(stock_smooth(:,j,:)) + ...
% kron(stock_ys(j,:),ones(size(stock_smooth,1),1));
% Out1(jsmoo).time = [1:size(y0,1)];
@@ -514,7 +514,7 @@ if options_gsa.glue
end
tit(M_.exo_names_orig_ord,:) = M_.exo_names;
for j=1:M_.exo_nbr
- Exo(j).name = deblank(tit(j,:));
+ Exo(j).name = deblank(tit(j,:));
end
if ~options_gsa.ppost
Lik(length(options_.varobs)+1).name = 'logpo';
@@ -525,10 +525,10 @@ if options_gsa.glue
Sam.name = bayestopt_.name;
Sam.dim = [size(x) 0];
Sam.data = [x];
-
+
Rem.id = 'Original';
Rem.ind= [1:size(x,1)];
-
+
Info.dynare=M_.fname;
Info.order_var=dr_.order_var;
Out=Out1;
@@ -539,7 +539,7 @@ if options_gsa.glue
Info.TypeofSample='post';
save([OutputDirectoryName,'/',fname_,'_glue_post.mat'], 'Out', 'Sam', 'Lik', 'Obs', 'Rem','Info', 'Exo')
%save([fname_,'_post_glue_smooth'], 'Out', 'Sam', 'Lik', 'Obs', 'Rem','Info')
-
+
else
if options_gsa.pprior
Info.TypeofSample='prior';
@@ -555,5 +555,5 @@ if options_gsa.glue
% save([OutputDirectoryName,'/',fname_,'_mc_glue_smooth'], 'Out', 'Sam', 'Lik', 'Obs', 'Rem')
end
end
-
+
end
diff --git a/matlab/dynare_solve.m b/matlab/dynare_solve.m
index 4e1c614e1e0e93986e53a6241c2a6dc979a46f39..b2b97819898b39040e116a2c19b4ed3a5984ebd2 100644
--- a/matlab/dynare_solve.m
+++ b/matlab/dynare_solve.m
@@ -141,19 +141,19 @@ if options.solve_algo == 0
if max(abs(fvec)) >= tolf
info = 1;
else
- info = 0;
+ info = 0;
end
else
info = 1;
end
elseif options.solve_algo == 1
- [x,info]=solve1(func,x,1:nn,1:nn,jacobian_flag,options.gstep, ...
+ [x,info]=solve1(func,x,1:nn,1:nn,jacobian_flag,options.gstep, ...
tolf,options.solve_tolx, ...
maxit,options.debug,varargin{:});
elseif options.solve_algo == 9
- [x,info]=trust_region(func,x,1:nn,1:nn,jacobian_flag,options.gstep, ...
- tolf,options.solve_tolx, ...
- maxit,options.debug,varargin{:});
+ [x,info]=trust_region(func,x,1:nn,1:nn,jacobian_flag,options.gstep, ...
+ tolf,options.solve_tolx, ...
+ maxit,options.debug,varargin{:});
elseif options.solve_algo == 2 || options.solve_algo == 4
if options.solve_algo == 2
@@ -233,4 +233,3 @@ elseif options.solve_algo == 11
else
error('DYNARE_SOLVE: option solve_algo must be one of [0,1,2,3,4,9,10,11]')
end
-
diff --git a/matlab/dynare_solve_block_or_bytecode.m b/matlab/dynare_solve_block_or_bytecode.m
index 6b3192f186136a136748f758f7faba737f4caa45..2a1f547b9bdca73af050837253a5a054d4d370c4 100644
--- a/matlab/dynare_solve_block_or_bytecode.m
+++ b/matlab/dynare_solve_block_or_bytecode.m
@@ -22,7 +22,7 @@ if options.block && ~options.bytecode
for b = 1:length(M.block_structure_stat.block)
ss = x;
if M.block_structure_stat.block(b).Simulation_Type ~= 1 && ...
- M.block_structure_stat.block(b).Simulation_Type ~= 2
+ M.block_structure_stat.block(b).Simulation_Type ~= 2
if options.solve_algo <= 4
[y, check] = dynare_solve('block_mfs_steadystate', ...
ss(M.block_structure_stat.block(b).variable), ...
@@ -60,7 +60,7 @@ elseif options.bytecode
elseif options.block
for b = 1:length(M.block_structure_stat.block)
if M.block_structure_stat.block(b).Simulation_Type ~= 1 && ...
- M.block_structure_stat.block(b).Simulation_Type ~= 2
+ M.block_structure_stat.block(b).Simulation_Type ~= 2
[y, check] = dynare_solve('block_bytecode_mfs_steadystate', ...
x(M.block_structure_stat ...
.block(b).variable), ...
diff --git a/matlab/dynare_squeeze.m b/matlab/dynare_squeeze.m
index 50fbec35622fb1d76f0cdd81494653d48502dddc..85e66f332e971b3e8bf22b5c0d21ae7528d1eae8 100644
--- a/matlab/dynare_squeeze.m
+++ b/matlab/dynare_squeeze.m
@@ -18,7 +18,7 @@ function B = dynare_squeeze(A)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-sizA = size(A);
+sizA = size(A);
dimA = length(sizA);
switch dimA
case 1
diff --git a/matlab/dynatype.m b/matlab/dynatype.m
index 35276028f42e90d2d20a3d105d5251c5b6064f1f..a65b2961b31b4703ff9b27f99fe3997a0051d536 100644
--- a/matlab/dynatype.m
+++ b/matlab/dynatype.m
@@ -1,12 +1,12 @@
function dynatype (s,var_list)
% function dynatype (s,var_list)
-% This optional command saves the simulation results in a text file. The name of each
+% This optional command saves the simulation results in a text file. The name of each
% variable preceeds the corresponding results. This command must follow SIMUL.
-%
+%
% INPUTS
% s: filename
% var_list: vector of selected endogenous variables
-%
+%
% OUTPUTS
% none
%
diff --git a/matlab/dyntable.m b/matlab/dyntable.m
index 86d92fc2b81043a3ee02aa548276e29881c9e2aa..b7a2262642ccfb81ecde7654c5e1af3f3da2850b 100644
--- a/matlab/dyntable.m
+++ b/matlab/dyntable.m
@@ -7,10 +7,10 @@ function dyntable(options_,title,headers,labels,values,label_width,val_width,val
% labels [n by nchar] character array of labels for label column
% values [matrix] matrix of values to display
% label_width [scalar] Width of the label
-% val_width [scalar] Width of value column
+% val_width [scalar] Width of value column
% val_precis [integer] precision of displayed values
-%
-%
+%
+%
% Copyright (C) 2002-2016 Dynare Team
%
% This file is part of Dynare.
@@ -33,9 +33,9 @@ if options_.noprint
end
%% get width of label column
-if ~isempty(label_width)
+if ~isempty(label_width)
label_width = max(size(deblank(char(headers(1,:),labels)),2)+2, ...
- label_width);
+ label_width);
else %use default length
label_width = max(size(deblank(char(headers(1,:),labels)),2))+2;
end
@@ -51,7 +51,7 @@ if any(values) < 0 %add one character for minus sign
values_length = values_length+1;
end
-%% get width of header strings
+%% get width of header strings
headers_length = max(size(deblank(headers(2:end,:)),2));
if ~isempty(val_width)
val_width = max(max(headers_length,values_length)+2,val_width);
diff --git a/matlab/endogenous_prior.m b/matlab/endogenous_prior.m
index c2930abad8a9b82e316df4ce2c4e26483bb0c85f..0c2fda36dcd7ed8a6ca7c1bc1c337c64c46cc5c9 100644
--- a/matlab/endogenous_prior.m
+++ b/matlab/endogenous_prior.m
@@ -2,17 +2,17 @@ function [lnpriormom] = endogenous_prior(data,Pstar,BayesInfo,H)
% Computes the endogenous log prior addition to the initial prior
%
% INPUTS
-% data [double] n*T vector of data observations
+% data [double] n*T vector of data observations
% Pstar [double] k*k matrix of
-% BayesInfo [structure]
+% BayesInfo [structure]
%
% OUTPUTS
% lnpriormom [double] scalar of log endogenous prior value
% Code to implement notes on endogenous priors by Lawrence Christiano,
% specified in the appendix of:
-% �Introducing Financial Frictions and Unemployment into a Small Open Economy Model�
-% by Lawrence J. Christiano, Mathias Trabandt and Karl Walentin (2011), Journal of Economic Dynamics and Control
+% Introducing Financial Frictions and Unemployment into a Small Open Economy Model
+% by Lawrence J. Christiano, Mathias Trabandt and Karl Walentin (2011), Journal of Economic Dynamics and Control
% this is the 'mother' of the priors on the model parameters.
% the priors include a metric across some choosen moments of the (supposedly
% pre-sample) data.
@@ -23,7 +23,7 @@ function [lnpriormom] = endogenous_prior(data,Pstar,BayesInfo,H)
% appropriate adjustment to the following code.
-% Copyright (C) 2011 Lawrence J. Christiano, Mathias Trabandt and Karl Walentin
+% Copyright (C) 2011 Lawrence J. Christiano, Mathias Trabandt and Karl Walentin
% Copyright (C) 2013 Dynare Team
%
% This file is part of Dynare.
@@ -75,7 +75,7 @@ end
% Finally, we have the sampling uncertainty measure Shat:
Shat=C0 +(1-1/(2+1))*(C1+C1')...
- +(1-2/(2+1))*(C2+C2');
+ +(1-2/(2+1))*(C2+C2');
% Model variances below:
mf=BayesInfo.mf1;
@@ -86,4 +86,3 @@ Ftheta=diag(Z*Pstar(:,mf)+H);
% below commented out line is for Del Negro Schorfheide style priors:
% lnpriormom=-.5*n*TT*log(2*pi)-.5*TT*log(det(sigma))-.5*TT*trace(inv(sigma)*(gamyy-2*phi'*gamxy+phi'*gamxx*phi));
lnpriormom=.5*n*log(Tsamp/(2*pi))-.5*log(det(Shat))-.5*Tsamp*(Fhat-Ftheta)'/Shat*(Fhat-Ftheta);
-
diff --git a/matlab/endogenous_prior_restrictions.m b/matlab/endogenous_prior_restrictions.m
index df38fd5468de97e7c2fa4f712d0a5ab2b1c631ed..ef734acd125ce59ce0c1c565f6a7ef72a72497eb 100644
--- a/matlab/endogenous_prior_restrictions.m
+++ b/matlab/endogenous_prior_restrictions.m
@@ -1,173 +1,173 @@
-function [info, info_irf, info_moment, data_irf, data_moment] = endogenous_prior_restrictions(T,R,Model,DynareOptions,DynareResults)
-% Check for prior (sign) restrictions on irf's and theoretical moments
-%
-% INPUTS
-% T [double] n*n state space matrix
-% R [double] n*k matrix of shocks
-% Model [structure]
-% DynareOptions [structure]
-% DynareResults [structure]
-
-% OUTPUTS
-% info [double] check if prior restrictions are matched by the
-% model and related info
-% info_irf [double] array of test checks for all individual irf restrictions
-% info_moment [double] array of test checks for all individual moment restrictions
-%
-
-% Copyright (C) 2013-2017 Dynare Team
-%
-% 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/>.
-
-info=[0 0];
-info_irf=[];
-info_moment=[];
-data_irf=[];
-data_moment=[];
-
-endo_prior_restrictions.irf= DynareOptions.endogenous_prior_restrictions.irf;
-endo_prior_restrictions.moment= DynareOptions.endogenous_prior_restrictions.moment;
-
-if ~isempty(endo_prior_restrictions.irf)
- data_irf=cell(size(endo_prior_restrictions.irf,1),1);
- if DynareOptions.order>1
- error('The algorithm for prior (sign) restrictions on irf''s is currently restricted to first-order decision rules')
- return
- end
- varlist=Model.endo_names(DynareResults.dr.order_var,:);
- if isempty(T)
- [T,R,SteadyState,infox,Model,DynareOptions,DynareResults] = dynare_resolve(Model,DynareOptions,DynareResults);
- else % check if T and R are given in the restricted form!!!
- if size(T,1)<size(varlist,1)
- varlist=varlist(DynareResults.dr.restrict_var_list,:);
- end
- % check if endo_prior_restrictions.irf{:,1} variables are in varlist
- varlistok=1;
- for j=1:size(endo_prior_restrictions.irf,1)
- if isempty(strmatch(endo_prior_restrictions.irf{j,1},varlist,'exact'))
- varlistok=0;
- end
- end
- if ~varlistok
- varlist=Model.endo_names(DynareResults.dr.order_var,:);
- [T,R,SteadyState,infox,Model,DynareOptions,DynareResults] = dynare_resolve(Model,DynareOptions,DynareResults);
- end
- end
- NT=1;
- for j=1:size(endo_prior_restrictions.irf,1)
- NT=max(NT,max(endo_prior_restrictions.irf{j,3}));
- end
- info_irf=ones(size(endo_prior_restrictions.irf,1),2);
- for t=1:NT
- if ~DynareOptions.relative_irf
- RR = T^(t-1)*R*diag(sqrt(diag(Model.Sigma_e)));
- else
- RR = T^(t-1)*R*100;
- end
- for j=1:size(endo_prior_restrictions.irf,1)
- if endo_prior_restrictions.irf{j,3}~=t
- continue
- end
- iendo=strmatch(endo_prior_restrictions.irf{j,1},varlist,'exact');
- iexo=strmatch(endo_prior_restrictions.irf{j,2},Model.exo_names,'exact');
- data_irf{j}=[data_irf{j}; [t RR(iendo,iexo)]];
- if (RR(iendo,iexo)>endo_prior_restrictions.irf{j,4}(1)) && (RR(iendo,iexo)<endo_prior_restrictions.irf{j,4}(2))
- info_irf(j,:)=info_irf(j,:).*[0, 0];
- else
- if RR(iendo,iexo)<endo_prior_restrictions.irf{j,4}(1)
- delt = (RR(iendo,iexo)-endo_prior_restrictions.irf{j,4}(1))^2;
- else
- delt = (RR(iendo,iexo)-endo_prior_restrictions.irf{j,4}(2))^2;
- end
- info_irf(j,:)=info_irf(j,:).*[49, delt];
- end
- end
- end
- if any(info_irf)
- info=[49,sum(info_irf(:,2))];
- end
-
-end
-
-if ~isempty(endo_prior_restrictions.moment)
- if DynareOptions.order>1
- error('The algorithm for prior (sign) restrictions on moments is currently restricted to first-order decision rules')
- return
- end
- data_moment=cell(size(endo_prior_restrictions.moment,1),1);
- var_list_=endo_prior_restrictions.moment{1,1};
- for j=1:size(endo_prior_restrictions.moment,1)
- tmp=endo_prior_restrictions.moment{j,1};
- if ~ismember(tmp,cellstr(var_list_))
- var_list_ = char(var_list_, tmp);
- end
- tmp=endo_prior_restrictions.moment{j,2};
- if ~ismember(tmp,cellstr(var_list_))
- var_list_ = char(var_list_, tmp);
- end
- end
- NTmax=0;
- NTmin=0;
- for j=1:size(endo_prior_restrictions.moment,1)
- NTmax=max(NTmax,max(endo_prior_restrictions.moment{j,3}));
- NTmin=min(NTmin,min(endo_prior_restrictions.moment{j,3}));
- end
- info_moment=ones(size(endo_prior_restrictions.moment,1),2);
- nvar = size(var_list_,1);
- ivar=zeros(nvar,1);
- for i=1:nvar
- i_tmp = strmatch(var_list_(i,:),Model.endo_names,'exact');
- if isempty(i_tmp)
- error (['One of the variable specified does not exist'])
- else
- ivar(i) = i_tmp;
- end
- end
- DynareOptions.ar = max(abs(NTmin),NTmax);
- [gamma_y,stationary_vars] = th_autocovariances(DynareResults.dr, ivar, Model, DynareOptions,1);
- for t=NTmin:NTmax
- RR = gamma_y{abs(t)+1};
- if t==0
- RR = RR./(sqrt(diag(RR))*sqrt(diag(RR))')-eye(nvar)+diag(diag(gamma_y{t+1})); % becomes correlation
- end
- for j=1:size(endo_prior_restrictions.moment,1)
- if endo_prior_restrictions.moment{j,3}~=t
- continue
- end
- iendo1 = strmatch(endo_prior_restrictions.moment{j,1},var_list_,'exact');
- iendo2 = strmatch(endo_prior_restrictions.moment{j,2},var_list_,'exact');
- if t>0
- tmp0 = iendo1;
- iendo1=iendo2;
- iendo2=tmp0;
- end
- data_moment{j}=[data_moment{j}; [t RR(iendo1,iendo2)]];
- if (RR(iendo1,iendo2)>endo_prior_restrictions.moment{j,4}(1)) && (RR(iendo1,iendo2)<endo_prior_restrictions.moment{j,4}(2))
- info_moment(j,:)=info_moment(j,:).*[0, 0];
- else
- if RR(iendo1,iendo2)<endo_prior_restrictions.moment{j,4}(1)
- delt = (RR(iendo1,iendo2)-endo_prior_restrictions.moment{j,4}(1))^2;
- else
- delt = (RR(iendo1,iendo2)-endo_prior_restrictions.moment{j,4}(2))^2;
- end
- info_moment(j,:)=info_moment(j,:).*[49, delt];
- end
- end
- end
- if any(info_moment)
- info=[49, info(2) + sum(info_moment(:,2))];
- end
+function [info, info_irf, info_moment, data_irf, data_moment] = endogenous_prior_restrictions(T,R,Model,DynareOptions,DynareResults)
+% Check for prior (sign) restrictions on irf's and theoretical moments
+%
+% INPUTS
+% T [double] n*n state space matrix
+% R [double] n*k matrix of shocks
+% Model [structure]
+% DynareOptions [structure]
+% DynareResults [structure]
+
+% OUTPUTS
+% info [double] check if prior restrictions are matched by the
+% model and related info
+% info_irf [double] array of test checks for all individual irf restrictions
+% info_moment [double] array of test checks for all individual moment restrictions
+%
+
+% Copyright (C) 2013-2017 Dynare Team
+%
+% 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/>.
+
+info=[0 0];
+info_irf=[];
+info_moment=[];
+data_irf=[];
+data_moment=[];
+
+endo_prior_restrictions.irf= DynareOptions.endogenous_prior_restrictions.irf;
+endo_prior_restrictions.moment= DynareOptions.endogenous_prior_restrictions.moment;
+
+if ~isempty(endo_prior_restrictions.irf)
+ data_irf=cell(size(endo_prior_restrictions.irf,1),1);
+ if DynareOptions.order>1
+ error('The algorithm for prior (sign) restrictions on irf''s is currently restricted to first-order decision rules')
+ return
+ end
+ varlist=Model.endo_names(DynareResults.dr.order_var,:);
+ if isempty(T)
+ [T,R,SteadyState,infox,Model,DynareOptions,DynareResults] = dynare_resolve(Model,DynareOptions,DynareResults);
+ else % check if T and R are given in the restricted form!!!
+ if size(T,1)<size(varlist,1)
+ varlist=varlist(DynareResults.dr.restrict_var_list,:);
+ end
+ % check if endo_prior_restrictions.irf{:,1} variables are in varlist
+ varlistok=1;
+ for j=1:size(endo_prior_restrictions.irf,1)
+ if isempty(strmatch(endo_prior_restrictions.irf{j,1},varlist,'exact'))
+ varlistok=0;
+ end
+ end
+ if ~varlistok
+ varlist=Model.endo_names(DynareResults.dr.order_var,:);
+ [T,R,SteadyState,infox,Model,DynareOptions,DynareResults] = dynare_resolve(Model,DynareOptions,DynareResults);
+ end
+ end
+ NT=1;
+ for j=1:size(endo_prior_restrictions.irf,1)
+ NT=max(NT,max(endo_prior_restrictions.irf{j,3}));
+ end
+ info_irf=ones(size(endo_prior_restrictions.irf,1),2);
+ for t=1:NT
+ if ~DynareOptions.relative_irf
+ RR = T^(t-1)*R*diag(sqrt(diag(Model.Sigma_e)));
+ else
+ RR = T^(t-1)*R*100;
+ end
+ for j=1:size(endo_prior_restrictions.irf,1)
+ if endo_prior_restrictions.irf{j,3}~=t
+ continue
+ end
+ iendo=strmatch(endo_prior_restrictions.irf{j,1},varlist,'exact');
+ iexo=strmatch(endo_prior_restrictions.irf{j,2},Model.exo_names,'exact');
+ data_irf{j}=[data_irf{j}; [t RR(iendo,iexo)]];
+ if (RR(iendo,iexo)>endo_prior_restrictions.irf{j,4}(1)) && (RR(iendo,iexo)<endo_prior_restrictions.irf{j,4}(2))
+ info_irf(j,:)=info_irf(j,:).*[0, 0];
+ else
+ if RR(iendo,iexo)<endo_prior_restrictions.irf{j,4}(1)
+ delt = (RR(iendo,iexo)-endo_prior_restrictions.irf{j,4}(1))^2;
+ else
+ delt = (RR(iendo,iexo)-endo_prior_restrictions.irf{j,4}(2))^2;
+ end
+ info_irf(j,:)=info_irf(j,:).*[49, delt];
+ end
+ end
+ end
+ if any(info_irf)
+ info=[49,sum(info_irf(:,2))];
+ end
+
+end
+
+if ~isempty(endo_prior_restrictions.moment)
+ if DynareOptions.order>1
+ error('The algorithm for prior (sign) restrictions on moments is currently restricted to first-order decision rules')
+ return
+ end
+ data_moment=cell(size(endo_prior_restrictions.moment,1),1);
+ var_list_=endo_prior_restrictions.moment{1,1};
+ for j=1:size(endo_prior_restrictions.moment,1)
+ tmp=endo_prior_restrictions.moment{j,1};
+ if ~ismember(tmp,cellstr(var_list_))
+ var_list_ = char(var_list_, tmp);
+ end
+ tmp=endo_prior_restrictions.moment{j,2};
+ if ~ismember(tmp,cellstr(var_list_))
+ var_list_ = char(var_list_, tmp);
+ end
+ end
+ NTmax=0;
+ NTmin=0;
+ for j=1:size(endo_prior_restrictions.moment,1)
+ NTmax=max(NTmax,max(endo_prior_restrictions.moment{j,3}));
+ NTmin=min(NTmin,min(endo_prior_restrictions.moment{j,3}));
+ end
+ info_moment=ones(size(endo_prior_restrictions.moment,1),2);
+ nvar = size(var_list_,1);
+ ivar=zeros(nvar,1);
+ for i=1:nvar
+ i_tmp = strmatch(var_list_(i,:),Model.endo_names,'exact');
+ if isempty(i_tmp)
+ error (['One of the variable specified does not exist'])
+ else
+ ivar(i) = i_tmp;
+ end
+ end
+ DynareOptions.ar = max(abs(NTmin),NTmax);
+ [gamma_y,stationary_vars] = th_autocovariances(DynareResults.dr, ivar, Model, DynareOptions,1);
+ for t=NTmin:NTmax
+ RR = gamma_y{abs(t)+1};
+ if t==0
+ RR = RR./(sqrt(diag(RR))*sqrt(diag(RR))')-eye(nvar)+diag(diag(gamma_y{t+1})); % becomes correlation
+ end
+ for j=1:size(endo_prior_restrictions.moment,1)
+ if endo_prior_restrictions.moment{j,3}~=t
+ continue
+ end
+ iendo1 = strmatch(endo_prior_restrictions.moment{j,1},var_list_,'exact');
+ iendo2 = strmatch(endo_prior_restrictions.moment{j,2},var_list_,'exact');
+ if t>0
+ tmp0 = iendo1;
+ iendo1=iendo2;
+ iendo2=tmp0;
+ end
+ data_moment{j}=[data_moment{j}; [t RR(iendo1,iendo2)]];
+ if (RR(iendo1,iendo2)>endo_prior_restrictions.moment{j,4}(1)) && (RR(iendo1,iendo2)<endo_prior_restrictions.moment{j,4}(2))
+ info_moment(j,:)=info_moment(j,:).*[0, 0];
+ else
+ if RR(iendo1,iendo2)<endo_prior_restrictions.moment{j,4}(1)
+ delt = (RR(iendo1,iendo2)-endo_prior_restrictions.moment{j,4}(1))^2;
+ else
+ delt = (RR(iendo1,iendo2)-endo_prior_restrictions.moment{j,4}(2))^2;
+ end
+ info_moment(j,:)=info_moment(j,:).*[49, delt];
+ end
+ end
+ end
+ if any(info_moment)
+ info=[49, info(2) + sum(info_moment(:,2))];
+ end
end
\ No newline at end of file
diff --git a/matlab/ep/ep_problem_2.m b/matlab/ep/ep_problem_2.m
index d31f0e7aa1da97e66a04521493fe6548661a3bd8..9168631bfe5a06927da76bb836f360d2357e36b6 100644
--- a/matlab/ep/ep_problem_2.m
+++ b/matlab/ep/ep_problem_2.m
@@ -78,7 +78,7 @@ for i = 1:order+1
else
k1 = (nnodes-1)*(i-1)+k;
end
- if hybrid_order == 2 && (k > 1 || i == order)
+ if hybrid_order == 2 && (k > 1 || i == order)
z = [Y(i_cols_p,1);
Y(i_cols_s,1);
Y(i_cols_f,k1)+h_correction(i_hc)];
@@ -93,10 +93,10 @@ for i = 1:order+1
% in first period we don't keep track of
% predetermined variables
i_cols_A = [i_cols_As - ny; i_cols_Af];
- A1(i_rows,i_cols_A) = A1(i_rows,i_cols_A) + weights(k)*jacobian(eq_index,i_cols_1);
+ A1(i_rows,i_cols_A) = A1(i_rows,i_cols_A) + weights(k)*jacobian(eq_index,i_cols_1);
else
i_cols_A = [i_cols_Ap; i_cols_As; i_cols_Af];
- A1(i_rows,i_cols_A) = A1(i_rows,i_cols_A) + weights(k)*jacobian(eq_index,i_cols_j);
+ A1(i_rows,i_cols_A) = A1(i_rows,i_cols_A) + weights(k)*jacobian(eq_index,i_cols_j);
end
else
d1 = dynamic_model(z,innovation,params,steady_state,i+1);
@@ -180,7 +180,7 @@ for j=1:world_nbr
nzA{i,j} = [offset_r+ir,offset_c+icA(ic), v]';
else
d1 = dynamic_model(Y(i_rows_y,j),x,params, ...
- steady_state,i+1);
+ steady_state,i+1);
end
res(:,i,j) = d1(eq_index);
i_rows_y = i_rows_y + ny;
diff --git a/matlab/ep/euler_equation_error.m b/matlab/ep/euler_equation_error.m
index 16de7a4b1681310943a1a6674235933ae50e2771..758f8785e350bf57b5948cfbb90c40feea627044 100644
--- a/matlab/ep/euler_equation_error.m
+++ b/matlab/ep/euler_equation_error.m
@@ -18,7 +18,7 @@ i_fwrd = find(M.lead_lag_incidence(3,:));
x1 = [x(2:end,:); zeros(1,M.exo_nbr)];
for i=1:length(nodes)
x2 = x1;
- x2(2,:) = x2(2,:) + nodes(i,:);
+ x2(2,:) = x2(2,:) + nodes(i,:);
[y2, info_convergence, endogenousvariablespaths] = ...
extended_path_core(ep.periods, M.endo_nbr, M.exo_nbr, ...
innovations.positive_var_indx, x2, ep.init, ...
@@ -26,7 +26,7 @@ for i=1:length(nodes)
ep.stochastic.order, M, pfm, ep.stochastic.algo, ...
ep.solve_algo, ep.stack_solve_algo, options.lmmcp, ...
options, oo, []);
-
+
z = [y0(i_pred); y1; y2(i_fwrd)];
res(:,i) = dynamic_model(z,x,M.params,oo.steady_state,2);
end
diff --git a/matlab/ep/extended_path.m b/matlab/ep/extended_path.m
index 03a7e4ed70dec7b8844f4f2979db4cf7b053b804..92f6be081936bb8a7b2e6e559fb34b331c35ac1f 100644
--- a/matlab/ep/extended_path.m
+++ b/matlab/ep/extended_path.m
@@ -3,7 +3,7 @@ function [ts, DynareResults] = extended_path(initialconditions, samplesize, exog
% Stochastic simulation of a non linear DSGE model using the Extended Path method (Fair and Taylor 1983). A time
% series of size T is obtained by solving T perfect foresight models.
%
-% INPUTS
+% INPUTS
% o initialconditions [double] m*1 array, where m is the number of endogenous variables in the model.
% o samplesize [integer] scalar, size of the sample to be simulated.
% o exogenousvariables [double] T*n array, values for the structural innovations.
@@ -11,7 +11,7 @@ function [ts, DynareResults] = extended_path(initialconditions, samplesize, exog
% o DynareModel [struct] M_
% o DynareResults [struct] oo_
%
-% OUTPUTS
+% OUTPUTS
% o ts [dseries] m*samplesize array, the simulations.
% o results [cell]
%
@@ -96,7 +96,7 @@ end
if any(isnan(endogenous_variables_paths(:)))
sl = find(~isnan(endogenous_variables_paths));
nn = size(endogenous_variables_paths, 1);
- endogenous_variables_paths = reshape(endogenous_variables_paths(sl), nn, length(sl)/nn);
+ endogenous_variables_paths = reshape(endogenous_variables_paths(sl), nn, length(sl)/nn);
end
ts = dseries(transpose(endogenous_variables_paths), initial_period, cellstr(DynareModel.endo_names));
diff --git a/matlab/ep/extended_path_core.m b/matlab/ep/extended_path_core.m
index 33c42b619c5ecc25506af06cc47e01740db7c66c..851a82202264d62d0d90d452efffff37cc74b2ae 100644
--- a/matlab/ep/extended_path_core.m
+++ b/matlab/ep/extended_path_core.m
@@ -1,8 +1,8 @@
function [y, info_convergence, endogenousvariablespaths] = extended_path_core(periods,endo_nbr,exo_nbr,positive_var_indx, ...
- exo_simul,init,initial_conditions,...
- steady_state, ...
- debug,bytecode_flag,order,M,pfm,algo,solve_algo,stack_solve_algo,...
- olmmcp,options,oo,initialguess)
+ exo_simul,init,initial_conditions,...
+ steady_state, ...
+ debug,bytecode_flag,order,M,pfm,algo,solve_algo,stack_solve_algo,...
+ olmmcp,options,oo,initialguess)
% Copyright (C) 2016 Dynare Team
%
@@ -58,7 +58,7 @@ if flag
options.solve_algo = solve_algo;
options.stack_solve_algo = stack_solve_algo;
tmp = perfect_foresight_solver_core(M, options, oo);
- if ~tmp.deterministic_simulation.status
+ if ~tmp.deterministic_simulation.status
info_convergence = false;
else
info_convergence = true;
diff --git a/matlab/ep/extended_path_homotopy.m b/matlab/ep/extended_path_homotopy.m
index 463003d2bfc5b76559106b46cf97a57c3742a952..47259765c035c04dc99ff0fbf1f7132a17b12d97 100644
--- a/matlab/ep/extended_path_homotopy.m
+++ b/matlab/ep/extended_path_homotopy.m
@@ -17,7 +17,7 @@ function [info_convergence, endo_simul] = extended_path_homotopy(endo_simul, exo
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
+
endo_simul0 = endo_simul;
if ismember(method, [1, 2])
noconvergence = true;
diff --git a/matlab/ep/extended_path_initialization.m b/matlab/ep/extended_path_initialization.m
index 1a9e6d22ec63a7b4338dd43f2c9599a7036e824f..fb8581bc687c0c265ef21214cfbb922ab851abbe 100644
--- a/matlab/ep/extended_path_initialization.m
+++ b/matlab/ep/extended_path_initialization.m
@@ -2,15 +2,15 @@ function [initial_conditions, innovations, pfm, ep, verbosity, DynareOptions, Dy
% Initialization of the extended path routines.
%
-% INPUTS
+% INPUTS
% o initial_conditions [double] m*1 array, where m is the number of endogenous variables in the model.
% o sample_size [integer] scalar, size of the sample to be simulated.
% o exogenousvariables [double] T*n array, values for the structural innovations.
-% o DynareOptions [struct] options_
+% o DynareOptions [struct] options_
% o DynareModel [struct] M_
% o DynareResults [struct] oo_
%
-% OUTPUTS
+% OUTPUTS
%
% ALGORITHM
%
@@ -118,7 +118,7 @@ pfm.nnzA = DynareModel.NNZDerivatives(1);
if ep.stochastic.order > 0
[nodes,weights,nnodes] = setup_integration_nodes(DynareOptions.ep,pfm);
pfm.nodes = nodes;
- pfm.weights = weights;
+ pfm.weights = weights;
pfm.nnodes = nnodes;
% compute number of blocks
[block_nbr,pfm.world_nbr] = get_block_world_nbr(ep.stochastic.algo,nnodes,ep.stochastic.order,ep.periods);
@@ -136,4 +136,3 @@ elseif DynareOptions.ep.solve_algo == 11
DynareOptions.mcppath.ub = repmat(ub,block_nbr,1);
end
pfm.block_nbr = block_nbr;
-
diff --git a/matlab/ep/extended_path_mc.m b/matlab/ep/extended_path_mc.m
index f03bca8aed605ea79d4aca87b71414c5c30706ef..72d3a55f4725f08b4945b320cdb211dc6b640a91 100644
--- a/matlab/ep/extended_path_mc.m
+++ b/matlab/ep/extended_path_mc.m
@@ -3,7 +3,7 @@ function Simulations = extended_path_mc(initialconditions, samplesize, replic, e
% Stochastic simulation of a non linear DSGE model using the Extended Path method (Fair and Taylor 1983). A time
% series of size T is obtained by solving T perfect foresight models.
%
-% INPUTS
+% INPUTS
% o initialconditions [double] m*1 array, where m is the number of endogenous variables in the model.
% o samplesize [integer] scalar, size of the sample to be simulated.
% o exogenousvariables [double] T*n array, values for the structural innovations.
@@ -11,7 +11,7 @@ function Simulations = extended_path_mc(initialconditions, samplesize, replic, e
% o DynareModel [struct] M_
% o DynareResults [struct] oo_
%
-% OUTPUTS
+% OUTPUTS
% o ts [dseries] m*samplesize array, the simulations.
% o results [cell]
%
@@ -65,7 +65,7 @@ vexo = NaN(innovations.effective_number_of_shocks, samplesize+1, replic);
info = NaN(replic, 1);
if ep.parallel
- % Use the Parallel toolbox.
+ % Use the Parallel toolbox.
parfor i=1:replic
innovations_ = innovations;
DynareResults_ = DynareResults;
diff --git a/matlab/ep/setup_integration_nodes.m b/matlab/ep/setup_integration_nodes.m
index 85d1be960c4bc5bdf93b0aa8862ea0c3b04234bc..5a6d206c1bbb7cd4cc1306823fd8c55c348661e1 100644
--- a/matlab/ep/setup_integration_nodes.m
+++ b/matlab/ep/setup_integration_nodes.m
@@ -1,39 +1,39 @@
function [nodes,weights,nnodes] = setup_integration_nodes(EpOptions,pfm)
- if EpOptions.stochastic.order
- % Compute weights and nodes for the stochastic version of the extended path.
- switch EpOptions.IntegrationAlgorithm
- case 'Tensor-Gaussian-Quadrature'
- % Get the nodes and weights from a univariate Gauss-Hermite quadrature.
- [nodes0,weights0] = gauss_hermite_weights_and_nodes(EpOptions.stochastic.quadrature.nodes);
- % Replicate the univariate nodes for each innovation and dates, and, if needed, correlate them.
- nodes0 = repmat(nodes0,1,pfm.number_of_shocks*pfm.stochastic_order)*kron(eye(pfm.stochastic_order),pfm.Omega);
- % Put the nodes and weights in cells
- for i=1:pfm.number_of_shocks
- rr(i) = {nodes0(:,i)};
- ww(i) = {weights0};
- end
- % Build the tensorial grid
- nodes = cartesian_product_of_sets(rr{:});
- weights = prod(cartesian_product_of_sets(ww{:}),2);
- nnodes = length(weights);
- case 'Stroud-Cubature-3'
- [nodes,weights] = cubature_with_gaussian_weight(pfm.number_of_shocks*pfm.stochastic_order,3,'Stroud')
- nodes = kron(eye(pfm.stochastic_order),transpose(pfm.Omega))*nodes;
- weights = weights;
- nnodes = length(weights);
- case 'Stroud-Cubature-5'
- [nodes,weights] = cubature_with_gaussian_weight(pfm.number_of_shocks*pfm.stochastic_order,5,'Stroud')
- nodes = kron(eye(pfm.stochastic_order),transpose(pfm.Omega))*nodes;
- weights = weights;
- nnodes = length(weights);
- case 'Unscented'
- p = pfm.number_of_shocks;
- k = EpOptions.ut.k;
- C = sqrt(pfm.number_of_shocks + k)*pfm.Omega';
- nodes = [zeros(1,p); -C; C];
- weights = [k/(p+k); (1/(2*(p+k)))*ones(2*p,1)];
- nnodes = 2*p+1;
- otherwise
- error('Stochastic extended path:: Unknown integration algorithm!')
+if EpOptions.stochastic.order
+ % Compute weights and nodes for the stochastic version of the extended path.
+ switch EpOptions.IntegrationAlgorithm
+ case 'Tensor-Gaussian-Quadrature'
+ % Get the nodes and weights from a univariate Gauss-Hermite quadrature.
+ [nodes0,weights0] = gauss_hermite_weights_and_nodes(EpOptions.stochastic.quadrature.nodes);
+ % Replicate the univariate nodes for each innovation and dates, and, if needed, correlate them.
+ nodes0 = repmat(nodes0,1,pfm.number_of_shocks*pfm.stochastic_order)*kron(eye(pfm.stochastic_order),pfm.Omega);
+ % Put the nodes and weights in cells
+ for i=1:pfm.number_of_shocks
+ rr(i) = {nodes0(:,i)};
+ ww(i) = {weights0};
end
+ % Build the tensorial grid
+ nodes = cartesian_product_of_sets(rr{:});
+ weights = prod(cartesian_product_of_sets(ww{:}),2);
+ nnodes = length(weights);
+ case 'Stroud-Cubature-3'
+ [nodes,weights] = cubature_with_gaussian_weight(pfm.number_of_shocks*pfm.stochastic_order,3,'Stroud')
+ nodes = kron(eye(pfm.stochastic_order),transpose(pfm.Omega))*nodes;
+ weights = weights;
+ nnodes = length(weights);
+ case 'Stroud-Cubature-5'
+ [nodes,weights] = cubature_with_gaussian_weight(pfm.number_of_shocks*pfm.stochastic_order,5,'Stroud')
+ nodes = kron(eye(pfm.stochastic_order),transpose(pfm.Omega))*nodes;
+ weights = weights;
+ nnodes = length(weights);
+ case 'Unscented'
+ p = pfm.number_of_shocks;
+ k = EpOptions.ut.k;
+ C = sqrt(pfm.number_of_shocks + k)*pfm.Omega';
+ nodes = [zeros(1,p); -C; C];
+ weights = [k/(p+k); (1/(2*(p+k)))*ones(2*p,1)];
+ nnodes = 2*p+1;
+ otherwise
+ error('Stochastic extended path:: Unknown integration algorithm!')
end
+end
diff --git a/matlab/ep/setup_stochastic_perfect_foresight_model_solver.m b/matlab/ep/setup_stochastic_perfect_foresight_model_solver.m
index 2bf6829eeb0e9a9f561967af543ee270206fc422..0ddf45360974919c687963bb652ec1bb7dd7361a 100644
--- a/matlab/ep/setup_stochastic_perfect_foresight_model_solver.m
+++ b/matlab/ep/setup_stochastic_perfect_foresight_model_solver.m
@@ -16,7 +16,7 @@ function pfm = setup_stochastic_perfect_foresight_model_solver(DynareModel,Dynar
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
+
pfm.lead_lag_incidence = DynareModel.lead_lag_incidence;
pfm.ny = DynareModel.endo_nbr;
pfm.Sigma = DynareModel.Sigma_e;
@@ -71,4 +71,3 @@ pfm.verbose = DynareOptions.ep.verbosity;
pfm.maxit_ = DynareOptions.simul.maxit;
pfm.tolerance = DynareOptions.dynatol.f;
pfm.use_bytecode = DynareOptions.ep.use_bytecode ;
-
diff --git a/matlab/ep/solve_stochastic_perfect_foresight_model.m b/matlab/ep/solve_stochastic_perfect_foresight_model.m
index ab13e3043ac6c89cd30f80aff549f6fb7f3b57b6..12373d15d9d66f2c37caac128e91948fe90a4ada 100644
--- a/matlab/ep/solve_stochastic_perfect_foresight_model.m
+++ b/matlab/ep/solve_stochastic_perfect_foresight_model.m
@@ -17,219 +17,219 @@ function [flag,endo_simul,err] = solve_stochastic_perfect_foresight_model(endo_s
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- flag = 0;
- err = 0;
- stop = 0;
+flag = 0;
+err = 0;
+stop = 0;
- params = pfm.params;
- steady_state = pfm.steady_state;
- ny = pfm.ny;
- periods = pfm.periods;
- dynamic_model = pfm.dynamic_model;
- lead_lag_incidence = pfm.lead_lag_incidence;
- nyp = pfm.nyp;
- nyf = pfm.nyf;
- i_cols_1 = pfm.i_cols_1;
- i_cols_A1 = pfm.i_cols_A1;
- i_cols_j = pfm.i_cols_j;
- i_cols_T = nonzeros(lead_lag_incidence(1:2,:)');
+params = pfm.params;
+steady_state = pfm.steady_state;
+ny = pfm.ny;
+periods = pfm.periods;
+dynamic_model = pfm.dynamic_model;
+lead_lag_incidence = pfm.lead_lag_incidence;
+nyp = pfm.nyp;
+nyf = pfm.nyf;
+i_cols_1 = pfm.i_cols_1;
+i_cols_A1 = pfm.i_cols_A1;
+i_cols_j = pfm.i_cols_j;
+i_cols_T = nonzeros(lead_lag_incidence(1:2,:)');
- maxit = pfm.maxit_;
- tolerance = pfm.tolerance;
- verbose = pfm.verbose;
+maxit = pfm.maxit_;
+tolerance = pfm.tolerance;
+verbose = pfm.verbose;
- number_of_shocks = size(exo_simul,2);
+number_of_shocks = size(exo_simul,2);
- [nodes,weights] = gauss_hermite_weights_and_nodes(nnodes);
+[nodes,weights] = gauss_hermite_weights_and_nodes(nnodes);
- if number_of_shocks>1
- nodes = repmat(nodes,1,number_of_shocks)*chol(pfm.Sigma);
- % to be fixed for Sigma ~= I
- for i=1:number_of_shocks
- rr(i) = {nodes(:,i)};
- ww(i) = {weights};
- end
- nodes = cartesian_product_of_sets(rr{:});
- weights = prod(cartesian_product_of_sets(ww{:}),2);
- nnodes = nnodes^number_of_shocks;
- else
- nodes = nodes*sqrt(pfm.Sigma);
+if number_of_shocks>1
+ nodes = repmat(nodes,1,number_of_shocks)*chol(pfm.Sigma);
+ % to be fixed for Sigma ~= I
+ for i=1:number_of_shocks
+ rr(i) = {nodes(:,i)};
+ ww(i) = {weights};
end
+ nodes = cartesian_product_of_sets(rr{:});
+ weights = prod(cartesian_product_of_sets(ww{:}),2);
+ nnodes = nnodes^number_of_shocks;
+else
+ nodes = nodes*sqrt(pfm.Sigma);
+end
- innovations = zeros(periods+2,number_of_shocks);
+innovations = zeros(periods+2,number_of_shocks);
- if verbose
- disp ([' -----------------------------------------------------']);
- disp (['MODEL SIMULATION :']);
- fprintf('\n');
- end
+if verbose
+ disp ([' -----------------------------------------------------']);
+ disp (['MODEL SIMULATION :']);
+ fprintf('\n');
+end
- z = endo_simul(find(lead_lag_incidence'));
- [d1,jacobian] = dynamic_model(z,exo_simul,params,steady_state,2);
+z = endo_simul(find(lead_lag_incidence'));
+[d1,jacobian] = dynamic_model(z,exo_simul,params,steady_state,2);
- % Each column of Y represents a different world
- % The upper right cells are unused
- % The first row block is ny x 1
- % The second row block is ny x nnodes
- % The third row block is ny x nnodes^2
- % and so on until size ny x nnodes^order
- world_nbr = nnodes^order;
- Y = repmat(endo_simul(:),1,world_nbr);
+% Each column of Y represents a different world
+% The upper right cells are unused
+% The first row block is ny x 1
+% The second row block is ny x nnodes
+% The third row block is ny x nnodes^2
+% and so on until size ny x nnodes^order
+world_nbr = nnodes^order;
+Y = repmat(endo_simul(:),1,world_nbr);
- % The columns of A map the elements of Y such that
- % each block of Y with ny rows are unfolded column wise
- dimension = ny*(sum(nnodes.^(0:order-1),2)+(periods-order)*world_nbr);
- if order == 0
- i_upd_r = (1:ny*periods);
- i_upd_y = i_upd_r + ny;
- else
- i_upd_r = zeros(dimension,1);
- i_upd_y = i_upd_r;
- i_upd_r(1:ny) = (1:ny);
- i_upd_y(1:ny) = ny+(1:ny);
- i1 = ny+1;
- i2 = 2*ny;
- n1 = ny+1;
- n2 = 2*ny;
- for i=2:periods
- k = n1:n2;
- for j=1:nnodes^min(i-1,order)
- i_upd_r(i1:i2) = (n1:n2)+(j-1)*ny*periods;
- i_upd_y(i1:i2) = (n1:n2)+ny+(j-1)*ny*(periods+2);
- i1 = i2+1;
- i2 = i2+ny;
- end
- n1 = n2+1;
- n2 = n2+ny;
+% The columns of A map the elements of Y such that
+% each block of Y with ny rows are unfolded column wise
+dimension = ny*(sum(nnodes.^(0:order-1),2)+(periods-order)*world_nbr);
+if order == 0
+ i_upd_r = (1:ny*periods);
+ i_upd_y = i_upd_r + ny;
+else
+ i_upd_r = zeros(dimension,1);
+ i_upd_y = i_upd_r;
+ i_upd_r(1:ny) = (1:ny);
+ i_upd_y(1:ny) = ny+(1:ny);
+ i1 = ny+1;
+ i2 = 2*ny;
+ n1 = ny+1;
+ n2 = 2*ny;
+ for i=2:periods
+ k = n1:n2;
+ for j=1:nnodes^min(i-1,order)
+ i_upd_r(i1:i2) = (n1:n2)+(j-1)*ny*periods;
+ i_upd_y(i1:i2) = (n1:n2)+ny+(j-1)*ny*(periods+2);
+ i1 = i2+1;
+ i2 = i2+ny;
end
+ n1 = n2+1;
+ n2 = n2+ny;
end
- icA = [find(lead_lag_incidence(1,:)) find(lead_lag_incidence(2,:))+world_nbr*ny ...
- find(lead_lag_incidence(3,:))+2*world_nbr*ny]';
- h1 = clock;
- for iter = 1:maxit
- h2 = clock;
- A1 = sparse([],[],[],ny*(sum(nnodes.^(0:order-1),2)+1),dimension,(order+1)*world_nbr*nnz(jacobian));
- res = zeros(ny,periods,world_nbr);
- i_rows = 1:ny;
- i_cols = find(lead_lag_incidence');
- i_cols_p = i_cols(1:nyp);
- i_cols_s = i_cols(nyp+(1:ny));
- i_cols_f = i_cols(nyp+ny+(1:nyf));
- i_cols_A = i_cols;
- i_cols_Ap = i_cols_p;
- i_cols_As = i_cols_s;
- i_cols_Af = i_cols_f - ny;
- for i = 1:order+1
- i_w_p = 1;
- for j = 1:nnodes^(i-1)
- innovation = exo_simul;
- if i > 1
- innovation(i+1,:) = nodes(mod(j-1,nnodes)+1,:);
- end
- if i <= order
- for k=1:nnodes
- y = [Y(i_cols_p,i_w_p);
- Y(i_cols_s,j);
- Y(i_cols_f,(j-1)*nnodes+k)];
- [d1,jacobian] = dynamic_model(y,innovation,params,steady_state,i+1);
- if i == 1
- % in first period we don't keep track of
- % predetermined variables
- i_cols_A = [i_cols_As - ny; i_cols_Af];
- A1(i_rows,i_cols_A) = A1(i_rows,i_cols_A) + weights(k)*jacobian(:,i_cols_1);
- else
- i_cols_A = [i_cols_Ap; i_cols_As; i_cols_Af];
- A1(i_rows,i_cols_A) = A1(i_rows,i_cols_A) + weights(k)*jacobian(:,i_cols_j);
- end
- res(:,i,j) = res(:,i,j)+weights(k)*d1;
- i_cols_Af = i_cols_Af + ny;
- end
- else
+end
+icA = [find(lead_lag_incidence(1,:)) find(lead_lag_incidence(2,:))+world_nbr*ny ...
+ find(lead_lag_incidence(3,:))+2*world_nbr*ny]';
+h1 = clock;
+for iter = 1:maxit
+ h2 = clock;
+ A1 = sparse([],[],[],ny*(sum(nnodes.^(0:order-1),2)+1),dimension,(order+1)*world_nbr*nnz(jacobian));
+ res = zeros(ny,periods,world_nbr);
+ i_rows = 1:ny;
+ i_cols = find(lead_lag_incidence');
+ i_cols_p = i_cols(1:nyp);
+ i_cols_s = i_cols(nyp+(1:ny));
+ i_cols_f = i_cols(nyp+ny+(1:nyf));
+ i_cols_A = i_cols;
+ i_cols_Ap = i_cols_p;
+ i_cols_As = i_cols_s;
+ i_cols_Af = i_cols_f - ny;
+ for i = 1:order+1
+ i_w_p = 1;
+ for j = 1:nnodes^(i-1)
+ innovation = exo_simul;
+ if i > 1
+ innovation(i+1,:) = nodes(mod(j-1,nnodes)+1,:);
+ end
+ if i <= order
+ for k=1:nnodes
y = [Y(i_cols_p,i_w_p);
Y(i_cols_s,j);
- Y(i_cols_f,j)];
+ Y(i_cols_f,(j-1)*nnodes+k)];
[d1,jacobian] = dynamic_model(y,innovation,params,steady_state,i+1);
if i == 1
% in first period we don't keep track of
% predetermined variables
i_cols_A = [i_cols_As - ny; i_cols_Af];
- A1(i_rows,i_cols_A) = jacobian(:,i_cols_1);
+ A1(i_rows,i_cols_A) = A1(i_rows,i_cols_A) + weights(k)*jacobian(:,i_cols_1);
else
i_cols_A = [i_cols_Ap; i_cols_As; i_cols_Af];
- A1(i_rows,i_cols_A) = jacobian(:,i_cols_j);
+ A1(i_rows,i_cols_A) = A1(i_rows,i_cols_A) + weights(k)*jacobian(:,i_cols_j);
end
- res(:,i,j) = d1;
+ res(:,i,j) = res(:,i,j)+weights(k)*d1;
i_cols_Af = i_cols_Af + ny;
end
- i_rows = i_rows + ny;
- if mod(j,nnodes) == 0
- i_w_p = i_w_p + 1;
- end
- if i > 1
- if mod(j,nnodes) == 0
- i_cols_Ap = i_cols_Ap + ny;
- end
- i_cols_As = i_cols_As + ny;
- end
- end
- i_cols_p = i_cols_p + ny;
- i_cols_s = i_cols_s + ny;
- i_cols_f = i_cols_f + ny;
- end
- nzA = cell(periods,world_nbr);
- for j=1:world_nbr
- i_rows_y = find(lead_lag_incidence')+(order+1)*ny;
- offset_c = ny*(sum(nnodes.^(0:order-1),2)+j-1);
- offset_r = (j-1)*ny;
- for i=order+2:periods
- [d1,jacobian] = dynamic_model(Y(i_rows_y,j), ...
- exo_simul,params, ...
- steady_state,i+1);
- if i == periods
- [ir,ic,v] = find(jacobian(:,i_cols_T));
+ else
+ y = [Y(i_cols_p,i_w_p);
+ Y(i_cols_s,j);
+ Y(i_cols_f,j)];
+ [d1,jacobian] = dynamic_model(y,innovation,params,steady_state,i+1);
+ if i == 1
+ % in first period we don't keep track of
+ % predetermined variables
+ i_cols_A = [i_cols_As - ny; i_cols_Af];
+ A1(i_rows,i_cols_A) = jacobian(:,i_cols_1);
else
- [ir,ic,v] = find(jacobian(:,i_cols_j));
+ i_cols_A = [i_cols_Ap; i_cols_As; i_cols_Af];
+ A1(i_rows,i_cols_A) = jacobian(:,i_cols_j);
end
- nzA{i,j} = [offset_r+ir,offset_c+icA(ic), v]';
res(:,i,j) = d1;
- i_rows_y = i_rows_y + ny;
- offset_c = offset_c + world_nbr*ny;
- offset_r = offset_r + world_nbr*ny;
+ i_cols_Af = i_cols_Af + ny;
+ end
+ i_rows = i_rows + ny;
+ if mod(j,nnodes) == 0
+ i_w_p = i_w_p + 1;
+ end
+ if i > 1
+ if mod(j,nnodes) == 0
+ i_cols_Ap = i_cols_Ap + ny;
+ end
+ i_cols_As = i_cols_As + ny;
end
end
- err = max(abs(res(i_upd_r)));
- if err < tolerance
- stop = 1;
- if verbose
- fprintf('\n') ;
- disp([' Total time of simulation :' num2str(etime(clock,h1))]) ;
- fprintf('\n') ;
- disp([' Convergency obtained.']) ;
- fprintf('\n') ;
+ i_cols_p = i_cols_p + ny;
+ i_cols_s = i_cols_s + ny;
+ i_cols_f = i_cols_f + ny;
+ end
+ nzA = cell(periods,world_nbr);
+ for j=1:world_nbr
+ i_rows_y = find(lead_lag_incidence')+(order+1)*ny;
+ offset_c = ny*(sum(nnodes.^(0:order-1),2)+j-1);
+ offset_r = (j-1)*ny;
+ for i=order+2:periods
+ [d1,jacobian] = dynamic_model(Y(i_rows_y,j), ...
+ exo_simul,params, ...
+ steady_state,i+1);
+ if i == periods
+ [ir,ic,v] = find(jacobian(:,i_cols_T));
+ else
+ [ir,ic,v] = find(jacobian(:,i_cols_j));
end
- flag = 0;% Convergency obtained.
- endo_simul = reshape(Y(:,1),ny,periods+2);%Y(ny+(1:ny),1);
- % figure;plot(Y(16:ny:(periods+2)*ny,:))
- % pause
- break
+ nzA{i,j} = [offset_r+ir,offset_c+icA(ic), v]';
+ res(:,i,j) = d1;
+ i_rows_y = i_rows_y + ny;
+ offset_c = offset_c + world_nbr*ny;
+ offset_r = offset_r + world_nbr*ny;
end
- A2 = [nzA{:}]';
- A = [A1; sparse(A2(:,1),A2(:,2),A2(:,3),ny*(periods-order-1)*world_nbr,dimension)];
- dy = -A\res(i_upd_r);
- Y(i_upd_y) = Y(i_upd_y) + dy;
end
-
- if ~stop
+ err = max(abs(res(i_upd_r)));
+ if err < tolerance
+ stop = 1;
if verbose
fprintf('\n') ;
- disp([' Total time of simulation :' num2str(etime(clock,h1))]) ;
+ disp([' Total time of simulation :' num2str(etime(clock,h1))]) ;
fprintf('\n') ;
- disp(['WARNING : maximum number of iterations is reached (modify options_.simul.maxit).']) ;
+ disp([' Convergency obtained.']) ;
fprintf('\n') ;
end
- flag = 1;% more iterations are needed.
- endo_simul = 1;
+ flag = 0;% Convergency obtained.
+ endo_simul = reshape(Y(:,1),ny,periods+2);%Y(ny+(1:ny),1);
+ % figure;plot(Y(16:ny:(periods+2)*ny,:))
+ % pause
+ break
end
+ A2 = [nzA{:}]';
+ A = [A1; sparse(A2(:,1),A2(:,2),A2(:,3),ny*(periods-order-1)*world_nbr,dimension)];
+ dy = -A\res(i_upd_r);
+ Y(i_upd_y) = Y(i_upd_y) + dy;
+end
+
+if ~stop
if verbose
- disp (['-----------------------------------------------------']) ;
+ fprintf('\n') ;
+ disp([' Total time of simulation :' num2str(etime(clock,h1))]) ;
+ fprintf('\n') ;
+ disp(['WARNING : maximum number of iterations is reached (modify options_.simul.maxit).']) ;
+ fprintf('\n') ;
end
+ flag = 1;% more iterations are needed.
+ endo_simul = 1;
+end
+if verbose
+ disp (['-----------------------------------------------------']) ;
+end
diff --git a/matlab/ep/solve_stochastic_perfect_foresight_model_1.m b/matlab/ep/solve_stochastic_perfect_foresight_model_1.m
index e884f155e04e0e6e2fb3fe42c429583c8a7df126..25a0e16736673df11d904f4915a6be782c3b899e 100644
--- a/matlab/ep/solve_stochastic_perfect_foresight_model_1.m
+++ b/matlab/ep/solve_stochastic_perfect_foresight_model_1.m
@@ -83,7 +83,7 @@ end
% and so on until size ny x nnodes^order
world_nbr = pfm.world_nbr;
Y = endo_simul(:,2:end-1);
-Y = repmat(Y,1,world_nbr);
+Y = repmat(Y,1,world_nbr);
pfm.y0 = endo_simul(:,1);
% The columns of A map the elements of Y such that
diff --git a/matlab/ep/stroud_judd_7.5.8.m b/matlab/ep/stroud_judd_7.5.8.m
index 40c9bc08db172e2a6d371d468f099ffa1817e73d..57d3f8d62fe278bac2d253f1d1b2753027b97a8c 100644
--- a/matlab/ep/stroud_judd_7.5.8.m
+++ b/matlab/ep/stroud_judd_7.5.8.m
@@ -1,9 +1,9 @@
-function [X,w]=stroud_judd_7.5.8(d)
-
- E = eye(d);
- X = cell(2*d,1);
- m = 1;
- for i=1:d
- X{m} = E(:,i);
- m = m+1;
- X{m} = -E(:,i);
+function [X,w]=stroud_judd_7.5.8.5.8(d)
+
+E = eye(d);
+X = cell(2*d,1);
+m = 1;
+for i=1:d
+ X{m} = E(:,i);
+ m = m+1;
+ X{m} = -E(:,i);
diff --git a/matlab/ep_residuals.m b/matlab/ep_residuals.m
index b2acd72942fddb7ac55a64164a192ce1e82b108e..2d3780cd328d6687fcde29653bd852e0a2b2ca94 100644
--- a/matlab/ep_residuals.m
+++ b/matlab/ep_residuals.m
@@ -1,20 +1,20 @@
function r = ep_residuals(x, y, ix, iy, steadystate, dr, maximum_lag, endo_nbr)
% Inversion of the extended path simulation approach. This routine computes the innovations needed to
-% reproduce the time path of a subset of endogenous variables.
-%
+% reproduce the time path of a subset of endogenous variables.
+%
% INPUTS
-% o x [double] n*1 vector, time t innovations.
+% o x [double] n*1 vector, time t innovations.
% o y [double] n*1 vector, time t restricted endogenous variables.
% o ix [integer] index of control innovations in the full vector of innovations.
-% o iy [integer] index of controlled variables in the full vector of endogenous variables.
-% o s [double] m*1 vector, endogenous variables at time t-1.
-%
-%
+% o iy [integer] index of controlled variables in the full vector of endogenous variables.
+% o s [double] m*1 vector, endogenous variables at time t-1.
+%
+%
% OUTPUTS
% o r [double] n*1 vector of residuals.
-%
+%
% ALGORITHM
-%
+%
% SPECIAL REQUIREMENTS
% Copyright (C) 2010-2014 Dynare Team.
diff --git a/matlab/evaluate_max_dynamic_residual.m b/matlab/evaluate_max_dynamic_residual.m
index 41aa4ec249c2fae2dcfb9721832e2aee20bcedef..ac4dbb32a0c5f08e4efeae0e24d737d9b4aa587b 100644
--- a/matlab/evaluate_max_dynamic_residual.m
+++ b/matlab/evaluate_max_dynamic_residual.m
@@ -16,7 +16,7 @@ function err = evaluate_max_dynamic_residual(model_dynamic, Y, exogenous_variabl
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
+
i_rows = 1:ny;
i_cols = find(lead_lag_incidence');
diff --git a/matlab/evaluate_planner_objective.m b/matlab/evaluate_planner_objective.m
index 40f1865903608528ecad2f69ded09cda08f74866..d547be57eda84dd6214e220260823c8f2098d54b 100644
--- a/matlab/evaluate_planner_objective.m
+++ b/matlab/evaluate_planner_objective.m
@@ -1,13 +1,13 @@
function planner_objective_value = evaluate_planner_objective(M,options,oo)
%function oo1 = evaluate_planner_objective(dr,M,oo,options)
-% computes value of planner objective function
-%
+% computes value of planner objective function
+%
% INPUTS
% M: (structure) model description
% options: (structure) options
% oo: (structure) output results
-%
+%
% SPECIAL REQUIREMENTS
% none
@@ -40,7 +40,7 @@ if nspred > 180
return
end
beta = get_optimal_policy_discount_factor(M.params,M.param_names);
-
+
Gy = dr.ghx(nstatic+(1:nspred),:);
Gu = dr.ghu(nstatic+(1:nspred),:);
gy(dr.order_var,:) = dr.ghx;
@@ -113,7 +113,7 @@ if ~options.noprint
disp('Approximated value of planner objective function')
if options.ramsey_policy
disp([' - with initial Lagrange multipliers set to 0: ' ...
- num2str(planner_objective_value(2)) ])
+ num2str(planner_objective_value(2)) ])
disp([' - with initial Lagrange multipliers set to steady state: ' ...
num2str(planner_objective_value(1)) ])
elseif options.discretionary_policy
diff --git a/matlab/evaluate_posterior_kernel.m b/matlab/evaluate_posterior_kernel.m
index 96ccac8d5d35726099a39188ad18a6e51186b3ab..ce4fd1a43f6d9e27e4548c33577d2e4b59482f11 100644
--- a/matlab/evaluate_posterior_kernel.m
+++ b/matlab/evaluate_posterior_kernel.m
@@ -2,20 +2,20 @@ function lpkern = evaluate_posterior_kernel(parameters,llik)
% Evaluate the prior density at parameters.
%
% INPUTS
-% o parameters a string ('posterior mode','posterior mean','posterior median','prior mode','prior mean') or a vector of values for
+% o parameters a string ('posterior mode','posterior mean','posterior median','prior mode','prior mean') or a vector of values for
% the (estimated) parameters of the model.
-%
-%
+%
+%
% OUTPUTS
% o lpkern [double] value of the logged posterior kernel.
-%
+%
% SPECIAL REQUIREMENTS
% None
%
% REMARKS
% [1] This function cannot evaluate the prior density of a dsge-var model...
-% [2] This function use persistent variables for the dataset and the description of the missing observations. Consequently, if this function
-% is called more than once (by changing the value of parameters) the sample *must not* change.
+% [2] This function use persistent variables for the dataset and the description of the missing observations. Consequently, if this function
+% is called more than once (by changing the value of parameters) the sample *must not* change.
% Copyright (C) 2009 Dynare Team
%
diff --git a/matlab/evaluate_prior.m b/matlab/evaluate_prior.m
index b8b2f7b0baeed35d10efde8038bf9263c61e07d5..32288062fc1831df2d425047c4678a9cb96fffdd 100644
--- a/matlab/evaluate_prior.m
+++ b/matlab/evaluate_prior.m
@@ -2,14 +2,14 @@ function [ldens,parameters] = evaluate_prior(parameters)
% Evaluate the prior density at parameters.
%
% INPUTS
-% o parameters a string ('posterior mode','posterior mean','posterior median','prior mode','prior mean') or a vector of values for
+% o parameters a string ('posterior mode','posterior mean','posterior median','prior mode','prior mean') or a vector of values for
% the (estimated) parameters of the model.
-%
-%
+%
+%
% OUTPUTS
% o ldens [double] value of the logged prior density.
% o parameters [double] vector of values for the estimated parameters.
-%
+%
% SPECIAL REQUIREMENTS
% None
%
diff --git a/matlab/evaluate_smoother.m b/matlab/evaluate_smoother.m
index fb3eaad7addb18272e853f35eea137faca78326b..9ab94654b1f42e0d134fa8ce5193c56817624ed5 100644
--- a/matlab/evaluate_smoother.m
+++ b/matlab/evaluate_smoother.m
@@ -23,7 +23,7 @@ function [oo_,M_,options_,bayestopt_,Smoothed_variables_declaration_order_deviat
% - FilteredVariablesKStepAheadVariances
% o M_ [structure] Definition of the model
% o options_ [structure] Options; returns options_.first_obs
-% o bayestopt_ [structure] describing the priors; returns fields like bayestopt_.smoother_var_list from the smoother
+% o bayestopt_ [structure] describing the priors; returns fields like bayestopt_.smoother_var_list from the smoother
% o Smoothed_variables_declaration_order_deviation_form
% Smoothed variables from the Kalman smoother in
% order of declaration of variables (M_.endo_names)
@@ -106,7 +106,7 @@ end
[oo_]=store_smoother_results(M_,oo_,options_,bayestopt_,dataset_,dataset_info,atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,P,PK,decomp,Trend,state_uncertainty);
if nargout>4
- Smoothed_variables_declaration_order_deviation_form=atT(oo_.dr.inv_order_var(bayestopt_.smoother_var_list),:);
+ Smoothed_variables_declaration_order_deviation_form=atT(oo_.dr.inv_order_var(bayestopt_.smoother_var_list),:);
end
%reset qz_criterium
diff --git a/matlab/evaluate_static_model.m b/matlab/evaluate_static_model.m
index 92251796423a0c79c6224b65efd0752acb313f13..2328f2b4ce941ff1da95502aff934182e7e18019 100644
--- a/matlab/evaluate_static_model.m
+++ b/matlab/evaluate_static_model.m
@@ -2,7 +2,7 @@ function [residuals,check1,jacob] = evaluate_static_model(ys,exo_ss,params,M,opt
% function [residuals,check1,jacob] = evaluate_static_model(ys,exo_ss,params,M,options)
% Evaluates the static model
-%
+%
% INPUTS
% ys vector initial values used to compute the steady
% state
@@ -10,7 +10,7 @@ function [residuals,check1,jacob] = evaluate_static_model(ys,exo_ss,params,M,opt
% params vector parameters
% M struct model structure
% options struct options
-%
+%
% OUTPUTS
% residuals vector residuals when ys is not
% the steady state
@@ -37,29 +37,29 @@ function [residuals,check1,jacob] = evaluate_static_model(ys,exo_ss,params,M,opt
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- check1 = 0;
- if options.bytecode
- [check1, residuals] = bytecode('evaluate','static',ys,...
- exo_ss, params, ys, 1);
- mexErrCheck('bytecode', check1);
- else
- fh_static = str2func([M.fname '_static']);
- if options.block
- residuals = zeros(M.endo_nbr,1);
- for b = 1:length(M.block_structure_stat.block)
- mfsb = M.block_structure_stat.block(b).variable;
- % blocks that can be directly evaluated (mfsb is empty)
- % have zero residuals by construction
- if M.block_structure_stat.block(b).Simulation_Type ~= 1 && ...
- M.block_structure_stat.block(b).Simulation_Type ~= 2
- residuals(mfsb) = feval(fh_static,b,ys,exo_ss,params);
- else
- %need to evaluate the recursive blocks to compute the
- %temporary terms
- feval(fh_static,b,ys,exo_ss,params);
- end
+check1 = 0;
+if options.bytecode
+ [check1, residuals] = bytecode('evaluate','static',ys,...
+ exo_ss, params, ys, 1);
+ mexErrCheck('bytecode', check1);
+else
+ fh_static = str2func([M.fname '_static']);
+ if options.block
+ residuals = zeros(M.endo_nbr,1);
+ for b = 1:length(M.block_structure_stat.block)
+ mfsb = M.block_structure_stat.block(b).variable;
+ % blocks that can be directly evaluated (mfsb is empty)
+ % have zero residuals by construction
+ if M.block_structure_stat.block(b).Simulation_Type ~= 1 && ...
+ M.block_structure_stat.block(b).Simulation_Type ~= 2
+ residuals(mfsb) = feval(fh_static,b,ys,exo_ss,params);
+ else
+ %need to evaluate the recursive blocks to compute the
+ %temporary terms
+ feval(fh_static,b,ys,exo_ss,params);
end
- else
- residuals = feval(fh_static,ys,exo_ss,params);
end
+ else
+ residuals = feval(fh_static,ys,exo_ss,params);
end
+end
diff --git a/matlab/evaluate_steady_state.m b/matlab/evaluate_steady_state.m
index e2bd1b86de3db14a6204736bb568e69388666fd1..afd8bb4c598e79040e1986a56c2f1993aad1c32c 100644
--- a/matlab/evaluate_steady_state.m
+++ b/matlab/evaluate_steady_state.m
@@ -39,120 +39,36 @@ function [ys,params,info] = evaluate_steady_state(ys_init,M,options,oo,steadysta
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- info = 0;
- check = 0;
+info = 0;
+check = 0;
- steadystate_flag = options.steadystate_flag;
- params = M.params;
- exo_ss = [oo.exo_steady_state; oo.exo_det_steady_state];
+steadystate_flag = options.steadystate_flag;
+params = M.params;
+exo_ss = [oo.exo_steady_state; oo.exo_det_steady_state];
- if length(M.aux_vars) > 0
- h_set_auxiliary_variables = str2func([M.fname '_set_auxiliary_variables']);
- if ~steadystate_flag
- ys_init = h_set_auxiliary_variables(ys_init,exo_ss,M.params);
- end
+if length(M.aux_vars) > 0
+ h_set_auxiliary_variables = str2func([M.fname '_set_auxiliary_variables']);
+ if ~steadystate_flag
+ ys_init = h_set_auxiliary_variables(ys_init,exo_ss,M.params);
end
+end
- if options.ramsey_policy
- if steadystate_flag
- % explicit steady state file
- [ys,params,info] = evaluate_steady_state_file(ys_init,exo_ss,M, ...
- options,steadystate_check_flag);
- %test whether it solves model conditional on the instruments
- resids = evaluate_static_model(ys,exo_ss,params,M,options);
- n_multipliers=M.ramsey_eq_nbr;
- nan_indices=find(isnan(resids(n_multipliers+1:end)));
-
- if ~isempty(nan_indices)
- if options.debug
- fprintf('\nevaluate_steady_state: The steady state file computation for the Ramsey problem resulted in NaNs.\n')
- fprintf('evaluate_steady_state: The steady state was computed conditional on the following initial instrument values: \n')
- for ii = 1:size(options.instruments,1)
- fprintf('\t %s \t %f \n',options.instruments(ii,:),ys_init(strmatch(options.instruments(ii,:),M.endo_names,'exact')))
- end
- fprintf('evaluate_steady_state: The problem occured in the following equations: \n')
- fprintf('\t Equation(s): ')
- for ii=1:length(nan_indices)
- fprintf('%d, ',nan_indices(ii));
- end
- skipline()
- fprintf('evaluate_steady_state: If those initial values are not admissable, change them using an initval-block.\n')
- skipline(2)
- end
- info(1) = 84;
- info(2) = resids'*resids;
- return
- end
-
- if any(imag(ys(n_multipliers+1:end)))
- if options.debug
- fprintf('\nevaluate_steady_state: The steady state file computation for the Ramsey problem resulted in complex numbers.\n')
- fprintf('evaluate_steady_state: The steady state was computed conditional on the following initial instrument values: \n')
- for ii = 1:size(options.instruments,1)
- fprintf('\t %s \t %f \n',options.instruments(ii,:),ys_init(strmatch(options.instruments(ii,:),M.endo_names,'exact')))
- end
- fprintf('evaluate_steady_state: If those initial values are not admissable, change them using an initval-block.\n')
- skipline(2)
- end
- info(1) = 86;
- info(2) = resids'*resids;
- return
- end
-
- if max(abs(resids(n_multipliers+1:end))) > options.solve_tolf %does it solve for all variables except for the Lagrange multipliers
- if options.debug
- fprintf('\nevaluate_steady_state: The steady state file does not solve the steady state for the Ramsey problem.\n')
- fprintf('evaluate_steady_state: Conditional on the following instrument values: \n')
- for ii = 1:size(options.instruments,1)
- fprintf('\t %s \t %f \n',options.instruments(ii,:),ys_init(strmatch(options.instruments(ii,:),M.endo_names,'exact')))
- end
- fprintf('evaluate_steady_state: the following equations have non-zero residuals: \n')
- for ii=n_multipliers+1:M.endo_nbr
- if abs(resids(ii)) > options.solve_tolf
- fprintf('\t Equation number %d: %f\n',ii-n_multipliers, resids(ii))
- end
- end
- skipline(2)
- end
- info(1) = 85;
- info(2) = resids'*resids;
- return
- end
- end
- if options.debug
- infrow=find(isinf(ys_init));
- if ~isempty(infrow)
- fprintf('\nevaluate_steady_state: The initial values for the steady state of the following variables are Inf:\n');
- for iter=1:length(infrow)
- fprintf('%s\n',M.endo_names(infrow(iter),:));
- end
- end
- nanrow=find(isnan(ys_init));
- if ~isempty(nanrow)
- fprintf('\nevaluate_steady_state: The initial values for the steady state of the following variables are NaN:\n');
- for iter=1:length(nanrow)
- fprintf('%s\n',M.endo_names(nanrow(iter),:));
- end
- end
- end
- %either if no steady state file or steady state file without problems
- [ys,params,info] = dyn_ramsey_static(ys_init,M,options,oo);
- if info
- return
- end
- %check whether steady state really solves the model
+if options.ramsey_policy
+ if steadystate_flag
+ % explicit steady state file
+ [ys,params,info] = evaluate_steady_state_file(ys_init,exo_ss,M, ...
+ options,steadystate_check_flag);
+ %test whether it solves model conditional on the instruments
resids = evaluate_static_model(ys,exo_ss,params,M,options);
-
n_multipliers=M.ramsey_eq_nbr;
- nan_indices_multiplier=find(isnan(resids(1:n_multipliers)));
nan_indices=find(isnan(resids(n_multipliers+1:end)));
if ~isempty(nan_indices)
if options.debug
- fprintf('\nevaluate_steady_state: The steady state computation for the Ramsey problem resulted in NaNs.\n')
- fprintf('evaluate_steady_state: The steady state computation resulted in the following instrument values: \n')
- for i = 1:size(options.instruments,1)
- fprintf('\t %s \t %f \n',options.instruments(i,:),ys(strmatch(options.instruments(i,:),M.endo_names,'exact')))
+ fprintf('\nevaluate_steady_state: The steady state file computation for the Ramsey problem resulted in NaNs.\n')
+ fprintf('evaluate_steady_state: The steady state was computed conditional on the following initial instrument values: \n')
+ for ii = 1:size(options.instruments,1)
+ fprintf('\t %s \t %f \n',options.instruments(ii,:),ys_init(strmatch(options.instruments(ii,:),M.endo_names,'exact')))
end
fprintf('evaluate_steady_state: The problem occured in the following equations: \n')
fprintf('\t Equation(s): ')
@@ -160,184 +76,268 @@ function [ys,params,info] = evaluate_steady_state(ys_init,M,options,oo,steadysta
fprintf('%d, ',nan_indices(ii));
end
skipline()
+ fprintf('evaluate_steady_state: If those initial values are not admissable, change them using an initval-block.\n')
+ skipline(2)
end
- info(1) = 82;
+ info(1) = 84;
+ info(2) = resids'*resids;
return
end
- if ~isempty(nan_indices_multiplier)
+ if any(imag(ys(n_multipliers+1:end)))
if options.debug
- fprintf('\nevaluate_steady_state: The steady state computation for the Ramsey problem resulted in NaNs in the auxiliary equations.\n')
- fprintf('evaluate_steady_state: The steady state computation resulted in the following instrument values: \n')
- for i = 1:size(options.instruments,1)
- fprintf('\t %s \t %f \n',options.instruments(i,:),ys(strmatch(options.instruments(i,:),M.endo_names,'exact')))
+ fprintf('\nevaluate_steady_state: The steady state file computation for the Ramsey problem resulted in complex numbers.\n')
+ fprintf('evaluate_steady_state: The steady state was computed conditional on the following initial instrument values: \n')
+ for ii = 1:size(options.instruments,1)
+ fprintf('\t %s \t %f \n',options.instruments(ii,:),ys_init(strmatch(options.instruments(ii,:),M.endo_names,'exact')))
end
- fprintf('evaluate_steady_state: The problem occured in the following equations: \n')
- fprintf('\t Auxiliary equation(s): ')
- for ii=1:length(nan_indices_multiplier)
- fprintf('%d, ',nan_indices_multiplier(ii));
- end
- skipline()
+ fprintf('evaluate_steady_state: If those initial values are not admissable, change them using an initval-block.\n')
+ skipline(2)
end
- info(1) = 83;
+ info(1) = 86;
+ info(2) = resids'*resids;
return
end
- if max(abs(resids)) > options.solve_tolf %does it solve for all variables including the auxiliary ones
+ if max(abs(resids(n_multipliers+1:end))) > options.solve_tolf %does it solve for all variables except for the Lagrange multipliers
if options.debug
- fprintf('\nevaluate_steady_state: The steady state for the Ramsey problem could not be computed.\n')
- fprintf('evaluate_steady_state: The steady state computation stopped with the following instrument values:: \n')
- for i = 1:size(options.instruments,1)
- fprintf('\t %s \t %f \n',options.instruments(i,:),ys(strmatch(options.instruments(i,:),M.endo_names,'exact')))
- end
- fprintf('evaluate_steady_state: The following equations have non-zero residuals: \n')
- for ii=1:n_multipliers
- if abs(resids(ii)) > options.solve_tolf/100
- fprintf('\t Auxiliary Ramsey equation number %d: %f\n',ii, resids(ii))
- end
+ fprintf('\nevaluate_steady_state: The steady state file does not solve the steady state for the Ramsey problem.\n')
+ fprintf('evaluate_steady_state: Conditional on the following instrument values: \n')
+ for ii = 1:size(options.instruments,1)
+ fprintf('\t %s \t %f \n',options.instruments(ii,:),ys_init(strmatch(options.instruments(ii,:),M.endo_names,'exact')))
end
+ fprintf('evaluate_steady_state: the following equations have non-zero residuals: \n')
for ii=n_multipliers+1:M.endo_nbr
- if abs(resids(ii)) > options.solve_tolf/100
+ if abs(resids(ii)) > options.solve_tolf
fprintf('\t Equation number %d: %f\n',ii-n_multipliers, resids(ii))
end
end
skipline(2)
end
- info(1) = 81;
+ info(1) = 85;
info(2) = resids'*resids;
return
end
- elseif steadystate_flag
- % explicit steady state file
- [ys,params,info] = evaluate_steady_state_file(ys_init,exo_ss,M, options,steadystate_check_flag);
- if size(ys,2)>size(ys,1)
- error('STEADY: steady_state-file must return a column vector, not a row vector.')
- end
- if info(1)
- return
+ end
+ if options.debug
+ infrow=find(isinf(ys_init));
+ if ~isempty(infrow)
+ fprintf('\nevaluate_steady_state: The initial values for the steady state of the following variables are Inf:\n');
+ for iter=1:length(infrow)
+ fprintf('%s\n',M.endo_names(infrow(iter),:));
+ end
end
- elseif (options.bytecode == 0 && options.block == 0)
- if options.linear == 0
- % non linear model
- static_model = str2func([M.fname '_static']);
- [ys,check] = dynare_solve(@static_problem,...
- ys_init,...
- options, exo_ss, params,...
- M.endo_nbr,...
- static_model);
- if check && options.debug
- [ys,check,fvec,fjac] = dynare_solve(@static_problem,...
- ys_init,...
- options, exo_ss, params,...
- M.endo_nbr,...
- static_model);
- [infrow,infcol]=find(isinf(fjac) | isnan(fjac));
- if ~isempty(infrow)
- fprintf('\nSTEADY: The Jacobian at the initial values contains Inf or NaN. The problem arises from: \n')
- display_problematic_vars_Jacobian(infrow,infcol,M,ys_init,'static','STEADY: ')
- end
- problematic_equation = find(~isfinite(fvec));
- if ~isempty(problematic_equation)
- fprintf('\nSTEADY: numerical initial values or parameters incompatible with the following equations\n')
- disp(problematic_equation')
- fprintf('Please check for example\n')
- fprintf(' i) if all parameters occurring in these equations are defined\n')
- fprintf(' ii) that no division by an endogenous variable initialized to 0 occurs\n')
- end
+ nanrow=find(isnan(ys_init));
+ if ~isempty(nanrow)
+ fprintf('\nevaluate_steady_state: The initial values for the steady state of the following variables are NaN:\n');
+ for iter=1:length(nanrow)
+ fprintf('%s\n',M.endo_names(nanrow(iter),:));
end
- else
- % linear model
- fh_static = str2func([M.fname '_static']);
- [fvec,jacob] = fh_static(ys_init,exo_ss, ...
- params);
+ end
+ end
+ %either if no steady state file or steady state file without problems
+ [ys,params,info] = dyn_ramsey_static(ys_init,M,options,oo);
+ if info
+ return
+ end
+ %check whether steady state really solves the model
+ resids = evaluate_static_model(ys,exo_ss,params,M,options);
- ii = find(~isfinite(fvec));
- if ~isempty(ii)
- ys=fvec;
- check=1;
- disp(['STEADY: numerical initial values or parameters incompatible with the following' ...
- ' equations'])
- disp(ii')
- disp('Check whether your model is truly linear. Put "resid(1);" before "steady;" to see the problematic equations.\n')
- elseif isempty(ii) && max(abs(fvec)) > 1e-12
- ys = ys_init-jacob\fvec;
- resid = evaluate_static_model(ys,exo_ss,params,M,options);
- if max(abs(resid)) > 1e-6
- check=1;
- fprintf('STEADY: No steady state for your model could be found\n')
- fprintf('STEADY: Check whether your model is truly linear. Put "resid(1);" before "steady;" to see the problematic equations.\n')
- end
- else
- ys = ys_init;
+ n_multipliers=M.ramsey_eq_nbr;
+ nan_indices_multiplier=find(isnan(resids(1:n_multipliers)));
+ nan_indices=find(isnan(resids(n_multipliers+1:end)));
+
+ if ~isempty(nan_indices)
+ if options.debug
+ fprintf('\nevaluate_steady_state: The steady state computation for the Ramsey problem resulted in NaNs.\n')
+ fprintf('evaluate_steady_state: The steady state computation resulted in the following instrument values: \n')
+ for i = 1:size(options.instruments,1)
+ fprintf('\t %s \t %f \n',options.instruments(i,:),ys(strmatch(options.instruments(i,:),M.endo_names,'exact')))
end
- if options.debug
- if any(any(isinf(jacob) | isnan(jacob)))
- [infrow,infcol]=find(isinf(jacob) | isnan(jacob));
- fprintf('\nSTEADY: The Jacobian contains Inf or NaN. The problem arises from: \n\n')
- for ii=1:length(infrow)
- fprintf('STEADY: Derivative of Equation %d with respect to Variable %s (initial value of %s: %g) \n',infrow(ii),deblank(M.endo_names(infcol(ii),:)),deblank(M.endo_names(infcol(ii),:)),ys_init(infcol(ii)))
- end
- fprintf('Check whether your model is truly linear. Put "resid(1);" before "steady;" to see the problematic equations.\n')
- end
+ fprintf('evaluate_steady_state: The problem occured in the following equations: \n')
+ fprintf('\t Equation(s): ')
+ for ii=1:length(nan_indices)
+ fprintf('%d, ',nan_indices(ii));
end
+ skipline()
end
- else
- % block or bytecode
- [ys,check] = dynare_solve_block_or_bytecode(ys_init,exo_ss, params, options, M);
+ info(1) = 82;
+ return
end
- if check
- info(1)= 20;
- %make sure ys contains auxiliary variables in case of problem with dynare_solve
- if length(M.aux_vars) > 0 && ~steadystate_flag
- ys = h_set_auxiliary_variables(ys,exo_ss,M.params);
+ if ~isempty(nan_indices_multiplier)
+ if options.debug
+ fprintf('\nevaluate_steady_state: The steady state computation for the Ramsey problem resulted in NaNs in the auxiliary equations.\n')
+ fprintf('evaluate_steady_state: The steady state computation resulted in the following instrument values: \n')
+ for i = 1:size(options.instruments,1)
+ fprintf('\t %s \t %f \n',options.instruments(i,:),ys(strmatch(options.instruments(i,:),M.endo_names,'exact')))
+ end
+ fprintf('evaluate_steady_state: The problem occured in the following equations: \n')
+ fprintf('\t Auxiliary equation(s): ')
+ for ii=1:length(nan_indices_multiplier)
+ fprintf('%d, ',nan_indices_multiplier(ii));
+ end
+ skipline()
end
- resid = evaluate_static_model(ys,exo_ss,params,M,options);
- info(2) = resid'*resid ;
- if isnan(info(2))
- info(1)=22;
+ info(1) = 83;
+ return
+ end
+
+ if max(abs(resids)) > options.solve_tolf %does it solve for all variables including the auxiliary ones
+ if options.debug
+ fprintf('\nevaluate_steady_state: The steady state for the Ramsey problem could not be computed.\n')
+ fprintf('evaluate_steady_state: The steady state computation stopped with the following instrument values:: \n')
+ for i = 1:size(options.instruments,1)
+ fprintf('\t %s \t %f \n',options.instruments(i,:),ys(strmatch(options.instruments(i,:),M.endo_names,'exact')))
+ end
+ fprintf('evaluate_steady_state: The following equations have non-zero residuals: \n')
+ for ii=1:n_multipliers
+ if abs(resids(ii)) > options.solve_tolf/100
+ fprintf('\t Auxiliary Ramsey equation number %d: %f\n',ii, resids(ii))
+ end
+ end
+ for ii=n_multipliers+1:M.endo_nbr
+ if abs(resids(ii)) > options.solve_tolf/100
+ fprintf('\t Equation number %d: %f\n',ii-n_multipliers, resids(ii))
+ end
+ end
+ skipline(2)
end
+ info(1) = 81;
+ info(2) = resids'*resids;
+ return
+ end
+elseif steadystate_flag
+ % explicit steady state file
+ [ys,params,info] = evaluate_steady_state_file(ys_init,exo_ss,M, options,steadystate_check_flag);
+ if size(ys,2)>size(ys,1)
+ error('STEADY: steady_state-file must return a column vector, not a row vector.')
+ end
+ if info(1)
return
end
+elseif (options.bytecode == 0 && options.block == 0)
+ if options.linear == 0
+ % non linear model
+ static_model = str2func([M.fname '_static']);
+ [ys,check] = dynare_solve(@static_problem,...
+ ys_init,...
+ options, exo_ss, params,...
+ M.endo_nbr,...
+ static_model);
+ if check && options.debug
+ [ys,check,fvec,fjac] = dynare_solve(@static_problem,...
+ ys_init,...
+ options, exo_ss, params,...
+ M.endo_nbr,...
+ static_model);
+ [infrow,infcol]=find(isinf(fjac) | isnan(fjac));
+ if ~isempty(infrow)
+ fprintf('\nSTEADY: The Jacobian at the initial values contains Inf or NaN. The problem arises from: \n')
+ display_problematic_vars_Jacobian(infrow,infcol,M,ys_init,'static','STEADY: ')
+ end
+ problematic_equation = find(~isfinite(fvec));
+ if ~isempty(problematic_equation)
+ fprintf('\nSTEADY: numerical initial values or parameters incompatible with the following equations\n')
+ disp(problematic_equation')
+ fprintf('Please check for example\n')
+ fprintf(' i) if all parameters occurring in these equations are defined\n')
+ fprintf(' ii) that no division by an endogenous variable initialized to 0 occurs\n')
+ end
+ end
+ else
+ % linear model
+ fh_static = str2func([M.fname '_static']);
+ [fvec,jacob] = fh_static(ys_init,exo_ss, ...
+ params);
- % If some equations are tagged [static] or [dynamic], verify consistency
- if M.static_and_dynamic_models_differ
- % Evaluate residual of *dynamic* model using the steady state
- % computed on the *static* one
- z = repmat(ys,1,M.maximum_lead + M.maximum_lag + 1);
- zx = repmat([exo_ss'], M.maximum_lead + M.maximum_lag + 1, 1);
- if options.bytecode
- [chck, r, junk]= bytecode('dynamic','evaluate', z, zx, M.params, ys, 1);
- mexErrCheck('bytecode', chck);
- elseif options.block
- [r, oo.dr] = feval([M.fname '_dynamic'], z', zx, M.params, ys, M.maximum_lag+1, oo.dr);
+ ii = find(~isfinite(fvec));
+ if ~isempty(ii)
+ ys=fvec;
+ check=1;
+ disp(['STEADY: numerical initial values or parameters incompatible with the following' ...
+ ' equations'])
+ disp(ii')
+ disp('Check whether your model is truly linear. Put "resid(1);" before "steady;" to see the problematic equations.\n')
+ elseif isempty(ii) && max(abs(fvec)) > 1e-12
+ ys = ys_init-jacob\fvec;
+ resid = evaluate_static_model(ys,exo_ss,params,M,options);
+ if max(abs(resid)) > 1e-6
+ check=1;
+ fprintf('STEADY: No steady state for your model could be found\n')
+ fprintf('STEADY: Check whether your model is truly linear. Put "resid(1);" before "steady;" to see the problematic equations.\n')
+ end
else
- iyv = M.lead_lag_incidence';
- iyr0 = find(iyv(:));
- xys = z(iyr0);
- r = feval([M.fname '_dynamic'], z(iyr0), zx, M.params, ys, M.maximum_lag + 1);
+ ys = ys_init;
end
- % Fail if residual greater than tolerance
- if max(abs(r)) > options.solve_tolf
- info(1) = 25;
- return
+ if options.debug
+ if any(any(isinf(jacob) | isnan(jacob)))
+ [infrow,infcol]=find(isinf(jacob) | isnan(jacob));
+ fprintf('\nSTEADY: The Jacobian contains Inf or NaN. The problem arises from: \n\n')
+ for ii=1:length(infrow)
+ fprintf('STEADY: Derivative of Equation %d with respect to Variable %s (initial value of %s: %g) \n',infrow(ii),deblank(M.endo_names(infcol(ii),:)),deblank(M.endo_names(infcol(ii),:)),ys_init(infcol(ii)))
+ end
+ fprintf('Check whether your model is truly linear. Put "resid(1);" before "steady;" to see the problematic equations.\n')
+ end
end
end
+else
+ % block or bytecode
+ [ys,check] = dynare_solve_block_or_bytecode(ys_init,exo_ss, params, options, M);
+end
- if ~isreal(ys)
- info(1) = 21;
- info(2) = sum(imag(ys).^2);
- ys = real(ys);
- return
+if check
+ info(1)= 20;
+ %make sure ys contains auxiliary variables in case of problem with dynare_solve
+ if length(M.aux_vars) > 0 && ~steadystate_flag
+ ys = h_set_auxiliary_variables(ys,exo_ss,M.params);
end
+ resid = evaluate_static_model(ys,exo_ss,params,M,options);
+ info(2) = resid'*resid ;
+ if isnan(info(2))
+ info(1)=22;
+ end
+ return
+end
- if ~isempty(find(isnan(ys)))
- info(1) = 22;
- info(2) = NaN;
+% If some equations are tagged [static] or [dynamic], verify consistency
+if M.static_and_dynamic_models_differ
+ % Evaluate residual of *dynamic* model using the steady state
+ % computed on the *static* one
+ z = repmat(ys,1,M.maximum_lead + M.maximum_lag + 1);
+ zx = repmat([exo_ss'], M.maximum_lead + M.maximum_lag + 1, 1);
+ if options.bytecode
+ [chck, r, junk]= bytecode('dynamic','evaluate', z, zx, M.params, ys, 1);
+ mexErrCheck('bytecode', chck);
+ elseif options.block
+ [r, oo.dr] = feval([M.fname '_dynamic'], z', zx, M.params, ys, M.maximum_lag+1, oo.dr);
+ else
+ iyv = M.lead_lag_incidence';
+ iyr0 = find(iyv(:));
+ xys = z(iyr0);
+ r = feval([M.fname '_dynamic'], z(iyr0), zx, M.params, ys, M.maximum_lag + 1);
+ end
+ % Fail if residual greater than tolerance
+ if max(abs(r)) > options.solve_tolf
+ info(1) = 25;
return
end
+end
+
+if ~isreal(ys)
+ info(1) = 21;
+ info(2) = sum(imag(ys).^2);
+ ys = real(ys);
+ return
+end
+
+if ~isempty(find(isnan(ys)))
+ info(1) = 22;
+ info(2) = NaN;
+ return
+end
function [resids,jac] = static_problem(y,x,params,nvar,fh_static_model)
- [r,j] = fh_static_model(y,x,params);
- resids = r(1:nvar);
- jac = j(1:nvar,1:nvar);
+[r,j] = fh_static_model(y,x,params);
+resids = r(1:nvar);
+jac = j(1:nvar,1:nvar);
diff --git a/matlab/evaluate_steady_state_file.m b/matlab/evaluate_steady_state_file.m
index 1bb2c018b9e690f415efc4223b54943aaad9e6fb..67a3b0c4dfe5f13a3d1b833b77fdd5b6f94c174a 100644
--- a/matlab/evaluate_steady_state_file.m
+++ b/matlab/evaluate_steady_state_file.m
@@ -1,14 +1,14 @@
function [ys,params,info] = evaluate_steady_state_file(ys_init,exo_ss,M,options,steady_state_checkflag)
% function [ys,params1,info] = evaluate_steady_state_file(ys_init,exo_ss,M,options,steady_state_checkflag)
-% Evaluates steady state files
-%
+% Evaluates steady state files
+%
% INPUTS
% ys_init vector initial values used to compute the steady
% state
% exo_ss vector exogenous steady state
% M struct model parameters
% options struct options
-% steady_state_checkflag boolean indicator whether to check steady state returned
+% steady_state_checkflag boolean indicator whether to check steady state returned
% OUTPUTS
% ys vector steady state
% params1 vector model parameters possibly
@@ -36,125 +36,123 @@ function [ys,params,info] = evaluate_steady_state_file(ys_init,exo_ss,M,options,
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- debug = false;
+debug = false;
- ys = [];
- params = M.params;
- info = 0;
+ys = [];
+params = M.params;
+info = 0;
- fname = M.fname;
+fname = M.fname;
- if options.steadystate_flag == 1
- % old format
- assignin('base','tmp_00_',params);
- evalin('base','M_.params=tmp_00_; clear(''tmp_00_'')');
- h_steadystate = str2func([fname '_steadystate']);
- [ys,check] = h_steadystate(ys_init, exo_ss);
- params1 = evalin('base','M_.params');
- else % steadystate_flag == 2
- % new format
- h_steadystate = str2func([fname '_steadystate2']);
- [ys,params1,check] = h_steadystate(ys_init, exo_ss, params);
- end
-
- if check
- info(1) = 19;
- info(2) = NaN;
- return
- end
-
- if M.param_nbr > 0
- updated_params_flag = max(abs(params1-params)) > 1e-12 ...
- || ~isequal(isnan(params1),isnan(params)); %checks whether numbers or NaN changed
- else
- updated_params_flag = 0;
- end
+if options.steadystate_flag == 1
+ % old format
+ assignin('base','tmp_00_',params);
+ evalin('base','M_.params=tmp_00_; clear(''tmp_00_'')');
+ h_steadystate = str2func([fname '_steadystate']);
+ [ys,check] = h_steadystate(ys_init, exo_ss);
+ params1 = evalin('base','M_.params');
+else % steadystate_flag == 2
+ % new format
+ h_steadystate = str2func([fname '_steadystate2']);
+ [ys,params1,check] = h_steadystate(ys_init, exo_ss, params);
+end
- h_set_auxiliary_variables = str2func([M.fname '_set_auxiliary_variables']);
- if isnan(updated_params_flag) || (updated_params_flag && any(isnan(params(~isnan(params))-params1(~isnan(params))))) %checks if new NaNs were added
- info(1) = 24;
- info(2) = NaN;
- ys = h_set_auxiliary_variables(ys,exo_ss,params);
- return
- end
+if check
+ info(1) = 19;
+ info(2) = NaN;
+ return
+end
- if updated_params_flag && ~isreal(params1)
- info(1) = 23;
- info(2) = sum(imag(params).^2);
- ys = h_set_auxiliary_variables(ys,exo_ss,params);
- return
+if M.param_nbr > 0
+ updated_params_flag = max(abs(params1-params)) > 1e-12 ...
+ || ~isequal(isnan(params1),isnan(params)); %checks whether numbers or NaN changed
+else
+ updated_params_flag = 0;
+end
+
+h_set_auxiliary_variables = str2func([M.fname '_set_auxiliary_variables']);
+if isnan(updated_params_flag) || (updated_params_flag && any(isnan(params(~isnan(params))-params1(~isnan(params))))) %checks if new NaNs were added
+ info(1) = 24;
+ info(2) = NaN;
+ ys = h_set_auxiliary_variables(ys,exo_ss,params);
+ return
+end
+
+if updated_params_flag && ~isreal(params1)
+ info(1) = 23;
+ info(2) = sum(imag(params).^2);
+ ys = h_set_auxiliary_variables(ys,exo_ss,params);
+ return
+end
+
+if updated_params_flag
+ params = params1;
+end
+
+% adding values for auxiliary variables
+if length(M.aux_vars) > 0 && ~options.ramsey_policy
+ if debug
+ ys0 = ys;
end
-
- if updated_params_flag
- params = params1;
+ ys = h_set_auxiliary_variables(ys,exo_ss,params);
+ if debug
+ ys1 = ys;
end
-
- % adding values for auxiliary variables
- if length(M.aux_vars) > 0 && ~options.ramsey_policy
- if debug
- ys0 = ys;
- end
+ ys = h_set_auxiliary_variables(ys,exo_ss,params);
+ if debug
+ ys2 = ys;
+ end
+ if debug
ys = h_set_auxiliary_variables(ys,exo_ss,params);
- if debug
- ys1 = ys;
+ ys3 = ys;
+ idx = find(abs(ys0-ys1)>0);
+ if ~isempty(idx)
+ M.endo_names(idx,:)
+ else
+ disp('1-invariant')
end
- ys = h_set_auxiliary_variables(ys,exo_ss,params);
- if debug
- ys2 = ys;
+ idx = find(abs(ys2-ys1)>0);
+ if ~isempty(idx)
+ M.endo_names(idx,:)
+ else
+ disp('2-invariant')
end
- if debug
- ys = h_set_auxiliary_variables(ys,exo_ss,params);
- ys3 = ys;
- idx = find(abs(ys0-ys1)>0);
- if ~isempty(idx)
- M.endo_names(idx,:)
- else
- disp('1-invariant')
- end
- idx = find(abs(ys2-ys1)>0);
- if ~isempty(idx)
- M.endo_names(idx,:)
- else
- disp('2-invariant')
- end
- idx = find(abs(ys3-ys3)>0);
- if ~isempty(idx)
- M.endo_names(idx,:)
- else
- disp('3-invariant')
- end
- pause
+ idx = find(abs(ys3-ys3)>0);
+ if ~isempty(idx)
+ M.endo_names(idx,:)
+ else
+ disp('3-invariant')
end
+ pause
end
+end
- check1 = 0;
- if steady_state_checkflag
- % Check whether the steady state obtained from the _steadystate file is a steady state.
- [residuals, check] = evaluate_static_model(ys, exo_ss, params, M, options);
- if check
- info(1) = 19;
- info(2) = check; % to be improved
- return
- end
- if max(abs(residuals)) > options.dynatol.f
- info(1) = 19;
- info(2) = residuals'*residuals;
- return
- end
- if any(isnan(residuals))
- info(1) = 22;
- return
- end
- elseif ~isempty(options.steadystate_partial)
- ssvar = options.steadystate_partial.ssvar;
- nov = length(ssvar);
- indv = zeros(nov,1);
- for i = 1:nov
- indv(i) = strmatch(ssvar(i),M.endo_names,'exact');
- end
- [ys,check] = dynare_solve('restricted_steadystate',...
- ys(indv),...
- options, exo_ss,indv);
+check1 = 0;
+if steady_state_checkflag
+ % Check whether the steady state obtained from the _steadystate file is a steady state.
+ [residuals, check] = evaluate_static_model(ys, exo_ss, params, M, options);
+ if check
+ info(1) = 19;
+ info(2) = check; % to be improved
+ return
end
-
-
+ if max(abs(residuals)) > options.dynatol.f
+ info(1) = 19;
+ info(2) = residuals'*residuals;
+ return
+ end
+ if any(isnan(residuals))
+ info(1) = 22;
+ return
+ end
+elseif ~isempty(options.steadystate_partial)
+ ssvar = options.steadystate_partial.ssvar;
+ nov = length(ssvar);
+ indv = zeros(nov,1);
+ for i = 1:nov
+ indv(i) = strmatch(ssvar(i),M.endo_names,'exact');
+ end
+ [ys,check] = dynare_solve('restricted_steadystate',...
+ ys(indv),...
+ options, exo_ss,indv);
+end
diff --git a/matlab/expand_group.m b/matlab/expand_group.m
index 7681ffeea9114955d7233b17a88d7b10d6ea6e8c..c20d38c37ca0429179b72d3377e6d4b3860d64a7 100644
--- a/matlab/expand_group.m
+++ b/matlab/expand_group.m
@@ -42,10 +42,8 @@ options.use_shock_groups = strrep(label,' ','_'); %[use_shock_groups_old int2str
for j=1:length(shocks)
M.shock_groups.(options.use_shock_groups).(['group' int2str(j)]).label=shocks{j};
M.shock_groups.(options.use_shock_groups).(['group' int2str(j)]).shocks=shocks(j);
-end
+end
options.shock_decomp.interactive=0;
options.shock_decomp.expand=1;
plot_shock_decomposition(M,oo,options,var_list_);
-
-
diff --git a/matlab/fastgensylv.m b/matlab/fastgensylv.m
index 332e46f2829c6cc635028aee5c56c04fdeba4c62..0f7340cd128b3afd2631669cd818b8b207f43637 100644
--- a/matlab/fastgensylv.m
+++ b/matlab/fastgensylv.m
@@ -95,19 +95,19 @@ end
% B
% C
% D
-% block : block number (for storage purpose)
+% block : block number (for storage purpose)
% tol : convergence criteria
% OUTPUTS
% X solution
-%
+%
% ALGORITHM
% fixed point method
% MARLLINY MONSALVE (2008): "Block linear method for large scale
-% Sylvester equations", Computational & Applied Mathematics, Vol 27, n�1,
+% Sylvester equations", Computational & Applied Mathematics, Vol 27, n°1,
% p47-59
% ||A^-1||.||B||.||C|| < 1 is a suffisant condition:
% - to get a unique solution for the Sylvester equation
% - to get a convergent fixed-point algorithm
%
% SPECIAL REQUIREMENTS
-% none.
+% none.
diff --git a/matlab/ff1_.m b/matlab/ff1_.m
index 4b6b666cef9c959abfabcc796e81eb9b68449274..6037809f03dfce792cc2f6d4d2cd5c7c15339f97 100644
--- a/matlab/ff1_.m
+++ b/matlab/ff1_.m
@@ -5,10 +5,10 @@ function y=ff1_(x)
%
% INPUTS
% x: argument splitted between endogenous and exogenous
-%
+%
% OUTPUTS
% y: 'static' function residuals
-%
+%
% SPECIAL REQUIREMENTS
% none
@@ -35,6 +35,3 @@ n1 = size(x,1) - M_.exo_nbr;
oo_.exo_simul(it_+M_.maximum_lag-M_.maximum_lag,:) = x(n1+1:end)';
fh = str2func([M_.fname '_static']);
y=feval(fh,x(1:n1),oo_.exo_simul, M_.params);
-
-
-
diff --git a/matlab/ffill.m b/matlab/ffill.m
index fc72c9f9f7a8cbaaab2cf168e81afa2db14f4101..3449ee2920712ba03f130eaa8b3deb46aadbe7a6 100644
--- a/matlab/ffill.m
+++ b/matlab/ffill.m
@@ -7,11 +7,11 @@ function [a,b] = ffill(x,ixc,y)
% x: matrix
% ixc: vector of indices
% y: matrix
-%
+%
% OUTPUTS
% a: concatenation results
% b: vector
-%
+%
% SPECIAL REQUIREMENTS
% none
diff --git a/matlab/flip_plan.m b/matlab/flip_plan.m
index 098e25949908cf506c081f5565d5adef890b69ed..5daf19fc7dc76edebb41eb91b61092df175c320f 100644
--- a/matlab/flip_plan.m
+++ b/matlab/flip_plan.m
@@ -31,71 +31,71 @@ function plan = flip_plan(plan, exogenous, endogenous, expectation_type, date, v
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- if ~ischar(expectation_type) || size(expectation_type,1) ~= 1
- error(['in flip_plan the fourth argument should be a string containing the simulation type (''perfect_foresight'' or ''surprise'')']);
- end
- exogenous = strtrim(exogenous);
- ix = find(strcmp(exogenous, plan.endo_names));
- if isempty(ix)
- error(['in flip_plan the second argument ' exogenous ' is not an endogenous variable']);
- end
- endogenous = strtrim(endogenous);
- iy = find(strcmp(endogenous, plan.exo_names));
- if isempty(iy)
- error(['in flip_plan the third argument ' endogenous ' is not an exogenous variable']);
- end
- sdate = length(date);
- if sdate > 1
- if date(1) < plan.date(1) || date(end) > plan.date(end)
- error(['in flip_plan the fifth argument (date=' date ') must lay inside the plan.date ' plan.date]);
- end
- else
- if date < plan.date(1) || date > plan.date(end)
- error(['in flip_plan the fifth argument (date=' date ') must lay iside the plan.date ' plan.date]);
- end
- end
- if ~isempty(plan.shock_vars_)
- common_var = find(iy == plan.shock_vars_);
- if ~isempty(common_var)
- common_date = intersect(date, plan.shock_date_{common_var});
- if ~isempty(common_date)
- if common_date.length > 1
- the_dates = [cell2mat(strings(common_date(1))) ':' cell2mat(strings(common_date(end)))];
- else
- the_dates = cell2mat(strings(common_date));
- end
- error(['Impossible case: ' plan.exo_names{plan.shock_vars_(common_var)} ' is used both as a shock and as an endogenous variable to control the path of ' plan.endo_names{ix} ' at the dates ' the_dates]);
- end
- end
- end
- i_ix = find(ix == plan.constrained_vars_);
- if isempty(i_ix)
+if ~ischar(expectation_type) || size(expectation_type,1) ~= 1
+ error(['in flip_plan the fourth argument should be a string containing the simulation type (''perfect_foresight'' or ''surprise'')']);
+end
+exogenous = strtrim(exogenous);
+ix = find(strcmp(exogenous, plan.endo_names));
+if isempty(ix)
+ error(['in flip_plan the second argument ' exogenous ' is not an endogenous variable']);
+end
+endogenous = strtrim(endogenous);
+iy = find(strcmp(endogenous, plan.exo_names));
+if isempty(iy)
+ error(['in flip_plan the third argument ' endogenous ' is not an exogenous variable']);
+end
+sdate = length(date);
+if sdate > 1
+ if date(1) < plan.date(1) || date(end) > plan.date(end)
+ error(['in flip_plan the fifth argument (date=' date ') must lay inside the plan.date ' plan.date]);
+ end
+else
+ if date < plan.date(1) || date > plan.date(end)
+ error(['in flip_plan the fifth argument (date=' date ') must lay iside the plan.date ' plan.date]);
+ end
+end
+if ~isempty(plan.shock_vars_)
+ common_var = find(iy == plan.shock_vars_);
+ if ~isempty(common_var)
+ common_date = intersect(date, plan.shock_date_{common_var});
+ if ~isempty(common_date)
+ if common_date.length > 1
+ the_dates = [cell2mat(strings(common_date(1))) ':' cell2mat(strings(common_date(end)))];
+ else
+ the_dates = cell2mat(strings(common_date));
+ end
+ error(['Impossible case: ' plan.exo_names{plan.shock_vars_(common_var)} ' is used both as a shock and as an endogenous variable to control the path of ' plan.endo_names{ix} ' at the dates ' the_dates]);
+ end
+ end
+end
+i_ix = find(ix == plan.constrained_vars_);
+if isempty(i_ix)
if isempty(plan.constrained_vars_)
- plan.constrained_vars_ = ix;
- plan.options_cond_fcst_.controlled_varexo = iy;
- if strcmp(expectation_type, 'perfect_foresight')
- plan.constrained_perfect_foresight_ = 1;
- else
- plan.constrained_perfect_foresight_ = 0;
- end
+ plan.constrained_vars_ = ix;
+ plan.options_cond_fcst_.controlled_varexo = iy;
+ if strcmp(expectation_type, 'perfect_foresight')
+ plan.constrained_perfect_foresight_ = 1;
+ else
+ plan.constrained_perfect_foresight_ = 0;
+ end
else
- plan.constrained_vars_ = [plan.constrained_vars_ ; ix];
- plan.options_cond_fcst_.controlled_varexo = [plan.options_cond_fcst_.controlled_varexo ; iy];
- if strcmp(expectation_type, 'perfect_foresight')
- plan.constrained_perfect_foresight_ = [plan.constrained_perfect_foresight_ ; 1];
- else
- plan.constrained_perfect_foresight_ = [plan.constrained_perfect_foresight_ ; 0];
- end
+ plan.constrained_vars_ = [plan.constrained_vars_ ; ix];
+ plan.options_cond_fcst_.controlled_varexo = [plan.options_cond_fcst_.controlled_varexo ; iy];
+ if strcmp(expectation_type, 'perfect_foresight')
+ plan.constrained_perfect_foresight_ = [plan.constrained_perfect_foresight_ ; 1];
+ else
+ plan.constrained_perfect_foresight_ = [plan.constrained_perfect_foresight_ ; 0];
+ end
end
plan.constrained_date_{length(plan.constrained_date_) + 1} = date;
plan.constrained_str_date_{length(plan.constrained_str_date_) + 1} = strings(date);
plan.constrained_int_date_{length(plan.constrained_int_date_) + 1} = date - plan.date(1) + 1;
plan.constrained_paths_{length(plan.constrained_paths_) + 1} = value;
- elseif plan.options_cond_fcst_.controlled_varexo(i_ix) == iy % same exogenous and endogenous hard tune
- [plan.constrained_str_date_{i_ix}, i1, i2] = union(strings(date), plan.constrained_str_date_{i_ix});
- plan.constrained_date_{i_ix} = [date(i1) plan.constrained_date_{i_ix}(i2)];
- plan.constrained_int_date_{i_ix} = [date(i1) - plan.date(1) + 1; plan.constrained_int_date_{i_ix}(i2)];
- plan.constrained_paths_{i_ix} = [value(i1)'; plan.constrained_paths_{i_ix}(i2)];
- else
+elseif plan.options_cond_fcst_.controlled_varexo(i_ix) == iy % same exogenous and endogenous hard tune
+[plan.constrained_str_date_{i_ix}, i1, i2] = union(strings(date), plan.constrained_str_date_{i_ix});
+plan.constrained_date_{i_ix} = [date(i1) plan.constrained_date_{i_ix}(i2)];
+plan.constrained_int_date_{i_ix} = [date(i1) - plan.date(1) + 1; plan.constrained_int_date_{i_ix}(i2)];
+plan.constrained_paths_{i_ix} = [value(i1)'; plan.constrained_paths_{i_ix}(i2)];
+else
error(['impossible case you have two conditional forecasts:\n - one involving ' plan.endo_names{plan.options_cond_fcst_.controlled_varexo(i_ix),:} ' as control and ' plan_exo_names{plan.constrained_vars_(ix_)} ' as constrined endogenous\n - the other involving ' plan.endo_names{plan.options_cond_fcst_.controlled_varexo(iy),:} ' as control and ' plan_exo_names{plan.constrained_vars_(ix)} ' as constrined endogenous\n']);
- end
+end
diff --git a/matlab/forcst.m b/matlab/forcst.m
index 9c7654ae62d8b6b416ab91fe3b57797c95cf31ca..de824258a45ca23cf81faaebac8e69385b186750 100644
--- a/matlab/forcst.m
+++ b/matlab/forcst.m
@@ -1,7 +1,7 @@
function [yf,int_width,int_width_ME]=forcst(dr,y0,horizon,var_list,M_,oo_,options_)
% function [yf,int_width,int_width_ME]=forecst(dr,y0,horizon,var_list,M_,oo_,options_)
% computes mean forecast for a given value of the parameters
-% computes also confidence band for the forecast
+% computes also confidence band for the forecast
%
% INPUTS:
% dr: structure containing decision rules
@@ -67,7 +67,7 @@ ghx1 = dr.ghx(inv_order_var(ivar),:);
ghu1 = dr.ghu(inv_order_var(ivar),:);
%initialize recursion
-sigma_u = B*M_.Sigma_e*B';
+sigma_u = B*M_.Sigma_e*B';
sigma_u1 = ghu1*M_.Sigma_e*ghu1';
sigma_y = 0; %no uncertainty about the states
diff --git a/matlab/forcst2.m b/matlab/forcst2.m
index 43c2b4c4e99332f43fc0726b32ef6adc64b795c6..621d44ec24490b9a2b487aa11b02aae66f3a8b96 100644
--- a/matlab/forcst2.m
+++ b/matlab/forcst2.m
@@ -1,6 +1,6 @@
function yf=forcst2(y0,horizon,dr,n)
% function yf=forcst2(y0,horizon,dr,n)
-%
+%
% computes forecasts based on first order model solution, given shocks
% drawn from the shock distribution, but not including measurement error
% Inputs:
@@ -11,7 +11,7 @@ function yf=forcst2(y0,horizon,dr,n)
%
% Outputs:
% - yf [horizon+ykmin_ by endo_nbr by n] array of forecasts
-%
+%
% Copyright (C) 2008-2016 Dynare Team
%
% This file is part of Dynare.
@@ -64,4 +64,4 @@ for i=ykmin_+(1:horizon)
end
yf(dr.order_var,:,:) = yf;
-yf=permute(yf,[2 1 3]);
+yf=permute(yf,[2 1 3]);
diff --git a/matlab/forcst2a.m b/matlab/forcst2a.m
index c5ede306d0d24c93ae1dc0b370da3a6f65d798db..72864ac3c08fe134a7aa8b128540ee3447b28bcc 100644
--- a/matlab/forcst2a.m
+++ b/matlab/forcst2a.m
@@ -1,6 +1,6 @@
function yf=forcst2a(y0,dr,e)
% function yf=forcst2a(y0,dr,e)
-% computes forecasts based on first order model solution, assuming the absence of shocks
+% computes forecasts based on first order model solution, assuming the absence of shocks
% Inputs:
% - y0 [endo_nbr by maximum_endo_lag] matrix of starting values
% - dr [structure] structure with Dynare decision rules
@@ -48,4 +48,3 @@ for i=ykmin_+(1:horizon)
end
yf(:,dr.order_var) = yf;
-
diff --git a/matlab/forecast_graphs.m b/matlab/forecast_graphs.m
index 108e0498b2d934cdd10806cc94812b22538cc229..5631ff4b4d4e2a738ed8d1a1d2074862d8db5322 100644
--- a/matlab/forecast_graphs.m
+++ b/matlab/forecast_graphs.m
@@ -1,7 +1,7 @@
function forecast_graphs(var_list,M_, oo_,options_)
% function forecast_graphs(var_list,M_, oo_,options_)
% Plots the classical forecasts created by dyn_forecast.m
-%
+%
% Inputs:
% o var_list character array with variable names
% o M_ model structure
@@ -73,7 +73,7 @@ for j= 1:nvar
fprintf(fidTeX,'\\caption{Mean forecasts and %2.0f\\%% confidence intervals}\n',options_.forecasts.conf_sig*100);
fprintf(fidTeX,'\\end{figure}\n');
fprintf(fidTeX,' \n');
- end
+ end
n_fig =n_fig+1;
eval(['hh=dyn_figure(options_.nodisplay,''Name'',''Forecasts (' int2str(n_fig) ')'');']);
m = 1;
@@ -81,7 +81,7 @@ for j= 1:nvar
subplot(nr,nc,m);
vn = deblank(endo_names(i_var(j),:));
obs = 0;
-% $$$ k = strmatch(vn,varobs,'exact');
+% $$$ k = strmatch(vn,varobs,'exact');
% $$$ if ~isempty(k)
% $$$ yy = y.(vn)(end-9:end) + repmat(ys(i_var(j)),10,1)+trend(k,:)';
% $$$ plot(yy);
@@ -118,11 +118,11 @@ end
if isfield(oo_.forecast,'HPDinf_ME')
var_names=fieldnames(oo_.forecast.HPDinf_ME);
-
+
if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
fidTeX=write_LaTeX_header([M_.dname '/graphs/',fname,'_forcst_ME.tex']);
end
-
+
m = 1;
n_fig = 1;
hh=dyn_figure(options_.nodisplay,'Name','Forecasts including ME (I)');
@@ -156,12 +156,12 @@ if isfield(oo_.forecast,'HPDinf_ME')
hold off
m = m + 1;
end
-
+
if m > 1
dyn_saveas(hh,[dname '/graphs/forcst_ME' int2str(n_fig)],options_.nodisplay,options_.graph_format);
if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
fprintf(fidTeX,'\\begin{figure}[H]\n');
- fprintf(fidTeX,'\\centering \n');
+ fprintf(fidTeX,'\\centering \n');
fprintf(fidTeX,'\\includegraphics[width=%2.2f\\textwidth]{%s/graphs/forcst_ME%d}\n',options_.figures.textwidth*min((m-1)/nc,1),dname,n_fig);
fprintf(fidTeX,'\\label{Fig:forcst_ME:%d}\n',n_fig);
fprintf(fidTeX,'\\caption{Mean forecasts and %2.0f\\%% confidence intervals accounting for measurement error}\n',options_.forecasts.conf_sig*100);
@@ -169,10 +169,10 @@ if isfield(oo_.forecast,'HPDinf_ME')
fprintf(fidTeX,' \n');
end
end
-
+
if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
write_LaTeX_footer(fidTeX);
- end
+ end
end
diff --git a/matlab/ftest.m b/matlab/ftest.m
index 97bedb6e56299b82552641b589f0d0f4c5e01af0..c72ed49d9611071cb07ec27ddb80de3e43688cc1 100644
--- a/matlab/ftest.m
+++ b/matlab/ftest.m
@@ -20,7 +20,7 @@ function ftest (s1,s2)
global nvx nvy x y lag1
if size(s1,1) ~= 2
- error ('Sp�cifiez deux fichiers pour la comparaison.') ;
+ error ('Spécifiez deux fichiers pour la comparaison.') ;
end
for i = 1:2
@@ -59,7 +59,7 @@ end
for i = 1:size(x,1)
if ~ strcmp(nvx(i,:),nvy(i,:))
- error ('FTEST: The two files don''t have the same variables.') ;
+ error ('FTEST: The two files don''t have the same variables.') ;
end
end
diff --git a/matlab/gauss_hermite_weights_and_nodes.m b/matlab/gauss_hermite_weights_and_nodes.m
index 8bbc08cca75181b340cfc72532d838b5943bf3c0..37f2ddf9278ccf263513e97a8fc4ab4d728d3a55 100644
--- a/matlab/gauss_hermite_weights_and_nodes.m
+++ b/matlab/gauss_hermite_weights_and_nodes.m
@@ -66,7 +66,7 @@ nodes = sqrt(2)*nodes;
%$ sum_of_weights = sum(weights);
%$
%$ % Expected nodes (taken from Judd (1998, table 7.4).
-%$ enodes = [-2.020182870; -0.9585724646; 0; 0.9585724646; 2.020182870];
+%$ enodes = [-2.020182870; -0.9585724646; 0; 0.9585724646; 2.020182870];
%$
%$ % Check the results.
%$ t(1) = dassert(1.0,sum_of_weights,1e-12);
@@ -79,7 +79,7 @@ nodes = sqrt(2)*nodes;
%$ [nodes,weights] = gauss_hermite_weights_and_nodes(n);
%$
%$ sum_of_weights = sum(weights);
-%$ expectation = sum(weights.*nodes);
+%$ expectation = sum(weights.*nodes);
%$ variance = sum(weights.*(nodes.^2));
%$
%$ % Check the results.
@@ -113,7 +113,7 @@ nodes = sqrt(2)*nodes;
%$ [nodes,weights] = gauss_hermite_weights_and_nodes(n);
%$
%$ sum_of_weights = sum(weights);
-%$ expectation = sum(weights.*nodes*.1);
+%$ expectation = sum(weights.*nodes*.1);
%$ variance = sum(weights.*((nodes*.1).^2));
%$
%$ % Check the results.
diff --git a/matlab/gauss_legendre_weights_and_nodes.m b/matlab/gauss_legendre_weights_and_nodes.m
index 6bc7067b153559f14bebc2fe89d1fd3d6637861f..fa42e916518a4c858a259a4942b2772c60430223 100644
--- a/matlab/gauss_legendre_weights_and_nodes.m
+++ b/matlab/gauss_legendre_weights_and_nodes.m
@@ -124,7 +124,7 @@ end
%@test:3
%$ [n,w] = gauss_legendre_weights_and_nodes(9,pi,2*pi);
-%$ % Check that the
+%$ % Check that the
%$ t(1) = all(n>pi);
%$ t(2) = all(n<2*pi);
%$ t(3) = dassert(sum(w),pi,1e-12);
diff --git a/matlab/gcompare.m b/matlab/gcompare.m
index d8a1e2ae831de756e29b8317de4d0f75e4fe5704..861aa894d9fe6f6628170fe5f1bc4c4ac7847662 100644
--- a/matlab/gcompare.m
+++ b/matlab/gcompare.m
@@ -1,5 +1,5 @@
function gcompare(s1,s2)
-% GCOMPARE : GCOMPARE ( [ 'file1' ; 'file2' ] , [ 'var1' ; 'var2' ...] )
+% GCOMPARE : GCOMPARE ( [ 'file1' ; 'file2' ] , [ 'var1' ; 'var2' ...] )
% This optional command plots the trajectories of a list of
% variables in two different simulations. One plot is drawn
% for each variable. The trajectories must have been previously
@@ -50,6 +50,3 @@ end
% 06/18/01 MJ corrected treatment of options_.smpl
% 06/24/01 MJ removed color specification
-
-
-
diff --git a/matlab/gensylv/gensylv.m b/matlab/gensylv/gensylv.m
index a67d7de5ee7b3573f5b73d2f33ee5c4c765f4a0b..2920aca4d2bd8d3f6f6cfb0842618cf669e75673 100644
--- a/matlab/gensylv/gensylv.m
+++ b/matlab/gensylv/gensylv.m
@@ -3,21 +3,21 @@ function [err, E] = gensylv(kron_prod,A,B,C0,D)
% Solves a Sylvester equation.
%
% INPUTS
-% kron_prod
+% kron_prod
% A
% B
% C
% D
-%
+%
% OUTPUTS
% err [double] scalar: 1 indicates failure, 0 indicates success
% E
-%
+%
% ALGORITHM
% none.
%
% SPECIAL REQUIREMENTS
-% none.
+% none.
% Copyright (C) 1996-2017 Dynare Team
%
@@ -37,7 +37,7 @@ function [err, E] = gensylv(kron_prod,A,B,C0,D)
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
C = C0;
for i=1:(kron_prod-1)
- C = kron(C0,C);
+ C = kron(C0,C);
end
x0 = sylvester3(A,B,C,D);
diff --git a/matlab/gensylv_fp.m b/matlab/gensylv_fp.m
index 1174fbc4b4a55e8226098208daeba626203f3cde..51c96fceb8c2e08ef179b3f5b5cb0683080105f6 100644
--- a/matlab/gensylv_fp.m
+++ b/matlab/gensylv_fp.m
@@ -7,22 +7,22 @@ function X = gensylv_fp(A, B, C, D, block, tol)
% B
% C
% D
-% block : block number (for storage purpose)
+% block : block number (for storage purpose)
% tol : convergence criteria
% OUTPUTS
% X solution
-%
+%
% ALGORITHM
% fixed point method
% MARLLINY MONSALVE (2008): "Block linear method for large scale
-% Sylvester equations", Computational & Applied Mathematics, Vol 27, n�1,
+% Sylvester equations", Computational & Applied Mathematics, Vol 27, n°1,
% p47-59
% ||A^-1||.||B||.||C|| < 1 is a suffisant condition:
% - to get a unique solution for the Sylvester equation
% - to get a convergent fixed-point algorithm
%
% SPECIAL REQUIREMENTS
-% none.
+% none.
% Copyright (C) 1996-2017 Dynare Team
%
% This file is part of Dynare.
@@ -62,7 +62,7 @@ while it_fp < maxit_fp && evol > tol
Z_old = Z;
Z = - (B * X * C + D);
evol = max(sum(abs(Z - Z_old))); %norm_1
- %evol = max(sum(abs(Z - Z_old)')); %norm_inf
+ %evol = max(sum(abs(Z - Z_old)')); %norm_inf
it_fp = it_fp + 1;
end
%fprintf('sylvester it_fp=%d evol=%g | ',it_fp,evol);
diff --git a/matlab/getH.m b/matlab/getH.m
index 33f890376674322021660a78046c0068c5d4cf4f..fcee9242e3cf19fb95f6fca712d2d4aee206320b 100644
--- a/matlab/getH.m
+++ b/matlab/getH.m
@@ -13,14 +13,14 @@ function [H, dA, dOm, Hss, gp, d2A, d2Om, H2ss] = getH(A, B, estim_params_,M_,oo
% not (-1 or -2)
% indx: Index of estimated parameters in M_.params
% indexo: Index of estimated standard deviations in M_.exo_names
-% iv: Index of considered variables
-%
+% iv: Index of considered variables
+%
% Outputs:
% H: dTAU/dTHETA: Jacobian of TAU, vectorized form of
% linearized reduced form state space model, given ys [steady state],
% A [transition matrix], B [matrix of shocks], Sigma [covariance of shocks]
-% TAU = [ys; vec(A); dyn_vech(B*Sigma*B')].
-% dA: [endo_nbr by endo_nbr by (indx+indexo)] Jacobian of transition matrix A
+% TAU = [ys; vec(A); dyn_vech(B*Sigma*B')].
+% dA: [endo_nbr by endo_nbr by (indx+indexo)] Jacobian of transition matrix A
% dOm: [endo_nbr by endo_nbr by (indx+indexo)] Jacobian of Omega = (B*Sigma*B')
% Hss: [endo_nbr by (indx)] Jacobian of steady state with respect to estimated
% structural parameters only (indx)
@@ -50,16 +50,16 @@ function [H, dA, dOm, Hss, gp, d2A, d2Om, H2ss] = getH(A, B, estim_params_,M_,oo
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
if nargin<7 || isempty(kronflag)
- kronflag = 0;
+ kronflag = 0;
end
if nargin<8 || isempty(indx)
- indx = [];
+ indx = [];
end
if nargin<9 || isempty(indexo)
- indexo = [];
+ indexo = [];
end
if nargin<10 || isempty(iv)
- iv = (1:length(A))';
+ iv = (1:length(A))';
end
[I,J]=find(M_.lead_lag_incidence');
@@ -96,7 +96,7 @@ if kronflag==-1 % perturbation
end
if nargout>5
H2 = hessian_sparse('thet2tau',[sqrt(diag(M_.Sigma_e(indexo,indexo))); M_.params(indx)], ...
- options_.gstep,estim_params_,M_, oo_, indx,indexo,0,[],[],[],iv);
+ options_.gstep,estim_params_,M_, oo_, indx,indexo,0,[],[],[],iv);
H2ss = zeros(m1,tot_param_nbr,tot_param_nbr);
iax=find(triu(rand(tot_param_nbr,tot_param_nbr)));
H2 = H2(:,iax);
@@ -109,23 +109,23 @@ if kronflag==-1 % perturbation
d2A(:,:) = H2(m1+1:m1+m1*m1,:);
d2Om(:,:) = H2(m1+m1*m1+1:end,:);
clear H2
-% tmp0=zeros(m1,m1);
-% tmp0(iv,iv)=1;
-% iax=find(tmp0);
-% d2A=d2a(iax,:);
-% iax=find(dyn_vech(tmp0));
-% d2Om=d2om(iax,:);
+ % tmp0=zeros(m1,m1);
+ % tmp0(iv,iv)=1;
+ % iax=find(tmp0);
+ % d2A=d2a(iax,:);
+ % iax=find(dyn_vech(tmp0));
+ % d2Om=d2om(iax,:);
end
-% assignin('base','M_', M_);
-% assignin('base','oo_', oo_);
+ % assignin('base','M_', M_);
+ % assignin('base','oo_', oo_);
return
end
if kronflag==-2
if nargout>5
[residual, g1, g2 ] = feval([M_.fname,'_dynamic'],yy0, oo_.exo_steady_state', ...
- M_.params, oo_.dr.ys, 1);
+ M_.params, oo_.dr.ys, 1);
g22 = hessian_sparse('thet2tau',[M_.params(indx)],options_.gstep,estim_params_,M_, oo_, indx,[],-1);
H2ss=full(g22(1:M_.endo_nbr,:));
H2ss = reshape(H2ss,[M_.endo_nbr param_nbr param_nbr]);
@@ -145,132 +145,132 @@ if kronflag==-2
clear gx22;
else
[residual, g1 ] = feval([M_.fname,'_dynamic'],yy0, oo_.exo_steady_state', ...
- M_.params, oo_.dr.ys, 1);
+ M_.params, oo_.dr.ys, 1);
end
gp = fjaco('thet2tau',[M_.params(indx)],estim_params_,M_, oo_, indx,[],-1);
Hss=gp(1:M_.endo_nbr,:);
gp=gp(M_.endo_nbr+1:end,:);
gp = reshape(gp,[size(g1) param_nbr]);
else
-dyssdtheta=zeros(length(oo_.dr.ys),M_.param_nbr);
-d2yssdtheta=zeros(length(oo_.dr.ys),M_.param_nbr,M_.param_nbr);
-[residual, gg1] = feval([M_.fname,'_static'],oo_.dr.ys, oo_.exo_steady_state', M_.params);
-df = feval([M_.fname,'_static_params_derivs'],oo_.dr.ys, repmat(oo_.exo_steady_state',[M_.maximum_exo_lag+M_.maximum_exo_lead+1]), ...
- M_.params);
-dyssdtheta = -gg1\df;
-if nargout>5
- [residual, gg1, gg2] = feval([M_.fname,'_static'],oo_.dr.ys, oo_.exo_steady_state', M_.params);
- [residual, g1, g2, g3] = feval([M_.fname,'_dynamic'],yy0, oo_.exo_steady_state', ...
- M_.params, oo_.dr.ys, 1);
- [nr, nc]=size(gg2);
-
- [df, gpx, d2f] = feval([M_.fname,'_static_params_derivs'],oo_.dr.ys, oo_.exo_steady_state', ...
- M_.params);%, oo_.dr.ys, 1, dyssdtheta*0, d2yssdtheta);
- d2f = get_all_resid_2nd_derivs(d2f,length(oo_.dr.ys),M_.param_nbr);
- if isempty(find(gg2))
- for j=1:M_.param_nbr
- d2yssdtheta(:,:,j) = -gg1\d2f(:,:,j);
- end
- else
- gam = d2f*0;
- for j=1:nr
- tmp1 = (squeeze(gpx(j,:,:))'*dyssdtheta);
- gam(j,:,:)=transpose(reshape(gg2(j,:),[nr nr])*dyssdtheta)*dyssdtheta ...
- + tmp1 + tmp1';
- end
- for j=1:M_.param_nbr
- d2yssdtheta(:,:,j) = -gg1\(d2f(:,:,j)+gam(:,:,j));
+ dyssdtheta=zeros(length(oo_.dr.ys),M_.param_nbr);
+ d2yssdtheta=zeros(length(oo_.dr.ys),M_.param_nbr,M_.param_nbr);
+ [residual, gg1] = feval([M_.fname,'_static'],oo_.dr.ys, oo_.exo_steady_state', M_.params);
+ df = feval([M_.fname,'_static_params_derivs'],oo_.dr.ys, repmat(oo_.exo_steady_state',[M_.maximum_exo_lag+M_.maximum_exo_lead+1]), ...
+ M_.params);
+ dyssdtheta = -gg1\df;
+ if nargout>5
+ [residual, gg1, gg2] = feval([M_.fname,'_static'],oo_.dr.ys, oo_.exo_steady_state', M_.params);
+ [residual, g1, g2, g3] = feval([M_.fname,'_dynamic'],yy0, oo_.exo_steady_state', ...
+ M_.params, oo_.dr.ys, 1);
+ [nr, nc]=size(gg2);
+
+ [df, gpx, d2f] = feval([M_.fname,'_static_params_derivs'],oo_.dr.ys, oo_.exo_steady_state', ...
+ M_.params);%, oo_.dr.ys, 1, dyssdtheta*0, d2yssdtheta);
+ d2f = get_all_resid_2nd_derivs(d2f,length(oo_.dr.ys),M_.param_nbr);
+ if isempty(find(gg2))
+ for j=1:M_.param_nbr
+ d2yssdtheta(:,:,j) = -gg1\d2f(:,:,j);
+ end
+ else
+ gam = d2f*0;
+ for j=1:nr
+ tmp1 = (squeeze(gpx(j,:,:))'*dyssdtheta);
+ gam(j,:,:)=transpose(reshape(gg2(j,:),[nr nr])*dyssdtheta)*dyssdtheta ...
+ + tmp1 + tmp1';
+ end
+ for j=1:M_.param_nbr
+ d2yssdtheta(:,:,j) = -gg1\(d2f(:,:,j)+gam(:,:,j));
+ end
+ clear tmp1 gpx gam
end
- clear tmp1 gpx gam
end
-end
-if any(any(isnan(dyssdtheta)))
- [U,T] = schur(gg1);
- qz_criterium=options_.qz_criterium;
- e1 = abs(ordeig(T)) < qz_criterium-1;
- k = sum(e1); % Number non stationary variables.
-% n = length(e1)-k; % Number of stationary variables.
- [U,T] = ordschur(U,T,e1);
- T = T(k+1:end,k+1:end);
- dyssdtheta = -U(:,k+1:end)*(T\U(:,k+1:end)')*df;
- if nargout>5
- for j=1:length(indx)
- d2yssdtheta(:,:,j) = -U(:,k+1:end)*(T\U(:,k+1:end)')*d2f(:,:,j);
+ if any(any(isnan(dyssdtheta)))
+ [U,T] = schur(gg1);
+ qz_criterium=options_.qz_criterium;
+ e1 = abs(ordeig(T)) < qz_criterium-1;
+ k = sum(e1); % Number non stationary variables.
+ % n = length(e1)-k; % Number of stationary variables.
+ [U,T] = ordschur(U,T,e1);
+ T = T(k+1:end,k+1:end);
+ dyssdtheta = -U(:,k+1:end)*(T\U(:,k+1:end)')*df;
+ if nargout>5
+ for j=1:length(indx)
+ d2yssdtheta(:,:,j) = -U(:,k+1:end)*(T\U(:,k+1:end)')*d2f(:,:,j);
+ end
end
end
-end
-if nargout>5
- [df, gp, d2f, gpp, hp] = feval([M_.fname,'_params_derivs'],yy0, oo_.exo_steady_state', ...
- M_.params, oo_.dr.ys, 1, dyssdtheta, d2yssdtheta);
- H2ss = d2yssdtheta(oo_.dr.order_var,indx,indx);
-else
- [df, gp] = feval([M_.fname,'_params_derivs'],yy0, repmat(oo_.exo_steady_state',[M_.maximum_exo_lag+M_.maximum_exo_lead+1,1]), ...
- M_.params, oo_.dr.ys, 1, dyssdtheta,d2yssdtheta);
- [residual, g1, g2 ] = feval([M_.fname,'_dynamic'],yy0, repmat(oo_.exo_steady_state',[M_.maximum_exo_lag+M_.maximum_exo_lead+1,1]), ...
- M_.params, oo_.dr.ys, 1);
-end
+ if nargout>5
+ [df, gp, d2f, gpp, hp] = feval([M_.fname,'_params_derivs'],yy0, oo_.exo_steady_state', ...
+ M_.params, oo_.dr.ys, 1, dyssdtheta, d2yssdtheta);
+ H2ss = d2yssdtheta(oo_.dr.order_var,indx,indx);
+ else
+ [df, gp] = feval([M_.fname,'_params_derivs'],yy0, repmat(oo_.exo_steady_state',[M_.maximum_exo_lag+M_.maximum_exo_lead+1,1]), ...
+ M_.params, oo_.dr.ys, 1, dyssdtheta,d2yssdtheta);
+ [residual, g1, g2 ] = feval([M_.fname,'_dynamic'],yy0, repmat(oo_.exo_steady_state',[M_.maximum_exo_lag+M_.maximum_exo_lead+1,1]), ...
+ M_.params, oo_.dr.ys, 1);
+ end
-[nr, nc]=size(g2);
-nc = sqrt(nc);
-Hss = dyssdtheta(oo_.dr.order_var,indx);
-dyssdtheta = dyssdtheta(I,:);
-ns = max(max(M_.lead_lag_incidence)); % retrieve the number of states excluding columns for shocks
-gp2 = gp*0;
-for j=1:nr
- [II JJ]=ind2sub([nc nc],find(g2(j,:)));
- for i=1:nc
- is = find(II==i);
- is = is(find(JJ(is)<=ns));
- if ~isempty(is)
- g20=full(g2(j,find(g2(j,:))));
- gp2(j,i,:)=g20(is)*dyssdtheta(JJ(is),:);
+ [nr, nc]=size(g2);
+ nc = sqrt(nc);
+ Hss = dyssdtheta(oo_.dr.order_var,indx);
+ dyssdtheta = dyssdtheta(I,:);
+ ns = max(max(M_.lead_lag_incidence)); % retrieve the number of states excluding columns for shocks
+ gp2 = gp*0;
+ for j=1:nr
+ [II JJ]=ind2sub([nc nc],find(g2(j,:)));
+ for i=1:nc
+ is = find(II==i);
+ is = is(find(JJ(is)<=ns));
+ if ~isempty(is)
+ g20=full(g2(j,find(g2(j,:))));
+ gp2(j,i,:)=g20(is)*dyssdtheta(JJ(is),:);
+ end
end
end
-end
-gp = gp+gp2;
-gp = gp(:,:,indx);
+ gp = gp+gp2;
+ gp = gp(:,:,indx);
-if nargout>5
-% h22 = get_all_hess_derivs(hp,nr,nc,M_.param_nbr);
- g22 = gpp;
- gp22 = sparse(nr*nc,param_nbr*param_nbr);
- tmp1 = reshape(g3,[nr*nc*nc nc]);
- tmp2=sparse(size(tmp1,1),M_.param_nbr);
-% tmp2=tmp1*[dyssdtheta; zeros(nc-ns,M_.param_nbr)];
- tmpa=[dyssdtheta; zeros(nc-ns,M_.param_nbr)];
- tmpa=sparse(tmpa);
- for j=1:M_.param_nbr
- tmp2(:,j)=tmp1*tmpa(:,j);
- end
-% tmp2=sparse(tmp2);
-% [i1 i2]=ind2sub([nc M_.param_nbr],[1:nc*M_.param_nbr]');
+ if nargout>5
+ % h22 = get_all_hess_derivs(hp,nr,nc,M_.param_nbr);
+ g22 = gpp;
+ gp22 = sparse(nr*nc,param_nbr*param_nbr);
+ tmp1 = reshape(g3,[nr*nc*nc nc]);
+ tmp2=sparse(size(tmp1,1),M_.param_nbr);
+ % tmp2=tmp1*[dyssdtheta; zeros(nc-ns,M_.param_nbr)];
+ tmpa=[dyssdtheta; zeros(nc-ns,M_.param_nbr)];
+ tmpa=sparse(tmpa);
+ for j=1:M_.param_nbr
+ tmp2(:,j)=tmp1*tmpa(:,j);
+ end
+ % tmp2=sparse(tmp2);
+ % [i1 i2]=ind2sub([nc M_.param_nbr],[1:nc*M_.param_nbr]');
- for j=1:nr
- tmp0=reshape(g2(j,:),[nc nc]);
- tmp0 = tmp0(:,1:ns)*reshape(d2yssdtheta(I,:,:),[ns,M_.param_nbr*M_.param_nbr]);
- for i=1:nc
- indo = sub2ind([nr nc nc], ones(nc,1)*j ,ones(nc,1)*i, (1:nc)');
- tmpx = (tmp2(indo,:))'*[dyssdtheta; zeros(nc-ns,M_.param_nbr)];
-% gp22(j,i,:,:)=squeeze(tmp1(j,i,:,:))'*[dyssdtheta; zeros(nc-ns,M_.param_nbr)];
- tmpu = (get_hess_deriv(hp,j,i,nc,M_.param_nbr))'*[dyssdtheta; zeros(nc-ns,M_.param_nbr)];
- tmpy = tmpx+tmpu+tmpu'+reshape(tmp0(i,:,:),[M_.param_nbr M_.param_nbr]);
- tmpy = tmpy + get_2nd_deriv_mat(gpp,j,i,M_.param_nbr);
- tmpy = tmpy(indx,indx);
- if any(any(tmpy))
- ina = find(triu(tmpy));
- gp22(sub2ind([nr nc],j,i),ina)=transpose(tmpy(ina));
-% gp22(j,i,:,:)= reshape(tmpy,[1 1 M_.param_nbr M_.param_nbr]);
+ for j=1:nr
+ tmp0=reshape(g2(j,:),[nc nc]);
+ tmp0 = tmp0(:,1:ns)*reshape(d2yssdtheta(I,:,:),[ns,M_.param_nbr*M_.param_nbr]);
+ for i=1:nc
+ indo = sub2ind([nr nc nc], ones(nc,1)*j ,ones(nc,1)*i, (1:nc)');
+ tmpx = (tmp2(indo,:))'*[dyssdtheta; zeros(nc-ns,M_.param_nbr)];
+ % gp22(j,i,:,:)=squeeze(tmp1(j,i,:,:))'*[dyssdtheta; zeros(nc-ns,M_.param_nbr)];
+ tmpu = (get_hess_deriv(hp,j,i,nc,M_.param_nbr))'*[dyssdtheta; zeros(nc-ns,M_.param_nbr)];
+ tmpy = tmpx+tmpu+tmpu'+reshape(tmp0(i,:,:),[M_.param_nbr M_.param_nbr]);
+ tmpy = tmpy + get_2nd_deriv_mat(gpp,j,i,M_.param_nbr);
+ tmpy = tmpy(indx,indx);
+ if any(any(tmpy))
+ ina = find(triu(tmpy));
+ gp22(sub2ind([nr nc],j,i),ina)=transpose(tmpy(ina));
+ % gp22(j,i,:,:)= reshape(tmpy,[1 1 M_.param_nbr M_.param_nbr]);
+ end
end
+ % gp22(j,:,:,:)=gp22(j,:,:,:)+reshape(tmp0(:,1:ns)*d2yssdtheta(I,:,:),[1 nc M_.param_nbr M_.param_nbr]);
end
-% gp22(j,:,:,:)=gp22(j,:,:,:)+reshape(tmp0(:,1:ns)*d2yssdtheta(I,:,:),[1 nc M_.param_nbr M_.param_nbr]);
- end
-% g22 = g22+gp22;
-% g22 = g22(:,:,indx,indx);
- clear tmp0 tmp1 tmp2 tmpu tmpx tmpy
+ % g22 = g22+gp22;
+ % g22 = g22(:,:,indx,indx);
+ clear tmp0 tmp1 tmp2 tmpu tmpx tmpy
inx=find(gp22);
gx22=zeros(length(inx),5);
for j=1:length(inx)
@@ -282,7 +282,7 @@ if nargout>5
g22 = gx22;
clear gx22 gp22;
-end
+ end
end
@@ -463,13 +463,13 @@ else % generalized sylvester equation
for j=1:length(indexo)
dSig(indexo(j),indexo(j),j) = 2*sqrt(M_.Sigma_e(indexo(j),indexo(j)));
y = B*dSig(:,:,j)*B';
-% y = y(nauxe+1:end,nauxe+1:end);
-% H(:,j) = [zeros((m-nauxe)^2,1); dyn_vech(y)];
+ % y = y(nauxe+1:end,nauxe+1:end);
+ % H(:,j) = [zeros((m-nauxe)^2,1); dyn_vech(y)];
H(:,j) = [zeros(m1^2,1); dyn_vech(y(iv,iv))];
if nargout>1
dOm(:,:,j) = y(iv,iv);
end
-% dSig(indexo(j),indexo(j)) = 0;
+ % dSig(indexo(j),indexo(j)) = 0;
end
end
for j=1:param_nbr
@@ -479,8 +479,8 @@ else % generalized sylvester equation
dB(:,:,j) = y;
end
y = y*M_.Sigma_e*B'+B*M_.Sigma_e*y';
-% x = x(nauxe+1:end,nauxe+1:end);
-% y = y(nauxe+1:end,nauxe+1:end);
+ % x = x(nauxe+1:end,nauxe+1:end);
+ % y = y(nauxe+1:end,nauxe+1:end);
if nargout>1
dA(:,:,j+length(indexo)) = x(iv,iv);
dOm(:,:,j+length(indexo)) = y(iv,iv);
@@ -521,7 +521,7 @@ if nargout > 5
cumjcount=0;
jinx = [];
x2x=sparse(m*m,param_nbr_2);
-% x2x=[];
+ % x2x=[];
for i=1:param_nbr
for j=1:i
elem1 = (get_2nd_deriv(D2g0,m,m,j,i)-get_2nd_deriv(D2g1,m,m,j,i)*A);
@@ -538,7 +538,7 @@ if nargout > 5
if (j*i)==param_nbr^2
d = d(:,:,1:jcount);
end
-% d(find(abs(d)<1.e-12))=0;
+ % d(find(abs(d)<1.e-12))=0;
xx2=sylvester3(a,b,c,d);
flag=1;
icount=0;
@@ -546,12 +546,12 @@ if nargout > 5
[xx2, flag]=sylvester3a(xx2,a,b,c,d);
icount = icount + 1;
end
-% inx = find(abs(xx2)>1.e-12);
-% xx2(find(abs(xx2)<1.e-12))=0;
+ % inx = find(abs(xx2)>1.e-12);
+ % xx2(find(abs(xx2)<1.e-12))=0;
x2x(:,cumjcount+1:cumjcount+jcount)=reshape(xx2,[m*m jcount]);
cumjcount=cumjcount+jcount;
-% [i1 i2 i3]=ind2sub(size(xx2),inx);
-% x2x = [x2x; [i1 i2 jinx(i3,:) xx2(inx)]];
+ % [i1 i2 i3]=ind2sub(size(xx2),inx);
+ % x2x = [x2x; [i1 i2 jinx(i3,:) xx2(inx)]];
jcount = 0;
jinx = [];
end
@@ -579,7 +579,7 @@ if nargout > 5
if i==j
d2Sig(indexo(j),indexo(j),j) = 2;
y = B*d2Sig(:,:,j)*B';
-% y(abs(y)<1.e-8)=0;
+ % y(abs(y)<1.e-8)=0;
d2Om_tmp(:,jcount) = dyn_vech(y(iv,iv));
end
else
@@ -587,12 +587,12 @@ if nargout > 5
iind = i-offset;
if i<=offset
y = dB(:,:,jind)*dSig(:,:,i)*B'+B*dSig(:,:,i)*dB(:,:,jind)';
-% y(abs(y)<1.e-8)=0;
+ % y(abs(y)<1.e-8)=0;
d2Om_tmp(:,jcount) = dyn_vech(y(iv,iv));
else
icount=icount+1;
x = reshape(x2x(:,icount),[m m]);
-% x = get_2nd_deriv(x2x,m,m,iind,jind);%xx2(:,:,jcount);
+ % x = get_2nd_deriv(x2x,m,m,iind,jind);%xx2(:,:,jcount);
elem1 = (get_2nd_deriv(D2g0,m,m,iind,jind)-get_2nd_deriv(D2g1,m,m,iind,jind)*A);
elem1 = elem1 -( Dg1(:,:,jind)*xx(:,:,iind) + Dg1(:,:,iind)*xx(:,:,jind) );
elemj0 = Dg0(:,:,jind)-Dg1(:,:,jind)*A-GAM1*xx(:,:,jind);
@@ -603,9 +603,9 @@ if nargout > 5
% d2B(:,:,i+length(indexo),j+length(indexo)) = y;
y = y*M_.Sigma_e*B'+B*M_.Sigma_e*y'+ ...
dB(:,:,jind)*M_.Sigma_e*dB(:,:,iind)'+dB(:,:,iind)*M_.Sigma_e*dB(:,:,jind)';
-% x(abs(x)<1.e-8)=0;
+ % x(abs(x)<1.e-8)=0;
d2A_tmp(:,jcount) = vec(x(iv,iv));
-% y(abs(y)<1.e-8)=0;
+ % y(abs(y)<1.e-8)=0;
d2Om_tmp(:,jcount) = dyn_vech(y(iv,iv));
end
end
@@ -624,7 +624,7 @@ if nargout > 5
% d2Om = sparse(m1*(m1+1)/2,tot_param_nbr*(tot_param_nbr+1)/2);
d2A_tmp = zeros(m1*m1,ncol);
d2Om_tmp = zeros(m1*(m1+1)/2,ncol);
-
+
end
end
end
@@ -663,23 +663,23 @@ end
if fsparse
g22=sparse(m*n,npar*npar);
else
-g22=zeros(m,n,npar,npar);
+ g22=zeros(m,n,npar,npar);
end
% c=ones(npar,npar);
% c=triu(c);
% ic=find(c);
for is=1:length(gpp)
-% d=zeros(npar,npar);
-% d(gpp(is,3),gpp(is,4))=1;
-% indx = find(ic==find(d));
+ % d=zeros(npar,npar);
+ % d(gpp(is,3),gpp(is,4))=1;
+ % indx = find(ic==find(d));
if fsparse
g22(sub2ind([m,n],gpp(is,1),gpp(is,2)),sub2ind([npar,npar],gpp(is,3),gpp(is,4)))=gpp(is,5);
g22(sub2ind([m,n],gpp(is,1),gpp(is,2)),sub2ind([npar,npar],gpp(is,4),gpp(is,3)))=gpp(is,5);
else
- g22(gpp(is,1),gpp(is,2),gpp(is,3),gpp(is,4))=gpp(is,5);
- g22(gpp(is,1),gpp(is,2),gpp(is,4),gpp(is,3))=gpp(is,5);
-end
+ g22(gpp(is,1),gpp(is,2),gpp(is,3),gpp(is,4))=gpp(is,5);
+ g22(gpp(is,1),gpp(is,2),gpp(is,4),gpp(is,3))=gpp(is,5);
+ end
end
return
@@ -692,9 +692,9 @@ r22=zeros(m,npar,npar);
% ic=find(c);
for is=1:length(rpp)
-% d=zeros(npar,npar);
-% d(rpp(is,2),rpp(is,3))=1;
-% indx = find(ic==find(d));
+ % d=zeros(npar,npar);
+ % d(rpp(is,2),rpp(is,3))=1;
+ % indx = find(ic==find(d));
r22(rpp(is,1),rpp(is,2),rpp(is,3))=rpp(is,4);
r22(rpp(is,1),rpp(is,3),rpp(is,2))=rpp(is,4);
end
diff --git a/matlab/getIrfShocksIndx.m b/matlab/getIrfShocksIndx.m
index 737bfcfb1db036b9655afcb01f5069e3c22209e5..3325a2dfca728b521a871794ccb4407ca3f8ef1d 100644
--- a/matlab/getIrfShocksIndx.m
+++ b/matlab/getIrfShocksIndx.m
@@ -34,7 +34,7 @@ else
for i=1:size(options_.irf_shocks,1)
irf_shocks_indx(i) = find(strcmp(deblank(options_.irf_shocks(i,:)), cellstr(M_.exo_names)));
end
- irf_shocks_indx_unique=unique(irf_shocks_indx);
+ irf_shocks_indx_unique=unique(irf_shocks_indx);
if options_.debug && (length(irf_shocks_indx_unique) ~= length(irf_shocks_indx))
fprintf('\nSTOCH_SIMUL: Warning: The IRFs for some shocks have been requested twice.\n')
fprintf('STOCH_SIMUL: The redundant entries will be ignored.\n')
diff --git a/matlab/getJJ.m b/matlab/getJJ.m
index c9dfe41ed040ab2346558f6293017d1e171665a2..7f934b81aedaaa8f084c26b00404e13701a24d63 100644
--- a/matlab/getJJ.m
+++ b/matlab/getJJ.m
@@ -2,7 +2,7 @@ function [JJ, H, gam, gp, dA, dOm, dYss] = getJJ(A, B, estim_params_, M_,oo_,opt
% function [JJ, H, gam, gp, dA, dOm, dYss] = getJJ(A, B, estim_params_, M_,oo_,options_,kronflag,indx,indexo,mf,nlags,useautocorr)
% computes derivatives of 1st and 2nd order moments of observables with
% respect to estimated parameters
-%
+%
% Inputs:
% A: Transition matrix of lagged states from Kalman filter
% B: Matrix in state transition equation mapping shocks today to
@@ -19,21 +19,21 @@ function [JJ, H, gam, gp, dA, dOm, dYss] = getJJ(A, B, estim_params_, M_,oo_,opt
% correlations
% useautocorr: Indicator on whether to use correlations (1) instead of
% covariances (0)
-%
+%
% Outputs:
-% JJ: Jacobian of 1st and 2nd order moments of observables, i.e. dgam/dTHETA
+% JJ: Jacobian of 1st and 2nd order moments of observables, i.e. dgam/dTHETA
% (see below for definition of gam)
% H: dTAU/dTHETA: Jacobian of TAU, vectorized form of
% linearized reduced form state space model, given ys [steady state],
% A [transition matrix], B [matrix of shocks], Sigma [covariance of shocks]
% TAU = [ys; vec(A); dyn_vech(B*Sigma*B')].
-% gam: vector of theoretical moments of observed variables mf [JJ is the Jacobian of gam].
-% gam = [ys(mf); dyn_vech(GAM{1}); vec(GAM{j+1})]; for j=1:ar and where
+% gam: vector of theoretical moments of observed variables mf [JJ is the Jacobian of gam].
+% gam = [ys(mf); dyn_vech(GAM{1}); vec(GAM{j+1})]; for j=1:ar and where
% GAM is the first output of th_autocovariances
% gp: Jacobian of linear rational expectation matrices [i.e.
% Jacobian of dynamic model] with respect to estimated
% structural parameters only (indx)
-% dA: [endo_nbr by endo_nbr by (indx+indexo)] Jacobian of transition matrix A
+% dA: [endo_nbr by endo_nbr by (indx+indexo)] Jacobian of transition matrix A
% dOm: Jacobian of Omega = (B*Sigma*B')
% dYss Jacobian of steady state with respect to estimated structural parameters only (indx)
@@ -55,16 +55,16 @@ function [JJ, H, gam, gp, dA, dOm, dYss] = getJJ(A, B, estim_params_, M_,oo_,opt
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
if nargin<8 || isempty(indx)
-% indx = [1:M_.param_nbr];
+ % indx = [1:M_.param_nbr];
end
if nargin<9 || isempty(indexo)
indexo = [];
end
if nargin<11 || isempty(nlags)
- nlags=3;
+ nlags=3;
end
if nargin<12 || isempty(useautocorr)
- useautocorr=0;
+ useautocorr=0;
end
% if useautocorr,
@@ -109,7 +109,7 @@ else
sdy = sqrt(diag(GAM));
sy = sdy*sdy';
% end
-
+
% BB = dOm*0;
% for j=1:length(indx),
% BB(:,:,j)= dA(:,:,j)*GAM*A'+A*GAM*dA(:,:,j)'+dOm(:,:,j);
@@ -167,9 +167,9 @@ else
end
JJ(:,j+nexo) = dumm;
end
-
+
JJ = [ [zeros(length(mf),nexo) dYss(mf,:)]; JJ];
-
+
end
if nargout >2
% sy=sy(mf,mf);
diff --git a/matlab/get_Hessian.m b/matlab/get_Hessian.m
index a878ad111786e9ec219bed303e2dd33e783c7b7c..fe74848dd683157a67215289b1d278e4ec53211b 100644
--- a/matlab/get_Hessian.m
+++ b/matlab/get_Hessian.m
@@ -28,146 +28,146 @@ function [Hess] = get_Hessian(T,R,Q,H,P,Y,DT,DYss,DOm,DH,DP,D2T,D2Yss,D2Om,D2H,D
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- k = size(DT,3); % number of structural parameters
- smpl = size(Y,2); % Sample size.
- pp = size(Y,1); % Maximum number of observed variables.
- mm = size(T,2); % Number of state variables.
- a = zeros(mm,1); % State vector.
- Om = R*Q*transpose(R); % Variance of R times the vector of structural innovations.
- t = 0; % Initialization of the time index.
- oldK = 0;
- notsteady = 1; % Steady state flag.
- F_singular = 1;
-
- Hess = zeros(k,k); % Initialization of the Hessian
- Da = zeros(mm,k); % State vector.
- Dv = zeros(length(mf),k);
- D2a = zeros(mm,k,k); % State vector.
- D2v = zeros(length(mf),k,k);
-
- C = zeros(length(mf),mm);
- for ii=1:length(mf); C(ii,mf(ii))=1;end % SELECTION MATRIX IN MEASUREMENT EQ. (FOR WHEN IT IS NOT CONSTANT)
- dC = zeros(length(mf),mm,k);
- d2C = zeros(length(mf),mm,k,k);
-
- s = zeros(pp,1); % CONSTANT TERM IN MEASUREMENT EQ. (FOR WHEN IT IS NOT CONSTANT)
- ds = zeros(pp,1,k);
- d2s = zeros(pp,1,k,k);
-
+k = size(DT,3); % number of structural parameters
+smpl = size(Y,2); % Sample size.
+pp = size(Y,1); % Maximum number of observed variables.
+mm = size(T,2); % Number of state variables.
+a = zeros(mm,1); % State vector.
+Om = R*Q*transpose(R); % Variance of R times the vector of structural innovations.
+t = 0; % Initialization of the time index.
+oldK = 0;
+notsteady = 1; % Steady state flag.
+F_singular = 1;
+
+Hess = zeros(k,k); % Initialization of the Hessian
+Da = zeros(mm,k); % State vector.
+Dv = zeros(length(mf),k);
+D2a = zeros(mm,k,k); % State vector.
+D2v = zeros(length(mf),k,k);
+
+C = zeros(length(mf),mm);
+for ii=1:length(mf); C(ii,mf(ii))=1;end % SELECTION MATRIX IN MEASUREMENT EQ. (FOR WHEN IT IS NOT CONSTANT)
+dC = zeros(length(mf),mm,k);
+d2C = zeros(length(mf),mm,k,k);
+
+s = zeros(pp,1); % CONSTANT TERM IN MEASUREMENT EQ. (FOR WHEN IT IS NOT CONSTANT)
+ds = zeros(pp,1,k);
+d2s = zeros(pp,1,k,k);
+
% for ii = 1:k
-% DOm = DR(:,:,ii)*Q*transpose(R) + R*DQ(:,:,ii)*transpose(R) + R*Q*transpose(DR(:,:,ii));
+% DOm = DR(:,:,ii)*Q*transpose(R) + R*DQ(:,:,ii)*transpose(R) + R*Q*transpose(DR(:,:,ii));
% end
-
- while notsteady & t<smpl
- t = t+1;
- v = Y(:,t)-a(mf);
- F = P(mf,mf) + H;
- if rcond(F) < kalman_tol
- if ~all(abs(F(:))<kalman_tol)
- return
- else
- a = T*a;
- P = T*P*transpose(T)+Om;
- end
+
+while notsteady & t<smpl
+ t = t+1;
+ v = Y(:,t)-a(mf);
+ F = P(mf,mf) + H;
+ if rcond(F) < kalman_tol
+ if ~all(abs(F(:))<kalman_tol)
+ return
else
- F_singular = 0;
- iF = inv(F);
- K = P(:,mf)*iF;
-
- [DK,DF,DP1] = computeDKalman(T,DT,DOm,P,DP,DH,mf,iF,K);
- [D2K,D2F,D2P1] = computeD2Kalman(T,DT,D2T,D2Om,P,DP,D2P,DH,mf,iF,K,DK);
- tmp = (a+K*v);
-
- for ii = 1:k
- Dv(:,ii) = -Da(mf,ii) - DYss(mf,ii);
- % dai = da(:,:,ii);
- dKi = DK(:,:,ii);
- diFi = -iF*DF(:,:,ii)*iF;
- dtmpi = Da(:,ii)+dKi*v+K*Dv(:,ii);
-
-
- for jj = 1:ii
- dFj = DF(:,:,jj);
- diFj = -iF*DF(:,:,jj)*iF;
- dKj = DK(:,:,jj);
- d2Kij = D2K(:,:,jj,ii);
- d2Fij = D2F(:,:,jj,ii);
- d2iFij = -diFi*dFj*iF -iF*d2Fij*iF -iF*dFj*diFi;
- dtmpj = Da(:,jj)+dKj*v+K*Dv(:,jj);
-
- d2vij = -D2Yss(mf,jj,ii) - D2a(mf,jj,ii);
- d2tmpij = D2a(:,jj,ii) + d2Kij*v + dKj*Dv(:,ii) + dKi*Dv(:,jj) + K*d2vij;
- D2a(:,jj,ii) = D2T(:,:,jj,ii)*tmp + DT(:,:,jj)*dtmpi + DT(:,:,ii)*dtmpj + T*d2tmpij;
-
- Hesst(ii,jj) = getHesst_ij(v,Dv(:,ii),Dv(:,jj),d2vij,iF,diFi,diFj,d2iFij,dFj,d2Fij);
+ a = T*a;
+ P = T*P*transpose(T)+Om;
end
- Da(:,ii) = DT(:,:,ii)*tmp + T*dtmpi;
- end
-% vecDPmf = reshape(DP(mf,mf,:),[],k);
-% iPmf = inv(P(mf,mf));
- if t>=start
- Hess = Hess + Hesst;
- end
- a = T*(a+K*v);
- P = T*(P-K*P(mf,:))*transpose(T)+Om;
- DP = DP1;
- D2P = D2P1;
+ else
+ F_singular = 0;
+ iF = inv(F);
+ K = P(:,mf)*iF;
+
+ [DK,DF,DP1] = computeDKalman(T,DT,DOm,P,DP,DH,mf,iF,K);
+ [D2K,D2F,D2P1] = computeD2Kalman(T,DT,D2T,D2Om,P,DP,D2P,DH,mf,iF,K,DK);
+ tmp = (a+K*v);
+
+ for ii = 1:k
+ Dv(:,ii) = -Da(mf,ii) - DYss(mf,ii);
+ % dai = da(:,:,ii);
+ dKi = DK(:,:,ii);
+ diFi = -iF*DF(:,:,ii)*iF;
+ dtmpi = Da(:,ii)+dKi*v+K*Dv(:,ii);
+
+
+ for jj = 1:ii
+ dFj = DF(:,:,jj);
+ diFj = -iF*DF(:,:,jj)*iF;
+ dKj = DK(:,:,jj);
+ d2Kij = D2K(:,:,jj,ii);
+ d2Fij = D2F(:,:,jj,ii);
+ d2iFij = -diFi*dFj*iF -iF*d2Fij*iF -iF*dFj*diFi;
+ dtmpj = Da(:,jj)+dKj*v+K*Dv(:,jj);
+
+ d2vij = -D2Yss(mf,jj,ii) - D2a(mf,jj,ii);
+ d2tmpij = D2a(:,jj,ii) + d2Kij*v + dKj*Dv(:,ii) + dKi*Dv(:,jj) + K*d2vij;
+ D2a(:,jj,ii) = D2T(:,:,jj,ii)*tmp + DT(:,:,jj)*dtmpi + DT(:,:,ii)*dtmpj + T*d2tmpij;
+
+ Hesst(ii,jj) = getHesst_ij(v,Dv(:,ii),Dv(:,jj),d2vij,iF,diFi,diFj,d2iFij,dFj,d2Fij);
+ end
+ Da(:,ii) = DT(:,:,ii)*tmp + T*dtmpi;
+ end
+ % vecDPmf = reshape(DP(mf,mf,:),[],k);
+ % iPmf = inv(P(mf,mf));
+ if t>=start
+ Hess = Hess + Hesst;
end
- notsteady = max(max(abs(K-oldK))) > riccati_tol;
- oldK = K;
+ a = T*(a+K*v);
+ P = T*(P-K*P(mf,:))*transpose(T)+Om;
+ DP = DP1;
+ D2P = D2P1;
end
+ notsteady = max(max(abs(K-oldK))) > riccati_tol;
+ oldK = K;
+end
- if F_singular
- error('The variance of the forecast error remains singular until the end of the sample')
- end
+if F_singular
+ error('The variance of the forecast error remains singular until the end of the sample')
+end
-
- if t < smpl
- t0 = t+1;
- while t < smpl
- t = t+1;
- v = Y(:,t)-a(mf);
- tmp = (a+K*v);
- for ii = 1:k
- Dv(:,ii) = -Da(mf,ii)-DYss(mf,ii);
- dKi = DK(:,:,ii);
- diFi = -iF*DF(:,:,ii)*iF;
- dtmpi = Da(:,ii)+dKi*v+K*Dv(:,ii);
-
- for jj = 1:ii
- dFj = DF(:,:,jj);
- diFj = -iF*DF(:,:,jj)*iF;
- dKj = DK(:,:,jj);
- d2Kij = D2K(:,:,jj,ii);
- d2Fij = D2F(:,:,jj,ii);
- d2iFij = -diFi*dFj*iF -iF*d2Fij*iF -iF*dFj*diFi;
- dtmpj = Da(:,jj)+dKj*v+K*Dv(:,jj);
-
- d2vij = -D2Yss(mf,jj,ii) - D2a(mf,jj,ii);
- d2tmpij = D2a(:,jj,ii) + d2Kij*v + dKj*Dv(:,ii) + dKi*Dv(:,jj) + K*d2vij;
- D2a(:,jj,ii) = D2T(:,:,jj,ii)*tmp + DT(:,:,jj)*dtmpi + DT(:,:,ii)*dtmpj + T*d2tmpij;
-
- Hesst(ii,jj) = getHesst_ij(v,Dv(:,ii),Dv(:,jj),d2vij,iF,diFi,diFj,d2iFij,dFj,d2Fij);
- end
- Da(:,ii) = DT(:,:,ii)*tmp + T*dtmpi;
- end
- if t>=start
- Hess = Hess + Hesst;
+
+if t < smpl
+ t0 = t+1;
+ while t < smpl
+ t = t+1;
+ v = Y(:,t)-a(mf);
+ tmp = (a+K*v);
+ for ii = 1:k
+ Dv(:,ii) = -Da(mf,ii)-DYss(mf,ii);
+ dKi = DK(:,:,ii);
+ diFi = -iF*DF(:,:,ii)*iF;
+ dtmpi = Da(:,ii)+dKi*v+K*Dv(:,ii);
+
+ for jj = 1:ii
+ dFj = DF(:,:,jj);
+ diFj = -iF*DF(:,:,jj)*iF;
+ dKj = DK(:,:,jj);
+ d2Kij = D2K(:,:,jj,ii);
+ d2Fij = D2F(:,:,jj,ii);
+ d2iFij = -diFi*dFj*iF -iF*d2Fij*iF -iF*dFj*diFi;
+ dtmpj = Da(:,jj)+dKj*v+K*Dv(:,jj);
+
+ d2vij = -D2Yss(mf,jj,ii) - D2a(mf,jj,ii);
+ d2tmpij = D2a(:,jj,ii) + d2Kij*v + dKj*Dv(:,ii) + dKi*Dv(:,jj) + K*d2vij;
+ D2a(:,jj,ii) = D2T(:,:,jj,ii)*tmp + DT(:,:,jj)*dtmpi + DT(:,:,ii)*dtmpj + T*d2tmpij;
+
+ Hesst(ii,jj) = getHesst_ij(v,Dv(:,ii),Dv(:,jj),d2vij,iF,diFi,diFj,d2iFij,dFj,d2Fij);
end
- a = T*(a+K*v);
+ Da(:,ii) = DT(:,:,ii)*tmp + T*dtmpi;
+ end
+ if t>=start
+ Hess = Hess + Hesst;
end
-% Hess = Hess + .5*(smpl+t0-1)*(vecDPmf' * kron(iPmf,iPmf) * vecDPmf);
- % for ii = 1:k;
- % for jj = 1:ii
- % H(ii,jj) = trace(iPmf*(.5*DP(mf,mf,ii)*iPmf*DP(mf,mf,jj) + Dv(:,ii)*Dv(:,jj)'));
- % end
- % end
+ a = T*(a+K*v);
end
-
+ % Hess = Hess + .5*(smpl+t0-1)*(vecDPmf' * kron(iPmf,iPmf) * vecDPmf);
+ % for ii = 1:k;
+ % for jj = 1:ii
+ % H(ii,jj) = trace(iPmf*(.5*DP(mf,mf,ii)*iPmf*DP(mf,mf,jj) + Dv(:,ii)*Dv(:,jj)'));
+ % end
+ % end
+end
+
Hess = Hess + tril(Hess,-1)';
-Hess = -Hess/2;
-% end of main function
+Hess = -Hess/2;
+% end of main function
function Hesst_ij = getHesst_ij(e,dei,dej,d2eij,iS,diSi,diSj,d2iSij,dSj,d2Sij);
% computes (i,j) term in the Hessian
@@ -178,11 +178,11 @@ Hesst_ij = trace(diSi*dSj + iS*d2Sij) + e'*d2iSij*e + 2*(dei'*diSj*e + dei'*iS*d
function [DK,DF,DP1] = computeDKalman(T,DT,DOm,P,DP,DH,mf,iF,K)
- k = size(DT,3);
- tmp = P-K*P(mf,:);
+k = size(DT,3);
+tmp = P-K*P(mf,:);
for ii = 1:k
- DF(:,:,ii) = DP(mf,mf,ii) + DH(:,:,ii);
+ DF(:,:,ii) = DP(mf,mf,ii) + DH(:,:,ii);
DiF(:,:,ii) = -iF*DF(:,:,ii)*iF;
DK(:,:,ii) = DP(:,mf,ii)*iF + P(:,mf)*DiF(:,:,ii);
Dtmp = DP(:,:,ii) - DK(:,:,ii)*P(mf,:) - K*DP(mf,:,ii);
@@ -194,9 +194,9 @@ end
function [d2K,d2S,d2P1] = computeD2Kalman(A,dA,d2A,d2Om,P0,dP0,d2P0,DH,mf,iF,K0,dK0)
% computes the second derivatives of the Kalman matrices
% note: A=T in main func.
-
- k = size(dA,3);
- tmp = P0-K0*P0(mf,:);
+
+k = size(dA,3);
+tmp = P0-K0*P0(mf,:);
[ns,no] = size(K0);
% CPC = C*P0*C'; CPC = .5*(CPC+CPC');iF = inv(CPC);
@@ -225,34 +225,31 @@ for ii = 1:k
d2Aij = d2A(:,:,jj,ii);
d2Pij = d2P0(:,:,jj,ii);
d2Omij = d2Om(:,:,jj,ii);
-
- % second order
-
- d2Fij = d2Pij(mf,mf) ;
-
-% d2APC = d2Aij*P0*C' + A*d2Pij*C' + A*P0*d2Cij' + dAi*dPj*C' + dAj*dPi*C' + A*dPj*dCi' + A*dPi*dCj' + dAi*P0*dCj' + dAj*P0*dCi';
- d2APC = d2Pij(:,mf);
-
- d2iF = -diFi*dFj*iF -iF*d2Fij*iF -iF*dFj*diFi;
-
- d2Kij= d2Pij(:,mf)*iF + P0(:,mf)*d2iF + dP0(:,mf,jj)*diFi + dP0(:,mf,ii)*diFj;
-
- d2KCP = d2Kij*P0(mf,:) + K0*d2Pij(mf,:) + dKi*dP0(mf,:,jj) + dKj*dP0(mf,:,ii) ;
-
- dtmpi = dP0(:,:,ii) - dK0(:,:,ii)*P0(mf,:) - K0*dP0(mf,:,ii);
- dtmpj = dP0(:,:,jj) - dK0(:,:,jj)*P0(mf,:) - K0*dP0(mf,:,jj);
- d2tmp = d2Pij - d2KCP;
-
- d2AtmpA = d2Aij*tmp*A' + A*d2tmp*A' + A*tmp*d2Aij' + dAi*dtmpj*A' + dAj*dtmpi*A' + A*dtmpj*dAi' + A*dtmpi*dAj' + dAi*tmp*dAj' + dAj*tmp*dAi';
-
- d2K(:,:,ii,jj) = d2Kij; %#ok<NASGU>
- d2P1(:,:,ii,jj) = d2AtmpA + d2Omij; %#ok<*NASGU>
- d2S(:,:,ii,jj) = d2Fij;
-% d2iS(:,:,ii,jj) = d2iF;
+
+ % second order
+
+ d2Fij = d2Pij(mf,mf) ;
+
+ % d2APC = d2Aij*P0*C' + A*d2Pij*C' + A*P0*d2Cij' + dAi*dPj*C' + dAj*dPi*C' + A*dPj*dCi' + A*dPi*dCj' + dAi*P0*dCj' + dAj*P0*dCi';
+ d2APC = d2Pij(:,mf);
+
+ d2iF = -diFi*dFj*iF -iF*d2Fij*iF -iF*dFj*diFi;
+
+ d2Kij= d2Pij(:,mf)*iF + P0(:,mf)*d2iF + dP0(:,mf,jj)*diFi + dP0(:,mf,ii)*diFj;
+
+ d2KCP = d2Kij*P0(mf,:) + K0*d2Pij(mf,:) + dKi*dP0(mf,:,jj) + dKj*dP0(mf,:,ii) ;
+
+ dtmpi = dP0(:,:,ii) - dK0(:,:,ii)*P0(mf,:) - K0*dP0(mf,:,ii);
+ dtmpj = dP0(:,:,jj) - dK0(:,:,jj)*P0(mf,:) - K0*dP0(mf,:,jj);
+ d2tmp = d2Pij - d2KCP;
+
+ d2AtmpA = d2Aij*tmp*A' + A*d2tmp*A' + A*tmp*d2Aij' + dAi*dtmpj*A' + dAj*dtmpi*A' + A*dtmpj*dAi' + A*dtmpi*dAj' + dAi*tmp*dAj' + dAj*tmp*dAi';
+
+ d2K(:,:,ii,jj) = d2Kij; %#ok<NASGU>
+ d2P1(:,:,ii,jj) = d2AtmpA + d2Omij; %#ok<*NASGU>
+ d2S(:,:,ii,jj) = d2Fij;
+ % d2iS(:,:,ii,jj) = d2iF;
end
end
% end of computeD2Kalman
-
-
-
\ No newline at end of file
diff --git a/matlab/get_all_parameters.m b/matlab/get_all_parameters.m
index 93bc56f2351e58b8d2d9d358f689b8cb835ceaa4..8857d6673710f0408537e4fc97dada6b8ddb3ff6 100644
--- a/matlab/get_all_parameters.m
+++ b/matlab/get_all_parameters.m
@@ -4,14 +4,14 @@ function xparam1=get_all_parameters(estim_params_,M_)
% gets parameters values from M_.params into xparam1 (inverse mapping to set_all_parameters)
% This is called if a model was calibrated before estimation to back out
% parameter values
-%
+%
% INPUTS
-% estim_params_: Dynare structure describing the estimated parameters.
-% M_: Dynare structure describing the model.
+% estim_params_: Dynare structure describing the estimated parameters.
+% M_: Dynare structure describing the model.
%
% OUTPUTS
% xparam1: N*1 double vector of parameters from calibrated model that are to be estimated
-%
+%
% SPECIAL REQUIREMENTS
% none
@@ -100,5 +100,5 @@ offset = nvx+ncx+nvn+ncn;
% structural parameters
if np
- xparam1(offset+1:end)=M_.params(estim_params_.param_vals(:,1));
+ xparam1(offset+1:end)=M_.params(estim_params_.param_vals(:,1));
end
\ No newline at end of file
diff --git a/matlab/get_dynare_random_generator_state.m b/matlab/get_dynare_random_generator_state.m
index 513919c220ff7de3f5a3d364cefd6637381d08e0..f5702e7043214786f8d5cf7a84d5770ab271b6d2 100644
--- a/matlab/get_dynare_random_generator_state.m
+++ b/matlab/get_dynare_random_generator_state.m
@@ -1,8 +1,8 @@
function [state_u,state_n] = get_dynare_random_generator_state()
% Get state of Matlab/Octave random generator depending on matlab
-% (octave) version.
+% (octave) version.
% In older versions, Matlab kept one generator for uniformly distributed numbers and
-% one for normally distributed numbers.
+% one for normally distributed numbers.
% For backward compatibility, we return two vectors, but, in recent
% versions of Matlab and in Octave, we return two identical vectors.
@@ -23,22 +23,22 @@ function [state_u,state_n] = get_dynare_random_generator_state()
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- matlab_random_streams = ~(isoctave || matlab_ver_less_than('7.7'));
+matlab_random_streams = ~(isoctave || matlab_ver_less_than('7.7'));
- if matlab_random_streams% Use new matlab interface.
- if matlab_ver_less_than('7.12')
- s = RandStream.getDefaultStream();
- else
- s = RandStream.getGlobalStream();
- end
- if isequal(s.Type,'legacy')
- state_u = rand('state');
- state_n = randn('state');
- else
- state_u = s.State;
- state_n = state_u;
- end
- else% Use old matlab interface.
+if matlab_random_streams% Use new matlab interface.
+ if matlab_ver_less_than('7.12')
+ s = RandStream.getDefaultStream();
+ else
+ s = RandStream.getGlobalStream();
+ end
+ if isequal(s.Type,'legacy')
state_u = rand('state');
state_n = randn('state');
- end
\ No newline at end of file
+ else
+ state_u = s.State;
+ state_n = state_u;
+ end
+else% Use old matlab interface.
+ state_u = rand('state');
+ state_n = randn('state');
+end
\ No newline at end of file
diff --git a/matlab/get_file_extension.m b/matlab/get_file_extension.m
index d922b36ac50150c4a8e925ad4446df03ad166ded..b4fe5a2ade8c6017d887f936706fbc406313ba68 100644
--- a/matlab/get_file_extension.m
+++ b/matlab/get_file_extension.m
@@ -2,15 +2,15 @@ function ext = get_file_extension(file)
% returns the extension of a file.
%
-% INPUTS
+% INPUTS
% o file string, name of the file
%
-% OUTPUTS
+% OUTPUTS
% o ext string, extension.
%
-% REMARKS
+% REMARKS
% If the provided file name has no extension, the routine will return an empty array.
-
+
% Copyright (C) 2013-2015 Dynare Team
%
% This file is part of Dynare.
diff --git a/matlab/get_moments_size.m b/matlab/get_moments_size.m
index aa8087c025500e342c8bfa5848765113ef9cf146..a036d1e9c3f27f19a31976501fc0ce4f787972c7 100644
--- a/matlab/get_moments_size.m
+++ b/matlab/get_moments_size.m
@@ -4,10 +4,10 @@ function s=get_moments_size(options)
%
% INPUTS
% options: structure of Dynare options
-%
+%
% OUTPUTS
% s: size of moments for a given model and options
-%
+%
% SPECIAL REQUIREMENTS
% none
diff --git a/matlab/get_name_of_the_last_mh_file.m b/matlab/get_name_of_the_last_mh_file.m
index 7634351251eb65c36fee1a16438fe2d8e1273c0b..357c7f88a76b38f014f1524b0a9be022dd6838cd 100644
--- a/matlab/get_name_of_the_last_mh_file.m
+++ b/matlab/get_name_of_the_last_mh_file.m
@@ -1,14 +1,14 @@
function [mhname,info] = get_name_of_the_last_mh_file(M_)
% This function returns the name of the last mh file and test if the metropolis was completed.
%
-% INPUTS
-% M_ [structure] Dynare structure specifying the model.
+% INPUTS
+% M_ [structure] Dynare structure specifying the model.
%
-% OUTPUTS
-% mhname [string] Name of the last mh file (with complete path).
+% OUTPUTS
+% mhname [string] Name of the last mh file (with complete path).
% info [integer] Scalar. If info is equal to 1 then the predicted name of the last
-% metropolis hastings matches the name of the name of the last mh
-% file. Otherwise info is equal to zero (a likely reason for this is
+% metropolis hastings matches the name of the name of the last mh
+% file. Otherwise info is equal to zero (a likely reason for this is
% that the mcmc simulations were not completed).
% Copyright (C) 2008-2013 Dynare Team
diff --git a/matlab/get_optimal_policy_discount_factor.m b/matlab/get_optimal_policy_discount_factor.m
index 67931b8ab1eb51fd7552c4288acd3b9c2023d657..61a418636b45063b142ab318ed42d152b0f8fe42 100644
--- a/matlab/get_optimal_policy_discount_factor.m
+++ b/matlab/get_optimal_policy_discount_factor.m
@@ -2,11 +2,11 @@ function discount_factor=get_optimal_policy_discount_factor(params,param_names)
%function discount_factor=get_optimal_policy_discount_factor(M)
% get the value of Ramsey policy discount factor
-%
+%
% INPUTS
-% params: (vector) value of parameters
+% params: (vector) value of parameters
% param_names: (char array) list of parameter names
-%
+%
% OUTPUTS
% discount_factor (double) discount factor
%
@@ -30,4 +30,4 @@ function discount_factor=get_optimal_policy_discount_factor(params,param_names)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- discount_factor = params(find(strcmp('optimal_policy_discount_factor',cellstr(param_names))));
+discount_factor = params(find(strcmp('optimal_policy_discount_factor',cellstr(param_names))));
diff --git a/matlab/get_param_by_name.m b/matlab/get_param_by_name.m
index 795ac88dff1c450d3f26735fc140c77dfa0926b5..1e5b907187552036b81a880d86b91b9412e31798 100644
--- a/matlab/get_param_by_name.m
+++ b/matlab/get_param_by_name.m
@@ -1,7 +1,7 @@
function x = get_param_by_name(pname)
% function x = get_param_by_name(pname)
% returns the value of a parameter identified by its name
-%
+%
% INPUTS:
% pname: parameter name
%
diff --git a/matlab/get_posterior_parameters.m b/matlab/get_posterior_parameters.m
index 495f365657623b228f78fef688607bf34e192a6f..3561f6f2469cd3b6f305c80ffe9bd1c9dd287f14 100644
--- a/matlab/get_posterior_parameters.m
+++ b/matlab/get_posterior_parameters.m
@@ -2,13 +2,13 @@ function xparam = get_posterior_parameters(type,field1)
% function xparam = get_posterior_parameters(type)
% Selects (estimated) parameters (posterior mode or posterior mean).
-%
-% INPUTS
+%
+% INPUTS
% o type [char] = 'mode' or 'mean'.
% o field_1 [char] optional field like 'mle_'.
-%
-% OUTPUTS
-% o xparam vector of estimated parameters
+%
+% OUTPUTS
+% o xparam vector of estimated parameters
%
% SPECIAL REQUIREMENTS
% None.
@@ -30,7 +30,7 @@ function xparam = get_posterior_parameters(type,field1)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-global estim_params_ oo_ options_ M_
+global estim_params_ oo_ options_ M_
if nargin<2
field1='posterior_';
diff --git a/matlab/get_subsamples_range_indx.m b/matlab/get_subsamples_range_indx.m
index 1d114c6869d38b01258bf262d60567f7055b53dd..cb9b971f5dfd5d59461be04f7ec247005b421901 100644
--- a/matlab/get_subsamples_range_indx.m
+++ b/matlab/get_subsamples_range_indx.m
@@ -39,5 +39,5 @@ range_indx = find(strcmp(range_label, estimation_info.subsamples(subsamples_indx
if size(range_indx,2) ~= 1
error(['Error: Index not found in estimation_info.subsamples(' ...
- num2str(subsamples_indx) ').range_index for label ' range_label]);
+ num2str(subsamples_indx) ').range_index for label ' range_label]);
end
diff --git a/matlab/get_variables_list.m b/matlab/get_variables_list.m
index e4a359225996ab0a042337e876c1943a4109d6b0..966ebf7698cc3f30611155a79dbd5fd8185a3556 100644
--- a/matlab/get_variables_list.m
+++ b/matlab/get_variables_list.m
@@ -1,17 +1,17 @@
function [ivar,vartan,options_] = get_variables_list(options_,M_)
% This function builds a vector of indices in varlist or varobs.
-%
-% INPUTS
-% o options_ [structure] Describes global options.
+%
+% INPUTS
+% o options_ [structure] Describes global options.
% o M_ [structure] Describes the model.
-% OUTPUTS
+% OUTPUTS
% o ivar [integer] nvar*1 vector of indices (nvar is the number
% of variables).
% o vartan [char] array of characters (with nvar rows).
% o options_ [structure] Describes global options.
-%
-% ALGORITHM
-% None.
+%
+% ALGORITHM
+% None.
%
% SPECIAL REQUIREMENTS
% None.
diff --git a/matlab/get_variance_of_endogenous_variables.m b/matlab/get_variance_of_endogenous_variables.m
index 33d3d9e22147c2e9803295822f3e9d71ca734e77..965319b1bdadf8960309f849c305f3b883be58fc 100644
--- a/matlab/get_variance_of_endogenous_variables.m
+++ b/matlab/get_variance_of_endogenous_variables.m
@@ -6,10 +6,10 @@ function vx1 = get_variance_of_endogenous_variables(dr,i_var)
% INPUTS
% dr: structure of decisions rules for stochastic simulations
% i_var: indices of a variables list
-%
+%
% OUTPUTS
% vx1: variance-covariance matrix
-%
+%
% SPECIAL REQUIREMENTS
% none
@@ -48,7 +48,7 @@ n = length(i_var);
[vx,u] = lyapunov_symm(A,B*Sigma_e*B',options_.lyapunov_fixed_point_tol,options_.qz_criterium,options_.lyapunov_complex_threshold, [], options_.debug);
if size(u,2) > 0
- i_stat = find(any(abs(ghx*u) < options_.Schur_vec_tol,2)); %only set those variances of objective function for which variance is finite
+ i_stat = find(any(abs(ghx*u) < options_.Schur_vec_tol,2)); %only set those variances of objective function for which variance is finite
ghx = ghx(i_stat,:);
ghu = ghu(i_stat,:);
else
@@ -57,4 +57,3 @@ end
vx1 = Inf*ones(n,n);
vx1(i_stat,i_stat) = ghx*vx*ghx'+ghu*Sigma_e*ghu';
-
diff --git a/matlab/global_initialization.m b/matlab/global_initialization.m
index cbeff6085ecd02c19f6722a16bd5edb5f0f80c08..d31c59e5406c03dff63460d17b44b5a225214d7e 100644
--- a/matlab/global_initialization.m
+++ b/matlab/global_initialization.m
@@ -165,9 +165,9 @@ options_.nocorr = 0;
options_.periods = 0;
options_.noprint = 0;
options_.SpectralDensity.trigger = 0;
-options_.SpectralDensity.plot = 1;
-options_.SpectralDensity.cutoff = 150;
-options_.SpectralDensity.sdl = 0.01;
+options_.SpectralDensity.plot = 1;
+options_.SpectralDensity.cutoff = 150;
+options_.SpectralDensity.sdl = 0.01;
options_.nofunctions = false;
options_.bandpass.indicator = 0;
@@ -282,7 +282,7 @@ particle.resampling.number_of_partitions = 200;
particle.mixture_state_variables = 5 ;
particle.mixture_structural_shocks = 1 ;
particle.mixture_measurement_shocks = 1 ;
-% Online approach
+% Online approach
particle.liu_west_delta = 0.99 ;
particle.liu_west_chol_sigma_bar = .01 ;
% Options for setting the weights in conditional particle filters.
@@ -471,7 +471,7 @@ options_.sub_draws = [];
options_.gradient_method = 2; %used by csminwel and newrat
options_.gradient_epsilon = 1e-6; %used by csminwel and newrat
options_.posterior_sampler_options.sampling_opt = []; %extended set of options for individual posterior samplers
-% Random Walk Metropolis-Hastings
+ % Random Walk Metropolis-Hastings
options_.posterior_sampler_options.posterior_sampling_method = 'random_walk_metropolis_hastings';
options_.posterior_sampler_options.rwmh.proposal_distribution = 'rand_multivariate_normal';
options_.posterior_sampler_options.rwmh.student_degrees_of_freedom = 3;
@@ -525,8 +525,8 @@ options_.estimation.moments_posterior_density.indicator = 1;
options_.estimation.moments_posterior_density.gridpoints = 2^9;
options_.estimation.moments_posterior_density.bandwidth = 0; % Rule of thumb optimal bandwidth parameter.
options_.estimation.moments_posterior_density.kernel_function = 'gaussian'; % Gaussian kernel for Fast Fourrier Transform approximaton.
-% Misc
-% options_.conf_sig = 0.6;
+ % Misc
+ % options_.conf_sig = 0.6;
oo_.exo_simul = [];
oo_.endo_simul = [];
ys0_ = [];
@@ -632,7 +632,7 @@ options_.simpsa = simpsa;
%solveopt optimizer
solveopt.minimizer_indicator=-1; %use minimizer
solveopt.TolX=1e-6; %accuracy of argument
-solveopt.TolFun=1e-6; %accuracy of function
+solveopt.TolFun=1e-6; %accuracy of function
solveopt.MaxIter=15000;
solveopt.verbosity=1;
solveopt.TolXConstraint=1.e-8;
@@ -841,4 +841,3 @@ set_dynare_seed('default');
if isfield(options_, 'global_init_file')
run(options_.global_init_file);
end
-
diff --git a/matlab/graph_decomp.m b/matlab/graph_decomp.m
index 2993699cf07a04ed1bebef7239db942d318db45f..f2c6705490602085d01b30e82b1292b36d462ef3 100644
--- a/matlab/graph_decomp.m
+++ b/matlab/graph_decomp.m
@@ -1,7 +1,7 @@
function []=graph_decomp(z,shock_names,endo_names,i_var,initial_date,DynareModel,DynareOptions)
%function []=graph_decomp(z,shock_names,endo_names,i_var,initial_date,DynareModel,DynareOptions)
% Plots the results from the shock_decomposition command
-%
+%
% Inputs
% z [n_var*(nshock+2)*nperiods] shock decomposition array, see shock_decomposition.m for details
% shock_names [endo_nbr*string length] shock names from M_.exo_names
@@ -131,8 +131,8 @@ for j=1:nvar
fhandle = dyn_figure(DynareOptions.plot_shock_decomp.nodisplay,'Name',[preamble_txt fig_name_long strrep(fig_mode1, '_', ' ') ': ' deblank(endo_names(i_var(j),:)) '.'], 'PaperPositionMode', 'auto','PaperOrientation','landscape','renderermode','auto');
set(fhandle,'position' ,[50 50 1500 750])
ax=axes('Position',[0.1 0.1 0.6 0.8],'box','on');
-% plot(ax,x(2:end),z1(end,:),'k-','LineWidth',2)
-% axis(ax,[xmin xmax ymin ymax]);
+ % plot(ax,x(2:end),z1(end,:),'k-','LineWidth',2)
+ % axis(ax,[xmin xmax ymin ymax]);
hold on;
for i=1:gend
i_1 = i-1;
@@ -175,7 +175,7 @@ for j=1:nvar
end
set(ax,'xlim',[xmin xmax]);
hold off;
-
+
axes('Position',[0.75 0.1 0.2 0.8]);
axis([0 1 0 1]);
axis off;
@@ -184,7 +184,7 @@ for j=1:nvar
height = 1/comp_nbr;
for i=comp_nbr:-1:1
-% for i=1:comp_nbr
+ % for i=1:comp_nbr
hl = fill([0 0 0.2 0.2],[y1 y1+0.7*height y1+0.7*height y1],i);
hold on
ht = text(0.3,y1+0.3*height,labels(i,:),'Interpreter','none');
@@ -222,7 +222,7 @@ for j=1:nvar
fprintf(fidTeX,['\\caption{' preamble_txt fig_name_long strrep(fig_mode1, '_', ' ') ': $ %s $.}\n'],deblank(DynareModel.endo_names_tex(i_var(j),:)));
fprintf(fidTeX,'\\end{figure}\n');
fprintf(fidTeX,' \n');
- end
+ end
end
%% write LaTeX-Footer
diff --git a/matlab/graph_decomp_detail.m b/matlab/graph_decomp_detail.m
index 5d42d0ce19d727f1c9a9dbfd5ff49ee6caa40bc0..75951a41135b97718454053b752727a2813fc357 100644
--- a/matlab/graph_decomp_detail.m
+++ b/matlab/graph_decomp_detail.m
@@ -1,7 +1,7 @@
function []=graph_decomp_detail(z,shock_names,endo_names,i_var,initial_date,DynareModel,DynareOptions)
%function []=graph_decomp_detail(z,shock_names,endo_names,i_var,initial_date,DynareModel,DynareOptions)
% Plots the results from the shock_decomposition command
-%
+%
% Inputs
% z [n_var*(nshock+2)*nperiods] shock decomposition array, see shock_decomposition.m for details
% shock_names [endo_nbr*string length] shock names from M_.exo_names
@@ -59,7 +59,7 @@ fig_name_long = opts_decomp.fig_name;
% fig_name = ['_' fig_name];
if screen_shocks
-% fig_name1 = [fig_name1 '_screen'];
+ % fig_name1 = [fig_name1 '_screen'];
fig_name_long = [fig_name_long ' SCREEN'];
end
@@ -83,7 +83,7 @@ else
x = initial_period-1/freq:(1/freq):initial_period+(gend-1)/freq;
end
-ind_yrs = find(floor(x)==x);
+ind_yrs = find(floor(x)==x);
dind_tick = 1;
if floor(length(ind_yrs)/3)
dind_tick = floor(length(ind_yrs)/3);
@@ -151,89 +151,89 @@ for j=1:nvar
continue
end
for jf = 1:nfigs
- fhandle = dyn_figure(DynareOptions.plot_shock_decomp.nodisplay,'Name',[preamble_txt fig_name_long strrep(fig_mode1, '_', ' ') ': ' deblank(endo_names(i_var(j),:)) ' (detail).'],'position',[200 100 650 850], 'PaperPositionMode', 'auto','PaperOrientation','portrait','renderermode','auto');
- a0=zeros(1,4);
- a0(3)=inf;
- a0(4)=-inf;
- for ic=1+nrow*ncol*(jf-1):min(nrow*ncol*jf,comp_nbr)
- i = ic-nrow*ncol*(jf-1);
- zz = z1(ic,:);
- zz(2,:)=z1(end,:)-zz;
- ipos=zz>0;
- ineg=zz<0;
- hax = subplot(nrow,ncol,i); set(gca,'box','on')
- hbar = bar(x(2:end),(zz.*ipos)','stacked');
- colormap([0.15 0.15 0.15;0.85 0.85 0.85]),
- set(hbar,'edgecolor','flat');
- hold on,
- hbar = bar(x(2:end),(zz.*ineg)','stacked');
- colormap([0.15 0.15 0.15;0.85 0.85 0.85]),
- set(hbar,'edgecolor','flat');
- title(deblank(labels(ic,:)),'Interpreter','none'),
- axis tight;
- a=axis;
- set(gca,'Xtick',xind_tick)
- set(gca,'xlim',[xmin xmax])
- a0(3)=min(a(3),a0(3));
- a0(4)=max(a(4),a0(4));
- set(gca,'ylim',a0(3:4))
- hold on, h1=plot(x(2:end),z1(end,:),'k-','LineWidth',2);
- if interactive & (~isoctave & use_shock_groups)
- mydata.fig_name = DynareOptions.plot_shock_decomp.fig_name(2:end);
- mydata.use_shock_groups = DynareOptions.plot_shock_decomp.use_shock_groups;
- mydata.shock_group = shock_groups.(shock_ind{ic});
- mydata.shock_decomp = DynareOptions.plot_shock_decomp;
- if ~isempty(mydata.shock_group.shocks{1})
- c = uicontextmenu;
- hax.UIContextMenu=c;
- browse_menu = uimenu(c,'Label','Browse group');
- expand_menu = uimenu(c,'Label','Expand group','Callback',['expand_group(''' mydata.use_shock_groups ''',''' deblank(mydata.shock_decomp.orig_varlist(j,:)) ''',' int2str(ic) ')']);
- set(expand_menu,'UserData',mydata,'Tag',['group' int2str(ic)]);
- for jmember = mydata.shock_group.shocks
- uimenu('parent',browse_menu,'Label',char(jmember))
+ fhandle = dyn_figure(DynareOptions.plot_shock_decomp.nodisplay,'Name',[preamble_txt fig_name_long strrep(fig_mode1, '_', ' ') ': ' deblank(endo_names(i_var(j),:)) ' (detail).'],'position',[200 100 650 850], 'PaperPositionMode', 'auto','PaperOrientation','portrait','renderermode','auto');
+ a0=zeros(1,4);
+ a0(3)=inf;
+ a0(4)=-inf;
+ for ic=1+nrow*ncol*(jf-1):min(nrow*ncol*jf,comp_nbr)
+ i = ic-nrow*ncol*(jf-1);
+ zz = z1(ic,:);
+ zz(2,:)=z1(end,:)-zz;
+ ipos=zz>0;
+ ineg=zz<0;
+ hax = subplot(nrow,ncol,i); set(gca,'box','on')
+ hbar = bar(x(2:end),(zz.*ipos)','stacked');
+ colormap([0.15 0.15 0.15;0.85 0.85 0.85]),
+ set(hbar,'edgecolor','flat');
+ hold on,
+ hbar = bar(x(2:end),(zz.*ineg)','stacked');
+ colormap([0.15 0.15 0.15;0.85 0.85 0.85]),
+ set(hbar,'edgecolor','flat');
+ title(deblank(labels(ic,:)),'Interpreter','none'),
+ axis tight;
+ a=axis;
+ set(gca,'Xtick',xind_tick)
+ set(gca,'xlim',[xmin xmax])
+ a0(3)=min(a(3),a0(3));
+ a0(4)=max(a(4),a0(4));
+ set(gca,'ylim',a0(3:4))
+ hold on, h1=plot(x(2:end),z1(end,:),'k-','LineWidth',2);
+ if interactive & (~isoctave & use_shock_groups)
+ mydata.fig_name = DynareOptions.plot_shock_decomp.fig_name(2:end);
+ mydata.use_shock_groups = DynareOptions.plot_shock_decomp.use_shock_groups;
+ mydata.shock_group = shock_groups.(shock_ind{ic});
+ mydata.shock_decomp = DynareOptions.plot_shock_decomp;
+ if ~isempty(mydata.shock_group.shocks{1})
+ c = uicontextmenu;
+ hax.UIContextMenu=c;
+ browse_menu = uimenu(c,'Label','Browse group');
+ expand_menu = uimenu(c,'Label','Expand group','Callback',['expand_group(''' mydata.use_shock_groups ''',''' deblank(mydata.shock_decomp.orig_varlist(j,:)) ''',' int2str(ic) ')']);
+ set(expand_menu,'UserData',mydata,'Tag',['group' int2str(ic)]);
+ for jmember = mydata.shock_group.shocks
+ uimenu('parent',browse_menu,'Label',char(jmember))
+ end
end
end
end
- end
- for isub=1:i
- subplot(nrow,ncol,isub),
- set(gca,'ylim',a0(3:4))
- end
-
-% make legend
- axes('Position',[0.1 0.01 0.8 0.02],'units','normalized');
- axis([0 1 0 1]);
- axis off;
- hold on;
- x1 = 0;
- width = 1/2;
- mylabels = {'Individual contrib.','Residual contrib.'};
-
- for i=1:2
-% for i=1:comp_nbr
- hl = fill([x1 x1 x1+0.3*width x1+0.3*width],[0 1 1 0],i);
- hold on
- ht = text(x1+0.4*width,0.3,mylabels{i},'Interpreter','none');
- hold on
- x1 = x1 + width;
- end
-
-
- if nfigs>1
- suffix = ['_detail_' int2str(jf)];
- else
- suffix = ['_detail'];
- end
- dyn_saveas(fhandle,[GraphDirectoryName, filesep, DynareModel.fname,'_shock_decomposition_',deblank(endo_names(i_var(j),:)),fig_mode1,fig_name suffix],DynareOptions.plot_shock_decomp.nodisplay,DynareOptions.plot_shock_decomp.graph_format);
- if DynareOptions.TeX && any(strcmp('eps',cellstr(DynareOptions.plot_shock_decomp.graph_format)))
- fprintf(fidTeX,'\\begin{figure}[H]\n');
- fprintf(fidTeX,'\\centering \n');
- fprintf(fidTeX,'\\includegraphics[width=0.8\\textwidth]{%s/graphs/%s_shock_decomposition_%s}\n',DynareModel.fname,DynareModel.fname,[deblank(endo_names(i_var(j),:)) fig_mode1 fig_name suffix]);
- fprintf(fidTeX,'\\label{Fig:shock_decomp_detail:%s}\n',[fig_mode deblank(endo_names(i_var(j),:)) fig_name suffix]);
- fprintf(fidTeX,['\\caption{' preamble_txt fig_name_long strrep(fig_mode1, '_', ' ') ': $ %s $ (detail).}\n'],deblank(DynareModel.endo_names_tex(i_var(j),:)));
- fprintf(fidTeX,'\\end{figure}\n');
- fprintf(fidTeX,' \n');
- end
+ for isub=1:i
+ subplot(nrow,ncol,isub),
+ set(gca,'ylim',a0(3:4))
+ end
+
+ % make legend
+ axes('Position',[0.1 0.01 0.8 0.02],'units','normalized');
+ axis([0 1 0 1]);
+ axis off;
+ hold on;
+ x1 = 0;
+ width = 1/2;
+ mylabels = {'Individual contrib.','Residual contrib.'};
+
+ for i=1:2
+ % for i=1:comp_nbr
+ hl = fill([x1 x1 x1+0.3*width x1+0.3*width],[0 1 1 0],i);
+ hold on
+ ht = text(x1+0.4*width,0.3,mylabels{i},'Interpreter','none');
+ hold on
+ x1 = x1 + width;
+ end
+
+
+ if nfigs>1
+ suffix = ['_detail_' int2str(jf)];
+ else
+ suffix = ['_detail'];
+ end
+ dyn_saveas(fhandle,[GraphDirectoryName, filesep, DynareModel.fname,'_shock_decomposition_',deblank(endo_names(i_var(j),:)),fig_mode1,fig_name suffix],DynareOptions.plot_shock_decomp.nodisplay,DynareOptions.plot_shock_decomp.graph_format);
+ if DynareOptions.TeX && any(strcmp('eps',cellstr(DynareOptions.plot_shock_decomp.graph_format)))
+ fprintf(fidTeX,'\\begin{figure}[H]\n');
+ fprintf(fidTeX,'\\centering \n');
+ fprintf(fidTeX,'\\includegraphics[width=0.8\\textwidth]{%s/graphs/%s_shock_decomposition_%s}\n',DynareModel.fname,DynareModel.fname,[deblank(endo_names(i_var(j),:)) fig_mode1 fig_name suffix]);
+ fprintf(fidTeX,'\\label{Fig:shock_decomp_detail:%s}\n',[fig_mode deblank(endo_names(i_var(j),:)) fig_name suffix]);
+ fprintf(fidTeX,['\\caption{' preamble_txt fig_name_long strrep(fig_mode1, '_', ' ') ': $ %s $ (detail).}\n'],deblank(DynareModel.endo_names_tex(i_var(j),:)));
+ fprintf(fidTeX,'\\end{figure}\n');
+ fprintf(fidTeX,' \n');
+ end
end
end
diff --git a/matlab/gsa/Morris_Measure_Groups.m b/matlab/gsa/Morris_Measure_Groups.m
index 1aa387f0745f3a73a0eeb5e6e0ec2d1a5c86747e..69dc1d713fe543f364b2d91d12018c1766586dbb 100644
--- a/matlab/gsa/Morris_Measure_Groups.m
+++ b/matlab/gsa/Morris_Measure_Groups.m
@@ -5,19 +5,19 @@ function [SAmeas, OutMatrix] = Morris_Measure_Groups(NumFact, Sample, Output, p,
% -------------------------------------------------------------------------
% INPUTS
% -------------------------------------------------------------------------
-% Group [NumFactor, NumGroups] := Matrix describing the groups.
-% Each column represents one group.
+% Group [NumFactor, NumGroups] := Matrix describing the groups.
+% Each column represents one group.
% The element of each column are zero if the factor is not in the
% group. Otherwise it is 1.
%
-% Sample := Matrix of the Morris sampled trajectories
+% Sample := Matrix of the Morris sampled trajectories
%
% Output := Matrix of the output(s) values in correspondence of each point
% of each trajectory
%
% k = Number of factors
% -------------------------------------------------------------------------
-% OUTPUTS
+% OUTPUTS
% OutMatrix (NumFactor*NumOutputs, 3)= [Mu*, Mu, StDev]
% for each output it gives the three measures of each factor
% -------------------------------------------------------------------------
@@ -45,9 +45,9 @@ function [SAmeas, OutMatrix] = Morris_Measure_Groups(NumFact, Sample, Output, p,
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
if nargin==0
- skipline()
- disp('[SAmeas, OutMatrix] = Morris_Measure_Groups(NumFact, Sample, Output, p, Group);')
- return
+ skipline()
+ disp('[SAmeas, OutMatrix] = Morris_Measure_Groups(NumFact, Sample, Output, p, Group);')
+ return
end
OutMatrix=[];
@@ -63,45 +63,45 @@ if NumGroups ~ 0
sizea = NumGroups; % Number of groups
GroupMat=Group;
GroupMat = GroupMat';
-else
- sizea = NumFact;
+else
+ sizea = NumFact;
end
r=size(Sample,1)/(sizea+1); % Number of trajectories
% For Each Output
for k=1:size(Output,2)
-
+
OutValues=Output(:,k);
-
+
% For each r trajectory
for i=1:r
-
+
% For each step j in the trajectory
% Read the orientation matrix fact for the r-th sampling
% Read the corresponding output values
- Single_Sample = Sample(i+(i-1)*sizea:i+(i-1)*sizea+sizea,:);
- Single_OutValues = OutValues(i+(i-1)*sizea:i+(i-1)*sizea+sizea,:);
+ Single_Sample = Sample(i+(i-1)*sizea:i+(i-1)*sizea+sizea,:);
+ Single_OutValues = OutValues(i+(i-1)*sizea:i+(i-1)*sizea+sizea,:);
A = (Single_Sample(2:sizea+1,:)-Single_Sample(1:sizea,:))';
Delta = A(find(A));
% For each point of the fixed trajectory compute the values of the Morris function. The function
% is partitioned in four parts, from order zero to order 4th.
- for j=1:sizea % For each point in the trajectory i.e for each factor
- % matrix of factor which changes
+ for j=1:sizea % For each point in the trajectory i.e for each factor
+ % matrix of factor which changes
if NumGroups ~ 0
AuxFind (:,1) = A(:,j);
-% AuxFind(find(A(:,j)),1)=1;
-% Pippo = sum((Group - repmat(AuxFind,1,NumGroups)),1);
-% Change_factor(j,i) = find(Pippo==0);
- Change_factor = find(abs(AuxFind)>1e-010);
+ % AuxFind(find(A(:,j)),1)=1;
+ % Pippo = sum((Group - repmat(AuxFind,1,NumGroups)),1);
+ % Change_factor(j,i) = find(Pippo==0);
+ Change_factor = find(abs(AuxFind)>1e-010);
% If we deal with groups we can only estimate the new mu*
% measure since factors in the same groups can move in
% opposite direction and the definition of the standard
- % Morris mu cannopt be applied.
+ % Morris mu cannopt be applied.
% In the new version the elementary effect is defined with
% the absolute value.
- %SAmeas(find(GroupMat(Change_factor(j,i),:)),i) = abs((Single_OutValues(j) - Single_OutValues(j+1) )/Delt); %(2/3));
- SAmeas(i,Change_factor') = abs((Single_OutValues(j) - Single_OutValues(j+1) )/Delt);
+ %SAmeas(find(GroupMat(Change_factor(j,i),:)),i) = abs((Single_OutValues(j) - Single_OutValues(j+1) )/Delt); %(2/3));
+ SAmeas(i,Change_factor') = abs((Single_OutValues(j) - Single_OutValues(j+1) )/Delt);
else
Change_factor(j,i) = find(Single_Sample(j+1,:)-Single_Sample(j,:));
% If no groups --> we compute both the original and
@@ -110,41 +110,41 @@ for k=1:size(Output,2)
SAmeas(Change_factor(j,i),i) = (Single_OutValues(j+1) - Single_OutValues(j) )/Delt; %(2/3);
else %=> -Delta
SAmeas(Change_factor(j,i),i) = (Single_OutValues(j) - Single_OutValues(j+1) )/Delt; %(2/3);
- end
+ end
end
end %for j=1:sizea
-
+
end %for i=1:r
-
+
if NumGroups ~ 0
SAmeas = SAmeas';
end
% Compute Mu AbsMu and StDev
if any(any(isnan(SAmeas)))
- for j=1:NumFact
- SAm = SAmeas(j,:);
- SAm = SAm(find(~isnan(SAm)));
- rr=length(SAm);
- AbsMu(j,1) = sum(abs(SAm),2)/rr;
- if NumGroups == 0
- Mu(j,1) = sum(SAm,2)/rr;
- StDev(j,1) = sum((SAm - repmat(Mu(j),1,rr)).^2/(rr*(rr-1)),2).^0.5;
- end
- end
+ for j=1:NumFact
+ SAm = SAmeas(j,:);
+ SAm = SAm(find(~isnan(SAm)));
+ rr=length(SAm);
+ AbsMu(j,1) = sum(abs(SAm),2)/rr;
+ if NumGroups == 0
+ Mu(j,1) = sum(SAm,2)/rr;
+ StDev(j,1) = sum((SAm - repmat(Mu(j),1,rr)).^2/(rr*(rr-1)),2).^0.5;
+ end
+ end
else
- AbsMu = sum(abs(SAmeas),2)/r;
- if NumGroups == 0
- Mu = sum(SAmeas,2)/r;
- StDev = sum((SAmeas - repmat(Mu,1,r)).^2/(r*(r-1)),2).^0.5;
- end
+ AbsMu = sum(abs(SAmeas),2)/r;
+ if NumGroups == 0
+ Mu = sum(SAmeas,2)/r;
+ StDev = sum((SAmeas - repmat(Mu,1,r)).^2/(r*(r-1)),2).^0.5;
+ end
end
% Define the output Matrix - if we have groups we cannot define the old
% measure mu, only mu* makes sense
if NumGroups > 0
- OutMatrix = [OutMatrix; AbsMu];
+ OutMatrix = [OutMatrix; AbsMu];
else
- OutMatrix = [OutMatrix; AbsMu, Mu, StDev];
+ OutMatrix = [OutMatrix; AbsMu, Mu, StDev];
end
end % For Each Output
diff --git a/matlab/gsa/Sampling_Function_2.m b/matlab/gsa/Sampling_Function_2.m
index 60255cafbb89b381041bd8eafb13758168155bf1..bdb8d03eb7d78f8dafac1109efead70218d224e0 100644
--- a/matlab/gsa/Sampling_Function_2.m
+++ b/matlab/gsa/Sampling_Function_2.m
@@ -1,42 +1,42 @@
function [Outmatrix, OutFact] = Sampling_Function_2(p, k, r, UB, LB, GroupMat)
%[Outmatrix, OutFact] = Sampling_Function_2(p, k, r, UB, LB, GroupMat)
-% Inputs: k (1,1) := number of factors examined or number of groups examined.
+% Inputs: k (1,1) := number of factors examined or number of groups examined.
% In case the groups are chosen the number of factors is stores in NumFact and
-% sizea becomes the number of created groups.
+% sizea becomes the number of created groups.
% NumFact (1,1) := number of factors examined in the case when groups are chosen
-% r (1,1) := sample size
+% r (1,1) := sample size
% p (1,1) := number of intervals considered in [0, 1]
-% UB(sizea,1) := Upper Bound for each factor
-% LB(sizea,1) := Lower Bound for each factor
+% UB(sizea,1) := Upper Bound for each factor
+% LB(sizea,1) := Lower Bound for each factor
% GroupNumber(1,1) := Number of groups (eventually 0)
-% GroupMat(NumFact,GroupNumber):= Matrix which describes the chosen groups. Each column represents a group and its elements
+% GroupMat(NumFact,GroupNumber):= Matrix which describes the chosen groups. Each column represents a group and its elements
% are set to 1 in correspondence of the factors that belong to the fixed group. All
% the other elements are zero.
-% Local Variables:
-% sizeb (1,1) := sizea+1
+% Local Variables:
+% sizeb (1,1) := sizea+1
% sizec (1,1) := 1
-% randmult (sizea,1) := vector of random +1 and -1
-% perm_e(1,sizea) := vector of sizea random permutated indeces
+% randmult (sizea,1) := vector of random +1 and -1
+% perm_e(1,sizea) := vector of sizea random permutated indeces
% fact(sizea) := vector containing the factor varied within each traj
-% DDo(sizea,sizea) := D* in Morris, 1991
-% A(sizeb,sizea) := Jk+1,k in Morris, 1991
-% B(sizeb,sizea) := B in Morris, 1991
-% Po(sizea,sizea) := P* in Morris, 1991
+% DDo(sizea,sizea) := D* in Morris, 1991
+% A(sizeb,sizea) := Jk+1,k in Morris, 1991
+% B(sizeb,sizea) := B in Morris, 1991
+% Po(sizea,sizea) := P* in Morris, 1991
% Bo(sizeb,sizea) := B* in Morris, 1991
-% Ao(sizeb,sizec) := Jk+1,1 in Morris, 1991
-% xo(sizec,sizea) := x* in Morris, 1991 (starting point for the trajectory)
+% Ao(sizeb,sizec) := Jk+1,1 in Morris, 1991
+% xo(sizec,sizea) := x* in Morris, 1991 (starting point for the trajectory)
% In(sizeb,sizea) := for each loop orientation matrix. It corresponds to a trajectory
% of k step in the parameter space and it provides a single elementary
-% effect per factor
+% effect per factor
% MyInt()
-% Fact(sizea,1) := for each loop vector indicating which factor or group of factors has been changed
+% Fact(sizea,1) := for each loop vector indicating which factor or group of factors has been changed
% in each step of the trajectory
% AuxMat(sizeb,sizea) := Delta*0.5*((2*B - A) * DD0 + A) in Morris, 1991. The AuxMat is used as in Morris design
% for single factor analysis, while it constitutes an intermediate step for the group analysis.
%
-% Output: Outmatrix(sizeb*r, sizea) := for the entire sample size computed In(i,j) matrices
+% Output: Outmatrix(sizeb*r, sizea) := for the entire sample size computed In(i,j) matrices
% OutFact(sizea*r,1) := for the entire sample size computed Fact(i,1) vectors
-%
+%
% Note: B0 is constructed as in Morris design when groups are not considered. When groups are considered the routine
% follows the following steps:
% 1- Creation of P0 and DD0 matrices defined in Morris for the groups. This means that the dimensions of these
@@ -44,7 +44,7 @@ function [Outmatrix, OutFact] = Sampling_Function_2(p, k, r, UB, LB, GroupMat)
% 2- Creation of AuxMat matrix with (GroupNumber+1,GroupNumber) elements.
% 3- Definition of GroupB0 starting from AuxMat, GroupMat and P0.
% 4- The final B0 for groups is obtained as [ones(sizeb,1)*x0' + GroupB0]. The P0 permutation is present in GroupB0
-% and it's not necessary to permute the matrix (ones(sizeb,1)*x0') because it's already randomly created.
+% and it's not necessary to permute the matrix (ones(sizeb,1)*x0') because it's already randomly created.
% Reference:
% A. Saltelli, K. Chan, E.M. Scott, "Sensitivity Analysis" on page 68 ss
%
@@ -82,76 +82,76 @@ GroupNumber = size(GroupMat,2);
if GroupNumber ~ 0;
sizea = size(GroupMat,2);
-end
+end
sizeb = sizea + 1;
sizec = 1;
Outmatrix = [];
OutFact = [];
-% For each i generate a trajectory
+% For each i generate a trajectory
for i=1:r
-
+
% Construct DD0 - OLD VERSION - it does not need communication toolbox
% RAND(N,M) is an NXM matrix with random entries, chosen from a uniform distribution on the interval (0.0,1.0).
% Note that DD0 tells if the factor have to be increased or ddecreased
% by Delta.
- randmult = ones(k,1);
- v = rand(k,1);
+ randmult = ones(k,1);
+ v = rand(k,1);
randmult (find(v < 0.5))=-1;
randmult = repmat(randmult,1,k);
DD0 = randmult .* eye(k);
-
+
% Construct DD0 - NEW VERSION - it needs communication toolbox
% randsrc(m) generates an m-by-m matrix, each of whose entries independently takes the value -1 with probability 1/2,
% and 1 with probability 1/2.
- % DD0 = randsrc(NumFact) .* eye(NumFact);
-
+ % DD0 = randsrc(NumFact) .* eye(NumFact);
+
% Construct B (lower triangular)
B = ones(sizeb,sizea);
for j = 1:sizea
- B(1:j,j)=0;
+ B(1:j,j)=0;
end
-
+
% Construct A0, A
A0 = ones(sizeb,1);
A = ones(sizeb,NumFact);
% Construct the permutation matrix P0. In each column of P0 one randomly chosen element equals 1
- % while all the others equal zero.
+ % while all the others equal zero.
% P0 tells the order in which order factors are changed in each
% trajectory. P0 is created as it follows:
% 1) All the elements of P0 are set equal to zero ==> P0 = zeros (sizea, sizea);
- % 2) The function randperm create a random permutation of integer 1:sizea, without repetitions ==> perm_e;
- % 3) In each column of P0 the element indicated in perm_e is set equal to one.
- % Note that P0 is then used reading it by rows.
+ % 2) The function randperm create a random permutation of integer 1:sizea, without repetitions ==> perm_e;
+ % 3) In each column of P0 the element indicated in perm_e is set equal to one.
+ % Note that P0 is then used reading it by rows.
P0 = zeros (sizea, sizea);
perm_e = randperm(sizea); % RANDPERM(n) is a random permutation of the integers from 1 to n.
for j = 1:sizea
- P0(perm_e(j),j) = 1;
- end
-
- % When groups are present the random permutation is done only on B. The effect is the same since
- % the added part (A0*x0') is completely random.
+ P0(perm_e(j),j) = 1;
+ end
+
+ % When groups are present the random permutation is done only on B. The effect is the same since
+ % the added part (A0*x0') is completely random.
if GroupNumber ~ 0
B = B * (GroupMat*P0')';
end
-
+
% Compute AuxMat both for single factors and groups analysis. For Single factors analysis
% AuxMat is added to (A0*X0) and then permutated through P0. When groups are active AuxMat is
% used to build GroupB0. AuxMat is created considering DD0. If the element on DD0 diagonal
- % is 1 then AuxMat will start with zero and add Delta. If the element on DD0 diagonal is -1
+ % is 1 then AuxMat will start with zero and add Delta. If the element on DD0 diagonal is -1
% then DD0 will start Delta and goes to zero.
AuxMat = Delta*0.5*((2*B - A) * DD0 + A);
-
+
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% a --> Define the random vector x0 for the factors. Note that x0 takes value in the hypercube
- % [0,...,1-Delta]*[0,...,1-Delta]*[0,...,1-Delta]*[0,...,1-Delta]
+ % [0,...,1-Delta]*[0,...,1-Delta]*[0,...,1-Delta]*[0,...,1-Delta]
MyInt = repmat([0:(1/(p-1)):(1-Delta)],NumFact,1); % Construct all possible values of the factors
-
+
% OLD VERSION - it needs communication toolbox
- % w = randint(NumFact,1,[1,size(MyInt,2)]);
-
+ % w = randint(NumFact,1,[1,size(MyInt,2)]);
+
% NEW VERSION - construct a version of random integers
% 1) create a vector of random integers
% 2) divide [0,1] into the needed steps
@@ -160,21 +160,21 @@ for i=1:r
v = repmat(rand(NumFact,1),1,size(MyInt,2)+1); % 1)
IntUsed = repmat([0:1/size(MyInt,2):1],NumFact,1); % 2)
DiffAuxVec = IntUsed - v; % 3)
-
+
for ii = 1:size(DiffAuxVec,1)
w(1,ii) = max(find(DiffAuxVec(ii,:)<0)); % 4)
end
- x0 = MyInt(1,w)'; % Define x0
- %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+ x0 = MyInt(1,w)'; % Define x0
+ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% b --> Compute the matrix B*, here indicated as B0. Each row in B0 is a
- % trajectory for Morris Calculations. The dimension of B0 is (Numfactors+1,Numfactors)
+ % trajectory for Morris Calculations. The dimension of B0 is (Numfactors+1,Numfactors)
if GroupNumber ~ 0
B0 = (A0*x0' + AuxMat);
else
B0 = (A0*x0' + AuxMat)*P0;
end
-
- %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% c --> Compute values in the original intervals
% B0 has values x(i,j) in [0, 1/(p -1), 2/(p -1), ... , 1].
% To obtain values in the original intervals [LB, UB] we compute
@@ -187,8 +187,8 @@ for i=1:r
Fact(1,j) = find(P0(j,:));
end
Fact(1,sizea+1) = 0;
-
+
Outmatrix = [Outmatrix; In];
OutFact = [OutFact; Fact'];
-
+
end
\ No newline at end of file
diff --git a/matlab/gsa/cumplot.m b/matlab/gsa/cumplot.m
index d210c81f955b98bc00c7d98f2861b09faa8e1c02..bc4eb969b20b5b2a290ee40f03249b5757c7d327 100644
--- a/matlab/gsa/cumplot.m
+++ b/matlab/gsa/cumplot.m
@@ -3,7 +3,7 @@ function h = cumplot(x)
% Written by Marco Ratto
% Joint Research Centre, The European Commission,
-% marco.ratto@ec.europa.eu
+% marco.ratto@ec.europa.eu
% Copyright (C) 2012 European Commission
% Copyright (C) 2012 Dynare Team
diff --git a/matlab/gsa/filt_mc_.m b/matlab/gsa/filt_mc_.m
index bf37c148b9246a402fe7cda51e434bf11f659dc9..b2dfb04117ed2ec29c3acb73644552ada46a349f 100644
--- a/matlab/gsa/filt_mc_.m
+++ b/matlab/gsa/filt_mc_.m
@@ -11,7 +11,7 @@ function [rmse_MC, ixx] = filt_mc_(OutDir,options_gsa_,dataset_,dataset_info)
%
% Written by Marco Ratto
% Joint Research Centre, The European Commission,
-% marco.ratto@ec.europa.eu
+% marco.ratto@ec.europa.eu
% Copyright (C) 2012-2016 European Commission
% Copyright (C) 2012-2017 Dynare Team
@@ -58,33 +58,33 @@ disp('Starting sensitivity analysis')
disp('for the fit of EACH observed series ...')
skipline()
if ~options_.nograph
-disp('Deleting old SA figures...')
-a=dir([OutDir,filesep,'*.*']);
-tmp1='0';
-if options_.opt_gsa.ppost
- tmp=['_rmse_post'];
-else
- if options_.opt_gsa.pprior
- tmp=['_rmse_prior'];
+ disp('Deleting old SA figures...')
+ a=dir([OutDir,filesep,'*.*']);
+ tmp1='0';
+ if options_.opt_gsa.ppost
+ tmp=['_rmse_post'];
else
- tmp=['_rmse_mc'];
- end
- if options_gsa_.lik_only
- tmp1 = [tmp,'_post_SA'];
- tmp = [tmp,'_lik_SA'];
- end
-end
-for j=1:length(a)
- if strmatch([fname_,tmp],a(j).name)
- disp(a(j).name)
- delete([OutDir,filesep,a(j).name])
+ if options_.opt_gsa.pprior
+ tmp=['_rmse_prior'];
+ else
+ tmp=['_rmse_mc'];
+ end
+ if options_gsa_.lik_only
+ tmp1 = [tmp,'_post_SA'];
+ tmp = [tmp,'_lik_SA'];
+ end
end
- if strmatch([fname_,tmp1],a(j).name)
- disp(a(j).name)
- delete([OutDir,filesep,a(j).name])
+ for j=1:length(a)
+ if strmatch([fname_,tmp],a(j).name)
+ disp(a(j).name)
+ delete([OutDir,filesep,a(j).name])
+ end
+ if strmatch([fname_,tmp1],a(j).name)
+ disp(a(j).name)
+ delete([OutDir,filesep,a(j).name])
+ end
end
-end
-disp('done !')
+ disp('done !')
end
nshock=estim_params_.nvx + estim_params_.nvn + estim_params_.ncx + estim_params_.ncn;
@@ -139,7 +139,7 @@ if ~loadSA
data_index = dataset_info.missing.aindex;
missing_value = dataset_info.missing.state;
for jx=1:gend
- data_indx(jx,data_index{jx})=true;
+ data_indx(jx,data_index{jx})=true;
end
%stock_gend=data_info.gend;
%stock_data = data_info.data;
@@ -175,7 +175,7 @@ if ~loadSA
disp('Computing RMSE''s...')
for i=1:size(vvarvecm,1)
vj=deblank(vvarvecm(i,:));
-
+
jxj(i) = strmatch(vj,lgy_(dr_.order_var,:),'exact');
js(i) = strmatch(vj,lgy_,'exact');
yss(i,:,:)=repmat(sto_ys(:,js(i))',[gend,1]);
@@ -227,7 +227,7 @@ if ~loadSA
end
likelihood=logpo2(:)-lnprior(:);
disp('... done!')
-
+
if options_.opt_gsa.ppost
save([OutDir,filesep,fnamtmp,'.mat'], 'x', 'logpo2', 'likelihood', 'rmse_MC', 'r2_MC', 'vvarvecm')
if exist('xparam1_mean','var')
@@ -322,7 +322,7 @@ if ~options_.opt_gsa.ppost && options_.opt_gsa.lik_only
options_mcf.beha_title = 'better posterior kernel';
options_mcf.nobeha_title = 'worse posterior kernel';
mcf_analysis(x, ipost(1:nfilt), ipost(nfilt+1:end), options_mcf, options_);
-
+
if options_.opt_gsa.pprior
anam = 'rmse_prior_lik';
atitle = 'RMSE prior: Log Likelihood Kernel';
@@ -336,7 +336,7 @@ if ~options_.opt_gsa.ppost && options_.opt_gsa.lik_only
options_mcf.beha_title = 'better likelihood';
options_mcf.nobeha_title = 'worse likelihood';
mcf_analysis(x, ilik(1:nfilt), ilik(nfilt+1:end), options_mcf, options_);
-
+
else
if options_.opt_gsa.ppost
rmse_txt=rmse_pmean;
@@ -425,19 +425,19 @@ else
if options_.opt_gsa.ppost
dyn_saveas(hh,[OutDir filesep fname_ '_rmse_post_lnprior',int2str(ifig)],options_.nodisplay,options_.graph_format);
if options_.TeX
- create_TeX_loader(options_,[OutDir '/' fname_ '_rmse_post_lnprior',int2str(ifig)],ifig,[temp_name,' ',int2str(ifig)],'rmse_post_lnprior',options_.figures.textwidth*min((i-9*(ifig-1))/3,1))
+ create_TeX_loader(options_,[OutDir '/' fname_ '_rmse_post_lnprior',int2str(ifig)],ifig,[temp_name,' ',int2str(ifig)],'rmse_post_lnprior',options_.figures.textwidth*min((i-9*(ifig-1))/3,1))
end
else
if options_.opt_gsa.pprior
dyn_saveas(hh,[OutDir filesep fname_ '_rmse_prior_lnprior',int2str(ifig) ],options_.nodisplay,options_.graph_format);
- if options_.TeX
- create_TeX_loader(options_,[OutDir '/' fname_ '_rmse_prior_lnprior',int2str(ifig)],ifig,[temp_name,' ',int2str(ifig)],'rmse_prior_lnprior',options_.figures.textwidth*min((i-9*(ifig-1))/3,1))
- end
+ if options_.TeX
+ create_TeX_loader(options_,[OutDir '/' fname_ '_rmse_prior_lnprior',int2str(ifig)],ifig,[temp_name,' ',int2str(ifig)],'rmse_prior_lnprior',options_.figures.textwidth*min((i-9*(ifig-1))/3,1))
+ end
else
dyn_saveas(hh,[OutDir filesep fname_ '_rmse_mc_lnprior',int2str(ifig) ],options_.nodisplay,options_.graph_format);
- if options_.TeX
- create_TeX_loader(options_,[OutDir '/' fname_ '_rmse_mc_lnprior',int2str(ifig)],ifig,[temp_name,' ',int2str(ifig)],'rmse_mc_lnprior',options_.figures.textwidth*min((i-9*(ifig-1))/3,1))
- end
+ if options_.TeX
+ create_TeX_loader(options_,[OutDir '/' fname_ '_rmse_mc_lnprior',int2str(ifig)],ifig,[temp_name,' ',int2str(ifig)],'rmse_mc_lnprior',options_.figures.textwidth*min((i-9*(ifig-1))/3,1))
+ end
end
end
end
@@ -545,7 +545,7 @@ else
end
end
end
-
+
if options_.TeX
[pnames,pnames_tex]=get_LaTeX_parameter_names(M_,options_,estim_params_,bayestopt_);
param_names = char(pnames);
@@ -556,7 +556,7 @@ else
param_names_tex = [];
end
-
+
skipline()
title_string='RMSE over the MC sample:';
data_mat=[min(rmse_MC)' max(rmse_MC)'];
@@ -566,7 +566,7 @@ else
headers_tex=strvcat('\text{Variable}','\text{min yr RMSE}','\text{max yr RMSE}');
dyn_latex_table(M_,options_,title_string,'RMSE_MC',headers_tex,vvarvecm_tex,data_mat,0,15,5);
end
-
+
invar = find( std(rmse_MC)./mean(rmse_MC)<=0.0001 );
if ~isempty(invar)
skipline(2)
@@ -580,7 +580,7 @@ else
ivar = find( std(rmse_MC)./mean(rmse_MC)>0.0001 );
vvarvecm=vvarvecm(ivar,:);
rmse_MC=rmse_MC(:,ivar);
-
+
skipline()
% if options_.opt_gsa.ppost==0 && options_.opt_gsa.pprior,
disp(['Sample filtered the ',num2str(pfilt*100),'% best RMSE''s for each observed series ...' ])
@@ -590,7 +590,7 @@ else
% figure, boxplot(rmse_MC)
% set(gca,'xticklabel',vvarvecm)
% saveas(gcf,[fname_,'_SA_RMSE'])
-
+
skipline(2)
disp('RMSE ranges after filtering:')
title_string='RMSE ranges after filtering:';
@@ -604,10 +604,10 @@ else
data_mat=NaN(size(vvarvecm,1),5);
for j=1:size(vvarvecm,1)
data_mat(j,:)=[min(rmse_MC(ixx(1:nfilt0(j),j),j)) ...
- max(rmse_MC(ixx(1:nfilt0(j),j),j)) ...
- min(rmse_MC(ixx(nfilt0(j)+1:end,j),j)) ...
- max(rmse_MC(ixx(nfilt0(j)+1:end,j),j)) ...
- rmse_txt(j)];
+ max(rmse_MC(ixx(1:nfilt0(j),j),j)) ...
+ min(rmse_MC(ixx(nfilt0(j)+1:end,j),j)) ...
+ max(rmse_MC(ixx(nfilt0(j)+1:end,j),j)) ...
+ rmse_txt(j)];
end
%get formatting for additional header line
val_width=15;
@@ -622,7 +622,7 @@ else
if any(data_mat) < 0 %add one character for minus sign
values_length = values_length+1;
end
-
+
headers_length = max(size(deblank(headers(2:end,:)),2));
if ~isempty(val_width)
val_width = max(max(headers_length,values_length)+2,val_width);
@@ -631,7 +631,7 @@ else
end
value_format = sprintf('%%%d.%df',val_width,val_precis);
header_string_format = sprintf('%%%ds',val_width);
-
+
if options_.opt_gsa.ppost==0 && options_.opt_gsa.pprior
optional_header=sprintf([label_format_leftbound,header_string_format,header_string_format,header_string_format,header_string_format],'','',['best ',num2str(pfilt*100),'% filtered'],'','remaining 90%');
else
@@ -646,7 +646,7 @@ else
end
dyn_latex_table(M_,options_,title_string,'RMSE_ranges_after_filtering',headers_tex,vvarvecm_tex,data_mat,0,val_width,val_precis,optional_header);
end
-
+
%%%%% R2 table
vvarvecm=vvarvecm0;
skipline()
@@ -658,13 +658,13 @@ else
headers_tex=strvcat('\text{Variable}','\text{min yr R2}','\text{max yr R2}');
dyn_latex_table(M_,options_,title_string,'R2_MC',headers_tex,vvarvecm_tex,data_mat,0,15,5);
end
-
+
r2_MC=r2_MC(:,ivar);
vvarvecm=vvarvecm(ivar,:);
-
+
skipline()
disp(['Sample filtered the ',num2str(pfilt*100),'% best R2''s for each observed series ...' ])
-
+
skipline()
disp('R2 ranges after filtering:')
title_string='R2 ranges after filtering:';
@@ -678,10 +678,10 @@ else
data_mat=NaN(size(vvarvecm,1),5);
for j=1:size(vvarvecm,1)
data_mat(j,:)=[min(r2_MC(ixx(1:nfilt0(j),j),j)) ...
- max(r2_MC(ixx(1:nfilt0(j),j),j)) ...
- min(r2_MC(ixx(nfilt0(j)+1:end,j),j)) ...
- max(r2_MC(ixx(nfilt0(j)+1:end,j),j)) ...
- r2_txt(j)];
+ max(r2_MC(ixx(1:nfilt0(j),j),j)) ...
+ min(r2_MC(ixx(nfilt0(j)+1:end,j),j)) ...
+ max(r2_MC(ixx(nfilt0(j)+1:end,j),j)) ...
+ r2_txt(j)];
end
%get formatting for additional header line
val_width=15;
@@ -696,7 +696,7 @@ else
if any(data_mat) < 0 %add one character for minus sign
values_length = values_length+1;
end
-
+
headers_length = max(size(deblank(headers(2:end,:)),2));
if ~isempty(val_width)
val_width = max(max(headers_length,values_length)+2,val_width);
@@ -705,7 +705,7 @@ else
end
value_format = sprintf('%%%d.%df',val_width,val_precis);
header_string_format = sprintf('%%%ds',val_width);
-
+
if options_.opt_gsa.ppost==0 && options_.opt_gsa.pprior
optional_header=sprintf([label_format_leftbound,header_string_format,header_string_format,header_string_format,header_string_format],'','',['best ',num2str(pfilt*100),'% filtered'],'','remaining 90%');
else
@@ -720,8 +720,8 @@ else
end
dyn_latex_table(M_,options_,title_string,'R2_ranges_after_filtering',headers_tex,vvarvecm_tex,data_mat,0,val_width,val_precis,optional_header);
end
-
-
+
+
%%%% R2 table
SP=zeros(npar+nshock,size(vvarvecm,1));
for j=1:size(vvarvecm,1)
@@ -729,7 +729,7 @@ else
SP(ns,j)=ones(size(ns));
SS(:,j)=SS(:,j).*SP(:,j);
end
-
+
for j=1:npar+nshock %estim_params_.np,
nsp(j)=length(find(SP(j,:)));
end
@@ -742,7 +742,7 @@ else
% snam2=bayestopt_.name(find(nsp>1));
% snam=bayestopt_.name(find(nsp>0));
nsnam=(find(nsp>1));
-
+
skipline(2)
disp('These parameters do not affect significantly the fit of ANY observed series:')
disp(snam0)
@@ -752,11 +752,11 @@ else
skipline()
disp('These parameters affect MORE THAN ONE observed series: trade off exists!')
disp(snam2)
-
-
+
+
%pnam=bayestopt_.name(end-estim_params_.np+1:end);
pnam=bayestopt_.name;
-
+
% plot trade-offs
if ~options_.nograph
a00=jet(size(vvarvecm,1));
@@ -856,7 +856,7 @@ else
end
end
end
-
+
% now I plot by individual parameters
for ix=1:ceil(length(nsnam)/5)
hh = dyn_figure(options_.nodisplay,'name',[temp_name,' estimated params and shocks ',int2str(ix)]);
@@ -923,66 +923,66 @@ else
end
end
end
-
-% for j=1:size(SP,2),
-% nsx(j)=length(find(SP(:,j)));
-% end
-
-% skipline(2)
-% disp('Sensitivity table (significance and direction):')
-% vav=char(zeros(1, size(param_names,2)+3 ));
-% ibl = 12-size(vvarvecm,2);
-% for j=1:size(vvarvecm,1),
-% vav = [vav, char(zeros(1,ibl)),vvarvecm(j,:)];
-% end
-% disp(vav)
-% for j=1:npar+nshock, %estim_params_.np,
-% %disp([param_names(j,:), sprintf('%8.5g',SP(j,:))])
-% disp([param_names(j,:),' ', sprintf('%12.3g',PP(j,:))])
-% disp([char(zeros(1, size(param_names,2)+3 )),sprintf(' (%6g)',SS(j,:))])
-% end
-
-
-% skipline()
-% disp('Starting bivariate analysis:')
-%
-% for i=1:size(vvarvecm,1)
-% if options_.opt_gsa.ppost
-% fnam = ['rmse_post_',deblank(vvarvecm(i,:))];
-% else
-% if options_.opt_gsa.pprior
-% fnam = ['rmse_prior_',deblank(vvarvecm(i,:))];
-% else
-% fnam = ['rmse_mc_',deblank(vvarvecm(i,:))];
-% end
-% end
-% stab_map_2(x(ixx(1:nfilt0(i),i),:),alpha2,pvalue,fnam, OutDir,[],[temp_name ' observed variable ' deblank(vvarvecm(i,:))]);
-%
-% % [pc,latent,explained] = pcacov(c0);
-% % %figure, bar([explained cumsum(explained)])
-% % ifig=0;
-% % j2=0;
-% % for j=1:npar+nshock,
-% % i2=find(abs(pc(:,j))>alphaPC);
-% % if ~isempty(i2),
-% % j2=j2+1;
-% % if mod(j2,12)==1,
-% % ifig=ifig+1;
-% % figure('name',['PCA of the filtered sample ',deblank(vvarvecm(i,:)),' ',num2str(ifig)]),
-% % end
-% % subplot(3,4,j2-(ifig-1)*12)
-% % bar(pc(i2,j)),
-% % set(gca,'xticklabel',bayestopt_.name(i2)),
-% % set(gca,'xtick',[1:length(i2)])
-% % title(['PC ',num2str(j),'. Explained ',num2str(explained(j)),'%'])
-% % end
-% % if (mod(j2,12)==0 | j==(npar+nshock)) & j2,
-% % saveas(gcf,[fname_,'_SA_PCA_',deblank(vvarvecm(i,:)),'_',int2str(ifig)])
-% % end
-% % end
-% % close all
-% end
-
+
+ % for j=1:size(SP,2),
+ % nsx(j)=length(find(SP(:,j)));
+ % end
+
+ % skipline(2)
+ % disp('Sensitivity table (significance and direction):')
+ % vav=char(zeros(1, size(param_names,2)+3 ));
+ % ibl = 12-size(vvarvecm,2);
+ % for j=1:size(vvarvecm,1),
+ % vav = [vav, char(zeros(1,ibl)),vvarvecm(j,:)];
+ % end
+ % disp(vav)
+ % for j=1:npar+nshock, %estim_params_.np,
+ % %disp([param_names(j,:), sprintf('%8.5g',SP(j,:))])
+ % disp([param_names(j,:),' ', sprintf('%12.3g',PP(j,:))])
+ % disp([char(zeros(1, size(param_names,2)+3 )),sprintf(' (%6g)',SS(j,:))])
+ % end
+
+
+ % skipline()
+ % disp('Starting bivariate analysis:')
+ %
+ % for i=1:size(vvarvecm,1)
+ % if options_.opt_gsa.ppost
+ % fnam = ['rmse_post_',deblank(vvarvecm(i,:))];
+ % else
+ % if options_.opt_gsa.pprior
+ % fnam = ['rmse_prior_',deblank(vvarvecm(i,:))];
+ % else
+ % fnam = ['rmse_mc_',deblank(vvarvecm(i,:))];
+ % end
+ % end
+ % stab_map_2(x(ixx(1:nfilt0(i),i),:),alpha2,pvalue,fnam, OutDir,[],[temp_name ' observed variable ' deblank(vvarvecm(i,:))]);
+ %
+ % % [pc,latent,explained] = pcacov(c0);
+ % % %figure, bar([explained cumsum(explained)])
+ % % ifig=0;
+ % % j2=0;
+ % % for j=1:npar+nshock,
+ % % i2=find(abs(pc(:,j))>alphaPC);
+ % % if ~isempty(i2),
+ % % j2=j2+1;
+ % % if mod(j2,12)==1,
+ % % ifig=ifig+1;
+ % % figure('name',['PCA of the filtered sample ',deblank(vvarvecm(i,:)),' ',num2str(ifig)]),
+ % % end
+ % % subplot(3,4,j2-(ifig-1)*12)
+ % % bar(pc(i2,j)),
+ % % set(gca,'xticklabel',bayestopt_.name(i2)),
+ % % set(gca,'xtick',[1:length(i2)])
+ % % title(['PC ',num2str(j),'. Explained ',num2str(explained(j)),'%'])
+ % % end
+ % % if (mod(j2,12)==0 | j==(npar+nshock)) & j2,
+ % % saveas(gcf,[fname_,'_SA_PCA_',deblank(vvarvecm(i,:)),'_',int2str(ifig)])
+ % % end
+ % % end
+ % % close all
+ % end
+
end
function []=create_TeX_loader(options_,figpath,label_number,caption,label_name,scale_factor)
@@ -1006,7 +1006,7 @@ end
function [pnames,pnames_tex]=get_LaTeX_parameter_names(M_,options_,estim_params_,bayestopt_)
np=size(bayestopt_.name,1);
pnames=cell(np,1);
-pnames_tex=cell(np,1);
+pnames_tex=cell(np,1);
for ii=1:length(bayestopt_.name)
if options_.TeX
[param_name_temp, param_name_tex_temp]= get_the_name(ii,options_.TeX,M_,estim_params_,options_);
@@ -1017,4 +1017,3 @@ for ii=1:length(bayestopt_.name)
pnames{ii,1} = param_name_temp;
end
end
-
diff --git a/matlab/gsa/ghx2transition.m b/matlab/gsa/ghx2transition.m
index fc957a7aa2d9b637fced9b3b60a39c51b779ddac..df404577a9cee5141cef2116cccab0233efe2ac5 100644
--- a/matlab/gsa/ghx2transition.m
+++ b/matlab/gsa/ghx2transition.m
@@ -3,7 +3,7 @@ function [A,B] = ghx2transition(mm,iv,ic,aux)
%
% Adapted by M. Ratto (from kalman_transition_matrix.m)
% Joint Research Centre, The European Commission,
-% marco.ratto@ec.europa.eu
+% marco.ratto@ec.europa.eu
%
% Copyright (C) 2012-2017 Dynare Team
@@ -25,10 +25,10 @@ function [A,B] = ghx2transition(mm,iv,ic,aux)
global oo_ M_
- [nr1, nc1] = size(mm);
- ghx = mm(:, [1:(nc1-M_.exo_nbr)]);
- ghu = mm(:, [(nc1-M_.exo_nbr+1):end] );
- if nargin == 1
+[nr1, nc1] = size(mm);
+ghx = mm(:, [1:(nc1-M_.exo_nbr)]);
+ghu = mm(:, [(nc1-M_.exo_nbr+1):end] );
+if nargin == 1
oo_.dr.ghx = ghx;
oo_.dr.ghu = ghu;
endo_nbr = M_.endo_nbr;
@@ -40,18 +40,18 @@ global oo_ M_
k = find(aux(:,2) > nspred);
aux(:,2) = aux(:,2) + nstatic;
aux(k,2) = aux(k,2) + M_.nfwrd;
- end
- n_iv = length(iv);
- n_ir1 = size(aux,1);
- nr = n_iv + n_ir1;
-
- A = zeros(nr,nr);
- B = zeros(nr,M_.exo_nbr);
-
- i_n_iv = 1:n_iv;
- A(i_n_iv,ic) = ghx(iv,:);
- if n_ir1 > 0
+end
+n_iv = length(iv);
+n_ir1 = size(aux,1);
+nr = n_iv + n_ir1;
+
+A = zeros(nr,nr);
+B = zeros(nr,M_.exo_nbr);
+
+i_n_iv = 1:n_iv;
+A(i_n_iv,ic) = ghx(iv,:);
+if n_ir1 > 0
A(n_iv+1:end,:) = sparse(aux(:,1),aux(:,2),ones(n_ir1,1),n_ir1,nr);
- end
-
- B(i_n_iv,:) = ghu(iv,:);
+end
+
+B(i_n_iv,:) = ghu(iv,:);
diff --git a/matlab/gsa/gsa_plotmatrix.m b/matlab/gsa/gsa_plotmatrix.m
index 7b6e44ded28c9844429ea5e3e2592fbc81f8299e..634ea4f453d5284f95a6ac6e0c8a9dcea41f60f7 100644
--- a/matlab/gsa/gsa_plotmatrix.m
+++ b/matlab/gsa/gsa_plotmatrix.m
@@ -1,10 +1,10 @@
function gsa_plotmatrix(type,varargin)
% function gsa_plotmatrix(type,varargin)
-% extended version of the standard MATLAB plotmatrix
+% extended version of the standard MATLAB plotmatrix
%
% Written by Marco Ratto
% Joint Research Centre, The European Commission,
-% marco.ratto@ec.europa.eu
+% marco.ratto@ec.europa.eu
% Copyright (C) 2011-2012 European Commission
% Copyright (C) 2011-2012 Dynare Team
@@ -38,31 +38,31 @@ end
iexplosive = iunstable(~ismember(iunstable,[iindeterm;iwrong]));
switch type
- case 'all'
- x=[lpmat0 lpmat];
- NumberOfDraws=size(x,1);
- B=NumberOfDraws;
- case 'stable'
- x=[lpmat0(istable,:) lpmat(istable,:)];
- NumberOfDraws=size(x,1);
- B=NumberOfDraws;
- case 'nosolution'
- x=[lpmat0(iunstable,:) lpmat(iunstable,:)];
- NumberOfDraws=size(x,1);
- B=NumberOfDraws;
- case 'unstable'
- x=[lpmat0(iexplosive,:) lpmat(iexplosive,:)];
- NumberOfDraws=size(x,1);
- B=NumberOfDraws;
- case 'indeterm'
- x=[lpmat0(iindeterm,:) lpmat(iindeterm,:)];
- NumberOfDraws=size(x,1);
- B=NumberOfDraws;
- case 'wrong'
- x=[lpmat0(iwrong,:) lpmat(iwrong,:)];
- NumberOfDraws=size(x,1);
- B=NumberOfDraws;
-
+ case 'all'
+ x=[lpmat0 lpmat];
+ NumberOfDraws=size(x,1);
+ B=NumberOfDraws;
+ case 'stable'
+ x=[lpmat0(istable,:) lpmat(istable,:)];
+ NumberOfDraws=size(x,1);
+ B=NumberOfDraws;
+ case 'nosolution'
+ x=[lpmat0(iunstable,:) lpmat(iunstable,:)];
+ NumberOfDraws=size(x,1);
+ B=NumberOfDraws;
+ case 'unstable'
+ x=[lpmat0(iexplosive,:) lpmat(iexplosive,:)];
+ NumberOfDraws=size(x,1);
+ B=NumberOfDraws;
+ case 'indeterm'
+ x=[lpmat0(iindeterm,:) lpmat(iindeterm,:)];
+ NumberOfDraws=size(x,1);
+ B=NumberOfDraws;
+ case 'wrong'
+ x=[lpmat0(iwrong,:) lpmat(iwrong,:)];
+ NumberOfDraws=size(x,1);
+ B=NumberOfDraws;
+
end
if isempty(x)
diff --git a/matlab/gsa/gsa_skewness.m b/matlab/gsa/gsa_skewness.m
index 24ced80133b81f6143a0b6c6d8199c2115a9acbd..4f09b91ca26c5766d0e7c6fdb11f3408fbd5c10e 100644
--- a/matlab/gsa/gsa_skewness.m
+++ b/matlab/gsa/gsa_skewness.m
@@ -2,7 +2,7 @@ function s=gsa_skewness(y)
% Written by Marco Ratto
% Joint Research Centre, The European Commission,
-% marco.ratto@ec.europa.eu
+% marco.ratto@ec.europa.eu
% Copyright (C) 2012 European Commission
% Copyright (C) 2012 Dynare Team
@@ -24,6 +24,6 @@ function s=gsa_skewness(y)
% y=stand_(y);
% s=mean(y.^3);
- m2=mean((y-mean(y)).^2);
- m3=mean((y-mean(y)).^3);
- s=m3/m2^1.5;
\ No newline at end of file
+m2=mean((y-mean(y)).^2);
+m3=mean((y-mean(y)).^3);
+s=m3/m2^1.5;
\ No newline at end of file
diff --git a/matlab/gsa/gsa_speed.m b/matlab/gsa/gsa_speed.m
index fbbf58e74b3d0dd891216be855ce1548de08bb95..d3b3d2b4605a946cb65e702bd543f176f31a38fd 100644
--- a/matlab/gsa/gsa_speed.m
+++ b/matlab/gsa/gsa_speed.m
@@ -3,7 +3,7 @@ function [tadj, iff] = gsa_speed(A,B,mf,p)
%
% Written by Marco Ratto
% Joint Research Centre, The European Commission,
-% marco.ratto@ec.europa.eu
+% marco.ratto@ec.europa.eu
% Copyright (C) 2012 European Commission
% Copyright (C) 2012 Dynare Team
@@ -34,24 +34,24 @@ disp('Computing speed of adjustement ...')
h = dyn_waitbar(0,'Speed of adjustement...');
for i=1:nrun
- irf=zeros(nvar,nshock);
- a=squeeze(A(:,:,i));
- b=squeeze(B(:,:,i));
- IFF=inv(eye(nstate)-a)*b;
- iff(:,:,i)=IFF(mf,:);
- IF=IFF-b;
-
- t=0;
- while any(any(irf<0.5))
- t=t+1;
- IFT=((eye(nstate)-a^(t+1))*inv(eye(nstate)-a))*b-b;
- irf=IFT(mf,:)./(IF(mf,:)+eps);
- irf = irf.*(abs(IF(mf,:))>1.e-7)+(abs(IF(mf,:))<=1.e-7);
- %irf=ft(mf,:);
- tt=(irf>0.5).*t;
- tadj(:,:,i)=((tt-tadj(:,:,i))==tt).*tt+tadj(:,:,i);
- end
- dyn_waitbar(i/nrun,h)
+ irf=zeros(nvar,nshock);
+ a=squeeze(A(:,:,i));
+ b=squeeze(B(:,:,i));
+ IFF=inv(eye(nstate)-a)*b;
+ iff(:,:,i)=IFF(mf,:);
+ IF=IFF-b;
+
+ t=0;
+ while any(any(irf<0.5))
+ t=t+1;
+ IFT=((eye(nstate)-a^(t+1))*inv(eye(nstate)-a))*b-b;
+ irf=IFT(mf,:)./(IF(mf,:)+eps);
+ irf = irf.*(abs(IF(mf,:))>1.e-7)+(abs(IF(mf,:))<=1.e-7);
+ %irf=ft(mf,:);
+ tt=(irf>0.5).*t;
+ tadj(:,:,i)=((tt-tadj(:,:,i))==tt).*tt+tadj(:,:,i);
+ end
+ dyn_waitbar(i/nrun,h)
end
skipline()
disp('.. done !')
diff --git a/matlab/gsa/log_trans_.m b/matlab/gsa/log_trans_.m
index f31b434d3c03ddd4c3948ea1c7576f16f9dada2c..5d35c1c6ad267aee91f3286b5d9cc1c31e75460b 100644
--- a/matlab/gsa/log_trans_.m
+++ b/matlab/gsa/log_trans_.m
@@ -2,7 +2,7 @@ function [yy, xdir, isig, lam]=log_trans_(y0,xdir0,isig,lam)
% Written by Marco Ratto
% Joint Research Centre, The European Commission,
-% marco.ratto@ec.europa.eu
+% marco.ratto@ec.europa.eu
% Copyright (C) 2012 European Commission
% Copyright (C) 2012 Dynare Team
@@ -29,57 +29,57 @@ if nargin==4
end
if nargin==1
- xdir0='';
+ xdir0='';
end
f=@(lam,y)gsa_skewness(log(y+lam));
isig=1;
if ~(max(y0)<0 | min(y0)>0)
- if gsa_skewness(y0)<0,
- isig=-1;
- y0=-y0;
- end
- n=hist(y0,10);
- if n(1)>20*n(end)
- try
- lam=fzero(f,[-min(y0)+10*eps -min(y0)+abs(median(y0))],[],y0);
- catch
- yl(1)=f(-min(y0)+10*eps,y0);
- yl(2)=f(-min(y0)+abs(median(y0)),y0);
- if abs(yl(1))<abs(yl(2))
- lam=-min(y0)+eps;
- else
- lam = -min(y0)+abs(median(y0)); %abs(100*(1+min(y0)));
- end
+ if gsa_skewness(y0)<0,
+ isig=-1;
+ y0=-y0;
end
- yy = log(y0+lam);
- xdir=[xdir0,'_logskew'];
- else
- isig=0;
- lam=0;
- yy = log(y0.^2);
- xdir=[xdir0,'_logsquared'];
- end
-else
- if max(y0)<0
- isig=-1;
- y0=-y0;
- %yy=log(-y0);
- xdir=[xdir0,'_minuslog'];
- elseif min(y0)>0
- %yy=log(y0);
- xdir=[xdir0,'_log'];
- end
- try
- lam=fzero(f,[-min(y0)+10*eps -min(y0)+median(y0)],[],y0);
- catch
- yl(1)=f(-min(y0)+10*eps,y0);
- yl(2)=f(-min(y0)+abs(median(y0)),y0);
- if abs(yl(1))<abs(yl(2))
- lam=-min(y0)+eps;
+ n=hist(y0,10);
+ if n(1)>20*n(end)
+ try
+ lam=fzero(f,[-min(y0)+10*eps -min(y0)+abs(median(y0))],[],y0);
+ catch
+ yl(1)=f(-min(y0)+10*eps,y0);
+ yl(2)=f(-min(y0)+abs(median(y0)),y0);
+ if abs(yl(1))<abs(yl(2))
+ lam=-min(y0)+eps;
+ else
+ lam = -min(y0)+abs(median(y0)); %abs(100*(1+min(y0)));
+ end
+ end
+ yy = log(y0+lam);
+ xdir=[xdir0,'_logskew'];
else
- lam = -min(y0)+abs(median(y0)); %abs(100*(1+min(y0)));
+ isig=0;
+ lam=0;
+ yy = log(y0.^2);
+ xdir=[xdir0,'_logsquared'];
+ end
+else
+ if max(y0)<0
+ isig=-1;
+ y0=-y0;
+ %yy=log(-y0);
+ xdir=[xdir0,'_minuslog'];
+ elseif min(y0)>0
+ %yy=log(y0);
+ xdir=[xdir0,'_log'];
+ end
+ try
+ lam=fzero(f,[-min(y0)+10*eps -min(y0)+median(y0)],[],y0);
+ catch
+ yl(1)=f(-min(y0)+10*eps,y0);
+ yl(2)=f(-min(y0)+abs(median(y0)),y0);
+ if abs(yl(1))<abs(yl(2))
+ lam=-min(y0)+eps;
+ else
+ lam = -min(y0)+abs(median(y0)); %abs(100*(1+min(y0)));
+ end
end
- end
- lam = max(lam,0);
- yy = log(y0+lam);
+ lam = max(lam,0);
+ yy = log(y0+lam);
end
diff --git a/matlab/gsa/map_calibration.m b/matlab/gsa/map_calibration.m
index 8a1729f0fd72ca7c37714374cd1047cf67436bb7..c7f707d4920f35ff5b3faec078255414d9278deb 100644
--- a/matlab/gsa/map_calibration.m
+++ b/matlab/gsa/map_calibration.m
@@ -2,7 +2,7 @@ function map_calibration(OutputDirectoryName, Model, DynareOptions, DynareResult
% Written by Marco Ratto
% Joint Research Centre, The European Commission,
-% marco.ratto@ec.europa.eu
+% marco.ratto@ec.europa.eu
% Copyright (C) 2014-2016 European Commission
% Copyright (C) 2014-2017 Dynare Team
@@ -28,7 +28,7 @@ np = EstimatedParameters.np;
nshock = EstimatedParameters.nvx + EstimatedParameters.nvn + EstimatedParameters.ncx + EstimatedParameters.ncn;
pnames=cell(np,1);
pnames_tex=cell(np,1);
-for jj=1:np
+for jj=1:np
if DynareOptions.TeX
[param_name_temp, param_name_tex_temp]= get_the_name(nshock+jj, DynareOptions.TeX, Model, EstimatedParameters, DynareOptions);
pnames_tex{jj,1} = strrep(param_name_tex_temp,'$','');
@@ -92,12 +92,12 @@ if init
for ij=1:nbr_irf_restrictions
mat_irf{ij}=NaN(Nsam,length(DynareOptions.endogenous_prior_restrictions.irf{ij,3}));
end
-
+
mat_moment=cell(nbr_moment_restrictions,1);
for ij=1:nbr_moment_restrictions
mat_moment{ij}=NaN(Nsam,length(DynareOptions.endogenous_prior_restrictions.moment{ij,3}));
end
-
+
irestrictions = [1:Nsam];
h = dyn_waitbar(0,'Please wait...');
for j=1:Nsam
@@ -127,7 +127,7 @@ if init
dyn_waitbar(j/Nsam,h,['MC iteration ',int2str(j),'/',int2str(Nsam)])
end
dyn_waitbar_close(h);
-
+
irestrictions=irestrictions(find(irestrictions));
xmat=lpmat(irestrictions,:);
skipline()
@@ -165,7 +165,7 @@ if ~isempty(indx_irf)
end
disp('done !')
skipline()
-
+
% For single legend search which has maximum nbr of restrictions
all_irf_couples = cellstr([char(endo_prior_restrictions.irf(:,1)) char(endo_prior_restrictions.irf(:,2))]);
irf_couples = unique(all_irf_couples);
@@ -188,7 +188,7 @@ if ~isempty(indx_irf)
time_matrix{plot_indx(ij)} = [time_matrix{plot_indx(ij)} endo_prior_restrictions.irf{ij,3}];
end
iplot_indx = ones(size(plot_indx));
-
+
indx_irf = indx_irf(irestrictions,:);
if ~DynareOptions.nograph
h1=dyn_figure(DynareOptions.nodisplay,'name',[type ' evaluation of irf restrictions']);
@@ -260,7 +260,7 @@ if ~isempty(indx_irf)
if ~isempty(indx1) && ~isempty(indx2)
mcf_analysis(xmat(:,nshock+1:end), indx1, indx2, options_mcf, DynareOptions);
end
-
+
% [proba, dproba] = stab_map_1(xmat, indx1, indx2, aname, 0);
% indplot=find(proba<pvalue_ks);
% if ~isempty(indplot)
@@ -360,7 +360,7 @@ if ~isempty(indx_moment)
end
disp('done !')
skipline()
-
+
%get parameter names including standard deviations
np=size(BayesInfo.name,1);
name=cell(np,1);
@@ -402,7 +402,7 @@ if ~isempty(indx_moment)
time_matrix{plot_indx(ij)} = [time_matrix{plot_indx(ij)} endo_prior_restrictions.moment{ij,3}];
end
iplot_indx = ones(size(plot_indx));
-
+
indx_moment = indx_moment(irestrictions,:);
if ~DynareOptions.nograph
h2=dyn_figure(DynareOptions.nodisplay,'name',[type ' evaluation of moment restrictions']);
@@ -412,7 +412,7 @@ if ~isempty(indx_moment)
ncol=nrow-1;
end
end
-
+
for ij=1:nbr_moment_restrictions
mat_moment{ij}=mat_moment{ij}(irestrictions,:);
moment_matrix{plot_indx(ij)} = [moment_matrix{plot_indx(ij)} mat_moment{ij}];
@@ -470,7 +470,7 @@ if ~isempty(indx_moment)
if ~isempty(indx1) && ~isempty(indx2)
mcf_analysis(xmat, indx1, indx2, options_mcf, DynareOptions);
end
-
+
% [proba, dproba] = stab_map_1(xmat, indx1, indx2, aname, 0);
% indplot=find(proba<pvalue_ks);
% if ~isempty(indplot)
@@ -538,7 +538,7 @@ if ~isempty(indx_moment)
dyn_saveas(h2,[OutputDirectoryName,filesep,fname_,'_',type,'_moment_restrictions'],DynareOptions.nodisplay,DynareOptions.graph_format);
create_TeX_loader(DynareOptions,[OutputDirectoryName,filesep,fname_,'_',type,'_moment_restrictions'],[type ' evaluation of moment restrictions'],'moment_restrictions',type,DynareOptions.figures.textwidth*min(ij/ncol,1))
end
-
+
skipline()
end
return
diff --git a/matlab/gsa/map_ident_.m b/matlab/gsa/map_ident_.m
index 50f27cc2daccc802465e3f7676f06cb2e12f5690..fa23574dd2cc99925e97050e12642cafb779090f 100644
--- a/matlab/gsa/map_ident_.m
+++ b/matlab/gsa/map_ident_.m
@@ -2,7 +2,7 @@ function map_ident_(OutputDirectoryName,opt_gsa)
% Written by Marco Ratto
% Joint Research Centre, The European Commission,
-% marco.ratto@ec.europa.eu
+% marco.ratto@ec.europa.eu
% Copyright (C) 2012-2016 European Commission
% Copyright (C) 2012-2017 Dynare Team
@@ -32,23 +32,23 @@ itrans = opt_gsa.trans_ident;
np = estim_params_.np;
if opt_gsa.load_ident_files
- gsa_flag=0;
+ gsa_flag=0;
else
- gsa_flag=-2;
+ gsa_flag=-2;
end
pnames = M_.param_names(estim_params_.param_vals(:,1),:);
- if opt_gsa.pprior
+if opt_gsa.pprior
-filetoload=[OutputDirectoryName '/' fname_ '_prior'];
- else
-filetoload=[OutputDirectoryName '/' fname_ '_mc'];
- end
+ filetoload=[OutputDirectoryName '/' fname_ '_prior'];
+else
+ filetoload=[OutputDirectoryName '/' fname_ '_mc'];
+end
load(filetoload,'lpmat','lpmat0','istable','T','yys','nspred','nboth','nfwrd')
if ~isempty(lpmat0)
- lpmatx=lpmat0(istable,:);
+ lpmatx=lpmat0(istable,:);
else
- lpmatx=[];
+ lpmatx=[];
end
Nsam = size(lpmat,1);
nshock = size(lpmat0,2);
@@ -57,139 +57,139 @@ npT = np+nshock;
fname_ = M_.fname;
if opt_gsa.load_ident_files==0
- % th moments
-% options_.ar = min(3,options_.ar);
-
- mss = yys(bayestopt_.mfys,:);
- mss = teff(mss(:,istable),Nsam,istable);
- yys = teff(yys(oo_.dr.order_var,istable),Nsam,istable);
- if exist('T')
- [vdec, cc, ac] = mc_moments(T, lpmatx, oo_.dr);
- else
- return
- end
-
-
- if opt_gsa.morris==2
- pdraws = dynare_identification(options_.options_ident,[lpmatx lpmat(istable,:)]);
-% [pdraws, TAU, GAM] = dynare_identification(options_.options_ident,[lpmatx lpmat(istable,:)]);
- if ~isempty(pdraws) && max(max(abs(pdraws-[lpmatx lpmat(istable,:)])))==0
- disp(['Sample check OK ', num2str(max(max(abs(pdraws-[lpmatx lpmat(istable,:)]))))]),
- clear pdraws;
+ % th moments
+ % options_.ar = min(3,options_.ar);
+
+ mss = yys(bayestopt_.mfys,:);
+ mss = teff(mss(:,istable),Nsam,istable);
+ yys = teff(yys(oo_.dr.order_var,istable),Nsam,istable);
+ if exist('T')
+ [vdec, cc, ac] = mc_moments(T, lpmatx, oo_.dr);
+ else
+ return
end
-% for j=1:length(istable), gas(:,j)=[vech(cc(:,:,j)); vec(ac(:,:,j))]; end
-% if ~isempty(mss),
-% gas = [mss(istable,:)'; gas];
-% end
-% if max(max(abs(GAM-gas)))<=1.e-8,
-% disp(['Moments check OK ',num2str(max(max(abs(GAM-gas))))]),
- clear GAM gas
-% end
- end
- if opt_gsa.morris~=1 & M_.exo_nbr>1
- ifig=0;
- for j=1:M_.exo_nbr
- if mod(j,6)==1
- hh=dyn_figure(options_.nodisplay,'name',['Variance decomposition shocks']);
- ifig=ifig+1;
- iplo=0;
- end
- iplo=iplo+1;
- subplot(2,3,iplo)
- myboxplot(squeeze(vdec(:,j,:))',[],'.',[],10)
- % boxplot(squeeze(vdec(:,j,:))','whis',10,'symbol','.r')
- set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:size(options_.varobs,1)])
- set(gca,'xlim',[0.5 size(options_.varobs,1)+0.5])
- set(gca,'ylim',[-2 102])
- for ip=1:size(options_.varobs,1)
- text(ip,-4,deblank(options_.varobs(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
- end
- xlabel(' ')
- ylabel(' ')
- title(M_.exo_names(j,:),'interpreter','none')
- if mod(j,6)==0 | j==M_.exo_nbr
- dyn_saveas(hh,[OutputDirectoryName,'/',fname_,'_vdec_exo_',int2str(ifig)],options_.nodisplay,options_.graph_format);
- create_TeX_loader(options_,[OutputDirectoryName,'/',fname_,'_vdec_exo_',int2str(ifig)],ifig,['Variance decomposition shocks'],'vdec_exo',options_.figures.textwidth*min(iplo/3,1))
- end
+
+
+ if opt_gsa.morris==2
+ pdraws = dynare_identification(options_.options_ident,[lpmatx lpmat(istable,:)]);
+ % [pdraws, TAU, GAM] = dynare_identification(options_.options_ident,[lpmatx lpmat(istable,:)]);
+ if ~isempty(pdraws) && max(max(abs(pdraws-[lpmatx lpmat(istable,:)])))==0
+ disp(['Sample check OK ', num2str(max(max(abs(pdraws-[lpmatx lpmat(istable,:)]))))]),
+ clear pdraws;
+ end
+ % for j=1:length(istable), gas(:,j)=[vech(cc(:,:,j)); vec(ac(:,:,j))]; end
+ % if ~isempty(mss),
+ % gas = [mss(istable,:)'; gas];
+ % end
+ % if max(max(abs(GAM-gas)))<=1.e-8,
+ % disp(['Moments check OK ',num2str(max(max(abs(GAM-gas))))]),
+ clear GAM gas
+ % end
+ end
+ if opt_gsa.morris~=1 & M_.exo_nbr>1
+ ifig=0;
+ for j=1:M_.exo_nbr
+ if mod(j,6)==1
+ hh=dyn_figure(options_.nodisplay,'name',['Variance decomposition shocks']);
+ ifig=ifig+1;
+ iplo=0;
+ end
+ iplo=iplo+1;
+ subplot(2,3,iplo)
+ myboxplot(squeeze(vdec(:,j,:))',[],'.',[],10)
+ % boxplot(squeeze(vdec(:,j,:))','whis',10,'symbol','.r')
+ set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:size(options_.varobs,1)])
+ set(gca,'xlim',[0.5 size(options_.varobs,1)+0.5])
+ set(gca,'ylim',[-2 102])
+ for ip=1:size(options_.varobs,1)
+ text(ip,-4,deblank(options_.varobs(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ end
+ xlabel(' ')
+ ylabel(' ')
+ title(M_.exo_names(j,:),'interpreter','none')
+ if mod(j,6)==0 | j==M_.exo_nbr
+ dyn_saveas(hh,[OutputDirectoryName,'/',fname_,'_vdec_exo_',int2str(ifig)],options_.nodisplay,options_.graph_format);
+ create_TeX_loader(options_,[OutputDirectoryName,'/',fname_,'_vdec_exo_',int2str(ifig)],ifig,['Variance decomposition shocks'],'vdec_exo',options_.figures.textwidth*min(iplo/3,1))
+ end
+ end
+ end
+ for j=1:size(cc,1)
+ cc(j,j,:)=stand_(squeeze(log(cc(j,j,:))))./2;
end
- end
- for j=1:size(cc,1)
- cc(j,j,:)=stand_(squeeze(log(cc(j,j,:))))./2;
- end
- [vdec, j0, ir_vdec, ic_vdec] = teff(vdec,Nsam,istable);
- [cc, j0, ir_cc, ic_cc] = teff(cc,Nsam,istable);
- [ac, j0, ir_ac, ic_ac] = teff(ac,Nsam,istable);
-
- [nr1, nc1, nnn] = size(T);
+ [vdec, j0, ir_vdec, ic_vdec] = teff(vdec,Nsam,istable);
+ [cc, j0, ir_cc, ic_cc] = teff(cc,Nsam,istable);
+ [ac, j0, ir_ac, ic_ac] = teff(ac,Nsam,istable);
+
+ [nr1, nc1, nnn] = size(T);
endo_nbr = M_.endo_nbr;
nstatic = M_.nstatic;
nspred = M_.nspred;
iv = (1:endo_nbr)';
ic = [ nstatic+(1:nspred) endo_nbr+(1:size(oo_.dr.ghx,2)-nspred) ]';
-
+
dr.ghx = T(:, [1:(nc1-M_.exo_nbr)],1);
dr.ghu = T(:, [(nc1-M_.exo_nbr+1):end], 1);
[Aa,Bb] = kalman_transition_matrix(dr,iv,ic,M_.exo_nbr);
-% bayestopt_.restrict_var_list, ...
-% bayestopt_.restrict_columns, ...
-% bayestopt_.restrict_aux, M_.exo_nbr);
- A = zeros(size(Aa,1),size(Aa,2)+size(Aa,1),length(istable));
- % Sig(estim_params_.var_exo(:,1))=lpmatx(1,:).^2;
- if ~isempty(lpmatx)
- set_shocks_param(lpmatx(1,:));
- end
- A(:,:,1)=[Aa, triu(Bb*M_.Sigma_e*Bb')];
- for j=2:length(istable)
- dr.ghx = T(:, [1:(nc1-M_.exo_nbr)],j);
- dr.ghu = T(:, [(nc1-M_.exo_nbr+1):end], j);
- [Aa,Bb] = kalman_transition_matrix(dr, iv, ic, M_.exo_nbr);
-% bayestopt_.restrict_var_list, ...
-% bayestopt_.restrict_columns, ...
-% bayestopt_.restrict_aux, M_.exo_nbr);
+ % bayestopt_.restrict_var_list, ...
+ % bayestopt_.restrict_columns, ...
+ % bayestopt_.restrict_aux, M_.exo_nbr);
+ A = zeros(size(Aa,1),size(Aa,2)+size(Aa,1),length(istable));
+ % Sig(estim_params_.var_exo(:,1))=lpmatx(1,:).^2;
if ~isempty(lpmatx)
- set_shocks_param(lpmatx(j,:));
+ set_shocks_param(lpmatx(1,:));
end
- A(:,:,j)=[Aa, triu(Bb*M_.Sigma_e*Bb')];
- end
- clear T
- clear lpmatx
-
- [nr,nc,nn]=size(A);
- io=bayestopt_.mf2;
- % T1=A(io,1:nr,:);
- % ino=find(~ismember([1:nr],io));
- % T2=A(ino,1:nr,:);
- R=A(:,nr+1:nc,:);
-% [tadj, iff] = gsa_speed(A(1:nr,1:nr,:),R,io,0.5);
-% [tadj, j0, ir_tadj, ic_tadj] = teff(tadj,Nsam,istable);
-% [iff, j0, ir_if, ic_if] = teff(iff,Nsam,istable);
-
-
- [yt, j0]=teff(A,Nsam,istable);
- yt = [yys yt];
- if opt_gsa.morris==2
-% iii=find(std(yt(istable,:))>1.e-8);
-% if max(max(abs(TAU-yt(istable,iii)')))<= 1.e-8,
-% err = max(max(abs(TAU-yt(istable,iii)')));
-% disp(['Model check OK ',num2str(err)]),
- clear TAU A
-% end
- else
- clear A
- end
- % [yt1, j01]=teff(T1,Nsam,istable);
- % [yt2, j02]=teff(T2,Nsam,istable);
- % [ytr, j0r]=teff(R,Nsam,istable);
- %
- % yt=[yt1 yt2 ytr];
- save([OutputDirectoryName,'/',fname_,'_main_eff.mat'],'ac','cc','vdec','yt','mss')
+ A(:,:,1)=[Aa, triu(Bb*M_.Sigma_e*Bb')];
+ for j=2:length(istable)
+ dr.ghx = T(:, [1:(nc1-M_.exo_nbr)],j);
+ dr.ghu = T(:, [(nc1-M_.exo_nbr+1):end], j);
+ [Aa,Bb] = kalman_transition_matrix(dr, iv, ic, M_.exo_nbr);
+ % bayestopt_.restrict_var_list, ...
+ % bayestopt_.restrict_columns, ...
+ % bayestopt_.restrict_aux, M_.exo_nbr);
+ if ~isempty(lpmatx)
+ set_shocks_param(lpmatx(j,:));
+ end
+ A(:,:,j)=[Aa, triu(Bb*M_.Sigma_e*Bb')];
+ end
+ clear T
+ clear lpmatx
+
+ [nr,nc,nn]=size(A);
+ io=bayestopt_.mf2;
+ % T1=A(io,1:nr,:);
+ % ino=find(~ismember([1:nr],io));
+ % T2=A(ino,1:nr,:);
+ R=A(:,nr+1:nc,:);
+ % [tadj, iff] = gsa_speed(A(1:nr,1:nr,:),R,io,0.5);
+ % [tadj, j0, ir_tadj, ic_tadj] = teff(tadj,Nsam,istable);
+ % [iff, j0, ir_if, ic_if] = teff(iff,Nsam,istable);
+
+
+ [yt, j0]=teff(A,Nsam,istable);
+ yt = [yys yt];
+ if opt_gsa.morris==2
+ % iii=find(std(yt(istable,:))>1.e-8);
+ % if max(max(abs(TAU-yt(istable,iii)')))<= 1.e-8,
+ % err = max(max(abs(TAU-yt(istable,iii)')));
+ % disp(['Model check OK ',num2str(err)]),
+ clear TAU A
+ % end
+ else
+ clear A
+ end
+ % [yt1, j01]=teff(T1,Nsam,istable);
+ % [yt2, j02]=teff(T2,Nsam,istable);
+ % [ytr, j0r]=teff(R,Nsam,istable);
+ %
+ % yt=[yt1 yt2 ytr];
+ save([OutputDirectoryName,'/',fname_,'_main_eff.mat'],'ac','cc','vdec','yt','mss')
else
- if opt_gsa.morris==2
-% [pdraws, TAU, GAM] = dynare_identification([1:npT]); %,[lpmatx lpmat(istable,:)]);
-% [pdraws, TAU, GAM] = dynare_identification(options_.options_ident);
- pdraws = dynare_identification(options_.options_ident);
- end
- load([OutputDirectoryName,'/',fname_,'_main_eff.mat'],'ac','cc','vdec','yt','mss')
+ if opt_gsa.morris==2
+ % [pdraws, TAU, GAM] = dynare_identification([1:npT]); %,[lpmatx lpmat(istable,:)]);
+ % [pdraws, TAU, GAM] = dynare_identification(options_.options_ident);
+ pdraws = dynare_identification(options_.options_ident);
+ end
+ load([OutputDirectoryName,'/',fname_,'_main_eff.mat'],'ac','cc','vdec','yt','mss')
end
% for j=1:nr,
@@ -206,1352 +206,1352 @@ end
% yt = yt(:,j0);
if opt_gsa.morris==1
- %OutputDir = CheckPath('gsa/screen');
- if ~isempty(vdec)
- if opt_gsa.load_ident_files==0
- SAMorris = [];
- for i=1:size(vdec,2)
- [SAmeas, SAMorris(:,:,i)] = Morris_Measure_Groups(npT, [lpmat0 lpmat], vdec(:,i),nliv);
- end
- SAvdec = squeeze(SAMorris(:,1,:))';
- save([OutputDirectoryName,'/',fname_,'_morris_IDE.mat'],'SAvdec','vdec','ir_vdec','ic_vdec')
- else
- load([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAvdec','vdec','ir_vdec','ic_vdec')
- end
-
- hh = dyn_figure(options_.nodisplay,'name','Screening identification: variance decomposition');
-% boxplot(SAvdec,'whis',10,'symbol','r.')
- myboxplot(SAvdec,[],'.',[],10)
- set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
- set(gca,'xlim',[0.5 npT+0.5])
- ydum = get(gca,'ylim');
- set(gca,'ylim',[0 ydum(2)])
- set(gca,'position',[0.13 0.2 0.775 0.7])
- for ip=1:npT
- text(ip,-2,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
- end
- xlabel(' ')
- title('Elementary effects variance decomposition')
- dyn_saveas(hh,[OutputDirectoryName,'/',fname_,'_morris_vdec'],options_.nodisplay,options_.graph_format);
- create_TeX_loader(options_,[OutputDirectoryName,'/',fname_,'_morris_vdec'],1,'Screening identification: variance decomposition','morris_vdec',1)
- else
- save([OutputDirectoryName,'/',fname_,'_morris_IDE.mat'],'vdec')
-
- end
-
-% ifig = 0;
-% for j=1:size(options_.varobs,1)
-% if mod(j,6)==1
-% figure('name',['EET variance decomposition observed variables']);
-% ifig=ifig+1;
-% iplo=0;
-% end
-% iplo=iplo+1;
-% subplot(3,2,iplo)
-% iv = find(ir_vdec==j);
-% if ~isempty(iv)
-% if length(iv)>1
-% % boxplot(SAvdec(iv,:),'whis',3,'symbol','r.');
-% myboxplot(SAvdec(iv,:),[],'.',[],3)
-% else
-% plot(SAvdec(iv,:),'r.');
-% end
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
-% set(gca,'xlim',[0.5 npT+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% for ip=1:npT,
-% text(ip,-2,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% end
-% title(options_.varobs(j,:),'interpreter','none')
-% if mod(j,6)==0 | j==size(options_.varobs,1)
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_vdec_varobs_',int2str(ifig)])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_vdec_varobs_',int2str(ifig)]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_vdec_varobs_',int2str(ifig)]);
-% close(gcf)
-% end
-% end
-%
-% ifig = 0;
-% for j=1:M_.exo_nbr,
-% if mod(j,6)==1
-% figure('name',['EET variance decomposition shocks']);
-% ifig=ifig+1;
-% iplo=0;
-% end
-% iplo=iplo+1;
-% subplot(3,2,iplo)
-% iv = find(ic_vdec==j);
-% if ~isempty(iv)
-% if length(iv)>1
-% % boxplot(SAvdec(iv,:),'whis',3,'symbol','r.');
-% myboxplot(SAvdec(iv,:),[],'.',[],3)
-% else
-% plot(SAvdec(iv,:),'r.');
-% end
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
-% set(gca,'xlim',[0.5 npT+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% for ip=1:npT,
-% text(ip,-2,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% end
-% title(M_.exo_names(j,:),'interpreter','none')
-% if mod(j,6)==0 | j==M_.exo_nbr,
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_vdec_exo_',int2str(ifig)])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_vdec_exo_',int2str(ifig)]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_vdec_exo_',int2str(ifig)]);
-% close(gcf),
-% end
-% end
+ %OutputDir = CheckPath('gsa/screen');
+ if ~isempty(vdec)
+ if opt_gsa.load_ident_files==0
+ SAMorris = [];
+ for i=1:size(vdec,2)
+ [SAmeas, SAMorris(:,:,i)] = Morris_Measure_Groups(npT, [lpmat0 lpmat], vdec(:,i),nliv);
+ end
+ SAvdec = squeeze(SAMorris(:,1,:))';
+ save([OutputDirectoryName,'/',fname_,'_morris_IDE.mat'],'SAvdec','vdec','ir_vdec','ic_vdec')
+ else
+ load([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAvdec','vdec','ir_vdec','ic_vdec')
+ end
+ hh = dyn_figure(options_.nodisplay,'name','Screening identification: variance decomposition');
+ % boxplot(SAvdec,'whis',10,'symbol','r.')
+ myboxplot(SAvdec,[],'.',[],10)
+ set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
+ set(gca,'xlim',[0.5 npT+0.5])
+ ydum = get(gca,'ylim');
+ set(gca,'ylim',[0 ydum(2)])
+ set(gca,'position',[0.13 0.2 0.775 0.7])
+ for ip=1:npT
+ text(ip,-2,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ end
+ xlabel(' ')
+ title('Elementary effects variance decomposition')
+ dyn_saveas(hh,[OutputDirectoryName,'/',fname_,'_morris_vdec'],options_.nodisplay,options_.graph_format);
+ create_TeX_loader(options_,[OutputDirectoryName,'/',fname_,'_morris_vdec'],1,'Screening identification: variance decomposition','morris_vdec',1)
+ else
+ save([OutputDirectoryName,'/',fname_,'_morris_IDE.mat'],'vdec')
- if opt_gsa.load_ident_files==0
- SAMorris = [];
- ccac = [mss cc ac];
- for i=1:size(ccac,2)
- [SAmeas, SAMorris(:,:,i)] = Morris_Measure_Groups(npT, [lpmat0 lpmat], [ccac(:,i)],nliv);
- end
- SAcc = squeeze(SAMorris(:,1,:))';
- SAcc = SAcc./(max(SAcc')'*ones(1,npT));
- save([OutputDirectoryName,'/',fname_,'_morris_IDE.mat'],'SAcc','cc','ir_cc','ic_cc','-append')
- save([OutputDirectoryName,'/',fname_,'_morris_IDE.mat'],'ac','ir_ac','ic_ac','-append')
- else
- load([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAcc','cc','ir_cc','ic_cc')
- load([OutputDirectoryName,'/',fname_,'_morris_IDE'],'ac','ir_ac','ic_ac')
- end
-
- hh=dyn_figure(options_.nodisplay,'name','Screening identification: theoretical moments');
-% boxplot(SAcc,'whis',10,'symbol','r.')
- myboxplot(SAcc,[],'.',[],10)
- set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
- set(gca,'xlim',[0.5 npT+0.5])
- ydum = get(gca,'ylim');
- set(gca,'ylim',[0 1])
- set(gca,'position',[0.13 0.2 0.775 0.7])
- for ip=1:npT
- text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
- end
- xlabel(' ')
- title('Elementary effects in the moments')
- dyn_saveas(hh,[OutputDirectoryName,'/',fname_,'_morris_moments'],options_.nodisplay,options_.graph_format);
- create_TeX_loader(options_,[OutputDirectoryName,'/',fname_,'_morris_moments'],1,'Screening identification: theoretical moments','morris_moments',1)
-
-% close(gcf),
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% MORRIS FOR DERIVATIVES
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-
-% if opt_gsa.load_ident_files==0,
-% for j=1:npT,
-% SAMorris = [];
-% ddd=NaN(size(lpmat,1),size(JJ,1));
-% ddd(istable,:) = squeeze(JJ(:,j,:))';
-% for i=1:size(ddd,2),
-% [SAmeas, SAMorris(:,:,i)] = Morris_Measure_Groups(npT, [lpmat0 lpmat], [ddd(:,i)],nliv);
-% end
-% SAddd(:,:,j) = squeeze(SAMorris(:,1,:))';
-% SAddd(:,:,j) = SAddd(:,:,j)./(max(SAddd(:,:,j)')'*ones(1,npT));
-% sad(:,j) = median(SAddd(find(~isnan(squeeze(SAddd(:,1,j)))),:,j))';
-% end
-% save([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAddd','sad','-append')
-% else
-% load([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAddd','sad')
-% end
-% figure,
-% contourf(sad,10), colorbar
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
-% set(gca,'yticklabel',' ','fontsize',10,'ytick',[1:npT])
-% for ip=1:npT,
-% text(ip,0.9,['D(',bayestopt_.name{ip},')'],'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% text(0.9,ip,[bayestopt_.name{ip}],'rotation',0,'HorizontalAlignment','right','interpreter','none')
-% end
-% [m,im]=max(sad);
-% iii = find((im-[1:npT])==0);
-% disp('Most identified params')
-% disp(bayestopt_.name(iii))
-
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% END OF MORRIS FOR DERIVATIVES
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-
-% ifig = 0;
-% for j=1:size(options_.varobs,1)
-% if mod(j,6)==1
-% figure('name',['EET cross-correlations']);
-% ifig=ifig+1;
-% iplo=0;
-% end
-% iplo=iplo+1;
-% subplot(3,2,iplo)
-% iv = find(ir_cc==j);
-% iv = [iv; find(ic_cc==j)];
-% if ~isempty(iv)
-% if length(iv)>1
-% % boxplot(SAcc(iv,:),'whis',3,'symbol','r.');
-% myboxplot(SAcc(iv,:),[],'.',[],3)
-% else
-% plot(SAcc(iv,:),'r.');
-% end
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
-% set(gca,'xlim',[0.5 npT+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% for ip=1:npT,
-% text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% end
-% title(options_.varobs(j,:),'interpreter','none')
-% if mod(j,6)==0 | j==size(options_.varobs,1)
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_cc_',int2str(ifig)])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_cc_',int2str(ifig)]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_cc_',int2str(ifig)]);
-% close(gcf),
-% end
-% end
+ end
+ % ifig = 0;
+ % for j=1:size(options_.varobs,1)
+ % if mod(j,6)==1
+ % figure('name',['EET variance decomposition observed variables']);
+ % ifig=ifig+1;
+ % iplo=0;
+ % end
+ % iplo=iplo+1;
+ % subplot(3,2,iplo)
+ % iv = find(ir_vdec==j);
+ % if ~isempty(iv)
+ % if length(iv)>1
+ % % boxplot(SAvdec(iv,:),'whis',3,'symbol','r.');
+ % myboxplot(SAvdec(iv,:),[],'.',[],3)
+ % else
+ % plot(SAvdec(iv,:),'r.');
+ % end
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
+ % set(gca,'xlim',[0.5 npT+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % for ip=1:npT,
+ % text(ip,-2,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % end
+ % title(options_.varobs(j,:),'interpreter','none')
+ % if mod(j,6)==0 | j==size(options_.varobs,1)
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_vdec_varobs_',int2str(ifig)])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_vdec_varobs_',int2str(ifig)]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_vdec_varobs_',int2str(ifig)]);
+ % close(gcf)
+ % end
+ % end
+ %
+ % ifig = 0;
+ % for j=1:M_.exo_nbr,
+ % if mod(j,6)==1
+ % figure('name',['EET variance decomposition shocks']);
+ % ifig=ifig+1;
+ % iplo=0;
+ % end
+ % iplo=iplo+1;
+ % subplot(3,2,iplo)
+ % iv = find(ic_vdec==j);
+ % if ~isempty(iv)
+ % if length(iv)>1
+ % % boxplot(SAvdec(iv,:),'whis',3,'symbol','r.');
+ % myboxplot(SAvdec(iv,:),[],'.',[],3)
+ % else
+ % plot(SAvdec(iv,:),'r.');
+ % end
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
+ % set(gca,'xlim',[0.5 npT+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % for ip=1:npT,
+ % text(ip,-2,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % end
+ % title(M_.exo_names(j,:),'interpreter','none')
+ % if mod(j,6)==0 | j==M_.exo_nbr,
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_vdec_exo_',int2str(ifig)])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_vdec_exo_',int2str(ifig)]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_vdec_exo_',int2str(ifig)]);
+ % close(gcf),
+ % end
+ % end
-% if opt_gsa.load_ident_files==0,
-% SAMorris = [];
-% for i=1:size(ac,2),
-% [SAmeas, SAMorris(:,:,i)] = Morris_Measure_Groups(npT, [lpmat0 lpmat], ac(:,i),nliv);
-% end
-% %end
-% SAac = squeeze(SAMorris(:,1,:))';
-% save([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAac','ac','ir_ac','ic_ac','-append')
-% else
-% load([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAac','ac','ir_ac','ic_ac')
-% end
-% figure,
-% % boxplot(SAac,'whis',10,'symbol','r.')
-% myboxplot(SAac,[],'.',[],10)
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
-% set(gca,'xlim',[0.5 npT+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% set(gca,'position',[0.13 0.2 0.775 0.7])
-% for ip=1:npT,
-% text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% title('EET All auto-correlations')
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_ac'])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_ac']);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_ac']);
-% close(gcf),
-
-% ifig = 0;
-% for j=1:size(options_.varobs,1)
-% if mod(j,6)==1
-% figure('name',['EET auto-correlations']);
-% ifig=ifig+1;
-% iplo=0;
-% end
-% iplo=iplo+1;
-% subplot(3,2,iplo)
-% iv = find(ir_ac==j);
-% if ~isempty(iv)
-% if length(iv)>1
-% % boxplot(SAac(iv,:),'whis',3,'symbol','r.');
-% myboxplot(SAac(iv,:),[],'.',[],3)
-% else
-% plot(SAac(iv,:),'r.');
-% end
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
-% set(gca,'xlim',[0.5 npT+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% for ip=1:npT,
-% text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% end
-% title(options_.varobs(j,:),'interpreter','none')
-% if mod(j,6)==0 | j==size(options_.varobs,1)
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_ac_',int2str(ifig)])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_ac_',int2str(ifig)]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_ac_',int2str(ifig)]);
-% close(gcf),
-% end
-% end
-% if opt_gsa.load_ident_files==0,
-% js=0;
-% %for j=1:size(tadj,1),
-% SAMorris = [];
-% for i=1:size(tadj,2),
-% js=js+1;
-% [SAmeas, SAMorris(:,:,js)] = Morris_Measure_Groups(npT, [lpmat0 lpmat], tadj(:,i),nliv);
-% end
-% %end
-% SAM = squeeze(SAMorris(nshock+1:end,1,:));
-% for j=1:js,
-% SAtadj(:,j)=SAM(:,j)./(max(SAM(:,j))+eps);
-% end
-% SAtadj = SAtadj';
-% save([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAtadj','tadj','ir_tadj','ic_tadj','-append')
-% else
-% load([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAtadj','tadj','ir_tadj','ic_tadj')
-% end
-% if opt_gsa.load_ident_files==0,
-% js=0;
-% SAMorris = [];
-% for i=1:size(iff,2),
-% js=js+1;
-% [SAmeas, SAMorriss(:,:,js)] = Morris_Measure_Groups(npT, [lpmat0 lpmat], iff(:,i),nliv);
-% end
-% SAM = squeeze(SAMorriss(nshock+1:end,1,:));
-% for j=1:js,
-% SAIF(:,j)=SAM(:,j)./(max(SAM(:,j))+eps);
-% end
-% SAIF = SAIF';
-% save([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAIF','iff','ir_if','ic_if','-append')
-% else
-% load([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAIF','iff','ir_if','ic_if')
-% end
-% figure,
-% %bar(SAtadj),
-% % boxplot(SAtadj,'whis',10,'symbol','r.')
-% myboxplot(SAtadj,[],'.',[],10)
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
-% set(gca,'xlim',[0.5 np+0.5])
-% set(gca,'ylim',[0 1])
-% set(gca,'position',[0.13 0.2 0.775 0.7])
-% for ip=1:np,
-% text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% title('All half-life')
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_tadj'])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_tadj']);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_tadj']);
-% close(gcf),
-
-% ifig = 0;
-% for j=1:size(options_.varobs,1)
-% if mod(j,6)==1
-% figure('name',['EET speed of adjustment observed variables']);
-% ifig=ifig+1;
-% iplo=0;
-% end
-% iplo=iplo+1;
-% subplot(3,2,iplo)
-% iv = find(ir_tadj==j);
-% if ~isempty(iv)
-% if length(iv)>1
-% % boxplot(SAtadj(iv,:),'whis',3,'symbol','r.');
-% myboxplot(SAtadj(iv,:),[],'.',[],3)
-% else
-% plot(SAtadj(iv,:),'r.');
-% end
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
-% set(gca,'xlim',[0.5 np+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% for ip=1:np,
-% text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% end
-% title(options_.varobs(j,:),'interpreter','none')
-% if mod(j,6)==0 | j==size(options_.varobs,1)
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_tadj_varobs_',int2str(ifig)])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_tadj_varobs_',int2str(ifig)]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_tadj_varobs_',int2str(ifig)]);
-% close(gcf),
-% end
-% end
+ if opt_gsa.load_ident_files==0
+ SAMorris = [];
+ ccac = [mss cc ac];
+ for i=1:size(ccac,2)
+ [SAmeas, SAMorris(:,:,i)] = Morris_Measure_Groups(npT, [lpmat0 lpmat], [ccac(:,i)],nliv);
+ end
+ SAcc = squeeze(SAMorris(:,1,:))';
+ SAcc = SAcc./(max(SAcc')'*ones(1,npT));
+ save([OutputDirectoryName,'/',fname_,'_morris_IDE.mat'],'SAcc','cc','ir_cc','ic_cc','-append')
+ save([OutputDirectoryName,'/',fname_,'_morris_IDE.mat'],'ac','ir_ac','ic_ac','-append')
+ else
+ load([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAcc','cc','ir_cc','ic_cc')
+ load([OutputDirectoryName,'/',fname_,'_morris_IDE'],'ac','ir_ac','ic_ac')
+ end
-% ifig = 0;
-% for j=1:M_.exo_nbr,
-% if mod(j,6)==1
-% figure('name',['EET speed of adjustment shocks']);
-% ifig=ifig+1;
-% iplo=0;
-% end
-% iplo=iplo+1;
-% subplot(3,2,iplo)
-% iv = find(ic_tadj==j);
-% if ~isempty(iv)
-% if length(iv)>1
-% % boxplot(SAtadj(iv,:),'whis',3,'symbol','r.');
-% myboxplot(SAtadj(iv,:),[],'.',[],3)
-% else
-% plot(SAtadj(iv,:),'r.');
-% end
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
-% set(gca,'xlim',[0.5 np+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% for ip=1:np,
-% text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% end
-% title(M_.exo_names(j,:),'interpreter','none')
-% if mod(j,6)==0 | j==M_.exo_nbr,
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_tadj_exo_',int2str(ifig)])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_tadj_exo_',int2str(ifig)]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_tadj_exo_',int2str(ifig)]);
-% close(gcf),
-% end
-% end
+ hh=dyn_figure(options_.nodisplay,'name','Screening identification: theoretical moments');
+ % boxplot(SAcc,'whis',10,'symbol','r.')
+ myboxplot(SAcc,[],'.',[],10)
+ set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
+ set(gca,'xlim',[0.5 npT+0.5])
+ ydum = get(gca,'ylim');
+ set(gca,'ylim',[0 1])
+ set(gca,'position',[0.13 0.2 0.775 0.7])
+ for ip=1:npT
+ text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ end
+ xlabel(' ')
+ title('Elementary effects in the moments')
+ dyn_saveas(hh,[OutputDirectoryName,'/',fname_,'_morris_moments'],options_.nodisplay,options_.graph_format);
+ create_TeX_loader(options_,[OutputDirectoryName,'/',fname_,'_morris_moments'],1,'Screening identification: theoretical moments','morris_moments',1)
-% figure,
-% %bar(SAIF),
-% % boxplot(SAIF,'whis',10,'symbol','r.')
-% myboxplot(SAIF,[],'.',[],10)
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
-% set(gca,'xlim',[0.5 np+0.5])
-% set(gca,'ylim',[0 1])
-% set(gca,'position',[0.13 0.2 0.775 0.7])
-% for ip=1:np,
-% text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% ylabel('Elementary Effects')
-% title('Steady state gains (impact factors)')
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_gain'])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_gain']);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_gain']);
-% close(gcf),
- %figure, bar(SAIF'), title('All Gain Relationships')
-% ifig = 0;
-% for j=1:size(options_.varobs,1)
-% if mod(j,6)==1
-% figure('name',['EET steady state gain observed series']);
-% ifig=ifig+1;
-% iplo=0;
-% end
-% iplo=iplo+1;
-% subplot(3,2,iplo)
-% iv = find(ir_if==j);
-% if ~isempty(iv)
-% if length(iv)>1
-% % boxplot(SAIF(iv,:),'whis',10,'symbol','r.');
-% myboxplot(SAIF(iv,:),[],'.',[],10)
-% else
-% plot(SAIF(iv,:),'r.');
-% end
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
-% set(gca,'xlim',[0.5 np+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% for ip=1:np,
-% text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% end
-% title(options_.varobs(j,:),'interpreter','none')
-% if mod(j,6)==0 | j==size(options_.varobs,1)
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_gain_varobs_',int2str(ifig)])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_gain_varobs_',int2str(ifig)]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_gain_varobs_',int2str(ifig)]);
-% close(gcf),
-% end
-% end
-%
-% ifig = 0;
-% for j=1:M_.exo_nbr,
-% if mod(j,6)==1
-% figure('name',['EET steady state gain shocks']);
-% ifig=ifig+1;
-% iplo=0;
-% end
-% iplo=iplo+1;
-% subplot(3,2,iplo)
-% iv = find(ic_if==j);
-% if ~isempty(iv)
-% if length(iv)>1
-% % boxplot(SAIF(iv,:),'whis',3,'symbol','r.');
-% myboxplot(SAIF(iv,:),[],'.',[],3)
-% else
-% plot(SAIF(iv,:),'r.');
-% end
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
-% set(gca,'xlim',[0.5 np+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% for ip=1:np,
-% text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% end
-% title(M_.exo_names(j,:),'interpreter','none')
-% if mod(j,6)==0 | j==M_.exo_nbr,
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_gain_exo_',int2str(ifig)])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_gain_exo_',int2str(ifig)]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_gain_exo_',int2str(ifig)]);
-% close(gcf),
-% end
-% end
+ % close(gcf),
+ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+ % MORRIS FOR DERIVATIVES
+ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
- if opt_gsa.load_ident_files==0
- SAMorris = [];
- for j=1:j0
- [SAmeas, SAMorris(:,:,j)] = Morris_Measure_Groups(npT, [lpmat0 lpmat], yt(:,j),nliv);
- end
-
-% SAM = squeeze(SAMorris(nshock+1:end,1,:));
- SAM = squeeze(SAMorris(1:end,1,:));
- for j=1:j0
- SAnorm(:,j)=SAM(:,j)./max(SAM(:,j));
- irex(j)=length(find(SAnorm(:,j)>0.01));
- end
- [dum, irel]=sort(irex);
-
-% SAMmu = squeeze(SAMorris(nshock+1:end,2,:));
- SAMmu = squeeze(SAMorris(1:end,2,:));
- for j=1:j0
- SAmunorm(:,j)=SAMmu(:,j)./max(SAM(:,j)); % normalised w.r.t. mu*
- end
-% SAMsig = squeeze(SAMorris(nshock+1:end,3,:));
- SAMsig = squeeze(SAMorris(1:end,3,:));
- for j=1:j0
- SAsignorm(:,j)=SAMsig(:,j)./max(SAMsig(:,j));
- end
- save([OutputDirectoryName,'/',fname_,'_morris_IDE.mat'],'SAnorm','SAmunorm','SAsignorm','-append')
- else
- load([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAnorm','SAmunorm','SAsignorm')
- end
- hh=dyn_figure(options_.nodisplay,'name','Screening identification: model'); %bar(SAnorm(:,irel))
-% boxplot(SAnorm','whis',10,'symbol','r.')
- myboxplot(SAnorm',[],'.',[],10)
- set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
- set(gca,'xlim',[0.5 npT+0.5])
- set(gca,'ylim',[0 1])
- set(gca,'position',[0.13 0.2 0.775 0.7])
- xlabel(' ')
- for ip=1:npT
-% text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
- text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
- end
- xlabel(' ')
- title('Elementary effects in the model')
- dyn_saveas(hh,[OutputDirectoryName,'/',fname_,'_morris_par'],options_.nodisplay,options_.graph_format);
- create_TeX_loader(options_,[OutputDirectoryName,'/',fname_,'_morris_par'],1,'Screening identification: model','morris_par',1)
-
-% hh=dyn_figure(options_.nodisplay); %bar(SAmunorm(:,irel))
-% % boxplot(SAmunorm','whis',10,'symbol','r.')
-% myboxplot(SAmunorm',[],'.',[],10)
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
-% set(gca,'xlim',[0.5 npT+0.5])
-% set(gca,'ylim',[-1 1])
-% set(gca,'position',[0.13 0.2 0.775 0.7])
-% xlabel(' ')
-% for ip=1:npT,
-% text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% title('\mu in the model')
-% dyn_saveas(hh,[OutputDirectoryName,'/',fname_,'_morrismu_par'],options_.nodisplay,options_.graph_format);
-%
-% hh=dyn_figure(options_.nodisplay); %bar(SAsignorm(:,irel))
-% % boxplot(SAsignorm','whis',10,'symbol','r.')
-% myboxplot(SAsignorm',[],'.',[],10)
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
-% set(gca,'xlim',[0.5 npT+0.5])
-% set(gca,'ylim',[0 1])
-% set(gca,'position',[0.13 0.2 0.775 0.7])
-% xlabel(' ')
-% for ip=1:npT,
-% text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% title('\sigma in the model')
-% dyn_saveas(hh,[OutputDirectoryName,'/',fname_,'_morrissig_par'],options_.nodisplay,options_.graph_format);
-
- % figure, bar(SAnorm(:,irel)')
- % set(gca,'xtick',[1:j0])
- % set(gca,'xlim',[0.5 j0+0.5])
- % title('Elementary effects relationships')
- % saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_redform'])
- % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_redform']);
- % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_redform']);
+ % if opt_gsa.load_ident_files==0,
+ % for j=1:npT,
+ % SAMorris = [];
+ % ddd=NaN(size(lpmat,1),size(JJ,1));
+ % ddd(istable,:) = squeeze(JJ(:,j,:))';
+ % for i=1:size(ddd,2),
+ % [SAmeas, SAMorris(:,:,i)] = Morris_Measure_Groups(npT, [lpmat0 lpmat], [ddd(:,i)],nliv);
+ % end
+ % SAddd(:,:,j) = squeeze(SAMorris(:,1,:))';
+ % SAddd(:,:,j) = SAddd(:,:,j)./(max(SAddd(:,:,j)')'*ones(1,npT));
+ % sad(:,j) = median(SAddd(find(~isnan(squeeze(SAddd(:,1,j)))),:,j))';
+ % end
+ % save([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAddd','sad','-append')
+ % else
+ % load([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAddd','sad')
+ % end
+ % figure,
+ % contourf(sad,10), colorbar
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
+ % set(gca,'yticklabel',' ','fontsize',10,'ytick',[1:npT])
+ % for ip=1:npT,
+ % text(ip,0.9,['D(',bayestopt_.name{ip},')'],'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % text(0.9,ip,[bayestopt_.name{ip}],'rotation',0,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % [m,im]=max(sad);
+ % iii = find((im-[1:npT])==0);
+ % disp('Most identified params')
+ % disp(bayestopt_.name(iii))
+
+
+ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+ % END OF MORRIS FOR DERIVATIVES
+ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+ % ifig = 0;
+ % for j=1:size(options_.varobs,1)
+ % if mod(j,6)==1
+ % figure('name',['EET cross-correlations']);
+ % ifig=ifig+1;
+ % iplo=0;
+ % end
+ % iplo=iplo+1;
+ % subplot(3,2,iplo)
+ % iv = find(ir_cc==j);
+ % iv = [iv; find(ic_cc==j)];
+ % if ~isempty(iv)
+ % if length(iv)>1
+ % % boxplot(SAcc(iv,:),'whis',3,'symbol','r.');
+ % myboxplot(SAcc(iv,:),[],'.',[],3)
+ % else
+ % plot(SAcc(iv,:),'r.');
+ % end
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
+ % set(gca,'xlim',[0.5 npT+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % for ip=1:npT,
+ % text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % end
+ % title(options_.varobs(j,:),'interpreter','none')
+ % if mod(j,6)==0 | j==size(options_.varobs,1)
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_cc_',int2str(ifig)])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_cc_',int2str(ifig)]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_cc_',int2str(ifig)]);
+ % close(gcf),
+ % end
+ % end
+
+
+ % if opt_gsa.load_ident_files==0,
+ % SAMorris = [];
+ % for i=1:size(ac,2),
+ % [SAmeas, SAMorris(:,:,i)] = Morris_Measure_Groups(npT, [lpmat0 lpmat], ac(:,i),nliv);
+ % end
+ % %end
+ % SAac = squeeze(SAMorris(:,1,:))';
+ % save([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAac','ac','ir_ac','ic_ac','-append')
+ % else
+ % load([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAac','ac','ir_ac','ic_ac')
+ % end
+ % figure,
+ % % boxplot(SAac,'whis',10,'symbol','r.')
+ % myboxplot(SAac,[],'.',[],10)
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
+ % set(gca,'xlim',[0.5 npT+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % set(gca,'position',[0.13 0.2 0.775 0.7])
+ % for ip=1:npT,
+ % text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % title('EET All auto-correlations')
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_ac'])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_ac']);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_ac']);
+ % close(gcf),
+
+ % ifig = 0;
+ % for j=1:size(options_.varobs,1)
+ % if mod(j,6)==1
+ % figure('name',['EET auto-correlations']);
+ % ifig=ifig+1;
+ % iplo=0;
+ % end
+ % iplo=iplo+1;
+ % subplot(3,2,iplo)
+ % iv = find(ir_ac==j);
+ % if ~isempty(iv)
+ % if length(iv)>1
+ % % boxplot(SAac(iv,:),'whis',3,'symbol','r.');
+ % myboxplot(SAac(iv,:),[],'.',[],3)
+ % else
+ % plot(SAac(iv,:),'r.');
+ % end
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
+ % set(gca,'xlim',[0.5 npT+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % for ip=1:npT,
+ % text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % end
+ % title(options_.varobs(j,:),'interpreter','none')
+ % if mod(j,6)==0 | j==size(options_.varobs,1)
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_ac_',int2str(ifig)])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_ac_',int2str(ifig)]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_ac_',int2str(ifig)]);
+ % close(gcf),
+ % end
+ % end
+
+ % if opt_gsa.load_ident_files==0,
+ % js=0;
+ % %for j=1:size(tadj,1),
+ % SAMorris = [];
+ % for i=1:size(tadj,2),
+ % js=js+1;
+ % [SAmeas, SAMorris(:,:,js)] = Morris_Measure_Groups(npT, [lpmat0 lpmat], tadj(:,i),nliv);
+ % end
+ % %end
+ % SAM = squeeze(SAMorris(nshock+1:end,1,:));
+ % for j=1:js,
+ % SAtadj(:,j)=SAM(:,j)./(max(SAM(:,j))+eps);
+ % end
+ % SAtadj = SAtadj';
+ % save([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAtadj','tadj','ir_tadj','ic_tadj','-append')
+ % else
+ % load([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAtadj','tadj','ir_tadj','ic_tadj')
+ % end
+ % if opt_gsa.load_ident_files==0,
+ % js=0;
+ % SAMorris = [];
+ % for i=1:size(iff,2),
+ % js=js+1;
+ % [SAmeas, SAMorriss(:,:,js)] = Morris_Measure_Groups(npT, [lpmat0 lpmat], iff(:,i),nliv);
+ % end
+ % SAM = squeeze(SAMorriss(nshock+1:end,1,:));
+ % for j=1:js,
+ % SAIF(:,j)=SAM(:,j)./(max(SAM(:,j))+eps);
+ % end
+ % SAIF = SAIF';
+ % save([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAIF','iff','ir_if','ic_if','-append')
+ % else
+ % load([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAIF','iff','ir_if','ic_if')
+ % end
+ % figure,
+ % %bar(SAtadj),
+ % % boxplot(SAtadj,'whis',10,'symbol','r.')
+ % myboxplot(SAtadj,[],'.',[],10)
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 np+0.5])
+ % set(gca,'ylim',[0 1])
+ % set(gca,'position',[0.13 0.2 0.775 0.7])
+ % for ip=1:np,
+ % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % title('All half-life')
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_tadj'])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_tadj']);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_tadj']);
+ % close(gcf),
+
+ % ifig = 0;
+ % for j=1:size(options_.varobs,1)
+ % if mod(j,6)==1
+ % figure('name',['EET speed of adjustment observed variables']);
+ % ifig=ifig+1;
+ % iplo=0;
+ % end
+ % iplo=iplo+1;
+ % subplot(3,2,iplo)
+ % iv = find(ir_tadj==j);
+ % if ~isempty(iv)
+ % if length(iv)>1
+ % % boxplot(SAtadj(iv,:),'whis',3,'symbol','r.');
+ % myboxplot(SAtadj(iv,:),[],'.',[],3)
+ % else
+ % plot(SAtadj(iv,:),'r.');
+ % end
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 np+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % for ip=1:np,
+ % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % end
+ % title(options_.varobs(j,:),'interpreter','none')
+ % if mod(j,6)==0 | j==size(options_.varobs,1)
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_tadj_varobs_',int2str(ifig)])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_tadj_varobs_',int2str(ifig)]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_tadj_varobs_',int2str(ifig)]);
+ % close(gcf),
+ % end
+ % end
+
+ % ifig = 0;
+ % for j=1:M_.exo_nbr,
+ % if mod(j,6)==1
+ % figure('name',['EET speed of adjustment shocks']);
+ % ifig=ifig+1;
+ % iplo=0;
+ % end
+ % iplo=iplo+1;
+ % subplot(3,2,iplo)
+ % iv = find(ic_tadj==j);
+ % if ~isempty(iv)
+ % if length(iv)>1
+ % % boxplot(SAtadj(iv,:),'whis',3,'symbol','r.');
+ % myboxplot(SAtadj(iv,:),[],'.',[],3)
+ % else
+ % plot(SAtadj(iv,:),'r.');
+ % end
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 np+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % for ip=1:np,
+ % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % end
+ % title(M_.exo_names(j,:),'interpreter','none')
+ % if mod(j,6)==0 | j==M_.exo_nbr,
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_tadj_exo_',int2str(ifig)])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_tadj_exo_',int2str(ifig)]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_tadj_exo_',int2str(ifig)]);
+ % close(gcf),
+ % end
+ % end
+
+ % figure,
+ % %bar(SAIF),
+ % % boxplot(SAIF,'whis',10,'symbol','r.')
+ % myboxplot(SAIF,[],'.',[],10)
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 np+0.5])
+ % set(gca,'ylim',[0 1])
+ % set(gca,'position',[0.13 0.2 0.775 0.7])
+ % for ip=1:np,
+ % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % ylabel('Elementary Effects')
+ % title('Steady state gains (impact factors)')
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_gain'])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_gain']);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_gain']);
+ % close(gcf),
+ %figure, bar(SAIF'), title('All Gain Relationships')
+ % ifig = 0;
+ % for j=1:size(options_.varobs,1)
+ % if mod(j,6)==1
+ % figure('name',['EET steady state gain observed series']);
+ % ifig=ifig+1;
+ % iplo=0;
+ % end
+ % iplo=iplo+1;
+ % subplot(3,2,iplo)
+ % iv = find(ir_if==j);
+ % if ~isempty(iv)
+ % if length(iv)>1
+ % % boxplot(SAIF(iv,:),'whis',10,'symbol','r.');
+ % myboxplot(SAIF(iv,:),[],'.',[],10)
+ % else
+ % plot(SAIF(iv,:),'r.');
+ % end
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 np+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % for ip=1:np,
+ % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % end
+ % title(options_.varobs(j,:),'interpreter','none')
+ % if mod(j,6)==0 | j==size(options_.varobs,1)
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_gain_varobs_',int2str(ifig)])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_gain_varobs_',int2str(ifig)]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_gain_varobs_',int2str(ifig)]);
+ % close(gcf),
+ % end
+ % end
+ %
+ % ifig = 0;
+ % for j=1:M_.exo_nbr,
+ % if mod(j,6)==1
+ % figure('name',['EET steady state gain shocks']);
+ % ifig=ifig+1;
+ % iplo=0;
+ % end
+ % iplo=iplo+1;
+ % subplot(3,2,iplo)
+ % iv = find(ic_if==j);
+ % if ~isempty(iv)
+ % if length(iv)>1
+ % % boxplot(SAIF(iv,:),'whis',3,'symbol','r.');
+ % myboxplot(SAIF(iv,:),[],'.',[],3)
+ % else
+ % plot(SAIF(iv,:),'r.');
+ % end
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 np+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % for ip=1:np,
+ % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % end
+ % title(M_.exo_names(j,:),'interpreter','none')
+ % if mod(j,6)==0 | j==M_.exo_nbr,
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_gain_exo_',int2str(ifig)])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_gain_exo_',int2str(ifig)]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_gain_exo_',int2str(ifig)]);
+ % close(gcf),
+ % end
+ % end
+
+
+ if opt_gsa.load_ident_files==0
+ SAMorris = [];
+ for j=1:j0
+ [SAmeas, SAMorris(:,:,j)] = Morris_Measure_Groups(npT, [lpmat0 lpmat], yt(:,j),nliv);
+ end
+
+ % SAM = squeeze(SAMorris(nshock+1:end,1,:));
+ SAM = squeeze(SAMorris(1:end,1,:));
+ for j=1:j0
+ SAnorm(:,j)=SAM(:,j)./max(SAM(:,j));
+ irex(j)=length(find(SAnorm(:,j)>0.01));
+ end
+ [dum, irel]=sort(irex);
+
+ % SAMmu = squeeze(SAMorris(nshock+1:end,2,:));
+ SAMmu = squeeze(SAMorris(1:end,2,:));
+ for j=1:j0
+ SAmunorm(:,j)=SAMmu(:,j)./max(SAM(:,j)); % normalised w.r.t. mu*
+ end
+ % SAMsig = squeeze(SAMorris(nshock+1:end,3,:));
+ SAMsig = squeeze(SAMorris(1:end,3,:));
+ for j=1:j0
+ SAsignorm(:,j)=SAMsig(:,j)./max(SAMsig(:,j));
+ end
+ save([OutputDirectoryName,'/',fname_,'_morris_IDE.mat'],'SAnorm','SAmunorm','SAsignorm','-append')
+ else
+ load([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAnorm','SAmunorm','SAsignorm')
+ end
+ hh=dyn_figure(options_.nodisplay,'name','Screening identification: model'); %bar(SAnorm(:,irel))
+ % boxplot(SAnorm','whis',10,'symbol','r.')
+ myboxplot(SAnorm',[],'.',[],10)
+ set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
+ set(gca,'xlim',[0.5 npT+0.5])
+ set(gca,'ylim',[0 1])
+ set(gca,'position',[0.13 0.2 0.775 0.7])
+ xlabel(' ')
+ for ip=1:npT
+ % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ end
+ xlabel(' ')
+ title('Elementary effects in the model')
+ dyn_saveas(hh,[OutputDirectoryName,'/',fname_,'_morris_par'],options_.nodisplay,options_.graph_format);
+ create_TeX_loader(options_,[OutputDirectoryName,'/',fname_,'_morris_par'],1,'Screening identification: model','morris_par',1)
+
+ % hh=dyn_figure(options_.nodisplay); %bar(SAmunorm(:,irel))
+ % % boxplot(SAmunorm','whis',10,'symbol','r.')
+ % myboxplot(SAmunorm',[],'.',[],10)
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
+ % set(gca,'xlim',[0.5 npT+0.5])
+ % set(gca,'ylim',[-1 1])
+ % set(gca,'position',[0.13 0.2 0.775 0.7])
+ % xlabel(' ')
+ % for ip=1:npT,
+ % text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % title('\mu in the model')
+ % dyn_saveas(hh,[OutputDirectoryName,'/',fname_,'_morrismu_par'],options_.nodisplay,options_.graph_format);
+ %
+ % hh=dyn_figure(options_.nodisplay); %bar(SAsignorm(:,irel))
+ % % boxplot(SAsignorm','whis',10,'symbol','r.')
+ % myboxplot(SAsignorm',[],'.',[],10)
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
+ % set(gca,'xlim',[0.5 npT+0.5])
+ % set(gca,'ylim',[0 1])
+ % set(gca,'position',[0.13 0.2 0.775 0.7])
+ % xlabel(' ')
+ % for ip=1:npT,
+ % text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % title('\sigma in the model')
+ % dyn_saveas(hh,[OutputDirectoryName,'/',fname_,'_morrissig_par'],options_.nodisplay,options_.graph_format);
+
+ % figure, bar(SAnorm(:,irel)')
+ % set(gca,'xtick',[1:j0])
+ % set(gca,'xlim',[0.5 j0+0.5])
+ % title('Elementary effects relationships')
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_redform'])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_redform']);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_redform']);
elseif opt_gsa.morris==3
return
-
- np=estim_params_.np;
- na=(4*np+1)*opt_gsa.Nsam;
- for j=1:j0
- [idex(j,:), yd(j,:)] = spop_ide(lpmat, yt(:,j), opt_gsa.Nsam, 5-1);
- end
- iok=find(~isnan(yt(1:opt_gsa.Nsam,1)));
- yr=NaN*ones(size(lpmat,1),j0);
- for j=1:j0,
- ys(j,:)=yd(j,:)./max(yd(j,:));
- [dum, is]=sort(yt(iok,j));
- yr(iok(is),j)=[1:length(iok)]'./length(iok);
- yr(istable(length(iok)+1:end),j) = interp1(yt(iok,j),yr(iok,j),yt(istable(length(iok)+1:end),j),'','extrap');
- ineg=find(yr(:,j)<0);
- if any(ineg)
- [dum, is]=sort(yr(ineg,j));
- yr(ineg(is),j)=-[length(ineg):-1:1]./length(iok);
+
+ np=estim_params_.np;
+ na=(4*np+1)*opt_gsa.Nsam;
+ for j=1:j0
+ [idex(j,:), yd(j,:)] = spop_ide(lpmat, yt(:,j), opt_gsa.Nsam, 5-1);
+ end
+ iok=find(~isnan(yt(1:opt_gsa.Nsam,1)));
+ yr=NaN*ones(size(lpmat,1),j0);
+ for j=1:j0,
+ ys(j,:)=yd(j,:)./max(yd(j,:));
+ [dum, is]=sort(yt(iok,j));
+ yr(iok(is),j)=[1:length(iok)]'./length(iok);
+ yr(istable(length(iok)+1:end),j) = interp1(yt(iok,j),yr(iok,j),yt(istable(length(iok)+1:end),j),'','extrap');
+ ineg=find(yr(:,j)<0);
+ if any(ineg)
+ [dum, is]=sort(yr(ineg,j));
+ yr(ineg(is),j)=-[length(ineg):-1:1]./length(iok);
+ end
+ [idex_r(j,:), yd_r(j,:)] = spop_ide(lpmat, yr(:,j), opt_gsa.Nsam, 5-1);
+ ys_r(j,:)=yd_r(j,:)./max(yd_r(j,:));
+
+ end,
+ figure, bar((idex.*ys)./opt_gsa.Nsam), title('Relationships')
+ figure, bar((idex.*ys)'./opt_gsa.Nsam), title('Parameters')
+ figure, bar((idex_r.*ys_r)./opt_gsa.Nsam), title('Relationships rank')
+ figure, bar((idex_r.*ys_r)'./opt_gsa.Nsam), title('Parameters rank')
+ [v0,d0]=eig(corrcoef(yt(iok,:)));
+ ee=diag(d0);
+ ee=ee([end:-1:1])./j0;
+ i0=length(find(ee>0.01));
+ v0=v0(:,[end:-1:1]);
+ for j=1:i0
+ [idex_pc(j,:), yd_pc(j,:)] = spop_ide(lpmat, yt*v0(:,j), opt_gsa.Nsam, 5-1);
+ end
+ for j=1:i0
+ ys_pc(j,:)=yd_pc(j,:)./max(yd_pc(j,:));
+ end,
+ figure, bar((idex_pc.*ys_pc)./opt_gsa.Nsam), title('Relationships PCA')
+ figure, bar((idex_pc.*ys_pc)'./opt_gsa.Nsam), title('Parameters PCA')
+
+ [vr,dr]=eig(corrcoef(yr(iok,:)));
+ er=diag(dr);
+ er=er([end:-1:1])./j0;
+ ir0=length(find(er>0.01));
+ vr=vr(:,[end:-1:1]);
+ for j=1:ir0
+ [idex_pcr(j,:), yd_pcr(j,:)] = spop_ide(lpmat, yr*vr(:,j), opt_gsa.Nsam, 5-1);
+ end
+ for j=1:ir0
+ ys_pcr(j,:)=yd_pcr(j,:)./max(yd_pcr(j,:));
end
- [idex_r(j,:), yd_r(j,:)] = spop_ide(lpmat, yr(:,j), opt_gsa.Nsam, 5-1);
- ys_r(j,:)=yd_r(j,:)./max(yd_r(j,:));
-
- end,
- figure, bar((idex.*ys)./opt_gsa.Nsam), title('Relationships')
- figure, bar((idex.*ys)'./opt_gsa.Nsam), title('Parameters')
- figure, bar((idex_r.*ys_r)./opt_gsa.Nsam), title('Relationships rank')
- figure, bar((idex_r.*ys_r)'./opt_gsa.Nsam), title('Parameters rank')
- [v0,d0]=eig(corrcoef(yt(iok,:)));
- ee=diag(d0);
- ee=ee([end:-1:1])./j0;
- i0=length(find(ee>0.01));
- v0=v0(:,[end:-1:1]);
- for j=1:i0
- [idex_pc(j,:), yd_pc(j,:)] = spop_ide(lpmat, yt*v0(:,j), opt_gsa.Nsam, 5-1);
- end
- for j=1:i0
- ys_pc(j,:)=yd_pc(j,:)./max(yd_pc(j,:));
- end,
- figure, bar((idex_pc.*ys_pc)./opt_gsa.Nsam), title('Relationships PCA')
- figure, bar((idex_pc.*ys_pc)'./opt_gsa.Nsam), title('Parameters PCA')
-
- [vr,dr]=eig(corrcoef(yr(iok,:)));
- er=diag(dr);
- er=er([end:-1:1])./j0;
- ir0=length(find(er>0.01));
- vr=vr(:,[end:-1:1]);
- for j=1:ir0
- [idex_pcr(j,:), yd_pcr(j,:)] = spop_ide(lpmat, yr*vr(:,j), opt_gsa.Nsam, 5-1);
- end
- for j=1:ir0
- ys_pcr(j,:)=yd_pcr(j,:)./max(yd_pcr(j,:));
- end
- figure, bar((idex_pcr.*ys_pcr)./opt_gsa.Nsam), title('Relationships rank PCA')
- figure, bar((idex_pcr.*ys_pcr)'./opt_gsa.Nsam), title('Parameters rank PCA')
-
+ figure, bar((idex_pcr.*ys_pcr)./opt_gsa.Nsam), title('Relationships rank PCA')
+ figure, bar((idex_pcr.*ys_pcr)'./opt_gsa.Nsam), title('Parameters rank PCA')
+
elseif opt_gsa.morris==2 % ISKREV staff
- return
+ return
+
-
else % main effects analysis
-
- if itrans==0
- fsuffix = '';
- elseif itrans==1
- fsuffix = '_log';
- else
- fsuffix = '_rank';
- end
-
- imap=[1:npT];
-
- if isempty(lpmat0)
- x0=lpmat(istable,:);
- else
-
- x0=[lpmat0(istable,:), lpmat(istable,:)];
- end
- nrun=length(istable);
- nest=min(250,nrun);
- nfit=min(1000,nrun);
-
-% opt_gsa.load_ident_files=0;
-
-% if opt_gsa.load_ident_files==0,
-% try
-% EET=load([OutputDirectoryName,'/SCREEN/',fname_,'_morris_IDE'],'SAvdec','vdec','ir_vdec','ic_vdec');
-% catch
-% EET=[];
-% end
-% SAvdec=zeros(size(vdec,2),npT);
-%
-% for j=1:size(vdec,2),
-% if itrans==0,
-% y0 = vdec(istable,j);
-% elseif itrans==1,
-% y0 = log_trans_(vdec(istable,j));
-% else
-% y0 = trank(vdec(istable,j));
-% end
-% if ~isempty(EET),
-% % imap=find(EET.SAvdec(j,:));
-% % [dum, isort]=sort(-EET.SAvdec(j,:));
-% imap=find(EET.SAvdec(j,:) >= (0.1.*max(EET.SAvdec(j,:))) );
-% end
-% gsa_(j) = gsa_sdp(y0(1:nest), x0(1:nest,imap), ...
-% 2, [],[],[],0,[OutputDirectoryName,'/map_vdec',fsuffix,int2str(j)], pnames);
-% if nfit>nest,
-% gsa_(j) = gsa_sdp(y0(1:nfit), x0(1:nfit,imap), ...
-% -2, gsa_(j).nvr*nest^3/nfit^3,[],[],0,[OutputDirectoryName,'/map_vdec',fsuffix,int2str(j)], pnames);
-% end
-%
-% SAvdec(j,imap)=gsa_(j).si;
-% imap_vdec{j}=imap;
-% end
-% save([OutputDirectoryName,'/',fname_,'_main_eff'],'imap_vdec','SAvdec','vdec','ir_vdec','ic_vdec','-append')
-% else
-% load([OutputDirectoryName,'/',fname_,'_main_eff'],'imap_vdec','SAvdec','vdec','ir_vdec','ic_vdec')
-% end
-% figure,
-% % boxplot(SAvdec,'whis',10,'symbol','r.')
-% myboxplot(SAvdec,[],'.',[],10)
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
-% set(gca,'xlim',[0.5 npT+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% set(gca,'position',[0.13 0.2 0.775 0.7])
-% for ip=1:npT,
-% text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% title(['Main effects variance decomposition ',fsuffix],'interpreter','none')
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_vdec',fsuffix])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_vdec',fsuffix]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_vdec',fsuffix]);
-% close(gcf),
-%
-% ifig = 0;
-% for j=1:size(options_.varobs,1)
-% if mod(j,6)==1
-% figure('name',['Main effects observed variance decomposition ',fsuffix]);
-% ifig=ifig+1;
-% iplo=0;
-% end
-% iplo=iplo+1;
-% subplot(3,2,iplo)
-% iv = find(ir_vdec==j);
-% if ~isempty(iv)
-% if length(iv)>1
-% % boxplot(SAvdec(iv,:),'whis',10,'symbol','r.');
-% myboxplot(SAvdec(iv,:),[],'.',[],10)
-% else
-% plot(SAvdec(iv,:),'r.');
-% end
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
-% set(gca,'xlim',[0.5 npT+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% for ip=1:npT,
-% text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% end
-% title(options_.varobs(j,:),'interpreter','none')
-% if mod(j,6)==0 | j==size(options_.varobs,1)
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_vdec',fsuffix,'_varobs_',int2str(ifig)])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_vdec',fsuffix,'_varobs_',int2str(ifig)]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_vdec',fsuffix,'_varobs_',int2str(ifig)]);
-% close(gcf),
-% end
-% end
-%
-% ifig = 0;
-% for j=1:M_.exo_nbr,
-% if mod(j,6)==1
-% figure('name',['Main effects shocks variance decomposition ',fsuffix]);
-% ifig=ifig+1;
-% iplo=0;
-% end
-% iplo=iplo+1;
-% subplot(3,2,iplo)
-% iv = find(ic_vdec==j);
-% if ~isempty(iv)
-% if length(iv)>1
-% % boxplot(SAvdec(iv,:),'whis',3,'symbol','r.');
-% myboxplot(SAvdec(iv,:),[],'.',[],10)
-% else
-% plot(SAvdec(iv,:),'r.');
-% end
-% set(gca,'xticklabel',' ','fontsize',3,'xtick',[1:np])
-% set(gca,'xlim',[0.5 npT+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% for ip=1:npT,
-% text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% set(gca,'fontsize',10)
-% end
-% title(M_.exo_names(j,:),'interpreter','none','fontsize',10)
-% if mod(j,6)==0 | j==M_.exo_nbr
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_vdec',fsuffix,'_exo_',int2str(ifig)])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_vdec',fsuffix,'_exo_',int2str(ifig)]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_vdec',fsuffix,'_exo_',int2str(ifig)]);
-% close(gcf),
-% end
-% end
- if opt_gsa.load_ident_files==0
- try
- EET=load([OutputDirectoryName,'/SCREEN/',fname_,'_morris_IDE'],'SAcc','ir_cc','ic_cc');
- catch
- EET=[];
- end
- ccac = stand_([mss cc ac]);
- [pcc, dd] = eig(cov(ccac(istable,:)));
- [latent, isort] = sort(-diag(dd));
- latent = -latent;
- figure, bar(latent)
- title('Eigenvalues in PCA')
- pcc=pcc(:,isort);
- ccac = ccac*pcc;
-% npca = min(40, max(find(cumsum(latent)./length(latent)<0.99))+1);
- npca = max(find(cumsum(latent)./length(latent)<0.99))+1;
- siPCA = (EET.SAcc'*abs(pcc'))';
-% siPCA = siPCA./(max(siPCA')'*ones(1,npT)).*(latent*ones(1,npT));
- siPCA = siPCA./(max(siPCA')'*ones(1,npT));
-% siPCA = sum(siPCA,1);
-% siPCA = siPCA./max(siPCA);
- SAcc=zeros(size(ccac,2),npT);
- for j=1:npca %size(ccac,2),
if itrans==0
- y0 = ccac(istable,j);
+ fsuffix = '';
elseif itrans==1
- y0 = log_trans_(ccac(istable,j));
+ fsuffix = '_log';
else
- y0 = trank(ccac(istable,j));
+ fsuffix = '_rank';
end
- if ~isempty(EET)
-% imap=find(EET.SAvdec(j,:));
-% [dum, isort]=sort(-EET.SAvdec(j,:));
- imap=find(siPCA(j,:) >= (0.1.*max(siPCA(j,:))) );
-% imap=find(EET.SAcc(j,:) >= (0.1.*max(EET.SAcc(j,:))) );
+
+ imap=[1:npT];
+
+ if isempty(lpmat0)
+ x0=lpmat(istable,:);
+ else
+
+ x0=[lpmat0(istable,:), lpmat(istable,:)];
end
- gsa_(j) = gsa_sdp(y0(1:nest), x0(1:nest,imap), ...
- 2, [],[],[],0,[OutputDirectoryName,'/map_cc',fsuffix,int2str(j)], pnames);
-% if nfit>nest,
-% gsa_(j) = gsa_sdp(y0(1:nfit), x0(1:nfit,imap), ...
-% -2, gsa_(j).nvr*nest^3/nfit^3,[],[],0,[OutputDirectoryName,'/map_cc',fsuffix,int2str(j)], pnames);
-% end
- SAcc(j,imap)=gsa_(j).si;
- imap_cc{j}=imap;
-
- end
- save([OutputDirectoryName,'/map_cc',fsuffix,'.mat'],'gsa_')
- save([OutputDirectoryName,'/',fname_,'_main_eff.mat'],'imap_cc','SAcc','ccac','-append')
- else
- load([OutputDirectoryName,'/',fname_,'_main_eff'],'imap_cc','SAcc','ccac')
-
- end
-% figure,
-% % boxplot(SAcc,'whis',10,'symbol','r.')
-% myboxplot(SAcc,[],'.',[],10)
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
-% set(gca,'xlim',[0.5 npT+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% set(gca,'position',[0.13 0.2 0.775 0.7])
-% for ip=1:npT,
-% text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% ylabel(' ')
-% title(['Main effects moments''s PCA ',fsuffix],'interpreter','none')
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_cc',fsuffix])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_moments',fsuffix]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_moments',fsuffix]);
-% close(gcf),
-
-% ifig = 0;
-% for j=1:size(options_.varobs,1)
-% if mod(j,6)==1
-% figure('name',['Main effects cross-covariances ',fsuffix]);
-% ifig=ifig+1;
-% iplo=0;
-% end
-% iplo=iplo+1;
-% subplot(3,2,iplo)
-% iv = find(ir_cc==j);
-% iv = [iv; find(ic_cc==j)];
-% if ~isempty(iv)
-% if length(iv)>1
-% % boxplot(SAcc(iv,:),'whis',10,'symbol','r.');
-% myboxplot(SAcc(iv,:),[],'.',[],10)
-% else
-% plot(SAcc(iv,:),'r.');
-% end
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
-% set(gca,'xlim',[0.5 npT+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% for ip=1:npT,
-% text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% set(gca,'fontsize',10)
-% end
-% title(options_.varobs(j,:),'interpreter','none','fontsize',10)
-% if mod(j,6)==0 | j==size(options_.varobs,1)
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_cc',fsuffix,'_',int2str(ifig)])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_cc',fsuffix,'_',int2str(ifig)]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_cc',fsuffix,'_',int2str(ifig)]);
-% close(gcf),
-% end
-% end
-%
-% if opt_gsa.load_ident_files==0,
-% try
-% EET=load([OutputDirectoryName,'/SCREEN/',fname_,'_morris_IDE'],'SAac','ir_ac','ic_ac');
-% catch
-% EET=[];
-% end
-% SAac=zeros(size(ac,2),npT);
-% for j=1:size(ac,2),
-% if itrans==0,
-% y0 = ac(istable,j);
-% elseif itrans==1,
-% y0 = log_trans_(ac(istable,j));
-% else
-% y0 = trank(ac(istable,j));
-% end
-% if ~isempty(EET),
-% imap=find(EET.SAac(j,:) >= (0.1.*max(EET.SAac(j,:))) );
-% end
-% % gsa_(j) = gsa_sdp_dyn( y0, lpmat(istable,:), ...
-% % gsa_flag, [],[],[],0,[OutputDirectoryName,'/map_ac',fsuffix,int2str(j)], pnames);
-% gsa_(j) = gsa_sdp(y0(1:nest), x0(1:nest,imap), ...
-% 2, [],[],[],0,[OutputDirectoryName,'/map_ac',fsuffix,int2str(j)], pnames);
-% if nfit>nest,
-% gsa_(j) = gsa_sdp(y0(1:nfit), x0(1:nfit,imap), ...
-% -2, gsa_(j).nvr*nest^3/nfit^3,[],[],0,[OutputDirectoryName,'/map_ac',fsuffix,int2str(j)], pnames);
-% end
-% SAac(j,imap)=gsa_(j).si;
-% imap_ac{j}=imap;
-%
-% end
-% save([OutputDirectoryName,'/',fname_,'_main_eff'],'imap_ac','SAac','ac','ir_ac','ic_ac','-append')
-% else
-% load([OutputDirectoryName,'/',fname_,'_main_eff'],'imap_ac','SAac','ac','ir_ac','ic_ac')
-% end
-%
-% figure,
-% % boxplot(SAac,'whis',10,'symbol','r.')
-% myboxplot(SAac,[],'.',[],10)
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
-% set(gca,'xlim',[0.5 npT+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% set(gca,'position',[0.13 0.2 0.775 0.7])
-% for ip=1:np,
-% text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% title(['Main effects 1 lag auto-covariances ',fsuffix],'interpreter','none')
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_ac',fsuffix])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_ac',fsuffix]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_ac',fsuffix]);
-% close(gcf),
-%
-% ifig = 0;
-% for j=1:size(options_.varobs,1)
-% if mod(j,6)==1
-% figure('name',['Main effects auto-covariances ',fsuffix]);
-% ifig=ifig+1;
-% iplo = 0;
-% end
-% iplo=iplo+1;
-% subplot(3,2,iplo)
-% iv = find(ir_ac==j);
-% %iv = [iv; find(ic_ac==j)];
-% if ~isempty(iv)
-% if length(iv)>1
-% % boxplot(SAac(iv,:),'whis',10,'symbol','r.');
-% myboxplot(SAac(iv,:),[],'.',[],10)
-% else
-% plot(SAac(iv,:),'r.');
-% end
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
-% set(gca,'xlim',[0.5 npT+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% for ip=1:npT,
-% text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% set(gca,'fontsize',10)
-% end
-% title(options_.varobs(j,:),'interpreter','none','fontsize',10)
-% if mod(j,6)==0 | j==size(options_.varobs,1)
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_ac',fsuffix,'_',int2str(ifig)])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_ac',fsuffix,'_',int2str(ifig)]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_ac',fsuffix,'_',int2str(ifig)]);
-% close(gcf),
-% end
-% end
-
-% x0=x0(:,nshock+1:end);
- imap=[1:npT];
-
-% if opt_gsa.load_ident_files==0,
-% try
-% EET=load([OutputDirectoryName,'/SCREEN/',fname_,'_morris_IDE'],'SAtadj','ir_tadj','ic_tadj');
-% ny=size(EET.SAtadj,1);
-% catch
-% EET=[];
-% end
-% SAtadj=zeros(size(tadj,2),np);
-% for j=1:size(tadj,2),
-% if itrans==0,
-% y0 = tadj(istable,j);
-% elseif itrans==1,
-% y0 = log_trans_(tadj(istable,j));
-% else
-% y0 = trank(tadj(istable,j));
-% end
-% if ~isempty(EET),
-% if size(tadj,2)~=ny,
-% jj=find(EET.ir_tadj==ir_tadj(j));
-% jj=jj(find(EET.ic_tadj(jj)==ic_tadj(j)));
-% if ~isempty(jj),
-% imap=find(EET.SAtadj(jj,:) >= (0.1.*max(EET.SAtadj(jj,:))) );
-% else
-% imap=[1:np];
-% end
-% else
-% imap=find(EET.SAtadj(j,:) >= (0.1.*max(EET.SAtadj(j,:))) );
-% end
-% end
-% % gsa_(j) = gsa_sdp_dyn( y0, lpmat(istable,:), ...
-% % gsa_flag, [],[],[],0,[OutputDirectoryName,'/map_tadj',fsuffix,int2str(j)], pnames);
-% gsa_(j) = gsa_sdp(y0(1:nest), x0(1:nest,imap), ...
-% 2, [],[],[],0,[OutputDirectoryName,'/map_tadj',fsuffix,int2str(j)], pnames);
-% if nfit>nest,
-% gsa_(j) = gsa_sdp(y0(1:nfit), x0(1:nfit,imap), ...
-% -2, gsa_(j).nvr*nest^3/nfit^3,[],[],0,[OutputDirectoryName,'/map_tadj',fsuffix,int2str(j)], pnames);
-% end
-% SAtadj(j,imap)=gsa_(j).si;
-% imap_tadj{j}=imap;
-%
-% end
-% save([OutputDirectoryName,'/',fname_,'_main_eff'],'imap_tadj','SAtadj','tadj','ir_tadj','ic_tadj','-append')
-% else
-% load([OutputDirectoryName,'/',fname_,'_main_eff'],'imap_tadj','SAtadj','tadj','ir_tadj','ic_tadj')
-% end
-%
-% figure,
-% % boxplot(SAtadj,'whis',10,'symbol','r.')
-% myboxplot(SAtadj,[],'.',[],10)
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
-% set(gca,'xlim',[0.5 np+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% set(gca,'position',[0.13 0.2 0.775 0.7])
-% for ip=1:np,
-% text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% title(['Main effects speed of adjustment ',fsuffix],'interpreter','none')
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_tadj',fsuffix])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_tadj',fsuffix]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_tadj',fsuffix]);
-% close(gcf),
-%
-% ifig = 0;
-% for j=1:size(options_.varobs,1)
-% if mod(j,6)==1
-% figure('name',['Main effects observed speed adjustment ',fsuffix]);
-% ifig=ifig+1;
-% iplo = 0;
-% end
-% iplo=iplo+1;
-% subplot(3,2,iplo)
-% iv = find(ir_tadj==j);
-% if ~isempty(iv)
-% if length(iv)>1
-% % boxplot(SAtadj(iv,:),'whis',3,'symbol','r.');
-% myboxplot(SAtadj(iv,:),[],'.',[],10)
-% else
-% plot(SAtadj(iv,:),'r.');
-% end
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
-% set(gca,'xlim',[0.5 np+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% for ip=1:np,
-% text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% end
-% title(options_.varobs(j,:),'interpreter','none')
-% if mod(j,6)==0 | j==size(options_.varobs,1)
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_tadj',fsuffix,'_varobs_',int2str(ifig)])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_tadj',fsuffix,'_varobs_',int2str(ifig)]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_tadj',fsuffix,'_varobs_',int2str(ifig)]);
-% close(gcf),
-% end
-% end
-%
-% ifig = 0;
-% for j=1:M_.exo_nbr,
-% if mod(j,6)==1
-% figure('name',['Main effects shocks speed of adjustment ',fsuffix]);
-% ifig=ifig+1;
-% iplo=0;
-% end
-% iplo=iplo+1;
-% subplot(3,2,iplo)
-% iv = find(ic_tadj==j);
-% if ~isempty(iv)
-% if length(iv)>1
-% % boxplot(SAtadj(iv,:),'whis',3,'symbol','r.');
-% myboxplot(SAtadj(iv,:),[],'.',[],10)
-% else
-% plot(SAtadj(iv,:),'r.');
-% end
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
-% set(gca,'xlim',[0.5 np+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% for ip=1:np,
-% text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% end
-% title(M_.exo_names(j,:),'interpreter','none')
-% if mod(j,6)==0 | j==M_.exo_nbr,
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_tadj',fsuffix,'_exo_',int2str(ifig)])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_tadj',fsuffix,'_exo_',int2str(ifig)]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_tadj',fsuffix,'_exo_',int2str(ifig)]);
-% close(gcf),
-% end
-% end
-%
-%
-% if opt_gsa.load_ident_files==0,
-% try
-% EET=load([OutputDirectoryName,'/SCREEN/',fname_,'_morris_IDE'],'SAIF','ir_if','ic_if');
-% catch
-% EET=[];
-% end
-% SAif=zeros(size(iff,2),np);
-% for j=1:size(iff,2),
-% if itrans==0,
-% y0 = iff(istable,j);
-% elseif itrans==1,
-% y0 = log_trans_(iff(istable,j));
-% else
-% y0 = trank(iff(istable,j));
-% end
-% if ~isempty(EET),
-% imap=find(EET.SAIF(j,:) >= (0.1.*max(EET.SAIF(j,:))) );
-% end
-% % gsa_(j) = gsa_sdp_dyn( y0, lpmat(istable,:), ...
-% % gsa_flag, [],[],[],0,[OutputDirectoryName,'/map_if',fsuffix,int2str(j)], pnames);
-% gsa_(j) = gsa_sdp(y0(1:nest), x0(1:nest,imap), ...
-% 2, [],[],[],0,[OutputDirectoryName,'/map_if',fsuffix,int2str(j)], pnames);
-% if nfit>nest,
-% gsa_(j) = gsa_sdp(y0(1:nfit), x0(1:nfit,imap), ...
-% -2, gsa_(j).nvr*nest^3/nfit^3,[],[],0,[OutputDirectoryName,'/map_if',fsuffix,int2str(j)], pnames);
-% end
-% SAif(j,imap)=gsa_(j).si;
-% imap_if{j}=imap;
-%
-% end
-% save([OutputDirectoryName,'/',fname_,'_main_eff'],'imap_if','SAif','iff','ir_if','ic_if','-append')
-% else
-% load([OutputDirectoryName,'/',fname_,'_main_eff'],'imap_if','SAif','iff','ir_if','ic_if')
-% end
-%
-% figure,
-% % boxplot(SAif,'whis',10,'symbol','r.')
-% myboxplot(SAif,[],'.',[],10)
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
-% set(gca,'xlim',[0.5 np+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% set(gca,'position',[0.13 0.2 0.775 0.7])
-% for ip=1:np,
-% text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% title(['Main effects impact factors ',fsuffix],'interpreter','none')
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_if',fsuffix])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_if',fsuffix]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_if',fsuffix]);
-% close(gcf),
-%
-% ifig = 0;
-% for j=1:size(options_.varobs,1)
-% if mod(j,6)==1
-% figure('name',['Main effects observed impact factors ',fsuffix]);
-% ifig=ifig+1;
-% iplo = 0;
-% end
-% iplo=iplo+1;
-% subplot(3,2,iplo)
-% iv = find(ir_if==j);
-% if ~isempty(iv)
-% if length(iv)>1
-% % boxplot(SAif(iv,:),'whis',3,'symbol','r.');
-% myboxplot(SAif(iv,:),[],'.',[],10)
-% else
-% plot(SAif(iv,:),'r.');
-% end
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
-% set(gca,'xlim',[0.5 np+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% for ip=1:np,
-% text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% end
-% title(options_.varobs(j,:),'interpreter','none')
-% if mod(j,6)==0 | j==size(options_.varobs,1)
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_if',fsuffix,'_varobs_',int2str(ifig)])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_if',fsuffix,'_varobs_',int2str(ifig)]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_if',fsuffix,'_varobs_',int2str(ifig)]);
-% close(gcf),
-% end
-% end
-%
-% ifig = 0;
-% for j=1:M_.exo_nbr,
-% if mod(j,6)==1
-% figure('name',['Main effects shocks impact factors ',fsuffix]);
-% ifig=ifig+1;
-% iplo=0;
-% end
-% iplo=iplo+1;
-% subplot(3,2,iplo)
-% iv = find(ic_if==j);
-% if ~isempty(iv)
-% if length(iv)>1
-% % boxplot(SAif(iv,:),'whis',3,'symbol','r.');
-% myboxplot(SAif(iv,:),[],'.',[],10)
-% else
-% plot(SAif(iv,:),'r.');
-% end
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
-% set(gca,'xlim',[0.5 np+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% for ip=1:np,
-% text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% end
-% title(M_.exo_names(j,:),'interpreter','none')
-% if mod(j,6)==0 | j==M_.exo_nbr
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_if',fsuffix,'_exo_',int2str(ifig)])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_if',fsuffix,'_exo_',int2str(ifig)]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_if',fsuffix,'_exo_',int2str(ifig)]);
-% close(gcf),
-% end
-% end
-% SAmom = [SAvdec' SAcc' SAac']';
-% SAdyn = [SAtadj' SAif']';
-% SAall = [SAmom(:,nshock+1:end)' SAdyn']';
-%
-% figure,
-% % boxplot(SAtadj,'whis',10,'symbol','r.')
-% myboxplot(SAmom,[],'.',[],10)
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
-% set(gca,'xlim',[0.5 npT+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% set(gca,'position',[0.13 0.2 0.775 0.7])
-% for ip=1:npT,
-% % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% title(['Main effects theoretical moments ',fsuffix],'interpreter','none')
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_moments',fsuffix])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_moments',fsuffix]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_moments',fsuffix]);
-% % close(gcf),
-%
-% figure,
-% % boxplot(SAtadj,'whis',10,'symbol','r.')
-% myboxplot(SAdyn,[],'.',[],10)
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
-% set(gca,'xlim',[0.5 np+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% set(gca,'position',[0.13 0.2 0.775 0.7])
-% for ip=1:np,
-% text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% % text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% title(['Main effects short-long term dynamics ',fsuffix],'interpreter','none')
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_dynamics',fsuffix])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_dynamics',fsuffix]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_dynamics',fsuffix]);
-% % close(gcf),
-%
-% figure,
-% % boxplot(SAtadj,'whis',10,'symbol','r.')
-% myboxplot(SAall,[],'.',[],10)
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
-% set(gca,'xlim',[0.5 np+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% set(gca,'position',[0.13 0.2 0.775 0.7])
-% for ip=1:np,
-% text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% % text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% title(['Main effects all ',fsuffix],'interpreter','none')
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_ALL',fsuffix])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_ALL',fsuffix]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_ALL',fsuffix]);
-% % close(gcf),
-
-% for j=1:size(SAall,1),
-% SAallN(j,:)=SAall(j,:)./max(SAall(j,:));
-% end
-% SAmean=mean(SAallN);
-% for j=1:size(SAmom,1),
-% SAmomN(j,:)=SAmom(j,1:nshock)./max(SAmom(j,1:nshock));
-% end
-% SAmomN(find(isnan(SAmomN)))=0;
-% SAmeanexo=mean(SAmomN(:,1:nshock));
-
-% figure, bar(latent'*SAcc),
- hh=dyn_figure(options_.nodisplay,'Name',['Identifiability indices in the ',fsuffix,' moments.']);
- bar(sum(SAcc))
- set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
- set(gca,'xlim',[0.5 npT+0.5])
- ydum = get(gca,'ylim');
- set(gca,'ylim',[0 ydum(2)])
- set(gca,'position',[0.13 0.2 0.775 0.7])
- for ip=1:npT
- text(ip,-0.02*(ydum(2)),bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ nrun=length(istable);
+ nest=min(250,nrun);
+ nfit=min(1000,nrun);
+
+ % opt_gsa.load_ident_files=0;
+
+ % if opt_gsa.load_ident_files==0,
+ % try
+ % EET=load([OutputDirectoryName,'/SCREEN/',fname_,'_morris_IDE'],'SAvdec','vdec','ir_vdec','ic_vdec');
+ % catch
+ % EET=[];
+ % end
+ % SAvdec=zeros(size(vdec,2),npT);
+ %
+ % for j=1:size(vdec,2),
+ % if itrans==0,
+ % y0 = vdec(istable,j);
+ % elseif itrans==1,
+ % y0 = log_trans_(vdec(istable,j));
+ % else
+ % y0 = trank(vdec(istable,j));
+ % end
+ % if ~isempty(EET),
+ % % imap=find(EET.SAvdec(j,:));
+ % % [dum, isort]=sort(-EET.SAvdec(j,:));
+ % imap=find(EET.SAvdec(j,:) >= (0.1.*max(EET.SAvdec(j,:))) );
+ % end
+ % gsa_(j) = gsa_sdp(y0(1:nest), x0(1:nest,imap), ...
+ % 2, [],[],[],0,[OutputDirectoryName,'/map_vdec',fsuffix,int2str(j)], pnames);
+ % if nfit>nest,
+ % gsa_(j) = gsa_sdp(y0(1:nfit), x0(1:nfit,imap), ...
+ % -2, gsa_(j).nvr*nest^3/nfit^3,[],[],0,[OutputDirectoryName,'/map_vdec',fsuffix,int2str(j)], pnames);
+ % end
+ %
+ % SAvdec(j,imap)=gsa_(j).si;
+ % imap_vdec{j}=imap;
+ % end
+ % save([OutputDirectoryName,'/',fname_,'_main_eff'],'imap_vdec','SAvdec','vdec','ir_vdec','ic_vdec','-append')
+ % else
+ % load([OutputDirectoryName,'/',fname_,'_main_eff'],'imap_vdec','SAvdec','vdec','ir_vdec','ic_vdec')
+ % end
+ % figure,
+ % % boxplot(SAvdec,'whis',10,'symbol','r.')
+ % myboxplot(SAvdec,[],'.',[],10)
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 npT+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % set(gca,'position',[0.13 0.2 0.775 0.7])
+ % for ip=1:npT,
% text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
- end
- xlabel(' ')
- title(['Identifiability indices in the ',fsuffix,' moments.'],'interpreter','none')
- dyn_saveas(hh,[OutputDirectoryName,'/',fname_,'_ident_ALL',fsuffix],options_.nodisplay,options_.graph_format);
- create_TeX_loader(options_,[OutputDirectoryName,'/',fname_,'_ident_ALL',fsuffix],1,['Identifiability indices in the ',fsuffix,' moments.'],['ident_ALL',fsuffix]',1)
-
-% figure, bar(SAmeanexo),
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:nshock])
-% set(gca,'xlim',[0.5 nshock+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% set(gca,'position',[0.13 0.2 0.775 0.7])
-% for ip=1:nshock,
-% % text(ip,-0.02*(ydum(2)),deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% text(ip,-0.02*(ydum(2)),bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% title(['Identifiability indices for shocks',fsuffix],'interpreter','none')
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_ident_SHOCKS',fsuffix])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_ident_SHOCKS',fsuffix]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_ident_SHOCKS',fsuffix]);
+ % % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % title(['Main effects variance decomposition ',fsuffix],'interpreter','none')
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_vdec',fsuffix])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_vdec',fsuffix]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_vdec',fsuffix]);
+ % close(gcf),
+ %
+ % ifig = 0;
+ % for j=1:size(options_.varobs,1)
+ % if mod(j,6)==1
+ % figure('name',['Main effects observed variance decomposition ',fsuffix]);
+ % ifig=ifig+1;
+ % iplo=0;
+ % end
+ % iplo=iplo+1;
+ % subplot(3,2,iplo)
+ % iv = find(ir_vdec==j);
+ % if ~isempty(iv)
+ % if length(iv)>1
+ % % boxplot(SAvdec(iv,:),'whis',10,'symbol','r.');
+ % myboxplot(SAvdec(iv,:),[],'.',[],10)
+ % else
+ % plot(SAvdec(iv,:),'r.');
+ % end
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 npT+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % for ip=1:npT,
+ % text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % end
+ % title(options_.varobs(j,:),'interpreter','none')
+ % if mod(j,6)==0 | j==size(options_.varobs,1)
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_vdec',fsuffix,'_varobs_',int2str(ifig)])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_vdec',fsuffix,'_varobs_',int2str(ifig)]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_vdec',fsuffix,'_varobs_',int2str(ifig)]);
+ % close(gcf),
+ % end
+ % end
+ %
+ % ifig = 0;
+ % for j=1:M_.exo_nbr,
+ % if mod(j,6)==1
+ % figure('name',['Main effects shocks variance decomposition ',fsuffix]);
+ % ifig=ifig+1;
+ % iplo=0;
+ % end
+ % iplo=iplo+1;
+ % subplot(3,2,iplo)
+ % iv = find(ic_vdec==j);
+ % if ~isempty(iv)
+ % if length(iv)>1
+ % % boxplot(SAvdec(iv,:),'whis',3,'symbol','r.');
+ % myboxplot(SAvdec(iv,:),[],'.',[],10)
+ % else
+ % plot(SAvdec(iv,:),'r.');
+ % end
+ % set(gca,'xticklabel',' ','fontsize',3,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 npT+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % for ip=1:npT,
+ % text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % set(gca,'fontsize',10)
+ % end
+ % title(M_.exo_names(j,:),'interpreter','none','fontsize',10)
+ % if mod(j,6)==0 | j==M_.exo_nbr
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_vdec',fsuffix,'_exo_',int2str(ifig)])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_vdec',fsuffix,'_exo_',int2str(ifig)]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_vdec',fsuffix,'_exo_',int2str(ifig)]);
+ % close(gcf),
+ % end
+ % end
+
+ if opt_gsa.load_ident_files==0
+ try
+ EET=load([OutputDirectoryName,'/SCREEN/',fname_,'_morris_IDE'],'SAcc','ir_cc','ic_cc');
+ catch
+ EET=[];
+ end
+ ccac = stand_([mss cc ac]);
+ [pcc, dd] = eig(cov(ccac(istable,:)));
+ [latent, isort] = sort(-diag(dd));
+ latent = -latent;
+ figure, bar(latent)
+ title('Eigenvalues in PCA')
+ pcc=pcc(:,isort);
+ ccac = ccac*pcc;
+ % npca = min(40, max(find(cumsum(latent)./length(latent)<0.99))+1);
+ npca = max(find(cumsum(latent)./length(latent)<0.99))+1;
+ siPCA = (EET.SAcc'*abs(pcc'))';
+ % siPCA = siPCA./(max(siPCA')'*ones(1,npT)).*(latent*ones(1,npT));
+ siPCA = siPCA./(max(siPCA')'*ones(1,npT));
+ % siPCA = sum(siPCA,1);
+ % siPCA = siPCA./max(siPCA);
+ SAcc=zeros(size(ccac,2),npT);
+ for j=1:npca %size(ccac,2),
+ if itrans==0
+ y0 = ccac(istable,j);
+ elseif itrans==1
+ y0 = log_trans_(ccac(istable,j));
+ else
+ y0 = trank(ccac(istable,j));
+ end
+ if ~isempty(EET)
+ % imap=find(EET.SAvdec(j,:));
+ % [dum, isort]=sort(-EET.SAvdec(j,:));
+ imap=find(siPCA(j,:) >= (0.1.*max(siPCA(j,:))) );
+ % imap=find(EET.SAcc(j,:) >= (0.1.*max(EET.SAcc(j,:))) );
+ end
+ gsa_(j) = gsa_sdp(y0(1:nest), x0(1:nest,imap), ...
+ 2, [],[],[],0,[OutputDirectoryName,'/map_cc',fsuffix,int2str(j)], pnames);
+ % if nfit>nest,
+ % gsa_(j) = gsa_sdp(y0(1:nfit), x0(1:nfit,imap), ...
+ % -2, gsa_(j).nvr*nest^3/nfit^3,[],[],0,[OutputDirectoryName,'/map_cc',fsuffix,int2str(j)], pnames);
+ % end
+ SAcc(j,imap)=gsa_(j).si;
+ imap_cc{j}=imap;
+
+ end
+ save([OutputDirectoryName,'/map_cc',fsuffix,'.mat'],'gsa_')
+ save([OutputDirectoryName,'/',fname_,'_main_eff.mat'],'imap_cc','SAcc','ccac','-append')
+ else
+ load([OutputDirectoryName,'/',fname_,'_main_eff'],'imap_cc','SAcc','ccac')
+
+ end
+ % figure,
+ % % boxplot(SAcc,'whis',10,'symbol','r.')
+ % myboxplot(SAcc,[],'.',[],10)
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 npT+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % set(gca,'position',[0.13 0.2 0.775 0.7])
+ % for ip=1:npT,
+ % text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % ylabel(' ')
+ % title(['Main effects moments''s PCA ',fsuffix],'interpreter','none')
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_cc',fsuffix])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_moments',fsuffix]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_moments',fsuffix]);
+ % close(gcf),
+
+ % ifig = 0;
+ % for j=1:size(options_.varobs,1)
+ % if mod(j,6)==1
+ % figure('name',['Main effects cross-covariances ',fsuffix]);
+ % ifig=ifig+1;
+ % iplo=0;
+ % end
+ % iplo=iplo+1;
+ % subplot(3,2,iplo)
+ % iv = find(ir_cc==j);
+ % iv = [iv; find(ic_cc==j)];
+ % if ~isempty(iv)
+ % if length(iv)>1
+ % % boxplot(SAcc(iv,:),'whis',10,'symbol','r.');
+ % myboxplot(SAcc(iv,:),[],'.',[],10)
+ % else
+ % plot(SAcc(iv,:),'r.');
+ % end
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 npT+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % for ip=1:npT,
+ % text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % set(gca,'fontsize',10)
+ % end
+ % title(options_.varobs(j,:),'interpreter','none','fontsize',10)
+ % if mod(j,6)==0 | j==size(options_.varobs,1)
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_cc',fsuffix,'_',int2str(ifig)])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_cc',fsuffix,'_',int2str(ifig)]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_cc',fsuffix,'_',int2str(ifig)]);
+ % close(gcf),
+ % end
+ % end
+ %
+ % if opt_gsa.load_ident_files==0,
+ % try
+ % EET=load([OutputDirectoryName,'/SCREEN/',fname_,'_morris_IDE'],'SAac','ir_ac','ic_ac');
+ % catch
+ % EET=[];
+ % end
+ % SAac=zeros(size(ac,2),npT);
+ % for j=1:size(ac,2),
+ % if itrans==0,
+ % y0 = ac(istable,j);
+ % elseif itrans==1,
+ % y0 = log_trans_(ac(istable,j));
+ % else
+ % y0 = trank(ac(istable,j));
+ % end
+ % if ~isempty(EET),
+ % imap=find(EET.SAac(j,:) >= (0.1.*max(EET.SAac(j,:))) );
+ % end
+ % % gsa_(j) = gsa_sdp_dyn( y0, lpmat(istable,:), ...
+ % % gsa_flag, [],[],[],0,[OutputDirectoryName,'/map_ac',fsuffix,int2str(j)], pnames);
+ % gsa_(j) = gsa_sdp(y0(1:nest), x0(1:nest,imap), ...
+ % 2, [],[],[],0,[OutputDirectoryName,'/map_ac',fsuffix,int2str(j)], pnames);
+ % if nfit>nest,
+ % gsa_(j) = gsa_sdp(y0(1:nfit), x0(1:nfit,imap), ...
+ % -2, gsa_(j).nvr*nest^3/nfit^3,[],[],0,[OutputDirectoryName,'/map_ac',fsuffix,int2str(j)], pnames);
+ % end
+ % SAac(j,imap)=gsa_(j).si;
+ % imap_ac{j}=imap;
+ %
+ % end
+ % save([OutputDirectoryName,'/',fname_,'_main_eff'],'imap_ac','SAac','ac','ir_ac','ic_ac','-append')
+ % else
+ % load([OutputDirectoryName,'/',fname_,'_main_eff'],'imap_ac','SAac','ac','ir_ac','ic_ac')
+ % end
+ %
+ % figure,
+ % % boxplot(SAac,'whis',10,'symbol','r.')
+ % myboxplot(SAac,[],'.',[],10)
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 npT+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % set(gca,'position',[0.13 0.2 0.775 0.7])
+ % for ip=1:np,
+ % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % title(['Main effects 1 lag auto-covariances ',fsuffix],'interpreter','none')
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_ac',fsuffix])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_ac',fsuffix]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_ac',fsuffix]);
+ % close(gcf),
+ %
+ % ifig = 0;
+ % for j=1:size(options_.varobs,1)
+ % if mod(j,6)==1
+ % figure('name',['Main effects auto-covariances ',fsuffix]);
+ % ifig=ifig+1;
+ % iplo = 0;
+ % end
+ % iplo=iplo+1;
+ % subplot(3,2,iplo)
+ % iv = find(ir_ac==j);
+ % %iv = [iv; find(ic_ac==j)];
+ % if ~isempty(iv)
+ % if length(iv)>1
+ % % boxplot(SAac(iv,:),'whis',10,'symbol','r.');
+ % myboxplot(SAac(iv,:),[],'.',[],10)
+ % else
+ % plot(SAac(iv,:),'r.');
+ % end
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 npT+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % for ip=1:npT,
+ % text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % set(gca,'fontsize',10)
+ % end
+ % title(options_.varobs(j,:),'interpreter','none','fontsize',10)
+ % if mod(j,6)==0 | j==size(options_.varobs,1)
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_ac',fsuffix,'_',int2str(ifig)])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_ac',fsuffix,'_',int2str(ifig)]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_ac',fsuffix,'_',int2str(ifig)]);
+ % close(gcf),
+ % end
+ % end
+
+ % x0=x0(:,nshock+1:end);
+ imap=[1:npT];
+
+ % if opt_gsa.load_ident_files==0,
+ % try
+ % EET=load([OutputDirectoryName,'/SCREEN/',fname_,'_morris_IDE'],'SAtadj','ir_tadj','ic_tadj');
+ % ny=size(EET.SAtadj,1);
+ % catch
+ % EET=[];
+ % end
+ % SAtadj=zeros(size(tadj,2),np);
+ % for j=1:size(tadj,2),
+ % if itrans==0,
+ % y0 = tadj(istable,j);
+ % elseif itrans==1,
+ % y0 = log_trans_(tadj(istable,j));
+ % else
+ % y0 = trank(tadj(istable,j));
+ % end
+ % if ~isempty(EET),
+ % if size(tadj,2)~=ny,
+ % jj=find(EET.ir_tadj==ir_tadj(j));
+ % jj=jj(find(EET.ic_tadj(jj)==ic_tadj(j)));
+ % if ~isempty(jj),
+ % imap=find(EET.SAtadj(jj,:) >= (0.1.*max(EET.SAtadj(jj,:))) );
+ % else
+ % imap=[1:np];
+ % end
+ % else
+ % imap=find(EET.SAtadj(j,:) >= (0.1.*max(EET.SAtadj(j,:))) );
+ % end
+ % end
+ % % gsa_(j) = gsa_sdp_dyn( y0, lpmat(istable,:), ...
+ % % gsa_flag, [],[],[],0,[OutputDirectoryName,'/map_tadj',fsuffix,int2str(j)], pnames);
+ % gsa_(j) = gsa_sdp(y0(1:nest), x0(1:nest,imap), ...
+ % 2, [],[],[],0,[OutputDirectoryName,'/map_tadj',fsuffix,int2str(j)], pnames);
+ % if nfit>nest,
+ % gsa_(j) = gsa_sdp(y0(1:nfit), x0(1:nfit,imap), ...
+ % -2, gsa_(j).nvr*nest^3/nfit^3,[],[],0,[OutputDirectoryName,'/map_tadj',fsuffix,int2str(j)], pnames);
+ % end
+ % SAtadj(j,imap)=gsa_(j).si;
+ % imap_tadj{j}=imap;
+ %
+ % end
+ % save([OutputDirectoryName,'/',fname_,'_main_eff'],'imap_tadj','SAtadj','tadj','ir_tadj','ic_tadj','-append')
+ % else
+ % load([OutputDirectoryName,'/',fname_,'_main_eff'],'imap_tadj','SAtadj','tadj','ir_tadj','ic_tadj')
+ % end
+ %
+ % figure,
+ % % boxplot(SAtadj,'whis',10,'symbol','r.')
+ % myboxplot(SAtadj,[],'.',[],10)
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 np+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % set(gca,'position',[0.13 0.2 0.775 0.7])
+ % for ip=1:np,
+ % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % title(['Main effects speed of adjustment ',fsuffix],'interpreter','none')
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_tadj',fsuffix])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_tadj',fsuffix]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_tadj',fsuffix]);
+ % close(gcf),
+ %
+ % ifig = 0;
+ % for j=1:size(options_.varobs,1)
+ % if mod(j,6)==1
+ % figure('name',['Main effects observed speed adjustment ',fsuffix]);
+ % ifig=ifig+1;
+ % iplo = 0;
+ % end
+ % iplo=iplo+1;
+ % subplot(3,2,iplo)
+ % iv = find(ir_tadj==j);
+ % if ~isempty(iv)
+ % if length(iv)>1
+ % % boxplot(SAtadj(iv,:),'whis',3,'symbol','r.');
+ % myboxplot(SAtadj(iv,:),[],'.',[],10)
+ % else
+ % plot(SAtadj(iv,:),'r.');
+ % end
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 np+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % for ip=1:np,
+ % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % end
+ % title(options_.varobs(j,:),'interpreter','none')
+ % if mod(j,6)==0 | j==size(options_.varobs,1)
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_tadj',fsuffix,'_varobs_',int2str(ifig)])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_tadj',fsuffix,'_varobs_',int2str(ifig)]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_tadj',fsuffix,'_varobs_',int2str(ifig)]);
+ % close(gcf),
+ % end
+ % end
+ %
+ % ifig = 0;
+ % for j=1:M_.exo_nbr,
+ % if mod(j,6)==1
+ % figure('name',['Main effects shocks speed of adjustment ',fsuffix]);
+ % ifig=ifig+1;
+ % iplo=0;
+ % end
+ % iplo=iplo+1;
+ % subplot(3,2,iplo)
+ % iv = find(ic_tadj==j);
+ % if ~isempty(iv)
+ % if length(iv)>1
+ % % boxplot(SAtadj(iv,:),'whis',3,'symbol','r.');
+ % myboxplot(SAtadj(iv,:),[],'.',[],10)
+ % else
+ % plot(SAtadj(iv,:),'r.');
+ % end
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 np+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % for ip=1:np,
+ % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % end
+ % title(M_.exo_names(j,:),'interpreter','none')
+ % if mod(j,6)==0 | j==M_.exo_nbr,
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_tadj',fsuffix,'_exo_',int2str(ifig)])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_tadj',fsuffix,'_exo_',int2str(ifig)]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_tadj',fsuffix,'_exo_',int2str(ifig)]);
+ % close(gcf),
+ % end
+ % end
+ %
+ %
+ % if opt_gsa.load_ident_files==0,
+ % try
+ % EET=load([OutputDirectoryName,'/SCREEN/',fname_,'_morris_IDE'],'SAIF','ir_if','ic_if');
+ % catch
+ % EET=[];
+ % end
+ % SAif=zeros(size(iff,2),np);
+ % for j=1:size(iff,2),
+ % if itrans==0,
+ % y0 = iff(istable,j);
+ % elseif itrans==1,
+ % y0 = log_trans_(iff(istable,j));
+ % else
+ % y0 = trank(iff(istable,j));
+ % end
+ % if ~isempty(EET),
+ % imap=find(EET.SAIF(j,:) >= (0.1.*max(EET.SAIF(j,:))) );
+ % end
+ % % gsa_(j) = gsa_sdp_dyn( y0, lpmat(istable,:), ...
+ % % gsa_flag, [],[],[],0,[OutputDirectoryName,'/map_if',fsuffix,int2str(j)], pnames);
+ % gsa_(j) = gsa_sdp(y0(1:nest), x0(1:nest,imap), ...
+ % 2, [],[],[],0,[OutputDirectoryName,'/map_if',fsuffix,int2str(j)], pnames);
+ % if nfit>nest,
+ % gsa_(j) = gsa_sdp(y0(1:nfit), x0(1:nfit,imap), ...
+ % -2, gsa_(j).nvr*nest^3/nfit^3,[],[],0,[OutputDirectoryName,'/map_if',fsuffix,int2str(j)], pnames);
+ % end
+ % SAif(j,imap)=gsa_(j).si;
+ % imap_if{j}=imap;
+ %
+ % end
+ % save([OutputDirectoryName,'/',fname_,'_main_eff'],'imap_if','SAif','iff','ir_if','ic_if','-append')
+ % else
+ % load([OutputDirectoryName,'/',fname_,'_main_eff'],'imap_if','SAif','iff','ir_if','ic_if')
+ % end
+ %
+ % figure,
+ % % boxplot(SAif,'whis',10,'symbol','r.')
+ % myboxplot(SAif,[],'.',[],10)
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 np+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % set(gca,'position',[0.13 0.2 0.775 0.7])
+ % for ip=1:np,
+ % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % title(['Main effects impact factors ',fsuffix],'interpreter','none')
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_if',fsuffix])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_if',fsuffix]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_if',fsuffix]);
+ % close(gcf),
+ %
+ % ifig = 0;
+ % for j=1:size(options_.varobs,1)
+ % if mod(j,6)==1
+ % figure('name',['Main effects observed impact factors ',fsuffix]);
+ % ifig=ifig+1;
+ % iplo = 0;
+ % end
+ % iplo=iplo+1;
+ % subplot(3,2,iplo)
+ % iv = find(ir_if==j);
+ % if ~isempty(iv)
+ % if length(iv)>1
+ % % boxplot(SAif(iv,:),'whis',3,'symbol','r.');
+ % myboxplot(SAif(iv,:),[],'.',[],10)
+ % else
+ % plot(SAif(iv,:),'r.');
+ % end
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 np+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % for ip=1:np,
+ % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % end
+ % title(options_.varobs(j,:),'interpreter','none')
+ % if mod(j,6)==0 | j==size(options_.varobs,1)
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_if',fsuffix,'_varobs_',int2str(ifig)])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_if',fsuffix,'_varobs_',int2str(ifig)]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_if',fsuffix,'_varobs_',int2str(ifig)]);
+ % close(gcf),
+ % end
+ % end
+ %
+ % ifig = 0;
+ % for j=1:M_.exo_nbr,
+ % if mod(j,6)==1
+ % figure('name',['Main effects shocks impact factors ',fsuffix]);
+ % ifig=ifig+1;
+ % iplo=0;
+ % end
+ % iplo=iplo+1;
+ % subplot(3,2,iplo)
+ % iv = find(ic_if==j);
+ % if ~isempty(iv)
+ % if length(iv)>1
+ % % boxplot(SAif(iv,:),'whis',3,'symbol','r.');
+ % myboxplot(SAif(iv,:),[],'.',[],10)
+ % else
+ % plot(SAif(iv,:),'r.');
+ % end
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 np+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % for ip=1:np,
+ % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % end
+ % title(M_.exo_names(j,:),'interpreter','none')
+ % if mod(j,6)==0 | j==M_.exo_nbr
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_if',fsuffix,'_exo_',int2str(ifig)])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_if',fsuffix,'_exo_',int2str(ifig)]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_if',fsuffix,'_exo_',int2str(ifig)]);
+ % close(gcf),
+ % end
+ % end
+ % SAmom = [SAvdec' SAcc' SAac']';
+ % SAdyn = [SAtadj' SAif']';
+ % SAall = [SAmom(:,nshock+1:end)' SAdyn']';
+ %
+ % figure,
+ % % boxplot(SAtadj,'whis',10,'symbol','r.')
+ % myboxplot(SAmom,[],'.',[],10)
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
+ % set(gca,'xlim',[0.5 npT+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % set(gca,'position',[0.13 0.2 0.775 0.7])
+ % for ip=1:npT,
+ % % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % title(['Main effects theoretical moments ',fsuffix],'interpreter','none')
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_moments',fsuffix])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_moments',fsuffix]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_moments',fsuffix]);
+ % % close(gcf),
+ %
+ % figure,
+ % % boxplot(SAtadj,'whis',10,'symbol','r.')
+ % myboxplot(SAdyn,[],'.',[],10)
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 np+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % set(gca,'position',[0.13 0.2 0.775 0.7])
+ % for ip=1:np,
+ % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % % text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % title(['Main effects short-long term dynamics ',fsuffix],'interpreter','none')
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_dynamics',fsuffix])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_dynamics',fsuffix]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_dynamics',fsuffix]);
+ % % close(gcf),
+ %
+ % figure,
+ % % boxplot(SAtadj,'whis',10,'symbol','r.')
+ % myboxplot(SAall,[],'.',[],10)
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 np+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % set(gca,'position',[0.13 0.2 0.775 0.7])
+ % for ip=1:np,
+ % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % % text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % title(['Main effects all ',fsuffix],'interpreter','none')
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_ALL',fsuffix])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_ALL',fsuffix]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_ALL',fsuffix]);
+ % % close(gcf),
+
+ % for j=1:size(SAall,1),
+ % SAallN(j,:)=SAall(j,:)./max(SAall(j,:));
+ % end
+ % SAmean=mean(SAallN);
+ % for j=1:size(SAmom,1),
+ % SAmomN(j,:)=SAmom(j,1:nshock)./max(SAmom(j,1:nshock));
+ % end
+ % SAmomN(find(isnan(SAmomN)))=0;
+ % SAmeanexo=mean(SAmomN(:,1:nshock));
+
+ % figure, bar(latent'*SAcc),
+ hh=dyn_figure(options_.nodisplay,'Name',['Identifiability indices in the ',fsuffix,' moments.']);
+ bar(sum(SAcc))
+ set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
+ set(gca,'xlim',[0.5 npT+0.5])
+ ydum = get(gca,'ylim');
+ set(gca,'ylim',[0 ydum(2)])
+ set(gca,'position',[0.13 0.2 0.775 0.7])
+ for ip=1:npT
+ text(ip,-0.02*(ydum(2)),bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ end
+ xlabel(' ')
+ title(['Identifiability indices in the ',fsuffix,' moments.'],'interpreter','none')
+ dyn_saveas(hh,[OutputDirectoryName,'/',fname_,'_ident_ALL',fsuffix],options_.nodisplay,options_.graph_format);
+ create_TeX_loader(options_,[OutputDirectoryName,'/',fname_,'_ident_ALL',fsuffix],1,['Identifiability indices in the ',fsuffix,' moments.'],['ident_ALL',fsuffix]',1)
+
+ % figure, bar(SAmeanexo),
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:nshock])
+ % set(gca,'xlim',[0.5 nshock+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % set(gca,'position',[0.13 0.2 0.775 0.7])
+ % for ip=1:nshock,
+ % % text(ip,-0.02*(ydum(2)),deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % text(ip,-0.02*(ydum(2)),bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % title(['Identifiability indices for shocks',fsuffix],'interpreter','none')
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_ident_SHOCKS',fsuffix])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_ident_SHOCKS',fsuffix]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_ident_SHOCKS',fsuffix]);
end
return
diff --git a/matlab/gsa/mc_moments.m b/matlab/gsa/mc_moments.m
index cee56c22d3a08b13cb4013c7afc19503cc2d8891..b7464a7e0d4a85404041278bbaede3090670018e 100644
--- a/matlab/gsa/mc_moments.m
+++ b/matlab/gsa/mc_moments.m
@@ -19,19 +19,19 @@ function [vdec, cc, ac] = mc_moments(mm, ss, dr)
global options_ M_ estim_params_ oo_
- [nr1, nc1, nsam] = size(mm);
- nobs=size(options_.varobs,2);
- disp('Computing theoretical moments ...')
- h = dyn_waitbar(0,'Theoretical moments ...');
- vdec = zeros(nobs,M_.exo_nbr,nsam);
- cc = zeros(nobs,nobs,nsam);
- ac = zeros(nobs,nobs*options_.ar,nsam);
-
- for j=1:nsam
+[nr1, nc1, nsam] = size(mm);
+nobs=size(options_.varobs,2);
+disp('Computing theoretical moments ...')
+h = dyn_waitbar(0,'Theoretical moments ...');
+vdec = zeros(nobs,M_.exo_nbr,nsam);
+cc = zeros(nobs,nobs,nsam);
+ac = zeros(nobs,nobs*options_.ar,nsam);
+
+for j=1:nsam
oo_.dr.ghx = mm(:, [1:(nc1-M_.exo_nbr)],j);
oo_.dr.ghu = mm(:, [(nc1-M_.exo_nbr+1):end], j);
if ~isempty(ss)
- set_shocks_param(ss(j,:));
+ set_shocks_param(ss(j,:));
end
[vdec(:,:,j), corr, autocorr, z, zz] = th_moments(oo_.dr,options_.varobs);
cc(:,:,j)=triu(corr);
@@ -41,7 +41,7 @@ global options_ M_ estim_params_ oo_
end
ac(:,:,j)=dum;
dyn_waitbar(j/nsam,h)
- end
- dyn_waitbar_close(h)
- skipline()
- disp('... done !')
+end
+dyn_waitbar_close(h)
+skipline()
+disp('... done !')
diff --git a/matlab/gsa/mcf_analysis.m b/matlab/gsa/mcf_analysis.m
index a8661722cae046d1ffeafa168f1729a74605b88e..5523d7d7b722d168baa78f956ccb11129c05b8f2 100644
--- a/matlab/gsa/mcf_analysis.m
+++ b/matlab/gsa/mcf_analysis.m
@@ -1,84 +1,84 @@
-function indmcf = mcf_analysis(lpmat, ibeha, inobeha, options_mcf, DynareOptions)
-%
-% Written by Marco Ratto
-% Joint Research Centre, The European Commission,
-% marco.ratto@ec.europa.eu
-%
-
-% Copyright (C) 2014 European Commission
-% Copyright (C) 2016 Dynare Team
-%
-% 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/>.
-
-pvalue_ks = options_mcf.pvalue_ks;
-pvalue_corr = options_mcf.pvalue_corr;
-alpha2 = options_mcf.alpha2;
-param_names = options_mcf.param_names;
-
-if DynareOptions.TeX
- if ~isfield(options_mcf,'param_names_tex')
- param_names_tex = options_mcf.param_names;
- else
- param_names_tex = options_mcf.param_names_tex;
- end
-end
-amcf_name = options_mcf.amcf_name;
-amcf_title = options_mcf.amcf_title;
-beha_title = options_mcf.beha_title;
-nobeha_title = options_mcf.nobeha_title;
-title = options_mcf.title;
-fname_ = options_mcf.fname_;
-xparam1=[];
-if isfield(options_mcf,'xparam1')
- xparam1=options_mcf.xparam1;
-end
-OutputDirectoryName = options_mcf.OutputDirectoryName;
-
-[proba, dproba] = stab_map_1(lpmat, ibeha, inobeha, [],0);
-indmcf=find(proba<pvalue_ks);
-[tmp,jtmp] = sort(proba(indmcf),2,'ascend');
-indmcf = indmcf(jtmp);
-if ~isempty(indmcf)
- skipline()
- headers=char('Parameter','d-stat','p-value');
- labels=char(param_names(indmcf,:));
- data_mat=[dproba(indmcf)' proba(indmcf)'];
- options_temp.noprint=0;
- dyntable(options_temp,['Smirnov statistics in driving ', title],headers,labels,data_mat,size(labels,2)+2,16,3);
- if DynareOptions.TeX
- labels_TeX=param_names_tex(indmcf,:);
- M_temp.dname=OutputDirectoryName ;
- M_temp.fname=fname_;
- dyn_latex_table(M_temp,options_temp,['Smirnov statistics in driving ', strrep(title,'_','\\_')],amcf_name,headers,labels_TeX,data_mat,size(labels,2)+2,16,6);
- end
-end
-
-
-if length(ibeha)>10 && length(inobeha)>10
- indcorr1 = stab_map_2(lpmat(ibeha,:),alpha2, pvalue_corr, beha_title);
- indcorr2 = stab_map_2(lpmat(inobeha,:),alpha2, pvalue_corr, nobeha_title);
- indcorr = union(indcorr1(:), indcorr2(:));
- indcorr = indcorr(~ismember(indcorr(:),indmcf));
- indmcf = [indmcf(:); indcorr(:)];
-end
-if ~isempty(indmcf) && ~DynareOptions.nograph
- skipline()
- xx=[];
- if ~ isempty(xparam1), xx=xparam1(indmcf); end
- scatter_mcf(lpmat(ibeha,indmcf),lpmat(inobeha,indmcf), param_names(indmcf,:), ...
- '.', [fname_,'_',amcf_name], OutputDirectoryName, amcf_title,xx, DynareOptions, ...
- beha_title, nobeha_title)
-end
+function indmcf = mcf_analysis(lpmat, ibeha, inobeha, options_mcf, DynareOptions)
+%
+% Written by Marco Ratto
+% Joint Research Centre, The European Commission,
+% marco.ratto@ec.europa.eu
+%
+
+% Copyright (C) 2014 European Commission
+% Copyright (C) 2016 Dynare Team
+%
+% 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/>.
+
+pvalue_ks = options_mcf.pvalue_ks;
+pvalue_corr = options_mcf.pvalue_corr;
+alpha2 = options_mcf.alpha2;
+param_names = options_mcf.param_names;
+
+if DynareOptions.TeX
+ if ~isfield(options_mcf,'param_names_tex')
+ param_names_tex = options_mcf.param_names;
+ else
+ param_names_tex = options_mcf.param_names_tex;
+ end
+end
+amcf_name = options_mcf.amcf_name;
+amcf_title = options_mcf.amcf_title;
+beha_title = options_mcf.beha_title;
+nobeha_title = options_mcf.nobeha_title;
+title = options_mcf.title;
+fname_ = options_mcf.fname_;
+xparam1=[];
+if isfield(options_mcf,'xparam1')
+ xparam1=options_mcf.xparam1;
+end
+OutputDirectoryName = options_mcf.OutputDirectoryName;
+
+[proba, dproba] = stab_map_1(lpmat, ibeha, inobeha, [],0);
+indmcf=find(proba<pvalue_ks);
+[tmp,jtmp] = sort(proba(indmcf),2,'ascend');
+indmcf = indmcf(jtmp);
+if ~isempty(indmcf)
+ skipline()
+ headers=char('Parameter','d-stat','p-value');
+ labels=char(param_names(indmcf,:));
+ data_mat=[dproba(indmcf)' proba(indmcf)'];
+ options_temp.noprint=0;
+ dyntable(options_temp,['Smirnov statistics in driving ', title],headers,labels,data_mat,size(labels,2)+2,16,3);
+ if DynareOptions.TeX
+ labels_TeX=param_names_tex(indmcf,:);
+ M_temp.dname=OutputDirectoryName ;
+ M_temp.fname=fname_;
+ dyn_latex_table(M_temp,options_temp,['Smirnov statistics in driving ', strrep(title,'_','\\_')],amcf_name,headers,labels_TeX,data_mat,size(labels,2)+2,16,6);
+ end
+end
+
+
+if length(ibeha)>10 && length(inobeha)>10
+ indcorr1 = stab_map_2(lpmat(ibeha,:),alpha2, pvalue_corr, beha_title);
+ indcorr2 = stab_map_2(lpmat(inobeha,:),alpha2, pvalue_corr, nobeha_title);
+ indcorr = union(indcorr1(:), indcorr2(:));
+ indcorr = indcorr(~ismember(indcorr(:),indmcf));
+ indmcf = [indmcf(:); indcorr(:)];
+end
+if ~isempty(indmcf) && ~DynareOptions.nograph
+ skipline()
+ xx=[];
+ if ~ isempty(xparam1), xx=xparam1(indmcf); end
+ scatter_mcf(lpmat(ibeha,indmcf),lpmat(inobeha,indmcf), param_names(indmcf,:), ...
+ '.', [fname_,'_',amcf_name], OutputDirectoryName, amcf_title,xx, DynareOptions, ...
+ beha_title, nobeha_title)
+end
diff --git a/matlab/gsa/myboxplot.m b/matlab/gsa/myboxplot.m
index 39eba89603b82ddd16da4c2474308c8fbc678eba..cdb4b4661d554408396c89e7b6fff32e88e182aa 100644
--- a/matlab/gsa/myboxplot.m
+++ b/matlab/gsa/myboxplot.m
@@ -3,7 +3,7 @@ function sout = myboxplot (data,notched,symbol,vertical,maxwhisker)
% Written by Marco Ratto
% Joint Research Centre, The European Commission,
-% marco.ratto@ec.europa.eu
+% marco.ratto@ec.europa.eu
% Copyright (C) 2012 European Commission
% Copyright (C) 2012 Dynare Team
@@ -36,10 +36,10 @@ a=1-notched;
% ## figure out how many data sets we have
if iscell(data)
- nc = length(data);
+ nc = length(data);
else
-% if isvector(data), data = data(:); end
- nc = size(data,2);
+ % if isvector(data), data = data(:); end
+ nc = size(data,2);
end
% ## compute statistics
@@ -63,60 +63,60 @@ for i=1:nc
else
col = data(:,i);
end
-% ## Skip missing data
-% % % % % % % col(isnan(col) | isna (col)) = [];
- col(isnan(col)) = [];
-
- % ## Remember the data length
- nd = length(col);
- box(i) = nd;
- if (nd > 1)
-% ## min,max and quartiles
-% s(1:5,i) = statistics(col)(1:5);
-s(1,i)=min(col);
-s(5,i)=max(col);
-s(2,i)=myprctilecol(col,25);
-s(3,i)=myprctilecol(col,50);
-s(4,i)=myprctilecol(col,75);
-
-
-
-
-
-
-
-
-% ## confidence interval for the median
- est = 1.57*(s(4,i)-s(2,i))/sqrt(nd);
- s(6,i) = max([s(3,i)-est, s(2,i)]);
- s(7,i) = min([s(3,i)+est, s(4,i)]);
-% ## whiskers out to the last point within the desired inter-quartile range
- IQR = maxwhisker*(s(4,i)-s(2,i));
- whisker_y(:,i) = [min(col(col >= s(2,i)-IQR)); s(2,i)];
- whisker_y(:,nc+i) = [max(col(col <= s(4,i)+IQR)); s(4,i)];
-% ## outliers beyond 1 and 2 inter-quartile ranges
- outliers = col((col < s(2,i)-IQR & col >= s(2,i)-2*IQR) | (col > s(4,i)+IQR & col <= s(4,i)+2*IQR));
- outliers2 = col(col < s(2,i)-2*IQR | col > s(4,i)+2*IQR);
- outliers_x = [outliers_x; i*ones(size(outliers))];
- outliers_y = [outliers_y; outliers];
- outliers2_x = [outliers2_x; i*ones(size(outliers2))];
- outliers2_y = [outliers2_y; outliers2];
- elseif (nd == 1)
-% ## all statistics collapse to the value of the point
- s(:,i) = col;
-% ## single point data sets are plotted as outliers.
- outliers_x = [outliers_x; i];
- outliers_y = [outliers_y; col];
- else
-% ## no statistics if no points
- s(:,i) = NaN;
- end
+ % ## Skip missing data
+ % % % % % % % col(isnan(col) | isna (col)) = [];
+ col(isnan(col)) = [];
+
+ % ## Remember the data length
+ nd = length(col);
+ box(i) = nd;
+ if (nd > 1)
+ % ## min,max and quartiles
+ % s(1:5,i) = statistics(col)(1:5);
+ s(1,i)=min(col);
+ s(5,i)=max(col);
+ s(2,i)=myprctilecol(col,25);
+ s(3,i)=myprctilecol(col,50);
+ s(4,i)=myprctilecol(col,75);
+
+
+
+
+
+
+
+
+ % ## confidence interval for the median
+ est = 1.57*(s(4,i)-s(2,i))/sqrt(nd);
+ s(6,i) = max([s(3,i)-est, s(2,i)]);
+ s(7,i) = min([s(3,i)+est, s(4,i)]);
+ % ## whiskers out to the last point within the desired inter-quartile range
+ IQR = maxwhisker*(s(4,i)-s(2,i));
+ whisker_y(:,i) = [min(col(col >= s(2,i)-IQR)); s(2,i)];
+ whisker_y(:,nc+i) = [max(col(col <= s(4,i)+IQR)); s(4,i)];
+ % ## outliers beyond 1 and 2 inter-quartile ranges
+ outliers = col((col < s(2,i)-IQR & col >= s(2,i)-2*IQR) | (col > s(4,i)+IQR & col <= s(4,i)+2*IQR));
+ outliers2 = col(col < s(2,i)-2*IQR | col > s(4,i)+2*IQR);
+ outliers_x = [outliers_x; i*ones(size(outliers))];
+ outliers_y = [outliers_y; outliers];
+ outliers2_x = [outliers2_x; i*ones(size(outliers2))];
+ outliers2_y = [outliers2_y; outliers2];
+ elseif (nd == 1)
+ % ## all statistics collapse to the value of the point
+ s(:,i) = col;
+ % ## single point data sets are plotted as outliers.
+ outliers_x = [outliers_x; i];
+ outliers_y = [outliers_y; col];
+ else
+ % ## no statistics if no points
+ s(:,i) = NaN;
+ end
end
% % % % if isempty(outliers2_y)
% % % % outliers2_y=
% ## Note which boxes don't have enough stats
chop = find(box <= 1);
-
+
% ## Draw a box around the quartiles, with width proportional to the number of
% ## items in the box. Draw notches if desired.
box = box*0.23/max(box);
@@ -135,7 +135,7 @@ whisker_x(:,[chop,chop+nc]) = [];
whisker_y(:,[chop,chop+nc]) = [];
median_x(:,chop) = [];
median_y(:,chop) = [];
-% % % %
+% % % %
% ## Add caps to the remaining whiskers
cap_x = whisker_x;
cap_x(1,:) =cap_x(1,:)- 0.05;
@@ -146,7 +146,7 @@ cap_y = whisker_y([1,1],:);
% #whisker_x,whisker_y
% #median_x,median_y
% #cap_x,cap_y
-%
+%
% ## Do the plot
mm=min(min(data));
@@ -154,25 +154,25 @@ MM=max(max(data));
if vertical
plot (quartile_x, quartile_y, 'b', ...
- whisker_x, whisker_y, 'b--', ...
- cap_x, cap_y, 'k', ...
- median_x, median_y, 'r', ...
- outliers_x, outliers_y, [symbol(1),'r'], ...
- outliers2_x, outliers2_y, [symbol(2),'r']);
- set(gca,'XTick',1:nc);
- set(gca, 'XLim', [0.5, nc+0.5]);
- set(gca, 'YLim', [mm-(MM-mm)*0.05-eps, MM+(MM-mm)*0.05+eps]);
+ whisker_x, whisker_y, 'b--', ...
+ cap_x, cap_y, 'k', ...
+ median_x, median_y, 'r', ...
+ outliers_x, outliers_y, [symbol(1),'r'], ...
+ outliers2_x, outliers2_y, [symbol(2),'r']);
+ set(gca,'XTick',1:nc);
+ set(gca, 'XLim', [0.5, nc+0.5]);
+ set(gca, 'YLim', [mm-(MM-mm)*0.05-eps, MM+(MM-mm)*0.05+eps]);
else
-% % % % % plot (quartile_y, quartile_x, "b;;",
-% % % % % whisker_y, whisker_x, "b;;",
-% % % % % cap_y, cap_x, "b;;",
-% % % % % median_y, median_x, "r;;",
-% % % % % outliers_y, outliers_x, [symbol(1),"r;;"],
-% % % % % outliers2_y, outliers2_x, [symbol(2),"r;;"]);
+ % % % % % plot (quartile_y, quartile_x, "b;;",
+ % % % % % whisker_y, whisker_x, "b;;",
+ % % % % % cap_y, cap_x, "b;;",
+ % % % % % median_y, median_x, "r;;",
+ % % % % % outliers_y, outliers_x, [symbol(1),"r;;"],
+ % % % % % outliers2_y, outliers2_x, [symbol(2),"r;;"]);
end
if nargout
- sout=s;
+ sout=s;
end
% % % endfunction
\ No newline at end of file
diff --git a/matlab/gsa/myprctilecol.m b/matlab/gsa/myprctilecol.m
index 21c139d6b9ff6ab34678ff830c3e78978c842b5f..2cc7f473f60816f71886d5b9ab1f02dc79dbbe71 100644
--- a/matlab/gsa/myprctilecol.m
+++ b/matlab/gsa/myprctilecol.m
@@ -2,7 +2,7 @@ function y = myprctilecol(x,p)
% Written by Marco Ratto
% Joint Research Centre, The European Commission,
-% marco.ratto@ec.europa.eu
+% marco.ratto@ec.europa.eu
% Copyright (C) 2012 European Commission
% Copyright (C) 2012 Dynare Team
@@ -27,10 +27,10 @@ xx = sort(x);
if m==1 | n==1
m = max(m,n);
- if m == 1
- y = x*ones(length(p),1);
- return
- end
+ if m == 1
+ y = x*ones(length(p),1);
+ return
+ end
n = 1;
q = 100*(0.5:m - 0.5)./m;
xx = [min(x); xx(:); max(x)];
diff --git a/matlab/gsa/pick.m b/matlab/gsa/pick.m
index 8a3f53a5564ee334da1cbfa2224289fc5b3669ca..9ff6e2c05ce3d9c159975fa531a4b2f75f1964f9 100644
--- a/matlab/gsa/pick.m
+++ b/matlab/gsa/pick.m
@@ -1,90 +1,90 @@
-function pick
-%
-% Copyright (C) 2001-2017 European Commission
-%
-% This file is part of GLUEWIN
-% GLUEWIN is a MATLAB code designed for analysing the output
-% of Monte Carlo runs when empirical observations of the model output are available
-% and implements the GSA-GLUE methodology by Ratto et al. [1], based on a combination
-% of GLUE (Generalised Likelihood Uncertainty Estimation) by K. Beven [2] and GSA
-% Global Sensitivity Analysis) [3].']
-% The program has been developed by M. Ratto, European Commission, Joint Research Centre,
-% Institute for the Protection and Security of The Citizen, Technological and Economic Risk Management,
-% Applied Statistics, as a deliverable of the IMPACT project
-% (EC Fifth Framework Programme, SCA Project, IST-1999-11313, DG-INFSO).
-%
-% The graphical layout of the code is inspired by the freeware GLUE package by K. Beven,
-% vailable at the Lancaster University web site on the page [4]:
-% http://www.es.lancs.ac.uk/hfdg/glue.html
-% to which the GLUEWIN code introduces several extensions and additional options.
-% Thanks are due to R. Girardi, A. Rossi, A. Saltelli, S. Tarantola and U. Callies for numerous
-% comments and suggestions.
-% For more information, please contact marco.ratto@ec.europa.eu
-%
-% Disclaimer: This software has been developed at the Joint Research Centre of European Commission
-% by officers in the course of their official duties. This software is not subject to copyright
-% protection and is in the public domain. It is an experimental system. The Joint Research Centre
-% of European Commission assumes no responsibility whatsoever for its use by other parties
-% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
-% characteristic. We would appreciate acknowledgement if the software is used.
-%
-% [1] Ratto, M., Tarantola, S., A. Saltelli, Sensitivity analysis in model calibration: GSA-GLUE approach.
-% 'Computer Physics Communications, 136, 2001, 212-224
-% [2] Beven K.J., Binley A., The Future of Distributed Models: Model Calibration and Uncertainty
-% 'Prediction, Hydrological Processes, 6, 279-298, 1992
-% [3] Saltelli, A., K. Chan, M. Scott, Editors, (2000), Sensitivity analysis, John Wiley & Sons
-% 'publishers, Probability and Statistics series.
-% [4] Beven K., GLUE for Windows User manual, 1998.
-
-
-
-pmenu=findobj(gcf,'type','uicontextmenu','Tag','Run viewer');
-button1=findobj(gcf,'type','uimenu','Tag','save params');
-button2=findobj(gcf,'type','uimenu','Tag','eval params');
-%button=get(pmenu,'children');
-gg=gco;
-ax0=gca;
-set(gg,'buttondownfcn',[]);
-c=get(gca,'currentpoint');
-x=c(1,1);
-y=c(1,2);
-X=get(gco,'xdata');
-Y=get(gco,'ydata');
-dx=get(gca,'xlim');
-dy=get(gca,'ylim');
-pos=get(gca,'position');
-scalex=dx(2)-dx(1);
-scaley=dy(2)-dy(1);
-if length(X)>1
- K = dsearchn([(Y./scaley)' (X./scalex)'],[y/scaley x/scalex]);
-else
- az=get(gca,'children');
- T =get(az(end),'ydata');
- [dum K]=max(T);
-end
-
-KK=K;
-
-set(button1,'Label',['Save ',num2str(K)],'Callback',['scatter_callback(',num2str(KK),',''save'')']);
-set(button2,'Label',['Eval ',num2str(K)],'Callback',['scatter_callback(',num2str(KK),',''eval'')']);
-hh=findobj(gcf,'type','axes','Tag','scatter');
-for k=1:length(hh)
- axes(hh(k));
- dum=get(gca,'children');
- dumx=get(dum(end),'xdata');
- dumy=get(dum(end),'ydata');
- xmid=min(dumx) + 0.5*(max(dumx)-min(dumx));
- hold on
- plot(dumx(KK),dumy(KK),'or');
- if dumx(KK) < xmid
- text(dumx(KK),dumy(KK),[' ',num2str(K)], ...
- 'FontWeight','Bold',...
- 'Color','r');
- else
- text(dumx(KK),dumy(KK),[num2str(K),' '], ...
- 'HorizontalAlignment','right', ...
- 'FontWeight','Bold',...
- 'Color','r');
- end
- hold off
+function pick
+%
+% Copyright (C) 2001-2017 European Commission
+%
+% This file is part of GLUEWIN
+% GLUEWIN is a MATLAB code designed for analysing the output
+% of Monte Carlo runs when empirical observations of the model output are available
+% and implements the GSA-GLUE methodology by Ratto et al. [1], based on a combination
+% of GLUE (Generalised Likelihood Uncertainty Estimation) by K. Beven [2] and GSA
+% Global Sensitivity Analysis) [3].']
+% The program has been developed by M. Ratto, European Commission, Joint Research Centre,
+% Institute for the Protection and Security of The Citizen, Technological and Economic Risk Management,
+% Applied Statistics, as a deliverable of the IMPACT project
+% (EC Fifth Framework Programme, SCA Project, IST-1999-11313, DG-INFSO).
+%
+% The graphical layout of the code is inspired by the freeware GLUE package by K. Beven,
+% vailable at the Lancaster University web site on the page [4]:
+% http://www.es.lancs.ac.uk/hfdg/glue.html
+% to which the GLUEWIN code introduces several extensions and additional options.
+% Thanks are due to R. Girardi, A. Rossi, A. Saltelli, S. Tarantola and U. Callies for numerous
+% comments and suggestions.
+% For more information, please contact marco.ratto@ec.europa.eu
+%
+% Disclaimer: This software has been developed at the Joint Research Centre of European Commission
+% by officers in the course of their official duties. This software is not subject to copyright
+% protection and is in the public domain. It is an experimental system. The Joint Research Centre
+% of European Commission assumes no responsibility whatsoever for its use by other parties
+% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
+% characteristic. We would appreciate acknowledgement if the software is used.
+%
+% [1] Ratto, M., Tarantola, S., A. Saltelli, Sensitivity analysis in model calibration: GSA-GLUE approach.
+% 'Computer Physics Communications, 136, 2001, 212-224
+% [2] Beven K.J., Binley A., The Future of Distributed Models: Model Calibration and Uncertainty
+% 'Prediction, Hydrological Processes, 6, 279-298, 1992
+% [3] Saltelli, A., K. Chan, M. Scott, Editors, (2000), Sensitivity analysis, John Wiley & Sons
+% 'publishers, Probability and Statistics series.
+% [4] Beven K., GLUE for Windows User manual, 1998.
+
+
+
+pmenu=findobj(gcf,'type','uicontextmenu','Tag','Run viewer');
+button1=findobj(gcf,'type','uimenu','Tag','save params');
+button2=findobj(gcf,'type','uimenu','Tag','eval params');
+%button=get(pmenu,'children');
+gg=gco;
+ax0=gca;
+set(gg,'buttondownfcn',[]);
+c=get(gca,'currentpoint');
+x=c(1,1);
+y=c(1,2);
+X=get(gco,'xdata');
+Y=get(gco,'ydata');
+dx=get(gca,'xlim');
+dy=get(gca,'ylim');
+pos=get(gca,'position');
+scalex=dx(2)-dx(1);
+scaley=dy(2)-dy(1);
+if length(X)>1
+ K = dsearchn([(Y./scaley)' (X./scalex)'],[y/scaley x/scalex]);
+else
+ az=get(gca,'children');
+ T =get(az(end),'ydata');
+ [dum K]=max(T);
+end
+
+KK=K;
+
+set(button1,'Label',['Save ',num2str(K)],'Callback',['scatter_callback(',num2str(KK),',''save'')']);
+set(button2,'Label',['Eval ',num2str(K)],'Callback',['scatter_callback(',num2str(KK),',''eval'')']);
+hh=findobj(gcf,'type','axes','Tag','scatter');
+for k=1:length(hh)
+ axes(hh(k));
+ dum=get(gca,'children');
+ dumx=get(dum(end),'xdata');
+ dumy=get(dum(end),'ydata');
+ xmid=min(dumx) + 0.5*(max(dumx)-min(dumx));
+ hold on
+ plot(dumx(KK),dumy(KK),'or');
+ if dumx(KK) < xmid
+ text(dumx(KK),dumy(KK),[' ',num2str(K)], ...
+ 'FontWeight','Bold',...
+ 'Color','r');
+ else
+ text(dumx(KK),dumy(KK),[num2str(K),' '], ...
+ 'HorizontalAlignment','right', ...
+ 'FontWeight','Bold',...
+ 'Color','r');
+ end
+ hold off
end
\ No newline at end of file
diff --git a/matlab/gsa/prior_draw_gsa.m b/matlab/gsa/prior_draw_gsa.m
index a54fda1b3b6d8b6d395caf0285e546e994f560fa..3078b3f0cdadfcefdcc834ad0497aaa68cad909b 100644
--- a/matlab/gsa/prior_draw_gsa.m
+++ b/matlab/gsa/prior_draw_gsa.m
@@ -1,22 +1,22 @@
function pdraw = prior_draw_gsa(init,rdraw)
% Draws from the prior distributions for use with Sensitivity Toolbox for DYNARE
-%
+%
% INPUTS
% o init [integer] scalar equal to 1 (first call) or 0.
-% o rdraw
-%
-% OUTPUTS
-% o pdraw [double] draw from the joint prior density.
+% o rdraw
+%
+% OUTPUTS
+% o pdraw [double] draw from the joint prior density.
%
-% ALGORITHM
-% ...
+% ALGORITHM
+% ...
%
% SPECIAL REQUIREMENTS
% MATLAB Statistics Toolbox
-%
+%
% Written by Marco Ratto
% Joint Research Centre, The European Commission,
-% marco.ratto@ec.europa.eu
+% marco.ratto@ec.europa.eu
% Copyright (C) 2012-2015 European Commission
% Copyright (C) 2012-2015 Dynare Team
@@ -38,7 +38,7 @@ function pdraw = prior_draw_gsa(init,rdraw)
global bayestopt_ options_ estim_params_ M_
persistent npar pshape p6 p7 p3 p4 lbcum ubcum
-
+
if init
pshape = bayestopt_.pshape;
p6 = bayestopt_.p6;
@@ -56,40 +56,40 @@ if init
bounds.lb = max(bounds.lb,lb);
bounds.ub = min(bounds.ub,ub);
else % estimated parameters but no declared priors
- % No priors are declared so Dynare will estimate the model by
- % maximum likelihood with inequality constraints for the parameters.
+ % No priors are declared so Dynare will estimate the model by
+ % maximum likelihood with inequality constraints for the parameters.
bounds.lb = lb;
bounds.ub = ub;
end
% set bounds for cumulative probabilities
for i = 1:npar
- switch pshape(i)
- case 5% Uniform prior.
- p4(i) = min(p4(i),bounds.ub(i));
- p3(i) = max(p3(i),bounds.lb(i));
- case 3% Gaussian prior.
- lbcum(i) = 0.5 * erfc(-(bounds.lb(i)-p6(i))/p7(i) ./ sqrt(2));
- ubcum(i) = 0.5 * erfc(-(bounds.ub(i)-p6(i))/p7(i) ./ sqrt(2));
- case 2% Gamma prior.
- lbcum(i) = gamcdf(bounds.lb(i)-p3(i),p6(i),p7(i));
- ubcum(i) = gamcdf(bounds.ub(i)-p3(i),p6(i),p7(i));
- case 1% Beta distribution (TODO: generalized beta distribution)
- lbcum(i) = betainc((bounds.lb(i)-p3(i))./(p4(i)-p3(i)),p6(i),p7(i));
- ubcum(i) = betainc((bounds.ub(i)-p3(i))./(p4(i)-p3(i)),p6(i),p7(i));
- case 4% INV-GAMMA1 distribution
- % TO BE CHECKED
- lbcum(i) = gamcdf(1/(bounds.ub(i)-p3(i))^2,p7(i)/2,2/p6(i));
- ubcum(i) = gamcdf(1/(bounds.lb(i)-p3(i))^2,p7(i)/2,2/p6(i));
- case 6% INV-GAMMA2 distribution
- % TO BE CHECKED
- lbcum(i) = gamcdf(1/(bounds.ub(i)-p3(i)),p7(i)/2,2/p6(i));
- ubcum(i) = gamcdf(1/(bounds.lb(i)-p3(i)),p7(i)/2,2/p6(i));
- case 8
- lbcum(i) = weibcdf(bounds.lb(i)-p3(i),p6(i),p7(i));
- ubcum(i) = weibcdf(bounds.ub(i)-p3(i),p6(i),p7(i));
- otherwise
- % Nothing to do here.
- end
+ switch pshape(i)
+ case 5% Uniform prior.
+ p4(i) = min(p4(i),bounds.ub(i));
+ p3(i) = max(p3(i),bounds.lb(i));
+ case 3% Gaussian prior.
+ lbcum(i) = 0.5 * erfc(-(bounds.lb(i)-p6(i))/p7(i) ./ sqrt(2));
+ ubcum(i) = 0.5 * erfc(-(bounds.ub(i)-p6(i))/p7(i) ./ sqrt(2));
+ case 2% Gamma prior.
+ lbcum(i) = gamcdf(bounds.lb(i)-p3(i),p6(i),p7(i));
+ ubcum(i) = gamcdf(bounds.ub(i)-p3(i),p6(i),p7(i));
+ case 1% Beta distribution (TODO: generalized beta distribution)
+ lbcum(i) = betainc((bounds.lb(i)-p3(i))./(p4(i)-p3(i)),p6(i),p7(i));
+ ubcum(i) = betainc((bounds.ub(i)-p3(i))./(p4(i)-p3(i)),p6(i),p7(i));
+ case 4% INV-GAMMA1 distribution
+ % TO BE CHECKED
+ lbcum(i) = gamcdf(1/(bounds.ub(i)-p3(i))^2,p7(i)/2,2/p6(i));
+ ubcum(i) = gamcdf(1/(bounds.lb(i)-p3(i))^2,p7(i)/2,2/p6(i));
+ case 6% INV-GAMMA2 distribution
+ % TO BE CHECKED
+ lbcum(i) = gamcdf(1/(bounds.ub(i)-p3(i)),p7(i)/2,2/p6(i));
+ ubcum(i) = gamcdf(1/(bounds.lb(i)-p3(i)),p7(i)/2,2/p6(i));
+ case 8
+ lbcum(i) = weibcdf(bounds.lb(i)-p3(i),p6(i),p7(i));
+ ubcum(i) = weibcdf(bounds.ub(i)-p3(i),p6(i),p7(i));
+ otherwise
+ % Nothing to do here.
+ end
end
return
end
@@ -106,11 +106,11 @@ for i = 1:npar
pdraw(:,i) = gaminv(rdraw(:,i),p6(i),p7(i))+p3(i);
case 1% Beta distribution (TODO: generalized beta distribution)
pdraw(:,i) = betainv(rdraw(:,i),p6(i),p7(i))*(p4(i)-p3(i))+p3(i);
- case 4% INV-GAMMA1 distribution
- % TO BE CHECKED
+ case 4% INV-GAMMA1 distribution
+ % TO BE CHECKED
pdraw(:,i) = sqrt(1./gaminv(rdraw(:,i),p7(i)/2,2/p6(i)))+p3(i);
- case 6% INV-GAMMA2 distribution
- % TO BE CHECKED
+ case 6% INV-GAMMA2 distribution
+ % TO BE CHECKED
pdraw(:,i) = 1./gaminv(rdraw(:,i),p7(i)/2,2/p6(i))+p3(i);
case 8
pdraw(:,i) = wblinv(rdraw(:,i),p6(i),p7(i))+p3(i);
@@ -118,5 +118,3 @@ for i = 1:npar
% Nothing to do here.
end
end
-
-
diff --git a/matlab/gsa/read_data.m b/matlab/gsa/read_data.m
index 85674bc5e28c88e749efc04a00cc59ebf71dc90a..9b8c16c40bc663d6a283f36b81c6c09601f3329e 100644
--- a/matlab/gsa/read_data.m
+++ b/matlab/gsa/read_data.m
@@ -1,7 +1,7 @@
function [gend, data] = read_data()
% Written by Marco Ratto
% Joint Research Centre, The European Commission,
-% marco.ratto@ec.europa.eu
+% marco.ratto@ec.europa.eu
% Copyright (C) 2012-2015 European Commission
% Copyright (C) 2012-2015 Dynare Team
@@ -30,15 +30,15 @@ gend = options_.nobs;
rawdata = rawdata(options_.first_obs:options_.first_obs+gend-1,:);
if options_.loglinear == 1 & ~options_.logdata
- rawdata = log(rawdata);
+ rawdata = log(rawdata);
end
if options_.prefilter == 1
- data = transpose(rawdata-ones(gend,1)* mean(rawdata,1));
+ data = transpose(rawdata-ones(gend,1)* mean(rawdata,1));
else
- data = transpose(rawdata);
+ data = transpose(rawdata);
end
if ~isreal(rawdata)
- error(['There are complex values in the data. Probably a wrong' ...
- ' transformation'])
+ error(['There are complex values in the data. Probably a wrong' ...
+ ' transformation'])
end
diff --git a/matlab/gsa/redform_map.m b/matlab/gsa/redform_map.m
index 7d1b3787d7ca96993ff8c46449fbdf46fdcb68a4..a7ae7f712dd44dfc89858be4556395177b3d6dc4 100644
--- a/matlab/gsa/redform_map.m
+++ b/matlab/gsa/redform_map.m
@@ -13,7 +13,7 @@ function redform_map(dirname,options_gsa_)
%
% Written by Marco Ratto
% Joint Research Centre, The European Commission,
-% marco.ratto@ec.europa.eu
+% marco.ratto@ec.europa.eu
% Copyright (C) 2012-2016 European Commission
% Copyright (C) 2012-2017 Dynare Team
@@ -55,7 +55,7 @@ np = estim_params_.np;
nshock = estim_params_.nvx + estim_params_.nvn + estim_params_.ncx + estim_params_.ncn;
pnames=cell(np,1);
pnames_tex=cell(np,1);
-for jj=1:np
+for jj=1:np
if options_.TeX
[param_name_temp, param_name_tex_temp]= get_the_name(nshock+jj,options_.TeX,M_,estim_params_,options_);
pnames_tex{jj,1} = strrep(param_name_tex_temp,'$','');
@@ -158,7 +158,7 @@ for j=1:size(anamendo,1)
skipline()
disp(['[', namendo,' vs ',namexo,']'])
-
+
if ~isempty(iexo)
%y0=squeeze(T(iendo,iexo+nspred,istable));
y0=squeeze(T(iendo,iexo+nspred,:));
@@ -193,7 +193,7 @@ for j=1:size(anamendo,1)
mkdir(xdir)
end
if ~options_.nograph
- hf=dyn_figure(options_.nodisplay,'name',['Reduced Form Mapping (Monte Carlo Filtering): ',namendo,' vs ', namexo]);
+ hf=dyn_figure(options_.nodisplay,'name',['Reduced Form Mapping (Monte Carlo Filtering): ',namendo,' vs ', namexo]);
hc = cumplot(y0);
a=axis; delete(hc);
% hist(mat_moment{ij}),
@@ -211,7 +211,7 @@ for j=1:size(anamendo,1)
end
si(:,js) = NaN(np,1);
delete([xdir, '/*threshold*.*'])
-
+
atitle0=['Reduced Form Mapping (Monte Carlo Filtering) for ',namendo,' vs ', namexo];
aname=[type '_' namendo '_vs_' namexo '_threshold'];
atitle=[type ' Reduced Form Mapping (Monte Carlo Filtering): Parameter(s) driving ',namendo,' vs ',namexo];
@@ -224,7 +224,7 @@ for j=1:size(anamendo,1)
if ~isempty(iy) && ~isempty(iyc)
fprintf(['%4.1f%% of the ',type,' support matches ',atitle0,'\n'],length(iy)/length(y0)*100)
icheck = mcf_analysis(x0, iy, iyc, options_mcf, options_);
-
+
lpmat=x0(iy,:);
if nshocks
lpmat0=xx0(iy,:);
@@ -239,7 +239,7 @@ for j=1:size(anamendo,1)
atitle0=['Monte Carlo Filtering for ',namendo,' vs ', namexo];
options_mcf.title = atitle0;
indmcf = redform_mcf(y0, x0, options_mcf, options_);
-
+
end
end
else
@@ -254,7 +254,7 @@ for j=1:size(anamendo,1)
options_map.OutputDirectoryName = xdir0;
silog(:,js) = redform_private(x0, y0, options_map, options_);
end
-
+
if isempty(threshold) && ~options_.nograph
figure(hfig)
subplot(3,3,iplo),
@@ -274,12 +274,12 @@ for j=1:size(anamendo,1)
text(ip,-0.02,deblank(pnames(iso(ip),:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
title([logflag,' ',namendo,' vs ',namexo],'interpreter','none')
- if iplo==9
+ if iplo==9
dyn_saveas(hfig,[dirname,filesep,M_.fname,'_redform_', namendo,'_vs_shocks_',logflag,num2str(ifig)],options_.nodisplay,options_.graph_format);
create_TeX_loader(options_,[dirname,filesep,M_.fname,'_redform_', namendo,'_vs_shocks_',logflag,num2str(ifig)],[logflag,' ',strrep(namendo,'_','\_'),' vs ',strrep(namexo,'_','\_')],['redform_', namendo,'_vs_shocks_',logflag,num2str(ifig)],1)
end
end
-
+
end
end
end
@@ -294,7 +294,7 @@ for j=1:size(anamendo,1)
ilagendo=strmatch(namlagendo,M_.endo_names(oo_.dr.order_var(M_.nstatic+1:M_.nstatic+nsok),:),'exact');
skipline()
disp(['[', namendo,' vs lagged ',namlagendo,']'])
-
+
if ~isempty(ilagendo)
%y0=squeeze(T(iendo,ilagendo,istable));
y0=squeeze(T(iendo,ilagendo,:));
@@ -329,7 +329,7 @@ for j=1:size(anamendo,1)
mkdir(xdir)
end
if ~options_.nograph
- hf=dyn_figure(options_.nodisplay,'name',['Reduced Form Mapping (Monte Carlo Filtering): ',namendo,' vs lagged ', namlagendo]);
+ hf=dyn_figure(options_.nodisplay,'name',['Reduced Form Mapping (Monte Carlo Filtering): ',namendo,' vs lagged ', namlagendo]);
hc = cumplot(y0);
a=axis; delete(hc);
% hist(mat_moment{ij}),
@@ -345,9 +345,9 @@ for j=1:size(anamendo,1)
dyn_saveas(hf,[xdir,filesep, fname_ '_' type '_' namendo,'_vs_', namlagendo],options_.nodisplay,options_.graph_format);
create_TeX_loader(options_,[xdir,filesep, fname_ '_' type '_' namendo,'_vs_', namlagendo],['Reduced Form Mapping (Monte Carlo Filtering): ',strrep(namendo,'_','\_'),' vs lagged ', strrep(namlagendo,'_','\_')],[type '_' namendo,'_vs_', namlagendo],1)
end
-
+
delete([xdir, '/*threshold*.*'])
-
+
atitle0=['Reduced Form Mapping (Monte Carlo Filtering) for ',namendo,' vs ', namlagendo];
aname=[type '_' namendo '_vs_' namlagendo '_threshold'];
atitle=[type ' Reduced Form Mapping (Monte Carlo Filtering): Parameter(s) driving ',namendo,' vs ',namlagendo];
@@ -361,7 +361,7 @@ for j=1:size(anamendo,1)
fprintf(['%4.1f%% of the ',type,' support matches ',atitle0,'\n'],length(iy)/length(y0)*100)
icheck = mcf_analysis(x0, iy, iyc, options_mcf, options_);
-
+
lpmat=x0(iy,:);
if nshocks
lpmat0=xx0(iy,:);
@@ -369,7 +369,7 @@ for j=1:size(anamendo,1)
istable=[1:length(iy)];
save([xdir,filesep, fname_ '_' type '_' namendo,'_vs_', namlagendo '_threshold' ],'lpmat','lpmat0','istable','y0','x0','xx0','iy','iyc')
lpmat=[]; lpmat0=[]; istable=[];
-
+
else
icheck = [];
end
@@ -391,7 +391,7 @@ for j=1:size(anamendo,1)
options_map.OutputDirectoryName = xdir0;
silog(:,js) = redform_private(x0, y0, options_map, options_);
end
-
+
if isempty(threshold) && ~options_.nograph
figure(hfig),
subplot(3,3,iplo),
@@ -416,7 +416,7 @@ for j=1:size(anamendo,1)
create_TeX_loader(options_,[dirname,filesep,M_.fname,'_redform_', namendo,'_vs_lags_',logflag,num2str(ifig)],[logflag,' ',strrep(namendo,'_','\_'),' vs ',strrep(namlagendo,'_','\_'),'(-1)'],['redform_', namendo,'_vs_lags_',logflag,':',num2str(ifig)],1)
end
end
-
+
end
end
end
@@ -429,7 +429,7 @@ end
if isempty(threshold) && ~options_.nograph
if ilog==0
hfig=dyn_figure(options_.nodisplay,'name','Reduced Form GSA'); %bar(si)
- % boxplot(si','whis',10,'symbol','r.')
+ % boxplot(si','whis',10,'symbol','r.')
myboxplot(si',[],'.',[],10)
xlabel(' ')
set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
@@ -445,7 +445,7 @@ if isempty(threshold) && ~options_.nograph
else
hfig=dyn_figure(options_.nodisplay,'name','Reduced Form GSA'); %bar(silog)
- % boxplot(silog','whis',10,'symbol','r.')
+ % boxplot(silog','whis',10,'symbol','r.')
myboxplot(silog',[],'.',[],10)
set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
xlabel(' ')
@@ -458,7 +458,7 @@ if isempty(threshold) && ~options_.nograph
title('Reduced form GSA - Log-transformed elements')
dyn_saveas(hfig,[dirname,filesep,M_.fname,'_redform_gsa_log'],options_.nodisplay,options_.graph_format);
create_TeX_loader(options_,[dirname,filesep,M_.fname,'_redform_gsa_log'],'Reduced form GSA - Log-transformed elements','redform_gsa_log')
-
+
end
end
@@ -494,15 +494,15 @@ if iload==0
nest=min(250,nest);
nfit=min(1000,nrun);
% dotheplots = (nfit<=nest);
-% gsa_ = gsa_sdp(y0(1:nest), x0(1:nest,:), 2, [],[-1 -1 -1 -1 -1 0],[],0,[fname,'_est'], pnames);
+ % gsa_ = gsa_sdp(y0(1:nest), x0(1:nest,:), 2, [],[-1 -1 -1 -1 -1 0],[],0,[fname,'_est'], pnames);
[ys,is] = sort(y0);
istep = ceil(nrun/nest);
if istep>1
- iest = is(floor(istep/2):istep:end);
- nest = length(iest);
- irest = is(setdiff([1:nrun],[floor(istep/2):istep:nrun]));
- istep = ceil(length(irest)/(nfit-nest));
- ifit = union(iest, irest(1:istep:end));
+ iest = is(floor(istep/2):istep:end);
+ nest = length(iest);
+ irest = is(setdiff([1:nrun],[floor(istep/2):istep:nrun]));
+ istep = ceil(length(irest)/(nfit-nest));
+ ifit = union(iest, irest(1:istep:end));
else
warning('the number of samples is too small for ANOVA estimation')
si=nan(np,1);
@@ -512,9 +512,9 @@ if iload==0
ifit = union(ifit, irest(end));
end
nfit=length(ifit);
-% ifit = union(iest, irest(randperm(nrun-nest,nfit-nest)));
-% ifit = iest;
-% nfit=nest;
+ % ifit = union(iest, irest(randperm(nrun-nest,nfit-nest)));
+ % ifit = iest;
+ % nfit=nest;
ipred = setdiff([1:nrun],ifit);
if ilog
@@ -522,7 +522,7 @@ if iload==0
y1 = log(y0*isig+lam);
end
if ~options_.nograph
- hfig=dyn_figure(options_.nodisplay,'name',options_map.figtitle);
+ hfig=dyn_figure(options_.nodisplay,'name',options_map.figtitle);
subplot(221)
if ilog
hist(y1,30)
@@ -544,61 +544,61 @@ if iload==0
if ilog
[gsa22, gsa1, gsax] = ss_anova_log(y1(iest), x0(iest,:), isig, lam, gsa0);
end
-% if (gsa1.out.bic-gsa0.out.bic) < 10,
-% y00=y0;
-% gsa00=gsa0;
-% gsa0=gsa1;
-% y0=y1;
-% ilog=1;
-% end
-if nfit>nest
- % gsa_ = gsa_sdp(y0(1:nfit), x0(1:nfit,:), -2, gsa_.nvr*nest^3/nfit^3,[-1 -1 -1 -1 -1 0],[],0,fname, pnames);
- nvr = gsa0.nvr*nest^3/nfit^3;
- nvr(gsa0.stat<2) = gsa0.nvr(gsa0.stat<2)*nest^5/nfit^5;
- gsa_ = ss_anova(y0(ifit), x0(ifit,:), 1, 0, 2, nvr);
- if ilog
- gsa0 = gsa_;
- nvr1 = gsa1.nvr*nest^3/nfit^3;
- nvr1(gsa1.stat<2) = gsa1.nvr(gsa1.stat<2)*nest^5/nfit^5;
- nvrx = gsax.nvr*nest^3/nfit^3;
- nvrx(gsax.stat<2) = gsax.nvr(gsax.stat<2)*nest^5/nfit^5;
- [gsa22, gsa1, gsax] = ss_anova_log(y1(ifit), x0(ifit,:), isig, lam, gsa0, [nvr1' nvrx']);
-% gsa1 = ss_anova(y1(ifit), x0(ifit,:), 1, 0, 2, nvr);
-% gsa2=gsa1;
-% gsa2.y = gsa0.y;
-% gsa2.fit = (exp(gsa1.fit)-lam)*isig;
-% gsa2.f0 = mean(gsa2.fit);
-% gsa2.out.SSE = sum((gsa2.fit-gsa2.y).^2);
-% gsa2.out.bic = gsa2.out.bic-nest*log(gsa1.out.SSE)+nest*log(gsa2.out.SSE);
-% gsa2.r2 = 1-cov(gsa2.fit-gsa2.y)/cov(gsa2.y);
-% for j=1:np,
-% gsa2.fs(:,j) = exp(gsa1.fs(:,j)).*mean(exp(gsa1.fit-gsa1.f(:,j)))*isig-lam*isig-gsa2.f0;
-% gsa2.f(:,j) = exp(gsa1.f(:,j)).*mean(exp(gsa1.fit-gsa1.f(:,j)))*isig-lam*isig-gsa2.f0;
-% gsa2.si(j) = var(gsa2.f(:,j))/var(gsa2.y);
-% end
-% nvr = gsax.nvr*nest^3/nfit^3;
-% nvr(gsax.stat<2) = gsax.nvr(gsax.stat<2)*nest^5/nfit^5;
-% gsax = ss_anova([gsa2.y-gsa2.fit], x0(ifit,:), 1, 0, 2, nvr);
-% gsa22=gsa2;
-% gsa22.fit = gsa2.fit+gsax.fit;
-% gsa22.f0 = mean(gsa22.fit);
-% gsa22.out.SSE = sum((gsa22.fit-gsa22.y).^2);
-% gsa22.out.bic = nest*log(gsa22.out.SSE/nest) + (gsax.out.df+gsa2.out.df-1)*log(nest);
-% gsa22.r2 = 1-sum((gsa22.fit-gsa22.y).^2)/sum((gsa22.y-mean(gsa22.y)).^2);
-% for j=1:np,
-% gsa22.fs(:,j) = gsa2.fs(:,j)+gsax.fs(:,j);
-% gsa22.f(:,j) = gsa2.f(:,j)+gsax.f(:,j);
-% gsa22.si(j) = var(gsa22.f(:,j))/var(gsa22.y);
-% end
- gsa_ = gsa22;
- end
-else
- if ilog
- gsa_ = gsa22;
+ % if (gsa1.out.bic-gsa0.out.bic) < 10,
+ % y00=y0;
+ % gsa00=gsa0;
+ % gsa0=gsa1;
+ % y0=y1;
+ % ilog=1;
+ % end
+ if nfit>nest
+ % gsa_ = gsa_sdp(y0(1:nfit), x0(1:nfit,:), -2, gsa_.nvr*nest^3/nfit^3,[-1 -1 -1 -1 -1 0],[],0,fname, pnames);
+ nvr = gsa0.nvr*nest^3/nfit^3;
+ nvr(gsa0.stat<2) = gsa0.nvr(gsa0.stat<2)*nest^5/nfit^5;
+ gsa_ = ss_anova(y0(ifit), x0(ifit,:), 1, 0, 2, nvr);
+ if ilog
+ gsa0 = gsa_;
+ nvr1 = gsa1.nvr*nest^3/nfit^3;
+ nvr1(gsa1.stat<2) = gsa1.nvr(gsa1.stat<2)*nest^5/nfit^5;
+ nvrx = gsax.nvr*nest^3/nfit^3;
+ nvrx(gsax.stat<2) = gsax.nvr(gsax.stat<2)*nest^5/nfit^5;
+ [gsa22, gsa1, gsax] = ss_anova_log(y1(ifit), x0(ifit,:), isig, lam, gsa0, [nvr1' nvrx']);
+ % gsa1 = ss_anova(y1(ifit), x0(ifit,:), 1, 0, 2, nvr);
+ % gsa2=gsa1;
+ % gsa2.y = gsa0.y;
+ % gsa2.fit = (exp(gsa1.fit)-lam)*isig;
+ % gsa2.f0 = mean(gsa2.fit);
+ % gsa2.out.SSE = sum((gsa2.fit-gsa2.y).^2);
+ % gsa2.out.bic = gsa2.out.bic-nest*log(gsa1.out.SSE)+nest*log(gsa2.out.SSE);
+ % gsa2.r2 = 1-cov(gsa2.fit-gsa2.y)/cov(gsa2.y);
+ % for j=1:np,
+ % gsa2.fs(:,j) = exp(gsa1.fs(:,j)).*mean(exp(gsa1.fit-gsa1.f(:,j)))*isig-lam*isig-gsa2.f0;
+ % gsa2.f(:,j) = exp(gsa1.f(:,j)).*mean(exp(gsa1.fit-gsa1.f(:,j)))*isig-lam*isig-gsa2.f0;
+ % gsa2.si(j) = var(gsa2.f(:,j))/var(gsa2.y);
+ % end
+ % nvr = gsax.nvr*nest^3/nfit^3;
+ % nvr(gsax.stat<2) = gsax.nvr(gsax.stat<2)*nest^5/nfit^5;
+ % gsax = ss_anova([gsa2.y-gsa2.fit], x0(ifit,:), 1, 0, 2, nvr);
+ % gsa22=gsa2;
+ % gsa22.fit = gsa2.fit+gsax.fit;
+ % gsa22.f0 = mean(gsa22.fit);
+ % gsa22.out.SSE = sum((gsa22.fit-gsa22.y).^2);
+ % gsa22.out.bic = nest*log(gsa22.out.SSE/nest) + (gsax.out.df+gsa2.out.df-1)*log(nest);
+ % gsa22.r2 = 1-sum((gsa22.fit-gsa22.y).^2)/sum((gsa22.y-mean(gsa22.y)).^2);
+ % for j=1:np,
+ % gsa22.fs(:,j) = gsa2.fs(:,j)+gsax.fs(:,j);
+ % gsa22.f(:,j) = gsa2.f(:,j)+gsax.f(:,j);
+ % gsa22.si(j) = var(gsa22.f(:,j))/var(gsa22.y);
+ % end
+ gsa_ = gsa22;
+ end
else
- gsa_ = gsa0;
+ if ilog
+ gsa_ = gsa22;
+ else
+ gsa_ = gsa0;
+ end
end
-end
save([fname,'_map.mat'],'gsa_')
[sidum, iii]=sort(-gsa_.si);
gsa_.x0=x00(ifit,:);
@@ -613,13 +613,13 @@ end
subplot(223),
plot(y0(ifit),[gsa_.fit y0(ifit)],'.'),
r2 = gsa_.r2;
-% if ilog,
-% plot(y00(ifit),[log_trans_(gsa_.fit,'',isig,lam) y00(ifit)],'.'),
-% r2 = 1 - cov(log_trans_(gsa_.fit,'',isig,lam)-y00(ifit))/cov(y00(ifit));
-% else
-% plot(y0(ifit),[gsa_.fit y0(ifit)],'.'),
-% r2 = gsa_.r2;
-% end
+ % if ilog,
+ % plot(y00(ifit),[log_trans_(gsa_.fit,'',isig,lam) y00(ifit)],'.'),
+ % r2 = 1 - cov(log_trans_(gsa_.fit,'',isig,lam)-y00(ifit))/cov(y00(ifit));
+ % else
+ % plot(y0(ifit),[gsa_.fit y0(ifit)],'.'),
+ % r2 = gsa_.r2;
+ % end
title(['Learning sample fit - R2=' num2str(r2,2)],'interpreter','none')
if nfit<nrun
if ilog
@@ -643,7 +643,7 @@ end
end
else
% gsa_ = gsa_sdp_dyn(y0, x0, 0, [],[],[],0,fname, pnames);
-% gsa_ = gsa_sdp(y0, x0, 0, [],[],[],0,fname, pnames);
+ % gsa_ = gsa_sdp(y0, x0, 0, [],[],[],0,fname, pnames);
load([fname,'_map.mat'],'gsa_')
if ~options_.nograph
yf = ss_anova_fcast(x0, gsa_);
@@ -727,7 +727,7 @@ function indmcf = redform_mcf(y0, x0, options_mcf, options_)
hfig=dyn_figure(options_.nodisplay,'name',options_mcf.amcf_title);
[post_mean, post_median, post_var, hpd_interval, post_deciles, ...
- density] = posterior_moments(y0,1,0.9);
+ density] = posterior_moments(y0,1,0.9);
post_deciles = [-inf; post_deciles; inf];
for jt=1:10
@@ -763,16 +763,16 @@ aa(2)=0.02;
set(hleg,'Position',aa);
if ~isoctave
annotation('textbox', [0.25,0.01,0.5,0.05], ...
- 'String', options_mcf.title, ...
- 'Color','black',...
- 'FontWeight','bold',...
- 'interpreter','none',...
- 'horizontalalignment','center');
+ 'String', options_mcf.title, ...
+ 'Color','black',...
+ 'FontWeight','bold',...
+ 'interpreter','none',...
+ 'horizontalalignment','center');
end
dyn_saveas(hfig,[options_mcf.OutputDirectoryName filesep options_mcf.fname_,'_',options_mcf.amcf_name],options_.nodisplay,options_.graph_format);
create_TeX_loader(options_,[options_mcf.OutputDirectoryName filesep options_mcf.fname_,'_',options_mcf.amcf_name],strrep(options_mcf.amcf_title,'_','\_'),[options_mcf.fname_,'_',options_mcf.amcf_name])
-
+
return
function []=create_TeX_loader(options_,figpath,caption,label_name,scale_factor)
diff --git a/matlab/gsa/redform_screen.m b/matlab/gsa/redform_screen.m
index 1a53b7e536afeca7835dab612f335caaf66b90c3..d4c007174674d177c7750dc034f5bff55dda1b6c 100644
--- a/matlab/gsa/redform_screen.m
+++ b/matlab/gsa/redform_screen.m
@@ -8,7 +8,7 @@ function redform_screen(dirname, options_gsa_)
%
% Written by Marco Ratto
% Joint Research Centre, The European Commission,
-% marco.ratto@ec.europa.eu
+% marco.ratto@ec.europa.eu
% Copyright (C) 2012-2016 European Commission
% Copyright (C) 2012-2017 Dynare Team
@@ -40,7 +40,7 @@ nliv = options_gsa_.morris_nliv;
pnames = M_.param_names(estim_params_.param_vals(:,1),:);
if nargin==0
- dirname='';
+ dirname='';
end
load([dirname,'/',M_.fname,'_prior'],'lpmat','lpmat0','istable','T');
@@ -54,95 +54,95 @@ nsok = length(find(M_.lead_lag_incidence(M_.maximum_lag,:)));
js=0;
for j=1:size(anamendo,1)
- namendo=deblank(anamendo(j,:));
- iendo=strmatch(namendo,M_.endo_names(oo_.dr.order_var,:),'exact');
-
- iplo=0;
- ifig=0;
- for jx=1:size(anamexo,1)
- namexo=deblank(anamexo(jx,:));
- iexo=strmatch(namexo,M_.exo_names,'exact');
-
- if ~isempty(iexo)
- y0=teff(T(iendo,iexo+nspred,:),kn,istable);
- if ~isempty(y0)
- if mod(iplo,9)==0
- ifig=ifig+1;
- hh=dyn_figure(options_.nodisplay,'name',[namendo,' vs. shocks ',int2str(ifig)]);
- iplo=0;
+ namendo=deblank(anamendo(j,:));
+ iendo=strmatch(namendo,M_.endo_names(oo_.dr.order_var,:),'exact');
+
+ iplo=0;
+ ifig=0;
+ for jx=1:size(anamexo,1)
+ namexo=deblank(anamexo(jx,:));
+ iexo=strmatch(namexo,M_.exo_names,'exact');
+
+ if ~isempty(iexo)
+ y0=teff(T(iendo,iexo+nspred,:),kn,istable);
+ if ~isempty(y0)
+ if mod(iplo,9)==0
+ ifig=ifig+1;
+ hh=dyn_figure(options_.nodisplay,'name',[namendo,' vs. shocks ',int2str(ifig)]);
+ iplo=0;
+ end
+ iplo=iplo+1;
+ js=js+1;
+ subplot(3,3,iplo),
+ [SAmeas, SAMorris] = Morris_Measure_Groups(np+nshock, [lpmat0 lpmat], y0,nliv);
+ SAM = squeeze(SAMorris(nshock+1:end,1));
+ SA(:,js)=SAM./(max(SAM)+eps);
+ [saso, iso] = sort(-SA(:,js));
+ bar(SA(iso(1:min(np,10)),js))
+ %set(gca,'xticklabel',pnames(iso(1:min(np,10)),:),'fontsize',8)
+ set(gca,'xticklabel',' ','fontsize',10)
+ set(gca,'xlim',[0.5 10.5])
+ for ip=1:min(np,10)
+ text(ip,-0.02,deblank(pnames(iso(ip),:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ end
+ title([namendo,' vs. ',namexo],'interpreter','none')
+ if iplo==9
+ dyn_saveas(hh,[dirname,'/',M_.fname,'_', namendo,'_vs_shock_',num2str(ifig)],options_.nodisplay,options_.graph_format);
+ create_TeX_loader(options_,[dirname,'/',M_.fname,'_', namendo,'_vs_shock_',num2str(ifig)],ifig,[namendo,' vs. shocks ',int2str(ifig)],[namendo,'_vs_shock'],1)
+ end
+
+ end
end
- iplo=iplo+1;
- js=js+1;
- subplot(3,3,iplo),
- [SAmeas, SAMorris] = Morris_Measure_Groups(np+nshock, [lpmat0 lpmat], y0,nliv);
- SAM = squeeze(SAMorris(nshock+1:end,1));
- SA(:,js)=SAM./(max(SAM)+eps);
- [saso, iso] = sort(-SA(:,js));
- bar(SA(iso(1:min(np,10)),js))
- %set(gca,'xticklabel',pnames(iso(1:min(np,10)),:),'fontsize',8)
- set(gca,'xticklabel',' ','fontsize',10)
- set(gca,'xlim',[0.5 10.5])
- for ip=1:min(np,10)
- text(ip,-0.02,deblank(pnames(iso(ip),:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
- end
- title([namendo,' vs. ',namexo],'interpreter','none')
- if iplo==9
- dyn_saveas(hh,[dirname,'/',M_.fname,'_', namendo,'_vs_shock_',num2str(ifig)],options_.nodisplay,options_.graph_format);
- create_TeX_loader(options_,[dirname,'/',M_.fname,'_', namendo,'_vs_shock_',num2str(ifig)],ifig,[namendo,' vs. shocks ',int2str(ifig)],[namendo,'_vs_shock'],1)
- end
-
- end
end
- end
- if iplo<9 && iplo>0 && ifig
- dyn_saveas(hh,[dirname,'/',M_.fname,'_', namendo,'_vs_shocks_',num2str(ifig)],options_.nodisplay,options_.graph_format);
- create_TeX_loader(options_,[dirname,'/',M_.fname,'_', namendo,'_vs_shock_',num2str(ifig)],ifig,[namendo,' vs. shocks ',int2str(ifig)],[namendo,'_vs_shock'],options_.figures.textwidth*min(iplo/3))
- end
-
- iplo=0;
- ifig=0;
- for je=1:size(anamlagendo,1)
- namlagendo=deblank(anamlagendo(je,:));
- ilagendo=strmatch(namlagendo,M_.endo_names(oo_.dr.order_var(M_.nstatic+1:M_.nstatic+nsok),:),'exact');
-
- if ~isempty(ilagendo)
- y0=teff(T(iendo,ilagendo,:),kn,istable);
- if ~isempty(y0)
- if mod(iplo,9)==0
- ifig=ifig+1;
- hh=dyn_figure(options_.nodisplay,'name',[namendo,' vs. lagged endogenous ',int2str(ifig)]);
- iplo=0;
- end
- iplo=iplo+1;
- js=js+1;
- subplot(3,3,iplo),
- [SAmeas, SAMorris] = Morris_Measure_Groups(np+nshock, [lpmat0 lpmat], y0,nliv);
- SAM = squeeze(SAMorris(nshock+1:end,1));
- SA(:,js)=SAM./(max(SAM)+eps);
- [saso, iso] = sort(-SA(:,js));
- bar(SA(iso(1:min(np,10)),js))
- %set(gca,'xticklabel',pnames(iso(1:min(np,10)),:),'fontsize',8)
- set(gca,'xticklabel',' ','fontsize',10)
- set(gca,'xlim',[0.5 10.5])
- for ip=1:min(np,10)
- text(ip,-0.02,deblank(pnames(iso(ip),:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
- end
+ if iplo<9 && iplo>0 && ifig
+ dyn_saveas(hh,[dirname,'/',M_.fname,'_', namendo,'_vs_shocks_',num2str(ifig)],options_.nodisplay,options_.graph_format);
+ create_TeX_loader(options_,[dirname,'/',M_.fname,'_', namendo,'_vs_shock_',num2str(ifig)],ifig,[namendo,' vs. shocks ',int2str(ifig)],[namendo,'_vs_shock'],options_.figures.textwidth*min(iplo/3))
+ end
- title([namendo,' vs. ',namlagendo,'(-1)'],'interpreter','none')
- if iplo==9
- dyn_saveas(hh,[dirname,'/',M_.fname,'_', namendo,'_vs_lags_',num2str(ifig)],options_.nodisplay,options_.graph_format);
- create_TeX_loader(options_,[dirname,'/',M_.fname,'_', namendo,'_vs_lags_',num2str(ifig)],ifig,[namendo,' vs. lagged endogenous ',int2str(ifig)],[namendo,'_vs_lags'],1)
+ iplo=0;
+ ifig=0;
+ for je=1:size(anamlagendo,1)
+ namlagendo=deblank(anamlagendo(je,:));
+ ilagendo=strmatch(namlagendo,M_.endo_names(oo_.dr.order_var(M_.nstatic+1:M_.nstatic+nsok),:),'exact');
+
+ if ~isempty(ilagendo)
+ y0=teff(T(iendo,ilagendo,:),kn,istable);
+ if ~isempty(y0)
+ if mod(iplo,9)==0
+ ifig=ifig+1;
+ hh=dyn_figure(options_.nodisplay,'name',[namendo,' vs. lagged endogenous ',int2str(ifig)]);
+ iplo=0;
+ end
+ iplo=iplo+1;
+ js=js+1;
+ subplot(3,3,iplo),
+ [SAmeas, SAMorris] = Morris_Measure_Groups(np+nshock, [lpmat0 lpmat], y0,nliv);
+ SAM = squeeze(SAMorris(nshock+1:end,1));
+ SA(:,js)=SAM./(max(SAM)+eps);
+ [saso, iso] = sort(-SA(:,js));
+ bar(SA(iso(1:min(np,10)),js))
+ %set(gca,'xticklabel',pnames(iso(1:min(np,10)),:),'fontsize',8)
+ set(gca,'xticklabel',' ','fontsize',10)
+ set(gca,'xlim',[0.5 10.5])
+ for ip=1:min(np,10)
+ text(ip,-0.02,deblank(pnames(iso(ip),:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ end
+
+ title([namendo,' vs. ',namlagendo,'(-1)'],'interpreter','none')
+ if iplo==9
+ dyn_saveas(hh,[dirname,'/',M_.fname,'_', namendo,'_vs_lags_',num2str(ifig)],options_.nodisplay,options_.graph_format);
+ create_TeX_loader(options_,[dirname,'/',M_.fname,'_', namendo,'_vs_lags_',num2str(ifig)],ifig,[namendo,' vs. lagged endogenous ',int2str(ifig)],[namendo,'_vs_lags'],1)
+ end
+ end
end
- end
end
- end
- if iplo<9 && iplo>0 && ifig
- dyn_saveas(hh,[dirname,'/',M_.fname,'_', namendo,'_vs_lags_',num2str(ifig)],options_.nodisplay,options_.graph_format);
- create_TeX_loader(options_,[dirname,'/',M_.fname,'_', namendo,'_vs_lags_',num2str(ifig)],ifig,[namendo,' vs. lagged endogenous ',int2str(ifig)],[namendo,'_vs_lags'],options_.figures.textwidth*min(iplo/3))
- end
+ if iplo<9 && iplo>0 && ifig
+ dyn_saveas(hh,[dirname,'/',M_.fname,'_', namendo,'_vs_lags_',num2str(ifig)],options_.nodisplay,options_.graph_format);
+ create_TeX_loader(options_,[dirname,'/',M_.fname,'_', namendo,'_vs_lags_',num2str(ifig)],ifig,[namendo,' vs. lagged endogenous ',int2str(ifig)],[namendo,'_vs_lags'],options_.figures.textwidth*min(iplo/3))
+ end
end
-hh=dyn_figure(options_.nodisplay,'Name','Reduced form screening');
+hh=dyn_figure(options_.nodisplay,'Name','Reduced form screening');
%bar(SA)
% boxplot(SA','whis',10,'symbol','r.')
myboxplot(SA',[],'.',[],10)
@@ -151,7 +151,7 @@ set(gca,'xlim',[0.5 np+0.5])
set(gca,'ylim',[0 1])
set(gca,'position',[0.13 0.2 0.775 0.7])
for ip=1:np
- text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
xlabel(' ')
ylabel('Elementary Effects')
@@ -177,4 +177,3 @@ if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
fprintf(fidTeX,'%% End Of TeX file. \n');
fclose(fidTeX);
end
-
diff --git a/matlab/gsa/scatter_analysis.m b/matlab/gsa/scatter_analysis.m
index 4550e58782a8fa4b16196e4de553646d89b98fb7..d526b3f0eafc5f7a9f28cb92b933c2de5716821d 100644
--- a/matlab/gsa/scatter_analysis.m
+++ b/matlab/gsa/scatter_analysis.m
@@ -39,7 +39,7 @@ fname_ = options_scatter.fname_;
xparam1=[];
if isfield(options_scatter,'xparam1')
xparam1=options_scatter.xparam1;
-end
+end
OutputDirectoryName = options_scatter.OutputDirectoryName;
if ~DynareOptions.nograph
diff --git a/matlab/gsa/scatter_callback.m b/matlab/gsa/scatter_callback.m
index 84b7d9ec0636ef8013f65106c9ebadbd2246e0a1..61345107caa1de5b2b7aed75e4739301770c428b 100644
--- a/matlab/gsa/scatter_callback.m
+++ b/matlab/gsa/scatter_callback.m
@@ -32,11 +32,11 @@ x=x{2};
xparam1=x(K,:)';
switch type
- case 'save'
- save(['my_params_' int2str(K)],'xparam1')
-
- case 'eval'
- disp('Evaluating smoother ...')
- [oo_, M_]=evaluate_smoother(xparam1,M_.endo_names,M_,oo_,options_,bayestopt_,estim_params_);
- % [rmse, lnam, r2,vv] = plot_fit(obsname{:});
+ case 'save'
+ save(['my_params_' int2str(K)],'xparam1')
+
+ case 'eval'
+ disp('Evaluating smoother ...')
+ [oo_, M_]=evaluate_smoother(xparam1,M_.endo_names,M_,oo_,options_,bayestopt_,estim_params_);
+ % [rmse, lnam, r2,vv] = plot_fit(obsname{:});
end
diff --git a/matlab/gsa/scatter_mcf.m b/matlab/gsa/scatter_mcf.m
index 0f48f636b83c2a31b6509759acfa0731b68ac99e..211b2e74bb47b8fb5fc3b2dad5263d72b035de47 100644
--- a/matlab/gsa/scatter_mcf.m
+++ b/matlab/gsa/scatter_mcf.m
@@ -1,183 +1,183 @@
-function scatter_mcf(X,Y,vnames,plotsymbol, fnam, dirname, figtitle, xparam1, DynareOptions, beha_name, non_beha_name)
-%
-% Written by Marco Ratto
-% Joint Research Centre, The European Commission,
-% marco.ratto@ec.europa.eu
-
-% Copyright (C) 2014-2016 European Commission
-% Copyright (C) 2014-2017 Dynare Team
-%
-% 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/>.
-
-% PURPOSE: Pairwise scatter plots of the columns of x and y after
-% Monte Carlo filtering
-%---------------------------------------------------
-% USAGE: scatter_mcf(x,y,vnames,pltsym,diagon)
-% or scatter_mcf(x,y) which relies on defaults
-% where:
-% x = an nxk matrix with columns containing behavioural sample
-% y = an mxk matrix with columns containing non-behavioural sample
-% vnames = a vector of variable names
-% (default = numeric labels 1,2,3 etc.)
-% pltsym = a plt symbol
-% (default = '.' for npts > 100, 'o' for npts < 100
-
-
-Z=[X;Y];
-[n,p] = size(X);
-% X = X - ones(n,1)*min(Z);
-% X = X ./ (ones(n,1)*max(Z));
-[n,p] = size(Y);
-% Y = Y - ones(n,1)*min(Z);
-% Y = Y ./ (ones(n,1)*max(Z));
-[n,p] = size(Z);
-clear Z;
-
-nflag = 0;
-if nargin >=3
- nflag = 1;
-end
-
-if nargin<4 || isempty(plotsymbol)
- if n*p<100, plotsymbol = 'o';
- else plotsymbol = '.';
- end
-end
-
-if nargin<5
- fnam='';
-end
-if nargin<6
- dirname='';
- nograph=1;
-else
- nograph=0;
-end
-if nargin<7
- figtitle=fnam;
-end
-if nargin<8
- xparam1=[];
-end
-if nargin<10
- beha_name = 'BEHAVIOUR';
- non_beha_name = 'NON-BEHAVIOUR';
-end
-if nargin==10
- non_beha_name = ['NON-' beha_name];
-end
-
-figtitle_tex=strrep(figtitle,'_','\_');
-
-fig_nam_=[fnam];
-if ~nograph
- hh=dyn_figure(DynareOptions.nodisplay,'name',figtitle);
-end
-
-bf = 0.1;
-ffs = 0.05/(p-1);
-ffl = (1-2*bf-0.05)/p;
-if p>1
- fL = linspace(bf,1-bf+ffs,p+1);
-else
- fL = bf;
-end
-for i = 1:p
- for j = 1:p
- h = axes('position',[fL(i),fL(p+1-j),ffl,ffl]);
- if i==j
- h1=cumplot(X(:,j));
- % set(h1,'color',[0 0 1], 'linestyle','--','LineWidth',1.5)
- set(h1,'color',[0 0 1],'LineWidth',1.5)
- hold on,
- h2=cumplot(Y(:,j));
- set(h2,'color',[1 0 0],'LineWidth',1.5)
- if ~isempty(xparam1)
- hold on, plot(xparam1([j j]),[0 1],'k--')
- end
- if j<p
- set(gca,'XTickLabel',[],'XTick',[]);
- else
- grid off
- end
- set(gca,'YTickLabel',[],'YTick',[]);
- else
- if j>i
- plot(X(:,i),X(:,j),[plotsymbol,'b'])
- hold on,
- plot(Y(:,i),Y(:,j),[plotsymbol,'r'])
- else
- plot(Y(:,i),Y(:,j),[plotsymbol,'r'])
- hold on,
- plot(X(:,i),X(:,j),[plotsymbol,'b'])
- end
- if ~isempty(xparam1)
- hold on, plot(xparam1(i),xparam1(j),'s','MarkerFaceColor',[0 0.75 0],'MarkerEdgeColor',[0 0.75 0])
- end
- hold off;
- % axis([-0.1 1.1 -0.1 1.1])
- if i<p
- set(gca,'YTickLabel',[],'YTick',[]);
- else
- set(gca,'yaxislocation','right');
- end
- if j<p
- set(gca,'XTickLabel',[],'XTick',[]);
- end
- end
- if nflag == 1
- set(gca,'fontsize',9);
- end
- if i==1
- if nflag == 1
- ylabel(vnames(j,:),'Rotation',45,'interpreter','none', ...
- 'HorizontalAlignment','right','VerticalAlignment','middle');
- else
- ylabel([num2str(j),' '],'Rotation',90)
- end
- end
- if j==1
- if nflag == 1
- title(vnames(i,:),'interpreter','none','Rotation',45, ...
- 'HorizontalAlignment','left','VerticalAlignment','bottom')
- else
- title(num2str(i))
- end
- end
- drawnow
- end
-end
-if ~isoctave
- annotation('textbox', [0.1,0,0.35,0.05],'String', beha_name,'Color','Blue','horizontalalignment','center','interpreter','none');
- annotation('textbox', [0.55,0,0.35,0.05],'String', non_beha_name,'Color','Red','horizontalalignment','center','interpreter','none');
-end
-
-if ~nograph
- dyn_saveas(hh,[dirname,filesep,fig_nam_],DynareOptions.nodisplay,DynareOptions.graph_format);
- if DynareOptions.TeX && any(strcmp('eps',cellstr(DynareOptions.graph_format)))
- fidTeX = fopen([dirname,'/',fig_nam_ '.tex'],'w');
- fprintf(fidTeX,'%% TeX eps-loader file generated by scatter_mcf.m (Dynare).\n');
- fprintf(fidTeX,['%% ' datestr(now,0) '\n\n']);
- fprintf(fidTeX,'\\begin{figure}[H]\n');
- fprintf(fidTeX,'\\centering \n');
- fprintf(fidTeX,'\\includegraphics[width=0.8\\textwidth]{%s}\n',strrep([dirname,'/',fig_nam_],'\','/'));
- fprintf(fidTeX,'\\caption{%s.}',figtitle_tex);
- fprintf(fidTeX,'\\label{Fig:%s}\n',fig_nam_);
- fprintf(fidTeX,'\\end{figure}\n\n');
- fprintf(fidTeX,'%% End Of TeX file. \n');
- fclose(fidTeX);
- end
+function scatter_mcf(X,Y,vnames,plotsymbol, fnam, dirname, figtitle, xparam1, DynareOptions, beha_name, non_beha_name)
+%
+% Written by Marco Ratto
+% Joint Research Centre, The European Commission,
+% marco.ratto@ec.europa.eu
+
+% Copyright (C) 2014-2016 European Commission
+% Copyright (C) 2014-2017 Dynare Team
+%
+% 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/>.
+
+% PURPOSE: Pairwise scatter plots of the columns of x and y after
+% Monte Carlo filtering
+%---------------------------------------------------
+% USAGE: scatter_mcf(x,y,vnames,pltsym,diagon)
+% or scatter_mcf(x,y) which relies on defaults
+% where:
+% x = an nxk matrix with columns containing behavioural sample
+% y = an mxk matrix with columns containing non-behavioural sample
+% vnames = a vector of variable names
+% (default = numeric labels 1,2,3 etc.)
+% pltsym = a plt symbol
+% (default = '.' for npts > 100, 'o' for npts < 100
+
+
+Z=[X;Y];
+[n,p] = size(X);
+% X = X - ones(n,1)*min(Z);
+% X = X ./ (ones(n,1)*max(Z));
+[n,p] = size(Y);
+% Y = Y - ones(n,1)*min(Z);
+% Y = Y ./ (ones(n,1)*max(Z));
+[n,p] = size(Z);
+clear Z;
+
+nflag = 0;
+if nargin >=3
+ nflag = 1;
+end
+
+if nargin<4 || isempty(plotsymbol)
+ if n*p<100, plotsymbol = 'o';
+ else plotsymbol = '.';
+ end
+end
+
+if nargin<5
+ fnam='';
+end
+if nargin<6
+ dirname='';
+ nograph=1;
+else
+ nograph=0;
+end
+if nargin<7
+ figtitle=fnam;
+end
+if nargin<8
+ xparam1=[];
+end
+if nargin<10
+ beha_name = 'BEHAVIOUR';
+ non_beha_name = 'NON-BEHAVIOUR';
+end
+if nargin==10
+ non_beha_name = ['NON-' beha_name];
+end
+
+figtitle_tex=strrep(figtitle,'_','\_');
+
+fig_nam_=[fnam];
+if ~nograph
+ hh=dyn_figure(DynareOptions.nodisplay,'name',figtitle);
+end
+
+bf = 0.1;
+ffs = 0.05/(p-1);
+ffl = (1-2*bf-0.05)/p;
+if p>1
+ fL = linspace(bf,1-bf+ffs,p+1);
+else
+ fL = bf;
+end
+for i = 1:p
+ for j = 1:p
+ h = axes('position',[fL(i),fL(p+1-j),ffl,ffl]);
+ if i==j
+ h1=cumplot(X(:,j));
+ % set(h1,'color',[0 0 1], 'linestyle','--','LineWidth',1.5)
+ set(h1,'color',[0 0 1],'LineWidth',1.5)
+ hold on,
+ h2=cumplot(Y(:,j));
+ set(h2,'color',[1 0 0],'LineWidth',1.5)
+ if ~isempty(xparam1)
+ hold on, plot(xparam1([j j]),[0 1],'k--')
+ end
+ if j<p
+ set(gca,'XTickLabel',[],'XTick',[]);
+ else
+ grid off
+ end
+ set(gca,'YTickLabel',[],'YTick',[]);
+ else
+ if j>i
+ plot(X(:,i),X(:,j),[plotsymbol,'b'])
+ hold on,
+ plot(Y(:,i),Y(:,j),[plotsymbol,'r'])
+ else
+ plot(Y(:,i),Y(:,j),[plotsymbol,'r'])
+ hold on,
+ plot(X(:,i),X(:,j),[plotsymbol,'b'])
+ end
+ if ~isempty(xparam1)
+ hold on, plot(xparam1(i),xparam1(j),'s','MarkerFaceColor',[0 0.75 0],'MarkerEdgeColor',[0 0.75 0])
+ end
+ hold off;
+ % axis([-0.1 1.1 -0.1 1.1])
+ if i<p
+ set(gca,'YTickLabel',[],'YTick',[]);
+ else
+ set(gca,'yaxislocation','right');
+ end
+ if j<p
+ set(gca,'XTickLabel',[],'XTick',[]);
+ end
+ end
+ if nflag == 1
+ set(gca,'fontsize',9);
+ end
+ if i==1
+ if nflag == 1
+ ylabel(vnames(j,:),'Rotation',45,'interpreter','none', ...
+ 'HorizontalAlignment','right','VerticalAlignment','middle');
+ else
+ ylabel([num2str(j),' '],'Rotation',90)
+ end
+ end
+ if j==1
+ if nflag == 1
+ title(vnames(i,:),'interpreter','none','Rotation',45, ...
+ 'HorizontalAlignment','left','VerticalAlignment','bottom')
+ else
+ title(num2str(i))
+ end
+ end
+ drawnow
+ end
+end
+if ~isoctave
+ annotation('textbox', [0.1,0,0.35,0.05],'String', beha_name,'Color','Blue','horizontalalignment','center','interpreter','none');
+ annotation('textbox', [0.55,0,0.35,0.05],'String', non_beha_name,'Color','Red','horizontalalignment','center','interpreter','none');
+end
+
+if ~nograph
+ dyn_saveas(hh,[dirname,filesep,fig_nam_],DynareOptions.nodisplay,DynareOptions.graph_format);
+ if DynareOptions.TeX && any(strcmp('eps',cellstr(DynareOptions.graph_format)))
+ fidTeX = fopen([dirname,'/',fig_nam_ '.tex'],'w');
+ fprintf(fidTeX,'%% TeX eps-loader file generated by scatter_mcf.m (Dynare).\n');
+ fprintf(fidTeX,['%% ' datestr(now,0) '\n\n']);
+ fprintf(fidTeX,'\\begin{figure}[H]\n');
+ fprintf(fidTeX,'\\centering \n');
+ fprintf(fidTeX,'\\includegraphics[width=0.8\\textwidth]{%s}\n',strrep([dirname,'/',fig_nam_],'\','/'));
+ fprintf(fidTeX,'\\caption{%s.}',figtitle_tex);
+ fprintf(fidTeX,'\\label{Fig:%s}\n',fig_nam_);
+ fprintf(fidTeX,'\\end{figure}\n\n');
+ fprintf(fidTeX,'%% End Of TeX file. \n');
+ fclose(fidTeX);
+ end
end
\ No newline at end of file
diff --git a/matlab/gsa/scatter_plots.m b/matlab/gsa/scatter_plots.m
index 852ae5b400019397a282044e2f580901f4ef1961..f9cf1fa4c45c27dba876a23951ca0a5baa646a5a 100644
--- a/matlab/gsa/scatter_plots.m
+++ b/matlab/gsa/scatter_plots.m
@@ -56,25 +56,25 @@ if nargin<5 || isempty(fnam)
fnam='scatter_plot';
end
if nargin<6 || isempty(dirname)
- dirname='';
- nograph=1;
- DynareOptions.nodisplay=0;
+ dirname='';
+ nograph=1;
+ DynareOptions.nodisplay=0;
else
- nograph=0;
+ nograph=0;
end
if nargin<7 || isempty(figtitle)
- figtitle=fnam;
+ figtitle=fnam;
end
if nargin<8
- xparam1=[];
+ xparam1=[];
end
figtitle_tex=strrep(figtitle,'_','\_');
fig_nam_=[fnam];
- hh=dyn_figure(DynareOptions.nodisplay,'name',figtitle);
- set(hh,'userdata',{X,xp})
+hh=dyn_figure(DynareOptions.nodisplay,'name',figtitle);
+set(hh,'userdata',{X,xp})
bf = 0.1;
ffs = 0.05/(p-1);
@@ -111,7 +111,7 @@ for i = 1:p
%%
if ~isoctave
- % Define a context menu; it is not attached to anything
+ % Define a context menu; it is not attached to anything
hcmenu = uicontextmenu('Callback','pick','Tag','Run viewer');
% Define callbacks for context menu
% items that change linestyle
@@ -150,7 +150,7 @@ for i = 1:p
if i==1
if nflag == 1
ylabel(vnames(j,:),'Rotation',45,'interpreter','none', ...
- 'HorizontalAlignment','right','VerticalAlignment','middle');
+ 'HorizontalAlignment','right','VerticalAlignment','middle');
else
ylabel([num2str(j),' '],'Rotation',90)
end
@@ -158,7 +158,7 @@ for i = 1:p
if j==1
if nflag == 1
title(vnames(i,:),'interpreter','none','Rotation',45, ...
- 'HorizontalAlignment','left','VerticalAlignment','bottom')
+ 'HorizontalAlignment','left','VerticalAlignment','bottom')
else
title(num2str(i))
end
diff --git a/matlab/gsa/set_shocks_param.m b/matlab/gsa/set_shocks_param.m
index 8902dfb102cd06f01a5830788cd4a25d0ecad8e4..485f64ec945e0cddd9f037b22c039705b68b21a9 100644
--- a/matlab/gsa/set_shocks_param.m
+++ b/matlab/gsa/set_shocks_param.m
@@ -67,7 +67,7 @@ if ncx
end
%build covariance matrix from correlation matrix and variances already on
%diagonal
-Sigma_e = diag(sqrt(diag(Sigma_e)))*Correlation_matrix*diag(sqrt(diag(Sigma_e)));
+Sigma_e = diag(sqrt(diag(Sigma_e)))*Correlation_matrix*diag(sqrt(diag(Sigma_e)));
%if calibrated covariances, set them now to their stored value
if isfield(estim_params_,'calibrated_covariances')
Sigma_e(estim_params_.calibrated_covariances.position)=estim_params_.calibrated_covariances.cov_value;
@@ -93,7 +93,7 @@ if isfield(estim_params_,'calibrated_covariances_ME')
H(estim_params_.calibrated_covariances_ME.position)=estim_params_.calibrated_covariances_ME.cov_value;
end
-
+
% updating matrices in M
if nvx || ncx
M_.Sigma_e = Sigma_e;
@@ -101,5 +101,5 @@ if nvx || ncx
end
if nvn || ncn
M_.H = H;
- M_.Correlation_matrix_ME=Correlation_matrix_ME;
-end
\ No newline at end of file
+ M_.Correlation_matrix_ME=Correlation_matrix_ME;
+end
\ No newline at end of file
diff --git a/matlab/gsa/smirnov.m b/matlab/gsa/smirnov.m
index 2a96a2a7dfb5d92d835806dac8504a7386045391..a2fa7f4727697f7e4dc8993bda5401193303f506 100644
--- a/matlab/gsa/smirnov.m
+++ b/matlab/gsa/smirnov.m
@@ -4,7 +4,7 @@ function [H,prob,d] = smirnov(x1 , x2 , alpha, iflag )
%
% Written by Marco Ratto
% Joint Research Centre, The European Commission,
-% marco.ratto@ec.europa.eu
+% marco.ratto@ec.europa.eu
% Copyright (C) 2012 European Commission
% Copyright (C) 2012 Dynare Team
@@ -64,10 +64,10 @@ end
%
lam = max((sqrt(n) + 0.12 + 0.11/sqrt(n)) * d , 0);
-if iflag == 0
+if iflag == 0
j = [1:101]';
prob = 2 * sum((-1).^(j-1).*exp(-2*lam*lam*j.^2));
-
+
prob=max(prob,0);
prob=min(1,prob);
else
diff --git a/matlab/gsa/stab_map_.m b/matlab/gsa/stab_map_.m
index 20a61b844f26ac55f53e8717341cd33035ca8db1..03ad56e880e115803f00b51e0401a197b615ec0b 100644
--- a/matlab/gsa/stab_map_.m
+++ b/matlab/gsa/stab_map_.m
@@ -32,7 +32,7 @@ function x0 = stab_map_(OutputDirectoryName,opt_gsa)
%
% Written by Marco Ratto
% Joint Research Centre, The European Commission,
-% marco.ratto@ec.europa.eu
+% marco.ratto@ec.europa.eu
% Copyright (C) 2012-2016 European Commission
% Copyright (C) 2012-2016 Dynare Team
@@ -99,8 +99,8 @@ if ~isempty(bayestopt_) && any(bayestopt_.pshape > 0)
bounds.lb = max(bounds.lb,lb);
bounds.ub = min(bounds.ub,ub);
else % estimated parameters but no declared priors
- % No priors are declared so Dynare will estimate the model by
- % maximum likelihood with inequality constraints for the parameters.
+ % No priors are declared so Dynare will estimate the model by
+ % maximum likelihood with inequality constraints for the parameters.
bounds.lb = lb;
bounds.ub = ub;
if opt_gsa.prior_range==0
@@ -119,7 +119,7 @@ options_mcf.alpha2 = alpha2;
name=cell(np,1);
name_tex=cell(np,1);
-for jj=1:np
+for jj=1:np
if options_.TeX
[param_name_temp, param_name_tex_temp]= get_the_name(nshock+jj,options_.TeX,M_,estim_params_,options_);
name_tex{jj,1} = strrep(param_name_tex_temp,'$','');
@@ -159,9 +159,9 @@ if fload==0
Nsam=size(lpmat,1);
lpmat0 = lpmat(:,1:nshock);
lpmat = lpmat(:,nshock+1:end);
-% elseif opt_gsa.morris==3,
-% lpmat = prep_ide(Nsam,np,5);
-% Nsam=size(lpmat,1);
+ % elseif opt_gsa.morris==3,
+ % lpmat = prep_ide(Nsam,np,5);
+ % Nsam=size(lpmat,1);
else
if np<52 && ilptau>0
[lpmat] = qmc_sequence(np, int64(1), 0, Nsam)';
@@ -180,14 +180,14 @@ if fload==0
end
% try
dummy=prior_draw_gsa(1); %initialize persistent variables
- % catch
- % if pprior,
- % if opt_gsa.prior_range==0;
- % error('Some unknown prior is specified or ML estimation,: use prior_range=1 option!!');
- % end
- % end
- %
- % end
+ % catch
+ % if pprior,
+ % if opt_gsa.prior_range==0;
+ % error('Some unknown prior is specified or ML estimation,: use prior_range=1 option!!');
+ % end
+ % end
+ %
+ % end
if pprior
for j=1:nshock
if opt_gsa.morris~=1
@@ -257,12 +257,12 @@ if fload==0
if neighborhood_width>0 && isempty(options_.mode_file)
xparam1 = get_all_parameters(estim_params_,M_);
else
- eval(['load ' options_.mode_file '.mat;']);
+ eval(['load ' options_.mode_file '.mat;']);
end
if neighborhood_width>0
for j=1:nshock
if opt_gsa.morris ~= 1
- lpmat0(:,j) = randperm(Nsam)'./(Nsam+1); %latin hypercube
+ lpmat0(:,j) = randperm(Nsam)'./(Nsam+1); %latin hypercube
end
ub=min([bounds.ub(j) xparam1(j)*(1+neighborhood_width)]);
lb=max([bounds.lb(j) xparam1(j)*(1-neighborhood_width)]);
@@ -335,7 +335,7 @@ if fload==0
egg=zeros(length(dr_.eigval),Nsam);
end
if infox(j,1)
-% disp('no solution'),
+ % disp('no solution'),
if isfield(oo_.dr,'ghx')
oo_.dr=rmfield(oo_.dr,'ghx');
end
@@ -457,23 +457,23 @@ if fload==0
if pprior
if ~prepSA
save([OutputDirectoryName filesep fname_ '_prior.mat'], ...
- 'bkpprior','lpmat','lpmat0','irestriction','iunstable','istable','iindeterm','iwrong','ixun', ...
- 'egg','yys','nspred','nboth','nfwrd','infox')
+ 'bkpprior','lpmat','lpmat0','irestriction','iunstable','istable','iindeterm','iwrong','ixun', ...
+ 'egg','yys','nspred','nboth','nfwrd','infox')
else
save([OutputDirectoryName filesep fname_ '_prior.mat'], ...
- 'bkpprior','lpmat','lpmat0','irestriction','iunstable','istable','iindeterm','iwrong','ixun', ...
- 'egg','yys','T','nspred','nboth','nfwrd','infox')
+ 'bkpprior','lpmat','lpmat0','irestriction','iunstable','istable','iindeterm','iwrong','ixun', ...
+ 'egg','yys','T','nspred','nboth','nfwrd','infox')
end
else
if ~prepSA
save([OutputDirectoryName filesep fname_ '_mc.mat'], ...
- 'lpmat','lpmat0','irestriction','iunstable','istable','iindeterm','iwrong','ixun', ...
- 'egg','yys','nspred','nboth','nfwrd','infox')
+ 'lpmat','lpmat0','irestriction','iunstable','istable','iindeterm','iwrong','ixun', ...
+ 'egg','yys','nspred','nboth','nfwrd','infox')
else
save([OutputDirectoryName filesep fname_ '_mc.mat'], ...
- 'lpmat','lpmat0','irestriction','iunstable','istable','iindeterm','iwrong','ixun', ...
- 'egg','yys','T','nspred','nboth','nfwrd','infox')
+ 'lpmat','lpmat0','irestriction','iunstable','istable','iindeterm','iwrong','ixun', ...
+ 'egg','yys','T','nspred','nboth','nfwrd','infox')
end
end
else
@@ -601,7 +601,7 @@ if length(iunstable)>0 || length(iwrong)>0
if length(iunstable)<Nsam || length(istable)>1
itot = [1:Nsam];
isolve = itot(find(~ismember(itot,iwrong))); % dynare could find a solution
- % Blanchard Kahn
+ % Blanchard Kahn
if neighborhood_width
options_mcf.xparam1 = xparam1(nshock+1:end);
end
@@ -622,7 +622,7 @@ if length(iunstable)>0 || length(iwrong)>0
options_mcf.title = 'indeterminacy';
mcf_analysis(lpmat, itmp, iindeterm, options_mcf, options_);
end
-
+
if ~isempty(ixun)
itmp = isolve(find(~ismember(isolve,ixun)));
options_mcf.amcf_name = aunstname;
@@ -632,7 +632,7 @@ if length(iunstable)>0 || length(iwrong)>0
options_mcf.title = 'instability';
mcf_analysis(lpmat, itmp, ixun, options_mcf, options_);
end
-
+
inorestriction = istable(find(~ismember(istable,irestriction))); % violation of prior restrictions
iwrong = iwrong(find(~ismember(iwrong,inorestriction))); % what went wrong beyond prior restrictions
if ~isempty(iwrong)
@@ -644,7 +644,7 @@ if length(iunstable)>0 || length(iwrong)>0
options_mcf.title = 'inability to find a solution';
mcf_analysis(lpmat, itmp, iwrong, options_mcf, options_);
end
-
+
if ~isempty(irestriction)
if neighborhood_width
options_mcf.xparam1 = xparam1;
@@ -679,7 +679,7 @@ if length(iunstable)>0 || length(iwrong)>0
x0=0.5.*(bounds.ub(1:nshock)-bounds.lb(1:nshock))+bounds.lb(1:nshock);
x0 = [x0; lpmat(iok,:)'];
end
-
+
M_ = set_all_parameters(x0,estim_params_,M_);
[oo_.dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
% stoch_simul([]);
@@ -704,7 +704,3 @@ if isfield(opt,'nomoments')
end
options_.irf=opt.irf;
options_.noprint=opt.noprint;
-
-
-
-
diff --git a/matlab/gsa/stab_map_1.m b/matlab/gsa/stab_map_1.m
index 0bd9888e7a4dbd92ee95d3d200c32709ff3279e3..092b50daa439d85b5bb12a17df1739ad0d44e7c0 100644
--- a/matlab/gsa/stab_map_1.m
+++ b/matlab/gsa/stab_map_1.m
@@ -8,7 +8,7 @@ function [proba, dproba] = stab_map_1(lpmat, ibehaviour, inonbehaviour, aname, i
% iplot = 1 plot cumulative distributions (default)
% iplot = 0 no plots
% ipar = index array of parameters to plot
-% dirname = (OPTIONAL) path of the directory where to save
+% dirname = (OPTIONAL) path of the directory where to save
% (default: current directory)
% pcrit = (OPTIONAL) critical value of the pvalue below which show the plots
%
@@ -18,7 +18,7 @@ function [proba, dproba] = stab_map_1(lpmat, ibehaviour, inonbehaviour, aname, i
%
% Written by Marco Ratto
% Joint Research Centre, The European Commission,
-% marco.ratto@ec.europa.eu
+% marco.ratto@ec.europa.eu
% Copyright (C) 2012-2016 European Commission
% Copyright (C) 2012-2017 Dynare Team
@@ -41,14 +41,14 @@ function [proba, dproba] = stab_map_1(lpmat, ibehaviour, inonbehaviour, aname, i
global estim_params_ bayestopt_ M_ options_
if nargin<5
- iplot=1;
+ iplot=1;
end
fname_ = M_.fname;
if nargin<7
- dirname='';
+ dirname='';
end
if nargin<9,
- atitle=aname;
+ atitle=aname;
end
nshock = estim_params_.nvx;
@@ -60,27 +60,27 @@ npar=size(lpmat,2);
ishock= npar>estim_params_.np;
if nargin<6
- ipar=[];
+ ipar=[];
end
if nargin<8 || isempty(pcrit)
- pcrit=1;
+ pcrit=1;
end
-% Smirnov test for Blanchard;
+% Smirnov test for Blanchard;
for j=1:npar
- [H,P,KSSTAT] = smirnov(lpmat(ibehaviour,j),lpmat(inonbehaviour,j));
- proba(j)=P;
- dproba(j)=KSSTAT;
+ [H,P,KSSTAT] = smirnov(lpmat(ibehaviour,j),lpmat(inonbehaviour,j));
+ proba(j)=P;
+ dproba(j)=KSSTAT;
end
if isempty(ipar)
-% ipar=find(dproba>dcrit);
+ % ipar=find(dproba>dcrit);
ipar=find(proba<pcrit);
end
nparplot=length(ipar);
if iplot && ~options_.nograph
lpmat=lpmat(:,ipar);
ftit=bayestopt_.name(ipar+nshock*(1-ishock));
-
+
for i=1:ceil(nparplot/12)
hh=dyn_figure(options_.nodisplay,'name',atitle);
for j=1+12*(i-1):min(nparplot,12*i)
diff --git a/matlab/gsa/stab_map_2.m b/matlab/gsa/stab_map_2.m
index d13435d11bc2d88bfc9c4c6e47c782f8e9407adc..f4b958355e276ed8af63a2cc7d6f53a014d7b788 100644
--- a/matlab/gsa/stab_map_2.m
+++ b/matlab/gsa/stab_map_2.m
@@ -3,7 +3,7 @@ function indcorr = stab_map_2(x,alpha2, pvalue_crit, fnam, dirname,xparam1,figti
%
% Written by Marco Ratto
% Joint Research Centre, The European Commission,
-% marco.ratto@ec.europa.eu
+% marco.ratto@ec.europa.eu
% Copyright (C) 2011-2016 European Commission
% Copyright (C) 2011-2017 Dynare Team
@@ -30,17 +30,17 @@ nsam=size(x,1);
ishock= npar>estim_params_.np;
nograph = options_.nograph;
if nargin<4
- fnam='';
+ fnam='';
end
if nargin<5
- dirname='';
- nograph=1;
+ dirname='';
+ nograph=1;
end
if nargin<6
- xparam1=[];
+ xparam1=[];
end
if nargin<7
- figtitle=fnam;
+ figtitle=fnam;
end
ys_ = oo_.dr.ys;
@@ -60,12 +60,12 @@ fig_nam_=strrep(fig_nam_,' ','_');
ifig=0;
j2=0;
if ishock==0
- npar=estim_params_.np;
- if ~isempty(xparam1)
- xparam1=xparam1(nshock+1:end);
- end
+ npar=estim_params_.np;
+ if ~isempty(xparam1)
+ xparam1=xparam1(nshock+1:end);
+ end
else
- npar=estim_params_.np+nshock;
+ npar=estim_params_.np+nshock;
end
skipline();
title_string=['Correlation analysis for ',fnam];
@@ -112,54 +112,54 @@ for j=1:npar
tmp_name=(['[',param_name_temp1,',',param_name_temp2,']']);
name{entry_iter,1}=tmp_name;
end
- end
+ end
data_mat(entry_iter,1)=c0(i2(jx),j);
entry_iter=entry_iter+1;
-
+
if ~nograph
if mod(j2,12)==1
- ifig=ifig+1;
- hh=dyn_figure(options_.nodisplay,'name',[figtitle,' sample bivariate projection ', num2str(ifig)]);
- end
- subplot(3,4,j2-(ifig-1)*12)
- % bar(c0(i2,j)),
- % set(gca,'xticklabel',bayestopt_.name(i2)),
- % set(gca,'xtick',[1:length(i2)])
- %plot(stock_par(ixx(nfilt+1:end,i),j),stock_par(ixx(nfilt+1:end,i),i2(jx)),'.k')
- %hold on,
- plot(x(:,j),x(:,i2(jx)),'.')
- if ~isempty(xparam1)
- hold on, plot(xparam1(j),xparam1(i2(jx)),'ro')
- end
- % xlabel(deblank(estim_params_.param_names(j,:)),'interpreter','none'),
- % ylabel(deblank(estim_params_.param_names(i2(jx),:)),'interpreter','none'),
- if ishock
- xlabel(bayestopt_.name{j},'interpreter','none'),
- ylabel(bayestopt_.name{i2(jx)},'interpreter','none'),
- else
- xlabel(bayestopt_.name{j+nshock},'interpreter','none'),
- ylabel(bayestopt_.name{i2(jx)+nshock},'interpreter','none'),
- end
- title(['cc = ',num2str(c0(i2(jx),j))])
- if (mod(j2,12)==0) && j2>0
- dyn_saveas(hh,[dirname,filesep,fig_nam_,int2str(ifig)],options_.nodisplay,options_.graph_format);
- if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
- fidTeX = fopen([dirname,filesep,fig_nam_,int2str(ifig),'.tex'],'w');
- fprintf(fidTeX,'%% TeX eps-loader file generated by stab_map_2.m (Dynare).\n');
- fprintf(fidTeX,['%% ' datestr(now,0) '\n\n']);
- fprintf(fidTeX,'\\begin{figure}[H]\n');
- fprintf(fidTeX,'\\centering \n');
- fprintf(fidTeX,'\\includegraphics[width=0.8\\textwidth]{%s}\n',strrep([dirname,'/',fig_nam_,int2str(ifig)],'\','/'));
- fprintf(fidTeX,'\\caption{%s.}',[figtitle,' sample bivariate projection ', num2str(ifig)]);
- fprintf(fidTeX,'\\label{Fig:%s:%u}\n',fig_nam_,ifig);
- fprintf(fidTeX,'\\end{figure}\n\n');
- fprintf(fidTeX,'%% End Of TeX file. \n');
- fclose(fidTeX);
+ ifig=ifig+1;
+ hh=dyn_figure(options_.nodisplay,'name',[figtitle,' sample bivariate projection ', num2str(ifig)]);
+ end
+ subplot(3,4,j2-(ifig-1)*12)
+ % bar(c0(i2,j)),
+ % set(gca,'xticklabel',bayestopt_.name(i2)),
+ % set(gca,'xtick',[1:length(i2)])
+ %plot(stock_par(ixx(nfilt+1:end,i),j),stock_par(ixx(nfilt+1:end,i),i2(jx)),'.k')
+ %hold on,
+ plot(x(:,j),x(:,i2(jx)),'.')
+ if ~isempty(xparam1)
+ hold on, plot(xparam1(j),xparam1(i2(jx)),'ro')
+ end
+ % xlabel(deblank(estim_params_.param_names(j,:)),'interpreter','none'),
+ % ylabel(deblank(estim_params_.param_names(i2(jx),:)),'interpreter','none'),
+ if ishock
+ xlabel(bayestopt_.name{j},'interpreter','none'),
+ ylabel(bayestopt_.name{i2(jx)},'interpreter','none'),
+ else
+ xlabel(bayestopt_.name{j+nshock},'interpreter','none'),
+ ylabel(bayestopt_.name{i2(jx)+nshock},'interpreter','none'),
+ end
+ title(['cc = ',num2str(c0(i2(jx),j))])
+ if (mod(j2,12)==0) && j2>0
+ dyn_saveas(hh,[dirname,filesep,fig_nam_,int2str(ifig)],options_.nodisplay,options_.graph_format);
+ if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
+ fidTeX = fopen([dirname,filesep,fig_nam_,int2str(ifig),'.tex'],'w');
+ fprintf(fidTeX,'%% TeX eps-loader file generated by stab_map_2.m (Dynare).\n');
+ fprintf(fidTeX,['%% ' datestr(now,0) '\n\n']);
+ fprintf(fidTeX,'\\begin{figure}[H]\n');
+ fprintf(fidTeX,'\\centering \n');
+ fprintf(fidTeX,'\\includegraphics[width=0.8\\textwidth]{%s}\n',strrep([dirname,'/',fig_nam_,int2str(ifig)],'\','/'));
+ fprintf(fidTeX,'\\caption{%s.}',[figtitle,' sample bivariate projection ', num2str(ifig)]);
+ fprintf(fidTeX,'\\label{Fig:%s:%u}\n',fig_nam_,ifig);
+ fprintf(fidTeX,'\\end{figure}\n\n');
+ fprintf(fidTeX,'%% End Of TeX file. \n');
+ fclose(fidTeX);
+ end
end
- end
end
end
-
+
end
end
if ~nograph && (j==(npar)) && j2>0 && (mod(j2,12)~=0)
@@ -177,7 +177,7 @@ for j=1:npar
fprintf(fidTeX,'%% End Of TeX file. \n');
fclose(fidTeX);
end
- end
+ end
end
if j2==0
diff --git a/matlab/gsa/stand_.m b/matlab/gsa/stand_.m
index b6a087a53137c3900b1289a9c1b48d324f3866bb..f4bb05ce1e090cae99172c3c4cb7f7750a9292e3 100644
--- a/matlab/gsa/stand_.m
+++ b/matlab/gsa/stand_.m
@@ -11,7 +11,7 @@ function [y, meany, stdy] = stand_(x)
%
% Written by Marco Ratto
% Joint Research Centre, The European Commission,
-% marco.ratto@ec.europa.eu
+% marco.ratto@ec.europa.eu
% Copyright (C) 2012 European Commission
% Copyright (C) 2012 Dynare Team%
@@ -35,8 +35,8 @@ if nargin==0
end
for j=1:size(x,2)
-meany(j)=mean(x(find(~isnan(x(:,j))),j));
-stdy(j)=std(x(find(~isnan(x(:,j))),j));
+ meany(j)=mean(x(find(~isnan(x(:,j))),j));
+ stdy(j)=std(x(find(~isnan(x(:,j))),j));
y(:,j)=(x(:,j)-meany(j))./stdy(j);
end
% end of m-file
\ No newline at end of file
diff --git a/matlab/gsa/tcrit.m b/matlab/gsa/tcrit.m
index ba5f85a9efc74177f2aca57d3522ca2ae3ff2b7b..cc7c8ad6afe5cd872bb856ffb98ee3972b048e89 100644
--- a/matlab/gsa/tcrit.m
+++ b/matlab/gsa/tcrit.m
@@ -1,12 +1,12 @@
function t_crit = tcrit(n,pval0)
% function t_crit = tcrit(n,pval0)
%
-% given the p-value pval0, the function givese the
-% critical value t_crit of the t-distribution with n degress of freedom
+% given the p-value pval0, the function givese the
+% critical value t_crit of the t-distribution with n degress of freedom
%
% Written by Marco Ratto
% Joint Research Centre, The European Commission,
-% marco.ratto@ec.europa.eu
+% marco.ratto@ec.europa.eu
% Copyright (C) 2011-2012 European Commission
% Copyright (C) 2011-2012 Dynare Team
@@ -43,108 +43,108 @@ pval0=max(pval0,min(pval));
ncol=min(find(pval<=pval0))+1;
t_crit=[
- 1 3.078 6.314 12.706 31.821 63.657 318.313
- 2 1.886 2.920 4.303 6.965 9.925 22.327
- 3 1.638 2.353 3.182 4.541 5.841 10.215
- 4 1.533 2.132 2.776 3.747 4.604 7.173
- 5 1.476 2.015 2.571 3.365 4.032 5.893
- 6 1.440 1.943 2.447 3.143 3.707 5.208
- 7 1.415 1.895 2.365 2.998 3.499 4.782
- 8 1.397 1.860 2.306 2.896 3.355 4.499
- 9 1.383 1.833 2.262 2.821 3.250 4.296
- 10 1.372 1.812 2.228 2.764 3.169 4.143
- 11 1.363 1.796 2.201 2.718 3.106 4.024
- 12 1.356 1.782 2.179 2.681 3.055 3.929
- 13 1.350 1.771 2.160 2.650 3.012 3.852
- 14 1.345 1.761 2.145 2.624 2.977 3.787
- 15 1.341 1.753 2.131 2.602 2.947 3.733
- 16 1.337 1.746 2.120 2.583 2.921 3.686
- 17 1.333 1.740 2.110 2.567 2.898 3.646
- 18 1.330 1.734 2.101 2.552 2.878 3.610
- 19 1.328 1.729 2.093 2.539 2.861 3.579
- 20 1.325 1.725 2.086 2.528 2.845 3.552
- 21 1.323 1.721 2.080 2.518 2.831 3.527
- 22 1.321 1.717 2.074 2.508 2.819 3.505
- 23 1.319 1.714 2.069 2.500 2.807 3.485
- 24 1.318 1.711 2.064 2.492 2.797 3.467
- 25 1.316 1.708 2.060 2.485 2.787 3.450
- 26 1.315 1.706 2.056 2.479 2.779 3.435
- 27 1.314 1.703 2.052 2.473 2.771 3.421
- 28 1.313 1.701 2.048 2.467 2.763 3.408
- 29 1.311 1.699 2.045 2.462 2.756 3.396
- 30 1.310 1.697 2.042 2.457 2.750 3.385
- 31 1.309 1.696 2.040 2.453 2.744 3.375
- 32 1.309 1.694 2.037 2.449 2.738 3.365
- 33 1.308 1.692 2.035 2.445 2.733 3.356
- 34 1.307 1.691 2.032 2.441 2.728 3.348
- 35 1.306 1.690 2.030 2.438 2.724 3.340
- 36 1.306 1.688 2.028 2.434 2.719 3.333
- 37 1.305 1.687 2.026 2.431 2.715 3.326
- 38 1.304 1.686 2.024 2.429 2.712 3.319
- 39 1.304 1.685 2.023 2.426 2.708 3.313
- 40 1.303 1.684 2.021 2.423 2.704 3.307
- 41 1.303 1.683 2.020 2.421 2.701 3.301
- 42 1.302 1.682 2.018 2.418 2.698 3.296
- 43 1.302 1.681 2.017 2.416 2.695 3.291
- 44 1.301 1.680 2.015 2.414 2.692 3.286
- 45 1.301 1.679 2.014 2.412 2.690 3.281
- 46 1.300 1.679 2.013 2.410 2.687 3.277
- 47 1.300 1.678 2.012 2.408 2.685 3.273
- 48 1.299 1.677 2.011 2.407 2.682 3.269
- 49 1.299 1.677 2.010 2.405 2.680 3.265
- 50 1.299 1.676 2.009 2.403 2.678 3.261
- 51 1.298 1.675 2.008 2.402 2.676 3.258
- 52 1.298 1.675 2.007 2.400 2.674 3.255
- 53 1.298 1.674 2.006 2.399 2.672 3.251
- 54 1.297 1.674 2.005 2.397 2.670 3.248
- 55 1.297 1.673 2.004 2.396 2.668 3.245
- 56 1.297 1.673 2.003 2.395 2.667 3.242
- 57 1.297 1.672 2.002 2.394 2.665 3.239
- 58 1.296 1.672 2.002 2.392 2.663 3.237
- 59 1.296 1.671 2.001 2.391 2.662 3.234
- 60 1.296 1.671 2.000 2.390 2.660 3.232
- 61 1.296 1.670 2.000 2.389 2.659 3.229
- 62 1.295 1.670 1.999 2.388 2.657 3.227
- 63 1.295 1.669 1.998 2.387 2.656 3.225
- 64 1.295 1.669 1.998 2.386 2.655 3.223
- 65 1.295 1.669 1.997 2.385 2.654 3.220
- 66 1.295 1.668 1.997 2.384 2.652 3.218
- 67 1.294 1.668 1.996 2.383 2.651 3.216
- 68 1.294 1.668 1.995 2.382 2.650 3.214
- 69 1.294 1.667 1.995 2.382 2.649 3.213
- 70 1.294 1.667 1.994 2.381 2.648 3.211
- 71 1.294 1.667 1.994 2.380 2.647 3.209
- 72 1.293 1.666 1.993 2.379 2.646 3.207
- 73 1.293 1.666 1.993 2.379 2.645 3.206
- 74 1.293 1.666 1.993 2.378 2.644 3.204
- 75 1.293 1.665 1.992 2.377 2.643 3.202
- 76 1.293 1.665 1.992 2.376 2.642 3.201
- 77 1.293 1.665 1.991 2.376 2.641 3.199
- 78 1.292 1.665 1.991 2.375 2.640 3.198
- 79 1.292 1.664 1.990 2.374 2.640 3.197
- 80 1.292 1.664 1.990 2.374 2.639 3.195
- 81 1.292 1.664 1.990 2.373 2.638 3.194
- 82 1.292 1.664 1.989 2.373 2.637 3.193
- 83 1.292 1.663 1.989 2.372 2.636 3.191
- 84 1.292 1.663 1.989 2.372 2.636 3.190
- 85 1.292 1.663 1.988 2.371 2.635 3.189
- 86 1.291 1.663 1.988 2.370 2.634 3.188
- 87 1.291 1.663 1.988 2.370 2.634 3.187
- 88 1.291 1.662 1.987 2.369 2.633 3.185
- 89 1.291 1.662 1.987 2.369 2.632 3.184
- 90 1.291 1.662 1.987 2.368 2.632 3.183
- 91 1.291 1.662 1.986 2.368 2.631 3.182
- 92 1.291 1.662 1.986 2.368 2.630 3.181
- 93 1.291 1.661 1.986 2.367 2.630 3.180
- 94 1.291 1.661 1.986 2.367 2.629 3.179
- 95 1.291 1.661 1.985 2.366 2.629 3.178
- 96 1.290 1.661 1.985 2.366 2.628 3.177
- 97 1.290 1.661 1.985 2.365 2.627 3.176
- 98 1.290 1.661 1.984 2.365 2.627 3.175
- 99 1.290 1.660 1.984 2.365 2.626 3.175
-100 1.290 1.660 1.984 2.364 2.626 3.174
-inf 1.282 1.645 1.960 2.326 2.576 3.090
-];
+ 1 3.078 6.314 12.706 31.821 63.657 318.313
+ 2 1.886 2.920 4.303 6.965 9.925 22.327
+ 3 1.638 2.353 3.182 4.541 5.841 10.215
+ 4 1.533 2.132 2.776 3.747 4.604 7.173
+ 5 1.476 2.015 2.571 3.365 4.032 5.893
+ 6 1.440 1.943 2.447 3.143 3.707 5.208
+ 7 1.415 1.895 2.365 2.998 3.499 4.782
+ 8 1.397 1.860 2.306 2.896 3.355 4.499
+ 9 1.383 1.833 2.262 2.821 3.250 4.296
+ 10 1.372 1.812 2.228 2.764 3.169 4.143
+ 11 1.363 1.796 2.201 2.718 3.106 4.024
+ 12 1.356 1.782 2.179 2.681 3.055 3.929
+ 13 1.350 1.771 2.160 2.650 3.012 3.852
+ 14 1.345 1.761 2.145 2.624 2.977 3.787
+ 15 1.341 1.753 2.131 2.602 2.947 3.733
+ 16 1.337 1.746 2.120 2.583 2.921 3.686
+ 17 1.333 1.740 2.110 2.567 2.898 3.646
+ 18 1.330 1.734 2.101 2.552 2.878 3.610
+ 19 1.328 1.729 2.093 2.539 2.861 3.579
+ 20 1.325 1.725 2.086 2.528 2.845 3.552
+ 21 1.323 1.721 2.080 2.518 2.831 3.527
+ 22 1.321 1.717 2.074 2.508 2.819 3.505
+ 23 1.319 1.714 2.069 2.500 2.807 3.485
+ 24 1.318 1.711 2.064 2.492 2.797 3.467
+ 25 1.316 1.708 2.060 2.485 2.787 3.450
+ 26 1.315 1.706 2.056 2.479 2.779 3.435
+ 27 1.314 1.703 2.052 2.473 2.771 3.421
+ 28 1.313 1.701 2.048 2.467 2.763 3.408
+ 29 1.311 1.699 2.045 2.462 2.756 3.396
+ 30 1.310 1.697 2.042 2.457 2.750 3.385
+ 31 1.309 1.696 2.040 2.453 2.744 3.375
+ 32 1.309 1.694 2.037 2.449 2.738 3.365
+ 33 1.308 1.692 2.035 2.445 2.733 3.356
+ 34 1.307 1.691 2.032 2.441 2.728 3.348
+ 35 1.306 1.690 2.030 2.438 2.724 3.340
+ 36 1.306 1.688 2.028 2.434 2.719 3.333
+ 37 1.305 1.687 2.026 2.431 2.715 3.326
+ 38 1.304 1.686 2.024 2.429 2.712 3.319
+ 39 1.304 1.685 2.023 2.426 2.708 3.313
+ 40 1.303 1.684 2.021 2.423 2.704 3.307
+ 41 1.303 1.683 2.020 2.421 2.701 3.301
+ 42 1.302 1.682 2.018 2.418 2.698 3.296
+ 43 1.302 1.681 2.017 2.416 2.695 3.291
+ 44 1.301 1.680 2.015 2.414 2.692 3.286
+ 45 1.301 1.679 2.014 2.412 2.690 3.281
+ 46 1.300 1.679 2.013 2.410 2.687 3.277
+ 47 1.300 1.678 2.012 2.408 2.685 3.273
+ 48 1.299 1.677 2.011 2.407 2.682 3.269
+ 49 1.299 1.677 2.010 2.405 2.680 3.265
+ 50 1.299 1.676 2.009 2.403 2.678 3.261
+ 51 1.298 1.675 2.008 2.402 2.676 3.258
+ 52 1.298 1.675 2.007 2.400 2.674 3.255
+ 53 1.298 1.674 2.006 2.399 2.672 3.251
+ 54 1.297 1.674 2.005 2.397 2.670 3.248
+ 55 1.297 1.673 2.004 2.396 2.668 3.245
+ 56 1.297 1.673 2.003 2.395 2.667 3.242
+ 57 1.297 1.672 2.002 2.394 2.665 3.239
+ 58 1.296 1.672 2.002 2.392 2.663 3.237
+ 59 1.296 1.671 2.001 2.391 2.662 3.234
+ 60 1.296 1.671 2.000 2.390 2.660 3.232
+ 61 1.296 1.670 2.000 2.389 2.659 3.229
+ 62 1.295 1.670 1.999 2.388 2.657 3.227
+ 63 1.295 1.669 1.998 2.387 2.656 3.225
+ 64 1.295 1.669 1.998 2.386 2.655 3.223
+ 65 1.295 1.669 1.997 2.385 2.654 3.220
+ 66 1.295 1.668 1.997 2.384 2.652 3.218
+ 67 1.294 1.668 1.996 2.383 2.651 3.216
+ 68 1.294 1.668 1.995 2.382 2.650 3.214
+ 69 1.294 1.667 1.995 2.382 2.649 3.213
+ 70 1.294 1.667 1.994 2.381 2.648 3.211
+ 71 1.294 1.667 1.994 2.380 2.647 3.209
+ 72 1.293 1.666 1.993 2.379 2.646 3.207
+ 73 1.293 1.666 1.993 2.379 2.645 3.206
+ 74 1.293 1.666 1.993 2.378 2.644 3.204
+ 75 1.293 1.665 1.992 2.377 2.643 3.202
+ 76 1.293 1.665 1.992 2.376 2.642 3.201
+ 77 1.293 1.665 1.991 2.376 2.641 3.199
+ 78 1.292 1.665 1.991 2.375 2.640 3.198
+ 79 1.292 1.664 1.990 2.374 2.640 3.197
+ 80 1.292 1.664 1.990 2.374 2.639 3.195
+ 81 1.292 1.664 1.990 2.373 2.638 3.194
+ 82 1.292 1.664 1.989 2.373 2.637 3.193
+ 83 1.292 1.663 1.989 2.372 2.636 3.191
+ 84 1.292 1.663 1.989 2.372 2.636 3.190
+ 85 1.292 1.663 1.988 2.371 2.635 3.189
+ 86 1.291 1.663 1.988 2.370 2.634 3.188
+ 87 1.291 1.663 1.988 2.370 2.634 3.187
+ 88 1.291 1.662 1.987 2.369 2.633 3.185
+ 89 1.291 1.662 1.987 2.369 2.632 3.184
+ 90 1.291 1.662 1.987 2.368 2.632 3.183
+ 91 1.291 1.662 1.986 2.368 2.631 3.182
+ 92 1.291 1.662 1.986 2.368 2.630 3.181
+ 93 1.291 1.661 1.986 2.367 2.630 3.180
+ 94 1.291 1.661 1.986 2.367 2.629 3.179
+ 95 1.291 1.661 1.985 2.366 2.629 3.178
+ 96 1.290 1.661 1.985 2.366 2.628 3.177
+ 97 1.290 1.661 1.985 2.365 2.627 3.176
+ 98 1.290 1.661 1.984 2.365 2.627 3.175
+ 99 1.290 1.660 1.984 2.365 2.626 3.175
+ 100 1.290 1.660 1.984 2.364 2.626 3.174
+ inf 1.282 1.645 1.960 2.326 2.576 3.090
+ ];
if n<=100
t_crit=t_crit(n,ncol);
diff --git a/matlab/gsa/teff.m b/matlab/gsa/teff.m
index da2d047416c0718c58299ecd39cd5348a9a05b6f..e047256a029d466420ec583c8ca8dc1a77c7a92d 100644
--- a/matlab/gsa/teff.m
+++ b/matlab/gsa/teff.m
@@ -3,7 +3,7 @@ function [yt, j0, ir, ic]=teff(T,Nsam,istable)
%
% Written by Marco Ratto
% Joint Research Centre, The European Commission,
-% marco.ratto@ec.europa.eu
+% marco.ratto@ec.europa.eu
%
% Reference:
% M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
@@ -28,32 +28,32 @@ function [yt, j0, ir, ic]=teff(T,Nsam,istable)
ndim = (length(size(T)));
if ndim==3
-if nargin==1
- Nsam=size(T,3);
- istable = [1:Nsam]';
-end
-tmax=max(T,[],3);
-tmin=min(T,[],3);
-[ir, ic]=(find( (tmax-tmin)>1.e-8));
-j0 = length(ir);
-yt=zeros(Nsam, j0);
+ if nargin==1
+ Nsam=size(T,3);
+ istable = [1:Nsam]';
+ end
+ tmax=max(T,[],3);
+ tmin=min(T,[],3);
+ [ir, ic]=(find( (tmax-tmin)>1.e-8));
+ j0 = length(ir);
+ yt=zeros(Nsam, j0);
-for j=1:j0
- y0=squeeze(T(ir(j),ic(j),:));
- %y1=ones(size(lpmat,1),1)*NaN;
- y1=ones(Nsam,1)*NaN;
- y1(istable,1)=y0;
- yt(:,j)=y1;
-end
+ for j=1:j0
+ y0=squeeze(T(ir(j),ic(j),:));
+ %y1=ones(size(lpmat,1),1)*NaN;
+ y1=ones(Nsam,1)*NaN;
+ y1(istable,1)=y0;
+ yt(:,j)=y1;
+ end
else
-tmax=max(T,[],2);
-tmin=min(T,[],2);
-ir=(find( (tmax-tmin)>1.e-8));
-j0 = length(ir);
-yt=NaN(Nsam, j0);
-yt(istable,:)=T(ir,:)';
+ tmax=max(T,[],2);
+ tmin=min(T,[],2);
+ ir=(find( (tmax-tmin)>1.e-8));
+ j0 = length(ir);
+ yt=NaN(Nsam, j0);
+ yt(istable,:)=T(ir,:)';
+
-
end
%clear y0 y1;
diff --git a/matlab/gsa/th_moments.m b/matlab/gsa/th_moments.m
index 95df0b459cc4fb3899cfd8fb4bc387793b41bd03..9b886e85197fc4220c6de64d32edcdbd44ec1f26 100644
--- a/matlab/gsa/th_moments.m
+++ b/matlab/gsa/th_moments.m
@@ -79,5 +79,3 @@ if options_.ar > 0
zz(:,i) = diag(gamma_y{i+1}(i1,i1));
end
end
-
-
diff --git a/matlab/gsa/trank.m b/matlab/gsa/trank.m
index 42eff73ce8da919c8e709a7a01d3f2fe3d8a9117..914d3849ce2b905f55c9635fcbfeac93f4cbade4 100644
--- a/matlab/gsa/trank.m
+++ b/matlab/gsa/trank.m
@@ -29,6 +29,6 @@ function yr = trank(y)
[nr, nc] = size(y);
for j=1:nc
- [dum, is]=sort(y(:,j));
- yr(is,j)=[1:nr]'./nr;
+ [dum, is]=sort(y(:,j));
+ yr(is,j)=[1:nr]'./nr;
end
diff --git a/matlab/hessian.m b/matlab/hessian.m
index 4faa4c50008dee3f7f754e18e37bc315b88c5920..844251f7cf5c95a099f16b01ac7d29824845b1c2 100644
--- a/matlab/hessian.m
+++ b/matlab/hessian.m
@@ -12,19 +12,19 @@ function hessian_mat = hessian(func,x, gstep, varargin) % --*-- Unitary tests --
% hessian_mat [double] Hessian matrix
%
% ALGORITHM
-% Uses Abramowitz and Stegun (1965) formulas 25.3.23
+% Uses Abramowitz and Stegun (1965) formulas 25.3.23
% \[
% \frac{\partial^2 f_{0,0}}{\partial {x^2}} = \frac{1}{h^2}\left( f_{1,0} - 2f_{0,0} + f_{ - 1,0} \right)
% \]
% and 25.3.27 p. 884
-%
+%
% \[
% \frac{\partial ^2f_{0,0}}{\partial x\partial y} = \frac{-1}{2h^2}\left(f_{1,0} + f_{-1,0} + f_{0,1} + f_{0,-1} - 2f_{0,0} - f_{1,1} - f_{-1,-1} \right)
% \]
%
% SPECIAL REQUIREMENTS
% none
-%
+%
% Copyright (C) 2001-2017 Dynare Team
%
@@ -43,7 +43,7 @@ function hessian_mat = hessian(func,x, gstep, varargin) % --*-- Unitary tests --
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-if ~isa(func, 'function_handle')
+if ~isa(func, 'function_handle')
func = str2func(func);
end
@@ -67,7 +67,7 @@ for i=1:n
xh1(i) = x(i)-h_1(i);
f_1(:,i) = feval(func, xh1, varargin{:});
%reset parameter
- xh1(i) = x(i);
+ xh1(i) = x(i);
end
xh_1 = xh1;
@@ -75,14 +75,14 @@ temp = f1+f_1-f0*ones(1, n); %term f_(1,0)+f_(-1,0)-f_(0,0) used later
hessian_mat = zeros(size(f0,1), n*n);
-for i=1:n
- if i > 1
- %fill symmetric part of Hessian based on previously computed results
+for i=1:n
+ if i > 1
+ %fill symmetric part of Hessian based on previously computed results
k = [i:n:n*(i-1)];
hessian_mat(:,(i-1)*n+1:(i-1)*n+i-1) = hessian_mat(:,k);
- end
+ end
hessian_mat(:,(i-1)*n+i) = (f1(:,i)+f_1(:,i)-2*f0)./(h1(i)*h_1(i)); %formula 25.3.23
- for j=i+1:n
+ for j=i+1:n
%step in up direction
xh1(i) = x(i)+h1(i);
xh1(j) = x(j)+h_1(j);
@@ -90,12 +90,12 @@ for i=1:n
xh_1(i) = x(i)-h1(i);
xh_1(j) = x(j)-h_1(j);
hessian_mat(:,(i-1)*n+j) =-(-feval(func, xh1, varargin{:})-feval(func, xh_1, varargin{:})+temp(:,i)+temp(:,j))./(2*h1(i)*h_1(j)); %formula 25.3.27
- %reset grid points
+ %reset grid points
xh1(i) = x(i);
xh1(j) = x(j);
xh_1(i) = x(i);
xh_1(j) = x(j);
- end
+ end
end
diff --git a/matlab/hessian_sparse.m b/matlab/hessian_sparse.m
index eb447f8d34d9ac9c9237c021dba01762a708a911..a15024c34f0a8dfc36dd0ed339b64fa625e0b212 100644
--- a/matlab/hessian_sparse.m
+++ b/matlab/hessian_sparse.m
@@ -16,7 +16,7 @@ function hessian_mat = hessian_sparse(func,x,gstep,varargin)
%
% SPECIAL REQUIREMENTS
% none
-%
+%
% Copyright (C) 2001-2012 Dynare Team
%
@@ -35,7 +35,7 @@ function hessian_mat = hessian_sparse(func,x,gstep,varargin)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-if ~isa(func, 'function_handle')
+if ~isa(func, 'function_handle')
func = str2func(func);
end
n=size(x,1);
@@ -49,7 +49,7 @@ xh1=x;
f0=feval(func,x,varargin{:});
f1=zeros(size(f0,1),n);
f_1=f1;
-for i=1:n
+for i=1:n
xh1(i)=x(i)+h1(i);
f1(:,i)=feval(func,xh1,varargin{:});
xh1(i)=x(i)-h_1(i);
@@ -59,15 +59,15 @@ end
xh_1=xh1;
hessian_mat = sparse(size(f0,1),n*n);
-for i=1:n
-% if i > 1
-% k=[i:n:n*(i-1)];
-% hessian_mat(:,(i-1)*n+1:(i-1)*n+i-1)=hessian_mat(:,k);
-% hessian_mat(:,k)=0;
-% end
+for i=1:n
+ % if i > 1
+ % k=[i:n:n*(i-1)];
+ % hessian_mat(:,(i-1)*n+1:(i-1)*n+i-1)=hessian_mat(:,k);
+ % hessian_mat(:,k)=0;
+ % end
hessian_mat(:,(i-1)*n+i)=(f1(:,i)+f_1(:,i)-2*f0)./(h1(i)*h_1(i));
temp=f1+f_1-f0*ones(1,n);
- for j=1:i-1
+ for j=1:i-1
xh1(i)=x(i)+h1(i);
xh1(j)=x(j)+h_1(j);
xh_1(i)=x(i)-h1(i);
@@ -77,5 +77,5 @@ for i=1:n
xh1(j)=x(j);
xh_1(i)=x(i);
xh_1(j)=x(j);
- end
+ end
end
\ No newline at end of file
diff --git a/matlab/histvalf.m b/matlab/histvalf.m
index 6e2712cebcb0a403d85f1d81b0d3f4a37de5a106..75838ec531f6fa10e27a40de0c370da4e22df5f3 100644
--- a/matlab/histvalf.m
+++ b/matlab/histvalf.m
@@ -86,4 +86,3 @@ for i = 1:length(outvars)
end
M_.endo_histval(j, :) = getfield(S, ov_);
end
-
diff --git a/matlab/homotopy1.m b/matlab/homotopy1.m
index e7de1f16a73e6682703a2f2731ecbd045d8f4479..956a86431f5e736ffe648ed7cb6088191b4cdca1 100644
--- a/matlab/homotopy1.m
+++ b/matlab/homotopy1.m
@@ -90,7 +90,7 @@ for i=1:step_nbr+1
% if homotopy step is not successful, current values of steady
% state are not modified
oo.steady_state = steady_state;
- else
+ else
M.params = old_params;
oo.exo_steady_state = old_exo;
oo.exo_det_steady_state = old_exo_det;
diff --git a/matlab/homotopy2.m b/matlab/homotopy2.m
index 8e1318a86b38c568501d2a5eff01de59a31b1214..efcdbf85ee876f7649255658259ae6ec0eaf7a19 100644
--- a/matlab/homotopy2.m
+++ b/matlab/homotopy2.m
@@ -99,7 +99,7 @@ for i = 1:nv
end
disp([ 'HOMOTOPY mode 2: lauching solver with ' deblank(varname) ' = ' num2str(v) ' ...'])
-
+
oo_.steady_state = steady_(M_,options_,oo_);
end
end
diff --git a/matlab/homotopy3.m b/matlab/homotopy3.m
index a506bb6b66bc0f566893abdda58342b025a88e5e..2f3a4f3178f3a9375e418aafa3b0e979ccf038e1 100644
--- a/matlab/homotopy3.m
+++ b/matlab/homotopy3.m
@@ -91,11 +91,11 @@ disp('HOMOTOPY mode 3: launching solver at initial point...')
iter = 1;
while iter <= step_nbr
-
+
M.params(values(ip,2)) = curvalues(ip);
oo.exo_steady_state(values(ix,2)) = curvalues(ix);
oo.exo_det_steady_state(values(ixd,2)) = curvalues(ixd);
-
+
old_ss = oo.steady_state;
[steady_state,params,info] = steady_(M,options,oo);
@@ -121,8 +121,8 @@ while iter <= step_nbr
disp('HOMOTOPY mode 3: failed step, now dividing increment by 2...')
inc = inc/2;
oo.steady_state = old_ss;
- end
-
+ end
+
curvalues = last_values + inc;
kplus = find(curvalues(iplus) >= targetvalues(iplus));
curvalues(iplus(kplus)) = targetvalues(iplus(kplus));
@@ -136,7 +136,7 @@ while iter <= step_nbr
oo.exo_det_steady_state = old_exo_det_steady_state;
return
end
-
+
iter = iter + 1;
end
disp('HOMOTOPY mode 3: failed, maximum iterations reached')
diff --git a/matlab/ident_bruteforce.m b/matlab/ident_bruteforce.m
index 238e3a5a02e271af44d41b62ad55f81fd21cf425..34725ade4c17b0c0537ab5ef70d54d550fc49a50 100644
--- a/matlab/ident_bruteforce.m
+++ b/matlab/ident_bruteforce.m
@@ -12,7 +12,7 @@ function [pars, cosnJ] = ident_bruteforce(J,n,TeX, pnames_TeX,tittxt)
% pnames_TeX [char] list of tex names
% tittxt [string] string indicating the title text for
% graphs and figures
-%
+%
% OUTPUTS
% pars : cell array with groupf of params for each column of J for 1 to n
% cosnJ : the cosn of each column with the selected group of columns
@@ -85,7 +85,7 @@ for ll = 1:n
fprintf(fidTeX,['%% ' datestr(now,0)]);
fprintf(fidTeX,' \n');
fprintf(fidTeX,' \n');
-
+
fprintf(fidTeX,'{\\tiny \n');
fprintf(fidTeX,'\\begin{longtable}{llc} \n');
fprintf(fidTeX,['\\caption{Collinearity patterns with ',int2str(ll),' parameter(s): ',tittxt,'}\n ']);
@@ -111,9 +111,9 @@ for ll = 1:n
end
end
fprintf(fidTeX,'$%s$ & [%s] & %7.3f \\\\ \n',...
- pnames_TeX(i,:),...
- plist,...
- cosnJ(i,ll));
+ pnames_TeX(i,:),...
+ plist,...
+ cosnJ(i,ll));
end
fprintf(fidTeX,'\\bottomrule \n');
fprintf(fidTeX,'\\end{longtable}\n');
diff --git a/matlab/identification_analysis.m b/matlab/identification_analysis.m
index 1ea6b543082408fb6fb5505ee63adbd4819755d0..c59e08ec6d2091b652d4ea3d82433d6579ac587b 100644
--- a/matlab/identification_analysis.m
+++ b/matlab/identification_analysis.m
@@ -9,14 +9,14 @@ function [ide_hess, ide_moments, ide_model, ide_lre, derivatives_info, info, opt
% o options_ident [structure] identification options
% o dataset_ [structure] the dataset after required transformation
% o dataset_info [structure] Various informations about the dataset (descriptive statistics and missing observations) info for Kalman Filter
-% o prior_exist [integer]
+% o prior_exist [integer]
% =1 when prior exists and indentification is checked only for estimated params and shocks
% =0 when prior is not defined and indentification is checked for all params and shocks
% o name_tex [char] list of tex names
% o init [integer] flag for initialization of persistent vars
% o tittxt [string] string indicating the title text for
% graphs and figures
-%
+%
% OUTPUTS
% o ide_hess [structure] identification results using Asymptotic Hessian
% o ide_moments [structure] identification results using theoretical moments
@@ -24,7 +24,7 @@ function [ide_hess, ide_moments, ide_model, ide_lre, derivatives_info, info, opt
% o ide_lre [structure] identification results using LRE model
% o derivatives_info [structure] info about analytic derivs
% o info output from dynare resolve
-%
+%
% SPECIAL REQUIREMENTS
% None
@@ -42,7 +42,7 @@ function [ide_hess, ide_moments, ide_model, ide_lre, derivatives_info, info, opt
% 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
+% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
global oo_ M_ options_ bayestopt_ estim_params_
@@ -63,7 +63,7 @@ periods = options_ident.periods;
max_dim_cova_group = options_ident.max_dim_cova_group;
normalize_jacobians = options_ident.normalize_jacobians;
kron_flag = options_ident.analytic_derivation_mode;
-
+
[I,J]=find(M_.lead_lag_incidence');
ide_hess = struct();
@@ -78,8 +78,8 @@ if info(1)==0
tau=[oo_.dr.ys(oo_.dr.order_var); vec(A); dyn_vech(B*M_.Sigma_e*B')];
yy0=oo_.dr.ys(I);
[residual, g1 ] = feval([M_.fname,'_dynamic'],yy0, ...
- repmat(oo_.exo_steady_state',[M_.maximum_exo_lag+M_.maximum_exo_lead+1]), M_.params, ...
- oo_.dr.ys, 1);
+ repmat(oo_.exo_steady_state',[M_.maximum_exo_lag+M_.maximum_exo_lead+1]), M_.params, ...
+ oo_.dr.ys, 1);
vg1 = [oo_.dr.ys(oo_.dr.order_var); vec(g1)];
[JJ, H, gam, gp, dA, dOm, dYss] = getJJ(A, B, estim_params_, M_,oo0,options_,kron_flag,indx,indexo,bayestopt_.mf2,nlags,useautocorr);
@@ -95,7 +95,7 @@ if info(1)==0
end
while length(indJJ)<nparam && nlags<10
disp('The number of moments with non-zero derivative is smaller than the number of parameters')
- disp(['Try increasing ar = ', int2str(nlags+1)])
+ disp(['Try increasing ar = ', int2str(nlags+1)])
nlags=nlags+1;
[JJ, H, gam, gp, dA, dOm, dYss] = getJJ(A, B, estim_params_, M_,oo0,options_,kron_flag,indx,indexo,bayestopt_.mf2,nlags,useautocorr);
derivatives_info.DT=dA;
@@ -106,8 +106,8 @@ if info(1)==0
end
if length(indJJ)<nparam
disp('The number of moments with non-zero derivative is smaller than the number of parameters')
- disp('up to 10 lags: check your model')
- disp('Either further increase ar or reduce the list of estimated parameters')
+ disp('up to 10 lags: check your model')
+ disp('Either further increase ar or reduce the list of estimated parameters')
error('identification_analysis: there are not enough moments and too many parameters'),
end
indH = (find(max(abs(H'),[],1)>1.e-8));
@@ -117,7 +117,7 @@ if info(1)==0
LRE(:,1)=vg1(indLRE);
GAM(:,1)=gam(indJJ);
siJ = (JJ(indJJ,:));
- siH = (H(indH,:));
+ siH = (H(indH,:));
siLRE = (gp(indLRE,:));
ide_strength_J=NaN(1,nparam);
ide_strength_J_prior=NaN(1,nparam);
@@ -137,7 +137,7 @@ if info(1)==0
end
% normaliz = max([normaliz; normaliz1]);
normaliz1(isinf(normaliz1)) = 1;
-
+
else
normaliz1 = NaN(1,nparam);
end
@@ -153,17 +153,17 @@ if info(1)==0
analytic_derivation = options_.analytic_derivation;
options_.analytic_derivation = -2;
info = stoch_simul(char(options_.varobs));
- dataset_ = dseries(oo_.endo_simul(options_.varobs_id,100+1:end)',dates('1Q1'), options_.varobs);
+ dataset_ = dseries(oo_.endo_simul(options_.varobs_id,100+1:end)',dates('1Q1'), options_.varobs);
derivatives_info.no_DLIK=1;
%bounds = prior_bounds(bayestopt_, options_.prior_trunc);
- [fval,info,cost_flag,DLIK,AHess,ys,trend_coeff,M_,options_,bayestopt_,oo_] = dsge_likelihood(params',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_,derivatives_info);
-% fval = DsgeLikelihood(xparam1,data_info,options_,M_,estim_params_,bayestopt_,oo_);
+ [fval,info,cost_flag,DLIK,AHess,ys,trend_coeff,M_,options_,bayestopt_,oo_] = dsge_likelihood(params',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_,derivatives_info);
+ % fval = DsgeLikelihood(xparam1,data_info,options_,M_,estim_params_,bayestopt_,oo_);
options_.analytic_derivation = analytic_derivation;
AHess=-AHess;
if min(eig(AHess))<-1.e-10
error('identification_analysis: Analytic Hessian is not positive semi-definite!')
end
-% chol(AHess);
+ % chol(AHess);
ide_hess.AHess= AHess;
deltaM = sqrt(diag(AHess));
iflag=any((deltaM.*deltaM)==0);
@@ -181,23 +181,23 @@ if info(1)==0
cmm = siJ(:,ind1)*((AHess(ind1,ind1))\siJ(:,ind1)');
temp1=((AHess(ind1,ind1))\siH(:,ind1)');
diag_chh=sum(siH(:,ind1)'.*temp1)';
-% chh = siH(:,ind1)*((AHess(ind1,ind1))\siH(:,ind1)');
+ % chh = siH(:,ind1)*((AHess(ind1,ind1))\siH(:,ind1)');
ind1=ind1(ind1>offset);
clre = siLRE(:,ind1-offset)*((AHess(ind1,ind1))\siLRE(:,ind1-offset)');
rhoM=sqrt(1./diag(inv(tildaM(indok,indok))));
-% deltaM = deltaM.*abs(params');
+ % deltaM = deltaM.*abs(params');
flag_score=1;
catch
-% replic = max([replic, nparam*(nparam+1)/2*10]);
+ % replic = max([replic, nparam*(nparam+1)/2*10]);
replic = max([replic, length(indJJ)*3]);
cmm = simulated_moment_uncertainty(indJJ, periods, replic,options_,M_,oo_);
-% [V,D,W]=eig(cmm);
+ % [V,D,W]=eig(cmm);
sd=sqrt(diag(cmm));
cc=cmm./(sd*sd');
if isoctave || matlab_ver_less_than('8.3')
[V,D]=eig(cc);
%fix for older Matlab versions that do not support computing left eigenvalues, see http://mathworks.com/help/releases/R2012b/matlab/ref/eig.html
- [W,junk] = eig(cc.');
+ [W,junk] = eig(cc.');
W = conj(W);
else
[V,D,W]=eig(cc);
@@ -206,21 +206,21 @@ if info(1)==0
siTMP=siJ./repmat(sd,[1 nparam]);
MIM=(siTMP'*V(:,id))*(D(id,id)\(W(:,id)'*siTMP));
clear siTMP;
-% MIM=siJ(:,indok)'*(cmm\siJ(:,indok));
-% look for independent moments!
-% % % sd=sqrt(diag(cmm));
-% % % cc=cmm./(sd*sd');
-% % % ix=[];
-% % % for jc=1:length(cmm),
-% % % jcheck=find(abs(cc(:,jc))>(1-1.e-6));
-% % % ix=[ix; jcheck(jcheck>jc)];
-% % % end
-% % % iy=find(~ismember([1:length(cmm)],ix));
-% % % indJJ=indJJ(iy);
-% % % GAM=GAM(iy);
-% % % cmm=cmm(iy,iy);
-% % % siJ = (JJ(indJJ,:));
-% % % MIM=siJ'*(cmm\siJ);
+ % MIM=siJ(:,indok)'*(cmm\siJ(:,indok));
+ % look for independent moments!
+ % % % sd=sqrt(diag(cmm));
+ % % % cc=cmm./(sd*sd');
+ % % % ix=[];
+ % % % for jc=1:length(cmm),
+ % % % jcheck=find(abs(cc(:,jc))>(1-1.e-6));
+ % % % ix=[ix; jcheck(jcheck>jc)];
+ % % % end
+ % % % iy=find(~ismember([1:length(cmm)],ix));
+ % % % indJJ=indJJ(iy);
+ % % % GAM=GAM(iy);
+ % % % cmm=cmm(iy,iy);
+ % % % siJ = (JJ(indJJ,:));
+ % % % MIM=siJ'*(cmm\siJ);
ide_hess.AHess= MIM;
deltaM = sqrt(diag(MIM));
iflag=any((deltaM.*deltaM)==0);
@@ -231,12 +231,12 @@ if info(1)==0
[ide_hess.cond, ide_hess.ind0, ide_hess.indno, ide_hess.ino, ide_hess.Mco, ide_hess.Pco] = identification_checks(tildaM, 1);
end
indok = find(max(ide_hess.indno,[],1)==0);
-% rhoM=sqrt(1-1./diag(inv(tildaM)));
-% rhoM=(1-1./diag(inv(tildaM)));
+ % rhoM=sqrt(1-1./diag(inv(tildaM)));
+ % rhoM=(1-1./diag(inv(tildaM)));
ind1=find(ide_hess.ind0);
temp1=((MIM(ind1,ind1))\siH(:,ind1)');
diag_chh=sum(siH(:,ind1)'.*temp1)';
-% chh = siH(:,ind1)*((MIM(ind1,ind1))\siH(:,ind1)');
+ % chh = siH(:,ind1)*((MIM(ind1,ind1))\siH(:,ind1)');
ind1=ind1(ind1>offset);
clre = siLRE(:,ind1-offset)*((MIM(ind1,ind1))\siLRE(:,ind1-offset)');
if ~isempty(indok)
@@ -260,11 +260,11 @@ if info(1)==0
end
% siJnorm = vnorm(siJ(inok,:)).*normaliz;
quant=[];
-% inok = find((abs(TAU)<1.e-8));
-% isok = find((abs(TAU)>=1.e-8));
-% quant(isok,:) = siH(isok,:)./repmat(TAU(isok,1),1,nparam);
-% quant(inok,:) = siH(inok,:)./repmat(mean(abs(TAU)),length(inok),nparam);
-% quant = siH./repmat(sqrt(diag(chh)),1,nparam);
+ % inok = find((abs(TAU)<1.e-8));
+ % isok = find((abs(TAU)>=1.e-8));
+ % quant(isok,:) = siH(isok,:)./repmat(TAU(isok,1),1,nparam);
+ % quant(inok,:) = siH(inok,:)./repmat(mean(abs(TAU)),length(inok),nparam);
+ % quant = siH./repmat(sqrt(diag(chh)),1,nparam);
iy = find(diag_chh);
indH=indH(iy);
siH=siH(iy,:);
@@ -280,10 +280,10 @@ if info(1)==0
end
% siHnorm = vnorm(siH./repmat(TAU,1,nparam)).*normaliz;
quant=[];
-% inok = find((abs(LRE)<1.e-8));
-% isok = find((abs(LRE)>=1.e-8));
-% quant(isok,:) = siLRE(isok,:)./repmat(LRE(isok,1),1,np);
-% quant(inok,:) = siLRE(inok,:)./repmat(mean(abs(LRE)),length(inok),np);
+ % inok = find((abs(LRE)<1.e-8));
+ % isok = find((abs(LRE)>=1.e-8));
+ % quant(isok,:) = siLRE(isok,:)./repmat(LRE(isok,1),1,np);
+ % quant(inok,:) = siLRE(inok,:)./repmat(mean(abs(LRE)),length(inok),np);
diag_clre = diag(clre);
iy = find(diag_clre);
indLRE=indLRE(iy);
@@ -299,14 +299,14 @@ if info(1)==0
siLREnorm=[];
end
% siLREnorm = vnorm(siLRE./repmat(LRE,1,nparam-offset)).*normaliz(offset+1:end);
- ide_hess.ide_strength_J=ide_strength_J;
- ide_hess.ide_strength_J_prior=ide_strength_J_prior;
- ide_hess.deltaM=deltaM;
- ide_hess.deltaM_prior=deltaM_prior;
- ide_moments.siJnorm=siJnorm;
- ide_model.siHnorm=siHnorm;
- ide_lre.siLREnorm=siLREnorm;
- ide_hess.flag_score=flag_score;
+ ide_hess.ide_strength_J=ide_strength_J;
+ ide_hess.ide_strength_J_prior=ide_strength_J_prior;
+ ide_hess.deltaM=deltaM;
+ ide_hess.deltaM_prior=deltaM_prior;
+ ide_moments.siJnorm=siJnorm;
+ ide_model.siHnorm=siHnorm;
+ ide_lre.siLREnorm=siLREnorm;
+ ide_hess.flag_score=flag_score;
end
if normalize_jacobians
normH = max(abs(siH)')';
@@ -329,14 +329,14 @@ if info(1)==0
ide_moments.GAM=GAM;
ide_model.TAU=TAU;
ide_lre.LRE=LRE;
-% [ide_checks.idemodel_Mco, ide_checks.idemoments_Mco, ide_checks.idelre_Mco, ...
-% ide_checks.idemodel_Pco, ide_checks.idemoments_Pco, ide_checks.idelre_Pco, ...
-% ide_checks.idemodel_cond, ide_checks.idemoments_cond, ide_checks.idelre_cond, ...
-% ide_checks.idemodel_ee, ide_checks.idemoments_ee, ide_checks.idelre_ee, ...
-% ide_checks.idemodel_ind, ide_checks.idemoments_ind, ...
-% ide_checks.idemodel_indno, ide_checks.idemoments_indno, ...
-% ide_checks.idemodel_ino, ide_checks.idemoments_ino] = ...
-% identification_checks(H(indH,:)./normH(:,ones(nparam,1)),JJ(indJJ,:)./normJ(:,ones(nparam,1)), gp(indLRE,:)./normLRE(:,ones(size(gp,2),1)));
+ % [ide_checks.idemodel_Mco, ide_checks.idemoments_Mco, ide_checks.idelre_Mco, ...
+ % ide_checks.idemodel_Pco, ide_checks.idemoments_Pco, ide_checks.idelre_Pco, ...
+ % ide_checks.idemodel_cond, ide_checks.idemoments_cond, ide_checks.idelre_cond, ...
+ % ide_checks.idemodel_ee, ide_checks.idemoments_ee, ide_checks.idelre_ee, ...
+ % ide_checks.idemodel_ind, ide_checks.idemoments_ind, ...
+ % ide_checks.idemodel_indno, ide_checks.idemoments_indno, ...
+ % ide_checks.idemodel_ino, ide_checks.idemoments_ino] = ...
+ % identification_checks(H(indH,:)./normH(:,ones(nparam,1)),JJ(indJJ,:)./normJ(:,ones(nparam,1)), gp(indLRE,:)./normLRE(:,ones(size(gp,2),1)));
[ide_moments.cond, ide_moments.ind0, ide_moments.indno, ide_moments.ino, ide_moments.Mco, ide_moments.Pco, ide_moments.jweak, ide_moments.jweak_pair] = ...
identification_checks(JJ(indJJ,:)./normJ, 0);
[ide_model.cond, ide_model.ind0, ide_model.indno, ide_model.ino, ide_model.Mco, ide_model.Pco, ide_model.jweak, ide_model.jweak_pair] = ...
diff --git a/matlab/identification_checks.m b/matlab/identification_checks.m
index 19c4b987b152e8da04b804c4bfb89543ce8560c6..e52fa960d882b1b93514de450bffde6fa6481def 100644
--- a/matlab/identification_checks.m
+++ b/matlab/identification_checks.m
@@ -7,17 +7,17 @@ function [condJ, ind0, indnoJ, ixnoJ, McoJ, PcoJ, jweak, jweak_pair] = identific
% derivatives of output w.r.t. parameters and shocks
% o JJ [matrix] [nparams x nparams] IF hess_flag==1
% information matrix
-%
+%
% OUTPUTS
% o cond condition number of JJ
% o ind0 [array] binary indicator for non-zero columns of H
-% o indnoJ [matrix] index of non-identified params
+% o indnoJ [matrix] index of non-identified params
% o ixnoJ number of rows in indnoJ
% o Mco [array] multicollinearity coefficients
-% o Pco [matrix] pairwise correlations
+% o Pco [matrix] pairwise correlations
% o jweak [binary array] gives 1 if the parameter has Mco=1(with tolerance 1.e-10)
% o jweak_pair [binary matrix] gives 1 if a couple parameters has Pco=1(with tolerance 1.e-10)
-%
+%
% SPECIAL REQUIREMENTS
% None
@@ -49,7 +49,7 @@ if size(JJ,1)>1
ind1 = find(vnorm(JJ)>=eps); % take non-zero columns
else
ind1 = find(abs(JJ)>=eps); % take non-zero columns
-end
+end
JJ1 = JJ(:,ind1);
[eu,ee2,ee1] = svd( JJ1, 0 );
condJ= cond(JJ1);
@@ -70,17 +70,17 @@ if npar>0 && (rankJ<npar)
end
if icheck
-JJ1 = JJ(:,ind1);
-[eu,ee2,ee1] = svd( JJ1, 0 );
-condJ= cond(JJ1);
-rankJ = rank(JJ);
-rankJJ = rankJ;
+ JJ1 = JJ(:,ind1);
+ [eu,ee2,ee1] = svd( JJ1, 0 );
+ condJ= cond(JJ1);
+ rankJ = rank(JJ);
+ rankJJ = rankJ;
end
-
+
% if hess_flag==0,
% rankJJ = rank(JJ'*JJ);
-% end
+% end
ind0 = zeros(1,npar);
ind0(ind1) = 1;
@@ -96,7 +96,7 @@ else
tildaJ = JJ(ind1,ind1)./((deltaJ)*(deltaJ'));
McoJ(ind1,1)=(1-1./diag(inv(tildaJ)));
rhoM=sqrt(1-McoJ);
-% PcoJ=inv(tildaJ);
+ % PcoJ=inv(tildaJ);
PcoJ=NaN(npar,npar);
PcoJ(ind1,ind1)=inv(JJ(ind1,ind1));
sd=sqrt(diag(PcoJ));
@@ -130,27 +130,26 @@ jweak=zeros(1,npar);
jweak_pair=zeros(npar,npar);
if hess_flag==0,
-PcoJ = NaN(npar,npar);
+ PcoJ = NaN(npar,npar);
-for ii = 1:size(JJ1,2)
- PcoJ(ind1(ii),ind1(ii)) = 1;
- for jj = ii+1:size(JJ1,2)
- PcoJ(ind1(ii),ind1(jj)) = cosn([JJ1(:,ii),JJ1(:,jj)]);
- PcoJ(ind1(jj),ind1(ii)) = PcoJ(ind1(ii),ind1(jj));
+ for ii = 1:size(JJ1,2)
+ PcoJ(ind1(ii),ind1(ii)) = 1;
+ for jj = ii+1:size(JJ1,2)
+ PcoJ(ind1(ii),ind1(jj)) = cosn([JJ1(:,ii),JJ1(:,jj)]);
+ PcoJ(ind1(jj),ind1(ii)) = PcoJ(ind1(ii),ind1(jj));
+ end
end
-end
-for j=1:npar
- if McoJ(j)>(1-1.e-10)
- jweak(j)=1;
- [ipair, jpair] = find(PcoJ(j,j+1:end)>(1-1.e-10));
- for jx=1:length(jpair)
- jweak_pair(j, jpair(jx)+j)=1;
- jweak_pair(jpair(jx)+j, j)=1;
+ for j=1:npar
+ if McoJ(j)>(1-1.e-10)
+ jweak(j)=1;
+ [ipair, jpair] = find(PcoJ(j,j+1:end)>(1-1.e-10));
+ for jx=1:length(jpair)
+ jweak_pair(j, jpair(jx)+j)=1;
+ jweak_pair(jpair(jx)+j, j)=1;
+ end
end
end
end
-end
jweak_pair=dyn_vech(jweak_pair)';
-
diff --git a/matlab/imcforecast.m b/matlab/imcforecast.m
index bad87e981261499902e657b4661a29fc5f128ce5..66841319a847fd3bf43f1d5269cfc2ea6ab6a114 100644
--- a/matlab/imcforecast.m
+++ b/matlab/imcforecast.m
@@ -2,29 +2,29 @@ function imcforecast(constrained_paths, constrained_vars, options_cond_fcst)
% Computes conditional forecasts.
%
% INPUTS
-% o constrained_paths [double] m*p array, where m is the number of constrained endogenous variables and p is the number of constrained periods.
-% o constrained_vars [char] m*x array holding the names of the controlled endogenous variables.
+% o constrained_paths [double] m*p array, where m is the number of constrained endogenous variables and p is the number of constrained periods.
+% o constrained_vars [char] m*x array holding the names of the controlled endogenous variables.
% o options_cond_fcst [structure] containing the options. The fields are:
% + replic [integer] scalar, number of monte carlo simulations.
% + parameter_set [char] values of the estimated parameters:
-% "posterior_mode",
-% "posterior_mean",
-% "posterior_median",
-% "prior_mode" or
-% "prior mean".
+% "posterior_mode",
+% "posterior_mean",
+% "posterior_median",
+% "prior_mode" or
+% "prior mean".
% [double] np*1 array, values of the estimated parameters.
% + controlled_varexo [char] m*x array, list of controlled exogenous variables.
-% + conf_sig [double] scalar in [0,1], probability mass covered by the confidence bands.
+% + conf_sig [double] scalar in [0,1], probability mass covered by the confidence bands.
%
% OUTPUTS
% None.
-%
+%
% SPECIAL REQUIREMENTS
% This routine has to be called after an estimation statement or an estimated_params block.
%
% REMARKS
% [1] Results are stored in a structure which is saved in a mat file called conditional_forecasts.mat.
-% [2] Use the function plot_icforecast to plot the results.
+% [2] Use the function plot_icforecast to plot the results.
% Copyright (C) 2006-2017 Dynare Team
%
@@ -119,7 +119,7 @@ if estimated_model
data_index = dataset_info.missing.aindex;
gend = dataset_.nobs;
missing_value = dataset_info.missing.state;
-
+
%store qz_criterium
qz_criterium_old=options_.qz_criterium;
options_=select_qz_criterium_value(options_);
@@ -147,7 +147,7 @@ if estimated_model
% add trend to constant
for obs_iter=1:length(options_.varobs)
j = strmatch(options_.varobs{obs_iter},M_.endo_names,'exact');
- constant(j,:) = constant(j,:)+trend_addition(obs_iter,:);
+ constant(j,:) = constant(j,:)+trend_addition(obs_iter,:);
end
trend = constant(oo_.dr.order_var,:);
InitState(:,1) = atT(:,end);
@@ -225,7 +225,7 @@ jdx = [];
for i = 1:n1
idx = [idx ; constrained_vars(i,:)];
-% idx = [idx ; oo_.dr.inv_order_var(constrained_vars(i,:))];
+ % idx = [idx ; oo_.dr.inv_order_var(constrained_vars(i,:))];
jdx = [jdx ; strmatch(deblank(options_cond_fcst.controlled_varexo(i,:)),M_.exo_names,'exact')];
end
mv = zeros(n1,NumberOfStates);
diff --git a/matlab/indnv.m b/matlab/indnv.m
index 1df707786504f55a1038c7947bc88b7dda42bb11..71b40cd3ed284bb3bc1ea8f3d2995e58cda8adb8 100644
--- a/matlab/indnv.m
+++ b/matlab/indnv.m
@@ -40,6 +40,3 @@ for i = 1:size(x,1)
a(i) = j;
end
end
-
-
-
diff --git a/matlab/init_plan.m b/matlab/init_plan.m
index bc70e361a9db3643d06fcc15f801d389d934a70d..721ba40fb7777308ba2a217b2393e1b43527d018 100644
--- a/matlab/init_plan.m
+++ b/matlab/init_plan.m
@@ -1,4 +1,4 @@
-function plan = init_plan(date)
+function plan = init_plan(date)
% Creates and initializes a new forecast scenario
%
% INPUTS
@@ -25,28 +25,27 @@ function plan = init_plan(date)
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- global M_
- plan = struct();
- plan.date = date;
- plan.date_str = strings(date);
- endo_names_length = size(M_.endo_names,2);
- plan.endo_names = deblank(mat2cell(M_.endo_names(1:M_.orig_endo_nbr,:),ones(1,M_.orig_endo_nbr),endo_names_length));
- exo_names_length = size(M_.exo_names,2);
- plan.exo_names = deblank(mat2cell(M_.exo_names(1:M_.exo_nbr,:),ones(1,M_.exo_nbr),exo_names_length));
- plan.constrained_vars_ = [];
- plan.constrained_paths_ = [];
- plan.constrained_date_ = [];
- plan.constrained_int_date_ = [];
- plan.constrained_str_date_ = [];
- plan.constrained_perfect_foresight_ = [];
- plan.shock_vars_ = [];
- plan.shock_paths_ = [];
- plan.shock_date_ = [];
- plan.shock_int_date_ = [];
- plan.shock_str_date_ = [];
- plan.shock_perfect_foresight_ = [];
- plan.options_cond_fcst_ = struct();
- plan.options_cond_fcst_.parameter_set = 'calibration';
- plan.options_cond_fcst_.simulation_type = 'deterministic';
- plan.options_cond_fcst_.controlled_varexo = [];
-
+global M_
+plan = struct();
+plan.date = date;
+plan.date_str = strings(date);
+endo_names_length = size(M_.endo_names,2);
+plan.endo_names = deblank(mat2cell(M_.endo_names(1:M_.orig_endo_nbr,:),ones(1,M_.orig_endo_nbr),endo_names_length));
+exo_names_length = size(M_.exo_names,2);
+plan.exo_names = deblank(mat2cell(M_.exo_names(1:M_.exo_nbr,:),ones(1,M_.exo_nbr),exo_names_length));
+plan.constrained_vars_ = [];
+plan.constrained_paths_ = [];
+plan.constrained_date_ = [];
+plan.constrained_int_date_ = [];
+plan.constrained_str_date_ = [];
+plan.constrained_perfect_foresight_ = [];
+plan.shock_vars_ = [];
+plan.shock_paths_ = [];
+plan.shock_date_ = [];
+plan.shock_int_date_ = [];
+plan.shock_str_date_ = [];
+plan.shock_perfect_foresight_ = [];
+plan.options_cond_fcst_ = struct();
+plan.options_cond_fcst_.parameter_set = 'calibration';
+plan.options_cond_fcst_.simulation_type = 'deterministic';
+plan.options_cond_fcst_.controlled_varexo = [];
diff --git a/matlab/initial_condition_decomposition.m b/matlab/initial_condition_decomposition.m
index af92d65ac88f499fb9d71413a4579a0c3cfe62b1..cee5335a864da6f5d2ebeac0a3bf4a803e709ec7 100644
--- a/matlab/initial_condition_decomposition.m
+++ b/matlab/initial_condition_decomposition.m
@@ -4,7 +4,7 @@ function oo_ = initial_condition_decomposition(M_,oo_,options_,varlist,bayestopt
% oo_.initval_decomposition. It is a n_var by n_var+2 by nperiods array. The
% first n_var columns store the respective endogenous initval contribution, column n+1
% stores the role of the shocks, while column n+2 stores the
-% value of the smoothed variables. Variables are stored
+% value of the smoothed variables. Variables are stored
% in the order of declaration, i.e. M_.endo_names.
%
% INPUTS
@@ -61,9 +61,9 @@ parameter_set = options_.parameter_set;
if isempty(parameter_set)
if isfield(oo_,'posterior_mean')
parameter_set = 'posterior_mean';
- elseif isfield(oo_,'mle_mode')
+ elseif isfield(oo_,'mle_mode')
parameter_set = 'mle_mode';
- elseif isfield(oo_,'posterior')
+ elseif isfield(oo_,'posterior')
parameter_set = 'posterior_mode';
else
error(['shock_decomposition: option parameter_set is not specified ' ...
@@ -72,61 +72,61 @@ if isempty(parameter_set)
end
if ~isfield(oo_,'initval_decomposition')
-options_.selected_variables_only = 0; %make sure all variables are stored
-options_.plot_priors=0;
-[oo,junk1,junk2,Smoothed_Variables_deviation_from_mean] = evaluate_smoother(parameter_set,varlist,M_,oo_,options_,bayestopt_,estim_params_);
+ options_.selected_variables_only = 0; %make sure all variables are stored
+ options_.plot_priors=0;
+ [oo,junk1,junk2,Smoothed_Variables_deviation_from_mean] = evaluate_smoother(parameter_set,varlist,M_,oo_,options_,bayestopt_,estim_params_);
-% reduced form
-dr = oo.dr;
+ % reduced form
+ dr = oo.dr;
-% data reordering
-order_var = dr.order_var;
-inv_order_var = dr.inv_order_var;
+ % data reordering
+ order_var = dr.order_var;
+ inv_order_var = dr.inv_order_var;
-% coefficients
-A = dr.ghx;
-B = dr.ghu;
+ % coefficients
+ A = dr.ghx;
+ B = dr.ghu;
-% initialization
-gend = size(oo.SmoothedShocks.(deblank(M_.exo_names(1,:))),1); %+options_.forecast;
-z = zeros(endo_nbr,endo_nbr+2,gend);
-z(:,end,:) = Smoothed_Variables_deviation_from_mean;
+ % initialization
+ gend = size(oo.SmoothedShocks.(deblank(M_.exo_names(1,:))),1); %+options_.forecast;
+ z = zeros(endo_nbr,endo_nbr+2,gend);
+ z(:,end,:) = Smoothed_Variables_deviation_from_mean;
-for i=1:endo_nbr
- z(i,i,1) = Smoothed_Variables_deviation_from_mean(i,1);
-end
+ for i=1:endo_nbr
+ z(i,i,1) = Smoothed_Variables_deviation_from_mean(i,1);
+ end
-maximum_lag = M_.maximum_lag;
+ maximum_lag = M_.maximum_lag;
-k2 = dr.kstate(find(dr.kstate(:,2) <= maximum_lag+1),[1 2]);
-i_state = order_var(k2(:,1))+(min(i,maximum_lag)+1-k2(:,2))*M_.endo_nbr;
-for i=1:gend
- if i > 1 && i <= maximum_lag+1
- lags = min(i-1,maximum_lag):-1:1;
- end
-
- if i > 1
- tempx = permute(z(:,1:endo_nbr,lags),[1 3 2]);
- m = min(i-1,maximum_lag);
- tempx = [reshape(tempx,endo_nbr*m,endo_nbr); zeros(endo_nbr*(maximum_lag-i+1),endo_nbr)];
- z(:,1:endo_nbr,i) = A(inv_order_var,:)*tempx(i_state,:);
- lags = lags+1;
- end
- z(:,endo_nbr+1,i) = z(:,endo_nbr+2,i) - sum(z(:,1:endo_nbr,i),2);
+ k2 = dr.kstate(find(dr.kstate(:,2) <= maximum_lag+1),[1 2]);
+ i_state = order_var(k2(:,1))+(min(i,maximum_lag)+1-k2(:,2))*M_.endo_nbr;
+ for i=1:gend
+ if i > 1 && i <= maximum_lag+1
+ lags = min(i-1,maximum_lag):-1:1;
+ end
-end
+ if i > 1
+ tempx = permute(z(:,1:endo_nbr,lags),[1 3 2]);
+ m = min(i-1,maximum_lag);
+ tempx = [reshape(tempx,endo_nbr*m,endo_nbr); zeros(endo_nbr*(maximum_lag-i+1),endo_nbr)];
+ z(:,1:endo_nbr,i) = A(inv_order_var,:)*tempx(i_state,:);
+ lags = lags+1;
+ end
+ z(:,endo_nbr+1,i) = z(:,endo_nbr+2,i) - sum(z(:,1:endo_nbr,i),2);
+
+ end
-oo_.initval_decomposition = z;
+ oo_.initval_decomposition = z;
end
% if ~options_.no_graph.shock_decomposition
- oo=oo_;
- oo.shock_decomposition = oo_.initval_decomposition;
- M_.exo_names = M_.endo_names;
- M_.exo_nbr = M_.endo_nbr;
- options_.plot_shock_decomp.screen_shocks=1;
- options_.plot_shock_decomp.use_shock_groups = '';
- options_.plot_shock_decomp.fig_names='initval';
- plot_shock_decomposition(M_,oo,options_,varlist);
+oo=oo_;
+oo.shock_decomposition = oo_.initval_decomposition;
+M_.exo_names = M_.endo_names;
+M_.exo_nbr = M_.endo_nbr;
+options_.plot_shock_decomp.screen_shocks=1;
+options_.plot_shock_decomp.use_shock_groups = '';
+options_.plot_shock_decomp.fig_names='initval';
+plot_shock_decomposition(M_,oo,options_,varlist);
% end
\ No newline at end of file
diff --git a/matlab/initial_estimation_checks.m b/matlab/initial_estimation_checks.m
index 8741eaec0dcfa64db6fe05778d24f7a472850f9b..7dfc4d9a2a74e2bd2bdd7800b4a887c16234b944 100644
--- a/matlab/initial_estimation_checks.m
+++ b/matlab/initial_estimation_checks.m
@@ -54,7 +54,7 @@ if maximum_number_non_missing_observations>length(find(diag(Model.Sigma_e)))+Est
end
if (any(BayesInfo.pshape >0 ) && DynareOptions.mh_replic) && DynareOptions.mh_nblck<1
- error(['initial_estimation_checks:: Bayesian estimation cannot be conducted with mh_nblocks=0.'])
+ error(['initial_estimation_checks:: Bayesian estimation cannot be conducted with mh_nblocks=0.'])
end
old_steady_params=Model.params; %save initial parameters for check if steady state changes param values
@@ -69,35 +69,35 @@ if isfield(EstimatedParameters,'param_vals') && ~isempty(EstimatedParameters.par
[junk, new_steady_params_2] = evaluate_steady_state(DynareResults.steady_state,Model_par_varied,DynareOptions,DynareResults,DynareOptions.diffuse_filter==0);
changed_par_indices=find((old_steady_params(EstimatedParameters.param_vals(:,1))-new_steady_params(EstimatedParameters.param_vals(:,1))) ...
- | (Model_par_varied.params(EstimatedParameters.param_vals(:,1))-new_steady_params_2(EstimatedParameters.param_vals(:,1))));
+ | (Model_par_varied.params(EstimatedParameters.param_vals(:,1))-new_steady_params_2(EstimatedParameters.param_vals(:,1))));
if ~isempty(changed_par_indices)
fprintf('\nThe steady state file internally changed the values of the following estimated parameters:\n')
disp(Model.param_names(EstimatedParameters.param_vals(changed_par_indices,1),:));
fprintf('This will override the parameter values drawn from the proposal density and may lead to wrong results.\n')
- fprintf('Check whether this is really intended.\n')
+ fprintf('Check whether this is really intended.\n')
warning('The steady state file internally changes the values of the estimated parameters.')
end
end
if any(BayesInfo.pshape) % if Bayesian estimation
nvx=EstimatedParameters.nvx;
- if nvx && any(BayesInfo.p3(1:nvx)<0)
+ if nvx && any(BayesInfo.p3(1:nvx)<0)
warning('Your prior allows for negative standard deviations for structural shocks. Due to working with variances, Dynare will be able to continue, but it is recommended to change your prior.')
end
offset=nvx;
nvn=EstimatedParameters.nvn;
- if nvn && any(BayesInfo.p3(1+offset:offset+nvn)<0)
+ if nvn && any(BayesInfo.p3(1+offset:offset+nvn)<0)
warning('Your prior allows for negative standard deviations for measurement error. Due to working with variances, Dynare will be able to continue, but it is recommended to change your prior.')
end
offset = nvx+nvn;
- ncx=EstimatedParameters.ncx;
- if ncx && (any(BayesInfo.p3(1+offset:offset+ncx)<-1) || any(BayesInfo.p4(1+offset:offset+ncx)>1))
+ ncx=EstimatedParameters.ncx;
+ if ncx && (any(BayesInfo.p3(1+offset:offset+ncx)<-1) || any(BayesInfo.p4(1+offset:offset+ncx)>1))
warning('Your prior allows for correlations between structural shocks larger than +-1 and will not integrate to 1 due to truncation. Please change your prior')
end
offset = nvx+nvn+ncx;
- ncn=EstimatedParameters.ncn;
- if ncn && (any(BayesInfo.p3(1+offset:offset+ncn)<-1) || any(BayesInfo.p4(1+offset:offset+ncn)>1))
+ ncn=EstimatedParameters.ncn;
+ if ncn && (any(BayesInfo.p3(1+offset:offset+ncn)<-1) || any(BayesInfo.p4(1+offset:offset+ncn)>1))
warning('Your prior allows for correlations between measurement errors larger than +-1 and will not integrate to 1 due to truncation. Please change your prior')
end
end
@@ -142,9 +142,9 @@ if ~isequal(DynareOptions.mode_compute,11) || ...
fprintf('initial_estimation_checks:: If you think the latter is the case, you should try with different initial values for the estimated parameters.\n')
error('initial_estimation_checks:: The forecast error variance in the multivariate Kalman filter became singular.')
end
- %reset options
+ %reset options
DynareOptions.use_univariate_filters_if_singularity_is_detected=use_univariate_filters_if_singularity_is_detected_old;
-else
+else
info=0;
fval = 0;
end
@@ -171,8 +171,8 @@ if info(1) > 0
error('Your model has at least one unit root and you are using a nonlinear filter. Please set nonlinear_filter_initialization=3.')
end
else
- disp('Error in computing likelihood for initial parameter values')
- print_info(info, DynareOptions.noprint, DynareOptions)
+ disp('Error in computing likelihood for initial parameter values')
+ print_info(info, DynareOptions.noprint, DynareOptions)
end
end
diff --git a/matlab/initialize_from_mode.m b/matlab/initialize_from_mode.m
index d7c11a9d0d5f289705bb80556a2db3fe4c33fced..2e5707158f8836faeab9c84ad3617610bdf9ba97 100644
--- a/matlab/initialize_from_mode.m
+++ b/matlab/initialize_from_mode.m
@@ -1,13 +1,13 @@
function estim_params_ = initialize_from_mode(fname,M_,estim_params_)
% function estim_params_ = initialize_from_mode(fname,M_,estim_params_)
% initialize parameters and initial value of estimated parameters
-% from a *_mode.mat file
-%
+% from a *_mode.mat file
+%
% INPUTS
% fname: mode file name (*.mat file)
% M_: sructure of model characteristics
% estim_params_: structure of estimated parameters
-%
+%
% OUTPUTS
% estim_params: modified structure of estimated parameters
%
@@ -99,4 +99,3 @@ for i=1:length(parameter_names)
end
error([name 'doesn''t exist in this model'])
end
-
diff --git a/matlab/initvalf.m b/matlab/initvalf.m
index 91c023706080126c58bdb83bf30b5324f1771a6f..9d6ae9bc1000da88ced20be02e72b52060e9eb66 100644
--- a/matlab/initvalf.m
+++ b/matlab/initvalf.m
@@ -1,7 +1,7 @@
function initvalf(fname_)
% function initvalf(fname_)
%
-% Reads an initial path from the 'fname_' file for exogenous and endogenous variables
+% Reads an initial path from the 'fname_' file for exogenous and endogenous variables
%
% INPUTS
% fname_: name of the function or file containing the data
@@ -58,15 +58,15 @@ if ~exist(fullname)
end
switch (extension)
- case '.m'
- eval(basename);
- case '.mat'
- load(basename);
- case { '.xls', '.xlsx' }
- [data_,names_v_]=xlsread(fullname); % Octave needs the extension explicitly
- series_=0;
- otherwise
- error(['Unsupported extension for datafile: ' extension])
+ case '.m'
+ eval(basename);
+ case '.mat'
+ load(basename);
+ case { '.xls', '.xlsx' }
+ [data_,names_v_]=xlsread(fullname); % Octave needs the extension explicitly
+ series_=0;
+ otherwise
+ error(['Unsupported extension for datafile: ' extension])
end
options_.initval_file = 1;
@@ -79,7 +79,7 @@ for i_=1:size(M_.endo_names,1)
if size(x_,2)>size(x_,1) %oo_.endo_simul must be collection of row vectors
oo_.endo_simul = [oo_.endo_simul; x_];
else %transpose if column vector
- oo_.endo_simul = [oo_.endo_simul; x_'];
+ oo_.endo_simul = [oo_.endo_simul; x_'];
end
else
k_ = strmatch(deblank(M_.endo_names(i_,:)),names_v_,'exact');
@@ -87,7 +87,7 @@ for i_=1:size(M_.endo_names,1)
error(['INITVAL_FILE: ' deblank(M_.endo_names(i_,:)) ' not found'])
end
x_ = data_(:,k_);
- oo_.endo_simul = [oo_.endo_simul; x_'];
+ oo_.endo_simul = [oo_.endo_simul; x_'];
end
end
@@ -97,7 +97,7 @@ for i_=1:size(M_.exo_names,1)
if size(x_,2)>size(x_,1) %oo_.endo_simul must be collection of row vectors
oo_.exo_simul = [oo_.exo_simul x_'];
else %if column vector
- oo_.exo_simul = [oo_.exo_simul x_];
+ oo_.exo_simul = [oo_.exo_simul x_];
end
else
k_ = strmatch(deblank(M_.exo_names(i_,:)),names_v_,'exact');
@@ -105,6 +105,6 @@ for i_=1:size(M_.exo_names,1)
error(['INITVAL_FILE: ' deblank(M_.exo_names(i_,:)) ' not found'])
end
x_ = data_(:,k_);
- oo_.exo_simul = [oo_.exo_simul x_];
+ oo_.exo_simul = [oo_.exo_simul x_];
end
end
\ No newline at end of file
diff --git a/matlab/interpret_resol_info.m b/matlab/interpret_resol_info.m
index de16d47e093ffba38e9b35740a705bac521ff205..e35dbe696608c3d334c41329d68df438f1c42c3b 100644
--- a/matlab/interpret_resol_info.m
+++ b/matlab/interpret_resol_info.m
@@ -1,14 +1,14 @@
function message = interpret_resol_info(info)
-
+
% Returns a message describing problem encountered during the resolution of
% a model.
%
-% INPUTS
+% INPUTS
% - info [struct] Second output argument return by the resol routine
%
-% OUTPUTS
+% OUTPUTS
% - message [string] Description of the issue preventing model's resolution.
-
+
% Copyright (C) 2001-2016 Dynare Team
%
% This file is part of Dynare.
diff --git a/matlab/irf.m b/matlab/irf.m
index f4be40503a07b74a5b8b051b2f85e2609befea77..13c8f36c19847a0d8ee5f1bc0d1526be3bf8722b 100644
--- a/matlab/irf.m
+++ b/matlab/irf.m
@@ -2,7 +2,7 @@ function y = irf(dr, e1, long, drop, replic, iorder)
% function y = irf(dr, e1, long, drop, replic, iorder)
% Computes impulse response functions
-%
+%
% INPUTS
% dr: structure of decisions rules for stochastic simulations
% e1: exogenous variables value in time 1 after one shock
@@ -13,7 +13,7 @@ function y = irf(dr, e1, long, drop, replic, iorder)
%
% OUTPUTS
% y: impulse response matrix
-%
+%
% SPECIAL REQUIREMENTS
% none
@@ -60,7 +60,7 @@ else
for j = 1: replic
ex1(:,i_exo_var) = randn(long+drop,nxs)*chol_S;
ex2 = ex1;
- ex2(drop+1,:) = ex2(drop+1,:)+e1';
+ ex2(drop+1,:) = ex2(drop+1,:)+e1';
y1 = simult_(temps,dr,ex1,iorder);
y2 = simult_(temps,dr,ex2,iorder);
y = y+(y2(:,M_.maximum_lag+drop+1:end)-y1(:,M_.maximum_lag+drop+1:end));
diff --git a/matlab/isolder.m b/matlab/isolder.m
index 091455ad0781022e52a9e31472e7fcc2d683e5c0..534ba54387d82b2db90e991a62d02182d7d59e5b 100644
--- a/matlab/isolder.m
+++ b/matlab/isolder.m
@@ -2,12 +2,12 @@ function b = isolder(f, F) % --*-- Unitary tests --*--
% Returns true if f is older than any file in folder (and subfolders) F.
%
-% INPUTS
+% INPUTS
% - f [string] file name
% - F [string] folder name
%
-% OUTPUT
-% - b [logical]
+% OUTPUT
+% - b [logical]
% Copyright (C) 2015 Dynare Team
%
@@ -29,9 +29,9 @@ function b = isolder(f, F) % --*-- Unitary tests --*--
if nargin<2 || isempty(F)
F = pwd();
end
-
+
b = true;
-
+
files = dir(F);
tfile = dir(f);
tdate = tfile.datenum;
diff --git a/matlab/k_order_pert.m b/matlab/k_order_pert.m
index 6de7a7607a0763335c76469ce9636aad65fbe92b..05c56eb37abbe3770efa798da64a8344d4e52e96 100644
--- a/matlab/k_order_pert.m
+++ b/matlab/k_order_pert.m
@@ -27,45 +27,45 @@ endo_nbr = M.endo_nbr;
exo_nbr = M.exo_nbr;
nspred = M.nspred;
-if order>1 && options.loglinear
- error('The loglinear-option currently only works at order 1')
+if order>1 && options.loglinear
+ error('The loglinear-option currently only works at order 1')
end
if M.maximum_endo_lead == 0 && order>1
- error(['2nd and 3rd order approximation not implemented for purely ' ...
- 'backward models'])
+ error(['2nd and 3rd order approximation not implemented for purely ' ...
+ 'backward models'])
end
switch(order)
case 1
[err, g_1] = k_order_perturbation(dr,M,options);
if err
- info(1)=9;
- return
+ info(1)=9;
+ return
end
dr.g_1 = g_1;
case 2
[err, g_0, g_1, g_2] = k_order_perturbation(dr,M,options);
if err
- info(1)=9;
- return
+ info(1)=9;
+ return
end
dr.g_0 = g_0;
dr.g_1 = g_1;
dr.g_2 = g_2;
case 3
- if options.pruning
+ if options.pruning
[err, g_0, g_1, g_2, g_3, derivs] = k_order_perturbation(dr, ...
M,options);
if err
- info(1)=9;
- return
+ info(1)=9;
+ return
end
else
[err, g_0, g_1, g_2, g_3] = k_order_perturbation(dr, ...
M,options);
if err
- info(1)=9;
- return
+ info(1)=9;
+ return
end
end
dr.g_0 = g_0;
@@ -119,7 +119,7 @@ else
if s1 > s0
ghxx(:,s1*nspred+s0+1) = 2*g_2(:,i);
end
- elseif s0 < nspred && s1 < nspred+exo_nbr
+ elseif s0 < nspred && s1 < nspred+exo_nbr
ghxu(:,(s0*exo_nbr+s1-nspred+1)) = 2*g_2(:,i);
elseif s0 < nspred+exo_nbr && s1 < nspred+exo_nbr
ghuu(:,(s0-nspred)*exo_nbr+s1-nspred +1) = 2*g_2(:,i);
@@ -132,7 +132,7 @@ else
s1 = s1+1;
if s1 == nspred+exo_nbr
s0 = s0+1;
- s1 = s0;
+ s1 = s0;
end
end % for loop
dr.ghxx = ghxx;
@@ -186,7 +186,7 @@ for i=1:n1
m = m + 1;
end
end
-end
+end
function y = unfold12(x,n1,n2)
y = zeros(size(x,1),n1*n2*n2);
@@ -203,7 +203,3 @@ for i=1:n1
end
end
end
-
-
-
-
diff --git a/matlab/kalman/build_selection_matrix.m b/matlab/kalman/build_selection_matrix.m
index f78662ddb000dc34019450f775cb01dc9c7ee389..adec3f33066f2e5ba8cd3971f57cbd1cf217509f 100644
--- a/matlab/kalman/build_selection_matrix.m
+++ b/matlab/kalman/build_selection_matrix.m
@@ -17,7 +17,7 @@ function Z = build_selection_matrix(mf,m,p)
% 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/>.
+% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
Z = zeros(p,m);
for i=1:p
Z(i,mf(i)) = 1;
diff --git a/matlab/kalman/likelihood/computeDLIK.m b/matlab/kalman/likelihood/computeDLIK.m
index d30b3f607f80613ffbb1a2edf222c6df10f15990..0fc417220c2a3f43314138bd0f656f5395655dfa 100644
--- a/matlab/kalman/likelihood/computeDLIK.m
+++ b/matlab/kalman/likelihood/computeDLIK.m
@@ -22,19 +22,19 @@ function [Da,DP,DLIK,D2a,D2P,Hesst] = computeDLIK(k,tmp,Z,Zflag,v,T,K,P,iF,Da,DY
persistent DK DF D2K D2F
if notsteady
-if Zflag
- [DK,DF,DP1] = computeDKalmanZ(T,DT,DOm,P,DP,DH,Z,iF,K);
- if nargout>4
- [D2K,D2F,D2P] = computeD2KalmanZ(T,DT,D2T,D2Om,P,DP,D2P,DH,Z,iF,K,DK);
- end
-else
- [DK,DF,DP1] = computeDKalman(T,DT,DOm,P,DP,DH,Z,iF,K);
- if nargout>4
- [D2K,D2F,D2P] = computeD2Kalman(T,DT,D2T,D2Om,P,DP,D2P,DH,Z,iF,K,DK);
+ if Zflag
+ [DK,DF,DP1] = computeDKalmanZ(T,DT,DOm,P,DP,DH,Z,iF,K);
+ if nargout>4
+ [D2K,D2F,D2P] = computeD2KalmanZ(T,DT,D2T,D2Om,P,DP,D2P,DH,Z,iF,K,DK);
+ end
+ else
+ [DK,DF,DP1] = computeDKalman(T,DT,DOm,P,DP,DH,Z,iF,K);
+ if nargout>4
+ [D2K,D2F,D2P] = computeD2Kalman(T,DT,D2T,D2Om,P,DP,D2P,DH,Z,iF,K,DK);
+ end
end
-end
-DP=DP1;
-clear DP1
+ DP=DP1;
+ clear DP1
else
DP=DP;
if nargout>4
@@ -47,20 +47,20 @@ Dv=zeros(length(v),k);
for ii = 1:k
if Zflag
Dv(:,ii) = -Z*Da(:,ii) - Z*DYss(:,ii);
-% if nargout>4,
-% for jj = 1:ii
-% D2v(:,jj,ii) = -Z*D2Yss(:,jj,ii) - Z*D2a(:,jj,ii);
-% D2v(:,ii,jj) = D2v(:,jj,ii);
-% end
-% end
+ % if nargout>4,
+ % for jj = 1:ii
+ % D2v(:,jj,ii) = -Z*D2Yss(:,jj,ii) - Z*D2a(:,jj,ii);
+ % D2v(:,ii,jj) = D2v(:,jj,ii);
+ % end
+ % end
else
Dv(:,ii) = -Da(Z,ii) - DYss(Z,ii);
-% if nargout>4,
-% for jj = 1:ii
-% D2v(:,jj,ii) = -D2Yss(Z,jj,ii) - D2a(Z,jj,ii);
-% D2v(:,ii,jj) = D2v(:,jj,ii);
-% end
-% end
+ % if nargout>4,
+ % for jj = 1:ii
+ % D2v(:,jj,ii) = -D2Yss(Z,jj,ii) - D2a(Z,jj,ii);
+ % D2v(:,ii,jj) = D2v(:,jj,ii);
+ % end
+ % end
end
end
@@ -71,7 +71,7 @@ for ii = 1:k
% dai = da(:,:,ii);
dKi = DK(:,:,ii);
dtmp(:,ii) = Da(:,ii)+dKi*v+K*Dv(:,ii);
-
+
if nargout>4
diFi = -iF*DF(:,:,ii)*iF;
for jj = 1:ii
@@ -82,9 +82,9 @@ for ii = 1:k
d2Kij = D2K(:,:,jj,ii);
d2Fij = D2F(:,:,jj,ii);
d2iFij = -diFi*dFj*iF -iF*d2Fij*iF -iF*dFj*diFi;
-% dtmpj = Da(:,jj)+dKj*v+K*Dv(:,jj);
-
-% d2vij = D2v(:,ii,jj);
+ % dtmpj = Da(:,jj)+dKj*v+K*Dv(:,jj);
+
+ % d2vij = D2v(:,ii,jj);
if Zflag
d2vij = -Z*D2Yss(:,jj,ii) - Z*D2a(:,jj,ii);
else
@@ -93,13 +93,13 @@ for ii = 1:k
d2tmpij = D2a(:,jj,ii) + d2Kij*v + dKj*Dv(:,ii) + dKi*Dv(:,jj) + K*d2vij;
D2a(:,jj,ii) = reshape(D2T(:,jcount),size(T))*tmp + DT(:,:,jj)*dtmp(:,ii) + DT(:,:,ii)*dtmp(:,jj) + T*d2tmpij;
D2a(:,ii,jj) = D2a(:,jj,ii);
-
+
if nargout==6
Hesst(ii,jj) = getHesst_ij(v,Dv(:,ii),Dv(:,jj),d2vij,iF,diFi,diFj,d2iFij,dFj,d2Fij);
end
end
end
-
+
Da(:,ii) = DT(:,:,ii)*tmp + T*dtmp(:,ii);
DLIK(ii,1) = trace( iF*DF(:,:,ii) ) + 2*Dv(:,ii)'*iF*v - v'*(iF*DF(:,:,ii)*iF)*v;
end
@@ -108,16 +108,16 @@ if nargout==4
% Hesst(ii,jj) = getHesst_ij(v,Dv(:,ii),Dv(:,jj),0,iF,diFi,diFj,0,dFj,0);
vecDPmf = reshape(DF,[],k);
D2a = 2*Dv'*iF*Dv + (vecDPmf' * kron(iF,iF) * vecDPmf);
-% for ii = 1:k
-%
-% diFi = -iF*DF(:,:,ii)*iF;
-% for jj = 1:ii
-% dFj = DF(:,:,jj);
-% diFj = -iF*DF(:,:,jj)*iF;
-%
-% Hesst(ii,jj) = getHesst_ij(v*0,Dv(:,ii),Dv(:,jj),v*0,iF,diFi,diFj,0,-dFj,0);
-% end
-% end
+ % for ii = 1:k
+ %
+ % diFi = -iF*DF(:,:,ii)*iF;
+ % for jj = 1:ii
+ % dFj = DF(:,:,jj);
+ % diFj = -iF*DF(:,:,jj)*iF;
+ %
+ % Hesst(ii,jj) = getHesst_ij(v*0,Dv(:,ii),Dv(:,jj),v*0,iF,diFi,diFj,0,-dFj,0);
+ % end
+ % end
end
% end of computeDLIK
@@ -168,9 +168,9 @@ end
function [d2K,d2S,d2P1] = computeD2Kalman(A,dA,d2A,d2Om,P0,dP0,d2P1,DH,Z,iF,K0,dK0)
% computes the second derivatives of the Kalman matrices
% note: A=T in main func.
-
- k = size(dA,3);
- tmp = P0-K0*P0(Z,:);
+
+k = size(dA,3);
+tmp = P0-K0*P0(Z,:);
[ns,no] = size(K0);
% CPC = C*P0*C'; CPC = .5*(CPC+CPC');iF = inv(CPC);
@@ -186,14 +186,14 @@ jcount=0;
for ii = 1:k
dAi = dA(:,:,ii);
dFi = dP0(Z,Z,ii);
-% d2Omi = d2Om(:,:,ii);
+ % d2Omi = d2Om(:,:,ii);
diFi = -iF*dFi*iF;
dKi = dK0(:,:,ii);
for jj = 1:ii
jcount=jcount+1;
dAj = dA(:,:,jj);
dFj = dP0(Z,Z,jj);
-% d2Omj = d2Om(:,:,jj);
+ % d2Omj = d2Om(:,:,jj);
dFj = dP0(Z,Z,jj);
diFj = -iF*dFj*iF;
dKj = dK0(:,:,jj);
@@ -201,33 +201,33 @@ for ii = 1:k
d2Aij = reshape(d2A(:,jcount),[ns ns]);
d2Pij = dyn_unvech(d2P1(:,jcount));
d2Omij = dyn_unvech(d2Om(:,jcount));
-
- % second order
-
- d2Fij = d2Pij(Z,Z) ;
-
-% d2APC = d2Aij*P0*C' + A*d2Pij*C' + A*P0*d2Cij' + dAi*dPj*C' + dAj*dPi*C' + A*dPj*dCi' + A*dPi*dCj' + dAi*P0*dCj' + dAj*P0*dCi';
- d2APC = d2Pij(:,Z);
-
- d2iF = -diFi*dFj*iF -iF*d2Fij*iF -iF*dFj*diFi;
-
- d2Kij= d2Pij(:,Z)*iF + P0(:,Z)*d2iF + dP0(:,Z,jj)*diFi + dP0(:,Z,ii)*diFj;
-
- d2KCP = d2Kij*P0(Z,:) + K0*d2Pij(Z,:) + dKi*dP0(Z,:,jj) + dKj*dP0(Z,:,ii) ;
-
- dtmpi = dP0(:,:,ii) - dK0(:,:,ii)*P0(Z,:) - K0*dP0(Z,:,ii);
- dtmpj = dP0(:,:,jj) - dK0(:,:,jj)*P0(Z,:) - K0*dP0(Z,:,jj);
- d2tmp = d2Pij - d2KCP;
-
- d2AtmpA = d2Aij*tmp*A' + A*d2tmp*A' + A*tmp*d2Aij' + dAi*dtmpj*A' + dAj*dtmpi*A' + A*dtmpj*dAi' + A*dtmpi*dAj' + dAi*tmp*dAj' + dAj*tmp*dAi';
-
- d2K(:,:,ii,jj) = d2Kij; %#ok<NASGU>
- d2P1(:,jcount) = dyn_vech(d2AtmpA + d2Omij); %#ok<*NASGU>
- d2S(:,:,ii,jj) = d2Fij;
- d2K(:,:,jj,ii) = d2Kij; %#ok<NASGU>
-% d2P1(:,:,jj,ii) = d2AtmpA + d2Omij; %#ok<*NASGU>
- d2S(:,:,jj,ii) = d2Fij;
-% d2iS(:,:,ii,jj) = d2iF;
+
+ % second order
+
+ d2Fij = d2Pij(Z,Z) ;
+
+ % d2APC = d2Aij*P0*C' + A*d2Pij*C' + A*P0*d2Cij' + dAi*dPj*C' + dAj*dPi*C' + A*dPj*dCi' + A*dPi*dCj' + dAi*P0*dCj' + dAj*P0*dCi';
+ d2APC = d2Pij(:,Z);
+
+ d2iF = -diFi*dFj*iF -iF*d2Fij*iF -iF*dFj*diFi;
+
+ d2Kij= d2Pij(:,Z)*iF + P0(:,Z)*d2iF + dP0(:,Z,jj)*diFi + dP0(:,Z,ii)*diFj;
+
+ d2KCP = d2Kij*P0(Z,:) + K0*d2Pij(Z,:) + dKi*dP0(Z,:,jj) + dKj*dP0(Z,:,ii) ;
+
+ dtmpi = dP0(:,:,ii) - dK0(:,:,ii)*P0(Z,:) - K0*dP0(Z,:,ii);
+ dtmpj = dP0(:,:,jj) - dK0(:,:,jj)*P0(Z,:) - K0*dP0(Z,:,jj);
+ d2tmp = d2Pij - d2KCP;
+
+ d2AtmpA = d2Aij*tmp*A' + A*d2tmp*A' + A*tmp*d2Aij' + dAi*dtmpj*A' + dAj*dtmpi*A' + A*dtmpj*dAi' + A*dtmpi*dAj' + dAi*tmp*dAj' + dAj*tmp*dAi';
+
+ d2K(:,:,ii,jj) = d2Kij; %#ok<NASGU>
+ d2P1(:,jcount) = dyn_vech(d2AtmpA + d2Omij); %#ok<*NASGU>
+ d2S(:,:,ii,jj) = d2Fij;
+ d2K(:,:,jj,ii) = d2Kij; %#ok<NASGU>
+ % d2P1(:,:,jj,ii) = d2AtmpA + d2Omij; %#ok<*NASGU>
+ d2S(:,:,jj,ii) = d2Fij;
+ % d2iS(:,:,ii,jj) = d2iF;
end
end
@@ -236,9 +236,9 @@ end
function [d2K,d2S,d2P1] = computeD2KalmanZ(A,dA,d2A,d2Om,P0,dP0,d2P1,DH,Z,iF,K0,dK0)
% computes the second derivatives of the Kalman matrices
% note: A=T in main func.
-
- k = size(dA,3);
- tmp = P0-K0*Z*P0(:,:);
+
+k = size(dA,3);
+tmp = P0-K0*Z*P0(:,:);
[ns,no] = size(K0);
% CPC = C*P0*C'; CPC = .5*(CPC+CPC');iF = inv(CPC);
@@ -254,14 +254,14 @@ jcount=0;
for ii = 1:k
dAi = dA(:,:,ii);
dFi = Z*dP0(:,:,ii)*Z;
-% d2Omi = d2Om(:,:,ii);
+ % d2Omi = d2Om(:,:,ii);
diFi = -iF*dFi*iF;
dKi = dK0(:,:,ii);
for jj = 1:ii
jcount=jcount+1;
dAj = dA(:,:,jj);
dFj = Z*dP0(:,:,jj)*Z;
-% d2Omj = d2Om(:,:,jj);
+ % d2Omj = d2Om(:,:,jj);
dFj = Z*dP0(:,:,jj)*Z;
diFj = -iF*dFj*iF;
dKj = dK0(:,:,jj);
@@ -269,36 +269,34 @@ for ii = 1:k
d2Aij = reshape(d2A(:,jcount),[ns ns]);
d2Pij = dyn_unvech(d2P1(:,jcount));
d2Omij = dyn_unvech(d2Om(:,jcount));
-
- % second order
-
- d2Fij = Z*d2Pij(:,:)*Z ;
-
-% d2APC = d2Aij*P0*C' + A*d2Pij*C' + A*P0*d2Cij' + dAi*dPj*C' + dAj*dPi*C' + A*dPj*dCi' + A*dPi*dCj' + dAi*P0*dCj' + dAj*P0*dCi';
- d2APC = d2Pij(:,:)*Z;
-
- d2iF = -diFi*dFj*iF -iF*d2Fij*iF -iF*dFj*diFi;
-
- d2Kij= d2Pij(:,:)*Z*iF + P0(:,:)*Z*d2iF + dP0(:,:,jj)*Z*diFi + dP0(:,:,ii)*Z*diFj;
-
- d2KCP = d2Kij*Z*P0(:,:) + K0*Z*d2Pij(:,:) + dKi*Z*dP0(:,:,jj) + dKj*Z*dP0(:,:,ii) ;
-
- dtmpi = dP0(:,:,ii) - dK0(:,:,ii)*Z*P0(:,:) - K0*Z*dP0(:,:,ii);
- dtmpj = dP0(:,:,jj) - dK0(:,:,jj)*Z*P0(:,:) - K0*Z*dP0(:,:,jj);
- d2tmp = d2Pij - d2KCP;
-
- d2AtmpA = d2Aij*tmp*A' + A*d2tmp*A' + A*tmp*d2Aij' + dAi*dtmpj*A' + dAj*dtmpi*A' + A*dtmpj*dAi' + A*dtmpi*dAj' + dAi*tmp*dAj' + dAj*tmp*dAi';
-
- d2K(:,:,ii,jj) = d2Kij; %#ok<NASGU>
- d2P1(:,jcount) = dyn_vech(d2AtmpA + d2Omij); %#ok<*NASGU>
- d2S(:,:,ii,jj) = d2Fij;
-% d2iS(:,:,ii,jj) = d2iF;
- d2K(:,:,jj,ii) = d2Kij; %#ok<NASGU>
-% d2P1(:,:,jj,ii) = d2AtmpA + d2Omij; %#ok<*NASGU>
- d2S(:,:,jj,ii) = d2Fij;
+
+ % second order
+
+ d2Fij = Z*d2Pij(:,:)*Z ;
+
+ % d2APC = d2Aij*P0*C' + A*d2Pij*C' + A*P0*d2Cij' + dAi*dPj*C' + dAj*dPi*C' + A*dPj*dCi' + A*dPi*dCj' + dAi*P0*dCj' + dAj*P0*dCi';
+ d2APC = d2Pij(:,:)*Z;
+
+ d2iF = -diFi*dFj*iF -iF*d2Fij*iF -iF*dFj*diFi;
+
+ d2Kij= d2Pij(:,:)*Z*iF + P0(:,:)*Z*d2iF + dP0(:,:,jj)*Z*diFi + dP0(:,:,ii)*Z*diFj;
+
+ d2KCP = d2Kij*Z*P0(:,:) + K0*Z*d2Pij(:,:) + dKi*Z*dP0(:,:,jj) + dKj*Z*dP0(:,:,ii) ;
+
+ dtmpi = dP0(:,:,ii) - dK0(:,:,ii)*Z*P0(:,:) - K0*Z*dP0(:,:,ii);
+ dtmpj = dP0(:,:,jj) - dK0(:,:,jj)*Z*P0(:,:) - K0*Z*dP0(:,:,jj);
+ d2tmp = d2Pij - d2KCP;
+
+ d2AtmpA = d2Aij*tmp*A' + A*d2tmp*A' + A*tmp*d2Aij' + dAi*dtmpj*A' + dAj*dtmpi*A' + A*dtmpj*dAi' + A*dtmpi*dAj' + dAi*tmp*dAj' + dAj*tmp*dAi';
+
+ d2K(:,:,ii,jj) = d2Kij; %#ok<NASGU>
+ d2P1(:,jcount) = dyn_vech(d2AtmpA + d2Omij); %#ok<*NASGU>
+ d2S(:,:,ii,jj) = d2Fij;
+ % d2iS(:,:,ii,jj) = d2iF;
+ d2K(:,:,jj,ii) = d2Kij; %#ok<NASGU>
+ % d2P1(:,:,jj,ii) = d2AtmpA + d2Omij; %#ok<*NASGU>
+ d2S(:,:,jj,ii) = d2Fij;
end
end
% end of computeD2KalmanZ
-
-
diff --git a/matlab/kalman/likelihood/kalman_filter.m b/matlab/kalman/likelihood/kalman_filter.m
index 4802a00cf33db8c146316304aa5b8d3c91fa2c2a..441ee8525190e9a8a1cb1fd33acb3d931e2ceecc 100644
--- a/matlab/kalman/likelihood/kalman_filter.m
+++ b/matlab/kalman/likelihood/kalman_filter.m
@@ -132,7 +132,7 @@ else
DLIK = zeros(k,1); % Initialization of the score.
Da = zeros(mm,k); % Derivative State vector.
dlikk = zeros(smpl,k);
-
+
if Zflag==0
C = zeros(pp,mm);
for ii=1:pp, C(ii,Z(ii))=1; end % SELECTION MATRIX IN MEASUREMENT EQ. (FOR WHEN IT IS NOT CONSTANT)
@@ -237,7 +237,7 @@ if analytic_derivation
dlikk = dlikk/2;
if analytic_derivation==2 || asy_hess
if asy_hess==0
- Hess = Hess + tril(Hess,-1)';
+ Hess = Hess + tril(Hess,-1)';
end
Hess = -Hess/2;
end
diff --git a/matlab/kalman/likelihood/kalman_filter_d.m b/matlab/kalman/likelihood/kalman_filter_d.m
index a10cf6f4d2c243eac5011f56db5df5d2bb7fc399..0877442cfc01e98a5bcf79878709adb2df7e7203 100644
--- a/matlab/kalman/likelihood/kalman_filter_d.m
+++ b/matlab/kalman/likelihood/kalman_filter_d.m
@@ -1,7 +1,7 @@
function [dLIK,dlik,a,Pstar] = kalman_filter_d(Y, start, last, a, Pinf, Pstar, kalman_tol, diffuse_kalman_tol, riccati_tol, presample, T, R, Q, H, Z, mm, pp, rr)
% Computes the diffuse likelihood of a state space model.
%
-% INPUTS
+% INPUTS
% Y [double] pp*smpl matrix of (detrended) data, where pp is the number of observed variables.
% start [integer] scalar, first observation.
% last [integer] scalar, last observation.
@@ -10,28 +10,28 @@ function [dLIK,dlik,a,Pstar] = kalman_filter_d(Y, start, last, a, Pinf, Pstar, k
% Pstar [double] mm*mm matrix used to initialize the covariance matrix of the state vector.
% kalman_tol [double] scalar, tolerance parameter (rcond) of F_star.
% diffuse_kalman_tol [double] scalar, tolerance parameter (rcond) of Pinf to signify end of diffuse filtering and Finf.
-% riccati_tol [double] scalar, tolerance parameter (riccati iteration);
+% riccati_tol [double] scalar, tolerance parameter (riccati iteration);
% not used in this filter as usually diffuse phase will be left before convergence of filter to steady state.
-% presample [integer] scalar, presampling if strictly positive.
+% presample [integer] scalar, presampling if strictly positive.
% T [double] mm*mm matrix, transition matrix in the state equations.
% R [double] mm*rr matrix relating the structural innovations to the state vector.
% Q [double] rr*rr covariance matrix of the structural innovations.
-% H [double] pp*pp covariance matrix of the measurement errors (if H is equal to zero (scalar) there is no measurement error).
+% H [double] pp*pp covariance matrix of the measurement errors (if H is equal to zero (scalar) there is no measurement error).
% Z [double] pp*mm matrix, selection matrix or pp linear independent combinations of the state vector.
% mm [integer] scalar, number of state variables.
% pp [integer] scalar, number of observed variables.
-% rr [integer] scalar, number of structural innovations.
-%
-% OUTPUTS
+% rr [integer] scalar, number of structural innovations.
+%
+% OUTPUTS
% LIK [double] scalar, minus loglikelihood
% lik [double] smpl*1 vector, log density of each vector of observations.
% a [double] mm*1 vector, current estimate of the state vector.
-% Pstar [double] mm*mm matrix, covariance matrix of the state vector.
-%
-% REFERENCES
+% Pstar [double] mm*mm matrix, covariance matrix of the state vector.
+%
+% REFERENCES
% See "Filtering and Smoothing of State Vector for Diffuse State Space
-% Models", S.J. Koopman and J. Durbin (2003), in Journal of Time Series
-% Analysis, vol. 24(1), pp. 85-98.
+% Models", S.J. Koopman and J. Durbin (2003), in Journal of Time Series
+% Analysis, vol. 24(1), pp. 85-98.
% and
% Durbin/Koopman (2012): "Time Series Analysis by State Space Methods", Oxford University Press,
% Second Edition, Ch. 5 and 7.2
@@ -69,10 +69,10 @@ while rank(Pinf,diffuse_kalman_tol) && (t<=last)
s = t-start+1;
v = Y(:,t)-Z*a; %get prediction error v^(0) in (5.13) DK (2012)
Finf = Z*Pinf*Z'; % (5.7) in DK (2012)
- %do case distinction based on whether F_{\infty,t} has full rank or 0 rank
- if rcond(Finf) < diffuse_kalman_tol %F_{\infty,t} = 0
+ %do case distinction based on whether F_{\infty,t} has full rank or 0 rank
+ if rcond(Finf) < diffuse_kalman_tol %F_{\infty,t} = 0
if ~all(abs(Finf(:)) < diffuse_kalman_tol) %rank-deficient but not rank 0
- % The univariate diffuse kalman filter should be used instead.
+ % The univariate diffuse kalman filter should be used instead.
return
else %rank of F_{\infty,t} is 0
Fstar = Z*Pstar*Z' + H; % (5.7) in DK (2012)
@@ -81,8 +81,8 @@ while rank(Pinf,diffuse_kalman_tol) && (t<=last)
% The univariate diffuse kalman filter should be used.
return
else %rank 0
- %pathological case, discard draw
- return
+ %pathological case, discard draw
+ return
end
else
iFstar = inv(Fstar);
@@ -95,15 +95,15 @@ while rank(Pinf,diffuse_kalman_tol) && (t<=last)
end
end
else %F_{\infty,t} positive definite
- %To compare to DK (2012), this block makes use of the following transformation
- %Kstar=T^{-1}*K^{(1)}=M_{*}*F^{(1)}+M_{\infty}*F^{(2)}
- % =P_{*}*Z'*F^{(1)}+P_{\infty}*Z'*((-1)*(-F_{\infty}^{-1})*F_{*}*(F_{\infty}^{-1}))
- % =[P_{*}*Z'-Kinf*F_{*})]*F^{(1)}
- %Make use of L^{0}'=(T-K^{(0)}*Z)'=(T-T*M_{\infty}*F^{(1)}*Z)'
- % =(T-T*P_{\infty*Z'*F^{(1)}*Z)'=(T-T*Kinf*Z)'
- % = (T*(I-*Kinf*Z))'=(I-Z'*Kinf')*T'
- %P_{*}=T*P_{\infty}*L^{(1)}+T*P_{*}*L^{(0)}+RQR
- % =T*[(P_{\infty}*(-K^{(1)*Z}))+P_{*}*(I-Z'*Kinf')*T'+RQR]
+ %To compare to DK (2012), this block makes use of the following transformation
+ %Kstar=T^{-1}*K^{(1)}=M_{*}*F^{(1)}+M_{\infty}*F^{(2)}
+ % =P_{*}*Z'*F^{(1)}+P_{\infty}*Z'*((-1)*(-F_{\infty}^{-1})*F_{*}*(F_{\infty}^{-1}))
+ % =[P_{*}*Z'-Kinf*F_{*})]*F^{(1)}
+ %Make use of L^{0}'=(T-K^{(0)}*Z)'=(T-T*M_{\infty}*F^{(1)}*Z)'
+ % =(T-T*P_{\infty*Z'*F^{(1)}*Z)'=(T-T*Kinf*Z)'
+ % = (T*(I-*Kinf*Z))'=(I-Z'*Kinf')*T'
+ %P_{*}=T*P_{\infty}*L^{(1)}+T*P_{*}*L^{(0)}+RQR
+ % =T*[(P_{\infty}*(-K^{(1)*Z}))+P_{*}*(I-Z'*Kinf')*T'+RQR]
dlik(s)= log(det(Finf)); %set w_t to top case in bottom equation page 172, DK (2012)
iFinf = inv(Finf);
Kinf = Pinf*Z'*iFinf; %define Kinf=T^{-1}*K_0 with M_{\infty}=Pinf*Z'
@@ -117,7 +117,7 @@ while rank(Pinf,diffuse_kalman_tol) && (t<=last)
end
if t>last
- warning(['kalman_filter_d: There isn''t enough information to estimate the initial conditions of the nonstationary variables. The diffuse Kalman filter never left the diffuse stage.']);
+ warning(['kalman_filter_d: There isn''t enough information to estimate the initial conditions of the nonstationary variables. The diffuse Kalman filter never left the diffuse stage.']);
dLIK = NaN;
return
end
diff --git a/matlab/kalman/likelihood/kalman_filter_fast.m b/matlab/kalman/likelihood/kalman_filter_fast.m
index f5bb08bae3c2556c687f0e88886fad8f7b7e117f..db3dec3b755d406d55bf89005c80b6946b3a49e6 100644
--- a/matlab/kalman/likelihood/kalman_filter_fast.m
+++ b/matlab/kalman/likelihood/kalman_filter_fast.m
@@ -152,7 +152,7 @@ while notsteady && t<=last
end
if rcond(F) < kalman_tol
if ~all(abs(F(:))<kalman_tol)
- % The univariate diffuse kalman filter should be used.
+ % The univariate diffuse kalman filter should be used.
return
else
%pathological case, discard draw
@@ -199,7 +199,7 @@ if analytic_derivation
dlikk = dlikk/2;
if analytic_derivation==2 || asy_hess
if asy_hess==0
- Hess = Hess + tril(Hess,-1)';
+ Hess = Hess + tril(Hess,-1)';
end
Hess = -Hess/2;
end
@@ -210,10 +210,10 @@ if t <= last
if analytic_derivation
if analytic_derivation==2
[tmp, tmp2] = kalman_filter_ss(Y,t,last,a,T,K,iF,dF,Z,pp,Zflag, ...
- analytic_derivation,Da,DT,DYss,D2a,D2T,D2Yss);
+ analytic_derivation,Da,DT,DYss,D2a,D2T,D2Yss);
else
[tmp, tmp2] = kalman_filter_ss(Y,t,last,a,T,K,iF,dF,Z,pp,Zflag, ...
- analytic_derivation,Da,DT,DYss,asy_hess);
+ analytic_derivation,Da,DT,DYss,asy_hess);
end
likk(s+1:end)=tmp2{1};
dlikk(s+1:end,:)=tmp2{2};
diff --git a/matlab/kalman/likelihood/missing_observations_kalman_filter.m b/matlab/kalman/likelihood/missing_observations_kalman_filter.m
index 0b0c4853302e8965598390ee298adc33d5c5009b..f1bd40cb59e5e0ce3198019d3e83c599228635e1 100644
--- a/matlab/kalman/likelihood/missing_observations_kalman_filter.m
+++ b/matlab/kalman/likelihood/missing_observations_kalman_filter.m
@@ -5,32 +5,32 @@ function [LIK, lik, a, P] = missing_observations_kalman_filter(data_index,numbe
% data_index [cell] 1*smpl cell of column vectors of indices.
% number_of_observations [integer] scalar.
% no_more_missing_observations [integer] scalar.
-% Y [double] pp*smpl matrix of data.
+% Y [double] pp*smpl matrix of data.
% start [integer] scalar, index of the first observation.
% last [integer] scalar, index of the last observation.
% a [double] pp*1 vector, initial level of the state vector.
% P [double] pp*pp matrix, covariance matrix of the initial state vector.
% kalman_tol [double] scalar, tolerance parameter (rcond).
-% riccati_tol [double] scalar, tolerance parameter (riccati iteration).
+% riccati_tol [double] scalar, tolerance parameter (riccati iteration).
% presample [integer] scalar, presampling if strictly positive.
% T [double] mm*mm transition matrix of the state equation.
-% Q [double] rr*rr covariance matrix of the structural innovations.
+% Q [double] rr*rr covariance matrix of the structural innovations.
% R [double] mm*rr matrix, mapping structural innovations to state variables.
-% H [double] pp*pp (or 1*1 =0 if no measurement error) covariance matrix of the measurement errors.
-% Z [integer] pp*1 vector of indices for the observed variables.
+% H [double] pp*pp (or 1*1 =0 if no measurement error) covariance matrix of the measurement errors.
+% Z [integer] pp*1 vector of indices for the observed variables.
% mm [integer] scalar, dimension of the state vector.
% pp [integer] scalar, number of observed variables.
-% rr [integer] scalar, number of structural innovations.
-%
-% OUTPUTS
+% rr [integer] scalar, number of structural innovations.
+%
+% OUTPUTS
% LIK [double] scalar, MINUS loglikelihood
% lik [double] vector, density of observations in each period.
% a [double] mm*1 vector, estimated level of the states.
-% P [double] mm*mm matrix, covariance matrix of the states.
-%
+% P [double] mm*mm matrix, covariance matrix of the states.
+%
%
% NOTES
-% The vector "lik" is used to evaluate the jacobian of the likelihood.
+% The vector "lik" is used to evaluate the jacobian of the likelihood.
% Copyright (C) 2004-2017 Dynare Team
%
diff --git a/matlab/kalman/likelihood/missing_observations_kalman_filter_d.m b/matlab/kalman/likelihood/missing_observations_kalman_filter_d.m
index a497d6e53f1e2af7d998af1cdf120332f9d9025a..c290f179309810cf92bf7ed46282d85ee66eaeae 100644
--- a/matlab/kalman/likelihood/missing_observations_kalman_filter_d.m
+++ b/matlab/kalman/likelihood/missing_observations_kalman_filter_d.m
@@ -1,11 +1,11 @@
function [dLIK,dlik,a,Pstar] = missing_observations_kalman_filter_d(data_index,number_of_observations,no_more_missing_observations, ...
- Y, start, last, ...
- a, Pinf, Pstar, ...
- kalman_tol, diffuse_kalman_tol, riccati_tol, presample, ...
- T, R, Q, H, Z, mm, pp, rr)
+ Y, start, last, ...
+ a, Pinf, Pstar, ...
+ kalman_tol, diffuse_kalman_tol, riccati_tol, presample, ...
+ T, R, Q, H, Z, mm, pp, rr)
% Computes the diffuse likelihood of a state space model when some observations are missing.
%
-% INPUTS
+% INPUTS
% data_index [cell] 1*smpl cell of column vectors of indices.
% number_of_observations [integer] scalar.
% no_more_missing_observations [integer] scalar.
@@ -17,31 +17,31 @@ function [dLIK,dlik,a,Pstar] = missing_observations_kalman_filter_d(data_index,n
% Pstar [double] mm*mm matrix used to initialize the covariance matrix of the state vector.
% kalman_tol [double] scalar, tolerance parameter (rcond).
% riccati_tol [double] scalar, tolerance parameter (riccati iteration).
-% presample [integer] scalar, presampling if strictly positive.
+% presample [integer] scalar, presampling if strictly positive.
% T [double] mm*mm matrix, transition matrix in the state equations.
% R [double] mm*rr matrix relating the structural innovations to the state vector.
% Q [double] rr*rr covariance matrix of the structural innovations.
-% H [double] pp*pp covariance matrix of the measurement errors (if H is equal to zero (scalar) there is no measurement error).
+% H [double] pp*pp covariance matrix of the measurement errors (if H is equal to zero (scalar) there is no measurement error).
% Z [double] pp*mm matrix, selection matrix or pp linear independant combinations of the state vector.
% mm [integer] scalar, number of state variables.
% pp [integer] scalar, number of observed variables.
-% rr [integer] scalar, number of structural innovations.
-%
-% OUTPUTS
+% rr [integer] scalar, number of structural innovations.
+%
+% OUTPUTS
% dLIK [double] scalar, MINUS loglikelihood
% dlik [double] vector, density of observations in each period.
% a [double] mm*1 vector, estimated level of the states.
-% Pstar [double] mm*mm matrix, covariance matrix of the states.
-%
+% Pstar [double] mm*mm matrix, covariance matrix of the states.
+%
% REFERENCES
% See "Filtering and Smoothing of State Vector for Diffuse State Space
-% Models", S.J. Koopman and J. Durbin (2003), in Journal of Time Series
-% Analysis, vol. 24(1), pp. 85-98.
+% Models", S.J. Koopman and J. Durbin (2003), in Journal of Time Series
+% Analysis, vol. 24(1), pp. 85-98.
% and
% Durbin/Koopman (2012): "Time Series Analysis by State Space Methods", Oxford University Press,
% Second Edition, Ch. 5 and 7.2
-%
+%
% Copyright (C) 2004-2016 Dynare Team
%
% This file is part of Dynare.
@@ -87,11 +87,11 @@ while rank(Pinf,diffuse_kalman_tol) && (t<=last)
Pinf = T*Pinf*transpose(T);
else
ZZ = Z(d_index,:); %span selector matrix
- v = Y(d_index,t)-ZZ*a; %get prediction error v^(0) in (5.13) DK (2012)
+ v = Y(d_index,t)-ZZ*a; %get prediction error v^(0) in (5.13) DK (2012)
Finf = ZZ*Pinf*ZZ'; % (5.7) in DK (2012)
- if rcond(Finf) < diffuse_kalman_tol %F_{\infty,t} = 0
+ if rcond(Finf) < diffuse_kalman_tol %F_{\infty,t} = 0
if ~all(abs(Finf(:)) < diffuse_kalman_tol) %rank-deficient but not rank 0
- % The univariate diffuse kalman filter should be used.
+ % The univariate diffuse kalman filter should be used.
return
else %rank of F_{\infty,t} is 0
Fstar = ZZ*Pstar*ZZ' + H(d_index,d_index); % (5.7) in DK (2012)
@@ -100,7 +100,7 @@ while rank(Pinf,diffuse_kalman_tol) && (t<=last)
% The univariate diffuse kalman filter should be used.
return
else %rank 0
- %pathological case, discard draw
+ %pathological case, discard draw
return
end
else
@@ -129,7 +129,7 @@ while rank(Pinf,diffuse_kalman_tol) && (t<=last)
end
if t==(last+1)
- warning(['kalman_filter_d: There isn''t enough information to estimate the initial conditions of the nonstationary variables. The diffuse Kalman filter never left the diffuse stage.']);
+ warning(['kalman_filter_d: There isn''t enough information to estimate the initial conditions of the nonstationary variables. The diffuse Kalman filter never left the diffuse stage.']);
dLIK = NaN;
return
end
diff --git a/matlab/kalman/likelihood/univariate_computeDLIK.m b/matlab/kalman/likelihood/univariate_computeDLIK.m
index 009c0f6357a94481d0fda12f28745289436087f9..083fb12db92938c23ae10c6679809459573fa42d 100644
--- a/matlab/kalman/likelihood/univariate_computeDLIK.m
+++ b/matlab/kalman/likelihood/univariate_computeDLIK.m
@@ -47,7 +47,7 @@ if notsteady
end
end
end
-
+
else
Dv = -Da(Z,:) - DYss(Z,:);
DF = squeeze(DP(Z,Z,:))+DH';
@@ -67,7 +67,7 @@ if notsteady
D2K(:,i,j) = D2K(:,i,j) -PZ*D2F(j,i)/F^2 - squeeze(DP(:,Z,i))*DF(j)/F^2 - ...
squeeze(DP(:,Z,j))*DF(i)'/F^2 + 2/F^3*PZ*DF(i)'*DF(j);
D2K(:,j,i) = D2K(:,i,j);
-% D2K = squeeze(D2P(:,Z,:,:))/F;
+ % D2K = squeeze(D2P(:,Z,:,:))/F;
end
end
end
@@ -104,17 +104,17 @@ end
DLIK = DF/F + 2*Dv'/F*v - v^2/F^2*DF;
if nargout==6
Hesst = D2F/F-1/F^2*(DF*DF') + 2*D2v/F*v + 2*(Dv'*Dv)/F - 2*(DF*Dv)*v/F^2 ...
- - v^2/F^2*D2F - 2*v/F^2*(Dv'*DF') + 2*v^2/F^3*(DF*DF');
+ - v^2/F^2*D2F - 2*v/F^2*(Dv'*DF') + 2*v^2/F^3*(DF*DF');
elseif nargout==4
D2a = 1/F^2*(DF*DF') + 2*(Dv'*Dv)/F ;
-% D2a = -1/F^2*(DF*DF') + 2*(Dv'*Dv)/F + 2*v^2/F^3*(DF*DF') ...
-% - 2*(DF*Dv)*v/F^2 - 2*v/F^2*(Dv'*DF');
-% D2a = +2*(Dv'*Dv)/F + (DF' * DF)/F^2;
+ % D2a = -1/F^2*(DF*DF') + 2*(Dv'*Dv)/F + 2*v^2/F^3*(DF*DF') ...
+ % - 2*(DF*Dv)*v/F^2 - 2*v/F^2*(Dv'*DF');
+ % D2a = +2*(Dv'*Dv)/F + (DF' * DF)/F^2;
end
Da = Da + DK*v+K*Dv;
if nargout>4
-
+
D2a = D2a + D2K*v;
for j=1:k
% D2a(:,:,j) = D2a(:,:,j) + DK*Dv(j) + DK(:,j)*Dv + K*D2v(j,:);
@@ -123,8 +123,8 @@ if nargout>4
D2a(:,i,j) = D2a(:,j,i);
end
end
-
-
+
+
end
if notsteady
@@ -145,9 +145,9 @@ if notsteady
jcount = jcount+1;
tmp = dyn_unvech(D2P(:,jcount));
tmp = tmp - (tmp*Z')*K' - (DP(:,:,j)*Z')*DK(:,i)' ...
- - (DP(:,:,i)*Z')*DK(:,j)' -PZ*D2K(:,j,i)';
+ - (DP(:,:,i)*Z')*DK(:,j)' -PZ*D2K(:,j,i)';
D2P(:,jcount) = dyn_vech(tmp);
-% D2P(:,:,i,j) = D2P(:,:,j,i);
+ % D2P(:,:,i,j) = D2P(:,:,j,i);
end
end
else
@@ -160,14 +160,12 @@ if notsteady
D2PZ = tmp(:,Z);
tmp = tmp - D2PZ*K' - DPZ(:,j)*DK(:,i)'- DPZ(:,i)*DK(:,j)' - PZ*squeeze(D2K(:,j,i))';
D2P(:,jcount) = dyn_vech(tmp);
-% D2P(:,:,i,j) = D2P(:,:,j,i);
+ % D2P(:,:,i,j) = D2P(:,:,j,i);
end
end
-
+
end
end
else
DP1=DP;
end
-
-
diff --git a/matlab/kalman/likelihood/univariate_computeDstate.m b/matlab/kalman/likelihood/univariate_computeDstate.m
index c21acd21ba1da58214a5a215c5beda7cdf7e20d8..eecee428eb4f3bcf2608e4434dce2c7baf4c4d89 100644
--- a/matlab/kalman/likelihood/univariate_computeDstate.m
+++ b/matlab/kalman/likelihood/univariate_computeDstate.m
@@ -43,14 +43,14 @@ if nargout>2
if notsteady
tmp = dyn_unvech(D2P(:,jcount));
tmp = T*tmp*T' +DT(:,:,i)*DP(:,:,j)*T'+T*DP(:,:,j)*DT(:,:,i)' + ...
- DT(:,:,j)*DP(:,:,i)*T'+T*DP(:,:,i)*DT(:,:,j)' + ...
- DT(:,:,j)*P*DT(:,:,i)'+DT(:,:,i)*P*DT(:,:,j)'+ ...
- reshape(D2T(:,jcount),size(T))*P*T'+T*P*reshape(D2T(:,jcount),size(T))' + ...
- dyn_unvech(D2Om(:,jcount));
+ DT(:,:,j)*DP(:,:,i)*T'+T*DP(:,:,i)*DT(:,:,j)' + ...
+ DT(:,:,j)*P*DT(:,:,i)'+DT(:,:,i)*P*DT(:,:,j)'+ ...
+ reshape(D2T(:,jcount),size(T))*P*T'+T*P*reshape(D2T(:,jcount),size(T))' + ...
+ dyn_unvech(D2Om(:,jcount));
D2P(:,jcount) = dyn_vech(tmp);
-% D2P(:,:,i,j) = D2P(:,:,j,i);
+ % D2P(:,:,i,j) = D2P(:,:,j,i);
end
end
end
-
+
end
diff --git a/matlab/kalman/likelihood/univariate_kalman_filter.m b/matlab/kalman/likelihood/univariate_kalman_filter.m
index 2e831cc0deeca45a22b8d75d1daadd385407200f..add638a54760dc397a29b7371cf0d752b5170c35 100644
--- a/matlab/kalman/likelihood/univariate_kalman_filter.m
+++ b/matlab/kalman/likelihood/univariate_kalman_filter.m
@@ -37,7 +37,7 @@ function [LIK, lik,a,P] = univariate_kalman_filter(data_index,number_of_observat
%! @item Q
%! Matrix (@var{rr}*@var{rr}) of doubles, covariance matrix of the structural innovations (noise in the state equation).
%! @item R
-%! Matrix (@var{mm}*@var{rr}) of doubles,
+%! Matrix (@var{mm}*@var{rr}) of doubles,
%! @item H
%! Vector (@var{pp}) of doubles, diagonal of covariance matrix of the measurement errors (corelation among measurement errors is handled by a model transformation).
%! @item Z
@@ -76,9 +76,9 @@ function [LIK, lik,a,P] = univariate_kalman_filter(data_index,number_of_observat
%! @ref{univariate_kalman_filter_ss}
%! @end deftypefn
%@eod:
-%
+%
% Algorithm:
-%
+%
% Uses the univariate filter as described in Durbin/Koopman (2012): "Time
% Series Analysis by State Space Methods", Oxford University Press,
% Second Edition, Ch. 6.4 + 7.2.5
@@ -136,7 +136,7 @@ else
DLIK = zeros(k,1); % Initialization of the score.
Da = zeros(mm,k); % Derivative State vector.
dlik = zeros(smpl,k);
-
+
if Zflag==0
C = zeros(pp,mm);
for ii=1:pp, C(ii,Z(ii))=1; end % SELECTION MATRIX IN MEASUREMENT EQ. (FOR WHEN IT IS NOT CONSTANT)
@@ -172,13 +172,13 @@ while notsteady && t<=last %loop over t
oldP = P(:);
for i=1:rows(z) %loop over i
if Zflag
- prediction_error = Y(d_index(i),t) - z(i,:)*a; % nu_{t,i} in 6.13 in DK (2012)
+ prediction_error = Y(d_index(i),t) - z(i,:)*a; % nu_{t,i} in 6.13 in DK (2012)
PZ = P*z(i,:)'; % Z_{t,i}*P_{t,i}*Z_{t,i}'
- Fi = z(i,:)*PZ + H(d_index(i)); % F_{t,i} in 6.13 in DK (2012), relies on H being diagonal
+ Fi = z(i,:)*PZ + H(d_index(i)); % F_{t,i} in 6.13 in DK (2012), relies on H being diagonal
else
- prediction_error = Y(d_index(i),t) - a(z(i)); % nu_{t,i} in 6.13 in DK (2012)
+ prediction_error = Y(d_index(i),t) - a(z(i)); % nu_{t,i} in 6.13 in DK (2012)
PZ = P(:,z(i)); % Z_{t,i}*P_{t,i}*Z_{t,i}'
- Fi = PZ(z(i)) + H(d_index(i)); % F_{t,i} in 6.13 in DK (2012), relies on H being diagonal
+ Fi = PZ(z(i)) + H(d_index(i)); % F_{t,i} in 6.13 in DK (2012), relies on H being diagonal
end
if Fi>kalman_tol
Ki = PZ/Fi; %K_{t,i} in 6.13 in DK (2012)
@@ -214,7 +214,7 @@ while notsteady && t<=last %loop over t
[Da,DP] = univariate_computeDstate(k,a,P,T,Da,DP,DT,DOm,notsteady);
end
end
- a = T*a; %transition according to (6.14) in DK (2012)
+ a = T*a; %transition according to (6.14) in DK (2012)
P = T*P*T' + QQ; %transition according to (6.14) in DK (2012)
if t>=no_more_missing_observations
notsteady = max(abs(K(:)-oldK))>riccati_tol;
@@ -229,7 +229,7 @@ if analytic_derivation
DLIK = DLIK/2;
dlik = dlik/2;
if analytic_derivation==2 || asy_hess
-% Hess = (Hess + Hess')/2;
+ % Hess = (Hess + Hess')/2;
Hess = -Hess/2;
end
end
@@ -239,10 +239,10 @@ if t <= last
if analytic_derivation
if analytic_derivation==2
[tmp, tmp2] = univariate_kalman_filter_ss(Y,t,last,a,P,kalman_tol,T,H,Z,pp,Zflag, ...
- analytic_derivation,Da,DT,DYss,DP,DH,D2a,D2T,D2Yss,D2P);
+ analytic_derivation,Da,DT,DYss,DP,DH,D2a,D2T,D2Yss,D2P);
else
[tmp, tmp2] = univariate_kalman_filter_ss(Y,t,last,a,P,kalman_tol,T,H,Z,pp,Zflag, ...
- analytic_derivation,Da,DT,DYss,DP,DH,asy_hess);
+ analytic_derivation,Da,DT,DYss,DP,DH,asy_hess);
end
lik(s+1:end,:)=tmp2{1};
dlik(s+1:end,:)=tmp2{2};
diff --git a/matlab/kalman/likelihood/univariate_kalman_filter_d.m b/matlab/kalman/likelihood/univariate_kalman_filter_d.m
index ac121ee33861d98c1d476b4940eeadefe64ac587..9a29c498138f60f9be4375f2df96ae470aa0ad46 100644
--- a/matlab/kalman/likelihood/univariate_kalman_filter_d.m
+++ b/matlab/kalman/likelihood/univariate_kalman_filter_d.m
@@ -80,9 +80,9 @@ function [dLIK, dlikk, a, Pstar, llik] = univariate_kalman_filter_d(data_index,
%! @ref{univariate_kalman_filter_ss}
%! @end deftypefn
%@eod:
-%
+%
% Algorithm:
-%
+%
% Uses the diffuse univariate filter as described in Durbin/Koopman (2012): "Time
% Series Analysis by State Space Methods", Oxford University Press,
% Second Edition, Ch. 5, 6.4 + 7.2.5
@@ -125,15 +125,15 @@ while newRank && (t<=last)
d_index = data_index{t};
for i=1:length(d_index)
Zi = Z(d_index(i),:);
- prediction_error = Y(d_index(i),t) - Zi*a; % nu_{t,i} in 6.13 in DK (2012)
+ prediction_error = Y(d_index(i),t) - Zi*a; % nu_{t,i} in 6.13 in DK (2012)
Fstar = Zi*Pstar*Zi' + H(d_index(i)); % F_{*,t} in 5.7 in DK (2012), relies on H being diagonal
- Finf = Zi*Pinf*Zi'; % F_{\infty,t} in 5.7 in DK (2012), relies on H being diagonal
+ Finf = Zi*Pinf*Zi'; % F_{\infty,t} in 5.7 in DK (2012), relies on H being diagonal
Kstar = Pstar*Zi';
% Conduct check of rank
- % Pinf and Finf are always scaled such that their norm=1: Fstar/Pstar, instead,
+ % Pinf and Finf are always scaled such that their norm=1: Fstar/Pstar, instead,
% depends on the actual values of std errors in the model and can be badly scaled.
- % experience is that diffuse_kalman_tol has to be bigger than kalman_tol, to ensure
- % exiting the diffuse filter properly, avoiding tests that provide false non-zero rank for Pinf.
+ % experience is that diffuse_kalman_tol has to be bigger than kalman_tol, to ensure
+ % exiting the diffuse filter properly, avoiding tests that provide false non-zero rank for Pinf.
% Also the test for singularity is better set coarser for Finf than for Fstar for the same reason
if Finf>diffuse_kalman_tol && newRank % F_{\infty,t,i} = 0, use upper part of bracket on p. 175 DK (2012) for w_{t,i}
Kinf = Pinf*Zi';
@@ -141,7 +141,7 @@ while newRank && (t<=last)
a = a + Kinf_Finf*prediction_error;
Pstar = Pstar + Kinf*(Kinf_Finf'*(Fstar/Finf)) - Kstar*Kinf_Finf' - Kinf_Finf*Kstar';
Pinf = Pinf - Kinf*Kinf_Finf';
- llik(s,d_index(i)) = log(Finf) + l2pi;
+ llik(s,d_index(i)) = log(Finf) + l2pi;
dlikk(s) = dlikk(s) + llik(s,d_index(i));
elseif Fstar>kalman_tol
llik(s,d_index(i)) = log(Fstar) + (prediction_error*prediction_error/Fstar) + l2pi;
diff --git a/matlab/kalman/likelihood/univariate_kalman_filter_ss.m b/matlab/kalman/likelihood/univariate_kalman_filter_ss.m
index 3801ea90e1e0a6d90caf2daa8baff217933b6555..9d8cbff06fa912f12c69768b34c2f2338ff33c7a 100644
--- a/matlab/kalman/likelihood/univariate_kalman_filter_ss.m
+++ b/matlab/kalman/likelihood/univariate_kalman_filter_ss.m
@@ -54,7 +54,7 @@ function [LIK,likk,a] = univariate_kalman_filter_ss(Y,start,last,a,P,kalman_tol,
%! @sp 1
%! @end deftypefn
%@eod:
-%
+%
% Algorithm: See univariate_kalman_filter.m
% Copyright (C) 2011-2017 Dynare Team
@@ -171,7 +171,7 @@ if analytic_derivation
likk = {likk, dlikk};
end
if analytic_derivation==2 || asy_hess
-% Hess = (Hess + Hess')/2;
+ % Hess = (Hess + Hess')/2;
Hess = -Hess/2;
LIK={LIK,DLIK,Hess};
elseif analytic_derivation==1
diff --git a/matlab/kalman/steady_state_kalman_gain.m b/matlab/kalman/steady_state_kalman_gain.m
index 460534e1b3b1011ae8aa56e65125a616b1861b10..aa9344741fce89c6ca8b7168da2ebb64b54af985 100644
--- a/matlab/kalman/steady_state_kalman_gain.m
+++ b/matlab/kalman/steady_state_kalman_gain.m
@@ -1,19 +1,19 @@
function [K,iF,P] = steady_state_kalman_gain(T,R,Q,H,mf)
% Given the invariant state space representation of a model, this
% function computes the gain matrix and the covariance matrix of the
-% state vector at the steady state of the kalman filter.
-%
-% INPUTS
-% T [double] m*m transition matrix of the state vector.
+% state vector at the steady state of the kalman filter.
+%
+% INPUTS
+% T [double] m*m transition matrix of the state vector.
% R [double] m*q matrix (q is the number of structural innovations).
% Q [double] q*q covariance matrix of the structural innovations.
% H [double] p*p covariance matrix of the measurement error.
% mf [integer] p*1 vector, indices for the observed variables
-%
-% OUTPUTS
+%
+% OUTPUTS
% K [double] kalman gain matrix.
% P [double] covariance matrix of the state vector.
-%
+%
% SPECIAL REQUIREMENTS
% Needs a solver for Riccati equations (dare.m)
diff --git a/matlab/kalman_transition_matrix.m b/matlab/kalman_transition_matrix.m
index 77b3a4d3a7741981a52fbf302f5778f80dfc77fb..1b026cfe4ce182bd2cc767b512aecd079051bc87 100644
--- a/matlab/kalman_transition_matrix.m
+++ b/matlab/kalman_transition_matrix.m
@@ -1,17 +1,17 @@
function [A,B] = kalman_transition_matrix(dr,iv,ic,exo_nbr)
%function [A,B] = kalman_transition_matrix(dr,iv,ic,exo_nbr)
% Builds the transition equation of the state space representation out of ghx and ghu for Kalman filter
-%
+%
% INPUTS
% dr: structure of decisions rules for stochastic simulations
% iv: selected variables
% ic: state variables position in the transition matrix columns
% exo_nbr: number of exogenous variables
-%
+%
% OUTPUTS
% A: matrix of predetermined variables effects in linear solution (ghx)
% B: matrix of shocks effects in linear solution (ghu)
-%
+%
% SPECIAL REQUIREMENTS
% none
@@ -41,4 +41,3 @@ A(:,ic) = dr.ghx(iv,:);
if nargout>1
B = dr.ghu(iv,:);
end
-
diff --git a/matlab/kernel_density_estimate.m b/matlab/kernel_density_estimate.m
index 172097c870c7bb0acd8614ef717b4f0537758b67..e3e83d5167afb659f39ed9a072b4aac48d7cc9dd 100644
--- a/matlab/kernel_density_estimate.m
+++ b/matlab/kernel_density_estimate.m
@@ -1,27 +1,27 @@
-function [abscissa,f] = kernel_density_estimate(data,number_of_grid_points,number_of_draws,bandwidth,kernel_function)
-% Estimates a continuous density.
-%
+function [abscissa,f] = kernel_density_estimate(data,number_of_grid_points,number_of_draws,bandwidth,kernel_function)
+% Estimates a continuous density.
+%
% INPUTS
% data [double] Vector (number_of_draws*1) of draws.
% number_of_grid_points [integer] Scalar, number of points where the density is estimated.
-% This (positive) integer must be a power of two.
+% This (positive) integer must be a power of two.
% number_of_draws [integer] Scalar, number of draws.
-% bandwidth [double] Real positive scalar.
+% bandwidth [double] Real positive scalar.
% kernel_function [string] Name of the kernel function: 'gaussian', 'triweight',
-% 'uniform', 'triangle', 'epanechnikov', 'quartic',
+% 'uniform', 'triangle', 'epanechnikov', 'quartic',
% 'triweight' and 'cosinus'
%
% OUTPUTS
% abscissa [double] Vector (number_of_grid_points*1) of values on the abscissa axis.
-% f: [double] Vector (number_of_grid_points*1) of values on the ordinate axis,
+% f: [double] Vector (number_of_grid_points*1) of values on the ordinate axis,
% (density estimates).
-%
+%
% SPECIAL REQUIREMENTS
% none.
%
% REFERENCES
% A kernel density estimator is used (see Silverman [1986], "Density estimation for statistics and data analysis")
-% The code is adapted from Anders Holtsberg's matlab toolbox (stixbox).
+% The code is adapted from Anders Holtsberg's matlab toolbox (stixbox).
%
% Copyright (C) 2004-2008 Dynare Team
@@ -53,19 +53,19 @@ if (abs(test-round(test)) > 1e-12)
end
%% Kernel specification.
-if strcmpi(kernel_function,'gaussian')
+if strcmpi(kernel_function,'gaussian')
kernel = @(x) inv(sqrt(2*pi))*exp(-0.5*x.^2);
-elseif strcmpi(kernel_function,'uniform')
- kernel = @(x) 0.5*(abs(x) <= 1);
-elseif strcmpi(kernel_function,'triangle')
+elseif strcmpi(kernel_function,'uniform')
+ kernel = @(x) 0.5*(abs(x) <= 1);
+elseif strcmpi(kernel_function,'triangle')
kernel = @(x) (1-abs(x)).*(abs(x) <= 1);
-elseif strcmpi(kernel_function,'epanechnikov')
+elseif strcmpi(kernel_function,'epanechnikov')
kernel = @(x) 0.75*(1-x.^2).*(abs(x) <= 1);
-elseif strcmpi(kernel_function,'quartic')
+elseif strcmpi(kernel_function,'quartic')
kernel = @(x) 0.9375*((1-x.^2).^2).*(abs(x) <= 1);
-elseif strcmpi(kernel_function,'triweight')
+elseif strcmpi(kernel_function,'triweight')
kernel = @(x) 1.09375*((1-x.^2).^3).*(abs(x) <= 1);
-elseif strcmpi(kernel_function,'cosinus')
+elseif strcmpi(kernel_function,'cosinus')
kernel = @(x) (pi/4)*cos((pi/2)*x).*(abs(x) <= 1);
end
@@ -73,9 +73,9 @@ end
lower_bound = min(data) - (max(data)-min(data))/3;
upper_bound = max(data) + (max(data)-min(data))/3;
abscissa = linspace(lower_bound,upper_bound,number_of_grid_points)';
-inc = abscissa(2)-abscissa(1);
+inc = abscissa(2)-abscissa(1);
xi = zeros(number_of_grid_points,1);
-xa = (data-lower_bound)/(upper_bound-lower_bound)*number_of_grid_points;
+xa = (data-lower_bound)/(upper_bound-lower_bound)*number_of_grid_points;
for i = 1:number_of_draws
indx = floor(xa(i));
temp = xa(i)-indx;
diff --git a/matlab/list_of_parameters_calibrated_as_NaN.m b/matlab/list_of_parameters_calibrated_as_NaN.m
index 603a9f2e1f2dd3fb7e77ce498d5c9f21dbd024a2..4911190a520c2df664e6ae02889c6f9c50af2dc9 100644
--- a/matlab/list_of_parameters_calibrated_as_NaN.m
+++ b/matlab/list_of_parameters_calibrated_as_NaN.m
@@ -1,15 +1,15 @@
function list = list_of_parameters_calibrated_as_NaN(M_)
% The name of the function is explicit enough...
-%
+%
% INPUTS
% M_ [structure] Description of the (simulated or estimated) model.
-%
+%
% OUTPUTS
% list [char] n*p array of characters, each line is the name of parameter without value.
-%
+%
% ALGORITHM
% none
-%
+%
% SPECIAL REQUIREMENTS
% none
diff --git a/matlab/lmmcp/catstruct.m b/matlab/lmmcp/catstruct.m
index 014768ad9afa2867da0bdaacc1e7ea8b5261198b..ae40d15318d0d8def7330d09db23f09e3e5f808c 100644
--- a/matlab/lmmcp/catstruct.m
+++ b/matlab/lmmcp/catstruct.m
@@ -1,174 +1,171 @@
-function A = catstruct(varargin)
-% CATSTRUCT Concatenate or merge structures with different fieldnames
-% X = CATSTRUCT(S1,S2,S3,...) merges the structures S1, S2, S3 ...
-% into one new structure X. X contains all fields present in the various
-% structures. An example:
-%
-% A.name = 'Me' ;
-% B.income = 99999 ;
-% X = catstruct(A,B)
-% % -> X.name = 'Me' ;
-% % X.income = 99999 ;
-%
-% If a fieldname is not unique among structures (i.e., a fieldname is
-% present in more than one structure), only the value from the last
-% structure with this field is used. In this case, the fields are
-% alphabetically sorted. A warning is issued as well. An axample:
-%
-% S1.name = 'Me' ;
-% S2.age = 20 ; S3.age = 30 ; S4.age = 40 ;
-% S5.honest = false ;
-% Y = catstruct(S1,S2,S3,S4,S5) % use value from S4
-%
-% The inputs can be array of structures. All structures should have the
-% same size. An example:
-%
-% C(1).bb = 1 ; C(2).bb = 2 ;
-% D(1).aa = 3 ; D(2).aa = 4 ;
-% CD = catstruct(C,D) % CD is a 1x2 structure array with fields bb and aa
-%
-% The last input can be the string 'sorted'. In this case,
-% CATSTRUCT(S1,S2, ..., 'sorted') will sort the fieldnames alphabetically.
-% To sort the fieldnames of a structure A, you could use
-% CATSTRUCT(A,'sorted') but I recommend ORDERFIELDS for doing that.
-%
-% When there is nothing to concatenate, the result will be an empty
-% struct (0x0 struct array with no fields).
-%
-% NOTE: To concatenate similar arrays of structs, you can use simple
-% concatenation:
-% A = dir('*.mat') ; B = dir('*.m') ; C = [A ; B] ;
-%
-% See also CAT, STRUCT, FIELDNAMES, STRUCT2CELL, ORDERFIELDS
-
-% for Matlab R13 and up
-% version 3.0 (mar 2013)
-% Originally downloaded from MATLAB central:
-% http://www.mathworks.com/matlabcentral/fileexchange/7842-catstruct
-
-% Copyright (C) 2005 Jos van der Geest <jos@jasen.nl>
-% Copyright (C) 2013 Christophe Gouel
-% Copyright (C) 2016 Dynare Team
-%
-% Redistribution and use in source and binary forms, with or without
-% modification, are permitted provided that the following conditions are
-% met:
-%
-% * Redistributions of source code must retain the above copyright
-% notice, this list of conditions and the following disclaimer.
-% * Redistributions in binary form must reproduce the above copyright
-% notice, this list of conditions and the following disclaimer in
-% the documentation and/or other materials provided with the distribution
-%
-% THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-% AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-% IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-% ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-% LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-% CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-% SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-% INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-% CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-% ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-% POSSIBILITY OF SUCH DAMAGE.
-
-% History
-% Created in 2005
-% Revisions
-% 2.0 (sep 2007) removed bug when dealing with fields containing cell
-% arrays (Thanks to Rene Willemink)
-% 2.1 (sep 2008) added warning and error identifiers
-% 2.2 (oct 2008) fixed error when dealing with empty structs (Thanks to
-% Lars Barring)
-% 3.0 (mar 2013) fixed problem when the inputs were array of structures
-% (thanks to Tor Inge Birkenes for pointing this out).
-% Rephrased the help section as well.
-
-narginchk(1, Inf);
-N = nargin ;
-
-if ~isstruct(varargin{end})
- if isequal(varargin{end},'sorted')
- sorted = 1 ;
- N = N-1 ;
- if N<1
- error('catstruct: wrong number of input arguments') ;
- end
- else
- error('catstruct:InvalidArgument','Last argument should be a structure, or the string "sorted".') ;
- end
-else
- sorted = 0 ;
-end
-
-sz0 = [] ; % used to check that all inputs have the same size
-
-% used to check for a few trivial cases
-NonEmptyInputs = false(N,1) ;
-NonEmptyInputsN = 0 ;
-
-% used to collect the fieldnames and the inputs
-FN = cell(N,1) ;
-VAL = cell(N,1) ;
-
-% parse the inputs
-for ii=1:N
- X = varargin{ii} ;
- if ~isstruct(X)
- error('catstruct:InvalidArgument',['Argument #' num2str(ii) ' is not a structure.']) ;
- end
-
- if ~isempty(X)
- % empty structs are ignored
- if ii > 1 && ~isempty(sz0)
- if ~isequal(size(X), sz0)
- error('catstruct:UnequalSizes','All structures should have the same size.') ;
- end
- else
- sz0 = size(X) ;
- end
- NonEmptyInputsN = NonEmptyInputsN + 1 ;
- NonEmptyInputs(ii) = true ;
- FN{ii} = fieldnames(X) ;
- VAL{ii} = struct2cell(X) ;
- end
-end
-
-if NonEmptyInputsN == 0
- % all structures were empty
- A = struct([]) ;
-elseif NonEmptyInputsN == 1
- % there was only one non-empty structure
- A = varargin{NonEmptyInputs} ;
- if sorted
- A = orderfields(A) ;
- end
-else
- % there is actually something to concatenate
- FN = cat(1,FN{:}) ;
- VAL = cat(1,VAL{:}) ;
- FN = squeeze(FN) ;
- VAL = squeeze(VAL) ;
- MatlabVersion = version;
- if isoctave || str2double(MatlabVersion(end-5:end-2))<2013 % Equivalent to, but faster than if verLessThan('matlab','8.1')
- [UFN,ind] = unique(FN) ;
- else
- [UFN,ind] = unique(FN,'legacy') ;
- end
-
- if numel(UFN) ~= numel(FN)
- warning('catstruct:DuplicatesFound','Fieldnames are not unique between structures.') ;
- sorted = 1 ;
- end
-
- if sorted
- VAL = VAL(ind,:) ;
- FN = FN(ind,:) ;
- end
-
- A = cell2struct(VAL, FN);
- A = reshape(A, sz0) ; % reshape into original format
-end
-
-
-
+function A = catstruct(varargin)
+% CATSTRUCT Concatenate or merge structures with different fieldnames
+% X = CATSTRUCT(S1,S2,S3,...) merges the structures S1, S2, S3 ...
+% into one new structure X. X contains all fields present in the various
+% structures. An example:
+%
+% A.name = 'Me' ;
+% B.income = 99999 ;
+% X = catstruct(A,B)
+% % -> X.name = 'Me' ;
+% % X.income = 99999 ;
+%
+% If a fieldname is not unique among structures (i.e., a fieldname is
+% present in more than one structure), only the value from the last
+% structure with this field is used. In this case, the fields are
+% alphabetically sorted. A warning is issued as well. An axample:
+%
+% S1.name = 'Me' ;
+% S2.age = 20 ; S3.age = 30 ; S4.age = 40 ;
+% S5.honest = false ;
+% Y = catstruct(S1,S2,S3,S4,S5) % use value from S4
+%
+% The inputs can be array of structures. All structures should have the
+% same size. An example:
+%
+% C(1).bb = 1 ; C(2).bb = 2 ;
+% D(1).aa = 3 ; D(2).aa = 4 ;
+% CD = catstruct(C,D) % CD is a 1x2 structure array with fields bb and aa
+%
+% The last input can be the string 'sorted'. In this case,
+% CATSTRUCT(S1,S2, ..., 'sorted') will sort the fieldnames alphabetically.
+% To sort the fieldnames of a structure A, you could use
+% CATSTRUCT(A,'sorted') but I recommend ORDERFIELDS for doing that.
+%
+% When there is nothing to concatenate, the result will be an empty
+% struct (0x0 struct array with no fields).
+%
+% NOTE: To concatenate similar arrays of structs, you can use simple
+% concatenation:
+% A = dir('*.mat') ; B = dir('*.m') ; C = [A ; B] ;
+%
+% See also CAT, STRUCT, FIELDNAMES, STRUCT2CELL, ORDERFIELDS
+
+% for Matlab R13 and up
+% version 3.0 (mar 2013)
+% Originally downloaded from MATLAB central:
+% http://www.mathworks.com/matlabcentral/fileexchange/7842-catstruct
+
+% Copyright (C) 2005 Jos van der Geest <jos@jasen.nl>
+% Copyright (C) 2013 Christophe Gouel
+% Copyright (C) 2016 Dynare Team
+%
+% Redistribution and use in source and binary forms, with or without
+% modification, are permitted provided that the following conditions are
+% met:
+%
+% * Redistributions of source code must retain the above copyright
+% notice, this list of conditions and the following disclaimer.
+% * Redistributions in binary form must reproduce the above copyright
+% notice, this list of conditions and the following disclaimer in
+% the documentation and/or other materials provided with the distribution
+%
+% THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+% AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+% IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+% ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+% LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+% CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+% SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+% INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+% CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+% ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+% POSSIBILITY OF SUCH DAMAGE.
+
+% History
+% Created in 2005
+% Revisions
+% 2.0 (sep 2007) removed bug when dealing with fields containing cell
+% arrays (Thanks to Rene Willemink)
+% 2.1 (sep 2008) added warning and error identifiers
+% 2.2 (oct 2008) fixed error when dealing with empty structs (Thanks to
+% Lars Barring)
+% 3.0 (mar 2013) fixed problem when the inputs were array of structures
+% (thanks to Tor Inge Birkenes for pointing this out).
+% Rephrased the help section as well.
+
+narginchk(1, Inf);
+N = nargin ;
+
+if ~isstruct(varargin{end})
+ if isequal(varargin{end},'sorted')
+ sorted = 1 ;
+ N = N-1 ;
+ if N<1
+ error('catstruct: wrong number of input arguments') ;
+ end
+ else
+ error('catstruct:InvalidArgument','Last argument should be a structure, or the string "sorted".') ;
+ end
+else
+ sorted = 0 ;
+end
+
+sz0 = [] ; % used to check that all inputs have the same size
+
+% used to check for a few trivial cases
+NonEmptyInputs = false(N,1) ;
+NonEmptyInputsN = 0 ;
+
+% used to collect the fieldnames and the inputs
+FN = cell(N,1) ;
+VAL = cell(N,1) ;
+
+% parse the inputs
+for ii=1:N
+ X = varargin{ii} ;
+ if ~isstruct(X)
+ error('catstruct:InvalidArgument',['Argument #' num2str(ii) ' is not a structure.']) ;
+ end
+
+ if ~isempty(X)
+ % empty structs are ignored
+ if ii > 1 && ~isempty(sz0)
+ if ~isequal(size(X), sz0)
+ error('catstruct:UnequalSizes','All structures should have the same size.') ;
+ end
+ else
+ sz0 = size(X) ;
+ end
+ NonEmptyInputsN = NonEmptyInputsN + 1 ;
+ NonEmptyInputs(ii) = true ;
+ FN{ii} = fieldnames(X) ;
+ VAL{ii} = struct2cell(X) ;
+ end
+end
+
+if NonEmptyInputsN == 0
+ % all structures were empty
+ A = struct([]) ;
+elseif NonEmptyInputsN == 1
+ % there was only one non-empty structure
+ A = varargin{NonEmptyInputs} ;
+ if sorted
+ A = orderfields(A) ;
+ end
+else
+ % there is actually something to concatenate
+ FN = cat(1,FN{:}) ;
+ VAL = cat(1,VAL{:}) ;
+ FN = squeeze(FN) ;
+ VAL = squeeze(VAL) ;
+ MatlabVersion = version;
+ if isoctave || str2double(MatlabVersion(end-5:end-2))<2013 % Equivalent to, but faster than if verLessThan('matlab','8.1')
+ [UFN,ind] = unique(FN) ;
+ else
+ [UFN,ind] = unique(FN,'legacy') ;
+ end
+
+ if numel(UFN) ~= numel(FN)
+ warning('catstruct:DuplicatesFound','Fieldnames are not unique between structures.') ;
+ sorted = 1 ;
+ end
+
+ if sorted
+ VAL = VAL(ind,:) ;
+ FN = FN(ind,:) ;
+ end
+
+ A = cell2struct(VAL, FN);
+ A = reshape(A, sz0) ; % reshape into original format
+end
diff --git a/matlab/lmmcp/dyn_lmmcp_func.m b/matlab/lmmcp/dyn_lmmcp_func.m
index 936f98306dae861e8c01a7d30f90620bdeffb308..d34b6adb159029a430446a4810d09cfea4983cce 100644
--- a/matlab/lmmcp/dyn_lmmcp_func.m
+++ b/matlab/lmmcp/dyn_lmmcp_func.m
@@ -20,37 +20,36 @@ function [F,A] = dyn_lmmcp_func(x, model_dynamic, Y0, YT, exo_simul, params, ...
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- Y = [Y0; x; YT];
-
- F = zeros(periods*ny,1);
- if nargout == 2
- A = sparse([],[],[],periods*ny,periods*ny,periods*nnzA);
- end
+Y = [Y0; x; YT];
- i_rows = 1:ny;
- i_cols = find(lead_lag_incidence');
- i_cols_A = i_cols;
-
- for it = 2:(periods+1)
-
- [res,jacobian] = model_dynamic(Y(i_cols),exo_simul, params, ...
- steady_state,it);
- F(i_rows) = res(eq_index);
-
- if nargout == 2
- if it == 2
- A(i_rows,i_cols_A1) = jacobian(eq_index,i_cols_1);
- elseif it == periods+1
- A(i_rows,i_cols_A(i_cols_T)) = jacobian(eq_index,i_cols_T);
- else
- A(i_rows,i_cols_A) = jacobian(eq_index,i_cols_j);
- end
- end
-
- i_rows = i_rows + ny;
- i_cols = i_cols + ny;
- if nargout == 2 && it > 2
- i_cols_A = i_cols_A + ny;
+F = zeros(periods*ny,1);
+if nargout == 2
+ A = sparse([],[],[],periods*ny,periods*ny,periods*nnzA);
+end
+
+i_rows = 1:ny;
+i_cols = find(lead_lag_incidence');
+i_cols_A = i_cols;
+
+for it = 2:(periods+1)
+
+ [res,jacobian] = model_dynamic(Y(i_cols),exo_simul, params, ...
+ steady_state,it);
+ F(i_rows) = res(eq_index);
+
+ if nargout == 2
+ if it == 2
+ A(i_rows,i_cols_A1) = jacobian(eq_index,i_cols_1);
+ elseif it == periods+1
+ A(i_rows,i_cols_A(i_cols_T)) = jacobian(eq_index,i_cols_T);
+ else
+ A(i_rows,i_cols_A) = jacobian(eq_index,i_cols_j);
end
end
+ i_rows = i_rows + ny;
+ i_cols = i_cols + ny;
+ if nargout == 2 && it > 2
+ i_cols_A = i_cols_A + ny;
+ end
+end
diff --git a/matlab/lmmcp/get_complementarity_conditions.m b/matlab/lmmcp/get_complementarity_conditions.m
index d1ea1771fdd2031835f1a0130b8fc4c22b058bc2..340bd74cb66804ee74d87634f1de9fbfa26f992b 100644
--- a/matlab/lmmcp/get_complementarity_conditions.m
+++ b/matlab/lmmcp/get_complementarity_conditions.m
@@ -2,13 +2,13 @@ function [lb,ub,eq_index] = get_complementarity_conditions(M,ramsey_policy)
% [lb,ub,eq_index] = get_complementarity_conditions(M,ramsey_policy)
% INPUTS
% - M [struct] contains a description of the model.
-% - ramsey_policy [boolean] indicator whether a Ramsey problem is considered
+% - ramsey_policy [boolean] indicator whether a Ramsey problem is considered
% OUTPUTS
% - lb [double] endo_nbr*1 array of lower bounds for
% endogenous variables
% - ub [double] endo_nbr*1 array of upper bounds for
% endogenous variables
-% - eq_index [struct] endo_nbr*1 index vector describing residual mapping resulting
+% - eq_index [struct] endo_nbr*1 index vector describing residual mapping resulting
% from complementarity setup used in
% perfect_foresight_mcp_problem.m
@@ -55,14 +55,14 @@ for i=1:size(etags,1)
str = etags{i,3};
kop = strfind(etags{i,3},'<');
if ~isempty(kop)
- k = find(strcmp(strtrim(str(1:kop-1)),cellstr(M.endo_names))); %get variable index with restriction
- if isempty(k)
- error(sprintf(['Complementarity condition %s: variable %s is ' ...
- 'not recognized'],etags{i,3},strtrim(str(1:kop-1))))
- end
- ub(k) = str2num(str(kop+1:end));
- eq_index(etags{i,1}) = k;
- eq_index(k) = etags{i,1};
+ k = find(strcmp(strtrim(str(1:kop-1)),cellstr(M.endo_names))); %get variable index with restriction
+ if isempty(k)
+ error(sprintf(['Complementarity condition %s: variable %s is ' ...
+ 'not recognized'],etags{i,3},strtrim(str(1:kop-1))))
+ end
+ ub(k) = str2num(str(kop+1:end));
+ eq_index(etags{i,1}) = k;
+ eq_index(k) = etags{i,1};
else
kop = strfind(etags{i,3},'>');
if ~isempty(kop)
@@ -81,4 +81,3 @@ for i=1:size(etags,1)
end
end
end
-
diff --git a/matlab/lmmcp/lmmcp.m b/matlab/lmmcp/lmmcp.m
index 1ac1c5e0a444a916ba17bde44212f58d6bc54923..162e61a865dfd505d7e9890eb8978744c9f1d0c8 100644
--- a/matlab/lmmcp/lmmcp.m
+++ b/matlab/lmmcp/lmmcp.m
@@ -1,625 +1,625 @@
-function [x,FVAL,EXITFLAG,OUTPUT,JACOB] = lmmcp(FUN,x,lb,ub,options,varargin)
-% LMMCP solves a mixed complementarity problem.
-%
-% LMMCP uses a semismooth least squares formulation. The method applies a
-% Levenberg-Marquardt/Gauss-Newton algorithm to a least-squares formulation.
-%
-% X = LMMCP(FUN,X0) tries to solve the system of nonlinear equations F(X)=0 and
-% starts at the vector X0. FUN accepts a vector X and return a vector F of equation
-% values F evaluated at X and, as second output if required, a matrix J, the
-% Jacobian evaluated at X.
-%
-% X = LMMCP(FUN,X0,LB,UB) solves the mixed complementarity problem of the form:
-% LB =X => F(X)>0,
-% LB<=X<=UB => F(X)=0,
-% X =UB => F(X)<0.
-%
-% X = LMMCP(FUN,X0,LB,UB,OPTIONS) solves the MCP problem using the options
-% defined in the structure OPTIONS. Main fields are
-% Display : control the display of iterations, 'none' (default),
-% 'iter-detailed' or 'final-detailed'
-% Switch from phase I to phase II
-% preprocess : activate preprocessor for phase I (default = 1)
-% presteps : number of iterations in phase I (default = 20)
-% Termination parameters
-% MaxIter : Maximum number of iterations (default = 500)
-% tmin : safeguard stepsize (default = 1E-12)
-% TolFun : Termination tolerance on the function value, a positive
-% scalar (default = sqrt(eps))
-% Stepsize parameters
-% m : number of previous function values to use in the nonmonotone
-% line search rule (default = 10)
-% kwatch : maximum number of steps (default = 20 and should not be
-% smaller than m)
-% watchdog : activate the watchdog strategy (default = 1)
-% Ther are other minor parameters. Please see the code for their default values
-% and interpretation.
-%
-% [X,FVAL] = LMMCP(FUN,X0,...) returns the value of the equations FUN at X.
-%
-% [X,FVAL,EXITFLAG] = LMMCP(FUN,X0,...) returns EXITFLAG that describes the exit
-% conditions. Possible values are
-% 1 : LMMCP converged to a root
-% 0 : Too many iterations
-% -1 :
-%
-% [X,FVAL,EXITFLAG,OUTPUT] = LMMCP(FUN,X0,...) returns the structure OUTPUT that
-% contains the number of iterations (OUTPUT.iterations), the value of the merit
-% function (OUTPUT.Psix), and the norm of the derivative of the merit function
-% (OUTPUT.normDPsix).
-%
-% [X,FVAL,EXITFLAG,OUTPUT,JACOB] = LMMCP(FUN,X0,...) returns JACOB the Jacobian
-% of FUN evaluated at X.
-%
-% More details of the main program may be found in the following paper:
-%
-% Christian Kanzow and Stefania Petra: On a semismooth least squares formulation of
-% complementarity problems with gap reduction. Optimization Methods and Software
-% 19, 2004, pp. 507-525.
-%
-% In addition, the current implementation uses a preprocessor which is the
-% projected Levenberg-Marquardt step from the following preprint:
-%
-% Christian Kanzow and Stefania Petra: Projected filter trust region methods for a
-% semismooth least squares formulation of mixed complementarity
-% problems. Optimization Methods and Software
-% 22, 2007, pp. 713-735.
-%
-% A user's guide is also available:
-%
-% Christian Kanzow and Stefania Petra (2005).
-% LMMCP --- A Levenberg-Marquardt-type MATLAB Solver for Mixed Complementarity Problems.
-% University of Wuerzburg.
-% http://www.mathematik.uni-wuerzburg.de/~kanzow/software/UserGuide.pdf
-%
-% This is a modification by Christophe Gouel of the original files, which can be
-% downloaded from:
-% http://www.mathematik.uni-wuerzburg.de/~kanzow/software/LMMCP.zip
-%
-% Written by Christian Kanzow and Stefania Petra
-% Institute of Applied Mathematics and Statistics
-% University of Wuerzburg
-% Am Hubland
-% 97074 Wuerzburg
-% GERMANY
-%
-% e-mail: kanzow@mathematik.uni-wuerzburg.de
-% petra@mathematik.uni-wuerzburg.de
-%
-% Christian Kanzow sent a private message to Dynare Team on July 8, 2014,
-% confirming the free software status of lmmcp and granting unlimited
-% permission to use, copy, modifiy or redistribute the file.
-
-% Copyright (C) 2005 Christian Kanzow and Stefania Petra
-% Copyright (C) 2013 Christophe Gouel
-% Copyright (C) 2014 Dynare Team
-%
-% Unlimited permission is granted to everyone to use, copy, modify or
-% distribute this software.
-
-%% Initialization
-defaultopt = struct(...
- 'beta', 0.55,...
- 'Big', 1e10,...
- 'delta', 5,...
- 'deltamin', 1,...
- 'Display', 'none',...
- 'epsilon1', 1e-6,...
- 'eta', 0.95,...
- 'kwatch', 20,...
- 'lambda1', 0.1,...
- 'm', 10,...
- 'MaxIter', 500,...
- 'null', 1e-10,...
- 'preprocess', 1,...
- 'presteps', 20,...
- 'sigma', 1e-4,...
- 'sigma1', 0.5,...
- 'sigma2', 2,...
- 'tmin', 1e-12,...
- 'TolFun', sqrt(eps),...
- 'watchdog', 1);
-
-if nargin < 4
- ub = inf(size(x));
- if nargin < 3
- lb = -inf(size(x));
- end
-end
-
-if nargin < 5 || isempty(options) || ~isstruct(options)
- options = defaultopt;
-else
- warning('off','catstruct:DuplicatesFound')
- options = catstruct(defaultopt,options);
-end
-
-warning('off','MATLAB:rankDeficientMatrix')
-
-switch options.Display
- case {'off','none'}
- verbosity = 0;
- case {'iter','iter-detailed'}
- verbosity = 2;
- case {'final','final-detailed'}
- verbosity = 1;
- otherwise
- verbosity = 0;
-end
-
-% parameter settings
-eps1 = options.epsilon1;
-eps2 = 0.5*options.TolFun^2;
-null = options.null;
-Big = options.Big;
-
-% maximal number of iterations
-kmax = options.MaxIter;
-
-% choice of lambda
-lambda1 = options.lambda1;
-lambda2 = 1-lambda1;
-
-% steplength parameters
-beta = options.beta;
-sigma = options.sigma;
-tmin = options.tmin;
-
-% parameters watchdog and nonmonotone line search; redefined later
-m = options.m;
-kwatch = options.kwatch;
-watchdog = options.watchdog; % 1=watchdog strategy active, otherwise not
-
-% parameters for preprocessor
-preprocess = options.preprocess; % 1=preprocessor used, otherwise not
-presteps = options.presteps; % maximum number of preprocessing steps
-
-% trust-region parameters for preprocessor
-delta = options.delta;
-deltamin = options.deltamin;
-sigma1 = options.sigma1;
-sigma2 = options.sigma2;
-eta = options.eta;
-
-% initializations
-k = 0;
-k_main = 0;
-
-% compute a feasible starting point by projection
-x = max(lb,min(x,ub));
-
-n = length(x);
-OUTPUT.Dim = n;
-
-% definition of index sets I_l, I_u and I_lu
-Indexset = zeros(n,1);
-I_l = lb>-Big & ub>Big;
-I_u = lb<-Big & ub<Big;
-I_lu = lb>-Big & ub<Big;
-Indexset(I_l) = 1;
-Indexset(I_u) = 2;
-Indexset(I_lu) = 3;
-
-% function evaluations
-[Fx,DFx] = feval(FUN,x,varargin{:});
-
-% choice of NCP-function and corresponding evaluations
-Phix = Phi(x,Fx,lb,ub,lambda1,lambda2,n,Indexset);
-normPhix = norm(Phix);
-Psix = 0.5*(Phix'*Phix);
-DPhix = DPhi(x,Fx,DFx,lb,ub,lambda1,lambda2,n,Indexset);
-DPsix = DPhix'*Phix;
-normDPsix = norm(DPsix);
-
-% save initial values
-x0 = x;
-Phix0 = Phix;
-Psix0 = Psix;
-DPhix0 = DPhix;
-DPsix0 = DPsix;
-normDPsix0 = normDPsix;
-
-% watchdog strategy
-aux = zeros(m,1);
-aux(1) = Psix;
-MaxPsi = Psix;
-
-if watchdog==1
- kbest = k;
- xbest = x;
- Phibest = Phix;
- Psibest = Psix;
- DPhibest = DPhix;
- DPsibest = DPsix;
- normDPsibest = normDPsix;
-end
-
-% initial output
-if verbosity > 1
- fprintf(' k Psi(x) || DPsi(x) || stepsize\n');
- disp('====================================================================')
- disp('********************* Output at starting point *********************')
- fprintf('%4.0f %24.5e %24.5e\n',k,Psix,normDPsix);
-end
-
-%% Preprocessor using local method
-
-if preprocess==1
-
- if verbosity > 1
- disp('************************** Preprocessor ****************************')
- end
-
- normpLM=1;
- while (k < presteps) && (Psix > eps2) && (normpLM>null)
- k = k+1;
-
- % choice of Levenberg-Marquardt parameter, note that we do not use
- % the condition estimator for large-scale problems, although this
- % may cause numerical problems in some examples
-
- i = false;
- mu = 0;
- if n<100
- i = true;
- mu = 1e-16;
- if condest(DPhix'*DPhix)>1e25
- mu = 1e-6/(k+1);
- end
- end
- if i
- pLM = [DPhix; sqrt(mu)*speye(n)]\[-Phix; zeros(n,1)];
- else
- pLM = -DPhix\Phix;
- end
- normpLM = norm(pLM);
-
- % compute the projected Levenberg-Marquard step onto box Xk
- lbnew = max(min(lb-x,0),-delta);
- ubnew = min(max(ub-x,0),delta);
- d = max(lbnew,min(pLM,ubnew));
- xnew = x+d;
-
- % function evaluations etc.
- [Fxnew,DFxnew] = feval(FUN,xnew,varargin{:});
- Phixnew = Phi(xnew,Fxnew,lb,ub,lambda1,lambda2,n,Indexset);
- Psixnew = 0.5*(Phixnew'*Phixnew);
- normPhixnew = norm(Phixnew);
-
- % update of delta
- if normPhixnew<=eta*normPhix
- delta = max(deltamin,sigma2*delta);
- elseif normPhixnew>5*eta*normPhix
- delta = max(deltamin,sigma1*delta);
- end
-
- % update
- x = xnew;
- Fx = Fxnew;
- DFx = DFxnew;
- Phix = Phixnew;
- Psix = Psixnew;
- normPhix = normPhixnew;
- DPhix = DPhi(x,Fx,DFx,lb,ub,lambda1,lambda2,n,Indexset);
- DPsix = DPhix'*Phix;
- normDPsix = norm(DPsix,inf);
-
- % output at each iteration
- t=1;
- if verbosity > 1
- fprintf('%4.0f %24.5e %24.5e %11.7g\n',k,Psix,normDPsix,t);
- end
- end
-end
-
-% terminate program or redefine current iterate as original initial point
-if preprocess==1 && Psix<eps2
- if verbosity > 0
- fprintf('Psix = %1.4e\nnormDPsix = %1.4e\n',Psix,normDPsix);
- disp('Approximate solution found.')
- end
- EXITFLAG = 1;
- FVAL = Fx;
- OUTPUT.iterations = k;
- OUTPUT.Psix = Psix;
- OUTPUT.normDPsix = normDPsix;
- JACOB = DFx;
- return
-elseif preprocess==1 && Psix>=eps2
- x=x0;
- Phix=Phix0;
- Psix=Psix0;
- DPhix=DPhix0;
- DPsix=DPsix0;
- if verbosity > 1
- disp('******************** Restart with initial point ********************')
- fprintf('%4.0f %24.5e %24.5e\n',k_main,Psix0,normDPsix0);
- end
-end
-
-%% Main algorithm
-
-if verbosity > 1
- disp('************************** Main program ****************************')
-end
-
-while (k < kmax) && (Psix > eps2)
-
- % choice of Levenberg-Marquardt parameter, note that we do not use
- % the condition estimator for large-scale problems, although this
- % may cause numerical problems in some examples
-
- i = false;
- if n<100
- i = true;
- mu = 1e-16;
- if condest(DPhix'*DPhix)>1e25
- mu = 1e-1/(k+1);
- end
- end
-
- % compute a Levenberg-Marquard direction
-
- if i
- d = [DPhix; sqrt(mu)*speye(n)]\[-Phix; zeros(n,1)];
- else
- d = -DPhix\Phix;
- end
-
- % computation of steplength t using the nonmonotone Armijo-rule
- % starting with the 6-th iteration
-
- % computation of steplength t using the monotone Armijo-rule if
- % d is a 'good' descent direction or k<=5
-
- t = 1;
- xnew = x+d;
- Fxnew = feval(FUN,xnew,varargin{:});
- Phixnew = Phi(xnew,Fxnew,lb,ub,lambda1,lambda2,n,Indexset);
- Psixnew = 0.5*(Phixnew'*Phixnew);
- const = sigma*DPsix'*d;
-
- while (Psixnew > MaxPsi + const*t) && (t > tmin)
- t = t*beta;
- xnew = x+t*d;
- Fxnew = feval(FUN,xnew,varargin{:});
- Phixnew = Phi(xnew,Fxnew,lb,ub,lambda1,lambda2,n,Indexset);
- Psixnew = 0.5*(Phixnew'*Phixnew);
- end
-
- % updatings
- x = xnew;
- Fx = Fxnew;
- Phix = Phixnew;
- Psix = Psixnew;
- [junk,DFx] = feval(FUN,x,varargin{:});
- DPhix = DPhi(x,Fx,DFx,lb,ub,lambda1,lambda2,n,Indexset);
- DPsix = DPhix'*Phix;
- normDPsix = norm(DPsix);
- k = k+1;
- k_main = k_main+1;
-
- if k_main<=5
- aux(mod(k_main,m)+1) = Psix;
- MaxPsi = Psix;
- else
- aux(mod(k_main,m)+1) = Psix;
- MaxPsi = max(aux);
- end
-
- % updatings for the watchdog strategy
- if watchdog ==1
- if Psix<Psibest
- kbest = k;
- xbest = x;
- Phibest = Phix;
- Psibest = Psix;
- DPhibest = DPhix;
- DPsibest = DPsix;
- normDPsibest = normDPsix;
- elseif k-kbest>kwatch
- x=xbest;
- Phix=Phibest;
- Psix=Psibest;
- DPhix=DPhibest;
- DPsix=DPsibest;
- normDPsix=normDPsibest;
- MaxPsi=Psix;
- end
- end
-
- if verbosity > 1
- % output at each iteration
- fprintf('%4.0f %24.5e %24.5e %11.7g\n',k,Psix,normDPsix,t);
- end
-end
-
-%% Final output
-if Psix<=eps2
- EXITFLAG = 1;
- if verbosity > 0, disp('Approximate solution found.'); end
-elseif k>=kmax
- EXITFLAG = 0;
- if verbosity > 0, disp('Maximum iteration number reached.'); end
-elseif normDPsix<=eps1
- EXITFLAG = -1; % Provisoire
- if verbosity > 0, disp('Approximate stationary point found.'); end
-else
- EXITFLAG = -1; % Provisoire
- if verbosity > 0, disp('No solution found.'); end
-end
-
-FVAL = Fx;
-OUTPUT.iterations = k;
-OUTPUT.Psix = Psix;
-OUTPUT.normDPsix = normDPsix;
-JACOB = DFx;
-
-%% Subfunctions
-
-function y = Phi(x,Fx,lb,ub,lambda1,lambda2,n,Indexset)
-%% PHI
-
-y = zeros(2*n,1);
-phi_u = sqrt((ub-x).^2+Fx.^2)-ub+x+Fx;
-LZ = false(n,1); % logical zero
-
-I0 = Indexset==0;
-y(I0) = -lambda1*Fx(I0);
-y([LZ; I0]) = -lambda2*Fx(I0);
-
-I1 = Indexset==1;
-y(I1) = lambda1*(-x(I1)+lb(I1)-Fx(I1)+sqrt((x(I1)-lb(I1)).^2+Fx(I1).^2));
-y([LZ; I1]) = lambda2*max(0,x(I1)-lb(I1)).*max(0,Fx(I1));
-
-I2 = Indexset==2;
-y(I2) = -lambda1*phi_u(I2);
-y([LZ; I2]) = lambda2*max(0,ub(I2)-x(I2)).*max(0,-Fx(I2));
-
-I3 = Indexset==3;
-y(I3) = lambda1*(sqrt((x(I3)-lb(I3)).^2+phi_u(I3).^2)-x(I3)+lb(I3)-phi_u(I3));
-y([LZ; I3]) = lambda2*(max(0,x(I3)-lb(I3)).*max(0,Fx(I3))+max(0,ub(I3)-x(I3)).*max(0,-Fx(I3)));
-
-
-function H = DPhi(x,Fx,DFx,lb,ub,lambda1,lambda2,n,Indexset)
-%% DPHI evaluates an element of the C-subdifferential of operator Phi
-
-null = 1e-8;
-beta_l = zeros(n,1);
-beta_u = zeros(n,1);
-alpha_l = zeros(n,1);
-alpha_u = zeros(n,1);
-
-
-z = zeros(n,1);
-H2 = sparse(n,n);
-
-I = abs(x-lb)<=null & abs(Fx)<=null;
-beta_l(I) = 1;
-z(I) = 1;
-
-I = abs(ub-x)<=null & abs(Fx)<=null;
-beta_u(I) = 1;
-z(I) = 1;
-
-I = x-lb>=-null & Fx>=-null;
-alpha_l(I) = 1;
-
-I = ub-x>=-null & Fx<=null;
-alpha_u(I) = 1;
-
-Da = zeros(n,1);
-Db = zeros(n,1);
-
-I = 1:n;
-
-I0 = Indexset==0;
-Da(I0) = 0;
-Db(I0) = -1;
-H2(I0,:) = -DFx(I0,:);
-
-I1 = Indexset==1;
-denom1 = zeros(n,1);
-denom2 = zeros(n,1);
-if any(I1)
- denom1(I1) = max(null,sqrt((x(I1)-lb(I1)).^2+Fx(I1).^2));
- denom2(I1) = max(null,sqrt(z(I1).^2+(DFx(I1,:)*z).^2));
-end
-
-I1b = Indexset==1 & beta_l==0;
-Da(I1b) = (x(I1b)-lb(I1b))./denom1(I1b)-1;
-Db(I1b) = Fx(I1b)./denom1(I1b)-1;
-I1b = Indexset==1 & beta_l~=0;
-if any(I1b)
- Da(I1b) = z(I1b)./denom2(I1b)-1;
- Db(I1b) = (DFx(I1b,:)*z)./denom2(I1b)-1;
-end
-
-I1a = I(Indexset==1 & alpha_l==1);
-if any(I1a)
- H2(I1a,:) = spdiags(x(I1a)-lb(I1a), 0, length(I1a), length(I1a))*DFx(I1a,:) +...
- sparse(1:length(I1a),I1a,Fx(I1a),length(I1a),n,length(I1a));
-end
-
-I2 = Indexset==2;
-denom1 = zeros(n,1);
-denom2 = zeros(n,1);
-if any(I2)
- denom1(I2) = max(null,sqrt((ub(I2)-x(I2)).^2+Fx(I2).^2));
- denom2(I2) = max(null,sqrt(z(I2).^2+(DFx(I2,:)*z).^2));
-end
-
-I2b = Indexset==2 & beta_u==0;
-Da(I2b) = (ub(I2b)-x(I2b))./denom1(I2b)-1;
-Db(I2b) = -Fx(I2b)./denom1(I2b)-1;
-I2b = Indexset==2 & beta_u~=0;
-if any(I2b)
- Da(I2b) = -z(I2b)./denom2(I2b)-1;
- Db(I2b) = -(DFx(I2b,:)*z)./denom2(I2b)-1;
-end
-
-I2a = I(Indexset==2 & alpha_u==1);
-if any(I2a)
- H2(I2a,:) = bsxfun(@times,x(I2a)-ub(I2a),DFx(I2a,:))+...
- sparse(1:length(I2a),I2a,Fx(I2a),length(I2a),n,length(I2a));
-end
-
-I3 = Indexset==3;
-phi = zeros(n,1);
-ai = zeros(n,1);
-bi = zeros(n,1);
-ci = zeros(n,1);
-di = zeros(n,1);
-denom1 = zeros(n,1);
-denom2 = zeros(n,1);
-denom3 = zeros(n,1);
-denom4 = zeros(n,1);
-if any(I3)
- phi(I3) = -ub(I3)+x(I3)+Fx(I3)+sqrt((ub(I3)-x(I3)).^2+Fx(I3).^2);
- denom1(I3) = max(null,sqrt((x(I3)-lb(I3)).^2+phi(I3).^2));
- denom2(I3) = max(null,sqrt(z(I3).^2+(DFx(I3,:)*z).^2));
- denom3(I3) = max(null,sqrt((ub(I3)-x(I3)).^2+Fx(I3).^2));
- denom4(I3) = max(null,sqrt(z(I3).^2));
-end
-
-I3bu = Indexset==3 & beta_u==0;
-ci(I3bu) = (x(I3bu)-ub(I3bu))./denom3(I3bu)+1;
-di(I3bu) = Fx(I3bu)./denom3(I3bu)+1;
-I3bu = Indexset==3 & beta_u~=0;
-if any(I3bu)
- ci(I3bu) = 1+z(I3bu)./denom2(I3bu);
- di(I3bu) = 1+(DFx(I3bu,:)*z)./denom2(I3bu);
-end
-
-I3bl = Indexset==3 & beta_l==0;
-ai(I3bl) = (x(I3bl)-lb(I3bl))./denom1(I3bl)-1;
-bi(I3bl) = phi(I3bl)./denom1(I3bl)-1;
-I3bl = Indexset==3 & beta_l~=0;
-if any(I3bl)
- ai(I3bl) = z(I3bl)./denom4(I3bl)-1;
- bi(I3bl) = (ci(I3bl).*z(I3bl)+(di(I3bl,ones(1,n)).*DFx(I3bl,:))*z)./denom4(I3bl)-1;
-end
-
-Da(I3) = ai(I3)+bi(I3).*ci(I3);
-Db(I3) = bi(I3).*di(I3);
-
-I3a = I(Indexset==3 & alpha_l==1 & alpha_u==1);
-if any(I3a)
- H2(I3a,:) = bsxfun(@times,-lb(I3a)-ub(I3a)+2*x(I3a),DFx(I3a,:))+...
- 2*sparse(1:length(I3a),I3a,Fx(I3a),length(I3a),n,length(I3a));
-end
-I3a = I(Indexset==3 & alpha_l==1 & alpha_u~=1);
-if any(I3a)
- H2(I3a,:) = bsxfun(@times,x(I3a)-lb(I3a),DFx(I3a,:))+...
- sparse(1:length(I3a),I3a,Fx(I3a),length(I3a),n,length(I3a));
-end
-I3a = I(Indexset==3 & alpha_l~=1 & alpha_u==1);
-if any(I3a)
- H2(I3a,:) = bsxfun(@times,x(I3a)-ub(I3a),DFx(I3a,:))+...
- sparse(1:length(I3a),I3a,Fx(I3a),length(I3a),n,length(I3a));
-end
-
-H1 = spdiags(Db,0,length(Db),length(Db))*DFx;
-H1 = H1 + spdiags(Da, 0, length(Da), length(Da));
-
-H = [lambda1*H1; lambda2*H2];
+function [x,FVAL,EXITFLAG,OUTPUT,JACOB] = lmmcp(FUN,x,lb,ub,options,varargin)
+% LMMCP solves a mixed complementarity problem.
+%
+% LMMCP uses a semismooth least squares formulation. The method applies a
+% Levenberg-Marquardt/Gauss-Newton algorithm to a least-squares formulation.
+%
+% X = LMMCP(FUN,X0) tries to solve the system of nonlinear equations F(X)=0 and
+% starts at the vector X0. FUN accepts a vector X and return a vector F of equation
+% values F evaluated at X and, as second output if required, a matrix J, the
+% Jacobian evaluated at X.
+%
+% X = LMMCP(FUN,X0,LB,UB) solves the mixed complementarity problem of the form:
+% LB =X => F(X)>0,
+% LB<=X<=UB => F(X)=0,
+% X =UB => F(X)<0.
+%
+% X = LMMCP(FUN,X0,LB,UB,OPTIONS) solves the MCP problem using the options
+% defined in the structure OPTIONS. Main fields are
+% Display : control the display of iterations, 'none' (default),
+% 'iter-detailed' or 'final-detailed'
+% Switch from phase I to phase II
+% preprocess : activate preprocessor for phase I (default = 1)
+% presteps : number of iterations in phase I (default = 20)
+% Termination parameters
+% MaxIter : Maximum number of iterations (default = 500)
+% tmin : safeguard stepsize (default = 1E-12)
+% TolFun : Termination tolerance on the function value, a positive
+% scalar (default = sqrt(eps))
+% Stepsize parameters
+% m : number of previous function values to use in the nonmonotone
+% line search rule (default = 10)
+% kwatch : maximum number of steps (default = 20 and should not be
+% smaller than m)
+% watchdog : activate the watchdog strategy (default = 1)
+% Ther are other minor parameters. Please see the code for their default values
+% and interpretation.
+%
+% [X,FVAL] = LMMCP(FUN,X0,...) returns the value of the equations FUN at X.
+%
+% [X,FVAL,EXITFLAG] = LMMCP(FUN,X0,...) returns EXITFLAG that describes the exit
+% conditions. Possible values are
+% 1 : LMMCP converged to a root
+% 0 : Too many iterations
+% -1 :
+%
+% [X,FVAL,EXITFLAG,OUTPUT] = LMMCP(FUN,X0,...) returns the structure OUTPUT that
+% contains the number of iterations (OUTPUT.iterations), the value of the merit
+% function (OUTPUT.Psix), and the norm of the derivative of the merit function
+% (OUTPUT.normDPsix).
+%
+% [X,FVAL,EXITFLAG,OUTPUT,JACOB] = LMMCP(FUN,X0,...) returns JACOB the Jacobian
+% of FUN evaluated at X.
+%
+% More details of the main program may be found in the following paper:
+%
+% Christian Kanzow and Stefania Petra: On a semismooth least squares formulation of
+% complementarity problems with gap reduction. Optimization Methods and Software
+% 19, 2004, pp. 507-525.
+%
+% In addition, the current implementation uses a preprocessor which is the
+% projected Levenberg-Marquardt step from the following preprint:
+%
+% Christian Kanzow and Stefania Petra: Projected filter trust region methods for a
+% semismooth least squares formulation of mixed complementarity
+% problems. Optimization Methods and Software
+% 22, 2007, pp. 713-735.
+%
+% A user's guide is also available:
+%
+% Christian Kanzow and Stefania Petra (2005).
+% LMMCP --- A Levenberg-Marquardt-type MATLAB Solver for Mixed Complementarity Problems.
+% University of Wuerzburg.
+% http://www.mathematik.uni-wuerzburg.de/~kanzow/software/UserGuide.pdf
+%
+% This is a modification by Christophe Gouel of the original files, which can be
+% downloaded from:
+% http://www.mathematik.uni-wuerzburg.de/~kanzow/software/LMMCP.zip
+%
+% Written by Christian Kanzow and Stefania Petra
+% Institute of Applied Mathematics and Statistics
+% University of Wuerzburg
+% Am Hubland
+% 97074 Wuerzburg
+% GERMANY
+%
+% e-mail: kanzow@mathematik.uni-wuerzburg.de
+% petra@mathematik.uni-wuerzburg.de
+%
+% Christian Kanzow sent a private message to Dynare Team on July 8, 2014,
+% confirming the free software status of lmmcp and granting unlimited
+% permission to use, copy, modifiy or redistribute the file.
+
+% Copyright (C) 2005 Christian Kanzow and Stefania Petra
+% Copyright (C) 2013 Christophe Gouel
+% Copyright (C) 2014 Dynare Team
+%
+% Unlimited permission is granted to everyone to use, copy, modify or
+% distribute this software.
+
+%% Initialization
+defaultopt = struct(...
+ 'beta', 0.55,...
+ 'Big', 1e10,...
+ 'delta', 5,...
+ 'deltamin', 1,...
+ 'Display', 'none',...
+ 'epsilon1', 1e-6,...
+ 'eta', 0.95,...
+ 'kwatch', 20,...
+ 'lambda1', 0.1,...
+ 'm', 10,...
+ 'MaxIter', 500,...
+ 'null', 1e-10,...
+ 'preprocess', 1,...
+ 'presteps', 20,...
+ 'sigma', 1e-4,...
+ 'sigma1', 0.5,...
+ 'sigma2', 2,...
+ 'tmin', 1e-12,...
+ 'TolFun', sqrt(eps),...
+ 'watchdog', 1);
+
+if nargin < 4
+ ub = inf(size(x));
+ if nargin < 3
+ lb = -inf(size(x));
+ end
+end
+
+if nargin < 5 || isempty(options) || ~isstruct(options)
+ options = defaultopt;
+else
+ warning('off','catstruct:DuplicatesFound')
+ options = catstruct(defaultopt,options);
+end
+
+warning('off','MATLAB:rankDeficientMatrix')
+
+switch options.Display
+ case {'off','none'}
+ verbosity = 0;
+ case {'iter','iter-detailed'}
+ verbosity = 2;
+ case {'final','final-detailed'}
+ verbosity = 1;
+ otherwise
+ verbosity = 0;
+end
+
+% parameter settings
+eps1 = options.epsilon1;
+eps2 = 0.5*options.TolFun^2;
+null = options.null;
+Big = options.Big;
+
+% maximal number of iterations
+kmax = options.MaxIter;
+
+% choice of lambda
+lambda1 = options.lambda1;
+lambda2 = 1-lambda1;
+
+% steplength parameters
+beta = options.beta;
+sigma = options.sigma;
+tmin = options.tmin;
+
+% parameters watchdog and nonmonotone line search; redefined later
+m = options.m;
+kwatch = options.kwatch;
+watchdog = options.watchdog; % 1=watchdog strategy active, otherwise not
+
+% parameters for preprocessor
+preprocess = options.preprocess; % 1=preprocessor used, otherwise not
+presteps = options.presteps; % maximum number of preprocessing steps
+
+% trust-region parameters for preprocessor
+delta = options.delta;
+deltamin = options.deltamin;
+sigma1 = options.sigma1;
+sigma2 = options.sigma2;
+eta = options.eta;
+
+% initializations
+k = 0;
+k_main = 0;
+
+% compute a feasible starting point by projection
+x = max(lb,min(x,ub));
+
+n = length(x);
+OUTPUT.Dim = n;
+
+% definition of index sets I_l, I_u and I_lu
+Indexset = zeros(n,1);
+I_l = lb>-Big & ub>Big;
+I_u = lb<-Big & ub<Big;
+I_lu = lb>-Big & ub<Big;
+Indexset(I_l) = 1;
+Indexset(I_u) = 2;
+Indexset(I_lu) = 3;
+
+% function evaluations
+[Fx,DFx] = feval(FUN,x,varargin{:});
+
+% choice of NCP-function and corresponding evaluations
+Phix = Phi(x,Fx,lb,ub,lambda1,lambda2,n,Indexset);
+normPhix = norm(Phix);
+Psix = 0.5*(Phix'*Phix);
+DPhix = DPhi(x,Fx,DFx,lb,ub,lambda1,lambda2,n,Indexset);
+DPsix = DPhix'*Phix;
+normDPsix = norm(DPsix);
+
+% save initial values
+x0 = x;
+Phix0 = Phix;
+Psix0 = Psix;
+DPhix0 = DPhix;
+DPsix0 = DPsix;
+normDPsix0 = normDPsix;
+
+% watchdog strategy
+aux = zeros(m,1);
+aux(1) = Psix;
+MaxPsi = Psix;
+
+if watchdog==1
+ kbest = k;
+ xbest = x;
+ Phibest = Phix;
+ Psibest = Psix;
+ DPhibest = DPhix;
+ DPsibest = DPsix;
+ normDPsibest = normDPsix;
+end
+
+% initial output
+if verbosity > 1
+ fprintf(' k Psi(x) || DPsi(x) || stepsize\n');
+ disp('====================================================================')
+ disp('********************* Output at starting point *********************')
+ fprintf('%4.0f %24.5e %24.5e\n',k,Psix,normDPsix);
+end
+
+%% Preprocessor using local method
+
+if preprocess==1
+
+ if verbosity > 1
+ disp('************************** Preprocessor ****************************')
+ end
+
+ normpLM=1;
+ while (k < presteps) && (Psix > eps2) && (normpLM>null)
+ k = k+1;
+
+ % choice of Levenberg-Marquardt parameter, note that we do not use
+ % the condition estimator for large-scale problems, although this
+ % may cause numerical problems in some examples
+
+ i = false;
+ mu = 0;
+ if n<100
+ i = true;
+ mu = 1e-16;
+ if condest(DPhix'*DPhix)>1e25
+ mu = 1e-6/(k+1);
+ end
+ end
+ if i
+ pLM = [DPhix; sqrt(mu)*speye(n)]\[-Phix; zeros(n,1)];
+ else
+ pLM = -DPhix\Phix;
+ end
+ normpLM = norm(pLM);
+
+ % compute the projected Levenberg-Marquard step onto box Xk
+ lbnew = max(min(lb-x,0),-delta);
+ ubnew = min(max(ub-x,0),delta);
+ d = max(lbnew,min(pLM,ubnew));
+ xnew = x+d;
+
+ % function evaluations etc.
+ [Fxnew,DFxnew] = feval(FUN,xnew,varargin{:});
+ Phixnew = Phi(xnew,Fxnew,lb,ub,lambda1,lambda2,n,Indexset);
+ Psixnew = 0.5*(Phixnew'*Phixnew);
+ normPhixnew = norm(Phixnew);
+
+ % update of delta
+ if normPhixnew<=eta*normPhix
+ delta = max(deltamin,sigma2*delta);
+ elseif normPhixnew>5*eta*normPhix
+ delta = max(deltamin,sigma1*delta);
+ end
+
+ % update
+ x = xnew;
+ Fx = Fxnew;
+ DFx = DFxnew;
+ Phix = Phixnew;
+ Psix = Psixnew;
+ normPhix = normPhixnew;
+ DPhix = DPhi(x,Fx,DFx,lb,ub,lambda1,lambda2,n,Indexset);
+ DPsix = DPhix'*Phix;
+ normDPsix = norm(DPsix,inf);
+
+ % output at each iteration
+ t=1;
+ if verbosity > 1
+ fprintf('%4.0f %24.5e %24.5e %11.7g\n',k,Psix,normDPsix,t);
+ end
+ end
+end
+
+% terminate program or redefine current iterate as original initial point
+if preprocess==1 && Psix<eps2
+ if verbosity > 0
+ fprintf('Psix = %1.4e\nnormDPsix = %1.4e\n',Psix,normDPsix);
+ disp('Approximate solution found.')
+ end
+ EXITFLAG = 1;
+ FVAL = Fx;
+ OUTPUT.iterations = k;
+ OUTPUT.Psix = Psix;
+ OUTPUT.normDPsix = normDPsix;
+ JACOB = DFx;
+ return
+elseif preprocess==1 && Psix>=eps2
+ x=x0;
+ Phix=Phix0;
+ Psix=Psix0;
+ DPhix=DPhix0;
+ DPsix=DPsix0;
+ if verbosity > 1
+ disp('******************** Restart with initial point ********************')
+ fprintf('%4.0f %24.5e %24.5e\n',k_main,Psix0,normDPsix0);
+ end
+end
+
+%% Main algorithm
+
+if verbosity > 1
+ disp('************************** Main program ****************************')
+end
+
+while (k < kmax) && (Psix > eps2)
+
+ % choice of Levenberg-Marquardt parameter, note that we do not use
+ % the condition estimator for large-scale problems, although this
+ % may cause numerical problems in some examples
+
+ i = false;
+ if n<100
+ i = true;
+ mu = 1e-16;
+ if condest(DPhix'*DPhix)>1e25
+ mu = 1e-1/(k+1);
+ end
+ end
+
+ % compute a Levenberg-Marquard direction
+
+ if i
+ d = [DPhix; sqrt(mu)*speye(n)]\[-Phix; zeros(n,1)];
+ else
+ d = -DPhix\Phix;
+ end
+
+ % computation of steplength t using the nonmonotone Armijo-rule
+ % starting with the 6-th iteration
+
+ % computation of steplength t using the monotone Armijo-rule if
+ % d is a 'good' descent direction or k<=5
+
+ t = 1;
+ xnew = x+d;
+ Fxnew = feval(FUN,xnew,varargin{:});
+ Phixnew = Phi(xnew,Fxnew,lb,ub,lambda1,lambda2,n,Indexset);
+ Psixnew = 0.5*(Phixnew'*Phixnew);
+ const = sigma*DPsix'*d;
+
+ while (Psixnew > MaxPsi + const*t) && (t > tmin)
+ t = t*beta;
+ xnew = x+t*d;
+ Fxnew = feval(FUN,xnew,varargin{:});
+ Phixnew = Phi(xnew,Fxnew,lb,ub,lambda1,lambda2,n,Indexset);
+ Psixnew = 0.5*(Phixnew'*Phixnew);
+ end
+
+ % updatings
+ x = xnew;
+ Fx = Fxnew;
+ Phix = Phixnew;
+ Psix = Psixnew;
+ [junk,DFx] = feval(FUN,x,varargin{:});
+ DPhix = DPhi(x,Fx,DFx,lb,ub,lambda1,lambda2,n,Indexset);
+ DPsix = DPhix'*Phix;
+ normDPsix = norm(DPsix);
+ k = k+1;
+ k_main = k_main+1;
+
+ if k_main<=5
+ aux(mod(k_main,m)+1) = Psix;
+ MaxPsi = Psix;
+ else
+ aux(mod(k_main,m)+1) = Psix;
+ MaxPsi = max(aux);
+ end
+
+ % updatings for the watchdog strategy
+ if watchdog ==1
+ if Psix<Psibest
+ kbest = k;
+ xbest = x;
+ Phibest = Phix;
+ Psibest = Psix;
+ DPhibest = DPhix;
+ DPsibest = DPsix;
+ normDPsibest = normDPsix;
+ elseif k-kbest>kwatch
+ x=xbest;
+ Phix=Phibest;
+ Psix=Psibest;
+ DPhix=DPhibest;
+ DPsix=DPsibest;
+ normDPsix=normDPsibest;
+ MaxPsi=Psix;
+ end
+ end
+
+ if verbosity > 1
+ % output at each iteration
+ fprintf('%4.0f %24.5e %24.5e %11.7g\n',k,Psix,normDPsix,t);
+ end
+end
+
+%% Final output
+if Psix<=eps2
+ EXITFLAG = 1;
+ if verbosity > 0, disp('Approximate solution found.'); end
+elseif k>=kmax
+ EXITFLAG = 0;
+ if verbosity > 0, disp('Maximum iteration number reached.'); end
+elseif normDPsix<=eps1
+ EXITFLAG = -1; % Provisoire
+ if verbosity > 0, disp('Approximate stationary point found.'); end
+else
+ EXITFLAG = -1; % Provisoire
+ if verbosity > 0, disp('No solution found.'); end
+end
+
+FVAL = Fx;
+OUTPUT.iterations = k;
+OUTPUT.Psix = Psix;
+OUTPUT.normDPsix = normDPsix;
+JACOB = DFx;
+
+%% Subfunctions
+
+function y = Phi(x,Fx,lb,ub,lambda1,lambda2,n,Indexset)
+%% PHI
+
+y = zeros(2*n,1);
+phi_u = sqrt((ub-x).^2+Fx.^2)-ub+x+Fx;
+LZ = false(n,1); % logical zero
+
+I0 = Indexset==0;
+y(I0) = -lambda1*Fx(I0);
+y([LZ; I0]) = -lambda2*Fx(I0);
+
+I1 = Indexset==1;
+y(I1) = lambda1*(-x(I1)+lb(I1)-Fx(I1)+sqrt((x(I1)-lb(I1)).^2+Fx(I1).^2));
+y([LZ; I1]) = lambda2*max(0,x(I1)-lb(I1)).*max(0,Fx(I1));
+
+I2 = Indexset==2;
+y(I2) = -lambda1*phi_u(I2);
+y([LZ; I2]) = lambda2*max(0,ub(I2)-x(I2)).*max(0,-Fx(I2));
+
+I3 = Indexset==3;
+y(I3) = lambda1*(sqrt((x(I3)-lb(I3)).^2+phi_u(I3).^2)-x(I3)+lb(I3)-phi_u(I3));
+y([LZ; I3]) = lambda2*(max(0,x(I3)-lb(I3)).*max(0,Fx(I3))+max(0,ub(I3)-x(I3)).*max(0,-Fx(I3)));
+
+
+function H = DPhi(x,Fx,DFx,lb,ub,lambda1,lambda2,n,Indexset)
+%% DPHI evaluates an element of the C-subdifferential of operator Phi
+
+null = 1e-8;
+beta_l = zeros(n,1);
+beta_u = zeros(n,1);
+alpha_l = zeros(n,1);
+alpha_u = zeros(n,1);
+
+
+z = zeros(n,1);
+H2 = sparse(n,n);
+
+I = abs(x-lb)<=null & abs(Fx)<=null;
+beta_l(I) = 1;
+z(I) = 1;
+
+I = abs(ub-x)<=null & abs(Fx)<=null;
+beta_u(I) = 1;
+z(I) = 1;
+
+I = x-lb>=-null & Fx>=-null;
+alpha_l(I) = 1;
+
+I = ub-x>=-null & Fx<=null;
+alpha_u(I) = 1;
+
+Da = zeros(n,1);
+Db = zeros(n,1);
+
+I = 1:n;
+
+I0 = Indexset==0;
+Da(I0) = 0;
+Db(I0) = -1;
+H2(I0,:) = -DFx(I0,:);
+
+I1 = Indexset==1;
+denom1 = zeros(n,1);
+denom2 = zeros(n,1);
+if any(I1)
+ denom1(I1) = max(null,sqrt((x(I1)-lb(I1)).^2+Fx(I1).^2));
+ denom2(I1) = max(null,sqrt(z(I1).^2+(DFx(I1,:)*z).^2));
+end
+
+I1b = Indexset==1 & beta_l==0;
+Da(I1b) = (x(I1b)-lb(I1b))./denom1(I1b)-1;
+Db(I1b) = Fx(I1b)./denom1(I1b)-1;
+I1b = Indexset==1 & beta_l~=0;
+if any(I1b)
+ Da(I1b) = z(I1b)./denom2(I1b)-1;
+ Db(I1b) = (DFx(I1b,:)*z)./denom2(I1b)-1;
+end
+
+I1a = I(Indexset==1 & alpha_l==1);
+if any(I1a)
+ H2(I1a,:) = spdiags(x(I1a)-lb(I1a), 0, length(I1a), length(I1a))*DFx(I1a,:) +...
+ sparse(1:length(I1a),I1a,Fx(I1a),length(I1a),n,length(I1a));
+end
+
+I2 = Indexset==2;
+denom1 = zeros(n,1);
+denom2 = zeros(n,1);
+if any(I2)
+ denom1(I2) = max(null,sqrt((ub(I2)-x(I2)).^2+Fx(I2).^2));
+ denom2(I2) = max(null,sqrt(z(I2).^2+(DFx(I2,:)*z).^2));
+end
+
+I2b = Indexset==2 & beta_u==0;
+Da(I2b) = (ub(I2b)-x(I2b))./denom1(I2b)-1;
+Db(I2b) = -Fx(I2b)./denom1(I2b)-1;
+I2b = Indexset==2 & beta_u~=0;
+if any(I2b)
+ Da(I2b) = -z(I2b)./denom2(I2b)-1;
+ Db(I2b) = -(DFx(I2b,:)*z)./denom2(I2b)-1;
+end
+
+I2a = I(Indexset==2 & alpha_u==1);
+if any(I2a)
+ H2(I2a,:) = bsxfun(@times,x(I2a)-ub(I2a),DFx(I2a,:))+...
+ sparse(1:length(I2a),I2a,Fx(I2a),length(I2a),n,length(I2a));
+end
+
+I3 = Indexset==3;
+phi = zeros(n,1);
+ai = zeros(n,1);
+bi = zeros(n,1);
+ci = zeros(n,1);
+di = zeros(n,1);
+denom1 = zeros(n,1);
+denom2 = zeros(n,1);
+denom3 = zeros(n,1);
+denom4 = zeros(n,1);
+if any(I3)
+ phi(I3) = -ub(I3)+x(I3)+Fx(I3)+sqrt((ub(I3)-x(I3)).^2+Fx(I3).^2);
+ denom1(I3) = max(null,sqrt((x(I3)-lb(I3)).^2+phi(I3).^2));
+ denom2(I3) = max(null,sqrt(z(I3).^2+(DFx(I3,:)*z).^2));
+ denom3(I3) = max(null,sqrt((ub(I3)-x(I3)).^2+Fx(I3).^2));
+ denom4(I3) = max(null,sqrt(z(I3).^2));
+end
+
+I3bu = Indexset==3 & beta_u==0;
+ci(I3bu) = (x(I3bu)-ub(I3bu))./denom3(I3bu)+1;
+di(I3bu) = Fx(I3bu)./denom3(I3bu)+1;
+I3bu = Indexset==3 & beta_u~=0;
+if any(I3bu)
+ ci(I3bu) = 1+z(I3bu)./denom2(I3bu);
+ di(I3bu) = 1+(DFx(I3bu,:)*z)./denom2(I3bu);
+end
+
+I3bl = Indexset==3 & beta_l==0;
+ai(I3bl) = (x(I3bl)-lb(I3bl))./denom1(I3bl)-1;
+bi(I3bl) = phi(I3bl)./denom1(I3bl)-1;
+I3bl = Indexset==3 & beta_l~=0;
+if any(I3bl)
+ ai(I3bl) = z(I3bl)./denom4(I3bl)-1;
+ bi(I3bl) = (ci(I3bl).*z(I3bl)+(di(I3bl,ones(1,n)).*DFx(I3bl,:))*z)./denom4(I3bl)-1;
+end
+
+Da(I3) = ai(I3)+bi(I3).*ci(I3);
+Db(I3) = bi(I3).*di(I3);
+
+I3a = I(Indexset==3 & alpha_l==1 & alpha_u==1);
+if any(I3a)
+ H2(I3a,:) = bsxfun(@times,-lb(I3a)-ub(I3a)+2*x(I3a),DFx(I3a,:))+...
+ 2*sparse(1:length(I3a),I3a,Fx(I3a),length(I3a),n,length(I3a));
+end
+I3a = I(Indexset==3 & alpha_l==1 & alpha_u~=1);
+if any(I3a)
+ H2(I3a,:) = bsxfun(@times,x(I3a)-lb(I3a),DFx(I3a,:))+...
+ sparse(1:length(I3a),I3a,Fx(I3a),length(I3a),n,length(I3a));
+end
+I3a = I(Indexset==3 & alpha_l~=1 & alpha_u==1);
+if any(I3a)
+ H2(I3a,:) = bsxfun(@times,x(I3a)-ub(I3a),DFx(I3a,:))+...
+ sparse(1:length(I3a),I3a,Fx(I3a),length(I3a),n,length(I3a));
+end
+
+H1 = spdiags(Db,0,length(Db),length(Db))*DFx;
+H1 = H1 + spdiags(Da, 0, length(Da), length(Da));
+
+H = [lambda1*H1; lambda2*H2];
diff --git a/matlab/lnsrch1.m b/matlab/lnsrch1.m
index 36e9f3afb5f2b029b2c334070fb0782c80f882b8..09b128b918f4ae7abb9e899fccf7873cd1c49389 100644
--- a/matlab/lnsrch1.m
+++ b/matlab/lnsrch1.m
@@ -20,7 +20,7 @@ function [x,f,fvec,check]=lnsrch1(xold, fold, g, p, stpmax, func, j1, j2, tolx,
% fvec: residuals vector
% check=1: problem of the looping which continues indefinitely
%
-%
+%
% SPECIAL REQUIREMENTS
% none
@@ -48,7 +48,7 @@ x = xold;
nn = length(j2);
summ = sqrt(p'*p);
-if ~isfinite(summ)
+if ~isfinite(summ)
if ~isequal(func,@perfect_foresight_problem)
eq_number_string=[];
for ii=1:length(j1)-1
@@ -61,13 +61,13 @@ if ~isfinite(summ)
var_string=[var_string, deblank(Model.endo_names(j2(ii),:)), ', '];
end
var_string=[var_string, deblank(Model.endo_names(j2(end),:))];
- fprintf('\nAn infinite element was encountered when trying to solve equation(s) %s \n',eq_number_string)
+ fprintf('\nAn infinite element was encountered when trying to solve equation(s) %s \n',eq_number_string)
fprintf('with respect to the variable(s): %s.\n',var_string)
fprintf('The values of the endogenous variables when the problem was encountered were:\n')
for ii=1:length(xold)
fprintf('%-s % 8.4g \n',Model.endo_names(ii,:),xold(ii));
end
- skipline();
+ skipline();
end
error(['Some element of Newton direction isn''t finite. Jacobian maybe' ...
' singular or there is a problem with initial values'])
diff --git a/matlab/lnsrch1_wrapper_one_boundary.m b/matlab/lnsrch1_wrapper_one_boundary.m
index 22485104681cb7639e1fc975cc48365bebe2984c..5aca3de3daefae17c41f3f954e6cd22bca0bf016 100644
--- a/matlab/lnsrch1_wrapper_one_boundary.m
+++ b/matlab/lnsrch1_wrapper_one_boundary.m
@@ -12,14 +12,14 @@ function r = lnsrch1_wrapper_one_boundary(ya, y_index, fname, y, x, params, stea
% x [matrix] All the exogenous variables of the model
% params [vector] All the parameters of the model
% OUTPUTS
-% r [vector] The residuals of the current block
-%
+% r [vector] The residuals of the current block
+%
% ALGORITHM
% none.
-%
+%
% SPECIAL REQUIREMENTS
% none.
-%
+%
% Copyright (C) 2009-2011 Dynare Team
%
diff --git a/matlab/lnsrch1_wrapper_two_boundaries.m b/matlab/lnsrch1_wrapper_two_boundaries.m
index 3b55ca70351a1f992f714936ca33078577fc6ee5..75a9771cc17402f5f0a7134d31541ba884fa4e57 100644
--- a/matlab/lnsrch1_wrapper_two_boundaries.m
+++ b/matlab/lnsrch1_wrapper_two_boundaries.m
@@ -17,14 +17,14 @@ function ra = lnsrch1_wrapper_two_boundaries(ya, fname, y, y_index, x, ...
% y_size [int] The number of endogenous variables
% in the current block
% OUTPUTS
-% ra [vector] The residuals of the current block
-%
+% ra [vector] The residuals of the current block
+%
% ALGORITHM
% none.
-%
+%
% SPECIAL REQUIREMENTS
% none.
-%
+%
% Copyright (C) 2009-2015 Dynare Team
%
diff --git a/matlab/load_first_mh_history_file.m b/matlab/load_first_mh_history_file.m
index 3177fafe4590b3c74d645c749de1acd72a2de261..67bd70a1c5062e2859a2881ad57a95516c3c6423 100644
--- a/matlab/load_first_mh_history_file.m
+++ b/matlab/load_first_mh_history_file.m
@@ -1,7 +1,7 @@
function info = load_first_mh_history_file(MetropolisFolder, ModelName)
% This routine requires that the MCMC draws were obtained with a dynare version greater than 4.3.3.
-
+
% Copyright (C) 2013 Dynare Team
%
% This file is part of Dynare.
diff --git a/matlab/load_last_mh_history_file.m b/matlab/load_last_mh_history_file.m
index 92bf059dbb80a08844ce810bd3cddfa385544ae5..e22fb61528b340723a396d9567b290d02c6b676a 100644
--- a/matlab/load_last_mh_history_file.m
+++ b/matlab/load_last_mh_history_file.m
@@ -4,9 +4,9 @@ function info = load_last_mh_history_file(MetropolisFolder, ModelName)
% Inputs:
% MetropolisFolder [char] Name of the metropolis subfolder
% ModelName [char] Name of the mod-file
-% Outputs:
+% Outputs:
% info [struct] structure storing the MH history
-%
+%
% Notes: The record structure is written to the caller workspace via an
% assignin statement.
@@ -37,7 +37,7 @@ mh_history_files = dir([BaseName '_mh_history_*.mat']);
% Consistency with older versions of Dynare.
if isequal(length(mh_history_files),0)
if exist([BaseName '_mh_history.mat'])
- format_mh_history_file = 1; % old Dynare format
+ format_mh_history_file = 1; % old Dynare format
else
error(['Estimation::load_mh_file: I cannot find any mh-history file in ' MetropolisFolder '!'])
end
@@ -63,7 +63,7 @@ else
load([BaseName '_mh_history_' num2str(length(mh_history_files)-1) '.mat']);
% add fields that have later been introduced
if ~isfield(record,'MCMCConcludedSuccessfully')
- record.MCMCConcludedSuccessfully = NaN; % This information is forever lost...
+ record.MCMCConcludedSuccessfully = NaN; % This information is forever lost...
end
if ~isfield(record,'MAX_nruns')
record.MAX_nruns=NaN(size(record.MhDraws,1),1); % This information is forever lost...
diff --git a/matlab/load_m_file_data_legacy.m b/matlab/load_m_file_data_legacy.m
index 5a4cdecb904e82633aa935610a64b2278eace86e..9d4b8e34c8155bc27d52a5811e376ecf00afcdd5 100644
--- a/matlab/load_m_file_data_legacy.m
+++ b/matlab/load_m_file_data_legacy.m
@@ -81,82 +81,82 @@ o2WysrOISH = dseries(eval(sprintf(VbO4y7zOlh, U7ORsJ0vy3{:})), [], U7ORsJ0vy3);
return
%@test:1
- % Write a data file
- fid = fopen('example.m','w');
- fwriten(fid, 'a = randn(100,1);');
- fwriten(fid, 'b = randn(100,1);');
- fwriten(fid, 'c = transpose(randn(100,1));');
- fwriten(fid, 'd = randn(100,1);');
- fwriten(fid, 'e = randn(100,2);');
- fwriten(fid, ' ');
- fwriten(fid, 'f = NaN(100,1);');
- fwriten(fid, 'for i=1:100');
- fwriten(fid, ' f(i) = log(rand());')
- fwriten(fid, 'end');
- fclose(fid);
- % Define a set of variables to be loaded.
- listofvariablestobeloaded = {'b', 'a'};
- % Test if we can load the data.
- try
- data = load_m_file_data_legacy('example.m', listofvariablestobeloaded);
- delete example.m
- t(1) = 1;
- catch
- t(1) = 0;
- end
- T = all(t);
+% Write a data file
+fid = fopen('example.m','w');
+fwriten(fid, 'a = randn(100,1);');
+fwriten(fid, 'b = randn(100,1);');
+fwriten(fid, 'c = transpose(randn(100,1));');
+fwriten(fid, 'd = randn(100,1);');
+fwriten(fid, 'e = randn(100,2);');
+fwriten(fid, ' ');
+fwriten(fid, 'f = NaN(100,1);');
+fwriten(fid, 'for i=1:100');
+fwriten(fid, ' f(i) = log(rand());')
+fwriten(fid, 'end');
+fclose(fid);
+% Define a set of variables to be loaded.
+listofvariablestobeloaded = {'b', 'a'};
+% Test if we can load the data.
+try
+ data = load_m_file_data_legacy('example.m', listofvariablestobeloaded);
+ delete example.m
+ t(1) = 1;
+catch
+ t(1) = 0;
+end
+T = all(t);
%@eof:1
%@test:2
- % Write a data file
- fid = fopen('example.m','w');
- fwriten(fid, 'a = randn(100,1);');
- fwriten(fid, 'b = randn(100,1);');
- fwriten(fid, 'c = transpose(randn(100,1));');
- fwriten(fid, 'd = randn(100,1);');
- fwriten(fid, 'e = randn(100,2);');
- fwriten(fid, ' ');
- fwriten(fid, 'f = NaN(100,1);');
- fwriten(fid, 'for i=1:100');
- fwriten(fid, ' f(i) = log(rand());')
- fwriten(fid, 'end');
- fclose(fid);
- % Define a set of variables to be loaded.
- listofvariablestobeloaded = {'e', 'a'};
- % Test if we can load the data.
- try
- data = load_m_file_data_legacy('example.m', listofvariablestobeloaded);
- delete example.m
- t(1) = 0;
- catch
- t(1) = 1;
- end
- T = all(t);
+% Write a data file
+fid = fopen('example.m','w');
+fwriten(fid, 'a = randn(100,1);');
+fwriten(fid, 'b = randn(100,1);');
+fwriten(fid, 'c = transpose(randn(100,1));');
+fwriten(fid, 'd = randn(100,1);');
+fwriten(fid, 'e = randn(100,2);');
+fwriten(fid, ' ');
+fwriten(fid, 'f = NaN(100,1);');
+fwriten(fid, 'for i=1:100');
+fwriten(fid, ' f(i) = log(rand());')
+fwriten(fid, 'end');
+fclose(fid);
+% Define a set of variables to be loaded.
+listofvariablestobeloaded = {'e', 'a'};
+% Test if we can load the data.
+try
+ data = load_m_file_data_legacy('example.m', listofvariablestobeloaded);
+ delete example.m
+ t(1) = 0;
+catch
+ t(1) = 1;
+end
+T = all(t);
%@eof:2
%@test:3
- % Write a data file
- fid = fopen('example.m','w');
- fwriten(fid, 'a = randn(100,1);');
- fwriten(fid, 'b = randn(100,1);');
- fwriten(fid, 'c = transpose(randn(100,1));');
- fwriten(fid, 'd = randn(100,1);');
- fwriten(fid, 'e = randn(100,2);');
- fwriten(fid, ' ');
- fwriten(fid, 'f = NaN(100,1);');
- fwriten(fid, 'for i=1:100');
- fwriten(fid, ' f(i) = log(rand());')
- fwriten(fid, 'end');
- fclose(fid);
- % Define a set of variables to be loaded.
- listofvariablestobeloaded = {'c', 'a'};
- % Test if we can load the data.
- try
- data = load_m_file_data_legacy('example.m', listofvariablestobeloaded);
- delete example.m
- t(1) = 1;
- catch
- t(1) = 0;
- end
- T = all(t);
+% Write a data file
+fid = fopen('example.m','w');
+fwriten(fid, 'a = randn(100,1);');
+fwriten(fid, 'b = randn(100,1);');
+fwriten(fid, 'c = transpose(randn(100,1));');
+fwriten(fid, 'd = randn(100,1);');
+fwriten(fid, 'e = randn(100,2);');
+fwriten(fid, ' ');
+fwriten(fid, 'f = NaN(100,1);');
+fwriten(fid, 'for i=1:100');
+fwriten(fid, ' f(i) = log(rand());')
+fwriten(fid, 'end');
+fclose(fid);
+% Define a set of variables to be loaded.
+listofvariablestobeloaded = {'c', 'a'};
+% Test if we can load the data.
+try
+ data = load_m_file_data_legacy('example.m', listofvariablestobeloaded);
+ delete example.m
+ t(1) = 1;
+catch
+ t(1) = 0;
+end
+T = all(t);
%@eof:3
diff --git a/matlab/load_mat_file_data_legacy.m b/matlab/load_mat_file_data_legacy.m
index bf8f342b1d2cf4e8b8d07d19a114834f36b1a8b0..356c57237049c6d21737be67d75f0781ef3ced26 100644
--- a/matlab/load_mat_file_data_legacy.m
+++ b/matlab/load_mat_file_data_legacy.m
@@ -27,14 +27,14 @@ if ~all(ismember(varobs',names))
for ii=2:size(missing_variables,1)
disp_string=[disp_string,', ',missing_variables{ii,:}];
end
- error('makedataset: The variable(s) %s listed in varobs are not contained in the dataset %s',disp_string);
-else
+ error('makedataset: The variable(s) %s listed in varobs are not contained in the dataset %s',disp_string);
+else
data_mat=[];
for var_iter=1:length(varobs)
try
- data_mat=[data_mat vec(data_file.(varobs{1,var_iter}))];
+ data_mat=[data_mat vec(data_file.(varobs{1,var_iter}))];
catch
- error('makedataset: The variable %s does not have dimensions conformable with the previous one',varobs{1,var_iter});
+ error('makedataset: The variable %s does not have dimensions conformable with the previous one',varobs{1,var_iter});
end
end
end
diff --git a/matlab/lpdfgam.m b/matlab/lpdfgam.m
index c98addbb791baa30b4cd88c7a12c2a17e4949528..7dbcf2579ee907433f4a6f52bcd35e26a1c5f030 100644
--- a/matlab/lpdfgam.m
+++ b/matlab/lpdfgam.m
@@ -1,15 +1,15 @@
function [ldens,Dldens,D2ldens] = lpdfgam(x,a,b)
% Evaluates the logged GAMMA PDF at x.
%
-% INPUTS
+% INPUTS
% x [double] m*n matrix of locations,
% a [double] m*n matrix or scalar, First GAMMA distribution parameters (shape),
% b [double] m*n matrix or scalar, Second GAMMA distribution parameters (scale),
%
% OUTPUTS
% ldens [double] m*n matrix of logged GAMMA densities evaluated at x.
-%
-%
+%
+%
% SPECIAL REQUIREMENTS
% none
@@ -41,7 +41,7 @@ end
-if nargout >1
+if nargout >1
if length(a)==1
Dldens(idx) = (a-1)./(x(idx)) - ones(length(idx),1)/b ;
else
diff --git a/matlab/lpdfgbeta.m b/matlab/lpdfgbeta.m
index bcb3c1737185878e7c6dd3de611872c3940eb734..6f0dd0fd876b0d9d87bd3cb8e836e8ea028dcd81 100644
--- a/matlab/lpdfgbeta.m
+++ b/matlab/lpdfgbeta.m
@@ -1,16 +1,16 @@
function [ldens,Dldens,D2ldens] = lpdfgbeta(x,a,b,aa,bb)
-% Evaluates the logged BETA PDF at x.
+% Evaluates the logged BETA PDF at x.
%
-% INPUTS
+% INPUTS
% x [double] m*n matrix of loactions,
-% a [double] m*n matrix of First BETA distribution parameters,
-% b [double] m*n matrix of Second BETA distribution parameters,
-% aa [double] m*n matrix of lower bounds for (generalized) distribution,
+% a [double] m*n matrix of First BETA distribution parameters,
+% b [double] m*n matrix of Second BETA distribution parameters,
+% aa [double] m*n matrix of lower bounds for (generalized) distribution,
% bb [double] m*n matrix of upper bounds for (generalized) distribution
%
-% OUTPUTS
+% OUTPUTS
% ldens [double] m*n matrix of logged (generalized) BETA densities.
-%
+%
% SPECIAL REQUIREMENTS
% none
@@ -41,7 +41,7 @@ else
end
-if nargout >1
+if nargout >1
if length(a)==1
Dldens(idx) = (a-1)./(x(idx)-aa) - (b-1)./(bb-x(idx)) ;
else
@@ -50,7 +50,7 @@ if nargout >1
end
-if nargout == 3
+if nargout == 3
if length(a)==1
D2ldens(idx) = -(a-1)./(x(idx)-aa).^2 - (b-1)./(bb-x(idx)).^2 ;
else
diff --git a/matlab/lpdfig1.m b/matlab/lpdfig1.m
index 5aeb05f90c95e5352b42d608fe6075e579e9b752..11fd33b3632d1e2ba8c7d64329fd7c7603abcc8a 100644
--- a/matlab/lpdfig1.m
+++ b/matlab/lpdfig1.m
@@ -1,15 +1,15 @@
function [ldens,Dldens,D2ldens] = lpdfig1(x,s,nu)
% Evaluates the logged INVERSE-GAMMA-1 PDF at x.
%
-% X ~ IG1(s,nu) if X = sqrt(Y) where Y ~ IG2(s,nu) and Y = inv(Z) with Z ~ G(nu/2,2/s) (Gamma distribution)
+% X ~ IG1(s,nu) if X = sqrt(Y) where Y ~ IG2(s,nu) and Y = inv(Z) with Z ~ G(nu/2,2/s) (Gamma distribution)
%
% See L. Bauwens, M. Lubrano and J-F. Richard [1999, appendix A] for more details.
%
%
-% INPUTS
+% INPUTS
% x [double] m*n matrix of locations,
-% s [double] m*n matrix or scalar, First INVERSE-GAMMA-1 distribution parameters,
-% nu [double] m*n matrix or scalar, Second INVERSE-GAMMA-1 distribution parameters.
+% s [double] m*n matrix or scalar, First INVERSE-GAMMA-1 distribution parameters,
+% nu [double] m*n matrix or scalar, Second INVERSE-GAMMA-1 distribution parameters.
%
% OUTPUTS
% ldens [double] m*n matrix of logged INVERSE-GAMMA-1 densities evaluated at x.
@@ -35,7 +35,7 @@ function [ldens,Dldens,D2ldens] = lpdfig1(x,s,nu)
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
ldens = -Inf( size(x) ) ;
-idx = find( x>0 ) ;
+idx = find( x>0 ) ;
if length(s)==1
ldens(idx) = log(2) - gammaln(.5*nu) - .5*nu*(log(2)-log(s)) - (nu+1)*log(x(idx)) - .5*s./(x(idx).*x(idx)) ;
@@ -43,7 +43,7 @@ else
ldens(idx) = log(2) - gammaln(.5*nu(idx)) - .5*nu(idx).*(log(2)-log(s(idx))) - (nu(idx)+1).*log(x(idx)) - .5*s(idx)./(x(idx).*x(idx)) ;
end
-if nargout >1
+if nargout >1
if length(s)==1
Dldens(idx) = - (nu+1)./(x(idx)) + s./(x(idx).^3) ;
else
@@ -51,11 +51,10 @@ if nargout >1
end
end
-if nargout == 3
+if nargout == 3
if length(s)==1
D2ldens(idx) = (nu+1)./(x(idx).^2) - 3*s(idx)./(x(idx).^4) ;
else
D2ldens(idx) = (nu(idx)+1)./(x(idx).^2) - 3*s(idx)./(x(idx).^4) ;
end
end
-
diff --git a/matlab/lpdfig2.m b/matlab/lpdfig2.m
index 00de3abb12746e3396812d651d6216af2a460369..3f534fb05ef24eec68c8f368ac59925afdf60bd2 100644
--- a/matlab/lpdfig2.m
+++ b/matlab/lpdfig2.m
@@ -1,15 +1,15 @@
function [ldens,Dldens,D2ldens] = lpdfig2(x,s,nu)
% Evaluates the logged INVERSE-GAMMA-2 PDF at x.
%
-% X ~ IG2(s,nu) if X = inv(Z) where Z ~ G(nu/2,2/s) (Gamma distribution)
+% X ~ IG2(s,nu) if X = inv(Z) where Z ~ G(nu/2,2/s) (Gamma distribution)
%
% See L. Bauwens, M. Lubrano and J-F. Richard [1999, appendix A] for more details.
%
%
-% INPUTS
+% INPUTS
% x [double] m*n matrix of locations,
-% s [double] m*n matrix or scalar, First INVERSE-GAMMA-2 distribution parameters,
-% nu [double] m*n matrix or scalar, Second INVERSE-GAMMA-2 distribution parameters.
+% s [double] m*n matrix or scalar, First INVERSE-GAMMA-2 distribution parameters,
+% nu [double] m*n matrix or scalar, Second INVERSE-GAMMA-2 distribution parameters.
%
% OUTPUTS
% ldens [double] m*n matrix of logged INVERSE-GAMMA-2 densities evaluated at x.
@@ -35,7 +35,7 @@ function [ldens,Dldens,D2ldens] = lpdfig2(x,s,nu)
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
ldens = -Inf( size(x) ) ;
-idx = find( x>0 ) ;
+idx = find( x>0 ) ;
if length(s)==1
ldens(idx) = -gammaln(.5*nu) - (.5*nu)*(log(2)-log(s)) - .5*(nu+2)*log(x(idx)) -.5*s./x(idx);
@@ -43,7 +43,7 @@ else
ldens(idx) = -gammaln(.5*nu(idx)) - (.5*nu(idx)).*(log(2)-log(s(idx))) - .5*(nu(idx)+2).*log(x(idx)) -.5*s(idx)./x(idx);
end
-if nargout >1
+if nargout >1
if length(s)==1
Dldens(idx) = - .5*(nu+2)./(x(idx)) + .5*s./x(idx).^2;
else
@@ -51,7 +51,7 @@ if nargout >1
end
end
-if nargout == 3
+if nargout == 3
if length(s)==1
D2ldens(idx) = .5*(nu+2)./(x(idx)).^2 - s./x(idx).^3;
else
diff --git a/matlab/lpdfnorm.m b/matlab/lpdfnorm.m
index bb9da98d8e64ff4f522bda0feba1867e6e73bcb8..fff678da91cd403ebe3546739bf3ea0585b85b73 100644
--- a/matlab/lpdfnorm.m
+++ b/matlab/lpdfnorm.m
@@ -1,15 +1,15 @@
function [ldens,Dldens,D2ldens] = lpdfnorm(x,a,b)
% Evaluates the logged UNIVARIATE GAUSSIAN PDF at x.
%
-% INPUTS
+% INPUTS
% x [double] m*n matrix of locations,
-% a [double] m*n matrix or scalar, First GAUSSIAN distribution parameters (expectation)
-% b [double] m*n matrix or scalar, Second GAUSSIAN distribution parameters (standard deviation).
+% a [double] m*n matrix or scalar, First GAUSSIAN distribution parameters (expectation)
+% b [double] m*n matrix or scalar, Second GAUSSIAN distribution parameters (standard deviation).
%
-% OUTPUTS
+% OUTPUTS
% ldens [double] m*n matrix of logged GAUSSIAN densities evaluated at x.
-%
-%
+%
+%
% SPECIAL REQUIREMENTS
% none
@@ -34,10 +34,10 @@ if nargin<3, b=1; end
if nargin<2, a=0; end
ldens = -log(b) -.5*log(2*pi) - .5*((x-a)./b).*((x-a)./b) ;
-if nargout >1
+if nargout >1
Dldens = - (1./b).*((x-a)./b) ;
end
-if nargout == 3
+if nargout == 3
D2ldens = - (1./b).^2 ;
end
\ No newline at end of file
diff --git a/matlab/lyapunov_solver.m b/matlab/lyapunov_solver.m
index 2be9e3a2828c5df9764e578bab0a2cb27278a054..98d782cdf30d7ef239bd7968f9cc875b75217ba8 100644
--- a/matlab/lyapunov_solver.m
+++ b/matlab/lyapunov_solver.m
@@ -1,14 +1,14 @@
function P=lyapunov_solver(T,R,Q,DynareOptions) % --*-- Unitary tests --*--
% function P=lyapunov_solver(T,R,Q,DynareOptions)
% Solves the Lyapunov equation P-T*P*T' = R*Q*R' arising in a state-space
-% system, where P is the variance of the states
+% system, where P is the variance of the states
%
% Inputs
% T [double] n*n matrix.
% R [double] n*m matrix.
% Q [double] m*m matrix.
% DynareOptions [structure] Dynare options
-%
+%
% Outputs
% P [double] n*n matrix.
%
@@ -20,7 +20,7 @@ function P=lyapunov_solver(T,R,Q,DynareOptions) % --*-- Unitary tests --*--
% DynareOptions.lyapunov_db == 1
% doubling algorithm
% DynareOptions.lyapunov_srs == 1
-% Square-root solver for discrete-time Lyapunov equations (requires Matlab System Control toolbox
+% Square-root solver for discrete-time Lyapunov equations (requires Matlab System Control toolbox
% or Octave control package)
% Copyright (C) 2016-2017 Dynare Team
@@ -60,7 +60,7 @@ elseif DynareOptions.lyapunov_srs == 1
end
chol_Q = R*chol(Q,'lower');
R_P = dlyapchol(T,chol_Q);
- P = R_P' * R_P;
+ P = R_P' * R_P;
else
P = lyapunov_symm(T,R*Q*R',DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold, [], DynareOptions.debug);
end
@@ -81,7 +81,7 @@ end
%$ tmp2=randn(m_small,m_small);
%$ Q_small=tmp2*tmp2';
%$ R_small=randn(n_small,m_small);
-%$
+%$
%$ n_large=9;
%$ m_large=11;
%$ T_large=randn(n_large,n_large);
@@ -89,7 +89,7 @@ end
%$ tmp2=randn(m_large,m_large);
%$ Q_large=tmp2*tmp2';
%$ R_large=randn(n_large,m_large);
-%$
+%$
%$ % DynareOptions.lyapunov_fp == 1
%$ options_.lyapunov_fp = 1;
%$ try
diff --git a/matlab/lyapunov_symm.m b/matlab/lyapunov_symm.m
index 92f66d4e113fdd928a5e9082bd7aa17871745cbc..53eaf9e4c1810645ffb91bd09fb67ed57794807c 100644
--- a/matlab/lyapunov_symm.m
+++ b/matlab/lyapunov_symm.m
@@ -1,22 +1,22 @@
function [x,u] = lyapunov_symm(a,b,lyapunov_fixed_point_tol,qz_criterium,lyapunov_complex_threshold,method,debug) % --*-- Unitary tests --*--
% Solves the Lyapunov equation x-a*x*a' = b, for b and x symmetric matrices.
% If a has some unit roots, the function computes only the solution of the stable subsystem.
-%
+%
% INPUTS:
% a [double] n*n matrix.
% b [double] n*n matrix.
% qz_criterium [double] unit root threshold for eigenvalues
% lyapunov_fixed_point_tol [double] convergence criteria for fixed_point algorithm.
% lyapunov_complex_threshold [double] scalar, complex block threshold for the upper triangular matrix T.
-% method [integer] Scalar, if method=0 [default] then U, T, n and k are not persistent.
-% method=1 then U, T, n and k are declared as persistent
-% variables and the Schur decomposition is triggered.
-% method=2 then U, T, n and k are declared as persistent
+% method [integer] Scalar, if method=0 [default] then U, T, n and k are not persistent.
+% method=1 then U, T, n and k are declared as persistent
+% variables and the Schur decomposition is triggered.
+% method=2 then U, T, n and k are declared as persistent
% variables and the Schur decomposition is not performed.
% method=3 fixed point method
% OUTPUTS
% x: [double] m*m solution matrix of the lyapunov equation, where m is the dimension of the stable subsystem.
-% u: [double] Schur vectors associated with unit roots
+% u: [double] Schur vectors associated with unit roots
%
% ALGORITHM
% Uses reordered Schur decomposition (Bartels-Stewart algorithm)
@@ -74,7 +74,7 @@ if method == 3
X_old = X;
X = a * X * at + b;
evol = max(sum(abs(X - X_old))); %norm_1
- %evol = max(sum(abs(X - X_old)')); %norm_inf
+ %evol = max(sum(abs(X - X_old)')); %norm_inf
it_fp = it_fp + 1;
end
if debug
@@ -110,7 +110,7 @@ end
if method<2
[U,T] = schur(a);
e1 = abs(ordeig(T)) > 2-qz_criterium;
- k = sum(e1); % Number of unit roots.
+ k = sum(e1); % Number of unit roots.
n = length(e1)-k; % Number of stationary variables.
if k > 0
% Selects stable roots
diff --git a/matlab/marginal_density.m b/matlab/marginal_density.m
index da9c450434e5b8189f508393d8328dc149186d66..e5683da2c1a06e209ccfc259854e7031c165a602 100644
--- a/matlab/marginal_density.m
+++ b/matlab/marginal_density.m
@@ -64,7 +64,7 @@ end
% save the posterior mean and the inverse of the covariance matrix
% (usefull if the user wants to perform some computations using
-% the posterior mean instead of the posterior mode ==> ).
+% the posterior mean instead of the posterior mode ==> ).
parameter_names = bayestopt_.name;
save([M_.fname '_mean.mat'],'xparam1','hh','parameter_names','SIGMA');
@@ -106,7 +106,7 @@ while check_coverage
disp('Estimation::marginal density: The support of the weighting density function is not large enough...')
disp('Estimation::marginal density: I increase the variance of this distribution.')
increase = 1.2*increase;
- linee = 0;
+ linee = 0;
else
disp('Estimation::marginal density: Let me try again.')
increase = 1.2*increase;
@@ -114,9 +114,9 @@ while check_coverage
if increase > 20
check_coverage = 0;
clear invSIGMA detSIGMA increase;
- disp('Estimation::marginal density: There''s probably a problem with the modified harmonic mean estimator.')
+ disp('Estimation::marginal density: There''s probably a problem with the modified harmonic mean estimator.')
end
- end
+ end
else
check_coverage = 0;
clear invSIGMA detSIGMA increase;
@@ -130,17 +130,17 @@ return
function oo_=fill_mh_mode(xparam1,stdh,M_,options_,estim_params_,bayestopt_,oo_, field_name)
%function oo_=fill_mh_mode(xparam1,stdh,M_,options_,estim_params_,bayestopt_,oo_, field_name)
-%
-% INPUTS
+%
+% INPUTS
% o xparam1 [double] (p*1) vector of estimate parameters.
% o stdh [double] (p*1) vector of estimate parameters.
-% o M_ Matlab's structure describing the Model (initialized by dynare, see @ref{M_}).
+% o M_ Matlab's structure describing the Model (initialized by dynare, see @ref{M_}).
% o estim_params_ Matlab's structure describing the estimated_parameters (initialized by dynare, see @ref{estim_params_}).
% o options_ Matlab's structure describing the options (initialized by dynare, see @ref{options_}).
% o bayestopt_ Matlab's structure describing the priors (initialized by dynare, see @ref{bayesopt_}).
% o oo_ Matlab's structure gathering the results (initialized by dynare, see @ref{oo_}).
-%
-% OUTPUTS
+%
+% OUTPUTS
% o oo_ Matlab's structure gathering the results
%
% SPECIAL REQUIREMENTS
@@ -172,8 +172,8 @@ if nvx
eval(['oo_.' field_name '_std_at_mode.shocks_std.' name ' = stdh(ip);']);
ip = ip+1;
end
- end
- if nvn
+end
+if nvn
ip = nvx+1;
for i=1:nvn
name = options_.varobs{estim_params_.nvn_observable_correspondence(i,1)};
@@ -181,7 +181,7 @@ if nvx
eval(['oo_.' field_name '_std_at_mode.measurement_errors_std.' name ' = stdh(ip);']);
ip = ip+1;
end
- end
+end
if ncx
ip = nvx+nvn+1;
diff --git a/matlab/matlab_ver_less_than.m b/matlab/matlab_ver_less_than.m
index 53dd8d7ef5f6a0b92d0155d4d2b7df903790cee5..1b4b808768c8b1013032bd964ada7a22931b1a45 100644
--- a/matlab/matlab_ver_less_than.m
+++ b/matlab/matlab_ver_less_than.m
@@ -8,7 +8,7 @@ function r = matlab_ver_less_than(verstr)
%
% INPUTS
% verstr: a string of the format 'x.y' or 'x.y.z'
-%
+%
% OUTPUTS
% r: 0 or 1
%
diff --git a/matlab/maximize_prior_density.m b/matlab/maximize_prior_density.m
index 72df861a4768cd76436c740acd00241224392716..2c9fc4de9de5dd277a1fd6c3526083ab4c5c96c7 100644
--- a/matlab/maximize_prior_density.m
+++ b/matlab/maximize_prior_density.m
@@ -1,16 +1,16 @@
function [xparams,lpd,hessian_mat] = ...
maximize_prior_density(iparams, prior_shape, prior_hyperparameter_1, prior_hyperparameter_2, prior_inf_bound, prior_sup_bound,DynareOptions,DynareModel,BayesInfo,EstimatedParams,DynareResults)
% Maximizes the logged prior density using Chris Sims' optimization routine.
-%
-% INPUTS
+%
+% INPUTS
% iparams [double] vector of initial parameters.
% prior_shape [integer] vector specifying prior densities shapes.
% prior_hyperparameter_1 [double] vector, first hyperparameter.
% prior_hyperparameter_2 [double] vector, second hyperparameter.
% prior_inf_bound [double] vector, prior's lower bound.
-% prior_sup_bound [double] vector, prior's upper bound.
+% prior_sup_bound [double] vector, prior's upper bound.
%
-% OUTPUTS
+% OUTPUTS
% xparams [double] vector, prior mode.
% lpd [double] scalar, value of the logged prior density at the mode.
% hessian_mat [double] matrix, Hessian matrix at the prior mode.
diff --git a/matlab/mcforecast3.m b/matlab/mcforecast3.m
index e2fb7de8a6102b37afa109ef0cd59d36121fbaf0..a01dbe8d57259b9964f846c0eacd91fc3bc27da0 100644
--- a/matlab/mcforecast3.m
+++ b/matlab/mcforecast3.m
@@ -2,7 +2,7 @@ function [forcs, e]= mcforecast3(cL,H,mcValue,shocks,forcs,T,R,mv,mu)
% forcs = mcforecast3(cL,H,mcValue,shocks,forcs,T,R,mv,mu)
% Computes the shock values for constrained forecasts necessary to keep
% endogenous variables at their constrained paths
-%
+%
% INPUTS
% o cL [scalar] number of controlled periods
% o H [scalar] number of forecast periods
@@ -14,7 +14,7 @@ function [forcs, e]= mcforecast3(cL,H,mcValue,shocks,forcs,T,R,mv,mu)
% o mv [n_controlled_exo by n_endovars boolean] indicator vector selecting constrained endogenous variables
% o mu [n_controlled_vars by nexo boolean] indicator vector
% selecting controlled exogenous variables
-%
+%
% Algorithm:
% Relies on state-space form:
% y_t=T*y_{t-1}+R*shocks(:,t)
@@ -30,7 +30,7 @@ function [forcs, e]= mcforecast3(cL,H,mcValue,shocks,forcs,T,R,mv,mu)
% After obtaining the shocks, and for uncontrolled periods, the state-space representation
% y_t=T*y_{t-1}+R*shocks(:,t)
% is used for forecasting
-%
+%
% Copyright (C) 2006-2015 Dynare Team
%
% This file is part of Dynare.
diff --git a/matlab/mcompare.m b/matlab/mcompare.m
index 9bf0f1713f644d6ddf5603d5ed98d855f07de68d..a43d54cf91b077dd8919fc7f9bb33acee0c91e69 100644
--- a/matlab/mcompare.m
+++ b/matlab/mcompare.m
@@ -1,7 +1,7 @@
function mcompare(s1,s2)
-% MCOMPARE : MCOMPARE ( [ 'file1' ; 'file2' ] , [ 'var1' ; 'var2' ...] )
+% MCOMPARE : MCOMPARE ( [ 'file1' ; 'file2' ] , [ 'var1' ; 'var2' ...] )
% This optional command plots the relative differences between
-% two different simulations for a list of variables. One plot
+% two different simulations for a list of variables. One plot
% is drawn for each variable. The trajectories must have been
% previously saved by the instruction DYNASAVE. The simulation
% in file1 serves as the base simulation and the ploted quantity
diff --git a/matlab/mcp_func.m b/matlab/mcp_func.m
index 424b9203f9a187f0dc950a181757c5f3743060b3..f0af5189dad1c98f768399598a447f3c22686535 100644
--- a/matlab/mcp_func.m
+++ b/matlab/mcp_func.m
@@ -1,12 +1,12 @@
function [res,fjac,domer] = mcp_func(x,jacflag)
% function [res,fjac,domer] = mcp_func(x,jacflag)
% wrapper function for mixed complementarity problem when using PATH
-%
-% INPUTS
+%
+% INPUTS
% - x [double] N*T array, paths for the endogenous variables (initial guess).
% - jacflag [scalar] indicator whether Jacobian is requested
%
-% OUTPUTS
+% OUTPUTS
% - res [double] (N*T)*1 array, residuals of the stacked problem
% - fjac [double] (N*T)*(N*T) array, Jacobian of the stacked problem
% - domer [scalar] errorflag that is 1 if solution is not real
diff --git a/matlab/metropolis_draw.m b/matlab/metropolis_draw.m
index 783d2215a7ca420d481534f6260797efc269a5ed..94c4212783b81cc30ca46c926deaa63d96ca7000 100644
--- a/matlab/metropolis_draw.m
+++ b/matlab/metropolis_draw.m
@@ -1,12 +1,12 @@
function [xparams, logpost, options_]=metropolis_draw(init,options_,estim_params_,M_)
-% function [xparams, logpost]=metropolis_draw(init)
+% function [xparams, logpost]=metropolis_draw(init)
% Builds draws from metropolis
%
% INPUTS:
-% init: scalar equal to
-% 1: first call to store the required information
-% on files, lines, and chains to be read
-% in persistent variables to make them available
+% init: scalar equal to
+% 1: first call to store the required information
+% on files, lines, and chains to be read
+% in persistent variables to make them available
% for future calls
% 0: load a parameter draw
% Additional Inputs required for initialization
@@ -19,7 +19,7 @@ function [xparams, logpost, options_]=metropolis_draw(init,options_,estim_params
% if init==1: error flaog
% logpost: log of posterior density
% options_: [structure] Matlab's structure describing the options (initialized by dynare, see @ref{options_}).
-%
+%
% SPECIAL REQUIREMENTS
%
% Requires CutSample to be run before in order to set up mh_history-file
@@ -61,9 +61,9 @@ if init
%load mh_history-file with info on what to load
load_last_mh_history_file(MetropolisFolder, FileName);
FirstMhFile = record.KeepedDraws.FirstMhFile;
- FirstLine = record.KeepedDraws.FirstLine;
- TotalNumberOfMhFiles = sum(record.MhDraws(:,2));
- LastMhFile = TotalNumberOfMhFiles;
+ FirstLine = record.KeepedDraws.FirstLine;
+ TotalNumberOfMhFiles = sum(record.MhDraws(:,2));
+ LastMhFile = TotalNumberOfMhFiles;
TotalNumberOfMhDraws = sum(record.MhDraws(:,1));
NumberOfDraws = TotalNumberOfMhDraws-floor(options_.mh_drop*TotalNumberOfMhDraws);
MAX_nruns = ceil(options_.MaxNumberOfBytes/(npar+2)/8); %number of parameters plus posterior plus ?
@@ -82,16 +82,16 @@ if init
end
return
else %not initialization, return one draw
- %get random draw from random chain
+ %get random draw from random chain
ChainNumber = ceil(rand*mh_nblck);
DrawNumber = ceil(rand*NumberOfDraws);
if DrawNumber <= MAX_nruns-FirstLine+1 %draw in first file, needs to account for first line
MhFilNumber = FirstMhFile;
MhLine = FirstLine+DrawNumber-1;
- else %draw in other file
+ else %draw in other file
DrawNumber = DrawNumber-(MAX_nruns-FirstLine+1);
- MhFilNumber = FirstMhFile+ceil(DrawNumber/MAX_nruns);
+ MhFilNumber = FirstMhFile+ceil(DrawNumber/MAX_nruns);
MhLine = DrawNumber-(MhFilNumber-FirstMhFile-1)*MAX_nruns;
end
%load parameters and posterior
diff --git a/matlab/metropolis_hastings_initialization.m b/matlab/metropolis_hastings_initialization.m
index f62cf70233a0c278c80a1c9cebaf621cddde5f82..e9d97a0ea26d956b4f89a22f388823e2ae8dc6ea 100644
--- a/matlab/metropolis_hastings_initialization.m
+++ b/matlab/metropolis_hastings_initialization.m
@@ -3,13 +3,13 @@ function [ ix2, ilogpo2, ModelName, MetropolisFolder, FirstBlock, FirstLine, npa
% function [ ix2, ilogpo2, ModelName, MetropolisFolder, FirstBlock, FirstLine, npar, NumberOfBlocks, nruns, NewFile, MAX_nruns, d ] = ...
% metropolis_hastings_initialization(TargetFun, xparam1, vv, mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_)
% Metropolis-Hastings initialization.
-%
-% INPUTS
+%
+% INPUTS
% o TargetFun [char] string specifying the name of the objective
% function (posterior kernel).
% o xparam1 [double] (p*1) vector of parameters to be estimated (initial values).
% o vv [double] (p*p) matrix, posterior covariance matrix (at the mode).
-% o mh_bounds [double] (p*2) matrix defining lower and upper bounds for the parameters.
+% o mh_bounds [double] (p*2) matrix defining lower and upper bounds for the parameters.
% o dataset_ data structure
% o dataset_info dataset info structure
% o options_ options structure
@@ -17,17 +17,17 @@ function [ ix2, ilogpo2, ModelName, MetropolisFolder, FirstBlock, FirstLine, npa
% o estim_params_ estimated parameters structure
% o bayestopt_ estimation options structure
% o oo_ outputs structure
-%
-% OUTPUTS
+%
+% OUTPUTS
% o ix2 [double] (NumberOfBlocks*npar) vector of starting points for different chains
% o ilogpo2 [double] (NumberOfBlocks*1) vector of initial posterior values for different chains
% o ModelName [string] name of the mod-file
% o MetropolisFolder [string] path to the Metropolis subfolder
-% o FirstBlock [scalar] number of the first MH chain to be run (not equal to 1 in case of recovery)
-% o FirstLine [double] (NumberOfBlocks*1) vector of first draw in each chain (not equal to 1 in case of recovery)
+% o FirstBlock [scalar] number of the first MH chain to be run (not equal to 1 in case of recovery)
+% o FirstLine [double] (NumberOfBlocks*1) vector of first draw in each chain (not equal to 1 in case of recovery)
% o npar [scalar] number of parameters estimated
-% o NumberOfBlocks [scalar] Number of MCM chains requested
-% o nruns [double] (NumberOfBlocks*1) number of draws in each chain
+% o NumberOfBlocks [scalar] Number of MCM chains requested
+% o nruns [double] (NumberOfBlocks*1) number of draws in each chain
% o NewFile [scalar] (NumberOfBlocks*1) vector storing the number
% of the first MH-file to created for each chain when saving additional
% draws
@@ -57,7 +57,7 @@ function [ ix2, ilogpo2, ModelName, MetropolisFolder, FirstBlock, FirstLine, npa
%Initialize outputs
ix2 = [];
ilogpo2 = [];
-ModelName = [];
+ModelName = [];
MetropolisFolder = [];
FirstBlock = [];
FirstLine = [];
@@ -124,7 +124,7 @@ if ~options_.load_mh_file && ~options_.mh_recover
trial = 1;
while validate == 0 && trial <= 10
candidate = rand_multivariate_normal( transpose(xparam1), d * options_.mh_init_scale, npar);
- if all(candidate(:) >= mh_bounds.lb) && all(candidate(:) <= mh_bounds.ub)
+ if all(candidate(:) >= mh_bounds.lb) && all(candidate(:) <= mh_bounds.ub)
ix2(j,:) = candidate;
ilogpo2(j) = - feval(TargetFun,ix2(j,:)',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_);
if ~isfinite(ilogpo2(j)) % if returned log-density is
@@ -167,7 +167,7 @@ if ~options_.load_mh_file && ~options_.mh_recover
else% Case 2: one chain (we start from the posterior mode)
fprintf(fidlog,[' Initial values of the parameters:\n']);
candidate = transpose(xparam1(:));%
- if all(candidate(:) >= mh_bounds.lb) && all(candidate(:) <= mh_bounds.ub)
+ if all(candidate(:) >= mh_bounds.lb) && all(candidate(:) <= mh_bounds.ub)
ix2 = candidate;
ilogpo2 = - feval(TargetFun,ix2',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_);
disp('Estimation::mcmc: Initialization at the posterior mode.')
@@ -281,7 +281,7 @@ elseif options_.load_mh_file && ~options_.mh_recover
NumberOfDrawsWrittenInThePastLastFile = MAX_nruns - LastLineNumber;
NumberOfDrawsToBeSaved = nruns(1) - NumberOfDrawsWrittenInThePastLastFile;
AnticipatedNumberOfFiles = ceil(NumberOfDrawsToBeSaved/MAX_nruns);
- AnticipatedNumberOfLinesInTheLastFile = NumberOfDrawsToBeSaved - (AnticipatedNumberOfFiles-1)*MAX_nruns;
+ AnticipatedNumberOfLinesInTheLastFile = NumberOfDrawsToBeSaved - (AnticipatedNumberOfFiles-1)*MAX_nruns;
record.LastFileNumber = LastFileNumber + AnticipatedNumberOfFiles;
record.LastLineNumber = AnticipatedNumberOfLinesInTheLastFile;
record.MhDraws(end,1) = nruns(1);
@@ -300,15 +300,15 @@ elseif options_.mh_recover
load_last_mh_history_file(MetropolisFolder, ModelName);
NumberOfBlocks = record.Nblck;% Number of "parallel" mcmc chains.
options_.mh_nblck = NumberOfBlocks;
-
+
%% check consistency of options
if record.MhDraws(end,1)~=options_.mh_replic
fprintf('\nEstimation::mcmc: You cannot specify a different mh_replic than in the chain you are trying to recover\n')
fprintf('Estimation::mcmc: I am resetting mh_replic to %u\n',record.MhDraws(end,1))
options_.mh_replic=record.MhDraws(end,1);
nruns = ones(NumberOfBlocks,1)*options_.mh_replic;
- end
-
+ end
+
if ~isnan(record.MAX_nruns(end,1)) %field exists
if record.MAX_nruns(end,1)~=MAX_nruns
fprintf('\nEstimation::mcmc: You cannot specify a different MaxNumberOfBytes than in the chain you are trying to recover\n')
@@ -335,9 +335,9 @@ elseif options_.mh_recover
if OldMhExists
LastLineNumberInThePreviousMh = record.MhDraws(end-1,3);% Number of lines in the last mh files of the previous session.
LastFileNumberInThePreviousMh = sum(record.MhDraws(1:end-1,2),1);% Number of mh files in the the previous sessions.
- %Test if the last mh files of the previous session were not full yet
- if LastLineNumberInThePreviousMh < MAX_nruns%not full
- %store starting point if whole chain needs to be redone
+ %Test if the last mh files of the previous session were not full yet
+ if LastLineNumberInThePreviousMh < MAX_nruns%not full
+ %store starting point if whole chain needs to be redone
NewFile = ones(NumberOfBlocks,1)*LastFileNumberInThePreviousMh;
FirstLine = ones(NumberOfBlocks,1)*(LastLineNumberInThePreviousMh+1);
LastFileFullIndicator=0;
@@ -354,9 +354,9 @@ elseif options_.mh_recover
FirstLine = ones(NumberOfBlocks,1);
LastFileFullIndicator=1;
end
-
+
%% Now find out what exactly needs to be redone
- % 1. Check if really something needs to be done
+ % 1. Check if really something needs to be done
% How many mh files should we have ?
ExpectedNumberOfMhFilesPerBlock = sum(record.MhDraws(:,2),1);
ExpectedNumberOfMhFiles = ExpectedNumberOfMhFilesPerBlock*NumberOfBlocks;
@@ -366,7 +366,7 @@ elseif options_.mh_recover
% Quit if no crashed mcmc chain can be found as there are as many files as expected
if (TotalNumberOfMhFiles==ExpectedNumberOfMhFiles)
disp('Estimation::mcmc: It appears that you don''t need to use the mh_recover option!')
- disp(' You have to edit the mod file and remove the mh_recover option')
+ disp(' You have to edit the mod file and remove the mh_recover option')
disp(' in the estimation command')
error('Estimation::mcmc: mh_recover option not required!')
end
@@ -389,15 +389,15 @@ elseif options_.mh_recover
end
FirstBlock = FirstBlock+1;
end
-
- %% 3. Overwrite default settings for
+
+ %% 3. Overwrite default settings for
% How many mh-files are saved in this block?
NumberOfSavedMhFilesInTheCrashedBlck = NumberOfMhFilesPerBlock(FirstBlock);
ExistingDrawsInLastMCFile=0; %initialize: no MCMC draws of current MCMC are in file from last run
- % Check whether last present file is a file included in the last MCMC run
- if ~LastFileFullIndicator
+ % Check whether last present file is a file included in the last MCMC run
+ if ~LastFileFullIndicator
if NumberOfSavedMhFilesInTheCrashedBlck==NewFile(FirstBlock) %only that last file exists, but no files from current MCMC
- loaded_results=load([BaseName '_mh' int2str(NewFile(FirstBlock)) '_blck' int2str(FirstBlock) '.mat']);
+ loaded_results=load([BaseName '_mh' int2str(NewFile(FirstBlock)) '_blck' int2str(FirstBlock) '.mat']);
%check whether that last file was filled
if size(loaded_results.x2,1)==MAX_nruns %file is full
NewFile(FirstBlock)=NewFile(FirstBlock)+1; %set first file to be created to next one
@@ -407,20 +407,20 @@ elseif options_.mh_recover
ExistingDrawsInLastMCFile=0;
end
end
- elseif LastFileFullIndicator
+ elseif LastFileFullIndicator
ExistingDrawsInLastMCFile=0;
if NumberOfSavedMhFilesInTheCrashedBlck==NewFile(FirstBlock) %only the last file exists, but no files from current MCMC
NewFile(FirstBlock)=NewFile(FirstBlock)+1; %set first file to be created to next one
end
end
-% % Correct the number of saved mh files if the crashed Metropolis was not the first session (so
-% % that NumberOfSavedMhFilesInTheCrashedBlck is the number of saved mh files in the crashed chain
-% % of the current session).
-% if OldMhExists
-% NumberOfSavedMhFilesInTheCrashedBlck = NumberOfSavedMhFilesInTheCrashedBlck - LastFileNumberInThePreviousMh;
-% end
-% NumberOfSavedMhFiles = NumberOfSavedMhFilesInTheCrashedBlck+LastFileNumberInThePreviousMh;
-
+ % % Correct the number of saved mh files if the crashed Metropolis was not the first session (so
+ % % that NumberOfSavedMhFilesInTheCrashedBlck is the number of saved mh files in the crashed chain
+ % % of the current session).
+ % if OldMhExists
+ % NumberOfSavedMhFilesInTheCrashedBlck = NumberOfSavedMhFilesInTheCrashedBlck - LastFileNumberInThePreviousMh;
+ % end
+ % NumberOfSavedMhFiles = NumberOfSavedMhFilesInTheCrashedBlck+LastFileNumberInThePreviousMh;
+
% Correct initial conditions.
if NumberOfSavedMhFilesInTheCrashedBlck<ExpectedNumberOfMhFilesPerBlock
loaded_results=load([BaseName '_mh' int2str(NumberOfSavedMhFilesInTheCrashedBlck) '_blck' int2str(FirstBlock) '.mat']);
diff --git a/matlab/metropolis_run_analysis.m b/matlab/metropolis_run_analysis.m
index 99b589c820b0d2a2aeb086ea7654266650ab29a6..aa4ec29a3eff1b445b2e840b142b5f402364ba2c 100644
--- a/matlab/metropolis_run_analysis.m
+++ b/matlab/metropolis_run_analysis.m
@@ -1,12 +1,12 @@
function metropolis_run_analysis(M,basetopt,j)
%function metropolis_run_analysis(M)
% analizes Metropolis runs
-%
+%
% INPUTS
% M: (struct) Model structure
% basetopt: (struct) Estimated parameters structure
% j: (int) Index of estimated paramter
-%
+%
% OUTPUTS
% none
%
@@ -39,7 +39,7 @@ params = [];
blck = 1;
for i=1:record.LastFileNumber
fname = [M.fname '/metropolis/' M.fname '_mh' int2str(i) '_blck' ...
- int2str(blck) '.mat'];
+ int2str(blck) '.mat'];
if exist(fname,'file')
o=load(fname);
logPost = [logPost; o.logpo2];
diff --git a/matlab/mex/k_order_perturbation.m b/matlab/mex/k_order_perturbation.m
index 53359a9cf50dc448d0e6cbec0131f68ee5569629..7c10d0efdf86ac44d765b331cf6fc9a86d81f4b7 100644
--- a/matlab/mex/k_order_perturbation.m
+++ b/matlab/mex/k_order_perturbation.m
@@ -37,7 +37,7 @@
% The Taylor coefficient (1/6) is
% included. Inside the tensor, the variables are in
% dr.order_var order of state endogenous variables
-% and shocks
+% and shocks
% derivs struct contains the original derivatives of the
% decision function (ghx, ghu, ghxx, ghxu, ghuu,
% ghs2, ghxxx, ghxxu, ghxuu,ghuuu, ghxss, ghuss),
diff --git a/matlab/mex/qmc_sequence.m b/matlab/mex/qmc_sequence.m
index e5405699946722197ff5c5a559442cd1f4a54963..1b934927312f037ee5323a9130d9b6025b304d66 100644
--- a/matlab/mex/qmc_sequence.m
+++ b/matlab/mex/qmc_sequence.m
@@ -16,7 +16,7 @@
%! @sp 1
%! @table @ @samp
%! @item @var{t}=0
-%! Uniform numbers in a n-dimensional (unit by default) hypercube
+%! Uniform numbers in a n-dimensional (unit by default) hypercube
%! @item @var{t}=1
%! Gaussian numbers
%! @item @var{t}=2
@@ -125,7 +125,7 @@
%$
%$ [draws1, S] = qmc_sequence(d,s,0,n,[0 , 2; -1, 2]);
%$ [draws2, Q] = qmc_sequence(d,s,0,n);
-%$
+%$
%$ draws3 = draws2;
%$ draws3(1,:) = 2*draws2(1,:);
%$ draws3(2,:) = 3*draws2(2,:)-1;
diff --git a/matlab/mh_autocorrelation_function.m b/matlab/mh_autocorrelation_function.m
index b14178def48a053324123a5eea68aa87ea61ff5e..377de19786043a2d0f8e0312b8ea6e713f13473f 100644
--- a/matlab/mh_autocorrelation_function.m
+++ b/matlab/mh_autocorrelation_function.m
@@ -1,9 +1,9 @@
function mh_autocorrelation_function(options_,M_,estim_params_,type,blck,name1,name2)
% This function plots the autocorrelation of the sampled draws in the
% posterior distribution.
-%
%
-% INPUTS
+%
+% INPUTS
%
% options_ [structure] Dynare structure.
% M_ [structure] Dynare structure (related to model definition).
@@ -11,11 +11,11 @@ function mh_autocorrelation_function(options_,M_,estim_params_,type,blck,name1,n
% type [string] 'DeepParameter', 'MeasurementError' (for measurement equation error) or 'StructuralShock' (for structural shock).
% blck [integer] Number of the mh chain.
% name1 [string] Object name.
-% name2 [string] Object name.
-%
-% OUTPUTS
+% name2 [string] Object name.
+%
+% OUTPUTS
% None
-%
+%
% SPECIAL REQUIREMENTS
% Copyright (C) 2003-2017 Dynare Team
@@ -36,7 +36,7 @@ function mh_autocorrelation_function(options_,M_,estim_params_,type,blck,name1,n
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
% Cet the column index:
-if nargin<7
+if nargin<7
column = name2index(options_, M_, estim_params_, type, name1);
else
column = name2index(options_, M_, estim_params_, type, name1, name2);
@@ -88,7 +88,7 @@ else
end
if options_.mh_nblck>1
- FigureName = [ FigureName , ' (block number' int2str(blck) ').'];
+ FigureName = [ FigureName , ' (block number' int2str(blck) ').'];
end
hh=dyn_figure(options_.nodisplay,'Name',FigureName);
diff --git a/matlab/mh_optimal_bandwidth.m b/matlab/mh_optimal_bandwidth.m
index c9296ad623e8dc6e6e632b5bc9e6b6434825d225..7d1858285dac7760037e5483e3a94a5c59d44630 100644
--- a/matlab/mh_optimal_bandwidth.m
+++ b/matlab/mh_optimal_bandwidth.m
@@ -1,28 +1,28 @@
-function optimal_bandwidth = mh_optimal_bandwidth(data,number_of_draws,bandwidth,kernel_function)
+function optimal_bandwidth = mh_optimal_bandwidth(data,number_of_draws,bandwidth,kernel_function)
% This function gives the optimal bandwidth parameter of a kernel estimator
-% used to estimate a posterior univariate density from realisations of a
-% Metropolis-Hastings algorithm.
+% used to estimate a posterior univariate density from realisations of a
+% Metropolis-Hastings algorithm.
%
% INPUTS:
% data [double] Vector (number_of_draws*1) of draws.
% number_of_draws [integer] Scalar, number of draws.
-% bandwidth [integer] Scalar equal to 0,-1 or -2.
+% bandwidth [integer] Scalar equal to 0,-1 or -2.
% bandwidth = 0 => Silverman [1986] rule of thumb.
% bandwidth = -1 => Sheather and Jones [1991].
-% bandwidth = -2 => Local bandwith parameters.
+% bandwidth = -2 => Local bandwith parameters.
% kernel_function [string] Name of the kernel function: 'gaussian', 'triweight',
-% 'uniform', 'triangle', 'epanechnikov', 'quartic',
+% 'uniform', 'triangle', 'epanechnikov', 'quartic',
% 'triweight' and 'cosinus'
%
% OUTPUTS:
% optimal_bandwidth: [double] Scalar or vector, optimal window width.
-%
+%
% SPECIAL REQUIREMENTS:
% none.
%
% REFERENCES:
-% [1] M. Skoeld and G.O. Roberts [2003], "Density estimation for the Metropolis-Hastings algorithm".
-% [2] Silverman [1986], "Density estimation for statistics and data analysis".
+% [1] M. Skoeld and G.O. Roberts [2003], "Density estimation for the Metropolis-Hastings algorithm".
+% [2] Silverman [1986], "Density estimation for statistics and data analysis".
% Copyright (C) 2004-2017 Dynare Team
%
@@ -53,30 +53,30 @@ if strcmpi(kernel_function,'gaussian')
k6 = @(x)inv(sqrt(2*pi))*(-15*exp(-0.5*x.^2)+45*(x.^2).*exp(-0.5*x.^2)-15*(x.^4).*exp(-0.5*x.^2)+(x.^6).*exp(-0.5*x.^2));
mu02 = inv(2*sqrt(pi));
mu21 = 1;
-elseif strcmpi(kernel_function,'uniform')
+elseif strcmpi(kernel_function,'uniform')
k = @(x)0.5*(abs(x) <= 1);
mu02 = 0.5;
mu21 = 1/3;
-elseif strcmpi(kernel_function,'triangle')
+elseif strcmpi(kernel_function,'triangle')
k = @(x)(1-abs(x)).*(abs(x) <= 1);
mu02 = 2/3;
mu21 = 1/6;
-elseif strcmpi(kernel_function,'epanechnikov')
+elseif strcmpi(kernel_function,'epanechnikov')
k = @(x)0.75*(1-x.^2).*(abs(x) <= 1);
mu02 = 3/5;
mu21 = 1/5;
-elseif strcmpi(kernel_function,'quartic')
+elseif strcmpi(kernel_function,'quartic')
k = @(x)0.9375*((1-x.^2).^2).*(abs(x) <= 1);
mu02 = 15/21;
mu21 = 1/7;
-elseif strcmpi(kernel_function,'triweight')
+elseif strcmpi(kernel_function,'triweight')
k = @(x)1.09375*((1-x.^2).^3).*(abs(x) <= 1);
k2 = @(x)(105/4*(1-x.^2).*x.^2-105/16*(1-x.^2).^2).*(abs(x) <= 1);
k4 = @(x)(-1575/4*x.^2+315/4).*(abs(x) <= 1);
k6 = @(x)(-1575/2).*(abs(x) <= 1);
mu02 = 350/429;
mu21 = 1/9;
-elseif strcmpi(kernel_function,'cosinus')
+elseif strcmpi(kernel_function,'cosinus')
k = @(x)(pi/4)*cos((pi/2)*x).*(abs(x) <= 1);
k2 = @(x)(-1/16*cos(pi*x/2)*pi^3).*(abs(x) <= 1);
k4 = @(x)(1/64*cos(pi*x/2)*pi^5).*(abs(x) <= 1);
@@ -101,20 +101,20 @@ sigma = std(data);
if bandwidth == 0 % Rule of thumb bandwidth parameter (Silverman [1986].
h = 2*sigma*(sqrt(pi)*mu02/(12*(mu21^2)*number_of_draws))^(1/5);
h = h*correction^(1/5);
-elseif bandwidth == -1 % Sheather and Jones [1991] plug-in estimation of the optimal bandwidth parameter.
- if strcmp(kernel_function,'uniform') || ...
- strcmp(kernel_function,'triangle') || ...
- strcmp(kernel_function,'epanechnikov') || ...
+elseif bandwidth == -1 % Sheather and Jones [1991] plug-in estimation of the optimal bandwidth parameter.
+ if strcmp(kernel_function,'uniform') || ...
+ strcmp(kernel_function,'triangle') || ...
+ strcmp(kernel_function,'epanechnikov') || ...
strcmp(kernel_function,'quartic')
error(['I can''t compute the optimal bandwidth with this kernel...' ...
'Try the gaussian, triweight or cosinus kernels.']);
- end
+ end
Itilda4 = 8*7*6*5/(((2*sigma)^9)*sqrt(pi));
g3 = abs(2*correction*k6(0)/(mu21*Itilda4*number_of_draws))^(1/9);
Ihat3 = 0;
for i=1:number_of_draws
Ihat3 = Ihat3 + sum(k6((data(i,1)-data)/g3));
- end
+ end
Ihat3 = - Ihat3/((number_of_draws^2)*g3^7);
g2 = abs(2*correction*k4(0)/(mu21*Ihat3*number_of_draws))^(1/7);
Ihat2 = 0;
@@ -122,12 +122,12 @@ elseif bandwidth == -1 % Sheather and Jones [1991] plug-in estimation of the opt
Ihat2 = Ihat2 + sum(k4((data(i)-data)/g2));
end
Ihat2 = Ihat2/((number_of_draws^2)*g2^5);
- h = (correction*mu02/(number_of_draws*Ihat2*mu21^2))^(1/5); % equation (22) in Skold and Roberts [2003].
-elseif bandwidth == -2 % Bump killing... I compute local bandwith parameters in order to remove
- % spurious bumps introduced by long rejecting periods.
- if strcmp(kernel_function,'uniform') || ...
- strcmp(kernel_function,'triangle') || ...
- strcmp(kernel_function,'epanechnikov') || ...
+ h = (correction*mu02/(number_of_draws*Ihat2*mu21^2))^(1/5); % equation (22) in Skold and Roberts [2003].
+elseif bandwidth == -2 % Bump killing... I compute local bandwith parameters in order to remove
+ % spurious bumps introduced by long rejecting periods.
+ if strcmp(kernel_function,'uniform') || ...
+ strcmp(kernel_function,'triangle') || ...
+ strcmp(kernel_function,'epanechnikov') || ...
strcmp(kernel_function,'quartic')
error(['I can''t compute the optimal bandwidth with this kernel...' ...
'Try the gaussian, triweight or cosinus kernels.']);
@@ -137,7 +137,7 @@ elseif bandwidth == -2 % Bump killing... I compute local bandwith parameters
j = i;
while j<= n && (data(j,1)-data(i,1))<2*eps
j = j+1;
- end
+ end
T(i) = (j-i);
correction = correction + 2*T(i) - 1;
end
@@ -153,9 +153,9 @@ elseif bandwidth == -2 % Bump killing... I compute local bandwith parameters
Ihat2 = 0;
for i=1:number_of_draws
Ihat2 = Ihat2 + sum(k4((data(i)-data)/g2));
- end
+ end
Ihat2 = Ihat2/((number_of_draws^2)*g2^5);
- h = ((2*T-1)*mu02/(number_of_draws*Ihat2*mu21^2)).^(1/5); % h is a column vector (local banwidth parameters).
+ h = ((2*T-1)*mu02/(number_of_draws*Ihat2*mu21^2)).^(1/5); % h is a column vector (local banwidth parameters).
else
disp('mh_optimal_bandwidth:: ');
error('Parameter bandwidth must be equal to 0, -1 or -2.');
diff --git a/matlab/minus_logged_prior_density.m b/matlab/minus_logged_prior_density.m
index 38662541e1bfb192cbc8f0456b33ca0e1086b9c3..c36fbe2a819b752857caea78ee7dfd235af2de57 100644
--- a/matlab/minus_logged_prior_density.m
+++ b/matlab/minus_logged_prior_density.m
@@ -1,15 +1,15 @@
function [fval,info,exit_flag,fake_1,fake_2] = minus_logged_prior_density(xparams,pshape,p6,p7,p3,p4,DynareOptions,DynareModel,EstimatedParams,DynareResults)
% Evaluates minus the logged prior density.
-%
-% INPUTS
+%
+% INPUTS
% xparams [double] vector of parameters.
% pshape [integer] vector specifying prior densities shapes.
% p6 [double] vector, first hyperparameter.
% p7 [double] vector, second hyperparameter.
% p3 [double] vector, prior's lower bound.
-% p4 [double] vector, prior's upper bound.
+% p4 [double] vector, prior's upper bound.
%
-% OUTPUTS
+% OUTPUTS
% f [double] value of minus the logged prior density.
% info [double] vector: second entry stores penalty, first entry the error code.
%
@@ -128,8 +128,8 @@ M_ = set_all_parameters(xparams,EstimatedParams,DynareModel);
% Return, with endogenous penalty when possible, if dynare_resolve issues an error code (defined in resol).
if info(1)
if info(1) == 3 || info(1) == 4 || info(1) == 5 || info(1)==6 ||info(1) == 19 ...
- info(1) == 20 || info(1) == 21 || info(1) == 23 || info(1) == 26 || ...
- info(1) == 81 || info(1) == 84 || info(1) == 85
+ info(1) == 20 || info(1) == 21 || info(1) == 23 || info(1) == 26 || ...
+ info(1) == 81 || info(1) == 84 || info(1) == 85
%meaningful second entry of output that can be used
fval = Inf;
info(4) = info(2);
@@ -138,7 +138,7 @@ if info(1)
else
fval = Inf;
info(4) = 0.1;
- exit_flag = 0;
+ exit_flag = 0;
return
end
end
diff --git a/matlab/missing/corrcoef/corrcoef.m b/matlab/missing/corrcoef/corrcoef.m
index e997518ac9b6e0237cece625884719cf19a8cb27..722d969c0bacbb43666817c6665de9a63f194f1a 100644
--- a/matlab/missing/corrcoef/corrcoef.m
+++ b/matlab/missing/corrcoef/corrcoef.m
@@ -25,28 +25,28 @@ function [R,sig,ci1,ci2,nan_sig] = corrcoef(X,Y,varargin)
% This replaces RANKCORR.M
%
% [...] = CORRCOEF(..., param1, value1, param2, value2, ... );
-% param value
-% 'Mode' type of correlation
-% 'Pearson','parametric'
-% 'Spearman'
-% 'rank'
-% 'rows' how do deal with missing values encoded as NaN's.
-% 'complete': remove all rows with at least one NaN
-% 'pairwise': [default]
-% 'alpha' 0.01 : significance level to compute confidence interval
+% param value
+% 'Mode' type of correlation
+% 'Pearson','parametric'
+% 'Spearman'
+% 'rank'
+% 'rows' how do deal with missing values encoded as NaN's.
+% 'complete': remove all rows with at least one NaN
+% 'pairwise': [default]
+% 'alpha' 0.01 : significance level to compute confidence interval
%
% [R,p,ci1,ci2,nansig] = CORRCOEF(...);
% R is the correlation matrix
-% R(i,j) is the correlation coefficient r between X(:,i) and Y(:,j)
+% R(i,j) is the correlation coefficient r between X(:,i) and Y(:,j)
% p gives the significance of R
-% It tests the null hypothesis that the product moment correlation coefficient is zero
+% It tests the null hypothesis that the product moment correlation coefficient is zero
% using Student's t-test on the statistic t = r*sqrt(N-2)/sqrt(1-r^2)
% where N is the number of samples (Statistics, M. Spiegel, Schaum series).
% p > alpha: do not reject the Null hypothesis: 'R is zero'.
% p < alpha: The alternative hypothesis 'R is larger than zero' is true with probability (1-alpha).
-% ci1 lower (1-alpha) confidence interval
-% ci2 upper (1-alpha) confidence interval
-% If no alpha is provided, the default alpha is 0.01. This can be changed with function flag_implicit_significance.
+% ci1 lower (1-alpha) confidence interval
+% ci2 upper (1-alpha) confidence interval
+% If no alpha is provided, the default alpha is 0.01. This can be changed with function flag_implicit_significance.
% nan_sig p-value whether H0: 'NaN''s are not correlated' could be correct
% if nan_sig < alpha, H1 ('NaNs are correlated') is very likely.
%
@@ -65,7 +65,7 @@ function [R,sig,ci1,ci2,nan_sig] = corrcoef(X,Y,varargin)
% Further recommandation related to the correlation coefficient:
% + LOOK AT THE SCATTERPLOTS to make sure that the relationship is linear
% + Correlation is not causation because
-% it is not clear which parameter is 'cause' and which is 'effect' and
+% it is not clear which parameter is 'cause' and which is 'effect' and
% the observed correlation between two variables might be due to the action of other, unobserved variables.
%
% see also: SUMSKIPNAN, COVM, COV, COR, SPEARMAN, RANKCORR, RANKS,
@@ -124,54 +124,54 @@ function [R,sig,ci1,ci2,nan_sig] = corrcoef(X,Y,varargin)
global FLAG_NANS_OCCURED;
-NARG = nargout; % needed because nargout is not reentrant in Octave, and corrcoef is recursive
+NARG = nargout; % needed because nargout is not reentrant in Octave, and corrcoef is recursive
mode = [];
if nargin==1
- Y = [];
- Mode='Pearson';
+ Y = [];
+ Mode='Pearson';
elseif nargin==0
- fprintf(2,'Error CORRCOEF: Missing argument(s)\n');
+ fprintf(2,'Error CORRCOEF: Missing argument(s)\n');
elseif nargin>1
- if ischar(Y)
- varg = [Y,varargin];
- Y=[];
- else
- varg = varargin;
- end
+ if ischar(Y)
+ varg = [Y,varargin];
+ Y=[];
+ else
+ varg = varargin;
+ end
- if length(varg)<1
- Mode = 'Pearson';
- elseif length(varg)==1
- Mode = varg{1};
- else
- for k = 2:2:length(varg)
- mode = setfield(mode,lower(varg{k-1}),varg{k});
- end
- if isfield(mode,'mode')
- Mode = mode.mode;
- end
+ if length(varg)<1
+ Mode = 'Pearson';
+ elseif length(varg)==1
+ Mode = varg{1};
+ else
+ for k = 2:2:length(varg)
+ mode = setfield(mode,lower(varg{k-1}),varg{k});
+ end
+ if isfield(mode,'mode')
+ Mode = mode.mode;
end
+ end
end
if isempty(Mode), Mode='pearson'; end
Mode=[Mode,' '];
-FLAG_WARNING = warning; % save warning status
+FLAG_WARNING = warning; % save warning status
warning('off');
[r1,c1]=size(X);
if ~isempty(Y)
- [r2,c2]=size(Y);
- if r1~=r2
- fprintf(2,'Error CORRCOEF: X and Y must have the same number of observations (rows).\n');
- return
- end
- NN = real(~isnan(X)')*real(~isnan(Y));
+ [r2,c2]=size(Y);
+ if r1~=r2
+ fprintf(2,'Error CORRCOEF: X and Y must have the same number of observations (rows).\n');
+ return
+ end
+ NN = real(~isnan(X)')*real(~isnan(Y));
else
- [r2,c2]=size(X);
- NN = real(~isnan(X)')*real(~isnan(X));
+ [r2,c2]=size(X);
+ NN = real(~isnan(X)')*real(~isnan(X));
end
%%%%% generate combinations using indices for pairwise calculation of the correlation
@@ -196,117 +196,117 @@ if YESNAN
end
end
if isempty(Y)
- IX = ones(c1)-diag(ones(c1,1));
- [jx, jy ] = find(IX);
- [jxo,jyo] = find(IX);
+ IX = ones(c1)-diag(ones(c1,1));
+ [jx, jy ] = find(IX);
+ [jxo,jyo] = find(IX);
R = eye(c1);
else
- IX = sparse([],[],[],c1+c2,c1+c2,c1*c2);
- IX(1:c1,c1+(1:c2)) = 1;
- [jx,jy] = find(IX);
+ IX = sparse([],[],[],c1+c2,c1+c2,c1*c2);
+ IX(1:c1,c1+(1:c2)) = 1;
+ [jx,jy] = find(IX);
- IX = ones(c1,c2);
- [jxo,jyo] = find(IX);
+ IX = ones(c1,c2);
+ [jxo,jyo] = find(IX);
R = zeros(c1,c2);
end
if strcmp(lower(Mode(1:7)),'pearson')
- % see http://mathworld.wolfram.com/CorrelationCoefficient.html
+ % see http://mathworld.wolfram.com/CorrelationCoefficient.html
if ~YESNAN
- [S,N,SSQ] = sumskipnan(X,1);
- if ~isempty(Y)
- [S2,N2,SSQ2] = sumskipnan(Y,1);
- CC = X'*Y;
- M1 = S./N;
- M2 = S2./N2;
- cc = CC./NN - M1'*M2;
- R = cc./sqrt((SSQ./N-M1.*M1)'*(SSQ2./N2-M2.*M2));
- else
- CC = X'*X;
- M = S./N;
- cc = CC./NN - M'*M;
- v = SSQ./N - M.*M; %max(N-1,0);
- R = cc./sqrt(v'*v);
- end
+ [S,N,SSQ] = sumskipnan(X,1);
+ if ~isempty(Y)
+ [S2,N2,SSQ2] = sumskipnan(Y,1);
+ CC = X'*Y;
+ M1 = S./N;
+ M2 = S2./N2;
+ cc = CC./NN - M1'*M2;
+ R = cc./sqrt((SSQ./N-M1.*M1)'*(SSQ2./N2-M2.*M2));
else
- if ~isempty(Y)
- X = [X,Y];
- end
- for k = 1:length(jx)
- %ik = ~any(isnan(X(:,[jx(k),jy(k)])),2);
- ik = ~isnan(X(:,jx(k))) & ~isnan(X(:,jy(k)));
- [s,n,s2] = sumskipnan(X(ik,[jx(k),jy(k)]),1);
- v = (s2-s.*s./n)./n;
- cc = X(ik,jx(k))'*X(ik,jy(k));
- cc = cc/n(1) - prod(s./n);
- %r(k) = cc./sqrt(prod(v));
- R(jxo(k),jyo(k)) = cc./sqrt(prod(v));
- end
+ CC = X'*X;
+ M = S./N;
+ cc = CC./NN - M'*M;
+ v = SSQ./N - M.*M; %max(N-1,0);
+ R = cc./sqrt(v'*v);
+ end
+ else
+ if ~isempty(Y)
+ X = [X,Y];
+ end
+ for k = 1:length(jx)
+ %ik = ~any(isnan(X(:,[jx(k),jy(k)])),2);
+ ik = ~isnan(X(:,jx(k))) & ~isnan(X(:,jy(k)));
+ [s,n,s2] = sumskipnan(X(ik,[jx(k),jy(k)]),1);
+ v = (s2-s.*s./n)./n;
+ cc = X(ik,jx(k))'*X(ik,jy(k));
+ cc = cc/n(1) - prod(s./n);
+ %r(k) = cc./sqrt(prod(v));
+ R(jxo(k),jyo(k)) = cc./sqrt(prod(v));
+ end
end
elseif strcmp(lower(Mode(1:4)),'rank')
- % see [ 6] http://mathworld.wolfram.com/SpearmanRankCorrelationCoefficient.html
+ % see [ 6] http://mathworld.wolfram.com/SpearmanRankCorrelationCoefficient.html
if ~YESNAN
- if isempty(Y)
- R = corrcoef(ranks(X));
- else
- R = corrcoef(ranks(X),ranks(Y));
- end
+ if isempty(Y)
+ R = corrcoef(ranks(X));
else
- if ~isempty(Y)
- X = [X,Y];
- end
- for k = 1:length(jx)
- %ik = ~any(isnan(X(:,[jx(k),jy(k)])),2);
- ik = ~isnan(X(:,jx(k))) & ~isnan(X(:,jy(k)));
- il = ranks(X(ik,[jx(k),jy(k)]));
- R(jxo(k),jyo(k)) = corrcoef(il(:,1),il(:,2));
- end
+ R = corrcoef(ranks(X),ranks(Y));
+ end
+ else
+ if ~isempty(Y)
+ X = [X,Y];
+ end
+ for k = 1:length(jx)
+ %ik = ~any(isnan(X(:,[jx(k),jy(k)])),2);
+ ik = ~isnan(X(:,jx(k))) & ~isnan(X(:,jy(k)));
+ il = ranks(X(ik,[jx(k),jy(k)]));
+ R(jxo(k),jyo(k)) = corrcoef(il(:,1),il(:,2));
+ end
X = ranks(X);
end
elseif strcmp(lower(Mode(1:8)),'spearman')
- % see [ 6] http://mathworld.wolfram.com/SpearmanRankCorrelationCoefficient.html
- if ~isempty(Y)
- X = [X,Y];
- end
+ % see [ 6] http://mathworld.wolfram.com/SpearmanRankCorrelationCoefficient.html
+ if ~isempty(Y)
+ X = [X,Y];
+ end
- n = repmat(nan,c1,c2);
+ n = repmat(nan,c1,c2);
- if ~YESNAN
- iy = ranks(X); % calculates ranks;
+ if ~YESNAN
+ iy = ranks(X); % calculates ranks;
- for k = 1:length(jx)
- [R(jxo(k),jyo(k)),n(jxo(k),jyo(k))] = sumskipnan((iy(:,jx(k)) - iy(:,jy(k))).^2); % NN is the number of non-missing values
- end
- else
- for k = 1:length(jx)
- %ik = ~any(isnan(X(:,[jx(k),jy(k)])),2);
- ik = ~isnan(X(:,jx(k))) & ~isnan(X(:,jy(k)));
- il = ranks(X(ik,[jx(k),jy(k)]));
- % NN is the number of non-missing values
- [R(jxo(k),jyo(k)),n(jxo(k),jyo(k))] = sumskipnan((il(:,1) - il(:,2)).^2);
- end
- X = ranks(X);
+ for k = 1:length(jx)
+ [R(jxo(k),jyo(k)),n(jxo(k),jyo(k))] = sumskipnan((iy(:,jx(k)) - iy(:,jy(k))).^2); % NN is the number of non-missing values
end
- R = 1 - 6 * R ./ (n.*(n.*n-1));
+ else
+ for k = 1:length(jx)
+ %ik = ~any(isnan(X(:,[jx(k),jy(k)])),2);
+ ik = ~isnan(X(:,jx(k))) & ~isnan(X(:,jy(k)));
+ il = ranks(X(ik,[jx(k),jy(k)]));
+ % NN is the number of non-missing values
+ [R(jxo(k),jyo(k)),n(jxo(k),jyo(k))] = sumskipnan((il(:,1) - il(:,2)).^2);
+ end
+ X = ranks(X);
+ end
+ R = 1 - 6 * R ./ (n.*(n.*n-1));
elseif strcmp(lower(Mode(1:7)),'partial')
- fprintf(2,'Error CORRCOEF: use PARTCORRCOEF \n',Mode);
+ fprintf(2,'Error CORRCOEF: use PARTCORRCOEF \n',Mode);
- return
+ return
elseif strcmp(lower(Mode(1:7)),'kendall')
- fprintf(2,'Error CORRCOEF: mode ''%s'' not implemented yet.\n',Mode);
+ fprintf(2,'Error CORRCOEF: mode ''%s'' not implemented yet.\n',Mode);
- return
+ return
else
- fprintf(2,'Error CORRCOEF: unknown mode ''%s''\n',Mode);
+ fprintf(2,'Error CORRCOEF: unknown mode ''%s''\n',Mode);
end
if (NARG<2)
- warning(FLAG_WARNING); % restore warning status
- return
+ warning(FLAG_WARNING); % restore warning status
+ return
end
@@ -314,7 +314,7 @@ end
if isfield(mode,'alpha')
alpha = mode.alpha;
elseif exist('flag_implicit_significance','file')
- alpha = flag_implicit_significance;
+ alpha = flag_implicit_significance;
else
alpha = 0.01;
end
@@ -324,36 +324,36 @@ end
% SIGNIFICANCE TEST
R(isnan(R))=0;
tmp = 1 - R.*R;
-tmp(tmp<0) = 0; % prevent tmp<0 i.e. imag(t)~=0
+tmp(tmp<0) = 0; % prevent tmp<0 i.e. imag(t)~=0
t = R.*sqrt(max(NN-2,0)./tmp);
if exist('t_cdf','file')
- sig = t_cdf(t,NN-2);
+ sig = t_cdf(t,NN-2);
elseif exist('tcdf','file')>1
- sig = tcdf(t,NN-2);
+ sig = tcdf(t,NN-2);
else
- fprintf('CORRCOEF: significance test not completed because of missing TCDF-function\n')
- sig = repmat(nan,size(R));
+ fprintf('CORRCOEF: significance test not completed because of missing TCDF-function\n')
+ sig = repmat(nan,size(R));
end
sig = 2 * min(sig,1 - sig);
if NARG<3
warning(FLAG_WARNING); % restore warning status
- return
+ return
end
tmp = R;
-%tmp(ix1 | ix2) = nan; % avoid division-by-zero warning
+%tmp(ix1 | ix2) = nan; % avoid division-by-zero warning
z = log((1+tmp)./(1-tmp))/2; % Fisher transformation [21]
-%sz = 1./sqrt(NN-3); % standard error of z
-sz = sqrt(2)*erfinv(1-alpha)./sqrt(NN-3); % confidence interval for alpha of z
+ %sz = 1./sqrt(NN-3); % standard error of z
+sz = sqrt(2)*erfinv(1-alpha)./sqrt(NN-3); % confidence interval for alpha of z
ci1 = tanh(z-sz);
ci2 = tanh(z+sz);
-%ci1(isnan(ci1))=R(isnan(ci1)); % in case of isnan(ci), the interval limits are exactly the R value
+%ci1(isnan(ci1))=R(isnan(ci1)); % in case of isnan(ci), the interval limits are exactly the R value
%ci2(isnan(ci2))=R(isnan(ci2));
if (NARG<5) || ~YESNAN
@@ -371,16 +371,15 @@ nan_sig(isnan(nan_R)) = nan;
nan_R(isnan(nan_R)) = 0;
if 0, any(nan_sig(:) < alpha)
- tmp = nan_sig(:); % Hack to skip NaN's in MIN(X)
- min_sig = min(tmp(~isnan(tmp))); % Necessary, because Octave returns NaN rather than min(X) for min(NaN,X)
- fprintf(1,'CORRCOFF Warning: Missing Values (i.e. NaNs) are not independent of data (p-value=%f)\n', min_sig);
- fprintf(1,' Its recommended to remove all samples (i.e. rows) with any missing value (NaN).\n');
- fprintf(1,' The null-hypotheses (NaNs are uncorrelated) is rejected for the following parameter pair(s).\n');
- [ix,iy] = find(nan_sig < alpha);
- disp([ix,iy])
+ tmp = nan_sig(:); % Hack to skip NaN's in MIN(X)
+ min_sig = min(tmp(~isnan(tmp))); % Necessary, because Octave returns NaN rather than min(X) for min(NaN,X)
+ fprintf(1,'CORRCOFF Warning: Missing Values (i.e. NaNs) are not independent of data (p-value=%f)\n', min_sig);
+ fprintf(1,' Its recommended to remove all samples (i.e. rows) with any missing value (NaN).\n');
+ fprintf(1,' The null-hypotheses (NaNs are uncorrelated) is rejected for the following parameter pair(s).\n');
+ [ix,iy] = find(nan_sig < alpha);
+ disp([ix,iy])
end
%%%%% ----- end of independence check ------
warning(FLAG_WARNING); % restore warning status
-
diff --git a/matlab/missing/corrcoef/flag_implicit_skip_nan.m b/matlab/missing/corrcoef/flag_implicit_skip_nan.m
index c86580d56693333ee93b632e6d1b1c83ddfddd32..12795f529181bd5466608cd68de225adecf9dd51 100644
--- a/matlab/missing/corrcoef/flag_implicit_skip_nan.m
+++ b/matlab/missing/corrcoef/flag_implicit_skip_nan.m
@@ -1,6 +1,6 @@
function FLAG = flag_implicit_skip_nan(i)
% FLAG_IMPLICIT_SKIP_NAN sets and gets default mode for handling NaNs
-% 1 skips NaN's (the default mode if no mode is set)
+% 1 skips NaN's (the default mode if no mode is set)
% 0 NaNs are propagated; input NaN's give NaN's at the output
%
% FLAG = flag_implicit_skip_nan()
@@ -9,15 +9,15 @@ function FLAG = flag_implicit_skip_nan(i)
% flag_implicit_skip_nan(FLAG) % sets mode
%
% prevFLAG = flag_implicit_skip_nan(nextFLAG)
-% gets previous set FLAG and sets FLAG for the future
+% gets previous set FLAG and sets FLAG for the future
% flag_implicit_skip_nan(prevFLAG)
-% resets FLAG to previous mode
+% resets FLAG to previous mode
%
% It is used in:
-% SUMSKIPNAN, MEDIAN, QUANTILES, TRIMEAN
+% SUMSKIPNAN, MEDIAN, QUANTILES, TRIMEAN
% and affects many other functions like:
-% CENTER, KURTOSIS, MAD, MEAN, MOMENT, RMS, SEM, SKEWNESS,
-% STATISTIC, STD, VAR, ZSCORE etc.
+% CENTER, KURTOSIS, MAD, MEAN, MOMENT, RMS, SEM, SKEWNESS,
+% STATISTIC, STD, VAR, ZSCORE etc.
%
% The mode is stored in the global variable FLAG_implicit_skip_nan
% It is recommended to use flag_implicit_skip_nan(1) as default and
@@ -40,7 +40,7 @@ function FLAG = flag_implicit_skip_nan(i)
% along with this program; if not, write to the Free Software
% Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
-% $Id: flag_implicit_skip_nan.m 8351 2011-06-24 17:35:07Z carandraug $
+% $Id: flag_implicit_skip_nan.m 8351 2011-06-24 17:35:07Z carandraug $
% Copyright (C) 2001-2003,2009 by Alois Schloegl <alois.schloegl@gmail.com>
% Copyright (C) 2014 Dynare Team
% This function is part of the NaN-toolbox
@@ -49,7 +49,7 @@ function FLAG = flag_implicit_skip_nan(i)
persistent FLAG_implicit_skip_nan;
-%% if strcmp(version,'3.6'), FLAG_implicit_skip_nan=(1==1); end; %% hack for the use with Freemat3.6
+%% if strcmp(version,'3.6'), FLAG_implicit_skip_nan=(1==1); end; %% hack for the use with Freemat3.6
%%% set DEFAULT value of FLAG
if isempty(FLAG_implicit_skip_nan)
@@ -63,4 +63,3 @@ if nargin>0
warning('flag_implicit_skipnan(0): You are warned!!! You have turned off skipping NaN in sumskipnan. This is not recommended. Make sure you really know what you do.')
end
end
-
diff --git a/matlab/missing/corrcoef/sumskipnan.m b/matlab/missing/corrcoef/sumskipnan.m
index 1c53fee6d446b3e91183e4ea5943453949e443f8..cdff175d3962a8acec0f274cf70f426a6f62597b 100644
--- a/matlab/missing/corrcoef/sumskipnan.m
+++ b/matlab/missing/corrcoef/sumskipnan.m
@@ -13,13 +13,13 @@ function [o,count,SSQ] = sumskipnan(x, DIM, W)
% [Y,N,SSQ] = sumskipnan(x [,DIM])
% [...] = sumskipnan(x, DIM, W)
%
-% x input data
-% DIM dimension (default: [])
-% empty DIM sets DIM to first non singleton dimension
-% W weight vector for weighted sum, numel(W) must fit size(x,DIM)
-% Y resulting sum
-% N number of valid (not missing) elements
-% SSQ sum of squares
+% x input data
+% DIM dimension (default: [])
+% empty DIM sets DIM to first non singleton dimension
+% W weight vector for weighted sum, numel(W) must fit size(x,DIM)
+% Y resulting sum
+% N number of valid (not missing) elements
+% SSQ sum of squares
%
% the function FLAG_NANS_OCCURED() returns whether any value in x
% is a not-a-number (NaN)
@@ -47,7 +47,7 @@ function [o,count,SSQ] = sumskipnan(x, DIM, W)
% You should have received a copy of the GNU General Public License
% along with this program; If not, see <http://www.gnu.org/licenses/>.
-% $Id: sumskipnan.m 9033 2011-11-08 20:58:07Z schloegl $
+% $Id: sumskipnan.m 9033 2011-11-08 20:58:07Z schloegl $
% Copyright (C) 2000-2005,2009,2011 by Alois Schloegl <alois.schloegl@gmail.com>
% Copyright (C) 2014 Dynare Team
% This function is part of the NaN-toolbox
@@ -57,10 +57,10 @@ function [o,count,SSQ] = sumskipnan(x, DIM, W)
global FLAG_NANS_OCCURED;
if nargin<2
- DIM = [];
+ DIM = [];
end
if nargin<3
- W = [];
+ W = [];
end
% an efficient implementation in C of the following lines
@@ -73,14 +73,14 @@ end
% if ~isnan(i(k))
% { o += x(k);
% count += 1;
-% tmp = x(k)*x(k)
-% o2 += tmp;
-% o3 += tmp.*tmp;
+% tmp = x(k)*x(k)
+% o2 += tmp;
+% o3 += tmp.*tmp;
% };
if isempty(DIM)
- DIM = find(size(x)>1,1);
- if isempty(DIM), DIM = 1; end
+ DIM = find(size(x)>1,1);
+ if isempty(DIM), DIM = 1; end
end
if (DIM<1), DIM = 1; end %% Hack, because min([])=0 for FreeMat v3.5
@@ -169,7 +169,7 @@ end
% count non-NaN's
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if nargout>1
- count = sum(x==x,DIM);
+ count = sum(x==x,DIM);
FLAG_NANS_OCCURED = any(count(:)<size(x,DIM));
end
@@ -180,8 +180,8 @@ x(x~=x) = 0;
o = sum(x,DIM);
if nargout>2
- x = real(x).^2 + imag(x).^2;
- SSQ = sum(x,DIM);
+ x = real(x).^2 + imag(x).^2;
+ SSQ = sum(x,DIM);
end
%!assert(sumskipnan([1,2],1),[1,2])
@@ -189,6 +189,3 @@ end
%!assert(sumskipnan([1,NaN],2),1)
%!assert(sumskipnan([nan,1,4,5]),10)
%!assert(sumskipnan([nan,1,4,5]',1,[3;2;1;0]),6)
-
-
-
diff --git a/matlab/missing/corrcoef/tcdf.m b/matlab/missing/corrcoef/tcdf.m
index 1901c4f7d19a3f4d0526c325ea77b7ca1f94e4c4..38c8379ed6b99488ef42b1f7042ec274defae0a3 100644
--- a/matlab/missing/corrcoef/tcdf.m
+++ b/matlab/missing/corrcoef/tcdf.m
@@ -11,8 +11,8 @@ function p = tcdf(x,n)
% Reference(s):
-% $Id: tcdf.m 9033 2011-11-08 20:58:07Z schloegl $
-% Copyright (C) 2000-2003,2009 by Alois Schloegl <alois.schloegl@gmail.com>
+% $Id: tcdf.m 9033 2011-11-08 20:58:07Z schloegl $
+% Copyright (C) 2000-2003,2009 by Alois Schloegl <alois.schloegl@gmail.com>
% Copyright (C) 2014 Dynare Team
% This is part of the NaN-toolbox. For more details see
% http://pub.ist.ac.at/~schloegl/matlab/NaN/
diff --git a/matlab/missing/ilu/ilu.m b/matlab/missing/ilu/ilu.m
index 4b4fe233829b1ba8d1a7f82a44c670cdbd9d5078..0b8f8df737ca6ec4bea84ebb4362b557a5098134 100644
--- a/matlab/missing/ilu/ilu.m
+++ b/matlab/missing/ilu/ilu.m
@@ -18,20 +18,20 @@ function [L, U, P] = ilu(A, setup)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- if nargout ~= 2
- error('Only two output arguments supported')
- end
- if nargin ~= 2
- error('Only two input arguments supported')
- end
+if nargout ~= 2
+ error('Only two output arguments supported')
+end
+if nargin ~= 2
+ error('Only two input arguments supported')
+end
+
+if isfield(setup, 'milu')
+ if setup.milu == 'off'
+ setup.milu = 0;
+ else
+ error(['Unsupported milu: ' setup.milu ])
+ end
+end
- if isfield(setup, 'milu')
- if setup.milu == 'off'
- setup.milu = 0;
- else
- error(['Unsupported milu: ' setup.milu ])
- end
- end
-
- [L, U] = luinc(A, setup);
+[L, U] = luinc(A, setup);
end
diff --git a/matlab/missing/issquare/issquare.m b/matlab/missing/issquare/issquare.m
index 4d4eb2b572c8a771080d57d35f8ffdc0904131af..dfbfe892591ae3419fd453c89e7827cd1b98d479 100644
--- a/matlab/missing/issquare/issquare.m
+++ b/matlab/missing/issquare/issquare.m
@@ -46,7 +46,7 @@ function i = issquare(A)
d = size(A);
if (length(d)==2) && (d(1)==d(2))
- i = d(1);
+ i = d(1);
else
- i = 0;
+ i = 0;
end
diff --git a/matlab/missing/stats/betacdf.m b/matlab/missing/stats/betacdf.m
index 2ba7cc53ebfd183c7ab454a85a97d26b02e229bd..b15913fbb19d6db8a579a3488a84a799b1a954a7 100644
--- a/matlab/missing/stats/betacdf.m
+++ b/matlab/missing/stats/betacdf.m
@@ -61,4 +61,3 @@ if (any (k))
end
end
-
diff --git a/matlab/missing/stats/betarnd.m b/matlab/missing/stats/betarnd.m
index f27fca4413447169311e3287bb13818a2254baf3..02c9e5ad7397b61e43c7bc171c77a71a001e4ee7 100644
--- a/matlab/missing/stats/betarnd.m
+++ b/matlab/missing/stats/betarnd.m
@@ -1,16 +1,16 @@
function rnd = betarnd(a, b)
% This function produces independent random variates from the Beta distribution.
%
-% INPUTS
+% INPUTS
% a [double] n*1 vector of positive parameters.
% b [double] n*1 vector of positive parameters.
%
-% OUTPUT
+% OUTPUT
% rnd [double] n*1 vector of independent variates from the beta(a,b) distribution.
-% rnd(i) is beta distributed with variance a(i)/(a(i)+b(i)) and
-% variance a(i)b(i)/(a(i)+b(i))^2/(a(i)+b(i)+1).
-%
-% ALGORITHMS
+% rnd(i) is beta distributed with variance a(i)/(a(i)+b(i)) and
+% variance a(i)b(i)/(a(i)+b(i))^2/(a(i)+b(i)+1).
+%
+% ALGORITHMS
% Described and Devroye (1986, chapter 9).
% Copyright (C) 2008-2009 Dynare Team
diff --git a/matlab/missing/stats/corr.m b/matlab/missing/stats/corr.m
index 4f3ebfb7bf572700ce9fb114965530647af94227..6b5d35cbbdda434fb0b2354ef5bcffba618ffeee 100644
--- a/matlab/missing/stats/corr.m
+++ b/matlab/missing/stats/corr.m
@@ -25,7 +25,7 @@ function retval = corr(x, y) % --*-- Unitary tests --*--
%! the correlation between the columns of @var{x}.
%! @end deftypefn
%@eod:
-%
+%
% Notes: - the original Octave code has been rewritten to avoid calling cov, since
% there is a long-standing incompatiblity between Matlab's cov and Octave's cov
% (see https://savannah.gnu.org/bugs/?40751)
@@ -63,12 +63,12 @@ end
% Input validation
if (nargin==1 && ~(isnumeric (x) || islogical (x))) || ...
- (nargin==2 && ~(isnumeric (x) || islogical (x) || isnumeric (y) || islogical (y)))
+ (nargin==2 && ~(isnumeric (x) || islogical (x) || isnumeric (y) || islogical (y)))
error ('corr: X and Y must be numeric matrices or vectors');
end
if (nargin==1 && ~ismatrix(x)) || (nargin==2 && (~ismatrix(y) || ~ismatrix(x)))
-error ('corr: X and Y must be 2-D matrices or vectors');
+ error ('corr: X and Y must be 2-D matrices or vectors');
end
if (nargin == 2)
@@ -82,13 +82,13 @@ if isscalar(x)
if nargin==1
retval = NaN;
else
- retval = NaN(size(y));
+ retval = NaN(size(y));
end
return
end
if nargin==2 && isscalar(y)
- retval = NaN(size(x'));
+ retval = NaN(size(x'));
return
end
diff --git a/matlab/missing/stats/exprnd.m b/matlab/missing/stats/exprnd.m
index 65855db6e37a1c9bf1e840b44259df8b5d438b15..6fc088b77788718f73c60b4e95910aaf634c77b6 100644
--- a/matlab/missing/stats/exprnd.m
+++ b/matlab/missing/stats/exprnd.m
@@ -2,20 +2,20 @@ function rnd = exprnd(a)
% Random samples from the exponential distribution with expectation a
% and variance a^2.
%
-% INPUTS
+% INPUTS
% a [double] m*n matrix of positive parameters
%
-% OUTPUT
+% OUTPUT
% rnd [double] m*n matrix, independent draws from the exponential
-% distribution rnd(j,j) has expectation a(i,j) and
-% variance a(i,j)^2.
+% distribution rnd(j,j) has expectation a(i,j) and
+% variance a(i,j)^2.
%
-% ALGORITHM
+% ALGORITHM
% Inverse transform sampling.
%
% SPECIAL REQUIREMENTS
% None.
-%
+%
% Copyright (C) 2009 Dynare Team
%
diff --git a/matlab/missing/stats/gamrnd.m b/matlab/missing/stats/gamrnd.m
index b75b7868fd987e08ebfea913767ca91630085fed..06caff7f005b731d113b858379ea58ec1f8014f5 100644
--- a/matlab/missing/stats/gamrnd.m
+++ b/matlab/missing/stats/gamrnd.m
@@ -1,16 +1,16 @@
function rnd = gamrnd(a,b,method)
% This function produces independent random variates from the Gamma distribution.
%
-% INPUTS
+% INPUTS
% a [double] n*1 vector of positive parameters.
-% b [double] n*1 vector of positive parameters.
+% b [double] n*1 vector of positive parameters.
% method [string] 'BawensLubranoRichard' or anything else (see below).
%
-% OUTPUT
+% OUTPUT
% rnd [double] n*1 vector of independent variates from the gamma(a,b) distribution.
% rnd(i) is gamma distributed with mean a(i)b(i) and variance a(i)b(i)^2.
-%
-% ALGORITHMS
+%
+% ALGORITHMS
% Described in Bauwens, Lubrano and Richard (1999, page 316) and Devroye (1986, chapter 9).
% Copyright (C) 2006-2011 Dynare Team
@@ -39,13 +39,13 @@ if nargin==2
if any(a<1)
method = 'Devroye';
Devroye.small = 'Best'; % 'Weibull' , 'Johnk' , 'Berman' , 'GS' , 'Best'
- % REMARK: The first algorithm (Weibull) is producing too much extreme values.
+ % REMARK: The first algorithm (Weibull) is producing too much extreme values.
end
if ~strcmpi(method,'BauwensLubranoRichard')
- Devroye.big = 'Best'; % 'Cheng' , 'Best'
+ Devroye.big = 'Best'; % 'Cheng' , 'Best'
% REMARK 1: The first algorithm (Cheng) is still producing obviously wrong simulations.
- % REMARK 2: The second algorithm seems slightly slower than the algorithm advocated by Bauwens,
- % Lubrano and Richard, but the comparison depends on the value of a (this should be
+ % REMARK 2: The second algorithm seems slightly slower than the algorithm advocated by Bauwens,
+ % Lubrano and Richard, but the comparison depends on the value of a (this should be
% investigated further).
end
else
@@ -96,7 +96,7 @@ if number_of_double_a
if strcmpi(method,'BauwensLubranoRichard')
% Algorithm given in Bauwens, Lubrano & Richard (1999) page 316.
rnd(double_idx) = knuth_algorithm(a(double_idx),b(double_idx));
- else% Algorithm given in Devroye (1986, chapter 9)
+ else% Algorithm given in Devroye (1986, chapter 9)
small_idx = find(a(double_idx)<1);
big_idx = find(a(double_idx)>1);
number_of_small_a = length(small_idx);
@@ -180,7 +180,7 @@ end
gamma_variates = exprnd(ones(nn,1)).*X./(X+Y);
-function gamma_variates = berman_algorithm(a,b)
+function gamma_variates = berman_algorithm(a,b)
nn = length(a);
mm = nn;
aa = 1./a ;
@@ -206,7 +206,7 @@ Z = gamrnd(2*ones(nn,1),ones(nn,1));
gamma_variates = Z.*X.*b ;
-function gamma_variates = ahrens_dieter_algorithm(a,b)
+function gamma_variates = ahrens_dieter_algorithm(a,b)
nn = length(a);
mm = nn;
bb = (exp(1)+a)/exp(1);
@@ -246,7 +246,7 @@ end
gamma_variates = X.*b ;
-function gamma_variates = best_1983_algorithm(a,b)
+function gamma_variates = best_1983_algorithm(a,b)
nn = length(a);
mm = nn;
tt = .07 + .75*sqrt(1-a);
diff --git a/matlab/missing/stats/quantile.m b/matlab/missing/stats/quantile.m
index ec7dbfa64f08b7eb188cdc7096452ab0f0cc0798..2c0c683f92d39308b62cfeff07069a2df38ba7a1 100644
--- a/matlab/missing/stats/quantile.m
+++ b/matlab/missing/stats/quantile.m
@@ -8,18 +8,18 @@ function [q,N] = quantile(X, p, dim, method, weights) % --*-- Unitary tests --*-
% a matrix, the i-th row of Q contains the P(i)-th quantiles of each
% column of X. For N-D arrays, QUANTILE2 operates along the first
% non-singleton dimension.
-%
+%
% Q = QUANTILE2(X,P,DIM) calculates quantiles along dimension DIM. The
% DIM'th dimension of Q has length LENGTH(P).
-%
+%
% Q = QUANTILE2(X,P,DIM,METHOD) calculates quantiles using one of the
% methods described in http://en.wikipedia.org/wiki/Quantile. The method
% are designated 'R-1'...'R-9'; the default is R-8 as described in
% http://bit.ly/1kX4NcT, whereas Matlab uses 'R-5'.
-%
+%
% Q = QUANTILE2(X,P,[],METHOD) uses the specified METHOD, but calculates
% quantiles along the first non-singleton dimension.
-%
+%
% Q = QUANTILE2(X,P,[],METHOD,WEIGHTS) and QUANTILE2(X,P,[],[],WEIGHTS)
% uses the array WEIGHTS to weight the values in X when calculating
% quantiles. If no weighting is specified, the method determines the
@@ -33,12 +33,12 @@ function [q,N] = quantile(X, p, dim, method, weights) % --*-- Unitary tests --*-
% equal, then the weighted and unweighted index (and correpsonding
% quantile) are identical. The default method R-8 is used if METHOD is
% specified as an empty array ([]).
-%
+%
% [Q,N] = QUANTILE2(...) returns an array that is the same size as Q such
% that N(i) is the number of points used to calculate Q(i).
-%
+%
% Further reading
-%
+%
% Hyndman, R.J.; Fan, Y. (November 1996). "Sample Quantiles in
% Statistical Packages". The American Statistician 50 (4): 361-365.
% Frigge, Michael; Hoaglin, David C.; Iglewicz, Boris (February 1989).
@@ -47,8 +47,8 @@ function [q,N] = quantile(X, p, dim, method, weights) % --*-- Unitary tests --*-
% Original file downloaded from:
% http://fr.mathworks.com/matlabcentral/fileexchange/46555-quantile-calculation
-%
-% Copyright (C) 2014-2016 University of Surrey (Christopher Hummersone)
+%
+% Copyright (C) 2014-2016 University of Surrey (Christopher Hummersone)
% Copyright (C) 2016 Dynare Team
%
% This file is part of Dynare.
@@ -205,34 +205,34 @@ if numel(p)==numel(q)
end
function cumweights = accumulateWeights(weights, ind, n, N)
- % ACCUMULATEWEIGHTS accumulate the weights
- wSorted = weights(ind,n); % sort weights
- wSorted = wSorted*N/sum(wSorted); % normalize weights to sum to N
- cumweights = cumsum(wSorted); % cumulative weights
+% ACCUMULATEWEIGHTS accumulate the weights
+wSorted = weights(ind,n); % sort weights
+wSorted = wSorted*N/sum(wSorted); % normalize weights to sum to N
+cumweights = cumsum(wSorted); % cumulative weights
function hw = weightedIndex(huw, cumweights)
- % WEIGHTEDINDEX calculate index from cumulative weights
- ii = find(sign(cumweights-huw)<0,1,'last');
- jj = find(sign(cumweights-huw)>0,1,'first');
- if isempty(ii) || isempty(jj)
- hw = huw;
- else
- hw = ii + (huw-cumweights(ii))/(cumweights(jj)-cumweights(ii)); % weighted index
- end
+% WEIGHTEDINDEX calculate index from cumulative weights
+ii = find(sign(cumweights-huw)<0,1,'last');
+jj = find(sign(cumweights-huw)>0,1,'first');
+if isempty(ii) || isempty(jj)
+ hw = huw;
+else
+ hw = ii + (huw-cumweights(ii))/(cumweights(jj)-cumweights(ii)); % weighted index
+end
function y = isint(x)
- % ISINT check if input is whole number
- y = x==round(x);
+% ISINT check if input is whole number
+y = x==round(x);
function y = rearrange(x,order,shape)
- %REARRANGE reshape and permute to make target dim column
- y = permute(x,order);
- y = reshape(y,shape);
+%REARRANGE reshape and permute to make target dim column
+y = permute(x,order);
+y = reshape(y,shape);
function y = irearrange(x,order,shape)
- %IREARRANGE reshape and permute to original size
- y = reshape(x,shape);
- y = ipermute(y,order);
+%IREARRANGE reshape and permute to original size
+y = reshape(x,shape);
+y = ipermute(y,order);
%@test:1
diff --git a/matlab/missing/stats/stdnormal_inv.m b/matlab/missing/stats/stdnormal_inv.m
index bc52845a562c105858abe3cf38baecbd81471167..44cdebe1ff5205a8b34a316799f81a26a988e33f 100644
--- a/matlab/missing/stats/stdnormal_inv.m
+++ b/matlab/missing/stats/stdnormal_inv.m
@@ -34,4 +34,3 @@ end
inv = sqrt (2) * erfinv (2 * x - 1);
end
-
diff --git a/matlab/missing/stats/wblcdf.m b/matlab/missing/stats/wblcdf.m
index 14b0fc8e8fdeb85c8ffed3fd582ef844a933f2f9..701abe964e5bc3499bdcbd79a94045050d384d65 100644
--- a/matlab/missing/stats/wblcdf.m
+++ b/matlab/missing/stats/wblcdf.m
@@ -2,13 +2,13 @@ function p = wblcdf(x, scale, shape) % --*-- Unitary tests --*--
% Cumulative distribution function for the Weibull distribution.
%
-% INPUTS
+% INPUTS
% - x [double] Positive real scalar.
% - scale [double] Positive hyperparameter.
% - shape [double] Positive hyperparameter.
%
-% OUTPUTS
-% - p [double] Positive scalar between
+% OUTPUTS
+% - p [double] Positive scalar between
% Copyright (C) 2015 Dynare Team
%
@@ -58,7 +58,7 @@ if isinf(x)
return
end
-% Evaluate the CDF.
+% Evaluate the CDF.
p = 1-exp(-(x/scale)^shape);
@@ -146,4 +146,3 @@ p = 1-exp(-(x/scale)^shape);
%$ end
%$ T = all(t);
%@eof:4
-
diff --git a/matlab/missing/stats/wblinv.m b/matlab/missing/stats/wblinv.m
index 42d83ffe9467a330081dd1043a5473b959745f12..363d5180192bdb0262bad5c57f743790dc4dadca 100644
--- a/matlab/missing/stats/wblinv.m
+++ b/matlab/missing/stats/wblinv.m
@@ -2,12 +2,12 @@ function t = wblinv(proba, scale, shape) % --*-- Unitary tests --*--
% Inverse cumulative distribution function.
%
-% INPUTS
+% INPUTS
% - proba [double] Probability, scalar between 0 and 1.
% - scale [double] Positive hyperparameter.
% - shape [double] Positive hyperparameter.
%
-% OUTPUTS
+% OUTPUTS
% - t [double] scalar such that P(X<=t)=proba
% Copyright (C) 2015-2017 Dynare Team
@@ -156,7 +156,7 @@ t = exp(log(scale)+log(-log(1-proba))/shape);
%$ [s, abs(p-s)]
%$ end
%$ if isoctave
-%$ t(k) = abs(p-s)<1e-10;
+%$ t(k) = abs(p-s)<1e-10;
%$ else
%$ t(k) = abs(p-s)<1e-12;
%$ end
@@ -165,4 +165,3 @@ t = exp(log(scale)+log(-log(1-proba))/shape);
%$ end
%$ T = all(t);
%@eof:4
-
diff --git a/matlab/missing/stats/wblrnd.m b/matlab/missing/stats/wblrnd.m
index 2f55bc8b7709286efa2b94c9a43b8001e7be0275..ca31efa50c53ae105c660b8811fe8fe37249c0aa 100644
--- a/matlab/missing/stats/wblrnd.m
+++ b/matlab/missing/stats/wblrnd.m
@@ -1,11 +1,11 @@
function rnd = wblrnd(a, b)
% This function produces independent random variates from the Weibull distribution.
%
-% INPUTS
+% INPUTS
% a [double] m*n matrix of positive parameters (scale).
% b [double] m*n matrix of positive parameters (shape).
%
-% OUTPUT
+% OUTPUT
% rnd [double] m*n matrix of independent variates from the beta(a,b) distribution.
% Copyright (C) 2015 Dynare Team
diff --git a/matlab/missing/struct2array/struct2array.m b/matlab/missing/struct2array/struct2array.m
index b1c1370dd3b9e475c13cec32636aef38e6406215..92aaab35fe5801fe68ec66cc5b3a12551d0a7882 100644
--- a/matlab/missing/struct2array/struct2array.m
+++ b/matlab/missing/struct2array/struct2array.m
@@ -1,9 +1,9 @@
function a = struct2array(s)
-% INPUTS
+% INPUTS
% - s [struct] with N fields, field i contains a n_i*m_i array of doubles.
%
-% OUPUTS
+% OUPUTS
% - a [double] column vector with sum(n_i*m_i, i=1,...,N) elements.
% Copyright (C) 2017 Dynare Team
diff --git a/matlab/missing/vec/vec.m b/matlab/missing/vec/vec.m
index 225c5f9ec1e910bda2bd0f15013aa479f73e94a1..8565f86a4e306fe365f023431242378533588d40 100644
--- a/matlab/missing/vec/vec.m
+++ b/matlab/missing/vec/vec.m
@@ -1,10 +1,10 @@
function Vector = vec(Matrix)
% This function implements the vec operator.
-%
-% INPUTS
+%
+% INPUTS
% Matrix [double] a squared n*n matrix.
-%
-% OUTPUTS
+%
+% OUTPUTS
% Vector [double] a n*n vector.
% Copyright (C) 2009 Dynare Team
diff --git a/matlab/missing_DiffuseKalmanSmootherH1_Z.m b/matlab/missing_DiffuseKalmanSmootherH1_Z.m
index a19d3fff6392455391fc18d332add47d3d04d14e..5950f03d28a056551b38ec12e9164e4e689f8cf2 100644
--- a/matlab/missing_DiffuseKalmanSmootherH1_Z.m
+++ b/matlab/missing_DiffuseKalmanSmootherH1_Z.m
@@ -5,10 +5,10 @@ function [alphahat,epsilonhat,etahat,atilde,P,aK,PK,decomp,V] = missing_DiffuseK
%
% INPUTS
% T: mm*mm matrix
-% Z: pp*mm matrix
+% Z: pp*mm matrix
% R: mm*rr matrix
% Q: rr*rr matrix
-% H: pp*pp matrix variance of measurement errors
+% H: pp*pp matrix variance of measurement errors
% Pinf1: mm*mm diagonal matrix with with q ones and m-q zeros
% Pstar1: mm*mm variance-covariance matrix with stationary variables
% Y: pp*1 vector
@@ -22,7 +22,7 @@ function [alphahat,epsilonhat,etahat,atilde,P,aK,PK,decomp,V] = missing_DiffuseK
% decomp_flag if true, compute filter decomposition
% state_uncertainty_flag if true, compute uncertainty about smoothed
% state estimate
-%
+%
% OUTPUTS
% alphahat: smoothed variables (a_{t|T})
% epsilonhat:smoothed measurement errors
@@ -36,16 +36,16 @@ function [alphahat,epsilonhat,etahat,atilde,P,aK,PK,decomp,V] = missing_DiffuseK
% matrices (meaningless for periods 1:d)
% decomp: decomposition of the effect of shocks on filtered values
% V: 3D array of state uncertainty matrices
-%
+%
% Notes:
% Outputs are stored in decision-rule order, i.e. to get variables in order of declaration
% as in M_.endo_names, ones needs code along the lines of:
% variables_declaration_order(dr.order_var,:) = alphahat
-%
+%
% SPECIAL REQUIREMENTS
% See "Filtering and Smoothing of State Vector for Diffuse State Space
-% Models", S.J. Koopman and J. Durbin (2003, in Journal of Time Series
-% Analysis, vol. 24(1), pp. 85-98).
+% Models", S.J. Koopman and J. Durbin (2003, in Journal of Time Series
+% Analysis, vol. 24(1), pp. 85-98).
% Durbin/Koopman (2012): "Time Series Analysis by State Space Methods", Oxford University Press,
% Second Edition, Ch. 5
@@ -70,7 +70,7 @@ function [alphahat,epsilonhat,etahat,atilde,P,aK,PK,decomp,V] = missing_DiffuseK
% new output argument aK (1-step to k-step predictions)
% new options_.nk: the max step ahed prediction in aK (default is 4)
% new crit1 value for rank of Pinf
-% it is assured that P is symmetric
+% it is assured that P is symmetric
d = 0;
decomp = [];
@@ -92,9 +92,9 @@ Lstar = zeros(mm,mm,smpl);
Kstar = zeros(mm,pp,smpl);
Kinf = zeros(mm,pp,smpl);
P = zeros(mm,mm,smpl+1);
-Pstar = zeros(spstar(1),spstar(2),smpl+1);
+Pstar = zeros(spstar(1),spstar(2),smpl+1);
Pstar(:,:,1) = Pstar1;
-Pinf = zeros(spinf(1),spinf(2),smpl+1);
+Pinf = zeros(spinf(1),spinf(2),smpl+1);
Pinf(:,:,1) = Pinf1;
rr = size(Q,1);
QQ = R*Q*transpose(R);
@@ -127,9 +127,9 @@ while rank(Pinf(:,:,t+1),diffuse_kalman_tol) && t<smpl
ZZ = Z(di,:); %span selector matrix
v(di,t)= Y(di,t) - ZZ*a(:,t); %get prediction error v^(0) in (5.13) DK (2012)
Finf = ZZ*Pinf(:,:,t)*ZZ'; % (5.7) in DK (2012)
- if rcond(Finf) < diffuse_kalman_tol %F_{\infty,t} = 0
+ if rcond(Finf) < diffuse_kalman_tol %F_{\infty,t} = 0
if ~all(abs(Finf(:)) < diffuse_kalman_tol) %rank-deficient but not rank 0
- % The univariate diffuse kalman filter should be used.
+ % The univariate diffuse kalman filter should be used.
alphahat = Inf;
return
else %rank of F_{\infty,t} is 0
@@ -200,13 +200,13 @@ while notsteady && t<smpl
if any(diag(F)<kalman_tol) || rcond(F./(sig*sig')) < kalman_tol
alphahat = Inf;
return
- end
+ end
iF(di,di,t) = inv(F./(sig*sig'))./(sig*sig');
PZI = P(:,:,t)*ZZ'*iF(di,di,t);
atilde(:,t) = a(:,t) + PZI*v(di,t);
K(:,di,t) = T*PZI;
L(:,:,t) = T-K(:,di,t)*ZZ;
- P(:,:,t+1) = T*P(:,:,t)*L(:,:,t)' + QQ;
+ P(:,:,t+1) = T*P(:,:,t)*L(:,:,t)' + QQ;
end
a(:,t+1) = T*atilde(:,t);
Pf = P(:,:,t);
@@ -267,8 +267,8 @@ while t>d+1
end
end
if d %diffuse periods
- % initialize r_d^(0) and r_d^(1) as below DK (2012), eq. 5.23
- r0 = zeros(mm,d+1);
+ % initialize r_d^(0) and r_d^(1) as below DK (2012), eq. 5.23
+ r0 = zeros(mm,d+1);
r0(:,d+1) = r(:,d+1); %set r0_{d}, i.e. shifted by one period
r1 = zeros(mm,d+1); %set r1_{d}, i.e. shifted by one period
if state_uncertainty_flag
@@ -284,7 +284,7 @@ if d %diffuse periods
if ~Finf_singular(1,t)
r0(:,t) = Linf(:,:,t)'*r0(:,t+1); % DK (2012), eq. 5.21 where L^(0) is named Linf
r1(:,t) = Z(di,:)'*(iFinf(di,di,t)*v(di,t)-Kstar(:,di,t)'*T'*r0(:,t+1)) ...
- + Linf(:,:,t)'*r1(:,t+1); % DK (2012), eq. 5.21, noting that i) F^(1)=(F^Inf)^(-1)(see 5.10), ii) where L^(0) is named Linf, and iii) Kstar=T^{-1}*K^(1)
+ + Linf(:,:,t)'*r1(:,t+1); % DK (2012), eq. 5.21, noting that i) F^(1)=(F^Inf)^(-1)(see 5.10), ii) where L^(0) is named Linf, and iii) Kstar=T^{-1}*K^(1)
if state_uncertainty_flag
L_1=(-T*Kstar(:,di,t)*Z(di,:)); % noting that Kstar=T^{-1}*K^(1)
N(:,:,t)=Linf(:,:,t)'*N(:,:,t+1)*Linf(:,:,t); % DK (2012), eq. 5.19, noting that L^(0) is named Linf
@@ -301,7 +301,7 @@ if d %diffuse periods
r1(:,t) = T'*r1(:,t+1); % DK (2003), eq. (14)
if state_uncertainty_flag
N(:,:,t)=Z(di,:)'*iFstar(di,di,t)*Z(di,:)...
- +Lstar(:,:,t)'*N(:,:,t+1)*Lstar(:,:,t); % DK (2003), eq. (14)
+ +Lstar(:,:,t)'*N(:,:,t+1)*Lstar(:,:,t); % DK (2003), eq. (14)
N_1(:,:,t)=T'*N_1(:,:,t+1)*Lstar(:,:,t); % DK (2003), eq. (14)
N_2(:,:,t)=T'*N_2(:,:,t+1)*T'; % DK (2003), eq. (14)
end
@@ -311,9 +311,9 @@ if d %diffuse periods
etahat(:,t) = QRt*r0(:,t); % DK (2012), p. 135
if state_uncertainty_flag
V(:,:,t)=Pstar(:,:,t)-Pstar(:,:,t)*N(:,:,t)*Pstar(:,:,t)...
- -(Pinf(:,:,t)*N_1(:,:,t)*Pstar(:,:,t))'...
- - Pinf(:,:,t)*N_1(:,:,t)*Pstar(:,:,t)...
- - Pinf(:,:,t)*N_2(:,:,t)*Pinf(:,:,t); % DK (2012), eq. 5.30
+ -(Pinf(:,:,t)*N_1(:,:,t)*Pstar(:,:,t))'...
+ - Pinf(:,:,t)*N_1(:,:,t)*Pstar(:,:,t)...
+ - Pinf(:,:,t)*N_2(:,:,t)*Pinf(:,:,t); % DK (2012), eq. 5.30
end
end
end
diff --git a/matlab/missing_DiffuseKalmanSmootherH3_Z.m b/matlab/missing_DiffuseKalmanSmootherH3_Z.m
index 463d7f1aaea947204afe3efddf6f6d6cfd23fd18..de40741fcf29f4d0c203c1d61b6c12437700e9a6 100644
--- a/matlab/missing_DiffuseKalmanSmootherH3_Z.m
+++ b/matlab/missing_DiffuseKalmanSmootherH3_Z.m
@@ -43,18 +43,18 @@ function [alphahat,epsilonhat,etahat,a,P,aK,PK,decomp,V] = missing_DiffuseKalman
% variables_declaration_order(dr.order_var,:) = alphahat
%
% Algorithm:
-%
+%
% Uses the univariate filter as described in Durbin/Koopman (2012): "Time
% Series Analysis by State Space Methods", Oxford University Press,
% Second Edition, Ch. 6.4 + 7.2.5
-% and
+% and
% Koopman/Durbin (2000): "Fast Filtering and Smoothing for Multivariatze State Space
% Models", in Journal of Time Series Analysis, vol. 21(3), pp. 281-296.
%
% SPECIAL REQUIREMENTS
% See "Filtering and Smoothing of State Vector for Diffuse State Space
-% Models", S.J. Koopman and J. Durbin (2003), in Journal of Time Series
-% Analysis, vol. 24(1), pp. 85-98.
+% Models", S.J. Koopman and J. Durbin (2003), in Journal of Time Series
+% Analysis, vol. 24(1), pp. 85-98.
% Copyright (C) 2004-2017 Dynare Team
%
@@ -78,10 +78,10 @@ function [alphahat,epsilonhat,etahat,a,P,aK,PK,decomp,V] = missing_DiffuseKalman
% New input argument nk: max order of predictions in aK
-if size(H,2)>1
+if size(H,2)>1
error('missing_DiffuseKalmanSmootherH3_Z:: H is not a vector. This must not happens')
end
-
+
d = 0;
decomp = [];
spinf = size(Pinf1);
@@ -100,9 +100,9 @@ Kinf = zeros(spstar(1),pp,smpl);
P = zeros(mm,mm,smpl+1);
P1 = P;
PK = zeros(nk,mm,mm,smpl+nk);
-Pstar = zeros(spstar(1),spstar(2),smpl);
+Pstar = zeros(spstar(1),spstar(2),smpl);
Pstar(:,:,1) = Pstar1;
-Pinf = zeros(spinf(1),spinf(2),smpl);
+Pinf = zeros(spinf(1),spinf(2),smpl);
Pinf(:,:,1) = Pinf1;
Pstar1 = Pstar;
Pinf1 = Pinf;
@@ -135,13 +135,13 @@ while newRank && t < smpl
di = data_index{t}';
for i=di
Zi = Z(i,:);
- v(i,t) = Y(i,t)-Zi*a(:,t); % nu_{t,i} in 6.13 in DK (2012)
+ v(i,t) = Y(i,t)-Zi*a(:,t); % nu_{t,i} in 6.13 in DK (2012)
Fstar(i,t) = Zi*Pstar(:,:,t)*Zi' +H(i); % F_{*,t} in 5.7 in DK (2012), relies on H being diagonal
Finf(i,t) = Zi*Pinf(:,:,t)*Zi'; % F_{\infty,t} in 5.7 in DK (2012)
Kstar(:,i,t) = Pstar(:,:,t)*Zi'; % KD (2000), eq. (15)
if Finf(i,t) > diffuse_kalman_tol && newRank % F_{\infty,t,i} = 0, use upper part of bracket on p. 175 DK (2012) for w_{t,i}
icc=icc+1;
- Kinf(:,i,t) = Pinf(:,:,t)*Zi'; % KD (2000), eq. (15)
+ Kinf(:,i,t) = Pinf(:,:,t)*Zi'; % KD (2000), eq. (15)
Kinf_Finf = Kinf(:,i,t)/Finf(i,t);
a(:,t) = a(:,t) + Kinf_Finf*v(i,t); % KD (2000), eq. (16)
Pstar(:,:,t) = Pstar(:,:,t) + ...
@@ -149,14 +149,14 @@ while newRank && t < smpl
Kstar(:,i,t)*Kinf_Finf' - ...
Kinf_Finf*Kstar(:,i,t)'; % KD (2000), eq. (16)
Pinf(:,:,t) = Pinf(:,:,t) - Kinf(:,i,t)*Kinf(:,i,t)'/Finf(i,t); % KD (2000), eq. (16)
- elseif Fstar(i,t) > kalman_tol
+ elseif Fstar(i,t) > kalman_tol
a(:,t) = a(:,t) + Kstar(:,i,t)*v(i,t)/Fstar(i,t); % KD (2000), eq. (17)
Pstar(:,:,t) = Pstar(:,:,t) - Kstar(:,i,t)*Kstar(:,i,t)'/Fstar(i,t); % KD (2000), eq. (17)
- % Pinf is passed through unaltered, see eq. (17) of
- % Koopman/Durbin (2000)
+ % Pinf is passed through unaltered, see eq. (17) of
+ % Koopman/Durbin (2000)
else
% do nothing as a_{t,i+1}=a_{t,i} and P_{t,i+1}=P_{t,i}, see
- % p. 157, DK (2012)
+ % p. 157, DK (2012)
end
end
if newRank
@@ -164,12 +164,12 @@ while newRank && t < smpl
oldRank = rank(Z*Pinf(:,:,t)*Z',diffuse_kalman_tol);
else
oldRank = rank(Pinf(:,:,t),diffuse_kalman_tol);
- end
+ end
else
oldRank = 0;
end
a1(:,t+1) = T*a(:,t);
- aK(1,:,t+1) = a1(:,t+1);
+ aK(1,:,t+1) = a1(:,t+1);
for jnk=2:nk
aK(jnk,:,t+jnk) = T*dynare_squeeze(aK(jnk-1,:,t+jnk-1));
end
@@ -183,7 +183,7 @@ while newRank && t < smpl
end
end
if oldRank ~= newRank
- disp('univariate_diffuse_kalman_filter:: T does influence the rank of Pinf!')
+ disp('univariate_diffuse_kalman_filter:: T does influence the rank of Pinf!')
end
end
@@ -206,7 +206,7 @@ while notsteady && t<smpl
for i=di
Zi = Z(i,:);
v(i,t) = Y(i,t) - Zi*a(:,t); % nu_{t,i} in 6.13 in DK (2012)
- Fi(i,t) = Zi*P(:,:,t)*Zi' + H(i); % F_{t,i} in 6.13 in DK (2012), relies on H being diagonal
+ Fi(i,t) = Zi*P(:,:,t)*Zi' + H(i); % F_{t,i} in 6.13 in DK (2012), relies on H being diagonal
Ki(:,i,t) = P(:,:,t)*Zi'; % K_{t,i}*F_(i,t) in 6.13 in DK (2012)
if Fi(i,t) > kalman_tol
a(:,t) = a(:,t) + Ki(:,i,t)*v(i,t)/Fi(i,t); %filtering according to (6.13) in DK (2012)
@@ -216,18 +216,18 @@ while notsteady && t<smpl
% p. 157, DK (2012)
end
end
- a1(:,t+1) = T*a(:,t); %transition according to (6.14) in DK (2012)
+ a1(:,t+1) = T*a(:,t); %transition according to (6.14) in DK (2012)
Pf = P(:,:,t);
- aK(1,:,t+1) = a1(:,t+1);
+ aK(1,:,t+1) = a1(:,t+1);
for jnk=1:nk
Pf = T*Pf*T' + QQ;
PK(jnk,:,:,t+jnk) = Pf;
if jnk>1
- aK(jnk,:,t+jnk) = T*dynare_squeeze(aK(jnk-1,:,t+jnk-1));
+ aK(jnk,:,t+jnk) = T*dynare_squeeze(aK(jnk-1,:,t+jnk-1));
end
end
- P(:,:,t+1) = T*P(:,:,t)*T' + QQ; %transition according to (6.14) in DK (2012)
- % notsteady = ~(max(max(abs(P(:,:,t+1)-P(:,:,t))))<kalman_tol);
+ P(:,:,t+1) = T*P(:,:,t)*T' + QQ; %transition according to (6.14) in DK (2012)
+ % notsteady = ~(max(max(abs(P(:,:,t+1)-P(:,:,t))))<kalman_tol);
end
% $$$ P_s=tril(P(:,:,t))+tril(P(:,:,t),-1)';
% $$$ P1_s=tril(P1(:,:,t))+tril(P1(:,:,t),-1)';
@@ -279,18 +279,18 @@ while t > d+1
end
end
end
- r(:,t) = ri; % DK (2012), below 6.15, r_{t-1}=r_{t,0}
+ r(:,t) = ri; % DK (2012), below 6.15, r_{t-1}=r_{t,0}
alphahat(:,t) = a1(:,t) + P1(:,:,t)*r(:,t);
etahat(:,t) = QRt*r(:,t);
- ri = T'*ri; % KD (2003), eq. (23), equation for r_{t-1,p_{t-1}}
+ ri = T'*ri; % KD (2003), eq. (23), equation for r_{t-1,p_{t-1}}
if state_uncertainty_flag
- N(:,:,t) = Ni; % DK (2012), below 6.15, N_{t-1}=N_{t,0}
+ N(:,:,t) = Ni; % DK (2012), below 6.15, N_{t-1}=N_{t,0}
V(:,:,t) = P1(:,:,t)-P1(:,:,t)*N(:,:,t)*P1(:,:,t); % KD (2000), eq. (7) with N_{t-1} stored in N(:,:,t)
- Ni = T'*Ni*T; % KD (2000), eq. (23), equation for N_{t-1,p_{t-1}}
+ Ni = T'*Ni*T; % KD (2000), eq. (23), equation for N_{t-1,p_{t-1}}
end
end
if d
- r0 = zeros(mm,d);
+ r0 = zeros(mm,d);
r0(:,d) = ri;
r1 = zeros(mm,d);
if state_uncertainty_flag
@@ -304,10 +304,10 @@ if d
di = flipud(data_index{t})';
for i = di
if Finf(i,t) > diffuse_kalman_tol
- % recursions need to be from highest to lowest term in order to not
+ % recursions need to be from highest to lowest term in order to not
% overwrite lower terms still needed in this step
Linf = eye(mm) - Kinf(:,i,t)*Z(i,:)/Finf(i,t);
- L0 = (Kinf(:,i,t)*(Fstar(i,t)/Finf(i,t))-Kstar(:,i,t))*Z(i,:)/Finf(i,t);
+ L0 = (Kinf(:,i,t)*(Fstar(i,t)/Finf(i,t))-Kstar(:,i,t))*Z(i,:)/Finf(i,t);
r1(:,t) = Z(i,:)'*v(i,t)/Finf(i,t) + ...
L0'*r0(:,t) + ...
Linf'*r1(:,t); % KD (2000), eq. (25) for r_1
@@ -335,9 +335,9 @@ if d
etahat(:,t) = QRt*r(:,t); % KD (2000), eq. (27)
if state_uncertainty_flag
V(:,:,t)=Pstar(:,:,t)-Pstar(:,:,t)*N_0(:,:,t)*Pstar(:,:,t)...
- -(Pinf(:,:,t)*N_1(:,:,t)*Pstar(:,:,t))'...
- - Pinf(:,:,t)*N_1(:,:,t)*Pstar(:,:,t)...
- - Pinf(:,:,t)*N_2(:,:,t)*Pinf(:,:,t); % DK (2012), eq. 5.30
+ -(Pinf(:,:,t)*N_1(:,:,t)*Pstar(:,:,t))'...
+ - Pinf(:,:,t)*N_1(:,:,t)*Pstar(:,:,t)...
+ - Pinf(:,:,t)*N_2(:,:,t)*Pinf(:,:,t); % DK (2012), eq. 5.30
end
if t > 1
r0(:,t-1) = T'*r0(:,t); % KD (2000), below eq. (25) r_{t-1,p_{t-1}}=T'*r_{t,0}
@@ -362,11 +362,11 @@ if decomp_flag
ri_d = Z(i,:)'/Fi(i,t)*v(i,t)+ri_d-Ki(:,i,t)'*ri_d/Fi(i,t)*Z(i,:)';
end
end
-
+
% calculate eta_tm1t
eta_tm1t = QRt*ri_d;
% calculate decomposition
- Ttok = eye(mm,mm);
+ Ttok = eye(mm,mm);
AAA = P1(:,:,t)*Z'*ZRQinv*Z*R;
for h = 1:nk
BBB = Ttok*AAA;
@@ -382,6 +382,6 @@ epsilonhat = Y - Z*alphahat;
if (d==smpl)
- warning(['missing_DiffuseKalmanSmootherH3_Z:: There isn''t enough information to estimate the initial conditions of the nonstationary variables']);
+ warning(['missing_DiffuseKalmanSmootherH3_Z:: There isn''t enough information to estimate the initial conditions of the nonstationary variables']);
return
end
diff --git a/matlab/mode_check.m b/matlab/mode_check.m
index ac4497249efe65694dc4b41ccc0066e796d91e5b..2df26ff0c520a20b90e07a988c427e862ed922eb 100644
--- a/matlab/mode_check.m
+++ b/matlab/mode_check.m
@@ -121,7 +121,7 @@ for plt = 1:nbplt
elseif isequal(x(kk),BoundsInfo.ub(kk))
binding_upper_bound=1;
bound_value=BoundsInfo.ub(kk);
- end
+ end
if DynareOptions.mode_check.symmetric_plots && ~binding_lower_bound && ~binding_upper_bound
if l2<(1+ll)*x(kk) %test whether upper bound is too small due to prior binding
l1 = x(kk) - (l2-x(kk)); %adjust lower bound to become closer
@@ -203,7 +203,7 @@ for plt = 1:nbplt
end
end
if TeX && any(strcmp('eps',cellstr(DynareOptions.graph_format)))
- fclose(fidTeX);
+ fclose(fidTeX);
end
OutputDirectoryName = CheckPath('modecheck',Model.dname);
diff --git a/matlab/model_comparison.m b/matlab/model_comparison.m
index cafa90a681f062586a646563467917fa5d26f887..81afdf768c6cd76adfd1180e036dcb7fd9d52365 100644
--- a/matlab/model_comparison.m
+++ b/matlab/model_comparison.m
@@ -5,9 +5,9 @@ function oo = model_comparison(ModelNames,ModelPriors,oo,options_,fname)
%
% INPUTS
% ModelNames [string] m*1 cell array of string.
-% ModelPriors [double] m*1 vector of prior probabilities
+% ModelPriors [double] m*1 vector of prior probabilities
% oo [struct] Dynare results structure
-% options_ [struct] Dynare options structure
+% options_ [struct] Dynare options structure
% fname [string] name of the current mod-file
%
% OUTPUTS
@@ -61,14 +61,14 @@ if isfield(options_,'mc_marginal_density')
type = options_.mc_marginal_density;
if strcmp(type,'laplace') || strcmp(type,'Laplace')
type = 'LaplaceApproximation';
- title = 'Model Comparison (based on Laplace approximation)';
- elseif strcmp(type,'modifiedharmonicmean') || strcmp(type,'ModifiedHarmonicMean')
+ title = 'Model Comparison (based on Laplace approximation)';
+ elseif strcmp(type,'modifiedharmonicmean') || strcmp(type,'ModifiedHarmonicMean')
type = 'ModifiedHarmonicMean';
- title = 'Model Comparison (based on Modified Harmonic Mean Estimator)';
+ title = 'Model Comparison (based on Modified Harmonic Mean Estimator)';
end
else
type = 'LaplaceApproximation';
- title = 'Model Comparison (based on Laplace approximation)';
+ title = 'Model Comparison (based on Laplace approximation)';
end
% Get the estimated logged marginal densities.
@@ -87,7 +87,7 @@ for i=1:NumberOfModels
end
end
try
- eval(['MarginalLogDensity(i) = mstruct.oo_.MarginalDensity.' type ';'])
+ eval(['MarginalLogDensity(i) = mstruct.oo_.MarginalDensity.' type ';'])
catch
if strcmpi(type,'LaplaceApproximation')
if isfield(mstruct.oo_,'mle_mode')
@@ -119,13 +119,13 @@ if prior_flag
labels = char('Priors','Log Marginal Density','Bayes Ratio', ...
'Posterior Model Probability');
field_labels={'Prior','Log_Marginal_Density','Bayes_Ratio', ...
- 'Posterior_Model_Probability'};
+ 'Posterior_Model_Probability'};
values = [ModelPriors';MarginalLogDensity';exp(lmpd-lmpd(1))'; ...
elmpd'/sum(elmpd)];
else
labels = char('Priors','Log Marginal Density','Bayes Ratio','Posterior Odds Ratio', ...
'Posterior Model Probability');
- field_labels={'Prior','Log_Marginal_Density','Bayes_Ratio','Posterior_Odds_Ratio','Posterior_Model_Probability'};
+ field_labels={'Prior','Log_Marginal_Density','Bayes_Ratio','Posterior_Odds_Ratio','Posterior_Model_Probability'};
values = [ModelPriors';MarginalLogDensity'; exp(MarginalLogDensity-MarginalLogDensity(1))'; ...
exp(lmpd-lmpd(1))'; elmpd'/sum(elmpd)];
end
@@ -148,7 +148,7 @@ if options_.TeX
for ii=1:size(labels,1)
labels_tex=strvcat(labels_tex,strrep(labels(ii,:),' ', '\ '));
end
-
+
dyn_latex_table(M_temp,options_,title,['model_comparison',type],headers_tex,labels_tex,values,0,16,6);
end
diff --git a/matlab/model_diagnostics.m b/matlab/model_diagnostics.m
index 8725f1cdd437b667a1890c61dae3fae5df5bcabc..6734291150c15a64b4f3de89f829fb1aa44a6db5 100644
--- a/matlab/model_diagnostics.m
+++ b/matlab/model_diagnostics.m
@@ -1,20 +1,20 @@
function model_diagnostics(M,options,oo)
% function model_diagnostics(M,options,oo)
-% computes various diagnostics on the model
+% computes various diagnostics on the model
% INPUTS
-% M [matlab structure] Definition of the model.
+% M [matlab structure] Definition of the model.
% options [matlab structure] Global options.
-% oo [matlab structure] Results
-%
+% oo [matlab structure] Results
+%
% OUTPUTS
% none
-%
+%
% ALGORITHM
% ...
-%
+%
% SPECIAL REQUIREMENTS
% none.
-%
+%
% Copyright (C) 1996-2017 Dynare Team
%
@@ -108,7 +108,7 @@ for b=1:nb
if options.bytecode
if nb == 1
[chk, res, jacob] = bytecode(dr.ys, exo, M.params, dr.ys, 1, exo, ...
- 'evaluate', 'static');
+ 'evaluate', 'static');
else
[chk, res, jacob] = bytecode(dr.ys, exo, M.params, dr.ys, 1, exo, ...
'evaluate', 'static',['block=' ...
@@ -173,8 +173,8 @@ for b=1:nb
end
end
end
-
-if singularity_problem
+
+if singularity_problem
try
options_check=options;
options_check.noprint=1;
@@ -205,29 +205,29 @@ if ~options.block
if options.order == 1
if (options.bytecode)
[chck, junk, loc_dr] = bytecode('dynamic','evaluate', z,exo_simul, ...
- M.params, dr.ys, 1);
+ M.params, dr.ys, 1);
jacobia_ = [loc_dr.g1 loc_dr.g1_x loc_dr.g1_xd];
else
[junk,jacobia_] = feval([M.fname '_dynamic'],z(iyr0),exo_simul, ...
- M.params, dr.ys, it_);
+ M.params, dr.ys, it_);
end
elseif options.order >= 2
if (options.bytecode)
[chck, junk, loc_dr] = bytecode('dynamic','evaluate', z,exo_simul, ...
- M.params, dr.ys, 1);
+ M.params, dr.ys, 1);
jacobia_ = [loc_dr.g1 loc_dr.g1_x];
else
[junk,jacobia_,hessian1] = feval([M.fname '_dynamic'],z(iyr0),...
- exo_simul, ...
- M.params, dr.ys, it_);
+ exo_simul, ...
+ M.params, dr.ys, it_);
end
if options.use_dll
% In USE_DLL mode, the hessian is in the 3-column sparse representation
hessian1 = sparse(hessian1(:,1), hessian1(:,2), hessian1(:,3), ...
- size(jacobia_, 1), size(jacobia_, 2)*size(jacobia_, 2));
+ size(jacobia_, 1), size(jacobia_, 2)*size(jacobia_, 2));
end
end
-
+
if any(any(isinf(jacobia_) | isnan(jacobia_)))
problem_dummy=1;
[infrow,infcol]=find(isinf(jacobia_) | isnan(jacobia_));
@@ -245,7 +245,7 @@ if ~options.block
problem_dummy=1;
fprintf('\nMODEL_DIAGNOSTICS: The Hessian of the dynamic model contains Inf or NaN.\n')
end
- end
+ end
else
fprintf('\nMODEL_DIAGNOSTICS: This command currently does not support the block option for checking.\n')
fprintf('\nMODEL_DIAGNOSTICS: the dynamic model. You may want to disable it for doing model_diagnostics. Skipping this part.\n')
@@ -254,4 +254,3 @@ end
if problem_dummy==0
fprintf('MODEL_DIAGNOSTICS: No obvious problems with this mod-file were detected.\n')
end
-
diff --git a/matlab/model_info.m b/matlab/model_info.m
index 2a55376ba659332abecedd3f7db4f22e43af18c9..e277792253b1a842d9af335dd94bdbdcad45408c 100644
--- a/matlab/model_info.m
+++ b/matlab/model_info.m
@@ -30,14 +30,14 @@ else
incidence = 0;
end
if static_
- fprintf(' Informations about %s (static model)\n',M_.fname);
- block_structre_str = 'block_structure_stat';
- nb_leadlag = 1;
- else
- fprintf(' Informations about %s (dynamic model)\n',M_.fname);
- block_structre_str = 'block_structure';
- nb_leadlag = 3;
- end
+ fprintf(' Informations about %s (static model)\n',M_.fname);
+ block_structre_str = 'block_structure_stat';
+ nb_leadlag = 1;
+else
+ fprintf(' Informations about %s (dynamic model)\n',M_.fname);
+ block_structre_str = 'block_structure';
+ nb_leadlag = 3;
+end
if(isfield(M_,block_structre_str))
if static_
block_structure = M_.block_structure_stat;
@@ -108,7 +108,7 @@ if(isfield(M_,block_structre_str))
fprintf('\n\n');
end
end
-
+
%printing the gross incidence matrix
IM_star = char([kron(ones(M_.endo_nbr, M_.endo_nbr-1), double(blanks(3))) double(blanks(M_.endo_nbr)')]);
for i = 1:nb_leadlag
@@ -136,7 +136,7 @@ if(isfield(M_,block_structre_str))
fprintf('\n Gross incidence matrix\n');
fprintf(' =======================\n');
disp([topp; bott]);
-
+
%printing the reordered incidence matrix
IM_star_reordered = char([kron(ones(M_.endo_nbr, M_.endo_nbr-1), double(blanks(3))) double(blanks(M_.endo_nbr)')]);
eq(block_structure.equation_reordered) = seq;
@@ -179,7 +179,7 @@ if(isfield(M_,block_structre_str))
end
end
fprintf('1: non nul element, X: non nul element related to a state variable\n');
-
+
cur_block = 1;
i_last = 0;
block = {};
@@ -201,17 +201,17 @@ if(isfield(M_,block_structre_str))
end
if past_block ~= cur_block
for j = 1:i-1
- IM_star_reordered(j, 3 * (i - 1) - 1) = '|';
+ IM_star_reordered(j, 3 * (i - 1) - 1) = '|';
end
end
end
-
+
bott = [int2str(block_structure.equation_reordered') blanks(M_.endo_nbr)' blanks(M_.endo_nbr)' IM_star_reordered];
fprintf('\n Reordered incidence matrix\n');
fprintf(' ==========================\n');
disp([topp; bott]);
fprintf('1: non nul element, X: non nul element related to a state variable\n');
- end
+ end
else
fprintf('There is no block decomposition of the model.\nUse ''block'' model''s option.\n');
end
@@ -256,6 +256,3 @@ for i=1:n
s = [s '|'];
end
ret = s;
-
-
-
diff --git a/matlab/model_inversion.m b/matlab/model_inversion.m
index 6c873c655052eadb24cf29d9804b0f04c34758f4..4d01412f98d1306af26b539ae98b8f5140af0240 100644
--- a/matlab/model_inversion.m
+++ b/matlab/model_inversion.m
@@ -1,19 +1,19 @@
function [endogenousvariables, exogenousvariables] = model_inversion(constraints, ...
- exogenousvariables, ...
- initialconditions, DynareModel, DynareOptions, DynareOutput)
+ exogenousvariables, ...
+ initialconditions, DynareModel, DynareOptions, DynareOutput)
-% INPUTS
+% INPUTS
% - constraints [dseries] with N constrained endogenous variables from t1 to t2.
% - exogenousvariables [dseries] with Q exogenous variables.
% - initialconditions [dseries] with M endogenous variables starting before t1 (M initialcond must contain at least the state variables).
% - DynareModel [struct] M_, Dynare global structure containing informations related to the model.
% - DynareOptions [struct] options_, Dynare global structure containing all the options.
%
-% OUTPUTS
-% - endogenous [dseries]
+% OUTPUTS
+% - endogenous [dseries]
% - exogenous [dseries]
%
-% REMARKS
+% REMARKS
% Copyright (C) 2017 Dynare Team
%
@@ -52,7 +52,7 @@ if ~isstruct(DynareModel)
error('model_inversion: Last input argument must be structures (M_)!')
end
-% Set range where the endogenous variables are constrained.
+% Set range where the endogenous variables are constrained.
crange = constraints.dates;
% Check that the number of instruments match the number of constrained endogenous variables.
diff --git a/matlab/moment_function.m b/matlab/moment_function.m
index 9ebe62e7b07a92ea63be51626ec3518ee338f37b..fb42db9e888f99380249493ab75abb258ac49226 100644
--- a/matlab/moment_function.m
+++ b/matlab/moment_function.m
@@ -3,12 +3,12 @@ function [g,grad,hess,flag] = moment_function(xparams,sample_moments,dataset,opt
% ).
%
% INPUTS:
-% xparams [double] p*1 vector of estimated parameters.
+% xparams [double] p*1 vector of estimated parameters.
% sample_moments [double] n*1 vector of sample moments (n>=p).
% options [ ] Structure defining options for SMM.
% parallel [ ] Structure defining the parallel mode settings (optional).
%
-% OUTPUTS:
+% OUTPUTS:
% g [double] n*1 vector, the gap between simulated and sample moments.
% flag [intger] empty matrix.
%
@@ -30,7 +30,7 @@ function [g,grad,hess,flag] = moment_function(xparams,sample_moments,dataset,opt
% 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/>.
+% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
global M_ options_
persistent mainStream mainState
@@ -106,7 +106,7 @@ else% parallel mode.
end
end
for j=1:parallel(i).number_of_jobs
- if (strcmpi(hostname,machine) && j>1) || ~strcmpi(hostname,machine)
+ if (strcmpi(hostname,machine) && j>1) || ~strcmpi(hostname,machine)
job_number = job_number + 1;
unix(['ssh -A ' parallel(i).login '@' machine ' ./call_matlab_session.sh job' int2str(job_number) '.m &']);
end
@@ -117,7 +117,7 @@ else% parallel mode.
eval('job1;')
tElapsedMasterJob = etime(clock, tStartMasterJob);
TimeLimit = tElapsedMasterJob*1.2;
- % Master waits for the slaves' output...
+ % Master waits for the slaves' output...
tStart = clock;
tElapsed = 0;
while tElapsed<TimeLimit
@@ -132,7 +132,7 @@ else% parallel mode.
simulated_moments = load(['./intermediary_results_from_master_and_slaves/simulated_moments_slave_' int2str(i) '.dat'],'-ascii');
tmp = tmp + simulated_moments;
end
- simulated_moments = tmp / job_number;
+ simulated_moments = tmp / job_number;
catch
flag = 0;
return
diff --git a/matlab/moments.m b/matlab/moments.m
index f295336aa3e4aa206d7ebd758cb33a96421145bb..ac5ed4f27a316cfb081b55085e6bbe8870ebdc1f 100644
--- a/matlab/moments.m
+++ b/matlab/moments.m
@@ -1,7 +1,7 @@
function m = moments(X,order)
% Returns the central sample moment of X specified by the positive integer order.
%
-% Note that the cross moments are only computed if order=2, in this case the
+% Note that the cross moments are only computed if order=2, in this case the
% output is a matrix.
%
% INPUTS
diff --git a/matlab/ms-sbvar/clean_files_for_second_type_of_mex.m b/matlab/ms-sbvar/clean_files_for_second_type_of_mex.m
index d29b532bfaff3b021d2182938344794355cd5efa..5f6d7d43cdfd921388aa2021109f206e99ed98c7 100644
--- a/matlab/ms-sbvar/clean_files_for_second_type_of_mex.m
+++ b/matlab/ms-sbvar/clean_files_for_second_type_of_mex.m
@@ -34,12 +34,12 @@ if ~strcmp(tagtouse, options_.ms.output_file_tag)
end
switch type
- case 'irf'
- clean_ms_irf_files(tagtouse);
- case 'forecast'
- clean_ms_forecast_files(tagtouse);
- case 'variance_decomposition'
- clean_ms_variance_decomposition_files(tagtouse);
- otherwise
- error('clean_files_for_second_type_of_mex: should not arrive here');
+ case 'irf'
+ clean_ms_irf_files(tagtouse);
+ case 'forecast'
+ clean_ms_forecast_files(tagtouse);
+ case 'variance_decomposition'
+ clean_ms_variance_decomposition_files(tagtouse);
+ otherwise
+ error('clean_files_for_second_type_of_mex: should not arrive here');
end
diff --git a/matlab/ms-sbvar/dyn_save_graph.m b/matlab/ms-sbvar/dyn_save_graph.m
index cc8906cd139abbcb17ddba4da6e0db614a08890f..91782d71fe5a89f0fd7a2c2633fddb954182d378 100644
--- a/matlab/ms-sbvar/dyn_save_graph.m
+++ b/matlab/ms-sbvar/dyn_save_graph.m
@@ -9,7 +9,7 @@ function dyn_save_graph(dirname,graph_name,graph_formats,TeX,names,texnames,capt
%
% OUTPUTS
% none
-%
+%
% SPECIAL REQUIREMENTS
% none
%
@@ -30,40 +30,40 @@ function dyn_save_graph(dirname,graph_name,graph_formats,TeX,names,texnames,capt
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- create_dir(dirname);
- graph_name = [dirname filesep regexprep(graph_name,' ','_')];
- if nargin <= 2
- TeX = 0;
- elseif nargin <= 4
- names = {};
- texnames = {};
- elseif nargin <= 6
- caption = '';
- end
-
- if graph_formats.eps || TeX
- print([ graph_name '.eps' ],'-depsc2');
- end
- if graph_formats.pdf && ~isoctave
- print(graph_name,'-dpdf');
- end
- if graph_formats.fig && ~isoctave
- print(graph_name,'-dfig');
+create_dir(dirname);
+graph_name = [dirname filesep regexprep(graph_name,' ','_')];
+if nargin <= 2
+ TeX = 0;
+elseif nargin <= 4
+ names = {};
+ texnames = {};
+elseif nargin <= 6
+ caption = '';
+end
+
+if graph_formats.eps || TeX
+ print([ graph_name '.eps' ],'-depsc2');
+end
+if graph_formats.pdf && ~isoctave
+ print(graph_name,'-dpdf');
+end
+if graph_formats.fig && ~isoctave
+ print(graph_name,'-dfig');
+end
+
+if TeX
+ fh = fopen([graph_name '.tex'],'w');
+ for i=1:length(names)
+ fprintf(fh,'\\psfrag{%s}[1][][0.5][0]{%s}\n',names{i},texnames{i});
end
-
- if TeX
- fh = fopen([graph_name '.tex'],'w');
- for i=1:length(names)
- fprintf(fh,'\\psfrag{%s}[1][][0.5][0]{%s}\n',names{i},texnames{i});
- end
- fprintf(fh,'\\centering \n');
- fprintf(fh,'\\includegraphics[width=0.8\\textwidth]{%s}\n',graph_name);
- if caption
- fprintf(fh,'\\caption{%s}',caption);
- end
- fprintf(fh,'\\label{Fig:%s}\n',graph_name);
- fprintf(fh,'\\end{figure}\n');
- fprintf(fh,'\n');
- fprintf(fh,'%% End of TeX file.\n');
- fclose(fh);
+ fprintf(fh,'\\centering \n');
+ fprintf(fh,'\\includegraphics[width=0.8\\textwidth]{%s}\n',graph_name);
+ if caption
+ fprintf(fh,'\\caption{%s}',caption);
end
+ fprintf(fh,'\\label{Fig:%s}\n',graph_name);
+ fprintf(fh,'\\end{figure}\n');
+ fprintf(fh,'\n');
+ fprintf(fh,'%% End of TeX file.\n');
+ fclose(fh);
+end
diff --git a/matlab/ms-sbvar/identification/exclusions.m b/matlab/ms-sbvar/identification/exclusions.m
index 938fd92b41b604679c7e2b58b2aadeb33536c306..c695124a81bfd1c2e088a6a5a93bd7674bd741d1 100644
--- a/matlab/ms-sbvar/identification/exclusions.m
+++ b/matlab/ms-sbvar/identification/exclusions.m
@@ -47,25 +47,25 @@ np = zeros(nvar,1);
k = nlags*nvar+1;
for n=1:nvar
- Qi{n} = zeros(nvar,nvar);
- sQ = size(Qi1{n});
- if all(sQ) > 0
- Qi{n}(1:sQ(1),1:sQ(2)) = Qi1{n};
- end
- Ri{n} = zeros(k,k);
- sR = size(Ri1{n});
- if all(sR) > 0
- Ri{n}(1:sR(1),1:sR(2)) = Ri1{n};
- end
+ Qi{n} = zeros(nvar,nvar);
+ sQ = size(Qi1{n});
+ if all(sQ) > 0
+ Qi{n}(1:sQ(1),1:sQ(2)) = Qi1{n};
+ end
+ Ri{n} = zeros(k,k);
+ sR = size(Ri1{n});
+ if all(sR) > 0
+ Ri{n}(1:sR(1),1:sR(2)) = Ri1{n};
+ end
- if options_ms.constants_exclusion
+ if options_ms.constants_exclusion
Ri{n}(sR(1)+1,k) = 1;
- end
+ end
- Ui{n} = null(Qi{n});
- Vi{n} = null(Ri{n});
- n0(n) = size(Ui{n},2);
- np(n) = size(Vi{n},2);
+ Ui{n} = null(Qi{n});
+ Vi{n} = null(Ri{n});
+ n0(n) = size(Ui{n},2);
+ np(n) = size(Vi{n},2);
end
ixmC0Pres = NaN;
\ No newline at end of file
diff --git a/matlab/ms-sbvar/identification/lower_cholesky.m b/matlab/ms-sbvar/identification/lower_cholesky.m
index 2af96ca9b7b732011d0f2831eb02470293262c6e..3085e09ddc73a6cffd0bdccb6af2f56ed740858e 100644
--- a/matlab/ms-sbvar/identification/lower_cholesky.m
+++ b/matlab/ms-sbvar/identification/lower_cholesky.m
@@ -41,11 +41,11 @@ Vi = cell(nvar,1);
n0 = zeros(nvar,1);
np = zeros(nvar,1);
if (nargin==2)
- nexo = 1;
+ nexo = 1;
elseif (nargin==3)
- indxC0Pres = 0;
+ indxC0Pres = 0;
end
-k = lags*nvar+nexo;
+k = lags*nvar+nexo;
Qi = zeros(nvar,nvar,nvar);
Ri = zeros(k,k,nvar);
@@ -61,11 +61,11 @@ if options_ms.constants_exclusion
end
end
-for n=1:nvar
- Ui{n} = null(Qi(:,:,n));
- Vi{n} = null(Ri(:,:,n));
- n0(n) = size(Ui{n},2);
- np(n) = size(Vi{n},2);
+for n=1:nvar
+ Ui{n} = null(Qi(:,:,n));
+ Vi{n} = null(Ri(:,:,n));
+ n0(n) = size(Ui{n},2);
+ np(n) = size(Vi{n},2);
end
ixmC0Pres = NaN;
\ No newline at end of file
diff --git a/matlab/ms-sbvar/identification/upper_cholesky.m b/matlab/ms-sbvar/identification/upper_cholesky.m
index 7f4d2027c56b854b64fe1e8274eeb1170a65fa99..dbdd19c40065fce4506e1e1a1d5c1d22ae07f92b 100644
--- a/matlab/ms-sbvar/identification/upper_cholesky.m
+++ b/matlab/ms-sbvar/identification/upper_cholesky.m
@@ -42,9 +42,9 @@ n0 = zeros(nvar,1);
np = zeros(nvar,1);
if (nargin==2)
- nexo = 1;
+ nexo = 1;
elseif (nargin==3)
- indxC0Pres = 0;
+ indxC0Pres = 0;
end
k = lags*nvar+nexo;
@@ -52,8 +52,8 @@ Qi = zeros(nvar,nvar,nvar);
Ri = zeros(k,k,nvar);
for ii=2:nvar
- Qi(ii-1,ii-1,ii)=1;
- Qi(:,:,ii)=Qi(:,:,ii)+Qi(:,:,ii-1);
+ Qi(ii-1,ii-1,ii)=1;
+ Qi(:,:,ii)=Qi(:,:,ii)+Qi(:,:,ii-1);
end
if options_ms.constants_exclusion
@@ -62,11 +62,11 @@ if options_ms.constants_exclusion
end
end
-for n=1:nvar
- Ui{n} = null(Qi(:,:,n));
- Vi{n} = null(Ri(:,:,n));
- n0(n) = size(Ui{n},2);
- np(n) = size(Vi{n},2);
+for n=1:nvar
+ Ui{n} = null(Qi(:,:,n));
+ Vi{n} = null(Ri(:,:,n));
+ n0(n) = size(Ui{n},2);
+ np(n) = size(Vi{n},2);
end
ixmC0Pres = NaN;
\ No newline at end of file
diff --git a/matlab/ms-sbvar/ms_estimation.m b/matlab/ms-sbvar/ms_estimation.m
index 6d5eec32d704d61ff4de834515ab2cd9701788dc..67b8c34fc5de58ef6a019723288df017351ce76c 100644
--- a/matlab/ms-sbvar/ms_estimation.m
+++ b/matlab/ms-sbvar/ms_estimation.m
@@ -45,7 +45,7 @@ else
exist(['matlab_' options_.ms.file_tag '.prn'],'file') ~= 2 || ...
exist([options_.ms.file_tag '_markov_file.dat'],'file') ~= 2
error(['You must run ms_estimation without no_create_init as '...
- 'you are missing an initialization file it needs.']);
+ 'you are missing an initialization file it needs.']);
end
if ~strcmp(options_.ms.file_tag, options_.ms.output_file_tag)
clean_ms_estimation_files(options_.ms.output_file_tag);
diff --git a/matlab/ms-sbvar/ms_forecast.m b/matlab/ms-sbvar/ms_forecast.m
index 55030c9021729dcbde0a1110f920c86cf4ea9eab..e8ed8be20cd22dad4c91d171b461f95e1f1ac41b 100644
--- a/matlab/ms-sbvar/ms_forecast.m
+++ b/matlab/ms-sbvar/ms_forecast.m
@@ -89,30 +89,30 @@ if options_.ms.regimes
for regime_i=1:n_regimes
forecast_title = ['Forecast, Regimes ' num2str(regime_i)];
forecast_data = load([forecastdir filesep 'forecasts_percentiles_regime_' ...
- num2str(regime_i-1) '_' options_.ms.output_file_tag ...
- '.out'], '-ascii');
+ num2str(regime_i-1) '_' options_.ms.output_file_tag ...
+ '.out'], '-ascii');
forecast_data = reshape_ascii_forecast_data(M_.endo_nbr, ...
- percentiles_size, options_.ms.horizon, forecast_data);
+ percentiles_size, options_.ms.horizon, forecast_data);
save([forecastdir filesep 'forecast_regime_' num2str(regime_i-1) '.mat'], ...
- 'forecast_data');
+ 'forecast_data');
plot_ms_forecast(M_, options_, forecast_data, forecast_title);
end
else
if options_.ms.regime
forecast_data = load([forecastdir filesep 'forecasts_percentiles_regime_' ...
- num2str(options_.ms.regime-1) '_' options_.ms.output_file_tag ...
- '.out'], '-ascii');
+ num2str(options_.ms.regime-1) '_' options_.ms.output_file_tag ...
+ '.out'], '-ascii');
forecast_title = ['Forecast, Regime ' num2str(options_.ms.regime)];
save_filename = ['forecast_regime_' num2str(options_.ms.regime-1) '.mat'];
else
forecast_data = load([forecastdir filesep 'forecasts_percentiles_' ...
- options_.ms.output_file_tag '.out'], '-ascii');
+ options_.ms.output_file_tag '.out'], '-ascii');
forecast_title = 'Forecast';
save_filename = 'forecast.mat';
end
forecast_data = reshape_ascii_forecast_data(M_.endo_nbr, ...
- percentiles_size, options_.ms.horizon, forecast_data);
+ percentiles_size, options_.ms.horizon, forecast_data);
save([forecastdir filesep save_filename], 'forecast_data');
plot_ms_forecast(M_, options_, forecast_data, forecast_title);
end
diff --git a/matlab/ms-sbvar/ms_irf.m b/matlab/ms-sbvar/ms_irf.m
index 32718aa29c4473515c07d477cccf5498d5ce7364..9b13aaa5e17204e75edb0baee1c2c174b3a0431d 100644
--- a/matlab/ms-sbvar/ms_irf.m
+++ b/matlab/ms-sbvar/ms_irf.m
@@ -91,34 +91,34 @@ if options_.ms.regimes
for regime_i=1:n_regimes
irf_title = ['Impulse Responses, Regime ' num2str(regime_i)];
irf_data = load([irfdir filesep 'ir_percentiles_regime_' ...
- num2str(regime_i-1) '_' options_.ms.output_file_tag ...
- '.out'], '-ascii');
+ num2str(regime_i-1) '_' options_.ms.output_file_tag ...
+ '.out'], '-ascii');
irf_data = reshape_ascii_irf_data(M_.endo_nbr, percentiles_size, ...
- options_.ms.horizon, irf_data);
+ options_.ms.horizon, irf_data);
save([irfdir filesep 'irf_regime_' num2str(regime_i-1) '.mat'], 'irf_data');
plot_ms_irf(M_, options_, irf_data, irf_title, varlist);
end
else
if options_.ms.regime
irf_data = load([irfdir filesep 'ir_percentiles_regime_' ...
- num2str(options_.ms.regime-1) '_' options_.ms.output_file_tag ...
- '.out'], '-ascii');
+ num2str(options_.ms.regime-1) '_' options_.ms.output_file_tag ...
+ '.out'], '-ascii');
irf_title = ['Impulse Response, Regime ' num2str(options_.ms.regime)];
save_filename = ['irf_regime_' num2str(options_.ms.regime-1) '.mat'];
elseif options_.ms.filtered_probabilities
irf_data = load([irfdir filesep 'ir_percentiles_filtered_' ...
- options_.ms.output_file_tag '.out'], '-ascii');
+ options_.ms.output_file_tag '.out'], '-ascii');
irf_title = 'Impulse Response Filtered';
save_filename = 'irf.mat';
else
irf_data = load([irfdir filesep 'ir_percentiles_ergodic_' ...
- options_.ms.output_file_tag '.out'], '-ascii');
+ options_.ms.output_file_tag '.out'], '-ascii');
irf_title = 'Impulse Response Ergodic';
save_filename = 'irf.mat';
end
irf_data = reshape_ascii_irf_data(M_.endo_nbr, percentiles_size, ...
- options_.ms.horizon, irf_data);
+ options_.ms.horizon, irf_data);
save([irfdir filesep save_filename], 'irf_data');
plot_ms_irf(M_, options_, irf_data, irf_title, varlist);
end
diff --git a/matlab/ms-sbvar/ms_mardd.m b/matlab/ms-sbvar/ms_mardd.m
index 7ca2f304aa6d2725ae33da5e061b50ec03b6099b..090c69127c38a6f9b7633fae353c36bbfcc340de 100644
--- a/matlab/ms-sbvar/ms_mardd.m
+++ b/matlab/ms-sbvar/ms_mardd.m
@@ -25,26 +25,26 @@ function ms_mardd(options_)
msstart2 % start the program in which everyhting is initialized through msstart2.m
if ~options_.ms.indxestima
- warning(' ')
- disp('You must set IxEstima=1 in msstart to run this program')
- disp('Press ctrl-c to abort now')
- pause
+ warning(' ')
+ disp('You must set IxEstima=1 in msstart to run this program')
+ disp('Press ctrl-c to abort now')
+ pause
end
A0xhat = zeros(size(A0hat));
Apxhat = zeros(size(Aphat));
if (0)
- %Robustness check to see if the same result is obtained with the purterbation of the parameters.
- for k=1:nvar
- bk = Uiconst{k}'*A0hat(:,k);
- gk = Viconst{k}'*Aphat(:,k);
- A0xhat(:,k) = Uiconst{k}*(bk + 5.2*randn(size(bk))); % Perturbing the posterior estimate.
- Apxhat(:,k) = Viconst{k}*(gk + 5.2*randn(size(gk))); % Perturbing the posterior estimate.
- end
+ %Robustness check to see if the same result is obtained with the purterbation of the parameters.
+ for k=1:nvar
+ bk = Uiconst{k}'*A0hat(:,k);
+ gk = Viconst{k}'*Aphat(:,k);
+ A0xhat(:,k) = Uiconst{k}*(bk + 5.2*randn(size(bk))); % Perturbing the posterior estimate.
+ Apxhat(:,k) = Viconst{k}*(gk + 5.2*randn(size(gk))); % Perturbing the posterior estimate.
+ end
else
- %At the posterior estimate.
- A0xhat = A0hat; % ML estimate of A0
- Apxhat = Aphat; % ML estimate of A+
+ %At the posterior estimate.
+ A0xhat = A0hat; % ML estimate of A0
+ Apxhat = Aphat; % ML estimate of A+
end
%--- Rename variables.
YatYa = yty;
@@ -59,40 +59,40 @@ XatXa = xtx;
vlog_a0p = 0;
Yexpt=0; % exponential term for Y in p(Y|A0,A+) at some point such as the peak
Apexpt=0.0; % 0.0 because we have chosen posterior estimate of A+ as A+*. Exponential term for A+ conditional on A0 and Y
-%======= Computing the log prior pdf of a0a+ and the exponential term for Y in p(Y|A0,A+).
+ %======= Computing the log prior pdf of a0a+ and the exponential term for Y in p(Y|A0,A+).
for k=1:nvar
- a0k = A0xhat(:,k); % meaningful parameters in the kth equation.
- apk = Apxhat(:,k); % meaningful parameters in the kth equation.
-
- %--- Prior settings.
- S0bar = H0invtld{k}; %See Claim 2 on p.69b.
- Spbar = Hpinvtld{k};
- bk = Uiconst{k}'*a0k; % free parameters in the kth equation.
- gk = Viconst{k}'*apk; % free parameters in the kth equation.
- gbark = Ptld{k}*bk; % bar: prior
-
- %--- The exponential term for Y in p(Y|A0,A+)
- Yexpt = Yexpt - 0.5*(a0k'*YatYa*a0k - 2*apk'*XatYa*a0k + apk'*XatXa*apk);
- %--- The log prior pdf.
- vlog_a0p = vlog_a0p - 0.5*(size(Uiconst{k},2)+size(Viconst{k},2))*log(2*pi) + 0.5*log(abs(det(S0bar))) + ...
- 0.5*log(abs(det(Spbar))) - 0.5*(bk'*S0bar*bk+(gk-gbark)'*Spbar*(gk-gbark));
- %--- For p(A+|Y,a0) only.
- tmpd = gk - Pmat{k}*bk;
- Apexpt = Apexpt - 0.5*tmpd'*(Hpinv{k}*tmpd);
+ a0k = A0xhat(:,k); % meaningful parameters in the kth equation.
+ apk = Apxhat(:,k); % meaningful parameters in the kth equation.
+
+ %--- Prior settings.
+ S0bar = H0invtld{k}; %See Claim 2 on p.69b.
+ Spbar = Hpinvtld{k};
+ bk = Uiconst{k}'*a0k; % free parameters in the kth equation.
+ gk = Viconst{k}'*apk; % free parameters in the kth equation.
+ gbark = Ptld{k}*bk; % bar: prior
+
+ %--- The exponential term for Y in p(Y|A0,A+)
+ Yexpt = Yexpt - 0.5*(a0k'*YatYa*a0k - 2*apk'*XatYa*a0k + apk'*XatXa*apk);
+ %--- The log prior pdf.
+ vlog_a0p = vlog_a0p - 0.5*(size(Uiconst{k},2)+size(Viconst{k},2))*log(2*pi) + 0.5*log(abs(det(S0bar))) + ...
+ 0.5*log(abs(det(Spbar))) - 0.5*(bk'*S0bar*bk+(gk-gbark)'*Spbar*(gk-gbark));
+ %--- For p(A+|Y,a0) only.
+ tmpd = gk - Pmat{k}*bk;
+ Apexpt = Apexpt - 0.5*tmpd'*(Hpinv{k}*tmpd);
end
vlog_a0p
%--------- The log value of p(Y|A0,A+) at some point such as the peak. ----------
%--------- Note that logMarLHres is the same as vlog_Y_a, just to double check. ----------
vlog_Y_a = -0.5*nvar*fss*log(2*pi) + fss*log(abs(det(A0xhat))) + Yexpt
- % a: given a0 and a+
+% a: given a0 and a+
logMarLHres = 0; % Initialize log of the marginal likelihood (restricted or constant parameters).
for ki=1:fss %ndobs+1:fss % Forward recursion to get the marginal likelihood. See F on p.19 and pp. 48-49.
- %---- Restricted log marginal likelihood function (constant parameters).
- [A0l,A0u] = lu(A0xhat);
- ada = sum(log(abs(diag(A0u)))); % log|A0|
- termexp = y(ki,:)*A0xhat - phi(ki,:)*Apxhat; % 1-by-nvar
- logMarLHres = logMarLHres - (0.5*nvar)*log(2*pi) + ada - 0.5*termexp*termexp'; % log value
+ %---- Restricted log marginal likelihood function (constant parameters).
+ [A0l,A0u] = lu(A0xhat);
+ ada = sum(log(abs(diag(A0u)))); % log|A0|
+ termexp = y(ki,:)*A0xhat - phi(ki,:)*Apxhat; % 1-by-nvar
+ logMarLHres = logMarLHres - (0.5*nvar)*log(2*pi) + ada - 0.5*termexp*termexp'; % log value
end
logMarLHres
@@ -101,8 +101,8 @@ logMarLHres
totparsp = 0.0;
tmpd = 0.0;
for k=1:nvar
- totparsp = totparsp + size(Viconst{k},2);
- tmpd = tmpd + 0.5*log(abs(det(Hpinv{k})));
+ totparsp = totparsp + size(Viconst{k},2);
+ tmpd = tmpd + 0.5*log(abs(det(Hpinv{k})));
end
vlog_ap_Ya0 = -0.5*totparsp*log(2*pi) + tmpd + Apexpt;
@@ -117,65 +117,65 @@ vlog_ap_Ya0 = -0.5*totparsp*log(2*pi) + tmpd + Apexpt;
[Tinv,UT] = fn_gibbsrvar_setup(H0inv, Uiconst, Hpinv, Pmat, Viconst, nvar, fss);
%
vlog_a0_Yao = zeros(nvar,1);
- % the log value of p(a0k|Y,ao) where ao: other a's at some point such as the peak of ONLY some a0's
+% the log value of p(a0k|Y,ao) where ao: other a's at some point such as the peak of ONLY some a0's
vlog=zeros(ndraws2,1);
for k=1:nvar
- bk = Uiconst{k}'*A0xhat(:,k);
- indx_ks=[k:nvar]; % the columns that exclude 1-(k-1)th columns
- A0gbs0 = A0hat; % starting at some point such as the peak
- nk = n0(k);
-
- if k<nvar
- %--------- The 1st set of draws to be tossed away. ------------------
- for draws = 1:ndraws1
- if ~mod(draws,nbuffer)
- skipline()
- disp(sprintf('The %dth column or equation in A0 with %d 1st tossed-away draws in Gibbs',k,draws))
- end
- A0gbs1 = fn_gibbsrvar(A0gbs0,UT,nvar,fss,n0,indx_ks);
- A0gbs0=A0gbs1; % repeat the Gibbs sampling
- end
-
-
- %--------- The 2nd set of draws to be used. ------------------
- for draws = 1:ndraws2
- if ~mod(draws,nbuffer)
- skipline()
- disp(sprintf('The %dth column or equation in A0 with %d usable draws in Gibbs',k,draws))
- end
- [A0gbs1, Wcell] = fn_gibbsrvar(A0gbs0,UT,nvar,fss,n0,indx_ks);
- %------ See p.71, Forecast (II).
- %------ Computing p(a0_k|Y,a_others) at some point such as the peak along the dimensions of indx_ks.
- Vk = Tinv{k}\Wcell{k}; %V_k on p.71 of Forecast (II).
- gbeta = Vk\bk; % inv(V_k)*b_k on p.71 of Forecast (II) where alpha_k = b_k in our notation.
- [Vtq,Vtr]=qr(Vk',0); %To get inv(V_k)'*inv(V_k) in (*) on p.71 of Forecast (II).
- %
- vlog(draws) = 0.5*(fss+nk)*log(fss)-log(abs(det(Vk)))-0.5*(nk-1)*log(2*pi)-...
- 0.5*(fss+1)*log(2)-gammaln(0.5*(fss+1))+fss*log(abs(gbeta(1)))-...
- 0.5*fss*bk'*(Vtr\(Vtr'\bk));
-
- A0gbs0=A0gbs1; % repeat the Gibbs sampling
- end
- vlogm=max(vlog);
- qlog=vlog-vlogm;
- vlogxhat=vlogm-log(ndraws2)+log(sum(exp(qlog)));
- vlog_a0_Yao(k) = vlogxhat;
- % The log value of p(a0_k|Y,a_others) where a_others: other a's at some point such as the peak of ONLY some a0's
- else
- skipline()
- disp(sprintf('The last(6th) column or equation in A0 with no Gibbs draws'))
- [A0gbs1, Wcell] = fn_gibbsrvar(A0gbs0,UT,nvar,fss,n0,indx_ks)
- %------ See p.71, Forecast (II).
- %------ Computing p(a0_k|Y,a_others) at some point such as the peak along the dimensions of indx_ks.
- Vk = Tinv{k}\Wcell{k}; %V_k on p.71 of Forecast (II).
- gbeta = Vk\bk; % inv(V_k)*b_k on p.71 of Forecast (II) where alpha_k = b_k in our notation.
- [Vtq,Vtr]=qr(Vk',0); %To get inv(V_k)'*inv(V_k) in (*) on p.71 of Forecast (II).
- %
- vloglast = 0.5*(fss+nk)*log(fss)-log(abs(det(Vk)))-0.5*(nk-1)*log(2*pi)-...
- 0.5*(fss+1)*log(2)-gammaln(0.5*(fss+1))+fss*log(abs(gbeta(1)))-...
- 0.5*fss*bk'*(Vtr\(Vtr'\bk));
- vlog_a0_Yao(k) = vloglast;
- end
+ bk = Uiconst{k}'*A0xhat(:,k);
+ indx_ks=[k:nvar]; % the columns that exclude 1-(k-1)th columns
+ A0gbs0 = A0hat; % starting at some point such as the peak
+ nk = n0(k);
+
+ if k<nvar
+ %--------- The 1st set of draws to be tossed away. ------------------
+ for draws = 1:ndraws1
+ if ~mod(draws,nbuffer)
+ skipline()
+ disp(sprintf('The %dth column or equation in A0 with %d 1st tossed-away draws in Gibbs',k,draws))
+ end
+ A0gbs1 = fn_gibbsrvar(A0gbs0,UT,nvar,fss,n0,indx_ks);
+ A0gbs0=A0gbs1; % repeat the Gibbs sampling
+ end
+
+
+ %--------- The 2nd set of draws to be used. ------------------
+ for draws = 1:ndraws2
+ if ~mod(draws,nbuffer)
+ skipline()
+ disp(sprintf('The %dth column or equation in A0 with %d usable draws in Gibbs',k,draws))
+ end
+ [A0gbs1, Wcell] = fn_gibbsrvar(A0gbs0,UT,nvar,fss,n0,indx_ks);
+ %------ See p.71, Forecast (II).
+ %------ Computing p(a0_k|Y,a_others) at some point such as the peak along the dimensions of indx_ks.
+ Vk = Tinv{k}\Wcell{k}; %V_k on p.71 of Forecast (II).
+ gbeta = Vk\bk; % inv(V_k)*b_k on p.71 of Forecast (II) where alpha_k = b_k in our notation.
+ [Vtq,Vtr]=qr(Vk',0); %To get inv(V_k)'*inv(V_k) in (*) on p.71 of Forecast (II).
+ %
+ vlog(draws) = 0.5*(fss+nk)*log(fss)-log(abs(det(Vk)))-0.5*(nk-1)*log(2*pi)-...
+ 0.5*(fss+1)*log(2)-gammaln(0.5*(fss+1))+fss*log(abs(gbeta(1)))-...
+ 0.5*fss*bk'*(Vtr\(Vtr'\bk));
+
+ A0gbs0=A0gbs1; % repeat the Gibbs sampling
+ end
+ vlogm=max(vlog);
+ qlog=vlog-vlogm;
+ vlogxhat=vlogm-log(ndraws2)+log(sum(exp(qlog)));
+ vlog_a0_Yao(k) = vlogxhat;
+ % The log value of p(a0_k|Y,a_others) where a_others: other a's at some point such as the peak of ONLY some a0's
+ else
+ skipline()
+ disp(sprintf('The last(6th) column or equation in A0 with no Gibbs draws'))
+ [A0gbs1, Wcell] = fn_gibbsrvar(A0gbs0,UT,nvar,fss,n0,indx_ks)
+ %------ See p.71, Forecast (II).
+ %------ Computing p(a0_k|Y,a_others) at some point such as the peak along the dimensions of indx_ks.
+ Vk = Tinv{k}\Wcell{k}; %V_k on p.71 of Forecast (II).
+ gbeta = Vk\bk; % inv(V_k)*b_k on p.71 of Forecast (II) where alpha_k = b_k in our notation.
+ [Vtq,Vtr]=qr(Vk',0); %To get inv(V_k)'*inv(V_k) in (*) on p.71 of Forecast (II).
+ %
+ vloglast = 0.5*(fss+nk)*log(fss)-log(abs(det(Vk)))-0.5*(nk-1)*log(2*pi)-...
+ 0.5*(fss+1)*log(2)-gammaln(0.5*(fss+1))+fss*log(abs(gbeta(1)))-...
+ 0.5*fss*bk'*(Vtr\(Vtr'\bk));
+ vlog_a0_Yao(k) = vloglast;
+ end
end
ndraws2
diff --git a/matlab/ms-sbvar/ms_sbvar_setup.m b/matlab/ms-sbvar/ms_sbvar_setup.m
index 93b417035719979b2bdb75a31c6507a4d1440100..2bdefad15c0b3cc18e8c1756a8e78bdd26ba77e8 100644
--- a/matlab/ms-sbvar/ms_sbvar_setup.m
+++ b/matlab/ms-sbvar/ms_sbvar_setup.m
@@ -29,7 +29,7 @@ function ms_sbvar_setup(options_)
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
options_.data = read_variables(options_.datafile, ...
- options_.varobs, [], options_.xls_sheet, options_.xls_range);
+ options_.varobs, [], options_.xls_sheet, options_.xls_range);
[options_.ms.final_year,options_.ms.final_subperiod] = check_datafile_years_assigned(options_);
if options_.ms.upper_cholesky
@@ -243,10 +243,10 @@ if indxPrior
else
[Pi,H0multi,Hpmulti,H0invmulti,Hpinvmulti] = fn_rnrprior(nvar,q_m,options_.ms.nlags,xdgel,mu);
end
-
+
%*** Combines asymmetric prior with linear restrictions
[Ptld,H0invtld,Hpinvtld] = fn_rlrprior(Ui,Vi,Pi,H0multi,Hpmulti,nvar);
-
+
%*** Obtains the posterior matrices for estimation and inference
[Pmat,H0inv,Hpinv] = fn_rlrpostr(xtx,xty,yty,Ptld,H0invtld,Hpinvtld,Ui,Vi);
else
@@ -258,7 +258,7 @@ if Rform
%*** Obtain the ML estimate
A0hatinv = chol(H0inv{1}/fss); % upper triangular but lower triangular choleski
A0hat=inv(A0hatinv);
-
+
Aphat = Pmat{1}*A0hat;
else
%*** Obtain the ML estimate
@@ -271,16 +271,16 @@ else
[fhat,xhat,grad,Hhat,itct,fcount,retcodehat] = csminwel('fn_a0freefun',x,H0,'fn_a0freegrad',crit,nit,Ui,nvar,n0,fss,H0inv);
A0hat = fn_tran_b2a(xhat,Ui,nvar,n0);
-
+
xhat = fn_tran_a2b(A0hat,Ui,nvar,n0);
[Aphat,ghat] = fn_gfmean(xhat,Pmat,Vi,nvar,ncoef,n0,np);
if indxC0Pres
Fhatur0P = Fhat; % ur: unrestriced across A0 and A+
for ki = 1:size(ixmC0Pres,1) % loop through the number of equations in which
- % cross-A0-A+ restrictions occur. See St. Louis Note p.5.
+ % cross-A0-A+ restrictions occur. See St. Louis Note p.5.
ixeq = ixmC0Pres{ki}(1,1); % index for the jth equation in consideration.
Lit = Vi{ixeq}(ixmC0Pres{ki}(:,2),:); % transposed restriction matrix Li
- % V_j(i,:) in f_j(i) = V_j(i,:)*g_j
+ % V_j(i,:) in f_j(i) = V_j(i,:)*g_j
ci = ixmC0Pres{ki}(:,4) .* A0hat(ixmC0Pres{ki}(:,3),ixeq);
% s * a_j(h) in the restriction f_j(i) = s * a_j(h).
LtH = Lit/Hpinv{ixeq};
diff --git a/matlab/ms-sbvar/ms_variance_decomposition.m b/matlab/ms-sbvar/ms_variance_decomposition.m
index 553488acd184828aabc0f1b836df2601a7b1de84..83e270171ae2cfeae473b1319e7acc87dda165f9 100644
--- a/matlab/ms-sbvar/ms_variance_decomposition.m
+++ b/matlab/ms-sbvar/ms_variance_decomposition.m
@@ -85,7 +85,7 @@ if options_.ms.regime || options_.ms.regimes
outfile = [outfile 'regime_'];
if options_.ms.regime
outfile = [outfile num2str(options_.ms.regime-1) ...
- '_' options_.ms.output_file_tag '.out'];
+ '_' options_.ms.output_file_tag '.out'];
end
elseif options_.ms.filtered_probabilities
outfile = [outfile 'filtered_' options_.ms.output_file_tag '.out'];
@@ -103,7 +103,7 @@ if options_.ms.regimes
for regime_i=1:n_regimes
vd_title = ['Variance Decomposition, Regime ' num2str(regime_i)];
vd_data = load([outfile num2str(regime_i-1) '_' ...
- options_.ms.output_file_tag '.out'], '-ascii');
+ options_.ms.output_file_tag '.out'], '-ascii');
vd_data = reshape_ascii_variance_decomposition_data( ...
M_.endo_nbr, percentiles_size, options_.ms.horizon, vd_data);
save([vddir filesep 'variance_decomposition_regime_' num2str(regime_i-1) '.mat'], 'vd_data');
diff --git a/matlab/ms-sbvar/ms_write_markov_file.m b/matlab/ms-sbvar/ms_write_markov_file.m
index 322a0fbf56f03a702cda06631931a5188d41a14d..b35409cf0a12fd6e00ae22836a2898476cb53ede 100644
--- a/matlab/ms-sbvar/ms_write_markov_file.m
+++ b/matlab/ms-sbvar/ms_write_markov_file.m
@@ -27,136 +27,136 @@ function ms_write_markov_file(fname, options)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- n_chains = length(options.ms.ms_chain);
- nvars = length(options.varobs);
-
- fh = fopen(fname,'w');
- %/******************************************************************************/
- %/********************* Markov State Variable Information **********************/
- %/******************************************************************************/
-
- fprintf(fh,'//== Markov State Variables with Simple Restrictions ==//\n\n');
-
-
- %//This number is NOT used but read in.
- fprintf(fh,'//== Number Observations ==//\n');
- fprintf(fh,'0\n\n');
-
- fprintf(fh,'//== Number independent state variables ==//\n');
- fprintf(fh,'%d\n\n',n_chains);
+n_chains = length(options.ms.ms_chain);
+nvars = length(options.varobs);
- for i_chain = 1:n_chains
+fh = fopen(fname,'w');
+%/******************************************************************************/
+%/********************* Markov State Variable Information **********************/
+%/******************************************************************************/
- %//=====================================================//
- %//== state_variable[i] (1 <= i <= n_state_variables) ==//
- %//=====================================================//
- fprintf(fh,'//== Number of states for state_variable[%d] ==//\n', ...
- i_chain);
- n_states = length(options.ms.ms_chain(i_chain).regime);
- fprintf(fh,'%d\n\n',n_states);
-
- %//== 03/15/06: DW TVBVAR code reads the data below and overwrite the prior data read somewhere else if any.
- %//== Each column contains the parameters for a Dirichlet prior on the corresponding
- %//== column of the transition matrix. Each element must be positive. For each column,
- %//== the relative size of the prior elements determine the relative size of the elements
- %//== of the transition matrix and overall larger sizes implies a tighter prior.
- fprintf(fh,['//== Transition matrix prior for state_variable[%d] ==//\n'], ...
- i_chain);
- Alpha = ones(n_states,n_states);
- for i_state = 1:n_states
- p = 1-1/options.ms.ms_chain(i_chain).regime(i_state).duration;
- Alpha(i_state,i_state) = p*(n_states-1)/(1-p);
- fprintf(fh,'%22.16f',Alpha(i_state,:));
- fprintf(fh,'\n');
+fprintf(fh,'//== Markov State Variables with Simple Restrictions ==//\n\n');
+
+
+%//This number is NOT used but read in.
+fprintf(fh,'//== Number Observations ==//\n');
+fprintf(fh,'0\n\n');
+
+fprintf(fh,'//== Number independent state variables ==//\n');
+fprintf(fh,'%d\n\n',n_chains);
+
+for i_chain = 1:n_chains
+
+ %//=====================================================//
+ %//== state_variable[i] (1 <= i <= n_state_variables) ==//
+ %//=====================================================//
+ fprintf(fh,'//== Number of states for state_variable[%d] ==//\n', ...
+ i_chain);
+ n_states = length(options.ms.ms_chain(i_chain).regime);
+ fprintf(fh,'%d\n\n',n_states);
+
+ %//== 03/15/06: DW TVBVAR code reads the data below and overwrite the prior data read somewhere else if any.
+ %//== Each column contains the parameters for a Dirichlet prior on the corresponding
+ %//== column of the transition matrix. Each element must be positive. For each column,
+ %//== the relative size of the prior elements determine the relative size of the elements
+ %//== of the transition matrix and overall larger sizes implies a tighter prior.
+ fprintf(fh,['//== Transition matrix prior for state_variable[%d] ==//\n'], ...
+ i_chain);
+ Alpha = ones(n_states,n_states);
+ for i_state = 1:n_states
+ p = 1-1/options.ms.ms_chain(i_chain).regime(i_state).duration;
+ Alpha(i_state,i_state) = p*(n_states-1)/(1-p);
+ fprintf(fh,'%22.16f',Alpha(i_state,:));
+ fprintf(fh,'\n');
+ end
+
+ fprintf(fh,['\n//== Dirichlet dimensions for state_variable[%d] ' ...
+ '==//\n'],i_chain);
+ % fprintf(fh,'%d ',repmat(n_states,1,n_states));
+ fprintf(fh,'%d ',repmat(2,1,n_states));
+ fprintf(fh,'\n\n');
+
+ %//== The jth restriction matrix is n_states-by-free[j]. Each row of the restriction
+ %//== matrix has exactly one non-zero entry and the sum of each column must be one.
+ fprintf(fh,['//== Column restrictions for state_variable[%d] ' ...
+ '==//\n'],i_chain);
+ for i_state = 1:n_states
+ if i_state == 1
+ M = eye(n_states,2);
+ elseif i_state == n_states
+ M = [zeros(n_states-2,2); eye(2)];
+ else
+ M = zeros(n_states,2);
+ M(i_state+[-1 1],1) = ones(2,1)/2;
+ M(i_state,2) = 1;
+ disp(M)
end
-
- fprintf(fh,['\n//== Dirichlet dimensions for state_variable[%d] ' ...
- '==//\n'],i_chain);
- % fprintf(fh,'%d ',repmat(n_states,1,n_states));
- fprintf(fh,'%d ',repmat(2,1,n_states));
- fprintf(fh,'\n\n');
-
- %//== The jth restriction matrix is n_states-by-free[j]. Each row of the restriction
- %//== matrix has exactly one non-zero entry and the sum of each column must be one.
- fprintf(fh,['//== Column restrictions for state_variable[%d] ' ...
- '==//\n'],i_chain);
- for i_state = 1:n_states
- if i_state == 1
- M = eye(n_states,2);
- elseif i_state == n_states
- M = [zeros(n_states-2,2); eye(2)];
- else
- M = zeros(n_states,2);
- M(i_state+[-1 1],1) = ones(2,1)/2;
- M(i_state,2) = 1;
- disp(M)
- end
- for j_state = 1:n_states
- fprintf(fh,'%f ',M(j_state,:));
- fprintf(fh,'\n');
- end
+ for j_state = 1:n_states
+ fprintf(fh,'%f ',M(j_state,:));
fprintf(fh,'\n');
end
+ fprintf(fh,'\n');
end
+end
+
+%/******************************************************************************/
+%/******************************* VAR Parameters *******************************/
+%/******************************************************************************/
+%//NOT read
+fprintf(fh,'//== Number Variables ==//\n');
+fprintf(fh,'%d\n\n',nvars);
+
+%//NOT read
+fprintf(fh,'//== Number Lags ==//\n');
+fprintf(fh,'%d\n\n',options.ms.nlags);
+
+%//NOT read
+fprintf(fh,'//== Exogenous Variables ==//\n');
+fprintf(fh,'1\n\n');
+
+
+%//== nvar x n_state_variables matrix. In the jth row, a non-zero value implies that
+%this state variable controls the jth column of A0 and Aplus
+fprintf(fh,['//== Controlling states variables for coefficients ==//\' ...
+ 'n']);
- %/******************************************************************************/
- %/******************************* VAR Parameters *******************************/
- %/******************************************************************************/
- %//NOT read
- fprintf(fh,'//== Number Variables ==//\n');
- fprintf(fh,'%d\n\n',nvars);
-
- %//NOT read
- fprintf(fh,'//== Number Lags ==//\n');
- fprintf(fh,'%d\n\n',options.ms.nlags);
-
- %//NOT read
- fprintf(fh,'//== Exogenous Variables ==//\n');
- fprintf(fh,'1\n\n');
-
-
- %//== nvar x n_state_variables matrix. In the jth row, a non-zero value implies that
- %this state variable controls the jth column of A0 and Aplus
- fprintf(fh,['//== Controlling states variables for coefficients ==//\' ...
- 'n']);
-
- for i_var = 1:nvars
- for i_chain = 1:n_chains
- if ~isfield(options.ms.ms_chain(i_chain),'svar_coefficients') ...
- || isempty(options.ms.ms_chain(i_chain).svar_coefficients)
- i_equations = 0;
- else
- i_equations = ...
- options.ms.ms_chain(i_chain).svar_coefficients.equations;
- end
- if strcmp(i_equations,'ALL') || any(i_equations == i_var)
- fprintf(fh,'%d ',1);
- else
- fprintf(fh,'%d ',0);
- end
+for i_var = 1:nvars
+ for i_chain = 1:n_chains
+ if ~isfield(options.ms.ms_chain(i_chain),'svar_coefficients') ...
+ || isempty(options.ms.ms_chain(i_chain).svar_coefficients)
+ i_equations = 0;
+ else
+ i_equations = ...
+ options.ms.ms_chain(i_chain).svar_coefficients.equations;
+ end
+ if strcmp(i_equations,'ALL') || any(i_equations == i_var)
+ fprintf(fh,'%d ',1);
+ else
+ fprintf(fh,'%d ',0);
end
- fprintf(fh,'\n');
end
+ fprintf(fh,'\n');
+end
- %//== nvar x n_state_variables matrix. In the jth row, a non-zero value implies that
- %this state variable controls the jth diagonal element of Xi
- fprintf(fh,'\n//== Controlling states variables for variance ==//\n');
- for i_var = 1:nvars
- for i_chain = 1:n_chains
- if ~isfield(options.ms.ms_chain(i_chain),'svar_variances') ...
- || isempty(options.ms.ms_chain(i_chain).svar_variances)
- i_equations = 0;
- else
- i_equations = ...
- options.ms.ms_chain(i_chain).svar_variances.equations;
- end
- if strcmp(i_equations,'ALL') || any(i_equations == i_var)
- fprintf(fh,'%d ',1);
- else
- fprintf(fh,'%d ',0);
- end
+%//== nvar x n_state_variables matrix. In the jth row, a non-zero value implies that
+%this state variable controls the jth diagonal element of Xi
+fprintf(fh,'\n//== Controlling states variables for variance ==//\n');
+for i_var = 1:nvars
+ for i_chain = 1:n_chains
+ if ~isfield(options.ms.ms_chain(i_chain),'svar_variances') ...
+ || isempty(options.ms.ms_chain(i_chain).svar_variances)
+ i_equations = 0;
+ else
+ i_equations = ...
+ options.ms.ms_chain(i_chain).svar_variances.equations;
+ end
+ if strcmp(i_equations,'ALL') || any(i_equations == i_var)
+ fprintf(fh,'%d ',1);
+ else
+ fprintf(fh,'%d ',0);
end
- fprintf(fh,'\n');
end
+ fprintf(fh,'\n');
+end
- fclose(fh);
+fclose(fh);
diff --git a/matlab/ms-sbvar/ms_write_mhm_input.m b/matlab/ms-sbvar/ms_write_mhm_input.m
index 57854d59ae3f6958fb3ca785859467f73a2b3f51..1edd4b4a2d6ae84dcba23501a7004e5aa755bb5a 100644
--- a/matlab/ms-sbvar/ms_write_mhm_input.m
+++ b/matlab/ms-sbvar/ms_write_mhm_input.m
@@ -28,41 +28,41 @@ function ms_write_mhm_input(fname, options_ms)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- fh = fopen(fname,'w');
+fh = fopen(fname,'w');
- fprintf(fh,'/**********************************************************\n');
- fprintf(fh,' *** This input file is read by swzmsbvar_mhm_1 and swzmsbvar_mhm_1.exe only, NOT by swzmsbvar_printdraws.exe.\n');
- fprintf(fh,' ***\n');
- fprintf(fh,' **********************************************************/\n');
+fprintf(fh,'/**********************************************************\n');
+fprintf(fh,' *** This input file is read by swzmsbvar_mhm_1 and swzmsbvar_mhm_1.exe only, NOT by swzmsbvar_printdraws.exe.\n');
+fprintf(fh,' ***\n');
+fprintf(fh,' **********************************************************/\n');
- fprintf(fh,'\n\n//------------- 1st set of posterior draws to find optimal scales for Metropolis (30000). ---------------\n');
- fprintf(fh,'//== number draws for first burn-in ==// //For determining the Metropolis scales only.\n');
- fprintf(fh,'%d\n\n',options_ms.draws_nbr_burn_in_1);
+fprintf(fh,'\n\n//------------- 1st set of posterior draws to find optimal scales for Metropolis (30000). ---------------\n');
+fprintf(fh,'//== number draws for first burn-in ==// //For determining the Metropolis scales only.\n');
+fprintf(fh,'%d\n\n',options_ms.draws_nbr_burn_in_1);
- fprintf(fh,'//------------- MCMC burn-in draws once the Metropolis scales (previous stage) are fixed. --------------\n');
- fprintf(fh,'//------------- 2nd set of standard burn-in posterior draws to throw away the initial draws (10000). ---------------\n');
- fprintf(fh,'//== number draws for second burn-in ==//\n');
- fprintf(fh,'%d\n\n',options_ms.draws_nbr_burn_in_2);
+fprintf(fh,'//------------- MCMC burn-in draws once the Metropolis scales (previous stage) are fixed. --------------\n');
+fprintf(fh,'//------------- 2nd set of standard burn-in posterior draws to throw away the initial draws (10000). ---------------\n');
+fprintf(fh,'//== number draws for second burn-in ==//\n');
+fprintf(fh,'%d\n\n',options_ms.draws_nbr_burn_in_2);
- fprintf(fh,'//--------------- 1st set of posterior draws to compute the mean and variance for the weighting function in the MHM (200000) ----------------\n');
- fprintf(fh,'//== number draws to estimate mean and variance ==//\n');
- fprintf(fh,'%d\n\n',options_ms.draws_nbr_mean_var_estimate);
+fprintf(fh,'//--------------- 1st set of posterior draws to compute the mean and variance for the weighting function in the MHM (200000) ----------------\n');
+fprintf(fh,'//== number draws to estimate mean and variance ==//\n');
+fprintf(fh,'%d\n\n',options_ms.draws_nbr_mean_var_estimate);
- fprintf(fh,'//--------------- Only applied to mhm_2 process: total number of MCMC draws = thinning factor * 2nd set of saved posterior draws ----------------\n');
- fprintf(fh,'//== thinning factor for modified harmonic mean process ==//\n');
- fprintf(fh,'%d\n\n',options_ms.thinning_factor);
+fprintf(fh,'//--------------- Only applied to mhm_2 process: total number of MCMC draws = thinning factor * 2nd set of saved posterior draws ----------------\n');
+fprintf(fh,'//== thinning factor for modified harmonic mean process ==//\n');
+fprintf(fh,'%d\n\n',options_ms.thinning_factor);
- fprintf(fh,'//--------------- 2nd set of saved posterior draws from MHM_2 (second stage): saved draws AFTER thinning (1000000) ----------------\n');
- fprintf(fh,'//== number draws for modified harmonic mean process ==//\n');
- fprintf(fh,'%d\n\n',options_ms.draws_nbr_modified_harmonic_mean);
+fprintf(fh,'//--------------- 2nd set of saved posterior draws from MHM_2 (second stage): saved draws AFTER thinning (1000000) ----------------\n');
+fprintf(fh,'//== number draws for modified harmonic mean process ==//\n');
+fprintf(fh,'%d\n\n',options_ms.draws_nbr_modified_harmonic_mean);
- fprintf(fh,'//------- 1st stage: computing all three tightness factors for Dirichlet. ---------\n');
- fprintf(fh,'//------- 2nd stage: hard-code the second scale factor (in principle, we can do all three). ---------\n');
- fprintf(fh,'//------- It seems that Dan''s code only use the first element of the following scales. The scale applies to the Dirichlet''s hyperparameter alpha for the diagonal of the transition matrix in the weighting function. Note that the weighting function for the transition matrix parameters is Dirichlet. ---------\n');
-
- fprintf(fh,'//== scale values for Dirichlet distribution ==//\n');
- fprintf(fh,'3\n\n');
- fprintf(fh,'%f ',options_ms.dirichlet_scale);
- fprintf(fh,'\n');
- fclose(fh);
\ No newline at end of file
+fprintf(fh,'//------- 1st stage: computing all three tightness factors for Dirichlet. ---------\n');
+fprintf(fh,'//------- 2nd stage: hard-code the second scale factor (in principle, we can do all three). ---------\n');
+fprintf(fh,'//------- It seems that Dan''s code only use the first element of the following scales. The scale applies to the Dirichlet''s hyperparameter alpha for the diagonal of the transition matrix in the weighting function. Note that the weighting function for the transition matrix parameters is Dirichlet. ---------\n');
+
+fprintf(fh,'//== scale values for Dirichlet distribution ==//\n');
+fprintf(fh,'3\n\n');
+fprintf(fh,'%f ',options_ms.dirichlet_scale);
+fprintf(fh,'\n');
+fclose(fh);
\ No newline at end of file
diff --git a/matlab/ms-sbvar/msstart2.m b/matlab/ms-sbvar/msstart2.m
index 284f6eaeea15643b9bb327a23808224a399a939b..95491d5af1c345d9188d7fc598df5de6e0b9fa4e 100644
--- a/matlab/ms-sbvar/msstart2.m
+++ b/matlab/ms-sbvar/msstart2.m
@@ -45,17 +45,17 @@ msstart_setup
%options_.ms.Indxcol = [1:nvar]; % a vector of random columns in which MC draws are made.
%
%options_.ms.indxparr = 1; % 1: parameters random; 0: no randomness in parameters
- % Note, when 0, there is no effect from the values of options_.ms.IndxAp, options_.ms.Aband, etc.
+% Note, when 0, there is no effect from the values of options_.ms.IndxAp, options_.ms.Aband, etc.
%options_.ms.indxovr = 0; % 1: distributions for other variables of interest; 0: no distribution.
- % Example: joint distribution of a(1) and a(2). Only for specific purposes
+% Example: joint distribution of a(1) and a(2). Only for specific purposes
%options_.ms.Aband = 1; % 1: error bands with only A0 and A+ random.
%options_.ms.IndxAp = 1; % 1: generate draws of A+; 0: no such draws.
- % Note: when options_.ms.IndxAp=0, there is no effect from the values of options_.ms.options_.ms.options_.ms.options_.ms.indximf, IndxFore,
- % or options_.ms.apband.
+% Note: when options_.ms.IndxAp=0, there is no effect from the values of options_.ms.options_.ms.options_.ms.options_.ms.indximf, IndxFore,
+% or options_.ms.apband.
%options_.ms.apband = 1; % 1: error bands for A+; 0: no error bands for A+.
%*** The following (impulse responses and forecasts) is used only if options_.ms.IndxAp=1
%options_.ms.indximf = 1; % 1: generate draws of impulse responses; 0: no such draws (thus no effect
- % from options_.ms.imfband)
+% from options_.ms.imfband)
%options_.ms.imfband = 1; % 1: error bands for impulse responses; 0: no error bands
%options_.ms.indxfore = 0; % 1: generate draws of forecasts; 0: no such draws (thus no effect from options_.ms.foreband)
%options_.ms.foreband = 0; % 1: error bands for out-of-sample forecasts; 0: no error bands
@@ -64,23 +64,23 @@ msstart_setup
rnum = nvar; % number of rows in the graph
cnum = 1; % number of columns in the graph
if rnum*cnum<nvar
- warning('rnum*cnum must be at least as large as nvar')
- disp('Hit any key to continue, or ctrl-c to abort')
- pause
+ warning('rnum*cnum must be at least as large as nvar')
+ disp('Hit any key to continue, or ctrl-c to abort')
+ pause
end
%options_.ms.indxgimfhat = 1; % 1: plot ML impulse responses; 0: no plot
%options_.ms.indxestima = 1; %1: ML estimation; 0: no estimation and data only
%
IndxNmlr = [1 0 0 0 0 0]; % imported by nmlzvar.m
- % 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 let A0inu=[])
- % IndxNmlr(6): Assigned postive rule (such as off-diagonal elements). Added 1/3/00
+ % 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 let A0inu=[])
+ % IndxNmlr(6): Assigned postive rule (such as off-diagonal elements). Added 1/3/00
%%%%----------------------------------------
@@ -90,7 +90,7 @@ IndxNmlr = [1 0 0 0 0 0]; % imported by nmlzvar.m
nconstr1=nfqm; % number of the 1st set of constraints
nconstr2=options_.forecast ; % number of the 2nd set of constraints
nconstr=nconstr1+nconstr2; % q: 4 years -- 4*12 months.
- % When 0, no conditions directly on variables <<>>
+ % When 0, no conditions directly on variables <<>>
nconstr=0 ; %6*nconstr1;
options_.ms.eq_ms = []; % location of MS equation; if [], all shocks
PorR = [4*ones(nconstr1,1);2*ones(nconstr1,1);3*ones(nconstr1,1)]; % the variable conditioned. 1: Pcm; 3: FFR; 4: CPI
@@ -102,32 +102,32 @@ PorR = [PorR;1*ones(nconstr1,1);5*ones(nconstr1,1);6*ones(nconstr1,1)];
% Conditions directly on future shocks
%
%options_.ms.cms = 0 % 1: condition on ms shocks; 0: disable this and "fidcnderr.m" gives
- % unconditional forecasts if nconstr = 0 as well; <<>>
+% unconditional forecasts if nconstr = 0 as well; <<>>
%options_.ms.ncms = 0; % number of the stance of policy; 0 if no tightening or loosening
%options_.ms.eq_cms = 1; % location of MS shocks
options_.ms.tlindx = 1*ones(1,options_.ms.ncms); % 1-by-options_.ms.ncms vector; 1: tightening; 0: loosen
options_.ms.tlnumber = [0.5 0.5 0 0]; %94:4 % [2 2 1.5 1.5]; %79:9 %[1.5 1.5 1 1]; 90:9
- % 1-by-options_.ms.ncms vector; cut-off point for MS shocks
+ % 1-by-options_.ms.ncms vector; cut-off point for MS shocks
TLmean = zeros(1,options_.ms.ncms);
- % unconditional, i.e., 0 mean, for the final report in the paper
+% unconditional, i.e., 0 mean, for the final report in the paper
if options_.ms.cms
- options_.ms.eq_ms = [];
- % At least at this point, it makes no sense to have DLS type of options_.ms.eq_ms; 10/12/98
- if all(isfinite(options_.ms.tlnumber))
- for k=1:options_.ms.ncms
- TLmean(k) = lcnmean(options_.ms.tlnumber(k),options_.ms.tlindx(k));
- % shock mean magnitude. 1: tight; 0: loose
- % Never used for any subsequent computation but
- % simply used for the final report in the paper.
- %options_.ms.tlnumber(k) = fzero('lcutoff',0,[],[],TLmean(k))
- % get an idea about the cutoff point given TLmean instead
-
- end
- end
+ options_.ms.eq_ms = [];
+ % At least at this point, it makes no sense to have DLS type of options_.ms.eq_ms; 10/12/98
+ if all(isfinite(options_.ms.tlnumber))
+ for k=1:options_.ms.ncms
+ TLmean(k) = lcnmean(options_.ms.tlnumber(k),options_.ms.tlindx(k));
+ % shock mean magnitude. 1: tight; 0: loose
+ % Never used for any subsequent computation but
+ % simply used for the final report in the paper.
+ %options_.ms.tlnumber(k) = fzero('lcutoff',0,[],[],TLmean(k))
+ % get an idea about the cutoff point given TLmean instead
+
+ end
+ end
else
- options_.ms.ncms = 0; % only for the use of the graph by msprobg.m
- options_.ms.tlnumber = NaN*ones(1,options_.ms.ncms);
- % -infinity, only for the use of the graph by msprobg.m
+ options_.ms.ncms = 0; % only for the use of the graph by msprobg.m
+ options_.ms.tlnumber = NaN*ones(1,options_.ms.ncms);
+ % -infinity, only for the use of the graph by msprobg.m
end
@@ -135,22 +135,22 @@ end
% Soft conditions on variables
%
%cnum = 0 % # of band condtions; when 0, disable this option
- % Note (different from "fidencon") that each condition corres. to variable
+% Note (different from "fidencon") that each condition corres. to variable
%options_.ms.banact = 1; % 1: use infor on actual; 0: preset without infor on actual
if cnum
- banindx = cell(cnum,1); % index for each variable or conditon
- banstp = cell(cnum,1); % steps: annual in general
- banvar = zeros(cnum,1); % varables: annual in general
- banval = cell(cnum,1); % band value (each variable occupy a cell)
- badval{1} = zeros(length(banstp{1}),2); % 2: lower or higher bound
-
- banstp{1} = 1:4; % 3 or 4 years
- banvar(1) = 3; % 3: FFR; 5: CPI
- if ~options_.ms.banact
- for i=1:length(banstp{1})
- banval{1}(i,:) = [5.0 10.0];
- end
- end
+ banindx = cell(cnum,1); % index for each variable or conditon
+ banstp = cell(cnum,1); % steps: annual in general
+ banvar = zeros(cnum,1); % varables: annual in general
+ banval = cell(cnum,1); % band value (each variable occupy a cell)
+ badval{1} = zeros(length(banstp{1}),2); % 2: lower or higher bound
+
+ banstp{1} = 1:4; % 3 or 4 years
+ banvar(1) = 3; % 3: FFR; 5: CPI
+ if ~options_.ms.banact
+ for i=1:length(banstp{1})
+ banval{1}(i,:) = [5.0 10.0];
+ end
+ end
end
%
pause(1)
@@ -171,60 +171,60 @@ pause(1)
%(1)--------------------------------------
%
if (options_.ms.freq==12)
- nStart=(yrStart-options_.ms.initial_year )*12+qmStart-options_.ms.initial_subperiod ; % positive number of months at the start
- nEnd=(yrEnd-options_.ms.final_year )*12+qmEnd-options_.ms.final_subperiod ; % negative number of months towards end
+ nStart=(yrStart-options_.ms.initial_year )*12+qmStart-options_.ms.initial_subperiod ; % positive number of months at the start
+ nEnd=(yrEnd-options_.ms.final_year )*12+qmEnd-options_.ms.final_subperiod ; % negative number of months towards end
elseif (options_.ms.freq==4)
- nStart=(yrStart-options_.ms.initial_year )*4+qmStart-options_.ms.initial_subperiod ; % positive number of months at the start
- nEnd=(yrEnd-options_.ms.final_year )*4+qmEnd-options_.ms.final_subperiod ; % negative number of months towards end
+ nStart=(yrStart-options_.ms.initial_year )*4+qmStart-options_.ms.initial_subperiod ; % positive number of months at the start
+ nEnd=(yrEnd-options_.ms.final_year )*4+qmEnd-options_.ms.final_subperiod ; % negative number of months towards end
elseif (options_.ms.freq==1)
- nStart=(yrStart-options_.ms.initial_year )*1+qmStart-options_.ms.initial_subperiod ; % positive number of months at the start
- nEnd=(yrEnd-options_.ms.final_year )*1+qmEnd-options_.ms.final_subperiod ; % negative number of months towards end
+ nStart=(yrStart-options_.ms.initial_year )*1+qmStart-options_.ms.initial_subperiod ; % positive number of months at the start
+ nEnd=(yrEnd-options_.ms.final_year )*1+qmEnd-options_.ms.final_subperiod ; % negative number of months towards end
else
- error('Error: this code is only good for monthly/quarterly/yearly data!!!')
- return
+ error('Error: this code is only good for monthly/quarterly/yearly data!!!')
+ return
end
%
if nEnd>0 || nStart<0
- disp('Warning: this particular sample consider is out of bounds of the data!!!')
- return
+ disp('Warning: this particular sample consider is out of bounds of the data!!!')
+ return
end
%*** Note, both xdgel and xdata have the same start with the specific sample
xdgel=options_.data(nStart+1:nData+nEnd,options_.ms.vlist);
- % gel: general options_.data within sample (nSample)
+% gel: general options_.data within sample (nSample)
if ~(nSample==size(xdgel,1))
- warning('The sample size (including options_.ms.nlags ) and data are incompatible')
- disp('Check to make sure nSample and size(xdgel,1) are the same')
- return
+ warning('The sample size (including options_.ms.nlags ) and data are incompatible')
+ disp('Check to make sure nSample and size(xdgel,1) are the same')
+ return
end
%
baddata = find(isnan(xdgel));
if ~isempty(baddata)
- warning('Some data for this selected sample are actually unavailable.')
- disp('Hit any key to continue, or ctrl-c to abort')
- pause
+ warning('Some data for this selected sample are actually unavailable.')
+ disp('Hit any key to continue, or ctrl-c to abort')
+ pause
end
%
if options_.ms.initial_subperiod ==1
- yrB = options_.ms.initial_year ; qmB = options_.ms.initial_subperiod ;
+ yrB = options_.ms.initial_year ; qmB = options_.ms.initial_subperiod ;
else
- yrB = options_.ms.initial_year +1; qmB = 1;
+ yrB = options_.ms.initial_year +1; qmB = 1;
end
yrF = options_.ms.final_year ; qmF = options_.ms.final_subperiod ;
[Mdate,tmp] = fn_calyrqm(options_.ms.freq,[options_.ms.initial_year options_.ms.initial_subperiod ],[options_.ms.final_year options_.ms.final_subperiod ]);
xdatae=[Mdate options_.data(1:nData,options_.ms.vlist)];
- % beyond sample into forecast horizon until the end of the data options_.ms.final_year :options_.ms.final_subperiod
- % Note: may contain NaN data. So must be careful about its use
+% beyond sample into forecast horizon until the end of the data options_.ms.final_year :options_.ms.final_subperiod
+% Note: may contain NaN data. So must be careful about its use
%=========== Obtain prior-period, period-to-last period, and annual growth rates
[yactyrge,yactyre,yactqmyge,yactqmge,yactqme] = fn_datana(xdatae,options_.ms.freq,options_.ms.log_var,options_.ms.percent_var,[yrB qmB],[yrF qmF]);
qdates = zeros(size(yactqmyge,1),1);
for ki=1:length(qdates)
- qdates(ki) = yactqmyge(1,1) + (yactqmyge(1,2)+ki-2)/options_.ms.freq;
+ qdates(ki) = yactqmyge(1,1) + (yactqmyge(1,2)+ki-2)/options_.ms.freq;
end
for ki=1:nvar
- figure
- plot(qdates, yactqmyge(:,2+ki)/100)
- xlabel(options_.ms.varlist{ki})
+ figure
+ plot(qdates, yactqmyge(:,2+ki)/100)
+ xlabel(options_.ms.varlist{ki})
end
save outactqmygdata.prn yactqmyge -ascii
@@ -236,11 +236,11 @@ spstr1 = 'disp([sprintf(';
spstr2 = '%4.0f %2.0f';
yactyrget=yactyrge';
for ki=1:length(options_.ms.vlist)
- if ki==length(options_.ms.vlist)
- spstr2 = [spstr2 ' %8.3f\n'];
- else
- spstr2 = [spstr2 ' %8.3f'];
- end
+ if ki==length(options_.ms.vlist)
+ spstr2 = [spstr2 ' %8.3f\n'];
+ else
+ spstr2 = [spstr2 ' %8.3f'];
+ end
end
spstr = [spstr1 'spstr2' ', yactyrget)])'];
eval(spstr)
@@ -251,11 +251,11 @@ fid = fopen('outyrqm.prn','w');
fpstr1 = 'fprintf(fid,';
fpstr2 = '%4.0f %2.0f';
for ki=1:nvar
- if ki==nvar
- fpstr2 = [fpstr2 ' %8.3f\n'];
- else
- fpstr2 = [fpstr2 ' %8.3f'];
- end
+ if ki==nvar
+ fpstr2 = [fpstr2 ' %8.3f\n'];
+ else
+ fpstr2 = [fpstr2 ' %8.3f'];
+ end
end
fpstr = [fpstr1 'fpstr2' ', yactyrget);'];
eval(fpstr)
@@ -264,479 +264,479 @@ fclose(fid);
if options_.ms.indxestima
- %(2)----------------------------------------------------------------------------
- % Estimation
- % ML forecast and impulse responses
- % Hard or soft conditions for conditional forecasts
- %(2)----------------------------------------------------------------------------
- %
- %* Arranged data information, WITHOUT dummy obs when 0 after mu is used. See fn_rnrprior_covres_dobs.m for using the dummy
- % observations as part of an explicit prior.
- [xtx,xty,yty,fss,phi,y,ncoef,xr,Bh] = fn_dataxy(nvar,options_.ms.nlags ,xdgel,mu,0,nexo);
- if qmStart+options_.ms.nlags -options_.ms.dummy_obs >0
- qmStartEsti = rem(qmStart+options_.ms.nlags -options_.ms.dummy_obs ,options_.ms.freq); % dummy observations are included in the sample.
- if (~qmStartEsti)
- qmStartEsti = options_.ms.freq;
- end
- yrStartEsti = yrStart + floor((qmStart+options_.ms.nlags -options_.ms.dummy_obs )/(options_.ms.freq+0.01));
+ %(2)----------------------------------------------------------------------------
+ % Estimation
+ % ML forecast and impulse responses
+ % Hard or soft conditions for conditional forecasts
+ %(2)----------------------------------------------------------------------------
+ %
+ %* Arranged data information, WITHOUT dummy obs when 0 after mu is used. See fn_rnrprior_covres_dobs.m for using the dummy
+ % observations as part of an explicit prior.
+ [xtx,xty,yty,fss,phi,y,ncoef,xr,Bh] = fn_dataxy(nvar,options_.ms.nlags ,xdgel,mu,0,nexo);
+ if qmStart+options_.ms.nlags -options_.ms.dummy_obs >0
+ qmStartEsti = rem(qmStart+options_.ms.nlags -options_.ms.dummy_obs ,options_.ms.freq); % dummy observations are included in the sample.
+ if (~qmStartEsti)
+ qmStartEsti = options_.ms.freq;
+ end
+ yrStartEsti = yrStart + floor((qmStart+options_.ms.nlags -options_.ms.dummy_obs )/(options_.ms.freq+0.01));
% + 0.01 (or any number < 1) is used so that qmStart+options_.ms.nlags -options_.ms.dummy_obs ==?*options_.ms.freq doesn't give us an extra year forward.
- else
- qmStartEsti = options_.ms.freq + rem(qmStart+options_.ms.nlags -options_.ms.dummy_obs ,options_.ms.freq); % dummy observations are included in the sample.
- if (qmStart+options_.ms.nlags -options_.ms.dummy_obs ==0)
- yrStartEsti = yrStart - 1; % one year back.
- else
- yrStartEsti = yrStart + floor((qmStart+options_.ms.nlags -options_.ms.dummy_obs )/(options_.ms.freq-0.01));
- % - 0.01 (or any number < 1) is used so that qmStart+options_.ms.nlags -options_.ms.dummy_obs ==-?*options_.ms.freq give us an extra year back.
- end
- end
- dateswd = fn_dataext([yrStartEsti qmStartEsti],[yrEnd qmEnd],xdatae(:,[1:2])); % dates with dummies
- phie = [dateswd phi];
- ye = [dateswd y];
-
- %* Obtain linear restrictions
- [Uiconst,Viconst,n0,np,ixmC0Pres] = feval(options_.ms.restriction_fname,nvar,nexo,options_.ms );
- if min(n0)==0
- skipline()
- warning('A0: restrictions in dlrprior.m give no free parameter in one of equations')
- disp('Press ctrl-c to abort')
- pause
- elseif min(np)==0
- skipline()
- warning('Ap: Restrictions in dlrprior.m give no free parameter in one of equations')
- disp('Press ctrl-c to abort')
- pause
- end
-
- if options_.ms.contemp_reduced_form
- Uiconst=cell(nvar,1); Viconst=cell(ncoef,1);
- for kj=1:nvar
- Uiconst{kj} = eye(nvar); Viconst{kj} = eye(ncoef);
- end
- end
-
- if options_.ms.bayesian_prior
- %*** Obtains asymmetric prior (with no linear restrictions) with dummy observations as part of an explicit prior (i.e,
- % reflected in Hpmulti and Hpinvmulti). See Forecast II, pp.69a-69b for details.
- if 1 % Liquidity effect prior on both MS and MD equations.
- [Pi,H0multi,Hpmulti,H0invmulti,Hpinvmulti] = fn_rnrprior_covres_dobs(nvar,options_.ms.freq,options_.ms.nlags ,xdgel,mu,indxDummy,hpmsmd,indxmsmdeqn);
- else
- [Pi,H0multi,Hpmulti,H0invmulti,Hpinvmulti] = fn_rnrprior(nvar,options_.ms.freq,options_.ms.nlags ,xdgel,mu);
- end
-
- %*** Combines asymmetric prior with linear restrictions
- [Ptld,H0invtld,Hpinvtld] = fn_rlrprior(Uiconst,Viconst,Pi,H0multi,Hpmulti,nvar);
-
- %*** Obtains the posterior matrices for estimation and inference
- [Pmat,H0inv,Hpinv] = fn_rlrpostr(xtx,xty,yty,Ptld,H0invtld,Hpinvtld,Uiconst,Viconst);
-
- if options_.ms.contemp_reduced_form
- %*** Obtain the ML estimate
- A0hatinv = chol(H0inv{1}/fss); % upper triangular but lower triangular choleski
- A0hat=inv(A0hatinv);
- a0indx = find(A0hat);
- else
- %*** Obtain the ML estimate
- % load idenml
- x = 10*rand(sum(n0),1);
- H0 = eye(sum(n0));
- crit = 1.0e-9;
- nit = 10000;
- %
- [fhat,xhat,grad,Hhat,itct,fcount,retcodehat] = ...
- csminwel('fn_a0freefun',x,H0,'fn_a0freegrad',crit,nit,Uiconst,nvar,n0,fss,H0inv);
-
- A0hat = fn_tran_b2a(xhat,Uiconst,nvar,n0)
- A0hatinv = inv(A0hat);
- fhat
- xhat
- grad
- itct
- fcount
- retcodehat
- save outm.mat xhat A0hat A0hatinv grad fhat itct itct fcount retcodehat
- end
- else
- %*** Obtain the posterior matrices for estimation and inference
- [Pmat,H0inv,Hpinv] = fn_dlrpostr(xtx,xty,yty,Uiconst,Viconst);
-
- if options_.ms.contemp_reduced_form
- %*** Obtain the ML estimate
- A0hatinv = chol(H0inv{1}/fss); % upper triangular but lower triangular choleski
- A0hat=inv(A0hatinv);
- a0indx = find(A0hat);
- else
- %*** Obtain the ML estimate
- % load idenml
- x = 10*rand(sum(n0),1);
- H0 = eye(sum(n0));
- crit = 1.0e-9;
- nit = 10000;
- %
- [fhat,xhat,grad,Hhat,itct,fcount,retcodehat] = ...
- csminwel('fn_a0freefun',x,H0,'fn_a0freegrad',crit,nit,Uiconst,nvar,n0,fss,H0inv);
-
- A0hat = fn_tran_b2a(xhat,Uiconst,nvar,n0)
- A0hatinv = inv(A0hat);
- fhat
- xhat
- grad
- itct
- fcount
- retcodehat
- save outm.mat xhat A0hat A0hatinv grad fhat itct itct fcount retcodehat
- end
- end
-
- %**** impulse responses
- swish = A0hatinv; % each column corresponds to an equation
- if options_.ms.contemp_reduced_form
- xhat = A0hat(a0indx);
- Bhat=Pmat{1};
- Fhat = Bhat*A0hat
- ghat = NaN;
- else
- xhat = fn_tran_a2b(A0hat,Uiconst,nvar,n0);
- [Fhat,ghat] = fn_gfmean(xhat,Pmat,Viconst,nvar,ncoef,n0,np);
- if options_.ms.cross_restrictions
- Fhatur0P = Fhat; % ur: unrestriced across A0 and A+
- for ki = 1:size(ixmC0Pres,1) % loop through the number of equations in which
- % cross-A0-A+ restrictions occur. See St. Louis Note p.5.
- ixeq = ixmC0Pres{ki}(1,1); % index for the jth equation in consideration.
- Lit = Viconst{ixeq}(ixmC0Pres{ki}(:,2),:); % transposed restriction matrix Li
- % V_j(i,:) in f_j(i) = V_j(i,:)*g_j
- ci = ixmC0Pres{ki}(:,4) .* A0hat(ixmC0Pres{ki}(:,3),ixeq);
- % s * a_j(h) in the restriction f_j(i) = s * a_j(h).
- LtH = Lit/Hpinv{ixeq};
- HLV = LtH'/(LtH*Lit');
- gihat = Viconst{ixeq}'*Fhatur0P(:,ixeq);
- Fhat(:,ixeq) = Viconst{ixeq}*(gihat + HLV*(ci-Lit*gihat));
- end
- end
- Fhat
- Bhat = Fhat/A0hat; % ncoef-by-nvar reduced form lagged parameters.
- end
- nn = [nvar options_.ms.nlags imstp];
- imfhat = fn_impulse(Bhat,swish,nn); % in the form that is congenial to RATS
- imf3hat=reshape(imfhat,size(imfhat,1),nvar,nvar);
- % imf3: row--steps, column--nvar responses, 3rd dimension--nvar shocks
- imf3shat=permute(imf3hat,[1 3 2]);
- % imf3s: permuted so that row--steps, column--nvar shocks,
- % 3rd dimension--nvar responses
- % Note: reshape(imf3s(1,:,:),nvar,nvar) = A0in (columns -- equations)
- if options_.ms.indxgimfhat
- figure
- end
- scaleout = fn_imcgraph(imfhat,nvar,imstp,xlab,ylab,options_.ms.indxgimfhat);
- imfstd = max(abs(scaleout)'); % row: nvar (largest number); used for standard deviations
-
- %
- % %**** save stds. of both data and impulse responses in idfile1
- % temp = [std(yactqmyge(:,3:end)); std(yactyrge(:,3:end)); imfstd]; %<<>>
- % save idenyimstd.prn temp -ascii % export forecast and impulse response to the file "idenyimstd.prn", 3-by-nvar
- % %
- % %**** save stds. of both data and impulse responses in idfile1
- % temp = [std(yactqmyge(:,3:end)); std(yactyrge(:,3:end)); imfstd]; %<<>>
- % save idenyimstd.prn temp -ascii % export forecast and impulse response to the file "idenyimstd.prn", 3-by-nvar
- % if options_.ms.indxparr
- % idfile1='idenyimstd';
- % end
-
- %=====================================
- % Now, out-of-sample forecasts. Note: Hm1t does not change with A0.
- %=====================================
- %
- % * updating the last row of X (phi) with the current (last row of) y.
- tcwx = nvar*options_.ms.nlags ; % total coefficeint without exogenous variables
- phil = phi(size(phi,1),:);
- phil(nvar+1:tcwx) = phil(1:tcwx-nvar);
- phil(1:nvar) = y(end,:);
- %*** exogenous variables excluding constant terms
- if (nexo>1)
- Xexoe = fn_dataext([yrEnd qmEnd],[yrEnd qmEnd],xdatae(:,[1:2 2+nvar+1:2+nvar+nexo-1]));
- phil(1,tcwx+1:tcwx+nexo-1) = Xexoe(1,3:end);
- end
- %
- %*** ML unconditional point forecast
- nn = [nvar options_.ms.nlags nfqm];
- if nexo<2
- yforehat = fn_forecast(Bhat,phil,nn); % nfqm-by-nvar, in log
- else
- Xfexoe = fn_dataext(fdates(1,:),fdates(numel(fdates),:),xdatae(:,[1:2 2+nvar+1:2+nvar+nexo-1]));
- %Xfexoe = fn_dataext(fdates(1,:),fdates(end,:),xdatae(:,[1:2 2+nvar+1:2+nvar+nexo-1]));
- yforehat = fn_forecast(Bhat,phil,nn,nexo,Xfexoe(:,3:end)); % nfqm-by-nvar, in log
- end
- yforehate = [fdates yforehat];
- %
- yact1e = fn_dataext([yrEnd-nayr 1],[yrEnd qmEnd],xdatae(:,1:nvar+2));
- if options_.ms.real_pseudo_forecast
- %yact2e = fn_dataext([yrEnd-nayr 1],E2yrqm,xdatae);
- yact2e = fn_dataext([yrEnd-nayr 1],[fdates(end,1) options_.ms.freq],xdatae(:,1:nvar+2));
- else
- yact2e=yact1e;
- end
- yafhate = [yact1e; yforehate]; % actual and forecast
- %
- %===== Converted to mg, qg, and calendar yg
- %
- [yafyrghate,yafyrhate,yafqmyghate] = fn_datana(yafhate,options_.ms.freq,options_.ms.log_var(1:nlogeno),options_.ms.percent_var(1:npereno));
- % actual and forecast growth rates
- [yact2yrge,yact2yre,yact2qmyge] = fn_datana(yact2e,options_.ms.freq,options_.ms.log_var(1:nlogeno),options_.ms.percent_var(1:npereno));
- % only actual growth rates
- yafyrghate
- if options_.ms.indxgforhat
- keyindx = [1:nvar];
- conlab=['unconditional'];
-
- figure
- yafyrghate(:,3:end) = yafyrghate(:,3:end)/100;
- yact2yrge(:,3:end) = yact2yrge(:,3:end)/100;
- fn_foregraph(yafyrghate,yact2yrge,keyindx,rnum,cnum,options_.ms.freq,ylab,forelabel,conlab)
- end
-
- %-------------------------------------------------
- % Setup for point conditional forecast
- % ML Conditional Forecast
- %-------------------------------------------------
- %
- %% 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.
- %
-
- if (nconstr > 0)
- %*** 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
- varcon(:)=PorR; % 1: Pcm; 3: FFR; 5: CPI
-
- %
- for i=1:nconstr
- if i<=nconstr1
- stepcon{i}=i; % FFR
- elseif i<=2*nconstr1
- stepcon{i}=i-nconstr1; % FFR
- elseif i<=3*nconstr1
- stepcon{i}=i-2*nconstr1; % FFR
- elseif i<=4*nconstr1
- stepcon{i}=i-3*nconstr1; % FFR
- elseif i<=5*nconstr1
- stepcon{i}=i-4*nconstr1; % FFR
- elseif i<=6*nconstr1
- stepcon{i}=i-5*nconstr1; % FFR
- end
- end
-
-% for i=1:nconstr
-% stepcon{i}=i; % FFR
-% end
-
-% bend=12;
-% stepcon{1}=[1:bend]'; % average over
-% stepcon{nconstr1+1}=[1:options_.ms.freq-qmSub]'; % average over the remaing months in 1st forecast year
-% stepcon{nconstr1+2}=[options_.ms.freq-qmSub+1:options_.ms.freq-qmSub+12]'; % average over 12 months next year
-% stepcon{nconstr1+3}=[options_.ms.freq-qmSub+13:options_.ms.freq-qmSub+24]'; % average over 12 months. 3rd year
-% stepcon{nconstr1+4}=[options_.ms.freq-qmSub+25:options_.ms.freq-qmSub+36]'; % average over 12 months. 4th year
-
-% %**** avearage condition over, say, options_.ms.freq periods
-% if qmEnd==options_.ms.freq
-% stepcon{1}=[1:options_.ms.freq]'; % average over the remaing periods in 1st forecast year
-% else
-% stepcon{1}=[1:options_.ms.freq-qmEnd]'; % average over the remaing periods in 1st forecast year
-% end
-% for kj=2:nconstr
-% stepcon{kj}=[length(stepcon{kj-1})+1:length(stepcon{kj-1})+options_.ms.freq]'; % average over 12 months next year
-% end
-
- if options_.ms.real_pseudo_forecast
-% %*** conditions in every period
-% for i=1:nconstr
-% valuecon(i) = yact(actup+i,varcon(i));
-% %valuecon(i) = mean( yact(actup+1:actup+bend,varcon(i)) );
-% %valuecon(i) = 0.060; % 95:01
-% %valuecon(i) = (0.0475+0.055)/2; % 94:10
-% end
-
-% %*** average condtions over,say, options_.ms.freq periods.
-% for i=nconstr1+1:nconstr1+nconstr2
-% i=1;
-% valuecon(nconstr1+i) = ( ( mean(ylast12Cal(:,varcon(nconstr1+i)),1) + ...
-% log(1+yactCalyg(yAg-yFg+i,varcon(nconstr1+i))/100) )*options_.ms.freq - ...
-% yCal_1(:,varcon(nconstr1+i)) ) ./ length(stepcon{nconstr1+i});
-% % the same as unconditional "yactCalyg" 1st calendar year
-% i=2;
-% valuecon(nconstr1+i) = mean(ylast12Cal(:,varcon(nconstr1+i))) + ...
-% log(1+yactCalyg(yAg-yFg+1,varcon(nconstr1+i))/100) ...
-% + log(1+yactCalyg(yAg-yFg+i,varcon(nconstr1+i))/100);
-% % the same as actual "yactCalgy" 2nd calendar year
-% i=3;
-% valuecon(nconstr1+i) = valuecon(nconstr1+i-1) + ...
-% log(1+yactCalyg(yAg-yFg+i,varcon(nconstr1+i))/100);
-% % the same as actual "yactCalgy" 3rd calendar year
-% %i=4;
-% %valuecon(nconstr1+i) = valuecon(nconstr1+i-1) + ...
-% % log(1+yactCalyg(yAg-yFg+i,varcon(nconstr1+i))/100);
-% % the same as actual "yactCalgy" 4th calendar year
-% end
-
- %*** conditions in every period
- vpntM = fn_dataext(E1yrqm, E2yrqm,xdatae); % point value matrix with dates
- % vaveM = fn_dataext([yrEnd+1 0],[yrEnd+options_.forecast 0],yact2yre); % average value matrix with dates
- for i=1:nconstr
+ else
+ qmStartEsti = options_.ms.freq + rem(qmStart+options_.ms.nlags -options_.ms.dummy_obs ,options_.ms.freq); % dummy observations are included in the sample.
+ if (qmStart+options_.ms.nlags -options_.ms.dummy_obs ==0)
+ yrStartEsti = yrStart - 1; % one year back.
+ else
+ yrStartEsti = yrStart + floor((qmStart+options_.ms.nlags -options_.ms.dummy_obs )/(options_.ms.freq-0.01));
+ % - 0.01 (or any number < 1) is used so that qmStart+options_.ms.nlags -options_.ms.dummy_obs ==-?*options_.ms.freq give us an extra year back.
+ end
+ end
+ dateswd = fn_dataext([yrStartEsti qmStartEsti],[yrEnd qmEnd],xdatae(:,[1:2])); % dates with dummies
+ phie = [dateswd phi];
+ ye = [dateswd y];
+
+ %* Obtain linear restrictions
+ [Uiconst,Viconst,n0,np,ixmC0Pres] = feval(options_.ms.restriction_fname,nvar,nexo,options_.ms );
+ if min(n0)==0
+ skipline()
+ warning('A0: restrictions in dlrprior.m give no free parameter in one of equations')
+ disp('Press ctrl-c to abort')
+ pause
+ elseif min(np)==0
+ skipline()
+ warning('Ap: Restrictions in dlrprior.m give no free parameter in one of equations')
+ disp('Press ctrl-c to abort')
+ pause
+ end
+
+ if options_.ms.contemp_reduced_form
+ Uiconst=cell(nvar,1); Viconst=cell(ncoef,1);
+ for kj=1:nvar
+ Uiconst{kj} = eye(nvar); Viconst{kj} = eye(ncoef);
+ end
+ end
+
+ if options_.ms.bayesian_prior
+ %*** Obtains asymmetric prior (with no linear restrictions) with dummy observations as part of an explicit prior (i.e,
+ % reflected in Hpmulti and Hpinvmulti). See Forecast II, pp.69a-69b for details.
+ if 1 % Liquidity effect prior on both MS and MD equations.
+ [Pi,H0multi,Hpmulti,H0invmulti,Hpinvmulti] = fn_rnrprior_covres_dobs(nvar,options_.ms.freq,options_.ms.nlags ,xdgel,mu,indxDummy,hpmsmd,indxmsmdeqn);
+ else
+ [Pi,H0multi,Hpmulti,H0invmulti,Hpinvmulti] = fn_rnrprior(nvar,options_.ms.freq,options_.ms.nlags ,xdgel,mu);
+ end
+
+ %*** Combines asymmetric prior with linear restrictions
+ [Ptld,H0invtld,Hpinvtld] = fn_rlrprior(Uiconst,Viconst,Pi,H0multi,Hpmulti,nvar);
+
+ %*** Obtains the posterior matrices for estimation and inference
+ [Pmat,H0inv,Hpinv] = fn_rlrpostr(xtx,xty,yty,Ptld,H0invtld,Hpinvtld,Uiconst,Viconst);
+
+ if options_.ms.contemp_reduced_form
+ %*** Obtain the ML estimate
+ A0hatinv = chol(H0inv{1}/fss); % upper triangular but lower triangular choleski
+ A0hat=inv(A0hatinv);
+ a0indx = find(A0hat);
+ else
+ %*** Obtain the ML estimate
+ % load idenml
+ x = 10*rand(sum(n0),1);
+ H0 = eye(sum(n0));
+ crit = 1.0e-9;
+ nit = 10000;
+ %
+ [fhat,xhat,grad,Hhat,itct,fcount,retcodehat] = ...
+ csminwel('fn_a0freefun',x,H0,'fn_a0freegrad',crit,nit,Uiconst,nvar,n0,fss,H0inv);
+
+ A0hat = fn_tran_b2a(xhat,Uiconst,nvar,n0)
+ A0hatinv = inv(A0hat);
+ fhat
+ xhat
+ grad
+ itct
+ fcount
+ retcodehat
+ save outm.mat xhat A0hat A0hatinv grad fhat itct itct fcount retcodehat
+ end
+ else
+ %*** Obtain the posterior matrices for estimation and inference
+ [Pmat,H0inv,Hpinv] = fn_dlrpostr(xtx,xty,yty,Uiconst,Viconst);
+
+ if options_.ms.contemp_reduced_form
+ %*** Obtain the ML estimate
+ A0hatinv = chol(H0inv{1}/fss); % upper triangular but lower triangular choleski
+ A0hat=inv(A0hatinv);
+ a0indx = find(A0hat);
+ else
+ %*** Obtain the ML estimate
+ % load idenml
+ x = 10*rand(sum(n0),1);
+ H0 = eye(sum(n0));
+ crit = 1.0e-9;
+ nit = 10000;
+ %
+ [fhat,xhat,grad,Hhat,itct,fcount,retcodehat] = ...
+ csminwel('fn_a0freefun',x,H0,'fn_a0freegrad',crit,nit,Uiconst,nvar,n0,fss,H0inv);
+
+ A0hat = fn_tran_b2a(xhat,Uiconst,nvar,n0)
+ A0hatinv = inv(A0hat);
+ fhat
+ xhat
+ grad
+ itct
+ fcount
+ retcodehat
+ save outm.mat xhat A0hat A0hatinv grad fhat itct itct fcount retcodehat
+ end
+ end
+
+ %**** impulse responses
+ swish = A0hatinv; % each column corresponds to an equation
+ if options_.ms.contemp_reduced_form
+ xhat = A0hat(a0indx);
+ Bhat=Pmat{1};
+ Fhat = Bhat*A0hat
+ ghat = NaN;
+ else
+ xhat = fn_tran_a2b(A0hat,Uiconst,nvar,n0);
+ [Fhat,ghat] = fn_gfmean(xhat,Pmat,Viconst,nvar,ncoef,n0,np);
+ if options_.ms.cross_restrictions
+ Fhatur0P = Fhat; % ur: unrestriced across A0 and A+
+ for ki = 1:size(ixmC0Pres,1) % loop through the number of equations in which
+ % cross-A0-A+ restrictions occur. See St. Louis Note p.5.
+ ixeq = ixmC0Pres{ki}(1,1); % index for the jth equation in consideration.
+ Lit = Viconst{ixeq}(ixmC0Pres{ki}(:,2),:); % transposed restriction matrix Li
+ % V_j(i,:) in f_j(i) = V_j(i,:)*g_j
+ ci = ixmC0Pres{ki}(:,4) .* A0hat(ixmC0Pres{ki}(:,3),ixeq);
+ % s * a_j(h) in the restriction f_j(i) = s * a_j(h).
+ LtH = Lit/Hpinv{ixeq};
+ HLV = LtH'/(LtH*Lit');
+ gihat = Viconst{ixeq}'*Fhatur0P(:,ixeq);
+ Fhat(:,ixeq) = Viconst{ixeq}*(gihat + HLV*(ci-Lit*gihat));
+ end
+ end
+ Fhat
+ Bhat = Fhat/A0hat; % ncoef-by-nvar reduced form lagged parameters.
+ end
+ nn = [nvar options_.ms.nlags imstp];
+ imfhat = fn_impulse(Bhat,swish,nn); % in the form that is congenial to RATS
+ imf3hat=reshape(imfhat,size(imfhat,1),nvar,nvar);
+ % imf3: row--steps, column--nvar responses, 3rd dimension--nvar shocks
+ imf3shat=permute(imf3hat,[1 3 2]);
+ % imf3s: permuted so that row--steps, column--nvar shocks,
+ % 3rd dimension--nvar responses
+ % Note: reshape(imf3s(1,:,:),nvar,nvar) = A0in (columns -- equations)
+ if options_.ms.indxgimfhat
+ figure
+ end
+ scaleout = fn_imcgraph(imfhat,nvar,imstp,xlab,ylab,options_.ms.indxgimfhat);
+ imfstd = max(abs(scaleout)'); % row: nvar (largest number); used for standard deviations
+
+ %
+ % %**** save stds. of both data and impulse responses in idfile1
+ % temp = [std(yactqmyge(:,3:end)); std(yactyrge(:,3:end)); imfstd]; %<<>>
+ % save idenyimstd.prn temp -ascii % export forecast and impulse response to the file "idenyimstd.prn", 3-by-nvar
+ % %
+ % %**** save stds. of both data and impulse responses in idfile1
+ % temp = [std(yactqmyge(:,3:end)); std(yactyrge(:,3:end)); imfstd]; %<<>>
+ % save idenyimstd.prn temp -ascii % export forecast and impulse response to the file "idenyimstd.prn", 3-by-nvar
+ % if options_.ms.indxparr
+ % idfile1='idenyimstd';
+ % end
+
+ %=====================================
+ % Now, out-of-sample forecasts. Note: Hm1t does not change with A0.
+ %=====================================
+ %
+ % * updating the last row of X (phi) with the current (last row of) y.
+ tcwx = nvar*options_.ms.nlags ; % total coefficeint without exogenous variables
+ phil = phi(size(phi,1),:);
+ phil(nvar+1:tcwx) = phil(1:tcwx-nvar);
+ phil(1:nvar) = y(end,:);
+ %*** exogenous variables excluding constant terms
+ if (nexo>1)
+ Xexoe = fn_dataext([yrEnd qmEnd],[yrEnd qmEnd],xdatae(:,[1:2 2+nvar+1:2+nvar+nexo-1]));
+ phil(1,tcwx+1:tcwx+nexo-1) = Xexoe(1,3:end);
+ end
+ %
+ %*** ML unconditional point forecast
+ nn = [nvar options_.ms.nlags nfqm];
+ if nexo<2
+ yforehat = fn_forecast(Bhat,phil,nn); % nfqm-by-nvar, in log
+ else
+ Xfexoe = fn_dataext(fdates(1,:),fdates(numel(fdates),:),xdatae(:,[1:2 2+nvar+1:2+nvar+nexo-1]));
+ %Xfexoe = fn_dataext(fdates(1,:),fdates(end,:),xdatae(:,[1:2 2+nvar+1:2+nvar+nexo-1]));
+ yforehat = fn_forecast(Bhat,phil,nn,nexo,Xfexoe(:,3:end)); % nfqm-by-nvar, in log
+ end
+ yforehate = [fdates yforehat];
+ %
+ yact1e = fn_dataext([yrEnd-nayr 1],[yrEnd qmEnd],xdatae(:,1:nvar+2));
+ if options_.ms.real_pseudo_forecast
+ %yact2e = fn_dataext([yrEnd-nayr 1],E2yrqm,xdatae);
+ yact2e = fn_dataext([yrEnd-nayr 1],[fdates(end,1) options_.ms.freq],xdatae(:,1:nvar+2));
+ else
+ yact2e=yact1e;
+ end
+ yafhate = [yact1e; yforehate]; % actual and forecast
+ %
+ %===== Converted to mg, qg, and calendar yg
+ %
+ [yafyrghate,yafyrhate,yafqmyghate] = fn_datana(yafhate,options_.ms.freq,options_.ms.log_var(1:nlogeno),options_.ms.percent_var(1:npereno));
+ % actual and forecast growth rates
+ [yact2yrge,yact2yre,yact2qmyge] = fn_datana(yact2e,options_.ms.freq,options_.ms.log_var(1:nlogeno),options_.ms.percent_var(1:npereno));
+ % only actual growth rates
+ yafyrghate
+ if options_.ms.indxgforhat
+ keyindx = [1:nvar];
+ conlab=['unconditional'];
+
+ figure
+ yafyrghate(:,3:end) = yafyrghate(:,3:end)/100;
+ yact2yrge(:,3:end) = yact2yrge(:,3:end)/100;
+ fn_foregraph(yafyrghate,yact2yrge,keyindx,rnum,cnum,options_.ms.freq,ylab,forelabel,conlab)
+ end
+
+ %-------------------------------------------------
+ % Setup for point conditional forecast
+ % ML Conditional Forecast
+ %-------------------------------------------------
+ %
+ %% 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.
+ %
+
+ if (nconstr > 0)
+ %*** 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
+ varcon(:)=PorR; % 1: Pcm; 3: FFR; 5: CPI
+
+ %
+ for i=1:nconstr
if i<=nconstr1
- valuecon(i) = vpntM(i,2+varcon(i)); % 2: first 2 elements are dates
+ stepcon{i}=i; % FFR
elseif i<=2*nconstr1
- valuecon(i) = vpntM(i-nconstr1,2+varcon(i));
+ stepcon{i}=i-nconstr1; % FFR
elseif i<=3*nconstr1
- valuecon(i) = vpntM(i-2*nconstr1,2+varcon(i));
+ stepcon{i}=i-2*nconstr1; % FFR
elseif i<=4*nconstr1
- valuecon(i) = vpntM(i-3*nconstr1,2+varcon(i));
+ stepcon{i}=i-3*nconstr1; % FFR
elseif i<=5*nconstr1
- valuecon(i) = vpntM(i-4*nconstr1,2+varcon(i));
+ stepcon{i}=i-4*nconstr1; % FFR
elseif i<=6*nconstr1
- valuecon(i) = vpntM(i-5*nconstr1,2+varcon(i));
+ stepcon{i}=i-5*nconstr1; % FFR
end
- end
-
-% %*** average condtions over,say, options_.ms.freq periods.
-% if qmEnd==options_.ms.freq
-% vaveM = fn_dataext([yrEnd+1 0],[yrEnd+options_.forecast 0],yact2yre); % average value matrix with dates
-% valuecon(1) = vaveM(1,2+varcon(1)); % 2: first 2 elements are dates
-% else
-% vaveM = fn_dataext([yrEnd 0],[yrEnd+options_.forecast 0],yact2yre); % average value matrix with dates
-% yactrem = fn_dataext([yrEnd qmEnd+1],[yrEnd options_.ms.freq],xdatae);
-% valuecon(1) = sum(yactrem(:,2+varcon(1)),1)/length(stepcon{1});
-% % 2: first 2 elements are dates
-% end
-% for kj=2:nconstr
-% valuecon(kj) = vaveM(kj,2+varcon(kj)); % 2: first 2 elements are dates
-% end
- else
- vpntM = dataext([yrEnd qmEnd+1],[yrEnd qmEnd+2],xdatae); % point value matrix with dates
- for i=1:nconstr
- if i<=nconstr1
- valuecon(i) = vpntM(i,2+varcon(i)); % 2: first 2 elements are dates; Poil
- elseif i<=2*nconstr1
- valuecon(i) = vpntM(i-nconstr1,2+varcon(i)); % 2: first 2 elements are dates; M2
- elseif i<=3*nconstr1
- valuecon(i) = vpntM(i-2*nconstr1,2+varcon(i)); % 2: first 2 elements are dates; FFR
- elseif i<=4*nconstr1
- valuecon(i) = vpntM(i-3*nconstr1,2+varcon(i)); % 2: first 2 elements are dates; CPI
- elseif i<=5*nconstr1
- valuecon(i) = vpntM(i-4*nconstr1,2+varcon(i)); % 2: first 2 elements are dates; U
- elseif i<=5*nconstr1+nconstr2
- valuecon(i)=xdata(end,5)+(i-5*nconstr1)*log(1.001)/options_.ms.freq; %CPI
- elseif i<=5*nconstr1+2*nconstr2
- valuecon(i)=0.0725; %FFR
- else
- valuecon(i)=xdata(end,6)+(i-5*nconstr1-2*nconstr2)*0.01/nfqm; %U
+ end
+
+ % for i=1:nconstr
+ % stepcon{i}=i; % FFR
+ % end
+
+ % bend=12;
+ % stepcon{1}=[1:bend]'; % average over
+ % stepcon{nconstr1+1}=[1:options_.ms.freq-qmSub]'; % average over the remaing months in 1st forecast year
+ % stepcon{nconstr1+2}=[options_.ms.freq-qmSub+1:options_.ms.freq-qmSub+12]'; % average over 12 months next year
+ % stepcon{nconstr1+3}=[options_.ms.freq-qmSub+13:options_.ms.freq-qmSub+24]'; % average over 12 months. 3rd year
+ % stepcon{nconstr1+4}=[options_.ms.freq-qmSub+25:options_.ms.freq-qmSub+36]'; % average over 12 months. 4th year
+
+ % %**** avearage condition over, say, options_.ms.freq periods
+ % if qmEnd==options_.ms.freq
+ % stepcon{1}=[1:options_.ms.freq]'; % average over the remaing periods in 1st forecast year
+ % else
+ % stepcon{1}=[1:options_.ms.freq-qmEnd]'; % average over the remaing periods in 1st forecast year
+ % end
+ % for kj=2:nconstr
+ % stepcon{kj}=[length(stepcon{kj-1})+1:length(stepcon{kj-1})+options_.ms.freq]'; % average over 12 months next year
+ % end
+
+ if options_.ms.real_pseudo_forecast
+ % %*** conditions in every period
+ % for i=1:nconstr
+ % valuecon(i) = yact(actup+i,varcon(i));
+ % %valuecon(i) = mean( yact(actup+1:actup+bend,varcon(i)) );
+ % %valuecon(i) = 0.060; % 95:01
+ % %valuecon(i) = (0.0475+0.055)/2; % 94:10
+ % end
+
+ % %*** average condtions over,say, options_.ms.freq periods.
+ % for i=nconstr1+1:nconstr1+nconstr2
+ % i=1;
+ % valuecon(nconstr1+i) = ( ( mean(ylast12Cal(:,varcon(nconstr1+i)),1) + ...
+ % log(1+yactCalyg(yAg-yFg+i,varcon(nconstr1+i))/100) )*options_.ms.freq - ...
+ % yCal_1(:,varcon(nconstr1+i)) ) ./ length(stepcon{nconstr1+i});
+ % % the same as unconditional "yactCalyg" 1st calendar year
+ % i=2;
+ % valuecon(nconstr1+i) = mean(ylast12Cal(:,varcon(nconstr1+i))) + ...
+ % log(1+yactCalyg(yAg-yFg+1,varcon(nconstr1+i))/100) ...
+ % + log(1+yactCalyg(yAg-yFg+i,varcon(nconstr1+i))/100);
+ % % the same as actual "yactCalgy" 2nd calendar year
+ % i=3;
+ % valuecon(nconstr1+i) = valuecon(nconstr1+i-1) + ...
+ % log(1+yactCalyg(yAg-yFg+i,varcon(nconstr1+i))/100);
+ % % the same as actual "yactCalgy" 3rd calendar year
+ % %i=4;
+ % %valuecon(nconstr1+i) = valuecon(nconstr1+i-1) + ...
+ % % log(1+yactCalyg(yAg-yFg+i,varcon(nconstr1+i))/100);
+ % % the same as actual "yactCalgy" 4th calendar year
+ % end
+
+ %*** conditions in every period
+ vpntM = fn_dataext(E1yrqm, E2yrqm,xdatae); % point value matrix with dates
+ % vaveM = fn_dataext([yrEnd+1 0],[yrEnd+options_.forecast 0],yact2yre); % average value matrix with dates
+ for i=1:nconstr
+ if i<=nconstr1
+ valuecon(i) = vpntM(i,2+varcon(i)); % 2: first 2 elements are dates
+ elseif i<=2*nconstr1
+ valuecon(i) = vpntM(i-nconstr1,2+varcon(i));
+ elseif i<=3*nconstr1
+ valuecon(i) = vpntM(i-2*nconstr1,2+varcon(i));
+ elseif i<=4*nconstr1
+ valuecon(i) = vpntM(i-3*nconstr1,2+varcon(i));
+ elseif i<=5*nconstr1
+ valuecon(i) = vpntM(i-4*nconstr1,2+varcon(i));
+ elseif i<=6*nconstr1
+ valuecon(i) = vpntM(i-5*nconstr1,2+varcon(i));
+ end
+ end
+
+ % %*** average condtions over,say, options_.ms.freq periods.
+ % if qmEnd==options_.ms.freq
+ % vaveM = fn_dataext([yrEnd+1 0],[yrEnd+options_.forecast 0],yact2yre); % average value matrix with dates
+ % valuecon(1) = vaveM(1,2+varcon(1)); % 2: first 2 elements are dates
+ % else
+ % vaveM = fn_dataext([yrEnd 0],[yrEnd+options_.forecast 0],yact2yre); % average value matrix with dates
+ % yactrem = fn_dataext([yrEnd qmEnd+1],[yrEnd options_.ms.freq],xdatae);
+ % valuecon(1) = sum(yactrem(:,2+varcon(1)),1)/length(stepcon{1});
+ % % 2: first 2 elements are dates
+ % end
+ % for kj=2:nconstr
+ % valuecon(kj) = vaveM(kj,2+varcon(kj)); % 2: first 2 elements are dates
+ % end
+ else
+ vpntM = dataext([yrEnd qmEnd+1],[yrEnd qmEnd+2],xdatae); % point value matrix with dates
+ for i=1:nconstr
+ if i<=nconstr1
+ valuecon(i) = vpntM(i,2+varcon(i)); % 2: first 2 elements are dates; Poil
+ elseif i<=2*nconstr1
+ valuecon(i) = vpntM(i-nconstr1,2+varcon(i)); % 2: first 2 elements are dates; M2
+ elseif i<=3*nconstr1
+ valuecon(i) = vpntM(i-2*nconstr1,2+varcon(i)); % 2: first 2 elements are dates; FFR
+ elseif i<=4*nconstr1
+ valuecon(i) = vpntM(i-3*nconstr1,2+varcon(i)); % 2: first 2 elements are dates; CPI
+ elseif i<=5*nconstr1
+ valuecon(i) = vpntM(i-4*nconstr1,2+varcon(i)); % 2: first 2 elements are dates; U
+ elseif i<=5*nconstr1+nconstr2
+ valuecon(i)=xdata(end,5)+(i-5*nconstr1)*log(1.001)/options_.ms.freq; %CPI
+ elseif i<=5*nconstr1+2*nconstr2
+ valuecon(i)=0.0725; %FFR
+ else
+ valuecon(i)=xdata(end,6)+(i-5*nconstr1-2*nconstr2)*0.01/nfqm; %U
+ end
+ end
+ %valuecon(i) = 0.060; % 95:01
+ end
+ else
+ valuecon = [];
+ stepcon = [];
+ varcon = [];
+ end
+
+ nstepsm = 0; % initializing, the maximum step in all constraints
+ for i=1:nconstr
+ nstepsm = max([nstepsm max(stepcon{i})]);
+ end
+
+ if cnum
+ if options_.ms.real_pseudo_forecast && options_.ms.banact
+ for i=1:length(banstp{1})
+ banval{1}(1:length(banstp{1}),1) = ...
+ yactCalyg(yAg-yFg+1:yAg-yFg+length(banstp{1}),banvar(1)) - 2;
+ banval{1}(1:length(banstp{1}),2) = ...
+ yactCalyg(yAg-yFg+1:yAg-yFg+length(banstp{1}),banvar(1)) + 2;
end
- end
- %valuecon(i) = 0.060; % 95:01
- end
- else
- valuecon = [];
- stepcon = [];
- varcon = [];
- end
-
- nstepsm = 0; % initializing, the maximum step in all constraints
- for i=1:nconstr
- nstepsm = max([nstepsm max(stepcon{i})]);
- end
-
- if cnum
- if options_.ms.real_pseudo_forecast && options_.ms.banact
- for i=1:length(banstp{1})
- banval{1}(1:length(banstp{1}),1) = ...
- yactCalyg(yAg-yFg+1:yAg-yFg+length(banstp{1}),banvar(1)) - 2;
- banval{1}(1:length(banstp{1}),2) = ...
- yactCalyg(yAg-yFg+1:yAg-yFg+length(banstp{1}),banvar(1)) + 2;
- end
- end
- end
-
-
- %===================================================
- % ML conditional forecast
- %===================================================
- %/*
- [ychat,Estr,rcon] = fn_fcstidcnd(valuecon,stepcon,varcon,nstepsm,...
- nconstr,options_.ms.eq_ms,nvar,options_.ms.nlags ,phil,0,0,yforehat,imf3shat,A0hat,Bhat,...
- nfqm,options_.ms.tlindx,options_.ms.tlnumber,options_.ms.ncms,options_.ms.eq_cms);
- ychate = [fdates ychat];
- yachate = [yact1e; ychate]; % actual and condtional forecast
- %===== Converted to mg, qg, and calendar yg
- [yacyrghate,yacyrhate,yacqmyghate] = fn_datana(yachate,options_.ms.freq,options_.ms.log_var(1:nlogeno),options_.ms.percent_var(1:npereno));
- % actual and conditional forecast growth rates
- if options_.ms.indxgdls && nconstr
- keyindx = [1:nvar];
- % conlab=['conditional on' ylab{PorR(1)}];
- conlab=['v-conditions'];
-
- figure
- fn_foregraph(yafyrghate,yact2yrge,keyindx,rnum,cnum,options_.ms.freq,ylab,forelabel,conlab)
- end
-
- if options_.ms.ncsk
- Estr = zeros(nfqm,nvar);
- Estr(1:2,:) = [
- -2.1838 -1.5779 0.53064 -0.099425 -0.69269 -1.0391
- 1.9407 3.3138 -0.10563 -0.55457 -0.68772 1.3534
- ];
- Estr(3:6,3) = [0.5*ones(1,4)]'; % MD shocks
-
- Estr(3:10,2) = [1.5 1.5 1.5*ones(1,6)]'; % MS shocks
-
- %Estr(3:6,6) = 1*ones(4,1); % U shocks
- %Estr(8:11,4) = 1*ones(4,1); % y shocks
-
- %Estr(3:10,2) = [2.5 2.5 1.5*ones(1,6)]'; % MS shocks alone
-
- nn = [nvar options_.ms.noptions_.ms.nlags nfqm];
- ycEhat = forefixe(A0hat,Bhat,phil,nn,Estr);
- ycEhate = [fdates ycEhat];
- yacEhate = [yact1e; ycEhate]; % actual and condtional forecast
- %===== Converted to mg, qg, and calendar yg
- [yacEyrghate,yacEyrhate,yacEqmyghate] = datana(yacEhate,options_.ms.freq,options_.ms.log_var(1:nlogeno),options_.ms.percent_var(1:npereno));
- % actual and conditional forecast growth rates
- disp([sprintf('%4.0f %2.0f %8.3f %8.3f %8.3f %8.3f %8.3f %8.3f\n',yacEyrghate')])
-
- if 1
- keyindx = [1:nvar];
- % conlab=['conditional on' ylab{PorR(1)}];
- conlab=['shock-conditions'];
-
- figure
- gyrfore(yacEyrghate,yact2yrge,keyindx,rnum,cnum,ylab,forelabel,conlab)
- end
- end
-
- %-----------------------------------------------------------
- % Compute structural shocks for the whole sample period excluding dummy observations.
- %-----------------------------------------------------------
- ywod = y(options_.ms.dummy_obs +1:end,:); % without dummy observations
- phiwod=phi(options_.ms.dummy_obs +1:end,:); % without dummy observations
- eplhat=ywod*A0hat-phiwod*Fhat;
- qmStartWod = mod(qmStart+options_.ms.nlags ,options_.ms.freq);
- if (~qmStartWod)
- qmStartWod = options_.ms.freq;
- end
- yrStartWod = yrStart + floor((qmStart+options_.ms.nlags -1)/options_.ms.freq);
- dateswod = fn_dataext([yrStartWod qmStartWod],[yrEnd qmEnd],xdatae(:,[1:2]));
- eplhate = [dateswod eplhat];
-
- Aphat = Fhat;
+ end
+ end
+
+
+ %===================================================
+ % ML conditional forecast
+ %===================================================
+ %/*
+ [ychat,Estr,rcon] = fn_fcstidcnd(valuecon,stepcon,varcon,nstepsm,...
+ nconstr,options_.ms.eq_ms,nvar,options_.ms.nlags ,phil,0,0,yforehat,imf3shat,A0hat,Bhat,...
+ nfqm,options_.ms.tlindx,options_.ms.tlnumber,options_.ms.ncms,options_.ms.eq_cms);
+ ychate = [fdates ychat];
+ yachate = [yact1e; ychate]; % actual and condtional forecast
+ %===== Converted to mg, qg, and calendar yg
+ [yacyrghate,yacyrhate,yacqmyghate] = fn_datana(yachate,options_.ms.freq,options_.ms.log_var(1:nlogeno),options_.ms.percent_var(1:npereno));
+ % actual and conditional forecast growth rates
+ if options_.ms.indxgdls && nconstr
+ keyindx = [1:nvar];
+ % conlab=['conditional on' ylab{PorR(1)}];
+ conlab=['v-conditions'];
+
+ figure
+ fn_foregraph(yafyrghate,yact2yrge,keyindx,rnum,cnum,options_.ms.freq,ylab,forelabel,conlab)
+ end
+
+ if options_.ms.ncsk
+ Estr = zeros(nfqm,nvar);
+ Estr(1:2,:) = [
+ -2.1838 -1.5779 0.53064 -0.099425 -0.69269 -1.0391
+ 1.9407 3.3138 -0.10563 -0.55457 -0.68772 1.3534
+ ];
+ Estr(3:6,3) = [0.5*ones(1,4)]'; % MD shocks
+
+ Estr(3:10,2) = [1.5 1.5 1.5*ones(1,6)]'; % MS shocks
+
+ %Estr(3:6,6) = 1*ones(4,1); % U shocks
+ %Estr(8:11,4) = 1*ones(4,1); % y shocks
+
+ %Estr(3:10,2) = [2.5 2.5 1.5*ones(1,6)]'; % MS shocks alone
+
+ nn = [nvar options_.ms.noptions_.ms.nlags nfqm];
+ ycEhat = forefixe(A0hat,Bhat,phil,nn,Estr);
+ ycEhate = [fdates ycEhat];
+ yacEhate = [yact1e; ycEhate]; % actual and condtional forecast
+ %===== Converted to mg, qg, and calendar yg
+ [yacEyrghate,yacEyrhate,yacEqmyghate] = datana(yacEhate,options_.ms.freq,options_.ms.log_var(1:nlogeno),options_.ms.percent_var(1:npereno));
+ % actual and conditional forecast growth rates
+ disp([sprintf('%4.0f %2.0f %8.3f %8.3f %8.3f %8.3f %8.3f %8.3f\n',yacEyrghate')])
+
+ if 1
+ keyindx = [1:nvar];
+ % conlab=['conditional on' ylab{PorR(1)}];
+ conlab=['shock-conditions'];
+
+ figure
+ gyrfore(yacEyrghate,yact2yrge,keyindx,rnum,cnum,ylab,forelabel,conlab)
+ end
+ end
+
+ %-----------------------------------------------------------
+ % Compute structural shocks for the whole sample period excluding dummy observations.
+ %-----------------------------------------------------------
+ ywod = y(options_.ms.dummy_obs +1:end,:); % without dummy observations
+ phiwod=phi(options_.ms.dummy_obs +1:end,:); % without dummy observations
+ eplhat=ywod*A0hat-phiwod*Fhat;
+ qmStartWod = mod(qmStart+options_.ms.nlags ,options_.ms.freq);
+ if (~qmStartWod)
+ qmStartWod = options_.ms.freq;
+ end
+ yrStartWod = yrStart + floor((qmStart+options_.ms.nlags -1)/options_.ms.freq);
+ dateswod = fn_dataext([yrStartWod qmStartWod],[yrEnd qmEnd],xdatae(:,[1:2]));
+ eplhate = [dateswod eplhat];
+
+ Aphat = Fhat;
end
%----------------------------------------
@@ -745,11 +745,11 @@ end
% from different idfile's.
%----------------------------------------
%
-%if ~options_.ms.contemp_reduced_form
+%if ~options_.ms.contemp_reduced_form
% SpHR=A0in'*A0in;
%end
%%
-%if ~isnan(SpHR) && ~options_.ms.contemp_reduced_form
+%if ~isnan(SpHR) && ~options_.ms.contemp_reduced_form
% warning(' ')
% disp('Make sure you run the program with options_.ms.contemp_reduced_form =1 first.')
% disp('Otherwise, the following test results such as Schwartz are incorrect.')
diff --git a/matlab/ms-sbvar/msstart_setup.m b/matlab/ms-sbvar/msstart_setup.m
index 79c8024d40ea51c5ec506984c9e4bea0e7566a85..88c307fe805822d74594f78e523d52d89c4ba9fc 100644
--- a/matlab/ms-sbvar/msstart_setup.m
+++ b/matlab/ms-sbvar/msstart_setup.m
@@ -26,14 +26,14 @@
% Exordium I
%===========================================
format short g % format
-%
-%options_.ms.freq = 4; % quarters or months
-%options_.ms.initial_year=1959; % beginning of the year
-%options_.ms.initial_subperiod=1; % begining of the quarter or month
-%options_.ms.final_year=2005; % final year
-%options_.ms.final_subperiod=4; % final month or quarter
+ %
+ %options_.ms.freq = 4; % quarters or months
+ %options_.ms.initial_year=1959; % beginning of the year
+ %options_.ms.initial_subperiod=1; % begining of the quarter or month
+ %options_.ms.final_year=2005; % final year
+ %options_.ms.final_subperiod=4; % final month or quarter
nData=(options_.ms.final_year-options_.ms.initial_year)*options_.ms.freq + (options_.ms.final_subperiod-options_.ms.initial_subperiod+1);
- % total number of the available data -- this is all you have
+% total number of the available data -- this is all you have
%*** Load data and series
%load datainf_argen.prn % the default name for the variable is "options_.ms.data".
@@ -61,19 +61,19 @@ nvar = length(options_.varobs); % number of endogenous variables
nlogeno = length(options_.ms.log_var); % number of endogenous variables in options_.ms.log_var
npereno = length(options_.ms.percent_var); % number of endogenous variables in options_.ms.percent_var
if (nvar~=(nlogeno+npereno))
- skipline()
- warning('Check xlab, nlogeno or npereno to make sure of endogenous variables in options_.ms.vlist')
- disp('Press ctrl-c to abort')
- return
+ skipline()
+ warning('Check xlab, nlogeno or npereno to make sure of endogenous variables in options_.ms.vlist')
+ disp('Press ctrl-c to abort')
+ return
elseif (nvar==length(options_.ms.vlist))
- nexo=1; % only constants as an exogenous variable. The default setting.
+ nexo=1; % only constants as an exogenous variable. The default setting.
elseif (nvar<length(options_.ms.vlist))
- nexo=length(options_.ms.vlist)-nvar+1;
+ nexo=length(options_.ms.vlist)-nvar+1;
else
- skipline()
- warning('Make sure there are only nvar endogenous variables in options_.ms.vlist')
- disp('Press ctrl-c to abort')
- return
+ skipline()
+ warning('Make sure there are only nvar endogenous variables in options_.ms.vlist')
+ disp('Press ctrl-c to abort')
+ return
end
@@ -84,24 +84,24 @@ yrEnd=options_.ms.final_year;
qmEnd=options_.ms.final_subperiod;
%options_.forecast = 4; % number of years for forecasting
if options_.forecast<1
- error('To be safe, the number of forecast years should be at least 1')
+ error('To be safe, the number of forecast years should be at least 1')
end
forelabel = [num2str(yrEnd) ':' num2str(qmEnd) ' Forecast'];
nSample=(yrEnd-yrStart)*options_.ms.freq + (qmEnd-qmStart+1);
if qmEnd==options_.ms.freq
- E1yrqm = [yrEnd+1 1]; % first year and quarter (month) after the sample
+ E1yrqm = [yrEnd+1 1]; % first year and quarter (month) after the sample
else
- E1yrqm = [yrEnd qmEnd+1]; % first year and quarter (month) after the sample
+ E1yrqm = [yrEnd qmEnd+1]; % first year and quarter (month) after the sample
end
E2yrqm = [yrEnd+options_.forecast qmEnd]; % end at the last month (quarter) of a calendar year after the sample
[fdates,nfqm]=fn_calyrqm(options_.ms.freq,E1yrqm,E2yrqm); % forecast dates and number of forecast dates
[sdates,nsqm] = fn_calyrqm(options_.ms.freq,[yrStart qmStart],[yrEnd qmEnd]);
- % sdates: dates for the whole sample (including options_.ms.nlags)
+% sdates: dates for the whole sample (including options_.ms.nlags)
if nSample~=nsqm
- warning('Make sure that nSample is consistent with the size of sdates')
- disp('Hit any key to continue, or ctrl-c to abort')
- pause
+ warning('Make sure that nSample is consistent with the size of sdates')
+ disp('Hit any key to continue, or ctrl-c to abort')
+ pause
end
imstp = 4*options_.ms.freq; % <<>> impulse responses (4 years)
nayr = 4; %options_.forecast; % number of years before forecasting for plotting.
@@ -110,18 +110,18 @@ nayr = 4; %options_.forecast; % number of years before forecasting for plotting
%------- Prior, etc. -------
%options_.ms.nlags = 4; % number of options_.ms.nlags
%options_.ms.cross_restrictions = 0; % 1: cross-A0-and-A+ restrictions; 0: options_.ms.restriction_fname is all we have
- % Example for indxOres==1: restrictions of the form P(t) = P(t-1).
+% Example for indxOres==1: restrictions of the form P(t) = P(t-1).
%options_.ms.contemp_reduced_form = 0; % 1: contemporaneous recursive reduced form; 0: restricted (non-recursive) form
%options_.ms.real_pseudo_forecast = 0; % 1: options_.ms.real_pseudo_forecast forecasts; 0: real time forecasts
%options_.ms.bayesian_prior = 1; % 1: Bayesian prior; 0: no prior
indxDummy = options_.ms.bayesian_prior; % 1: add dummy observations to the data; 0: no dummy added.
-%options_.ms.dummy_obs = 0; % No dummy observations for xtx, phi, fss, xdatae, etc. Dummy observations are used as an explicit prior in fn_rnrprior_covres_dobs.m.
-%if indxDummy
-% options_.ms.dummy_obs=nvar+1; % number of dummy observations
-%else
-% options_.ms.dummy_obs=0; % no dummy observations
-%end
-%=== The following mu is effective only if options_.ms.bayesian_prior==1.
+ %options_.ms.dummy_obs = 0; % No dummy observations for xtx, phi, fss, xdatae, etc. Dummy observations are used as an explicit prior in fn_rnrprior_covres_dobs.m.
+ %if indxDummy
+ % options_.ms.dummy_obs=nvar+1; % number of dummy observations
+ %else
+ % options_.ms.dummy_obs=0; % no dummy observations
+ %end
+ %=== The following mu is effective only if options_.ms.bayesian_prior==1.
mu = options_.ms.coefficients_prior_hyperparameters;
@@ -142,20 +142,15 @@ tdf = 3; % degrees of freedom for t-dist for initial draw of the MC loo
nbuffer = 1000; % a block or buffer of draws (buffer) that is saved to the disk (not memory)
ndraws1=1*nbuffer; % 1st part of Monte Carlo draws
ndraws2=10*ndraws1; % 2nd part of Monte Carlo draws
-% seednumber = options_.DynareRandomStreams.seed; %7910; %472534; % if 0, random state at each clock time
-% % good one 420 for [29 45], [29 54]
-% if seednumber
-% randn('state',seednumber);
-% rand('state',seednumber);
-% else
-% randn('state',fix(100*sum(clock)));
-% rand('state',fix(100*sum(clock)));
-% end
-% nstarts=1 % number of starting points
-% imndraws = nstarts*ndraws2; % total draws for impulse responses or forecasts
-%<<<<<<<<<<<<<<<<<<<
-
-
-
-
-
+ % seednumber = options_.DynareRandomStreams.seed; %7910; %472534; % if 0, random state at each clock time
+ % % good one 420 for [29 45], [29 54]
+ % if seednumber
+ % randn('state',seednumber);
+ % rand('state',seednumber);
+ % else
+ % randn('state',fix(100*sum(clock)));
+ % rand('state',fix(100*sum(clock)));
+ % end
+ % nstarts=1 % number of starting points
+ % imndraws = nstarts*ndraws2; % total draws for impulse responses or forecasts
+ %<<<<<<<<<<<<<<<<<<<
diff --git a/matlab/ms-sbvar/plot_ms_forecast.m b/matlab/ms-sbvar/plot_ms_forecast.m
index c6f0661f3ca43062a02725298b4baf910cd14713..c76f8af89599892185dfca6469fb41bfa8795765 100644
--- a/matlab/ms-sbvar/plot_ms_forecast.m
+++ b/matlab/ms-sbvar/plot_ms_forecast.m
@@ -32,122 +32,122 @@ function plot_ms_forecast(M_, options_, forecast, figure_name)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- nc = 2;
- nr = 2;
- nvars = M_.endo_nbr;
- endo_names = M_.endo_names;
- var_list = endo_names(1:M_.orig_endo_nbr,:);
- names = {};
- tex_names = {};
- m = 1;
- for i = 1:size(var_list)
- tmp = strmatch(var_list(i,:),endo_names,'exact');
- if isempty(tmp)
- error([var_list(i,:) ' isn''t and endogenous variable'])
- end
- tex_name = deblank(M_.endo_names_tex(i,:));
- if ~isempty(tex_name)
- names{m} = deblank(var_list(i,:));
- tex_names{m} = tex_name;
- m = m + 1;
- end
+nc = 2;
+nr = 2;
+nvars = M_.endo_nbr;
+endo_names = M_.endo_names;
+var_list = endo_names(1:M_.orig_endo_nbr,:);
+names = {};
+tex_names = {};
+m = 1;
+for i = 1:size(var_list)
+ tmp = strmatch(var_list(i,:),endo_names,'exact');
+ if isempty(tmp)
+ error([var_list(i,:) ' isn''t and endogenous variable'])
end
-
- dims = size(forecast);
- if (length(dims) == 2)
- % Point Forecast (horizon x nvars )
- horizon = dims(1);
- num_percentiles = 1;
- elseif (length(dims) == 3)
- % Banded Forecast
- horizon = dims(2);
- num_percentiles = dims(1);
- else
- error('The impulse response matrix passed to be plotted does not appear to be the correct size');
+ tex_name = deblank(M_.endo_names_tex(i,:));
+ if ~isempty(tex_name)
+ names{m} = deblank(var_list(i,:));
+ tex_names{m} = tex_name;
+ m = m + 1;
end
+end
- if num_percentiles == 1
- plot_point_forecast(forecast, nvars, nr, nc, var_list, figure_name, ...
- options_.graph_save_formats, options_.TeX, names, tex_names, ...
- [options_.ms.output_file_tag filesep 'Output' filesep 'Forecast']);
- else
- plot_banded_forecast(forecast, nvars, nr, nc, var_list, num_percentiles, ...
- figure_name, options_.graph_save_formats, options_.TeX, names, tex_names, ...
- [options_.ms.output_file_tag filesep 'Output' filesep 'Forecast']);
- end
+dims = size(forecast);
+if (length(dims) == 2)
+ % Point Forecast (horizon x nvars )
+ horizon = dims(1);
+ num_percentiles = 1;
+elseif (length(dims) == 3)
+ % Banded Forecast
+ horizon = dims(2);
+ num_percentiles = dims(1);
+else
+ error('The impulse response matrix passed to be plotted does not appear to be the correct size');
+end
+
+if num_percentiles == 1
+ plot_point_forecast(forecast, nvars, nr, nc, var_list, figure_name, ...
+ options_.graph_save_formats, options_.TeX, names, tex_names, ...
+ [options_.ms.output_file_tag filesep 'Output' filesep 'Forecast']);
+else
+ plot_banded_forecast(forecast, nvars, nr, nc, var_list, num_percentiles, ...
+ figure_name, options_.graph_save_formats, options_.TeX, names, tex_names, ...
+ [options_.ms.output_file_tag filesep 'Output' filesep 'Forecast']);
+end
end
function plot_point_forecast(forecast,nvars,nr,nc,endo_names,figure_name,save_graph_formats,TeX,names,tex_names,dirname)
- if nvars > nr*nc
- graph_name = ['MS (1) ' figure_name];
- figure('Name', graph_name);
- else
- graph_name = figure_name;
- figure('Name', graph_name);
- end
- m = 1;
- n_fig = 1;
- for j=1:nvars
- if m > nr*nc
- graph_name = ['MS (' int2str(n_fig) ') ' figure_name];
- dyn_save_graph(dirname,['MS-forecast-' int2str(n_fig)],...
- save_graph_formats,TeX,names,tex_names,graph_name);
- n_fig =n_fig+1;
- figure('Name', graph_name);
- m = 1;
- end
- subplot(nr,nc,m);
- vn = deblank(endo_names(j,:));
- plot(forecast(:,j))
- title(vn,'Interpreter','none');
- grid on;
- m = m+1;
- end
- if m > 1
+if nvars > nr*nc
+ graph_name = ['MS (1) ' figure_name];
+ figure('Name', graph_name);
+else
+ graph_name = figure_name;
+ figure('Name', graph_name);
+end
+m = 1;
+n_fig = 1;
+for j=1:nvars
+ if m > nr*nc
+ graph_name = ['MS (' int2str(n_fig) ') ' figure_name];
dyn_save_graph(dirname,['MS-forecast-' int2str(n_fig)],...
save_graph_formats,TeX,names,tex_names,graph_name);
+ n_fig =n_fig+1;
+ figure('Name', graph_name);
+ m = 1;
end
+ subplot(nr,nc,m);
+ vn = deblank(endo_names(j,:));
+ plot(forecast(:,j))
+ title(vn,'Interpreter','none');
+ grid on;
+ m = m+1;
+end
+if m > 1
+ dyn_save_graph(dirname,['MS-forecast-' int2str(n_fig)],...
+ save_graph_formats,TeX,names,tex_names,graph_name);
+end
end
function plot_banded_forecast(forecast,nvars,nr,nc,endo_names,num_percentiles,figure_name,save_graph_formats,TeX,names,tex_names,dirname)
- if nvars > nr*nc
- graph_name = ['MS (1) ' figure_name];
- figure('Name', graph_name);
- else
- graph_name = figure_name;
- figure('Name', graph_name);
+if nvars > nr*nc
+ graph_name = ['MS (1) ' figure_name];
+ figure('Name', graph_name);
+else
+ graph_name = figure_name;
+ figure('Name', graph_name);
+end
+m = 1;
+n_fig = 1;
+for j=1:nvars
+ if m > nr*nc
+ graph_name = ['MS (' int2str(n_fig) ') ' figure_name];
+ dyn_save_graph(dirname,['MS-forecast-' int2str(n_fig)],...
+ save_graph_formats,TeX,names,tex_names,graph_name);
+ n_fig =n_fig+1;
+ figure('Name',graph_name);
+ m = 1;
end
- m = 1;
- n_fig = 1;
- for j=1:nvars
- if m > nr*nc
- graph_name = ['MS (' int2str(n_fig) ') ' figure_name];
- dyn_save_graph(dirname,['MS-forecast-' int2str(n_fig)],...
- save_graph_formats,TeX,names,tex_names,graph_name);
- n_fig =n_fig+1;
- figure('Name',graph_name);
- m = 1;
+ subplot(nr,nc,m);
+ vn = deblank(endo_names(j,:));
+ for k=1:num_percentiles
+ if ceil(num_percentiles/2) == k
+ plot(forecast(k,:,j),'LineWidth',1.5)
+ else
+ plot(forecast(k,:,j),'LineWidth',1.1)
end
- subplot(nr,nc,m);
- vn = deblank(endo_names(j,:));
- for k=1:num_percentiles
- if ceil(num_percentiles/2) == k
- plot(forecast(k,:,j),'LineWidth',1.5)
- else
- plot(forecast(k,:,j),'LineWidth',1.1)
- end
- if k==1
- hold on;
- end
+ if k==1
+ hold on;
end
- title(vn,'Interpreter','none');
- hold off
- grid on;
- m = m+1;
- end
- if m > 1
- dyn_save_graph(dirname,['MS-forecast-' int2str(n_fig)],...
- save_graph_formats,TeX,names,tex_names,graph_name);
end
+ title(vn,'Interpreter','none');
+ hold off
+ grid on;
+ m = m+1;
+end
+if m > 1
+ dyn_save_graph(dirname,['MS-forecast-' int2str(n_fig)],...
+ save_graph_formats,TeX,names,tex_names,graph_name);
+end
end
diff --git a/matlab/ms-sbvar/plot_ms_irf.m b/matlab/ms-sbvar/plot_ms_irf.m
index 82c97274791d8a2a434806e5652c4d96a73a8de6..39ca642cff802a043a6d9a98a9102581f97fd085 100644
--- a/matlab/ms-sbvar/plot_ms_irf.m
+++ b/matlab/ms-sbvar/plot_ms_irf.m
@@ -32,108 +32,108 @@ function plot_ms_irf(M_, options_, irf, figure_name, varlist)
% 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
- figure_name = '';
- end
+if nargin < 4
+ figure_name = '';
+end
- nvars = M_.endo_nbr;
- endo_names = M_.endo_names;
+nvars = M_.endo_nbr;
+endo_names = M_.endo_names;
- if isempty(varlist)
- var_list = endo_names(1:M_.orig_endo_nbr,:);
- end
+if isempty(varlist)
+ var_list = endo_names(1:M_.orig_endo_nbr,:);
+end
- names = {};
- tex_names = {};
- m = 1;
- for i = 1:size(var_list)
- tmp = strmatch(var_list(i,:),endo_names,'exact');
- if isempty(tmp)
- error([var_list(i,:) ' isn''t and endogenous variable'])
- end
- tex_name = deblank(M_.endo_names_tex(tmp,:));
- if ~isempty(tex_name)
- names{m} = deblank(var_list(i,:));
- tex_names{m} = tex_name;
- m = m + 1;
- end
+names = {};
+tex_names = {};
+m = 1;
+for i = 1:size(var_list)
+ tmp = strmatch(var_list(i,:),endo_names,'exact');
+ if isempty(tmp)
+ error([var_list(i,:) ' isn''t and endogenous variable'])
end
-
- for i=1:M_.exo_nbr
- tex_name = deblank(M_.exo_names_tex(i,:));
- if ~isempty(tex_name)
- names{m} = deblank(M_.exo_names(i,:));
- tex_names{m} = tex_name;
- m = m + 1;
- end
+ tex_name = deblank(M_.endo_names_tex(tmp,:));
+ if ~isempty(tex_name)
+ names{m} = deblank(var_list(i,:));
+ tex_names{m} = tex_name;
+ m = m + 1;
end
+end
- dims = size(irf);
- if (length(dims) == 2)
- % Point IRF (horizon x (nvarsxnvars) )
- horizon = dims(1);
- num_percentiles = 1;
- elseif (length(dims) == 3)
- % Banded IRF
- horizon = dims(2);
- num_percentiles = dims(1);
- else
- error('The impulse response matrix passed to be plotted does not appear to be the correct size');
+for i=1:M_.exo_nbr
+ tex_name = deblank(M_.exo_names_tex(i,:));
+ if ~isempty(tex_name)
+ names{m} = deblank(M_.exo_names(i,:));
+ tex_names{m} = tex_name;
+ m = m + 1;
end
+end
- if size(endo_names,1) ~= nvars
- error('The names passed are not the same length as the number of variables');
- end
+dims = size(irf);
+if (length(dims) == 2)
+ % Point IRF (horizon x (nvarsxnvars) )
+ horizon = dims(1);
+ num_percentiles = 1;
+elseif (length(dims) == 3)
+ % Banded IRF
+ horizon = dims(2);
+ num_percentiles = dims(1);
+else
+ error('The impulse response matrix passed to be plotted does not appear to be the correct size');
+end
- if num_percentiles == 1
- % loop through the shocks
- for s=1:nvars
- shock = zeros(horizon,nvars);
- for i=1:nvars
- shock(:,i) = irf(:,((i-1) + ((s-1)*nvars)+1));
- end
- plot_point_irf_for_shock(shock, nvars,endo_names, deblank(endo_names(s,:)), ...
- figure_name, [options_.ms.output_file_tag filesep 'Output' filesep 'IRF'], options_, names, tex_names);
+if size(endo_names,1) ~= nvars
+ error('The names passed are not the same length as the number of variables');
+end
+
+if num_percentiles == 1
+ % loop through the shocks
+ for s=1:nvars
+ shock = zeros(horizon,nvars);
+ for i=1:nvars
+ shock(:,i) = irf(:,((i-1) + ((s-1)*nvars)+1));
end
- else
- for s=1:nvars
- shock = zeros(horizon,nvars,num_percentiles);
- for n=1:num_percentiles
- for i=1:nvars
- shock(:,i,n) = irf(n,:,((i-1) + ((s-1)*nvars)+1));
- end
+ plot_point_irf_for_shock(shock, nvars,endo_names, deblank(endo_names(s,:)), ...
+ figure_name, [options_.ms.output_file_tag filesep 'Output' filesep 'IRF'], options_, names, tex_names);
+ end
+else
+ for s=1:nvars
+ shock = zeros(horizon,nvars,num_percentiles);
+ for n=1:num_percentiles
+ for i=1:nvars
+ shock(:,i,n) = irf(n,:,((i-1) + ((s-1)*nvars)+1));
end
- plot_banded_irf_for_shock(shock, nvars,endo_names, deblank(endo_names(s,:)), ...
- figure_name, [options_.ms.output_file_tag filesep 'Output' filesep 'IRF'], options_, names, tex_names);
end
+ plot_banded_irf_for_shock(shock, nvars,endo_names, deblank(endo_names(s,:)), ...
+ figure_name, [options_.ms.output_file_tag filesep 'Output' filesep 'IRF'], options_, names, tex_names);
end
end
+end
function [fig] = plot_point_irf_for_shock(irf,nvars,endo_names,shock_name,figure_name,dirname,options_,names,tex_names)
- fig = figure('Name',figure_name);
- for k=1:nvars
- subplot(ceil(sqrt(nvars)), ceil(sqrt(nvars)),k);
- plot(irf(:,k))
- disp([endo_names(k,:) ' shock from ' shock_name]);
- title([endo_names(k,:) ' shock from ' shock_name]);
- end
- dyn_save_graph(dirname,[figure_name ' ' shock_name],options_.graph_save_formats, ...
- options_.TeX,names,tex_names,[figure_name ' ' shock_name]);
+fig = figure('Name',figure_name);
+for k=1:nvars
+ subplot(ceil(sqrt(nvars)), ceil(sqrt(nvars)),k);
+ plot(irf(:,k))
+ disp([endo_names(k,:) ' shock from ' shock_name]);
+ title([endo_names(k,:) ' shock from ' shock_name]);
+end
+dyn_save_graph(dirname,[figure_name ' ' shock_name],options_.graph_save_formats, ...
+ options_.TeX,names,tex_names,[figure_name ' ' shock_name]);
end
function [fig] = plot_banded_irf_for_shock(irf,nvars, endo_names, shock_name,figure_name,dirname,options_,names,tex_names)
- fig = figure('Name',figure_name);
- npercentiles = size(irf,3);
- for k=1:nvars
- subplot(ceil(sqrt(nvars)), ceil(sqrt(nvars)),k);
- for nn=1:npercentiles
- plot(irf(:,k,nn))
- hold on
- end
- hold off
- disp([endo_names(k,:) ' shock from ' shock_name]);
- title([endo_names(k,:) ' shock from ' shock_name]);
+fig = figure('Name',figure_name);
+npercentiles = size(irf,3);
+for k=1:nvars
+ subplot(ceil(sqrt(nvars)), ceil(sqrt(nvars)),k);
+ for nn=1:npercentiles
+ plot(irf(:,k,nn))
+ hold on
end
- dyn_save_graph(dirname,[figure_name ' ' shock_name],options_.graph_save_formats, ...
- options_.TeX,names,tex_names,[figure_name ' ' shock_name]);
+ hold off
+ disp([endo_names(k,:) ' shock from ' shock_name]);
+ title([endo_names(k,:) ' shock from ' shock_name]);
+end
+dyn_save_graph(dirname,[figure_name ' ' shock_name],options_.graph_save_formats, ...
+ options_.TeX,names,tex_names,[figure_name ' ' shock_name]);
end
diff --git a/matlab/ms-sbvar/plot_ms_probabilities.m b/matlab/ms-sbvar/plot_ms_probabilities.m
index 023ecfd4499ee346954a3ce63d42a739354d8aaa..0b1125de30c57c349801a6638df22baa2140fb26 100644
--- a/matlab/ms-sbvar/plot_ms_probabilities.m
+++ b/matlab/ms-sbvar/plot_ms_probabilities.m
@@ -3,7 +3,7 @@ function plot_ms_probabilities(computed_probabilities, options_)
% Plots the regime probablities for each graph
%
% INPUTS
-% computed_probabilities: Txnstates
+% computed_probabilities: Txnstates
%
% OUTPUTS
% none
@@ -27,41 +27,41 @@ function plot_ms_probabilities(computed_probabilities, options_)
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
- [T,num_grand_regimes] = size(computed_probabilities);
- num_chains = length(options_.ms.ms_chain);
- for i=1:num_chains
+
+[T,num_grand_regimes] = size(computed_probabilities);
+num_chains = length(options_.ms.ms_chain);
+for i=1:num_chains
chains(i).num_regimes = length(options_.ms.ms_chain(i).regime);
chains(i).probabilities = zeros([T,chains(i).num_regimes]);
- end
-
- for t=1:T
+end
+
+for t=1:T
chains = iterate_chain(computed_probabilities(t,:), t, chains, 1, num_chains);
- end
-
- for i=1:num_chains
+end
+
+for i=1:num_chains
graph_name = ['MS-Probabilities, Chain ' int2str(i)];
figure('Name',graph_name)
plot(chains(i).probabilities,'LineWidth', 1.2);
ltxt = {};
for j=1:chains(i).num_regimes
- ltxt{j} = ['Regime ' int2str(j)];
+ ltxt{j} = ['Regime ' int2str(j)];
end
legend(ltxt{:});
title(['Chain ' int2str(i)]);
ylim([0 1.0]);
dyn_save_graph([options_.ms.output_file_tag filesep 'Output' filesep 'Probabilities'], ['MS-Probabilities-Chain-' int2str(i)], ...
- options_.graph_save_formats,options_.TeX,[],[],graph_name);
- end
+ options_.graph_save_formats,options_.TeX,[],[],graph_name);
+end
end
function [chains] = iterate_chain(probs, t, chains, chain, num_chains)
- offset_length = length(probs)/chains(chain).num_regimes;
- for i=1:chains(chain).num_regimes
+offset_length = length(probs)/chains(chain).num_regimes;
+for i=1:chains(chain).num_regimes
p = probs( (i-1)*offset_length+1 : i*offset_length );
chains(chain).probabilities(t, i) = chains(chain).probabilities(t, i) + sum( p );
if chain < num_chains
- chains = iterate_chain(p, t, chains, chain+1, num_chains);
+ chains = iterate_chain(p, t, chains, chain+1, num_chains);
end
- end
+end
end
diff --git a/matlab/ms-sbvar/plot_ms_variance_decomposition.m b/matlab/ms-sbvar/plot_ms_variance_decomposition.m
index a1998b8e4605bdbbc89b0a6733ff52a91dfd07b9..81aab9263fe5611b61a224617347e9be17755811 100644
--- a/matlab/ms-sbvar/plot_ms_variance_decomposition.m
+++ b/matlab/ms-sbvar/plot_ms_variance_decomposition.m
@@ -9,12 +9,12 @@ function plot_ms_variance_decomposition(M_, options_, vd, figure_name, varargin)
% figure_name: (string) graph name
%
% OPTIONAL INPUTS
-% 'data': the actual data, TxK with K=number of data series
-% 'steady': the steady state value, TxK
-% 'shock_names': to specify the names of the shocks
-% 'series_names': to specify the names of the different series
-% 'dates': pass a date vector to use, otherwise will just index on 1:T
-% 'colors': Jx3 list of the rgb colors to use for each shock
+% 'data': the actual data, TxK with K=number of data series
+% 'steady': the steady state value, TxK
+% 'shock_names': to specify the names of the shocks
+% 'series_names': to specify the names of the different series
+% 'dates': pass a date vector to use, otherwise will just index on 1:T
+% 'colors': Jx3 list of the rgb colors to use for each shock
%
% OUTPUTS
% none
@@ -44,130 +44,130 @@ if length(size(vd)) == 3
return;
end
- nvars = M_.endo_nbr;
- endo_names = M_.endo_names;
+nvars = M_.endo_nbr;
+endo_names = M_.endo_names;
- names = {};
- tex_names = {};
- m = 1;
- for i=1:M_.orig_endo_nbr
- tex_name = deblank(M_.endo_names_tex(i,:));
- if ~isempty(tex_name)
- names{m} = deblank(endo_names(i,:));
- tex_names{m} = tex_name;
- m = m + 1;
- end
+names = {};
+tex_names = {};
+m = 1;
+for i=1:M_.orig_endo_nbr
+ tex_name = deblank(M_.endo_names_tex(i,:));
+ if ~isempty(tex_name)
+ names{m} = deblank(endo_names(i,:));
+ tex_names{m} = tex_name;
+ m = m + 1;
end
+end
- dims = size(vd);
- if length(dims) == 3
- T = dims(1);
- K = dims(2);
- J = dims(3);
- shocks = vd;
- else
- T = dims(1);
- K = nvars;
- J = nvars;
- temp_vd = zeros(T,K,J);
- for i=1:nvars
- for j=1:nvars
- temp_vd(:,i,j) = vd(:,((j-1) + ((i-1)*nvars)+1));
- end
+dims = size(vd);
+if length(dims) == 3
+ T = dims(1);
+ K = dims(2);
+ J = dims(3);
+ shocks = vd;
+else
+ T = dims(1);
+ K = nvars;
+ J = nvars;
+ temp_vd = zeros(T,K,J);
+ for i=1:nvars
+ for j=1:nvars
+ temp_vd(:,i,j) = vd(:,((j-1) + ((i-1)*nvars)+1));
end
- shocks = temp_vd;
end
+ shocks = temp_vd;
+end
- for i=1:nvars
- shock_names{i} = endo_names(i,:);
- series_names{i} = endo_names(i,:);
- end
+for i=1:nvars
+ shock_names{i} = endo_names(i,:);
+ series_names{i} = endo_names(i,:);
+end
- x = [1:T];
- plot_dates = 0;
- data = 0;
- steady = 0;
- colors = [ .1 .1 .75
- .8 0 0
- 1 .7 .25
- 1 1 0
- .5 1 .5
- .7 .7 .1
- .5 .6 .2
- .1 .5 .1];
+x = [1:T];
+plot_dates = 0;
+data = 0;
+steady = 0;
+colors = [ .1 .1 .75
+ .8 0 0
+ 1 .7 .25
+ 1 1 0
+ .5 1 .5
+ .7 .7 .1
+ .5 .6 .2
+ .1 .5 .1];
- % overide the defaults with optional inputs
- for i=1:length(varargin)
- if strcmpi(varargin{i},'data')
- data = varargin{i+1};
- elseif strcmpi(varargin{i},'steady')
- steady = varargin{i+1};
- elseif strcmpi(varargin{i},'shock_names')
- shock_names = varargin{i+1};
- elseif strcmpi(varargin{i},'series_names')
- series_names = varargin{i+1};
- elseif strcmpi(varargin{i}, 'dates')
- x = varargin{i+1}; plot_dates = 1;
- elseif strcmpi(varargin{i},'colors')
- colors = varargin{i+1};
- end
+% overide the defaults with optional inputs
+for i=1:length(varargin)
+ if strcmpi(varargin{i},'data')
+ data = varargin{i+1};
+ elseif strcmpi(varargin{i},'steady')
+ steady = varargin{i+1};
+ elseif strcmpi(varargin{i},'shock_names')
+ shock_names = varargin{i+1};
+ elseif strcmpi(varargin{i},'series_names')
+ series_names = varargin{i+1};
+ elseif strcmpi(varargin{i}, 'dates')
+ x = varargin{i+1}; plot_dates = 1;
+ elseif strcmpi(varargin{i},'colors')
+ colors = varargin{i+1};
end
+end
- % add an extra period to the time series
- x(T+1) = x(T) + (x(T) - x(T-1));
+% add an extra period to the time series
+x(T+1) = x(T) + (x(T) - x(T-1));
- figure('Name',figure_name)
- for k=1:K
- % Go through each series
- subplot(K,1,k);
- sshocks = shocks(:,k,:);
- hold on
- % plot the stacked shocks
- for t=1:T
- % step through each time period
- pos_position = 0; neg_position = 0;
- xt = [x(t) x(t) x(t+1) x(t+1)];
- for j=1:J
- % stack each shock
- st = sshocks(t,1,j);
- if st < 0
- yi = st+neg_position;
- y = [neg_position yi yi neg_position];
- neg_position = yi;
- else
- yi = st+pos_position;
- y = [pos_position yi yi pos_position];
- pos_position = yi;
- end
- fill(xt,y,colors(j,:));
- XY(t,j,:) = y;
- end
- end
- if data
- plot(x(2:end)',data(:,k),'k','LineWidth',2.5);
- end
- if steady
- plot(x(2:end)',steady(:,k), '--k','LineWidth',2.25);
- end
- if k==K
- if isoctave
- legend(shock_names,'Location','SouthOutside');
+figure('Name',figure_name)
+for k=1:K
+ % Go through each series
+ subplot(K,1,k);
+ sshocks = shocks(:,k,:);
+ hold on
+ % plot the stacked shocks
+ for t=1:T
+ % step through each time period
+ pos_position = 0; neg_position = 0;
+ xt = [x(t) x(t) x(t+1) x(t+1)];
+ for j=1:J
+ % stack each shock
+ st = sshocks(t,1,j);
+ if st < 0
+ yi = st+neg_position;
+ y = [neg_position yi yi neg_position];
+ neg_position = yi;
else
- legend(shock_names,'Location','BestOutside','Orientation','horizontal');
+ yi = st+pos_position;
+ y = [pos_position yi yi pos_position];
+ pos_position = yi;
end
+ fill(xt,y,colors(j,:));
+ XY(t,j,:) = y;
end
-
- hold off
- if plot_dates
- datetick 'x';
+ end
+ if data
+ plot(x(2:end)',data(:,k),'k','LineWidth',2.5);
+ end
+ if steady
+ plot(x(2:end)',steady(:,k), '--k','LineWidth',2.25);
+ end
+ if k==K
+ if isoctave
+ legend(shock_names,'Location','SouthOutside');
+ else
+ legend(shock_names,'Location','BestOutside','Orientation','horizontal');
end
- xlim([min(x),max(x)])
- ylim([0 , 1])
- grid on
- title(series_names{k});
end
- dyn_save_graph([options_.ms.output_file_tag filesep 'Output' ...
- filesep 'Variance_Decomposition'], 'MS-Variance-Decomposition', ...
- options_.graph_save_formats, options_.TeX, names, tex_names, ...
- 'Variance decomposition');
+
+ hold off
+ if plot_dates
+ datetick 'x';
+ end
+ xlim([min(x),max(x)])
+ ylim([0 , 1])
+ grid on
+ title(series_names{k});
+end
+dyn_save_graph([options_.ms.output_file_tag filesep 'Output' ...
+ filesep 'Variance_Decomposition'], 'MS-Variance-Decomposition', ...
+ options_.graph_save_formats, options_.TeX, names, tex_names, ...
+ 'Variance decomposition');
end
diff --git a/matlab/ms-sbvar/plot_ms_variance_decomposition_error_bands.m b/matlab/ms-sbvar/plot_ms_variance_decomposition_error_bands.m
index cc03e87c7f31682b03734d78f4fde4803aa8265a..193c9b59ff0600a13c38712b4a1691a63da04ab5 100644
--- a/matlab/ms-sbvar/plot_ms_variance_decomposition_error_bands.m
+++ b/matlab/ms-sbvar/plot_ms_variance_decomposition_error_bands.m
@@ -79,14 +79,14 @@ for s=1:nvars
end
end
plot_banded_vddata_for_shock(shock, nvars, endo_names, ...
- deblank(endo_names(s,:)), figure_name, ...
- [options_.ms.output_file_tag filesep 'Output' filesep 'Variance_Decomposition'], ...
- options_, names, tex_names);
+ deblank(endo_names(s,:)), figure_name, ...
+ [options_.ms.output_file_tag filesep 'Output' filesep 'Variance_Decomposition'], ...
+ options_, names, tex_names);
end
end
function [fig] = plot_banded_vddata_for_shock(vddata, nvars, endo_names, ...
- shock_name, figure_name, dirname, options_, names, tex_names)
+ shock_name, figure_name, dirname, options_, names, tex_names)
fig = figure('Name', figure_name);
npercentiles = size(vddata,3);
for k=1:nvars
@@ -100,6 +100,6 @@ for k=1:nvars
title([endo_names(k,:) ' contribution to ' shock_name]);
end
dyn_save_graph(dirname, [figure_name ' ' shock_name], ...
- options_.graph_save_formats, options_.TeX, names, tex_names, ...
- [figure_name ' ' shock_name]);
+ options_.graph_save_formats, options_.TeX, names, tex_names, ...
+ [figure_name ' ' shock_name]);
end
diff --git a/matlab/ms-sbvar/svar_global_identification_check.m b/matlab/ms-sbvar/svar_global_identification_check.m
index 549d5c7fd40150a46bd27d185de9662586c588cf..b0884defe57dbb39083f3d5d265b41c74d04dcc7 100644
--- a/matlab/ms-sbvar/svar_global_identification_check.m
+++ b/matlab/ms-sbvar/svar_global_identification_check.m
@@ -47,7 +47,7 @@ if isequal(options_.ms.restriction_fname, 'upper_cholesky') || ...
end
nvar = length(options_.varobs); % number of endogenous variables
nexo = 1;
-
+
[Uiconst,Viconst,n0,np,ixmC0Pres,Qi,Ri] = exclusions(nvar,nexo,options_.ms );
% order column constraints by rank
diff --git a/matlab/mult_elimination.m b/matlab/mult_elimination.m
index 17f0d18db51d46f7a350b37dbc9eab0d59d90a17..347c702cb2f3653b0f9cff2b916a9d9885dc5948 100644
--- a/matlab/mult_elimination.m
+++ b/matlab/mult_elimination.m
@@ -4,7 +4,7 @@ function dr=mult_elimination(varlist,M_, options_, oo_)
% and shock variables
%
% INPUT
-% none
+% none
%
% OUTPUT
% dr: a structure with the new decision rule
@@ -107,13 +107,13 @@ if nvar > 0 && options_.noprint == 0
for i=1:length(varlist)
k = strmatch(varlist{i},M_.endo_names(dr.order_var,:),'exact');
headers = char(headers,varlist{i});
-
+
res_table(1:nm_nbr,i) = M1(k,:)';
res_table(nm_nbr+(1:nm_nbr),i) = M2(k,:)';
res_table(2*nm_nbr+(1:M_.exo_nbr),i) = M3(k,:)';
res_table(2*nm_nbr+M_.exo_nbr+(1:M_.exo_nbr),i) = M4(k,:)';
end
-
+
my_title='ELIMINATION OF THE MULTIPLIERS';
lab = nstates(nil,:);
labels = strcat(deblank(lab(i,:)),'(-1)');
diff --git a/matlab/multivariate_sample_autocovariance.m b/matlab/multivariate_sample_autocovariance.m
index 6bc0ea32eae54f191fde28177e4990d1ced7fb47..3b35db5f3b889468872b8f6ff4608ad2a088ac5c 100644
--- a/matlab/multivariate_sample_autocovariance.m
+++ b/matlab/multivariate_sample_autocovariance.m
@@ -1,12 +1,12 @@
function autocov = multivariate_sample_autocovariance(data,q)
% Computes the autocovariance function of multivariate time series.
-%
%
-% INPUTS
+%
+% INPUTS
% data [double] T*m matrix of data.
-% q [integer] Order of the autocovariance function.
-%
-% OUTPUTS
+% q [integer] Order of the autocovariance function.
+%
+% OUTPUTS
% autocov [double] m*m*(q+1) array, autocovariance function.
%
% SPECIAL REQUIREMENTS
diff --git a/matlab/myboxplot.m b/matlab/myboxplot.m
index 5d513c4cc97e05e48b10e7aede74c6e93ff10936..b3490a4b39eb77da5c61a500f5850e6b8cf9c43f 100644
--- a/matlab/myboxplot.m
+++ b/matlab/myboxplot.m
@@ -33,7 +33,7 @@ if notched==1, notched=0.25; end
a=1-notched;
% ## figure out how many data sets we have
-if iscell(data)
+if iscell(data)
nc = length(data);
else
% if isvector(data), data = data(:); end
@@ -133,7 +133,7 @@ whisker_x(:,[chop,chop+nc]) = [];
whisker_y(:,[chop,chop+nc]) = [];
median_x(:,chop) = [];
median_y(:,chop) = [];
-% % % %
+% % % %
% ## Add caps to the remaining whiskers
cap_x = whisker_x;
cap_x(1,:) =cap_x(1,:)- 0.05;
@@ -144,7 +144,7 @@ cap_y = whisker_y([1,1],:);
% #whisker_x,whisker_y
% #median_x,median_y
% #cap_x,cap_y
-%
+%
% ## Do the plot
mm=min(min(data));
diff --git a/matlab/name2index.m b/matlab/name2index.m
index b653e71c62a9feca12a5880ecab11b8e385185e0..29723160b6597cbc76796b83eea071c459499567 100644
--- a/matlab/name2index.m
+++ b/matlab/name2index.m
@@ -2,14 +2,14 @@ function i = name2index(options_, M_, estim_params_, type, name1, name2 )
% Returns the index associated to an estimated object (deep parameter,
% variance of a structural shock or measurement error, covariance between
% two structural shocks, covariance between two measurement errors).
-%
+%
% INPUTS:
% options_ [structure] Dynare structure.
% M_ [structure] Dynare structure (related to model definition).
% estim_params_ [structure] Dynare structure (related to estimation).
% type [string] 'DeepParameter', 'MeasurementError' (for measurement equation error) or 'StructuralShock' (for structural shock).
-% name1 [string]
-% name2 [string]
+% name1 [string]
+% name2 [string]
% OUTPUTS
% i [integer] column index (in x2, an array of mh draws) associated to name1[,name2].
%
@@ -63,7 +63,7 @@ if strcmpi(type,'StructuralShock')
end
else% Covariance matrix off-diagonal term
offset = nvx+nvn;
- try
+ try
list_of_structural_shocks = { M_.exo_names(estim_params_.corrx(:,1),:) , M_.exo_names(estim_params_.corrx(:,2),:) };
k1 = strmatch(name1,list_of_structural_shocks(:,1),'exact');
k2 = strmatch(name2,list_of_structural_shocks(:,2),'exact');
diff --git a/matlab/non_linear_dsge_likelihood.m b/matlab/non_linear_dsge_likelihood.m
index 57e02a0b7a0a6c0968ebcfbe592c95911fdfd869..1f56c3be41e46866b1ff5bfb5023ea327c0ea2ca 100644
--- a/matlab/non_linear_dsge_likelihood.m
+++ b/matlab/non_linear_dsge_likelihood.m
@@ -229,8 +229,8 @@ end
if info(1)
if info(1) == 3 || info(1) == 4 || info(1) == 5 || info(1)==6 ||info(1) == 19 || ...
- info(1) == 20 || info(1) == 21 || info(1) == 23 || info(1) == 26 || ...
- info(1) == 81 || info(1) == 84 || info(1) == 85
+ info(1) == 20 || info(1) == 21 || info(1) == 23 || info(1) == 26 || ...
+ info(1) == 81 || info(1) == 84 || info(1) == 85
%meaningful second entry of output that can be used
fval = Inf;
info(4) = info(2);
@@ -376,4 +376,3 @@ if isinf(LIK)~=0
exit_flag = 0;
return
end
-
diff --git a/matlab/occbin/call_solve_one_constraint.m b/matlab/occbin/call_solve_one_constraint.m
index c613b50346fc9620c008b6d39179ba767fa08cb6..65b98c4319590a11db0ff9b8a914df05d78a269e 100755
--- a/matlab/occbin/call_solve_one_constraint.m
+++ b/matlab/occbin/call_solve_one_constraint.m
@@ -1,21 +1,21 @@
-% Solve model, generate model IRFs
-[zdatalinear, zdatapiecewise, zdatass, oobase_, Mbase_ ] = ...
- solve_one_constraint(modnam,modnamstar,...
- constraint, constraint_relax,...
- shockssequence,irfshock,nperiods,maxiter);
-
-
-
-% unpack the IRFs
-for i=1:Mbase_.endo_nbr
- eval([deblank(Mbase_.endo_names(i,:)),'_uncdifference=zdatalinear(:,i);']);
- eval([deblank(Mbase_.endo_names(i,:)),'_difference=zdatapiecewise(:,i);']);
- eval([deblank(Mbase_.endo_names(i,:)),'_ss=zdatass(i);']);
-end
-
-
-nparams = size(Mbase_.param_names,1);
-
-for i = 1:nparams
- eval([Mbase_.param_names(i,:),'= Mbase_.params(i);']);
-end
+% Solve model, generate model IRFs
+[zdatalinear, zdatapiecewise, zdatass, oobase_, Mbase_ ] = ...
+ solve_one_constraint(modnam,modnamstar,...
+ constraint, constraint_relax,...
+ shockssequence,irfshock,nperiods,maxiter);
+
+
+
+% unpack the IRFs
+for i=1:Mbase_.endo_nbr
+ eval([deblank(Mbase_.endo_names(i,:)),'_uncdifference=zdatalinear(:,i);']);
+ eval([deblank(Mbase_.endo_names(i,:)),'_difference=zdatapiecewise(:,i);']);
+ eval([deblank(Mbase_.endo_names(i,:)),'_ss=zdatass(i);']);
+end
+
+
+nparams = size(Mbase_.param_names,1);
+
+for i = 1:nparams
+ eval([Mbase_.param_names(i,:),'= Mbase_.params(i);']);
+end
diff --git a/matlab/occbin/call_solve_two_constraints.m b/matlab/occbin/call_solve_two_constraints.m
index 3adda90d73e14b92e0ac2c836acfea3f658847a0..5bbc1beb868e001b1126389e36e182249d0116a7 100755
--- a/matlab/occbin/call_solve_two_constraints.m
+++ b/matlab/occbin/call_solve_two_constraints.m
@@ -1,21 +1,21 @@
-[zdatalinear, zdatapiecewise, zdatass, oobase_, Mbase_] = solve_two_constraints(...
- modnam_00,modnam_10,modnam_01,modnam_11,...
- constraint1, constraint2,...
- constraint_relax1, constraint_relax2,...
- scalefactormod,irfshock,nperiods,curb_retrench,maxiter);
-
-
-for i=1:Mbase_.endo_nbr
- eval([deblank(Mbase_.endo_names(i,:)),'_uncdifference=zdatalinear(:,i);']);
- eval([deblank(Mbase_.endo_names(i,:)),'_difference=zdatapiecewise(:,i);']);
- eval([deblank(Mbase_.endo_names(i,:)),'_ss=zdatass(i);']);
-end
-
-constraint1_difference = process_constraint(constraint1,'_difference',Mbase_.endo_names,0);
-constraint2_difference = process_constraint(constraint2,'_difference',Mbase_.endo_names,0);
-
-nparams = size(Mbase_.param_names,1);
-
-for i = 1:nparams
- eval([Mbase_.param_names(i,:),'= Mbase_.params(i);']);
-end
+[zdatalinear, zdatapiecewise, zdatass, oobase_, Mbase_] = solve_two_constraints(...
+ modnam_00,modnam_10,modnam_01,modnam_11,...
+ constraint1, constraint2,...
+ constraint_relax1, constraint_relax2,...
+ scalefactormod,irfshock,nperiods,curb_retrench,maxiter);
+
+
+for i=1:Mbase_.endo_nbr
+ eval([deblank(Mbase_.endo_names(i,:)),'_uncdifference=zdatalinear(:,i);']);
+ eval([deblank(Mbase_.endo_names(i,:)),'_difference=zdatapiecewise(:,i);']);
+ eval([deblank(Mbase_.endo_names(i,:)),'_ss=zdatass(i);']);
+end
+
+constraint1_difference = process_constraint(constraint1,'_difference',Mbase_.endo_names,0);
+constraint2_difference = process_constraint(constraint2,'_difference',Mbase_.endo_names,0);
+
+nparams = size(Mbase_.param_names,1);
+
+for i = 1:nparams
+ eval([Mbase_.param_names(i,:),'= Mbase_.params(i);']);
+end
diff --git a/matlab/occbin/get_deriv.m b/matlab/occbin/get_deriv.m
index d1fe7803285ff2b94189b172a5d4eaeb02da36d1..214b1cf42572ce693436e8f817cedca1f6510096 100755
--- a/matlab/occbin/get_deriv.m
+++ b/matlab/occbin/get_deriv.m
@@ -1,83 +1,82 @@
-function [hm1,h,hl1,j,resid] = get_deriv(M_,ys_)
-
-iy_ = M_.lead_lag_incidence;
-it_ = 1;
-
-x = zeros(1,M_.exo_nbr);
-
-% For most models, there are leads, lags and current values of variables
-if size(iy_,1)==3
- % find non-zero columns of hm1
- lag_cols = find(iy_(1,:)~=0);
- % find non-zero columns of h
- con_cols = find(iy_(2,:));
- % find non-zero columns of hl1
- lea_cols = find(iy_(3,:));
-
-% If models either lacks leads or lags, iy_ will have two rows
-% In this case, we guess that the row with more nonzeros is the row with current variables
-elseif size(iy_,1)==2
- % if first row has more nonzero entries than the second, assume model lacks lagged variables
- if length(find(iy_(1,:)))>length(find(iy_(2,:)))
- warning('Model does not have lagged endogenous variables')
- con_cols = find(iy_(1,:));
- lea_cols = find(iy_(2,:));
- lag_cols = [];
- else
- warning('Model does not have expected future endogenous variables')
- lag_cols = find(iy_(1,:));
- con_cols = find(iy_(2,:));
- lea_cols = [];
- end
-
-end
-
-
-
-% find number of entries for y vector
-ny = length(find(iy_~=0));
-
-% build steady state y
-y = ys_(lag_cols);
-y = [y;ys_(con_cols)];
-y = [y;ys_(lea_cols)];
-
-
-if ismac
-eval(['[resid,g1]=',M_.fname,'_dynamic(y,x, M_.params, ys_, it_);']);
-% Older versions of DYNARE for Mac did not include ys_ in the call structure
-%eval(['[resid,g1]=',M_.fname,'_dynamic(y,x, M_.params, it_);']);
-else
-eval(['[resid,g1]=',M_.fname,'_dynamic(y,x, M_.params, ys_, it_);']);
-end
-
-
-hm1=zeros(M_.endo_nbr);
-h = hm1;
-hl1 = hm1;
-j = zeros(M_.endo_nbr,M_.exo_nbr);
-
-
-% build hm1
-nlag_cols = length(lag_cols);
-for i=1:nlag_cols
- hm1(:,lag_cols(i)) = g1(:,i);
-end
-
-% build h
-ncon_cols = length(con_cols);
-for i=1:ncon_cols
- h(:,con_cols(i)) = g1(:,i+nlag_cols);
-end
-
-% build hl1
-nlea_cols = length(lea_cols);
-for i=1:nlea_cols
- hl1(:,lea_cols(i)) = g1(:,i+nlag_cols+ncon_cols);
-end
-
-
-for i = 1:M_.exo_nbr
- j(:,i) =g1(:,i+ny);
-end
-
+function [hm1,h,hl1,j,resid] = get_deriv(M_,ys_)
+
+iy_ = M_.lead_lag_incidence;
+it_ = 1;
+
+x = zeros(1,M_.exo_nbr);
+
+% For most models, there are leads, lags and current values of variables
+if size(iy_,1)==3
+ % find non-zero columns of hm1
+ lag_cols = find(iy_(1,:)~=0);
+ % find non-zero columns of h
+ con_cols = find(iy_(2,:));
+ % find non-zero columns of hl1
+ lea_cols = find(iy_(3,:));
+
+ % If models either lacks leads or lags, iy_ will have two rows
+ % In this case, we guess that the row with more nonzeros is the row with current variables
+elseif size(iy_,1)==2
+ % if first row has more nonzero entries than the second, assume model lacks lagged variables
+ if length(find(iy_(1,:)))>length(find(iy_(2,:)))
+ warning('Model does not have lagged endogenous variables')
+ con_cols = find(iy_(1,:));
+ lea_cols = find(iy_(2,:));
+ lag_cols = [];
+ else
+ warning('Model does not have expected future endogenous variables')
+ lag_cols = find(iy_(1,:));
+ con_cols = find(iy_(2,:));
+ lea_cols = [];
+ end
+
+end
+
+
+
+% find number of entries for y vector
+ny = length(find(iy_~=0));
+
+% build steady state y
+y = ys_(lag_cols);
+y = [y;ys_(con_cols)];
+y = [y;ys_(lea_cols)];
+
+
+if ismac
+ eval(['[resid,g1]=',M_.fname,'_dynamic(y,x, M_.params, ys_, it_);']);
+ % Older versions of DYNARE for Mac did not include ys_ in the call structure
+ %eval(['[resid,g1]=',M_.fname,'_dynamic(y,x, M_.params, it_);']);
+else
+ eval(['[resid,g1]=',M_.fname,'_dynamic(y,x, M_.params, ys_, it_);']);
+end
+
+
+hm1=zeros(M_.endo_nbr);
+h = hm1;
+hl1 = hm1;
+j = zeros(M_.endo_nbr,M_.exo_nbr);
+
+
+% build hm1
+nlag_cols = length(lag_cols);
+for i=1:nlag_cols
+ hm1(:,lag_cols(i)) = g1(:,i);
+end
+
+% build h
+ncon_cols = length(con_cols);
+for i=1:ncon_cols
+ h(:,con_cols(i)) = g1(:,i+nlag_cols);
+end
+
+% build hl1
+nlea_cols = length(lea_cols);
+for i=1:nlea_cols
+ hl1(:,lea_cols(i)) = g1(:,i+nlag_cols+ncon_cols);
+end
+
+
+for i = 1:M_.exo_nbr
+ j(:,i) =g1(:,i+ny);
+end
diff --git a/matlab/occbin/get_pq.m b/matlab/occbin/get_pq.m
index 2924eacf5eda79e465fc8d28e877b882a46ec910..99888b8068b68e26d2ad22461463019e169fb4a7 100755
--- a/matlab/occbin/get_pq.m
+++ b/matlab/occbin/get_pq.m
@@ -1,28 +1,28 @@
-function [p,q]=get_pq(dr_,nstatic,nfwrd)
-
-nvars = size(dr_.ghx,1);
-nshocks = size(dr_.ghu,2);
-statevar_pos = (nstatic +1):(nvars-nfwrd);
-
-p = zeros(nvars);
-% interlace matrix
-nnotzero = length(statevar_pos);
-for i=1:nnotzero
- p(:,statevar_pos(i)) = dr_.ghx(:,i);
-end
-
-% reorder p matrix according to order in lgy_
-inverse_order = zeros(nvars,1);
-for i=1:nvars
- inverse_order(i) = find(i==dr_.order_var);
-end
-
-p_reordered = zeros(nvars);
-q = zeros(nvars,nshocks);
-for i=1:nvars
- for j=1:nvars
- p_reordered(i,j)=p(inverse_order(i),inverse_order(j));
- end
- q(i,:)=dr_.ghu(inverse_order(i),:);
-end
+function [p,q]=get_pq(dr_,nstatic,nfwrd)
+
+nvars = size(dr_.ghx,1);
+nshocks = size(dr_.ghu,2);
+statevar_pos = (nstatic +1):(nvars-nfwrd);
+
+p = zeros(nvars);
+% interlace matrix
+nnotzero = length(statevar_pos);
+for i=1:nnotzero
+ p(:,statevar_pos(i)) = dr_.ghx(:,i);
+end
+
+% reorder p matrix according to order in lgy_
+inverse_order = zeros(nvars,1);
+for i=1:nvars
+ inverse_order(i) = find(i==dr_.order_var);
+end
+
+p_reordered = zeros(nvars);
+q = zeros(nvars,nshocks);
+for i=1:nvars
+ for j=1:nvars
+ p_reordered(i,j)=p(inverse_order(i),inverse_order(j));
+ end
+ q(i,:)=dr_.ghu(inverse_order(i),:);
+end
p=p_reordered;
\ No newline at end of file
diff --git a/matlab/occbin/makechart.m b/matlab/occbin/makechart.m
index c3ea7719ee666a5eacea6491f7239c7b84d504bd..934e8463bf1fb8620ba0ede35d5dc2e27600e329 100755
--- a/matlab/occbin/makechart.m
+++ b/matlab/occbin/makechart.m
@@ -1,81 +1,81 @@
-function makechart(titlelist,legendlist,figlabel,ylabels,zdata1,zdata2,zdata3)
-
-
-
-figure
-
-titlelist = char(strrep(cellstr(titlelist),'_','.'));
-
-ndsets=3; % default, changed below as applicable
-if nargin==5
- zdata2=nan*zdata1;
- zdata3=nan*zdata1;
- ndsets =1;
-elseif nargin == 6
- zdata3 =nan*zdata1;
- ndsets=2;
-elseif ((nargin>8) | (nargin <=4))
- error ('makechart takes 5 to 6 arguments')
-end
-
-nobs = size(zdata1,1);
-xvalues = (1:nobs)';
-
-nvars = size(titlelist,1);
-if nvars==1
- nrows=1;
- ncols = 1;
-elseif nvars==2
- nrows =2;
- ncols = 1;
-elseif (nvars == 3 | nvars ==4)
- nrows = 2;
- ncols =2;
-elseif (nvars==5 |nvars ==6)
- nrows = 3;
- ncols = 2;
-elseif (nvars==7 | nvars==8)
- nrows = 4;
- ncols = 2;
-elseif (nvars==9 | nvars==10)
- nrows = 5;
- ncols = 2;
-else
- error('too many variables (makechart)')
-end
-
-for i = 1:nvars
- subplot(nrows,ncols,i)
- h1=plot(xvalues,zdata1(:,i),'b-','linewidth',2); hold on
- h1=plot(xvalues,zdata2(:,i),'r--','linewidth',2); hold on
- h2=plot(xvalues,zdata3(:,i),'b-','LineWidth',3);
- [x0, x1, y10, y11] = pickaxes(xvalues,zdata1(:,i));
- [x0, x1, y20, y21] = pickaxes(xvalues,zdata2(:,i));
- [x0, x1, y30, y31] = pickaxes(xvalues,zdata3(:,i));
- y0 = min([y10,y20,y30]);
- y1 = max([y11,y21,y31]);
- if y0==y1
- y1=y0+1;
- end
-
- axis([x0 x1 y0 y1])
- set(h1);
-
- if i==1 && isempty(legendlist)==0
- legend(legendlist)
- text('String',figlabel,'Units','normalized','Position',[1.2 1.24],...
- 'FontSize',14,'FontWeight','bold','HorizontalAlignment','center');
- end
-
- if i==nvars | i==nvars-1
- xlabel('Time');
- end
-% set(gca,'XTick',xtick)
-% set(gca,'XTickLabel',xticklabel)
-
- title([num2str(i),'. ',titlelist(i,:)]);
- ylabel(ylabels(i,:))
-end
-
-% sets printing preferences
-%printpref
+function makechart(titlelist,legendlist,figlabel,ylabels,zdata1,zdata2,zdata3)
+
+
+
+figure
+
+titlelist = char(strrep(cellstr(titlelist),'_','.'));
+
+ndsets=3; % default, changed below as applicable
+if nargin==5
+ zdata2=nan*zdata1;
+ zdata3=nan*zdata1;
+ ndsets =1;
+elseif nargin == 6
+ zdata3 =nan*zdata1;
+ ndsets=2;
+elseif ((nargin>8) | (nargin <=4))
+ error ('makechart takes 5 to 6 arguments')
+end
+
+nobs = size(zdata1,1);
+xvalues = (1:nobs)';
+
+nvars = size(titlelist,1);
+if nvars==1
+ nrows=1;
+ ncols = 1;
+elseif nvars==2
+ nrows =2;
+ ncols = 1;
+elseif (nvars == 3 | nvars ==4)
+ nrows = 2;
+ ncols =2;
+elseif (nvars==5 |nvars ==6)
+ nrows = 3;
+ ncols = 2;
+elseif (nvars==7 | nvars==8)
+ nrows = 4;
+ ncols = 2;
+elseif (nvars==9 | nvars==10)
+ nrows = 5;
+ ncols = 2;
+else
+ error('too many variables (makechart)')
+end
+
+for i = 1:nvars
+ subplot(nrows,ncols,i)
+ h1=plot(xvalues,zdata1(:,i),'b-','linewidth',2); hold on
+ h1=plot(xvalues,zdata2(:,i),'r--','linewidth',2); hold on
+ h2=plot(xvalues,zdata3(:,i),'b-','LineWidth',3);
+ [x0, x1, y10, y11] = pickaxes(xvalues,zdata1(:,i));
+ [x0, x1, y20, y21] = pickaxes(xvalues,zdata2(:,i));
+ [x0, x1, y30, y31] = pickaxes(xvalues,zdata3(:,i));
+ y0 = min([y10,y20,y30]);
+ y1 = max([y11,y21,y31]);
+ if y0==y1
+ y1=y0+1;
+ end
+
+ axis([x0 x1 y0 y1])
+ set(h1);
+
+ if i==1 && isempty(legendlist)==0
+ legend(legendlist)
+ text('String',figlabel,'Units','normalized','Position',[1.2 1.24],...
+ 'FontSize',14,'FontWeight','bold','HorizontalAlignment','center');
+ end
+
+ if i==nvars | i==nvars-1
+ xlabel('Time');
+ end
+ % set(gca,'XTick',xtick)
+ % set(gca,'XTickLabel',xticklabel)
+
+ title([num2str(i),'. ',titlelist(i,:)]);
+ ylabel(ylabels(i,:))
+end
+
+% sets printing preferences
+%printpref
diff --git a/matlab/occbin/makechart9.m b/matlab/occbin/makechart9.m
index 8f9ae54793be9c536e5b26c13c8b49668718d535..264f9f58f73a0535c61adc47b6ff2954e617553b 100755
--- a/matlab/occbin/makechart9.m
+++ b/matlab/occbin/makechart9.m
@@ -1,136 +1,136 @@
-function makechart9(titlelist,legendlist,figlabel,yearshock,ylabels,...
- zdata1,zdata2,zdata3,zdata4,zdata5,zdata6,zdata7)
-
-
-
-figure
-
-titlelist = char(strrep(cellstr(titlelist),'_','.'));
-
-ndsets=7; % default, changed below as applicable
-if nargin==6
- zdata2=nan*zdata1;
- zdata3=nan*zdata1;
- zdata4=nan*zdata1;
- zdata5=nan*zdata1;
- zdata6=nan*zdata1;
- zdata7=nan*zdata1;
- ndsets =1;
-elseif nargin==7
- zdata3=nan*zdata1;
- zdata4=nan*zdata1;
- zdata5=nan*zdata1;
- zdata6=nan*zdata1;
- zdata7=nan*zdata1;
- ndsets =2;
-elseif nargin == 8
- zdata4 =nan*zdata1;
- zdata5 =nan*zdata1;
- zdata6=nan*zdata1;
- zdata7=nan*zdata1;
- ndsets=3;
-elseif nargin == 9
- zdata5 =nan*zdata1;
- zdata6=nan*zdata1;
- zdata7=nan*zdata1;
- ndsets=4;
-elseif nargin == 10
- zdata6 =nan*zdata1;
- zdata7=nan*zdata1;
- ndsets=5;
-elseif nargin == 11
- zdata7=nan*zdata1;
- ndsets=6;
-elseif ((nargin>=13) | (nargin <=3))
- error ('makechart takes 4 to 10 arguments')
-end
-
-nobs = size(zdata1,1);
-
-if yearshock>-100
-xvalues = yearshock+(0:nobs-1)'/4; % Matteo plot year on x axis
-else
-xvalues = (1:nobs)'; % Matteo plot year on x axis
-end
-
-nvars = size(titlelist,1);
-if nvars==1
- nrows=1;
- ncols = 1;
-elseif nvars==2
- nrows =2;
- ncols = 1;
-elseif nvars == 3
- nrows = 3;
- ncols = 1;
-elseif nvars==4
- nrows = 2;
- ncols = 2;
-elseif (nvars==5 | nvars ==6)
- nrows = 3;
- ncols = 2;
-elseif (nvars==7 | nvars==8)
- nrows = 4;
- ncols = 2;
-elseif nvars>8 & nvars<=12
- nrows = 3;
- ncols = 4;
-elseif nvars>12 & nvars<=15
- nrows = 5;
- ncols = 3;
-else
- error('too many variables (makechart)')
-end
-
-
-for i = 1:nvars
- subplot(nrows,ncols,i)
- h1=plot(xvalues,zdata1(:,i),'k',...
- xvalues,zdata2(:,i),'r',...
- xvalues,zdata3(:,i),'b',...
- xvalues,zdata4(:,i),'g',...
- xvalues,zdata5(:,i),'g',...
- xvalues,zdata6(:,i),'c',...
- xvalues,zdata7(:,i),'y');
- [x0, x1, y10, y11] = pickaxes(xvalues,zdata1(:,i));
- [x0, x1, y20, y21] = pickaxes(xvalues,zdata2(:,i));
- [x0, x1, y30, y31] = pickaxes(xvalues,zdata3(:,i));
- [x0, x1, y40, y41] = pickaxes(xvalues,zdata4(:,i));
- [x0, x1, y50, y51] = pickaxes(xvalues,zdata5(:,i));
- [x0, x1, y60, y61] = pickaxes(xvalues,zdata6(:,i));
- [x0, x1, y70, y71] = pickaxes(xvalues,zdata7(:,i));
- grid on
- y0 = min([y10,y20,y30,y40,y50,y60,y70]);
- y1 = max([y11,y21,y31,y41,y51,y61,y71]);
- if y0==y1
- y1=y0+1;
- end
-
- axis([x0 x1 y0 y1])
- set(h1,'linewidth',2);
- if i==1
- if numel(strvcat(legendlist(1,:)))
- h=legend(legendlist,'Location','Northwest');
- set(h,'Fontsize',8)
- end
- end
- if i==1
- if nvars>3
- text('String',figlabel,'Units','normalized','Position',[1.2 1.21],...
- 'FontSize',13,'FontWeight','bold','HorizontalAlignment','center');
- else
- text('String',figlabel,'Units','normalized','Position',[0.4 1.24],...
- 'FontSize',13,'FontWeight','bold','HorizontalAlignment','center');
- end
- end
-
- %set(gca,'XTick',xtick)
- %set(gca,'XTickLabel',xticklabel)
-
- title(titlelist(i,:),'Fontsize',11);
- ylabel(ylabels(i,:))
-
-end
-
-% sets printing preferences
-%printpref
+function makechart9(titlelist,legendlist,figlabel,yearshock,ylabels,...
+ zdata1,zdata2,zdata3,zdata4,zdata5,zdata6,zdata7)
+
+
+
+figure
+
+titlelist = char(strrep(cellstr(titlelist),'_','.'));
+
+ndsets=7; % default, changed below as applicable
+if nargin==6
+ zdata2=nan*zdata1;
+ zdata3=nan*zdata1;
+ zdata4=nan*zdata1;
+ zdata5=nan*zdata1;
+ zdata6=nan*zdata1;
+ zdata7=nan*zdata1;
+ ndsets =1;
+elseif nargin==7
+ zdata3=nan*zdata1;
+ zdata4=nan*zdata1;
+ zdata5=nan*zdata1;
+ zdata6=nan*zdata1;
+ zdata7=nan*zdata1;
+ ndsets =2;
+elseif nargin == 8
+ zdata4 =nan*zdata1;
+ zdata5 =nan*zdata1;
+ zdata6=nan*zdata1;
+ zdata7=nan*zdata1;
+ ndsets=3;
+elseif nargin == 9
+ zdata5 =nan*zdata1;
+ zdata6=nan*zdata1;
+ zdata7=nan*zdata1;
+ ndsets=4;
+elseif nargin == 10
+ zdata6 =nan*zdata1;
+ zdata7=nan*zdata1;
+ ndsets=5;
+elseif nargin == 11
+ zdata7=nan*zdata1;
+ ndsets=6;
+elseif ((nargin>=13) | (nargin <=3))
+ error ('makechart takes 4 to 10 arguments')
+end
+
+nobs = size(zdata1,1);
+
+if yearshock>-100
+ xvalues = yearshock+(0:nobs-1)'/4; % Matteo plot year on x axis
+else
+ xvalues = (1:nobs)'; % Matteo plot year on x axis
+end
+
+nvars = size(titlelist,1);
+if nvars==1
+ nrows=1;
+ ncols = 1;
+elseif nvars==2
+ nrows =2;
+ ncols = 1;
+elseif nvars == 3
+ nrows = 3;
+ ncols = 1;
+elseif nvars==4
+ nrows = 2;
+ ncols = 2;
+elseif (nvars==5 | nvars ==6)
+ nrows = 3;
+ ncols = 2;
+elseif (nvars==7 | nvars==8)
+ nrows = 4;
+ ncols = 2;
+elseif nvars>8 & nvars<=12
+ nrows = 3;
+ ncols = 4;
+elseif nvars>12 & nvars<=15
+ nrows = 5;
+ ncols = 3;
+else
+ error('too many variables (makechart)')
+end
+
+
+for i = 1:nvars
+ subplot(nrows,ncols,i)
+ h1=plot(xvalues,zdata1(:,i),'k',...
+ xvalues,zdata2(:,i),'r',...
+ xvalues,zdata3(:,i),'b',...
+ xvalues,zdata4(:,i),'g',...
+ xvalues,zdata5(:,i),'g',...
+ xvalues,zdata6(:,i),'c',...
+ xvalues,zdata7(:,i),'y');
+ [x0, x1, y10, y11] = pickaxes(xvalues,zdata1(:,i));
+ [x0, x1, y20, y21] = pickaxes(xvalues,zdata2(:,i));
+ [x0, x1, y30, y31] = pickaxes(xvalues,zdata3(:,i));
+ [x0, x1, y40, y41] = pickaxes(xvalues,zdata4(:,i));
+ [x0, x1, y50, y51] = pickaxes(xvalues,zdata5(:,i));
+ [x0, x1, y60, y61] = pickaxes(xvalues,zdata6(:,i));
+ [x0, x1, y70, y71] = pickaxes(xvalues,zdata7(:,i));
+ grid on
+ y0 = min([y10,y20,y30,y40,y50,y60,y70]);
+ y1 = max([y11,y21,y31,y41,y51,y61,y71]);
+ if y0==y1
+ y1=y0+1;
+ end
+
+ axis([x0 x1 y0 y1])
+ set(h1,'linewidth',2);
+ if i==1
+ if numel(strvcat(legendlist(1,:)))
+ h=legend(legendlist,'Location','Northwest');
+ set(h,'Fontsize',8)
+ end
+ end
+ if i==1
+ if nvars>3
+ text('String',figlabel,'Units','normalized','Position',[1.2 1.21],...
+ 'FontSize',13,'FontWeight','bold','HorizontalAlignment','center');
+ else
+ text('String',figlabel,'Units','normalized','Position',[0.4 1.24],...
+ 'FontSize',13,'FontWeight','bold','HorizontalAlignment','center');
+ end
+ end
+
+ %set(gca,'XTick',xtick)
+ %set(gca,'XTickLabel',xticklabel)
+
+ title(titlelist(i,:),'Fontsize',11);
+ ylabel(ylabels(i,:))
+
+end
+
+% sets printing preferences
+%printpref
diff --git a/matlab/occbin/map_regime.m b/matlab/occbin/map_regime.m
index 1c84871bd05e1188363ecfbd9071edc5c9ce9253..702b011f9fb516114539a7c6530ba44fec2bd378 100755
--- a/matlab/occbin/map_regime.m
+++ b/matlab/occbin/map_regime.m
@@ -1,25 +1,24 @@
-function [regime, regimestart]=map_regimes(violvecbool)
-
-nperiods = length(violvecbool)-1;
-
-% analyse violvec and isolate contiguous periods in the other regime.
- regime(1) = violvecbool(1);
- regimeindx = 1;
- regimestart(1) = 1;
- for i=2:nperiods
- if violvecbool(i)~=regime(regimeindx)
- regimeindx=regimeindx+1;
- regime(regimeindx) = violvecbool(i);
- regimestart(regimeindx)=i;
- end
- end
-
-
- if (regime(1) == 1 & length(regimestart)==1)
- warning('Increase nperiods');
- end
-
- if (regime(end)==1)
- warning('Increase nperiods');
- end
-
+function [regime, regimestart]=map_regime(violvecbool)
+
+nperiods = length(violvecbool)-1;
+
+% analyse violvec and isolate contiguous periods in the other regime.
+regime(1) = violvecbool(1);
+regimeindx = 1;
+regimestart(1) = 1;
+for i=2:nperiods
+ if violvecbool(i)~=regime(regimeindx)
+ regimeindx=regimeindx+1;
+ regime(regimeindx) = violvecbool(i);
+ regimestart(regimeindx)=i;
+ end
+end
+
+
+if (regime(1) == 1 & length(regimestart)==1)
+ warning('Increase nperiods');
+end
+
+if (regime(end)==1)
+ warning('Increase nperiods');
+end
diff --git a/matlab/occbin/mkdata.m b/matlab/occbin/mkdata.m
index a7f855743ddb228ecf8c75e65823849f054fd8b4..f22fb45f6574a12d6740ad53c6d8f5b0e6e6ce79 100755
--- a/matlab/occbin/mkdata.m
+++ b/matlab/occbin/mkdata.m
@@ -1,63 +1,63 @@
-function [zdata]=mkdata(nperiods,decrulea,decruleb,endog_,exog_,wishlist,irfshock,scalefactormod,init)
-
-%[nsim, ksim, ysim, isim, csim] = mkdata(nperiods,cofb,endog_)
-
-% given decision rule
-neqs = size(endog_,1);
-
-if nargin<9
- init = zeros(neqs,1);
-end
-
-if nargin<8
- scalefactormod=1;
-end
-
-if nargin<7
- error('Not enough inputs')
-end
-
-history = zeros(neqs,nperiods+1);
-
- nshocks = size(irfshock,1);
- for i = 1:nshocks
- shockpos = strmatch(irfshock(i,:),exog_,'exact');
- if ~isempty(shockpos)
- irfshockpos(i) = shockpos;
- else
- error(['Shock ',irfshock(i,:),' is not in the model']);
- end
- end
-
-
-% generate data
-% history will contain data, the state vector at each period in time will
-% be stored columnwise.
-history = zeros(neqs,nperiods);
-history(:,1)= init;
-
-lengthshock = size(scalefactormod,1);
-
-errvec = zeros(size(exog_,1),1);
-
-for i = 2:nperiods+1
- if i<=(lengthshock+1)
- for j = 1:nshocks
- errvec(irfshockpos(j)) = scalefactormod(i-1,j);
- end
- history(:,i) = decrulea * history(:,i-1)+decruleb*errvec;
- else
- % update endogenous variables
- history(:,i) = decrulea * history(:,i-1);
- end
-end
-
-% extract desired variables
-nwish=size(wishlist,1);
-wishpos = zeros(nwish,1);
-
-history=history';
-for i=1:nwish
- wishpos(i) = strmatch(wishlist(i,:),endog_,'exact');
-end
+function [zdata]=mkdata(nperiods,decrulea,decruleb,endog_,exog_,wishlist,irfshock,scalefactormod,init)
+
+%[nsim, ksim, ysim, isim, csim] = mkdata(nperiods,cofb,endog_)
+
+% given decision rule
+neqs = size(endog_,1);
+
+if nargin<9
+ init = zeros(neqs,1);
+end
+
+if nargin<8
+ scalefactormod=1;
+end
+
+if nargin<7
+ error('Not enough inputs')
+end
+
+history = zeros(neqs,nperiods+1);
+
+nshocks = size(irfshock,1);
+for i = 1:nshocks
+ shockpos = strmatch(irfshock(i,:),exog_,'exact');
+ if ~isempty(shockpos)
+ irfshockpos(i) = shockpos;
+ else
+ error(['Shock ',irfshock(i,:),' is not in the model']);
+ end
+end
+
+
+% generate data
+% history will contain data, the state vector at each period in time will
+% be stored columnwise.
+history = zeros(neqs,nperiods);
+history(:,1)= init;
+
+lengthshock = size(scalefactormod,1);
+
+errvec = zeros(size(exog_,1),1);
+
+for i = 2:nperiods+1
+ if i<=(lengthshock+1)
+ for j = 1:nshocks
+ errvec(irfshockpos(j)) = scalefactormod(i-1,j);
+ end
+ history(:,i) = decrulea * history(:,i-1)+decruleb*errvec;
+ else
+ % update endogenous variables
+ history(:,i) = decrulea * history(:,i-1);
+ end
+end
+
+% extract desired variables
+nwish=size(wishlist,1);
+wishpos = zeros(nwish,1);
+
+history=history';
+for i=1:nwish
+ wishpos(i) = strmatch(wishlist(i,:),endog_,'exact');
+end
zdata = history(2:end,wishpos);
\ No newline at end of file
diff --git a/matlab/occbin/mkdatap_anticipated.m b/matlab/occbin/mkdatap_anticipated.m
index 6fef8c7f2dac0576873613d0c99c1d15c356bad2..a9c9249663e52b5e80efb9d9937614dfdd361527 100755
--- a/matlab/occbin/mkdatap_anticipated.m
+++ b/matlab/occbin/mkdatap_anticipated.m
@@ -1,125 +1,125 @@
-function [zdata]=mkdatap_anticipated(nperiods,decrulea,decruleb,...
- cof,Jbarmat,cofstar,Jstarbarmat,Dstarbarmat,...
- regime,regimestart,violvecbool,...
- endog_,exog_,irfshock,scalefactormod,init)
-
-
-
-nvars = size(endog_,1);
-
-
-if nargin<16
- init=zeros(nvars,1);
-end
-
-if nargin<15
- scalefactormod=1;
-end
-
-
-nshocks = size(irfshock,1);
-for i = 1:nshocks
- shockpos = strmatch(irfshock(i,:),exog_,'exact');
- if ~isempty(shockpos)
- irfshockpos(i) = shockpos;
- else
- error(['Shock ',irfshock(i,:),' is not in the model']);
- end
-end
-
-
-nregimes = length(regime);
-
-Cbarmat = cof(:,1:nvars);
-Bbarmat = cof(:,nvars+1:2*nvars);
-Abarmat = cof(:,2*nvars+1:3*nvars);
-
-
-% cofstar contains the system for the model when the constraint binds
-Cstarbarmat = cofstar(:,1:nvars);
-Bstarbarmat = cofstar(:,nvars+1:2*nvars);
-Astarbarmat = cofstar(:,2*nvars+1:3*nvars);
-
-% get the time-dependent decision rules
-
-Tmax = regimestart(nregimes)-1; % Tmax is the position of the last period
-% when the constraint binds
-
-if Tmax > 0
- P = zeros(nvars,nvars,Tmax);
- D = zeros(nvars,Tmax);
-
-
- invmat = inv((Astarbarmat*decrulea+Bstarbarmat));
- P(:,:,Tmax) = -invmat*Cstarbarmat;
- D(:,Tmax) = -invmat*Dstarbarmat;
-
-
- % equivalent to pre-multiplying by the inverse above if the target
- % matrix is invertible. Otherwise it yields the minimum state solution
- %P(:,:,Tmax) = -(Astarbarmat*decrulea+Bstarbarmat)\Cstarbarmat;
- %D(:,Tmax) = -(Astarbarmat*decrulea+Bstarbarmat)\Dstarbarmat;
-
-
- for i = Tmax-1:-1:1
-
- if violvecbool(i)
- invmat = inv(Bstarbarmat+Astarbarmat*P(:,:,i+1));
- P(:,:,i)=-invmat*Cstarbarmat;
- D(:,i) = -invmat*(Astarbarmat*D(:,i+1)+Dstarbarmat);
- else
- invmat = inv(Bbarmat+Abarmat*P(:,:,i+1));
- P(:,:,i)=-invmat*Cbarmat;
- D(:,i) = -invmat*(Abarmat*D(:,i+1));
- end
- end
-
-if Tmax > 1
-if violvecbool(1)
- E = -invmat*Jstarbarmat;
-else
- E = -invmat*Jbarmat;
-end
-else
- invmat = inv(Astarbarmat*decrulea+Bstarbarmat);
- E = -invmat*Jstarbarmat;
-
-end
-
-
-end
-
-% generate data
-% history will contain data, the state vector at each period in time will
-% be stored columnwise.
-history = zeros(nvars,nperiods+1);
-history(:,1) = init;
-errvec = zeros(size(exog_,1),1);
-
-% deal with predetermined conditions
-for i = 1:nshocks
- errvec(irfshockpos(i)) = scalefactormod(i);
-end
-
-% deal with shocks
-irfpos =1;
-if irfpos <=Tmax
- history(:,irfpos+1) = P(:,:,irfpos)* history(:,irfpos)+...
- D(:,irfpos) + E*errvec;
-else
- history(:,irfpos+1) = decrulea*history(:,irfpos)+decruleb*errvec;
-end
-
-% all other periods
-for irfpos=2:nperiods+1
- if irfpos <=Tmax
- history(:,irfpos+1) = P(:,:,irfpos)* history(:,irfpos)+...
- D(:,irfpos);
- else
- history(:,irfpos+1) = decrulea*history(:,irfpos);
- end
-end
-
-
-history=history';
+function [zdata]=mkdatap_anticipated(nperiods,decrulea,decruleb,...
+ cof,Jbarmat,cofstar,Jstarbarmat,Dstarbarmat,...
+ regime,regimestart,violvecbool,...
+ endog_,exog_,irfshock,scalefactormod,init)
+
+
+
+nvars = size(endog_,1);
+
+
+if nargin<16
+ init=zeros(nvars,1);
+end
+
+if nargin<15
+ scalefactormod=1;
+end
+
+
+nshocks = size(irfshock,1);
+for i = 1:nshocks
+ shockpos = strmatch(irfshock(i,:),exog_,'exact');
+ if ~isempty(shockpos)
+ irfshockpos(i) = shockpos;
+ else
+ error(['Shock ',irfshock(i,:),' is not in the model']);
+ end
+end
+
+
+nregimes = length(regime);
+
+Cbarmat = cof(:,1:nvars);
+Bbarmat = cof(:,nvars+1:2*nvars);
+Abarmat = cof(:,2*nvars+1:3*nvars);
+
+
+% cofstar contains the system for the model when the constraint binds
+Cstarbarmat = cofstar(:,1:nvars);
+Bstarbarmat = cofstar(:,nvars+1:2*nvars);
+Astarbarmat = cofstar(:,2*nvars+1:3*nvars);
+
+% get the time-dependent decision rules
+
+Tmax = regimestart(nregimes)-1; % Tmax is the position of the last period
+ % when the constraint binds
+
+if Tmax > 0
+ P = zeros(nvars,nvars,Tmax);
+ D = zeros(nvars,Tmax);
+
+
+ invmat = inv((Astarbarmat*decrulea+Bstarbarmat));
+ P(:,:,Tmax) = -invmat*Cstarbarmat;
+ D(:,Tmax) = -invmat*Dstarbarmat;
+
+
+ % equivalent to pre-multiplying by the inverse above if the target
+ % matrix is invertible. Otherwise it yields the minimum state solution
+ %P(:,:,Tmax) = -(Astarbarmat*decrulea+Bstarbarmat)\Cstarbarmat;
+ %D(:,Tmax) = -(Astarbarmat*decrulea+Bstarbarmat)\Dstarbarmat;
+
+
+ for i = Tmax-1:-1:1
+
+ if violvecbool(i)
+ invmat = inv(Bstarbarmat+Astarbarmat*P(:,:,i+1));
+ P(:,:,i)=-invmat*Cstarbarmat;
+ D(:,i) = -invmat*(Astarbarmat*D(:,i+1)+Dstarbarmat);
+ else
+ invmat = inv(Bbarmat+Abarmat*P(:,:,i+1));
+ P(:,:,i)=-invmat*Cbarmat;
+ D(:,i) = -invmat*(Abarmat*D(:,i+1));
+ end
+ end
+
+ if Tmax > 1
+ if violvecbool(1)
+ E = -invmat*Jstarbarmat;
+ else
+ E = -invmat*Jbarmat;
+ end
+ else
+ invmat = inv(Astarbarmat*decrulea+Bstarbarmat);
+ E = -invmat*Jstarbarmat;
+
+ end
+
+
+end
+
+% generate data
+% history will contain data, the state vector at each period in time will
+% be stored columnwise.
+history = zeros(nvars,nperiods+1);
+history(:,1) = init;
+errvec = zeros(size(exog_,1),1);
+
+% deal with predetermined conditions
+for i = 1:nshocks
+ errvec(irfshockpos(i)) = scalefactormod(i);
+end
+
+% deal with shocks
+irfpos =1;
+if irfpos <=Tmax
+ history(:,irfpos+1) = P(:,:,irfpos)* history(:,irfpos)+...
+ D(:,irfpos) + E*errvec;
+else
+ history(:,irfpos+1) = decrulea*history(:,irfpos)+decruleb*errvec;
+end
+
+% all other periods
+for irfpos=2:nperiods+1
+ if irfpos <=Tmax
+ history(:,irfpos+1) = P(:,:,irfpos)* history(:,irfpos)+...
+ D(:,irfpos);
+ else
+ history(:,irfpos+1) = decrulea*history(:,irfpos);
+ end
+end
+
+
+history=history';
zdata = history(2:end,:);
\ No newline at end of file
diff --git a/matlab/occbin/mkdatap_anticipated_2constraints.m b/matlab/occbin/mkdatap_anticipated_2constraints.m
index 85da740e5557f32c6c9ab498511e561a92f73845..496f5a931c8777970f3e991e6012ab455e188aaa 100755
--- a/matlab/occbin/mkdatap_anticipated_2constraints.m
+++ b/matlab/occbin/mkdatap_anticipated_2constraints.m
@@ -1,180 +1,180 @@
-function [zdata]=mkdatap_anticipated_2constraints_alt(nperiods,decrulea,decruleb,...
- cof,Jbarmat,...
- cof10,Jbarmat10,Dbarmat10,...
- cof01,Jbarmat01,Dbarmat01,...
- cof11,Jbarmat11,Dbarmat11,...
- regime1,regimestart1,...
- regime2,regimestart2,...
- violvecbool,endog_,exog_,...
- irfshock,scalefactormod,init)
-
-
-nvars = size(endog_,1);
-
-
-if nargin<16
- init=zeros(nvars,1);
-end
-
-if nargin<15
- scalefactormod=1;
-end
-
-
-nshocks = size(irfshock,1);
-for i = 1:nshocks
- shockpos = strmatch(irfshock(i,:),exog_,'exact');
- if ~isempty(shockpos)
- irfshockpos(i) = shockpos;
- else
- error(['Shock ',irfshock(i,:),' is not in the model']);
- end
-end
-
-
-
-Cbarmat = cof(:,1:nvars);
-Bbarmat = cof(:,nvars+1:2*nvars);
-Abarmat = cof(:,2*nvars+1:3*nvars);
-
-
-% cofstar contains the system for the model when the constraint binds
-
-
-Cbarmat10 = cof10(:,1:nvars);
-Bbarmat10 = cof10(:,nvars+1:2*nvars);
-Abarmat10 = cof10(:,2*nvars+1:3*nvars);
-
-Cbarmat01 = cof01(:,1:nvars);
-Bbarmat01 = cof01(:,nvars+1:2*nvars);
-Abarmat01 = cof01(:,2*nvars+1:3*nvars);
-
-Cbarmat11 = cof11(:,1:nvars);
-Bbarmat11 = cof11(:,nvars+1:2*nvars);
-Abarmat11 = cof11(:,2*nvars+1:3*nvars);
-
-% get the time-dependent decision rules
-nregimes1 = length(regime1);
-nregimes2 = length(regime2);
-
-Tmax = max([regimestart1(nregimes1) regimestart2(nregimes2)])-1; % Tmax is the position of the last period
-% when the constraint binds
-
-if Tmax > 0
- P = zeros(nvars,nvars,Tmax);
- D = zeros(nvars,Tmax);
-
-% invmat = inv((Astarbarmat*decrulea+Bstarbarmat));
-% P(:,:,Tmax) = -invmat*Cstarbarmat;
-% D(:,Tmax) = -invmat*Dstarbarmat;
-
-
- if (violvecbool(Tmax,1) & ~violvecbool(Tmax,2))
- %XXX fix next three lines
- invmat = inv((Abarmat10*decrulea+Bbarmat10));
- P(:,:,Tmax) = -invmat*Cbarmat10;
- D(:,Tmax) = -invmat*Dbarmat10;
- elseif (violvecbool(Tmax,1) & violvecbool(Tmax,2))
- invmat = inv((Abarmat11*decrulea+Bbarmat11));
- P(:,:,Tmax) = -invmat*Cbarmat11;
- D(:,Tmax) = -invmat*Dbarmat11;
- else
- invmat = inv((Abarmat01*decrulea+Bbarmat01));
- P(:,:,Tmax) = -invmat*Cbarmat01;
- D(:,Tmax) = -invmat*Dbarmat01;
- end
-
-
-
-
- for i = Tmax-1:-1:1
-
- if (violvecbool(i,1) & ~violvecbool(i,2))
- invmat = inv(Bbarmat10+Abarmat10*P(:,:,i+1));
- P(:,:,i)=-invmat*Cbarmat10;
- D(:,i) = -invmat*(Abarmat10*D(:,i+1)+Dbarmat10);
- elseif (~violvecbool(i,1) & violvecbool(i,2))
- invmat = inv(Bbarmat01+Abarmat01*P(:,:,i+1));
- P(:,:,i)=-invmat*Cbarmat01;
- D(:,i) = -invmat*(Abarmat01*D(:,i+1)+Dbarmat01);
- elseif (violvecbool(i,1) & violvecbool(i,2))
- invmat = inv(Bbarmat11+Abarmat11*P(:,:,i+1));
- P(:,:,i)=-invmat*Cbarmat11;
- D(:,i) = -invmat*(Abarmat11*D(:,i+1)+Dbarmat11);
- else
- invmat = inv(Bbarmat+Abarmat*P(:,:,i+1));
- P(:,:,i)=-invmat*Cbarmat;
- D(:,i) = -invmat*(Abarmat*D(:,i+1));
- end
-
- end
-
-
-% Double check the appropriate invmat in each case
-% right now -- inherited from previous loop
-if Tmax > 1
-
- if ( ~violvecbool(1,1) & violvecbool(1,2) )
- E = -invmat*Jbarmat01;
- elseif ( violvecbool(1,1) & ~violvecbool(1,2) )
- E = -invmat*Jbarmat10;
- elseif ( violvecbool(1,1) & violvecbool(1,2) )
- E = -invmat*Jbarmat11;
- else
- E = -invmat*Jbarmat;
- end
-
-else % Tmax is equal to 1
-% invmat = inv((Astarbarmat*decrulea+Bstarbarmat));
-% E = -invmat*Jstarbarmat;
-
- if ( ~violvecbool(1,1) & violvecbool(1,2) )
- invmat = inv((Abarmat01*decrulea+Bbarmat01));
- E = -invmat*Jbarmat01;
- elseif ( violvecbool(1,1) & violvecbool(1,2) )
- invmat = inv((Abarmat11*decrulea+Bbarmat11));
- E = -invmat*Jbarmat11;
- else
- invmat = inv((Abarmat10*decrulea+Bbarmat10));
- E = -invmat*Jbarmat10;
-
- end
-
-end
-
-
-end
-
-% generate data
-% history will contain data, the state vector at each period in time will
-% be stored columnwise.
-history = zeros(nvars,nperiods+1);
-history(:,1) = init;
-errvec = zeros(size(exog_,1),1);
-
-for i = 1:nshocks
- errvec(irfshockpos(i)) = scalefactormod(i);
-end
-
-% deal with shocks
-irfpos =1;
-if irfpos <=Tmax
- history(:,irfpos+1) = P(:,:,irfpos)* history(:,irfpos)+...
- D(:,irfpos) + E*errvec;
-else
- history(:,irfpos+1) = decrulea*history(:,irfpos)+decruleb*errvec;
-end
-
-% all other periods
-for irfpos=2:nperiods+1
- if irfpos <=Tmax
- history(:,irfpos+1) = P(:,:,irfpos)* history(:,irfpos)+...
- D(:,irfpos);
- else
- history(:,irfpos+1) = decrulea*history(:,irfpos);
- end
-end
-
-
-history=history';
+function [zdata]=mkdatap_anticipated_2constraints(nperiods,decrulea,decruleb,...
+ cof,Jbarmat,...
+ cof10,Jbarmat10,Dbarmat10,...
+ cof01,Jbarmat01,Dbarmat01,...
+ cof11,Jbarmat11,Dbarmat11,...
+ regime1,regimestart1,...
+ regime2,regimestart2,...
+ violvecbool,endog_,exog_,...
+ irfshock,scalefactormod,init)
+
+
+nvars = size(endog_,1);
+
+
+if nargin<16
+ init=zeros(nvars,1);
+end
+
+if nargin<15
+ scalefactormod=1;
+end
+
+
+nshocks = size(irfshock,1);
+for i = 1:nshocks
+ shockpos = strmatch(irfshock(i,:),exog_,'exact');
+ if ~isempty(shockpos)
+ irfshockpos(i) = shockpos;
+ else
+ error(['Shock ',irfshock(i,:),' is not in the model']);
+ end
+end
+
+
+
+Cbarmat = cof(:,1:nvars);
+Bbarmat = cof(:,nvars+1:2*nvars);
+Abarmat = cof(:,2*nvars+1:3*nvars);
+
+
+% cofstar contains the system for the model when the constraint binds
+
+
+Cbarmat10 = cof10(:,1:nvars);
+Bbarmat10 = cof10(:,nvars+1:2*nvars);
+Abarmat10 = cof10(:,2*nvars+1:3*nvars);
+
+Cbarmat01 = cof01(:,1:nvars);
+Bbarmat01 = cof01(:,nvars+1:2*nvars);
+Abarmat01 = cof01(:,2*nvars+1:3*nvars);
+
+Cbarmat11 = cof11(:,1:nvars);
+Bbarmat11 = cof11(:,nvars+1:2*nvars);
+Abarmat11 = cof11(:,2*nvars+1:3*nvars);
+
+% get the time-dependent decision rules
+nregimes1 = length(regime1);
+nregimes2 = length(regime2);
+
+Tmax = max([regimestart1(nregimes1) regimestart2(nregimes2)])-1; % Tmax is the position of the last period
+ % when the constraint binds
+
+if Tmax > 0
+ P = zeros(nvars,nvars,Tmax);
+ D = zeros(nvars,Tmax);
+
+ % invmat = inv((Astarbarmat*decrulea+Bstarbarmat));
+ % P(:,:,Tmax) = -invmat*Cstarbarmat;
+ % D(:,Tmax) = -invmat*Dstarbarmat;
+
+
+ if (violvecbool(Tmax,1) & ~violvecbool(Tmax,2))
+ %XXX fix next three lines
+ invmat = inv((Abarmat10*decrulea+Bbarmat10));
+ P(:,:,Tmax) = -invmat*Cbarmat10;
+ D(:,Tmax) = -invmat*Dbarmat10;
+ elseif (violvecbool(Tmax,1) & violvecbool(Tmax,2))
+ invmat = inv((Abarmat11*decrulea+Bbarmat11));
+ P(:,:,Tmax) = -invmat*Cbarmat11;
+ D(:,Tmax) = -invmat*Dbarmat11;
+ else
+ invmat = inv((Abarmat01*decrulea+Bbarmat01));
+ P(:,:,Tmax) = -invmat*Cbarmat01;
+ D(:,Tmax) = -invmat*Dbarmat01;
+ end
+
+
+
+
+ for i = Tmax-1:-1:1
+
+ if (violvecbool(i,1) & ~violvecbool(i,2))
+ invmat = inv(Bbarmat10+Abarmat10*P(:,:,i+1));
+ P(:,:,i)=-invmat*Cbarmat10;
+ D(:,i) = -invmat*(Abarmat10*D(:,i+1)+Dbarmat10);
+ elseif (~violvecbool(i,1) & violvecbool(i,2))
+ invmat = inv(Bbarmat01+Abarmat01*P(:,:,i+1));
+ P(:,:,i)=-invmat*Cbarmat01;
+ D(:,i) = -invmat*(Abarmat01*D(:,i+1)+Dbarmat01);
+ elseif (violvecbool(i,1) & violvecbool(i,2))
+ invmat = inv(Bbarmat11+Abarmat11*P(:,:,i+1));
+ P(:,:,i)=-invmat*Cbarmat11;
+ D(:,i) = -invmat*(Abarmat11*D(:,i+1)+Dbarmat11);
+ else
+ invmat = inv(Bbarmat+Abarmat*P(:,:,i+1));
+ P(:,:,i)=-invmat*Cbarmat;
+ D(:,i) = -invmat*(Abarmat*D(:,i+1));
+ end
+
+ end
+
+
+ % Double check the appropriate invmat in each case
+ % right now -- inherited from previous loop
+ if Tmax > 1
+
+ if ( ~violvecbool(1,1) & violvecbool(1,2) )
+ E = -invmat*Jbarmat01;
+ elseif ( violvecbool(1,1) & ~violvecbool(1,2) )
+ E = -invmat*Jbarmat10;
+ elseif ( violvecbool(1,1) & violvecbool(1,2) )
+ E = -invmat*Jbarmat11;
+ else
+ E = -invmat*Jbarmat;
+ end
+
+ else % Tmax is equal to 1
+ % invmat = inv((Astarbarmat*decrulea+Bstarbarmat));
+ % E = -invmat*Jstarbarmat;
+
+ if ( ~violvecbool(1,1) & violvecbool(1,2) )
+ invmat = inv((Abarmat01*decrulea+Bbarmat01));
+ E = -invmat*Jbarmat01;
+ elseif ( violvecbool(1,1) & violvecbool(1,2) )
+ invmat = inv((Abarmat11*decrulea+Bbarmat11));
+ E = -invmat*Jbarmat11;
+ else
+ invmat = inv((Abarmat10*decrulea+Bbarmat10));
+ E = -invmat*Jbarmat10;
+
+ end
+
+ end
+
+
+end
+
+% generate data
+% history will contain data, the state vector at each period in time will
+% be stored columnwise.
+history = zeros(nvars,nperiods+1);
+history(:,1) = init;
+errvec = zeros(size(exog_,1),1);
+
+for i = 1:nshocks
+ errvec(irfshockpos(i)) = scalefactormod(i);
+end
+
+% deal with shocks
+irfpos =1;
+if irfpos <=Tmax
+ history(:,irfpos+1) = P(:,:,irfpos)* history(:,irfpos)+...
+ D(:,irfpos) + E*errvec;
+else
+ history(:,irfpos+1) = decrulea*history(:,irfpos)+decruleb*errvec;
+end
+
+% all other periods
+for irfpos=2:nperiods+1
+ if irfpos <=Tmax
+ history(:,irfpos+1) = P(:,:,irfpos)* history(:,irfpos)+...
+ D(:,irfpos);
+ else
+ history(:,irfpos+1) = decrulea*history(:,irfpos);
+ end
+end
+
+
+history=history';
zdata = history(2:end,:);
\ No newline at end of file
diff --git a/matlab/occbin/pickaxes.m b/matlab/occbin/pickaxes.m
index 46c4b79c6db0176bb5a076573d4a95fdc7869378..26c9b6b1a397d0eb019f20b4670e8c0317e54a8a 100755
--- a/matlab/occbin/pickaxes.m
+++ b/matlab/occbin/pickaxes.m
@@ -1,16 +1,16 @@
-function [x0,x1,y0,y1] = pickaxes(xvalues,yvalues)
-
-x0=xvalues(1);
-nobs = length(xvalues);
-x1=xvalues(nobs);
-
-maxy = max(yvalues);
-miny = min(yvalues);
-
-
-y0 = miny - .05*abs(miny);
-if (miny>0 & y0<0)
- y0 = 0;
-end
-
-y1 = maxy + .05*abs(maxy);
+function [x0,x1,y0,y1] = pickaxes(xvalues,yvalues)
+
+x0=xvalues(1);
+nobs = length(xvalues);
+x1=xvalues(nobs);
+
+maxy = max(yvalues);
+miny = min(yvalues);
+
+
+y0 = miny - .05*abs(miny);
+if (miny>0 & y0<0)
+ y0 = 0;
+end
+
+y1 = maxy + .05*abs(maxy);
diff --git a/matlab/occbin/process_constraint.m b/matlab/occbin/process_constraint.m
index d209111755fde2bd1e9e192397fae953fd092624..2d2b35f364299ed84cc072d35311eedeef3675c8 100755
--- a/matlab/occbin/process_constraint.m
+++ b/matlab/occbin/process_constraint.m
@@ -1,38 +1,38 @@
-% this function looks for occurrences of the endogenous variables in
-% endo_names in the input string constraint
-% all occurrences of the endogenous variables are appended a suffix
-% if the invert_switch is true, the direction of the inequality in the
-% constraint is inverted
-
-function constraint1 = process_constraint(constraint,suffix,endo_names,invert_switch)
-
-% create a list of delimiters that can separate parameters and endogenoous
-% variables in the string that expresses the constraint
-delimiters = char(',',';','(',')','+','-','^','*','/',' ','>','<','=');
-
-% split the string that holds the constraint into tokens
-tokens = tokenize(constraint,delimiters);
-
-ntokens = length(tokens);
-
-% search for tokens that match the list of endogenous variables
-for i=1:ntokens
- if ~isempty(find(strcmp(tokens(i),endo_names)))
- % when there is a match with an endogenous variable append the
- % suffix
- tokens(i) = cellstr([char(tokens(i)),suffix]);
- end
-
- % if the invert_switch is true
- % reverse the direction of the inequality
- if invert_switch
- if strcmp(tokens(i),cellstr('>'))
- tokens(i) = cellstr('<');
- elseif strcmp(tokens(i),cellstr('<'))
- tokens(i) = cellstr('>');
- end
- end
-end
-
-% reassemble the tokens to create a string that expresses the constraint
+% this function looks for occurrences of the endogenous variables in
+% endo_names in the input string constraint
+% all occurrences of the endogenous variables are appended a suffix
+% if the invert_switch is true, the direction of the inequality in the
+% constraint is inverted
+
+function constraint1 = process_constraint(constraint,suffix,endo_names,invert_switch)
+
+% create a list of delimiters that can separate parameters and endogenoous
+% variables in the string that expresses the constraint
+delimiters = char(',',';','(',')','+','-','^','*','/',' ','>','<','=');
+
+% split the string that holds the constraint into tokens
+tokens = tokenize(constraint,delimiters);
+
+ntokens = length(tokens);
+
+% search for tokens that match the list of endogenous variables
+for i=1:ntokens
+ if ~isempty(find(strcmp(tokens(i),endo_names)))
+ % when there is a match with an endogenous variable append the
+ % suffix
+ tokens(i) = cellstr([char(tokens(i)),suffix]);
+ end
+
+ % if the invert_switch is true
+ % reverse the direction of the inequality
+ if invert_switch
+ if strcmp(tokens(i),cellstr('>'))
+ tokens(i) = cellstr('<');
+ elseif strcmp(tokens(i),cellstr('<'))
+ tokens(i) = cellstr('>');
+ end
+ end
+end
+
+% reassemble the tokens to create a string that expresses the constraint
constraint1 = strmerge(tokens);
\ No newline at end of file
diff --git a/matlab/occbin/setss.m b/matlab/occbin/setss.m
index d1c0e440ac6b23f14a6726bad535274a3dfc8eaa..7fc52f6c0ec32e373251e1db2a80dbdf5707cda4 100755
--- a/matlab/occbin/setss.m
+++ b/matlab/occbin/setss.m
@@ -1,14 +1,13 @@
-% Script that retrieves parameter values once model is solved
-
-nendog = size(Mbase_.endo_names,1);
-
-for i=1:nendog
- eval([deblank(Mbase_.endo_names(i,:)) '_ss = oo_.dr.ys(i); ']);
-end
-
-nparams = size(Mbase_.param_names);
-
-for i = 1:nparams
- eval([Mbase_.param_names(i,:),'= M_.params(i);']);
-end
-
+% Script that retrieves parameter values once model is solved
+
+nendog = size(Mbase_.endo_names,1);
+
+for i=1:nendog
+ eval([deblank(Mbase_.endo_names(i,:)) '_ss = oo_.dr.ys(i); ']);
+end
+
+nparams = size(Mbase_.param_names);
+
+for i = 1:nparams
+ eval([Mbase_.param_names(i,:),'= M_.params(i);']);
+end
diff --git a/matlab/occbin/solve_no_constraint.m b/matlab/occbin/solve_no_constraint.m
index d9671409d26bc41ef12b7fda8e6ca6f8d2ea930b..27af64f1ce1fa0ed5554911fab109e347bc4f0bb 100755
--- a/matlab/occbin/solve_no_constraint.m
+++ b/matlab/occbin/solve_no_constraint.m
@@ -1,50 +1,49 @@
-function [zdata_, zdatass_, oobase_, Mbase_ ] = ...
- solve_no_constraint(modnam,...
- shockssequence,irfshock,nperiods)
-
-global M_ oo_
-
-errlist = [];
-
-% solve model
-eval(['dynare ',modnam,' noclearall'])
-oobase_ = oo_;
-Mbase_ = M_;
-
-ys_ = oobase_.dr.ys;
-nvars = numel(ys_);
-zdatass_ = ys_ ;
-
-for i=1:Mbase_.endo_nbr
- eval([deblank(Mbase_.endo_names(i,:)) '_ss = oo_.dr.ys(i); ']);
-end
-
-for i = 1:size(Mbase_.param_names)
- eval([Mbase_.param_names(i,:),'= M_.params(i);']);
-end
-
-
-
-
-
-
-
-[hm1,h,hl1,Jbarmat] = get_deriv(Mbase_,ys_);
-cof = [hm1,h,hl1];
-
-[decrulea,decruleb]=get_pq(oobase_.dr);
-endog_ = M_.endo_names;
-exog_ = M_.exo_names;
-
-nvars = numel(Mbase_.endo_nbr);
-
-
-nshocks = size(shockssequence,1);
-init = zeros(nvars,1);
-
-wishlist = endog_;
-nwishes = size(wishlist,1);
-
-
-zdata_ = mkdata(nperiods,decrulea,decruleb,endog_,exog_,wishlist,irfshock,shockssequence);
-
+function [zdata_, zdatass_, oobase_, Mbase_ ] = ...
+ solve_no_constraint(modnam,...
+ shockssequence,irfshock,nperiods)
+
+global M_ oo_
+
+errlist = [];
+
+% solve model
+eval(['dynare ',modnam,' noclearall'])
+oobase_ = oo_;
+Mbase_ = M_;
+
+ys_ = oobase_.dr.ys;
+nvars = numel(ys_);
+zdatass_ = ys_ ;
+
+for i=1:Mbase_.endo_nbr
+ eval([deblank(Mbase_.endo_names(i,:)) '_ss = oo_.dr.ys(i); ']);
+end
+
+for i = 1:size(Mbase_.param_names)
+ eval([Mbase_.param_names(i,:),'= M_.params(i);']);
+end
+
+
+
+
+
+
+
+[hm1,h,hl1,Jbarmat] = get_deriv(Mbase_,ys_);
+cof = [hm1,h,hl1];
+
+[decrulea,decruleb]=get_pq(oobase_.dr);
+endog_ = M_.endo_names;
+exog_ = M_.exo_names;
+
+nvars = numel(Mbase_.endo_nbr);
+
+
+nshocks = size(shockssequence,1);
+init = zeros(nvars,1);
+
+wishlist = endog_;
+nwishes = size(wishlist,1);
+
+
+zdata_ = mkdata(nperiods,decrulea,decruleb,endog_,exog_,wishlist,irfshock,shockssequence);
diff --git a/matlab/occbin/solve_no_constraint_noclear.m b/matlab/occbin/solve_no_constraint_noclear.m
index 0dab8c8165565a617ceaecdf14475f9119ed7efb..8012f9126cc0c85d7cf61b43d8c9b471f00b2790 100755
--- a/matlab/occbin/solve_no_constraint_noclear.m
+++ b/matlab/occbin/solve_no_constraint_noclear.m
@@ -1,48 +1,47 @@
-function [zdata, oobase_, Mbase_ ] = ...
- solve_no_constraint_noclear(modnam,...
- shockssequence,irfshock,nperiods)
-
-global M_ oo_
-
-errlist = [];
-
-% solve model
-eval(['dynare ',modnam,' nolog']);
-oobase_ = oo_;
-Mbase_ = M_;
-
-ys_ = oobase_.dr.ys;
-
-for i=1:Mbase_.endo_nbr
- eval([deblank(Mbase_.endo_names(i,:)) '_ss = oo_.dr.ys(i); ']);
-end
-
-for i = 1:size(Mbase_.param_names)
- eval([Mbase_.param_names(i,:),'= M_.params(i);']);
-end
-
-setss
-
-
-
-
-
-[hm1,h,hl1,Jbarmat] = get_deriv(Mbase_,ys_);
-cof = [hm1,h,hl1];
-
-[decrulea,decruleb]=get_pq(oobase_.dr);
-endog_ = M_.endo_names;
-exog_ = M_.exo_names;
-
-
-nvars = numel(Mbase_.endo_nbr);
-
-nshocks = size(shockssequence,1);
-init = zeros(nvars,1);
-
-wishlist = endog_;
-nwishes = size(wishlist,1);
-
-
-zdata = mkdata(nperiods,decrulea,decruleb,endog_,exog_,wishlist,irfshock,shockssequence);
-
+function [zdata, oobase_, Mbase_ ] = ...
+ solve_no_constraint_noclear(modnam,...
+ shockssequence,irfshock,nperiods)
+
+global M_ oo_
+
+errlist = [];
+
+% solve model
+eval(['dynare ',modnam,' nolog']);
+oobase_ = oo_;
+Mbase_ = M_;
+
+ys_ = oobase_.dr.ys;
+
+for i=1:Mbase_.endo_nbr
+ eval([deblank(Mbase_.endo_names(i,:)) '_ss = oo_.dr.ys(i); ']);
+end
+
+for i = 1:size(Mbase_.param_names)
+ eval([Mbase_.param_names(i,:),'= M_.params(i);']);
+end
+
+setss
+
+
+
+
+
+[hm1,h,hl1,Jbarmat] = get_deriv(Mbase_,ys_);
+cof = [hm1,h,hl1];
+
+[decrulea,decruleb]=get_pq(oobase_.dr);
+endog_ = M_.endo_names;
+exog_ = M_.exo_names;
+
+
+nvars = numel(Mbase_.endo_nbr);
+
+nshocks = size(shockssequence,1);
+init = zeros(nvars,1);
+
+wishlist = endog_;
+nwishes = size(wishlist,1);
+
+
+zdata = mkdata(nperiods,decrulea,decruleb,endog_,exog_,wishlist,irfshock,shockssequence);
diff --git a/matlab/occbin/solve_one_constraint.1.m b/matlab/occbin/solve_one_constraint.1.m
index 34e296496b4c535a01cb2eb05b16536fded1cf42..aa155c234fb4033230b0cc06522c7922e14ce6bf 100755
--- a/matlab/occbin/solve_one_constraint.1.m
+++ b/matlab/occbin/solve_one_constraint.1.m
@@ -1,192 +1,192 @@
-% solve_one_constraint [zdatalinear zdatapiecewise zdatass oo base M base] = solve one constraint(modnam, modnamstar, constraint, constraint relax, shockssequence, irfshock, nperiods, maxiter, init);
-%
-% Inputs:
-% modnam: name of .mod file for the reference regime (excludes the .mod extension).
-% modnamstar: name of .mod file for the alternative regime (excludes the .mod exten- sion).
-% constraint: the constraint (see notes 1 and 2 below). When the condition in constraint evaluates to true, the solution switches from the reference to the alternative regime.
-% constraint relax: when the condition in constraint relax evaluates to true, the solution returns to the reference regime.
-% shockssequence: a sequence of unforeseen shocks under which one wants to solve the model (size T�nshocks).
-% irfshock: label for innovation for IRFs, from Dynare .mod file (one or more of the ?varexo?).
-% nperiods: simulation horizon (can be longer than the sequence of shocks defined in shockssequence; must be long enough to ensure convergence back to the reference model at the end of the simulation horizon and may need to be varied depending on the sequence of shocks).
-% maxiter: maximum number of iterations allowed for the solution algorithm (20 if not specified).
-% init: the initial position for the vector of state variables, in deviation from steady state (if not specified, the default is steady state). The ordering follows the definition order in the .mod files.
-%
-% Outputs:
-% zdatalinear: an array containing paths for all endogenous variables ignoring the occasionally binding constraint (the linear solution), in deviation from steady state. Each column is a variable, the order is the definition order in the .mod files.
-% zdatapiecewise: an array containing paths for all endogenous variables satisfying the occasionally binding constraint (the occbin/piecewise solution), in deviation from steady state. Each column is a variable, the order is the definition order in the .mod files.
-% zdatass: theinitialpositionforthevectorofstatevariables,indeviationfromsteady state (if not specified, the default is a vectors of zero implying that the initial conditions coincide with the steady state). The ordering follows the definition order in the .mod files.
-% oobase,Mbase: structures produced by Dynare for the reference model ? see Dynare User Guide.
-
-% Log of changes:
-% 6/17/2013 -- Luca added a trailing underscore to local variables in an
-% attempt to avoid conflicts with parameter names defined in the .mod files
-% to be processed.
-% 6/17/2013 -- Luca replaced external .m file setss.m
-
-
-function [zdatalinear_ zdatapiecewise_ zdatass_ oobase_ Mbase_ ] = ...
- solve_one_constraint(modnam_,modnamstar_,...
- constraint_, constraint_relax_,...
- shockssequence_,irfshock_,nperiods_,maxiter_,init_)
-
-global M_ oo_
-
-errlist_ = [];
-
-% solve the reference model linearly
-eval(['dynare ',modnam_,' noclearall nolog '])
-oobase_ = oo_;
-Mbase_ = M_;
-
-% import locally the values of parameters assigned in the reference .mod
-% file
-for i_indx_ = 1:Mbase_.param_nbr
- eval([Mbase_.param_names(i_indx_,:),'= M_.params(i_indx_);']);
-end
-
-% Create steady state values of the variables if needed for processing the constraint
-for i=1:Mbase_.endo_nbr
- eval([deblank(Mbase_.endo_names(i,:)) '_ss = oobase_.dr.ys(i); ']);
-end
-
-
-% parse the .mod file for the alternative regime
-eval(['dynare ',modnamstar_,' noclearall nolog '])
-oostar_ = oo_;
-Mstar_ = M_;
-
-
-% check inputs
-if ~strcmp(Mbase_.endo_names,Mstar_.endo_names)
- error('The two .mod files need to have exactly the same endogenous variables declared in the same order')
-end
-
-if ~strcmp(Mbase_.exo_names,Mstar_.exo_names)
- error('The two .mod files need to have exactly the same exogenous variables declared in the same order')
-end
-
-if ~strcmp(Mbase_.param_names,Mstar_.param_names)
- warning('The parameter list does not match across .mod files')
-end
-
-% ensure that the two models have the same parameters
-% use the parameters for the base model.
-Mstar_.params = Mbase_.params;
-
-nvars_ = Mbase_.endo_nbr;
-zdatass_ = oobase_.dr.ys;
-
-
-% get the matrices holding the first derivatives for the model
-% each regime is treated separately
-[hm1_,h_,hl1_,Jbarmat_] = get_deriv(Mbase_,zdatass_);
-cof_ = [hm1_,h_,hl1_];
-
-[hm1_,h_,hl1_,Jstarbarmat_,resid_] = get_deriv(Mstar_,zdatass_);
-cofstar_ = [hm1_,h_,hl1_];
-Dstartbarmat_ = resid_;
-
-[decrulea_,decruleb_]=get_pq(oobase_.dr);
-endog_ = M_.endo_names;
-exog_ = M_.exo_names;
-
-
-% processes the constraints specified in the call to this function
-% uppend a suffix to each endogenous variable
-constraint_difference_ = process_constraint(constraint_,'_difference',Mbase_.endo_names,0);
-
-constraint_relax_difference_ = process_constraint(constraint_relax_,'_difference',Mbase_.endo_names,0);
-
-
-
-nshocks_ = size(shockssequence_,1);
-
-% if necessary, set default values for optional arguments
-if ~exist('init_')
- init_ = zeros(nvars_,1);
-end
-
-if ~exist('maxiter_')
- maxiter_ = 20;
-end
-
-if ~exist('nperiods_')
- nperiods_ = 100;
-end
-
-
-% set some initial conditions and loop through the shocks
-% period by period
-init_orig_ = init_;
-zdatapiecewise_ = zeros(nperiods_,nvars_);
-wishlist_ = endog_;
-nwishes_ = size(wishlist_,1);
-violvecbool_ = zeros(nperiods_+1,1);
-
-
-for ishock_ = 1:nshocks_
-
- changes_=1;
- iter_ = 0;
-
-
- while (changes_ & iter_<maxiter_)
- iter_ = iter_ +1;
-
- % analyze when each regime starts based on current guess
- [regime regimestart]=map_regime(violvecbool_);
-
-
- % get the hypothesized piece wise linear solution
- [zdatalinear_]=mkdatap_anticipated(nperiods_,decrulea_,decruleb_,...
- cof_,Jbarmat_,cofstar_,Jstarbarmat_,Dstartbarmat_,...
- regime,regimestart,violvecbool_,...
- endog_,exog_,irfshock_,shockssequence_(ishock_,:),init_);
-
- for i_indx_=1:nwishes_
- eval([deblank(wishlist_(i_indx_,:)),'_difference=zdatalinear_(:,i_indx_);']);
- end
-
-
-
- newviolvecbool_ = eval(constraint_difference_);
- relaxconstraint_ = eval(constraint_relax_difference_);
-
-
-
- % check if changes to the hypothesis of the duration for each
- % regime
- if (max(newviolvecbool_-violvecbool_>0)) | sum(relaxconstraint_(find(violvecbool_==1))>0)
- changes_ = 1;
- else
- changes_ = 0;
- end
-
-
- violvecbool_ = (violvecbool_|newviolvecbool_)-(relaxconstraint_ & violvecbool_);
-
-
- end
-
- init_ = zdatalinear_(1,:);
- zdatapiecewise_(ishock_,:)=init_;
- init_= init_';
-
- % reset violvecbool_ for next period's shock -- this resetting is
- % consistent with expecting no additional shocks
- violvecbool_=[violvecbool_(2:end);0];
-
-end
-
-% if necessary, fill in the rest of the path with the remainder of the
-% last IRF computed.
-zdatapiecewise_(ishock_+1:end,:)=zdatalinear_(2:nperiods_-ishock_+1,:);
-
-% get the linear responses
-zdatalinear_ = mkdata(max(nperiods_,size(shockssequence_,1)),...
- decrulea_,decruleb_,endog_,exog_,...
- wishlist_,irfshock_,shockssequence_,init_orig_);
-
-if changes_ ==1
- display('Did not converge -- increase maxiter_')
-end
+% solve_one_constraint [zdatalinear zdatapiecewise zdatass oo base M base] = solve one constraint(modnam, modnamstar, constraint, constraint relax, shockssequence, irfshock, nperiods, maxiter, init);
+%
+% Inputs:
+% modnam: name of .mod file for the reference regime (excludes the .mod extension).
+% modnamstar: name of .mod file for the alternative regime (excludes the .mod exten- sion).
+% constraint: the constraint (see notes 1 and 2 below). When the condition in constraint evaluates to true, the solution switches from the reference to the alternative regime.
+% constraint relax: when the condition in constraint relax evaluates to true, the solution returns to the reference regime.
+% shockssequence: a sequence of unforeseen shocks under which one wants to solve the model (size T×nshocks).
+% irfshock: label for innovation for IRFs, from Dynare .mod file (one or more of the ?varexo?).
+% nperiods: simulation horizon (can be longer than the sequence of shocks defined in shockssequence; must be long enough to ensure convergence back to the reference model at the end of the simulation horizon and may need to be varied depending on the sequence of shocks).
+% maxiter: maximum number of iterations allowed for the solution algorithm (20 if not specified).
+% init: the initial position for the vector of state variables, in deviation from steady state (if not specified, the default is steady state). The ordering follows the definition order in the .mod files.
+%
+% Outputs:
+% zdatalinear: an array containing paths for all endogenous variables ignoring the occasionally binding constraint (the linear solution), in deviation from steady state. Each column is a variable, the order is the definition order in the .mod files.
+% zdatapiecewise: an array containing paths for all endogenous variables satisfying the occasionally binding constraint (the occbin/piecewise solution), in deviation from steady state. Each column is a variable, the order is the definition order in the .mod files.
+% zdatass: theinitialpositionforthevectorofstatevariables,indeviationfromsteady state (if not specified, the default is a vectors of zero implying that the initial conditions coincide with the steady state). The ordering follows the definition order in the .mod files.
+% oobase,Mbase: structures produced by Dynare for the reference model ? see Dynare User Guide.
+
+% Log of changes:
+% 6/17/2013 -- Luca added a trailing underscore to local variables in an
+% attempt to avoid conflicts with parameter names defined in the .mod files
+% to be processed.
+% 6/17/2013 -- Luca replaced external .m file setss.m
+
+
+function [zdatalinear_ zdatapiecewise_ zdatass_ oobase_ Mbase_ ] = ...
+ solve_one_constraint.1(modnam_,modnamstar_,...
+ constraint_, constraint_relax_,...
+ shockssequence_,irfshock_,nperiods_,maxiter_,init_)
+
+global M_ oo_
+
+errlist_ = [];
+
+% solve the reference model linearly
+eval(['dynare ',modnam_,' noclearall nolog '])
+oobase_ = oo_;
+Mbase_ = M_;
+
+% import locally the values of parameters assigned in the reference .mod
+% file
+for i_indx_ = 1:Mbase_.param_nbr
+ eval([Mbase_.param_names(i_indx_,:),'= M_.params(i_indx_);']);
+end
+
+% Create steady state values of the variables if needed for processing the constraint
+for i=1:Mbase_.endo_nbr
+ eval([deblank(Mbase_.endo_names(i,:)) '_ss = oobase_.dr.ys(i); ']);
+end
+
+
+% parse the .mod file for the alternative regime
+eval(['dynare ',modnamstar_,' noclearall nolog '])
+oostar_ = oo_;
+Mstar_ = M_;
+
+
+% check inputs
+if ~strcmp(Mbase_.endo_names,Mstar_.endo_names)
+ error('The two .mod files need to have exactly the same endogenous variables declared in the same order')
+end
+
+if ~strcmp(Mbase_.exo_names,Mstar_.exo_names)
+ error('The two .mod files need to have exactly the same exogenous variables declared in the same order')
+end
+
+if ~strcmp(Mbase_.param_names,Mstar_.param_names)
+ warning('The parameter list does not match across .mod files')
+end
+
+% ensure that the two models have the same parameters
+% use the parameters for the base model.
+Mstar_.params = Mbase_.params;
+
+nvars_ = Mbase_.endo_nbr;
+zdatass_ = oobase_.dr.ys;
+
+
+% get the matrices holding the first derivatives for the model
+% each regime is treated separately
+[hm1_,h_,hl1_,Jbarmat_] = get_deriv(Mbase_,zdatass_);
+cof_ = [hm1_,h_,hl1_];
+
+[hm1_,h_,hl1_,Jstarbarmat_,resid_] = get_deriv(Mstar_,zdatass_);
+cofstar_ = [hm1_,h_,hl1_];
+Dstartbarmat_ = resid_;
+
+[decrulea_,decruleb_]=get_pq(oobase_.dr);
+endog_ = M_.endo_names;
+exog_ = M_.exo_names;
+
+
+% processes the constraints specified in the call to this function
+% uppend a suffix to each endogenous variable
+constraint_difference_ = process_constraint(constraint_,'_difference',Mbase_.endo_names,0);
+
+constraint_relax_difference_ = process_constraint(constraint_relax_,'_difference',Mbase_.endo_names,0);
+
+
+
+nshocks_ = size(shockssequence_,1);
+
+% if necessary, set default values for optional arguments
+if ~exist('init_')
+ init_ = zeros(nvars_,1);
+end
+
+if ~exist('maxiter_')
+ maxiter_ = 20;
+end
+
+if ~exist('nperiods_')
+ nperiods_ = 100;
+end
+
+
+% set some initial conditions and loop through the shocks
+% period by period
+init_orig_ = init_;
+zdatapiecewise_ = zeros(nperiods_,nvars_);
+wishlist_ = endog_;
+nwishes_ = size(wishlist_,1);
+violvecbool_ = zeros(nperiods_+1,1);
+
+
+for ishock_ = 1:nshocks_
+
+ changes_=1;
+ iter_ = 0;
+
+
+ while (changes_ & iter_<maxiter_)
+ iter_ = iter_ +1;
+
+ % analyze when each regime starts based on current guess
+ [regime regimestart]=map_regime(violvecbool_);
+
+
+ % get the hypothesized piece wise linear solution
+ [zdatalinear_]=mkdatap_anticipated(nperiods_,decrulea_,decruleb_,...
+ cof_,Jbarmat_,cofstar_,Jstarbarmat_,Dstartbarmat_,...
+ regime,regimestart,violvecbool_,...
+ endog_,exog_,irfshock_,shockssequence_(ishock_,:),init_);
+
+ for i_indx_=1:nwishes_
+ eval([deblank(wishlist_(i_indx_,:)),'_difference=zdatalinear_(:,i_indx_);']);
+ end
+
+
+
+ newviolvecbool_ = eval(constraint_difference_);
+ relaxconstraint_ = eval(constraint_relax_difference_);
+
+
+
+ % check if changes to the hypothesis of the duration for each
+ % regime
+ if (max(newviolvecbool_-violvecbool_>0)) | sum(relaxconstraint_(find(violvecbool_==1))>0)
+ changes_ = 1;
+ else
+ changes_ = 0;
+ end
+
+
+ violvecbool_ = (violvecbool_|newviolvecbool_)-(relaxconstraint_ & violvecbool_);
+
+
+ end
+
+ init_ = zdatalinear_(1,:);
+ zdatapiecewise_(ishock_,:)=init_;
+ init_= init_';
+
+ % reset violvecbool_ for next period's shock -- this resetting is
+ % consistent with expecting no additional shocks
+ violvecbool_=[violvecbool_(2:end);0];
+
+end
+
+% if necessary, fill in the rest of the path with the remainder of the
+% last IRF computed.
+zdatapiecewise_(ishock_+1:end,:)=zdatalinear_(2:nperiods_-ishock_+1,:);
+
+% get the linear responses
+zdatalinear_ = mkdata(max(nperiods_,size(shockssequence_,1)),...
+ decrulea_,decruleb_,endog_,exog_,...
+ wishlist_,irfshock_,shockssequence_,init_orig_);
+
+if changes_ ==1
+ display('Did not converge -- increase maxiter_')
+end
diff --git a/matlab/occbin/solve_one_constraint.m b/matlab/occbin/solve_one_constraint.m
index c8a7c2494d45da8b090edfcb223622b5c6aab9e0..5959636bf76c40936ac5947262895dee217bcef6 100755
--- a/matlab/occbin/solve_one_constraint.m
+++ b/matlab/occbin/solve_one_constraint.m
@@ -1,200 +1,200 @@
-% solve_one_constraint [zdatalinear zdatapiecewise zdatass oo base M base] = solve one constraint(modnam, modnamstar, constraint, constraint relax, shockssequence, irfshock, nperiods, maxiter, init);
-%
-% Inputs:
-% modnam: name of .mod file for the reference regime (excludes the .mod extension).
-% modnamstar: name of .mod file for the alternative regime (excludes the .mod exten- sion).
-% constraint: the constraint (see notes 1 and 2 below). When the condition in constraint evaluates to true, the solution switches from the reference to the alternative regime.
-% constraint relax: when the condition in constraint relax evaluates to true, the solution returns to the reference regime.
-% shockssequence: a sequence of unforeseen shocks under which one wants to solve the model (size T�nshocks).
-% irfshock: label for innovation for IRFs, from Dynare .mod file (one or more of the ?varexo?).
-% nperiods: simulation horizon (can be longer than the sequence of shocks defined in shockssequence; must be long enough to ensure convergence back to the reference model at the end of the simulation horizon and may need to be varied depending on the sequence of shocks).
-% maxiter: maximum number of iterations allowed for the solution algorithm (20 if not specified).
-% init: the initial position for the vector of state variables, in deviation from steady state (if not specified, the default is steady state). The ordering follows the definition order in the .mod files.
-%
-% Outputs:
-% zdatalinear: an array containing paths for all endogenous variables ignoring the occasionally binding constraint (the linear solution), in deviation from steady state. Each column is a variable, the order is the definition order in the .mod files.
-% zdatapiecewise: an array containing paths for all endogenous variables satisfying the occasionally binding constraint (the occbin/piecewise solution), in deviation from steady state. Each column is a variable, the order is the definition order in the .mod files.
-% zdatass: theinitialpositionforthevectorofstatevariables,indeviationfromsteady state (if not specified, the default is a vectors of zero implying that the initial conditions coincide with the steady state). The ordering follows the definition order in the .mod files.
-% oobase,Mbase: structures produced by Dynare for the reference model ? see Dynare User Guide.
-
-% Log of changes:
-% 6/17/2013 -- Luca added a trailing underscore to local variables in an
-% attempt to avoid conflicts with parameter names defined in the .mod files
-% to be processed.
-% 6/17/2013 -- Luca replaced external .m file setss.m
-
-
-function [zdatalinear_, zdatapiecewise_, zdatass_, oobase_, Mbase_ ] = ...
- solve_one_constraint(modnam_,modnamstar_,...
- constraint_, constraint_relax_,...
- shockssequence_,irfshock_,nperiods_,maxiter_,init_)
-
-global M_ oo_
-
-errlist_ = [];
-
-% solve the reference model linearly
-eval(['dynare ',modnam_,' noclearall nolog '])
-oobase_ = oo_;
-Mbase_ = M_;
-
-% import locally the values of parameters assigned in the reference .mod
-% file
-for i_indx_ = 1:Mbase_.param_nbr
- eval([Mbase_.param_names(i_indx_,:),'= M_.params(i_indx_);']);
-end
-
-% Create steady state values of the variables if needed for processing the constraint
-for i=1:Mbase_.endo_nbr
- eval([deblank(Mbase_.endo_names(i,:)) '_ss = oobase_.dr.ys(i); ']);
-end
-
-
-% parse the .mod file for the alternative regime
-eval(['dynare ',modnamstar_,' noclearall nolog '])
-oostar_ = oo_;
-Mstar_ = M_;
-
-
-% check inputs
-if ~strcmp(Mbase_.endo_names,Mstar_.endo_names)
- error('The two .mod files need to have exactly the same endogenous variables declared in the same order')
-end
-
-if ~strcmp(Mbase_.exo_names,Mstar_.exo_names)
- error('The two .mod files need to have exactly the same exogenous variables declared in the same order')
-end
-
-if ~strcmp(Mbase_.param_names,Mstar_.param_names)
- warning('The parameter list does not match across .mod files')
-end
-
-% ensure that the two models have the same parameters
-% use the parameters for the base model.
-Mstar_.params = Mbase_.params;
-
-nvars_ = Mbase_.endo_nbr;
-zdatass_ = oobase_.dr.ys;
-
-
-% get the matrices holding the first derivatives for the model
-% each regime is treated separately
-[hm1_,h_,hl1_,Jbarmat_] = get_deriv(Mbase_,zdatass_);
-cof_ = [hm1_,h_,hl1_];
-
-[hm1_,h_,hl1_,Jstarbarmat_,resid_] = get_deriv(Mstar_,zdatass_);
-cofstar_ = [hm1_,h_,hl1_];
-Dstartbarmat_ = resid_;
-
-if isfield(Mbase_,'nfwrd')
- % the latest Dynare distributions have moved nstatic and nfwrd
- [decrulea_,decruleb_]=get_pq(oobase_.dr,Mbase_.nstatic,Mbase_.nfwrd);
-else
- [decrulea_,decruleb_]=get_pq(oobase_.dr,oobase_.dr.nstatic,oobase_.dr.nfwrd);
-end
-
-endog_ = M_.endo_names;
-exog_ = M_.exo_names;
-
-
-% processes the constraints specified in the call to this function
-% uppend a suffix to each endogenous variable
-constraint_difference_ = process_constraint(constraint_,'_difference',Mbase_.endo_names,0);
-
-constraint_relax_difference_ = process_constraint(constraint_relax_,'_difference',Mbase_.endo_names,0);
-
-
-
-nshocks_ = size(shockssequence_,1);
-
-% if necessary, set default values for optional arguments
-if ~exist('init_')
- init_ = zeros(nvars_,1);
-end
-
-if ~exist('maxiter_')
- maxiter_ = 20;
-end
-
-if ~exist('nperiods_')
- nperiods_ = 100;
-end
-
-
-% set some initial conditions and loop through the shocks
-% period by period
-init_orig_ = init_;
-zdatapiecewise_ = zeros(nperiods_,nvars_);
-wishlist_ = endog_;
-nwishes_ = size(wishlist_,1);
-violvecbool_ = zeros(nperiods_+1,1);
-
-
-for ishock_ = 1:nshocks_
-
- changes_=1;
- iter_ = 0;
-
-
- while (changes_ & iter_<maxiter_)
- iter_ = iter_ +1;
-
- % analyze when each regime starts based on current guess
- [regime, regimestart]=map_regime(violvecbool_);
-
-
-
- % get the hypothesized piece wise linear solution
- [zdatalinear_]=mkdatap_anticipated(nperiods_,decrulea_,decruleb_,...
- cof_,Jbarmat_,cofstar_,Jstarbarmat_,Dstartbarmat_,...
- regime,regimestart,violvecbool_,...
- endog_,exog_,irfshock_,shockssequence_(ishock_,:),init_);
-
- for i_indx_=1:nwishes_
- eval([deblank(wishlist_(i_indx_,:)),'_difference=zdatalinear_(:,i_indx_);']);
- end
-
-
-
- newviolvecbool_ = eval(constraint_difference_);
- relaxconstraint_ = eval(constraint_relax_difference_);
-
-
-
- % check if changes to the hypothesis of the duration for each
- % regime
- if (max(newviolvecbool_-violvecbool_>0)) | sum(relaxconstraint_(find(violvecbool_==1))>0)
- changes_ = 1;
- else
- changes_ = 0;
- end
-
-
-
- violvecbool_ = (violvecbool_|newviolvecbool_)-(relaxconstraint_ & violvecbool_);
-
-
- end
-
- init_ = zdatalinear_(1,:);
- zdatapiecewise_(ishock_,:)=init_;
- init_= init_';
-
- % reset violvecbool_ for next period's shock -- this resetting is
- % consistent with expecting no additional shocks
- violvecbool_=[violvecbool_(2:end);0];
-
-end
-
-% if necessary, fill in the rest of the path with the remainder of the
-% last IRF computed.
-zdatapiecewise_(ishock_+1:end,:)=zdatalinear_(2:nperiods_-ishock_+1,:);
-
-% get the linear responses
-zdatalinear_ = mkdata(max(nperiods_,size(shockssequence_,1)),...
- decrulea_,decruleb_,endog_,exog_,...
- wishlist_,irfshock_,shockssequence_,init_orig_);
-
-if changes_ ==1
- display('Did not converge -- increase maxiter_')
-end
+% solve_one_constraint [zdatalinear zdatapiecewise zdatass oo base M base] = solve one constraint(modnam, modnamstar, constraint, constraint relax, shockssequence, irfshock, nperiods, maxiter, init);
+%
+% Inputs:
+% modnam: name of .mod file for the reference regime (excludes the .mod extension).
+% modnamstar: name of .mod file for the alternative regime (excludes the .mod exten- sion).
+% constraint: the constraint (see notes 1 and 2 below). When the condition in constraint evaluates to true, the solution switches from the reference to the alternative regime.
+% constraint relax: when the condition in constraint relax evaluates to true, the solution returns to the reference regime.
+% shockssequence: a sequence of unforeseen shocks under which one wants to solve the model (size T×nshocks).
+% irfshock: label for innovation for IRFs, from Dynare .mod file (one or more of the ?varexo?).
+% nperiods: simulation horizon (can be longer than the sequence of shocks defined in shockssequence; must be long enough to ensure convergence back to the reference model at the end of the simulation horizon and may need to be varied depending on the sequence of shocks).
+% maxiter: maximum number of iterations allowed for the solution algorithm (20 if not specified).
+% init: the initial position for the vector of state variables, in deviation from steady state (if not specified, the default is steady state). The ordering follows the definition order in the .mod files.
+%
+% Outputs:
+% zdatalinear: an array containing paths for all endogenous variables ignoring the occasionally binding constraint (the linear solution), in deviation from steady state. Each column is a variable, the order is the definition order in the .mod files.
+% zdatapiecewise: an array containing paths for all endogenous variables satisfying the occasionally binding constraint (the occbin/piecewise solution), in deviation from steady state. Each column is a variable, the order is the definition order in the .mod files.
+% zdatass: theinitialpositionforthevectorofstatevariables,indeviationfromsteady state (if not specified, the default is a vectors of zero implying that the initial conditions coincide with the steady state). The ordering follows the definition order in the .mod files.
+% oobase,Mbase: structures produced by Dynare for the reference model ? see Dynare User Guide.
+
+% Log of changes:
+% 6/17/2013 -- Luca added a trailing underscore to local variables in an
+% attempt to avoid conflicts with parameter names defined in the .mod files
+% to be processed.
+% 6/17/2013 -- Luca replaced external .m file setss.m
+
+
+function [zdatalinear_, zdatapiecewise_, zdatass_, oobase_, Mbase_ ] = ...
+ solve_one_constraint(modnam_,modnamstar_,...
+ constraint_, constraint_relax_,...
+ shockssequence_,irfshock_,nperiods_,maxiter_,init_)
+
+global M_ oo_
+
+errlist_ = [];
+
+% solve the reference model linearly
+eval(['dynare ',modnam_,' noclearall nolog '])
+oobase_ = oo_;
+Mbase_ = M_;
+
+% import locally the values of parameters assigned in the reference .mod
+% file
+for i_indx_ = 1:Mbase_.param_nbr
+ eval([Mbase_.param_names(i_indx_,:),'= M_.params(i_indx_);']);
+end
+
+% Create steady state values of the variables if needed for processing the constraint
+for i=1:Mbase_.endo_nbr
+ eval([deblank(Mbase_.endo_names(i,:)) '_ss = oobase_.dr.ys(i); ']);
+end
+
+
+% parse the .mod file for the alternative regime
+eval(['dynare ',modnamstar_,' noclearall nolog '])
+oostar_ = oo_;
+Mstar_ = M_;
+
+
+% check inputs
+if ~strcmp(Mbase_.endo_names,Mstar_.endo_names)
+ error('The two .mod files need to have exactly the same endogenous variables declared in the same order')
+end
+
+if ~strcmp(Mbase_.exo_names,Mstar_.exo_names)
+ error('The two .mod files need to have exactly the same exogenous variables declared in the same order')
+end
+
+if ~strcmp(Mbase_.param_names,Mstar_.param_names)
+ warning('The parameter list does not match across .mod files')
+end
+
+% ensure that the two models have the same parameters
+% use the parameters for the base model.
+Mstar_.params = Mbase_.params;
+
+nvars_ = Mbase_.endo_nbr;
+zdatass_ = oobase_.dr.ys;
+
+
+% get the matrices holding the first derivatives for the model
+% each regime is treated separately
+[hm1_,h_,hl1_,Jbarmat_] = get_deriv(Mbase_,zdatass_);
+cof_ = [hm1_,h_,hl1_];
+
+[hm1_,h_,hl1_,Jstarbarmat_,resid_] = get_deriv(Mstar_,zdatass_);
+cofstar_ = [hm1_,h_,hl1_];
+Dstartbarmat_ = resid_;
+
+if isfield(Mbase_,'nfwrd')
+ % the latest Dynare distributions have moved nstatic and nfwrd
+ [decrulea_,decruleb_]=get_pq(oobase_.dr,Mbase_.nstatic,Mbase_.nfwrd);
+else
+ [decrulea_,decruleb_]=get_pq(oobase_.dr,oobase_.dr.nstatic,oobase_.dr.nfwrd);
+end
+
+endog_ = M_.endo_names;
+exog_ = M_.exo_names;
+
+
+% processes the constraints specified in the call to this function
+% uppend a suffix to each endogenous variable
+constraint_difference_ = process_constraint(constraint_,'_difference',Mbase_.endo_names,0);
+
+constraint_relax_difference_ = process_constraint(constraint_relax_,'_difference',Mbase_.endo_names,0);
+
+
+
+nshocks_ = size(shockssequence_,1);
+
+% if necessary, set default values for optional arguments
+if ~exist('init_')
+ init_ = zeros(nvars_,1);
+end
+
+if ~exist('maxiter_')
+ maxiter_ = 20;
+end
+
+if ~exist('nperiods_')
+ nperiods_ = 100;
+end
+
+
+% set some initial conditions and loop through the shocks
+% period by period
+init_orig_ = init_;
+zdatapiecewise_ = zeros(nperiods_,nvars_);
+wishlist_ = endog_;
+nwishes_ = size(wishlist_,1);
+violvecbool_ = zeros(nperiods_+1,1);
+
+
+for ishock_ = 1:nshocks_
+
+ changes_=1;
+ iter_ = 0;
+
+
+ while (changes_ & iter_<maxiter_)
+ iter_ = iter_ +1;
+
+ % analyze when each regime starts based on current guess
+ [regime, regimestart]=map_regime(violvecbool_);
+
+
+
+ % get the hypothesized piece wise linear solution
+ [zdatalinear_]=mkdatap_anticipated(nperiods_,decrulea_,decruleb_,...
+ cof_,Jbarmat_,cofstar_,Jstarbarmat_,Dstartbarmat_,...
+ regime,regimestart,violvecbool_,...
+ endog_,exog_,irfshock_,shockssequence_(ishock_,:),init_);
+
+ for i_indx_=1:nwishes_
+ eval([deblank(wishlist_(i_indx_,:)),'_difference=zdatalinear_(:,i_indx_);']);
+ end
+
+
+
+ newviolvecbool_ = eval(constraint_difference_);
+ relaxconstraint_ = eval(constraint_relax_difference_);
+
+
+
+ % check if changes to the hypothesis of the duration for each
+ % regime
+ if (max(newviolvecbool_-violvecbool_>0)) | sum(relaxconstraint_(find(violvecbool_==1))>0)
+ changes_ = 1;
+ else
+ changes_ = 0;
+ end
+
+
+
+ violvecbool_ = (violvecbool_|newviolvecbool_)-(relaxconstraint_ & violvecbool_);
+
+
+ end
+
+ init_ = zdatalinear_(1,:);
+ zdatapiecewise_(ishock_,:)=init_;
+ init_= init_';
+
+ % reset violvecbool_ for next period's shock -- this resetting is
+ % consistent with expecting no additional shocks
+ violvecbool_=[violvecbool_(2:end);0];
+
+end
+
+% if necessary, fill in the rest of the path with the remainder of the
+% last IRF computed.
+zdatapiecewise_(ishock_+1:end,:)=zdatalinear_(2:nperiods_-ishock_+1,:);
+
+% get the linear responses
+zdatalinear_ = mkdata(max(nperiods_,size(shockssequence_,1)),...
+ decrulea_,decruleb_,endog_,exog_,...
+ wishlist_,irfshock_,shockssequence_,init_orig_);
+
+if changes_ ==1
+ display('Did not converge -- increase maxiter_')
+end
diff --git a/matlab/occbin/solve_two_constraints.m b/matlab/occbin/solve_two_constraints.m
index ff5f734edd7c85cb6c5e5bd081a05e0a08dc6427..1b436ad00fb38b909bec99442a48716841270d60 100755
--- a/matlab/occbin/solve_two_constraints.m
+++ b/matlab/occbin/solve_two_constraints.m
@@ -1,305 +1,304 @@
-% [zdatalinear zdatapiecewise zdatass oo 00 M 00] = solve two constraints(modnam 00,modnam 10,modnam 01,modnam 11,... constraint1, constraint2,... constraint relax1, constraint relax2,... shockssequence,irfshock,nperiods,curb retrench,maxiter,init);
-%
-% Inputs:
-% modnam 00: name of the .mod file for reference regime (excludes the .mod extension). modnam10: name of the .mod file for the alternative regime governed by the first
-% constraint.
-% modnam01: name of the .mod file for the alternative regime governed by the second constraint.
-% modnam 11: name of the .mod file for the case in which both constraints force a switch to their alternative regimes.
-% constraint1: the first constraint (see notes 1 and 2 below). If constraint1 evaluates to true, then the solution switches to the alternative regime for condition 1. In thatcase, if constraint2 (described below) evaluates to false, then the model solution switches to enforcing the conditions for an equilibrium in modnam 10. Otherwise, if constraint2 also evaluates to true, then the model solution switches to enforcing the conditions for an equilibrium in modnam 11.
-% constraint relax1: when the condition in constraint relax1 evaluates to true, the solution returns to the reference regime for constraint1.
-% constraint2: the second constraint (see notes 1 and 2 below). constraint relax2: when the condition in constraint relax2 evaluates to true, the
-% solution returns to the reference regime for constraint2. shockssequence: a sequence of unforeseen shocks under which one wants to solve the
-% model
-% irfshock: label for innovation for IRFs, from Dynare .mod file (one or more of the ?varexo?)
-% nperiods: simulation horizon (can be longer than the sequence of shocks defined in shockssequence; must be long enough to ensure convergence back to the reference model at the end of the simulation horizon and may need to be varied depending on the sequence of shocks).
-% curb retrench: a scalar equal to 0 or 1. Default is 0. When set to 0, it updates the guess based of regimes based on the previous iteration. When set to 1, it updates in a manner similar to a Gauss-Jacobi scheme, slowing the iterations down by updating the guess of regimes only one period at a time.
-% maxiter: maximum number of iterations allowed for the solution algorithm (20 if not specified).
-% init: the initial position for the vector of state variables, in deviation from steady state (if not specified, the default is a vector of zero implying that the initial conditions coincide with the steady state). The ordering follows the definition order in the .mod files.
-%
-% Outputs:
-% zdatalinear: an array containing paths for all endogenous variables ignoring the occasionally binding constraint (the linear solution), in deviation from steady state. Each column is a variable, the order is the definition order in the .mod files.
-% zdatapiecewise: an array containing paths for all endogenous variables satisfying the occasionally binding constraint (the occbin/piecewise solution), in deviation from steady state. Each column is a variable, the order is the definition order in the .mod files.
-% zdatass: a vector that holds the steady state values of the endogenous variables ( following the definition order in the .mod file).
-% oo00 , M00 : structures produced by Dynare for the reference model ? see Dynare User Guide.
-
-
-% Log of changes
-% 6/17/2013 -- Luca added a trailing underscore to local variables in an
-% attempt to avoid conflicts with parameter names defined in the .mod files
-% to be processed.
-% 6/17/2013 -- Luca replaced external .m file setss.m
-
-function [ zdatalinear_, zdatapiecewise_, zdatass_, oo00_ , M00_ ] = ...
- solve_two_constraints(modnam_00_,modnam_10_,modnam_01_,modnam_11_,...
- constrain1_, constrain2_,...
- constraint_relax1_, constraint_relax2_,...
- shockssequence_,irfshock_,nperiods_,curb_retrench_,maxiter_,init_)
-
-global M_ oo_
-
-
-
-% solve model
-eval(['dynare ',modnam_00_,' noclearall nolog'])
-oo00_ = oo_;
-M00_ = M_;
-
-
-for i=1:M00_.endo_nbr
- eval([deblank(M00_.endo_names(i,:)) '_ss = oo00_.dr.ys(i); ']);
-end
-
-for i_indx_ = 1:M00_.param_nbr
- eval([M00_.param_names(i_indx_,:),'= M00_.params(i_indx_);']);
-end
-
-
-
-eval(['dynare ',modnam_10_,' noclearall'])
-oo10_ = oo_;
-M10_ = M_;
-
-eval(['dynare ',modnam_01_,' noclearall'])
-oo01_ = oo_;
-M01_ = M_;
-
-eval(['dynare ',modnam_11_,' noclearall'])
-oo11_ = oo_;
-M11_ = M_;
-
-
-% do some error checking
-
-% check inputs
-if ~strcmp(M00_.endo_names,M10_.endo_names)
- error([modnam_00_,' and ',modnam_10_,' need to have exactly the same endogenous variables and they need to be declared in the same order'])
-end
-
-if ~strcmp(M00_.exo_names,M10_.exo_names)
- error([modnam_00_,' and ',modnam_10_,' need to have exactly the same exogenous variables and they need to be declared in the same order'])
-end
-
-if ~strcmp(M00_.param_names,M10_.param_names)
- warning(['The parameter list does not match across the files ',modnam_00_,' and ',modnam_10_])
-end
-
-
-if ~strcmp(M00_.endo_names,M01_.endo_names)
- error([modnam_00,' and ',modnam_01_,' need to have exactly the same endogenous variables and they need to be declared in the same order'])
-end
-
-if ~strcmp(M00_.exo_names,M01_.exo_names)
- error([modnam_00_,' and ',modnam_01_,' need to have exactly the same exogenous variables and they need to be declared in the same order'])
-end
-
-if ~strcmp(M00_.param_names,M01_.param_names)
- warning(['The parameter list does not match across the files ',modnam_00_,' and ',modnam_01_])
-end
-
-
-if ~strcmp(M00_.endo_names,M11_.endo_names)
- error([modnam_00_,' and ',modnam_11_,' need to have exactly the same endogenous variables and they need to be declared in the same order'])
-end
-
-if ~strcmp(M00_.exo_names,M11_.exo_names)
- error([modnam_00_,' and ',modnam_11_,' need to have exactly the same exogenous variables and they need to be declared in the same order'])
-end
-
-if ~strcmp(M00_.param_names,M11_.param_names)
- warning(['The parameter list does not match across the files ',modnam_00_,' and ',modnam_11_])
-end
-
-
-
-
-
-nvars_ = M00_.endo_nbr;
-zdatass_ = oo00_.dr.ys;
-
-
-[hm1_,h_,hl1_,Jbarmat_] = get_deriv(M00_,zdatass_);
-cof_ = [hm1_,h_,hl1_];
-
-
-M10_.params = M00_.params;
-[hm1_,h_,hl1_,Jbarmat10_,resid_] = get_deriv(M10_,zdatass_);
-cof10_ = [hm1_,h_,hl1_];
-Dbarmat10_ = resid_;
-
-M01_.params = M00_.params;
-[hm1_,h_,hl1_,Jbarmat01_,resid_] = get_deriv(M01_,zdatass_);
-cof01_ = [hm1_,h_,hl1_];
-Dbarmat01_ = resid_;
-
-M11_.params = M00_.params;
-[hm1_,h_,hl1_,Jbarmat11_,resid_] = get_deriv(M11_,zdatass_);
-cof11_ = [hm1_,h_,hl1_];
-Dbarmat11_ = resid_;
-
-
-if isfield(M00_,'nfwrd') % needed for bakward compatibility with older Dynare releases
-[decrulea,decruleb]=get_pq(oo00_.dr,M00_.nstatic,M00_.nfwrd);
-else
-[decrulea,decruleb]=get_pq(oo00_.dr,oo00_.dr.nstatic,oo00_.dr.nfwrd);
-end
-endog_ = M00_.endo_names;
-exog_ = M00_.exo_names;
-
-
-% processes the constrain so as to uppend a suffix to each
-% endogenous variables
-constraint1_difference_ = process_constraint(constrain1_,'_difference',M00_.endo_names,0);
-
-% when the last argument in process_constraint is set to 1, the
-% direction of the inequality in the constraint is inverted
-constraint_relax1_difference_ = process_constraint(constraint_relax1_,'_difference',M00_.endo_names,0);
-
-
-% processes the constrain so as to uppend a suffix to each
-% endogenous variables
-constraint2_difference_ = process_constraint(constrain2_,'_difference',M00_.endo_names,0);
-
-% when the last argument in process_constraint is set to 1, the
-% direction of the inequality in the constraint is inverted
-constraint_relax2_difference_ = process_constraint(constraint_relax2_,'_difference',M00_.endo_names,0);
-
-
-
-nshocks = size(shockssequence_,1);
-
-
-
-
-if ~exist('init_')
- init_ = zeros(nvars_,1);
-end
-
-if ~exist('maxiter_')
- maxiter_ = 20;
-end
-
-if ~exist('curb_retrench_')
- curb_retrench_ = 0;
-end
-
-init_orig_ = init_;
-
-
-
-
-
-
-zdatapiecewise_ = zeros(nperiods_,nvars_);
-
-
-violvecbool_ = zeros(nperiods_+1,2); % This sets the first guess for when
-% the constraints are going to hold.
-% The variable is a boolean with two
-% columns. The first column refers to
-% constrain1_; the second to
-% constrain2_.
-% Each row is a period in time.
-% If the boolean is true it indicates
-% the relevant constraint is expected
-% to evaluate to true.
-% The default initial guess is
-% consistent with the base model always
-% holding -- equivalent to the linear
-% solution.
-
-wishlist_ = endog_;
-nwishes_ = size(wishlist_,1);
-for ishock_ = 1:nshocks
-
-
- changes_=1;
- iter_ = 0;
-
- while (changes_ & iter_<maxiter_)
- iter_ = iter_ +1;
-
- % analyse violvec and isolate contiguous periods in the other
- % regime.
- [regime1, regimestart1]=map_regime(violvecbool_(:,1));
- [regime2, regimestart2]=map_regime(violvecbool_(:,2));
-
-
- [zdatalinear_]=mkdatap_anticipated_2constraints(nperiods_,decrulea,decruleb,...
- cof_,Jbarmat_,...
- cof10_,Jbarmat10_,Dbarmat10_,...
- cof01_,Jbarmat01_,Dbarmat01_,...
- cof11_,Jbarmat11_,Dbarmat11_,...
- regime1,regimestart1,...
- regime2,regimestart2,...
- violvecbool_,endog_,exog_,...
- irfshock_,shockssequence_(ishock_,:),init_);
-
- for i_indx_=1:nwishes_
- eval([deblank(wishlist_(i_indx_,:)),'_difference=zdatalinear_(:,i_indx_);']);
- end
-
-
-
-
- newviolvecbool1_ = eval(constraint1_difference_);
- relaxconstraint1_ = eval(constraint_relax1_difference_);
-
- newviolvecbool2_ = eval(constraint2_difference_);
- relaxconstraint2_ = eval(constraint_relax2_difference_);
-
-
-
- newviolvecbool_ = [newviolvecbool1_;newviolvecbool2_];
- relaxconstraint_ = [relaxconstraint1_;relaxconstraint2_];
-
-
-
- % check if changes_
- if (max(newviolvecbool_(:)-violvecbool_(:)>0)) | sum(relaxconstraint_(find(violvecbool_==1))>0)
- changes_ = 1;
- else
- changes_ = 0;
- end
-
- if curb_retrench_ % apply Gauss-Sidel idea of slowing down the change in the guess
- % for the constraint -- only relax one
- % period at a time starting from the last
- % one when each of the constraints is true.
- retrench = 0*violvecbool_(:);
- if ~isempty(find(relaxconstraint1_ & violvecbool_(:,1)))
- retrenchpos = max(find(relaxconstraint1_ & violvecbool_(:,1)));
- retrench(retrenchpos) = 1;
- end
- if ~isempty(find(relaxconstraint2_ & violvecbool_(:,2)))
- retrenchpos = max(find(relaxconstraint2_ & violvecbool_(:,2)));
- retrench(retrenchpos+nperiods_+1) = 1;
- end
- violvecbool_ = (violvecbool_(:) | newviolvecbool_(:))-retrench(:);
- else
- violvecbool_ = (violvecbool_(:) | newviolvecbool_(:))-(relaxconstraint_(:) & violvecbool_(:));
- end
-
- violvecbool_ = reshape(violvecbool_,nperiods_+1,2);
-
-
-
- end
- if changes_ ==1
- display('Did not converge -- increase maxiter')
- end
-
- init_ = zdatalinear_(1,:);
- zdatapiecewise_(ishock_,:)=init_;
- init_= init_';
-
- % update the guess for constraint violations for next period
- % update is consistent with expecting no additional shocks next period
- violvecbool_=[violvecbool_(2:end,:);zeros(1,2)];
-
-end
-
-
-zdatapiecewise_(ishock_+1:end,:)=zdatalinear_(2:nperiods_-ishock_+1,:);
-
-zdatalinear_ = mkdata(nperiods_,decrulea,decruleb,endog_,exog_,wishlist_,irfshock_,shockssequence_,init_orig_);
-
+% [zdatalinear zdatapiecewise zdatass oo 00 M 00] = solve two constraints(modnam 00,modnam 10,modnam 01,modnam 11,... constraint1, constraint2,... constraint relax1, constraint relax2,... shockssequence,irfshock,nperiods,curb retrench,maxiter,init);
+%
+% Inputs:
+% modnam 00: name of the .mod file for reference regime (excludes the .mod extension). modnam10: name of the .mod file for the alternative regime governed by the first
+% constraint.
+% modnam01: name of the .mod file for the alternative regime governed by the second constraint.
+% modnam 11: name of the .mod file for the case in which both constraints force a switch to their alternative regimes.
+% constraint1: the first constraint (see notes 1 and 2 below). If constraint1 evaluates to true, then the solution switches to the alternative regime for condition 1. In thatcase, if constraint2 (described below) evaluates to false, then the model solution switches to enforcing the conditions for an equilibrium in modnam 10. Otherwise, if constraint2 also evaluates to true, then the model solution switches to enforcing the conditions for an equilibrium in modnam 11.
+% constraint relax1: when the condition in constraint relax1 evaluates to true, the solution returns to the reference regime for constraint1.
+% constraint2: the second constraint (see notes 1 and 2 below). constraint relax2: when the condition in constraint relax2 evaluates to true, the
+% solution returns to the reference regime for constraint2. shockssequence: a sequence of unforeseen shocks under which one wants to solve the
+% model
+% irfshock: label for innovation for IRFs, from Dynare .mod file (one or more of the ?varexo?)
+% nperiods: simulation horizon (can be longer than the sequence of shocks defined in shockssequence; must be long enough to ensure convergence back to the reference model at the end of the simulation horizon and may need to be varied depending on the sequence of shocks).
+% curb retrench: a scalar equal to 0 or 1. Default is 0. When set to 0, it updates the guess based of regimes based on the previous iteration. When set to 1, it updates in a manner similar to a Gauss-Jacobi scheme, slowing the iterations down by updating the guess of regimes only one period at a time.
+% maxiter: maximum number of iterations allowed for the solution algorithm (20 if not specified).
+% init: the initial position for the vector of state variables, in deviation from steady state (if not specified, the default is a vector of zero implying that the initial conditions coincide with the steady state). The ordering follows the definition order in the .mod files.
+%
+% Outputs:
+% zdatalinear: an array containing paths for all endogenous variables ignoring the occasionally binding constraint (the linear solution), in deviation from steady state. Each column is a variable, the order is the definition order in the .mod files.
+% zdatapiecewise: an array containing paths for all endogenous variables satisfying the occasionally binding constraint (the occbin/piecewise solution), in deviation from steady state. Each column is a variable, the order is the definition order in the .mod files.
+% zdatass: a vector that holds the steady state values of the endogenous variables ( following the definition order in the .mod file).
+% oo00 , M00 : structures produced by Dynare for the reference model ? see Dynare User Guide.
+
+
+% Log of changes
+% 6/17/2013 -- Luca added a trailing underscore to local variables in an
+% attempt to avoid conflicts with parameter names defined in the .mod files
+% to be processed.
+% 6/17/2013 -- Luca replaced external .m file setss.m
+
+function [ zdatalinear_, zdatapiecewise_, zdatass_, oo00_ , M00_ ] = ...
+ solve_two_constraints(modnam_00_,modnam_10_,modnam_01_,modnam_11_,...
+ constrain1_, constrain2_,...
+ constraint_relax1_, constraint_relax2_,...
+ shockssequence_,irfshock_,nperiods_,curb_retrench_,maxiter_,init_)
+
+global M_ oo_
+
+
+
+% solve model
+eval(['dynare ',modnam_00_,' noclearall nolog'])
+oo00_ = oo_;
+M00_ = M_;
+
+
+for i=1:M00_.endo_nbr
+ eval([deblank(M00_.endo_names(i,:)) '_ss = oo00_.dr.ys(i); ']);
+end
+
+for i_indx_ = 1:M00_.param_nbr
+ eval([M00_.param_names(i_indx_,:),'= M00_.params(i_indx_);']);
+end
+
+
+
+eval(['dynare ',modnam_10_,' noclearall'])
+oo10_ = oo_;
+M10_ = M_;
+
+eval(['dynare ',modnam_01_,' noclearall'])
+oo01_ = oo_;
+M01_ = M_;
+
+eval(['dynare ',modnam_11_,' noclearall'])
+oo11_ = oo_;
+M11_ = M_;
+
+
+% do some error checking
+
+% check inputs
+if ~strcmp(M00_.endo_names,M10_.endo_names)
+ error([modnam_00_,' and ',modnam_10_,' need to have exactly the same endogenous variables and they need to be declared in the same order'])
+end
+
+if ~strcmp(M00_.exo_names,M10_.exo_names)
+ error([modnam_00_,' and ',modnam_10_,' need to have exactly the same exogenous variables and they need to be declared in the same order'])
+end
+
+if ~strcmp(M00_.param_names,M10_.param_names)
+ warning(['The parameter list does not match across the files ',modnam_00_,' and ',modnam_10_])
+end
+
+
+if ~strcmp(M00_.endo_names,M01_.endo_names)
+ error([modnam_00,' and ',modnam_01_,' need to have exactly the same endogenous variables and they need to be declared in the same order'])
+end
+
+if ~strcmp(M00_.exo_names,M01_.exo_names)
+ error([modnam_00_,' and ',modnam_01_,' need to have exactly the same exogenous variables and they need to be declared in the same order'])
+end
+
+if ~strcmp(M00_.param_names,M01_.param_names)
+ warning(['The parameter list does not match across the files ',modnam_00_,' and ',modnam_01_])
+end
+
+
+if ~strcmp(M00_.endo_names,M11_.endo_names)
+ error([modnam_00_,' and ',modnam_11_,' need to have exactly the same endogenous variables and they need to be declared in the same order'])
+end
+
+if ~strcmp(M00_.exo_names,M11_.exo_names)
+ error([modnam_00_,' and ',modnam_11_,' need to have exactly the same exogenous variables and they need to be declared in the same order'])
+end
+
+if ~strcmp(M00_.param_names,M11_.param_names)
+ warning(['The parameter list does not match across the files ',modnam_00_,' and ',modnam_11_])
+end
+
+
+
+
+
+nvars_ = M00_.endo_nbr;
+zdatass_ = oo00_.dr.ys;
+
+
+[hm1_,h_,hl1_,Jbarmat_] = get_deriv(M00_,zdatass_);
+cof_ = [hm1_,h_,hl1_];
+
+
+M10_.params = M00_.params;
+[hm1_,h_,hl1_,Jbarmat10_,resid_] = get_deriv(M10_,zdatass_);
+cof10_ = [hm1_,h_,hl1_];
+Dbarmat10_ = resid_;
+
+M01_.params = M00_.params;
+[hm1_,h_,hl1_,Jbarmat01_,resid_] = get_deriv(M01_,zdatass_);
+cof01_ = [hm1_,h_,hl1_];
+Dbarmat01_ = resid_;
+
+M11_.params = M00_.params;
+[hm1_,h_,hl1_,Jbarmat11_,resid_] = get_deriv(M11_,zdatass_);
+cof11_ = [hm1_,h_,hl1_];
+Dbarmat11_ = resid_;
+
+
+if isfield(M00_,'nfwrd') % needed for bakward compatibility with older Dynare releases
+ [decrulea,decruleb]=get_pq(oo00_.dr,M00_.nstatic,M00_.nfwrd);
+else
+ [decrulea,decruleb]=get_pq(oo00_.dr,oo00_.dr.nstatic,oo00_.dr.nfwrd);
+end
+endog_ = M00_.endo_names;
+exog_ = M00_.exo_names;
+
+
+% processes the constrain so as to uppend a suffix to each
+% endogenous variables
+constraint1_difference_ = process_constraint(constrain1_,'_difference',M00_.endo_names,0);
+
+% when the last argument in process_constraint is set to 1, the
+% direction of the inequality in the constraint is inverted
+constraint_relax1_difference_ = process_constraint(constraint_relax1_,'_difference',M00_.endo_names,0);
+
+
+% processes the constrain so as to uppend a suffix to each
+% endogenous variables
+constraint2_difference_ = process_constraint(constrain2_,'_difference',M00_.endo_names,0);
+
+% when the last argument in process_constraint is set to 1, the
+% direction of the inequality in the constraint is inverted
+constraint_relax2_difference_ = process_constraint(constraint_relax2_,'_difference',M00_.endo_names,0);
+
+
+
+nshocks = size(shockssequence_,1);
+
+
+
+
+if ~exist('init_')
+ init_ = zeros(nvars_,1);
+end
+
+if ~exist('maxiter_')
+ maxiter_ = 20;
+end
+
+if ~exist('curb_retrench_')
+ curb_retrench_ = 0;
+end
+
+init_orig_ = init_;
+
+
+
+
+
+
+zdatapiecewise_ = zeros(nperiods_,nvars_);
+
+
+violvecbool_ = zeros(nperiods_+1,2); % This sets the first guess for when
+ % the constraints are going to hold.
+ % The variable is a boolean with two
+ % columns. The first column refers to
+ % constrain1_; the second to
+ % constrain2_.
+ % Each row is a period in time.
+ % If the boolean is true it indicates
+ % the relevant constraint is expected
+ % to evaluate to true.
+ % The default initial guess is
+ % consistent with the base model always
+ % holding -- equivalent to the linear
+ % solution.
+
+wishlist_ = endog_;
+nwishes_ = size(wishlist_,1);
+for ishock_ = 1:nshocks
+
+
+ changes_=1;
+ iter_ = 0;
+
+ while (changes_ & iter_<maxiter_)
+ iter_ = iter_ +1;
+
+ % analyse violvec and isolate contiguous periods in the other
+ % regime.
+ [regime1, regimestart1]=map_regime(violvecbool_(:,1));
+ [regime2, regimestart2]=map_regime(violvecbool_(:,2));
+
+
+ [zdatalinear_]=mkdatap_anticipated_2constraints(nperiods_,decrulea,decruleb,...
+ cof_,Jbarmat_,...
+ cof10_,Jbarmat10_,Dbarmat10_,...
+ cof01_,Jbarmat01_,Dbarmat01_,...
+ cof11_,Jbarmat11_,Dbarmat11_,...
+ regime1,regimestart1,...
+ regime2,regimestart2,...
+ violvecbool_,endog_,exog_,...
+ irfshock_,shockssequence_(ishock_,:),init_);
+
+ for i_indx_=1:nwishes_
+ eval([deblank(wishlist_(i_indx_,:)),'_difference=zdatalinear_(:,i_indx_);']);
+ end
+
+
+
+
+ newviolvecbool1_ = eval(constraint1_difference_);
+ relaxconstraint1_ = eval(constraint_relax1_difference_);
+
+ newviolvecbool2_ = eval(constraint2_difference_);
+ relaxconstraint2_ = eval(constraint_relax2_difference_);
+
+
+
+ newviolvecbool_ = [newviolvecbool1_;newviolvecbool2_];
+ relaxconstraint_ = [relaxconstraint1_;relaxconstraint2_];
+
+
+
+ % check if changes_
+ if (max(newviolvecbool_(:)-violvecbool_(:)>0)) | sum(relaxconstraint_(find(violvecbool_==1))>0)
+ changes_ = 1;
+ else
+ changes_ = 0;
+ end
+
+ if curb_retrench_ % apply Gauss-Sidel idea of slowing down the change in the guess
+ % for the constraint -- only relax one
+ % period at a time starting from the last
+ % one when each of the constraints is true.
+ retrench = 0*violvecbool_(:);
+ if ~isempty(find(relaxconstraint1_ & violvecbool_(:,1)))
+ retrenchpos = max(find(relaxconstraint1_ & violvecbool_(:,1)));
+ retrench(retrenchpos) = 1;
+ end
+ if ~isempty(find(relaxconstraint2_ & violvecbool_(:,2)))
+ retrenchpos = max(find(relaxconstraint2_ & violvecbool_(:,2)));
+ retrench(retrenchpos+nperiods_+1) = 1;
+ end
+ violvecbool_ = (violvecbool_(:) | newviolvecbool_(:))-retrench(:);
+ else
+ violvecbool_ = (violvecbool_(:) | newviolvecbool_(:))-(relaxconstraint_(:) & violvecbool_(:));
+ end
+
+ violvecbool_ = reshape(violvecbool_,nperiods_+1,2);
+
+
+
+ end
+ if changes_ ==1
+ display('Did not converge -- increase maxiter')
+ end
+
+ init_ = zdatalinear_(1,:);
+ zdatapiecewise_(ishock_,:)=init_;
+ init_= init_';
+
+ % update the guess for constraint violations for next period
+ % update is consistent with expecting no additional shocks next period
+ violvecbool_=[violvecbool_(2:end,:);zeros(1,2)];
+
+end
+
+
+zdatapiecewise_(ishock_+1:end,:)=zdatalinear_(2:nperiods_-ishock_+1,:);
+
+zdatalinear_ = mkdata(nperiods_,decrulea,decruleb,endog_,exog_,wishlist_,irfshock_,shockssequence_,init_orig_);
diff --git a/matlab/occbin/strmerge.m b/matlab/occbin/strmerge.m
index b8585df04d40537b79f5762d5a6f7c7a346ad104..8aebb009956fb505bf4aedf678b07f1e0cf08945 100755
--- a/matlab/occbin/strmerge.m
+++ b/matlab/occbin/strmerge.m
@@ -1,9 +1,9 @@
-function string = strmerge(tokens)
-
-ntokens = length(tokens);
-
-string = char(tokens(1));
-
-for i=2:ntokens
- string = [string,char(tokens(i))];
+function string = strmerge(tokens)
+
+ntokens = length(tokens);
+
+string = char(tokens(1));
+
+for i=2:ntokens
+ string = [string,char(tokens(i))];
end
\ No newline at end of file
diff --git a/matlab/occbin/tokenize.m b/matlab/occbin/tokenize.m
index b711ca0820019460b42821210a68e8c337d62452..2cd8fb41031491d0a9d812a6d42e55fb2f02c07f 100755
--- a/matlab/occbin/tokenize.m
+++ b/matlab/occbin/tokenize.m
@@ -1,55 +1,54 @@
-function tokens = tokenize(source,delimiter)
-% syntax
-% tokens = tokenize(source,delimiters)
-%
-% source is a string to be broken into tokens
-% delimiters is a character array of single character delimiters
-% tokens is a cell string array containing the tokens
-
-
-posdelims = [];
-
-% assumes that delimiter cannot be in the first position or the last
-% position
-ndelimiters = size(delimiter,1);
-for i=1:ndelimiters
- newpositions = strfind(source,delimiter(i,:));
- if ~isempty(newpositions)
- posdelims =[posdelims, newpositions];
- end
-end
-
-% reorder posdelims in ascending order
-posdelims = sort(posdelims);
-
-if isempty(posdelims)
- tokens = cellstr(source);
-else
- ndelims = length(posdelims);
- % build positions for substrings
- delims = zeros(ndelims+1,2);
- for i=1:ndelims+1
- if i==1
- if posdelims(1) == 1
- tokens = cellstr(source(1));
- else
- delims(i,:) = [1,posdelims(i)-1];
- tokens = cellstr(source([delims(i,1):delims(i,2)]));
- tokens = [tokens, source(posdelims(i))];
- end
- elseif i==ndelims+1
- if (posdelims(i-1) < length(source))
- delims(i,:) = [posdelims(i-1)+1,length(source)];
- tokens = [tokens, cellstr(source([delims(i,1):delims(i,2)]))];
- end
- else
- if posdelims(i)>posdelims(i-1)+1
- delims(i,:) = [posdelims(i-1)+1,posdelims(i)-1];
- tokens = [tokens, cellstr(source([delims(i,1):delims(i,2)]))];
- end
- tokens = [tokens, source(posdelims(i))];
- end
- end
-
-end
-
+function tokens = tokenize(source,delimiter)
+% syntax
+% tokens = tokenize(source,delimiters)
+%
+% source is a string to be broken into tokens
+% delimiters is a character array of single character delimiters
+% tokens is a cell string array containing the tokens
+
+
+posdelims = [];
+
+% assumes that delimiter cannot be in the first position or the last
+% position
+ndelimiters = size(delimiter,1);
+for i=1:ndelimiters
+ newpositions = strfind(source,delimiter(i,:));
+ if ~isempty(newpositions)
+ posdelims =[posdelims, newpositions];
+ end
+end
+
+% reorder posdelims in ascending order
+posdelims = sort(posdelims);
+
+if isempty(posdelims)
+ tokens = cellstr(source);
+else
+ ndelims = length(posdelims);
+ % build positions for substrings
+ delims = zeros(ndelims+1,2);
+ for i=1:ndelims+1
+ if i==1
+ if posdelims(1) == 1
+ tokens = cellstr(source(1));
+ else
+ delims(i,:) = [1,posdelims(i)-1];
+ tokens = cellstr(source([delims(i,1):delims(i,2)]));
+ tokens = [tokens, source(posdelims(i))];
+ end
+ elseif i==ndelims+1
+ if (posdelims(i-1) < length(source))
+ delims(i,:) = [posdelims(i-1)+1,length(source)];
+ tokens = [tokens, cellstr(source([delims(i,1):delims(i,2)]))];
+ end
+ else
+ if posdelims(i)>posdelims(i-1)+1
+ delims(i,:) = [posdelims(i-1)+1,posdelims(i)-1];
+ tokens = [tokens, cellstr(source([delims(i,1):delims(i,2)]))];
+ end
+ tokens = [tokens, source(posdelims(i))];
+ end
+ end
+
+end
diff --git a/matlab/octave_ver_less_than.m b/matlab/octave_ver_less_than.m
index 9d702d5972e4c22fbc57c53b84745bb210f3111b..d60bea88da85c06c5d1e67cbc0bbaa8e45b0dcce 100644
--- a/matlab/octave_ver_less_than.m
+++ b/matlab/octave_ver_less_than.m
@@ -8,7 +8,7 @@ function r = octave_ver_less_than(verstr)
%
% INPUTS
% verstr: a string of the format 'x.y' or 'x.y.z'
-%
+%
% OUTPUTS
% r: 0 or 1
%
diff --git a/matlab/one_sided_hp_filter.m b/matlab/one_sided_hp_filter.m
index 38af32ea28cb8265c9003836ce8851bb48853738..53e494a9adfc1b00bfeec1e1d93b54ad380c052f 100644
--- a/matlab/one_sided_hp_filter.m
+++ b/matlab/one_sided_hp_filter.m
@@ -32,8 +32,8 @@ function [ytrend,ycycle]=one_sided_hp_filter(y,lambda,x_user,P_user,discard)
%
% y_t=tau_t+epsilon_t
% (1-L)^2 tau_t=eta_t"
-%
-% The Kalman filter notation follows Chapter 13 of Hamilton, J.D. (1994).
+%
+% The Kalman filter notation follows Chapter 13 of Hamilton, J.D. (1994).
% Time Series Analysis, with the exception of H, which is equivalent to his H'.
@@ -63,26 +63,26 @@ end
%Set up state space
q=1/lambda; % the signal-to-noise ration: i.e. var eta_t / var epsilon_t
F=[2,-1;
- 1,0]; % state transition matrix
+ 1,0]; % state transition matrix
H=[1,0]; % observation matrix
Q=[q,0;
- 0,0]; % covariance matrix state equation errors
+ 0,0]; % covariance matrix state equation errors
R=1; % variance observation equation error
for k=1:n %Run the Kalman filter for each variable
if nargin < 4 || isempty(x_user) %no intial value for state, extrapolate back two periods from the observations
- x=[2*y(1,k)-y(2,k);
- 3*y(1,k)-2*y(2,k)];
+ x=[2*y(1,k)-y(2,k);
+ 3*y(1,k)-2*y(2,k)];
else
x=x_user(:,k);
- end
+ end
if nargin < 4 || isempty(P_user) %no initial value for the MSE, set a rather high one
P= [1e5 0;
- 0 1e5];
+ 0 1e5];
else
P=P_user{k};
- end
-
+ end
+
for j=1:T %Get the estimates for each period
[x,P]=kalman_update(F,H,Q,R,y(j,k),x,P); %get new state estimate and update recursion
ytrend(j,k)=x(2);%second state is trend estimate
@@ -103,9 +103,9 @@ end
function [x,P]=kalman_update(F,H,Q,R,obs,x,P)
% Updates the Kalman filter estimation of the state and MSE
-S=H*P*H'+R;
-K=F*P*H';
-K=K/S;
+S=H*P*H'+R;
+K=F*P*H';
+K=K/S;
x=F*x+K*(obs -H*x); %State estimate
Temp=F-K*H;
P=Temp*P*Temp';
diff --git a/matlab/optimization/apprgrdn.m b/matlab/optimization/apprgrdn.m
index 633cfbf9c7188aff8214b56073a5886527e3ff81..a1956908689030009ce19c361f21da377d963d1e 100644
--- a/matlab/optimization/apprgrdn.m
+++ b/matlab/optimization/apprgrdn.m
@@ -2,7 +2,7 @@ function g = apprgrdn(x,f,fun,deltax,obj,varargin)
% g = apprgrdn(x,f,fun,deltax,obj,varargin)
% Performs the finite difference approximation of the gradient <g> at a
% point <x> used in solveopt
-%
+%
% Inputs:
% x: point at which to evaluate gradient
% f: calculated function value at a point x;
@@ -14,8 +14,8 @@ function g = apprgrdn(x,f,fun,deltax,obj,varargin)
% Modified by Giovanni Lombardo and Johannes Pfeifer to accomodate Dynare
% structure
%
-%
-% Copyright (C) 1997-2008, Alexei Kuntsevich and Franz Kappel
+%
+% Copyright (C) 1997-2008, Alexei Kuntsevich and Franz Kappel
% Copyright (C) 2008-2015 Giovanni Lombardo
% Copyright (C) 2015 Dynare Team
%
@@ -38,10 +38,10 @@ n=max(size(x)); ee=ones(size(x));
di=abs(x); idx=find(di<5e-15); di(idx)=5e-15*ee(idx);
di=deltax.*di;
if obj
- idx=find(abs(di)<2e-10);
+ idx=find(abs(di)<2e-10);
di(idx)=2e-10*sign(di(idx));
else
- idx=find(abs(di)<5e-15);
+ idx=find(abs(di)<5e-15);
di(idx)=5e-15*sign(di(idx));
end
y=x;
diff --git a/matlab/optimization/bfgsi1.m b/matlab/optimization/bfgsi1.m
index 696ee410b1ae82930b51c58ff3221e9c02d3bae0..7ed5b0db08c3c87cb29265f78db6d33919d316d5 100644
--- a/matlab/optimization/bfgsi1.m
+++ b/matlab/optimization/bfgsi1.m
@@ -1,20 +1,20 @@
function H = bfgsi1(H0,dg,dx,Verbose,Save_files)
% H = bfgsi1(H0,dg,dx,Verbose,Save_files)
% Update Inverse Hessian matrix
-%
+%
% Inputs:
% H0 [npar by npar] initial inverse Hessian matrix
% dg [npar by 1] previous change in gradient
% dx [npar by 1] previous change in x;
% Verbose [scalar] Indicator for silent mode
-% Save_files [scalar] Indicator whether to save files
-%
+% Save_files [scalar] Indicator whether to save files
+%
% 6/8/93 version that updates inverse Hessian instead of Hessian
% itself.
-%
+%
% Original file downloaded from:
% http://sims.princeton.edu/yftp/optimize/mfiles/bfgsi.m
-%
+%
% Copyright (C) 1993-2009 Christopher Sims
% Copyright (C) 2009-2015 Dynare Team
%
diff --git a/matlab/optimization/cmaes.m b/matlab/optimization/cmaes.m
index 8ec94e5ea63cbede9822883b53623ece0083572c..db6869306207645e6141eb4d8c16ed214cabdf3a 100644
--- a/matlab/optimization/cmaes.m
+++ b/matlab/optimization/cmaes.m
@@ -1,163 +1,163 @@
function [xmin, ... % minimum search point of last iteration
- fmin, ... % function value of xmin
- counteval, ... % number of function evaluations done
- stopflag, ... % stop criterion reached
- out, ... % struct with various histories and solutions
- bestever ... % struct containing overall best solution (for convenience)
- ] = cmaes( ...
- fitfun, ... % name of objective/fitness function
- xstart, ... % objective variables initial point, determines N
- insigma, ... % initial coordinate wise standard deviation(s)
- inopts, ... % options struct, see defopts below
- varargin ) % arguments passed to objective function
-% cmaes.m, Version 3.56.beta, last change: February, 2012
-% CMAES implements an Evolution Strategy with Covariance Matrix
-% Adaptation (CMA-ES) for nonlinear function minimization. For
-% introductory comments and copyright (GPL) see end of file (type
-% 'type cmaes'). cmaes.m runs with MATLAB (Windows, Linux) and,
-% without data logging and plotting, it should run under Octave
-% (Linux, package octave-forge is needed).
-%
-% OPTS = CMAES returns default options.
-% OPTS = CMAES('defaults') returns default options quietly.
-% OPTS = CMAES('displayoptions') displays options.
-% OPTS = CMAES('defaults', OPTS) supplements options OPTS with default
-% options.
-%
-% XMIN = CMAES(FUN, X0, SIGMA[, OPTS]) locates the minimum XMIN of
-% function FUN starting from column vector X0 with the initial
-% coordinate wise search standard deviation SIGMA.
-%
-% Input arguments:
-%
-% FUN is a string function name like 'myfun'. FUN takes as argument a
-% column vector of size of X0 and returns a scalar. An easy way to
-% implement a hard non-linear constraint is to return NaN. Then,
-% this function evaluation is not counted and a newly sampled
-% point is tried immediately.
-%
-% X0 is a column vector, or a matrix, or a string. If X0 is a matrix,
-% mean(X0, 2) is taken as initial point. If X0 is a string like
-% '2*rand(10,1)-1', the string is evaluated first.
-%
-% SIGMA is a scalar, or a column vector of size(X0,1), or a string
-% that can be evaluated into one of these. SIGMA determines the
-% initial coordinate wise standard deviations for the search.
-% Setting SIGMA one third of the initial search region is
-% appropriate, e.g., the initial point in [0, 6]^10 and SIGMA=2
-% means cmaes('myfun', 3*rand(10,1), 2). If SIGMA is missing and
-% size(X0,2) > 1, SIGMA is set to sqrt(var(X0')'). That is, X0 is
-% used as a sample for estimating initial mean and variance of the
-% search distribution.
-%
-% OPTS (an optional argument) is a struct holding additional input
-% options. Valid field names and a short documentation can be
-% discovered by looking at the default options (type 'cmaes'
-% without arguments, see above). Empty or missing fields in OPTS
-% invoke the default value, i.e. OPTS needs not to have all valid
-% field names. Capitalization does not matter and unambiguous
-% abbreviations can be used for the field names. If a string is
-% given where a numerical value is needed, the string is evaluated
-% by eval, where 'N' expands to the problem dimension
-% (==size(X0,1)) and 'popsize' to the population size.
-%
-% [XMIN, FMIN, COUNTEVAL, STOPFLAG, OUT, BESTEVER] = ...
-% CMAES(FITFUN, X0, SIGMA)
-% returns the best (minimal) point XMIN (found in the last
-% generation); function value FMIN of XMIN; the number of needed
-% function evaluations COUNTEVAL; a STOPFLAG value as cell array,
-% where possible entries are 'fitness', 'tolx', 'tolupx', 'tolfun',
-% 'maxfunevals', 'maxiter', 'stoptoresume', 'manual',
-% 'warnconditioncov', 'warnnoeffectcoord', 'warnnoeffectaxis',
-% 'warnequalfunvals', 'warnequalfunvalhist', 'bug' (use
-% e.g. any(strcmp(STOPFLAG, 'tolx')) or findstr(strcat(STOPFLAG,
-% 'tolx')) for further processing); a record struct OUT with some
-% more output, where the struct SOLUTIONS.BESTEVER contains the overall
-% best evaluated point X with function value F evaluated at evaluation
-% count EVALS. The last output argument BESTEVER equals
-% OUT.SOLUTIONS.BESTEVER. Moreover a history of solutions and
-% parameters is written to files according to the Log-options.
-%
-% A regular manual stop can be achieved via the file signals.par. The
-% program is terminated if the first two non-white sequences in any
-% line of this file are 'stop' and the value of the LogFilenamePrefix
-% option (by default 'outcmaes'). Also a run can be skipped.
-% Given, for example, 'skip outcmaes run 2', skips the second run
-% if option Restarts is at least 2, and another run will be started.
-%
-% To run the code completely silently set Disp, Save, and Log options
-% to 0. With OPTS.LogModulo > 0 (1 by default) the most important
-% data are written to ASCII files permitting to investigate the
-% results (e.g. plot with function plotcmaesdat) even while CMAES is
-% still running (which can be quite useful on expensive objective
-% functions). When OPTS.SaveVariables==1 (default) everything is saved
-% in file OPTS.SaveFilename (default 'variablescmaes.mat') allowing to
-% resume the search afterwards by using the resume option.
-%
-% To find the best ever evaluated point load the variables typing
-% "es=load('variablescmaes')" and investigate the variable
-% es.out.solutions.bestever.
-%
-% In case of a noisy objective function (uncertainties) set
-% OPTS.Noise.on = 1. This option interferes presumably with some
-% termination criteria, because the step-size sigma will presumably
-% not converge to zero anymore. If CMAES was provided with a
-% fifth argument (P1 in the below example, which is passed to the
-% objective function FUN), this argument is multiplied with the
-% factor given in option Noise.alphaevals, each time the detected
-% noise exceeds a threshold. This argument can be used within
-% FUN, for example, as averaging number to reduce the noise level.
-%
-% OPTS.DiagonalOnly > 1 defines the number of initial iterations,
-% where the covariance matrix remains diagonal and the algorithm has
-% internally linear time complexity. OPTS.DiagonalOnly = 1 means
-% keeping the covariance matrix always diagonal and this setting
-% also exhibits linear space complexity. This can be particularly
-% useful for dimension > 100. The default is OPTS.DiagonalOnly = 0.
-%
-% OPTS.CMA.active = 1 turns on "active CMA" with a negative update
-% of the covariance matrix and checks for positive definiteness.
-% OPTS.CMA.active = 2 does not check for pos. def. and is numerically
-% faster. Active CMA usually speeds up the adaptation and might
-% become a default in near future.
-%
-% The primary strategy parameter to play with is OPTS.PopSize, which
-% can be increased from its default value. Increasing the population
-% size (by default linked to increasing parent number OPTS.ParentNumber)
-% improves global search properties in exchange to speed. Speed
-% decreases, as a rule, at most linearely with increasing population
-% size. It is advisable to begin with the default small population
-% size. The options Restarts and IncPopSize can be used for an
-% automated multistart where the population size is increased by the
-% factor IncPopSize (two by default) before each restart. X0 (given as
-% string) is reevaluated for each restart. Stopping options
-% StopFunEvals, StopIter, MaxFunEvals, and Fitness terminate the
-% program, all others including MaxIter invoke another restart, where
-% the iteration counter is reset to zero.
-%
-% Examples:
-%
-% XMIN = cmaes('myfun', 5*ones(10,1), 1.5); starts the search at
-% 10D-point 5 and initially searches mainly between 5-3 and 5+3
-% (+- two standard deviations), but this is not a strict bound.
-% 'myfun' is a name of a function that returns a scalar from a 10D
-% column vector.
-%
-% opts.LBounds = 0; opts.UBounds = 10;
-% X=cmaes('myfun', 10*rand(10,1), 5, opts);
-% search within lower bound of 0 and upper bound of 10. Bounds can
-% also be given as column vectors. If the optimum is not located
-% on the boundary, use rather a penalty approach to handle bounds.
-%
-% opts=cmaes; opts.StopFitness=1e-10;
-% X=cmaes('myfun', rand(5,1), 0.5, opts); stops the search, if
-% the function value is smaller than 1e-10.
-%
-% [X, F, E, STOP, OUT] = cmaes('myfun2', 'rand(5,1)', 1, [], P1, P2);
-% passes two additional parameters to the function MYFUN2.
-%
-
-% Copyright (C) 2001-2012 Nikolaus Hansen,
+ fmin, ... % function value of xmin
+ counteval, ... % number of function evaluations done
+ stopflag, ... % stop criterion reached
+ out, ... % struct with various histories and solutions
+ bestever ... % struct containing overall best solution (for convenience)
+ ] = cmaes( ...
+ fitfun, ... % name of objective/fitness function
+ xstart, ... % objective variables initial point, determines N
+ insigma, ... % initial coordinate wise standard deviation(s)
+ inopts, ... % options struct, see defopts below
+ varargin ) % arguments passed to objective function
+ % cmaes.m, Version 3.56.beta, last change: February, 2012
+ % CMAES implements an Evolution Strategy with Covariance Matrix
+ % Adaptation (CMA-ES) for nonlinear function minimization. For
+ % introductory comments and copyright (GPL) see end of file (type
+ % 'type cmaes'). cmaes.m runs with MATLAB (Windows, Linux) and,
+ % without data logging and plotting, it should run under Octave
+ % (Linux, package octave-forge is needed).
+ %
+ % OPTS = CMAES returns default options.
+ % OPTS = CMAES('defaults') returns default options quietly.
+ % OPTS = CMAES('displayoptions') displays options.
+ % OPTS = CMAES('defaults', OPTS) supplements options OPTS with default
+ % options.
+ %
+ % XMIN = CMAES(FUN, X0, SIGMA[, OPTS]) locates the minimum XMIN of
+ % function FUN starting from column vector X0 with the initial
+ % coordinate wise search standard deviation SIGMA.
+ %
+ % Input arguments:
+ %
+ % FUN is a string function name like 'myfun'. FUN takes as argument a
+ % column vector of size of X0 and returns a scalar. An easy way to
+ % implement a hard non-linear constraint is to return NaN. Then,
+ % this function evaluation is not counted and a newly sampled
+ % point is tried immediately.
+ %
+ % X0 is a column vector, or a matrix, or a string. If X0 is a matrix,
+ % mean(X0, 2) is taken as initial point. If X0 is a string like
+ % '2*rand(10,1)-1', the string is evaluated first.
+ %
+ % SIGMA is a scalar, or a column vector of size(X0,1), or a string
+ % that can be evaluated into one of these. SIGMA determines the
+ % initial coordinate wise standard deviations for the search.
+ % Setting SIGMA one third of the initial search region is
+ % appropriate, e.g., the initial point in [0, 6]^10 and SIGMA=2
+ % means cmaes('myfun', 3*rand(10,1), 2). If SIGMA is missing and
+ % size(X0,2) > 1, SIGMA is set to sqrt(var(X0')'). That is, X0 is
+ % used as a sample for estimating initial mean and variance of the
+ % search distribution.
+ %
+ % OPTS (an optional argument) is a struct holding additional input
+ % options. Valid field names and a short documentation can be
+ % discovered by looking at the default options (type 'cmaes'
+ % without arguments, see above). Empty or missing fields in OPTS
+ % invoke the default value, i.e. OPTS needs not to have all valid
+ % field names. Capitalization does not matter and unambiguous
+ % abbreviations can be used for the field names. If a string is
+ % given where a numerical value is needed, the string is evaluated
+ % by eval, where 'N' expands to the problem dimension
+ % (==size(X0,1)) and 'popsize' to the population size.
+ %
+ % [XMIN, FMIN, COUNTEVAL, STOPFLAG, OUT, BESTEVER] = ...
+ % CMAES(FITFUN, X0, SIGMA)
+ % returns the best (minimal) point XMIN (found in the last
+ % generation); function value FMIN of XMIN; the number of needed
+ % function evaluations COUNTEVAL; a STOPFLAG value as cell array,
+ % where possible entries are 'fitness', 'tolx', 'tolupx', 'tolfun',
+ % 'maxfunevals', 'maxiter', 'stoptoresume', 'manual',
+ % 'warnconditioncov', 'warnnoeffectcoord', 'warnnoeffectaxis',
+ % 'warnequalfunvals', 'warnequalfunvalhist', 'bug' (use
+ % e.g. any(strcmp(STOPFLAG, 'tolx')) or findstr(strcat(STOPFLAG,
+ % 'tolx')) for further processing); a record struct OUT with some
+ % more output, where the struct SOLUTIONS.BESTEVER contains the overall
+ % best evaluated point X with function value F evaluated at evaluation
+ % count EVALS. The last output argument BESTEVER equals
+ % OUT.SOLUTIONS.BESTEVER. Moreover a history of solutions and
+ % parameters is written to files according to the Log-options.
+ %
+ % A regular manual stop can be achieved via the file signals.par. The
+ % program is terminated if the first two non-white sequences in any
+ % line of this file are 'stop' and the value of the LogFilenamePrefix
+ % option (by default 'outcmaes'). Also a run can be skipped.
+ % Given, for example, 'skip outcmaes run 2', skips the second run
+ % if option Restarts is at least 2, and another run will be started.
+ %
+ % To run the code completely silently set Disp, Save, and Log options
+ % to 0. With OPTS.LogModulo > 0 (1 by default) the most important
+ % data are written to ASCII files permitting to investigate the
+ % results (e.g. plot with function plotcmaesdat) even while CMAES is
+ % still running (which can be quite useful on expensive objective
+ % functions). When OPTS.SaveVariables==1 (default) everything is saved
+ % in file OPTS.SaveFilename (default 'variablescmaes.mat') allowing to
+ % resume the search afterwards by using the resume option.
+ %
+ % To find the best ever evaluated point load the variables typing
+ % "es=load('variablescmaes')" and investigate the variable
+ % es.out.solutions.bestever.
+ %
+ % In case of a noisy objective function (uncertainties) set
+ % OPTS.Noise.on = 1. This option interferes presumably with some
+ % termination criteria, because the step-size sigma will presumably
+ % not converge to zero anymore. If CMAES was provided with a
+ % fifth argument (P1 in the below example, which is passed to the
+ % objective function FUN), this argument is multiplied with the
+ % factor given in option Noise.alphaevals, each time the detected
+ % noise exceeds a threshold. This argument can be used within
+ % FUN, for example, as averaging number to reduce the noise level.
+ %
+ % OPTS.DiagonalOnly > 1 defines the number of initial iterations,
+ % where the covariance matrix remains diagonal and the algorithm has
+ % internally linear time complexity. OPTS.DiagonalOnly = 1 means
+ % keeping the covariance matrix always diagonal and this setting
+ % also exhibits linear space complexity. This can be particularly
+ % useful for dimension > 100. The default is OPTS.DiagonalOnly = 0.
+ %
+ % OPTS.CMA.active = 1 turns on "active CMA" with a negative update
+ % of the covariance matrix and checks for positive definiteness.
+ % OPTS.CMA.active = 2 does not check for pos. def. and is numerically
+ % faster. Active CMA usually speeds up the adaptation and might
+ % become a default in near future.
+ %
+ % The primary strategy parameter to play with is OPTS.PopSize, which
+ % can be increased from its default value. Increasing the population
+ % size (by default linked to increasing parent number OPTS.ParentNumber)
+ % improves global search properties in exchange to speed. Speed
+ % decreases, as a rule, at most linearely with increasing population
+ % size. It is advisable to begin with the default small population
+ % size. The options Restarts and IncPopSize can be used for an
+ % automated multistart where the population size is increased by the
+ % factor IncPopSize (two by default) before each restart. X0 (given as
+ % string) is reevaluated for each restart. Stopping options
+ % StopFunEvals, StopIter, MaxFunEvals, and Fitness terminate the
+ % program, all others including MaxIter invoke another restart, where
+ % the iteration counter is reset to zero.
+ %
+ % Examples:
+ %
+ % XMIN = cmaes('myfun', 5*ones(10,1), 1.5); starts the search at
+ % 10D-point 5 and initially searches mainly between 5-3 and 5+3
+ % (+- two standard deviations), but this is not a strict bound.
+ % 'myfun' is a name of a function that returns a scalar from a 10D
+ % column vector.
+ %
+ % opts.LBounds = 0; opts.UBounds = 10;
+ % X=cmaes('myfun', 10*rand(10,1), 5, opts);
+ % search within lower bound of 0 and upper bound of 10. Bounds can
+ % also be given as column vectors. If the optimum is not located
+ % on the boundary, use rather a penalty approach to handle bounds.
+ %
+ % opts=cmaes; opts.StopFitness=1e-10;
+ % X=cmaes('myfun', rand(5,1), 0.5, opts); stops the search, if
+ % the function value is smaller than 1e-10.
+ %
+ % [X, F, E, STOP, OUT] = cmaes('myfun2', 'rand(5,1)', 1, [], P1, P2);
+ % passes two additional parameters to the function MYFUN2.
+ %
+
+% Copyright (C) 2001-2012 Nikolaus Hansen,
% Copyright (C) 2012 Dynare Team
%
% This file is part of Dynare.
@@ -176,7 +176,7 @@ function [xmin, ... % minimum search point of last iteration
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-cmaVersion = '3.60.beta';
+cmaVersion = '3.60.beta';
% ----------- Set Defaults for Input Parameters and Options -------------
% These defaults may be edited for convenience
@@ -197,1596 +197,1596 @@ defopts.TolFun = '1e-10 % stop if fun-changes smaller TolFun';
defopts.TolHistFun = '1e-11 % stop if back fun-changes smaller TolHistFun';
defopts.StopOnStagnation = 'on % stop when fitness stagnates for a long time';
defopts.StopOnWarnings = 'yes % ''no''==''off''==0, ''on''==''yes''==1 ';
-defopts.StopOnEqualFunctionValues = '2 + N/3 % number of iterations';
+defopts.StopOnEqualFunctionValues = '2 + N/3 % number of iterations';
% Options defaults: Other
defopts.DiffMaxChange = 'Inf % maximal variable change(s), can be Nx1-vector';
defopts.DiffMinChange = '0 % minimal variable change(s), can be Nx1-vector';
defopts.WarnOnEqualFunctionValues = ...
'yes % ''no''==''off''==0, ''on''==''yes''==1 ';
-defopts.LBounds = '-Inf % lower bounds, scalar or Nx1-vector';
-defopts.UBounds = 'Inf % upper bounds, scalar or Nx1-vector';
+defopts.LBounds = '-Inf % lower bounds, scalar or Nx1-vector';
+defopts.UBounds = 'Inf % upper bounds, scalar or Nx1-vector';
defopts.EvalParallel = 'no % objective function FUN accepts NxM matrix, with M>1?';
defopts.EvalInitialX = 'yes % evaluation of initial solution';
defopts.Restarts = '0 % number of restarts ';
defopts.IncPopSize = '2 % multiplier for population size before each restart';
-defopts.PopSize = '(4 + floor(3*log(N))) % population size, lambda';
+defopts.PopSize = '(4 + floor(3*log(N))) % population size, lambda';
defopts.ParentNumber = 'floor(popsize/2) % AKA mu, popsize equals lambda';
defopts.RecombinationWeights = 'superlinear decrease % or linear, or equal';
-defopts.DiagonalOnly = '0*(1+100*N/sqrt(popsize))+(N>=1000) % C is diagonal for given iterations, 1==always';
-defopts.Noise.on = '0 % uncertainty handling is off by default';
+defopts.DiagonalOnly = '0*(1+100*N/sqrt(popsize))+(N>=1000) % C is diagonal for given iterations, 1==always';
+defopts.Noise.on = '0 % uncertainty handling is off by default';
defopts.Noise.reevals = '1*ceil(0.05*lambda) % nb. of re-evaluated for uncertainty measurement';
defopts.Noise.theta = '0.5 % threshold to invoke uncertainty treatment'; % smaller: more likely to diverge
-defopts.Noise.cum = '0.3 % cumulation constant for uncertainty';
+defopts.Noise.cum = '0.3 % cumulation constant for uncertainty';
defopts.Noise.cutoff = '2*lambda/3 % rank change cutoff for summation';
defopts.Noise.alphasigma = '1+2/(N+10) % factor for increasing sigma'; % smaller: slower adaptation
defopts.Noise.epsilon = '1e-7 % additional relative perturbation before reevaluation';
-defopts.Noise.minmaxevals = '[1 inf] % min and max value of 2nd arg to fitfun, start value is 5th arg to cmaes';
-defopts.Noise.alphaevals = '1+2/(N+10) % factor for increasing 2nd arg to fitfun';
+defopts.Noise.minmaxevals = '[1 inf] % min and max value of 2nd arg to fitfun, start value is 5th arg to cmaes';
+defopts.Noise.alphaevals = '1+2/(N+10) % factor for increasing 2nd arg to fitfun';
defopts.Noise.callback = '[] % callback function when uncertainty threshold is exceeded';
-% defopts.TPA = 0;
-defopts.CMA.cs = '(mueff+2)/(N+mueff+3) % cumulation constant for step-size';
- %qqq cs = (mueff^0.5)/(N^0.5+mueff^0.5) % the short time horizon version
+% defopts.TPA = 0;
+defopts.CMA.cs = '(mueff+2)/(N+mueff+3) % cumulation constant for step-size';
+%qqq cs = (mueff^0.5)/(N^0.5+mueff^0.5) % the short time horizon version
defopts.CMA.damps = '1 + 2*max(0,sqrt((mueff-1)/(N+1))-1) + cs % damping for step-size';
-% defopts.CMA.ccum = '4/(N+4) % cumulation constant for covariance matrix';
+% defopts.CMA.ccum = '4/(N+4) % cumulation constant for covariance matrix';
defopts.CMA.ccum = '(4 + mueff/N) / (N+4 + 2*mueff/N) % cumulation constant for pc';
-defopts.CMA.ccov1 = '2 / ((N+1.3)^2+mueff) % learning rate for rank-one update';
-defopts.CMA.ccovmu = '2 * (mueff-2+1/mueff) / ((N+2)^2+mueff) % learning rate for rank-mu update';
-defopts.CMA.on = 'yes';
-defopts.CMA.active = '0 % active CMA 1: neg. updates with pos. def. check, 2: neg. updates';
+defopts.CMA.ccov1 = '2 / ((N+1.3)^2+mueff) % learning rate for rank-one update';
+defopts.CMA.ccovmu = '2 * (mueff-2+1/mueff) / ((N+2)^2+mueff) % learning rate for rank-mu update';
+defopts.CMA.on = 'yes';
+defopts.CMA.active = '0 % active CMA 1: neg. updates with pos. def. check, 2: neg. updates';
flg_future_setting = 0; % testing for possible future variant(s)
-if flg_future_setting
- disp('in the future')
+if flg_future_setting
+ disp('in the future')
- % damps setting from Brockhoff et al 2010
- % this damps diverges with popsize 400:
- % cmaeshtml('benchmarkszero', ones(20,1)*2, 5, o, 15);
- defopts.CMA.damps = '2*mueff/lambda + 0.3 + cs % damping for step-size'; % cs: for large mueff
- % how about:
- % defopts.CMA.damps = '2*mueff/lambda + 0.3 + 2*max(0,sqrt((mueff-1)/(N+1))-1) + cs % damping for step-size';
+ % damps setting from Brockhoff et al 2010
+ % this damps diverges with popsize 400:
+ % cmaeshtml('benchmarkszero', ones(20,1)*2, 5, o, 15);
+ defopts.CMA.damps = '2*mueff/lambda + 0.3 + cs % damping for step-size'; % cs: for large mueff
+ % how about:
+ % defopts.CMA.damps = '2*mueff/lambda + 0.3 + 2*max(0,sqrt((mueff-1)/(N+1))-1) + cs % damping for step-size';
- % ccum adjusted for large mueff, better on schefelmult?
- % TODO: this should also depend on diagonal option!?
- defopts.CMA.ccum = '(4 + mueff/N) / (N+4 + 2*mueff/N) % cumulation constant for pc';
+ % ccum adjusted for large mueff, better on schefelmult?
+ % TODO: this should also depend on diagonal option!?
+ defopts.CMA.ccum = '(4 + mueff/N) / (N+4 + 2*mueff/N) % cumulation constant for pc';
- defopts.CMA.active = '1 % active CMA 1: neg. updates with pos. def. check, 2: neg. updates';
+ defopts.CMA.active = '1 % active CMA 1: neg. updates with pos. def. check, 2: neg. updates';
end
-
-defopts.Resume = 'no % resume former run from SaveFile';
-defopts.Science = 'on % off==do some additional (minor) problem capturing, NOT IN USE';
+
+defopts.Resume = 'no % resume former run from SaveFile';
+defopts.Science = 'on % off==do some additional (minor) problem capturing, NOT IN USE';
defopts.ReadSignals = 'on % from file signals.par for termination, yet a stumb';
defopts.Seed = 'sum(100*clock) % evaluated if it is a string';
defopts.DispFinal = 'on % display messages like initial and final message';
defopts.DispModulo = '100 % [0:Inf], disp messages after every i-th iteration';
defopts.SaveVariables = 'on % [on|final|off][-v6] save variables to .mat file';
-defopts.SaveFilename = 'variablescmaes.mat % save all variables, see SaveVariables';
+defopts.SaveFilename = 'variablescmaes.mat % save all variables, see SaveVariables';
defopts.LogModulo = '1 % [0:Inf] if >1 record data less frequently after gen=100';
defopts.LogTime = '25 % [0:100] max. percentage of time for recording data';
-defopts.LogFilenamePrefix = 'outcmaes % files for output data';
+defopts.LogFilenamePrefix = 'outcmaes % files for output data';
defopts.LogPlot = 'off % plot while running using output data files';
-%qqqkkk
-%defopts.varopt1 = ''; % 'for temporary and hacking purposes';
-%defopts.varopt2 = ''; % 'for temporary and hacking purposes';
+%qqqkkk
+%defopts.varopt1 = ''; % 'for temporary and hacking purposes';
+%defopts.varopt2 = ''; % 'for temporary and hacking purposes';
defopts.UserData = 'for saving data/comments associated with the run';
defopts.UserDat2 = ''; 'for saving data/comments associated with the run';
% ---------------------- Handling Input Parameters ----------------------
if nargin < 1 || isequal(fitfun, 'defaults') % pass default options
- if nargin < 1
- disp('Default options returned (type "help cmaes" for help).');
- end
- xmin = defopts;
- if nargin > 1 % supplement second argument with default options
- xmin = getoptions(xstart, defopts);
- end
- return
+ if nargin < 1
+ disp('Default options returned (type "help cmaes" for help).');
+ end
+ xmin = defopts;
+ if nargin > 1 % supplement second argument with default options
+ xmin = getoptions(xstart, defopts);
+ end
+ return
end
if isequal(fitfun, 'displayoptions')
- names = fieldnames(defopts);
- for name = names'
- disp([name{:} repmat(' ', 1, 20-length(name{:})) ': ''' defopts.(name{:}) '''']);
- end
- return
+ names = fieldnames(defopts);
+ for name = names'
+ disp([name{:} repmat(' ', 1, 20-length(name{:})) ': ''' defopts.(name{:}) '''']);
+ end
+ return
end
input.fitfun = fitfun; % record used input
if isempty(fitfun)
- % fitfun = definput.fitfun;
- % warning(['Objective function not determined, ''' fitfun ''' used']);
- error(['Objective function not determined']);
+ % fitfun = definput.fitfun;
+ % warning(['Objective function not determined, ''' fitfun ''' used']);
+ error(['Objective function not determined']);
end
if ~ischar(fitfun)
- error('first argument FUN must be a string');
+ error('first argument FUN must be a string');
end
-if nargin < 2
- xstart = [];
+if nargin < 2
+ xstart = [];
end
input.xstart = xstart;
if isempty(xstart)
- % xstart = definput.xstart; % objective variables initial point
- % warning('Initial search point, and problem dimension, not determined');
- error('Initial search point, and problem dimension, not determined');
+ % xstart = definput.xstart; % objective variables initial point
+ % warning('Initial search point, and problem dimension, not determined');
+ error('Initial search point, and problem dimension, not determined');
end
-if nargin < 3
- insigma = [];
+if nargin < 3
+ insigma = [];
end
if isa(insigma, 'struct')
- error(['Third argument SIGMA must be (or eval to) a scalar '...
- 'or a column vector of size(X0,1)']);
+ error(['Third argument SIGMA must be (or eval to) a scalar '...
+ 'or a column vector of size(X0,1)']);
end
input.sigma = insigma;
if isempty(insigma)
- if all(size(myeval(xstart)) > 1)
- insigma = std(xstart, 0, 2);
- if any(insigma == 0)
- error(['Initial search volume is zero, choose SIGMA or X0 appropriate']);
+ if all(size(myeval(xstart)) > 1)
+ insigma = std(xstart, 0, 2);
+ if any(insigma == 0)
+ error(['Initial search volume is zero, choose SIGMA or X0 appropriate']);
+ end
+ else
+ % will be captured later
+ % error(['Initial step sizes (SIGMA) not determined']);
end
- else
- % will be captured later
- % error(['Initial step sizes (SIGMA) not determined']);
- end
end
% Compose options opts
if nargin < 4 || isempty(inopts) % no input options available
- inopts = [];
- opts = defopts;
+ inopts = [];
+ opts = defopts;
else
- opts = getoptions(inopts, defopts);
+ opts = getoptions(inopts, defopts);
end
i = strfind(opts.SaveFilename, ' '); % remove everything after white space
if ~isempty(i)
- opts.SaveFilename = opts.SaveFilename(1:i(1)-1);
+ opts.SaveFilename = opts.SaveFilename(1:i(1)-1);
end
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-counteval = 0; countevalNaN = 0;
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+counteval = 0; countevalNaN = 0;
irun = 0;
while irun <= myeval(opts.Restarts) % for-loop does not work with resume
- irun = irun + 1;
-
-% ------------------------ Initialization -------------------------------
-
-% Handle resuming of old run
-flgresume = myevalbool(opts.Resume);
-xmean = myeval(xstart);
-if all(size(xmean) > 1)
- xmean = mean(xmean, 2); % in case if xstart is a population
-elseif size(xmean, 2) > 1
- xmean = xmean';
-end
-if ~flgresume % not resuming a former run
- % Assign settings from input parameters and options for myeval...
- N = size(xmean, 1); numberofvariables = N;
- lambda0 = floor(myeval(opts.PopSize) * myeval(opts.IncPopSize)^(irun-1));
- % lambda0 = floor(myeval(opts.PopSize) * 3^floor((irun-1)/2));
- popsize = lambda0;
- lambda = lambda0;
- insigma = myeval(insigma);
- if all(size(insigma) == [N 2])
- insigma = 0.5 * (insigma(:,2) - insigma(:,1));
- end
-else % flgresume is true, do resume former run
- tmp = whos('-file', opts.SaveFilename);
- for i = 1:length(tmp)
- if strcmp(tmp(i).name, 'localopts')
- error('Saved variables include variable "localopts", please remove');
- end
- end
- local.opts = opts; % keep stopping and display options
- local.varargin = varargin;
- load(opts.SaveFilename);
- varargin = local.varargin;
- flgresume = 1;
-
- % Overwrite old stopping and display options
- opts.StopFitness = local.opts.StopFitness;
- %%opts.MaxFunEvals = local.opts.MaxFunEvals;
- %%opts.MaxIter = local.opts.MaxIter;
- opts.StopFunEvals = local.opts.StopFunEvals;
- opts.StopIter = local.opts.StopIter;
- opts.TolX = local.opts.TolX;
- opts.TolUpX = local.opts.TolUpX;
- opts.TolFun = local.opts.TolFun;
- opts.TolHistFun = local.opts.TolHistFun;
- opts.StopOnStagnation = local.opts.StopOnStagnation;
- opts.StopOnWarnings = local.opts.StopOnWarnings;
- opts.ReadSignals = local.opts.ReadSignals;
- opts.DispFinal = local.opts.DispFinal;
- opts.LogPlot = local.opts.LogPlot;
- opts.DispModulo = local.opts.DispModulo;
- opts.SaveVariables = local.opts.SaveVariables;
- opts.LogModulo = local.opts.LogModulo;
- opts.LogTime = local.opts.LogTime;
- clear local; % otherwise local would be overwritten during load
-end
-
-%--------------------------------------------------------------
-% Evaluate options
-stopFitness = myeval(opts.StopFitness);
-stopMaxFunEvals = myeval(opts.MaxFunEvals);
-stopMaxIter = myeval(opts.MaxIter);
-stopFunEvals = myeval(opts.StopFunEvals);
-stopIter = myeval(opts.StopIter);
-stopTolX = myeval(opts.TolX);
-stopTolUpX = myeval(opts.TolUpX);
-stopTolFun = myeval(opts.TolFun);
-stopTolHistFun = myeval(opts.TolHistFun);
-stopOnStagnation = myevalbool(opts.StopOnStagnation);
-stopOnWarnings = myevalbool(opts.StopOnWarnings);
-flgreadsignals = myevalbool(opts.ReadSignals);
-flgWarnOnEqualFunctionValues = myevalbool(opts.WarnOnEqualFunctionValues);
-flgEvalParallel = myevalbool(opts.EvalParallel);
-stopOnEqualFunctionValues = myeval(opts.StopOnEqualFunctionValues);
-arrEqualFunvals = zeros(1,10+N);
-flgDiagonalOnly = myeval(opts.DiagonalOnly);
-flgActiveCMA = myeval(opts.CMA.active);
-noiseHandling = myevalbool(opts.Noise.on);
-noiseMinMaxEvals = myeval(opts.Noise.minmaxevals);
-noiseAlphaEvals = myeval(opts.Noise.alphaevals);
-noiseCallback = myeval(opts.Noise.callback);
-flgdisplay = myevalbool(opts.DispFinal);
-flgplotting = myevalbool(opts.LogPlot);
-verbosemodulo = myeval(opts.DispModulo);
-flgscience = myevalbool(opts.Science);
-flgsaving = [];
-strsaving = [];
-if strfind(opts.SaveVariables, '-v6')
- i = strfind(opts.SaveVariables, '%');
- if isempty(i) || i == 0 || strfind(opts.SaveVariables, '-v6') < i
- strsaving = '-v6';
- flgsaving = 1;
- flgsavingfinal = 1;
- end
-end
-if strncmp('final', opts.SaveVariables, 5)
- flgsaving = 0;
- flgsavingfinal = 1;
-end
-if isempty(flgsaving)
- flgsaving = myevalbool(opts.SaveVariables);
- flgsavingfinal = flgsaving;
-end
-savemodulo = myeval(opts.LogModulo);
-savetime = myeval(opts.LogTime);
-
-i = strfind(opts.LogFilenamePrefix, ' '); % remove everything after white space
-if ~isempty(i)
- opts.LogFilenamePrefix = opts.LogFilenamePrefix(1:i(1)-1);
-end
-
-% TODO here silent option? set disp, save and log options to 0
-
-%--------------------------------------------------------------
-
-if (isfinite(stopFunEvals) || isfinite(stopIter)) && ~flgsaving
- warning('To resume later the saving option needs to be set');
-end
-
-
-% Do more checking and initialization
-if flgresume % resume is on
- time.t0 = clock;
- if flgdisplay
- disp([' resumed from ' opts.SaveFilename ]);
- end
- if counteval >= stopMaxFunEvals
- error(['MaxFunEvals exceeded, use StopFunEvals as stopping ' ...
- 'criterion before resume']);
- end
- if countiter >= stopMaxIter
- error(['MaxIter exceeded, use StopIter as stopping criterion ' ...
- 'before resume']);
- end
-
-else % flgresume
- % xmean = mean(myeval(xstart), 2); % evaluate xstart again, because of irun
- maxdx = myeval(opts.DiffMaxChange); % maximal sensible variable change
- mindx = myeval(opts.DiffMinChange); % minimal sensible variable change
- % can both also be defined as Nx1 vectors
- lbounds = myeval(opts.LBounds);
- ubounds = myeval(opts.UBounds);
- if length(lbounds) == 1
- lbounds = repmat(lbounds, N, 1);
- end
- if length(ubounds) == 1
- ubounds = repmat(ubounds, N, 1);
- end
- if isempty(insigma) % last chance to set insigma
- if all(lbounds > -Inf) && all(ubounds < Inf)
- if any(lbounds>=ubounds)
- error('upper bound must be greater than lower bound');
- end
- insigma = 0.3*(ubounds-lbounds);
- stopTolX = myeval(opts.TolX); % reevaluate these
- stopTolUpX = myeval(opts.TolUpX);
- else
- error(['Initial step sizes (SIGMA) not determined']);
- end
- end
-
- % Check all vector sizes
- if size(xmean, 2) > 1 || size(xmean,1) ~= N
- error(['intial search point should be a column vector of size ' ...
- num2str(N)]);
- elseif ~(all(size(insigma) == [1 1]) || all(size(insigma) == [N 1]))
- error(['input parameter SIGMA should be (or eval to) a scalar '...
- 'or a column vector of size ' num2str(N)] );
- elseif size(stopTolX, 2) > 1 || ~ismember(size(stopTolX, 1), [1 N])
- error(['option TolX should be (or eval to) a scalar '...
- 'or a column vector of size ' num2str(N)] );
- elseif size(stopTolUpX, 2) > 1 || ~ismember(size(stopTolUpX, 1), [1 N])
- error(['option TolUpX should be (or eval to) a scalar '...
- 'or a column vector of size ' num2str(N)] );
- elseif size(maxdx, 2) > 1 || ~ismember(size(maxdx, 1), [1 N])
- error(['option DiffMaxChange should be (or eval to) a scalar '...
- 'or a column vector of size ' num2str(N)] );
- elseif size(mindx, 2) > 1 || ~ismember(size(mindx, 1), [1 N])
- error(['option DiffMinChange should be (or eval to) a scalar '...
- 'or a column vector of size ' num2str(N)] );
- elseif size(lbounds, 2) > 1 || ~ismember(size(lbounds, 1), [1 N])
- error(['option lbounds should be (or eval to) a scalar '...
- 'or a column vector of size ' num2str(N)] );
- elseif size(ubounds, 2) > 1 || ~ismember(size(ubounds, 1), [1 N])
- error(['option ubounds should be (or eval to) a scalar '...
- 'or a column vector of size ' num2str(N)] );
- end
-
- % Initialize dynamic internal state parameters
- if any(insigma <= 0)
- error(['Initial search volume (SIGMA) must be greater than zero']);
- end
- if max(insigma)/min(insigma) > 1e6
- error(['Initial search volume (SIGMA) badly conditioned']);
- end
- sigma = max(insigma); % overall standard deviation
- pc = zeros(N,1); ps = zeros(N,1); % evolution paths for C and sigma
-
- if length(insigma) == 1
- insigma = insigma * ones(N,1) ;
- end
- diagD = insigma/max(insigma); % diagonal matrix D defines the scaling
- diagC = diagD.^2;
- if flgDiagonalOnly ~= 1 % use at some point full covariance matrix
- B = eye(N,N); % B defines the coordinate system
- BD = B.*repmat(diagD',N,1); % B*D for speed up only
- C = diag(diagC); % covariance matrix == BD*(BD)'
- end
- if flgDiagonalOnly
- B = 1;
- end
-
- fitness.hist=NaN*ones(1,10+ceil(3*10*N/lambda)); % history of fitness values
- fitness.histsel=NaN*ones(1,10+ceil(3*10*N/lambda)); % history of fitness values
- fitness.histbest=[]; % history of fitness values
- fitness.histmedian=[]; % history of fitness values
-
- % Initialize boundary handling
- bnd.isactive = any(lbounds > -Inf) || any(ubounds < Inf);
- if bnd.isactive
- if any(lbounds>ubounds)
- error('lower bound found to be greater than upper bound');
+ irun = irun + 1;
+
+ % ------------------------ Initialization -------------------------------
+
+ % Handle resuming of old run
+ flgresume = myevalbool(opts.Resume);
+ xmean = myeval(xstart);
+ if all(size(xmean) > 1)
+ xmean = mean(xmean, 2); % in case if xstart is a population
+ elseif size(xmean, 2) > 1
+ xmean = xmean';
end
- [xmean, ti] = xintobounds(xmean, lbounds, ubounds); % just in case
- if any(ti)
- warning('Initial point was out of bounds, corrected');
+ if ~flgresume % not resuming a former run
+ % Assign settings from input parameters and options for myeval...
+ N = size(xmean, 1); numberofvariables = N;
+ lambda0 = floor(myeval(opts.PopSize) * myeval(opts.IncPopSize)^(irun-1));
+ % lambda0 = floor(myeval(opts.PopSize) * 3^floor((irun-1)/2));
+ popsize = lambda0;
+ lambda = lambda0;
+ insigma = myeval(insigma);
+ if all(size(insigma) == [N 2])
+ insigma = 0.5 * (insigma(:,2) - insigma(:,1));
+ end
+ else % flgresume is true, do resume former run
+ tmp = whos('-file', opts.SaveFilename);
+ for i = 1:length(tmp)
+ if strcmp(tmp(i).name, 'localopts')
+ error('Saved variables include variable "localopts", please remove');
+ end
+ end
+ local.opts = opts; % keep stopping and display options
+ local.varargin = varargin;
+ load(opts.SaveFilename);
+ varargin = local.varargin;
+ flgresume = 1;
+
+ % Overwrite old stopping and display options
+ opts.StopFitness = local.opts.StopFitness;
+ %%opts.MaxFunEvals = local.opts.MaxFunEvals;
+ %%opts.MaxIter = local.opts.MaxIter;
+ opts.StopFunEvals = local.opts.StopFunEvals;
+ opts.StopIter = local.opts.StopIter;
+ opts.TolX = local.opts.TolX;
+ opts.TolUpX = local.opts.TolUpX;
+ opts.TolFun = local.opts.TolFun;
+ opts.TolHistFun = local.opts.TolHistFun;
+ opts.StopOnStagnation = local.opts.StopOnStagnation;
+ opts.StopOnWarnings = local.opts.StopOnWarnings;
+ opts.ReadSignals = local.opts.ReadSignals;
+ opts.DispFinal = local.opts.DispFinal;
+ opts.LogPlot = local.opts.LogPlot;
+ opts.DispModulo = local.opts.DispModulo;
+ opts.SaveVariables = local.opts.SaveVariables;
+ opts.LogModulo = local.opts.LogModulo;
+ opts.LogTime = local.opts.LogTime;
+ clear local; % otherwise local would be overwritten during load
end
- bnd.weights = zeros(N,1); % weights for bound penalty
- % scaling is better in axis-parallel case, worse in rotated
- bnd.flgscale = 0; % scaling will be omitted if zero
- if bnd.flgscale ~= 0
- bnd.scale = diagC/mean(diagC);
- else
- bnd.scale = ones(N,1);
+
+ %--------------------------------------------------------------
+ % Evaluate options
+ stopFitness = myeval(opts.StopFitness);
+ stopMaxFunEvals = myeval(opts.MaxFunEvals);
+ stopMaxIter = myeval(opts.MaxIter);
+ stopFunEvals = myeval(opts.StopFunEvals);
+ stopIter = myeval(opts.StopIter);
+ stopTolX = myeval(opts.TolX);
+ stopTolUpX = myeval(opts.TolUpX);
+ stopTolFun = myeval(opts.TolFun);
+ stopTolHistFun = myeval(opts.TolHistFun);
+ stopOnStagnation = myevalbool(opts.StopOnStagnation);
+ stopOnWarnings = myevalbool(opts.StopOnWarnings);
+ flgreadsignals = myevalbool(opts.ReadSignals);
+ flgWarnOnEqualFunctionValues = myevalbool(opts.WarnOnEqualFunctionValues);
+ flgEvalParallel = myevalbool(opts.EvalParallel);
+ stopOnEqualFunctionValues = myeval(opts.StopOnEqualFunctionValues);
+ arrEqualFunvals = zeros(1,10+N);
+ flgDiagonalOnly = myeval(opts.DiagonalOnly);
+ flgActiveCMA = myeval(opts.CMA.active);
+ noiseHandling = myevalbool(opts.Noise.on);
+ noiseMinMaxEvals = myeval(opts.Noise.minmaxevals);
+ noiseAlphaEvals = myeval(opts.Noise.alphaevals);
+ noiseCallback = myeval(opts.Noise.callback);
+ flgdisplay = myevalbool(opts.DispFinal);
+ flgplotting = myevalbool(opts.LogPlot);
+ verbosemodulo = myeval(opts.DispModulo);
+ flgscience = myevalbool(opts.Science);
+ flgsaving = [];
+ strsaving = [];
+ if strfind(opts.SaveVariables, '-v6')
+ i = strfind(opts.SaveVariables, '%');
+ if isempty(i) || i == 0 || strfind(opts.SaveVariables, '-v6') < i
+ strsaving = '-v6';
+ flgsaving = 1;
+ flgsavingfinal = 1;
+ end
end
-
- idx = (lbounds > -Inf) | (ubounds < Inf);
- if length(idx) == 1
- idx = idx * ones(N,1);
+ if strncmp('final', opts.SaveVariables, 5)
+ flgsaving = 0;
+ flgsavingfinal = 1;
end
- bnd.isbounded = zeros(N,1);
- bnd.isbounded(find(idx)) = 1;
- maxdx = min(maxdx, (ubounds - lbounds)/2);
- if any(sigma*sqrt(diagC) > maxdx)
- fac = min(maxdx ./ sqrt(diagC))/sigma;
- sigma = min(maxdx ./ sqrt(diagC));
- warning(['Initial SIGMA multiplied by the factor ' num2str(fac) ...
- ', because it was larger than half' ...
- ' of one of the boundary intervals']);
+ if isempty(flgsaving)
+ flgsaving = myevalbool(opts.SaveVariables);
+ flgsavingfinal = flgsaving;
end
- idx = (lbounds > -Inf) & (ubounds < Inf);
- dd = diagC;
- if any(5*sigma*sqrt(dd(idx)) < ubounds(idx) - lbounds(idx))
- warning(['Initial SIGMA is, in at least one coordinate, ' ...
- 'much smaller than the '...
- 'given boundary intervals. For reasonable ' ...
- 'global search performance SIGMA should be ' ...
- 'between 0.2 and 0.5 of the bounded interval in ' ...
- 'each coordinate. If all coordinates have ' ...
- 'lower and upper bounds SIGMA can be empty']);
+ savemodulo = myeval(opts.LogModulo);
+ savetime = myeval(opts.LogTime);
+
+ i = strfind(opts.LogFilenamePrefix, ' '); % remove everything after white space
+ if ~isempty(i)
+ opts.LogFilenamePrefix = opts.LogFilenamePrefix(1:i(1)-1);
end
- bnd.dfithist = 1; % delta fit for setting weights
- bnd.aridxpoints = []; % remember complete outside points
- bnd.arfitness = []; % and their fitness
- bnd.validfitval = 0;
- bnd.iniphase = 1;
- end
-
- % ooo initial feval, for output only
- if irun == 1
- out.solutions.bestever.x = xmean;
- out.solutions.bestever.f = Inf; % for simpler comparison below
- out.solutions.bestever.evals = counteval;
- bestever = out.solutions.bestever;
- end
- if myevalbool(opts.EvalInitialX)
- fitness.hist(1)=feval(fitfun, xmean, varargin{:});
- fitness.histsel(1)=fitness.hist(1);
- counteval = counteval + 1;
- if fitness.hist(1) < out.solutions.bestever.f
- out.solutions.bestever.x = xmean;
- out.solutions.bestever.f = fitness.hist(1);
- out.solutions.bestever.evals = counteval;
- bestever = out.solutions.bestever;
+
+ % TODO here silent option? set disp, save and log options to 0
+
+ %--------------------------------------------------------------
+
+ if (isfinite(stopFunEvals) || isfinite(stopIter)) && ~flgsaving
+ warning('To resume later the saving option needs to be set');
end
- else
- fitness.hist(1)=NaN;
- fitness.histsel(1)=NaN;
- end
-
- % initialize random number generator
+
+
+ % Do more checking and initialization
+ if flgresume % resume is on
+ time.t0 = clock;
+ if flgdisplay
+ disp([' resumed from ' opts.SaveFilename ]);
+ end
+ if counteval >= stopMaxFunEvals
+ error(['MaxFunEvals exceeded, use StopFunEvals as stopping ' ...
+ 'criterion before resume']);
+ end
+ if countiter >= stopMaxIter
+ error(['MaxIter exceeded, use StopIter as stopping criterion ' ...
+ 'before resume']);
+ end
+
+ else % flgresume
+ % xmean = mean(myeval(xstart), 2); % evaluate xstart again, because of irun
+ maxdx = myeval(opts.DiffMaxChange); % maximal sensible variable change
+ mindx = myeval(opts.DiffMinChange); % minimal sensible variable change
+ % can both also be defined as Nx1 vectors
+ lbounds = myeval(opts.LBounds);
+ ubounds = myeval(opts.UBounds);
+ if length(lbounds) == 1
+ lbounds = repmat(lbounds, N, 1);
+ end
+ if length(ubounds) == 1
+ ubounds = repmat(ubounds, N, 1);
+ end
+ if isempty(insigma) % last chance to set insigma
+ if all(lbounds > -Inf) && all(ubounds < Inf)
+ if any(lbounds>=ubounds)
+ error('upper bound must be greater than lower bound');
+ end
+ insigma = 0.3*(ubounds-lbounds);
+ stopTolX = myeval(opts.TolX); % reevaluate these
+ stopTolUpX = myeval(opts.TolUpX);
+ else
+ error(['Initial step sizes (SIGMA) not determined']);
+ end
+ end
+
+ % Check all vector sizes
+ if size(xmean, 2) > 1 || size(xmean,1) ~= N
+ error(['intial search point should be a column vector of size ' ...
+ num2str(N)]);
+ elseif ~(all(size(insigma) == [1 1]) || all(size(insigma) == [N 1]))
+ error(['input parameter SIGMA should be (or eval to) a scalar '...
+ 'or a column vector of size ' num2str(N)] );
+ elseif size(stopTolX, 2) > 1 || ~ismember(size(stopTolX, 1), [1 N])
+ error(['option TolX should be (or eval to) a scalar '...
+ 'or a column vector of size ' num2str(N)] );
+ elseif size(stopTolUpX, 2) > 1 || ~ismember(size(stopTolUpX, 1), [1 N])
+ error(['option TolUpX should be (or eval to) a scalar '...
+ 'or a column vector of size ' num2str(N)] );
+ elseif size(maxdx, 2) > 1 || ~ismember(size(maxdx, 1), [1 N])
+ error(['option DiffMaxChange should be (or eval to) a scalar '...
+ 'or a column vector of size ' num2str(N)] );
+ elseif size(mindx, 2) > 1 || ~ismember(size(mindx, 1), [1 N])
+ error(['option DiffMinChange should be (or eval to) a scalar '...
+ 'or a column vector of size ' num2str(N)] );
+ elseif size(lbounds, 2) > 1 || ~ismember(size(lbounds, 1), [1 N])
+ error(['option lbounds should be (or eval to) a scalar '...
+ 'or a column vector of size ' num2str(N)] );
+ elseif size(ubounds, 2) > 1 || ~ismember(size(ubounds, 1), [1 N])
+ error(['option ubounds should be (or eval to) a scalar '...
+ 'or a column vector of size ' num2str(N)] );
+ end
+
+ % Initialize dynamic internal state parameters
+ if any(insigma <= 0)
+ error(['Initial search volume (SIGMA) must be greater than zero']);
+ end
+ if max(insigma)/min(insigma) > 1e6
+ error(['Initial search volume (SIGMA) badly conditioned']);
+ end
+ sigma = max(insigma); % overall standard deviation
+ pc = zeros(N,1); ps = zeros(N,1); % evolution paths for C and sigma
+
+ if length(insigma) == 1
+ insigma = insigma * ones(N,1) ;
+ end
+ diagD = insigma/max(insigma); % diagonal matrix D defines the scaling
+ diagC = diagD.^2;
+ if flgDiagonalOnly ~= 1 % use at some point full covariance matrix
+ B = eye(N,N); % B defines the coordinate system
+ BD = B.*repmat(diagD',N,1); % B*D for speed up only
+ C = diag(diagC); % covariance matrix == BD*(BD)'
+ end
+ if flgDiagonalOnly
+ B = 1;
+ end
+
+ fitness.hist=NaN*ones(1,10+ceil(3*10*N/lambda)); % history of fitness values
+ fitness.histsel=NaN*ones(1,10+ceil(3*10*N/lambda)); % history of fitness values
+ fitness.histbest=[]; % history of fitness values
+ fitness.histmedian=[]; % history of fitness values
+
+ % Initialize boundary handling
+ bnd.isactive = any(lbounds > -Inf) || any(ubounds < Inf);
+ if bnd.isactive
+ if any(lbounds>ubounds)
+ error('lower bound found to be greater than upper bound');
+ end
+ [xmean, ti] = xintobounds(xmean, lbounds, ubounds); % just in case
+ if any(ti)
+ warning('Initial point was out of bounds, corrected');
+ end
+ bnd.weights = zeros(N,1); % weights for bound penalty
+ % scaling is better in axis-parallel case, worse in rotated
+ bnd.flgscale = 0; % scaling will be omitted if zero
+ if bnd.flgscale ~= 0
+ bnd.scale = diagC/mean(diagC);
+ else
+ bnd.scale = ones(N,1);
+ end
+
+ idx = (lbounds > -Inf) | (ubounds < Inf);
+ if length(idx) == 1
+ idx = idx * ones(N,1);
+ end
+ bnd.isbounded = zeros(N,1);
+ bnd.isbounded(find(idx)) = 1;
+ maxdx = min(maxdx, (ubounds - lbounds)/2);
+ if any(sigma*sqrt(diagC) > maxdx)
+ fac = min(maxdx ./ sqrt(diagC))/sigma;
+ sigma = min(maxdx ./ sqrt(diagC));
+ warning(['Initial SIGMA multiplied by the factor ' num2str(fac) ...
+ ', because it was larger than half' ...
+ ' of one of the boundary intervals']);
+ end
+ idx = (lbounds > -Inf) & (ubounds < Inf);
+ dd = diagC;
+ if any(5*sigma*sqrt(dd(idx)) < ubounds(idx) - lbounds(idx))
+ warning(['Initial SIGMA is, in at least one coordinate, ' ...
+ 'much smaller than the '...
+ 'given boundary intervals. For reasonable ' ...
+ 'global search performance SIGMA should be ' ...
+ 'between 0.2 and 0.5 of the bounded interval in ' ...
+ 'each coordinate. If all coordinates have ' ...
+ 'lower and upper bounds SIGMA can be empty']);
+ end
+ bnd.dfithist = 1; % delta fit for setting weights
+ bnd.aridxpoints = []; % remember complete outside points
+ bnd.arfitness = []; % and their fitness
+ bnd.validfitval = 0;
+ bnd.iniphase = 1;
+ end
+
+ % ooo initial feval, for output only
+ if irun == 1
+ out.solutions.bestever.x = xmean;
+ out.solutions.bestever.f = Inf; % for simpler comparison below
+ out.solutions.bestever.evals = counteval;
+ bestever = out.solutions.bestever;
+ end
+ if myevalbool(opts.EvalInitialX)
+ fitness.hist(1)=feval(fitfun, xmean, varargin{:});
+ fitness.histsel(1)=fitness.hist(1);
+ counteval = counteval + 1;
+ if fitness.hist(1) < out.solutions.bestever.f
+ out.solutions.bestever.x = xmean;
+ out.solutions.bestever.f = fitness.hist(1);
+ out.solutions.bestever.evals = counteval;
+ bestever = out.solutions.bestever;
+ end
+ else
+ fitness.hist(1)=NaN;
+ fitness.histsel(1)=NaN;
+ end
+
+ % initialize random number generator
% $$$ if ischar(opts.Seed)
% $$$ randn('state', eval(opts.Seed)); % random number generator state
% $$$ else
% $$$ randn('state', opts.Seed);
% $$$ end
- %qqq
+%qqq
% load(opts.SaveFilename, 'startseed');
% randn('state', startseed);
% disp(['SEED RELOADED FROM ' opts.SaveFilename]);
% startseed = randn('state'); % for retrieving in saved variables
- % Initialize further constants
- chiN=N^0.5*(1-1/(4*N)+1/(21*N^2)); % expectation of
- % ||N(0,I)|| == norm(randn(N,1))
-
- countiter = 0;
- % Initialize records and output
- if irun == 1
- time.t0 = clock;
-
- % TODO: keep also median solution?
- out.evals = counteval; % should be first entry
- out.stopflag = {};
-
- outiter = 0;
-
- % Write headers to output data files
- filenameprefix = opts.LogFilenamePrefix;
- if savemodulo && savetime
- filenames = {};
- filenames(end+1) = {'axlen'};
- filenames(end+1) = {'fit'};
- filenames(end+1) = {'stddev'};
- filenames(end+1) = {'xmean'};
- filenames(end+1) = {'xrecentbest'};
- str = [' (startseed=' num2str(startseed(2)) ...
- ', ' num2str(clock, '%d/%02d/%d %d:%d:%2.2f') ')'];
- for namecell = filenames(:)'
- name = namecell{:};
-
- [fid, err] = fopen(['./' filenameprefix name '.dat'], 'w');
- if fid < 1 % err ~= 0
- warning(['could not open ' filenameprefix name '.dat']);
- filenames(find(strcmp(filenames,name))) = [];
- else
-% fprintf(fid, '%s\n', ...
-% ['<CMAES-OUTPUT version="' cmaVersion '">']);
-% fprintf(fid, [' <NAME>' name '</NAME>\n']);
-% fprintf(fid, [' <DATE>' date() '</DATE>\n']);
-% fprintf(fid, ' <PARAMETERS>\n');
-% fprintf(fid, [' dimension=' num2str(N) '\n']);
-% fprintf(fid, ' </PARAMETERS>\n');
- % different cases for DATA columns annotations here
-% fprintf(fid, ' <DATA');
- if strcmp(name, 'axlen')
- fprintf(fid, ['%% columns="iteration, evaluation, sigma, ' ...
- 'max axis length, min axis length, ' ...
- 'all principal axes lengths (sorted square roots ' ...
- 'of eigenvalues of C)"' str]);
- elseif strcmp(name, 'fit')
- fprintf(fid, ['%% columns="iteration, evaluation, sigma, axis ratio, bestever,' ...
- ' best, median, worst fitness function value,' ...
- ' further objective values of best"' str]);
- elseif strcmp(name, 'stddev')
- fprintf(fid, ['%% columns=["iteration, evaluation, sigma, void, void, ' ...
- 'stds==sigma*sqrt(diag(C))"' str]);
- elseif strcmp(name, 'xmean')
- fprintf(fid, ['%% columns="iteration, evaluation, void, ' ...
- 'void, void, xmean"' str]);
- elseif strcmp(name, 'xrecentbest')
- fprintf(fid, ['%% columns="iteration, evaluation, fitness, ' ...
- 'void, void, xrecentbest"' str]);
- end
- fprintf(fid, '\n'); % DATA
- if strcmp(name, 'xmean')
- fprintf(fid, '%ld %ld 0 0 0 ', 0, counteval);
- % fprintf(fid, '%ld %ld 0 0 %e ', countiter, counteval, fmean);
-%qqq fprintf(fid, msprintf('%e ', genophenotransform(out.genopheno, xmean)) + '\n');
- fprintf(fid, '%e ', xmean);
- fprintf(fid, '\n');
- end
- fclose(fid);
- clear fid; % preventing
- end
- end % for files
- end % savemodulo
- end % irun == 1
-
-end % else flgresume
-
-% -------------------- Generation Loop --------------------------------
-stopflag = {};
-while isempty(stopflag)
- % set internal parameters
- if countiter == 0 || lambda ~= lambda_last
- if countiter > 0 && floor(log10(lambda)) ~= floor(log10(lambda_last)) ...
- && flgdisplay
- disp([' lambda = ' num2str(lambda)]);
- lambda_hist(:,end+1) = [countiter+1; lambda];
- else
- lambda_hist = [countiter+1; lambda];
- end
- lambda_last = lambda;
- % Strategy internal parameter setting: Selection
- mu = myeval(opts.ParentNumber); % number of parents/points for recombination
- if strncmp(lower(opts.RecombinationWeights), 'equal', 3)
- weights = ones(mu,1); % (mu_I,lambda)-CMA-ES
- elseif strncmp(lower(opts.RecombinationWeights), 'linear', 3)
- weights = mu+0.5-(1:mu)';
- elseif strncmp(lower(opts.RecombinationWeights), 'superlinear', 3)
- weights = log(mu+0.5)-log(1:mu)'; % muXone array for weighted recombination
- % qqq mu can be non-integer and
- % should become ceil(mu-0.5) (minor correction)
- else
- error(['Recombination weights to be "' opts.RecombinationWeights ...
- '" is not implemented']);
- end
- mueff=sum(weights)^2/sum(weights.^2); % variance-effective size of mu
- weights = weights/sum(weights); % normalize recombination weights array
- if mueff == lambda
- error(['Combination of values for PopSize, ParentNumber and ' ...
- ' and RecombinationWeights is not reasonable']);
- end
-
- % Strategy internal parameter setting: Adaptation
- cc = myeval(opts.CMA.ccum); % time constant for cumulation for covariance matrix
- cs = myeval(opts.CMA.cs);
-
- % old way TODO: remove this at some point
- % mucov = mueff; % size of mu used for calculating learning rate ccov
- % ccov = (1/mucov) * 2/(N+1.41)^2 ... % learning rate for covariance matrix
- % + (1-1/mucov) * min(1,(2*mucov-1)/((N+2)^2+mucov));
-
- % new way
- if myevalbool(opts.CMA.on)
- ccov1 = myeval(opts.CMA.ccov1);
- ccovmu = min(1-ccov1, myeval(opts.CMA.ccovmu));
- else
- ccov1 = 0;
- ccovmu = 0;
- end
-
- % flgDiagonalOnly = -lambda*4*1/ccov; % for ccov==1 it is not needed
- % 0 : C will never be diagonal anymore
- % 1 : C will always be diagonal
- % >1: C is diagonal for first iterations, set to 0 afterwards
- if flgDiagonalOnly < 1
- flgDiagonalOnly = 0;
- end
- if flgDiagonalOnly
- ccov1_sep = min(1, ccov1 * (N+1.5) / 3);
- ccovmu_sep = min(1-ccov1_sep, ccovmu * (N+1.5) / 3);
- elseif N > 98 && flgdisplay && countiter == 0
- disp('consider option DiagonalOnly for high-dimensional problems');
- end
-
- % ||ps|| is close to sqrt(mueff/N) for mueff large on linear fitness
- %damps = ... % damping for step size control, usually close to one
- % (1 + 2*max(0,sqrt((mueff-1)/(N+1))-1)) ... % limit sigma increase
- % * max(0.3, ... % reduce damps, if max. iteration number is small
- % 1 - N/min(stopMaxIter,stopMaxFunEvals/lambda)) + cs;
- damps = myeval(opts.CMA.damps);
- if noiseHandling
- noiseReevals = min(myeval(opts.Noise.reevals), lambda);
- noiseAlpha = myeval(opts.Noise.alphasigma);
- noiseEpsilon = myeval(opts.Noise.epsilon);
- noiseTheta = myeval(opts.Noise.theta);
- noisecum = myeval(opts.Noise.cum);
- noiseCutOff = myeval(opts.Noise.cutoff); % arguably of minor relevance
- else
- noiseReevals = 0; % more convenient in later coding
- end
+ % Initialize further constants
+ chiN=N^0.5*(1-1/(4*N)+1/(21*N^2)); % expectation of
+ % ||N(0,I)|| == norm(randn(N,1))
+
+ countiter = 0;
+ % Initialize records and output
+ if irun == 1
+ time.t0 = clock;
+
+ % TODO: keep also median solution?
+ out.evals = counteval; % should be first entry
+ out.stopflag = {};
+
+ outiter = 0;
+
+ % Write headers to output data files
+ filenameprefix = opts.LogFilenamePrefix;
+ if savemodulo && savetime
+ filenames = {};
+ filenames(end+1) = {'axlen'};
+ filenames(end+1) = {'fit'};
+ filenames(end+1) = {'stddev'};
+ filenames(end+1) = {'xmean'};
+ filenames(end+1) = {'xrecentbest'};
+ str = [' (startseed=' num2str(startseed(2)) ...
+ ', ' num2str(clock, '%d/%02d/%d %d:%d:%2.2f') ')'];
+ for namecell = filenames(:)'
+ name = namecell{:};
+
+ [fid, err] = fopen(['./' filenameprefix name '.dat'], 'w');
+ if fid < 1 % err ~= 0
+ warning(['could not open ' filenameprefix name '.dat']);
+ filenames(find(strcmp(filenames,name))) = [];
+ else
+ % fprintf(fid, '%s\n', ...
+ % ['<CMAES-OUTPUT version="' cmaVersion '">']);
+ % fprintf(fid, [' <NAME>' name '</NAME>\n']);
+ % fprintf(fid, [' <DATE>' date() '</DATE>\n']);
+ % fprintf(fid, ' <PARAMETERS>\n');
+ % fprintf(fid, [' dimension=' num2str(N) '\n']);
+ % fprintf(fid, ' </PARAMETERS>\n');
+ % different cases for DATA columns annotations here
+ % fprintf(fid, ' <DATA');
+ if strcmp(name, 'axlen')
+ fprintf(fid, ['%% columns="iteration, evaluation, sigma, ' ...
+ 'max axis length, min axis length, ' ...
+ 'all principal axes lengths (sorted square roots ' ...
+ 'of eigenvalues of C)"' str]);
+ elseif strcmp(name, 'fit')
+ fprintf(fid, ['%% columns="iteration, evaluation, sigma, axis ratio, bestever,' ...
+ ' best, median, worst fitness function value,' ...
+ ' further objective values of best"' str]);
+ elseif strcmp(name, 'stddev')
+ fprintf(fid, ['%% columns=["iteration, evaluation, sigma, void, void, ' ...
+ 'stds==sigma*sqrt(diag(C))"' str]);
+ elseif strcmp(name, 'xmean')
+ fprintf(fid, ['%% columns="iteration, evaluation, void, ' ...
+ 'void, void, xmean"' str]);
+ elseif strcmp(name, 'xrecentbest')
+ fprintf(fid, ['%% columns="iteration, evaluation, fitness, ' ...
+ 'void, void, xrecentbest"' str]);
+ end
+ fprintf(fid, '\n'); % DATA
+ if strcmp(name, 'xmean')
+ fprintf(fid, '%ld %ld 0 0 0 ', 0, counteval);
+ % fprintf(fid, '%ld %ld 0 0 %e ', countiter, counteval, fmean);
+ %qqq fprintf(fid, msprintf('%e ', genophenotransform(out.genopheno, xmean)) + '\n');
+ fprintf(fid, '%e ', xmean);
+ fprintf(fid, '\n');
+ end
+ fclose(fid);
+ clear fid; % preventing
+ end
+ end % for files
+ end % savemodulo
+ end % irun == 1
+
+ end % else flgresume
+
+ % -------------------- Generation Loop --------------------------------
+ stopflag = {};
+ while isempty(stopflag)
+ % set internal parameters
+ if countiter == 0 || lambda ~= lambda_last
+ if countiter > 0 && floor(log10(lambda)) ~= floor(log10(lambda_last)) ...
+ && flgdisplay
+ disp([' lambda = ' num2str(lambda)]);
+ lambda_hist(:,end+1) = [countiter+1; lambda];
+ else
+ lambda_hist = [countiter+1; lambda];
+ end
+ lambda_last = lambda;
+ % Strategy internal parameter setting: Selection
+ mu = myeval(opts.ParentNumber); % number of parents/points for recombination
+ if strncmp(lower(opts.RecombinationWeights), 'equal', 3)
+ weights = ones(mu,1); % (mu_I,lambda)-CMA-ES
+ elseif strncmp(lower(opts.RecombinationWeights), 'linear', 3)
+ weights = mu+0.5-(1:mu)';
+ elseif strncmp(lower(opts.RecombinationWeights), 'superlinear', 3)
+ weights = log(mu+0.5)-log(1:mu)'; % muXone array for weighted recombination
+ % qqq mu can be non-integer and
+ % should become ceil(mu-0.5) (minor correction)
+ else
+ error(['Recombination weights to be "' opts.RecombinationWeights ...
+ '" is not implemented']);
+ end
+ mueff=sum(weights)^2/sum(weights.^2); % variance-effective size of mu
+ weights = weights/sum(weights); % normalize recombination weights array
+ if mueff == lambda
+ error(['Combination of values for PopSize, ParentNumber and ' ...
+ ' and RecombinationWeights is not reasonable']);
+ end
+
+ % Strategy internal parameter setting: Adaptation
+ cc = myeval(opts.CMA.ccum); % time constant for cumulation for covariance matrix
+ cs = myeval(opts.CMA.cs);
+
+ % old way TODO: remove this at some point
+ % mucov = mueff; % size of mu used for calculating learning rate ccov
+ % ccov = (1/mucov) * 2/(N+1.41)^2 ... % learning rate for covariance matrix
+ % + (1-1/mucov) * min(1,(2*mucov-1)/((N+2)^2+mucov));
+
+ % new way
+ if myevalbool(opts.CMA.on)
+ ccov1 = myeval(opts.CMA.ccov1);
+ ccovmu = min(1-ccov1, myeval(opts.CMA.ccovmu));
+ else
+ ccov1 = 0;
+ ccovmu = 0;
+ end
+
+ % flgDiagonalOnly = -lambda*4*1/ccov; % for ccov==1 it is not needed
+ % 0 : C will never be diagonal anymore
+ % 1 : C will always be diagonal
+ % >1: C is diagonal for first iterations, set to 0 afterwards
+ if flgDiagonalOnly < 1
+ flgDiagonalOnly = 0;
+ end
+ if flgDiagonalOnly
+ ccov1_sep = min(1, ccov1 * (N+1.5) / 3);
+ ccovmu_sep = min(1-ccov1_sep, ccovmu * (N+1.5) / 3);
+ elseif N > 98 && flgdisplay && countiter == 0
+ disp('consider option DiagonalOnly for high-dimensional problems');
+ end
+
+ % ||ps|| is close to sqrt(mueff/N) for mueff large on linear fitness
+ %damps = ... % damping for step size control, usually close to one
+ % (1 + 2*max(0,sqrt((mueff-1)/(N+1))-1)) ... % limit sigma increase
+ % * max(0.3, ... % reduce damps, if max. iteration number is small
+ % 1 - N/min(stopMaxIter,stopMaxFunEvals/lambda)) + cs;
+ damps = myeval(opts.CMA.damps);
+ if noiseHandling
+ noiseReevals = min(myeval(opts.Noise.reevals), lambda);
+ noiseAlpha = myeval(opts.Noise.alphasigma);
+ noiseEpsilon = myeval(opts.Noise.epsilon);
+ noiseTheta = myeval(opts.Noise.theta);
+ noisecum = myeval(opts.Noise.cum);
+ noiseCutOff = myeval(opts.Noise.cutoff); % arguably of minor relevance
+ else
+ noiseReevals = 0; % more convenient in later coding
+ end
+
+ %qqq hacking of a different parameter setting, e.g. for ccov or damps,
+ % can be done here, but is not necessary anymore, see opts.CMA.
+ % ccov1 = 0.0*ccov1; disp(['CAVE: ccov1=' num2str(ccov1)]);
+ % ccovmu = 0.0*ccovmu; disp(['CAVE: ccovmu=' num2str(ccovmu)]);
+ % damps = inf*damps; disp(['CAVE: damps=' num2str(damps)]);
+ % cc = 1; disp(['CAVE: cc=' num2str(cc)]);
- %qqq hacking of a different parameter setting, e.g. for ccov or damps,
- % can be done here, but is not necessary anymore, see opts.CMA.
- % ccov1 = 0.0*ccov1; disp(['CAVE: ccov1=' num2str(ccov1)]);
- % ccovmu = 0.0*ccovmu; disp(['CAVE: ccovmu=' num2str(ccovmu)]);
- % damps = inf*damps; disp(['CAVE: damps=' num2str(damps)]);
- % cc = 1; disp(['CAVE: cc=' num2str(cc)]);
-
- end
-
- % Display initial message
- if countiter == 0 && flgdisplay
- if mu == 1
- strw = '100';
- elseif mu < 8
- strw = [sprintf('%.0f', 100*weights(1)) ...
- sprintf(' %.0f', 100*weights(2:end)')];
- else
- strw = [sprintf('%.2g ', 100*weights(1:2)') ...
- sprintf('%.2g', 100*weights(3)') '...' ...
- sprintf(' %.2g', 100*weights(end-1:end)') ']%, '];
- end
- if irun > 1
- strrun = [', run ' num2str(irun)];
- else
- strrun = '';
- end
- disp([' n=' num2str(N) ': (' num2str(mu) ',' ...
- num2str(lambda) ')-CMA-ES(w=[' ...
- strw ']%, ' ...
- 'mu_eff=' num2str(mueff,'%.1f') ...
- ') on function ' ...
- (fitfun) strrun]);
- if flgDiagonalOnly == 1
- disp(' C is diagonal');
- elseif flgDiagonalOnly
- disp([' C is diagonal for ' num2str(floor(flgDiagonalOnly)) ' iterations']);
- end
- end
-
- flush;
-
- countiter = countiter + 1;
-
- % Generate and evaluate lambda offspring
-
- fitness.raw = repmat(NaN, 1, lambda + noiseReevals);
-
- % parallel evaluation
- if flgEvalParallel
- arz = randn(N,lambda);
-
- if ~flgDiagonalOnly
- arx = repmat(xmean, 1, lambda) + sigma * (BD * arz); % Eq. (1)
- else
- arx = repmat(xmean, 1, lambda) + repmat(sigma * diagD, 1, lambda) .* arz;
- end
-
- if noiseHandling
- if noiseEpsilon == 0
- arx = [arx arx(:,1:noiseReevals)];
- elseif flgDiagonalOnly
- arx = [arx arx(:,1:noiseReevals) + ...
- repmat(noiseEpsilon * sigma * diagD, 1, noiseReevals) ...
- .* randn(N,noiseReevals)];
- else
- arx = [arx arx(:,1:noiseReevals) + ...
- noiseEpsilon * sigma * ...
- (BD * randn(N,noiseReevals))];
- end
- end
-
- % You may handle constraints here. You may either resample
- % arz(:,k) and/or multiply it with a factor between -1 and 1
- % (the latter will decrease the overall step size) and
- % recalculate arx accordingly. Do not change arx or arz in any
- % other way.
-
- if ~bnd.isactive
- arxvalid = arx;
- else
- arxvalid = xintobounds(arx, lbounds, ubounds);
- end
- % You may handle constraints here. You may copy and alter
- % (columns of) arxvalid(:,k) only for the evaluation of the
- % fitness function. arx and arxvalid should not be changed.
- fitness.raw = feval(fitfun, arxvalid, varargin{:});
- countevalNaN = countevalNaN + sum(isnan(fitness.raw));
- counteval = counteval + sum(~isnan(fitness.raw));
- end
-
- % non-parallel evaluation and remaining NaN-values
- % set also the reevaluated solution to NaN
- fitness.raw(lambda + find(isnan(fitness.raw(1:noiseReevals)))) = NaN;
- for k=find(isnan(fitness.raw))
- % fitness.raw(k) = NaN;
- tries = 0;
- % Resample, until fitness is not NaN
- while isnan(fitness.raw(k))
- if k <= lambda % regular samples (not the re-evaluation-samples)
- arz(:,k) = randn(N,1); % (re)sample
-
- if flgDiagonalOnly
- arx(:,k) = xmean + sigma * diagD .* arz(:,k); % Eq. (1)
- else
- arx(:,k) = xmean + sigma * (BD * arz(:,k)); % Eq. (1)
end
- else % re-evaluation solution with index > lambda
- if flgDiagonalOnly
- arx(:,k) = arx(:,k-lambda) + (noiseEpsilon * sigma) * diagD .* randn(N,1);
- else
- arx(:,k) = arx(:,k-lambda) + (noiseEpsilon * sigma) * (BD * randn(N,1));
- end
- end
-
- % You may handle constraints here. You may either resample
- % arz(:,k) and/or multiply it with a factor between -1 and 1
- % (the latter will decrease the overall step size) and
- % recalculate arx accordingly. Do not change arx or arz in any
- % other way.
-
- if ~bnd.isactive
- arxvalid(:,k) = arx(:,k);
- else
- arxvalid(:,k) = xintobounds(arx(:,k), lbounds, ubounds);
- end
- % You may handle constraints here. You may copy and alter
- % (columns of) arxvalid(:,k) only for the evaluation of the
- % fitness function. arx should not be changed.
- fitness.raw(k) = feval(fitfun, arxvalid(:,k), varargin{:});
- tries = tries + 1;
- if isnan(fitness.raw(k))
- countevalNaN = countevalNaN + 1;
- end
- if mod(tries, 100) == 0
- warning([num2str(tries) ...
- ' NaN objective function values at evaluation ' ...
- num2str(counteval)]);
- end
- end
- counteval = counteval + 1; % retries due to NaN are not counted
- end
-
- fitness.sel = fitness.raw;
-
- % ----- handle boundaries -----
- if 1 < 3 && bnd.isactive
- % Get delta fitness values
- val = myprctile(fitness.raw, [25 75]);
- % more precise would be exp(mean(log(diagC)))
- val = (val(2) - val(1)) / N / mean(diagC) / sigma^2;
- %val = (myprctile(fitness.raw, 75) - myprctile(fitness.raw, 25)) ...
- % / N / mean(diagC) / sigma^2;
- % Catch non-sensible values
- if ~isfinite(val)
- warning('Non-finite fitness range');
- val = max(bnd.dfithist);
- elseif val == 0 % happens if all points are out of bounds
- val = min(bnd.dfithist(bnd.dfithist>0)); % seems not to make sense, given all solutions are out of bounds
- elseif bnd.validfitval == 0 % flag that first sensible val was found
- bnd.dfithist = [];
- bnd.validfitval = 1;
- end
- % Store delta fitness values
- if length(bnd.dfithist) < 20+(3*N)/lambda
- bnd.dfithist = [bnd.dfithist val];
- else
- bnd.dfithist = [bnd.dfithist(2:end) val];
- end
-
- [tx, ti] = xintobounds(xmean, lbounds, ubounds);
-
- % Set initial weights
- if bnd.iniphase
- if any(ti)
- bnd.weights(find(bnd.isbounded)) = 2.0002 * median(bnd.dfithist);
- if bnd.flgscale == 0 % scale only initial weights then
- dd = diagC;
- idx = find(bnd.isbounded);
- dd = dd(idx) / mean(dd); % remove mean scaling
- bnd.weights(idx) = bnd.weights(idx) ./ dd;
- end
- if bnd.validfitval && countiter > 2
- bnd.iniphase = 0;
- end
- end
- end
-
- % Increase weights
- if 1 < 3 && any(ti) % any coordinate of xmean out of bounds
- % judge distance of xmean to boundary
- tx = xmean - tx;
- idx = (ti ~= 0 & abs(tx) > 3*max(1,sqrt(N)/mueff) ...
- * sigma*sqrt(diagC)) ;
- % only increase if xmean is moving away
- idx = idx & (sign(tx) == sign(xmean - xold));
- if ~isempty(idx) % increase
- % the factor became 1.2 instead of 1.1, because
- % changed from max to min in version 3.52
- bnd.weights(idx) = 1.2^(min(1, mueff/10/N)) * bnd.weights(idx);
- end
- end
-
- % Calculate scaling biased to unity, product is one
- if bnd.flgscale ~= 0
- bnd.scale = exp(0.9*(log(diagC)-mean(log(diagC))));
- end
+ % Display initial message
+ if countiter == 0 && flgdisplay
+ if mu == 1
+ strw = '100';
+ elseif mu < 8
+ strw = [sprintf('%.0f', 100*weights(1)) ...
+ sprintf(' %.0f', 100*weights(2:end)')];
+ else
+ strw = [sprintf('%.2g ', 100*weights(1:2)') ...
+ sprintf('%.2g', 100*weights(3)') '...' ...
+ sprintf(' %.2g', 100*weights(end-1:end)') ']%, '];
+ end
+ if irun > 1
+ strrun = [', run ' num2str(irun)];
+ else
+ strrun = '';
+ end
+ disp([' n=' num2str(N) ': (' num2str(mu) ',' ...
+ num2str(lambda) ')-CMA-ES(w=[' ...
+ strw ']%, ' ...
+ 'mu_eff=' num2str(mueff,'%.1f') ...
+ ') on function ' ...
+ (fitfun) strrun]);
+ if flgDiagonalOnly == 1
+ disp(' C is diagonal');
+ elseif flgDiagonalOnly
+ disp([' C is diagonal for ' num2str(floor(flgDiagonalOnly)) ' iterations']);
+ end
+ end
- % Assigned penalized fitness
- bnd.arpenalty = (bnd.weights ./ bnd.scale)' * (arxvalid - arx).^2;
-
- fitness.sel = fitness.raw + bnd.arpenalty;
-
- end % handle boundaries
- % ----- end handle boundaries -----
-
- % compute noise measurement and reduce fitness arrays to size lambda
- if noiseHandling
- [noiseS] = local_noisemeasurement(fitness.sel(1:lambda), ...
- fitness.sel(lambda+(1:noiseReevals)), ...
- noiseReevals, noiseTheta, noiseCutOff);
- if countiter == 1 % TODO: improve this very rude way of initialization
- noiseSS = 0;
- noiseN = 0; % counter for mean
- end
- noiseSS = noiseSS + noisecum * (noiseS - noiseSS);
-
- % noise-handling could be done here, but the original sigma is still needed
- % disp([noiseS noiseSS noisecum])
-
- fitness.rawar12 = fitness.raw; % just documentary
- fitness.selar12 = fitness.sel; % just documentary
- % qqq refine fitness based on both values
- if 11 < 3 % TODO: in case of outliers this mean is counterproductive
- % median out of three would be ok
- fitness.raw(1:noiseReevals) = ... % not so raw anymore
- (fitness.raw(1:noiseReevals) + fitness.raw(lambda+(1:noiseReevals))) / 2;
- fitness.sel(1:noiseReevals) = ...
- (fitness.sel(1:noiseReevals) + fitness.sel(lambda+(1:noiseReevals))) / 2;
- end
- fitness.raw = fitness.raw(1:lambda);
- fitness.sel = fitness.sel(1:lambda);
- end
-
- % Sort by fitness
- [fitness.raw, fitness.idx] = sort(fitness.raw);
- [fitness.sel, fitness.idxsel] = sort(fitness.sel); % minimization
- fitness.hist(2:end) = fitness.hist(1:end-1); % record short history of
- fitness.hist(1) = fitness.raw(1); % best fitness values
- if length(fitness.histbest) < 120+ceil(30*N/lambda) || ...
- (mod(countiter, 5) == 0 && length(fitness.histbest) < 2e4) % 20 percent of 1e5 gen.
- fitness.histbest = [fitness.raw(1) fitness.histbest]; % best fitness values
- fitness.histmedian = [median(fitness.raw) fitness.histmedian]; % median fitness values
- else
- fitness.histbest(2:end) = fitness.histbest(1:end-1);
- fitness.histmedian(2:end) = fitness.histmedian(1:end-1);
- fitness.histbest(1) = fitness.raw(1); % best fitness values
- fitness.histmedian(1) = median(fitness.raw); % median fitness values
- end
- fitness.histsel(2:end) = fitness.histsel(1:end-1); % record short history of
- fitness.histsel(1) = fitness.sel(1); % best sel fitness values
-
- % Calculate new xmean, this is selection and recombination
- xold = xmean; % for speed up of Eq. (2) and (3)
- xmean = arx(:,fitness.idxsel(1:mu))*weights;
- zmean = arz(:,fitness.idxsel(1:mu))*weights;%==D^-1*B'*(xmean-xold)/sigma
- if mu == 1
- fmean = fitness.sel(1);
- else
- fmean = NaN; % [] does not work in the latter assignment
- % fmean = feval(fitfun, xintobounds(xmean, lbounds, ubounds), varargin{:});
- % counteval = counteval + 1;
- end
-
- % Cumulation: update evolution paths
- ps = (1-cs)*ps + sqrt(cs*(2-cs)*mueff) * (B*zmean); % Eq. (4)
- hsig = norm(ps)/sqrt(1-(1-cs)^(2*countiter))/chiN < 1.4 + 2/(N+1);
- if flg_future_setting
- hsig = sum(ps.^2) / (1-(1-cs)^(2*countiter)) / N < 2 + 4/(N+1); % just simplified
- end
-% hsig = norm(ps)/sqrt(1-(1-cs)^(2*countiter))/chiN < 1.4 + 2/(N+1);
-% hsig = norm(ps)/sqrt(1-(1-cs)^(2*countiter))/chiN < 1.5 + 1/(N-0.5);
-% hsig = norm(ps) < 1.5 * sqrt(N);
-% hsig = 1;
-
- pc = (1-cc)*pc ...
- + hsig*(sqrt(cc*(2-cc)*mueff)/sigma) * (xmean-xold); % Eq. (2)
- if hsig == 0
- % disp([num2str(countiter) ' ' num2str(counteval) ' pc update stalled']);
- end
-
- % Adapt covariance matrix
- neg.ccov = 0; % TODO: move parameter setting upwards at some point
- if ccov1 + ccovmu > 0 % Eq. (3)
- if flgDiagonalOnly % internal linear(?) complexity
- diagC = (1-ccov1_sep-ccovmu_sep+(1-hsig)*ccov1_sep*cc*(2-cc)) * diagC ... % regard old matrix
- + ccov1_sep * pc.^2 ... % plus rank one update
- + ccovmu_sep ... % plus rank mu update
- * (diagC .* (arz(:,fitness.idxsel(1:mu)).^2 * weights));
-% * (repmat(diagC,1,mu) .* arz(:,fitness.idxsel(1:mu)).^2 * weights);
- diagD = sqrt(diagC); % replaces eig(C)
- else
- arpos = (arx(:,fitness.idxsel(1:mu))-repmat(xold,1,mu)) / sigma;
- % "active" CMA update: negative update, in case controlling pos. definiteness
- if flgActiveCMA > 0
- % set parameters
- neg.mu = mu;
- neg.mueff = mueff;
- if flgActiveCMA > 10 % flat weights with mu=lambda/2
- neg.mu = floor(lambda/2);
- neg.mueff = neg.mu;
+ flush;
+
+ countiter = countiter + 1;
+
+ % Generate and evaluate lambda offspring
+
+ fitness.raw = repmat(NaN, 1, lambda + noiseReevals);
+
+ % parallel evaluation
+ if flgEvalParallel
+ arz = randn(N,lambda);
+
+ if ~flgDiagonalOnly
+ arx = repmat(xmean, 1, lambda) + sigma * (BD * arz); % Eq. (1)
+ else
+ arx = repmat(xmean, 1, lambda) + repmat(sigma * diagD, 1, lambda) .* arz;
+ end
+
+ if noiseHandling
+ if noiseEpsilon == 0
+ arx = [arx arx(:,1:noiseReevals)];
+ elseif flgDiagonalOnly
+ arx = [arx arx(:,1:noiseReevals) + ...
+ repmat(noiseEpsilon * sigma * diagD, 1, noiseReevals) ...
+ .* randn(N,noiseReevals)];
+ else
+ arx = [arx arx(:,1:noiseReevals) + ...
+ noiseEpsilon * sigma * ...
+ (BD * randn(N,noiseReevals))];
+ end
+ end
+
+ % You may handle constraints here. You may either resample
+ % arz(:,k) and/or multiply it with a factor between -1 and 1
+ % (the latter will decrease the overall step size) and
+ % recalculate arx accordingly. Do not change arx or arz in any
+ % other way.
+
+ if ~bnd.isactive
+ arxvalid = arx;
+ else
+ arxvalid = xintobounds(arx, lbounds, ubounds);
+ end
+ % You may handle constraints here. You may copy and alter
+ % (columns of) arxvalid(:,k) only for the evaluation of the
+ % fitness function. arx and arxvalid should not be changed.
+ fitness.raw = feval(fitfun, arxvalid, varargin{:});
+ countevalNaN = countevalNaN + sum(isnan(fitness.raw));
+ counteval = counteval + sum(~isnan(fitness.raw));
end
- % neg.mu = ceil(min([N, lambda/4, mueff])); neg.mueff = mu; % i.e. neg.mu <= N
- % Parameter study: in 3-D lambda=50,100, 10-D lambda=200,400, 30-D lambda=1000,2000 a
- % three times larger neg.ccov does not work.
- % increasing all ccov rates three times does work (probably because of the factor (1-ccovmu))
- % in 30-D to looks fine
-
- neg.ccov = (1 - ccovmu) * 0.25 * neg.mueff / ((N+2)^1.5 + 2*neg.mueff);
- neg.minresidualvariance = 0.66; % keep at least 0.66 in all directions, small popsize are most critical
- neg.alphaold = 0.5; % where to make up for the variance loss, 0.5 means no idea what to do
- % 1 is slightly more robust and gives a better "guaranty" for pos. def.,
- % but does it make sense from the learning perspective for large ccovmu?
-
- neg.ccovfinal = neg.ccov;
-
- % prepare vectors, compute negative updating matrix Cneg and checking matrix Ccheck
- arzneg = arz(:,fitness.idxsel(lambda:-1:lambda - neg.mu + 1));
- % i-th longest becomes i-th shortest
- % TODO: this is not in compliance with the paper Hansen&Ros2010,
- % where simply arnorms = arnorms(end:-1:1) ?
- [arnorms, idxnorms] = sort(sqrt(sum(arzneg.^2, 1)));
- [ignore, idxnorms] = sort(idxnorms); % inverse index
- arnormfacs = arnorms(end:-1:1) ./ arnorms;
- % arnormfacs = arnorms(randperm(neg.mu)) ./ arnorms;
- arnorms = arnorms(end:-1:1); % for the record
- if flgActiveCMA < 20
- arzneg = arzneg .* repmat(arnormfacs(idxnorms), N, 1); % E x*x' is N
- % arzneg = sqrt(N) * arzneg ./ repmat(sqrt(sum(arzneg.^2, 1)), N, 1); % E x*x' is N
+
+ % non-parallel evaluation and remaining NaN-values
+ % set also the reevaluated solution to NaN
+ fitness.raw(lambda + find(isnan(fitness.raw(1:noiseReevals)))) = NaN;
+ for k=find(isnan(fitness.raw))
+ % fitness.raw(k) = NaN;
+ tries = 0;
+ % Resample, until fitness is not NaN
+ while isnan(fitness.raw(k))
+ if k <= lambda % regular samples (not the re-evaluation-samples)
+ arz(:,k) = randn(N,1); % (re)sample
+
+ if flgDiagonalOnly
+ arx(:,k) = xmean + sigma * diagD .* arz(:,k); % Eq. (1)
+ else
+ arx(:,k) = xmean + sigma * (BD * arz(:,k)); % Eq. (1)
+ end
+ else % re-evaluation solution with index > lambda
+ if flgDiagonalOnly
+ arx(:,k) = arx(:,k-lambda) + (noiseEpsilon * sigma) * diagD .* randn(N,1);
+ else
+ arx(:,k) = arx(:,k-lambda) + (noiseEpsilon * sigma) * (BD * randn(N,1));
+ end
+ end
+
+ % You may handle constraints here. You may either resample
+ % arz(:,k) and/or multiply it with a factor between -1 and 1
+ % (the latter will decrease the overall step size) and
+ % recalculate arx accordingly. Do not change arx or arz in any
+ % other way.
+
+ if ~bnd.isactive
+ arxvalid(:,k) = arx(:,k);
+ else
+ arxvalid(:,k) = xintobounds(arx(:,k), lbounds, ubounds);
+ end
+ % You may handle constraints here. You may copy and alter
+ % (columns of) arxvalid(:,k) only for the evaluation of the
+ % fitness function. arx should not be changed.
+ fitness.raw(k) = feval(fitfun, arxvalid(:,k), varargin{:});
+ tries = tries + 1;
+ if isnan(fitness.raw(k))
+ countevalNaN = countevalNaN + 1;
+ end
+ if mod(tries, 100) == 0
+ warning([num2str(tries) ...
+ ' NaN objective function values at evaluation ' ...
+ num2str(counteval)]);
+ end
+ end
+ counteval = counteval + 1; % retries due to NaN are not counted
end
- if flgActiveCMA < 10 && neg.mu == mu % weighted sum
- if mod(flgActiveCMA, 10) == 1 % TODO: prevent this with a less tight but more efficient check (see below)
- Ccheck = arzneg * diag(weights) * arzneg'; % in order to check the largest EV
- end
- artmp = BD * arzneg;
- Cneg = artmp * diag(weights) * artmp';
- else % simple sum
- if mod(flgActiveCMA, 10) == 1
- Ccheck = (1/neg.mu) * arzneg*arzneg'; % in order to check largest EV
- end
- artmp = BD * arzneg;
- Cneg = (1/neg.mu) * artmp*artmp';
+ fitness.sel = fitness.raw;
+
+ % ----- handle boundaries -----
+ if 1 < 3 && bnd.isactive
+ % Get delta fitness values
+ val = myprctile(fitness.raw, [25 75]);
+ % more precise would be exp(mean(log(diagC)))
+ val = (val(2) - val(1)) / N / mean(diagC) / sigma^2;
+ %val = (myprctile(fitness.raw, 75) - myprctile(fitness.raw, 25)) ...
+ % / N / mean(diagC) / sigma^2;
+ % Catch non-sensible values
+ if ~isfinite(val)
+ warning('Non-finite fitness range');
+ val = max(bnd.dfithist);
+ elseif val == 0 % happens if all points are out of bounds
+ val = min(bnd.dfithist(bnd.dfithist>0)); % seems not to make sense, given all solutions are out of bounds
+ elseif bnd.validfitval == 0 % flag that first sensible val was found
+ bnd.dfithist = [];
+ bnd.validfitval = 1;
+ end
+
+ % Store delta fitness values
+ if length(bnd.dfithist) < 20+(3*N)/lambda
+ bnd.dfithist = [bnd.dfithist val];
+ else
+ bnd.dfithist = [bnd.dfithist(2:end) val];
+ end
+
+ [tx, ti] = xintobounds(xmean, lbounds, ubounds);
+
+ % Set initial weights
+ if bnd.iniphase
+ if any(ti)
+ bnd.weights(find(bnd.isbounded)) = 2.0002 * median(bnd.dfithist);
+ if bnd.flgscale == 0 % scale only initial weights then
+ dd = diagC;
+ idx = find(bnd.isbounded);
+ dd = dd(idx) / mean(dd); % remove mean scaling
+ bnd.weights(idx) = bnd.weights(idx) ./ dd;
+ end
+ if bnd.validfitval && countiter > 2
+ bnd.iniphase = 0;
+ end
+ end
+ end
+
+ % Increase weights
+ if 1 < 3 && any(ti) % any coordinate of xmean out of bounds
+ % judge distance of xmean to boundary
+ tx = xmean - tx;
+ idx = (ti ~= 0 & abs(tx) > 3*max(1,sqrt(N)/mueff) ...
+ * sigma*sqrt(diagC)) ;
+ % only increase if xmean is moving away
+ idx = idx & (sign(tx) == sign(xmean - xold));
+ if ~isempty(idx) % increase
+ % the factor became 1.2 instead of 1.1, because
+ % changed from max to min in version 3.52
+ bnd.weights(idx) = 1.2^(min(1, mueff/10/N)) * bnd.weights(idx);
+ end
+ end
+
+ % Calculate scaling biased to unity, product is one
+ if bnd.flgscale ~= 0
+ bnd.scale = exp(0.9*(log(diagC)-mean(log(diagC))));
+ end
+
+ % Assigned penalized fitness
+ bnd.arpenalty = (bnd.weights ./ bnd.scale)' * (arxvalid - arx).^2;
+
+ fitness.sel = fitness.raw + bnd.arpenalty;
+
+ end % handle boundaries
+ % ----- end handle boundaries -----
+
+ % compute noise measurement and reduce fitness arrays to size lambda
+ if noiseHandling
+ [noiseS] = local_noisemeasurement(fitness.sel(1:lambda), ...
+ fitness.sel(lambda+(1:noiseReevals)), ...
+ noiseReevals, noiseTheta, noiseCutOff);
+ if countiter == 1 % TODO: improve this very rude way of initialization
+ noiseSS = 0;
+ noiseN = 0; % counter for mean
+ end
+ noiseSS = noiseSS + noisecum * (noiseS - noiseSS);
+
+ % noise-handling could be done here, but the original sigma is still needed
+ % disp([noiseS noiseSS noisecum])
+
+ fitness.rawar12 = fitness.raw; % just documentary
+ fitness.selar12 = fitness.sel; % just documentary
+ % qqq refine fitness based on both values
+ if 11 < 3 % TODO: in case of outliers this mean is counterproductive
+ % median out of three would be ok
+ fitness.raw(1:noiseReevals) = ... % not so raw anymore
+ (fitness.raw(1:noiseReevals) + fitness.raw(lambda+(1:noiseReevals))) / 2;
+ fitness.sel(1:noiseReevals) = ...
+ (fitness.sel(1:noiseReevals) + fitness.sel(lambda+(1:noiseReevals))) / 2;
+ end
+ fitness.raw = fitness.raw(1:lambda);
+ fitness.sel = fitness.sel(1:lambda);
+ end
+
+ % Sort by fitness
+ [fitness.raw, fitness.idx] = sort(fitness.raw);
+ [fitness.sel, fitness.idxsel] = sort(fitness.sel); % minimization
+ fitness.hist(2:end) = fitness.hist(1:end-1); % record short history of
+ fitness.hist(1) = fitness.raw(1); % best fitness values
+ if length(fitness.histbest) < 120+ceil(30*N/lambda) || ...
+ (mod(countiter, 5) == 0 && length(fitness.histbest) < 2e4) % 20 percent of 1e5 gen.
+ fitness.histbest = [fitness.raw(1) fitness.histbest]; % best fitness values
+ fitness.histmedian = [median(fitness.raw) fitness.histmedian]; % median fitness values
+ else
+ fitness.histbest(2:end) = fitness.histbest(1:end-1);
+ fitness.histmedian(2:end) = fitness.histmedian(1:end-1);
+ fitness.histbest(1) = fitness.raw(1); % best fitness values
+ fitness.histmedian(1) = median(fitness.raw); % median fitness values
+ end
+ fitness.histsel(2:end) = fitness.histsel(1:end-1); % record short history of
+ fitness.histsel(1) = fitness.sel(1); % best sel fitness values
+
+ % Calculate new xmean, this is selection and recombination
+ xold = xmean; % for speed up of Eq. (2) and (3)
+ xmean = arx(:,fitness.idxsel(1:mu))*weights;
+ zmean = arz(:,fitness.idxsel(1:mu))*weights;%==D^-1*B'*(xmean-xold)/sigma
+ if mu == 1
+ fmean = fitness.sel(1);
+ else
+ fmean = NaN; % [] does not work in the latter assignment
+ % fmean = feval(fitfun, xintobounds(xmean, lbounds, ubounds), varargin{:});
+ % counteval = counteval + 1;
+ end
+
+ % Cumulation: update evolution paths
+ ps = (1-cs)*ps + sqrt(cs*(2-cs)*mueff) * (B*zmean); % Eq. (4)
+ hsig = norm(ps)/sqrt(1-(1-cs)^(2*countiter))/chiN < 1.4 + 2/(N+1);
+ if flg_future_setting
+ hsig = sum(ps.^2) / (1-(1-cs)^(2*countiter)) / N < 2 + 4/(N+1); % just simplified
+ end
+ % hsig = norm(ps)/sqrt(1-(1-cs)^(2*countiter))/chiN < 1.4 + 2/(N+1);
+ % hsig = norm(ps)/sqrt(1-(1-cs)^(2*countiter))/chiN < 1.5 + 1/(N-0.5);
+ % hsig = norm(ps) < 1.5 * sqrt(N);
+ % hsig = 1;
+
+ pc = (1-cc)*pc ...
+ + hsig*(sqrt(cc*(2-cc)*mueff)/sigma) * (xmean-xold); % Eq. (2)
+ if hsig == 0
+ % disp([num2str(countiter) ' ' num2str(counteval) ' pc update stalled']);
+ end
+
+ % Adapt covariance matrix
+ neg.ccov = 0; % TODO: move parameter setting upwards at some point
+ if ccov1 + ccovmu > 0 % Eq. (3)
+ if flgDiagonalOnly % internal linear(?) complexity
+ diagC = (1-ccov1_sep-ccovmu_sep+(1-hsig)*ccov1_sep*cc*(2-cc)) * diagC ... % regard old matrix
+ + ccov1_sep * pc.^2 ... % plus rank one update
+ + ccovmu_sep ... % plus rank mu update
+ * (diagC .* (arz(:,fitness.idxsel(1:mu)).^2 * weights));
+ % * (repmat(diagC,1,mu) .* arz(:,fitness.idxsel(1:mu)).^2 * weights);
+ diagD = sqrt(diagC); % replaces eig(C)
+ else
+ arpos = (arx(:,fitness.idxsel(1:mu))-repmat(xold,1,mu)) / sigma;
+ % "active" CMA update: negative update, in case controlling pos. definiteness
+ if flgActiveCMA > 0
+ % set parameters
+ neg.mu = mu;
+ neg.mueff = mueff;
+ if flgActiveCMA > 10 % flat weights with mu=lambda/2
+ neg.mu = floor(lambda/2);
+ neg.mueff = neg.mu;
+ end
+ % neg.mu = ceil(min([N, lambda/4, mueff])); neg.mueff = mu; % i.e. neg.mu <= N
+ % Parameter study: in 3-D lambda=50,100, 10-D lambda=200,400, 30-D lambda=1000,2000 a
+ % three times larger neg.ccov does not work.
+ % increasing all ccov rates three times does work (probably because of the factor (1-ccovmu))
+ % in 30-D to looks fine
+
+ neg.ccov = (1 - ccovmu) * 0.25 * neg.mueff / ((N+2)^1.5 + 2*neg.mueff);
+ neg.minresidualvariance = 0.66; % keep at least 0.66 in all directions, small popsize are most critical
+ neg.alphaold = 0.5; % where to make up for the variance loss, 0.5 means no idea what to do
+ % 1 is slightly more robust and gives a better "guaranty" for pos. def.,
+ % but does it make sense from the learning perspective for large ccovmu?
+
+ neg.ccovfinal = neg.ccov;
+
+ % prepare vectors, compute negative updating matrix Cneg and checking matrix Ccheck
+ arzneg = arz(:,fitness.idxsel(lambda:-1:lambda - neg.mu + 1));
+ % i-th longest becomes i-th shortest
+ % TODO: this is not in compliance with the paper Hansen&Ros2010,
+ % where simply arnorms = arnorms(end:-1:1) ?
+ [arnorms, idxnorms] = sort(sqrt(sum(arzneg.^2, 1)));
+ [ignore, idxnorms] = sort(idxnorms); % inverse index
+ arnormfacs = arnorms(end:-1:1) ./ arnorms;
+ % arnormfacs = arnorms(randperm(neg.mu)) ./ arnorms;
+ arnorms = arnorms(end:-1:1); % for the record
+ if flgActiveCMA < 20
+ arzneg = arzneg .* repmat(arnormfacs(idxnorms), N, 1); % E x*x' is N
+ % arzneg = sqrt(N) * arzneg ./ repmat(sqrt(sum(arzneg.^2, 1)), N, 1); % E x*x' is N
+ end
+ if flgActiveCMA < 10 && neg.mu == mu % weighted sum
+ if mod(flgActiveCMA, 10) == 1 % TODO: prevent this with a less tight but more efficient check (see below)
+ Ccheck = arzneg * diag(weights) * arzneg'; % in order to check the largest EV
+ end
+ artmp = BD * arzneg;
+ Cneg = artmp * diag(weights) * artmp';
+ else % simple sum
+ if mod(flgActiveCMA, 10) == 1
+ Ccheck = (1/neg.mu) * arzneg*arzneg'; % in order to check largest EV
+ end
+ artmp = BD * arzneg;
+ Cneg = (1/neg.mu) * artmp*artmp';
+
+ end
+
+ % check pos.def. and set learning rate neg.ccov accordingly,
+ % this check makes the original choice of neg.ccov extremly failsafe
+ % still assuming C == BD*BD', which is only approxim. correct
+ if mod(flgActiveCMA, 10) == 1 && 1 - neg.ccov * arnorms(idxnorms).^2 * weights < neg.minresidualvariance
+ % TODO: the simple and cheap way would be to set
+ % fac = 1 - ccovmu - ccov1 OR 1 - mueff/lambda and
+ % neg.ccov = fac*(1 - neg.minresidualvariance) / (arnorms(idxnorms).^2 * weights)
+ % this is the more sophisticated way:
+ % maxeigenval = eigs(arzneg * arzneg', 1, 'lm', eigsopts); % not faster
+ maxeigenval = max(eig(Ccheck)); % norm is much slower, because (norm()==max(svd())
+ %disp([countiter log10([neg.ccov, maxeigenval, arnorms(idxnorms).^2 * weights, max(arnorms)^2]), ...
+ % neg.ccov * arnorms(idxnorms).^2 * weights])
+ % pause
+ % remove less than ??34*(1-cmu)%?? of variance in any direction
+ % 1-ccovmu is the variance left from the old C
+ neg.ccovfinal = min(neg.ccov, (1-ccovmu)*(1-neg.minresidualvariance)/maxeigenval);
+ % -ccov1 removed to avoid error message??
+ if neg.ccovfinal < neg.ccov
+ disp(['active CMA at iteration ' num2str(countiter) ...
+ ': max EV ==', num2str([maxeigenval, neg.ccov, neg.ccovfinal])]);
+ end
+ end
+ % xmean = xold; % the distribution does not degenerate!?
+ % update C
+ C = (1-ccov1-ccovmu+neg.alphaold*neg.ccovfinal+(1-hsig)*ccov1*cc*(2-cc)) * C ... % regard old matrix
+ + ccov1 * pc*pc' ... % plus rank one update
+ + (ccovmu + (1-neg.alphaold)*neg.ccovfinal) ... % plus rank mu update
+ * arpos * (repmat(weights,1,N) .* arpos') ...
+ - neg.ccovfinal * Cneg; % minus rank mu update
+ else % no active (negative) update
+ C = (1-ccov1-ccovmu+(1-hsig)*ccov1*cc*(2-cc)) * C ... % regard old matrix
+ + ccov1 * pc*pc' ... % plus rank one update
+ + ccovmu ... % plus rank mu update
+ * arpos * (repmat(weights,1,N) .* arpos');
+ % is now O(mu*N^2 + mu*N), was O(mu*N^2 + mu^2*N) when using diag(weights)
+ % for mu=30*N it is now 10 times faster, overall 3 times faster
+ end
+ diagC = diag(C);
+ end
+ end
+
+ % the following is de-preciated and will be removed in future
+ % better setting for cc makes this hack obsolete
+ if 11 < 2 && ~flgscience
+ % remove momentum in ps, if ps is large and fitness is getting worse.
+ % this should rarely happen.
+ % this might very well be counterproductive in dynamic environments
+ if sum(ps.^2)/N > 1.5 + 10*(2/N)^.5 && ...
+ fitness.histsel(1) > max(fitness.histsel(2:3))
+ ps = ps * sqrt(N*(1+max(0,log(sum(ps.^2)/N))) / sum(ps.^2));
+ if flgdisplay
+ disp(['Momentum in ps removed at [niter neval]=' ...
+ num2str([countiter counteval]) ']']);
+ end
+ end
+ end
+
+ % Adapt sigma
+ if flg_future_setting % according to a suggestion from Dirk Arnold (2000)
+ % exp(1) is still not reasonably small enough
+ sigma = sigma * exp(min(1, (sum(ps.^2)/N - 1)/2 * cs/damps)); % Eq. (5)
+ else
+ % exp(1) is still not reasonably small enough
+ sigma = sigma * exp(min(1, (sqrt(sum(ps.^2))/chiN - 1) * cs/damps)); % Eq. (5)
+ end
+ % disp([countiter norm(ps)/chiN]);
+
+ if 11 < 3 % testing with optimal step-size
+ if countiter == 1
+ disp('*********** sigma set to const * ||x|| ******************');
+ end
+ sigma = 0.04 * mueff * sqrt(sum(xmean.^2)) / N; % 20D,lam=1000:25e3
+ sigma = 0.3 * mueff * sqrt(sum(xmean.^2)) / N; % 20D,lam=(40,1000):17e3
+ % 75e3 with def (1.5)
+ % 35e3 with damps=0.25
+ end
+ if 11 < 3
+ if countiter == 1
+ disp('*********** xmean set to const ******************');
+ end
+ xmean = ones(N,1);
+ end
+
+ % Update B and D from C
+
+ if ~flgDiagonalOnly && (ccov1+ccovmu+neg.ccov) > 0 && mod(countiter, 1/(ccov1+ccovmu+neg.ccov)/N/10) < 1
+ C=triu(C)+triu(C,1)'; % enforce symmetry to prevent complex numbers
+ [B,tmp] = eig(C); % eigen decomposition, B==normalized eigenvectors
+ % effort: approx. 15*N matrix-vector multiplications
+ diagD = diag(tmp);
+
+ if any(~isfinite(diagD))
+ clear idx; % prevents error under octave
+ save(['tmp' opts.SaveFilename]);
+ error(['function eig returned non-finited eigenvalues, cond(C)=' ...
+ num2str(cond(C)) ]);
+ end
+ if any(any(~isfinite(B)))
+ clear idx; % prevents error under octave
+ save(['tmp' opts.SaveFilename]);
+ error(['function eig returned non-finited eigenvectors, cond(C)=' ...
+ num2str(cond(C)) ]);
+ end
+
+ % limit condition of C to 1e14 + 1
+ if min(diagD) <= 0
+ if stopOnWarnings
+ stopflag(end+1) = {'warnconditioncov'};
+ else
+ warning(['Iteration ' num2str(countiter) ...
+ ': Eigenvalue (smaller) zero']);
+ diagD(diagD<0) = 0;
+ tmp = max(diagD)/1e14;
+ C = C + tmp*eye(N,N); diagD = diagD + tmp*ones(N,1);
+ end
+ end
+ if max(diagD) > 1e14*min(diagD)
+ if stopOnWarnings
+ stopflag(end+1) = {'warnconditioncov'};
+ else
+ warning(['Iteration ' num2str(countiter) ': condition of C ' ...
+ 'at upper limit' ]);
+ tmp = max(diagD)/1e14 - min(diagD);
+ C = C + tmp*eye(N,N); diagD = diagD + tmp*ones(N,1);
+ end
+ end
+
+ diagC = diag(C);
+ diagD = sqrt(diagD); % D contains standard deviations now
+ % diagD = diagD / prod(diagD)^(1/N); C = C / prod(diagD)^(2/N);
+ BD = B.*repmat(diagD',N,1); % O(n^2)
+ end % if mod
+
+ % Align/rescale order of magnitude of scales of sigma and C for nicer output
+ % not a very usual case
+ if 1 < 2 && sigma > 1e10*max(diagD)
+ fac = sigma / max(diagD);
+ sigma = sigma/fac;
+ pc = fac * pc;
+ diagD = fac * diagD;
+ if ~flgDiagonalOnly
+ C = fac^2 * C; % disp(fac);
+ BD = B.*repmat(diagD',N,1); % O(n^2), but repmat might be inefficient todo?
+ end
+ diagC = fac^2 * diagC;
+ end
+
+ if flgDiagonalOnly > 1 && countiter > flgDiagonalOnly
+ % full covariance matrix from now on
+ flgDiagonalOnly = 0;
+ B = eye(N,N);
+ BD = diag(diagD);
+ C = diag(diagC); % is better, because correlations are spurious anyway
+ end
+
+ if noiseHandling
+ if countiter == 1 % assign firstvarargin for noise treatment e.g. as #reevaluations
+ if ~isempty(varargin) && length(varargin{1}) == 1 && isnumeric(varargin{1})
+ if irun == 1
+ firstvarargin = varargin{1};
+ else
+ varargin{1} = firstvarargin; % reset varargin{1}
+ end
+ else
+ firstvarargin = 0;
+ end
+ end
+ if noiseSS < 0 && noiseMinMaxEvals(2) > noiseMinMaxEvals(1) && firstvarargin
+ varargin{1} = max(noiseMinMaxEvals(1), varargin{1} / noiseAlphaEvals^(1/4)); % still experimental
+ elseif noiseSS > 0
+ if ~isempty(noiseCallback) % to be removed?
+ res = feval(noiseCallback); % should also work without output argument!?
+ if ~isempty(res) && res > 1 % TODO: decide for interface of callback
+ % also a dynamic popsize could be done here
+ sigma = sigma * noiseAlpha;
+ end
+ else
+ if noiseMinMaxEvals(2) > noiseMinMaxEvals(1) && firstvarargin
+ varargin{1} = min(noiseMinMaxEvals(2), varargin{1} * noiseAlphaEvals);
+ end
+
+ sigma = sigma * noiseAlpha;
+ % lambda = ceil(0.1 * sqrt(lambda) + lambda);
+ % TODO: find smallest increase of lambda with log-linear
+ % convergence in iterations
+ end
+ % qqq experimental: take a mean to estimate the true optimum
+ noiseN = noiseN + 1;
+ if noiseN == 1
+ noiseX = xmean;
+ else
+ noiseX = noiseX + (3/noiseN) * (xmean - noiseX);
+ end
+ end
+ end
+
+ % ----- numerical error management -----
+ % Adjust maximal coordinate axis deviations
+ if any(sigma*sqrt(diagC) > maxdx)
+ sigma = min(maxdx ./ sqrt(diagC));
+ %warning(['Iteration ' num2str(countiter) ': coordinate axis std ' ...
+ % 'deviation at upper limit of ' num2str(maxdx)]);
+ % stopflag(end+1) = {'maxcoorddev'};
+ end
+ % Adjust minimal coordinate axis deviations
+ if any(sigma*sqrt(diagC) < mindx)
+ sigma = max(mindx ./ sqrt(diagC)) * exp(0.05+cs/damps);
+ %warning(['Iteration ' num2str(countiter) ': coordinate axis std ' ...
+ % 'deviation at lower limit of ' num2str(mindx)]);
+ % stopflag(end+1) = {'mincoorddev'};;
+ end
+ % Adjust too low coordinate axis deviations
+ if any(xmean == xmean + 0.2*sigma*sqrt(diagC))
+ if stopOnWarnings
+ stopflag(end+1) = {'warnnoeffectcoord'};
+ else
+ warning(['Iteration ' num2str(countiter) ': coordinate axis std ' ...
+ 'deviation too low' ]);
+ if flgDiagonalOnly
+ diagC = diagC + (ccov1_sep+ccovmu_sep) * (diagC .* ...
+ (xmean == xmean + 0.2*sigma*sqrt(diagC)));
+ else
+ C = C + (ccov1+ccovmu) * diag(diagC .* ...
+ (xmean == xmean + 0.2*sigma*sqrt(diagC)));
+ end
+ sigma = sigma * exp(0.05+cs/damps);
+ end
+ end
+ % Adjust step size in case of (numerical) precision problem
+ if flgDiagonalOnly
+ tmp = 0.1*sigma*diagD;
+ else
+ tmp = 0.1*sigma*BD(:,1+floor(mod(countiter,N)));
+ end
+ if all(xmean == xmean + tmp)
+ i = 1+floor(mod(countiter,N));
+ if stopOnWarnings
+ stopflag(end+1) = {'warnnoeffectaxis'};
+ else
+ warning(['Iteration ' num2str(countiter) ...
+ ': main axis standard deviation ' ...
+ num2str(sigma*diagD(i)) ' has no effect' ]);
+ sigma = sigma * exp(0.2+cs/damps);
+ end
+ end
+ % Adjust step size in case of equal function values (flat fitness)
+ % isequalfuncvalues = 0;
+ if fitness.sel(1) == fitness.sel(1+ceil(0.1+lambda/4))
+ % isequalfuncvalues = 1;
+ if stopOnEqualFunctionValues
+ arrEqualFunvals = [countiter arrEqualFunvals(1:end-1)];
+ % stop if this happens in more than 33%
+ if arrEqualFunvals(end) > countiter - 3 * length(arrEqualFunvals)
+ stopflag(end+1) = {'equalfunvals'};
+ end
+ else
+ if flgWarnOnEqualFunctionValues
+ warning(['Iteration ' num2str(countiter) ...
+ ': equal function values f=' num2str(fitness.sel(1)) ...
+ ' at maximal main axis sigma ' ...
+ num2str(sigma*max(diagD))]);
+ end
+ sigma = sigma * exp(0.2+cs/damps);
+ end
+ end
+ % Adjust step size in case of equal function values
+ if countiter > 2 && myrange([fitness.hist fitness.sel(1)]) == 0
+ if stopOnWarnings
+ stopflag(end+1) = {'warnequalfunvalhist'};
+ else
+ warning(['Iteration ' num2str(countiter) ...
+ ': equal function values in history at maximal main ' ...
+ 'axis sigma ' num2str(sigma*max(diagD))]);
+ sigma = sigma * exp(0.2+cs/damps);
+ end
+ end
+
+ % ----- end numerical error management -----
+
+ % Keep overall best solution
+ out.evals = counteval;
+ out.solutions.evals = counteval;
+ out.solutions.mean.x = xmean;
+ out.solutions.mean.f = fmean;
+ out.solutions.mean.evals = counteval;
+ out.solutions.recentbest.x = arxvalid(:, fitness.idx(1));
+ out.solutions.recentbest.f = fitness.raw(1);
+ out.solutions.recentbest.evals = counteval + fitness.idx(1) - lambda;
+ out.solutions.recentworst.x = arxvalid(:, fitness.idx(end));
+ out.solutions.recentworst.f = fitness.raw(end);
+ out.solutions.recentworst.evals = counteval + fitness.idx(end) - lambda;
+ if fitness.hist(1) < out.solutions.bestever.f
+ out.solutions.bestever.x = arxvalid(:, fitness.idx(1));
+ out.solutions.bestever.f = fitness.hist(1);
+ out.solutions.bestever.evals = counteval + fitness.idx(1) - lambda;
+ bestever = out.solutions.bestever;
+ end
+
+ % Set stop flag
+ if fitness.raw(1) <= stopFitness, stopflag(end+1) = {'fitness'}; end
+ if counteval >= stopMaxFunEvals, stopflag(end+1) = {'maxfunevals'}; end
+ if countiter >= stopMaxIter, stopflag(end+1) = {'maxiter'}; end
+ if all(sigma*(max(abs(pc), sqrt(diagC))) < stopTolX)
+ stopflag(end+1) = {'tolx'};
+ end
+ if any(sigma*sqrt(diagC) > stopTolUpX)
+ stopflag(end+1) = {'tolupx'};
+ end
+ if sigma*max(diagD) == 0 % should never happen
+ stopflag(end+1) = {'bug'};
+ end
+ if countiter > 2 && myrange([fitness.sel fitness.hist]) <= stopTolFun
+ stopflag(end+1) = {'tolfun'};
+ end
+ if countiter >= length(fitness.hist) && myrange(fitness.hist) <= stopTolHistFun
+ stopflag(end+1) = {'tolhistfun'};
+ end
+ l = floor(length(fitness.histbest)/3);
+ if 1 < 2 && stopOnStagnation && ... % leads sometimes early stop on ftablet, fcigtab
+ countiter > N * (5+100/lambda) && ...
+ length(fitness.histbest) > 100 && ...
+ median(fitness.histmedian(1:l)) >= median(fitness.histmedian(end-l:end)) && ...
+ median(fitness.histbest(1:l)) >= median(fitness.histbest(end-l:end))
+ stopflag(end+1) = {'stagnation'};
+ end
+
+ if counteval >= stopFunEvals || countiter >= stopIter
+ stopflag(end+1) = {'stoptoresume'};
+ if length(stopflag) == 1 && flgsaving == 0
+ error('To resume later the saving option needs to be set');
+ end
+ end
+ % read stopping message from file signals.par
+ if flgreadsignals
+ fid = fopen('./signals.par', 'rt'); % can be performance critical
+ while fid > 0
+ strline = fgetl(fid); %fgets(fid, 300);
+ if strline < 0 % fgets and fgetl returns -1 at end of file
+ break
+ end
+ % 'stop filename' sets stopflag to manual
+ str = sscanf(strline, ' %s %s', 2);
+ if strcmp(str, ['stop' opts.LogFilenamePrefix])
+ stopflag(end+1) = {'manual'};
+ break
+ end
+ % 'skip filename run 3' skips a run, but not the last
+ str = sscanf(strline, ' %s %s %s', 3);
+ if strcmp(str, ['skip' opts.LogFilenamePrefix 'run'])
+ i = strfind(strline, 'run');
+ if irun == sscanf(strline(i+3:end), ' %d ', 1) && irun <= myeval(opts.Restarts)
+ stopflag(end+1) = {'skipped'};
+ end
+ end
+ end % while, break
+ if fid > 0
+ fclose(fid);
+ clear fid; % prevents strange error under octave
+ end
+ end
+
+ out.stopflag = stopflag;
+
+ % ----- output generation -----
+ if verbosemodulo > 0 && isfinite(verbosemodulo)
+ if countiter == 1 || mod(countiter, 10*verbosemodulo) < 1
+ disp(['Iterat, #Fevals: Function Value (median,worst) ' ...
+ '|Axis Ratio|' ...
+ 'idx:Min SD idx:Max SD']);
+ end
+ if mod(countiter, verbosemodulo) < 1 ...
+ || (verbosemodulo > 0 && isfinite(verbosemodulo) && ...
+ (countiter < 3 || ~isempty(stopflag)))
+ [minstd, minstdidx] = min(sigma*sqrt(diagC));
+ [maxstd, maxstdidx] = max(sigma*sqrt(diagC));
+ % format display nicely
+ disp([repmat(' ',1,4-floor(log10(countiter))) ...
+ num2str(countiter) ' , ' ...
+ repmat(' ',1,5-floor(log10(counteval))) ...
+ num2str(counteval) ' : ' ...
+ num2str(fitness.hist(1), '%.13e') ...
+ ' +(' num2str(median(fitness.raw)-fitness.hist(1), '%.0e ') ...
+ ',' num2str(max(fitness.raw)-fitness.hist(1), '%.0e ') ...
+ ') | ' ...
+ num2str(max(diagD)/min(diagD), '%4.2e') ' | ' ...
+ repmat(' ',1,1-floor(log10(minstdidx))) num2str(minstdidx) ':' ...
+ num2str(minstd, ' %.1e') ' ' ...
+ repmat(' ',1,1-floor(log10(maxstdidx))) num2str(maxstdidx) ':' ...
+ num2str(maxstd, ' %.1e')]);
+ end
+ end
+
+ % measure time for recording data
+ if countiter < 3
+ time.c = 0.05;
+ time.nonoutput = 0;
+ time.recording = 0;
+ time.saving = 0.15; % first saving after 3 seconds of 100 iterations
+ time.plotting = 0;
+ elseif countiter > 300
+ % set backward horizon, must be long enough to cover infrequent plotting etc
+ % time.c = min(1, time.nonoutput/3 + 1e-9);
+ time.c = max(1e-5, 0.1/sqrt(countiter)); % mean over all or 1e-5
+ end
+ % get average time per iteration
+ time.t1 = clock;
+ time.act = max(0,etime(time.t1, time.t0));
+ time.nonoutput = (1-time.c) * time.nonoutput ...
+ + time.c * time.act;
+
+ time.recording = (1-time.c) * time.recording; % per iteration
+ time.saving = (1-time.c) * time.saving;
+ time.plotting = (1-time.c) * time.plotting;
+
+ % record output data, concerning time issues
+ if savemodulo && savetime && (countiter < 1e2 || ~isempty(stopflag) || ...
+ countiter >= outiter + savemodulo)
+ outiter = countiter;
+ % Save output data to files
+ for namecell = filenames(:)'
+ name = namecell{:};
+
+ [fid, err] = fopen(['./' filenameprefix name '.dat'], 'a');
+ if fid < 1 % err ~= 0
+ warning(['could not open ' filenameprefix name '.dat']);
+ else
+ if strcmp(name, 'axlen')
+ fprintf(fid, '%d %d %e %e %e ', countiter, counteval, sigma, ...
+ max(diagD), min(diagD));
+ fprintf(fid, '%e ', sort(diagD));
+ fprintf(fid, '\n');
+ elseif strcmp(name, 'disp') % TODO
+ elseif strcmp(name, 'fit')
+ fprintf(fid, '%ld %ld %e %e %25.18e %25.18e %25.18e %25.18e', ...
+ countiter, counteval, sigma, max(diagD)/min(diagD), ...
+ out.solutions.bestever.f, ...
+ fitness.raw(1), median(fitness.raw), fitness.raw(end));
+ if ~isempty(varargin) && length(varargin{1}) == 1 && isnumeric(varargin{1}) && varargin{1} ~= 0
+ fprintf(fid, ' %f', varargin{1});
+ end
+ fprintf(fid, '\n');
+ elseif strcmp(name, 'stddev')
+ fprintf(fid, '%ld %ld %e 0 0 ', countiter, counteval, sigma);
+ fprintf(fid, '%e ', sigma*sqrt(diagC));
+ fprintf(fid, '\n');
+ elseif strcmp(name, 'xmean')
+ if isnan(fmean)
+ fprintf(fid, '%ld %ld 0 0 0 ', countiter, counteval);
+ else
+ fprintf(fid, '%ld %ld 0 0 %e ', countiter, counteval, fmean);
+ end
+ fprintf(fid, '%e ', xmean);
+ fprintf(fid, '\n');
+ elseif strcmp(name, 'xrecentbest')
+ % TODO: fitness is inconsistent with x-value
+ fprintf(fid, '%ld %ld %25.18e 0 0 ', countiter, counteval, fitness.raw(1));
+ fprintf(fid, '%e ', arx(:,fitness.idx(1)));
+ fprintf(fid, '\n');
+ end
+ fclose(fid);
+ end
+ end
+
+ % get average time for recording data
+ time.t2 = clock;
+ time.recording = time.recording + time.c * max(0,etime(time.t2, time.t1));
+
+ % plot
+ if flgplotting && countiter > 1
+ if countiter == 2
+ iterplotted = 0;
+ end
+ if ~isempty(stopflag) || ...
+ ((time.nonoutput+time.recording) * (countiter - iterplotted) > 1 && ...
+ time.plotting < 0.05 * (time.nonoutput+time.recording))
+ local_plotcmaesdat(324, filenameprefix);
+ iterplotted = countiter;
+ % outplot(out); % outplot defined below
+ if time.plotting == 0 % disregard opening of the window
+ time.plotting = time.nonoutput+time.recording;
+ else
+ time.plotting = time.plotting + time.c * max(0,etime(clock, time.t2));
+ end
+ end
+ end
+ if countiter > 100 + 20 && savemodulo && ...
+ time.recording * countiter > 0.1 && ... % absolute time larger 0.1 second
+ time.recording > savetime * (time.nonoutput+time.recording) / 100
+ savemodulo = floor(1.1 * savemodulo) + 1;
+ % disp('++savemodulo'); %qqq
+ end
+ end % if output
+
+ % save everything
+ time.t3 = clock;
+ if ~isempty(stopflag) || time.saving < 0.05 * time.nonoutput || countiter == 100
+ xmin = arxvalid(:, fitness.idx(1));
+ fmin = fitness.raw(1);
+ if flgsaving && countiter > 2
+ clear idx; % prevents error under octave
+ % -v6 : non-compressed non-unicode for version 6 and earlier
+ if ~isempty(strsaving) && ~isoctave
+ save('-mat', strsaving, opts.SaveFilename); % for inspection and possible restart
+ else
+ save('-mat', opts.SaveFilename); % for inspection and possible restart
+ end
+ time.saving = time.saving + time.c * max(0,etime(clock, time.t3));
+ end
+ end
+ time.t0 = clock;
+
+ % ----- end output generation -----
+
+ end % while, end generation loop
+
+ % -------------------- Final Procedures -------------------------------
+
+ % Evaluate xmean and return best recent point in xmin
+ fmin = fitness.raw(1);
+ xmin = arxvalid(:, fitness.idx(1)); % Return best point of last generation.
+ if length(stopflag) > sum(strcmp(stopflag, 'stoptoresume')) % final stopping
+ out.solutions.mean.f = ...
+ feval(fitfun, xintobounds(xmean, lbounds, ubounds), varargin{:});
+ counteval = counteval + 1;
+ out.solutions.mean.evals = counteval;
+ if out.solutions.mean.f < fitness.raw(1)
+ fmin = out.solutions.mean.f;
+ xmin = xintobounds(xmean, lbounds, ubounds); % Return xmean as best point
+ end
+ if out.solutions.mean.f < out.solutions.bestever.f
+ out.solutions.bestever = out.solutions.mean; % Return xmean as bestever point
+ out.solutions.bestever.x = xintobounds(xmean, lbounds, ubounds);
+ bestever = out.solutions.bestever;
+ end
end
- % check pos.def. and set learning rate neg.ccov accordingly,
- % this check makes the original choice of neg.ccov extremly failsafe
- % still assuming C == BD*BD', which is only approxim. correct
- if mod(flgActiveCMA, 10) == 1 && 1 - neg.ccov * arnorms(idxnorms).^2 * weights < neg.minresidualvariance
- % TODO: the simple and cheap way would be to set
- % fac = 1 - ccovmu - ccov1 OR 1 - mueff/lambda and
- % neg.ccov = fac*(1 - neg.minresidualvariance) / (arnorms(idxnorms).^2 * weights)
- % this is the more sophisticated way:
- % maxeigenval = eigs(arzneg * arzneg', 1, 'lm', eigsopts); % not faster
- maxeigenval = max(eig(Ccheck)); % norm is much slower, because (norm()==max(svd())
- %disp([countiter log10([neg.ccov, maxeigenval, arnorms(idxnorms).^2 * weights, max(arnorms)^2]), ...
- % neg.ccov * arnorms(idxnorms).^2 * weights])
- % pause
- % remove less than ??34*(1-cmu)%?? of variance in any direction
- % 1-ccovmu is the variance left from the old C
- neg.ccovfinal = min(neg.ccov, (1-ccovmu)*(1-neg.minresidualvariance)/maxeigenval);
- % -ccov1 removed to avoid error message??
- if neg.ccovfinal < neg.ccov
- disp(['active CMA at iteration ' num2str(countiter) ...
- ': max EV ==', num2str([maxeigenval, neg.ccov, neg.ccovfinal])]);
- end
+ % Save everything and display final message
+ if flgsavingfinal
+ clear idx; % prevents error under octave
+ if ~isempty(strsaving) && ~isoctave
+ save('-mat', strsaving, opts.SaveFilename); % for inspection and possible restart
+ else
+ save('-mat', opts.SaveFilename); % for inspection and possible restart
+ end
+ message = [' (saved to ' opts.SaveFilename ')'];
+ else
+ message = [];
end
- % xmean = xold; % the distribution does not degenerate!?
- % update C
- C = (1-ccov1-ccovmu+neg.alphaold*neg.ccovfinal+(1-hsig)*ccov1*cc*(2-cc)) * C ... % regard old matrix
- + ccov1 * pc*pc' ... % plus rank one update
- + (ccovmu + (1-neg.alphaold)*neg.ccovfinal) ... % plus rank mu update
- * arpos * (repmat(weights,1,N) .* arpos') ...
- - neg.ccovfinal * Cneg; % minus rank mu update
- else % no active (negative) update
- C = (1-ccov1-ccovmu+(1-hsig)*ccov1*cc*(2-cc)) * C ... % regard old matrix
- + ccov1 * pc*pc' ... % plus rank one update
- + ccovmu ... % plus rank mu update
- * arpos * (repmat(weights,1,N) .* arpos');
- % is now O(mu*N^2 + mu*N), was O(mu*N^2 + mu^2*N) when using diag(weights)
- % for mu=30*N it is now 10 times faster, overall 3 times faster
- end
- diagC = diag(C);
- end
- end
-
- % the following is de-preciated and will be removed in future
- % better setting for cc makes this hack obsolete
- if 11 < 2 && ~flgscience
- % remove momentum in ps, if ps is large and fitness is getting worse.
- % this should rarely happen.
- % this might very well be counterproductive in dynamic environments
- if sum(ps.^2)/N > 1.5 + 10*(2/N)^.5 && ...
- fitness.histsel(1) > max(fitness.histsel(2:3))
- ps = ps * sqrt(N*(1+max(0,log(sum(ps.^2)/N))) / sum(ps.^2));
- if flgdisplay
- disp(['Momentum in ps removed at [niter neval]=' ...
- num2str([countiter counteval]) ']']);
- end
- end
- end
-
- % Adapt sigma
- if flg_future_setting % according to a suggestion from Dirk Arnold (2000)
- % exp(1) is still not reasonably small enough
- sigma = sigma * exp(min(1, (sum(ps.^2)/N - 1)/2 * cs/damps)); % Eq. (5)
- else
- % exp(1) is still not reasonably small enough
- sigma = sigma * exp(min(1, (sqrt(sum(ps.^2))/chiN - 1) * cs/damps)); % Eq. (5)
- end
- % disp([countiter norm(ps)/chiN]);
-
- if 11 < 3 % testing with optimal step-size
- if countiter == 1
- disp('*********** sigma set to const * ||x|| ******************');
- end
- sigma = 0.04 * mueff * sqrt(sum(xmean.^2)) / N; % 20D,lam=1000:25e3
- sigma = 0.3 * mueff * sqrt(sum(xmean.^2)) / N; % 20D,lam=(40,1000):17e3
- % 75e3 with def (1.5)
- % 35e3 with damps=0.25
- end
- if 11 < 3
- if countiter == 1
- disp('*********** xmean set to const ******************');
- end
- xmean = ones(N,1);
- end
-
- % Update B and D from C
-
- if ~flgDiagonalOnly && (ccov1+ccovmu+neg.ccov) > 0 && mod(countiter, 1/(ccov1+ccovmu+neg.ccov)/N/10) < 1
- C=triu(C)+triu(C,1)'; % enforce symmetry to prevent complex numbers
- [B,tmp] = eig(C); % eigen decomposition, B==normalized eigenvectors
- % effort: approx. 15*N matrix-vector multiplications
- diagD = diag(tmp);
-
- if any(~isfinite(diagD))
- clear idx; % prevents error under octave
- save(['tmp' opts.SaveFilename]);
- error(['function eig returned non-finited eigenvalues, cond(C)=' ...
- num2str(cond(C)) ]);
- end
- if any(any(~isfinite(B)))
- clear idx; % prevents error under octave
- save(['tmp' opts.SaveFilename]);
- error(['function eig returned non-finited eigenvectors, cond(C)=' ...
- num2str(cond(C)) ]);
- end
- % limit condition of C to 1e14 + 1
- if min(diagD) <= 0
- if stopOnWarnings
- stopflag(end+1) = {'warnconditioncov'};
- else
- warning(['Iteration ' num2str(countiter) ...
- ': Eigenvalue (smaller) zero']);
- diagD(diagD<0) = 0;
- tmp = max(diagD)/1e14;
- C = C + tmp*eye(N,N); diagD = diagD + tmp*ones(N,1);
- end
- end
- if max(diagD) > 1e14*min(diagD)
- if stopOnWarnings
- stopflag(end+1) = {'warnconditioncov'};
- else
- warning(['Iteration ' num2str(countiter) ': condition of C ' ...
- 'at upper limit' ]);
- tmp = max(diagD)/1e14 - min(diagD);
- C = C + tmp*eye(N,N); diagD = diagD + tmp*ones(N,1);
- end
- end
+ if flgdisplay
+ disp(['#Fevals: f(returned x) | bestever.f | stopflag' ...
+ message]);
+ if isoctave
+ strstop = stopflag(:);
+ else
+ strcat(stopflag(:), '.');
+ end
+ strstop = stopflag(:); %strcat(stopflag(:), '.');
+ disp([repmat(' ',1,6-floor(log10(counteval))) ...
+ num2str(counteval, '%6.0f') ': ' num2str(fmin, '%.11e') ' | ' ...
+ num2str(out.solutions.bestever.f, '%.11e') ' | ' ...
+ strstop{1:end}]);
+ if N < 102
+ disp(['mean solution:' sprintf(' %+.1e', xmean)]);
+ disp(['std deviation:' sprintf(' %.1e', sigma*sqrt(diagC))]);
+ disp(sprintf('use plotcmaesdat.m for plotting the output at any time (option LogModulo must not be zero)'));
+ end
+ if exist('sfile', 'var')
+ disp(['Results saved in ' sfile]);
+ end
+ end
- diagC = diag(C);
- diagD = sqrt(diagD); % D contains standard deviations now
- % diagD = diagD / prod(diagD)^(1/N); C = C / prod(diagD)^(2/N);
- BD = B.*repmat(diagD',N,1); % O(n^2)
- end % if mod
-
- % Align/rescale order of magnitude of scales of sigma and C for nicer output
- % not a very usual case
- if 1 < 2 && sigma > 1e10*max(diagD)
- fac = sigma / max(diagD);
- sigma = sigma/fac;
- pc = fac * pc;
- diagD = fac * diagD;
- if ~flgDiagonalOnly
- C = fac^2 * C; % disp(fac);
- BD = B.*repmat(diagD',N,1); % O(n^2), but repmat might be inefficient todo?
- end
- diagC = fac^2 * diagC;
- end
-
- if flgDiagonalOnly > 1 && countiter > flgDiagonalOnly
- % full covariance matrix from now on
- flgDiagonalOnly = 0;
- B = eye(N,N);
- BD = diag(diagD);
- C = diag(diagC); % is better, because correlations are spurious anyway
- end
-
- if noiseHandling
- if countiter == 1 % assign firstvarargin for noise treatment e.g. as #reevaluations
- if ~isempty(varargin) && length(varargin{1}) == 1 && isnumeric(varargin{1})
- if irun == 1
- firstvarargin = varargin{1};
- else
- varargin{1} = firstvarargin; % reset varargin{1}
- end
- else
- firstvarargin = 0;
- end
- end
- if noiseSS < 0 && noiseMinMaxEvals(2) > noiseMinMaxEvals(1) && firstvarargin
- varargin{1} = max(noiseMinMaxEvals(1), varargin{1} / noiseAlphaEvals^(1/4)); % still experimental
- elseif noiseSS > 0
- if ~isempty(noiseCallback) % to be removed?
- res = feval(noiseCallback); % should also work without output argument!?
- if ~isempty(res) && res > 1 % TODO: decide for interface of callback
- % also a dynamic popsize could be done here
- sigma = sigma * noiseAlpha;
- end
- else
- if noiseMinMaxEvals(2) > noiseMinMaxEvals(1) && firstvarargin
- varargin{1} = min(noiseMinMaxEvals(2), varargin{1} * noiseAlphaEvals);
- end
-
- sigma = sigma * noiseAlpha;
- % lambda = ceil(0.1 * sqrt(lambda) + lambda);
- % TODO: find smallest increase of lambda with log-linear
- % convergence in iterations
- end
- % qqq experimental: take a mean to estimate the true optimum
- noiseN = noiseN + 1;
- if noiseN == 1
- noiseX = xmean;
- else
- noiseX = noiseX + (3/noiseN) * (xmean - noiseX);
- end
- end
- end
-
- % ----- numerical error management -----
- % Adjust maximal coordinate axis deviations
- if any(sigma*sqrt(diagC) > maxdx)
- sigma = min(maxdx ./ sqrt(diagC));
- %warning(['Iteration ' num2str(countiter) ': coordinate axis std ' ...
- % 'deviation at upper limit of ' num2str(maxdx)]);
- % stopflag(end+1) = {'maxcoorddev'};
- end
- % Adjust minimal coordinate axis deviations
- if any(sigma*sqrt(diagC) < mindx)
- sigma = max(mindx ./ sqrt(diagC)) * exp(0.05+cs/damps);
- %warning(['Iteration ' num2str(countiter) ': coordinate axis std ' ...
- % 'deviation at lower limit of ' num2str(mindx)]);
- % stopflag(end+1) = {'mincoorddev'};;
- end
- % Adjust too low coordinate axis deviations
- if any(xmean == xmean + 0.2*sigma*sqrt(diagC))
- if stopOnWarnings
- stopflag(end+1) = {'warnnoeffectcoord'};
- else
- warning(['Iteration ' num2str(countiter) ': coordinate axis std ' ...
- 'deviation too low' ]);
- if flgDiagonalOnly
- diagC = diagC + (ccov1_sep+ccovmu_sep) * (diagC .* ...
- (xmean == xmean + 0.2*sigma*sqrt(diagC)));
- else
- C = C + (ccov1+ccovmu) * diag(diagC .* ...
- (xmean == xmean + 0.2*sigma*sqrt(diagC)));
- end
- sigma = sigma * exp(0.05+cs/damps);
- end
- end
- % Adjust step size in case of (numerical) precision problem
- if flgDiagonalOnly
- tmp = 0.1*sigma*diagD;
- else
- tmp = 0.1*sigma*BD(:,1+floor(mod(countiter,N)));
- end
- if all(xmean == xmean + tmp)
- i = 1+floor(mod(countiter,N));
- if stopOnWarnings
- stopflag(end+1) = {'warnnoeffectaxis'};
- else
- warning(['Iteration ' num2str(countiter) ...
- ': main axis standard deviation ' ...
- num2str(sigma*diagD(i)) ' has no effect' ]);
- sigma = sigma * exp(0.2+cs/damps);
- end
- end
- % Adjust step size in case of equal function values (flat fitness)
- % isequalfuncvalues = 0;
- if fitness.sel(1) == fitness.sel(1+ceil(0.1+lambda/4))
- % isequalfuncvalues = 1;
- if stopOnEqualFunctionValues
- arrEqualFunvals = [countiter arrEqualFunvals(1:end-1)];
- % stop if this happens in more than 33%
- if arrEqualFunvals(end) > countiter - 3 * length(arrEqualFunvals)
- stopflag(end+1) = {'equalfunvals'};
- end
- else
- if flgWarnOnEqualFunctionValues
- warning(['Iteration ' num2str(countiter) ...
- ': equal function values f=' num2str(fitness.sel(1)) ...
- ' at maximal main axis sigma ' ...
- num2str(sigma*max(diagD))]);
- end
- sigma = sigma * exp(0.2+cs/damps);
- end
- end
- % Adjust step size in case of equal function values
- if countiter > 2 && myrange([fitness.hist fitness.sel(1)]) == 0
- if stopOnWarnings
- stopflag(end+1) = {'warnequalfunvalhist'};
- else
- warning(['Iteration ' num2str(countiter) ...
- ': equal function values in history at maximal main ' ...
- 'axis sigma ' num2str(sigma*max(diagD))]);
- sigma = sigma * exp(0.2+cs/damps);
- end
- end
-
- % ----- end numerical error management -----
-
- % Keep overall best solution
- out.evals = counteval;
- out.solutions.evals = counteval;
- out.solutions.mean.x = xmean;
- out.solutions.mean.f = fmean;
- out.solutions.mean.evals = counteval;
- out.solutions.recentbest.x = arxvalid(:, fitness.idx(1));
- out.solutions.recentbest.f = fitness.raw(1);
- out.solutions.recentbest.evals = counteval + fitness.idx(1) - lambda;
- out.solutions.recentworst.x = arxvalid(:, fitness.idx(end));
- out.solutions.recentworst.f = fitness.raw(end);
- out.solutions.recentworst.evals = counteval + fitness.idx(end) - lambda;
- if fitness.hist(1) < out.solutions.bestever.f
- out.solutions.bestever.x = arxvalid(:, fitness.idx(1));
- out.solutions.bestever.f = fitness.hist(1);
- out.solutions.bestever.evals = counteval + fitness.idx(1) - lambda;
- bestever = out.solutions.bestever;
- end
-
- % Set stop flag
- if fitness.raw(1) <= stopFitness, stopflag(end+1) = {'fitness'}; end
- if counteval >= stopMaxFunEvals, stopflag(end+1) = {'maxfunevals'}; end
- if countiter >= stopMaxIter, stopflag(end+1) = {'maxiter'}; end
- if all(sigma*(max(abs(pc), sqrt(diagC))) < stopTolX)
- stopflag(end+1) = {'tolx'};
- end
- if any(sigma*sqrt(diagC) > stopTolUpX)
- stopflag(end+1) = {'tolupx'};
- end
- if sigma*max(diagD) == 0 % should never happen
- stopflag(end+1) = {'bug'};
- end
- if countiter > 2 && myrange([fitness.sel fitness.hist]) <= stopTolFun
- stopflag(end+1) = {'tolfun'};
- end
- if countiter >= length(fitness.hist) && myrange(fitness.hist) <= stopTolHistFun
- stopflag(end+1) = {'tolhistfun'};
- end
- l = floor(length(fitness.histbest)/3);
- if 1 < 2 && stopOnStagnation && ... % leads sometimes early stop on ftablet, fcigtab
- countiter > N * (5+100/lambda) && ...
- length(fitness.histbest) > 100 && ...
- median(fitness.histmedian(1:l)) >= median(fitness.histmedian(end-l:end)) && ...
- median(fitness.histbest(1:l)) >= median(fitness.histbest(end-l:end))
- stopflag(end+1) = {'stagnation'};
- end
-
- if counteval >= stopFunEvals || countiter >= stopIter
- stopflag(end+1) = {'stoptoresume'};
- if length(stopflag) == 1 && flgsaving == 0
- error('To resume later the saving option needs to be set');
- end
- end
- % read stopping message from file signals.par
- if flgreadsignals
- fid = fopen('./signals.par', 'rt'); % can be performance critical
- while fid > 0
- strline = fgetl(fid); %fgets(fid, 300);
- if strline < 0 % fgets and fgetl returns -1 at end of file
- break
- end
- % 'stop filename' sets stopflag to manual
- str = sscanf(strline, ' %s %s', 2);
- if strcmp(str, ['stop' opts.LogFilenamePrefix])
- stopflag(end+1) = {'manual'};
- break
- end
- % 'skip filename run 3' skips a run, but not the last
- str = sscanf(strline, ' %s %s %s', 3);
- if strcmp(str, ['skip' opts.LogFilenamePrefix 'run'])
- i = strfind(strline, 'run');
- if irun == sscanf(strline(i+3:end), ' %d ', 1) && irun <= myeval(opts.Restarts)
- stopflag(end+1) = {'skipped'};
- end
- end
- end % while, break
- if fid > 0
- fclose(fid);
- clear fid; % prevents strange error under octave
- end
- end
-
- out.stopflag = stopflag;
-
- % ----- output generation -----
- if verbosemodulo > 0 && isfinite(verbosemodulo)
- if countiter == 1 || mod(countiter, 10*verbosemodulo) < 1
- disp(['Iterat, #Fevals: Function Value (median,worst) ' ...
- '|Axis Ratio|' ...
- 'idx:Min SD idx:Max SD']);
- end
- if mod(countiter, verbosemodulo) < 1 ...
- || (verbosemodulo > 0 && isfinite(verbosemodulo) && ...
- (countiter < 3 || ~isempty(stopflag)))
- [minstd, minstdidx] = min(sigma*sqrt(diagC));
- [maxstd, maxstdidx] = max(sigma*sqrt(diagC));
- % format display nicely
- disp([repmat(' ',1,4-floor(log10(countiter))) ...
- num2str(countiter) ' , ' ...
- repmat(' ',1,5-floor(log10(counteval))) ...
- num2str(counteval) ' : ' ...
- num2str(fitness.hist(1), '%.13e') ...
- ' +(' num2str(median(fitness.raw)-fitness.hist(1), '%.0e ') ...
- ',' num2str(max(fitness.raw)-fitness.hist(1), '%.0e ') ...
- ') | ' ...
- num2str(max(diagD)/min(diagD), '%4.2e') ' | ' ...
- repmat(' ',1,1-floor(log10(minstdidx))) num2str(minstdidx) ':' ...
- num2str(minstd, ' %.1e') ' ' ...
- repmat(' ',1,1-floor(log10(maxstdidx))) num2str(maxstdidx) ':' ...
- num2str(maxstd, ' %.1e')]);
- end
- end
-
- % measure time for recording data
- if countiter < 3
- time.c = 0.05;
- time.nonoutput = 0;
- time.recording = 0;
- time.saving = 0.15; % first saving after 3 seconds of 100 iterations
- time.plotting = 0;
- elseif countiter > 300
- % set backward horizon, must be long enough to cover infrequent plotting etc
- % time.c = min(1, time.nonoutput/3 + 1e-9);
- time.c = max(1e-5, 0.1/sqrt(countiter)); % mean over all or 1e-5
- end
- % get average time per iteration
- time.t1 = clock;
- time.act = max(0,etime(time.t1, time.t0));
- time.nonoutput = (1-time.c) * time.nonoutput ...
- + time.c * time.act;
-
- time.recording = (1-time.c) * time.recording; % per iteration
- time.saving = (1-time.c) * time.saving;
- time.plotting = (1-time.c) * time.plotting;
-
- % record output data, concerning time issues
- if savemodulo && savetime && (countiter < 1e2 || ~isempty(stopflag) || ...
- countiter >= outiter + savemodulo)
- outiter = countiter;
- % Save output data to files
- for namecell = filenames(:)'
- name = namecell{:};
-
- [fid, err] = fopen(['./' filenameprefix name '.dat'], 'a');
- if fid < 1 % err ~= 0
- warning(['could not open ' filenameprefix name '.dat']);
- else
- if strcmp(name, 'axlen')
- fprintf(fid, '%d %d %e %e %e ', countiter, counteval, sigma, ...
- max(diagD), min(diagD));
- fprintf(fid, '%e ', sort(diagD));
- fprintf(fid, '\n');
- elseif strcmp(name, 'disp') % TODO
- elseif strcmp(name, 'fit')
- fprintf(fid, '%ld %ld %e %e %25.18e %25.18e %25.18e %25.18e', ...
- countiter, counteval, sigma, max(diagD)/min(diagD), ...
- out.solutions.bestever.f, ...
- fitness.raw(1), median(fitness.raw), fitness.raw(end));
- if ~isempty(varargin) && length(varargin{1}) == 1 && isnumeric(varargin{1}) && varargin{1} ~= 0
- fprintf(fid, ' %f', varargin{1});
- end
- fprintf(fid, '\n');
- elseif strcmp(name, 'stddev')
- fprintf(fid, '%ld %ld %e 0 0 ', countiter, counteval, sigma);
- fprintf(fid, '%e ', sigma*sqrt(diagC));
- fprintf(fid, '\n');
- elseif strcmp(name, 'xmean')
- if isnan(fmean)
- fprintf(fid, '%ld %ld 0 0 0 ', countiter, counteval);
- else
- fprintf(fid, '%ld %ld 0 0 %e ', countiter, counteval, fmean);
- end
- fprintf(fid, '%e ', xmean);
- fprintf(fid, '\n');
- elseif strcmp(name, 'xrecentbest')
- % TODO: fitness is inconsistent with x-value
- fprintf(fid, '%ld %ld %25.18e 0 0 ', countiter, counteval, fitness.raw(1));
- fprintf(fid, '%e ', arx(:,fitness.idx(1)));
- fprintf(fid, '\n');
- end
- fclose(fid);
- end
- end
-
- % get average time for recording data
- time.t2 = clock;
- time.recording = time.recording + time.c * max(0,etime(time.t2, time.t1));
-
- % plot
- if flgplotting && countiter > 1
- if countiter == 2
- iterplotted = 0;
- end
- if ~isempty(stopflag) || ...
- ((time.nonoutput+time.recording) * (countiter - iterplotted) > 1 && ...
- time.plotting < 0.05 * (time.nonoutput+time.recording))
- local_plotcmaesdat(324, filenameprefix);
- iterplotted = countiter;
- % outplot(out); % outplot defined below
- if time.plotting == 0 % disregard opening of the window
- time.plotting = time.nonoutput+time.recording;
- else
- time.plotting = time.plotting + time.c * max(0,etime(clock, time.t2));
+ out.arstopflags{irun} = stopflag;
+ if any(strcmp(stopflag, 'fitness')) ...
+ || any(strcmp(stopflag, 'maxfunevals')) ...
+ || any(strcmp(stopflag, 'stoptoresume')) ...
+ || any(strcmp(stopflag, 'manual'))
+ break
end
- end
- end
- if countiter > 100 + 20 && savemodulo && ...
- time.recording * countiter > 0.1 && ... % absolute time larger 0.1 second
- time.recording > savetime * (time.nonoutput+time.recording) / 100
- savemodulo = floor(1.1 * savemodulo) + 1;
- % disp('++savemodulo'); %qqq
- end
- end % if output
-
- % save everything
- time.t3 = clock;
- if ~isempty(stopflag) || time.saving < 0.05 * time.nonoutput || countiter == 100
- xmin = arxvalid(:, fitness.idx(1));
- fmin = fitness.raw(1);
- if flgsaving && countiter > 2
- clear idx; % prevents error under octave
- % -v6 : non-compressed non-unicode for version 6 and earlier
- if ~isempty(strsaving) && ~isoctave
- save('-mat', strsaving, opts.SaveFilename); % for inspection and possible restart
- else
- save('-mat', opts.SaveFilename); % for inspection and possible restart
- end
- time.saving = time.saving + time.c * max(0,etime(clock, time.t3));
- end
- end
- time.t0 = clock;
-
- % ----- end output generation -----
-
-end % while, end generation loop
-
-% -------------------- Final Procedures -------------------------------
-
-% Evaluate xmean and return best recent point in xmin
-fmin = fitness.raw(1);
-xmin = arxvalid(:, fitness.idx(1)); % Return best point of last generation.
-if length(stopflag) > sum(strcmp(stopflag, 'stoptoresume')) % final stopping
- out.solutions.mean.f = ...
- feval(fitfun, xintobounds(xmean, lbounds, ubounds), varargin{:});
- counteval = counteval + 1;
- out.solutions.mean.evals = counteval;
- if out.solutions.mean.f < fitness.raw(1)
- fmin = out.solutions.mean.f;
- xmin = xintobounds(xmean, lbounds, ubounds); % Return xmean as best point
- end
- if out.solutions.mean.f < out.solutions.bestever.f
- out.solutions.bestever = out.solutions.mean; % Return xmean as bestever point
- out.solutions.bestever.x = xintobounds(xmean, lbounds, ubounds);
- bestever = out.solutions.bestever;
- end
-end
-
-% Save everything and display final message
-if flgsavingfinal
- clear idx; % prevents error under octave
- if ~isempty(strsaving) && ~isoctave
- save('-mat', strsaving, opts.SaveFilename); % for inspection and possible restart
- else
- save('-mat', opts.SaveFilename); % for inspection and possible restart
- end
- message = [' (saved to ' opts.SaveFilename ')'];
-else
- message = [];
-end
-
-if flgdisplay
- disp(['#Fevals: f(returned x) | bestever.f | stopflag' ...
- message]);
- if isoctave
- strstop = stopflag(:);
- else
- strcat(stopflag(:), '.');
- end
- strstop = stopflag(:); %strcat(stopflag(:), '.');
- disp([repmat(' ',1,6-floor(log10(counteval))) ...
- num2str(counteval, '%6.0f') ': ' num2str(fmin, '%.11e') ' | ' ...
- num2str(out.solutions.bestever.f, '%.11e') ' | ' ...
- strstop{1:end}]);
- if N < 102
- disp(['mean solution:' sprintf(' %+.1e', xmean)]);
- disp(['std deviation:' sprintf(' %.1e', sigma*sqrt(diagC))]);
- disp(sprintf('use plotcmaesdat.m for plotting the output at any time (option LogModulo must not be zero)'));
- end
- if exist('sfile', 'var')
- disp(['Results saved in ' sfile]);
- end
-end
-
- out.arstopflags{irun} = stopflag;
- if any(strcmp(stopflag, 'fitness')) ...
- || any(strcmp(stopflag, 'maxfunevals')) ...
- || any(strcmp(stopflag, 'stoptoresume')) ...
- || any(strcmp(stopflag, 'manual'))
- break
- end
end % while irun <= Restarts
-% ---------------------------------------------------------------
-% ---------------------------------------------------------------
+% ---------------------------------------------------------------
+% ---------------------------------------------------------------
function [x, idx] = xintobounds(x, lbounds, ubounds)
%
% x can be a column vector or a matrix consisting of column vectors
%
- if ~isempty(lbounds)
+if ~isempty(lbounds)
if length(lbounds) == 1
- idx = x < lbounds;
- x(idx) = lbounds;
+ idx = x < lbounds;
+ x(idx) = lbounds;
else
- arbounds = repmat(lbounds, 1, size(x,2));
- idx = x < arbounds;
- x(idx) = arbounds(idx);
+ arbounds = repmat(lbounds, 1, size(x,2));
+ idx = x < arbounds;
+ x(idx) = arbounds(idx);
end
- else
+else
idx = 0;
- end
- if ~isempty(ubounds)
+end
+if ~isempty(ubounds)
if length(ubounds) == 1
- idx2 = x > ubounds;
- x(idx2) = ubounds;
+ idx2 = x > ubounds;
+ x(idx2) = ubounds;
else
- arbounds = repmat(ubounds, 1, size(x,2));
- idx2 = x > arbounds;
- x(idx2) = arbounds(idx2);
+ arbounds = repmat(ubounds, 1, size(x,2));
+ idx2 = x > arbounds;
+ x(idx2) = arbounds(idx2);
end
- else
+else
idx2 = 0;
- end
- idx = idx2-idx;
-
-% ---------------------------------------------------------------
-% ---------------------------------------------------------------
+end
+idx = idx2-idx;
+
+% ---------------------------------------------------------------
+% ---------------------------------------------------------------
function opts=getoptions(inopts, defopts)
% OPTS = GETOPTIONS(INOPTS, DEFOPTS) handles an arbitrary number of
% optional arguments to a function. The given arguments are collected
@@ -1812,7 +1812,7 @@ function opts=getoptions(inopts, defopts)
% defopts.ParentNumber = 50;
% defopts.MaxIterations = 1e6;
% defopts.MaxSigma = 1;
-%
+%
% % merge default options with input options
% opts = getoptions(inopts, defopts);
%
@@ -1823,148 +1823,148 @@ function opts=getoptions(inopts, defopts)
% % do whatever
% end
% end
-%
+%
% For calling the function myfunction with default options:
% myfunction(argument1, []);
% For calling the function myfunction with modified options:
% opt.pop = 100; % redefine PopulationSize option
% opt.PAR = 10; % redefine ParentNumber option
-% opt.maxiter = 2; % opt.max=2 is ambiguous and would result in an error
+% opt.maxiter = 2; % opt.max=2 is ambiguous and would result in an error
% myfunction(argument1, opt);
%
% 04/07/19: Entries can be structs itself leading to a recursive
-% call to getoptions.
+% call to getoptions.
%
if nargin < 2 || isempty(defopts) % no default options available
- opts=inopts;
- return
+ opts=inopts;
+ return
elseif isempty(inopts) % empty inopts invoke default options
- opts = defopts;
- return
-elseif ~isstruct(defopts) % handle a single option value
- if isempty(inopts)
opts = defopts;
- elseif ~isstruct(inopts)
- opts = inopts;
- else
- error('Input options are a struct, while default options are not');
- end
- return
+ return
+elseif ~isstruct(defopts) % handle a single option value
+ if isempty(inopts)
+ opts = defopts;
+ elseif ~isstruct(inopts)
+ opts = inopts;
+ else
+ error('Input options are a struct, while default options are not');
+ end
+ return
elseif ~isstruct(inopts) % no valid input options
- error('The options need to be a struct or empty');
+ error('The options need to be a struct or empty');
end
- opts = defopts; % start from defopts
- % if necessary overwrite opts fields by inopts values
- defnames = fieldnames(defopts);
- idxmatched = []; % indices of defopts that already matched
- for name = fieldnames(inopts)'
+opts = defopts; % start from defopts
+ % if necessary overwrite opts fields by inopts values
+defnames = fieldnames(defopts);
+idxmatched = []; % indices of defopts that already matched
+for name = fieldnames(inopts)'
name = name{1}; % name of i-th inopts-field
if isoctave
- for i = 1:size(defnames, 1)
- idx(i) = strncmp(lower(defnames(i)), lower(name), length(name));
- end
+ for i = 1:size(defnames, 1)
+ idx(i) = strncmp(lower(defnames(i)), lower(name), length(name));
+ end
else
- idx = strncmpi(defnames, name, length(name));
+ idx = strncmpi(defnames, name, length(name));
end
if sum(idx) > 1
- error(['option "' name '" is not an unambigous abbreviation. ' ...
- 'Use opts=RMFIELD(opts, ''' name, ...
- ''') to remove the field from the struct.']);
+ error(['option "' name '" is not an unambigous abbreviation. ' ...
+ 'Use opts=RMFIELD(opts, ''' name, ...
+ ''') to remove the field from the struct.']);
end
if sum(idx) == 1
- defname = defnames{find(idx)};
- if ismember(find(idx), idxmatched)
- error(['input options match more than ones with "' ...
- defname '". ' ...
- 'Use opts=RMFIELD(opts, ''' name, ...
- ''') to remove the field from the struct.']);
- end
- idxmatched = [idxmatched find(idx)];
- val = getfield(inopts, name);
- % next line can replace previous line from MATLAB version 6.5.0 on and in octave
- % val = inopts.(name);
- if isstruct(val) % valid syntax only from version 6.5.0
- opts = setfield(opts, defname, ...
- getoptions(val, getfield(defopts, defname)));
- elseif isstruct(getfield(defopts, defname))
- % next three lines can replace previous three lines from MATLAB
- % version 6.5.0 on
- % opts.(defname) = ...
- % getoptions(val, defopts.(defname));
- % elseif isstruct(defopts.(defname))
- warning(['option "' name '" disregarded (must be struct)']);
- elseif ~isempty(val) % empty value: do nothing, i.e. stick to default
- opts = setfield(opts, defnames{find(idx)}, val);
- % next line can replace previous line from MATLAB version 6.5.0 on
- % opts.(defname) = inopts.(name);
- end
+ defname = defnames{find(idx)};
+ if ismember(find(idx), idxmatched)
+ error(['input options match more than ones with "' ...
+ defname '". ' ...
+ 'Use opts=RMFIELD(opts, ''' name, ...
+ ''') to remove the field from the struct.']);
+ end
+ idxmatched = [idxmatched find(idx)];
+ val = getfield(inopts, name);
+ % next line can replace previous line from MATLAB version 6.5.0 on and in octave
+ % val = inopts.(name);
+ if isstruct(val) % valid syntax only from version 6.5.0
+ opts = setfield(opts, defname, ...
+ getoptions(val, getfield(defopts, defname)));
+ elseif isstruct(getfield(defopts, defname))
+ % next three lines can replace previous three lines from MATLAB
+ % version 6.5.0 on
+ % opts.(defname) = ...
+ % getoptions(val, defopts.(defname));
+ % elseif isstruct(defopts.(defname))
+ warning(['option "' name '" disregarded (must be struct)']);
+ elseif ~isempty(val) % empty value: do nothing, i.e. stick to default
+ opts = setfield(opts, defnames{find(idx)}, val);
+ % next line can replace previous line from MATLAB version 6.5.0 on
+ % opts.(defname) = inopts.(name);
+ end
else
- warning(['option "' name '" disregarded (unknown field name)']);
+ warning(['option "' name '" disregarded (unknown field name)']);
end
- end
+end
-% ---------------------------------------------------------------
-% ---------------------------------------------------------------
+% ---------------------------------------------------------------
+% ---------------------------------------------------------------
function res=myeval(s)
- if ischar(s)
+if ischar(s)
res = evalin('caller', s);
- else
+else
res = s;
- end
-
-% ---------------------------------------------------------------
-% ---------------------------------------------------------------
+end
+
+% ---------------------------------------------------------------
+% ---------------------------------------------------------------
function res=myevalbool(s)
- if ~ischar(s) % s may not and cannot be empty
+if ~ischar(s) % s may not and cannot be empty
res = s;
- else % evaluation string s
+else % evaluation string s
if strncmpi(lower(s), 'yes', 3) || strncmpi(s, 'on', 2) ...
- || strncmpi(s, 'true', 4) || strncmp(s, '1 ', 2)
- res = 1;
+ || strncmpi(s, 'true', 4) || strncmp(s, '1 ', 2)
+ res = 1;
elseif strncmpi(s, 'no', 2) || strncmpi(s, 'off', 3) ...
- || strncmpi(s, 'false', 5) || strncmp(s, '0 ', 2)
- res = 0;
+ || strncmpi(s, 'false', 5) || strncmp(s, '0 ', 2)
+ res = 0;
else
- try res = evalin('caller', s); catch
- error(['String value "' s '" cannot be evaluated']);
- end
- try res ~= 0; catch
- error(['String value "' s '" cannot be evaluated reasonably']);
- end
+ try res = evalin('caller', s); catch
+ error(['String value "' s '" cannot be evaluated']);
+ end
+ try res ~= 0; catch
+ error(['String value "' s '" cannot be evaluated reasonably']);
+ end
end
- end
-
+end
-% ---------------------------------------------------------------
-% ---------------------------------------------------------------
+
+% ---------------------------------------------------------------
+% ---------------------------------------------------------------
function res = isoctave
% any hack to find out whether we are running octave
- s = version;
- res = 0;
- if exist('fflush', 'builtin') && eval(s(1)) < 7
+s = version;
+res = 0;
+if exist('fflush', 'builtin') && eval(s(1)) < 7
res = 1;
- end
+end
-% ---------------------------------------------------------------
-% ---------------------------------------------------------------
+% ---------------------------------------------------------------
+% ---------------------------------------------------------------
function flush
- if isoctave
+if isoctave
feval('fflush', stdout);
- end
+end
-% ---------------------------------------------------------------
-% ---------------------------------------------------------------
+% ---------------------------------------------------------------
+% ---------------------------------------------------------------
% ----- replacements for statistic toolbox functions ------------
-% ---------------------------------------------------------------
-% ---------------------------------------------------------------
+% ---------------------------------------------------------------
+% ---------------------------------------------------------------
function res=myrange(x)
- res = max(x) - min(x);
-
-% ---------------------------------------------------------------
-% ---------------------------------------------------------------
+res = max(x) - min(x);
+
+% ---------------------------------------------------------------
+% ---------------------------------------------------------------
function res = myprctile(inar, perc, idx)
%
% Computes the percentiles in vector perc from vector inar
@@ -1975,105 +1975,105 @@ function res = myprctile(inar, perc, idx)
N = length(inar);
flgtranspose = 0;
-% sizes
+% sizes
if size(perc,1) > 1
- perc = perc';
- flgtranspose = 1;
- if size(perc,1) > 1
- error('perc must not be a matrix');
- end
+ perc = perc';
+ flgtranspose = 1;
+ if size(perc,1) > 1
+ error('perc must not be a matrix');
+ end
end
if size(inar, 1) > 1 && size(inar,2) > 1
- error('data inar must not be a matrix');
+ error('data inar must not be a matrix');
end
-
+
% sort inar
if nargin < 3 || isempty(idx)
- [sar, idx] = sort(inar);
+ [sar, idx] = sort(inar);
else
- sar = inar(idx);
+ sar = inar(idx);
end
res = [];
for p = perc
- if p <= 100*(0.5/N)
- res(end+1) = sar(1);
- elseif p >= 100*((N-0.5)/N)
- res(end+1) = sar(N);
- else
- % find largest index smaller than required percentile
- availablepercentiles = 100*((1:N)-0.5)/N;
- i = max(find(p > availablepercentiles));
- % interpolate linearly
- res(end+1) = sar(i) ...
- + (sar(i+1)-sar(i))*(p - availablepercentiles(i)) ...
- / (availablepercentiles(i+1) - availablepercentiles(i));
-
- end
+ if p <= 100*(0.5/N)
+ res(end+1) = sar(1);
+ elseif p >= 100*((N-0.5)/N)
+ res(end+1) = sar(N);
+ else
+ % find largest index smaller than required percentile
+ availablepercentiles = 100*((1:N)-0.5)/N;
+ i = max(find(p > availablepercentiles));
+ % interpolate linearly
+ res(end+1) = sar(i) ...
+ + (sar(i+1)-sar(i))*(p - availablepercentiles(i)) ...
+ / (availablepercentiles(i+1) - availablepercentiles(i));
+
+ end
end
if flgtranspose
- res = res';
+ res = res';
end
-% ---------------------------------------------------------------
-% ---------------------------------------------------------------
-% ---------------------------------------------------------------
-% ---------------------------------------------------------------
+% ---------------------------------------------------------------
+% ---------------------------------------------------------------
+% ---------------------------------------------------------------
+% ---------------------------------------------------------------
function [s, ranks, rankDelta] = local_noisemeasurement(arf1, arf2, lamreev, theta, cutlimit)
% function [s ranks rankDelta] = noisemeasurement(arf1, arf2, lamreev, theta)
%
-% Input:
-% arf1, arf2 : two arrays of function values. arf1 is of size 1xlambda,
-% arf2 may be of size 1xlamreev or 1xlambda. The first lamreev values
-% in arf2 are (re-)evaluations of the respective solutions, i.e.
+% Input:
+% arf1, arf2 : two arrays of function values. arf1 is of size 1xlambda,
+% arf2 may be of size 1xlamreev or 1xlambda. The first lamreev values
+% in arf2 are (re-)evaluations of the respective solutions, i.e.
% arf1(1) and arf2(1) are two evaluations of "the first" solution.
-% lamreev: number of reevaluated individuals in arf2
-% theta : parameter theta for the rank change limit, between 0 and 1,
-% typically between 0.2 and 0.7.
-% cutlimit (optional): output s is computed as a mean of rankchange minus
-% threshold, where rankchange is <=2*(lambda-1). cutlimit limits
-% abs(rankchange minus threshold) in this calculation to cutlimit.
-% cutlimit=1 evaluates basically the sign only. cutlimit=2 could be
-% the rank change with one solution (both evaluations of it).
-%
-% Output:
+% lamreev: number of reevaluated individuals in arf2
+% theta : parameter theta for the rank change limit, between 0 and 1,
+% typically between 0.2 and 0.7.
+% cutlimit (optional): output s is computed as a mean of rankchange minus
+% threshold, where rankchange is <=2*(lambda-1). cutlimit limits
+% abs(rankchange minus threshold) in this calculation to cutlimit.
+% cutlimit=1 evaluates basically the sign only. cutlimit=2 could be
+% the rank change with one solution (both evaluations of it).
+%
+% Output:
% s : noise measurement, s>0 means the noise measure is above the
% acceptance threshold
-% ranks : 2xlambda array, corresponding to [arf1; arf2], of ranks
+% ranks : 2xlambda array, corresponding to [arf1; arf2], of ranks
% of arf1 and arf2 in the set [arf1 arf2], values are in [1:2*lambda]
% rankDelta: 1xlambda array of rank movements of arf2 compared to
% arf1. rankDelta(i) agrees with the number of values from
% the set [arf1 arf2] that lie between arf1(i) and arf2(i).
%
% Note: equal function values might lead to somewhat spurious results.
-% For this case a revision is advisable.
+% For this case a revision is advisable.
%%% verify input argument sizes
if size(arf1,1) ~= 1
- error('arf1 must be an 1xlambda array');
+ error('arf1 must be an 1xlambda array');
elseif size(arf2,1) ~= 1
- error('arf2 must be an 1xsomething array');
+ error('arf2 must be an 1xsomething array');
elseif size(arf1,2) < size(arf2,2) % not really necessary, but saver
- error('arf2 must not be smaller than arf1 in length');
+ error('arf2 must not be smaller than arf1 in length');
end
lam = size(arf1, 2);
if size(arf1,2) ~= size(arf2,2)
- arf2(end+1:lam) = arf1((size(arf2,2)+1):lam);
+ arf2(end+1:lam) = arf1((size(arf2,2)+1):lam);
end
if nargin < 5
- cutlimit = inf;
+ cutlimit = inf;
end
%%% capture unusual values
if any(diff(arf1) == 0)
- % this will presumably interpreted as rank change, because
- % sort(ones(...)) returns 1,2,3,...
- warning([num2str(sum(diff(arf1)==0)) ' equal function values']);
+ % this will presumably interpreted as rank change, because
+ % sort(ones(...)) returns 1,2,3,...
+ warning([num2str(sum(diff(arf1)==0)) ' equal function values']);
end
%%% compute rank changes into rankDelta
@@ -2086,12 +2086,12 @@ rankDelta = ranks(1,:) - ranks(2,:) - sign(ranks(1,:) - ranks(2,:));
%%% compute rank change limits using both ranks(1,...) and ranks(2,...)
for i = 1:lamreev
- sumlim(i) = ...
- 0.5 * (...
- myprctile(abs((1:2*lam-1) - (ranks(1,i) - (ranks(1,i)>ranks(2,i)))), ...
- theta*50) ...
- + myprctile(abs((1:2*lam-1) - (ranks(2,i) - (ranks(2,i)>ranks(1,i)))), ...
- theta*50));
+ sumlim(i) = ...
+ 0.5 * (...
+ myprctile(abs((1:2*lam-1) - (ranks(1,i) - (ranks(1,i)>ranks(2,i)))), ...
+ theta*50) ...
+ + myprctile(abs((1:2*lam-1) - (ranks(2,i) - (ranks(2,i)>ranks(1,i)))), ...
+ theta*50));
end
%%% compute measurement
@@ -2101,235 +2101,235 @@ end
s = abs(rankDelta(1:lamreev)) - max(1, sumlim); % lives roughly in 0..2*lambda
% cut-off limit
-idx = abs(s) > cutlimit;
+idx = abs(s) > cutlimit;
s(idx) = sign(s(idx)) * cutlimit;
s = mean(s);
-% ---------------------------------------------------------------
-% ---------------------------------------------------------------
-% ---------------------------------------------------------------
-% ---------------------------------------------------------------
+% ---------------------------------------------------------------
+% ---------------------------------------------------------------
+% ---------------------------------------------------------------
+% ---------------------------------------------------------------
% just a "local" copy of plotcmaesdat.m, with manual_mode set to zero
function local_plotcmaesdat(figNb, filenameprefix, filenameextension, objectvarname)
% PLOTCMAESDAT;
% PLOTCMAES(FIGURENUMBER_iBEGIN_iEND, FILENAMEPREFIX, FILENAMEEXTENSION, OBJECTVARNAME);
-% plots output from CMA-ES, e.g. cmaes.m, Java class CMAEvolutionStrategy...
-% mod(figNb,100)==1 plots versus iterations.
+% plots output from CMA-ES, e.g. cmaes.m, Java class CMAEvolutionStrategy...
+% mod(figNb,100)==1 plots versus iterations.
%
-% PLOTCMAES([101 300]) plots versus iteration, from iteration 300.
-% PLOTCMAES([100 150 800]) plots versus function evaluations, between iteration 150 and 800.
+% PLOTCMAES([101 300]) plots versus iteration, from iteration 300.
+% PLOTCMAES([100 150 800]) plots versus function evaluations, between iteration 150 and 800.
%
-% Upper left subplot: blue/red: function value of the best solution in the
+% Upper left subplot: blue/red: function value of the best solution in the
% recent population, cyan: same function value minus best
-% ever seen function value, green: sigma, red: ratio between
+% ever seen function value, green: sigma, red: ratio between
% longest and shortest principal axis length which is equivalent
-% to sqrt(cond(C)).
+% to sqrt(cond(C)).
% Upper right plot: time evolution of the distribution mean (default) or
-% the recent best solution vector.
-% Lower left: principal axes lengths of the distribution ellipsoid,
-% equivalent with the sqrt(eig(C)) square root eigenvalues of C.
-% Lower right: magenta: minimal and maximal "true" standard deviation
-% (with sigma included) in the coordinates, other colors: sqrt(diag(C))
+% the recent best solution vector.
+% Lower left: principal axes lengths of the distribution ellipsoid,
+% equivalent with the sqrt(eig(C)) square root eigenvalues of C.
+% Lower right: magenta: minimal and maximal "true" standard deviation
+% (with sigma included) in the coordinates, other colors: sqrt(diag(C))
% of all diagonal elements of C, if C is diagonal they equal to the
-% lower left.
+% lower left.
%
-% Files [FILENAMEPREFIX name FILENAMEEXTENSION] are used, where
+% Files [FILENAMEPREFIX name FILENAMEEXTENSION] are used, where
% name = axlen, OBJECTVARNAME (xmean|xrecentbest), fit, or stddev.
%
manual_mode = 0;
- if nargin < 1 || isempty(figNb)
+if nargin < 1 || isempty(figNb)
figNb = 325;
- end
- if nargin < 2 || isempty(filenameprefix)
+end
+if nargin < 2 || isempty(filenameprefix)
filenameprefix = 'outcmaes';
- end
- if nargin < 3 || isempty(filenameextension)
+end
+if nargin < 3 || isempty(filenameextension)
filenameextension = '.dat';
- end
- if nargin < 4 || isempty(objectvarname)
- objectvarname = 'xmean';
+end
+if nargin < 4 || isempty(objectvarname)
+ objectvarname = 'xmean';
objectvarname = 'xrecentbest';
- end
- % load data
- d.x = load([filenameprefix objectvarname filenameextension]);
- % d.x = load([filenameprefix 'xmean' filenameextension]);
- % d.x = load([filenameprefix 'xrecentbest' filenameextension]);
- d.f = load([filenameprefix 'fit' filenameextension]);
- d.std = load([filenameprefix 'stddev' filenameextension]);
- d.D = load([filenameprefix 'axlen' filenameextension]);
-
- % interpret entries in figNb for cutting out some data
- if length(figNb) > 1
+end
+% load data
+d.x = load([filenameprefix objectvarname filenameextension]);
+% d.x = load([filenameprefix 'xmean' filenameextension]);
+% d.x = load([filenameprefix 'xrecentbest' filenameextension]);
+d.f = load([filenameprefix 'fit' filenameextension]);
+d.std = load([filenameprefix 'stddev' filenameextension]);
+d.D = load([filenameprefix 'axlen' filenameextension]);
+
+% interpret entries in figNb for cutting out some data
+if length(figNb) > 1
iend = inf;
istart = figNb(2);
if length(figNb) > 2
- iend = figNb(3);
+ iend = figNb(3);
end
figNb = figNb(1);
d.x = d.x(d.x(:,1) >= istart & d.x(:,1) <= iend, :);
d.f = d.f(d.f(:,1) >= istart & d.f(:,1) <= iend, :);
d.std = d.std(d.std(:,1) >= istart & d.std(:,1) <= iend, :);
d.D = d.D(d.D(:,1) >= istart & d.D(:,1) <= iend, :);
- end
+end
- % decide for x-axis
- iabscissa = 2; % 1== versus iterations, 2==versus fevals
- if mod(figNb,100) == 1
+% decide for x-axis
+iabscissa = 2; % 1== versus iterations, 2==versus fevals
+if mod(figNb,100) == 1
iabscissa = 1; % a short hack
- end
- if iabscissa == 1
- xlab ='iterations';
- elseif iabscissa == 2
- xlab = 'function evaluations';
- end
-
- if size(d.x, 2) < 1000
+end
+if iabscissa == 1
+ xlab ='iterations';
+elseif iabscissa == 2
+ xlab = 'function evaluations';
+end
+
+if size(d.x, 2) < 1000
minxend = 1.03*d.x(end, iabscissa);
- else
+else
minxend = 0;
- end
+end
- % set up figure window
- if manual_mode
+% set up figure window
+if manual_mode
figure(figNb); % just create and raise the figure window
- else
- if 1 < 3 && evalin('caller', 'iterplotted') == 0 && evalin('caller', 'irun') == 1
- figure(figNb); % incomment this, if figure raise in the beginning is not desired
+else
+ if 1 < 3 && evalin('caller', 'iterplotted') == 0 && evalin('caller', 'irun') == 1
+ figure(figNb); % incomment this, if figure raise in the beginning is not desired
elseif ismember(figNb, findobj('Type', 'figure'))
- set(0, 'CurrentFigure', figNb); % prevents raise of existing figure window
+ set(0, 'CurrentFigure', figNb); % prevents raise of existing figure window
else
- figure(figNb);
+ figure(figNb);
end
- end
-
- % plot fitness etc
- foffset = 1e-99;
- dfit = d.f(:,6)-min(d.f(:,6));
- [ignore, idxbest] = min(dfit);
- dfit(dfit<1e-98) = NaN;
- subplot(2,2,1); hold off;
- dd = abs(d.f(:,7:8)) + foffset;
- dd(d.f(:,7:8)==0) = NaN;
- semilogy(d.f(:,iabscissa), dd, '-k'); hold on;
- % additional fitness data, for example constraints values
- if size(d.f,2) > 8
+end
+
+% plot fitness etc
+foffset = 1e-99;
+dfit = d.f(:,6)-min(d.f(:,6));
+[ignore, idxbest] = min(dfit);
+dfit(dfit<1e-98) = NaN;
+subplot(2,2,1); hold off;
+dd = abs(d.f(:,7:8)) + foffset;
+dd(d.f(:,7:8)==0) = NaN;
+semilogy(d.f(:,iabscissa), dd, '-k'); hold on;
+% additional fitness data, for example constraints values
+if size(d.f,2) > 8
dd = abs(d.f(:,9:end)) + 10*foffset; % a hack
- % dd(d.f(:,9:end)==0) = NaN;
- semilogy(d.f(:,iabscissa), dd, '-m'); hold on;
+ % dd(d.f(:,9:end)==0) = NaN;
+ semilogy(d.f(:,iabscissa), dd, '-m'); hold on;
if size(d.f,2) > 12
- semilogy(d.f(:,iabscissa),abs(d.f(:,[7 8 11 13]))+foffset,'-k'); hold on;
+ semilogy(d.f(:,iabscissa),abs(d.f(:,[7 8 11 13]))+foffset,'-k'); hold on;
end
- end
-
- idx = find(d.f(:,6)>1e-98); % positive values
- if ~isempty(idx) % otherwise non-log plot gets hold
- semilogy(d.f(idx,iabscissa), d.f(idx,6)+foffset, '.b'); hold on;
- end
- idx = find(d.f(:,6) < -1e-98); % negative values
- if ~isempty(idx)
- semilogy(d.f(idx, iabscissa), abs(d.f(idx,6))+foffset,'.r'); hold on;
- end
- semilogy(d.f(:,iabscissa),abs(d.f(:,6))+foffset,'-b'); hold on;
- semilogy(d.f(:,iabscissa),dfit,'-c'); hold on;
- semilogy(d.f(:,iabscissa),(d.f(:,4)),'-r'); hold on; % AR
- semilogy(d.std(:,iabscissa), [max(d.std(:,6:end)')' ...
- min(d.std(:,6:end)')'], '-m'); % max,min std
- maxval = max(d.std(end,6:end));
- minval = min(d.std(end,6:end));
- text(d.std(end,iabscissa), maxval, sprintf('%.0e', maxval));
- text(d.std(end,iabscissa), minval, sprintf('%.0e', minval));
-
- semilogy(d.std(:,iabscissa),(d.std(:,3)),'-g'); % sigma
- % plot best f
- semilogy(d.f(idxbest,iabscissa),min(dfit),'*c'); hold on;
- semilogy(d.f(idxbest,iabscissa),abs(d.f(idxbest,6))+foffset,'*r'); hold on;
-
- ax = axis;
- ax(2) = max(minxend, ax(2));
- axis(ax);
-
- yannote = 10^(log10(ax(3)) + 0.05*(log10(ax(4))-log10(ax(3))));
- text(ax(1), yannote, ...
- [ 'f=' num2str(d.f(end,6), '%.15g') ]);
-
- title('blue:abs(f), cyan:f-min(f), green:sigma, red:axis ratio');
- grid on;
-
- subplot(2,2,2); hold off;
- plot(d.x(:,iabscissa), d.x(:,6:end),'-'); hold on;
- ax = axis;
- ax(2) = max(minxend, ax(2));
- axis(ax);
-
- % add some annotation lines
- [ignore idx] = sort(d.x(end,6:end));
- % choose no more than 25 indices
- idxs = round(linspace(1, size(d.x,2)-5, min(size(d.x,2)-5, 25)));
- yy = repmat(NaN, 2, size(d.x,2)-5);
- yy(1,:) = d.x(end, 6:end);
- yy(2,idx(idxs)) = linspace(ax(3), ax(4), length(idxs));
- plot([d.x(end,iabscissa) ax(2)], yy, '-');
- plot(repmat(d.x(end,iabscissa),2), [ax(3) ax(4)], 'k-');
- for i = idx(idxs)
+end
+
+idx = find(d.f(:,6)>1e-98); % positive values
+if ~isempty(idx) % otherwise non-log plot gets hold
+ semilogy(d.f(idx,iabscissa), d.f(idx,6)+foffset, '.b'); hold on;
+end
+idx = find(d.f(:,6) < -1e-98); % negative values
+if ~isempty(idx)
+ semilogy(d.f(idx, iabscissa), abs(d.f(idx,6))+foffset,'.r'); hold on;
+end
+semilogy(d.f(:,iabscissa),abs(d.f(:,6))+foffset,'-b'); hold on;
+semilogy(d.f(:,iabscissa),dfit,'-c'); hold on;
+semilogy(d.f(:,iabscissa),(d.f(:,4)),'-r'); hold on; % AR
+semilogy(d.std(:,iabscissa), [max(d.std(:,6:end)')' ...
+ min(d.std(:,6:end)')'], '-m'); % max,min std
+maxval = max(d.std(end,6:end));
+minval = min(d.std(end,6:end));
+text(d.std(end,iabscissa), maxval, sprintf('%.0e', maxval));
+text(d.std(end,iabscissa), minval, sprintf('%.0e', minval));
+
+semilogy(d.std(:,iabscissa),(d.std(:,3)),'-g'); % sigma
+ % plot best f
+semilogy(d.f(idxbest,iabscissa),min(dfit),'*c'); hold on;
+semilogy(d.f(idxbest,iabscissa),abs(d.f(idxbest,6))+foffset,'*r'); hold on;
+
+ax = axis;
+ax(2) = max(minxend, ax(2));
+axis(ax);
+
+yannote = 10^(log10(ax(3)) + 0.05*(log10(ax(4))-log10(ax(3))));
+text(ax(1), yannote, ...
+ [ 'f=' num2str(d.f(end,6), '%.15g') ]);
+
+title('blue:abs(f), cyan:f-min(f), green:sigma, red:axis ratio');
+grid on;
+
+subplot(2,2,2); hold off;
+plot(d.x(:,iabscissa), d.x(:,6:end),'-'); hold on;
+ax = axis;
+ax(2) = max(minxend, ax(2));
+axis(ax);
+
+% add some annotation lines
+[ignore idx] = sort(d.x(end,6:end));
+% choose no more than 25 indices
+idxs = round(linspace(1, size(d.x,2)-5, min(size(d.x,2)-5, 25)));
+yy = repmat(NaN, 2, size(d.x,2)-5);
+yy(1,:) = d.x(end, 6:end);
+yy(2,idx(idxs)) = linspace(ax(3), ax(4), length(idxs));
+plot([d.x(end,iabscissa) ax(2)], yy, '-');
+plot(repmat(d.x(end,iabscissa),2), [ax(3) ax(4)], 'k-');
+for i = idx(idxs)
text(ax(2), yy(2,i), ...
['x(' num2str(i) ')=' num2str(yy(1,i))]);
- end
-
- lam = 'NA';
- if size(d.x, 1) > 1 && d.x(end, 1) > d.x(end-1, 1)
+end
+
+lam = 'NA';
+if size(d.x, 1) > 1 && d.x(end, 1) > d.x(end-1, 1)
lam = num2str((d.x(end, 2) - d.x(end-1, 2)) / (d.x(end, 1) - d.x(end-1, 1)));
- end
- title(['Object Variables (' num2str(size(d.x, 2)-5) ...
- '-D, popsize~' lam ')']);grid on;
-
- subplot(2,2,3); hold off; semilogy(d.D(:,iabscissa), d.D(:,6:end), '-');
- ax = axis;
- ax(2) = max(minxend, ax(2));
- axis(ax);
- title('Principal Axes Lengths');grid on;
- xlabel(xlab);
-
- subplot(2,2,4); hold off;
- % semilogy(d.std(:,iabscissa), d.std(:,6:end), 'k-'); hold on;
- % remove sigma from stds
- d.std(:,6:end) = d.std(:,6:end) ./ (d.std(:,3) * ones(1,size(d.std,2)-5));
- semilogy(d.std(:,iabscissa), d.std(:,6:end), '-'); hold on;
- if 11 < 3 % max and min std
+end
+title(['Object Variables (' num2str(size(d.x, 2)-5) ...
+ '-D, popsize~' lam ')']);grid on;
+
+subplot(2,2,3); hold off; semilogy(d.D(:,iabscissa), d.D(:,6:end), '-');
+ax = axis;
+ax(2) = max(minxend, ax(2));
+axis(ax);
+title('Principal Axes Lengths');grid on;
+xlabel(xlab);
+
+subplot(2,2,4); hold off;
+% semilogy(d.std(:,iabscissa), d.std(:,6:end), 'k-'); hold on;
+% remove sigma from stds
+d.std(:,6:end) = d.std(:,6:end) ./ (d.std(:,3) * ones(1,size(d.std,2)-5));
+semilogy(d.std(:,iabscissa), d.std(:,6:end), '-'); hold on;
+if 11 < 3 % max and min std
semilogy(d.std(:,iabscissa), [d.std(:,3).*max(d.std(:,6:end)')' ...
d.std(:,3).*min(d.std(:,6:end)')'], '-m', 'linewidth', 2);
maxval = max(d.std(end,6:end));
minval = min(d.std(end,6:end));
text(d.std(end,iabscissa), d.std(end,3)*maxval, sprintf('max=%.0e', maxval));
text(d.std(end,iabscissa), d.std(end,3)*minval, sprintf('min=%.0e', minval));
- end
- ax = axis;
- ax(2) = max(minxend, ax(2));
- axis(ax);
- % add some annotation lines
- [ignore, idx] = sort(d.std(end,6:end));
- % choose no more than 25 indices
- idxs = round(linspace(1, size(d.x,2)-5, min(size(d.x,2)-5, 25)));
- yy = repmat(NaN, 2, size(d.std,2)-5);
- yy(1,:) = d.std(end, 6:end);
- yy(2,idx(idxs)) = logspace(log10(ax(3)), log10(ax(4)), length(idxs));
- semilogy([d.std(end,iabscissa) ax(2)], yy, '-');
- semilogy(repmat(d.std(end,iabscissa),2), [ax(3) ax(4)], 'k-');
- for i = idx(idxs)
+end
+ax = axis;
+ax(2) = max(minxend, ax(2));
+axis(ax);
+% add some annotation lines
+[ignore, idx] = sort(d.std(end,6:end));
+% choose no more than 25 indices
+idxs = round(linspace(1, size(d.x,2)-5, min(size(d.x,2)-5, 25)));
+yy = repmat(NaN, 2, size(d.std,2)-5);
+yy(1,:) = d.std(end, 6:end);
+yy(2,idx(idxs)) = logspace(log10(ax(3)), log10(ax(4)), length(idxs));
+semilogy([d.std(end,iabscissa) ax(2)], yy, '-');
+semilogy(repmat(d.std(end,iabscissa),2), [ax(3) ax(4)], 'k-');
+for i = idx(idxs)
text(ax(2), yy(2,i), [' ' num2str(i)]);
- end
- title('Standard Deviations in Coordinates divided by sigma');grid on;
- xlabel(xlab);
+end
+title('Standard Deviations in Coordinates divided by sigma');grid on;
+xlabel(xlab);
- if figNb ~= 324
+if figNb ~= 324
% zoom on; % does not work in Octave
- end
- drawnow;
+end
+drawnow;
-% ---------------------------------------------------------------
-% --------------- TEST OBJECTIVE FUNCTIONS ----------------------
-% ---------------------------------------------------------------
+% ---------------------------------------------------------------
+% --------------- TEST OBJECTIVE FUNCTIONS ----------------------
+% ---------------------------------------------------------------
%%% Unimodal functions
@@ -2337,652 +2337,652 @@ function f=fjens1(x)
%
% use population size about 2*N
%
- f = sum((x>0) .* x.^1, 1);
- if any(any(x<0))
+f = sum((x>0) .* x.^1, 1);
+if any(any(x<0))
idx = sum(x < 0, 1) > 0;
f(idx) = 1e3;
-% f = f + 1e3 * sum(x<0, 1);
-% f = f + 10 * sum((x<0) .* x.^2, 1);
+ % f = f + 1e3 * sum(x<0, 1);
+ % f = f + 10 * sum((x<0) .* x.^2, 1);
f(idx) = f(idx) + 1e-3*abs(randn(1,sum(idx)));
-% f(idx) = NaN;
- end
+ % f(idx) = NaN;
+end
function f=fsphere(x)
- f = sum(x.^2,1);
+f = sum(x.^2,1);
function f=fmax(x)
- f = max(abs(x), [], 1);
+f = max(abs(x), [], 1);
function f=fssphere(x)
- f=sqrt(sum(x.^2, 1));
+f=sqrt(sum(x.^2, 1));
-% lb = -0.512; ub = 512;
-% xfeas = x;
+% lb = -0.512; ub = 512;
+% xfeas = x;
% xfeas(x<lb) = lb;
-% xfeas(x>ub) = ub;
+% xfeas(x>ub) = ub;
% f=sum(xfeas.^2, 1);
-% f = f + 1e-9 * sum((xfeas-x).^2);
-
+% f = f + 1e-9 * sum((xfeas-x).^2);
+
function f=fspherenoise(x, Nevals)
- if nargin < 2 || isempty(Nevals)
+if nargin < 2 || isempty(Nevals)
Nevals = 1;
- end
- [N,popsi] = size(x);
+end
+[N,popsi] = size(x);
% x = x .* (1 + 0.3e-0 * randn(N, popsi)/(2*N)); % actuator noise
- fsum = 10.^(0*(0:N-1)/(N-1)) * x.^2;
+fsum = 10.^(0*(0:N-1)/(N-1)) * x.^2;
% f = 0*rand(1,1) ...
% + fsum ...
% + fsum .* (2*randn(1,popsi) ./ randn(1,popsi).^0 / (2*N)) ...
-% + 1*fsum.^0.9 .* 2*randn(1,popsi) / (2*N); %
+% + 1*fsum.^0.9 .* 2*randn(1,popsi) / (2*N); %
% f = fsum .* exp(0.1*randn(1,popsi));
- f = fsum .* (1 + (10/(N+10)/sqrt(Nevals))*randn(1,popsi));
+f = fsum .* (1 + (10/(N+10)/sqrt(Nevals))*randn(1,popsi));
% f = fsum .* (1 + (0.1/N)*randn(1,popsi)./randn(1,popsi).^1);
- idx = rand(1,popsi) < 0.0;
- if sum(idx) > 0
+idx = rand(1,popsi) < 0.0;
+if sum(idx) > 0
f(idx) = f(idx) + 1e3*exp(randn(1,sum(idx)));
- end
-
+end
+
function f=fmixranks(x)
- N = size(x,1);
- f=(10.^(0*(0:(N-1))/(N-1))*x.^2).^0.5;
- if size(x, 2) > 1 % compute ranks, if it is a population
+N = size(x,1);
+f=(10.^(0*(0:(N-1))/(N-1))*x.^2).^0.5;
+if size(x, 2) > 1 % compute ranks, if it is a population
[ignore, idx] = sort(f);
[ignore, ranks] = sort(idx);
k = 9; % number of solutions randomly permuted, lambda/2-1
% works still quite well (two time slower)
for i = k+1:k-0:size(x,2)
- idx(i-k+(1:k)) = idx(i-k+randperm(k));
+ idx(i-k+(1:k)) = idx(i-k+randperm(k));
end
%disp([ranks' f'])
[ignore, ranks] = sort(idx);
%disp([ranks' f'])
%pause
f = ranks+1e-9*randn(1,1);
- end
-
+end
+
function f = fsphereoneax(x)
- f = x(1)^2;
- f = mean(x)^2;
-
+f = x(1)^2;
+f = mean(x)^2;
+
function f=frandsphere(x)
- N = size(x,1);
- idx = ceil(N*rand(7,1));
- f=sum(x(idx).^2);
+N = size(x,1);
+idx = ceil(N*rand(7,1));
+f=sum(x(idx).^2);
function f=fspherelb0(x, M) % lbound at zero for 1:M needed
- if nargin < 2 M = 0; end
- N = size(x,1);
- % M active bounds, f_i = 1 for x = 0
- f = -M + sum((x(1:M) + 1).^2);
- f = f + sum(x(M+1:N).^2);
-
+if nargin < 2 M = 0; end
+N = size(x,1);
+% M active bounds, f_i = 1 for x = 0
+f = -M + sum((x(1:M) + 1).^2);
+f = f + sum(x(M+1:N).^2);
+
function f=fspherehull(x)
- % Patton, Dexter, Goodman, Punch
- % in -500..500
- % spherical ridge through zeros(N,1)
- % worst case start point seems x = 2*100*sqrt(N)
- % and small step size
- N = size(x,1);
- f = norm(x) + (norm(x-100*sqrt(N)) - 100*N)^2;
-
+% Patton, Dexter, Goodman, Punch
+% in -500..500
+% spherical ridge through zeros(N,1)
+% worst case start point seems x = 2*100*sqrt(N)
+% and small step size
+N = size(x,1);
+f = norm(x) + (norm(x-100*sqrt(N)) - 100*N)^2;
+
function f=fellilb0(x, idxM, scal) % lbound at zero for 1:M needed
- N = size(x,1);
- if nargin < 3 || isempty(scal)
+N = size(x,1);
+if nargin < 3 || isempty(scal)
scal = 100;
- end
- scale=scal.^((0:N-1)/(N-1));
- if nargin < 2 || isempty(idxM)
+end
+scale=scal.^((0:N-1)/(N-1));
+if nargin < 2 || isempty(idxM)
idxM = 1:N;
- end
- %scale(N) = 1e0;
- % M active bounds
- xopt = 0.1;
- x(idxM) = x(idxM) + xopt;
- f = scale.^2*x.^2;
- f = f - sum((xopt*scale(idxM)).^2);
+end
+%scale(N) = 1e0;
+% M active bounds
+xopt = 0.1;
+x(idxM) = x(idxM) + xopt;
+f = scale.^2*x.^2;
+f = f - sum((xopt*scale(idxM)).^2);
% f = exp(f) - 1;
% f = log10(f+1e-19) + 19;
- f = f + 1e-19;
-
+f = f + 1e-19;
+
function f=fcornersphere(x)
- w = ones(size(x,1));
- w(1) = 2.5; w(2)=2.5;
- idx = x < 0;
- f = sum(x(idx).^2);
- idx = x > 0;
- f = f + 2^2*sum(w(idx).*x(idx).^2);
-
+w = ones(size(x,1));
+w(1) = 2.5; w(2)=2.5;
+idx = x < 0;
+f = sum(x(idx).^2);
+idx = x > 0;
+f = f + 2^2*sum(w(idx).*x(idx).^2);
+
function f=fsectorsphere(x, scal)
%
% This is deceptive for cumulative sigma control CSA in large dimension:
% The strategy (initially) diverges for N=50 and popsize = 150. (Even
% for cs==1 this can be observed for larger settings of N and
-% popsize.) The reason is obvious from the function topology.
+% popsize.) The reason is obvious from the function topology.
% Divergence can be avoided by setting boundaries or adding a
-% penalty for large ||x||. Then, convergence can be observed again.
+% penalty for large ||x||. Then, convergence can be observed again.
% Conclusion: for popsize>N cumulative sigma control is not completely
% reasonable, but I do not know better alternatives. In particular:
-% TPA takes longer to converge than CSA when the latter still works.
+% TPA takes longer to converge than CSA when the latter still works.
%
- if nargin < 2 || isempty (scal)
+if nargin < 2 || isempty (scal)
scal = 1e3;
- end
- f=sum(x.^2,1);
- idx = x<0;
- f = f + (scal^2 - 1) * sum((idx.*x).^2,1);
- if 11 < 3
+end
+f=sum(x.^2,1);
+idx = x<0;
+f = f + (scal^2 - 1) * sum((idx.*x).^2,1);
+if 11 < 3
idxpen = find(f>1e9);
if ~isempty(idxpen)
- f(idxpen) = f(idxpen) + 1e8*sum(x(:,idxpen).^2,1);
+ f(idxpen) = f(idxpen) + 1e8*sum(x(:,idxpen).^2,1);
end
- end
-
+end
+
function f=fstepsphere(x, scal)
- if nargin < 2 || isempty (scal)
+if nargin < 2 || isempty (scal)
scal = 1e0;
- end
- N = size(x,1);
- f=1e-11+sum(scal.^((0:N-1)/(N-1))*floor(x+0.5).^2);
- f=1e-11+sum(floor(scal.^((0:N-1)/(N-1))'.*x+0.5).^2);
+end
+N = size(x,1);
+f=1e-11+sum(scal.^((0:N-1)/(N-1))*floor(x+0.5).^2);
+f=1e-11+sum(floor(scal.^((0:N-1)/(N-1))'.*x+0.5).^2);
% f=1e-11+sum(floor(x+0.5).^2);
function f=fstep(x)
- % in -5.12..5.12 (bounded)
- N = size(x,1);
- f=1e-11+6*N+sum(floor(x));
+% in -5.12..5.12 (bounded)
+N = size(x,1);
+f=1e-11+6*N+sum(floor(x));
function f=flnorm(x, scal, e)
if nargin < 2 || isempty(scal)
- scal = 1;
+ scal = 1;
end
if nargin < 3 || isempty(e)
- e = 1;
+ e = 1;
end
if e==inf
- f = max(abs(x));
+ f = max(abs(x));
else
- N = size(x,1);
- scale = scal.^((0:N-1)/(N-1))';
- f=sum(abs(scale.*x).^e);
+ N = size(x,1);
+ scale = scal.^((0:N-1)/(N-1))';
+ f=sum(abs(scale.*x).^e);
end
-function f=fneumaier3(x)
- % in -n^2..n^2
- % x^*-i = i(n+1-i)
- N = size(x,1);
+function f=fneumaier3(x)
+% in -n^2..n^2
+% x^*-i = i(n+1-i)
+N = size(x,1);
% f = N*(N+4)*(N-1)/6 + sum((x-1).^2) - sum(x(1:N-1).*x(2:N));
- f = sum((x-1).^2) - sum(x(1:N-1).*x(2:N));
+f = sum((x-1).^2) - sum(x(1:N-1).*x(2:N));
function f = fmaxmindist(y)
- % y in [-1,1], y(1:2) is first point on a plane, y(3:4) second etc
- % points best
- % 5 1.4142
- % 8 1.03527618
- % 10 0.842535997
- % 20 0.5997
- pop = size(y,2);
- N = size(y,1)/2;
- f = [];
- for ipop = 1:pop
+% y in [-1,1], y(1:2) is first point on a plane, y(3:4) second etc
+% points best
+% 5 1.4142
+% 8 1.03527618
+% 10 0.842535997
+% 20 0.5997
+pop = size(y,2);
+N = size(y,1)/2;
+f = [];
+for ipop = 1:pop
if any(abs(y(:,ipop)) > 1)
- f(ipop) = NaN;
+ f(ipop) = NaN;
else
- x = reshape(y(:,ipop), [2, N]);
- f(ipop) = inf;
- for i = 1:N
- f(ipop) = min(f(ipop), min(sqrt(sum((x(:,[1:i-1 i+1:N]) - repmat(x(:,i), 1, N-1)).^2, 1))));
- end
+ x = reshape(y(:,ipop), [2, N]);
+ f(ipop) = inf;
+ for i = 1:N
+ f(ipop) = min(f(ipop), min(sqrt(sum((x(:,[1:i-1 i+1:N]) - repmat(x(:,i), 1, N-1)).^2, 1))));
+ end
end
- end
- f = -f;
+end
+f = -f;
function f=fchangingsphere(x)
- N = size(x,1);
- global scale_G; global count_G; if isempty(count_G) count_G=-1; end
- count_G = count_G+1;
- if mod(count_G,10) == 0
+N = size(x,1);
+global scale_G; global count_G; if isempty(count_G) count_G=-1; end
+count_G = count_G+1;
+if mod(count_G,10) == 0
scale_G = 10.^(2*rand(1,N));
- end
- %disp(scale(1));
- f = scale_G*x.^2;
-
+end
+%disp(scale(1));
+f = scale_G*x.^2;
+
function f= flogsphere(x)
- f = 1-exp(-sum(x.^2));
-
+f = 1-exp(-sum(x.^2));
+
function f= fexpsphere(x)
- f = exp(sum(x.^2)) - 1;
-
+f = exp(sum(x.^2)) - 1;
+
function f=fbaluja(x)
- % in [-0.16 0.16]
- y = x(1);
- for i = 2:length(x)
+% in [-0.16 0.16]
+y = x(1);
+for i = 2:length(x)
y(i) = x(i) + y(i-1);
- end
- f = 1e5 - 1/(1e-5 + sum(abs(y)));
+end
+f = 1e5 - 1/(1e-5 + sum(abs(y)));
function f=fschwefel(x)
- f = 0;
- for i = 1:size(x,1)
+f = 0;
+for i = 1:size(x,1)
f = f+sum(x(1:i))^2;
- end
+end
function f=fcigar(x, ar)
- if nargin < 2 || isempty(ar)
+if nargin < 2 || isempty(ar)
ar = 1e3;
- end
- f = x(1,:).^2 + ar^2*sum(x(2:end,:).^2,1);
-
+end
+f = x(1,:).^2 + ar^2*sum(x(2:end,:).^2,1);
+
function f=fcigtab(x)
- f = x(1,:).^2 + 1e8*x(end,:).^2 + 1e4*sum(x(2:(end-1),:).^2, 1);
-
+f = x(1,:).^2 + 1e8*x(end,:).^2 + 1e4*sum(x(2:(end-1),:).^2, 1);
+
function f=ftablet(x)
- f = 1e6*x(1,:).^2 + sum(x(2:end,:).^2, 1);
+f = 1e6*x(1,:).^2 + sum(x(2:end,:).^2, 1);
function f=felli(x, lgscal, expon, expon2)
- % lgscal: log10(axis ratio)
- % expon: x_i^expon, sphere==2
- N = size(x,1); if N < 2 error('dimension must be greater one'); end
+% lgscal: log10(axis ratio)
+% expon: x_i^expon, sphere==2
+N = size(x,1); if N < 2 error('dimension must be greater one'); end
% x = x - repmat(-0.5+(1:N)',1,size(x,2)); % optimum in 1:N
- if nargin < 2 || isempty(lgscal), lgscal = 3; end
- if nargin < 3 || isempty(expon), expon = 2; end
- if nargin < 4 || isempty(expon2), expon2 = 1; end
+if nargin < 2 || isempty(lgscal), lgscal = 3; end
+if nargin < 3 || isempty(expon), expon = 2; end
+if nargin < 4 || isempty(expon2), expon2 = 1; end
- f=((10^(lgscal*expon)).^((0:N-1)/(N-1)) * abs(x).^expon).^(1/expon2);
+f=((10^(lgscal*expon)).^((0:N-1)/(N-1)) * abs(x).^expon).^(1/expon2);
% if rand(1,1) > 0.015
% f = NaN;
% end
% f = f + randn(size(f));
function f=fellitest(x)
- beta = 0.9;
- N = size(x,1);
- f = (1e6.^((0:(N-1))/(N-1))).^beta * (x.^2).^beta;
+beta = 0.9;
+N = size(x,1);
+f = (1e6.^((0:(N-1))/(N-1))).^beta * (x.^2).^beta;
+
-
function f=fellii(x, scal)
- N = size(x,1); if N < 2 error('dimension must be greater one'); end
- if nargin < 2
+N = size(x,1); if N < 2 error('dimension must be greater one'); end
+if nargin < 2
scal = 1;
- end
- f= (scal*(1:N)).^2 * (x).^2;
+end
+f= (scal*(1:N)).^2 * (x).^2;
function f=fellirot(x)
- N = size(x,1);
- global ORTHOGONALCOORSYSTEM_G
- if isempty(ORTHOGONALCOORSYSTEM_G) ...
- || length(ORTHOGONALCOORSYSTEM_G) < N ...
- || isempty(ORTHOGONALCOORSYSTEM_G{N})
+N = size(x,1);
+global ORTHOGONALCOORSYSTEM_G
+if isempty(ORTHOGONALCOORSYSTEM_G) ...
+ || length(ORTHOGONALCOORSYSTEM_G) < N ...
+ || isempty(ORTHOGONALCOORSYSTEM_G{N})
coordinatesystem(N);
- end
- f = felli(ORTHOGONALCOORSYSTEM_G{N}*x);
-
+end
+f = felli(ORTHOGONALCOORSYSTEM_G{N}*x);
+
function f=frot(x, fun, varargin)
- N = size(x,1);
- global ORTHOGONALCOORSYSTEM_G
- if isempty(ORTHOGONALCOORSYSTEM_G) ...
- || length(ORTHOGONALCOORSYSTEM_G) < N ...
- || isempty(ORTHOGONALCOORSYSTEM_G{N})
+N = size(x,1);
+global ORTHOGONALCOORSYSTEM_G
+if isempty(ORTHOGONALCOORSYSTEM_G) ...
+ || length(ORTHOGONALCOORSYSTEM_G) < N ...
+ || isempty(ORTHOGONALCOORSYSTEM_G{N})
coordinatesystem(N);
- end
- f = feval(fun, ORTHOGONALCOORSYSTEM_G{N}*x, varargin{:});
-
+end
+f = feval(fun, ORTHOGONALCOORSYSTEM_G{N}*x, varargin{:});
+
function coordinatesystem(N)
- if nargin < 1 || isempty(N)
+if nargin < 1 || isempty(N)
arN = 2:30;
- else
+else
arN = N;
- end
- global ORTHOGONALCOORSYSTEM_G
- ORTHOGONALCOORSYSTEM_G{1} = 1;
- for N = arN
+end
+global ORTHOGONALCOORSYSTEM_G
+ORTHOGONALCOORSYSTEM_G{1} = 1;
+for N = arN
ar = randn(N,N);
- for i = 1:N
- for j = 1:i-1
- ar(:,i) = ar(:,i) - ar(:,i)'*ar(:,j) * ar(:,j);
- end
- ar(:,i) = ar(:,i) / norm(ar(:,i));
+ for i = 1:N
+ for j = 1:i-1
+ ar(:,i) = ar(:,i) - ar(:,i)'*ar(:,j) * ar(:,j);
+ end
+ ar(:,i) = ar(:,i) / norm(ar(:,i));
end
- ORTHOGONALCOORSYSTEM_G{N} = ar;
- end
+ ORTHOGONALCOORSYSTEM_G{N} = ar;
+end
function f=fplane(x)
- f=x(1);
+f=x(1);
function f=ftwoaxes(x)
- f = sum(x(1:floor(end/2),:).^2, 1) + 1e6*sum(x(floor(1+end/2):end,:).^2, 1);
+f = sum(x(1:floor(end/2),:).^2, 1) + 1e6*sum(x(floor(1+end/2):end,:).^2, 1);
function f=fparabR(x)
- f = -x(1,:) + 100*sum(x(2:end,:).^2,1);
+f = -x(1,:) + 100*sum(x(2:end,:).^2,1);
function f=fsharpR(x)
- f = abs(-x(1, :)).^2 + 100 * sqrt(sum(x(2:end,:).^2, 1));
-
+f = abs(-x(1, :)).^2 + 100 * sqrt(sum(x(2:end,:).^2, 1));
+
function f=frosen(x)
- if size(x,1) < 2, error('dimension must be greater one'); end
- N = size(x,1);
- popsi = size(x,2);
- f = 1e2*sum((x(1:end-1,:).^2 - x(2:end,:)).^2,1) + sum((x(1:end-1,:)-1).^2,1);
- % f = f + f^0.9 .* (2*randn(1,popsi) ./ randn(1,popsi).^0 / (2*N));
+if size(x,1) < 2, error('dimension must be greater one'); end
+N = size(x,1);
+popsi = size(x,2);
+f = 1e2*sum((x(1:end-1,:).^2 - x(2:end,:)).^2,1) + sum((x(1:end-1,:)-1).^2,1);
+% f = f + f^0.9 .* (2*randn(1,popsi) ./ randn(1,popsi).^0 / (2*N));
function f=frosenlin(x)
- if size(x,1) < 2 error('dimension must be greater one'); end
+if size(x,1) < 2 error('dimension must be greater one'); end
- x_org = x;
- x(x>30) = 30;
- x(x<-30) = -30;
+x_org = x;
+x(x>30) = 30;
+x(x<-30) = -30;
- f = 1e2*sum(-(x(1:end-1,:).^2 - x(2:end,:)),1) + ...
- sum((x(1:end-1,:)-1).^2,1);
+f = 1e2*sum(-(x(1:end-1,:).^2 - x(2:end,:)),1) + ...
+ sum((x(1:end-1,:)-1).^2,1);
- f = f + sum((x-x_org).^2,1);
-% f(any(abs(x)>30,1)) = NaN;
+f = f + sum((x-x_org).^2,1);
+% f(any(abs(x)>30,1)) = NaN;
function f=frosenrot(x)
- N = size(x,1);
- global ORTHOGONALCOORSYSTEM_G
- if isempty(ORTHOGONALCOORSYSTEM_G) ...
- || length(ORTHOGONALCOORSYSTEM_G) < N ...
- || isempty(ORTHOGONALCOORSYSTEM_G{N})
+N = size(x,1);
+global ORTHOGONALCOORSYSTEM_G
+if isempty(ORTHOGONALCOORSYSTEM_G) ...
+ || length(ORTHOGONALCOORSYSTEM_G) < N ...
+ || isempty(ORTHOGONALCOORSYSTEM_G{N})
coordinatesystem(N);
- end
- f = frosen(ORTHOGONALCOORSYSTEM_G{N}*x);
+end
+f = frosen(ORTHOGONALCOORSYSTEM_G{N}*x);
function f=frosenmodif(x)
- f = 74 + 100*(x(2)-x(1)^2)^2 + (1-x(1))^2 ...
- - 400*exp(-sum((x+1).^2)/2/0.05);
-
+f = 74 + 100*(x(2)-x(1)^2)^2 + (1-x(1))^2 ...
+ - 400*exp(-sum((x+1).^2)/2/0.05);
+
function f=fschwefelrosen1(x)
- % in [-10 10]
- f=sum((x.^2-x(1)).^2 + (x-1).^2);
-
+% in [-10 10]
+f=sum((x.^2-x(1)).^2 + (x-1).^2);
+
function f=fschwefelrosen2(x)
- % in [-10 10]
- f=sum((x(2:end).^2-x(1)).^2 + (x(2:end)-1).^2);
+% in [-10 10]
+f=sum((x(2:end).^2-x(1)).^2 + (x(2:end)-1).^2);
function f=fdiffpow(x)
- [N, popsi] = size(x); if N < 2 error('dimension must be greater one'); end
+[N, popsi] = size(x); if N < 2 error('dimension must be greater one'); end
- f = sum(abs(x).^repmat(2+10*(0:N-1)'/(N-1), 1, popsi), 1);
- f = sqrt(f);
+f = sum(abs(x).^repmat(2+10*(0:N-1)'/(N-1), 1, popsi), 1);
+f = sqrt(f);
function f=fabsprod(x)
- f = sum(abs(x),1) + prod(abs(x),1);
+f = sum(abs(x),1) + prod(abs(x),1);
function f=ffloor(x)
- f = sum(floor(x+0.5).^2,1);
+f = sum(floor(x+0.5).^2,1);
function f=fmaxx(x)
- f = max(abs(x), [], 1);
+f = max(abs(x), [], 1);
-%%% Multimodal functions
+%%% Multimodal functions
function f=fbirastrigin(x)
-% todo: the volume needs to be a constant
- N = size(x,1);
- idx = (sum(x, 1) < 0.5*N); % global optimum
- f = zeros(1,size(x,2));
- f(idx) = 10*(N-sum(cos(2*pi*x(:,idx)),1)) + sum(x(:,idx).^2,1);
- idx = ~idx;
- f(idx) = 0.1 + 10*(N-sum(cos(2*pi*(x(:,idx)-2)),1)) + sum((x(:,idx)-2).^2,1);
+% todo: the volume needs to be a constant
+N = size(x,1);
+idx = (sum(x, 1) < 0.5*N); % global optimum
+f = zeros(1,size(x,2));
+f(idx) = 10*(N-sum(cos(2*pi*x(:,idx)),1)) + sum(x(:,idx).^2,1);
+idx = ~idx;
+f(idx) = 0.1 + 10*(N-sum(cos(2*pi*(x(:,idx)-2)),1)) + sum((x(:,idx)-2).^2,1);
function f=fackley(x)
- % -32.768..32.768
- % Adding a penalty outside the interval is recommended,
- % because for large step sizes, fackley imposes like frand
- %
- N = size(x,1);
- f = 20-20*exp(-0.2*sqrt(sum(x.^2)/N));
- f = f + (exp(1) - exp(sum(cos(2*pi*x))/N));
- % add penalty outside the search interval
- f = f + sum((x(x>32.768)-32.768).^2) + sum((x(x<-32.768)+32.768).^2);
-
+% -32.768..32.768
+% Adding a penalty outside the interval is recommended,
+% because for large step sizes, fackley imposes like frand
+%
+N = size(x,1);
+f = 20-20*exp(-0.2*sqrt(sum(x.^2)/N));
+f = f + (exp(1) - exp(sum(cos(2*pi*x))/N));
+% add penalty outside the search interval
+f = f + sum((x(x>32.768)-32.768).^2) + sum((x(x<-32.768)+32.768).^2);
+
function f = fbohachevsky(x)
- % -15..15
- f = sum(x(1:end-1).^2 + 2 * x(2:end).^2 - 0.3 * cos(3*pi*x(1:end-1)) ...
- - 0.4 * cos(4*pi*x(2:end)) + 0.7);
-
+% -15..15
+f = sum(x(1:end-1).^2 + 2 * x(2:end).^2 - 0.3 * cos(3*pi*x(1:end-1)) ...
+ - 0.4 * cos(4*pi*x(2:end)) + 0.7);
+
function f=fconcentric(x)
- % in +-600
- s = sum(x.^2);
- f = s^0.25 * (sin(50*s^0.1)^2 + 1);
+% in +-600
+s = sum(x.^2);
+f = s^0.25 * (sin(50*s^0.1)^2 + 1);
function f=fgriewank(x)
- % in [-600 600]
- [N, P] = size(x);
- f = 1 - prod(cos(x'./sqrt(1:N))) + sum(x.^2)/4e3;
- scale = repmat(sqrt(1:N)', 1, P);
- f = 1 - prod(cos(x./scale), 1) + sum(x.^2, 1)/4e3;
- % f = f + 1e4*sum(x(abs(x)>5).^2);
- % if sum(x(abs(x)>5).^2) > 0
- % f = 1e4 * sum(x(abs(x)>5).^2) + 1e8 * sum(x(x>5)).^2;
- % end
+% in [-600 600]
+[N, P] = size(x);
+f = 1 - prod(cos(x'./sqrt(1:N))) + sum(x.^2)/4e3;
+scale = repmat(sqrt(1:N)', 1, P);
+f = 1 - prod(cos(x./scale), 1) + sum(x.^2, 1)/4e3;
+% f = f + 1e4*sum(x(abs(x)>5).^2);
+% if sum(x(abs(x)>5).^2) > 0
+% f = 1e4 * sum(x(abs(x)>5).^2) + 1e8 * sum(x(x>5)).^2;
+% end
function f=fgriewrosen(x)
% F13 or F8F2
- [N, P] = size(x);
- scale = repmat(sqrt(1:N)', 1, P);
- y = [x(2:end,:); x(1,:)];
- x = 100 * (x.^2 - y) + (x - 1).^2; % Rosenbrock part
- f = 1 - prod(cos(x./scale), 1) + sum(x.^2, 1)/4e3;
- f = sum(1 - cos(x) + x.^2/4e3, 1);
+[N, P] = size(x);
+scale = repmat(sqrt(1:N)', 1, P);
+y = [x(2:end,:); x(1,:)];
+x = 100 * (x.^2 - y) + (x - 1).^2; % Rosenbrock part
+f = 1 - prod(cos(x./scale), 1) + sum(x.^2, 1)/4e3;
+f = sum(1 - cos(x) + x.^2/4e3, 1);
function f=fspallpseudorastrigin(x, scal, skewfac, skewstart, amplitude)
% by default multi-modal about between -30 and 30
- if nargin < 5 || isempty(amplitude)
+if nargin < 5 || isempty(amplitude)
amplitude = 40;
- end
- if nargin < 4 || isempty(skewstart)
+end
+if nargin < 4 || isempty(skewstart)
skewstart = 0;
- end
- if nargin < 3 || isempty(skewfac)
+end
+if nargin < 3 || isempty(skewfac)
skewfac = 1;
- end
- if nargin < 2 || isempty(scal)
+end
+if nargin < 2 || isempty(scal)
scal = 1;
- end
- N = size(x,1);
- scale = 1;
- if N > 1
- scale=scal.^((0:N-1)'/(N-1));
- end
- % simple version:
- % f = amplitude*(N - sum(cos(2*pi*(scale.*x)))) + sum((scale.*x).^2);
-
- % skew version:
- y = repmat(scale, 1, size(x,2)) .* x;
- idx = find(x > skewstart);
- if ~isempty(idx)
+end
+N = size(x,1);
+scale = 1;
+if N > 1
+ scale=scal.^((0:N-1)'/(N-1));
+end
+% simple version:
+% f = amplitude*(N - sum(cos(2*pi*(scale.*x)))) + sum((scale.*x).^2);
+
+% skew version:
+y = repmat(scale, 1, size(x,2)) .* x;
+idx = find(x > skewstart);
+if ~isempty(idx)
y(idx) = skewfac*y(idx);
- end
- f = amplitude * (0*N-prod(cos((2*pi)^0*y),1)) + 0.05 * sum(y.^2,1) ...
- + randn(1,1);
+end
+f = amplitude * (0*N-prod(cos((2*pi)^0*y),1)) + 0.05 * sum(y.^2,1) ...
+ + randn(1,1);
function f=frastrigin(x, scal, skewfac, skewstart, amplitude)
% by default multi-modal about between -30 and 30
- if nargin < 5 || isempty(amplitude)
+if nargin < 5 || isempty(amplitude)
amplitude = 10;
- end
- if nargin < 4 || isempty(skewstart)
+end
+if nargin < 4 || isempty(skewstart)
skewstart = 0;
- end
- if nargin < 3 || isempty(skewfac)
+end
+if nargin < 3 || isempty(skewfac)
skewfac = 1;
- end
- if nargin < 2 || isempty(scal)
+end
+if nargin < 2 || isempty(scal)
scal = 1;
- end
- N = size(x,1);
- scale = 1;
- if N > 1
- scale=scal.^((0:N-1)'/(N-1));
- end
- % simple version:
- % f = amplitude*(N - sum(cos(2*pi*(scale.*x)))) + sum((scale.*x).^2);
-
- % skew version:
- y = repmat(scale, 1, size(x,2)) .* x;
- idx = find(x > skewstart);
- % idx = intersect(idx, 2:2:10);
- if ~isempty(idx)
+end
+N = size(x,1);
+scale = 1;
+if N > 1
+ scale=scal.^((0:N-1)'/(N-1));
+end
+% simple version:
+% f = amplitude*(N - sum(cos(2*pi*(scale.*x)))) + sum((scale.*x).^2);
+
+% skew version:
+y = repmat(scale, 1, size(x,2)) .* x;
+idx = find(x > skewstart);
+% idx = intersect(idx, 2:2:10);
+if ~isempty(idx)
y(idx) = skewfac*y(idx);
- end
- f = amplitude * (N-sum(cos(2*pi*y),1)) + sum(y.^2,1);
-
+end
+f = amplitude * (N-sum(cos(2*pi*y),1)) + sum(y.^2,1);
+
function f=frastriginmax(x)
- N = size(x,1);
- f = (N/20)*807.06580387678 - (10 * (N-sum(cos(2*pi*x),1)) + sum(x.^2,1));
- f(any(abs(x) > 5.12)) = 1e2*N;
+N = size(x,1);
+f = (N/20)*807.06580387678 - (10 * (N-sum(cos(2*pi*x),1)) + sum(x.^2,1));
+f(any(abs(x) > 5.12)) = 1e2*N;
function f = fschaffer(x)
- % -100..100
- N = size(x,1);
- s = x(1:N-1,:).^2 + x(2:N,:).^2;
- f = sum(s.^0.25 .* (sin(50*s.^0.1).^2+1), 1);
+% -100..100
+N = size(x,1);
+s = x(1:N-1,:).^2 + x(2:N,:).^2;
+f = sum(s.^0.25 .* (sin(50*s.^0.1).^2+1), 1);
function f=fschwefelmult(x)
- % -500..500
- %
- N = size(x,1);
- f = - sum(x.*sin(sqrt(abs(x))), 1);
- f = 418.9829*N - 1.27275661e-5*N - sum(x.*sin(sqrt(abs(x))), 1);
- % penalty term
- f = f + 1e4*sum((abs(x)>500) .* (abs(x)-500).^2, 1);
-
+% -500..500
+%
+N = size(x,1);
+f = - sum(x.*sin(sqrt(abs(x))), 1);
+f = 418.9829*N - 1.27275661e-5*N - sum(x.*sin(sqrt(abs(x))), 1);
+% penalty term
+f = f + 1e4*sum((abs(x)>500) .* (abs(x)-500).^2, 1);
+
function f=ftwomax(x)
- % Boundaries at +/-5
- N = size(x,1);
- f = -abs(sum(x)) + 5*N;
+% Boundaries at +/-5
+N = size(x,1);
+f = -abs(sum(x)) + 5*N;
function f=ftwomaxtwo(x)
- % Boundaries at +/-10
- N = size(x,1);
- f = abs(sum(x));
- if f > 30
+% Boundaries at +/-10
+N = size(x,1);
+f = abs(sum(x));
+if f > 30
f = f - 30;
- end
- f = -f;
-
+end
+f = -f;
+
function f=frand(x)
- f=1./(1-rand(1, size(x,2))) - 1;
+f=1./(1-rand(1, size(x,2))) - 1;
% CHANGES
% 12/02/19: "future" setting of ccum, correcting for large mueff, is default now
% 11/11/15: bug-fix: max value for ccovmu_sep setting corrected
-% 10/11/11: (3.52.beta) boundary handling: replace max with min in change
-% rate formula. Active CMA: check of pos.def. improved.
-% Plotting: value of lambda appears in the title.
+% 10/11/11: (3.52.beta) boundary handling: replace max with min in change
+% rate formula. Active CMA: check of pos.def. improved.
+% Plotting: value of lambda appears in the title.
% 10/04/03: (3.51.beta) active CMA cleaned up. Equal fitness detection
-% looks into history now.
+% looks into history now.
% 10/03/08: (3.50.beta) "active CMA" revised and bug-fix of ambiguous
-% option Noise.alpha -> Noise.alphasigma.
-% 09/10/12: (3.40.beta) a slightly modified version of "active CMA",
+% option Noise.alpha -> Noise.alphasigma.
+% 09/10/12: (3.40.beta) a slightly modified version of "active CMA",
% that is a negative covariance matrix update, use option
-% CMA.active. In 10;30;90-D the gain on ftablet is a factor
-% of 1.6;2.5;4.4 (the scaling improves by sqrt(N)). On
-% Rosenbrock the gain is about 25%. On sharp ridge the
-% behavior is improved. Cigar is unchanged.
-% 09/08/10: local plotcmaesdat remains in backround
-% 09/08/10: bug-fix in time management for data writing, logtime was not
-% considered properly (usually not at all).
-% 09/07/05: V3.24: stagnation termination added
+% CMA.active. In 10;30;90-D the gain on ftablet is a factor
+% of 1.6;2.5;4.4 (the scaling improves by sqrt(N)). On
+% Rosenbrock the gain is about 25%. On sharp ridge the
+% behavior is improved. Cigar is unchanged.
+% 09/08/10: local plotcmaesdat remains in backround
+% 09/08/10: bug-fix in time management for data writing, logtime was not
+% considered properly (usually not at all).
+% 09/07/05: V3.24: stagnation termination added
% 08/09/27: V3.23: momentum alignment is out-commented and de-preciated
% 08/09/25: V3.22: re-alignment of sigma and C was buggy
% 08/07/15: V3.20, CMA-parameters are options now. ccov and mucov were replaced
% by ccov1 \approx ccov/mucov and ccovmu \approx (1-1/mucov)*ccov
-% 08/06/30: file name xrecent was change to xrecentbest (compatible with other
+% 08/06/30: file name xrecent was change to xrecentbest (compatible with other
% versions)
-% 08/06/29: time stamp added to output files
-% 08/06/28: bug fixed with resume option, commentary did not work
+% 08/06/29: time stamp added to output files
+% 08/06/28: bug fixed with resume option, commentary did not work
% 08/06/28: V3.10, uncertainty (noise) handling added (re-implemented), according
% to reference "A Method for Handling Uncertainty..." from below.
-% 08/06/28: bug fix: file xrecent was empty
-% 08/06/01: diagonalonly clean up. >1 means some iterations.
+% 08/06/28: bug fix: file xrecent was empty
+% 08/06/01: diagonalonly clean up. >1 means some iterations.
% 08/05/05: output is written to file preventing an increasing data
-% array and ease long runs.
-% 08/03/27: DiagonalOnly<0 learns for -DiagonalOnly iterations only the
-% diagonal with a larger learning rate.
-% 08/03 (2.60): option DiagonalOnly>=1 invokes a time- and space-linear
-% variant with only diagonal elements of the covariance matrix
-% updating. This can be useful for large dimensions, say > 100.
+% array and ease long runs.
+% 08/03/27: DiagonalOnly<0 learns for -DiagonalOnly iterations only the
+% diagonal with a larger learning rate.
+% 08/03 (2.60): option DiagonalOnly>=1 invokes a time- and space-linear
+% variant with only diagonal elements of the covariance matrix
+% updating. This can be useful for large dimensions, say > 100.
% 08/02: diag(weights) * ... replaced with repmat(weights,1,N) .* ...
-% in C update, implies O(mu*N^2) instead of O(mu^2*N + mu*N^2).
+% in C update, implies O(mu*N^2) instead of O(mu^2*N + mu*N^2).
% 07/09: tolhistfun as termination criterion added, "<" changed to
-% "<=" also for TolFun to allow for stopping on zero difference.
-% Name tolfunhist clashes with option tolfun.
-% 07/07: hsig threshold made slighly smaller for large dimension,
-% useful for lambda < lambda_default.
+% "<=" also for TolFun to allow for stopping on zero difference.
+% Name tolfunhist clashes with option tolfun.
+% 07/07: hsig threshold made slighly smaller for large dimension,
+% useful for lambda < lambda_default.
% 07/06: boundary handling: scaling in the boundary handling
% is omitted now, see bnd.flgscale. This seems not to
% have a big impact. Using the scaling is worse on rotated
-% functions, but better on separable ones.
+% functions, but better on separable ones.
% 07/05: boundary handling: weight i is not incremented anymore
% if xmean(i) moves towards the feasible space. Increment
-% factor changed to 1.2 instead of 1.1.
+% factor changed to 1.2 instead of 1.1.
% 07/05: boundary handling code simplified not changing the algorithm
% 07/04: bug removed for saving in octave
% 06/11/10: more testing of outcome of eig, fixed max(D) to max(diag(D))
-% 06/10/21: conclusive final bestever assignment in the end
+% 06/10/21: conclusive final bestever assignment in the end
% 06/10/21: restart and incpopsize option implemented for restarts
-% with increasing population size, version 2.50.
+% with increasing population size, version 2.50.
% 06/09/16: output argument bestever inserted again for convenience and
% backward compatibility
-% 06/08: output argument out and struct out reorganized.
+% 06/08: output argument out and struct out reorganized.
% 06/01: Possible parallel evaluation included as option EvalParallel
% 05/11: Compatibility to octave implemented, package octave-forge
-% is needed.
+% is needed.
% 05/09: Raise of figure and waiting for first plots improved
-% 05/01: Function coordinatesystem cleaned up.
+% 05/01: Function coordinatesystem cleaned up.
% 05/01: Function prctile, which requires the statistics toolbox,
-% replaced by myprctile.
-% 05/01: Option warnonequalfunctionvalues included.
-% 04/12: Decrease of sigma removed. Problems on fsectorsphere can
-% be addressed better by adding search space boundaries.
-% 04/12: Boundary handling simpyfied.
-% 04/12: Bug when stopping criteria tolx or tolupx are vectors.
-% 04/11: Three input parameters are obligatory now.
-% 04/11: Bug in boundary handling removed: Boundary weights can decrease now.
-% 04/11: Normalization for boundary weights scale changed.
-% 04/11: VerboseModulo option bug removed. Documentation improved.
+% replaced by myprctile.
+% 05/01: Option warnonequalfunctionvalues included.
+% 04/12: Decrease of sigma removed. Problems on fsectorsphere can
+% be addressed better by adding search space boundaries.
+% 04/12: Boundary handling simpyfied.
+% 04/12: Bug when stopping criteria tolx or tolupx are vectors.
+% 04/11: Three input parameters are obligatory now.
+% 04/11: Bug in boundary handling removed: Boundary weights can decrease now.
+% 04/11: Normalization for boundary weights scale changed.
+% 04/11: VerboseModulo option bug removed. Documentation improved.
% 04/11: Condition for increasing boundary weights changed.
% 04/10: Decrease of sigma when fitness is getting consistenly
% worse. Addresses the problems appearing on fsectorsphere for
% large population size.
-% 04/10: VerboseModulo option included.
+% 04/10: VerboseModulo option included.
% 04/10: Bug for condition for increasing boundary weights removed.
% 04/07: tolx depends on initial sigma to achieve scale invariance
-% for this stopping criterion.
+% for this stopping criterion.
% 04/06: Objective function value NaN is not counted as function
-% evaluation and invokes resampling of the search point.
+% evaluation and invokes resampling of the search point.
% 04/06: Error handling for eigenvalue beeing zero (never happens
% with default parameter setting)
-% 04/05: damps further tuned for large mueff
-% o Details for stall of pc-adaptation added (variable hsig
-% introduced).
+% 04/05: damps further tuned for large mueff
+% o Details for stall of pc-adaptation added (variable hsig
+% introduced).
% 04/05: Bug in boundary handling removed: A large initial SIGMA was
% corrected not until *after* the first iteration, which could
% lead to a complete failure.
-% 04/05: Call of function range (works with stats toolbox only)
-% changed to myrange.
+% 04/05: Call of function range (works with stats toolbox only)
+% changed to myrange.
% 04/04: Parameter cs depends on mueff now and damps \propto sqrt(mueff)
-% instead of \propto mueff.
+% instead of \propto mueff.
% o Initial stall to adapt C (flginiphase) is removed and
% adaptation of pc is stalled for large norm(ps) instead.
-% o Returned default options include documentation.
+% o Returned default options include documentation.
% o Resume part reorganized.
-% 04/03: Stopflag becomes cell-array.
+% 04/03: Stopflag becomes cell-array.
% ---------------------------------------------------------------
% CMA-ES: Evolution Strategy with Covariance Matrix Adaptation for
% nonlinear function minimization. To be used under the terms of the
% GNU General Public License (http://www.gnu.org/copyleft/gpl.html).
-% Author (copyright): Nikolaus Hansen, 2001-2008.
+% Author (copyright): Nikolaus Hansen, 2001-2008.
% e-mail: nikolaus.hansen AT inria.fr
% URL:http://www.bionik.tu-berlin.de/user/niko
-% References: See below.
+% References: See below.
% ---------------------------------------------------------------
%
% GENERAL PURPOSE: The CMA-ES (Evolution Strategy with Covariance
@@ -2999,7 +2999,7 @@ function f=frand(x)
% expected to take at least 30*N^2 and up to 300*N^2 function
% evaluations.
%
-% SOME MORE COMMENTS:
+% SOME MORE COMMENTS:
% The adaptation of the covariance matrix (e.g. by the CMA) is
% equivalent to a general linear transformation of the problem
% coding. Nevertheless every problem specific knowlegde about the best
@@ -3022,13 +3022,13 @@ function f=frand(x)
% ---------------------------------------------------------------
%%% REFERENCES
%
-% The equation numbers refer to
+% The equation numbers refer to
% Hansen, N. and S. Kern (2004). Evaluating the CMA Evolution
% Strategy on Multimodal Test Functions. Eighth International
% Conference on Parallel Problem Solving from Nature PPSN VIII,
-% Proceedings, pp. 282-291, Berlin: Springer.
+% Proceedings, pp. 282-291, Berlin: Springer.
% (http://www.bionik.tu-berlin.de/user/niko/ppsn2004hansenkern.pdf)
-%
+%
% Further references:
% Hansen, N. and A. Ostermeier (2001). Completely Derandomized
% Self-Adaptation in Evolution Strategies. Evolutionary Computation,
@@ -3050,6 +3050,3 @@ function f=frand(x)
% Optimization with an Application to Feedback Control of
% Combustion. To appear in IEEE Transactions on Evolutionary
% Computation.
-
-
-
diff --git a/matlab/optimization/csminit1.m b/matlab/optimization/csminit1.m
index 4184af2df6578102545c5f58e448614f47c05564..0ad54a3a74fcf50ca60dec8176b8251de26debc9 100644
--- a/matlab/optimization/csminit1.m
+++ b/matlab/optimization/csminit1.m
@@ -1,7 +1,7 @@
function [fhat,xhat,fcount,retcode] = csminit1(fcn,x0,penalty,f0,g0,badg,H0,Verbose,varargin)
% [fhat,xhat,fcount,retcode] = csminit1(fcn,x0,penalty,f0,g0,badg,H0,Verbose,varargin)
-%
-% Inputs:
+%
+% Inputs:
% fcn: [string] string naming the objective function to be minimized
% x0: [npar by 1] initial value of the parameter vector
% penalty: [scalar] variable penalty in case of failure of objective function
@@ -20,7 +20,7 @@ function [fhat,xhat,fcount,retcode] = csminit1(fcn,x0,penalty,f0,g0,badg,H0,Verb
% retcode [scalar] 0: normal step
% 1: zero gradient.
% 5: largest step still improves too fast.
-% 2,4: back and forth adjustment of stepsize didn't finish.
+% 2,4: back and forth adjustment of stepsize didn't finish.
% 3: smallest stepsize still improves too slow
% 6: no improvement found
%---------------------
@@ -33,10 +33,10 @@ function [fhat,xhat,fcount,retcode] = csminit1(fcn,x0,penalty,f0,g0,badg,H0,Verb
%
% Fixed 7/19/93 to flip eigenvalues of H to get better performance when
% it's not psd.
-%
+%
% Original file downloaded from:
% http://sims.princeton.edu/yftp/optimize/mfiles/csminit.m
-%
+%
% Copyright (C) 1993-2007 Christopher Sims
% Copyright (C) 2008-2016 Dynare Team
%
@@ -122,7 +122,7 @@ else
disp_verbose(sprintf('Correct for low angle: %g',a),Verbose)
end
end
- disp_verbose(sprintf('Predicted improvement: %18.9f',-dfhat/2),Verbose)
+ disp_verbose(sprintf('Predicted improvement: %18.9f',-dfhat/2),Verbose)
%
% Have OK dx, now adjust length of step (lambda) until min and
% max improvement rate criteria are met.
@@ -145,7 +145,7 @@ else
%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);
- disp_verbose(sprintf('lambda = %10.5g; f = %20.7f',lambda,f ),Verbose)
+ disp_verbose(sprintf('lambda = %10.5g; f = %20.7f',lambda,f ),Verbose)
%debug
%disp(sprintf('Improvement too great? f0-f: %g, criterion: %g',f0-f,-(1-THETA)*dfhat*lambda))
if f<fhat
@@ -183,7 +183,7 @@ else
if Verbose
lambda = -lambda*factor^6
else
- lambda = -lambda*factor^6;
+ lambda = -lambda*factor^6;
end
else
if lambda < 0
diff --git a/matlab/optimization/csminwel1.m b/matlab/optimization/csminwel1.m
index 7b067a0a81a2e8ffc05d8d482616881a15e79689..33270c11783dcae9fdd8514ddb1be587b44ed415 100644
--- a/matlab/optimization/csminwel1.m
+++ b/matlab/optimization/csminwel1.m
@@ -14,13 +14,13 @@ function [fh,xh,gh,H,itct,fcount,retcodeh] = csminwel1(fcn,x0,H0,grad,crit,nit,m
% epsilon: [scalar] scalar double, numerical differentiation increment
% varargin: Optional additional inputs 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
% writes 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.)
-%
+%
% Outputs:
% fh: [scalar] function value at minimum
% xh: [npar by 1] parameter vector at minimum
@@ -30,17 +30,17 @@ function [fh,xh,gh,H,itct,fcount,retcodeh] = csminwel1(fcn,x0,H0,grad,crit,nit,m
% fcount [scalar] function iteration count upon termination
% retcodeh [scalar] return code:
% 0: normal step
-% 1: zero gradient
+% 1: zero gradient
% 2: back and forth on step length never finished
% 3: smallest step still improving too slow
% 4: back and forth on step length never finished
% 5: largest step still improving too fast
% 6: smallest step still improving too slow, reversed gradient
% 7: warning: possible inaccuracy in H matrix
-%
+%
% Original file downloaded from:
% http://sims.princeton.edu/yftp/optimize/mfiles/csminwel.m
-%
+%
% Copyright (C) 1993-2007 Christopher Sims
% Copyright (C) 2006-2015 Dynare Team
%
@@ -59,7 +59,7 @@ function [fh,xh,gh,H,itct,fcount,retcodeh] = csminwel1(fcn,x0,H0,grad,crit,nit,m
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-% initialize variable penalty
+% initialize variable penalty
penalty = 1e8;
fh = [];
xh = [];
@@ -193,7 +193,7 @@ while ~done
[f3, x3, fc, retcode3] = csminit1(fcn,x,penalty,f,gcliff,0,eye(nx),Verbose,varargin{:});
fcount = fcount+fc; % put by Jinill
if retcode3==2 || retcode3==4
- wall3=1;
+ wall3=1;
badg3=1;
else
if NumGrad
@@ -302,7 +302,7 @@ while ~done
error('Unaccounted Case, please contact the developers',Verbose)
end
end
-
+
f=fh;
x=xh;
g=gh;
@@ -312,18 +312,18 @@ end
end
function [g, badg]=get_num_grad(method,fcn,penalty,f0,x0,epsilon,varargin)
- switch method
- case 2
- [g,badg] = numgrad2(fcn, f0, x0, penalty, epsilon, varargin{:});
- case 3
- [g,badg] = numgrad3(fcn, f0, x0, penalty, epsilon, varargin{:});
- case 5
- [g,badg] = numgrad5(fcn, f0, x0, penalty, epsilon, varargin{:});
- case 13
- [g,badg] = numgrad3_(fcn, f0, x0, penalty, epsilon, varargin{:});
- case 15
- [g,badg] = numgrad5_(fcn, f0, x0, penalty, epsilon, varargin{:});
- otherwise
- error('csminwel1: Unknown method for gradient evaluation!')
- end
+switch method
+ case 2
+ [g,badg] = numgrad2(fcn, f0, x0, penalty, epsilon, varargin{:});
+ case 3
+ [g,badg] = numgrad3(fcn, f0, x0, penalty, epsilon, varargin{:});
+ case 5
+ [g,badg] = numgrad5(fcn, f0, x0, penalty, epsilon, varargin{:});
+ case 13
+ [g,badg] = numgrad3_(fcn, f0, x0, penalty, epsilon, varargin{:});
+ case 15
+ [g,badg] = numgrad5_(fcn, f0, x0, penalty, epsilon, varargin{:});
+ otherwise
+ error('csminwel1: Unknown method for gradient evaluation!')
+end
end
\ No newline at end of file
diff --git a/matlab/optimization/dynare_minimize_objective.m b/matlab/optimization/dynare_minimize_objective.m
index 54b84b84c876939abceafbab541256c85974c56f..025d411899bb4112ad484210ca61ba9d4c0055a8 100644
--- a/matlab/optimization/dynare_minimize_objective.m
+++ b/matlab/optimization/dynare_minimize_objective.m
@@ -8,12 +8,12 @@ function [opt_par_values,fval,exitflag,hessian_mat,options_,Scale,new_rat_hess_i
% minimizer_algorithm [scalar double, or string] code of the optimizer algorithm, or string for the name of a user defined optimization routine (not shipped with dynare).
% options_ [matlab structure] Dynare options structure
% bounds [n_params by 2] vector of doubles 2 row vectors containing lower and upper bound for parameters
-% parameter_names [n_params by 1] cell array strings containing the parameters names
+% parameter_names [n_params by 1] cell array strings containing the parameters names
% prior_information [matlab structure] Dynare prior information structure (bayestopt_) provided for algorithm 6
% Initial_Hessian [n_params by n_params] matrix initial hessian matrix provided for algorithm 6
% new_rat_hess_info [matlab structure] step size info used by algorith 5
% varargin [cell array] Input arguments for objective function
-%
+%
% OUTPUTS
% opt_par_values [n_params by 1] vector of doubles optimal parameter values minimizing the objective
% fval [scalar double] value of the objective function at the minimum
@@ -21,11 +21,11 @@ function [opt_par_values,fval,exitflag,hessian_mat,options_,Scale,new_rat_hess_i
% hessian_mat [n_params by n_params] matrix hessian matrix at the mode returned by optimizer
% options_ [matlab structure] Dynare options structure (to return options set by algorithms 5)
% Scale [scalar double] scaling parameter returned by algorith 6
-%
+%
% SPECIAL REQUIREMENTS
% none.
-%
-%
+%
+%
% Copyright (C) 2014-2017 Dynare Team
%
% This file is part of Dynare.
@@ -188,16 +188,16 @@ switch minimizer_algorithm
case 'verbosity'
Verbose = options_list{i,2};
case 'SaveFiles'
- Save_files = options_list{i,2};
+ Save_files = options_list{i,2};
otherwise
warning(['csminwel: Unknown option (' options_list{i,1} ')!'])
end
end
end
if options_.silent_optimizer
- Save_files = 0;
+ Save_files = 0;
Verbose = 0;
- end
+ end
% Set flag for analytical gradient.
if options_.analytic_derivation
analytic_grad=1;
@@ -232,23 +232,23 @@ switch minimizer_algorithm
if options_.analytic_derivation && flag~=0
error('newrat: analytic_derivation is incompatible with numerical Hessian. Using analytic Hessian')
else
- newratflag=flag;
+ newratflag=flag;
end
case 'TolFun'
crit = options_list{i,2};
case 'verbosity'
Verbose = options_list{i,2};
case 'SaveFiles'
- Save_files = options_list{i,2};
+ Save_files = options_list{i,2};
otherwise
warning(['newrat: Unknown option (' options_list{i,1} ')!'])
end
end
end
if options_.silent_optimizer
- Save_files = 0;
+ Save_files = 0;
Verbose = 0;
- end
+ end
hess_info.gstep=options_.gstep;
hess_info.htol = 1.e-4;
hess_info.h1=options_.gradient_epsilon*ones(n_params,1);
@@ -307,13 +307,13 @@ switch minimizer_algorithm
end
if options_.silent_optimizer
simplexOptions.verbose = options_list{i,2};
- end
+ end
[opt_par_values,fval,exitflag] = simplex_optimization_routine(objective_function,start_par_value,simplexOptions,parameter_names,varargin{:});
case 9
% Set defaults
H0 = (bounds(:,2)-bounds(:,1))*0.2;
H0(~isfinite(H0)) = 0.01;
- while max(H0)/min(H0)>1e6 %make sure initial search volume (SIGMA) is not badly conditioned
+ while max(H0)/min(H0)>1e6 %make sure initial search volume (SIGMA) is not badly conditioned
H0(H0==max(H0))=0.9*H0(H0==max(H0));
end
cmaesOptions = options_.cmaes;
@@ -339,14 +339,14 @@ switch minimizer_algorithm
end
case 'SaveFiles'
if options_list{i,2}==0
- cmaesOptions.SaveVariables='off';
- cmaesOptions.LogModulo = '0'; % [0:Inf] if >1 record data less frequently after gen=100';
- cmaesOptions.LogTime = '0'; % [0:100] max. percentage of time for recording data';
+ cmaesOptions.SaveVariables='off';
+ cmaesOptions.LogModulo = '0'; % [0:Inf] if >1 record data less frequently after gen=100';
+ cmaesOptions.LogTime = '0'; % [0:100] max. percentage of time for recording data';
end
case 'CMAESResume'
- if options_list{i,2}==1
+ if options_list{i,2}==1
cmaesOptions.Resume = 'yes';
- end
+ end
otherwise
warning(['cmaes: Unknown option (' options_list{i,1} ')!'])
end
@@ -358,7 +358,7 @@ switch minimizer_algorithm
cmaesOptions.SaveVariables='off';
cmaesOptions.LogModulo = '0'; % [0:Inf] if >1 record data less frequently after gen=100';
cmaesOptions.LogTime = '0'; % [0:100] max. percentage of time for recording data';
- end
+ end
warning('off','CMAES:NonfinitenessRange');
warning('off','CMAES:InitialSigma');
[x, fval, COUNTEVAL, STOPFLAG, OUT, BESTEVER] = cmaes(func2str(objective_function),start_par_value,H0,cmaesOptions,varargin{:});
@@ -386,11 +386,11 @@ switch minimizer_algorithm
case 'MaxFunEvals'
simpsaOptions.MAX_FUN_EVALS = options_list{i,2};
case 'verbosity'
- if options_list{i,2} == 0
+ if options_list{i,2} == 0
simpsaOptions.DISPLAY = 'none';
- else
+ else
simpsaOptions.DISPLAY = 'iter';
- end
+ end
otherwise
warning(['simpsa: Unknown option (' options_list{i,1} ')!'])
end
@@ -414,10 +414,10 @@ switch minimizer_algorithm
if ~isempty(options_.optim_opt)
options_list = read_key_value_string(options_.optim_opt);
SupportedListOfOptions = {'CreationFcn', 'Display', 'DisplayInterval', 'FunctionTolerance', ...
- 'FunValCheck', 'HybridFcn', 'InertiaRange', 'InitialSwarmMatrix', 'InitialSwarmSpan', ...
- 'MaxIterations', 'MaxStallIterations', 'MaxStallTime', 'MaxTime', ...
- 'MinNeighborsFraction', 'ObjectiveLimit', 'OutputFcn', 'PlotFcn', 'SelfAdjustmentWeight', ...
- 'SocialAdjustmentWeight', 'SwarmSize', 'UseParallel', 'UseVectorized'};
+ 'FunValCheck', 'HybridFcn', 'InertiaRange', 'InitialSwarmMatrix', 'InitialSwarmSpan', ...
+ 'MaxIterations', 'MaxStallIterations', 'MaxStallTime', 'MaxTime', ...
+ 'MinNeighborsFraction', 'ObjectiveLimit', 'OutputFcn', 'PlotFcn', 'SelfAdjustmentWeight', ...
+ 'SocialAdjustmentWeight', 'SwarmSize', 'UseParallel', 'UseVectorized'};
for i=1:rows(options_list)
if ismember(options_list{i,1}, SupportedListOfOptions)
particleswarmOptions = optimoptions(particleswarmOptions, options_list{i,1}, options_list{i,2});
@@ -514,8 +514,8 @@ end
end
function [LB, UB]=set_bounds_to_finite_values(bounds, huge_number)
- LB=bounds(:,1);
- LB(isinf(LB))=-huge_number;
- UB=bounds(:,2);
- UB(isinf(UB))=huge_number;
+LB=bounds(:,1);
+LB(isinf(LB))=-huge_number;
+UB=bounds(:,2);
+UB(isinf(UB))=huge_number;
end
diff --git a/matlab/optimization/gmhmaxlik.m b/matlab/optimization/gmhmaxlik.m
index b26088a163aee6d1b81c04f8c993d256fd3a96f3..4f1db628d06cf6070678076d47a8d36daa8dbe08 100644
--- a/matlab/optimization/gmhmaxlik.m
+++ b/matlab/optimization/gmhmaxlik.m
@@ -16,7 +16,7 @@ function [PostMode, HessianMatrix, Scale, ModeValue] = gmhmaxlik(fun, xinit, Hin
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
+
% Set default options
if ~isempty(Hinit)
diff --git a/matlab/optimization/gmhmaxlik_core.m b/matlab/optimization/gmhmaxlik_core.m
index 2d7a3cb814fe1db262ff8ab597ecf24049e5bcff..eae0f931a4442533587d48ae31ad7e2ba1751bb4 100644
--- a/matlab/optimization/gmhmaxlik_core.m
+++ b/matlab/optimization/gmhmaxlik_core.m
@@ -1,8 +1,8 @@
-function [PostMod,PostVar,Scale,PostMean] = gmhmaxlik_core(ObjFun,xparam1,mh_bounds,options,iScale,info,MeanPar,VarCov,varargin)
+function [PostMod,PostVar,Scale,PostMean] = gmhmaxlik_core(ObjFun,xparam1,mh_bounds,options,iScale,info,MeanPar,VarCov,varargin)
-% (Dirty) Global minimization routine of (minus) a likelihood (or posterior density) function.
-%
-% INPUTS
+% (Dirty) Global minimization routine of (minus) a likelihood (or posterior density) function.
+%
+% INPUTS
% o ObjFun [char] string specifying the name of the objective function.
% o xparam1 [double] (p*1) vector of parameters to be estimated.
% o mh_bounds [double] (p*2) matrix defining lower and upper bounds for the parameters.
@@ -10,29 +10,29 @@ function [PostMod,PostVar,Scale,PostMean] = gmhmaxlik_core(ObjFun,xparam1,mh_bou
% o iScale [double] scalar specifying the initial of the jumping distribution's scale parameter.
% o info [char] string, empty or equal to 'LastCall'.
% o MeanPar [double] (p*1) vector specifying the initial posterior mean.
-% o VarCov [double] (p*p) matrix specifying the initial posterior covariance matrix.
+% o VarCov [double] (p*p) matrix specifying the initial posterior covariance matrix.
% o gend [integer] scalar specifying the number of observations ==> varargin{1}.
% o data [double] (T*n) matrix of data ==> varargin{2}.
-%
-% OUTPUTS
+%
+% OUTPUTS
% o PostMod [double] (p*1) vector, evaluation of the posterior mode.
% o PostVar [double] (p*p) matrix, evaluation of the posterior covariance matrix.
-% o Scale [double] scalar specifying the scale parameter that should be used in
-% an eventual metropolis-hastings algorithm.
-% o PostMean [double] (p*1) vector, evaluation of the posterior mean.
+% o Scale [double] scalar specifying the scale parameter that should be used in
+% an eventual metropolis-hastings algorithm.
+% o PostMean [double] (p*1) vector, evaluation of the posterior mean.
%
-% ALGORITHM
+% ALGORITHM
% Metropolis-Hastings with an constantly updated covariance matrix for
% the jump distribution. The posterior mean, variance and mode are
% updated (in step 2) with the following rules:
%
-% \[
-% \mu_t = \mu_{t-1} + \frac{1}{t}\left(\theta_t-\mu_{t-1}\right)
-% \]
+% \[
+% \mu_t = \mu_{t-1} + \frac{1}{t}\left(\theta_t-\mu_{t-1}\right)
+% \]
%
-% \[
-% \Sigma_t = \Sigma_{t-1} + \mu_{t-1}\mu_{t-1}'-\mu_{t}\mu_{t}' +
-% \frac{1}{t}\left(\theta_t\theta_t'-\Sigma_{t-1}-\mu_{t-1}\mu_{t-1}'\right)
+% \[
+% \Sigma_t = \Sigma_{t-1} + \mu_{t-1}\mu_{t-1}'-\mu_{t}\mu_{t}' +
+% \frac{1}{t}\left(\theta_t\theta_t'-\Sigma_{t-1}-\mu_{t-1}\mu_{t-1}'\right)
% \]
%
% and
@@ -43,7 +43,7 @@ function [PostMod,PostVar,Scale,PostMean] = gmhmaxlik_core(ObjFun,xparam1,mh_bou
% \theta_t, & \hbox{if } p(\theta_t|\mathcal Y) > p(\mathrm{mode}_{t-1}|\mathcal Y) \\
% \mathrm{mode}_{t-1}, & \hbox{otherwise.}
% \end{array}
-% \right.
+% \right.
% \]
%
% where $t$ is the iteration, $\mu_t$ the estimate of the posterior mean
@@ -51,7 +51,7 @@ function [PostMod,PostVar,Scale,PostMean] = gmhmaxlik_core(ObjFun,xparam1,mh_bou
% covariance matrix after $t$ iterations, $\mathrm{mode}_t$ is the
% evaluation of the posterior mode after $t$ iterations and
% $p(\theta_t|\mathcal Y)$ is the posterior density of parameters
-% (specified by the user supplied function "fun").
+% (specified by the user supplied function "fun").
%
% SPECIAL REQUIREMENTS
% None.
@@ -94,7 +94,7 @@ isux = 0; jsux = 0; test = 0;
ix2 = ModePar;% initial condition!
ilogpo2 = - feval(ObjFun,ix2,varargin{:});% initial posterior density
mlogpo2 = ilogpo2;
-try
+try
dd = transpose(chol(CovJump));
catch
dd = eye(length(CovJump));
@@ -108,7 +108,7 @@ while j<=MaxNumberOfTuningSimulations
end
% I move if the proposal is enough likely...
if logpo2 > -inf && log(rand) < logpo2 - ilogpo2
- ix2 = proposal;
+ ix2 = proposal;
if logpo2 > mlogpo2
ModePar = proposal;
mlogpo2 = logpo2;
@@ -165,7 +165,7 @@ while j<= NumberOfIterations
ilogpo2 = logpo2;
isux = isux + 1;
jsux = jsux + 1;
- end% ... otherwise I don't move.
+ end% ... otherwise I don't move.
prtfrc = j/NumberOfIterations;
if mod(j, 10)==0
dyn_waitbar(prtfrc,hh,sprintf('Acceptance ratio: %f',isux/j));
@@ -184,7 +184,7 @@ PostMean = MeanPar;
%% this is the last call to the routine, and I climb the hill (without
%% updating the covariance matrix)...
if strcmpi(info,'LastCall')
-
+
hh = dyn_waitbar(0,'Tuning of the scale parameter...');
set(hh,'Name','Tuning of the scale parameter.'),
j = 1; jj = 1;
@@ -214,8 +214,8 @@ if strcmpi(info,'LastCall')
if mod(j, 10)==0
dyn_waitbar(prtfrc,hh,sprintf('Acceptance ratio [during last 1000]: %f [%f]',isux/j,jsux/jj));
end
- if j/1000 == round(j/1000)
- test1 = jsux/jj;
+ if j/1000 == round(j/1000)
+ test1 = jsux/jj;
cfactor = test1/AcceptanceTarget;
iScale = iScale*cfactor;
jsux = 0; jj = 0;
diff --git a/matlab/optimization/mr_gstep.m b/matlab/optimization/mr_gstep.m
index 58507527149f0640509e997d1461bb374c66eabd..0836a7a87efb99a604844ab967a06000466d1e82 100644
--- a/matlab/optimization/mr_gstep.m
+++ b/matlab/optimization/mr_gstep.m
@@ -103,5 +103,3 @@ inx = find(x<=bounds(:,1));
if ~isempty(inx)
x(inx) = bounds(inx,1)+eps;
end
-
-
diff --git a/matlab/optimization/mr_hessian.m b/matlab/optimization/mr_hessian.m
index 9b336c1225dde3f2da4ff8edafb33530ac4bf5f1..b234df9b7c2c19c265294cbb22381e53728c7bd1 100644
--- a/matlab/optimization/mr_hessian.m
+++ b/matlab/optimization/mr_hessian.m
@@ -33,7 +33,7 @@ function [hessian_mat, gg, htol1, ihh, hh_mat0, hh1, hess_info] = mr_hessian(x,f
% varargin{6} --> BayesInfo
% varargin{7} --> Bounds
% varargin{8} --> DynareResults
-%
+%
% Outputs
% - hessian_mat hessian
% - gg Jacobian
@@ -223,7 +223,7 @@ if outer_product_gradient
hh_mat=gga'*gga; % rescaled outer product hessian
hh_mat0=ggh'*ggh; % outer product hessian
A=diag(2.*hess_info.h1); % rescaling matrix
- % igg=inv(hh_mat); % inverted rescaled outer product hessian
+ % igg=inv(hh_mat); % inverted rescaled outer product hessian
ihh=A'*(hh_mat\A); % inverted outer product hessian
if hflag>0 && min(eig(reshape(hessian_mat,n,n)))>0
hh0 = A*reshape(hessian_mat,n,n)*A'; %rescaled second order derivatives
@@ -244,12 +244,12 @@ if outer_product_gradient
igg=inv(A)'*ihh*inv(A); % inverted rescaled outer product hessian with modified std's
hh_mat=inv(igg); % outer product rescaled hessian with modified std's
hh_mat0=inv(A)'*hh_mat*inv(A); % outer product hessian with modified std's
- % sd0=sqrt(1./diag(hh0)); %rescaled 'standard errors' using second order derivatives
- % sd=sqrt(diag(igg)); %rescaled 'standard errors' using outer product
- % igg=igg./(sd*sd').*(sd0*sd0'); %rescaled inverse outer product with 'true' std's
- % hh_mat=inv(igg); % rescaled outer product hessian with 'true' std's
- % ihh=A'*igg*A; % inverted outer product hessian
- % hh_mat0=inv(A)'*hh_mat*inv(A); % outer product hessian with 'true' std's
+ % sd0=sqrt(1./diag(hh0)); %rescaled 'standard errors' using second order derivatives
+ % sd=sqrt(diag(igg)); %rescaled 'standard errors' using outer product
+ % igg=igg./(sd*sd').*(sd0*sd0'); %rescaled inverse outer product with 'true' std's
+ % hh_mat=inv(igg); % rescaled outer product hessian with 'true' std's
+ % ihh=A'*igg*A; % inverted outer product hessian
+ % hh_mat0=inv(A)'*hh_mat*inv(A); % outer product hessian with 'true' std's
end
if hflag<2
hessian_mat=hh_mat0(:);
diff --git a/matlab/optimization/newrat.m b/matlab/optimization/newrat.m
index 8dbdf5d330e9826f17f131cca84e933d2223f74f..c554a22dc14a5111342e9c9281f7749d6eca3a9d 100644
--- a/matlab/optimization/newrat.m
+++ b/matlab/optimization/newrat.m
@@ -11,14 +11,14 @@ function [xparam1, hh, gg, fval, igg, hess_info] = newrat(func0, x, bounds, anal
% - analytic_derivation 1 if analytic derivatives, 0 otherwise
% - ftol0 termination criterion for function change
% - nit maximum number of iterations
-% - flagg Indicator how to compute final Hessian (In each iteration, Hessian is computed with outer product gradient)
+% - flagg Indicator how to compute final Hessian (In each iteration, Hessian is computed with outer product gradient)
% 0: final Hessian computed with outer product gradient
-% 1: final 'mixed' Hessian: diagonal elements computed with
-% numerical second order derivatives with correlation structure
+% 1: final 'mixed' Hessian: diagonal elements computed with
+% numerical second order derivatives with correlation structure
% as from outer product gradient
% 2: full numerical Hessian
% - Verbose 1 if explicit output is requested
-% - Save_files 1 if intermediate output is to be saved
+% - Save_files 1 if intermediate output is to be saved
% - hess_info structure storing the step sizes for
% computation of Hessian
% - varargin other inputs:
@@ -30,7 +30,7 @@ function [xparam1, hh, gg, fval, igg, hess_info] = newrat(func0, x, bounds, anal
% varargin{6} --> BayesInfo
% varargin{7} --> Bounds
% varargin{8} --> DynareResults
-%
+%
% Outputs
% - xparam1 parameter vector at optimum
% - hh hessian
@@ -56,7 +56,7 @@ function [xparam1, hh, gg, fval, igg, hess_info] = newrat(func0, x, bounds, anal
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-% initialize variable penalty
+% initialize variable penalty
penalty = 1e8;
icount=0;
@@ -200,21 +200,21 @@ while norm(gg)>gtol && check==0 && jit<nit
hhg=hh;
H = inv(hh);
else
- if flagit==2
- hh=hh0;
- elseif flagg>0
- [dum, gg, htol0, igg, hhg, h1, hess_info]=mr_hessian(xparam1,func0,penalty,flagg,ftol0,hess_info,varargin{:});
- if flagg==2
- hh = reshape(dum,nx,nx);
- ee=eig(hh);
- if min(ee)<0
+ if flagit==2
+ hh=hh0;
+ elseif flagg>0
+ [dum, gg, htol0, igg, hhg, h1, hess_info]=mr_hessian(xparam1,func0,penalty,flagg,ftol0,hess_info,varargin{:});
+ if flagg==2
+ hh = reshape(dum,nx,nx);
+ ee=eig(hh);
+ if min(ee)<0
+ hh=hhg;
+ end
+ else
hh=hhg;
end
- else
- hh=hhg;
end
end
- end
disp_verbose(['Actual dxnorm ',num2str(norm(x(:,end)-x(:,end-1)))],Verbose)
disp_verbose(['FVAL ',num2str(fval)],Verbose)
disp_verbose(['Improvement ',num2str(fval0(icount)-fval)],Verbose)
diff --git a/matlab/optimization/penalty_objective_function.m b/matlab/optimization/penalty_objective_function.m
index c245800c2f2ff93eef829c7d245a44594150034d..e0c24925123eaeeae03c06d9eeca8972fa2fe8d1 100644
--- a/matlab/optimization/penalty_objective_function.m
+++ b/matlab/optimization/penalty_objective_function.m
@@ -2,13 +2,13 @@ function [fval, exit_flag, arg1, arg2] = penalty_objective_function(x, fcn, base
% Encapsulates an objective function to be minimized, adding a penalty if necessary.
%
-% INPUTS
+% INPUTS
% - x [double] n*1 vector of instrument values.
% - fcn [fhandle] objective function.
% - base_penalty [double] scalar, base of the penality (typically the value of the objective at the previous iteration).
% - varagin [cell] additional parameters for fcn.
%
-% OUTPUTS
+% OUTPUTS
% - fval [double] scalar, value of the objective function at x.
% - exit_flag [integer] scalar, flag returned by fcn (third output).
% - arg1, arg2 fourth and fifth output arguments of the objective function.
diff --git a/matlab/optimization/simplex_optimization_routine.m b/matlab/optimization/simplex_optimization_routine.m
index 465528d84291bbb0d38627a0cd5e92b802af3091..ccd81367eb4a329ff116fa37cac8d65abee83dbe 100644
--- a/matlab/optimization/simplex_optimization_routine.m
+++ b/matlab/optimization/simplex_optimization_routine.m
@@ -7,7 +7,7 @@ function [x,fval,exitflag] = simplex_optimization_routine(objective_function,x,o
%
% The routine automatically restarts from the current solution while amelioration is possible.
%
-% INPUTS
+% INPUTS
% o objective_function [string] Name of the objective function to be minimized.
% o x [double] n*1 vector, starting guess of the optimization routine.
% o options [structure] Options of this implementation of the simplex algorithm.
@@ -23,7 +23,7 @@ function [x,fval,exitflag] = simplex_optimization_routine(objective_function,x,o
% varargin{6} --> BayesInfo
% varargin{1} --> DynareResults
%
-% OUTPUTS
+% OUTPUTS
% o x [double] n*1 vector, estimate of the optimal inputs.
% o fval [double] scalar, value of the objective at the optimum.
% o exitflag [integer] scalar equal to 0 or 1 (0 if the algorithm did not converge to
@@ -251,7 +251,7 @@ while (func_count < max_func_calls) && (iter_count < max_iterations) && (simplex
fxr = feval(objective_function,x,varargin{:});
func_count = func_count+1;
if fxr < fv(1)% xr is better than previous best point v(:,1).
- % Calculate the expansion point
+ % Calculate the expansion point
xe = xbar + rho*chi*(xbar-v(:,end));
x = xe;
fxe = feval(objective_function,x,varargin{:});
@@ -499,7 +499,7 @@ while (func_count < max_func_calls) && (iter_count < max_iterations) && (simplex
else
break
end
- end
+ end
end% while loop.
x(:) = v(:,1);
@@ -520,43 +520,43 @@ end
function [v,fv,delta] = simplex_initialization(objective_function,point,point_score,delta,zero_delta,check_delta,varargin)
- n = length(point);
- v = zeros(n,n+1);
- v(:,1) = point;
- fv = zeros(n+1,1);
- fv(1) = point_score;
- if length(delta)==1
- delta = repmat(delta,n,1);
+n = length(point);
+v = zeros(n,n+1);
+v(:,1) = point;
+fv = zeros(n+1,1);
+fv(1) = point_score;
+if length(delta)==1
+ delta = repmat(delta,n,1);
+end
+for j = 1:n
+ y = point;
+ if y(j) ~= 0
+ y(j) = (1 + delta(j))*y(j);
+ else
+ y(j) = zero_delta;
end
- for j = 1:n
- y = point;
- if y(j) ~= 0
- y(j) = (1 + delta(j))*y(j);
- else
- y(j) = zero_delta;
- end
- v(:,j+1) = y;
- x = y;
- [fv(j+1),junk1,junk2,nopenalty_flag] = feval(objective_function,x,varargin{:});
- if check_delta
- while ~nopenalty_flag
- if y(j)~=0
- delta(j) = delta(j)/1.1;
- else
- zero_delta = zero_delta/1.1;
- end
- y = point;
- if y(j) ~= 0
- y(j) = (1 + delta(j))*y(j);
- else
- y(j) = zero_delta;
- end
- v(:,j+1) = y;
- x = y;
- [fv(j+1),junk1,junk2,nopenalty_flag] = feval(objective_function,x,varargin{:});
+ v(:,j+1) = y;
+ x = y;
+ [fv(j+1),junk1,junk2,nopenalty_flag] = feval(objective_function,x,varargin{:});
+ if check_delta
+ while ~nopenalty_flag
+ if y(j)~=0
+ delta(j) = delta(j)/1.1;
+ else
+ zero_delta = zero_delta/1.1;
end
+ y = point;
+ if y(j) ~= 0
+ y(j) = (1 + delta(j))*y(j);
+ else
+ y(j) = zero_delta;
+ end
+ v(:,j+1) = y;
+ x = y;
+ [fv(j+1),junk1,junk2,nopenalty_flag] = feval(objective_function,x,varargin{:});
end
end
- % Sort by increasing order of the objective function values.
- [fv,sort_idx] = sort(fv);
- v = v(:,sort_idx);
\ No newline at end of file
+end
+% Sort by increasing order of the objective function values.
+[fv,sort_idx] = sort(fv);
+v = v(:,sort_idx);
\ No newline at end of file
diff --git a/matlab/optimization/simpsa.m b/matlab/optimization/simpsa.m
index b389b37bdae7501917e732217e50b0c9c05ed539..814618d415cb48febd3c48739328abbe5b3de64c 100644
--- a/matlab/optimization/simpsa.m
+++ b/matlab/optimization/simpsa.m
@@ -1,8 +1,8 @@
function [X,FVAL,EXITFLAG,OUTPUT] = simpsa(FUN,X0,LB,UB,OPTIONS,varargin)
-% Finds a minimum of a function of several variables using an algorithm
-% that is based on the combination of the non-linear smplex and the simulated
-% annealing algorithm (the SIMPSA algorithm, Cardoso et al., 1996).
+% Finds a minimum of a function of several variables using an algorithm
+% that is based on the combination of the non-linear smplex and the simulated
+% annealing algorithm (the SIMPSA algorithm, Cardoso et al., 1996).
% In this paper, the algorithm is shown to be adequate for the global optimi-
% zation of an example set of unconstrained and constrained NLP functions.
%
@@ -11,48 +11,48 @@ function [X,FVAL,EXITFLAG,OUTPUT] = simpsa(FUN,X0,LB,UB,OPTIONS,varargin)
% X
%
% Algorithm partly is based on paper of Cardoso et al, 1996.
-%
-% X=SIMPSA(FUN,X0) start at X0 and finds a minimum X to the function FUN.
+%
+% X=SIMPSA(FUN,X0) start at X0 and finds a minimum X to the function FUN.
% FUN accepts input X and returns a scalar function value F evaluated at X.
% X0 may be a scalar, vector, or matrix.
-%
-% X=SIMPSA(FUN,X0,LB,UB) defines a set of lower and upper bounds on the
-% design variables, X, so that a solution is found in the range
-% LB <= X <= UB. Use empty matrices for LB and UB if no bounds exist.
-% Set LB(i) = -Inf if X(i) is unbounded below; set UB(i) = Inf if X(i) is
+%
+% X=SIMPSA(FUN,X0,LB,UB) defines a set of lower and upper bounds on the
+% design variables, X, so that a solution is found in the range
+% LB <= X <= UB. Use empty matrices for LB and UB if no bounds exist.
+% Set LB(i) = -Inf if X(i) is unbounded below; set UB(i) = Inf if X(i) is
% unbounded above.
-%
+%
% X=SIMPSA(FUN,X0,LB,UB,OPTIONS) minimizes with the default optimization
-% parameters replaced by values in the structure OPTIONS, an argument
-% created with the SIMPSASET function. See SIMPSASET for details.
+% parameters replaced by values in the structure OPTIONS, an argument
+% created with the SIMPSASET function. See SIMPSASET for details.
% Used options are TEMP_START, TEMP_END, COOL_RATE, INITIAL_ACCEPTANCE_RATIO,
% MIN_COOLING_FACTOR, MAX_ITER_TEMP_FIRST, MAX_ITER_TEMP_LAST, MAX_ITER_TEMP,
% MAX_ITER_TOTAL, MAX_TIME, MAX_FUN_EVALS, TOLX, TOLFUN, DISPLAY and OUTPUT_FCN.
% Use OPTIONS = [] as a place holder if no options are set.
-%
-% X=SIMPSA(FUN,X0,LB,UB,OPTIONS,varargin) is used to supply a variable
+%
+% X=SIMPSA(FUN,X0,LB,UB,OPTIONS,varargin) is used to supply a variable
% number of input arguments to the objective function FUN.
-%
-% [X,FVAL]=SIMPSA(FUN,X0,...) returns the value of the objective
+%
+% [X,FVAL]=SIMPSA(FUN,X0,...) returns the value of the objective
% function FUN at the solution X.
-%
-% [X,FVAL,EXITFLAG]=SIMPSA(FUN,X0,...) returns an EXITFLAG that describes the
-% exit condition of SIMPSA. Possible values of EXITFLAG and the corresponding
+%
+% [X,FVAL,EXITFLAG]=SIMPSA(FUN,X0,...) returns an EXITFLAG that describes the
+% exit condition of SIMPSA. Possible values of EXITFLAG and the corresponding
% exit conditions are:
-%
+%
% 1 Change in the objective function value less than the specified tolerance.
% 2 Change in X less than the specified tolerance.
% 0 Maximum number of function evaluations or iterations reached.
% -1 Maximum time exceeded.
-%
-% [X,FVAL,EXITFLAG,OUTPUT]=SIMPSA(FUN,X0,...) returns a structure OUTPUT with
+%
+% [X,FVAL,EXITFLAG,OUTPUT]=SIMPSA(FUN,X0,...) returns a structure OUTPUT with
% the number of iterations taken in OUTPUT.nITERATIONS, the number of function
% evaluations in OUTPUT.nFUN_EVALS, the temperature profile in OUTPUT.TEMPERATURE,
-% the simplexes that were evaluated in OUTPUT.SIMPLEX and the best one in
-% OUTPUT.SIMPLEX_BEST, the costs associated with each simplex in OUTPUT.COSTS and
-% from the best simplex at that iteration in OUTPUT.COST_BEST, the amount of time
+% the simplexes that were evaluated in OUTPUT.SIMPLEX and the best one in
+% OUTPUT.SIMPLEX_BEST, the costs associated with each simplex in OUTPUT.COSTS and
+% from the best simplex at that iteration in OUTPUT.COST_BEST, the amount of time
% needed in OUTPUT.TIME and the options used in OUTPUT.OPTIONS.
-%
+%
% See also SIMPSASET, SIMPSAGET
@@ -122,20 +122,20 @@ NDIM = length(X0);
% set default options
DEFAULT_OPTIONS = simpsaset('TEMP_START',[],... % starting temperature (if none provided, an optimal one will be estimated)
- 'TEMP_END',.1,... % end temperature
- 'COOL_RATE',10,... % small values (<1) means slow convergence,large values (>1) means fast convergence
- 'INITIAL_ACCEPTANCE_RATIO',0.95,... % when initial temperature is estimated, this will be the initial acceptance ratio in the first round
- 'MIN_COOLING_FACTOR',0.9,... % minimum cooling factor (<1)
- 'MAX_ITER_TEMP_FIRST',50,... % number of iterations in the preliminary temperature loop
- 'MAX_ITER_TEMP_LAST',2000,... % number of iterations in the last temperature loop (pure simplex)
- 'MAX_ITER_TEMP',10,... % number of iterations in the remaining temperature loops
- 'MAX_ITER_TOTAL',2500,... % maximum number of iterations tout court
- 'MAX_TIME',2500,... % maximum duration of optimization
- 'MAX_FUN_EVALS',20000,... % maximum number of function evaluations
- 'TOLX',1e-6,... % maximum difference between best and worst function evaluation in simplex
- 'TOLFUN',1e-6,... % maximum difference between the coordinates of the vertices
- 'DISPLAY','iter',... % 'iter' or 'none' indicating whether user wants feedback
- 'OUTPUT_FCN',[]); % string with output function name
+ 'TEMP_END',.1,... % end temperature
+ 'COOL_RATE',10,... % small values (<1) means slow convergence,large values (>1) means fast convergence
+ 'INITIAL_ACCEPTANCE_RATIO',0.95,... % when initial temperature is estimated, this will be the initial acceptance ratio in the first round
+ 'MIN_COOLING_FACTOR',0.9,... % minimum cooling factor (<1)
+ 'MAX_ITER_TEMP_FIRST',50,... % number of iterations in the preliminary temperature loop
+ 'MAX_ITER_TEMP_LAST',2000,... % number of iterations in the last temperature loop (pure simplex)
+ 'MAX_ITER_TEMP',10,... % number of iterations in the remaining temperature loops
+ 'MAX_ITER_TOTAL',2500,... % maximum number of iterations tout court
+ 'MAX_TIME',2500,... % maximum duration of optimization
+ 'MAX_FUN_EVALS',20000,... % maximum number of function evaluations
+ 'TOLX',1e-6,... % maximum difference between best and worst function evaluation in simplex
+ 'TOLFUN',1e-6,... % maximum difference between the coordinates of the vertices
+ 'DISPLAY','iter',... % 'iter' or 'none' indicating whether user wants feedback
+ 'OUTPUT_FCN',[]); % string with output function name
% update default options with supplied options
@@ -196,25 +196,25 @@ nITERATIONS = 0;
% -----------------------------------------------------------
while 1
-
+
% detect if termination criterium was met
% ---------------------------------------
-
+
% if a termination criterium was met, the value of EXITFLAG should have changed
% from its default value of -2 to -1, 0, 1 or 2
-
+
if EXITFLAG ~= -2
break
end
-
+
% set MAXITERTEMP: maximum number of iterations at current temperature
% --------------------------------------------------------------------
-
+
if TEMP_LOOP_NUMBER == 1
MAXITERTEMP = OPTIONS.MAX_ITER_TEMP_FIRST*NDIM;
- % The initial temperature is estimated (is requested) as described in
- % Cardoso et al. (1996). Therefore, we need to store the number of
- % successful and unsuccessful moves, as well as the increase in cost
+ % The initial temperature is estimated (is requested) as described in
+ % Cardoso et al. (1996). Therefore, we need to store the number of
+ % successful and unsuccessful moves, as well as the increase in cost
% for the unsuccessful moves.
if isempty(OPTIONS.TEMP_START)
[SUCCESSFUL_MOVES,UNSUCCESSFUL_MOVES,UNSUCCESSFUL_COSTS] = deal(0);
@@ -225,19 +225,19 @@ while 1
else
MAXITERTEMP = OPTIONS.MAX_ITER_TEMP*NDIM;
end
-
+
% construct initial simplex
% -------------------------
-
+
% 1st vertex of initial simplex
P(1,:) = PBEST;
Y(1) = CALCULATE_COST(FUN,P(1,:),LB,UB,varargin{:});
-
+
% if output function given then run output function to plot intermediate result
if ~isempty(OPTIONS.OUTPUT_FCN)
feval(OPTIONS.OUTPUT_FCN,transpose(P(1,:)),Y(1));
end
-
+
% remaining vertices of simplex
for k = 1:NDIM
% copy first vertex in new vertex
@@ -247,17 +247,17 @@ while 1
% calculate value of objective function at new vertex
Y(k+1) = CALCULATE_COST(FUN,P(k+1,:),LB,UB,varargin{:});
end
-
+
% store information on what step the algorithm just did
ALGOSTEP = 'initial simplex';
-
+
% add NDIM+1 to number of function evaluations
nFUN_EVALS = nFUN_EVALS + NDIM;
-
+
% note:
% dimensions of matrix P: (NDIM+1) x NDIM
% dimensions of vector Y: (NDIM+1) x 1
-
+
% give user feedback if requested
if strcmp(OPTIONS.DISPLAY,'iter')
if nITERATIONS == 0
@@ -269,67 +269,67 @@ while 1
% run full metropolis cycle at current temperature
% ------------------------------------------------
-
+
% initialize vector COSTS, needed to calculate new temperature using cooling
% schedule as described by Cardoso et al. (1996)
COSTS = zeros((NDIM+1)*MAXITERTEMP,1);
-
+
% initialize ITERTEMP to zero
-
+
ITERTEMP = 0;
-
+
% start
for ITERTEMP = 1:MAXITERTEMP
-
+
% add one to number of iterations
nITERATIONS = nITERATIONS + 1;
-
+
% Press and Teukolsky (1991) add a positive logarithmic distributed variable,
- % proportional to the control temperature T to the function value associated with
- % every vertex of the simplex. Likewise,they subtract a similar random variable
+ % proportional to the control temperature T to the function value associated with
+ % every vertex of the simplex. Likewise,they subtract a similar random variable
% from the function value at every new replacement point.
% Thus, if the replacement point corresponds to a lower cost, this method always
- % accepts a true down hill step. If, on the other hand, the replacement point
+ % accepts a true down hill step. If, on the other hand, the replacement point
% corresponds to a higher cost, an uphill move may be accepted, depending on the
% relative COSTS of the perturbed values.
% (taken from Cardoso et al.,1996)
-
+
% add random fluctuations to function values of current vertices
YFLUCT = Y+TEMP*abs(log(rand(NDIM+1,1)));
-
+
% reorder YFLUCT, Y and P so that the first row corresponds to the lowest YFLUCT value
help = sortrows([YFLUCT,Y,P],1);
YFLUCT = help(:,1);
Y = help(:,2);
P = help(:,3:end);
-
+
% store temperature at current iteration
OUTPUT.TEMPERATURE(nITERATIONS) = TEMP;
% store information about simplex at the current iteration
OUTPUT.SIMPLEX(:,:,nITERATIONS) = P;
OUTPUT.SIMPLEX_BEST(nITERATIONS,:) = PBEST;
-
+
% store cost function value of best vertex in current iteration
OUTPUT.COSTS(nITERATIONS,:) = Y;
OUTPUT.COST_BEST(nITERATIONS) = YBEST;
-
+
if strcmp(OPTIONS.DISPLAY,'iter')
disp(sprintf('%5.0f %5.0f %12.6g %15.6g %12.6g %s',nITERATIONS,nFUN_EVALS,Y(1),YBEST,TEMP,ALGOSTEP));
end
-
+
% if output function given then run output function to plot intermediate result
if ~isempty(OPTIONS.OUTPUT_FCN)
feval(OPTIONS.OUTPUT_FCN,transpose(P(1,:)),Y(1));
end
-
+
% end the optimization if one of the stopping criteria is met
- %% 1. difference between best and worst function evaluation in simplex is smaller than TOLFUN
+ %% 1. difference between best and worst function evaluation in simplex is smaller than TOLFUN
%% 2. maximum difference between the coordinates of the vertices in simplex is less than TOLX
%% 3. no convergence,but maximum number of iterations has been reached
%% 4. no convergence,but maximum time has been reached
-
+
if (abs(max(Y)-min(Y)) < OPTIONS.TOLFUN) && (TEMP_LOOP_NUMBER ~= 1)
if strcmp(OPTIONS.DISPLAY,'iter')
disp('Change in the objective function value less than the specified tolerance (TOLFUN).')
@@ -337,7 +337,7 @@ while 1
EXITFLAG = 1;
break
end
-
+
if (max(max(abs(P(2:NDIM+1,:)-P(1:NDIM,:)))) < OPTIONS.TOLX) && (TEMP_LOOP_NUMBER ~= 1)
if strcmp(OPTIONS.DISPLAY,'iter')
disp('Change in X less than the specified tolerance (TOLX).')
@@ -345,7 +345,7 @@ while 1
EXITFLAG = 2;
break
end
-
+
if (nITERATIONS >= OPTIONS.MAX_ITER_TOTAL*NDIM) || (nFUN_EVALS >= OPTIONS.MAX_FUN_EVALS*NDIM*(NDIM+1))
if strcmp(OPTIONS.DISPLAY,'iter')
disp('Maximum number of function evaluations or iterations reached.');
@@ -353,7 +353,7 @@ while 1
EXITFLAG = 0;
break
end
-
+
if toc/60 > OPTIONS.MAX_TIME
if strcmp(OPTIONS.DISPLAY,'iter')
disp('Exceeded maximum time.');
@@ -361,13 +361,13 @@ while 1
EXITFLAG = -1;
break
end
-
+
% begin a new iteration
-
+
%% first extrapolate by a factor -1 through the face of the simplex
%% across from the high point,i.e.,reflect the simplex from the high point
[YFTRY,YTRY,PTRY] = AMOTRY(FUN,P,-1,LB,UB,varargin{:});
-
+
%% check the result
if YFTRY <= YFLUCT(1)
%% gives a result better than the best point,so try an additional
@@ -407,11 +407,11 @@ while 1
Y(end) = YTRY;
ALGOSTEP = 'reflection';
end
-
- % the initial temperature is estimated in the first loop from
- % the number of successfull and unsuccesfull moves, and the average
+
+ % the initial temperature is estimated in the first loop from
+ % the number of successfull and unsuccesfull moves, and the average
% increase in cost associated with the unsuccessful moves
-
+
if TEMP_LOOP_NUMBER == 1 && isempty(OPTIONS.TEMP_START)
if Y(1) > Y(end)
SUCCESSFUL_MOVES = SUCCESSFUL_MOVES+1;
@@ -427,14 +427,14 @@ while 1
if ITERTEMP < MAXITERTEMP
break
end
-
+
% store cost function values in COSTS vector
COSTS((ITERTEMP-1)*NDIM+1:ITERTEMP*NDIM+1) = Y;
-
- % calculated initial temperature or recalculate temperature
+
+ % calculated initial temperature or recalculate temperature
% using cooling schedule as proposed by Cardoso et al. (1996)
% -----------------------------------------------------------
-
+
if TEMP_LOOP_NUMBER == 1 && isempty(OPTIONS.TEMP_START)
TEMP = -(UNSUCCESSFUL_COSTS/(SUCCESSFUL_MOVES+UNSUCCESSFUL_MOVES))/log(((SUCCESSFUL_MOVES+UNSUCCESSFUL_MOVES)*OPTIONS.INITIAL_ACCEPTANCE_RATIO-SUCCESSFUL_MOVES)/UNSUCCESSFUL_MOVES);
elseif TEMP_LOOP_NUMBER ~= 0
@@ -442,10 +442,10 @@ while 1
COOLING_FACTOR = 1/(1+TEMP*log(1+OPTIONS.COOL_RATE)/(3*STDEV_Y));
TEMP = TEMP*min(OPTIONS.MIN_COOLING_FACTOR,COOLING_FACTOR);
end
-
+
% add one to temperature loop number
TEMP_LOOP_NUMBER = TEMP_LOOP_NUMBER+1;
-
+
end
% return solution
@@ -476,8 +476,8 @@ return
% ---------------
function [YFTRY,YTRY,PTRY] = AMOTRY(FUN,P,fac,LB,UB,varargin)
-% Extrapolates by a factor fac through the face of the simplex across from
-% the high point, tries it, and replaces the high point if the new point is
+% Extrapolates by a factor fac through the face of the simplex across from
+% the high point, tries it, and replaces the high point if the new point is
% better.
global NDIM TEMP
diff --git a/matlab/optimization/simpsaget.m b/matlab/optimization/simpsaget.m
index e1c775495ec2ce9a944d9babfe3fcbc26ad66c0f..318ea4313413da3e011850ea5b996e68afa6d52e 100644
--- a/matlab/optimization/simpsaget.m
+++ b/matlab/optimization/simpsaget.m
@@ -38,25 +38,25 @@ function o = simpsaget(options,name,default,flag)
% undocumented usage for fast access with no error checking
if (nargin == 4) && isequal(flag,'fast')
- o = getknownfield(options,name,default);
- return
+ o = getknownfield(options,name,default);
+ return
end
if nargin < 2
- error('MATLAB:odeget:NotEnoughInputs','Not enough input arguments.');
+ error('MATLAB:odeget:NotEnoughInputs','Not enough input arguments.');
end
if nargin < 3
- default = [];
+ default = [];
end
if ~isempty(options) && ~isa(options,'struct')
- error('MATLAB:odeget:Arg1NotODESETstruct',...
- 'First argument must be an options structure created with ODESET.');
+ error('MATLAB:odeget:Arg1NotODESETstruct',...
+ 'First argument must be an options structure created with ODESET.');
end
if isempty(options)
- o = default;
- return
+ o = default;
+ return
end
Names = [
@@ -75,39 +75,39 @@ Names = [
'TOLFUN '
'DISPLAY '
'OUTPUT_FCN '
- ];
+ ];
names = lower(Names);
lowName = lower(name);
j = strmatch(lowName,names);
if isempty(j) % if no matches
- error('MATLAB:odeget:InvalidPropName',...
- ['Unrecognized property name ''%s''. ' ...
- 'See ODESET for possibilities.'], name);
+ error('MATLAB:odeget:InvalidPropName',...
+ ['Unrecognized property name ''%s''. ' ...
+ 'See ODESET for possibilities.'], name);
elseif length(j) > 1 % if more than one match
- % Check for any exact matches (in case any names are subsets of others)
- k = strmatch(lowName,names,'exact');
- if length(k) == 1
- j = k;
- else
- msg = sprintf('Ambiguous property name ''%s'' ', name);
- msg = [msg '(' deblank(Names(j(1),:))];
- for k = j(2:length(j))'
- msg = [msg ', ' deblank(Names(k,:))];
+ % Check for any exact matches (in case any names are subsets of others)
+ k = strmatch(lowName,names,'exact');
+ if length(k) == 1
+ j = k;
+ else
+ msg = sprintf('Ambiguous property name ''%s'' ', name);
+ msg = [msg '(' deblank(Names(j(1),:))];
+ for k = j(2:length(j))'
+ msg = [msg ', ' deblank(Names(k,:))];
+ end
+ msg = sprintf('%s).', msg);
+ error('MATLAB:odeget:AmbiguousPropName', msg);
end
- msg = sprintf('%s).', msg);
- error('MATLAB:odeget:AmbiguousPropName', msg);
- end
end
if any(strcmp(fieldnames(options),deblank(Names(j,:))))
- o = options.(deblank(Names(j,:)));
- if isempty(o)
- o = default;
- end
+ o = options.(deblank(Names(j,:)));
+ if isempty(o)
+ o = default;
+ end
else
- o = default;
+ o = default;
end
% --------------------------------------------------------------------------
@@ -115,11 +115,10 @@ function v = getknownfield(s, f, d)
%GETKNOWNFIELD Get field f from struct s, or else yield default d.
if isfield(s,f) % s could be empty.
- v = subsref(s, struct('type','.','subs',f));
- if isempty(v)
- v = d;
- end
+ v = subsref(s, struct('type','.','subs',f));
+ if isempty(v)
+ v = d;
+ end
else
- v = d;
+ v = d;
end
-
diff --git a/matlab/optimization/simpsaset.m b/matlab/optimization/simpsaset.m
index dc0aade4faa87e72dab2f15a21708f696080dedd..16e1919c1f502b22c0d56e1ec4a99ce4df77f83d 100644
--- a/matlab/optimization/simpsaset.m
+++ b/matlab/optimization/simpsaset.m
@@ -64,7 +64,7 @@ if (nargin == 0) && (nargout == 0)
fprintf(' DISPLAY: [ ''iter'' or ''none'' {''iter''} ]\n');
fprintf(' OUTPUT_FCN: [ function_handle ]\n');
fprintf('\n');
-return
+ return
end
Names = [
@@ -83,7 +83,7 @@ Names = [
'TOLFUN '
'DISPLAY '
'OUTPUT_FCN '
- ];
+ ];
m = size(Names,1);
names = lower(Names);
@@ -91,82 +91,82 @@ names = lower(Names);
% Combine all leading options structures o1, o2, ... in odeset(o1,o2,...).
options = [];
for j = 1:m
- options.(deblank(Names(j,:))) = [];
+ options.(deblank(Names(j,:))) = [];
end
i = 1;
while i <= nargin
- arg = varargin{i};
- if ischar(arg) % arg is an option name
- break
- end
- if ~isempty(arg) % [] is a valid options argument
- if ~isa(arg,'struct')
- error('MATLAB:odeset:NoPropNameOrStruct',...
- ['Expected argument %d to be a string property name ' ...
- 'or an options structure\ncreated with SIMANSET.'], i);
+ arg = varargin{i};
+ if ischar(arg) % arg is an option name
+ break
end
- for j = 1:m
- if any(strcmp(fieldnames(arg),deblank(Names(j,:))))
- val = arg.(deblank(Names(j,:)));
- else
- val = [];
- end
- if ~isempty(val)
- options.(deblank(Names(j,:))) = val;
- end
+ if ~isempty(arg) % [] is a valid options argument
+ if ~isa(arg,'struct')
+ error('MATLAB:odeset:NoPropNameOrStruct',...
+ ['Expected argument %d to be a string property name ' ...
+ 'or an options structure\ncreated with SIMANSET.'], i);
+ end
+ for j = 1:m
+ if any(strcmp(fieldnames(arg),deblank(Names(j,:))))
+ val = arg.(deblank(Names(j,:)));
+ else
+ val = [];
+ end
+ if ~isempty(val)
+ options.(deblank(Names(j,:))) = val;
+ end
+ end
end
- end
- i = i + 1;
+ i = i + 1;
end
% A finite state machine to parse name-value pairs.
if rem(nargin-i+1,2) ~= 0
- error('MATLAB:odeset:ArgNameValueMismatch',...
- 'Arguments must occur in name-value pairs.');
+ error('MATLAB:odeset:ArgNameValueMismatch',...
+ 'Arguments must occur in name-value pairs.');
end
expectval = 0; % start expecting a name, not a value
while i <= nargin
- arg = varargin{i};
-
- if ~expectval
- if ~ischar(arg)
- error('MATLAB:odeset:NoPropName',...
- 'Expected argument %d to be a string property name.', i);
- end
-
- lowArg = lower(arg);
- j = strmatch(lowArg,names);
- if isempty(j) % if no matches
- error('MATLAB:odeset:InvalidPropName',...
- 'Unrecognized property name ''%s''.', arg);
- elseif length(j) > 1 % if more than one match
- % Check for any exact matches (in case any names are subsets of others)
- k = strmatch(lowArg,names,'exact');
- if length(k) == 1
- j = k;
- else
- msg = sprintf('Ambiguous property name ''%s'' ', arg);
- msg = [msg '(' deblank(Names(j(1),:))];
- for k = j(2:length(j))'
- msg = [msg ', ' deblank(Names(k,:))];
+ arg = varargin{i};
+
+ if ~expectval
+ if ~ischar(arg)
+ error('MATLAB:odeset:NoPropName',...
+ 'Expected argument %d to be a string property name.', i);
end
- msg = sprintf('%s).', msg);
- error('MATLAB:odeset:AmbiguousPropName', msg);
- end
+
+ lowArg = lower(arg);
+ j = strmatch(lowArg,names);
+ if isempty(j) % if no matches
+ error('MATLAB:odeset:InvalidPropName',...
+ 'Unrecognized property name ''%s''.', arg);
+ elseif length(j) > 1 % if more than one match
+ % Check for any exact matches (in case any names are subsets of others)
+ k = strmatch(lowArg,names,'exact');
+ if length(k) == 1
+ j = k;
+ else
+ msg = sprintf('Ambiguous property name ''%s'' ', arg);
+ msg = [msg '(' deblank(Names(j(1),:))];
+ for k = j(2:length(j))'
+ msg = [msg ', ' deblank(Names(k,:))];
+ end
+ msg = sprintf('%s).', msg);
+ error('MATLAB:odeset:AmbiguousPropName', msg);
+ end
+ end
+ expectval = 1; % we expect a value next
+
+ else
+ options.(deblank(Names(j,:))) = arg;
+ expectval = 0;
+
end
- expectval = 1; % we expect a value next
-
- else
- options.(deblank(Names(j,:))) = arg;
- expectval = 0;
-
- end
- i = i + 1;
+ i = i + 1;
end
if expectval
- error('MATLAB:odeset:NoValueForProp',...
- 'Expected value for property ''%s''.', arg);
+ error('MATLAB:odeset:NoValueForProp',...
+ 'Expected value for property ''%s''.', arg);
end
end
diff --git a/matlab/optimization/simulated_annealing.m b/matlab/optimization/simulated_annealing.m
index 35f0240e00db767b028ce4a851ede1c78ebdc9de..9637e64e92d7da924b5c7b6b2355696ecbcd52fd 100644
--- a/matlab/optimization/simulated_annealing.m
+++ b/matlab/optimization/simulated_annealing.m
@@ -5,7 +5,7 @@ function [xopt, fopt,exitflag, n_accepted_draws, n_total_draws, n_out_of_bounds_
%
% Implements the continuous simulated annealing global optimization
% algorithm described in Corana et al. (1987)
-%
+%
% A very quick (perhaps too quick) overview of SA:
% SA tries to find the global optimum of an N dimensional function.
% It moves both up and downhill and as the optimization process
@@ -49,17 +49,17 @@ function [xopt, fopt,exitflag, n_accepted_draws, n_total_draws, n_out_of_bounds_
% Note: The suggested values generally come from Corana et al. To
% drastically reduce runtime, see Goffe et al., pp. 90-1 for
% suggestions on choosing the appropriate RT and NT.
-%
+%
% fcn - function to be optimized.
% x - The starting values for the variables of the function to be
% optimized. (N)
% optim: Options structure with fields
-%
+%
% optim.maximizer_indicator - Denotes whether the function should be maximized or
% minimized. A value =1 denotes maximization while a
% value =0 denotes minimization. Intermediate output (see verbosity)
% takes this into account.
-% optim.RT - The temperature reduction factor
+% optim.RT - The temperature reduction factor
% optim.TolFun - Error tolerance for termination. If the final function
% values from the last neps temperatures differ from the
% corresponding value at the current temperature by less than
@@ -104,11 +104,11 @@ function [xopt, fopt,exitflag, n_accepted_draws, n_total_draws, n_out_of_bounds_
% of all points are accepted, the input value is not very
% important (i.e. is the value is off, SA adjusts VM to the
% correct value)
-%
-% lb - The lower bound for the allowable solution variables.
-% ub - The upper bound for the allowable solution variables.
+%
+% lb - The lower bound for the allowable solution variables.
+% ub - The upper bound for the allowable solution variables.
% If the algorithm chooses X(I) < LB(I) or X(I) > UB(I),
-% I = 1, N, a point is from inside is randomly selected.
+% I = 1, N, a point is from inside is randomly selected.
% This focuses the algorithm on the region inside UB and LB.
% Unless the user wishes to concentrate the search to a par-
% ticular region, UB and LB should be set to very large positive
@@ -116,8 +116,8 @@ function [xopt, fopt,exitflag, n_accepted_draws, n_total_draws, n_out_of_bounds_
% vector X should be inside this region. Also note that LB and
% UB are fixed in position, while VM is centered on the last
% accepted trial set of variables that optimizes the function.
-%
-%
+%
+%
% Input/Output Parameters:
%
% Output Parameters:
@@ -142,26 +142,26 @@ function [xopt, fopt,exitflag, n_accepted_draws, n_total_draws, n_out_of_bounds_
% t: On output, the final temperature.
% vm: Final step length vector
%
-% Algorithm:
+% Algorithm:
% This routine implements the continuous simulated annealing global
% optimization algorithm described in Corana et al.'s article
% "Minimizing Multimodal Functions of Continuous Variables with the
% "Simulated Annealing" Algorithm" in the September 1987 (vol. 13,
% no. 3, pp. 262-280) issue of the ACM Transactions on Mathematical
% Software.
-%
+%
% For modifications to the algorithm and many details on its use,
% (particularly for econometric applications) see Goffe, Ferrier
% and Rogers, "Global Optimization of Statistical Functions with
% Simulated Annealing," Journal of Econometrics, vol. 60, no. 1/2,
% Jan./Feb. 1994, pp. 65-100.
-%
+%
% Based on the Matlab code written by Thomas Werner (Bundesbank December
-% 2002), which in turn is based on the GAUSS version of Bill Goffe's simulated annealing
+% 2002), which in turn is based on the GAUSS version of Bill Goffe's simulated annealing
% program for global optimization, written by E.G.Tsionas (9/4/95).
-%
-% Copyright (C) 1995 E.G.Tsionas
-% Copyright (C) 1995-2002 Thomas Werner
+%
+% Copyright (C) 1995 E.G.Tsionas
+% Copyright (C) 1995-2002 Thomas Werner
% Copyright (C) 2002-2015 Giovanni Lombardo
% Copyright (C) 2015 Dynare Team
%
@@ -314,9 +314,9 @@ while (1>0)
if(pp<p)
if(optim.verbosity >=3)
if(optim.maximizer_indicator)
- fprintf('though lower, point accepted\n');
+ fprintf('though lower, point accepted\n');
else
- fprintf('though higher, point accepted\n');
+ fprintf('though higher, point accepted\n');
end
end
x=xp;
@@ -383,7 +383,7 @@ while (1>0)
if(optim.verbosity >=1)
fprintf('SA achieved termination criteria.exitflag=0\n');
end
- return
+ return
end
%* If termination criteria are not met, prepare for another loop. *
t=optim.rt*t;
@@ -401,7 +401,7 @@ end
function print_current_invalid_try(max,xp,x,fp,f)
fprintf('\n');
- disp(['Current x ' num2str(x(:)')]);
+disp(['Current x ' num2str(x(:)')]);
if(max)
disp(['Current f ' num2str(f)]);
else
diff --git a/matlab/optimization/solvopt.m b/matlab/optimization/solvopt.m
index 9976ad8502f14ae9a7e96b0b2fe3b821a158d18a..079a6d5b794befd92f899ec0a16cd1581f9e3ce3 100644
--- a/matlab/optimization/solvopt.m
+++ b/matlab/optimization/solvopt.m
@@ -1,10 +1,10 @@
function [x,f,exitflag,n_f_evals,n_grad_evals,n_constraint_evals,n_constraint_gradient_evals]=solvopt(x,fun,grad,func,gradc,optim,varargin)
% [x,f,options]=solvopt(x,fun,grad,func,gradc,options,varargin)
-%
-% The function SOLVOPT, developed by Alexei Kuntsevich and Franz Kappe,
+%
+% The function SOLVOPT, developed by Alexei Kuntsevich and Franz Kappe,
% performs a modified version of Shor's r-algorithm in
% order to find a local minimum resp. maximum of a nonlinear function
-% defined on the n-dimensional Euclidean space or % a solution of a nonlinear
+% defined on the n-dimensional Euclidean space or % a solution of a nonlinear
% constrained problem:
% min { f(x): g(x) (<)= 0, g(x) in R(m), x in R(n) }
%
@@ -41,7 +41,7 @@ function [x,f,exitflag,n_f_evals,n_grad_evals,n_constraint_evals,n_constraint_gr
% *optim.LBGradientStep= lower bound for the stepsize used for the difference
% approximation of gradients (1e-12 by default, see more in the manual).
% (* ... changes should be done with care)
-%
+%
% Outputs:
% x optimal parameter vector (row resp. column),
% f optimum function value
@@ -51,13 +51,13 @@ function [x,f,exitflag,n_f_evals,n_grad_evals,n_constraint_evals,n_constraint_gr
% n_grad_evals: number of gradient evaluations,
% n_constraint_evals: number of constraint function evaluations,
% n_constraint_gradient_evals number of constraint gradient evaluations.
-%
-%
-% Algorithm: Kuntsevich, A.V., Kappel, F., SolvOpt - The solver for local nonlinear optimization problems
-% (version 1.1, Matlab, C, FORTRAN). University of Graz, Graz, 1997.
-%
-%
-% Copyright (C) 1997-2008, Alexei Kuntsevich and Franz Kappel
+%
+%
+% Algorithm: Kuntsevich, A.V., Kappel, F., SolvOpt - The solver for local nonlinear optimization problems
+% (version 1.1, Matlab, C, FORTRAN). University of Graz, Graz, 1997.
+%
+%
+% Copyright (C) 1997-2008, Alexei Kuntsevich and Franz Kappel
% Copyright (C) 2008-2015 Giovanni Lombardo
% Copyright (C) 2015 Dynare Team
%
@@ -217,7 +217,7 @@ ajp=20;
ajpp=ajp; % Start value for the power
ajs=1.15; % Base II
knorms=0; gnorms=zeros(1,10); % Gradient norms stored
-%---}
+ %---}
%Display control ---{
if optim.verbosity<=0, dispdata=0
@@ -243,7 +243,7 @@ else
des=3.3;
end
mxtc=3; % Number of trial cycles (steep wall detect)
-%---}
+ %---}
termx=0; limxterm=50; % Counter and limit for x-criterion
ddx =max(1e-11,optim.LBGradientStep); % stepsize for gradient approximation
@@ -253,7 +253,7 @@ low_bound=-1+1e-4; % Lower bound cosine used to detect a ravine
ZeroGrad=n*1.e-16; % Lower bound for a gradient norm
nzero=0; % Zero-gradient events counter
-% Lower bound for values of variables taking into account
+ % Lower bound for values of variables taking into account
lowxbound=max([optim.TolX,1e-3]);
% Lower bound for function values to be considered as making difference
lowfbound=optim.TolFun^2;
@@ -282,7 +282,7 @@ if isempty(f)
disp(error30)
end
exitflag=-3;
- if trx
+ if trx
x=x';
end
return
@@ -310,7 +310,7 @@ elseif abs(f)==Inf
return
end
xrec=x; frec=f; % record point and function value
-% Constrained problem
+ % Constrained problem
if constr, fp=f; kless=0
if trx
fc=feval(func,x');
@@ -367,13 +367,13 @@ if app
if trx
g=apprgrdn(x',fp,fun,deltax',1,varargin{:});
else
- g=apprgrdn(x ,fp,fun,deltax,1,varargin{:});
+ g=apprgrdn(x ,fp,fun,deltax,1,varargin{:});
end
else
if trx
g=apprgrdn(x',f,fun,deltax',1,varargin{:});
else
- g=apprgrdn(x ,f,fun,deltax,1,varargin{:});
+ g=apprgrdn(x ,f,fun,deltax,1,varargin{:});
end
end
n_f_evals=n_f_evals+n;
@@ -381,7 +381,7 @@ else
if trx
g=feval(grad,x',varargin{:});
else
- g=feval(grad,x,varargin{:});
+ g=feval(grad,x,varargin{:});
end
n_grad_evals=n_grad_evals+1;
end
@@ -395,7 +395,7 @@ if size(g,2)~=n
exitflag=-4;
if trx
x=x';
- end
+ end
return
elseif isnan(ng)
if dispwarn
@@ -435,24 +435,24 @@ if constr
if ~FP
if appconstr
deltax=sign(x); idx=find(deltax==0);
- deltax(idx)=ones(size(idx));
+ deltax(idx)=ones(size(idx));
deltax=ddx*deltax;
if trx
gc=apprgrdn(x',fc,func,deltax',0);
else
- gc=apprgrdn(x ,fc,func,deltax ,0);
+ gc=apprgrdn(x ,fc,func,deltax ,0);
end
n_constraint_evals=n_constraint_evals+n;
else
if trx
gc=feval(gradc,x');
else
- gc=feval(gradc,x);
+ gc=feval(gradc,x);
end
n_constraint_gradient_evals=n_constraint_gradient_evals+1;
end
if size(gc,2)==1
- gc=gc';
+ gc=gc';
end
ngc=norm(gc);
if size(gc,2)~=n
@@ -460,7 +460,7 @@ if constr
disp(errmes)
disp(error60)
end
- exitflag=-6;
+ exitflag=-6;
if trx
x=x';
end
@@ -482,7 +482,7 @@ if constr
disp(error62)
disp(error6)
end
- exitflag=-6;
+ exitflag=-6;
if trx
x=x';
end
@@ -492,7 +492,7 @@ if constr
disp(errmes)
disp(error63)
end
- exitflag=-6;
+ exitflag=-6;
if trx
x=x';
end
@@ -519,13 +519,13 @@ while 1
B=eye(n); % re-set transf. matrix to identity
fst=f; g1=g; dx=0;
% ----}
-
+
% MAIN ITERATIONS ----{
-
+
while 1
k=k+1;kcheck=kcheck+1;
laststep=dx;
-
+
% ADJUST GAMMA --{
gamma=1+max([ajb^((ajp-kcheck)*n),2*optim.TolFun]);
gamma=min([gamma,ajs^max([1,log10(nng+1)])]);
@@ -534,8 +534,8 @@ while 1
% JUMPING OVER A RAVINE ----{
if (gt/norm(gt))*(g1'/norm(g1))<low_bound
if kj==2
- xx=x;
- end
+ xx=x;
+ end
if kj==0
kd=4
end
@@ -561,8 +561,8 @@ while 1
z=z/nrmz;
g1=gt+w*(z*gt')*z; B=B+w*(B*z')*z;
else
- z=zeros(1,n);
- nrmz=0;
+ z=zeros(1,n);
+ nrmz=0;
g1=gt;
end
d1=norm(g1); g0=(g1/d1)*B';
@@ -579,9 +579,9 @@ while 1
if abs(fst-f)<abs(f)*.01
ajp=ajp-10*n;
else
- ajp=ajpp;
+ ajp=ajpp;
end
- h=h1*dx/3;
+ h=h1*dx/3;
k=k-1;
break
end
@@ -603,15 +603,15 @@ while 1
if trx
f=feval(fun,x',varargin{:});
else
- f=feval(fun,x,varargin{:});
+ f=feval(fun,x,varargin{:});
end
n_f_evals=n_f_evals+1;
if h1*f==Inf
if dispwarn
disp(errmes)
- disp(error5)
+ disp(error5)
end
- exitflag=-7;
+ exitflag=-7;
if trx
x=x';
end
@@ -648,7 +648,7 @@ while 1
return
end
if fc<=cnteps
- FP=1;
+ FP=1;
fc=0;
else
FP=0;
@@ -669,7 +669,7 @@ while 1
if isnan(f)
disp(error31)
else
- disp(error32)
+ disp(error32)
end
end
if ksm || kc>=mxtc
@@ -681,13 +681,13 @@ while 1
else
k2=k2+1;
k1=0;
- hp=hp/dq;
+ hp=hp/dq;
x=x1;
- f=f1;
+ f=f1;
knan=1;
if constr
- FP=FP1;
- fp=fp1;
+ FP=FP1;
+ fp=fp1;
end
end
% STEP SIZE IS ZERO TO THE EXTENT OF EPSNORM
@@ -704,20 +704,20 @@ while 1
end
return
else
- x=x1;
- f=f1;
- hp=hp*10;
+ x=x1;
+ f=f1;
+ hp=hp*10;
ksm=1;
if constr
- FP=FP1;
- fp=fp1;
+ FP=FP1;
+ fp=fp1;
end
end
% USE SMALLER STEP
elseif h1*f<h1*gamma^sign(f1)*f1
if ksm
break
- end
+ end
k2=k2+1;k1=0; hp=hp/dq; x=x1;f=f1;
if constr
FP=FP1;
@@ -725,12 +725,12 @@ while 1
end
if kc>=mxtc, break, end
% 1-D OPTIMIZER IS LEFT BEHIND
- else
+ else
if h1*f<=h1*f1
break
end
% USE LARGER STEP
- k1=k1+1;
+ k1=k1+1;
if k2>0
kc=kc+1;
end
@@ -759,12 +759,12 @@ while 1
if kg==1
h=h*(kk-des+1);
else
- h=h*sqrt(kk-des+1);
+ h=h*sqrt(kk-des+1);
end
elseif kk<des
h=h*sqrt(kk/des);
end
-
+
stepvanish=stepvanish+ksm;
% ----}
% COMPUTE THE GRADIENT ----{
@@ -775,13 +775,13 @@ while 1
if trx
g=apprgrdn(x',fp,fun,deltax',1,varargin{:});
else
- g=apprgrdn(x ,fp,fun,deltax,1,varargin{:});
+ g=apprgrdn(x ,fp,fun,deltax,1,varargin{:});
end
else
if trx
g=apprgrdn(x',f,fun,deltax',1,varargin{:});
else
- g=apprgrdn(x ,f,fun,deltax ,1,varargin{:});
+ g=apprgrdn(x ,f,fun,deltax ,1,varargin{:});
end
end
n_f_evals=n_f_evals+n;
@@ -789,7 +789,7 @@ while 1
if trx
g=feval(grad,x',varargin{:});
else
- g=feval(grad,x,varargin{:});
+ g=feval(grad,x,varargin{:});
end
n_grad_evals=n_grad_evals+1;
end
@@ -843,14 +843,14 @@ while 1
if trx
gc=apprgrdn(x',fc,func,deltax',0);
else
- gc=apprgrdn(x ,fc,func,deltax ,0);
+ gc=apprgrdn(x ,fc,func,deltax ,0);
end
n_constraint_evals=n_constraint_evals+n;
else
if trx
gc=feval(gradc,x');
else
- gc=feval(gradc,x );
+ gc=feval(gradc,x );
end
n_constraint_gradient_evals=n_constraint_gradient_evals+1;
end
@@ -891,7 +891,7 @@ while 1
end
g=g+PenCoef*gc; ng=norm(g);
if Reset
- if dispwarn
+ if dispwarn
disp(wrnmes)
disp(warn21)
end
@@ -915,7 +915,7 @@ while 1
gnorms(1)=ng;
nng=(prod(gnorms(1:knorms)))^(1/knorms);
end
-
+
% DISPLAY THE CURRENT VALUES ----{
if k==ld
disp('Iter.# ..... Function ... Step Value ... Gradient Norm ');
@@ -954,20 +954,20 @@ while 1
disp(wrnmes)
disp(warn09)
end
- x=xrec;
- f=frec;
- g=grec;
- ng=norm(g);
+ x=xrec;
+ f=frec;
+ g=grec;
+ ng=norm(g);
krerun=krerun+1;
h=h1*max([dx,detxr*norm(x)])/krerun;
- warnno=2;
+ warnno=2;
break
else
h=h*10;
end
elseif abs(f-frec)> optim.TolFun*abs(f) && ...
norm(x-xrec)<optim.TolX*norm(x) && constr
-
+
elseif abs(f-fopt)<=optim.TolFun*abs(f) || ...
abs(f)<=lowfbound || ...
(abs(f-fopt)<=optim.TolFun && termx>=limxterm )
@@ -992,7 +992,7 @@ while 1
else
exitflag=k;
if dispwarn
- disp(termwarn0);
+ disp(termwarn0);
end
end
if trx
@@ -1024,9 +1024,9 @@ while 1
end
% ITERATIONS LIMIT
if(k==optim.MaxIter)
- exitflag=-9;
+ exitflag=-9;
if trx
- x=x';
+ x=x';
end
if dispwarn
disp(wrnmes)
@@ -1042,7 +1042,7 @@ while 1
disp(termwarn1)
disp(warn1)
end
- exitflag=-8;
+ exitflag=-8;
if trx
x=x';
end
@@ -1056,7 +1056,7 @@ while 1
disp(warn1)
end
if nzero>=3
- exitflag=-8;
+ exitflag=-8;
if trx
x=x';
end
@@ -1068,13 +1068,13 @@ while 1
if trx
f=feval(fun,x',varargin{:});
else
- f=feval(fun,x,varargin{:});
+ f=feval(fun,x,varargin{:});
end
n_f_evals=n_f_evals+1;
if abs(f)==Inf
if dispwarn
disp(errmes)
- disp(error32)
+ disp(error32)
end
exitflag=-3;
if trx
@@ -1083,8 +1083,8 @@ while 1
return
elseif isnan(f)
if dispwarn
- disp(errmes)
- disp(error32)
+ disp(errmes)
+ disp(error32)
end
exitflag=-3;
if trx
@@ -1093,46 +1093,46 @@ while 1
return
end
if app
- deltax=sign(g0);
+ deltax=sign(g0);
idx=find(deltax==0);
- deltax(idx)=ones(size(idx));
+ deltax(idx)=ones(size(idx));
deltax=h1*ddx*deltax;
- if trx
+ if trx
g=apprgrdn(x',f,fun,deltax',1,varargin{:});
else
g=apprgrdn(x,f,fun,deltax,1,varargin{:});
end
n_f_evals=n_f_evals+n;
else
- if trx
+ if trx
g=feval(grad,x',varargin{:});
else
- g=feval(grad,x,varargin{:});
+ g=feval(grad,x,varargin{:});
end
n_grad_evals=n_grad_evals+1;
end
if size(g,2)==1
- g=g';
+ g=g';
end
ng=norm(g);
if ng==Inf
if dispwarn
disp(errmes)
- disp(error42)
+ disp(error42)
end
- exitflag=-4;
+ exitflag=-4;
if trx
- x=x';
+ x=x';
end
return
elseif isnan(ng)
if dispwarn
- disp(errmes)
- disp(error41)
+ disp(errmes)
+ disp(error41)
end
- exitflag=-4;
+ exitflag=-4;
if trx
- x=x';
+ x=x';
end
return
end
@@ -1142,23 +1142,23 @@ while 1
end
if ng<=ZeroGrad
if dispwarn
- disp(termwarn1)
+ disp(termwarn1)
disp(warn1)
end
- exitflag=-8;
+ exitflag=-8;
if trx
x=x';
end
return
end
- h=h1*dx;
+ h=h1*dx;
break
end
end
% ----}
% FUNCTION IS FLAT AT THE POINT ----{
if ~constr && abs(f-fopt)<abs(fopt)*optim.TolFun && kcheck>5 && ng<1
- idx=find(abs(g)<=epsnorm2);
+ idx=find(abs(g)<=epsnorm2);
ni=size(idx,2);
if ni>=1 && ni<=n/2 && kflat<=3
kflat=kflat+1;
@@ -1174,7 +1174,7 @@ while 1
x1(j)=1;
elseif abs(y)<1
x1(j)=sign(y);
- else
+ else
x1(j)=y;
end
for i=1:20
@@ -1182,12 +1182,12 @@ while 1
if trx
f1=feval(fun,x1',varargin{:});
else
- f1=feval(fun,x1,varargin{:});
+ f1=feval(fun,x1,varargin{:});
end
n_f_evals=n_f_evals+1;
if abs(f1)~=Inf && ~isnan(f1)
if h1*f1>h1*fm
- y=x1(j);
+ y=x1(j);
fm=f1;
elseif h1*f2>h1*f1
break
@@ -1205,19 +1205,19 @@ while 1
if trx
gt=apprgrdn(x1',fm,fun,deltax',1,varargin{:});
else
- gt=apprgrdn(x1 ,fm,fun,deltax ,1,varargin{:});
+ gt=apprgrdn(x1 ,fm,fun,deltax ,1,varargin{:});
end
n_f_evals=n_f_evals+n;
else
if trx
gt=feval(grad,x1',varargin{:});
else
- gt=feval(grad,x1,varargin{:});
+ gt=feval(grad,x1,varargin{:});
end
n_grad_evals=n_grad_evals+1;
end
if size(gt,2)==1
- gt=gt';
+ gt=gt';
end
ngt=norm(gt);
if ~isnan(ngt) && ngt>epsnorm2
@@ -1225,11 +1225,11 @@ while 1
disp(warn32)
end
optim.TolFun=optim.TolFun/5;
- x=x1;
- g=gt;
- ng=ngt;
- f=fm;
- h=h1*dx/3;
+ x=x1;
+ g=gt;
+ ng=ngt;
+ f=fm;
+ h=h1*dx/3;
break
end
end
@@ -1238,6 +1238,6 @@ while 1
% ----}
end % iterations
end % restart
-% end of the function
-%
-end
\ No newline at end of file
+ % end of the function
+ %
+ end
\ No newline at end of file
diff --git a/matlab/options2cell.m b/matlab/options2cell.m
index 3ff8ef22741e7e39030dd162f9afde04acd8c8b5..dcf87748a67b787793f8d7651ebdd57c61a6ce70 100644
--- a/matlab/options2cell.m
+++ b/matlab/options2cell.m
@@ -2,10 +2,10 @@ function c = options2cell(o)
% Converts an option structure as a cell of NAME and VALUE pairs.
%
-% INPUTS
+% INPUTS
% o o matlab's structure holding a set of options (each field name is the name of an option and the associated content is the value of the option).
%
-% OUTPUTS
+% OUTPUTS
% o c matlab's cell row array of the form {NAME1, VALUE1, NAME2, VALUE2, NAME3, VALUE3, ...}.
% Copyright (C) 2013 Dynare Team.
@@ -27,10 +27,10 @@ function c = options2cell(o)
s = fieldnames(o);
c = {};
-j = 1;
-
+j = 1;
+
for i=1:length(s)
- c(j) = {s{i}};
- c(j+1) = {o.(s{i})};
- j = j+2;
+ c(j) = {s{i}};
+ c(j+1) = {o.(s{i})};
+ j = j+2;
end
\ No newline at end of file
diff --git a/matlab/osr.m b/matlab/osr.m
index d579c63ec90b20c6dc650c1ed47b300175ec01d5..a34df099aebc9493e5333c07a43171fcf7fd9397 100644
--- a/matlab/osr.m
+++ b/matlab/osr.m
@@ -9,18 +9,18 @@ function osr_res = osr(var_list,params,i_var,W)
% optimal simple rule
% i_var [n_osr_vars by 1 double] indices of osr-variable in
% specified in optim_weights in declaration order
-% W [M_.endo_nbr by M_.endo_nbr sparse matrix] Weighting matrix for variance of endogenous variables
-%
+% W [M_.endo_nbr by M_.endo_nbr sparse matrix] Weighting matrix for variance of endogenous variables
+%
% OUTPUTS
% osr_res: [structure] results structure containing:
% - objective_function [scalar double] value of the objective
% function at the optimum
-% - optim_params [structure] parameter values at the optimum
-%
-%
+% - optim_params [structure] parameter values at the optimum
+%
+%
% SPECIAL REQUIREMENTS
% none.
-%
+%
% Copyright (C) 2001-2016 Dynare Team
%
% This file is part of Dynare.
@@ -38,7 +38,7 @@ function osr_res = osr(var_list,params,i_var,W)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-global M_ options_ oo_
+global M_ options_ oo_
options_.order = 1;
diff --git a/matlab/osr1.m b/matlab/osr1.m
index ae7c6afeab664067506beb875015ba950bc6606f..bd19412ca30204219baa4e50a19a9b42fca8007c 100644
--- a/matlab/osr1.m
+++ b/matlab/osr1.m
@@ -9,13 +9,13 @@ function osr_res = osr1(i_params,i_var,weights)
% osr_res: [structure] results structure containing:
% - objective_function [scalar double] value of the objective
% function at the optimum
-% - optim_params [structure] parameter values at the optimum
-%
+% - optim_params [structure] parameter values at the optimum
+%
% Algorithm:
-%
+%
% Uses Newton-type optimizer csminwel to directly solve quadratic
% osr-problem
-%
+%
% Copyright (C) 2005-2016 Dynare Team
%
% This file is part of Dynare.
@@ -80,7 +80,7 @@ if isfield(options_.osr,'maxit') || isfield(options_.osr,'tolf')
options_.optim_opt=[options_.optim_opt,','];
end
options_.optim_opt=[options_.optim_opt,'''TolFun'',',num2str(options_.osr.tolf),''];
- end
+ end
end
end
@@ -101,17 +101,17 @@ i_var=unique(i_var);
%% do initial checks
[loss,info,exit_flag,vx]=osr_obj(t0,i_params,inv_order_var(i_var),weights(i_var,i_var));
if info~=0
- print_info(info, options_.noprint, options_);
- else
- if ~options_.noprint
- fprintf('\nOSR: Initial value of the objective function: %g \n\n',loss);
- end
+ print_info(info, options_.noprint, options_);
+else
+ if ~options_.noprint
+ fprintf('\nOSR: Initial value of the objective function: %g \n\n',loss);
+ end
end
-if ~options_.noprint && isinf(loss)
- fprintf('\nOSR: The initial value of the objective function is infinite.\n');
- fprintf('\nOSR: Check whether the unconditional variance of a target variable is infinite\n');
- fprintf('\nOSR: due to the presence of a unit root.\n');
- error('OSR: Initial likelihood is infinite')
+if ~options_.noprint && isinf(loss)
+ fprintf('\nOSR: The initial value of the objective function is infinite.\n');
+ fprintf('\nOSR: Check whether the unconditional variance of a target variable is infinite\n');
+ fprintf('\nOSR: due to the presence of a unit root.\n');
+ error('OSR: Initial likelihood is infinite')
end
@@ -120,17 +120,17 @@ if isequal(options_.osr.opt_algo,5)
elseif isequal(options_.osr.opt_algo,6)
error('OSR: OSR does not support opt_algo=6.')
elseif isequal(options_.osr.opt_algo,10)
- error('OSR: OSR does not support opt_algo=10.')
+ error('OSR: OSR does not support opt_algo=10.')
elseif isequal(options_.osr.opt_algo,11)
- error('OSR: OSR does not support opt_algo=11.')
+ error('OSR: OSR does not support opt_algo=11.')
else
-
-if ~isempty(M_.osr.param_bounds) && ~(ismember(options_.osr.opt_algo,[1,2,5,9]) || ischar(options_.osr.opt_algo))
- error('OSR: OSR with bounds on parameters requires a constrained optimizer, i.e. 1,2,5, or 9.')
-end
-%%do actual optimization
-[p, f, exitflag] = dynare_minimize_objective(str2func('osr_obj'),t0,options_.osr.opt_algo,options_,M_.osr.param_bounds,cellstr(M_.param_names(i_params,:)),[],[], i_params,...
- inv_order_var(i_var),weights(i_var,i_var));
+
+ if ~isempty(M_.osr.param_bounds) && ~(ismember(options_.osr.opt_algo,[1,2,5,9]) || ischar(options_.osr.opt_algo))
+ error('OSR: OSR with bounds on parameters requires a constrained optimizer, i.e. 1,2,5, or 9.')
+ end
+ %%do actual optimization
+ [p, f, exitflag] = dynare_minimize_objective(str2func('osr_obj'),t0,options_.osr.opt_algo,options_,M_.osr.param_bounds,cellstr(M_.param_names(i_params,:)),[],[], i_params,...
+ inv_order_var(i_var),weights(i_var,i_var));
end
osr_res.objective_function = f;
diff --git a/matlab/osr_obj.m b/matlab/osr_obj.m
index 67efbbbc08a6a669413d4ab2d6aa9768a9af4f53..d29c7e530737a7e4f50533278e865782daf36fbe 100644
--- a/matlab/osr_obj.m
+++ b/matlab/osr_obj.m
@@ -51,14 +51,14 @@ it_ = M_.maximum_lag+1;
if info(1)
if info(1) == 3 || info(1) == 4 || info(1) == 5 || info(1)==6 ||info(1) == 19 ||...
- info(1) == 20 || info(1) == 21 || info(1) == 23 || info(1) == 26 || ...
- info(1) == 81 || info(1) == 84 || info(1) == 85
+ info(1) == 20 || info(1) == 21 || info(1) == 23 || info(1) == 26 || ...
+ info(1) == 81 || info(1) == 84 || info(1) == 85
loss = 1e8;
info(4)=info(2);
return
else
loss = 1e8;
- info(4)=0.1;
+ info(4)=0.1;
return
end
end
diff --git a/matlab/parallel/AnalyseComputationalEnvironment.m b/matlab/parallel/AnalyseComputationalEnvironment.m
index e91e3b3e14770c8a25ba90c03a9e255fbfa35b04..d00ca1b699c5144ea7a72f364dc49dc3f89024ed 100644
--- a/matlab/parallel/AnalyseComputationalEnvironment.m
+++ b/matlab/parallel/AnalyseComputationalEnvironment.m
@@ -1,658 +1,658 @@
-function [ErrorCode] = AnalyseComputationalEnvironment(DataInput, DataInputAdd)
-% PARALLEL CONTEXT
-% In a parallel context, this function is used to check the cluster defined by the user.
-% If no error happen the function returns 0. The function complies with
-% Windows/Linux operating systems and Matlab/Octave software.
-%
-%
-% INPUT/OUTPUT description:
-%
-%
-% DataInput
-% is the strcture option_.parallel, with the follow fields:
-%
-% Local 1 is on local machine, 0 remote
-% ComputerName the computer name.
-% CPUnbr the CPU's
-% UserName the user name for the ComputerName.
-% Password the password for the user name in ComputerName.
-% RemoteDrive Drive used for Remote computation (data exchange, etc): must be contain 'RemoteFolder'.
-% RemoteDirectory Folder in RemoteDrive used for Remote computation.
-% MatlabOctavePath Path to MATLAB or Octave executable.
-% DynarePath Path to matlab directory within the Dynare installation directory.
-%
-% This information is typed by the user in the DYNARE configuration file and is parsed by the preprocessor,
-% the goal of this function is to check if configuration is correct and if dynare
-% can be executed successfully in parallel mode.
-%
-%
-% DataInputAdd
-% it is the structure options_.parallel_info. Currently , only the string in the
-% field RemoteTmpFolder (the temporary directory created/destroyed on remote
-% computer) is used.
-
-if ispc
- [tempo, MasterName]=system('hostname');
- MasterName=deblank(MasterName);
-end
-
-RemoteTmpFolder=DataInputAdd.RemoteTmpFolder;
-dynareParallelMkDir(RemoteTmpFolder,DataInput);
-
-
-% The variable ErrorCode is initialized at 0. If there are non problems with
-% Local, ComputerName connections,... in general with parallel software execution,
-% the ErrorCode is unchanged, in the others cases 1, 2 , ... The values
-% table is below.
-%
-%
-% Table for ErrorCode Values.
-%
-% ErrorCode -> 0 Initial Value -> No Error Detected!!!
-% ErrorCode -> 1 ... When an error is detected, the values 1, 2, 3... are
-% used to specify the type of error or warning.
-%
-% Value 1: The variable 'Local' has a bad value!
-%
-% Value 2: The variable 'CPUnbr' has a bad value. For more information
-% see http://www.dynare.org/DynareWiki/ParallelDynare.
-% 2.1 [warning] The user asks to use more CPU's than those available.
-% 2.2 [warning] There are unused CPU's!
-% 2.3 [error] NumberOfThreadsPerJob is not a divisor of CPUnbr
-%
-%
-% Value 3: The remote computer is unreachable!!!
-%
-% Value 4: The fields user name and/or password are/is empty!
-%
-% Value 5: Remote Drive and/or Remote Folder do not exist!
-%
-% Value 6: It is impossible write/read files on the remote computer.
-%
-% Value 7: The values user and/or passwd are incorrect or the user has
-% no permissions to execute a Matlab session. Or simply
-% Matlab path (MatlabOctavePath) is incorrect!
-%
-% Value 8: Dynare path (DynarePath) is incorrect!
-%
-% Value 9: It is impossible delete remote computational temporary files!
-%
-%
-%
-%
-% Currently when errors are detected execution simply stops and users can
-% fix configuration errors according to the error type.
-
-% Copyright (C) 2009-2017 Dynare Team
-%
-% 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/>.
-
-
-ErrorCode=0;
-
-
-for Node=1:length(DataInput) % To obtain a recoursive function remove the 'for'
- % and use AnalyseComputationalEnvironment with differents input!
-
-
- % Determine the operating system or software version when necessary
- % for different command types.
-
- OScallerUnix=~ispc;
- OScallerWindows=ispc;
- OStargetUnix=strcmpi('unix',DataInput(Node).OperatingSystem);
- if isempty(DataInput(Node).OperatingSystem)
- OStargetUnix=OScallerUnix;
- end
- OStargetWindows=strcmpi('windows',DataInput(Node).OperatingSystem);
- if isempty(DataInput(Node).OperatingSystem)
- OStargetWindows=OScallerWindows;
- end
-
- Environment= (OScallerUnix || OStargetUnix);
-
- skipline(2)
- disp(['Testing computer -> ',DataInput(Node).ComputerName,' <- ...']);
- skipline(2)
-
- % The function is composed by two main blocks, determined by the 'Local'
- % variable.
-
- % This check can be removed ... according to the dynare parser
- % strategy.
-
- if ((DataInput(Node).Local == 0) |(DataInput(Node).Local == 1))
- % Continue it is Ok!
- disp('Check on Local Variable ..... Ok!');
- skipline()
- else
- disp('The variable "Local" has a bad value!');
- skipline()
- disp('ErrorCode 1.');
- skipline()
- ErrorCode=1;
- return
- end
-
- % %%%%%%%%%% Local (No Network) Computing %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
- % Here only the multi-core, or multi-processor avaiable on local
- % machine are involved in parallel computing. No network
- % comunications are required!
-
-
- % In this case we need to check only the variable 'CPUnbr'.
-
- % We run the parallel code on local computer, so the others fields are automatically
- % fixed by Dynare parser. Then the user can also fill them with wrong values.
-
-
- % %%%%%%%%%% Cluster Computing %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
- % Here we can have many computer with multi-core, or multi-processor avaiable on the
- % network and involved in parallel computing.
- % So in this case we need more sophisticated check.
-
-
- if (DataInput(Node).Local == 0)
-
- % Now we verify if it is possibile to be connected with the
- % remote computer.
-
- si1=[];
- de1=[];
-
- if Environment
- if OScallerWindows
- [si1 de1]=system(['ping ', DataInput(Node).ComputerName]);
- else
- [si1 de1]=system(['ping ', DataInput(Node).ComputerName, ' -c 4']);
- end
- else
- [si1 de1]=system(['ping ', DataInput(Node).ComputerName]);
- end
-
- if (si1)
- disp(['It is impossibile to ping to the computer with name "',DataInput(Node).ComputerName,'" using the network!'])
- skipline()
- disp('ErrorCode 3.')
- ErrorCode=3;
- skipline(2)
- else
- disp('Check on ComputerName Variable ..... Ok!')
- skipline(2)
- end
-
-
- % Now we verify if user name and password are correct and if remote
- % drive and remote folder exist on the remote computer and it is
- % possible to exchange data with them.
-
- if Environment
- % This check can be removed ... according to the dynare parser
- % strategy.
-
- if (isempty(DataInput(Node).UserName))
- disp('The fields UserName is empty!')
- skipline()
- disp('ErrorCode 4.')
- skipline(2)
- ErrorCode=4;
- return
- end
- disp('Check on UserName Variable ..... Ok!')
- skipline(2)
-
- % This check can be removed ... according to the dynare parser
- % strategy.
- if (~isempty(DataInput(Node).Password))
- disp('[WARNING] The field Password should be empty under unix or mac!');
- skipline()
- disp(['Remove the string ',DataInput(Node).Password,' from this field!'])
- skipline()
- disp('ErrorCode 4.')
- skipline(2)
- ErrorCode=4;
- else
- disp('Check on Password Variable ..... Ok!')
- skipline(2)
- end
- else
-
- % This check can be removed ... according to the dynare parser
- % strategy.
-
- if (isempty(DataInput(Node).UserName)) || (isempty(DataInput(Node).Password))
- disp('The fields UserName and/or Password are/is empty!');
- skipline()
- disp('ErrorCode 4.')
- skipline(2)
- ErrorCode=4;
- return
- end
- disp('Check on UserName Variable ..... Ok!');
- skipline()
- disp('Check on Password Variable ..... Ok!');
- skipline()
- end
-
- % Now we very if RemoteDrive and/or RemoteDirectory exist on remote
- % computer!
-
- if Environment
-
- % This check can be removed ... according to the dynare parser
- % strategy.
-
- if isempty(DataInput(Node).RemoteDirectory)
- disp('The field RemoteDirectory is empty!')
- skipline()
- disp('ErrorCode 5.')
- skipline()
- ErrorCode=5;
- return
- end
-
- % This check can be removed ... according to the dynare parser
- % strategy.
-
- if (~isempty(DataInput(Node).RemoteDrive))
- disp('[WARNING] The fields RemoteDrive should be empty under unix or mac!')
- skipline()
- disp(['remove the string ',DataInput(Node).RemoteDrive,' from this field!'])
- skipline()
- disp('ErrorCode 5.')
- skipline(2)
- ErrorCode=5;
- end
-
- si2=[];
- de2=[];
- if ~isempty(DataInput(Node).Port)
- ssh_token = ['-p ',DataInput(Node).Port];
- else
- ssh_token = '';
- end
-
- [si2 de2]=system(['ssh ',ssh_token,' ',DataInput(Node).UserName,'@',DataInput(Node).ComputerName,' ls ',DataInput(Node).RemoteDirectory,'/',RemoteTmpFolder,'/']);
-
- if (si2)
- disp ('Remote Directory does not exist or is not reachable!')
- skipline()
- disp('ErrorCode 5.')
- skipline(2)
- ErrorCode=5;
- return
- end
-
- disp('Check on RemoteDirectory Variable ..... Ok!')
- skipline(2)
- disp('Check on RemoteDrive Variable ..... Ok!')
- skipline(2)
-
- else
- % This check can be removed ... according to the dynare parser
- % strategy.
-
- if (isempty(DataInput(Node).RemoteDrive)||isempty(DataInput(Node).RemoteDirectory))
- disp('Remote RemoteDrive and/or RemoteDirectory is/are empty!')
- skipline()
- disp('ErrorCode 5.')
- skipline(2)
- ErrorCode=5;
- return
- end
-
-
- si2=[];
- de2=[];
- [si2 de2]=system(['dir \\',DataInput(Node).ComputerName,'\',DataInput(Node).RemoteDrive,'$\',DataInput(Node).RemoteDirectory,'\',RemoteTmpFolder]);
-
- if (si2)
- disp ('Remote Directory does not exist or it is not reachable!')
- skipline()
- disp('ErrorCode 5.')
- skipline(2)
- ErrorCode=5;
- return
- end
-
- disp('Check on RemoteDirectory Variable ..... Ok!')
- skipline(2)
- disp('Check on RemoteDrive Variable ..... Ok!')
- skipline(2)
-
- end
-
-
- % Now we verify if it possible to exchange data with the remote
- % computer:
-
-
- % Build a command file to test the matlab execution and dynare path ...
-
- fid = fopen('Tracing.m', 'w+');
- s1=(['fT = fopen(''MatlabOctaveIsOk.txt'',''w+'');\n']);
- s2='fclose(fT);\n';
- SBS=strfind(DataInput(Node).DynarePath,'\');
- DPStr=DataInput(Node).DynarePath;
- if isempty(SBS)
- DPStrNew=DPStr;
- else
- DPStrNew=[DPStr(1:SBS(1)),'\'];
- for j=2:length(SBS)
- DPStrNew=[DPStrNew,DPStr(SBS(j-1)+1:SBS(j)),'\'];
- end
- DPStrNew=[DPStrNew,DPStr(SBS(end)+1:end)];
- end
- s3=['addpath(''',DPStrNew,'''),\n'];
- s4=['try,\n dynareroot = dynare_config();\n'];
- s41=([' fT = fopen(''DynareIsOk.txt'',''w+'');\n']);
- s42=' fclose(fT);\n';
- s5=['catch,\n'];
- s51=([' fT = fopen(''DynareFailed.txt'',''w+'');\n']);
- s52=' fclose(fT);\n';
- s6=['end,\n'];
- s7=['if ismac,\n'];
- s71=([' fT = fopen(''IsMac.txt'',''w+'');\n']);
- s72=' fclose(fT);\n';
- s8=['end,\n'];
- send='exit';
- StrCommand=([s1,s2,s3,s4,s41,s42,s5,s51,s52,s6,s7,s71,s72,s8,send]);
-
- % Mettere controllo su NbW ...
- % if isoctave
- % NbW = fprintf(fid,StrCommand, '%s');
- % else
- NbW = fprintf(fid,StrCommand, '%s');
- % end
- fclose(fid);
-
- dynareParallelSendFiles('Tracing.m', RemoteTmpFolder,DataInput(Node));
- FindTracing = dynareParallelDir('Tracing.m', RemoteTmpFolder,DataInput(Node));
-
- delete ('Tracing.m');
-
- if (isempty(FindTracing))
- disp('It is impossible to exchange data with Remote Drive and/or Remote Directory! ErrorCode 6.')
- skipline()
- disp('ErrorCode 6.')
- skipline(2)
- ErrorCode=6;
- return
- else
- disp('Check on Exchange File with Remote Computer ..... Ok!')
- skipline(2)
- end
-
-
- % Now we verify if it is possible execute a matlab/octave section on remote
- % machine when the user is .UserName with password .Password and
- % the path is MatlabOctavePath.
-
- if Environment
- if ~isempty(DataInput(Node).Port)
- ssh_token = ['-p ',DataInput(Node).Port];
- else
- ssh_token = '';
- end
- if strfind([DataInput(Node).MatlabOctavePath], 'octave') % Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
- system(['ssh ',ssh_token,' ',DataInput(Node).UserName,'@',DataInput(Node).ComputerName,' "cd ',DataInput(Node).RemoteDirectory,'/',RemoteTmpFolder, '; ', DataInput(Node).MatlabOctavePath, ' Tracing.m;" &']);
- else
- system(['ssh ',ssh_token,' ',DataInput(Node).UserName,'@',DataInput(Node).ComputerName,' "cd ',DataInput(Node).RemoteDirectory,'/',RemoteTmpFolder, '; ', DataInput(Node).MatlabOctavePath, ' -nosplash -nodesktop -minimize -r Tracing;" &']);
- end
- else
- if ~strcmp(DataInput(Node).ComputerName,MasterName) % run on remote machine
- if strfind([DataInput(Node).MatlabOctavePath], 'octave') % Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
- [NonServeS NenServeD]=system(['start /B psexec \\',DataInput(Node).ComputerName,' -e -u ',DataInput(Node).UserName,' -p ',DataInput(Node).Password,' -W ',DataInput(Node).RemoteDrive,':\',DataInput(Node).RemoteDirectory,'\',RemoteTmpFolder ' -low ',DataInput(Node).MatlabOctavePath,' Tracing.m']);
- else
- [NonServeS NenServeD]=system(['start /B psexec \\',DataInput(Node).ComputerName,' -e -u ',DataInput(Node).UserName,' -p ',DataInput(Node).Password,' -W ',DataInput(Node).RemoteDrive,':\',DataInput(Node).RemoteDirectory,'\',RemoteTmpFolder ' -low ',DataInput(Node).MatlabOctavePath,' -nosplash -nodesktop -minimize -r Tracing']);
- end
- else % run on local machine via the network: user and passwd cannot be used!
- if strfind([DataInput(Node).MatlabOctavePath], 'octave') % Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
- [NonServeS NenServeD]=system(['start /B psexec \\',DataInput(Node).ComputerName,' -e ',' -W ',DataInput(Node).RemoteDrive,':\',DataInput(Node).RemoteDirectory,'\',RemoteTmpFolder ' -low ',DataInput(Node).MatlabOctavePath,' Tracing.m']);
- else
- [NonServeS NenServeD]=system(['start /B psexec \\',DataInput(Node).ComputerName,' -e ',' -W ',DataInput(Node).RemoteDrive,':\',DataInput(Node).RemoteDirectory,'\',RemoteTmpFolder ' -low ',DataInput(Node).MatlabOctavePath,' -nosplash -nodesktop -minimize -r Tracing']);
- end
- end
-
- end
-
- % Timer da fissare, nei valori di attesa!
-
- t1=fix(clock);
-
- if t1(5)+1>60
- t2=2;
- else
- t2=t1(5)+1;
- end
-
- Flag=0;
-
- while (1)
- if Flag==0
- disp('Try to run matlab/octave on remote machine ... ')
- skipline()
- disp('please wait ... ')
- skipline()
- Flag=1;
- end
- nt=fix(clock);
- nt(5)-t2;
-
- if (~isempty (dynareParallelDir('MatlabOctaveIsOk.txt',RemoteTmpFolder,DataInput(Node)))) || ((nt(5)-t2)>0)
- if ((nt(5)-t2)>0)
- ErrorCode=7;
- end
- break
- end
-
- end
-
- if (ErrorCode==7)
-
- disp ('It is not possible execute a matlab session on remote machine!')
- skipline()
- disp('ErrorCode 7.')
- skipline(2)
- ErrorCode=7;
- dynareParallelRmDir(RemoteTmpFolder,DataInput(Node));
- return
- else
- disp('Check on MatlabOctave Path and MatlabOctave Program Execution on remote machine ..... Ok!')
- skipline(2)
-
- % Now we verify if the DynarePath is correct ...
- disp('Check the Dynare path on remote machine ... ')
- skipline()
- disp('please wait ... ')
- skipline(2)
- pause(2)
-
- if isempty(dynareParallelDir('DynareIsOk.txt',RemoteTmpFolder,DataInput(Node)))
- ErrorCode=8;
- end
-
- if (ErrorCode==8)
- disp ('The DynarePath is incorrect!')
- skipline()
- disp('ErrorCode 8.')
- skipline(2)
- ErrorCode=8;
- dynareParallelRmDir(RemoteTmpFolder,DataInput(Node));
- return
- else
- disp('Check on Dynare Path remote machine ..... Ok!')
- if isempty(dynareParallelDir('IsMac.txt',RemoteTmpFolder,DataInput(Node)))
- RemoteEnvironment=Environment;
- else
- RemoteEnvironment=2;
- end
- skipline(2)
- end
- end
-
-
- % Now we verify if it is possible delete remote computational traces!
-
- dynareParallelRmDir(RemoteTmpFolder,DataInput(Node));
-
- si3=[];
-
- si3=dynareParallelDir('Tracing.m', RemoteTmpFolder,DataInput(Node));
-
- if (isempty(si3))
- disp ('Check on Delete Remote Computational Traces ..... Ok!')
- skipline(2)
- else
- disp ('It is impossible to delete temporary files on remote machine!')
- skipline()
- disp('ErrorCode 9.')
- skipline(2)
- ErrorCode=9;
- return
- end
-
-
- end
- % Now we check the variable 'CPUnbr'.
-
- % This check can be removed ... according to the dynare parser
- % strategy.
-
- yn=isempty(DataInput(Node).CPUnbr);
-
- if yn==1
- % The field is empty!
- disp('The field "CPUnbr" is empty!')
- skipline()
- disp('ErrorCode 2.')
- skipline(2)
- ErrorCode=2;
- return
- end
-
- % This check can be removed ... according to the dynare parser
- % strategy.
-
-
-
- % We look for the information on local computer hardware.
-
- si0=[];
- de0=[];
-
- Environment1=Environment;
- disp('Checking Hardware please wait ...');
- if (DataInput(Node).Local == 1)
- if Environment
- if ~ismac
- [si0 de0]=system('grep processor /proc/cpuinfo');
- else
- [si0 de0]=system('sysctl -n hw.ncpu');
- Environment1=2;
- end
- else
- [si0 de0]=system(['psinfo \\']);
- end
- else
- if Environment
- if ~isempty(DataInput(Node).Port)
- ssh_token = ['-p ',DataInput(Node).Port];
- else
- ssh_token = '';
- end
- if OStargetUnix
- if RemoteEnvironment ==1
- [si0 de0]=system(['ssh ',ssh_token,' ',DataInput(Node).UserName,'@',DataInput(Node).ComputerName,' grep processor /proc/cpuinfo']);
- else % it is MAC
- [si0 de0]=system(['ssh ',ssh_token,' ',DataInput(Node).UserName,'@',DataInput(Node).ComputerName,' sysctl -n hw.ncpu']);
- Environment1=2;
- end
- else
- [si0 de0]=system(['ssh ',ssh_token,' ',DataInput(Node).UserName,'@',DataInput(Node).ComputerName,' psinfo']);
- end
- else
- [si0 de0]=system(['psinfo \\',DataInput(Node).ComputerName,' -u ',DataInput(Node).UserName,' -p ',DataInput(Node).Password]);
- end
- end
-
-
- RealCPUnbr='';
-% keyboard;
- RealCPUnbr=GiveCPUnumber(de0,Environment1);
-
- % Questo controllo penso che si possa MIGLIORARE!!!!!
- if isempty (RealCPUnbr) && Environment1==0
- [si0 de0]=system(['psinfo \\',DataInput(Node).ComputerName]);
- end
- RealCPUnbr=GiveCPUnumber(de0,Environment1);
-
- if isempty (RealCPUnbr)
- % An error occurred when we try to know the Cpu/Cores
- % numbers.
- disp('It is impossible determine the number of Cpu/Processor avaiable on this machine!')
- skipline()
- disp('ErrorCode 2.')
- skipline()
- if Environment
- disp('Check the command "$less /proc/cpuinfo" ... !')
- else
- disp('Check if the pstools are installed and are in machine path! And check the command "psinfo \\"')
- end
- skipline()
- ErrorCode=2;
- return
- end
-
-
- % Trasforming the input data provided in a form [n1:n2] in a single numerical
- % value.
-
-
- CPUnbrUser=length(DataInput(Node).CPUnbr);
- maxCPUnbrUser=max(DataInput(Node).CPUnbr)+1;
-
- disp(['Hardware has ', num2str(RealCPUnbr),' Cpu/Cores!'])
- disp(['User requires ',num2str(CPUnbrUser),' Cpu/Cores!'])
- if CPUnbrUser==RealCPUnbr
- % It is Ok!
- disp('Check on CPUnbr Variable ..... Ok!')
- skipline(3)
- end
-
- if CPUnbrUser > RealCPUnbr
- disp('Warning! The user asks to use more CPU''s than those available.')
- skipline(2)
- ErrorCode=2.1;
- end
- if CPUnbrUser < RealCPUnbr
- disp('Warning! There are unused CPU''s!')
- skipline(2)
- ErrorCode=2.2;
- end
-
- if mod(length(DataInput(Node).CPUnbr),DataInput(Node).NumberOfThreadsPerJob)
- skipline()
- disp(['NumberOfThreadsPerJob = ',int2str(DataInput(Node).NumberOfThreadsPerJob),' is not an exact divisor of number of CPUs = ',int2str(DataInput(Node).CPUnbr),'!'])
- disp([' You must re-set properly NumberOfThreadsPerJob of node ' int2str(Node) ' ' DataInput(Node).ComputerName])
- disp(' in your configuration file')
- skipline()
- ErrorCode=2.3;
- end
-
- disp(['Test for Cluster computation, computer ',DataInput(Node).ComputerName, ' ..... Passed!'])
- skipline(2)
+function [ErrorCode] = AnalyseComputationalEnvironment(DataInput, DataInputAdd)
+% PARALLEL CONTEXT
+% In a parallel context, this function is used to check the cluster defined by the user.
+% If no error happen the function returns 0. The function complies with
+% Windows/Linux operating systems and Matlab/Octave software.
+%
+%
+% INPUT/OUTPUT description:
+%
+%
+% DataInput
+% is the strcture option_.parallel, with the follow fields:
+%
+% Local 1 is on local machine, 0 remote
+% ComputerName the computer name.
+% CPUnbr the CPU's
+% UserName the user name for the ComputerName.
+% Password the password for the user name in ComputerName.
+% RemoteDrive Drive used for Remote computation (data exchange, etc): must be contain 'RemoteFolder'.
+% RemoteDirectory Folder in RemoteDrive used for Remote computation.
+% MatlabOctavePath Path to MATLAB or Octave executable.
+% DynarePath Path to matlab directory within the Dynare installation directory.
+%
+% This information is typed by the user in the DYNARE configuration file and is parsed by the preprocessor,
+% the goal of this function is to check if configuration is correct and if dynare
+% can be executed successfully in parallel mode.
+%
+%
+% DataInputAdd
+% it is the structure options_.parallel_info. Currently , only the string in the
+% field RemoteTmpFolder (the temporary directory created/destroyed on remote
+% computer) is used.
+
+if ispc
+ [tempo, MasterName]=system('hostname');
+ MasterName=deblank(MasterName);
+end
+
+RemoteTmpFolder=DataInputAdd.RemoteTmpFolder;
+dynareParallelMkDir(RemoteTmpFolder,DataInput);
+
+
+% The variable ErrorCode is initialized at 0. If there are non problems with
+% Local, ComputerName connections,... in general with parallel software execution,
+% the ErrorCode is unchanged, in the others cases 1, 2 , ... The values
+% table is below.
+%
+%
+% Table for ErrorCode Values.
+%
+% ErrorCode -> 0 Initial Value -> No Error Detected!!!
+% ErrorCode -> 1 ... When an error is detected, the values 1, 2, 3... are
+% used to specify the type of error or warning.
+%
+% Value 1: The variable 'Local' has a bad value!
+%
+% Value 2: The variable 'CPUnbr' has a bad value. For more information
+% see http://www.dynare.org/DynareWiki/ParallelDynare.
+% 2.1 [warning] The user asks to use more CPU's than those available.
+% 2.2 [warning] There are unused CPU's!
+% 2.3 [error] NumberOfThreadsPerJob is not a divisor of CPUnbr
+%
+%
+% Value 3: The remote computer is unreachable!!!
+%
+% Value 4: The fields user name and/or password are/is empty!
+%
+% Value 5: Remote Drive and/or Remote Folder do not exist!
+%
+% Value 6: It is impossible write/read files on the remote computer.
+%
+% Value 7: The values user and/or passwd are incorrect or the user has
+% no permissions to execute a Matlab session. Or simply
+% Matlab path (MatlabOctavePath) is incorrect!
+%
+% Value 8: Dynare path (DynarePath) is incorrect!
+%
+% Value 9: It is impossible delete remote computational temporary files!
+%
+%
+%
+%
+% Currently when errors are detected execution simply stops and users can
+% fix configuration errors according to the error type.
+
+% Copyright (C) 2009-2017 Dynare Team
+%
+% 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/>.
+
+
+ErrorCode=0;
+
+
+for Node=1:length(DataInput) % To obtain a recoursive function remove the 'for'
+ % and use AnalyseComputationalEnvironment with differents input!
+
+
+ % Determine the operating system or software version when necessary
+ % for different command types.
+
+ OScallerUnix=~ispc;
+ OScallerWindows=ispc;
+ OStargetUnix=strcmpi('unix',DataInput(Node).OperatingSystem);
+ if isempty(DataInput(Node).OperatingSystem)
+ OStargetUnix=OScallerUnix;
+ end
+ OStargetWindows=strcmpi('windows',DataInput(Node).OperatingSystem);
+ if isempty(DataInput(Node).OperatingSystem)
+ OStargetWindows=OScallerWindows;
+ end
+
+ Environment= (OScallerUnix || OStargetUnix);
+
+ skipline(2)
+ disp(['Testing computer -> ',DataInput(Node).ComputerName,' <- ...']);
+ skipline(2)
+
+ % The function is composed by two main blocks, determined by the 'Local'
+ % variable.
+
+ % This check can be removed ... according to the dynare parser
+ % strategy.
+
+ if ((DataInput(Node).Local == 0) |(DataInput(Node).Local == 1))
+ % Continue it is Ok!
+ disp('Check on Local Variable ..... Ok!');
+ skipline()
+ else
+ disp('The variable "Local" has a bad value!');
+ skipline()
+ disp('ErrorCode 1.');
+ skipline()
+ ErrorCode=1;
+ return
+ end
+
+ % %%%%%%%%%% Local (No Network) Computing %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+ % Here only the multi-core, or multi-processor avaiable on local
+ % machine are involved in parallel computing. No network
+ % comunications are required!
+
+
+ % In this case we need to check only the variable 'CPUnbr'.
+
+ % We run the parallel code on local computer, so the others fields are automatically
+ % fixed by Dynare parser. Then the user can also fill them with wrong values.
+
+
+ % %%%%%%%%%% Cluster Computing %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+ % Here we can have many computer with multi-core, or multi-processor avaiable on the
+ % network and involved in parallel computing.
+ % So in this case we need more sophisticated check.
+
+
+ if (DataInput(Node).Local == 0)
+
+ % Now we verify if it is possibile to be connected with the
+ % remote computer.
+
+ si1=[];
+ de1=[];
+
+ if Environment
+ if OScallerWindows
+ [si1 de1]=system(['ping ', DataInput(Node).ComputerName]);
+ else
+ [si1 de1]=system(['ping ', DataInput(Node).ComputerName, ' -c 4']);
+ end
+ else
+ [si1 de1]=system(['ping ', DataInput(Node).ComputerName]);
+ end
+
+ if (si1)
+ disp(['It is impossibile to ping to the computer with name "',DataInput(Node).ComputerName,'" using the network!'])
+ skipline()
+ disp('ErrorCode 3.')
+ ErrorCode=3;
+ skipline(2)
+ else
+ disp('Check on ComputerName Variable ..... Ok!')
+ skipline(2)
+ end
+
+
+ % Now we verify if user name and password are correct and if remote
+ % drive and remote folder exist on the remote computer and it is
+ % possible to exchange data with them.
+
+ if Environment
+ % This check can be removed ... according to the dynare parser
+ % strategy.
+
+ if (isempty(DataInput(Node).UserName))
+ disp('The fields UserName is empty!')
+ skipline()
+ disp('ErrorCode 4.')
+ skipline(2)
+ ErrorCode=4;
+ return
+ end
+ disp('Check on UserName Variable ..... Ok!')
+ skipline(2)
+
+ % This check can be removed ... according to the dynare parser
+ % strategy.
+ if (~isempty(DataInput(Node).Password))
+ disp('[WARNING] The field Password should be empty under unix or mac!');
+ skipline()
+ disp(['Remove the string ',DataInput(Node).Password,' from this field!'])
+ skipline()
+ disp('ErrorCode 4.')
+ skipline(2)
+ ErrorCode=4;
+ else
+ disp('Check on Password Variable ..... Ok!')
+ skipline(2)
+ end
+ else
+
+ % This check can be removed ... according to the dynare parser
+ % strategy.
+
+ if (isempty(DataInput(Node).UserName)) || (isempty(DataInput(Node).Password))
+ disp('The fields UserName and/or Password are/is empty!');
+ skipline()
+ disp('ErrorCode 4.')
+ skipline(2)
+ ErrorCode=4;
+ return
+ end
+ disp('Check on UserName Variable ..... Ok!');
+ skipline()
+ disp('Check on Password Variable ..... Ok!');
+ skipline()
+ end
+
+ % Now we very if RemoteDrive and/or RemoteDirectory exist on remote
+ % computer!
+
+ if Environment
+
+ % This check can be removed ... according to the dynare parser
+ % strategy.
+
+ if isempty(DataInput(Node).RemoteDirectory)
+ disp('The field RemoteDirectory is empty!')
+ skipline()
+ disp('ErrorCode 5.')
+ skipline()
+ ErrorCode=5;
+ return
+ end
+
+ % This check can be removed ... according to the dynare parser
+ % strategy.
+
+ if (~isempty(DataInput(Node).RemoteDrive))
+ disp('[WARNING] The fields RemoteDrive should be empty under unix or mac!')
+ skipline()
+ disp(['remove the string ',DataInput(Node).RemoteDrive,' from this field!'])
+ skipline()
+ disp('ErrorCode 5.')
+ skipline(2)
+ ErrorCode=5;
+ end
+
+ si2=[];
+ de2=[];
+ if ~isempty(DataInput(Node).Port)
+ ssh_token = ['-p ',DataInput(Node).Port];
+ else
+ ssh_token = '';
+ end
+
+ [si2 de2]=system(['ssh ',ssh_token,' ',DataInput(Node).UserName,'@',DataInput(Node).ComputerName,' ls ',DataInput(Node).RemoteDirectory,'/',RemoteTmpFolder,'/']);
+
+ if (si2)
+ disp ('Remote Directory does not exist or is not reachable!')
+ skipline()
+ disp('ErrorCode 5.')
+ skipline(2)
+ ErrorCode=5;
+ return
+ end
+
+ disp('Check on RemoteDirectory Variable ..... Ok!')
+ skipline(2)
+ disp('Check on RemoteDrive Variable ..... Ok!')
+ skipline(2)
+
+ else
+ % This check can be removed ... according to the dynare parser
+ % strategy.
+
+ if (isempty(DataInput(Node).RemoteDrive)||isempty(DataInput(Node).RemoteDirectory))
+ disp('Remote RemoteDrive and/or RemoteDirectory is/are empty!')
+ skipline()
+ disp('ErrorCode 5.')
+ skipline(2)
+ ErrorCode=5;
+ return
+ end
+
+
+ si2=[];
+ de2=[];
+ [si2 de2]=system(['dir \\',DataInput(Node).ComputerName,'\',DataInput(Node).RemoteDrive,'$\',DataInput(Node).RemoteDirectory,'\',RemoteTmpFolder]);
+
+ if (si2)
+ disp ('Remote Directory does not exist or it is not reachable!')
+ skipline()
+ disp('ErrorCode 5.')
+ skipline(2)
+ ErrorCode=5;
+ return
+ end
+
+ disp('Check on RemoteDirectory Variable ..... Ok!')
+ skipline(2)
+ disp('Check on RemoteDrive Variable ..... Ok!')
+ skipline(2)
+
+ end
+
+
+ % Now we verify if it possible to exchange data with the remote
+ % computer:
+
+
+ % Build a command file to test the matlab execution and dynare path ...
+
+ fid = fopen('Tracing.m', 'w+');
+ s1=(['fT = fopen(''MatlabOctaveIsOk.txt'',''w+'');\n']);
+ s2='fclose(fT);\n';
+ SBS=strfind(DataInput(Node).DynarePath,'\');
+ DPStr=DataInput(Node).DynarePath;
+ if isempty(SBS)
+ DPStrNew=DPStr;
+ else
+ DPStrNew=[DPStr(1:SBS(1)),'\'];
+ for j=2:length(SBS)
+ DPStrNew=[DPStrNew,DPStr(SBS(j-1)+1:SBS(j)),'\'];
+ end
+ DPStrNew=[DPStrNew,DPStr(SBS(end)+1:end)];
+ end
+ s3=['addpath(''',DPStrNew,'''),\n'];
+ s4=['try,\n dynareroot = dynare_config();\n'];
+ s41=([' fT = fopen(''DynareIsOk.txt'',''w+'');\n']);
+ s42=' fclose(fT);\n';
+ s5=['catch,\n'];
+ s51=([' fT = fopen(''DynareFailed.txt'',''w+'');\n']);
+ s52=' fclose(fT);\n';
+ s6=['end,\n'];
+ s7=['if ismac,\n'];
+ s71=([' fT = fopen(''IsMac.txt'',''w+'');\n']);
+ s72=' fclose(fT);\n';
+ s8=['end,\n'];
+ send='exit';
+ StrCommand=([s1,s2,s3,s4,s41,s42,s5,s51,s52,s6,s7,s71,s72,s8,send]);
+
+ % Mettere controllo su NbW ...
+ % if isoctave
+ % NbW = fprintf(fid,StrCommand, '%s');
+ % else
+ NbW = fprintf(fid,StrCommand, '%s');
+ % end
+ fclose(fid);
+
+ dynareParallelSendFiles('Tracing.m', RemoteTmpFolder,DataInput(Node));
+ FindTracing = dynareParallelDir('Tracing.m', RemoteTmpFolder,DataInput(Node));
+
+ delete ('Tracing.m');
+
+ if (isempty(FindTracing))
+ disp('It is impossible to exchange data with Remote Drive and/or Remote Directory! ErrorCode 6.')
+ skipline()
+ disp('ErrorCode 6.')
+ skipline(2)
+ ErrorCode=6;
+ return
+ else
+ disp('Check on Exchange File with Remote Computer ..... Ok!')
+ skipline(2)
+ end
+
+
+ % Now we verify if it is possible execute a matlab/octave section on remote
+ % machine when the user is .UserName with password .Password and
+ % the path is MatlabOctavePath.
+
+ if Environment
+ if ~isempty(DataInput(Node).Port)
+ ssh_token = ['-p ',DataInput(Node).Port];
+ else
+ ssh_token = '';
+ end
+ if strfind([DataInput(Node).MatlabOctavePath], 'octave') % Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
+ system(['ssh ',ssh_token,' ',DataInput(Node).UserName,'@',DataInput(Node).ComputerName,' "cd ',DataInput(Node).RemoteDirectory,'/',RemoteTmpFolder, '; ', DataInput(Node).MatlabOctavePath, ' Tracing.m;" &']);
+ else
+ system(['ssh ',ssh_token,' ',DataInput(Node).UserName,'@',DataInput(Node).ComputerName,' "cd ',DataInput(Node).RemoteDirectory,'/',RemoteTmpFolder, '; ', DataInput(Node).MatlabOctavePath, ' -nosplash -nodesktop -minimize -r Tracing;" &']);
+ end
+ else
+ if ~strcmp(DataInput(Node).ComputerName,MasterName) % run on remote machine
+ if strfind([DataInput(Node).MatlabOctavePath], 'octave') % Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
+ [NonServeS NenServeD]=system(['start /B psexec \\',DataInput(Node).ComputerName,' -e -u ',DataInput(Node).UserName,' -p ',DataInput(Node).Password,' -W ',DataInput(Node).RemoteDrive,':\',DataInput(Node).RemoteDirectory,'\',RemoteTmpFolder ' -low ',DataInput(Node).MatlabOctavePath,' Tracing.m']);
+ else
+ [NonServeS NenServeD]=system(['start /B psexec \\',DataInput(Node).ComputerName,' -e -u ',DataInput(Node).UserName,' -p ',DataInput(Node).Password,' -W ',DataInput(Node).RemoteDrive,':\',DataInput(Node).RemoteDirectory,'\',RemoteTmpFolder ' -low ',DataInput(Node).MatlabOctavePath,' -nosplash -nodesktop -minimize -r Tracing']);
+ end
+ else % run on local machine via the network: user and passwd cannot be used!
+ if strfind([DataInput(Node).MatlabOctavePath], 'octave') % Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
+ [NonServeS NenServeD]=system(['start /B psexec \\',DataInput(Node).ComputerName,' -e ',' -W ',DataInput(Node).RemoteDrive,':\',DataInput(Node).RemoteDirectory,'\',RemoteTmpFolder ' -low ',DataInput(Node).MatlabOctavePath,' Tracing.m']);
+ else
+ [NonServeS NenServeD]=system(['start /B psexec \\',DataInput(Node).ComputerName,' -e ',' -W ',DataInput(Node).RemoteDrive,':\',DataInput(Node).RemoteDirectory,'\',RemoteTmpFolder ' -low ',DataInput(Node).MatlabOctavePath,' -nosplash -nodesktop -minimize -r Tracing']);
+ end
+ end
+
+ end
+
+ % Timer da fissare, nei valori di attesa!
+
+ t1=fix(clock);
+
+ if t1(5)+1>60
+ t2=2;
+ else
+ t2=t1(5)+1;
+ end
+
+ Flag=0;
+
+ while (1)
+ if Flag==0
+ disp('Try to run matlab/octave on remote machine ... ')
+ skipline()
+ disp('please wait ... ')
+ skipline()
+ Flag=1;
+ end
+ nt=fix(clock);
+ nt(5)-t2;
+
+ if (~isempty (dynareParallelDir('MatlabOctaveIsOk.txt',RemoteTmpFolder,DataInput(Node)))) || ((nt(5)-t2)>0)
+ if ((nt(5)-t2)>0)
+ ErrorCode=7;
+ end
+ break
+ end
+
+ end
+
+ if (ErrorCode==7)
+
+ disp ('It is not possible execute a matlab session on remote machine!')
+ skipline()
+ disp('ErrorCode 7.')
+ skipline(2)
+ ErrorCode=7;
+ dynareParallelRmDir(RemoteTmpFolder,DataInput(Node));
+ return
+ else
+ disp('Check on MatlabOctave Path and MatlabOctave Program Execution on remote machine ..... Ok!')
+ skipline(2)
+
+ % Now we verify if the DynarePath is correct ...
+ disp('Check the Dynare path on remote machine ... ')
+ skipline()
+ disp('please wait ... ')
+ skipline(2)
+ pause(2)
+
+ if isempty(dynareParallelDir('DynareIsOk.txt',RemoteTmpFolder,DataInput(Node)))
+ ErrorCode=8;
+ end
+
+ if (ErrorCode==8)
+ disp ('The DynarePath is incorrect!')
+ skipline()
+ disp('ErrorCode 8.')
+ skipline(2)
+ ErrorCode=8;
+ dynareParallelRmDir(RemoteTmpFolder,DataInput(Node));
+ return
+ else
+ disp('Check on Dynare Path remote machine ..... Ok!')
+ if isempty(dynareParallelDir('IsMac.txt',RemoteTmpFolder,DataInput(Node)))
+ RemoteEnvironment=Environment;
+ else
+ RemoteEnvironment=2;
+ end
+ skipline(2)
+ end
+ end
+
+
+ % Now we verify if it is possible delete remote computational traces!
+
+ dynareParallelRmDir(RemoteTmpFolder,DataInput(Node));
+
+ si3=[];
+
+ si3=dynareParallelDir('Tracing.m', RemoteTmpFolder,DataInput(Node));
+
+ if (isempty(si3))
+ disp ('Check on Delete Remote Computational Traces ..... Ok!')
+ skipline(2)
+ else
+ disp ('It is impossible to delete temporary files on remote machine!')
+ skipline()
+ disp('ErrorCode 9.')
+ skipline(2)
+ ErrorCode=9;
+ return
+ end
+
+
+ end
+ % Now we check the variable 'CPUnbr'.
+
+ % This check can be removed ... according to the dynare parser
+ % strategy.
+
+ yn=isempty(DataInput(Node).CPUnbr);
+
+ if yn==1
+ % The field is empty!
+ disp('The field "CPUnbr" is empty!')
+ skipline()
+ disp('ErrorCode 2.')
+ skipline(2)
+ ErrorCode=2;
+ return
+ end
+
+ % This check can be removed ... according to the dynare parser
+ % strategy.
+
+
+
+ % We look for the information on local computer hardware.
+
+ si0=[];
+ de0=[];
+
+ Environment1=Environment;
+ disp('Checking Hardware please wait ...');
+ if (DataInput(Node).Local == 1)
+ if Environment
+ if ~ismac
+ [si0 de0]=system('grep processor /proc/cpuinfo');
+ else
+ [si0 de0]=system('sysctl -n hw.ncpu');
+ Environment1=2;
+ end
+ else
+ [si0 de0]=system(['psinfo \\']);
+ end
+ else
+ if Environment
+ if ~isempty(DataInput(Node).Port)
+ ssh_token = ['-p ',DataInput(Node).Port];
+ else
+ ssh_token = '';
+ end
+ if OStargetUnix
+ if RemoteEnvironment ==1
+ [si0 de0]=system(['ssh ',ssh_token,' ',DataInput(Node).UserName,'@',DataInput(Node).ComputerName,' grep processor /proc/cpuinfo']);
+ else % it is MAC
+ [si0 de0]=system(['ssh ',ssh_token,' ',DataInput(Node).UserName,'@',DataInput(Node).ComputerName,' sysctl -n hw.ncpu']);
+ Environment1=2;
+ end
+ else
+ [si0 de0]=system(['ssh ',ssh_token,' ',DataInput(Node).UserName,'@',DataInput(Node).ComputerName,' psinfo']);
+ end
+ else
+ [si0 de0]=system(['psinfo \\',DataInput(Node).ComputerName,' -u ',DataInput(Node).UserName,' -p ',DataInput(Node).Password]);
+ end
+ end
+
+
+ RealCPUnbr='';
+ % keyboard;
+ RealCPUnbr=GiveCPUnumber(de0,Environment1);
+
+ % Questo controllo penso che si possa MIGLIORARE!!!!!
+ if isempty (RealCPUnbr) && Environment1==0
+ [si0 de0]=system(['psinfo \\',DataInput(Node).ComputerName]);
+ end
+ RealCPUnbr=GiveCPUnumber(de0,Environment1);
+
+ if isempty (RealCPUnbr)
+ % An error occurred when we try to know the Cpu/Cores
+ % numbers.
+ disp('It is impossible determine the number of Cpu/Processor avaiable on this machine!')
+ skipline()
+ disp('ErrorCode 2.')
+ skipline()
+ if Environment
+ disp('Check the command "$less /proc/cpuinfo" ... !')
+ else
+ disp('Check if the pstools are installed and are in machine path! And check the command "psinfo \\"')
+ end
+ skipline()
+ ErrorCode=2;
+ return
+ end
+
+
+ % Trasforming the input data provided in a form [n1:n2] in a single numerical
+ % value.
+
+
+ CPUnbrUser=length(DataInput(Node).CPUnbr);
+ maxCPUnbrUser=max(DataInput(Node).CPUnbr)+1;
+
+ disp(['Hardware has ', num2str(RealCPUnbr),' Cpu/Cores!'])
+ disp(['User requires ',num2str(CPUnbrUser),' Cpu/Cores!'])
+ if CPUnbrUser==RealCPUnbr
+ % It is Ok!
+ disp('Check on CPUnbr Variable ..... Ok!')
+ skipline(3)
+ end
+
+ if CPUnbrUser > RealCPUnbr
+ disp('Warning! The user asks to use more CPU''s than those available.')
+ skipline(2)
+ ErrorCode=2.1;
+ end
+ if CPUnbrUser < RealCPUnbr
+ disp('Warning! There are unused CPU''s!')
+ skipline(2)
+ ErrorCode=2.2;
+ end
+
+ if mod(length(DataInput(Node).CPUnbr),DataInput(Node).NumberOfThreadsPerJob)
+ skipline()
+ disp(['NumberOfThreadsPerJob = ',int2str(DataInput(Node).NumberOfThreadsPerJob),' is not an exact divisor of number of CPUs = ',int2str(DataInput(Node).CPUnbr),'!'])
+ disp([' You must re-set properly NumberOfThreadsPerJob of node ' int2str(Node) ' ' DataInput(Node).ComputerName])
+ disp(' in your configuration file')
+ skipline()
+ ErrorCode=2.3;
+ end
+
+ disp(['Test for Cluster computation, computer ',DataInput(Node).ComputerName, ' ..... Passed!'])
+ skipline(2)
end
\ No newline at end of file
diff --git a/matlab/parallel/CreateTimeString.m b/matlab/parallel/CreateTimeString.m
index 8b82b1cc6d0e5c7681fdcb3006ac34bc5f45cc50..53251d4186c7c7d562ee58d79dbbb076b17b4ef0 100644
--- a/matlab/parallel/CreateTimeString.m
+++ b/matlab/parallel/CreateTimeString.m
@@ -6,7 +6,7 @@ function [TiSt] = CreateTimeString()
% None
%
% OUTPUTS
-% o TiSt [] ...
+% o TiSt [] ...
%
% Copyright (C) 2009-2010 Dynare Team
%
diff --git a/matlab/parallel/GiveCPUnumber.m b/matlab/parallel/GiveCPUnumber.m
index 4d3988bece0dcb7fe390fc5d805c883187be17e5..cc2d45aa38e3674b234239e9d1f93a1edd65ef49 100644
--- a/matlab/parallel/GiveCPUnumber.m
+++ b/matlab/parallel/GiveCPUnumber.m
@@ -1,76 +1,76 @@
-function [nCPU]= GiveCPUnumber (ComputerInformations, Environment)
-% PARALLEL CONTEXT
-% In a parallel context this function return the CPUs or cores numer avaiable
-% on the computer used for run parallel code.
-%
-% INPUTS
-% an array contained several fields that describe the hardaware
-% software enviroments of a generic computer.
-%
-% OUTPUTS
-% The CPUs or Cores numbers of computer.
-%
-% SPECIAL REQUIREMENTS
-% none
-
-% Copyright (C) 2010-2017 Dynare Team
-%
-% 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/>.
-
-
-nCPU='';
-
-if nargin < 2
-% Determine a specific operating system or software version when necessary
-% for different command (sintax, name, ...).
-Environment=~ispc;
-end
-
-switch Environment
- case 0 %WINDOWS OPERATING SYSTEM
-
- OffSet=27;
-
- SringPosition=strfind(ComputerInformations, 'Processors:');
- nCPU=ComputerInformations(SringPosition+OffSet);
-
- % We check if there are Processors/Cores more than 9.
-
-
- t0=ComputerInformations(SringPosition+OffSet+1);
- t1=str2num(t0);
- t1=isempty(t1);
-
- % if t1 is 0 the machine have more than 9 CPU.
-
- if t1==0
- nCPU=strcat(nCPU,t0);
- end
-
- nCPU=str2num(nCPU);
-
- return
- case 1 %LIKE UNIX OPERATING SYSTEM
-
- % Da generalizzare a un numero di CPu maggiore di 9!!!
-
- nCPU=str2num(ComputerInformations(length(ComputerInformations)-1))+1;
-
- case 2 %MAC-OS OPERATING SYSTEM
-
- nCPU=str2num(ComputerInformations);
-
-end
+function [nCPU]= GiveCPUnumber (ComputerInformations, Environment)
+% PARALLEL CONTEXT
+% In a parallel context this function return the CPUs or cores numer avaiable
+% on the computer used for run parallel code.
+%
+% INPUTS
+% an array contained several fields that describe the hardaware
+% software enviroments of a generic computer.
+%
+% OUTPUTS
+% The CPUs or Cores numbers of computer.
+%
+% SPECIAL REQUIREMENTS
+% none
+
+% Copyright (C) 2010-2017 Dynare Team
+%
+% 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/>.
+
+
+nCPU='';
+
+if nargin < 2
+ % Determine a specific operating system or software version when necessary
+ % for different command (sintax, name, ...).
+ Environment=~ispc;
+end
+
+switch Environment
+ case 0 %WINDOWS OPERATING SYSTEM
+
+ OffSet=27;
+
+ SringPosition=strfind(ComputerInformations, 'Processors:');
+ nCPU=ComputerInformations(SringPosition+OffSet);
+
+ % We check if there are Processors/Cores more than 9.
+
+
+ t0=ComputerInformations(SringPosition+OffSet+1);
+ t1=str2num(t0);
+ t1=isempty(t1);
+
+ % if t1 is 0 the machine have more than 9 CPU.
+
+ if t1==0
+ nCPU=strcat(nCPU,t0);
+ end
+
+ nCPU=str2num(nCPU);
+
+ return
+ case 1 %LIKE UNIX OPERATING SYSTEM
+
+ % Da generalizzare a un numero di CPu maggiore di 9!!!
+
+ nCPU=str2num(ComputerInformations(length(ComputerInformations)-1))+1;
+
+ case 2 %MAC-OS OPERATING SYSTEM
+
+ nCPU=str2num(ComputerInformations);
+
+end
diff --git a/matlab/parallel/InitializeComputationalEnvironment.m b/matlab/parallel/InitializeComputationalEnvironment.m
index 5a824cd72d085489e349a343e95f0b3658e481c1..18ce19f113331bfb87e7e5c325c8b331685e6db9 100644
--- a/matlab/parallel/InitializeComputationalEnvironment.m
+++ b/matlab/parallel/InitializeComputationalEnvironment.m
@@ -37,7 +37,7 @@ function InitializeComputationalEnvironment()
% Comment the line 'warning('off');' in order to view the warning message
% in Octave!
-if isoctave
+if isoctave
warning('off');
end
diff --git a/matlab/parallel/closeSlave.m b/matlab/parallel/closeSlave.m
index f6552e0e0ca9542b88b42142d7b684767e33145d..49c14deb3eac395502e8979b8e87b1275f7d12bc 100644
--- a/matlab/parallel/closeSlave.m
+++ b/matlab/parallel/closeSlave.m
@@ -10,7 +10,7 @@ function closeSlave(Parallel,TmpFolder,partial)
% This directory is named using current date
% and is used only one time and then deleted.
% If islocal==1, TmpFolder=''.
-%
+%
%
% OUTPUTS
% None
@@ -45,7 +45,7 @@ if partial==1
dynareParallelSendFiles('slaveParallel_break.mat',TmpFolder,Parallel(indPC));
end
end
-% delete('slaveParallel_break')
+ % delete('slaveParallel_break')
return
end
if partial==-1
@@ -55,7 +55,7 @@ if partial==-1
dynareParallelDelete( 'slaveParallel_break.mat',TmpFolder,Parallel(indPC));
end
end
-% delete('slaveParallel_break')
+ % delete('slaveParallel_break')
return
end
@@ -63,14 +63,14 @@ for indPC=1:length(Parallel)
if (Parallel(indPC).Local==0)
dynareParallelDelete( 'slaveParallel_input*.mat',TmpFolder,Parallel(indPC));
end
-
-
+
+
delete( 'slaveParallel_input*.mat');
delete( 'slaveJob*.mat');
pause(1)
delete(['slaveParallel_*.log']);
delete ConcurrentCommand1.bat;
-
+
end
while(1)
@@ -81,7 +81,7 @@ while(1)
end
end
break
-
+
end
end
diff --git a/matlab/parallel/distributeJobs.m b/matlab/parallel/distributeJobs.m
index b1276dd1160239f6d63c135858adacce87d3fe25..c0c41925f7db2b3b96005d8741d81799ecbcdb89 100644
--- a/matlab/parallel/distributeJobs.m
+++ b/matlab/parallel/distributeJobs.m
@@ -40,7 +40,7 @@ function [nCPU, totCPU, nBlockPerCPU, totSLAVES] = distributeJobs(Parallel, fBlo
% The Parallel vector has already been sorted
-% (in accord with the CPUWeight values) in DESCENDING order in
+% (in accord with the CPUWeight values) in DESCENDING order in
% InitializeComputationalEnvironment!
totCPU=0;
@@ -57,7 +57,7 @@ for j=1:lP
error(['PARALLEL_ERROR:: NumberOfThreadsPerJob is not an exact divisor of CPUnbr'])
end
nCPU(j)=length(Parallel(j).CPUnbr)/Parallel(j).NumberOfThreadsPerJob;
- totCPU=totCPU+nCPU(j);
+ totCPU=totCPU+nCPU(j);
CPUWeight(j)=str2num(Parallel(j).NodeWeight);
end
@@ -85,15 +85,15 @@ CPUWeight=CPUWeight./sum(CPUWeight);
% Redistributing the jobs among the cluster nodes according to the
% CPUWeight.
for i=1:nC
-
+
JobsForNode(i)=CPUWeight(i)*NumbersOfJobs;
-
+
% Many choices are possible:
-
+
% JobsForNode(i)=round(JobsForNode(i));
% JobsForNode(i)=floor(JobsForNode(i));
- JobsForNode(i)=ceil(JobsForNode(i));
-
+ JobsForNode(i)=ceil(JobsForNode(i));
+
end
% Check if there are more (fewer) jobs.
@@ -101,19 +101,19 @@ end
SumOfJobs=sum(JobsForNode);
if SumOfJobs~=NumbersOfJobs
-
+
if SumOfJobs>NumbersOfJobs
-
+
% Many choices are possible:
-
+
% - Remove the excess works at the node that has the greatest
% number of jobs.
% - Remove the excess works at the node slower.
-
+
VerySlow=nC;
-
+
while SumOfJobs>NumbersOfJobs
-
+
if JobsForNode(VerySlow)==0
VerySlow=VerySlow-1;
continue
@@ -121,21 +121,21 @@ if SumOfJobs~=NumbersOfJobs
JobsForNode(VerySlow)=JobsForNode(VerySlow)-1;
SumOfJobs=SumOfJobs-1;
end
-
+
end
-
+
if SumOfJobs<NumbersOfJobs
-
+
% Many choices are possible:
% - ... (see above).
-
+
[NonServe VeryFast]= min(CPUWeight);
-
+
while SumOfJobs<NumbersOfJobs
JobsForNode(VeryFast)=JobsForNode(VeryFast)+1;
SumOfJobs=SumOfJobs+1;
end
-
+
end
end
@@ -148,36 +148,36 @@ JobAssignedCpu=0;
RelativePosition=1;
for i=1:nC
-
+
% Many choices are possible:
% - ... (see above).
-
+
JobAssignedCpu=max(1,floor(JobsForNode(i)/nCPUoriginal(i)));
-
+
ChekOverFlow=0;
-
+
for j=RelativePosition:nCPU(i)
JobsForCpu(j)=JobAssignedCpu;
ChekOverFlow=ChekOverFlow+JobAssignedCpu;
-
+
if ChekOverFlow>=JobsForNode(i)
break
end
-
+
end
-
+
% Check if there are more (fewer) jobs.
% This can happen when we use ceil, round, ... functions.
-
+
if ChekOverFlow ~=(JobsForNode(i))
-
+
if ChekOverFlow >(JobsForNode(i))
while ChekOverFlow>JobsForNode(i)
JobsForCpu(nCPU(i))=JobsForCpu(nCPU(i))-1;
ChekOverFlow=ChekOverFlow-1;
end
end
-
+
if ChekOverFlow <(JobsForNode(i))
while ChekOverFlow<JobsForNode(i)
JobsForCpu(nCPU(i))=JobsForCpu(nCPU(i))+1;
@@ -185,9 +185,9 @@ for i=1:nC
end
end
end
-
+
RelativePosition=nCPU(i)+1;
-
+
end
% Reshape the variables totCPU,totSLAVES and nBlockPerCPU in accord with
@@ -216,5 +216,3 @@ for i=1:nCPU(nC)
RelativeCounter=RelativeCounter+1;
end
end
-
-
diff --git a/matlab/parallel/dynareParallelDelete.m b/matlab/parallel/dynareParallelDelete.m
index 7c028043670be7d8f24a8eed3ad1b67c5c454bfd..478b8c8e46ba30db186b75a523066feeff20329b 100644
--- a/matlab/parallel/dynareParallelDelete.m
+++ b/matlab/parallel/dynareParallelDelete.m
@@ -4,8 +4,8 @@ function dynareParallelDelete(fname,pname,Parallel)
%
% INPUTS
% o fname [] ...
-% o pname [] ...
-% o Parallel [] ...
+% o pname [] ...
+% o Parallel [] ...
%
% OUTPUTS
% None
@@ -53,5 +53,5 @@ for indPC=1:length(Parallel)
delete(fname_temp);
end
end
-
+
end
diff --git a/matlab/parallel/dynareParallelDeleteNewFiles.m b/matlab/parallel/dynareParallelDeleteNewFiles.m
index 9f88e6599922b54b5e0561f02c16a8965796aff2..34fdab44be274f5434a34401ca4c8fe42196c6df 100644
--- a/matlab/parallel/dynareParallelDeleteNewFiles.m
+++ b/matlab/parallel/dynareParallelDeleteNewFiles.m
@@ -36,7 +36,7 @@ NewFilesFromSlaves={};
% try
for indPC=1:length(Parallel)
-
+
if Parallel(indPC).Local==0
[NewFilesFromSlaves, PRCDirSnapshot{indPC}]=dynareParallelFindNewFiles(PRCDirSnapshot{indPC},Parallel(indPC), PRCDir);
if ~ispc || strcmpi('unix',Parallel(indPC).OperatingSystem)
@@ -60,11 +60,11 @@ for indPC=1:length(Parallel)
exception_flag=exception_flag+(~isempty(strfind(fileaddress{2},varargin{indexc})));
end
if exception_flag==0
- dynareParallelDelete(fileaddress{2},[PRCDir,fS,fileaddress{1}],Parallel(indPC));
+ dynareParallelDelete(fileaddress{2},[PRCDir,fS,fileaddress{1}],Parallel(indPC));
- disp('New file deleted in remote -->');
- disp(fileaddress{2});
- disp('<--');
+ disp('New file deleted in remote -->');
+ disp(fileaddress{2});
+ disp('<--');
end
end
@@ -75,6 +75,3 @@ for indPC=1:length(Parallel)
end
end
-
-
-
diff --git a/matlab/parallel/dynareParallelDir.m b/matlab/parallel/dynareParallelDir.m
index 8db2d6fe39eaae7f729186f75e1990a7915b29ce..0625dd8e7b97b7588fb3e16f1c444ae8a44e870d 100644
--- a/matlab/parallel/dynareParallelDir.m
+++ b/matlab/parallel/dynareParallelDir.m
@@ -37,10 +37,10 @@ for indPC=1:length(Parallel)
ssh_token = '';
end
if isoctave % Patch for peculiar behaviour of ssh-ls under Linux.
- % It is necessary to capture the ls warning message.
- % To do it under the ssh protocol it is necessary to redirect the ls message in a text file.
- % The file is 'OctaveStandardOutputMessage.txt' and it is
- % saved in the Model directory.
+ % It is necessary to capture the ls warning message.
+ % To do it under the ssh protocol it is necessary to redirect the ls message in a text file.
+ % The file is 'OctaveStandardOutputMessage.txt' and it is
+ % saved in the Model directory.
[check, ax]=system(['ssh ',ssh_token,' ',Parallel(indPC).UserName,'@',Parallel(indPC).ComputerName,' ls ',Parallel(indPC).RemoteDirectory,'/',PRCDir,'/',filename, ' 2> OctaveStandardOutputMessage.txt']);
else
[check, ax]=system(['ssh ',ssh_token,' ',Parallel(indPC).UserName,'@',Parallel(indPC).ComputerName,' ls ',Parallel(indPC).RemoteDirectory,'/',PRCDir,'/',filename]);
@@ -57,10 +57,10 @@ for indPC=1:length(Parallel)
else
if isoctave % Patch for peculiar behaviour of ls under Linux.
-
+
% It is necessary to capture the ls warning message and properly manage the jolly char '*'!
[check ax]=system(['ls ' ,filename, ' 2> OctaveStandardOutputMessage.txt']);
-
+
if check ~= 0 || ~isempty(strfind(ax,'No such file or directory'))
ax=[];
end
diff --git a/matlab/parallel/dynareParallelFindNewFiles.m b/matlab/parallel/dynareParallelFindNewFiles.m
index 4c6dabf36e3781ef039d8a916e48c9860ddf6cb5..ba93d6245b9ea9d71434a3e5650dd80f1dc635a1 100644
--- a/matlab/parallel/dynareParallelFindNewFiles.m
+++ b/matlab/parallel/dynareParallelFindNewFiles.m
@@ -36,7 +36,7 @@ function [NewFilesFrom, NewFileList]=dynareParallelFindNewFiles(FileList,Paralle
NewFilesFrom={};
LfL=length(FileList);
- % The first call ...
+% The first call ...
NewFileList = dynareParallelListAllFiles('Root',PRCDir,Parallel);
@@ -45,35 +45,34 @@ LnFl=length(NewFileList);
RelativePosition=1;
for i=1:LnFl
-
+
% Exception Handling
-
+
% If you comment the code below all new file will be copied!
-
+
% 1. The comp_status* files are managed separately.
-
+
FiCoS=strfind(NewFileList{i},'comp_status_');
if ~isempty(FiCoS)
continue
end
-
+
% 2. For the time being is not necessary to always copy *.log
% and *.txt files.
-
+
nC1 = strfind(NewFileList{i}, '.log');
nC2 = strfind(NewFileList{i}, '.txt');
-
+
if (~isempty(nC1) || ~isempty(nC2))
continue
end
-
+
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-
-
+
+
N=strmatch(NewFileList{i},FileList,'exact');
if isempty(N)
NewFilesFrom{RelativePosition}=NewFileList{i};
RelativePosition=RelativePosition+1;
end
end
-
diff --git a/matlab/parallel/dynareParallelGetFiles.m b/matlab/parallel/dynareParallelGetFiles.m
index 53e9c4ef0f1c45f6cb1aca4daabfa71651ecb61b..6669f21a85f0e5315540d9afaf8fc0663ed8fb06 100644
--- a/matlab/parallel/dynareParallelGetFiles.m
+++ b/matlab/parallel/dynareParallelGetFiles.m
@@ -55,10 +55,10 @@ for indPC=1:length(Parallel)
for jfil=1:size(NamFileInput,1)
if isoctave % Patch for peculiar behaviour of ls under Linux.
- % It is necessary to manage the jolly char '*'!
+ % It is necessary to manage the jolly char '*'!
FindAst=strfind(NamFileInput{jfil,2},'comp_status_posterior_sampler_core*');
-
+
if isempty (FindAst)
[NonServeL NonServeR]= system(['scp ',scp_token,' ',Parallel(indPC).UserName,'@',Parallel(indPC).ComputerName,':',Parallel(indPC).RemoteDirectory,'/',PRCDir,'/',NamFileInput{jfil,1},NamFileInput{jfil,2},' ',NamFileInput{jfil,1}]);
@@ -66,9 +66,9 @@ for indPC=1:length(Parallel)
else
filenameTemp=NamFileInput{jfil,2};
-
+
[NotUsed FlI]=system(['ssh ',ssh_token,' ',Parallel(indPC).UserName,'@',Parallel(indPC).ComputerName,' ls ',Parallel(indPC).RemoteDirectory,'/',PRCDir,'/',filenameTemp, ' 2> OctaveStandardOutputMessage.txt']);
-
+
if isempty (FlI)
return
end
diff --git a/matlab/parallel/dynareParallelGetNewFiles.m b/matlab/parallel/dynareParallelGetNewFiles.m
index fb9b0ae91781eba6a60da4d9fb5c945a00fcfb39..8d42bd3759f3379e7785e481c288b7e9757c8e18 100644
--- a/matlab/parallel/dynareParallelGetNewFiles.m
+++ b/matlab/parallel/dynareParallelGetNewFiles.m
@@ -37,7 +37,7 @@ NewFilesFromSlaves={};
% try
for indPC=1:length(Parallel)
-
+
if Parallel(indPC).Local==0
[NewFilesFromSlaves, PRCDirSnapshot{indPC}]=dynareParallelFindNewFiles(PRCDirSnapshot{indPC},Parallel(indPC), PRCDir);
if ~ispc || strcmpi('unix',Parallel(indPC).OperatingSystem)
@@ -71,6 +71,3 @@ for indPC=1:length(Parallel)
end
end
-
-
-
diff --git a/matlab/parallel/dynareParallelListAllFiles.m b/matlab/parallel/dynareParallelListAllFiles.m
index ebdbb78810e82628bb0f867b0e293fd2b8cfab15..eab7134223aff237ddd42c190908943dbbdba5b1 100644
--- a/matlab/parallel/dynareParallelListAllFiles.m
+++ b/matlab/parallel/dynareParallelListAllFiles.m
@@ -34,7 +34,7 @@ function fileList = dynareParallelListAllFiles(dirName,PRCDir,Parallel)
if (~ispc || strcmpi('unix',Parallel.OperatingSystem))
-
+
fileList={};
currentPath=[];
if ~isempty(Parallel.Port)
@@ -43,24 +43,24 @@ if (~ispc || strcmpi('unix',Parallel.OperatingSystem))
ssh_token = '';
end
- % Get the data for the current remote directory.
+ % Get the data for the current remote directory.
[Flag, fL]=system(['ssh ',ssh_token,' ',' ',Parallel.UserName,'@',Parallel.ComputerName,' ls ',Parallel.RemoteDirectory,'/',PRCDir, ' -R -p -1']);
% Format the return value fL.
-
+
nL=regexp(fL,'\n');
start=1;
j=1;
for (i=1:length(nL))
-
+
stringProcessed=fL(start:nL(i)-1);
-
+
if isempty(stringProcessed)
start=nL(i)+1;
continue
end
-
+
if strfind(stringProcessed,'/')
if strfind(stringProcessed,PRCDir)
DD=strfind(stringProcessed,':');
@@ -70,11 +70,11 @@ if (~ispc || strcmpi('unix',Parallel.OperatingSystem))
start=nL(i)+1;
continue
end
-
+
fileList{j,1}=[currentPath stringProcessed];
start=nL(i)+1;
j=j+1;
- end
+ end
else
@@ -92,7 +92,7 @@ else
% Build the path files:
if ~isempty(fileList)
fileList = cellfun(@(x) fullfile(dirName,x),...
- fileList,'UniformOutput',false);
+ fileList,'UniformOutput',false);
end
% Get a list of the subdirectories:
@@ -112,5 +112,3 @@ else
fileList = [fileList; dynareParallelListAllFiles(nextDir,PRCDir,Parallel)];
end
end
-
-
diff --git a/matlab/parallel/dynareParallelMkDir.m b/matlab/parallel/dynareParallelMkDir.m
index 9cb0a41a563489725d5030a86c1b37061cff7cbc..e85d1af3a206dfd49315efc4bd86788c354b8b0a 100644
--- a/matlab/parallel/dynareParallelMkDir.m
+++ b/matlab/parallel/dynareParallelMkDir.m
@@ -3,8 +3,8 @@ function dynareParallelMkDir(PRCDir,Parallel)
% In a parallel context, this is a specialized version of rmdir() function.
%
% INPUTS
-% o PRCDir [] ...
-% o Parallel [] ...
+% o PRCDir [] ...
+% o Parallel [] ...
%
% OUTPUTS
% None
diff --git a/matlab/parallel/dynareParallelRmDir.m b/matlab/parallel/dynareParallelRmDir.m
index 11b6e03608cb3309562b2eb4abb63915e2900cb6..64f3c4fdecba38b0c93514aa9fbe408f242f3ad9 100644
--- a/matlab/parallel/dynareParallelRmDir.m
+++ b/matlab/parallel/dynareParallelRmDir.m
@@ -3,8 +3,8 @@ function dynareParallelRmDir(PRCDir,Parallel)
% In a parallel context, this is a specialized version of rmdir() function.
%
% INPUTS
-% o PRCDir [] ...
-% o Parallel [] ...
+% o PRCDir [] ...
+% o Parallel [] ...
%
% OUTPUTS
% None
diff --git a/matlab/parallel/dynareParallelSendFiles.m b/matlab/parallel/dynareParallelSendFiles.m
index 6a12a2328c8d25c412b6f24097a47f79728955c4..6a3c8904cee2a34223d225c211c441b57b5afb6b 100644
--- a/matlab/parallel/dynareParallelSendFiles.m
+++ b/matlab/parallel/dynareParallelSendFiles.m
@@ -62,15 +62,15 @@ for indPC=1:length(Parallel)
for jfil=1:size(NamFileInput,1)
if ~isempty(NamFileInput{jfil,1})
if isempty(dynareParallelDir(NamFileInput{jfil,1},PRCDir,Parallel(indPC)))
-
+
if isoctave % Patch for peculiar behaviour of mkdir under Windows.
-
+
% It is Necessary because Octave is not able to
% create two nested directory at the same time.
-
- % Remove (if present) the '/' chars. Can be easily transformed
- % in a function.
-
+
+ % Remove (if present) the '/' chars. Can be easily transformed
+ % in a function.
+
NamFileInputTemp=NamFileInput{jfil,1};
while(1)
Bs=strfind(NamFileInputTemp,'/');
@@ -80,7 +80,7 @@ for indPC=1:length(Parallel)
NamFileInputTemp(1,Bs)='\';
end
end
-
+
[NonServeL, NonServeR]=system(['mkdir \\',Parallel(indPC).ComputerName,'\',Parallel(indPC).RemoteDrive,'$\',Parallel(indPC).RemoteDirectory,'\',PRCDir,'\',NamFileInputTemp]);
else
@@ -88,15 +88,15 @@ for indPC=1:length(Parallel)
end
end
end
-
+
if isoctave % Patch for peculiar behaviour copyfile ls under Windows.
-
+
% It is Necessary because Octave is not able to
% use the jolly char '*' with copyfile.
-
- % Remove (if present) the '/' chars. Can be easily transformed
- % in a function.
-
+
+ % Remove (if present) the '/' chars. Can be easily transformed
+ % in a function.
+
NamFileInputTemp=NamFileInput{jfil,1};
while(1)
Bs=strfind(NamFileInputTemp,'/');
@@ -106,9 +106,9 @@ for indPC=1:length(Parallel)
NamFileInputTemp(1,Bs)='\';
end
end
-
+
[NonServeS, NonServeD]=system(['copy ',NamFileInputTemp, NamFileInput{jfil,2},' \\',Parallel(indPC).ComputerName,'\',Parallel(indPC).RemoteDrive,'$\',Parallel(indPC).RemoteDirectory,'\',PRCDir,'\',NamFileInputTemp]);
-
+
else
copyfile([NamFileInput{jfil,1},NamFileInput{jfil,2}],['\\',Parallel(indPC).ComputerName,'\',Parallel(indPC).RemoteDrive,'$\',Parallel(indPC).RemoteDirectory,'\',PRCDir,'\',NamFileInput{jfil,1}]);
end
diff --git a/matlab/parallel/dynareParallelSnapshot.m b/matlab/parallel/dynareParallelSnapshot.m
index 522ffa989b92e8dfa569d8080903209c286abd0b..8566ebfe05b61bc7646456acaae5d98a34cdd6a4 100644
--- a/matlab/parallel/dynareParallelSnapshot.m
+++ b/matlab/parallel/dynareParallelSnapshot.m
@@ -35,8 +35,8 @@ PRCDirSnapshot={};
for indPC=1:length(Parallel)
if Parallel(indPC).Local==0
- % The first call ...
+ % The first call ...
PRCDirSnapshot{indPC}=dynareParallelListAllFiles('Root',PRCDir,Parallel(indPC));
-
+
end
end
diff --git a/matlab/parallel/fMessageStatus.m b/matlab/parallel/fMessageStatus.m
index 0d19960465fc2824a7dd490088589b36e893a4b5..2c9f8ab5f4a5643f16e7e7ae78c7c696bdf4cf8d 100644
--- a/matlab/parallel/fMessageStatus.m
+++ b/matlab/parallel/fMessageStatus.m
@@ -3,16 +3,16 @@ function fMessageStatus(prtfrc, njob, waitbarString, waitbarTitle, Parallel)
% In parallel context, this function is launched on slave
% machines, and acts as a message passing device for the master machine.
-% INPUTS
+% INPUTS
% o prtfrc [double] fraction of iteration performed
% o njob [int] index number of this CPU among all CPUs in the
% cluster
-% o waitbarString [char] running message string to be displayed in the monitor window on master machine
+% o waitbarString [char] running message string to be displayed in the monitor window on master machine
% o waitbarTitle [char] title to be displayed in the monitor window on master machine
% o Parallel [struct] options_.parallel(ThisMatlab), i.e. the parallel settings for this slave machine in the cluster.
%
-% OUTPUTS
-% o None
+% OUTPUTS
+% o None
% Copyright (C) 2006-2017 Dynare Team
%
@@ -39,7 +39,7 @@ end
try
save(['comp_status_',funcName,int2str(njob),'.mat'],'prtfrc','njob','waitbarString','waitbarTitle');
-catch
+catch
end
fslave = dir( ['slaveParallel_input',int2str(njob),'.mat']);
@@ -47,4 +47,3 @@ fbreak = dir( ['slaveParallel_break.mat']);
if isempty(fslave) || ~isempty(fbreak)
error('Master asked to break the job');
end
-
diff --git a/matlab/parallel/fParallel.m b/matlab/parallel/fParallel.m
index 67752fe0901a539a456bc23f73f5388ed5f201b0..4bce41008496693d7b03c4b2e5376a344ea38898 100644
--- a/matlab/parallel/fParallel.m
+++ b/matlab/parallel/fParallel.m
@@ -69,7 +69,7 @@ fInputVar.Parallel = Parallel;
% Launch the routine to be run in parallel.
try
tic
-
+
fOutputVar = feval(fname, fInputVar ,fblck, nblck, whoiam, ThisMatlab);
toc
if isfield(fOutputVar,'OutputFileName')
@@ -87,7 +87,7 @@ try
save([ fname,'_output_',int2str(whoiam),'.mat'],'fOutputVar' )
save(['comp_status_',funcName,int2str(whoiam),'.mat'],'CloseAllSlaves');
end
-
+
disp(['fParallel ',int2str(whoiam),' completed.'])
catch
theerror = lasterror;
@@ -108,7 +108,7 @@ catch
end
fMessageStatus(NaN,whoiam,waitbarString, waitbarTitle, Parallel(ThisMatlab));
end
-
+
end
diary off;
delete(['P_',fname,'_',int2str(whoiam),'End.txt']);
diff --git a/matlab/parallel/masterParallel.m b/matlab/parallel/masterParallel.m
index f9b47dea939e0088e4de897704b6e756d45c8274..e51c6d02335c4637b3a612d51f8174b9cd10b173 100644
--- a/matlab/parallel/masterParallel.m
+++ b/matlab/parallel/masterParallel.m
@@ -3,37 +3,37 @@ function [fOutVar,nBlockPerCPU, totCPU] = masterParallel(Parallel,fBlock,nBlock,
% This is the most important function for the management of DYNARE parallel
% computing.
% It is the top-level function called on the master computer when parallelizing a task.
-%
+%
% This function has two main computational strategies for managing the
% matlab worker (slave process):
-%
+%
% 0 Simple Close/Open Stategy:
% In this case the new Matlab instances (slave process) are open when
% necessary and then closed. This can happen many times during the
% simulation of a model.
-%
+%
% 1 Always Open Strategy:
% In this case we have a more sophisticated management of slave processes,
% which are no longer closed at the end of each job. The slave processes
% wait for a new job (if it exists). If a slave does not receive a new job after a
% fixed time it is destroyed. This solution removes the computational
% time necessary to Open/Close new Matlab instances.
-%
+%
% The first (point 0) is the default Strategy
% i.e.(Parallel_info.leaveSlaveOpen=0). This value can be changed by the
% user in xxx.mod file or it is changed by the programmer if it is necessary to
% reduce the overall computational time. See for example the
% prior_posterior_statistics.m.
-%
+%
% The number of parallelized threads will be equal to (nBlock-fBlock+1).
%
% Treatment of global variables:
-% Global variables used within the called function are wrapped and passed by storing their
+% Global variables used within the called function are wrapped and passed by storing their
% values at the start of the parallel computation in a file via
% storeGlobalVars.m. This file is then loaded in the separate,
% independent slave Matlab sessions. By keeping them separate, no
-% interaction via global variables can take place.
-%
+% interaction via global variables can take place.
+%
% INPUTS
% o Parallel [struct vector] copy of options_.parallel
% o fBlock [int] index number of the first thread
@@ -134,17 +134,17 @@ if isHybridMatlabOctave || isoctave
fInputVar.(fInputNames{j})=TargetVar;
end
end
-
+
if exist('fGlobalVar','var') && ~isempty(fGlobalVar)
- fInputNames = fieldnames(fGlobalVar);
- for j=1:length(fInputNames)
- TargetVar = fGlobalVar.(fInputNames{j});
- if isa(TargetVar,'function_handle')
- TargetVar=func2str(TargetVar);
- fGlobalVar.(fInputNames{j})=TargetVar;
+ fInputNames = fieldnames(fGlobalVar);
+ for j=1:length(fInputNames)
+ TargetVar = fGlobalVar.(fInputNames{j});
+ if isa(TargetVar,'function_handle')
+ TargetVar=func2str(TargetVar);
+ fGlobalVar.(fInputNames{j})=TargetVar;
+ end
end
end
- end
end
if Strategy==1
@@ -165,18 +165,18 @@ end
% Save input data for use by the slaves.
switch Strategy
- case 0
- storeGlobalVars([fname,'_input.mat']);
- save([fname,'_input.mat'],'fInputVar','Parallel','-append')
-
- case 1
- if exist('fGlobalVar','var')
- save(['temp_input.mat'],'fInputVar','fGlobalVar')
- else
- save(['temp_input.mat'],'fInputVar')
- end
- save(['temp_input.mat'],'Parallel','-append')
- closeSlave(Parallel,PRCDir,-1);
+ case 0
+ storeGlobalVars([fname,'_input.mat']);
+ save([fname,'_input.mat'],'fInputVar','Parallel','-append')
+
+ case 1
+ if exist('fGlobalVar','var')
+ save(['temp_input.mat'],'fInputVar','fGlobalVar')
+ else
+ save(['temp_input.mat'],'fInputVar')
+ end
+ save(['temp_input.mat'],'Parallel','-append')
+ closeSlave(Parallel,PRCDir,-1);
end
@@ -239,26 +239,26 @@ end
% End
for j=1:totCPU
-
+
if Strategy==1
command1 = ' ';
end
-
+
indPC=min(find(nCPU>=j));
-
+
% According to the information contained in configuration file, compThread can limit MATLAB
% to a single computational thread. By default, MATLAB makes use of the multithreading
% capabilities of the computer on which it is running. Nevertheless
% exsperimental results show as matlab native
% multithreading limit the performaces when the parallel computing is active.
-
-
+
+
if strcmp('true',Parallel(indPC).SingleCompThread)
compThread = '-singleCompThread';
else
compThread = '';
end
-
+
nthreads=Parallel(indPC).NumberOfThreadsPerJob;
if indPC>1
nCPU0 = nCPU(indPC-1);
@@ -266,15 +266,15 @@ for j=1:totCPU
nCPU0=0;
end
offset = sum(nBlockPerCPU(1:j-1))+offset0;
-
+
% Create a file used to monitoring if a parallel block (core)
% computation is finished or not.
-
+
fid1=fopen(['P_',fname,'_',int2str(j),'End.txt'],'w+');
fclose(fid1);
-
+
if Strategy==1
-
+
fblck = offset+1;
nblck = sum(nBlockPerCPU(1:j));
save temp_input.mat fblck nblck fname -append;
@@ -289,7 +289,7 @@ for j=1:totCPU
% internal criteria.
pause(1);
newInstance = 0;
-
+
% Check if j CPU is already alive.
if isempty(dynareParallelDir(['P_slave_',int2str(j),'End.txt'],PRCDir,Parallel(indPC)))
fid1=fopen(['P_slave_',int2str(j),'End.txt'],'w+');
@@ -298,66 +298,141 @@ for j=1:totCPU
dynareParallelSendFiles(['P_slave_',int2str(j),'End.txt'],PRCDir,Parallel(indPC));
delete(['P_slave_',int2str(j),'End.txt']);
end
-
+
newInstance = 1;
storeGlobalVars( ['slaveParallel_input',int2str(j),'.mat']);
save( ['slaveParallel_input',int2str(j),'.mat'],'Parallel','-append');
% Prepare global vars for Slave.
end
else
-
+
% If the computation is executed remotely all the necessary files
% are created localy, then copied in remote directory and then
% deleted (loacal)!
-
+
save( ['slaveParallel_input',int2str(j),'.mat'],'j');
-
+
if Parallel(indPC).Local==0
dynareParallelSendFiles(['P_',fname,'_',int2str(j),'End.txt'],PRCDir,Parallel(indPC));
delete(['P_',fname,'_',int2str(j),'End.txt']);
-
+
dynareParallelSendFiles(['slaveParallel_input',int2str(j),'.mat'],PRCDir,Parallel(indPC));
delete(['slaveParallel_input',int2str(j),'.mat']);
-
+
end
-
+
end
-
+
% set affinity range on win CPU's
affinity_range = [1:nthreads]+(j-1-nCPU0)*nthreads;
my_affinity = int2str(Parallel(indPC).CPUnbr(affinity_range(1)));
for jaff=2:length(affinity_range)
my_affinity = [my_affinity ',' int2str(Parallel(indPC).CPUnbr(affinity_range(jaff)))];
end
-% % % int2str(Parallel(indPC).CPUnbr(j-nCPU0))
+ % % % int2str(Parallel(indPC).CPUnbr(j-nCPU0))
% DA SINTETIZZARE:
-
+
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% The following 'switch - case' code is the core of this function!
switch Strategy
- case 0
-
- if Parallel(indPC).Local == 1 % 0.1 Run on the local machine (localhost).
-
- if ~ispc || strcmpi('unix',Parallel(indPC).OperatingSystem) % Hybrid computing Windows <-> Unix!
- if regexpi([Parallel(indPC).MatlabOctavePath], 'octave') % Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
- command1=[Parallel(indPC).MatlabOctavePath,' -f --eval "default_save_options(''-v7''); addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); fParallel(',int2str(offset+1),',',int2str(sum(nBlockPerCPU(1:j))),',',int2str(j),',',int2str(indPC),',''',fname,''')" &'];
+ case 0
+
+ if Parallel(indPC).Local == 1 % 0.1 Run on the local machine (localhost).
+
+ if ~ispc || strcmpi('unix',Parallel(indPC).OperatingSystem) % Hybrid computing Windows <-> Unix!
+ if regexpi([Parallel(indPC).MatlabOctavePath], 'octave') % Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
+ command1=[Parallel(indPC).MatlabOctavePath,' -f --eval "default_save_options(''-v7''); addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); fParallel(',int2str(offset+1),',',int2str(sum(nBlockPerCPU(1:j))),',',int2str(j),',',int2str(indPC),',''',fname,''')" &'];
+ else
+ command1=[Parallel(indPC).MatlabOctavePath,' -nosplash -nodesktop -minimize ',compThread,' -r "addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); fParallel(',int2str(offset+1),',',int2str(sum(nBlockPerCPU(1:j))),',',int2str(j),',',int2str(indPC),',''',fname,''')" &'];
+ end
+ else % Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
+ if regexpi([Parallel(indPC).MatlabOctavePath], 'octave')
+ command1=['psexec -d -W "',DyMo, '" -a ',my_affinity,' -low ',Parallel(indPC).MatlabOctavePath,' -f --eval "default_save_options(''-v7''); addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); fParallel(',int2str(offset+1),',',int2str(sum(nBlockPerCPU(1:j))),',',int2str(j),',',int2str(indPC),',''',fname,''')"'];
+ else
+ command1=['psexec -d -W "',DyMo, '" -a ',my_affinity,' -low ',Parallel(indPC).MatlabOctavePath,' -nosplash -nodesktop -minimize ',compThread,' -r "addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); fParallel(',int2str(offset+1),',',int2str(sum(nBlockPerCPU(1:j))),',',int2str(j),',',int2str(indPC),',''',fname,''')"'];
+ end
+ end
+ else % 0.2 Parallel(indPC).Local==0: Run using network on remote machine or also on local machine.
+ if j==nCPU0+1
+ dynareParallelSendFiles([fname,'_input.mat'],PRCDir,Parallel(indPC));
+ dynareParallelSendFiles(NamFileInput,PRCDir,Parallel(indPC));
+ end
+
+ if (~ispc || strcmpi('unix',Parallel(indPC).OperatingSystem)) % Hybrid computing Windows <-> Unix!
+ if ispc
+ token='start /B ';
+ else
+ token = '';
+ end
+ if ~isempty(Parallel(indPC).Port)
+ ssh_token = ['-p ',Parallel(indPC).Port];
+ else
+ ssh_token = '';
+ end
+ % To manage the diferences in Unix/Windows OS syntax.
+ remoteFile=['remoteDynare',int2str(j)];
+ fidRemote=fopen([remoteFile,'.m'],'w+');
+ if regexpi([Parallel(indPC).MatlabOctavePath], 'octave') % Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
+ remoteString=['default_save_options(''-v7''); addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); fParallel(',int2str(offset+1),',',int2str(sum(nBlockPerCPU(1:j))),',',int2str(j),',',int2str(indPC),',''',fname,''')'];
+ command1=[token, 'ssh ',ssh_token,' ',Parallel(indPC).UserName,'@',Parallel(indPC).ComputerName,' "cd ',Parallel(indPC).RemoteDirectory,'/',PRCDir, '; ',Parallel(indPC).MatlabOctavePath,' -f --eval ',remoteFile,' " &'];
+ else
+ remoteString=['addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); fParallel(',int2str(offset+1),',',int2str(sum(nBlockPerCPU(1:j))),',',int2str(j),',',int2str(indPC),',''',fname,''')'];
+ command1=[token, 'ssh ',ssh_token,' ',Parallel(indPC).UserName,'@',Parallel(indPC).ComputerName,' "cd ',Parallel(indPC).RemoteDirectory,'/',PRCDir, '; ',Parallel(indPC).MatlabOctavePath,' -nosplash -nodesktop -minimize ',compThread,' -r ',remoteFile,';" &'];
+ end
+ fprintf(fidRemote,'%s\n',remoteString);
+ fclose(fidRemote);
+ dynareParallelSendFiles([remoteFile,'.m'],PRCDir,Parallel(indPC));
+ delete([remoteFile,'.m']);
+ else
+ if ~strcmpi(Parallel(indPC).ComputerName,MasterName) % 0.3 Run on a remote machine!
+ % Hybrid computing Matlab(Master)-> Octave(Slaves) and Vice Versa!
+ if regexpi([Parallel(indPC).MatlabOctavePath], 'octave')
+ command1=['psexec \\',Parallel(indPC).ComputerName,' -d -e -u ',Parallel(indPC).UserName,' -p ',Parallel(indPC).Password,' -W "',Parallel(indPC).RemoteDrive,':\',Parallel(indPC).RemoteDirectory,'\',PRCDir,'\" -a ',my_affinity, ...
+ ' -low ',Parallel(indPC).MatlabOctavePath,' -f --eval "default_save_options(''-v7''); addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); fParallel(',int2str(offset+1),',',int2str(sum(nBlockPerCPU(1:j))),',',int2str(j),',',int2str(indPC),',''',fname,''')"'];
else
- command1=[Parallel(indPC).MatlabOctavePath,' -nosplash -nodesktop -minimize ',compThread,' -r "addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); fParallel(',int2str(offset+1),',',int2str(sum(nBlockPerCPU(1:j))),',',int2str(j),',',int2str(indPC),',''',fname,''')" &'];
+
+ command1=['psexec \\',Parallel(indPC).ComputerName,' -d -e -u ',Parallel(indPC).UserName,' -p ',Parallel(indPC).Password,' -W "',Parallel(indPC).RemoteDrive,':\',Parallel(indPC).RemoteDirectory,'\',PRCDir,'\" -a ',my_affinity, ...
+ ' -low ',Parallel(indPC).MatlabOctavePath,' -nosplash -nodesktop -minimize ',compThread,' -r "addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); fParallel(',int2str(offset+1),',',int2str(sum(nBlockPerCPU(1:j))),',',int2str(j),',',int2str(indPC),',''',fname,''')"'];
end
- else % Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
+ else % 0.4 Run on the local machine via the network
+ % Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
if regexpi([Parallel(indPC).MatlabOctavePath], 'octave')
- command1=['psexec -d -W "',DyMo, '" -a ',my_affinity,' -low ',Parallel(indPC).MatlabOctavePath,' -f --eval "default_save_options(''-v7''); addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); fParallel(',int2str(offset+1),',',int2str(sum(nBlockPerCPU(1:j))),',',int2str(j),',',int2str(indPC),',''',fname,''')"'];
+ command1=['psexec \\',Parallel(indPC).ComputerName,' -d -e -W "',Parallel(indPC).RemoteDrive,':\',Parallel(indPC).RemoteDirectory,'\',PRCDir,'\" -a ',my_affinity, ...
+ ' -low ',Parallel(indPC).MatlabOctavePath,' -f --eval "default_save_options(''-v7''); addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); fParallel(',int2str(offset+1),',',int2str(sum(nBlockPerCPU(1:j))),',',int2str(j),',',int2str(indPC),',''',fname,''')"'];
else
- command1=['psexec -d -W "',DyMo, '" -a ',my_affinity,' -low ',Parallel(indPC).MatlabOctavePath,' -nosplash -nodesktop -minimize ',compThread,' -r "addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); fParallel(',int2str(offset+1),',',int2str(sum(nBlockPerCPU(1:j))),',',int2str(j),',',int2str(indPC),',''',fname,''')"'];
+ command1=['psexec \\',Parallel(indPC).ComputerName,' -d -e -W "',Parallel(indPC).RemoteDrive,':\',Parallel(indPC).RemoteDirectory,'\',PRCDir,'\" -a ',my_affinity, ...
+ ' -low ',Parallel(indPC).MatlabOctavePath,' -nosplash -nodesktop -minimize ',compThread,' -r "addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); fParallel(',int2str(offset+1),',',int2str(sum(nBlockPerCPU(1:j))),',',int2str(j),',',int2str(indPC),',''',fname,''')"'];
end
end
- else % 0.2 Parallel(indPC).Local==0: Run using network on remote machine or also on local machine.
- if j==nCPU0+1
- dynareParallelSendFiles([fname,'_input.mat'],PRCDir,Parallel(indPC));
- dynareParallelSendFiles(NamFileInput,PRCDir,Parallel(indPC));
+ end
+ end
+
+
+ case 1
+ if Parallel(indPC).Local == 1 && newInstance % 1.1 Run on the local machine.
+ if (~ispc || strcmpi('unix',Parallel(indPC).OperatingSystem)) % Hybrid computing Windows <-> Unix!
+ if regexpi([Parallel(indPC).MatlabOctavePath], 'octave') % Hybrid computing Matlab(Master)-> Octave(Slaves) and Vice Versa!
+ command1=[Parallel(indPC).MatlabOctavePath,' -f --eval "default_save_options(''-v7''); addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); slaveParallel(',int2str(j),',',int2str(indPC),')" &'];
+ else
+ command1=[Parallel(indPC).MatlabOctavePath,' -nosplash -nodesktop -minimize ',compThread,' -r "addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); slaveParallel(',int2str(j),',',int2str(indPC),')" &'];
+ end
+ else % Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
+ if regexpi([Parallel(indPC).MatlabOctavePath], 'octave')
+ command1=['psexec -d -W "',DyMo, '" -a ',my_affinity,' -low ',Parallel(indPC).MatlabOctavePath,' -f --eval "default_save_options(''-v7'');addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); slaveParallel(',int2str(j),',',int2str(indPC),')"'];
+ else
+ command1=['psexec -d -W "',DyMo, '" -a ',my_affinity,' -low ',Parallel(indPC).MatlabOctavePath,' -nosplash -nodesktop -minimize ',compThread,' -r "addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); slaveParallel(',int2str(j),',',int2str(indPC),')"'];
end
-
+ end
+ elseif Parallel(indPC).Local==0 % 1.2 Run using network on remote machine or also on local machine.
+ if j==nCPU0+1
+ dynareParallelSendFiles(NamFileInput,PRCDir,Parallel(indPC));
+ end
+ dynareParallelSendFiles(['P_',fname,'_',int2str(j),'End.txt'],PRCDir,Parallel(indPC));
+ delete(['P_',fname,'_',int2str(j),'End.txt']);
+ if newInstance
+ dynareParallelSendFiles(['slaveJob',int2str(j),'.mat'],PRCDir,Parallel(indPC));
+ delete(['slaveJob',int2str(j),'.mat']);
+ dynareParallelSendFiles(['slaveParallel_input',int2str(j),'.mat'],PRCDir,Parallel(indPC))
if (~ispc || strcmpi('unix',Parallel(indPC).OperatingSystem)) % Hybrid computing Windows <-> Unix!
if ispc
token='start /B ';
@@ -372,134 +447,59 @@ for j=1:totCPU
% To manage the diferences in Unix/Windows OS syntax.
remoteFile=['remoteDynare',int2str(j)];
fidRemote=fopen([remoteFile,'.m'],'w+');
- if regexpi([Parallel(indPC).MatlabOctavePath], 'octave') % Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
- remoteString=['default_save_options(''-v7''); addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); fParallel(',int2str(offset+1),',',int2str(sum(nBlockPerCPU(1:j))),',',int2str(j),',',int2str(indPC),',''',fname,''')'];
- command1=[token, 'ssh ',ssh_token,' ',Parallel(indPC).UserName,'@',Parallel(indPC).ComputerName,' "cd ',Parallel(indPC).RemoteDirectory,'/',PRCDir, '; ',Parallel(indPC).MatlabOctavePath,' -f --eval ',remoteFile,' " &'];
+ if regexpi([Parallel(indPC).MatlabOctavePath], 'octave') % Hybrid computing Matlab(Master)-> Octave(Slaves) and Vice Versa!
+ remoteString=['default_save_options(''-v7''); addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); slaveParallel(',int2str(j),',',int2str(indPC),');'];
+ command1=[token, 'ssh ',ssh_token,' ',Parallel(indPC).UserName,'@',Parallel(indPC).ComputerName,' "cd ',Parallel(indPC).RemoteDirectory,'/',PRCDir '; ',Parallel(indPC).MatlabOctavePath,' -f --eval ',remoteFile,' " &'];
else
- remoteString=['addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); fParallel(',int2str(offset+1),',',int2str(sum(nBlockPerCPU(1:j))),',',int2str(j),',',int2str(indPC),',''',fname,''')'];
- command1=[token, 'ssh ',ssh_token,' ',Parallel(indPC).UserName,'@',Parallel(indPC).ComputerName,' "cd ',Parallel(indPC).RemoteDirectory,'/',PRCDir, '; ',Parallel(indPC).MatlabOctavePath,' -nosplash -nodesktop -minimize ',compThread,' -r ',remoteFile,';" &'];
+ remoteString=['addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); slaveParallel(',int2str(j),',',int2str(indPC),');'];
+ command1=[token, 'ssh ',ssh_token,' ',Parallel(indPC).UserName,'@',Parallel(indPC).ComputerName,' "cd ',Parallel(indPC).RemoteDirectory,'/',PRCDir '; ',Parallel(indPC).MatlabOctavePath,' -nosplash -nodesktop -minimize ',compThread,' -r ',remoteFile,';" &'];
end
fprintf(fidRemote,'%s\n',remoteString);
fclose(fidRemote);
dynareParallelSendFiles([remoteFile,'.m'],PRCDir,Parallel(indPC));
delete([remoteFile,'.m']);
else
- if ~strcmpi(Parallel(indPC).ComputerName,MasterName) % 0.3 Run on a remote machine!
- % Hybrid computing Matlab(Master)-> Octave(Slaves) and Vice Versa!
+ if ~strcmpi(Parallel(indPC).ComputerName,MasterName) % 1.3 Run on a remote machine.
+ % Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
if regexpi([Parallel(indPC).MatlabOctavePath], 'octave')
command1=['psexec \\',Parallel(indPC).ComputerName,' -d -e -u ',Parallel(indPC).UserName,' -p ',Parallel(indPC).Password,' -W "',Parallel(indPC).RemoteDrive,':\',Parallel(indPC).RemoteDirectory,'\',PRCDir,'\" -a ',my_affinity, ...
- ' -low ',Parallel(indPC).MatlabOctavePath,' -f --eval "default_save_options(''-v7''); addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); fParallel(',int2str(offset+1),',',int2str(sum(nBlockPerCPU(1:j))),',',int2str(j),',',int2str(indPC),',''',fname,''')"'];
+ ' -low ',Parallel(indPC).MatlabOctavePath,' -f --eval "default_save_options(''-v7'');addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); slaveParallel(',int2str(j),',',int2str(indPC),')"'];
else
-
command1=['psexec \\',Parallel(indPC).ComputerName,' -d -e -u ',Parallel(indPC).UserName,' -p ',Parallel(indPC).Password,' -W "',Parallel(indPC).RemoteDrive,':\',Parallel(indPC).RemoteDirectory,'\',PRCDir,'\" -a ',my_affinity, ...
- ' -low ',Parallel(indPC).MatlabOctavePath,' -nosplash -nodesktop -minimize ',compThread,' -r "addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); fParallel(',int2str(offset+1),',',int2str(sum(nBlockPerCPU(1:j))),',',int2str(j),',',int2str(indPC),',''',fname,''')"'];
+ ' -low ',Parallel(indPC).MatlabOctavePath,' -nosplash -nodesktop -minimize ',compThread,' -r "addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); slaveParallel(',int2str(j),',',int2str(indPC),')"'];
end
- else % 0.4 Run on the local machine via the network
- % Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
+ else % 1.4 Run on the local machine via the network.
+ % Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
if regexpi([Parallel(indPC).MatlabOctavePath], 'octave')
command1=['psexec \\',Parallel(indPC).ComputerName,' -d -e -W "',Parallel(indPC).RemoteDrive,':\',Parallel(indPC).RemoteDirectory,'\',PRCDir,'\" -a ',my_affinity, ...
- ' -low ',Parallel(indPC).MatlabOctavePath,' -f --eval "default_save_options(''-v7''); addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); fParallel(',int2str(offset+1),',',int2str(sum(nBlockPerCPU(1:j))),',',int2str(j),',',int2str(indPC),',''',fname,''')"'];
+ ' -low ',Parallel(indPC).MatlabOctavePath,' -f --eval "default_save_options(''-v7''); addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); slaveParallel(',int2str(j),',',int2str(indPC),')"'];
else
command1=['psexec \\',Parallel(indPC).ComputerName,' -d -e -W "',Parallel(indPC).RemoteDrive,':\',Parallel(indPC).RemoteDirectory,'\',PRCDir,'\" -a ',my_affinity, ...
- ' -low ',Parallel(indPC).MatlabOctavePath,' -nosplash -nodesktop -minimize ',compThread,' -r "addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); fParallel(',int2str(offset+1),',',int2str(sum(nBlockPerCPU(1:j))),',',int2str(j),',',int2str(indPC),',''',fname,''')"'];
+ ' -low ',Parallel(indPC).MatlabOctavePath,' -nosplash -nodesktop -minimize ',compThread,' -r "addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); slaveParallel(',int2str(j),',',int2str(indPC),')"'];
end
end
end
- end
-
-
- case 1
- if Parallel(indPC).Local == 1 && newInstance % 1.1 Run on the local machine.
- if (~ispc || strcmpi('unix',Parallel(indPC).OperatingSystem)) % Hybrid computing Windows <-> Unix!
- if regexpi([Parallel(indPC).MatlabOctavePath], 'octave') % Hybrid computing Matlab(Master)-> Octave(Slaves) and Vice Versa!
- command1=[Parallel(indPC).MatlabOctavePath,' -f --eval "default_save_options(''-v7''); addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); slaveParallel(',int2str(j),',',int2str(indPC),')" &'];
- else
- command1=[Parallel(indPC).MatlabOctavePath,' -nosplash -nodesktop -minimize ',compThread,' -r "addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); slaveParallel(',int2str(j),',',int2str(indPC),')" &'];
- end
- else % Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
- if regexpi([Parallel(indPC).MatlabOctavePath], 'octave')
- command1=['psexec -d -W "',DyMo, '" -a ',my_affinity,' -low ',Parallel(indPC).MatlabOctavePath,' -f --eval "default_save_options(''-v7'');addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); slaveParallel(',int2str(j),',',int2str(indPC),')"'];
- else
- command1=['psexec -d -W "',DyMo, '" -a ',my_affinity,' -low ',Parallel(indPC).MatlabOctavePath,' -nosplash -nodesktop -minimize ',compThread,' -r "addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); slaveParallel(',int2str(j),',',int2str(indPC),')"'];
- end
- end
- elseif Parallel(indPC).Local==0 % 1.2 Run using network on remote machine or also on local machine.
- if j==nCPU0+1
- dynareParallelSendFiles(NamFileInput,PRCDir,Parallel(indPC));
- end
- dynareParallelSendFiles(['P_',fname,'_',int2str(j),'End.txt'],PRCDir,Parallel(indPC));
- delete(['P_',fname,'_',int2str(j),'End.txt']);
- if newInstance
- dynareParallelSendFiles(['slaveJob',int2str(j),'.mat'],PRCDir,Parallel(indPC));
- delete(['slaveJob',int2str(j),'.mat']);
- dynareParallelSendFiles(['slaveParallel_input',int2str(j),'.mat'],PRCDir,Parallel(indPC))
- if (~ispc || strcmpi('unix',Parallel(indPC).OperatingSystem)) % Hybrid computing Windows <-> Unix!
- if ispc
- token='start /B ';
- else
- token = '';
- end
- if ~isempty(Parallel(indPC).Port)
- ssh_token = ['-p ',Parallel(indPC).Port];
- else
- ssh_token = '';
- end
- % To manage the diferences in Unix/Windows OS syntax.
- remoteFile=['remoteDynare',int2str(j)];
- fidRemote=fopen([remoteFile,'.m'],'w+');
- if regexpi([Parallel(indPC).MatlabOctavePath], 'octave') % Hybrid computing Matlab(Master)-> Octave(Slaves) and Vice Versa!
- remoteString=['default_save_options(''-v7''); addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); slaveParallel(',int2str(j),',',int2str(indPC),');'];
- command1=[token, 'ssh ',ssh_token,' ',Parallel(indPC).UserName,'@',Parallel(indPC).ComputerName,' "cd ',Parallel(indPC).RemoteDirectory,'/',PRCDir '; ',Parallel(indPC).MatlabOctavePath,' -f --eval ',remoteFile,' " &'];
- else
- remoteString=['addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); slaveParallel(',int2str(j),',',int2str(indPC),');'];
- command1=[token, 'ssh ',ssh_token,' ',Parallel(indPC).UserName,'@',Parallel(indPC).ComputerName,' "cd ',Parallel(indPC).RemoteDirectory,'/',PRCDir '; ',Parallel(indPC).MatlabOctavePath,' -nosplash -nodesktop -minimize ',compThread,' -r ',remoteFile,';" &'];
- end
- fprintf(fidRemote,'%s\n',remoteString);
- fclose(fidRemote);
- dynareParallelSendFiles([remoteFile,'.m'],PRCDir,Parallel(indPC));
- delete([remoteFile,'.m']);
- else
- if ~strcmpi(Parallel(indPC).ComputerName,MasterName) % 1.3 Run on a remote machine.
- % Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
- if regexpi([Parallel(indPC).MatlabOctavePath], 'octave')
- command1=['psexec \\',Parallel(indPC).ComputerName,' -d -e -u ',Parallel(indPC).UserName,' -p ',Parallel(indPC).Password,' -W "',Parallel(indPC).RemoteDrive,':\',Parallel(indPC).RemoteDirectory,'\',PRCDir,'\" -a ',my_affinity, ...
- ' -low ',Parallel(indPC).MatlabOctavePath,' -f --eval "default_save_options(''-v7'');addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); slaveParallel(',int2str(j),',',int2str(indPC),')"'];
- else
- command1=['psexec \\',Parallel(indPC).ComputerName,' -d -e -u ',Parallel(indPC).UserName,' -p ',Parallel(indPC).Password,' -W "',Parallel(indPC).RemoteDrive,':\',Parallel(indPC).RemoteDirectory,'\',PRCDir,'\" -a ',my_affinity, ...
- ' -low ',Parallel(indPC).MatlabOctavePath,' -nosplash -nodesktop -minimize ',compThread,' -r "addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); slaveParallel(',int2str(j),',',int2str(indPC),')"'];
- end
- else % 1.4 Run on the local machine via the network.
- % Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
- if regexpi([Parallel(indPC).MatlabOctavePath], 'octave')
- command1=['psexec \\',Parallel(indPC).ComputerName,' -d -e -W "',Parallel(indPC).RemoteDrive,':\',Parallel(indPC).RemoteDirectory,'\',PRCDir,'\" -a ',my_affinity, ...
- ' -low ',Parallel(indPC).MatlabOctavePath,' -f --eval "default_save_options(''-v7''); addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); slaveParallel(',int2str(j),',',int2str(indPC),')"'];
- else
- command1=['psexec \\',Parallel(indPC).ComputerName,' -d -e -W "',Parallel(indPC).RemoteDrive,':\',Parallel(indPC).RemoteDirectory,'\',PRCDir,'\" -a ',my_affinity, ...
- ' -low ',Parallel(indPC).MatlabOctavePath,' -nosplash -nodesktop -minimize ',compThread,' -r "addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); slaveParallel(',int2str(j),',',int2str(indPC),')"'];
- end
- end
- end
+ else
+ % When the user user strategy is equal to 1, you must
+ % do PRCDirSnapshot here to to avoid problems of
+ % synchronization.
+
+ if isempty(PRCDirSnapshot{indPC})
+ PRCDirSnapshot(indPC)=dynareParallelSnapshot(PRCDir,Parallel(indPC));
+ PRCDirSnapshotInit(indPC) = PRCDirSnapshot(indPC);
else
- % When the user user strategy is equal to 1, you must
- % do PRCDirSnapshot here to to avoid problems of
- % synchronization.
-
- if isempty(PRCDirSnapshot{indPC})
- PRCDirSnapshot(indPC)=dynareParallelSnapshot(PRCDir,Parallel(indPC));
- PRCDirSnapshotInit(indPC) = PRCDirSnapshot(indPC);
- else
- PRCDirSnapshot(indPC)=dynareParallelGetNewFiles(PRCDir,Parallel(indPC),PRCDirSnapshot(indPC));
- end
- dynareParallelSendFiles(['slaveJob',int2str(j),'.mat'],PRCDir,Parallel(indPC));
- delete(['slaveJob',int2str(j),'.mat']);
-
+ PRCDirSnapshot(indPC)=dynareParallelGetNewFiles(PRCDir,Parallel(indPC),PRCDirSnapshot(indPC));
end
+ dynareParallelSendFiles(['slaveJob',int2str(j),'.mat'],PRCDir,Parallel(indPC));
+ delete(['slaveJob',int2str(j),'.mat']);
+
end
-
+ end
+
end
-
+
fprintf(fid,'%s\n',command1);
-
+
end
% In This way we are sure that the file 'ConcurrentCommand1.bat' is
@@ -518,13 +518,13 @@ end
if Strategy==0 || newInstance % See above.
PRCDirSnapshot=dynareParallelSnapshot(PRCDir,Parallel(1:totSlaves));
PRCDirSnapshotInit = PRCDirSnapshot;
-
+
% Run the slaves.
if ~ispc
system('sh ConcurrentCommand1.bat &');
pause(1)
else
-
+
if isoctave
% Redirect the standard output to the file 'OctaveStandardOutputMessage.txt'!
% This file is saved in the Model directory.
@@ -553,18 +553,18 @@ if isoctave || options_.console_mode
end
else
hfigstatus = figure('name',['Parallel ',fname],...
- 'DockControls','off', ...
- 'IntegerHandle','off', ...
- 'Interruptible','off', ...
- 'MenuBar', 'none', ...
- 'NumberTitle','off', ...
- 'Renderer','Painters', ...
- 'Resize','off');
-
+ 'DockControls','off', ...
+ 'IntegerHandle','off', ...
+ 'Interruptible','off', ...
+ 'MenuBar', 'none', ...
+ 'NumberTitle','off', ...
+ 'Renderer','Painters', ...
+ 'Resize','off');
+
ncol = ceil(totCPU/10);
hspace = 0.9/ncol;
hstatus(1) = axes('position',[0.05/ncol 0.92 0.9/ncol 0.03], ...
- 'box','on','xtick',[],'ytick',[],'xlim',[0 1],'ylim',[0 1]);
+ 'box','on','xtick',[],'ytick',[],'xlim',[0 1],'ylim',[0 1]);
set(hstatus(1),'Units','pixels')
hpixel = get(hstatus(1),'Position');
hfigure = get(hfigstatus,'Position');
@@ -577,12 +577,12 @@ else
jrow = mod(j-1,10)+1;
jcol = ceil(j/10);
hstatus(j) = axes('position',[0.05/ncol+(jcol-1)/ncol vstart-vspace*(jrow-1) 0.9/ncol 0.3*vspace], ...
- 'box','on','xtick',[],'ytick',[],'xlim',[0 1],'ylim',[0 1]);
+ 'box','on','xtick',[],'ytick',[],'xlim',[0 1],'ylim',[0 1]);
hpat(j) = patch([0 0 0 0],[0 1 1 0],'r','EdgeColor','r');
htit(j) = title(['Initialize ...']);
-
+
end
-
+
cumBlockPerCPU = cumsum(nBlockPerCPU);
end
pcerdone = NaN(1,totCPU);
@@ -596,20 +596,20 @@ idCPU = NaN(1,totCPU);
% Caption for console mode computing ...
if options_.console_mode || isoctave
-
+
if ~isoctave
if strcmpi([Parallel(indPC).MatlabOctavePath], 'octave')
RjInformation='Hybrid Computing Is Active: Remote jobs are computed by Octave!';
fprintf([RjInformation,'\n\n']);
end
end
-
+
fnameTemp=fname;
-
+
L=length(fnameTemp);
-
+
PoCo=strfind(fnameTemp,'_core');
-
+
for i=PoCo:L
if i==PoCo
fnameTemp(i)=' ';
@@ -617,27 +617,27 @@ if options_.console_mode || isoctave
fnameTemp(i)='.';
end
end
-
+
for i=1:L
if fnameTemp(i)=='_'
fnameTemp(i)=' ';
end
end
-
+
fnameTemp(L)='';
-
+
Information=['Parallel ' fnameTemp ' Computing ...'];
if isoctave
if (~ispc || strcmpi('unix',Parallel(indPC).OperatingSystem)) && (Strategy==0)
printf('\n');
pause(2);
end
-
+
printf([Information,'\n\n']);
else
fprintf([Information,'\n\n']);
end
-
+
end
@@ -653,25 +653,25 @@ statusString = '';
flag_CloseAllSlaves=0;
while (ForEver)
-
+
waitbarString = '';
statusString0 = repmat('\b',1,length(sprintf(statusString, 100 .* pcerdone)));
statusString = '';
-
+
pause(1)
-
+
try
if islocal ==0
dynareParallelGetFiles(['comp_status_',fname,'*.mat'],PRCDir,Parallel(1:totSlaves));
end
catch
end
-
+
for j=1:totCPU
try
if ~isempty(['comp_status_',fname,int2str(j),'.mat'])
load(['comp_status_',fname,int2str(j),'.mat']);
-% whoCloseAllSlaves = who(['comp_status_',fname,int2str(j),'.mat','CloseAllSlaves']);
+ % whoCloseAllSlaves = who(['comp_status_',fname,int2str(j),'.mat','CloseAllSlaves']);
if exist('CloseAllSlaves') && flag_CloseAllSlaves==0
flag_CloseAllSlaves=1;
whoiamCloseAllSlaves=j;
@@ -691,7 +691,7 @@ while (ForEver)
status_Title{j} = waitbarTitle;
end
catch % ME
- % To define!
+ % To define!
if isoctave || options_.console_mode
if (~ispc || strcmpi('unix',Parallel(indPC).OperatingSystem))
statusString = [statusString, int2str(j), ' %3.f%% done! '];
@@ -715,15 +715,15 @@ while (ForEver)
set(hpat(j),'XData',[0 0 pcerdone(j) pcerdone(j)]);
set(htit(j),'String',[status_Title{j},' - ',status_String{j}]);
catch
-
+
end
end
end
-
+
% Check if the slave(s) has generated some new files remotely.
% 1. The files .log and .txt are not copied.
% 2. The comp_status_*.mat files are managed separately.
-
+
if isoctave % to avoid synchronism problems
try
PRCDirSnapshot=dynareParallelGetNewFiles(PRCDir,Parallel(1:totSlaves),PRCDirSnapshot);
@@ -732,11 +732,11 @@ while (ForEver)
else
PRCDirSnapshot=dynareParallelGetNewFiles(PRCDir,Parallel(1:totSlaves),PRCDirSnapshot);
end
-
+
if isempty(dynareParallelDir(['P_',fname,'_*End.txt'],PRCDir,Parallel(1:totSlaves)))
HoTuttiGliOutput=0;
for j=1:totCPU
-
+
% Checking if the remote computation is finished and if we copied all the output here.
if ~isempty(dir([fname,'_output_',int2str(j),'.mat']))
HoTuttiGliOutput=HoTuttiGliOutput+1;
@@ -745,7 +745,7 @@ while (ForEver)
dynareParallelGetFiles([fname,'_output_',int2str(j),'.mat'],PRCDir,Parallel(indPC));
end
end
-
+
if HoTuttiGliOutput==totCPU
mydelete(['comp_status_',fname,'*.mat']);
if isoctave || options_.console_mode
@@ -760,13 +760,13 @@ while (ForEver)
else
close(hfigstatus)
end
-
+
break
else
disp('Waiting for output files from slaves ...')
end
end
-
+
end
@@ -780,7 +780,7 @@ for j=1:totCPU
delete([fname,'_output_',int2str(j),'.mat']);
if isfield(fOutputVar,'OutputFileName') && Parallel(indPC).Local==0
% Check if input files have been updated!
- OutputFileName=fOutputVar.OutputFileName;
+ OutputFileName=fOutputVar.OutputFileName;
tmp0='';
for i=1:size(NamFileInput,1)
FileList = regexp(strrep(PRCDirSnapshot{indPC},'\','/'),strrep(strrep([NamFileInput{i,:}],'\','/'),'*','(\w*)'),'match');
@@ -808,7 +808,7 @@ for j=1:totCPU
end
end
for k=1:size(tmp1,1)
- dynareParallelGetFiles([OutputFileName(i,1) {tmp1(k,:)}],PRCDir,Parallel(indPC));
+ dynareParallelGetFiles([OutputFileName(i,1) {tmp1(k,:)}],PRCDir,Parallel(indPC));
end
end
% check if some output file is missing!
@@ -842,7 +842,7 @@ for j=1:totCPU
elseif flag_CloseAllSlaves==0
fOutVar(j)=fOutputVar;
elseif j==whoiamCloseAllSlaves
- fOutVar=fOutputVar;
+ fOutVar=fOutputVar;
end
end
@@ -860,46 +860,46 @@ pause(1) % Wait for all remote diary off completed
dynareParallelGetFiles('*.log',PRCDir,Parallel(1:totSlaves));
switch Strategy
- case 0
- for indPC=1:min(find(nCPU>=totCPU))
- if Parallel(indPC).Local == 0
- dynareParallelRmDir(PRCDir,Parallel(indPC));
- end
-
- if isempty(dir('dynareParallelLogFiles'))
- [A B C]=rmdir('dynareParallelLogFiles');
- mkdir('dynareParallelLogFiles');
- end
- try
- copyfile('*.log','dynareParallelLogFiles');
- mydelete([fname,'*.log']);
- catch
- end
-
- mydelete(['*_core*_input*.mat']);
-
+ case 0
+ for indPC=1:min(find(nCPU>=totCPU))
+ if Parallel(indPC).Local == 0
+ dynareParallelRmDir(PRCDir,Parallel(indPC));
end
-
- delete ConcurrentCommand1.bat
- case 1
- delete(['temp_input.mat'])
- if newInstance
- if isempty(dir('dynareParallelLogFiles'))
- [A B C]=rmdir('dynareParallelLogFiles');
- mkdir('dynareParallelLogFiles');
- end
+
+ if isempty(dir('dynareParallelLogFiles'))
+ [A B C]=rmdir('dynareParallelLogFiles');
+ mkdir('dynareParallelLogFiles');
end
- copyfile('*.log','dynareParallelLogFiles');
- if newInstance
- delete ConcurrentCommand1.bat
+ try
+ copyfile('*.log','dynareParallelLogFiles');
+ mydelete([fname,'*.log']);
+ catch
end
- dynareParallelDelete(['comp_status_',fname,'*.mat'],PRCDir,Parallel);
- for indPC=1:min(find(nCPU>=totCPU))
- if Parallel(indPC).Local == 0
- dynareParallelDeleteNewFiles(PRCDir,Parallel(indPC),PRCDirSnapshotInit(indPC),'.log');
- for ifil=1:size(NamFileInput,1)
- dynareParallelDelete([NamFileInput{ifil,:}],PRCDir,Parallel(indPC));
- end
+
+ mydelete(['*_core*_input*.mat']);
+
+ end
+
+ delete ConcurrentCommand1.bat
+ case 1
+ delete(['temp_input.mat'])
+ if newInstance
+ if isempty(dir('dynareParallelLogFiles'))
+ [A B C]=rmdir('dynareParallelLogFiles');
+ mkdir('dynareParallelLogFiles');
+ end
+ end
+ copyfile('*.log','dynareParallelLogFiles');
+ if newInstance
+ delete ConcurrentCommand1.bat
+ end
+ dynareParallelDelete(['comp_status_',fname,'*.mat'],PRCDir,Parallel);
+ for indPC=1:min(find(nCPU>=totCPU))
+ if Parallel(indPC).Local == 0
+ dynareParallelDeleteNewFiles(PRCDir,Parallel(indPC),PRCDirSnapshotInit(indPC),'.log');
+ for ifil=1:size(NamFileInput,1)
+ dynareParallelDelete([NamFileInput{ifil,:}],PRCDir,Parallel(indPC));
end
end
+ end
end
\ No newline at end of file
diff --git a/matlab/parallel/slaveParallel.m b/matlab/parallel/slaveParallel.m
index 15b5351a179566ddc40fe45190b50a872c15e16c..5e3fe460cea6960cba4c62f478bc18c41162e000 100644
--- a/matlab/parallel/slaveParallel.m
+++ b/matlab/parallel/slaveParallel.m
@@ -2,7 +2,7 @@ function slaveParallel(whoiam,ThisMatlab)
% PARALLEL CONTEXT
% In a parallelization context, this function is launched on slave
% machines, to initialize MATLAB and DYNARE environment and waits for
-% instructions sent by the Master.
+% instructions sent by the Master.
% This function is invoked by masterParallel only when the strategy (1),
% i.e. always open, is actived.
%
@@ -12,8 +12,8 @@ function slaveParallel(whoiam,ThisMatlab)
% cluster.
% o ThisMatlab [int] index number of this slave machine in the cluster.
%
-% OUTPUTS
-% None
+% OUTPUTS
+% None
% Copyright (C) 2006-2017 Dynare Team
%
@@ -72,36 +72,36 @@ while (etime(clock,t0)<1200 && ~isempty(fslave)) || ~isempty(dir(['stayalive',in
end
% I wait for 20 min or while mater asks to exit (i.e. it cancels fslave file)
pause(1)
-
+
fjob = dir(['slaveJob',int2str(whoiam),'.mat']);
-
+
if ~isempty(fjob)
clear fGlobalVar fInputVar fblck nblck fname
-
+
while(1)
Go=0;
-
+
Go=fopen(['slaveJob',int2str(whoiam),'.mat']);
-
- if Go>0
+
+ if Go>0
fclose(Go);
pause(1)
load(['slaveJob',int2str(whoiam),'.mat']);
break
else
% Only for testing, will be remouved!
-
+
% if isunix
% E1=fopen('/home/ivano/Works/Errore-slaveParallel.txt','w+');
% fclose(E1);
- % else
+ % else
% E1=fopen('c:\dynare_calcs\Errore-slaveParallel.txt','w+');
% fclose(E1);
% end
-
+
end
end
-
+
funcName=fname; % Update global job name.
if exist('fGlobalVar') && ~isempty (fGlobalVar)
@@ -120,7 +120,7 @@ while (etime(clock,t0)<1200 && ~isempty(fslave)) || ~isempty(dir(['stayalive',in
end
delete(['slaveJob',int2str(whoiam),'.mat']);
fInputVar.Parallel = Parallel;
-
+
% Launch the routine to be run in parallel.
try
tic
@@ -136,7 +136,7 @@ while (etime(clock,t0)<1200 && ~isempty(fslave)) || ~isempty(dir(['stayalive',in
% Save the output result.
save([ fname,'_output_',int2str(whoiam),'.mat'],'fOutputVar' );
-% keyboard,
+ % keyboard,
if isfield(fOutputVar,'CloseAllSlaves')
CloseAllSlaves = 1;
fOutputVar = rmfield(fOutputVar,'CloseAllSlaves');
@@ -171,7 +171,7 @@ while (etime(clock,t0)<1200 && ~isempty(fslave)) || ~isempty(dir(['stayalive',in
delete(['P_',fname,'_',int2str(whoiam),'End.txt']);
break
end
-
+
end
end
fslave = dir( ['slaveParallel_input',int2str(whoiam),'.mat']); % Check if Master asks to exit
diff --git a/matlab/parallel/storeGlobalVars.m b/matlab/parallel/storeGlobalVars.m
index 9d0426f22143269f1b5a573b4b4352b0e7e1bcf6..79b86aa35198cb1d143f962f88345b2afe83b8c7 100644
--- a/matlab/parallel/storeGlobalVars.m
+++ b/matlab/parallel/storeGlobalVars.m
@@ -1,6 +1,6 @@
function storeGlobalVars(fname,append)
% PARALLEL CONTEXT
-% In a parallel context, this function stores all global vars in structure
+% In a parallel context, this function stores all global vars in structure
% fGlobalVar and saves it in the file fname.mat
%
% INPUTS
diff --git a/matlab/parallel/struct2local.m b/matlab/parallel/struct2local.m
index 16cbd7432f2d5fbc75b85ffc81bc9b976cf802e1..d45bc12b8c47bd55935f71b287546981f4942763 100644
--- a/matlab/parallel/struct2local.m
+++ b/matlab/parallel/struct2local.m
@@ -6,7 +6,7 @@ function struct2local(S)
%
% INPUTS
% o S [struc] This structure possibly containing several fields ...
-%
+%
% OUTPUTS
% None
%
diff --git a/matlab/partial_information/PCL_Part_info_irf.m b/matlab/partial_information/PCL_Part_info_irf.m
index f0b8ad6ee7d64f9a22450f3cf95d331562de99fe..0c3c17c2be822567c1cee3067f3d0520356cd693 100644
--- a/matlab/partial_information/PCL_Part_info_irf.m
+++ b/matlab/partial_information/PCL_Part_info_irf.m
@@ -1,9 +1,9 @@
function y=PCL_Part_info_irf( H, varobs, ivar, M_, dr, irfpers,ii)
% sets up parameters and calls part-info kalman filter
-% developed by G Perendia, July 2006 for implementation from notes by Prof. Joe Pearlman to
-% suit partial information RE solution in accordance with, and based on, the
+% developed by G Perendia, July 2006 for implementation from notes by Prof. Joe Pearlman to
+% suit partial information RE solution in accordance with, and based on, the
% Pearlman, Currie and Levine 1986 solution.
-% 22/10/06 - Version 2 for new Riccati with 4 params instead 5
+% 22/10/06 - Version 2 for new Riccati with 4 params instead 5
% Copyright (C) 2006-2017 Dynare Team
%
@@ -23,8 +23,8 @@ function y=PCL_Part_info_irf( H, varobs, ivar, M_, dr, irfpers,ii)
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-% Recall that the state space is given by the
-% predetermined variables s(t-1), x(t-1)
+% Recall that the state space is given by the
+% predetermined variables s(t-1), x(t-1)
% and the jump variables x(t).
% The jump variables have dimension NETA
@@ -66,7 +66,7 @@ U22=0;
% determine K1 and K2 observation mapping matrices
% This uses the fact that measurements are given by L1*s(t)+L2*x(t)
% and s(t) is expressed in the dynamics as
-% H1*eps(t)+G11*s(t-1)+G12*x(t-1)+G13*x(t).
+% H1*eps(t)+G11*s(t-1)+G12*x(t-1)+G13*x(t).
% Thus the observations o(t) can be written in the form
% o(t)=K1*[eps(t)' s(t-1)' x(t-1)']' + K2*x(t) where
% K1=[L1*H1 L1*G11 L1*G12] K2=L1*G13+L2
@@ -84,18 +84,18 @@ A21=G1(pd+1:end,1:pd);
Lambda= nmat*A12+A22;
I_L=inv(Lambda);
BB=A12*inv(A22);
-FF=K2*inv(A22);
-QQ=BB*U22*BB' + U11;
+FF=K2*inv(A22);
+QQ=BB*U22*BB' + U11;
UFT=U22*FF';
AA=A11-BB*A21;
CCCC=A11-A12*nmat; % F in new notation
DD=K1-FF*A21; % H in new notation
EE=K1-K2*nmat;
RR=FF*UFT+VV;
-if ~any(RR)
- % if zero add some dummy measurement err. variance-covariances
+if ~any(RR)
+ % if zero add some dummy measurement err. variance-covariances
% with diagonals 0.000001. This would not be needed if we used
- % the slow solver, or the generalised eigenvalue approach,
+ % the slow solver, or the generalised eigenvalue approach,
% but these are both slower.
RR=eye(size(RR,1))*1.0e-6;
end
@@ -128,14 +128,14 @@ imp=[impact(1:ss-FL_RANK,:); impact(1:ss-FL_RANK,:)];
I_PD=(eye(ss-FL_RANK)-PDIDPDRD);
LL0=[ EE (DD-EE)*I_PD];
VV = [ dr.PI_TT1 dr.PI_TT2];
-stderr=diag(M_.Sigma_e^0.5);
+stderr=diag(M_.Sigma_e^0.5);
irfmat=zeros(size(dr.PI_TT1 ,1),irfpers+1);
-irfst=zeros(size(GG,1),irfpers+1);
+irfst=zeros(size(GG,1),irfpers+1);
irfst(:,1)=stderr(ii)*imp(:,ii);
for jj=2:irfpers+1
irfst(:,jj)=GG*irfst(:,jj-1);
irfmat(:,jj-1)=VV*irfst(NX+1:ss-FL_RANK,jj);
-end
+end
y = irfmat(:,1:irfpers);
save ([M_.fname '_PCL_PtInfoIRFs_' num2str(ii) '_' deblank(exo_names(ii,:))], 'irfmat','irfst');
diff --git a/matlab/partial_information/PCL_Part_info_moments.m b/matlab/partial_information/PCL_Part_info_moments.m
index 67612f15873497686bf44993d3747361f62f5eb6..bb16b66043251ccb2b49b214d41a6ba726dc4494 100644
--- a/matlab/partial_information/PCL_Part_info_moments.m
+++ b/matlab/partial_information/PCL_Part_info_moments.m
@@ -1,9 +1,9 @@
function AutoCOR_YRk=PCL_Part_info_moments( H, varobs, dr,ivar)
% sets up parameters and calls part-info kalman filter
-% developed by G Perendia, July 2006 for implementation from notes by Prof. Joe Pearlman to
-% suit partial information RE solution in accordance with, and based on, the
+% developed by G Perendia, July 2006 for implementation from notes by Prof. Joe Pearlman to
+% suit partial information RE solution in accordance with, and based on, the
% Pearlman, Currie and Levine 1986 solution.
-% 22/10/06 - Version 2 for new Riccati with 4 params instead 5
+% 22/10/06 - Version 2 for new Riccati with 4 params instead 5
% Copyright (C) 2006-2017 Dynare Team
%
@@ -22,8 +22,8 @@ function AutoCOR_YRk=PCL_Part_info_moments( H, varobs, dr,ivar)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-% Recall that the state space is given by the
-% predetermined variables s(t-1), x(t-1)
+% Recall that the state space is given by the
+% predetermined variables s(t-1), x(t-1)
% and the jump variables x(t).
% The jump variables have dimension NETA
@@ -54,7 +54,7 @@ if exist( 'irfpers')==1
end
else
irfpers=20;
-end
+end
ss=size(G1,1);
@@ -78,7 +78,7 @@ U22=0;
% determine K1 and K2 observation mapping matrices
% This uses the fact that measurements are given by L1*s(t)+L2*x(t)
% and s(t) is expressed in the dynamics as
-% H1*eps(t)+G11*s(t-1)+G12*x(t-1)+G13*x(t).
+% H1*eps(t)+G11*s(t-1)+G12*x(t-1)+G13*x(t).
% Thus the observations o(t) can be written in the form
% o(t)=K1*[eps(t)' s(t-1)' x(t-1)']' + K2*x(t) where
% K1=[L1*H1 L1*G11 L1*G12] K2=L1*G13+L2
@@ -96,8 +96,8 @@ A21=G1(pd+1:end,1:pd);
Lambda= nmat*A12+A22;
I_L=inv(Lambda);
BB=A12*inv(A22);
-FF=K2*inv(A22);
-QQ=BB*U22*BB' + U11;
+FF=K2*inv(A22);
+QQ=BB*U22*BB' + U11;
UFT=U22*FF';
% kf_param structure:
AA=A11-BB*A21;
@@ -105,10 +105,10 @@ CCCC=A11-A12*nmat; % F in new notation
DD=K1-FF*A21; % H in new notation
EE=K1-K2*nmat;
RR=FF*UFT+VV;
-if ~any(RR)
- % if zero add some dummy measurement err. variance-covariances
+if ~any(RR)
+ % if zero add some dummy measurement err. variance-covariances
% with diagonals 0.000001. This would not be needed if we used
- % the slow solver, or the generalised eigenvalue approach,
+ % the slow solver, or the generalised eigenvalue approach,
% but these are both slower.
RR=eye(size(RR,1))*1.0e-6;
end
@@ -150,7 +150,7 @@ diagCovYR0=diag(COV_YR0);
labels = deblank(M_.endo_names(ivar,:));
if options_.nomoments == 0
- z = [ sqrt(diagCovYR0(ivar)) diagCovYR0(ivar) ];
+ z = [ sqrt(diagCovYR0(ivar)) diagCovYR0(ivar) ];
title='THEORETICAL MOMENTS';
headers=char('VARIABLE','STD. DEV.','VARIANCE');
dyntable(options_,title,headers,labels,z,size(labels,2)+2,16,10);
@@ -168,8 +168,8 @@ end
ar = options_.ar;
if ar > 0
- COV_YRk= zeros(nn,ar);
- AutoCOR_YRk= zeros(nn,ar);
+ COV_YRk= zeros(nn,ar);
+ AutoCOR_YRk= zeros(nn,ar);
for k=1:ar
COV_P=GAM*COV_P;
COV_OMEGA= COV_P( end-nn+1:end, end-nn+1:end);
diff --git a/matlab/partial_information/PCL_resol.m b/matlab/partial_information/PCL_resol.m
index 95b57a00d926346b113adf58ccbe4335a55315f1..6bf4a7243a05833a364f905b6ad8e964fabca7eb 100644
--- a/matlab/partial_information/PCL_resol.m
+++ b/matlab/partial_information/PCL_resol.m
@@ -17,7 +17,7 @@ function [dr,info]=PCL_resol(ys,check_flag)
% info=6: The jacobian evaluated at the steady state is complex.
% info=19: The steadystate file did not compute the steady state (inconsistent deep parameters).
% info=20: can't find steady state info(2) contains sum of sqare residuals
-% info=21: steady state is complex
+% info=21: steady state is complex
% info(2) contains sum of sqare of
% imaginary part of steady state
% info=30: Variance can't be computed
@@ -45,7 +45,7 @@ function [dr,info]=PCL_resol(ys,check_flag)
global M_ options_ oo_
global it_
-jacobian_flag = 0;
+jacobian_flag = 0;
info = 0;
@@ -58,7 +58,7 @@ end
% check if ys is steady state
tempex = oo_.exo_simul;
oo_.exo_simul = repmat(oo_.exo_steady_state',M_.maximum_lag+M_.maximum_lead+1,1);
-if M_.exo_det_nbr > 0
+if M_.exo_det_nbr > 0
tempexdet = oo_.exo_det_simul;
oo_.exo_det_simul = repmat(oo_.exo_det_steady_state',M_.maximum_lag+M_.maximum_lead+1,1);
end
@@ -70,7 +70,7 @@ if options_.steadystate_flag
[dr.ys,check1] = feval([M_.fname '_steadystate'],dr.ys,...
[oo_.exo_steady_state; ...
oo_.exo_det_steady_state]);
- if size(dr.ys,1) < M_.endo_nbr
+ if size(dr.ys,1) < M_.endo_nbr
if length(M_.aux_vars) > 0
dr.ys = add_auxiliary_variables_to_steadystate(dr.ys,M_.aux_vars,...
M_.fname,...
@@ -143,5 +143,5 @@ tempex = [];
% 01/01/2003 MJ added dr_algo == 1
% 08/24/2001 MJ uses Schmitt-Grohe and Uribe (2001) constant correction
-% in dr.ghs2
+% in dr.ghs2
% 05/26/2003 MJ added temporary values for oo_.exo_simul
diff --git a/matlab/partial_information/PI_gensys.m b/matlab/partial_information/PI_gensys.m
index eb8ba6a94301016007e3aeb0f32afe63fc351a44..edc4e5da14fc2821991b4eb8e1c48a3ed1fa6edb 100644
--- a/matlab/partial_information/PI_gensys.m
+++ b/matlab/partial_information/PI_gensys.m
@@ -83,12 +83,12 @@ try
end
if singular == 1 || strcmp('MATLAB:nearlySingularMatrix',LastWarningID) == 1 || ...
strcmp('MATLAB:illConditionedMatrix',LastWarningID)==1 || ...
- strcmp('MATLAB:singularMatrix',LastWarningID)==1
+ strcmp('MATLAB:singularMatrix',LastWarningID)==1
[C1,C2,C3,C4, C5, F1, F2, F3, F4, F5, M1, M2, UAVinv, FL_RANK, V01, V02] = PI_gensys_singularC(C1,C2,C3,C4, C5, F1, F2, F3, F4, F5, V01, V02, 0);
end
warning('on','MATLAB:singularMatrix');
warning('on','MATLAB:nearlySingularMatrix');
- if (any(any(isinf(UAVinv))) || any(any(isnan(UAVinv))))
+ if (any(any(isinf(UAVinv))) || any(any(isnan(UAVinv))))
if(options_.ACES_solver==1)
disp('ERROR! saving PI_gensys_data_dump');
save PI_gensys_data_dump
@@ -131,8 +131,8 @@ G23=eye(FL_RANK);
num_inst=0;
% New Definitions
-Ze11=zeros(NX,NX);
-Ze12=zeros(NX,(n-FL_RANK));
+Ze11=zeros(NX,NX);
+Ze12=zeros(NX,(n-FL_RANK));
Ze134=zeros(NX,FL_RANK);
Ze31=zeros(FL_RANK,NX);
@@ -149,7 +149,7 @@ impact=[eye(NX,NX); zeros(n+FL_RANK,NX)];
if(options_.ACES_solver==1)
if isfield(lq_instruments,'names')
num_inst=size(lq_instruments.names,1);
- if num_inst>0
+ if num_inst>0
i_var=lq_instruments.inst_var_indices;
N1=UAVinv*U02'*lq_instruments.B1;
N3=-FF*N1+Sinv*U01'*lq_instruments.B1;
@@ -176,9 +176,9 @@ end
G0pi=eye(n+FL_RANK+NX);
try
- % In Matlab: [aa bb q z v w] = qz(a,b) s.t. qaz = aa, qbz = bb %
+ % In Matlab: [aa bb q z v w] = qz(a,b) s.t. qaz = aa, qbz = bb %
% In Octave: [aa bb q z v w] = qz(a,b) s.t. q'az = aa, q'bz=bb %
- % and qzcomplex() extension based on lapack zgges produces same
+ % and qzcomplex() extension based on lapack zgges produces same
% qz output for Octave as Matlab qz() does for Matlab thus:
if isoctave
[a b q z]=qzcomplex(G0pi,G1pi);
@@ -192,12 +192,12 @@ catch
disp(['PI_Gensys: ' lerror.message]);
if 0==strcmp('MATLAB:qz:matrixWithNaNInf',lerror.identifier)
disp '** Unexpected Error PI_Gensys:qz: ** :';
- button=questdlg('Continue Y/N?','Unexpected Error in qz','No','Yes','Yes');
- switch button
- case 'No'
+ button=questdlg('Continue Y/N?','Unexpected Error in qz','No','Yes','Yes');
+ switch button
+ case 'No'
error ('Terminated')
%case 'Yes'
-
+
end
end
G1pi=[];impact=[];nmat=[]; gev=[];
@@ -206,11 +206,11 @@ catch
catch
disp '** Unexpected Error in qz ** :';
disp lerror.message;
- button=questdlg('Continue Y/N?','Unexpected Error in qz','No','Yes','Yes');
- switch button
- case 'No'
- error ('Terminated')
- case 'Yes'
+ button=questdlg('Continue Y/N?','Unexpected Error in qz','No','Yes','Yes');
+ switch button
+ case 'No'
+ error ('Terminated')
+ case 'Yes'
G1pi=[];impact=[];nmat=[]; gev=[];
eu=[-2;-2];
return
@@ -257,7 +257,7 @@ if zxz
end
if (FL_RANK ~= nunstab && options_.ACES_solver~=1)
disp(['Number of unstable variables ' num2str(nunstab)]);
- disp( ['does not match number of expectational equations ' num2str(FL_RANK)]);
+ disp( ['does not match number of expectational equations ' num2str(FL_RANK)]);
nmat=[];% gev=[];
eu=[-2;-2];
return
diff --git a/matlab/partial_information/PI_gensys_singularC.m b/matlab/partial_information/PI_gensys_singularC.m
index 5802346b97b695aee2b0df07af589cfe59f9cbfa..b0c085fafcf3a6e98106f871b2a657805ce389c7 100644
--- a/matlab/partial_information/PI_gensys_singularC.m
+++ b/matlab/partial_information/PI_gensys_singularC.m
@@ -1,9 +1,9 @@
function [C1,C2,C3,C4, C5, F1, F2, F3, F4, F5, M1, M2, UAVinv, FL_RANK, V01, V02]=PI_gensys_singularC(C1in, C2in, C3in, C4in, C5in, F1, F2, F3, F4, F5, V01, V02, level)
% [C1,C2,C3,C4, C5, F1, F2, F3, F4, F5, M1, M2, UAVinv,FL_RANK, V01, V02]...
% =PI_gensys_singularC(C1in, C2in, C3in, C4in, C5in, F1, F2, F3, F4, F5, V01, V02, level)
-%
+%
% Recursive extension for PI_gensys function PCL general DSGE solver
-% devised by Prof. Joseph Pearlman
+% devised by Prof. Joseph Pearlman
% developed by George Perendia
% December 2010
diff --git a/matlab/partial_information/disc_riccati_fast.m b/matlab/partial_information/disc_riccati_fast.m
index 4a0bd0401838a17796c692fca2b617b996278d5e..c73adb5b2e816b65005140037721675390fb9ee5 100644
--- a/matlab/partial_information/disc_riccati_fast.m
+++ b/matlab/partial_information/disc_riccati_fast.m
@@ -1,12 +1,12 @@
function Z=disc_riccati_fast(F,D,R,H,ch)
% function Z=disc_riccati_fast(F,D,R,H,ch)
-%
-% Solves discrete Riccati Equation:
+%
+% Solves discrete Riccati Equation:
% Z=FZF' - FZD'inv(DZD'+R)DZF' + H
-% Using the Doubling Algorithm
+% Using the Doubling Algorithm
%
-% George Perendia: based on the doubling algorithm for Riccati
-% and the disclyap_fast function provided by Prof. Joe Pearlman
+% George Perendia: based on the doubling algorithm for Riccati
+% and the disclyap_fast function provided by Prof. Joe Pearlman
% V.1 19/5/2006
% V.2 22/10/06
% =================================================================
@@ -28,16 +28,16 @@ function Z=disc_riccati_fast(F,D,R,H,ch)
% 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 || isempty( ch ) == 1
- flag_ch = 0;
-else
- flag_ch = 1;
-end
+if nargin == 4 || isempty( ch ) == 1
+ flag_ch = 0;
+else
+ flag_ch = 1;
+end
% intialisation
tol = 1e-10; % iteration convergence threshold
-P0=H;
+P0=H;
X0=F;
if ~any(R) % i.e. ==0
warning('Dangerously evading inversion of zero matrix!');
@@ -50,21 +50,21 @@ I=eye(size(POYO));
clear POYO;
% iterate Riccati equation solution
-matd=1;
+matd=1;
count=0;
while matd > tol && count < 100
INVPY=inv(I+P0*Y0);
- P1=X0*INVPY*P0*X0'+ P0;
- Y1=X0'*Y0*INVPY*X0+ Y0;
- X1=X0*INVPY*X0;
- matd=sum( sum(abs( P1 - P0 )));
+ P1=X0*INVPY*P0*X0'+ P0;
+ Y1=X0'*Y0*INVPY*X0+ Y0;
+ X1=X0*INVPY*X0;
+ matd=sum( sum(abs( P1 - P0 )));
% P0=(P1+P1')/2
- P0=P1;
+ P0=P1;
X0=X1;
Y0=Y1;
count=count+1;
% matd;
-end
+end
Z=(P0+P0')/2;
%Z=P0
@@ -75,17 +75,17 @@ if count==100
% error.identifier='Riccati not converged!'
% error
end
-%if count >5
+%if count >5
% disp('Riccati count= ');
% count
%end
-clear X0 X1 Y0 Y1 P1 I INVPY;
+clear X0 X1 Y0 Y1 P1 I INVPY;
-% Check that X is positive definite
-if flag_ch==1
- [C,p]=chol(Z);
- if p ~= 0
+% Check that X is positive definite
+if flag_ch==1
+ [C,p]=chol(Z);
+ if p ~= 0
error('Z is not positive definite')
- end
-end
+ end
+end
diff --git a/matlab/partial_information/disclyap_fast.m b/matlab/partial_information/disclyap_fast.m
index fd4516f2b51201fea86ca6d22b0d9d04f84d2657..5dfcfe4b5657ec806d4a6953fae971d7b80efb40 100644
--- a/matlab/partial_information/disclyap_fast.m
+++ b/matlab/partial_information/disclyap_fast.m
@@ -3,21 +3,21 @@ function [X,exitflag]=disclyap_fast(G,V,tol,check_flag)
% Inputs:
% - G [double] first input matrix
% - V [double] second input matrix
-% - tol [scalar] tolerance criterion
+% - tol [scalar] tolerance criterion
% - check_flag empty of non-empty if non-empty: check positive-definiteness
% Outputs:
% - X [double] solution matrix
% - exitflag [scalar] 0 if solution is found, 1 otherwise
%
-% Solve the discrete Lyapunov Equation
-% X=G*X*G'+V
-% Using the Doubling Algorithm
+% Solve the discrete Lyapunov Equation
+% X=G*X*G'+V
+% Using the Doubling Algorithm
%
-% If check_flag is defined then the code will check if the resulting X
-% is positive definite and generate an error message if it is not
-%
-% Joe Pearlman and Alejandro Justiniano
-% 3/5/2005
+% If check_flag is defined then the code will check if the resulting X
+% is positive definite and generate an error message if it is not
+%
+% Joe Pearlman and Alejandro Justiniano
+% 3/5/2005
% Copyright (C) 2010-2017 Dynare Team
%
@@ -36,40 +36,40 @@ function [X,exitflag]=disclyap_fast(G,V,tol,check_flag)
% 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 || isempty( check_flag ) == 1
- flag_ch = 0;
-else
- flag_ch = 1;
-end
+if nargin <= 3 || isempty( check_flag ) == 1
+ flag_ch = 0;
+else
+ flag_ch = 1;
+end
exitflag=0;
-P0=V;
-A0=G;
+P0=V;
+A0=G;
-matd=1;
+matd=1;
iter=1;
-while matd > tol && iter< 2000
- P1=P0+A0*P0*A0';
- A1=A0*A0;
- matd=max( max( abs( P1 - P0 ) ) );
- P0=P1;
- A0=A1;
+while matd > tol && iter< 2000
+ P1=P0+A0*P0*A0';
+ A1=A0*A0;
+ matd=max( max( abs( P1 - P0 ) ) );
+ P0=P1;
+ A0=A1;
iter=iter+1;
-end
+end
if iter==5000
X=NaN(P0);
exitflag=1;
return
end
-clear A0 A1 P1;
+clear A0 A1 P1;
-X=(P0+P0')/2;
+X=(P0+P0')/2;
-% Check that X is positive definite
-if flag_ch==1
- [C,p]=chol(X);
- if p ~= 0
+% Check that X is positive definite
+if flag_ch==1
+ [C,p]=chol(X);
+ if p ~= 0
exitflag=1;
error('X is not positive definite')
- end
-end
\ No newline at end of file
+ end
+end
\ No newline at end of file
diff --git a/matlab/partial_information/dr1_PI.m b/matlab/partial_information/dr1_PI.m
index 8ae7ff2ff1efae618abae5edfe1a8062ed0f35f0..04fe8d92674be5255da148e260d4627fb9ffff4c 100644
--- a/matlab/partial_information/dr1_PI.m
+++ b/matlab/partial_information/dr1_PI.m
@@ -6,7 +6,7 @@ function [dr,info,M_,options_,oo_] = dr1_PI(dr,task,M_,options_,oo_)
% Prepares System as
% A0*E_t[y(t+1])+A1*y(t)=A2*y(t-1)+c+psi*eps(t)
% with z an exogenous variable process.
-% and calls PI_Gensys.m solver
+% and calls PI_Gensys.m solver
% based on Pearlman et al 1986 paper and derived from
% C.Sims' gensys linear solver.
% to return solution in format
@@ -16,29 +16,29 @@ function [dr,info,M_,options_,oo_] = dr1_PI(dr,task,M_,options_,oo_)
% dr [matlab structure] Decision rules for stochastic simulations.
% task [integer] if task = 0 then dr1 computes decision rules.
% if task = 1 then dr1 computes eigenvalues.
-% M_ [matlab structure] Definition of the model.
+% M_ [matlab structure] Definition of the model.
% options_ [matlab structure] Global options.
-% oo_ [matlab structure] Results
-%
+% oo_ [matlab structure] Results
+%
% OUTPUTS
% dr [matlab structure] Decision rules for stochastic simulations.
% info [integer] info=1: the model doesn't define current variables uniquely
-% info=2: problem in mjdgges.dll info(2) contains error code.
+% info=2: problem in mjdgges.dll info(2) contains error code.
% info=3: BK order condition not satisfied info(2) contains "distance"
% absence of stable trajectory.
% info=4: BK order condition not satisfied info(2) contains "distance"
% indeterminacy.
% info=5: BK rank condition not satisfied.
-% M_ [matlab structure]
+% M_ [matlab structure]
% options_ [matlab structure]
% oo_ [matlab structure]
-%
+%
% ALGORITHM
% ...
-%
+%
% SPECIAL REQUIREMENTS
% none.
-%
+%
% Copyright (C) 1996-2017 Dynare Team
%
@@ -66,9 +66,9 @@ xlen = M_.maximum_endo_lead + M_.maximum_endo_lag + 1;
if (options_.aim_solver == 1)
options_.aim_solver == 0;
- warning('You can not use AIM with Part Info solver. AIM ignored');
+ warning('You can not use AIM with Part Info solver. AIM ignored');
end
-if (options_.order > 1)
+if (options_.order > 1)
warning('You can not use order higher than 1 with Part Info solver. Order 1 assumed');
options_.order =1;
end
@@ -101,11 +101,11 @@ else
iyv = iyv(:);
iyr0 = find(iyv) ;
it_ = M_.maximum_lag + 1 ;
-
+
if M_.exo_nbr == 0
oo_.exo_steady_state = [] ;
end
-
+
if options_.ACES_solver == 1
sim_ruleids=[];
@@ -172,15 +172,15 @@ sdyn = M_.endo_nbr - nstatic;
k0 = M_.lead_lag_incidence(M_.maximum_endo_lag+1,order_var);
k1 = M_.lead_lag_incidence(find([1:klen] ~= M_.maximum_endo_lag+1),:);
-if (options_.aim_solver == 1)
+if (options_.aim_solver == 1)
error('Anderson and Moore AIM solver is not compatible with Partial Information models');
end % end if useAIM and...
% If required, try PCL86 solver, that is, if not the check being
- % performed only and if it is 1st order
+ % performed only and if it is 1st order
% create sparse, extended jacobia AA:
nendo=M_.endo_nbr; % = size(aa,1)
-
+
if(options_.ACES_solver==1)
%if ~isfield(lq_instruments,'names')
@@ -239,8 +239,8 @@ end % end if useAIM and...
% E_t z(t)
% E_t z(t+1)
% E_t z(t+2)
- % E_t z(t+3)
-
+ % E_t z(t+3)
+
% partition jacobian:
jlen=M_.nspred+M_.nsfwrd+M_.endo_nbr+M_.exo_nbr; % length of jacobian
PSI=-jacobia_(:, jlen-M_.exo_nbr+1:end); % exog
@@ -253,12 +253,12 @@ end % end if useAIM and...
AA2=AA0; % empty A2 and A3
AA3=AA0;
if xlen==nendo % && M_.maximum_lag <=1 && M_.maximum_lead <=1 % apply a shortcut
- AA1=jacobia_(:,nspred+1:nspred+nendo);
+ AA1=jacobia_(:,nspred+1:nspred+nendo);
if M_.maximum_lead ==1
fnd = find(lead_lag(:,M_.maximum_lag+2));
AA0(:, fnd)= jacobia_(:,nonzeros(lead_lag(:,M_.maximum_lag+2))); %forwd jacobian
end
- if nspred>0 && M_.maximum_lag ==1
+ if nspred>0 && M_.maximum_lag ==1
fnd = find(lead_lag(:,1));
AA2(:, fnd)= jacobia_(:,nonzeros(lead_lag(:,1))); %backward
end
@@ -266,7 +266,7 @@ end % end if useAIM and...
if nendo-xlen==num_inst
PSI=[PSI;zeros(num_inst, M_.exo_nbr)];
% AA1 contemporary
- AA_all=jacobia_(:,nspred+1:nspred+nendo);
+ AA_all=jacobia_(:,nspred+1:nspred+nendo);
AA1=AA_all(:,lq_instruments.m_var); % endo without instruments
lq_instruments.ij1=AA_all(:,lq_instruments.inst_var_indices); % instruments only
lq_instruments.B1=-[lq_instruments.ij1; eye(num_inst)];
@@ -281,7 +281,7 @@ end % end if useAIM and...
lq_instruments.B0=[lq_instruments.ij0; eye(num_inst)];
AA0=[AA0, zeros(xlen,num_inst); zeros(num_inst,xlen+num_inst)];
end
- if nspred>0 && M_.maximum_lag ==1
+ if nspred>0 && M_.maximum_lag ==1
AA_all(:,:)=0.0;
fnd = find(lead_lag(:,1));
AA_all(:, fnd)= jacobia_(:,nonzeros(lead_lag(:,1))); %backward
@@ -312,7 +312,7 @@ end % end if useAIM and...
NX=M_.exo_nbr; % no of exogenous varexo shock variables.
NETA=nfwrd+nboth; % total no of exp. errors set to no of forward looking equations
FL_RANK=rank(AA0); % nfwrd+nboth; % min total no of forward looking equations and vars
-
+
try
% call [G1pi,C,impact,nmat,TT1,TT2,gev,eu]=PI_gensys(a0,a1,a2,c,PSI,NX,NETA,NO_FL_EQS)
% System given as
@@ -348,13 +348,13 @@ end % end if useAIM and...
[G1pi,CC,impact,nmat,TT1,TT2,gev,eu, DD, E2,E5, GAMMA, FL_RANK]=PI_gensys(AA0,AA1,-AA2,AA3,cc,PSI,NX,NETA,FL_RANK, M_, options_);
end
- % reuse some of the bypassed code and tests that may be needed
+ % reuse some of the bypassed code and tests that may be needed
if (eu(1) ~= 1 || eu(2) ~= 1) && options_.ACES_solver==0
info(1) = abs(eu(1)+eu(2));
info(2) = 1.0e+8;
% return
end
-
+
dr.PI_ghx=G1pi;
dr.PI_ghu=impact;
dr.PI_TT1=TT1;
@@ -369,7 +369,7 @@ end % end if useAIM and...
dr.ghu=impact;
dr.eigval = eig(G1pi);
dr.rank=FL_RANK;
-
+
if options_.ACES_solver==1
betap=options_.planner_discount;
sigma_cov=M_.Sigma_e;
@@ -390,8 +390,8 @@ end % end if useAIM and...
ACES.Q=DYN_Q;
ACES.W=W;
NY=nendo-num_inst;
-
- % save the followings in a subdirectory - BY
+
+ % save the followings in a subdirectory - BY
save ([ACES_DirectoryName,'/',M_.fname '_ACESLQ_Matrices'], 'ACES');
save ([ACES_DirectoryName,'/',M_.fname '_ACESLQ_GAMMA'], 'GAMMA');
save ([ACES_DirectoryName,'/',M_.fname '_ACESLQ_A.txt'], 'G1pi', '-ascii', '-double', '-tabs');
@@ -449,6 +449,6 @@ end % end if useAIM and...
end
end
- % TODO:
+ % TODO:
% if options_.loglinear == 1
% if exogenous deterministic variables
\ No newline at end of file
diff --git a/matlab/perfect-foresight-models/det_cond_forecast.m b/matlab/perfect-foresight-models/det_cond_forecast.m
index ac2a86c0333b3c8924e2cf6e481c52ac1ca2fc42..ee177d8306ae5e6ff8bc3f270c236e3e76f53989 100644
--- a/matlab/perfect-foresight-models/det_cond_forecast.m
+++ b/matlab/perfect-foresight-models/det_cond_forecast.m
@@ -34,9 +34,9 @@ pp = 2;
initial_conditions = oo_.steady_state;
verbosity = options_.verbosity;
if options_.periods == 0
- options_.periods = 25;
+ options_.periods = 25;
end
-%We have to get an initial guess for the conditional forecast
+%We have to get an initial guess for the conditional forecast
% and terminal conditions for the non-stationary variables, we
% use the first order approximation of the rational expectation solution.
if ~isfield(oo_,'dr') || (isempty(oo_.dr))
@@ -82,7 +82,7 @@ if length(varargin) > 3
error(['Unknown exogenous variable ' controlled_varexo(i,:)]);
end
end
-
+
else
% alternative way to call: plan, dset, dates_of_frcst
plan = varargin{1};
@@ -102,10 +102,10 @@ else
s2 = strings(range(range.ndat));
error(['the dseries ' inputname(2) ' finish at time ' s1{1} ' before the last period of forecast ' s2{1}]);
end
-
+
sym_dset = dset(dates(-range(1)):dates(range(range.ndat)));
periods = options_.periods + M_.maximum_lag + M_.maximum_lead;
- total_periods = periods + range.ndat;
+ total_periods = periods + range.ndat;
if isfield(oo_, 'exo_simul')
if size(oo_.exo_simul, 1) ~= total_periods
oo_.exo_simul = repmat(oo_.exo_steady_state',total_periods,1);
@@ -113,22 +113,22 @@ else
else
oo_.exo_simul = repmat(oo_.exo_steady_state',total_periods,1);
end
-
+
oo_.endo_simul = repmat(oo_.steady_state, 1, total_periods);
-
+
for i = 1:sym_dset.vobs
iy = find(strcmp(strtrim(sym_dset.name{i}), strtrim(plan.endo_names)));
if ~isempty(iy)
oo_.endo_simul(iy,1:sym_dset.nobs) = sym_dset.data(:, i);
initial_conditions(iy) = sym_dset.data(1, i);
- else
- ix = find(strcmp(strtrim(sym_dset.name{i}), strtrim(plan.exo_names)));
- if ~isempty(ix)
- oo_.exo_simul(1, ix) = sym_dset.data(1, i)';
+ else
+ ix = find(strcmp(strtrim(sym_dset.name{i}), strtrim(plan.exo_names)));
+ if ~isempty(ix)
+ oo_.exo_simul(1, ix) = sym_dset.data(1, i)';
else
%warning(['The variable ' sym_dset.name{i} ' in the dataset ' inputname(2) ' is not a endogenous neither an exogenous variable!!']);
end
- end
+ end
end
for i = 1:length(M_.aux_vars)
if M_.aux_vars(i).type == 1 %lag variable
@@ -146,8 +146,8 @@ else
%Compute the initial path using the the steady-state
% steady-state
%for jj = 2 : (options_.periods + 2)
- for jj = 2 : (range.ndat + 2)
- oo_.endo_simul(:, jj) = oo_.steady_state;
+ for jj = 2 : (range.ndat + 2)
+ oo_.endo_simul(:, jj) = oo_.steady_state;
end
missings = isnan(oo_.endo_simul(:,1));
if any(missings)
@@ -165,11 +165,11 @@ else
options_.dynatol.f = save_options_dynatol_f;
if Info == 0
- oo_.endo_simul = endo;
- oo_.exo_simul = exo;
+ oo_.endo_simul = endo;
+ oo_.exo_simul = exo;
end
endo = endo';
- endo_l = size(endo(1+M_.maximum_lag:end,:),1);
+ endo_l = size(endo(1+M_.maximum_lag:end,:),1);
jrng = dates(plan.date(1)):dates(plan.date(1)+endo_l);
data_set = dseries(nan(endo_l, dset.vobs), plan.date(1), dset.name);
for i = 1:length(dset.name)
@@ -179,7 +179,7 @@ else
else
pos = find(strcmp(dset.name{i},plan.exo_names));
if ~isempty(pos)
- data_set{dset.name{i}} = dseries(exo(1+M_.maximum_lag:end,pos), plan.date(1),dset.name{i});
+ data_set{dset.name{i}} = dseries(exo(1+M_.maximum_lag:end,pos), plan.date(1),dset.name{i});
end
end
end
@@ -209,14 +209,14 @@ else
return
end
else
- error('impossible case');
+ error('impossible case');
end
-
+
else
oo_.exo_simul = repmat(oo_.exo_steady_state',options_.periods+2,1);
oo_.endo_simul = repmat(oo_.steady_state, 1, options_.periods+2);
end
-
+
direct_mode = 1;
constrained_paths = plan.constrained_paths_;
constrained_vars = plan.constrained_vars_;
@@ -232,16 +232,16 @@ else
else
shocks_present = 0;
end
-
+
total_periods = plan.date;
-
+
end
if ~isfield(options_cond_fcst,'periods') || isempty(options_cond_fcst.periods)
options_cond_fcst.periods = 100;
end
-options_.periods = 10;
+options_.periods = 10;
if direct_mode == 1
n_periods = length(constrained_periods);
@@ -272,7 +272,7 @@ if direct_mode == 1
tp = tp + 1;
j = j + 1;
end
- if tp - tp0 > max_periods_simulation
+ if tp - tp0 > max_periods_simulation
max_periods_simulation = tp - tp0;
end
end
@@ -311,7 +311,7 @@ if direct_mode == 1
tp = tp + 1;
j = j + 1;
end
- if tp - tp0 > max_periods_simulation
+ if tp - tp0 > max_periods_simulation
max_periods_simulation = tp - tp0;
end
end
@@ -336,7 +336,7 @@ ny = size(ys,1);
xs = [oo_.exo_steady_state ; oo_.exo_det_steady_state];
nx = size(xs,1);
-constrained_periods = max_periods_simulation;
+constrained_periods = max_periods_simulation;
n_endo_constrained = size(constrained_vars,1);
if isfield(options_cond_fcst,'controlled_varexo')
n_control_exo = size(options_cond_fcst.controlled_varexo, 1);
@@ -453,7 +453,7 @@ if pf && ~surprise
not_achieved = 0;
end
end
-
+
per = 30;
z = oo_.endo_simul(:, 1 : per + 2 );
zx = oo_.exo_simul(1: per + 2,:);
@@ -485,7 +485,7 @@ if pf && ~surprise
end
mexErrCheck('bytecode', chck);
end
- if k == 1
+ if k == 1
g1(1:M_.endo_nbr,-M_.endo_nbr + [cur_indx lead_indx]) = data1.g1(:,M_.nspred + 1:end);
elseif k == per
g1(M_.endo_nbr * (k - 1) + 1 :M_.endo_nbr * k,M_.endo_nbr * (k -2) + [lag_indx cur_indx]) = data1.g1(:,1:M_.nspred + M_.endo_nbr);
@@ -514,7 +514,7 @@ if pf && ~surprise
end
end
end
-
+
d_y_x = - g1 \ g1_x;
cum_l1 = 0;
@@ -534,24 +534,24 @@ if pf && ~surprise
end
cum_l1 = cum_l1 + length(constrained_vars(j1));
end
-
-
-% col_count = 1;
-% for j = controlled_varexo'
-% for time = time_index_constraint
-% saved = oo_.exo_simul(time,j);
-% oo_.exo_simul(time,j) = oo_.exo_simul(time,j) + eps1;
-% simul();
-% J1(:,col_count) = (oo_.endo_simul(indx_endo) - ys) / eps1;
-% oo_.exo_simul(time,j) = saved;
-% col_count = col_count + 1;
-% end
-% end
-% J1
-% sdfmlksdf;
-
+
+
+ % col_count = 1;
+ % for j = controlled_varexo'
+ % for time = time_index_constraint
+ % saved = oo_.exo_simul(time,j);
+ % oo_.exo_simul(time,j) = oo_.exo_simul(time,j) + eps1;
+ % simul();
+ % J1(:,col_count) = (oo_.endo_simul(indx_endo) - ys) / eps1;
+ % oo_.exo_simul(time,j) = saved;
+ % col_count = col_count + 1;
+ % end
+ % end
+ % J1
+ % sdfmlksdf;
+
disp(['iteration ' int2str(it) ' error = ' num2str(normr)]);
-
+
if normr <= eps
convg = 1;
disp('convergence achieved');
@@ -573,8 +573,8 @@ if pf && ~surprise
exo = oo_.exo_simul;
else
for t = 1:constrained_periods
-
- if direct_mode && ~isempty(is_constraint)
+
+ if direct_mode && ~isempty(is_constraint)
[pos_constrained_pf, junk] = find(constrained_perfect_foresight .* is_constraint(t, :)');
indx_endo_solve_pf = constrained_vars(pos_constrained_pf);
if isempty(indx_endo_solve_pf)
@@ -582,7 +582,7 @@ else
else
pf = length(indx_endo_solve_pf);
end
-
+
[pos_constrained_surprise, junk] = find((1-constrained_perfect_foresight) .* is_constraint(t, :)');
indx_endo_solve_surprise = constrained_vars(pos_constrained_surprise);
@@ -592,8 +592,8 @@ else
surprise = length(indx_endo_solve_surprise);
end
end
-
- if direct_mode && ~isempty(is_shock)
+
+ if direct_mode && ~isempty(is_shock)
[pos_shock_pf, junk] = find(shock_perfect_foresight .* is_shock(t, :)');
indx_endo_solve_pf = shock_vars(pos_shock_pf);
if isempty(indx_endo_solve_pf)
@@ -601,7 +601,7 @@ else
else
b_pf = length(indx_endo_solve_pf);
end
-
+
[pos_shock_surprise, junk] = find((1-shock_perfect_foresight) .* is_shock(t, :)');
indx_endo_solve_surprise = shock_vars(pos_shock_surprise);
@@ -611,7 +611,7 @@ else
b_surprise = length(indx_endo_solve_surprise);
end
end
-
+
disp('===============================================================================================');
disp(['t=' int2str(t) ' conditional (surprise=' int2str(surprise) ' perfect foresight=' int2str(pf) ') unconditional (surprise=' int2str(b_surprise) ' perfect foresight=' int2str(b_pf) ')']);
disp('===============================================================================================');
@@ -627,16 +627,16 @@ else
%exo_init
oo_.exo_simul = exo_init;
oo_.endo_simul(:,1) = initial_conditions;
-
+
time_index_constraint = maximum_lag + 1:maximum_lag + constrained_periods - t + 1;
-
+
if direct_mode
nb_shocks = length(plan.shock_vars_);
if nb_shocks > 0
shock_index_t = shock_index{t};
shock_vars_t = shock_vars(shock_index_t);
for shock_indx = shock_index_t
- if shock_perfect_foresight(shock_indx)
+ if shock_perfect_foresight(shock_indx)
oo_.exo_simul(time_index_constraint,shock_vars_t(shock_indx)) = shock_paths(shock_indx,t:constrained_periods);
else
oo_.exo_simul(maximum_lag + 1,shock_vars_t(shock_indx)) = shock_paths(shock_indx,t);
@@ -653,11 +653,11 @@ else
for jj = 1 : (options_.periods + 2)
oo_.endo_simul(:,jj) = oo_.steady_state + oo_.endo_simul(:,jj);
end
-
+
constraint_index_t = constraint_index{t};
controlled_varexo_t = controlled_varexo(constraint_index_t);
constrained_vars_t = constrained_vars(constraint_index_t);
-
+
second_system_size = surprise + pf * (constrained_periods - t + 1);
indx_endo = zeros(second_system_size,1);
col_count = 1;
@@ -670,9 +670,9 @@ else
col_count = col_count + 1;
end
end
-
+
r = zeros(second_system_size,1);
-
+
convg = 0;
it = 1;
while ~convg && it <= maxit
@@ -709,7 +709,7 @@ else
end
yc = oo_.endo_simul(:,maximum_lag + 1);
ys = oo_.endo_simul(indx_endo);
-
+
col_count = 1;
for j = constraint_index_t
if constrained_perfect_foresight(j)
@@ -722,7 +722,7 @@ else
col_count = col_count + 1;
end
end
-
+
disp('computation of derivatives w.r. to exogenous shocks');
per = 30;
@@ -757,14 +757,14 @@ else
end
mexErrCheck('bytecode', chck);
end
- if k == 1
+ if k == 1
g1(1:M_.endo_nbr,-M_.endo_nbr + [cur_indx lead_indx]) = data1.g1(:,M_.nspred + 1:end);
elseif k == per
g1(M_.endo_nbr * (k - 1) + 1 :M_.endo_nbr * k,M_.endo_nbr * (k -2) + [lag_indx cur_indx]) = data1.g1(:,1:M_.nspred + M_.endo_nbr);
else
g1(M_.endo_nbr * (k - 1) + 1 :M_.endo_nbr * k, M_.endo_nbr * (k -2) + [lag_indx cur_indx lead_indx]) = data1.g1;
end
-
+
l2 = 1;
pf_c = 1;
if t + k - 1 <= constrained_periods
@@ -794,10 +794,10 @@ else
end
end
end
-
+
d_y_x = - g1 \ g1_x;
-
+
cum_l1 = 0;
count_col = 1;
cum_index_J = 1:length(cum_index_d_y_x(indx_x));
@@ -821,39 +821,39 @@ else
cum_l1 = cum_l1 + length(constrained_vars_t(j1));
end
-
-% % Numerical computation of the derivatives in the second systme
-% J1 = [];
-% col_count = 1;
-% for j = constraint_index_t
-% j_pos = controlled_varexo(j);
-% if constrained_perfect_foresight(j)
-% for time = time_index_constraint
-% saved = oo_.exo_simul(time,j_pos);
-% oo_.exo_simul(time,j_pos) = oo_.exo_simul(time,j_pos) + eps1;
-% simul();
-% J1(:,col_count) = (oo_.endo_simul(indx_endo) - ys) / eps1;
-% oo_.exo_simul(time,j_pos) = saved;
-% col_count = col_count + 1;
-% end
-% else
-% saved = oo_.exo_simul(maximum_lag+1,j_pos);
-% oo_.exo_simul(maximum_lag+1,j_pos) = oo_.exo_simul(maximum_lag+1,j_pos) + eps1;
-% simul();
-% % indx_endo
-% J1(:,col_count) = (oo_.endo_simul(indx_endo) - ys) / eps1;
-% % J(:,col_count) = (oo_.endo_simul((pp - 1) * M_.endo_nbr + 1: pp * M_.endo_nbr) - ys) / eps1;
-% oo_.exo_simul(maximum_lag+1,j_pos) = saved;
-% col_count = col_count + 1;
-% end
-% end
-% disp('J1');
-% disp(full(J1));
-% sdfmlk;
-
+
+ % % Numerical computation of the derivatives in the second systme
+ % J1 = [];
+ % col_count = 1;
+ % for j = constraint_index_t
+ % j_pos = controlled_varexo(j);
+ % if constrained_perfect_foresight(j)
+ % for time = time_index_constraint
+ % saved = oo_.exo_simul(time,j_pos);
+ % oo_.exo_simul(time,j_pos) = oo_.exo_simul(time,j_pos) + eps1;
+ % simul();
+ % J1(:,col_count) = (oo_.endo_simul(indx_endo) - ys) / eps1;
+ % oo_.exo_simul(time,j_pos) = saved;
+ % col_count = col_count + 1;
+ % end
+ % else
+ % saved = oo_.exo_simul(maximum_lag+1,j_pos);
+ % oo_.exo_simul(maximum_lag+1,j_pos) = oo_.exo_simul(maximum_lag+1,j_pos) + eps1;
+ % simul();
+ % % indx_endo
+ % J1(:,col_count) = (oo_.endo_simul(indx_endo) - ys) / eps1;
+ % % J(:,col_count) = (oo_.endo_simul((pp - 1) * M_.endo_nbr + 1: pp * M_.endo_nbr) - ys) / eps1;
+ % oo_.exo_simul(maximum_lag+1,j_pos) = saved;
+ % col_count = col_count + 1;
+ % end
+ % end
+ % disp('J1');
+ % disp(full(J1));
+ % sdfmlk;
+
normr = norm(r, 1);
-
+
disp(['iteration ' int2str(it) ' error = ' num2str(normr) ' at time ' int2str(t)]);
if normr <= eps
@@ -862,7 +862,7 @@ else
else
% Newton update on exogenous shocks
try
- D_exo = - J \ r;
+ D_exo = - J \ r;
catch
[V, D] = eig(full(J));
ev = diag(D);
diff --git a/matlab/perfect-foresight-models/linear_approximation_accuracy.m b/matlab/perfect-foresight-models/linear_approximation_accuracy.m
index e3f9eb521529941ad8c4f96e60f99dd2156c7e94..552310a691b4b4dde037ca96fc8d4ca29110ffaf 100644
--- a/matlab/perfect-foresight-models/linear_approximation_accuracy.m
+++ b/matlab/perfect-foresight-models/linear_approximation_accuracy.m
@@ -2,12 +2,12 @@ function err = linear_approximation_accuracy(options_, M_, oo_)
% Evaluates the accuracy of the linear approximation when solving perfect foresight models, by
% reporting the max absolute value of the dynamic residuals.
%
-% INPUTS
+% INPUTS
% - options_ [struct] contains various options.
% - M_ [struct] contains a description of the model.
% - oo_ [struct] contains results.
%
-% OUTPUTS
+% OUTPUTS
% - err [double] n*1 vector, evaluation of the accuracy (n is the number of equations).
% Copyright (C) 2015 Dynare Team
diff --git a/matlab/perfect-foresight-models/linear_perfect_foresight_problem.m b/matlab/perfect-foresight-models/linear_perfect_foresight_problem.m
index 8361ffcb97cea7451f8be11431291ae91e33ae70..78d9fed418d182339acfa25b89e247e2346288a4 100644
--- a/matlab/perfect-foresight-models/linear_perfect_foresight_problem.m
+++ b/matlab/perfect-foresight-models/linear_perfect_foresight_problem.m
@@ -1,11 +1,11 @@
function [residuals,JJacobian] = linear_perfect_foresight_problem(y, dynamicjacobian, Y0, YT, ...
- exo_simul, params, steady_state, ...
- maximum_lag, T, ny, i_cols, ...
- i_cols_J1, i_cols_1, i_cols_T, ...
- i_cols_j,nnzJ,jendo,jexog)
+ exo_simul, params, steady_state, ...
+ maximum_lag, T, ny, i_cols, ...
+ i_cols_J1, i_cols_1, i_cols_T, ...
+ i_cols_j,nnzJ,jendo,jexog)
% function [residuals,JJacobian] = perfect_foresight_problem(x, model_dynamic, Y0, YT,exo_simul,
% params, steady_state, maximum_lag, periods, ny, i_cols,i_cols_J1, i_cols_1,
-% i_cols_T, i_cols_j, nnzA)
+% i_cols_T, i_cols_j, nnzA)
% computes the residuals and th Jacobian matrix
% for a perfect foresight problem over T periods.
%
diff --git a/matlab/perfect-foresight-models/make_ex_.m b/matlab/perfect-foresight-models/make_ex_.m
index 73ab28f436093c9ebe172d10165a47295d4ab453..1eec29ac5445ba6e5ce80891d2624ab0139e5c91 100644
--- a/matlab/perfect-foresight-models/make_ex_.m
+++ b/matlab/perfect-foresight-models/make_ex_.m
@@ -5,14 +5,14 @@ function oo_=make_ex_(M_,options_,oo_)
% M_: Dynare model structure
% options_: Dynare options structure
% oo_: Dynare results structure
-%
+%
% OUTPUTS
% oo_: Dynare results structure
%
% ALGORITHM
-%
+%
% SPECIAL REQUIREMENTS
-%
+%
% Copyright (C) 1996-2016 Dynare Team
%
@@ -53,7 +53,7 @@ else
else
error('histval and endval cannot be used simultaneously')
end
-end
+end
% Initialize oo_.exo_det_simul
if M_.exo_det_nbr > 0
diff --git a/matlab/perfect-foresight-models/make_y_.m b/matlab/perfect-foresight-models/make_y_.m
index f3c8f686f679c88ef60e5183acdbc0ec1861b5d1..bbe41db71696f1468fb75a0680faaa4e412e9cd2 100644
--- a/matlab/perfect-foresight-models/make_y_.m
+++ b/matlab/perfect-foresight-models/make_y_.m
@@ -1,20 +1,20 @@
function oo_=make_y_(M_,options_,oo_)
% function oo_=make_y_(M_,options_,oo_)
% forms oo_.endo_simul as guess values for deterministic simulations
-%
+%
% INPUTS
% M_: Dynare model structure
% options_: Dynare options structure
% oo_: Dynare results structure
-%
+%
% OUTPUTS
% oo_: Dynare results structure
-%
+%
% ALGORITHM
% ...
% SPECIAL REQUIREMENTS
% none
-%
+%
% Copyright (C) 1996-2016 Dynare Team
%
@@ -33,7 +33,7 @@ function oo_=make_y_(M_,options_,oo_)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-global ys0_
+global ys0_
if options_.steadystate_flag
[oo_.steady_state,M_.params,check] = ...
diff --git a/matlab/perfect-foresight-models/perfect_foresight_mcp_problem.m b/matlab/perfect-foresight-models/perfect_foresight_mcp_problem.m
index d4b419308659cec1ac240f50657f66cbc3f6f9f1..5160ce71f69f8473b8ab0dc04fd9e8e0123dfc15 100644
--- a/matlab/perfect-foresight-models/perfect_foresight_mcp_problem.m
+++ b/matlab/perfect-foresight-models/perfect_foresight_mcp_problem.m
@@ -1,8 +1,8 @@
function [residuals,JJacobian] = perfect_foresight_mcp_problem(y, dynamic_function, Y0, YT, ...
- exo_simul, params, steady_state, ...
- maximum_lag, T, ny, i_cols, ...
- i_cols_J1, i_cols_1, i_cols_T, ...
- i_cols_j,nnzJ,eq_index)
+ exo_simul, params, steady_state, ...
+ maximum_lag, T, ny, i_cols, ...
+ i_cols_J1, i_cols_1, i_cols_T, ...
+ i_cols_j,nnzJ,eq_index)
% function [residuals,JJacobian] = perfect_foresight_mcp_problem(y, dynamic_function, Y0, YT, ...
% exo_simul, params, steady_state, ...
% maximum_lag, T, ny, i_cols, ...
@@ -17,7 +17,7 @@ function [residuals,JJacobian] = perfect_foresight_mcp_problem(y, dynamic_functi
% Y0 [double] N*1 array, initial conditions for the endogenous variables
% YT [double] N*1 array, terminal conditions for the endogenous variables
% exo_simul [double] nperiods*M_.exo_nbr matrix of exogenous variables (in declaration order)
-% for all simulation periods
+% for all simulation periods
% params [double] nparams*1 array, parameter values
% steady_state [double] endo_nbr*1 vector of steady state values
% maximum_lag [scalar] maximum lag present in the model
@@ -25,7 +25,7 @@ function [residuals,JJacobian] = perfect_foresight_mcp_problem(y, dynamic_functi
% ny [scalar] number of endogenous variables
% i_cols [double] indices of variables appearing in M.lead_lag_incidence
% and that need to be passed to _dynamic-file
-% i_cols_J1 [double] indices of contemporaneous and forward looking variables
+% i_cols_J1 [double] indices of contemporaneous and forward looking variables
% appearing in M.lead_lag_incidence
% i_cols_1 [double] indices of contemporaneous and forward looking variables in
% M.lead_lag_incidence in dynamic Jacobian (relevant in first period)
@@ -33,8 +33,8 @@ function [residuals,JJacobian] = perfect_foresight_mcp_problem(y, dynamic_functi
% variables (relevant in last period)
% i_cols_j [double] indices of variables in M.lead_lag_incidence
% in dynamic Jacobian (relevant in intermediate periods)
-% nnzJ [scalar] number of non-zero elements in Jacobian
-% eq_index [double] N*1 array, index vector describing residual mapping resulting
+% nnzJ [scalar] number of non-zero elements in Jacobian
+% eq_index [double] N*1 array, index vector describing residual mapping resulting
% from complementarity setup
% OUTPUTS
% residuals [double] (N*T)*1 array, residuals of the stacked problem
@@ -77,11 +77,11 @@ i_cols_J = i_cols;
for it = 2:(T+1)
if nargout == 1
res = dynamic_function(YY(i_cols),exo_simul, params, ...
- steady_state,it);
+ steady_state,it);
residuals(i_rows) = res(eq_index);
elseif nargout == 2
[res,jacobian] = dynamic_function(YY(i_cols),exo_simul, params, ...
- steady_state,it);
+ steady_state,it);
residuals(i_rows) = res(eq_index);
if it == 2
[rows,cols,vals] = find(jacobian(eq_index,i_cols_1));
@@ -96,7 +96,7 @@ for it = 2:(T+1)
end
offset = offset + ny;
end
-
+
i_rows = i_rows + ny;
i_cols = i_cols + ny;
end
@@ -104,5 +104,5 @@ end
if nargout == 2
iJacobian = cat(1,iJacobian{:});
JJacobian = sparse(iJacobian(:,1),iJacobian(:,2),iJacobian(:,3),T* ...
- ny,T*ny);
+ ny,T*ny);
end
\ No newline at end of file
diff --git a/matlab/perfect-foresight-models/perfect_foresight_problem.m b/matlab/perfect-foresight-models/perfect_foresight_problem.m
index 95611909453fa57a6652972ecb50ed3f96bcc697..a853d65149564829cbf8a5122ce83ecbd5764bd6 100644
--- a/matlab/perfect-foresight-models/perfect_foresight_problem.m
+++ b/matlab/perfect-foresight-models/perfect_foresight_problem.m
@@ -1,8 +1,8 @@
function [residuals,JJacobian] = perfect_foresight_problem(y, dynamic_function, Y0, YT, ...
- exo_simul, params, steady_state, ...
- maximum_lag, T, ny, i_cols, ...
- i_cols_J1, i_cols_1, i_cols_T, ...
- i_cols_j,nnzJ)
+ exo_simul, params, steady_state, ...
+ maximum_lag, T, ny, i_cols, ...
+ i_cols_J1, i_cols_1, i_cols_T, ...
+ i_cols_j,nnzJ)
% function [residuals,JJacobian] = perfect_foresight_problem(y, dynamic_function, Y0, YT, ...
% exo_simul, params, steady_state, ...
% maximum_lag, T, ny, i_cols, ...
@@ -16,7 +16,7 @@ function [residuals,JJacobian] = perfect_foresight_problem(y, dynamic_function,
% Y0 [double] N*1 array, initial conditions for the endogenous variables
% YT [double] N*1 array, terminal conditions for the endogenous variables
% exo_simul [double] nperiods*M_.exo_nbr matrix of exogenous variables (in declaration order)
-% for all simulation periods
+% for all simulation periods
% params [double] nparams*1 array, parameter values
% steady_state [double] endo_nbr*1 vector of steady state values
% maximum_lag [scalar] maximum lag present in the model
@@ -24,7 +24,7 @@ function [residuals,JJacobian] = perfect_foresight_problem(y, dynamic_function,
% ny [scalar] number of endogenous variables
% i_cols [double] indices of variables appearing in M.lead_lag_incidence
% and that need to be passed to _dynamic-file
-% i_cols_J1 [double] indices of contemporaneous and forward looking variables
+% i_cols_J1 [double] indices of contemporaneous and forward looking variables
% appearing in M.lead_lag_incidence
% i_cols_1 [double] indices of contemporaneous and forward looking variables in
% M.lead_lag_incidence in dynamic Jacobian (relevant in first period)
@@ -32,7 +32,7 @@ function [residuals,JJacobian] = perfect_foresight_problem(y, dynamic_function,
% variables (relevant in last period)
% i_cols_j [double] indices of variables in M.lead_lag_incidence
% in dynamic Jacobian (relevant in intermediate periods)
-% nnzJ [scalar] number of non-zero elements in Jacobian
+% nnzJ [scalar] number of non-zero elements in Jacobian
% OUTPUTS
% residuals [double] (N*T)*1 array, residuals of the stacked problem
% JJacobian [double] (N*T)*(N*T) array, Jacobian of the stacked problem
@@ -60,44 +60,44 @@ function [residuals,JJacobian] = perfect_foresight_problem(y, dynamic_function,
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- YY = [Y0; y; YT];
-
- residuals = zeros(T*ny,1);
- if nargout == 2
- iJacobian = cell(T,1);
- end
+YY = [Y0; y; YT];
- i_rows = 1:ny;
- i_cols_J = i_cols;
- offset = 0;
-
- for it = 2:(T+1)
- if nargout == 1
- residuals(i_rows) = dynamic_function(YY(i_cols),exo_simul, params, ...
- steady_state,it);
- elseif nargout == 2
- [residuals(i_rows),jacobian] = dynamic_function(YY(i_cols),exo_simul, params, ...
- steady_state,it);
- if it == 2
- [rows,cols,vals] = find(jacobian(:,i_cols_1));
- iJacobian{1} = [offset+rows, i_cols_J1(cols), vals];
- elseif it == T + 1
- [rows,cols,vals] = find(jacobian(:,i_cols_T));
- iJacobian{T} = [offset+rows, i_cols_J(i_cols_T(cols)), vals];
- else
- [rows,cols,vals] = find(jacobian(:,i_cols_j));
- iJacobian{it-1} = [offset+rows, i_cols_J(cols), vals];
- i_cols_J = i_cols_J + ny;
- end
- offset = offset + ny;
- end
+residuals = zeros(T*ny,1);
+if nargout == 2
+ iJacobian = cell(T,1);
+end
+
+i_rows = 1:ny;
+i_cols_J = i_cols;
+offset = 0;
- i_rows = i_rows + ny;
- i_cols = i_cols + ny;
+for it = 2:(T+1)
+ if nargout == 1
+ residuals(i_rows) = dynamic_function(YY(i_cols),exo_simul, params, ...
+ steady_state,it);
+ elseif nargout == 2
+ [residuals(i_rows),jacobian] = dynamic_function(YY(i_cols),exo_simul, params, ...
+ steady_state,it);
+ if it == 2
+ [rows,cols,vals] = find(jacobian(:,i_cols_1));
+ iJacobian{1} = [offset+rows, i_cols_J1(cols), vals];
+ elseif it == T + 1
+ [rows,cols,vals] = find(jacobian(:,i_cols_T));
+ iJacobian{T} = [offset+rows, i_cols_J(i_cols_T(cols)), vals];
+ else
+ [rows,cols,vals] = find(jacobian(:,i_cols_j));
+ iJacobian{it-1} = [offset+rows, i_cols_J(cols), vals];
+ i_cols_J = i_cols_J + ny;
+ end
+ offset = offset + ny;
end
- if nargout == 2
- iJacobian = cat(1,iJacobian{:});
- JJacobian = sparse(iJacobian(:,1),iJacobian(:,2),iJacobian(:,3),T* ...
- ny,T*ny);
- end
\ No newline at end of file
+ i_rows = i_rows + ny;
+ i_cols = i_cols + ny;
+end
+
+if nargout == 2
+ iJacobian = cat(1,iJacobian{:});
+ JJacobian = sparse(iJacobian(:,1),iJacobian(:,2),iJacobian(:,3),T* ...
+ ny,T*ny);
+end
\ No newline at end of file
diff --git a/matlab/perfect-foresight-models/perfect_foresight_setup.m b/matlab/perfect-foresight-models/perfect_foresight_setup.m
index 3b28254326f2642f46cfa098ec61b210e9336f8d..a9cddb0e597049b90a47e584e2387fa65df4eb9f 100644
--- a/matlab/perfect-foresight-models/perfect_foresight_setup.m
+++ b/matlab/perfect-foresight-models/perfect_foresight_setup.m
@@ -1,14 +1,14 @@
function perfect_foresight_setup()
% Prepares a deterministic simulation, by filling oo_.exo_simul and oo_.endo_simul
-%
+%
% INPUTS
% None
-%
+%
% OUTPUTS
% none
-%
+%
% ALGORITHM
-%
+%
% SPECIAL REQUIREMENTS
% none
@@ -35,14 +35,14 @@ test_for_deep_parameters_calibration(M_);
if size(M_.lead_lag_incidence,2)-nnz(M_.lead_lag_incidence(M_.maximum_endo_lag+1,:)) > 0
mess = ['PERFECT_FORESIGHT_SETUP: error in model specification : the variable(s) '];
- var_list=M_.endo_names(find(M_.lead_lag_incidence(M_.maximum_lag+1,:)==0),:);
- for i=1:size(var_list,1)
- if i<size(var_list,1)
- mess = [mess, deblank(var_list(i,:)) ', '];
- else
- mess = [mess, deblank(var_list(i,:)) ];
- end
+ var_list=M_.endo_names(find(M_.lead_lag_incidence(M_.maximum_lag+1,:)==0),:);
+ for i=1:size(var_list,1)
+ if i<size(var_list,1)
+ mess = [mess, deblank(var_list(i,:)) ', '];
+ else
+ mess = [mess, deblank(var_list(i,:)) ];
end
+ end
mess = [mess ' don''t appear as current period variables.'];
error(mess)
end
diff --git a/matlab/perfect-foresight-models/perfect_foresight_solver.m b/matlab/perfect-foresight-models/perfect_foresight_solver.m
index b5fc7e4f21402b04c7fdbbb23c7bd55838662958..8bf52c28b25558621d9febe6c448b7f063794c85 100644
--- a/matlab/perfect-foresight-models/perfect_foresight_solver.m
+++ b/matlab/perfect-foresight-models/perfect_foresight_solver.m
@@ -184,20 +184,20 @@ if ~isreal(oo_.endo_simul(:)) %can only happen without bytecode
[i_cols_J1,junk,i_cols_1] = find(illi(:));
i_cols_T = nonzeros(M_.lead_lag_incidence(1:2,:)');
residuals = perfect_foresight_problem(yy(:),str2func([M_.fname '_dynamic']), y0, yT, ...
- oo_.exo_simul,M_.params,oo_.steady_state, ...
- M_.maximum_lag,options_.periods,M_.endo_nbr,i_cols, ...
- i_cols_J1, i_cols_1, i_cols_T, i_cols_j, ...
- M_.NNZDerivatives(1));
+ oo_.exo_simul,M_.params,oo_.steady_state, ...
+ M_.maximum_lag,options_.periods,M_.endo_nbr,i_cols, ...
+ i_cols_J1, i_cols_1, i_cols_T, i_cols_j, ...
+ M_.NNZDerivatives(1));
if max(abs(residuals))< options_.dynatol.f
oo_.deterministic_simulation.status = 1;
oo_.endo_simul=real(oo_.endo_simul);
else
- oo_.deterministic_simulation.status = 0;
+ oo_.deterministic_simulation.status = 0;
disp('Simulation terminated with imaginary parts in the residuals or endogenous variables.')
- end
+ end
end
-if oo_.deterministic_simulation.status == 1
+if oo_.deterministic_simulation.status == 1
disp('Perfect foresight solution found.')
else
disp('Failed to solve perfect foresight model')
diff --git a/matlab/perfect-foresight-models/perfect_foresight_solver_core.m b/matlab/perfect-foresight-models/perfect_foresight_solver_core.m
index da267b7dae697716ccffc3b4e8537c01375d33d4..94e978316ed97d141cdf41bb3401be99dc61421f 100644
--- a/matlab/perfect-foresight-models/perfect_foresight_solver_core.m
+++ b/matlab/perfect-foresight-models/perfect_foresight_solver_core.m
@@ -2,12 +2,12 @@ function [oo_, maxerror] = perfect_foresight_solver_core(M_, options_, oo_)
%function [oo_, maxerror] = perfect_foresight_solver_core(M_, options_, oo_)
% Core function calling solvers for perfect foresight model
%
-% INPUTS
+% INPUTS
% - M_ [struct] contains a description of the model.
% - options_ [struct] contains various options.
% - oo_ [struct] contains results
%
-% OUTPUTS
+% OUTPUTS
% - oo_ [struct] contains results
% - maxerror [double] contains the maximum absolute error
@@ -81,8 +81,8 @@ else
[oo_.endo_simul, oo_.deterministic_simulation] = ...
sim1_purely_backward(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_);
elseif M_.maximum_endo_lag == 0 % Purely forward model
- [oo_.endo_simul, oo_.deterministic_simulation] = ...
- sim1_purely_forward(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_);
+ [oo_.endo_simul, oo_.deterministic_simulation] = ...
+ sim1_purely_forward(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_);
else % General case
switch options_.stack_solve_algo
case 0
@@ -143,5 +143,3 @@ if nargout>1
maxerror = max(max(abs(residuals)));
end
end
-
-
diff --git a/matlab/perfect-foresight-models/private/check_input_arguments.m b/matlab/perfect-foresight-models/private/check_input_arguments.m
index affb808e081591bf1fc84b0e1a0026af030454b2..81c3da8ce4e2f040459d9fecd482c7ab2446a15d 100644
--- a/matlab/perfect-foresight-models/private/check_input_arguments.m
+++ b/matlab/perfect-foresight-models/private/check_input_arguments.m
@@ -43,7 +43,7 @@ end
if isempty(DynareResults.endo_simul) || any(size(DynareResults.endo_simul) ~= [ DynareModel.endo_nbr, DynareModel.maximum_lag+DynareOptions.periods+DynareModel.maximum_lead ])
-
+
if DynareOptions.initval_file
fprintf('PERFECT_FORESIGHT_SOLVER: ''oo_.endo_simul'' has wrong size. Check whether your initval-file provides %d periods.',DynareModel.maximum_endo_lag+DynareOptions.periods+DynareModel.maximum_endo_lead)
error('perfect_foresight_solver:ArgCheck','PERFECT_FORESIGHT_SOLVER: ''oo_.endo_simul'' has wrong size. Did you run ''perfect_foresight_setup'' ?')
diff --git a/matlab/perfect-foresight-models/private/initialize_stacked_problem.m b/matlab/perfect-foresight-models/private/initialize_stacked_problem.m
index 64beba34e2734456a69da08bcee6a4ecea70b88c..3d6c9abdc092e38ba2bdf6709d67d0166b307ea6 100644
--- a/matlab/perfect-foresight-models/private/initialize_stacked_problem.m
+++ b/matlab/perfect-foresight-models/private/initialize_stacked_problem.m
@@ -4,19 +4,19 @@ function [options, y0, yT, z, i_cols, i_cols_J1, i_cols_T, i_cols_j, i_cols_1, .
% dynamicmodel] = initialize_stacked_problem(endogenousvariables, options, M, steadystate_y)
% Sets up the stacked perfect foresight problem for use with dynare_solve.m
%
-% INPUTS
+% INPUTS
% - endogenousvariables [double] N*T array, paths for the endogenous variables (initial guess).
% - options [struct] contains various options.
% - M [struct] contains a description of the model.
% - steadystate_y [double] N*1 array, steady state for the endogenous variables.
-% OUTPUTS
+% OUTPUTS
% - options [struct] contains various options.
% - y0 [double] N*1 array, initial conditions for the endogenous variables
% - yT [double] N*1 array, terminal conditions for the endogenous variables
% - z [double] T*M array, paths for the exogenous variables.
% - i_cols [double] indices of variables appearing in M.lead_lag_incidence
% and that need to be passed to _dynamic-file
-% - i_cols_J1 [double] indices of contemporaneous and forward looking variables
+% - i_cols_J1 [double] indices of contemporaneous and forward looking variables
% appearing in M.lead_lag_incidence
% - i_cols_T [double] columns of dynamic Jacobian related to
% contemporaneous and backward-looking
@@ -43,7 +43,7 @@ function [options, y0, yT, z, i_cols, i_cols_J1, i_cols_T, i_cols_j, i_cols_1, .
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
+
periods = options.periods;
if (options.solve_algo == 10)
if ~isfield(options.lmmcp,'lb')
diff --git a/matlab/perfect-foresight-models/private/simulation_core.m b/matlab/perfect-foresight-models/private/simulation_core.m
index 3c8f12604672322c7edb7c261086d2aa917a166b..947ebd9a59d2a8bcf85d2b7554a1a737550ba445 100644
--- a/matlab/perfect-foresight-models/private/simulation_core.m
+++ b/matlab/perfect-foresight-models/private/simulation_core.m
@@ -66,8 +66,8 @@ else
[oo_.endo_simul, oo_.deterministic_simulation] = ...
sim1_purely_backward(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_);
elseif M_.maximum_endo_lag == 0 % Purely forward model
- [oo_.endo_simul, oo_.deterministic_simulation] = ...
- sim1_purely_forward(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_);
+ [oo_.endo_simul, oo_.deterministic_simulation] = ...
+ sim1_purely_forward(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_);
else % General case
if options_.stack_solve_algo == 0
if options_.linear_approximation
diff --git a/matlab/perfect-foresight-models/sim1.m b/matlab/perfect-foresight-models/sim1.m
index 1a173b2115580e422a6bcb67e74bf254a8f2ba51..b8d09a808693cb5318a4e70ca95ea619304ab087 100644
--- a/matlab/perfect-foresight-models/sim1.m
+++ b/matlab/perfect-foresight-models/sim1.m
@@ -154,13 +154,13 @@ for iter = 1:options.simul.maxit
if endogenous_terminal_period && iter>1
dy = ZERO;
if options.simul.robust_lin_solve
- dy(1:i_rows(end)) = -lin_solve_robust( A(1:i_rows(end),1:i_rows(end)), res(1:i_rows(end)),verbose );
+ dy(1:i_rows(end)) = -lin_solve_robust( A(1:i_rows(end),1:i_rows(end)), res(1:i_rows(end)),verbose );
else
dy(1:i_rows(end)) = -lin_solve( A(1:i_rows(end),1:i_rows(end)), res(1:i_rows(end)), verbose );
end
else
if options.simul.robust_lin_solve
- dy = -lin_solve_robust( A, res, verbose );
+ dy = -lin_solve_robust( A, res, verbose );
else
dy = -lin_solve( A, res, verbose );
end
@@ -228,108 +228,106 @@ if verbose
end
function x = lin_solve( A, b,verbose)
- if norm( b ) < sqrt( eps ) % then x = 0 is a solution
- x = 0;
- return
- end
-
- x = A\b;
- x( ~isfinite( x ) ) = 0;
- relres = norm( b - A * x ) / norm( b );
- if relres > 1e-6 && verbose
- fprintf( 'WARNING : Failed to find a solution to the linear system.\n' );
- end
-
+if norm( b ) < sqrt( eps ) % then x = 0 is a solution
+ x = 0;
+ return
+end
+
+x = A\b;
+x( ~isfinite( x ) ) = 0;
+relres = norm( b - A * x ) / norm( b );
+if relres > 1e-6 && verbose
+ fprintf( 'WARNING : Failed to find a solution to the linear system.\n' );
+end
+
function [ x, flag, relres ] = lin_solve_robust( A, b , verbose)
- if norm( b ) < sqrt( eps ) % then x = 0 is a solution
- x = 0;
- flag = 0;
- relres = 0;
- return
- end
-
- x = A\b;
- x( ~isfinite( x ) ) = 0;
- [ x, flag, relres ] = bicgstab( A, b, [], [], [], [], x ); % returns immediately if x is a solution
- if flag == 0
- return
- end
+if norm( b ) < sqrt( eps ) % then x = 0 is a solution
+ x = 0;
+ flag = 0;
+ relres = 0;
+ return
+end
- disp( relres );
+x = A\b;
+x( ~isfinite( x ) ) = 0;
+[ x, flag, relres ] = bicgstab( A, b, [], [], [], [], x ); % returns immediately if x is a solution
+if flag == 0
+ return
+end
- if verbose
- fprintf( 'Initial bicgstab failed, trying alternative start point.\n' );
- end
- old_x = x;
- old_relres = relres;
- [ x, flag, relres ] = bicgstab( A, b );
- if flag == 0
- return
- end
+disp( relres );
- if verbose
- fprintf( 'Alternative start point also failed with bicgstab, trying gmres.\n' );
- end
- if old_relres < relres
- x = old_x;
- end
- [ x, flag, relres ] = gmres( A, b, [], [], [], [], [], x );
- if flag == 0
- return
- end
+if verbose
+ fprintf( 'Initial bicgstab failed, trying alternative start point.\n' );
+end
+old_x = x;
+old_relres = relres;
+[ x, flag, relres ] = bicgstab( A, b );
+if flag == 0
+ return
+end
- if verbose
- fprintf( 'Initial gmres failed, trying alternative start point.\n' );
- end
- old_x = x;
- old_relres = relres;
- [ x, flag, relres ] = gmres( A, b );
- if flag == 0
- return
- end
+if verbose
+ fprintf( 'Alternative start point also failed with bicgstab, trying gmres.\n' );
+end
+if old_relres < relres
+ x = old_x;
+end
+[ x, flag, relres ] = gmres( A, b, [], [], [], [], [], x );
+if flag == 0
+ return
+end
- if verbose
- fprintf( 'Alternative start point also failed with gmres, using the (SLOW) Moore-Penrose Pseudo-Inverse.\n' );
- end
- if old_relres < relres
- x = old_x;
- relres = old_relres;
- end
- old_x = x;
- old_relres = relres;
- x = pinv( full( A ) ) * b;
- relres = norm( b - A * x ) / norm( b );
- if old_relres < relres
- x = old_x;
- relres = old_relres;
- end
- flag = relres > 1e-6;
- if flag ~= 0 && verbose
- fprintf( 'WARNING : Failed to find a solution to the linear system\n' );
- end
-
-function display_critical_variables(dyy, M)
+if verbose
+ fprintf( 'Initial gmres failed, trying alternative start point.\n' );
+end
+old_x = x;
+old_relres = relres;
+[ x, flag, relres ] = gmres( A, b );
+if flag == 0
+ return
+end
- if any(isnan(dyy))
- indx = find(any(isnan(dyy)));
- endo_names=cellstr(M.endo_names(indx,:));
- disp('Last iteration provided NaN for the following variables:')
- fprintf('%s, ',endo_names{:}),
- fprintf('\n'),
- end
- if any(isinf(dyy))
- indx = find(any(isinf(dyy)));
- endo_names=cellstr(M.endo_names(indx,:));
- disp('Last iteration diverged (Inf) for the following variables:')
- fprintf('%s, ',endo_names{:}),
- fprintf('\n'),
- end
- if any(~isreal(dyy))
- indx = find(any(~isreal(dyy)));
- endo_names=cellstr(M.endo_names(indx,:));
- disp('Last iteration provided complex number for the following variables:')
- fprintf('%s, ',endo_names{:}),
- fprintf('\n'),
- end
+if verbose
+ fprintf( 'Alternative start point also failed with gmres, using the (SLOW) Moore-Penrose Pseudo-Inverse.\n' );
+end
+if old_relres < relres
+ x = old_x;
+ relres = old_relres;
+end
+old_x = x;
+old_relres = relres;
+x = pinv( full( A ) ) * b;
+relres = norm( b - A * x ) / norm( b );
+if old_relres < relres
+ x = old_x;
+ relres = old_relres;
+end
+flag = relres > 1e-6;
+if flag ~= 0 && verbose
+ fprintf( 'WARNING : Failed to find a solution to the linear system\n' );
+end
+
+function display_critical_variables(dyy, M)
-
\ No newline at end of file
+if any(isnan(dyy))
+ indx = find(any(isnan(dyy)));
+ endo_names=cellstr(M.endo_names(indx,:));
+ disp('Last iteration provided NaN for the following variables:')
+ fprintf('%s, ',endo_names{:}),
+ fprintf('\n'),
+end
+if any(isinf(dyy))
+ indx = find(any(isinf(dyy)));
+ endo_names=cellstr(M.endo_names(indx,:));
+ disp('Last iteration diverged (Inf) for the following variables:')
+ fprintf('%s, ',endo_names{:}),
+ fprintf('\n'),
+end
+if any(~isreal(dyy))
+ indx = find(any(~isreal(dyy)));
+ endo_names=cellstr(M.endo_names(indx,:));
+ disp('Last iteration provided complex number for the following variables:')
+ fprintf('%s, ',endo_names{:}),
+ fprintf('\n'),
+end
diff --git a/matlab/perfect-foresight-models/sim1_lbj.m b/matlab/perfect-foresight-models/sim1_lbj.m
index 50bcbc6fd60f306989be042df149bbb5de617aa6..4ff1cf9639f822272a29c4ba4146320f43549dcc 100644
--- a/matlab/perfect-foresight-models/sim1_lbj.m
+++ b/matlab/perfect-foresight-models/sim1_lbj.m
@@ -122,7 +122,7 @@ if ~stop
end
info.status = 0;% more iterations are needed.
info.error = err;
- info.errors = c/abs(err);
+ info.errors = c/abs(err);
info.iterations = options.simul.maxit;
end
diff --git a/matlab/perfect-foresight-models/sim1_linear.m b/matlab/perfect-foresight-models/sim1_linear.m
index 152c5933bd9ae6bcad244ed285d5c6dcdb8a89b5..e5a2d19ac9ba23653c6d775bc537fa19cb368bc9 100644
--- a/matlab/perfect-foresight-models/sim1_linear.m
+++ b/matlab/perfect-foresight-models/sim1_linear.m
@@ -2,7 +2,7 @@ function [endogenousvariables, info] = sim1_linear(endogenousvariables, exogenou
% Solves a linear approximation of a perfect foresight model using sparse matrix.
%
-% INPUTS
+% INPUTS
% - endogenousvariables [double] N*T array, paths for the endogenous variables (initial guess).
% - exogenousvariables [double] T*M array, paths for the exogenous variables.
% - steadystate_y [double] N*1 array, steady state for the endogenous variables.
@@ -10,16 +10,16 @@ function [endogenousvariables, info] = sim1_linear(endogenousvariables, exogenou
% - M [struct] contains a description of the model.
% - options [struct] contains various options.
%
-% OUTPUTS
+% OUTPUTS
% - endogenousvariables [double] N*T array, paths for the endogenous variables (solution of the perfect foresight model).
% - info [struct] contains informations about the results.
%
-% NOTATIONS
+% NOTATIONS
% - N is the number of endogenous variables.
% - M is the number of innovations.
% - T is the number of periods (including initial and/or terminal conditions).
%
-% REMARKS
+% REMARKS
% - The structure `M` describing the structure of the model, must contain the
% following informations:
% + lead_lag_incidence, incidence matrix (given by the preprocessor).
diff --git a/matlab/perfect-foresight-models/sim1_purely_backward.m b/matlab/perfect-foresight-models/sim1_purely_backward.m
index 48ac6e98622290840f6bf4e255ef1abc25a7b2b7..1c9d04179477d66e17884527dfbdcdb9c36bde6b 100644
--- a/matlab/perfect-foresight-models/sim1_purely_backward.m
+++ b/matlab/perfect-foresight-models/sim1_purely_backward.m
@@ -28,12 +28,12 @@ else
nyb = 0;
iyb = [];
end
-
+
if ny0 ~= M.endo_nbr
error('All endogenous variables must appear at the current period!')
end
-
+
dynamicmodel = str2func([M.fname,'_dynamic']);
info.status = 1;
diff --git a/matlab/perfect-foresight-models/sim1_purely_forward.m b/matlab/perfect-foresight-models/sim1_purely_forward.m
index fbb3e9ace7f4dc9cbdb7c7a84840704bd261b995..a1af2111744e714ee6230f18636564af17c11a6f 100644
--- a/matlab/perfect-foresight-models/sim1_purely_forward.m
+++ b/matlab/perfect-foresight-models/sim1_purely_forward.m
@@ -24,7 +24,7 @@ iyf = find(M.lead_lag_incidence(2,:)>0); % Indices of variables at next period
if ny0 ~= M.endo_nbr
error('All endogenous variables must appear at the current period!')
end
-
+
dynamicmodel = str2func([M.fname,'_dynamic']);
info.status = 1;
diff --git a/matlab/perfect-foresight-models/solve_stacked_linear_problem.m b/matlab/perfect-foresight-models/solve_stacked_linear_problem.m
index 9a2d2d3fc83dcaee96f37d942c0413454fad16f4..2ddc51a21c426b47d201364cff5975d496799a52 100644
--- a/matlab/perfect-foresight-models/solve_stacked_linear_problem.m
+++ b/matlab/perfect-foresight-models/solve_stacked_linear_problem.m
@@ -42,11 +42,11 @@ z = bsxfun(@minus, z, steadystate_y);
x = bsxfun(@minus, exogenousvariables, steadystate_x');
[y, check] = dynare_solve(@linear_perfect_foresight_problem,z(:), options, ...
- jacobian, y0-steadystate_y, yT-steadystate_y, ...
- x, M.params, steadystate_y, ...
- M.maximum_lag, options.periods, M.endo_nbr, i_cols, ...
- i_cols_J1, i_cols_1, i_cols_T, i_cols_j, ...
- M.NNZDerivatives(1), jendo, jexog);
+ jacobian, y0-steadystate_y, yT-steadystate_y, ...
+ x, M.params, steadystate_y, ...
+ M.maximum_lag, options.periods, M.endo_nbr, i_cols, ...
+ i_cols_J1, i_cols_1, i_cols_T, i_cols_j, ...
+ M.NNZDerivatives(1), jendo, jexog);
if all(imag(y)<.1*options.dynatol.x)
if ~isreal(y)
diff --git a/matlab/perfect-foresight-models/solve_stacked_problem.m b/matlab/perfect-foresight-models/solve_stacked_problem.m
index 66fa99ffe8dd1cad1726a2dce573f0e84abb092b..2e0885367fb40c333aad7b351698f1cd14b76576 100644
--- a/matlab/perfect-foresight-models/solve_stacked_problem.m
+++ b/matlab/perfect-foresight-models/solve_stacked_problem.m
@@ -2,14 +2,14 @@ function [endogenousvariables, info] = solve_stacked_problem(endogenousvariables
% [endogenousvariables, info] = solve_stacked_problem(endogenousvariables, exogenousvariables, steadystate, M, options);
% Solves the perfect foresight model using dynare_solve
%
-% INPUTS
+% INPUTS
% - endogenousvariables [double] N*T array, paths for the endogenous variables (initial guess).
% - exogenousvariables [double] T*M array, paths for the exogenous variables.
% - steadystate [double] N*1 array, steady state for the endogenous variables.
% - M [struct] contains a description of the model.
% - options [struct] contains various options.
%
-% OUTPUTS
+% OUTPUTS
% - endogenousvariables [double] N*T array, paths for the endogenous variables (solution of the perfect foresight model).
% - info [struct] contains informations about the results.
@@ -29,7 +29,7 @@ function [endogenousvariables, info] = solve_stacked_problem(endogenousvariables
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
+
[options, y0, yT, z, i_cols, i_cols_J1, i_cols_T, i_cols_j, i_cols_1, dynamicmodel] = ...
initialize_stacked_problem(endogenousvariables, options, M, steadystate);
@@ -74,6 +74,6 @@ endogenousvariables = [y0 reshape(y, M.endo_nbr, options.periods) yT];
if check
info.status = false;
else
-
+
info.status = true;
end
\ No newline at end of file
diff --git a/matlab/plot_icforecast.m b/matlab/plot_icforecast.m
index 1d4b61eb7526cef3c31d8e2592630e65dc43fd66..c4e3c2b2b995ba9ae5b102ff2a6a526c962e10df 100644
--- a/matlab/plot_icforecast.m
+++ b/matlab/plot_icforecast.m
@@ -1,12 +1,12 @@
function plot_icforecast(Variables,periods,options_)
% Build plots for the conditional forecasts.
%
-% INPUTS
-% o Variables [char] m*x array holding the names of the endogenous variables to be plotted.
+% INPUTS
+% o Variables [char] m*x array holding the names of the endogenous variables to be plotted.
%
% OUTPUTS
% None.
-%
+%
% SPECIAL REQUIREMENTS
% This routine has to be called after imcforecast.m.
diff --git a/matlab/plot_identification.m b/matlab/plot_identification.m
index 116909bb2395691c1cab0fe7e4387716d408f860..12da5680486cde9938eb2997db96f3bc3bbda753 100644
--- a/matlab/plot_identification.m
+++ b/matlab/plot_identification.m
@@ -8,14 +8,14 @@ function plot_identification(params,idemoments,idehess,idemodel, idelre, advance
% o idemodel [structure] identification results for the reduced form solution
% o idelre [structure] identification results for the LRE model
% o advanced [integer] flag for advanced identification checks
-% o tittxt [char] name of the results to plot
+% o tittxt [char] name of the results to plot
% o name [char] list of names
% o IdentifDirectoryName [char] directory name
-% o tittxt [char] TeX-name of the results to plot
+% o tittxt [char] TeX-name of the results to plot
% o name_tex [char] TeX-names of the parameters
% OUTPUTS
% None
-%
+%
% SPECIAL REQUIREMENTS
% None
@@ -41,8 +41,8 @@ global M_ options_
if nargin <10 || isempty(tit_TeX)
tit_TeX=tittxt;
end
-
-if nargin <11
+
+if nargin <11
name_TeX=name;
end
@@ -85,7 +85,7 @@ if SampleSize == 1
else
title('Identification strength with moments Information matrix (log-scale)')
end
-
+
subplot(212)
if ~all(isnan(idehess.deltaM_prior))
bar(log([idehess.deltaM(is) idehess.deltaM_prior(is)]))
@@ -123,7 +123,7 @@ if SampleSize == 1
fclose(fidTeX);
end
dyn_saveas(hh,[IdentifDirectoryName '/' M_.fname '_ident_strength_' tittxt1],options_.nodisplay,options_.graph_format);
-
+
if advanced
if ~options_.nodisplay
skipline()
@@ -169,9 +169,9 @@ if SampleSize == 1
% identificaton patterns
for j=1:size(idemoments.cosnJ,2)
pax=NaN(nparam,nparam);
-% fprintf('\n')
-% disp(['Collinearity patterns with ', int2str(j) ,' parameter(s)'])
-% fprintf('%-15s [%-*s] %10s\n','Parameter',(15+1)*j,' Expl. params ','cosn')
+ % fprintf('\n')
+ % disp(['Collinearity patterns with ', int2str(j) ,' parameter(s)'])
+ % fprintf('%-15s [%-*s] %10s\n','Parameter',(15+1)*j,' Expl. params ','cosn')
for i=1:nparam
namx='';
for in=1:j
@@ -183,7 +183,7 @@ if SampleSize == 1
pax(i,dumpindx)=idemoments.cosnJ(i,j);
end
end
-% fprintf('%-15s [%s] %10.3f\n',name{i},namx,idemoments.cosnJ(i,j))
+ % fprintf('%-15s [%s] %10.3f\n',name{i},namx,idemoments.cosnJ(i,j))
end
hh = dyn_figure(options_.nodisplay,'Name',[tittxt,' - Collinearity patterns with ', int2str(j) ,' parameter(s)']);
imagesc(pax,[0 1]);
@@ -234,8 +234,8 @@ if SampleSize == 1
tex_tit_2=[tittxt,' - Identification patterns (Information matrix): HIGHEST SV'];
end
else
-% S = idemoments.S;
-% V = idemoments.V;
+ % S = idemoments.S;
+ % V = idemoments.V;
if nparam<5
f1 = dyn_figure(options_.nodisplay,'Name',[tittxt,' - Identification patterns (moments Information matrix)']);
tex_tit_1=[tittxt,' - Identification patterns (moments Information matrix)'];
@@ -301,7 +301,7 @@ if SampleSize == 1
end
end
end
-
+
else
hh = dyn_figure(options_.nodisplay,'Name',['MC sensitivities']);
subplot(211)
@@ -316,8 +316,8 @@ else
offset=size(siHnorm,2)-size(siLREnorm,2);
mmm1 = [NaN(offset,1); mmm1./max(mmm1)];
mmm=[mmm mmm1];
- end
-
+ end
+
bar(mmm(is,:))
set(gca,'xlim',[0 nparam+1])
set(gca,'xticklabel','')
@@ -343,13 +343,13 @@ else
fprintf(fidTeX,'%% End Of TeX file. \n');
fclose(fidTeX);
end
-
+
if advanced
if ~options_.nodisplay,
skipline()
disp('Press ENTER to display advanced diagnostics'), pause(5),
end
-% options_.nograph=1;
+ % options_.nograph=1;
hh = dyn_figure(options_.nodisplay,'Name','MC Condition Number');
subplot(221)
hist(log10(idemodel.cond))
@@ -386,16 +386,16 @@ else
options_mcf.title = 'MC Highest Condition Number Model Moments';
[dum,is]=sort(idemoments.cond);
mcf_analysis(params, is(1:ncut), is(ncut+1:end), options_mcf, options_);
-% [proba, dproba] = stab_map_1(idemoments.Mco', is(1:ncut), is(ncut+1:end), 'HighestCondNumberMoments_vs_Mco', 1, [], IdentifDirectoryName);
-% for j=1:nparam,
-% % ibeh=find(idemoments.Mco(j,:)<0.9);
-% % inonbeh=find(idemoments.Mco(j,:)>=0.9);
-% % if ~isempty(ibeh) && ~isempty(inonbeh)
-% % [proba, dproba] = stab_map_1(params, ibeh, inonbeh, ['HighestMultiCollinearity_',name{j}], 1, [], IdentifDirectoryName);
-% % end
-% [~,is]=sort(idemoments.Mco(:,j));
-% [proba, dproba] = stab_map_1(params, is(1:ncut), is(ncut+1:end), ['MC_HighestMultiCollinearity_',name{j}], 1, [], IdentifDirectoryName, 0.15);
-% end
+ % [proba, dproba] = stab_map_1(idemoments.Mco', is(1:ncut), is(ncut+1:end), 'HighestCondNumberMoments_vs_Mco', 1, [], IdentifDirectoryName);
+ % for j=1:nparam,
+ % % ibeh=find(idemoments.Mco(j,:)<0.9);
+ % % inonbeh=find(idemoments.Mco(j,:)>=0.9);
+ % % if ~isempty(ibeh) && ~isempty(inonbeh)
+ % % [proba, dproba] = stab_map_1(params, ibeh, inonbeh, ['HighestMultiCollinearity_',name{j}], 1, [], IdentifDirectoryName);
+ % % end
+ % [~,is]=sort(idemoments.Mco(:,j));
+ % [proba, dproba] = stab_map_1(params, is(1:ncut), is(ncut+1:end), ['MC_HighestMultiCollinearity_',name{j}], 1, [], IdentifDirectoryName, 0.15);
+ % end
if nparam<5
f1 = dyn_figure(options_.nodisplay,'Name',[tittxt,' - MC Identification patterns (moments): HIGHEST SV']);
diff --git a/matlab/plot_shock_decomposition.m b/matlab/plot_shock_decomposition.m
index b7d3675fc373846b06cab26a932cc4589a9a535f..78488d6c60c6979aabe406e89937389082162594 100644
--- a/matlab/plot_shock_decomposition.m
+++ b/matlab/plot_shock_decomposition.m
@@ -77,7 +77,7 @@ if vintage_
end
initial_date = options_.initial_date;
-
+
if isfield(options_.plot_shock_decomp,'q2a') % private trap for aoa calls
q2a=options_.plot_shock_decomp.q2a;
if isstruct(q2a) && isempty(fieldnames(q2a))
@@ -88,60 +88,60 @@ else
end
switch realtime_
-
- case 0
- z = oo_.shock_decomposition;
- fig_name1=fig_name;
-
- case 1 % realtime
- if vintage_
- z = oo_.realtime_shock_decomposition.(['time_' int2str(vintage_)]);
- fig_name1=[fig_name ' realtime (vintage ' char(initial_date+vintage_-1) ')'];
- else
- z = oo_.realtime_shock_decomposition.pool;
- fig_name1=[fig_name ' realtime (rolling)'];
- end
-
- case 2 % conditional
- if vintage_
- z = oo_.realtime_conditional_shock_decomposition.(['time_' int2str(vintage_)]);
- initial_date = options_.initial_date+vintage_-1;
- fig_name1=[fig_name ' ' int2str(forecast_) '-step ahead conditional forecast (given ' char(initial_date) ')'];
- else
- z = oo_.conditional_shock_decomposition.pool;
- fig_name1=[fig_name ' 1-step ahead conditional forecast (rolling)'];
- end
-
- case 3 % forecast
- if vintage_
- z = oo_.realtime_forecast_shock_decomposition.(['time_' int2str(vintage_)]);
- initial_date = options_.initial_date+vintage_-1;
- fig_name1=[fig_name ' ' int2str(forecast_) '-step ahead forecast (given ' char(initial_date) ')'];
- else
- z = oo_.realtime_forecast_shock_decomposition.pool;
- fig_name1=[fig_name ' 1-step ahead forecast (rolling)'];
- end
+
+ case 0
+ z = oo_.shock_decomposition;
+ fig_name1=fig_name;
+
+ case 1 % realtime
+ if vintage_
+ z = oo_.realtime_shock_decomposition.(['time_' int2str(vintage_)]);
+ fig_name1=[fig_name ' realtime (vintage ' char(initial_date+vintage_-1) ')'];
+ else
+ z = oo_.realtime_shock_decomposition.pool;
+ fig_name1=[fig_name ' realtime (rolling)'];
+ end
+
+ case 2 % conditional
+ if vintage_
+ z = oo_.realtime_conditional_shock_decomposition.(['time_' int2str(vintage_)]);
+ initial_date = options_.initial_date+vintage_-1;
+ fig_name1=[fig_name ' ' int2str(forecast_) '-step ahead conditional forecast (given ' char(initial_date) ')'];
+ else
+ z = oo_.conditional_shock_decomposition.pool;
+ fig_name1=[fig_name ' 1-step ahead conditional forecast (rolling)'];
+ end
+
+ case 3 % forecast
+ if vintage_
+ z = oo_.realtime_forecast_shock_decomposition.(['time_' int2str(vintage_)]);
+ initial_date = options_.initial_date+vintage_-1;
+ fig_name1=[fig_name ' ' int2str(forecast_) '-step ahead forecast (given ' char(initial_date) ')'];
+ else
+ z = oo_.realtime_forecast_shock_decomposition.pool;
+ fig_name1=[fig_name ' 1-step ahead forecast (rolling)'];
+ end
end
steady_state = oo_.steady_state;
if isequal(type,'aoa') && isstruct(q2a) && realtime_
- if isempty(initial_date)
+ if isempty(initial_date)
+ t0=1;
+ initial_date = dates('1Y');
+ else
+ initial_date0 = dates([int2str(initial_date.time(1)) 'Y']);
+ if initial_date.time(2)==1
t0=1;
- initial_date = dates('1Y');
+ initial_date1=initial_date0;
else
- initial_date0 = dates([int2str(initial_date.time(1)) 'Y']);
- if initial_date.time(2)==1
- t0=1;
- initial_date1=initial_date0;
- else
- t0=(4-initial_date.time(2)+2);
- initial_date1=initial_date0+1;
- end
+ t0=(4-initial_date.time(2)+2);
+ initial_date1=initial_date0+1;
end
- t0=min(options_.plot_shock_decomp.save_realtime);
- ini1 = initial_date+t0-1;
- t0=t0+(4-ini1.time(2));
+ end
+ t0=min(options_.plot_shock_decomp.save_realtime);
+ ini1 = initial_date+t0-1;
+ t0=t0+(4-ini1.time(2));
if ~isfield(q2a,'var_type') % private trap for aoa calls
q2a.var_type=1;
end
@@ -160,21 +160,21 @@ if isequal(type,'aoa') && isstruct(q2a) && realtime_
if ~isfield(q2a,'plot') % private trap for aoa calls
q2a.plot=1; % growth rate
end
-
-% if isstruct(q2a.aux) && ischar(q2a.aux.y)
-% opts=options_;
-% opts.plot_shock_decomp.type='qoq';
-% [y_aux, steady_state_aux] = plot_shock_decomposition(M_,oo_,opts,q2a.aux.y);
-% q2a.aux.y=y_aux;
-% q2a.aux.yss=steady_state_aux;
-% end
+
+ % if isstruct(q2a.aux) && ischar(q2a.aux.y)
+ % opts=options_;
+ % opts.plot_shock_decomp.type='qoq';
+ % [y_aux, steady_state_aux] = plot_shock_decomposition(M_,oo_,opts,q2a.aux.y);
+ % q2a.aux.y=y_aux;
+ % q2a.aux.yss=steady_state_aux;
+ % end
[za, endo_names, endo_names_tex, steady_state, i_var, oo_] = ...
annualized_shock_decomposition(oo_,M_, options_, i_var, t0, options_.nobs, realtime_, vintage_, steady_state,q2a);
-% if realtime_<2
-% initial_date = initial_date1;
-% else
-% initial_date = initial_date0;
-% end
+ % if realtime_<2
+ % initial_date = initial_date1;
+ % else
+ % initial_date = initial_date0;
+ % end
end
@@ -190,7 +190,7 @@ if options_.plot_shock_decomp.use_shock_groups
fig_name=[fig_name ' group ' options_.plot_shock_decomp.use_shock_groups];
shock_names = shock_ind;
for i=1:ngroups
- shock_names{i} = (shock_groups.(shock_ind{i}).label);
+ shock_names{i} = (shock_groups.(shock_ind{i}).label);
end
zz = zeros(endo_nbr,ngroups+2,gend);
kcum=[];
@@ -208,7 +208,7 @@ if options_.plot_shock_decomp.use_shock_groups
M_.shock_groups.(options_.plot_shock_decomp.use_shock_groups)=shock_groups;
if any(any(zothers))
shock_names = [shock_names; {'Others + Initial Values'}];
- end
+ end
zz(:,ngroups+1,:) = sum(z(:,1:nshocks+1,:),2);
zz(:,ngroups+2,:) = z(:,nshocks+2,:);
z = zz;
@@ -216,10 +216,10 @@ else
shock_names = M_.exo_names;
end
- func = @(x) colorspace('RGB->Lab',x);
- MAP = distinguishable_colors(size(z,2)-1,'w',func);
+func = @(x) colorspace('RGB->Lab',x);
+MAP = distinguishable_colors(size(z,2)-1,'w',func);
% MAP = [MAP; MAP(end,:)];
- MAP(end,:) = [0.7 0.7 0.7];
+MAP(end,:) = [0.7 0.7 0.7];
% MAP = [MAP; [0.7 0.7 0.7]; [0.3 0.3 0.3]];
if isempty(options_.plot_shock_decomp.colormap)
@@ -228,36 +228,36 @@ end
switch type
- case '' % default
+ case '' % default
- case 'qoq'
+ case 'qoq'
- case 'yoy'
- z=z(:,:,1:end-3)+z(:,:,2:end-2)+z(:,:,3:end-1)+z(:,:,4:end);
- if ~isempty(initial_date),
- initial_date = initial_date+3;
- else
- initial_date = dates('1Q4');
- end
- steady_state = 4*steady_state;
-
- case 'aoa'
+ case 'yoy'
+ z=z(:,:,1:end-3)+z(:,:,2:end-2)+z(:,:,3:end-1)+z(:,:,4:end);
+ if ~isempty(initial_date),
+ initial_date = initial_date+3;
+ else
+ initial_date = dates('1Q4');
+ end
+ steady_state = 4*steady_state;
+
+ case 'aoa'
- if isempty(initial_date)
- t0=4;
- initial_date = dates('1Y');
+ if isempty(initial_date)
+ t0=4;
+ initial_date = dates('1Y');
+ else
+ initial_date0 = dates([int2str(initial_date.time(1)) 'Y']);
+ if initial_date.time(2)==1
+ t0=1;
+ initial_date1=initial_date0;
else
- initial_date0 = dates([int2str(initial_date.time(1)) 'Y']);
- if initial_date.time(2)==1
- t0=1;
- initial_date1=initial_date0;
- else
- t0=(4-initial_date.time(2)+2);
- initial_date1=initial_date0+1;
- end
+ t0=(4-initial_date.time(2)+2);
+ initial_date1=initial_date0+1;
end
- if isstruct(q2a)
- if realtime_ == 0
+ end
+ if isstruct(q2a)
+ if realtime_ == 0
if ~isfield(q2a,'var_type') % private trap for aoa calls
q2a.var_type=1;
end
@@ -276,7 +276,7 @@ switch type
if ~isfield(q2a,'plot') % private trap for aoa calls
q2a.plot=1; % growth rate
end
-
+
if isstruct(q2a.aux) && ischar(q2a.aux.y)
opts=options_;
opts.plot_shock_decomp.type='qoq';
@@ -286,33 +286,33 @@ switch type
end
[za, endo_names, endo_names_tex, steady_state, i_var, oo_] = ...
annualized_shock_decomposition(z,M_, options_, i_var, t0, options_.nobs, realtime_, vintage_, steady_state,q2a);
- end
- z = za;
- M_.endo_names = endo_names;
- M_.endo_names_tex = endo_names_tex;
-% endo_nbr = size(z,1);
- if realtime_<2
- initial_date = initial_date1;
- else
- initial_date = initial_date0;
- end
+ end
+ z = za;
+ M_.endo_names = endo_names;
+ M_.endo_names_tex = endo_names_tex;
+ % endo_nbr = size(z,1);
+ if realtime_<2
+ initial_date = initial_date1;
else
- t0=4-initial_date.time(2)+1;
initial_date = initial_date0;
- z=z(:,:,t0:4:end);
- end
-
- if ~isempty(options_.plot_shock_decomp.plot_init_date)
- options_.plot_shock_decomp.plot_init_date = dates([int2str(options_.plot_shock_decomp.plot_init_date.time(1)) 'Y']);
- end
- if ~isempty(options_.plot_shock_decomp.plot_end_date)
- options_.plot_shock_decomp.plot_end_date = dates([int2str(options_.plot_shock_decomp.plot_end_date.time(1)) 'Y']);
end
-
-
- otherwise
+ else
+ t0=4-initial_date.time(2)+1;
+ initial_date = initial_date0;
+ z=z(:,:,t0:4:end);
+ end
+
+ if ~isempty(options_.plot_shock_decomp.plot_init_date)
+ options_.plot_shock_decomp.plot_init_date = dates([int2str(options_.plot_shock_decomp.plot_init_date.time(1)) 'Y']);
+ end
+ if ~isempty(options_.plot_shock_decomp.plot_end_date)
+ options_.plot_shock_decomp.plot_end_date = dates([int2str(options_.plot_shock_decomp.plot_end_date.time(1)) 'Y']);
+ end
+
+
+ otherwise
- error('plot_shock_decomposition:: Wrong type')
+ error('plot_shock_decomposition:: Wrong type')
end
if steadystate
diff --git a/matlab/pltorg.m b/matlab/pltorg.m
index 7f100e4b2f1f66e29bf59d80198ceae3f0abeb47..dca9e66f1b6884ca029979f60c4b990ebc0fc201 100644
--- a/matlab/pltorg.m
+++ b/matlab/pltorg.m
@@ -48,11 +48,11 @@ elseif number == 5
elseif number == 6
nbplt = 1;
nr = 3;
- nc = 2;
+ nc = 2;
elseif number == 7
nbplt = 1;
nr = 3;
- nc = 3;
+ nc = 3;
elseif number == 8
nbplt = 1;
nr = 3;
@@ -67,7 +67,7 @@ else
nr = nrstar;
nc = ncstar;
lr = nr;
- lc = nc;
+ lc = nc;
else
nbplt = ceil(number/nstar);
nr = nrstar;
@@ -90,10 +90,10 @@ else
lc = 2;
elseif reste == 6
lr = 3;
- lc = 2;
+ lc = 2;
elseif reste == 7
lr = 3;
- lc = 3;
+ lc = 3;
elseif reste == 8
lr = 3;
lc = 3;
diff --git a/matlab/pm3.m b/matlab/pm3.m
index f1d9a4483f22005546f7779bd5523d3c6f357837..242df77c26c71244556723204e16eff79431f8a9 100644
--- a/matlab/pm3.m
+++ b/matlab/pm3.m
@@ -7,7 +7,7 @@ function pm3(n1,n2,ifil,B,tit1,tit2,tit3,tit_tex,names1,names2,name3,DirectoryNa
% n2 [scalar] size of second dimension of moment matrix
% ifil [scalar] number of moment files to load
% B [scalar] number of subdraws
-% tit1 [string] Figure title
+% tit1 [string] Figure title
% tit2 [string] not used
% tit3 [string] Save name for figure
% tit_tex [cell array] TeX-Names for Variables
@@ -63,7 +63,7 @@ if options_.TeX
if isempty(tit_tex)
tit_tex=names1;
end
-
+
varlist_TeX = [];
for i=1:nvar
if i==1
@@ -92,7 +92,7 @@ for file = 1:ifil
stock=loaded_file.stock;
if strcmp(var_type,'_filter_step_ahead')
if file==1 %on first run, initialize variable for storing filter_step_ahead
- stock1_filter_step_ahead=NaN(n1,n2,B,length(options_.filter_step_ahead));
+ stock1_filter_step_ahead=NaN(n1,n2,B,length(options_.filter_step_ahead));
stock1 = zeros(n1,n2,B);
end
filter_step_ahead_indicator=1;
@@ -289,112 +289,112 @@ if strcmp(var_type,'_trend_coeff') || max(max(abs(Mean(:,:))))<=10^(-6) || all(a
return %not do plots
end
%%
-%% Finally I build the plots.
+%% Finally I build the plots.
%%
if ~options_.nograph && ~options_.no_graph.posterior
-% Block of code executed in parallel, with the exception of file
-% .tex generation always run sequentially. This portion of code is execute in parallel by
-% pm3_core1.m function.
+ % Block of code executed in parallel, with the exception of file
+ % .tex generation always run sequentially. This portion of code is execute in parallel by
+ % pm3_core1.m function.
-% %%%%%%%%% PARALLEL BLOCK % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%
-% %%% The file .TeX! are not saved in parallel.
+ % %%%%%%%%% PARALLEL BLOCK % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+ %
+ % %%% The file .TeX! are not saved in parallel.
-% Store the variable mandatory for local/remote parallel computing.
+ % Store the variable mandatory for local/remote parallel computing.
-localVars=[];
+ localVars=[];
-localVars.tit1=tit1;
-localVars.nn=nn;
-localVars.n2=n2;
-localVars.Distrib=Distrib;
-localVars.varlist=varlist;
-localVars.MaxNumberOfPlotsPerFigure=MaxNumberOfPlotsPerFigure;
-localVars.name3=name3;
-localVars.tit3=tit3;
-localVars.Mean=Mean;
-% Like sequential execution!
-nvar0=nvar;
+ localVars.tit1=tit1;
+ localVars.nn=nn;
+ localVars.n2=n2;
+ localVars.Distrib=Distrib;
+ localVars.varlist=varlist;
+ localVars.MaxNumberOfPlotsPerFigure=MaxNumberOfPlotsPerFigure;
+ localVars.name3=name3;
+ localVars.tit3=tit3;
+ localVars.Mean=Mean;
+ % Like sequential execution!
+ nvar0=nvar;
-if ~isoctave
- % Commenting for testing!
- if isnumeric(options_.parallel) || ceil(size(varlist,1)/MaxNumberOfPlotsPerFigure)<4
- fout = pm3_core(localVars,1,nvar,0);
-
- % Parallel execution!
- else
- isRemoteOctave = 0;
- for indPC=1:length(options_.parallel)
- isRemoteOctave = isRemoteOctave + (findstr(options_.parallel(indPC).MatlabOctavePath, 'octave'));
- end
- if isRemoteOctave
+ if ~isoctave
+ % Commenting for testing!
+ if isnumeric(options_.parallel) || ceil(size(varlist,1)/MaxNumberOfPlotsPerFigure)<4
fout = pm3_core(localVars,1,nvar,0);
+
+ % Parallel execution!
else
- globalVars = struct('M_',M_, ...
- 'options_', options_, ...
- 'oo_', oo_);
- [fout, nvar0, totCPU] = masterParallel(options_.parallel, 1, nvar, [],'pm3_core', localVars,globalVars, options_.parallel_info);
+ isRemoteOctave = 0;
+ for indPC=1:length(options_.parallel)
+ isRemoteOctave = isRemoteOctave + (findstr(options_.parallel(indPC).MatlabOctavePath, 'octave'));
+ end
+ if isRemoteOctave
+ fout = pm3_core(localVars,1,nvar,0);
+ else
+ globalVars = struct('M_',M_, ...
+ 'options_', options_, ...
+ 'oo_', oo_);
+ [fout, nvar0, totCPU] = masterParallel(options_.parallel, 1, nvar, [],'pm3_core', localVars,globalVars, options_.parallel_info);
+ end
end
+ else
+ % For the time being in Octave enviroment the pm3.m is executed only in
+ % serial modality, to avoid problem with the plots.
+
+ fout = pm3_core(localVars,1,nvar,0);
end
-else
- % For the time being in Octave enviroment the pm3.m is executed only in
- % serial modality, to avoid problem with the plots.
-
- fout = pm3_core(localVars,1,nvar,0);
-end
-subplotnum = 0;
+ subplotnum = 0;
-if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
- fidTeX = fopen([M_.dname '/Output/' M_.fname '_' name3 '.tex'],'w');
- fprintf(fidTeX,'%% TeX eps-loader file generated by Dynare.\n');
- fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
- fprintf(fidTeX,' \n');
- nvar0=cumsum(nvar0);
+ if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
+ fidTeX = fopen([M_.dname '/Output/' M_.fname '_' name3 '.tex'],'w');
+ fprintf(fidTeX,'%% TeX eps-loader file generated by Dynare.\n');
+ fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
+ fprintf(fidTeX,' \n');
+ nvar0=cumsum(nvar0);
+
+ i=0;
+ for j=1:length(
- i=0;
- for j=1:length(
-
- NAMES = [];
- TEXNAMES = [];
- nvar=nvar0(j);
- while i<nvar
- i=i+1;
- if max(abs(Mean(:,i))) > 10^(-6)
- subplotnum = subplotnum+1;
- name = deblank(varlist(i,:));
- texname = deblank(varlist_TeX(i,:));
- if subplotnum==1
- NAMES = name;
- TEXNAMES = ['$' texname '$'];
- else
- NAMES = char(NAMES,name);
- TEXNAMES = char(TEXNAMES,['$' texname '$']);
- end
- end
- if subplotnum == MaxNumberOfPlotsPerFigure || i == nvar
- fprintf(fidTeX,'\\begin{figure}[H]\n');
- for jj = 1:size(TEXNAMES,1)
- fprintf(fidTeX,['\\psfrag{%s}[1][][0.5][0]{%s}\n'],deblank(NAMES(jj,:)),deblank(TEXNAMES(jj,:)));
- end
- fprintf(fidTeX,'\\centering \n');
- fprintf(fidTeX,['\\includegraphics[width=%2.2f\\textwidth]{%s/Output/%s_' name3 '_%s}\n'],options_.figures.textwidth*min(subplotnum/nn,1),M_.dname,M_.fname,deblank(tit3(i,:)));
- fprintf(fidTeX,'\\label{Fig:%s:%s}\n',name3,deblank(tit3(i,:)));
- fprintf(fidTeX,'\\caption{%s}\n',tit1);
- fprintf(fidTeX,'\\end{figure}\n');
- fprintf(fidTeX,' \n');
- subplotnum = 0;
NAMES = [];
TEXNAMES = [];
+ nvar=nvar0(j);
+ while i<nvar
+ i=i+1;
+ if max(abs(Mean(:,i))) > 10^(-6)
+ subplotnum = subplotnum+1;
+ name = deblank(varlist(i,:));
+ texname = deblank(varlist_TeX(i,:));
+ if subplotnum==1
+ NAMES = name;
+ TEXNAMES = ['$' texname '$'];
+ else
+ NAMES = char(NAMES,name);
+ TEXNAMES = char(TEXNAMES,['$' texname '$']);
+ end
+ end
+ if subplotnum == MaxNumberOfPlotsPerFigure || i == nvar
+ fprintf(fidTeX,'\\begin{figure}[H]\n');
+ for jj = 1:size(TEXNAMES,1)
+ fprintf(fidTeX,['\\psfrag{%s}[1][][0.5][0]{%s}\n'],deblank(NAMES(jj,:)),deblank(TEXNAMES(jj,:)));
+ end
+ fprintf(fidTeX,'\\centering \n');
+ fprintf(fidTeX,['\\includegraphics[width=%2.2f\\textwidth]{%s/Output/%s_' name3 '_%s}\n'],options_.figures.textwidth*min(subplotnum/nn,1),M_.dname,M_.fname,deblank(tit3(i,:)));
+ fprintf(fidTeX,'\\label{Fig:%s:%s}\n',name3,deblank(tit3(i,:)));
+ fprintf(fidTeX,'\\caption{%s}\n',tit1);
+ fprintf(fidTeX,'\\end{figure}\n');
+ fprintf(fidTeX,' \n');
+ subplotnum = 0;
+ NAMES = [];
+ TEXNAMES = [];
+ end
+ end
end
+ fprintf(fidTeX,'%% End of TeX file.\n');
+ fclose(fidTeX);
end
- end
- fprintf(fidTeX,'%% End of TeX file.\n');
- fclose(fidTeX);
-end
end
fprintf(['Estimation::mcmc: ' tit1 ', done!\n']);
diff --git a/matlab/pm3_core.m b/matlab/pm3_core.m
index e484304a822397e2a761988757523303d633c797..881c4b42350e22dd657127e02cd9461319864f9e 100644
--- a/matlab/pm3_core.m
+++ b/matlab/pm3_core.m
@@ -3,7 +3,7 @@ function myoutput=pm3_core(myinputs,fpar,nvar,whoiam, ThisMatlab)
% PARALLEL CONTEXT
% Core functionality for pm3.m function, which can be parallelized.
-% INPUTS
+% INPUTS
% See the comment in posterior_sampler_core.m funtion.
% OUTPUTS
@@ -100,13 +100,13 @@ for i=fpar:nvar
set(gca,'yticklabel',yticklabels_new)
end
end
-
+
if whoiam
if Parallel(ThisMatlab).Local==0
DirectoryName = CheckPath('Output',M_.dname);
end
end
-
+
if subplotnum == MaxNumberOfPlotsPerFigure || i == nvar
dyn_saveas(hh,[M_.dname '/Output/' M_.fname '_' name3 '_' deblank(tit3(i,:))],options_.nodisplay,options_.graph_format);
if RemoteFlag==1
@@ -118,14 +118,14 @@ for i=fpar:nvar
hh = dyn_figure(options_.nodisplay,'Name',[name3 ' ' int2str(figunumber+1)]);
end
end
-
+
if whoiam
-% waitbarString = [ 'Variable ' int2str(i) '/' int2str(nvar) ' done.'];
-% fMessageStatus((i-fpar+1)/(nvar-fpar+1),whoiam,waitbarString, waitbarTitle, Parallel(ThisMatlab));
+ % waitbarString = [ 'Variable ' int2str(i) '/' int2str(nvar) ' done.'];
+ % fMessageStatus((i-fpar+1)/(nvar-fpar+1),whoiam,waitbarString, waitbarTitle, Parallel(ThisMatlab));
dyn_waitbar((i-fpar+1)/(nvar-fpar+1),h);
end
-
-
+
+
end
if whoiam
diff --git a/matlab/posterior_analysis.m b/matlab/posterior_analysis.m
index ca8459c2fcaa55f7fd1e8d04ed4f1a294fbf1b2d..7e13059bf18ba69b2d36d8648fa6e8cad3064b22 100644
--- a/matlab/posterior_analysis.m
+++ b/matlab/posterior_analysis.m
@@ -1,4 +1,4 @@
-function oo_ = posterior_analysis(type,arg1,arg2,arg3,options_,M_,oo_)
+function oo_ = posterior_analysis(type,arg1,arg2,arg3,options_,M_,oo_)
% Copyright (C) 2008-2015 Dynare Team
%
% This file is part of Dynare.
@@ -55,14 +55,14 @@ switch type
dsge_simulated_theoretical_covariance(SampleSize,M_,options_,oo_,'posterior');
end
oo_ = covariance_mc_analysis(SampleSize,'posterior',M_.dname,M_.fname,...
- vartan,nvar,arg1,arg2,options_.mh_conf_sig,oo_,options_);
+ vartan,nvar,arg1,arg2,options_.mh_conf_sig,oo_,options_);
case 'decomposition'
if nargin==narg1
[nvar,vartan,NumberOfFiles] = ...
dsge_simulated_theoretical_variance_decomposition(SampleSize,M_,options_,oo_,'posterior');
end
oo_ = variance_decomposition_mc_analysis(SampleSize,'posterior',M_.dname,M_.fname,...
- M_.exo_names,arg2,vartan,arg1,options_.mh_conf_sig,oo_,options_);
+ M_.exo_names,arg2,vartan,arg1,options_.mh_conf_sig,oo_,options_);
case 'correlation'
if nargin==narg1
[nvar,vartan,NumberOfFiles] = ...
@@ -76,7 +76,7 @@ switch type
dsge_simulated_theoretical_conditional_variance_decomposition(SampleSize,arg3,M_,options_,oo_,'posterior');
end
oo_ = conditional_variance_decomposition_mc_analysis(SampleSize,'posterior',M_.dname,M_.fname,...
- arg3,M_.exo_names,arg2,vartan,arg1,options_.mh_conf_sig,oo_,options_);
+ arg3,M_.exo_names,arg2,vartan,arg1,options_.mh_conf_sig,oo_,options_);
otherwise
disp('Not yet implemented')
end
\ No newline at end of file
diff --git a/matlab/posterior_moments.m b/matlab/posterior_moments.m
index 75fd7a287d42c25ce2680cebed5dbaf92afbd553..e16992a08c8e7679a4d2ea4bde9f49f92c5d65bc 100644
--- a/matlab/posterior_moments.m
+++ b/matlab/posterior_moments.m
@@ -11,13 +11,13 @@ function [post_mean, post_median, post_var, hpd_interval, post_deciles, density]
% post_mean [double] Scalar, posterior mean.
% post_median [double] Scalar, posterior median.
% post_var [double] Scalar, posterior variance.
-% hpd_interval [double] Vector (1*2), Highest Probability Density interval
+% hpd_interval [double] Vector (1*2), Highest Probability Density interval
% post_deciles [double] Vector (9*1), deciles of the posterior distribution.
-% density [double] Matrix (n*2), non parametric estimate of the posterior density. First and second
-% columns are respectively abscissa and ordinate coordinates.
-%
+% density [double] Matrix (n*2), non parametric estimate of the posterior density. First and second
+% columns are respectively abscissa and ordinate coordinates.
+%
% SPECIAL REQUIREMENTS
-% Other matlab routines distributed with Dynare: mh_optimal_bandwidth.m
+% Other matlab routines distributed with Dynare: mh_optimal_bandwidth.m
% kernel_density_estimate.m.
%
@@ -41,7 +41,7 @@ function [post_mean, post_median, post_var, hpd_interval, post_deciles, density]
if nargin<4
number_of_grid_points = 2^9; % 2^9 = 512 !... Must be a power of two.
bandwidth = 0; % Rule of thumb optimal bandwidth parameter.
- kernel_function = 'gaussian'; % Gaussian kernel for Fast Fourrier Transform approximaton.
+ kernel_function = 'gaussian'; % Gaussian kernel for Fast Fourrier Transform approximaton.
else
number_of_grid_points = kernel_options.gridpoints;
bandwidth = kernel_options.bandwidth;
@@ -69,7 +69,7 @@ if hpd_draws>2
[kmin,idx] = min(kk);
hpd_interval = [xx(idx) xx(idx)+kmin];
else
- hpd_interval=NaN(1,2);
+ hpd_interval=NaN(1,2);
end
if length(xx)>9
post_deciles = xx([round(0.1*number_of_draws) ...
diff --git a/matlab/posterior_sampler.m b/matlab/posterior_sampler.m
index 5d8b9cdbe7b35d25486d3f74d6b3d4f91e3b2ed9..70f7b5de9f8786ee3edb7613803d2df8d350179b 100644
--- a/matlab/posterior_sampler.m
+++ b/matlab/posterior_sampler.m
@@ -1,8 +1,8 @@
function posterior_sampler(TargetFun,ProposalFun,xparam1,sampler_options,mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_)
% function posterior_sampler(TargetFun,ProposalFun,xparam1,sampler_options,mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_)
-% Random Walk Metropolis-Hastings algorithm.
-%
-% INPUTS
+% Random Walk Metropolis-Hastings algorithm.
+%
+% INPUTS
% o TargetFun [char] string specifying the name of the objective
% function (posterior kernel).
% o ProposalFun [char] string specifying the name of the proposal
@@ -10,7 +10,7 @@ function posterior_sampler(TargetFun,ProposalFun,xparam1,sampler_options,mh_boun
% o xparam1 [double] (p*1) vector of parameters to be estimated (initial values).
% o sampler_options structure
% .invhess [double] (p*p) matrix, posterior covariance matrix (at the mode).
-% o mh_bounds [double] (p*2) matrix defining lower and upper bounds for the parameters.
+% o mh_bounds [double] (p*2) matrix defining lower and upper bounds for the parameters.
% o dataset_ data structure
% o dataset_info dataset info structure
% o options_ options structure
@@ -27,7 +27,7 @@ function posterior_sampler(TargetFun,ProposalFun,xparam1,sampler_options,mh_boun
% in parallel. The code suitable to be executed in
% parallel on multi core or cluster machine (in general a 'for' cycle)
% has been removed from this function and been placed in the posterior_sampler_core.m funtion.
-%
+%
% The DYNARE parallel packages comprise a i) set of pairs of Matlab functions that can be executed in
% parallel and called name_function.m and name_function_core.m and ii) a second set of functions used
% to manage the parallel computations.
@@ -120,8 +120,8 @@ end
if isnumeric(options_.parallel) || (nblck-fblck)==0
fout = posterior_sampler_core(localVars, fblck, nblck, 0);
record = fout.record;
- % Parallel in Local or remote machine.
-else
+ % Parallel in Local or remote machine.
+else
% Global variables for parallel routines.
globalVars = struct();
% which files have to be copied to run remotely
diff --git a/matlab/posterior_sampler_core.m b/matlab/posterior_sampler_core.m
index cd140c52c4a7e10b5f67f8084445426dfbe6a2e9..b753391b4681cba17eaa27a19778d3023edd4cd1 100644
--- a/matlab/posterior_sampler_core.m
+++ b/matlab/posterior_sampler_core.m
@@ -31,7 +31,7 @@ function myoutput = posterior_sampler_core(myinputs,fblck,nblck,whoiam, ThisMatl
%
% SPECIAL REQUIREMENTS.
% None.
-%
+%
% PARALLEL CONTEXT
% See the comments in the posterior_sampler.m funtion.
@@ -132,9 +132,9 @@ for curr_block = fblck:nblck
set_dynare_seed(options_.DynareRandomStreams.seed+curr_block);
end
% Set the state of the RNG
- set_dynare_random_generator_state(record.InitialSeeds(curr_block).Unifor, record.InitialSeeds(curr_block).Normal);
+ set_dynare_random_generator_state(record.InitialSeeds(curr_block).Unifor, record.InitialSeeds(curr_block).Normal);
catch
- % If the state set by master is incompatible with the slave, we only reseed
+ % If the state set by master is incompatible with the slave, we only reseed
set_dynare_seed(options_.DynareRandomStreams.seed+curr_block);
end
if (options_.load_mh_file~=0) && (fline(curr_block)>1) && OpenOldFile(curr_block) %load previous draws and likelihood
@@ -164,9 +164,9 @@ for curr_block = fblck:nblck
feval_this_file = 0;
draw_index_current_file = fline(curr_block); %get location of first draw in current block
draw_iter = 1;
-
+
while draw_iter <= nruns(curr_block)
-
+
[par, logpost, accepted, neval] = posterior_sampler_iteration(TargetFun, last_draw(curr_block,:), last_posterior(curr_block), sampler_options,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_);
x2(draw_index_current_file,:) = par;
diff --git a/matlab/posterior_sampler_initialization.m b/matlab/posterior_sampler_initialization.m
index ed98fc96d0906f7b77375cf6a713fd5875a04acf..b8b65e8b8dc6a11a7d63300c99014bea463c109e 100644
--- a/matlab/posterior_sampler_initialization.m
+++ b/matlab/posterior_sampler_initialization.m
@@ -3,13 +3,13 @@ function [ ix2, ilogpo2, ModelName, MetropolisFolder, FirstBlock, FirstLine, npa
% function [ ix2, ilogpo2, ModelName, MetropolisFolder, FirstBlock, FirstLine, npar, NumberOfBlocks, nruns, NewFile, MAX_nruns, d, bayestopt_] = ...
% metropolis_hastings_initialization(TargetFun, xparam1, vv, mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_)
% Metropolis-Hastings initialization.
-%
-% INPUTS
+%
+% INPUTS
% o TargetFun [char] string specifying the name of the objective
% function (posterior kernel).
% o xparam1 [double] (p*1) vector of parameters to be estimated (initial values).
% o vv [double] (p*p) matrix, posterior covariance matrix (at the mode).
-% o mh_bounds [double] (p*2) matrix defining lower and upper bounds for the parameters.
+% o mh_bounds [double] (p*2) matrix defining lower and upper bounds for the parameters.
% o dataset_ data structure
% o dataset_info dataset info structure
% o options_ options structure
@@ -17,17 +17,17 @@ function [ ix2, ilogpo2, ModelName, MetropolisFolder, FirstBlock, FirstLine, npa
% o estim_params_ estimated parameters structure
% o bayestopt_ estimation options structure
% o oo_ outputs structure
-%
-% OUTPUTS
+%
+% OUTPUTS
% o ix2 [double] (NumberOfBlocks*npar) vector of starting points for different chains
% o ilogpo2 [double] (NumberOfBlocks*1) vector of initial posterior values for different chains
% o ModelName [string] name of the mod-file
% o MetropolisFolder [string] path to the Metropolis subfolder
-% o FirstBlock [scalar] number of the first MH chain to be run (not equal to 1 in case of recovery)
-% o FirstLine [double] (NumberOfBlocks*1) vector of first draw in each chain (not equal to 1 in case of recovery)
+% o FirstBlock [scalar] number of the first MH chain to be run (not equal to 1 in case of recovery)
+% o FirstLine [double] (NumberOfBlocks*1) vector of first draw in each chain (not equal to 1 in case of recovery)
% o npar [scalar] number of parameters estimated
-% o NumberOfBlocks [scalar] Number of MCM chains requested
-% o nruns [double] (NumberOfBlocks*1) number of draws in each chain
+% o NumberOfBlocks [scalar] Number of MCM chains requested
+% o nruns [double] (NumberOfBlocks*1) number of draws in each chain
% o NewFile [scalar] (NumberOfBlocks*1) vector storing the number
% of the first MH-file to created for each chain when saving additional
% draws
@@ -58,7 +58,7 @@ function [ ix2, ilogpo2, ModelName, MetropolisFolder, FirstBlock, FirstLine, npa
%Initialize outputs
ix2 = [];
ilogpo2 = [];
-ModelName = [];
+ModelName = [];
MetropolisFolder = [];
FirstBlock = [];
FirstLine = [];
@@ -132,7 +132,7 @@ if ~options_.load_mh_file && ~options_.mh_recover
else
candidate = rand_multivariate_normal( transpose(xparam1), d * options_.mh_init_scale, npar);
end
- if all(candidate(:) >= mh_bounds.lb) && all(candidate(:) <= mh_bounds.ub)
+ if all(candidate(:) >= mh_bounds.lb) && all(candidate(:) <= mh_bounds.ub)
ix2(j,:) = candidate;
ilogpo2(j) = - feval(TargetFun,ix2(j,:)',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_);
if ~isfinite(ilogpo2(j)) % if returned log-density is
@@ -175,7 +175,7 @@ if ~options_.load_mh_file && ~options_.mh_recover
else% Case 2: one chain (we start from the posterior mode)
fprintf(fidlog,[' Initial values of the parameters:\n']);
candidate = transpose(xparam1(:));%
- if all(candidate(:) >= mh_bounds.lb) && all(candidate(:) <= mh_bounds.ub)
+ if all(candidate(:) >= mh_bounds.lb) && all(candidate(:) <= mh_bounds.ub)
ix2 = candidate;
ilogpo2 = - feval(TargetFun,ix2',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_);
disp('Estimation::mcmc: Initialization at the posterior mode.')
@@ -293,7 +293,7 @@ elseif options_.load_mh_file && ~options_.mh_recover
NumberOfDrawsWrittenInThePastLastFile = MAX_nruns - LastLineNumber;
NumberOfDrawsToBeSaved = nruns(1) - NumberOfDrawsWrittenInThePastLastFile;
AnticipatedNumberOfFiles = ceil(NumberOfDrawsToBeSaved/MAX_nruns);
- AnticipatedNumberOfLinesInTheLastFile = NumberOfDrawsToBeSaved - (AnticipatedNumberOfFiles-1)*MAX_nruns;
+ AnticipatedNumberOfLinesInTheLastFile = NumberOfDrawsToBeSaved - (AnticipatedNumberOfFiles-1)*MAX_nruns;
record.LastFileNumber = LastFileNumber + AnticipatedNumberOfFiles;
record.LastLineNumber = AnticipatedNumberOfLinesInTheLastFile;
record.MhDraws(end,1) = nruns(1);
@@ -312,15 +312,15 @@ elseif options_.mh_recover
load_last_mh_history_file(MetropolisFolder, ModelName);
NumberOfBlocks = record.Nblck;% Number of "parallel" mcmc chains.
options_.mh_nblck = NumberOfBlocks;
-
+
%% check consistency of options
if record.MhDraws(end,1)~=options_.mh_replic
fprintf('\nEstimation::mcmc: You cannot specify a different mh_replic than in the chain you are trying to recover\n')
fprintf('Estimation::mcmc: I am resetting mh_replic to %u\n',record.MhDraws(end,1))
options_.mh_replic=record.MhDraws(end,1);
nruns = ones(NumberOfBlocks,1)*options_.mh_replic;
- end
-
+ end
+
if ~isnan(record.MAX_nruns(end,1)) %field exists
if record.MAX_nruns(end,1)~=MAX_nruns
fprintf('\nEstimation::mcmc: You cannot specify a different MaxNumberOfBytes than in the chain you are trying to recover\n')
@@ -347,9 +347,9 @@ elseif options_.mh_recover
if OldMhExists
LastLineNumberInThePreviousMh = record.MhDraws(end-1,3);% Number of lines in the last mh files of the previous session.
LastFileNumberInThePreviousMh = sum(record.MhDraws(1:end-1,2),1);% Number of mh files in the the previous sessions.
- %Test if the last mh files of the previous session were not full yet
- if LastLineNumberInThePreviousMh < MAX_nruns%not full
- %store starting point if whole chain needs to be redone
+ %Test if the last mh files of the previous session were not full yet
+ if LastLineNumberInThePreviousMh < MAX_nruns%not full
+ %store starting point if whole chain needs to be redone
NewFile = ones(NumberOfBlocks,1)*LastFileNumberInThePreviousMh;
FirstLine = ones(NumberOfBlocks,1)*(LastLineNumberInThePreviousMh+1);
LastFileFullIndicator=0;
@@ -368,7 +368,7 @@ elseif options_.mh_recover
end
[d,bayestopt_]=set_proposal_density_to_previous_value(record,options_,bayestopt_);
%% Now find out what exactly needs to be redone
- % 1. Check if really something needs to be done
+ % 1. Check if really something needs to be done
% How many mh files should we have ?
ExpectedNumberOfMhFilesPerBlock = sum(record.MhDraws(:,2),1);
ExpectedNumberOfMhFiles = ExpectedNumberOfMhFilesPerBlock*NumberOfBlocks;
@@ -378,7 +378,7 @@ elseif options_.mh_recover
% Quit if no crashed mcmc chain can be found as there are as many files as expected
if (TotalNumberOfMhFiles==ExpectedNumberOfMhFiles)
disp('Estimation::mcmc: It appears that you don''t need to use the mh_recover option!')
- disp(' You have to edit the mod file and remove the mh_recover option')
+ disp(' You have to edit the mod file and remove the mh_recover option')
disp(' in the estimation command')
error('Estimation::mcmc: mh_recover option not required!')
end
@@ -401,15 +401,15 @@ elseif options_.mh_recover
end
FirstBlock = FirstBlock+1;
end
-
- %% 3. Overwrite default settings for
+
+ %% 3. Overwrite default settings for
% How many mh-files are saved in this block?
NumberOfSavedMhFilesInTheCrashedBlck = NumberOfMhFilesPerBlock(FirstBlock);
ExistingDrawsInLastMCFile=0; %initialize: no MCMC draws of current MCMC are in file from last run
- % Check whether last present file is a file included in the last MCMC run
- if ~LastFileFullIndicator
+ % Check whether last present file is a file included in the last MCMC run
+ if ~LastFileFullIndicator
if NumberOfSavedMhFilesInTheCrashedBlck==NewFile(FirstBlock) %only that last file exists, but no files from current MCMC
- loaded_results=load([BaseName '_mh' int2str(NewFile(FirstBlock)) '_blck' int2str(FirstBlock) '.mat']);
+ loaded_results=load([BaseName '_mh' int2str(NewFile(FirstBlock)) '_blck' int2str(FirstBlock) '.mat']);
%check whether that last file was filled
if size(loaded_results.x2,1)==MAX_nruns %file is full
NewFile(FirstBlock)=NewFile(FirstBlock)+1; %set first file to be created to next one
@@ -419,20 +419,20 @@ elseif options_.mh_recover
ExistingDrawsInLastMCFile=0;
end
end
- elseif LastFileFullIndicator
+ elseif LastFileFullIndicator
ExistingDrawsInLastMCFile=0;
if NumberOfSavedMhFilesInTheCrashedBlck==NewFile(FirstBlock) %only the last file exists, but no files from current MCMC
NewFile(FirstBlock)=NewFile(FirstBlock)+1; %set first file to be created to next one
end
end
-% % Correct the number of saved mh files if the crashed Metropolis was not the first session (so
-% % that NumberOfSavedMhFilesInTheCrashedBlck is the number of saved mh files in the crashed chain
-% % of the current session).
-% if OldMhExists
-% NumberOfSavedMhFilesInTheCrashedBlck = NumberOfSavedMhFilesInTheCrashedBlck - LastFileNumberInThePreviousMh;
-% end
-% NumberOfSavedMhFiles = NumberOfSavedMhFilesInTheCrashedBlck+LastFileNumberInThePreviousMh;
-
+ % % Correct the number of saved mh files if the crashed Metropolis was not the first session (so
+ % % that NumberOfSavedMhFilesInTheCrashedBlck is the number of saved mh files in the crashed chain
+ % % of the current session).
+ % if OldMhExists
+ % NumberOfSavedMhFilesInTheCrashedBlck = NumberOfSavedMhFilesInTheCrashedBlck - LastFileNumberInThePreviousMh;
+ % end
+ % NumberOfSavedMhFiles = NumberOfSavedMhFilesInTheCrashedBlck+LastFileNumberInThePreviousMh;
+
% Correct initial conditions.
if NumberOfSavedMhFilesInTheCrashedBlck<ExpectedNumberOfMhFilesPerBlock
loaded_results=load([BaseName '_mh' int2str(NumberOfSavedMhFilesInTheCrashedBlck) '_blck' int2str(FirstBlock) '.mat']);
@@ -452,19 +452,19 @@ elseif options_.mh_recover
end
function [d,bayestopt_]=set_proposal_density_to_previous_value(record,options_,bayestopt_,d)
- if isfield(record,'ProposalCovariance') && isfield(record,'ProposalCovariance')
- if isfield(record,'MCMC_sampler')
- if ~strcmp(record.MCMC_sampler,options_.posterior_sampler_options.posterior_sampling_method)
- error(fprintf('Estimation::mcmc: The posterior_sampling_method differs from the one of the original chain. Please reset it to %s',record.MCMC_sampler))
- end
- end
- fprintf('Estimation::mcmc: Recovering the previous proposal density.\n')
- d=record.ProposalCovariance;
- bayestopt_.jscale=record.ProposalScaleVec;
- else
- if options_.mode_compute~=0
- fprintf('Estimation::mcmc: No stored previous proposal density found, continuing with the one implied by mode_compute\n.');
- elseif ~isempty(options_.mode_file)
- fprintf('Estimation::mcmc: No stored previous proposal density found, continuing with the one implied by the mode_file\n.');
+if isfield(record,'ProposalCovariance') && isfield(record,'ProposalCovariance')
+ if isfield(record,'MCMC_sampler')
+ if ~strcmp(record.MCMC_sampler,options_.posterior_sampler_options.posterior_sampling_method)
+ error(fprintf('Estimation::mcmc: The posterior_sampling_method differs from the one of the original chain. Please reset it to %s',record.MCMC_sampler))
end
end
+ fprintf('Estimation::mcmc: Recovering the previous proposal density.\n')
+ d=record.ProposalCovariance;
+ bayestopt_.jscale=record.ProposalScaleVec;
+else
+ if options_.mode_compute~=0
+ fprintf('Estimation::mcmc: No stored previous proposal density found, continuing with the one implied by mode_compute\n.');
+ elseif ~isempty(options_.mode_file)
+ fprintf('Estimation::mcmc: No stored previous proposal density found, continuing with the one implied by the mode_file\n.');
+ end
+end
diff --git a/matlab/posterior_sampler_iteration.m b/matlab/posterior_sampler_iteration.m
index 09e5d8851060d425cacd040ff90e1b19232b0de9..5b54e785ef20b72de18f04adfa316f629c60f541 100644
--- a/matlab/posterior_sampler_iteration.m
+++ b/matlab/posterior_sampler_iteration.m
@@ -1,173 +1,173 @@
-function [par, logpost, accepted, neval] = posterior_sampler_iteration(TargetFun,last_draw, last_posterior, sampler_options,varargin)
-
-% function [par, logpost, accepted, neval] = posterior_sampler_iteration(TargetFun,last_draw, last_posterior, sampler_options,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_)
-% posterior samplers
-%
-% INPUTS
-% posterior_sampler_options: posterior sampler options
-% options_: structure storing the options
-
-% OUTPUTS
-% posterior_sampler_options: checked posterior sampler options
-%
-% SPECIAL REQUIREMENTS
-% none
-
-% Copyright (C) 2015-16 Dynare Team
-%
-% 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/>.
-
-
-posterior_sampling_method = sampler_options.posterior_sampling_method;
-mh_bounds = sampler_options.bounds;
-
-switch posterior_sampling_method
- case 'slice'
-
- [par, logpost, neval] = slice_sampler(TargetFun,last_draw, [mh_bounds.lb mh_bounds.ub], sampler_options,varargin{:});
- accepted = 1;
- case 'random_walk_metropolis_hastings'
- neval = 1;
- ProposalFun = sampler_options.proposal_distribution;
- proposal_covariance_Cholesky_decomposition = sampler_options.proposal_covariance_Cholesky_decomposition;
- n = sampler_options.n;
-
- par = feval(ProposalFun, last_draw, proposal_covariance_Cholesky_decomposition, n);
- if all( par(:) > mh_bounds.lb ) && all( par(:) < mh_bounds.ub )
- try
- logpost = - feval(TargetFun, par(:),varargin{:});
- catch
- logpost = -inf;
- end
- else
- logpost = -inf;
- end
- r = logpost-last_posterior;
- if (logpost > -inf) && (log(rand) < r)
- accepted = 1;
- else
- accepted = 0;
- par = last_draw;
- logpost = last_posterior;
- end
- case 'tailored_random_block_metropolis_hastings'
- options_=varargin{3};
- bayestopt_=varargin{6};
- npar=length(last_draw);
- %% randomize indices for blocking in this iteration
- indices=randperm(npar)';
- blocks=[1; (1+cumsum((rand(length(indices)-1,1)>(1-sampler_options.new_block_probability))))];
- nblocks=blocks(end,1); %get number of blocks this iteration
- current_draw=last_draw'; %get starting point for current draw for updating
- blocked_draws_counter=0;
- accepted_draws_counter=0;
- for block_iter=1:nblocks
- blocked_draws_counter=blocked_draws_counter+1;
- nxopt=length(indices(blocks==block_iter,1)); %get size of current block
- par_start_current_block=current_draw(indices(blocks==block_iter,1));
- [xopt_current_block, fval, exitflag, hess_mat_optimizer, options_, Scale] = dynare_minimize_objective(@TaRB_optimizer_wrapper,par_start_current_block,sampler_options.mode_compute,options_,[mh_bounds.lb(indices(blocks==block_iter,1),1) mh_bounds.ub(indices(blocks==block_iter,1),1)],bayestopt_.name,bayestopt_,[],...
- current_draw,indices(blocks==block_iter,1),TargetFun,...% inputs for wrapper
- varargin{:}); %inputs for objective
- %% covariance for proposal density
- hessian_mat = reshape(hessian('TaRB_optimizer_wrapper',xopt_current_block, ...
- options_.gstep,...
- current_draw,indices(blocks==block_iter,1),TargetFun,...% inputs for wrapper
- varargin{:}),nxopt,nxopt);
-
- if any(any(isnan(hessian_mat))) || any(any(isinf(hessian_mat)))
- inverse_hessian_mat=eye(nxopt)*1e-4; %use diagonal
- else
- inverse_hessian_mat=inv(hessian_mat); %get inverse Hessian
- if any(any((isnan(inverse_hessian_mat)))) || any(any((isinf(inverse_hessian_mat))))
- inverse_hessian_mat=eye(nxopt)*1e-4; %use diagonal
- end
- end
- [proposal_covariance_Cholesky_decomposition_upper,negeigenvalues]=chol(inverse_hessian_mat);
- %if not positive definite, use generalized Cholesky of Eskow/Schnabel
- if negeigenvalues~=0
- proposal_covariance_Cholesky_decomposition_upper=chol_SE(inverse_hessian_mat,0);
- end
- proposal_covariance_Cholesky_decomposition_upper=proposal_covariance_Cholesky_decomposition_upper*diag(bayestopt_.jscale(indices(blocks==block_iter,1),:));
- %get proposal draw
- if strcmpi(sampler_options.proposal_distribution,'rand_multivariate_normal')
- n = nxopt;
- elseif strcmpi(sampler_options.proposal_distribution,'rand_multivariate_student')
- n = options_.student_degrees_of_freedom;
- end
-
- proposed_par = feval(sampler_options.proposal_distribution, xopt_current_block', proposal_covariance_Cholesky_decomposition_upper, n);
- % check whether draw is valid and compute posterior
- if all( proposed_par(:) > mh_bounds.lb(indices(blocks==block_iter,1),:) ) && all( proposed_par(:) < mh_bounds.ub(indices(blocks==block_iter,1),:) )
- try
- logpost = - feval('TaRB_optimizer_wrapper', proposed_par(:),...
- current_draw,indices(blocks==block_iter,1),TargetFun,...% inputs for wrapper
- varargin{:});
- catch
- logpost = -inf;
- end
- else
- logpost = -inf;
- end
- %get ratio of proposal densities, required because proposal depends
- %on current mode via Hessian and is thus not symmetric anymore
- if strcmpi(sampler_options.proposal_distribution,'rand_multivariate_normal')
- proposal_density_proposed_move_forward=multivariate_normal_pdf(proposed_par,xopt_current_block',proposal_covariance_Cholesky_decomposition_upper,n);
- proposal_density_proposed_move_backward=multivariate_normal_pdf(par_start_current_block',xopt_current_block',proposal_covariance_Cholesky_decomposition_upper,n);
- elseif strcmpi(sampler_options.proposal_distribution,'rand_multivariate_student')
- proposal_density_proposed_move_forward=multivariate_student_pdf(proposed_par,xopt_current_block',proposal_covariance_Cholesky_decomposition_upper,n);
- proposal_density_proposed_move_backward=multivariate_student_pdf(par_start_current_block',xopt_current_block',proposal_covariance_Cholesky_decomposition_upper,n);
- end
- accprob=logpost-last_posterior+ log(proposal_density_proposed_move_backward)-log(proposal_density_proposed_move_forward); %Formula (6), Chib/Ramamurthy
-
- if (logpost > -inf) && (log(rand) < accprob)
- current_draw(indices(blocks==block_iter,1))=proposed_par;
- last_posterior=logpost;
- accepted_draws_counter =accepted_draws_counter +1;
- else %no updating
- %do nothing, keep old value
- end
- end
- accepted=accepted_draws_counter/blocked_draws_counter;
- par = current_draw;
- neval=1;
- case 'independent_metropolis_hastings'
- neval = 1;
- ProposalFun = sampler_options.proposal_distribution;
- ProposalDensity = sampler_options.ProposalDensity;
- proposal_covariance_Cholesky_decomposition = sampler_options.proposal_covariance_Cholesky_decomposition;
- n = sampler_options.n;
- xparam1 = sampler_options.xparam1';
- par = feval(ProposalFun, xparam1, proposal_covariance_Cholesky_decomposition, n);
- if all( par(:) > mh_bounds.lb ) && all( par(:) < mh_bounds.ub )
- try
- logpost = - feval(TargetFun, par(:),varargin{:});
- catch
- logpost = -inf;
- end
- else
- logpost = -inf;
- end
- r = logpost - last_posterior + ...
- log(feval(ProposalDensity, last_draw, xparam1, proposal_covariance_Cholesky_decomposition, n)) - ...
- log(feval(ProposalDensity, par, xparam1, proposal_covariance_Cholesky_decomposition, n));
- if (logpost > -inf) && (log(rand) < r)
- accepted = 1;
- else
- accepted = 0;
- par = last_draw;
- logpost = last_posterior;
- end
+function [par, logpost, accepted, neval] = posterior_sampler_iteration(TargetFun,last_draw, last_posterior, sampler_options,varargin)
+
+% function [par, logpost, accepted, neval] = posterior_sampler_iteration(TargetFun,last_draw, last_posterior, sampler_options,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_)
+% posterior samplers
+%
+% INPUTS
+% posterior_sampler_options: posterior sampler options
+% options_: structure storing the options
+
+% OUTPUTS
+% posterior_sampler_options: checked posterior sampler options
+%
+% SPECIAL REQUIREMENTS
+% none
+
+% Copyright (C) 2015-16 Dynare Team
+%
+% 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/>.
+
+
+posterior_sampling_method = sampler_options.posterior_sampling_method;
+mh_bounds = sampler_options.bounds;
+
+switch posterior_sampling_method
+ case 'slice'
+
+ [par, logpost, neval] = slice_sampler(TargetFun,last_draw, [mh_bounds.lb mh_bounds.ub], sampler_options,varargin{:});
+ accepted = 1;
+ case 'random_walk_metropolis_hastings'
+ neval = 1;
+ ProposalFun = sampler_options.proposal_distribution;
+ proposal_covariance_Cholesky_decomposition = sampler_options.proposal_covariance_Cholesky_decomposition;
+ n = sampler_options.n;
+
+ par = feval(ProposalFun, last_draw, proposal_covariance_Cholesky_decomposition, n);
+ if all( par(:) > mh_bounds.lb ) && all( par(:) < mh_bounds.ub )
+ try
+ logpost = - feval(TargetFun, par(:),varargin{:});
+ catch
+ logpost = -inf;
+ end
+ else
+ logpost = -inf;
+ end
+ r = logpost-last_posterior;
+ if (logpost > -inf) && (log(rand) < r)
+ accepted = 1;
+ else
+ accepted = 0;
+ par = last_draw;
+ logpost = last_posterior;
+ end
+ case 'tailored_random_block_metropolis_hastings'
+ options_=varargin{3};
+ bayestopt_=varargin{6};
+ npar=length(last_draw);
+ %% randomize indices for blocking in this iteration
+ indices=randperm(npar)';
+ blocks=[1; (1+cumsum((rand(length(indices)-1,1)>(1-sampler_options.new_block_probability))))];
+ nblocks=blocks(end,1); %get number of blocks this iteration
+ current_draw=last_draw'; %get starting point for current draw for updating
+ blocked_draws_counter=0;
+ accepted_draws_counter=0;
+ for block_iter=1:nblocks
+ blocked_draws_counter=blocked_draws_counter+1;
+ nxopt=length(indices(blocks==block_iter,1)); %get size of current block
+ par_start_current_block=current_draw(indices(blocks==block_iter,1));
+ [xopt_current_block, fval, exitflag, hess_mat_optimizer, options_, Scale] = dynare_minimize_objective(@TaRB_optimizer_wrapper,par_start_current_block,sampler_options.mode_compute,options_,[mh_bounds.lb(indices(blocks==block_iter,1),1) mh_bounds.ub(indices(blocks==block_iter,1),1)],bayestopt_.name,bayestopt_,[],...
+ current_draw,indices(blocks==block_iter,1),TargetFun,...% inputs for wrapper
+ varargin{:}); %inputs for objective
+ %% covariance for proposal density
+ hessian_mat = reshape(hessian('TaRB_optimizer_wrapper',xopt_current_block, ...
+ options_.gstep,...
+ current_draw,indices(blocks==block_iter,1),TargetFun,...% inputs for wrapper
+ varargin{:}),nxopt,nxopt);
+
+ if any(any(isnan(hessian_mat))) || any(any(isinf(hessian_mat)))
+ inverse_hessian_mat=eye(nxopt)*1e-4; %use diagonal
+ else
+ inverse_hessian_mat=inv(hessian_mat); %get inverse Hessian
+ if any(any((isnan(inverse_hessian_mat)))) || any(any((isinf(inverse_hessian_mat))))
+ inverse_hessian_mat=eye(nxopt)*1e-4; %use diagonal
+ end
+ end
+ [proposal_covariance_Cholesky_decomposition_upper,negeigenvalues]=chol(inverse_hessian_mat);
+ %if not positive definite, use generalized Cholesky of Eskow/Schnabel
+ if negeigenvalues~=0
+ proposal_covariance_Cholesky_decomposition_upper=chol_SE(inverse_hessian_mat,0);
+ end
+ proposal_covariance_Cholesky_decomposition_upper=proposal_covariance_Cholesky_decomposition_upper*diag(bayestopt_.jscale(indices(blocks==block_iter,1),:));
+ %get proposal draw
+ if strcmpi(sampler_options.proposal_distribution,'rand_multivariate_normal')
+ n = nxopt;
+ elseif strcmpi(sampler_options.proposal_distribution,'rand_multivariate_student')
+ n = options_.student_degrees_of_freedom;
+ end
+
+ proposed_par = feval(sampler_options.proposal_distribution, xopt_current_block', proposal_covariance_Cholesky_decomposition_upper, n);
+ % check whether draw is valid and compute posterior
+ if all( proposed_par(:) > mh_bounds.lb(indices(blocks==block_iter,1),:) ) && all( proposed_par(:) < mh_bounds.ub(indices(blocks==block_iter,1),:) )
+ try
+ logpost = - feval('TaRB_optimizer_wrapper', proposed_par(:),...
+ current_draw,indices(blocks==block_iter,1),TargetFun,...% inputs for wrapper
+ varargin{:});
+ catch
+ logpost = -inf;
+ end
+ else
+ logpost = -inf;
+ end
+ %get ratio of proposal densities, required because proposal depends
+ %on current mode via Hessian and is thus not symmetric anymore
+ if strcmpi(sampler_options.proposal_distribution,'rand_multivariate_normal')
+ proposal_density_proposed_move_forward=multivariate_normal_pdf(proposed_par,xopt_current_block',proposal_covariance_Cholesky_decomposition_upper,n);
+ proposal_density_proposed_move_backward=multivariate_normal_pdf(par_start_current_block',xopt_current_block',proposal_covariance_Cholesky_decomposition_upper,n);
+ elseif strcmpi(sampler_options.proposal_distribution,'rand_multivariate_student')
+ proposal_density_proposed_move_forward=multivariate_student_pdf(proposed_par,xopt_current_block',proposal_covariance_Cholesky_decomposition_upper,n);
+ proposal_density_proposed_move_backward=multivariate_student_pdf(par_start_current_block',xopt_current_block',proposal_covariance_Cholesky_decomposition_upper,n);
+ end
+ accprob=logpost-last_posterior+ log(proposal_density_proposed_move_backward)-log(proposal_density_proposed_move_forward); %Formula (6), Chib/Ramamurthy
+
+ if (logpost > -inf) && (log(rand) < accprob)
+ current_draw(indices(blocks==block_iter,1))=proposed_par;
+ last_posterior=logpost;
+ accepted_draws_counter =accepted_draws_counter +1;
+ else %no updating
+ %do nothing, keep old value
+ end
+ end
+ accepted=accepted_draws_counter/blocked_draws_counter;
+ par = current_draw;
+ neval=1;
+ case 'independent_metropolis_hastings'
+ neval = 1;
+ ProposalFun = sampler_options.proposal_distribution;
+ ProposalDensity = sampler_options.ProposalDensity;
+ proposal_covariance_Cholesky_decomposition = sampler_options.proposal_covariance_Cholesky_decomposition;
+ n = sampler_options.n;
+ xparam1 = sampler_options.xparam1';
+ par = feval(ProposalFun, xparam1, proposal_covariance_Cholesky_decomposition, n);
+ if all( par(:) > mh_bounds.lb ) && all( par(:) < mh_bounds.ub )
+ try
+ logpost = - feval(TargetFun, par(:),varargin{:});
+ catch
+ logpost = -inf;
+ end
+ else
+ logpost = -inf;
+ end
+ r = logpost - last_posterior + ...
+ log(feval(ProposalDensity, last_draw, xparam1, proposal_covariance_Cholesky_decomposition, n)) - ...
+ log(feval(ProposalDensity, par, xparam1, proposal_covariance_Cholesky_decomposition, n));
+ if (logpost > -inf) && (log(rand) < r)
+ accepted = 1;
+ else
+ accepted = 0;
+ par = last_draw;
+ logpost = last_posterior;
+ end
end
\ No newline at end of file
diff --git a/matlab/print_bytecode_dynamic_model.m b/matlab/print_bytecode_dynamic_model.m
index f4e381ed1514e5036179085d5153cf69e8837fed..688f9cadb9f60f7d9c88ccb11cf92e3c80963f4d 100644
--- a/matlab/print_bytecode_dynamic_model.m
+++ b/matlab/print_bytecode_dynamic_model.m
@@ -1,10 +1,10 @@
function print_bytecode_dynamic_model()
% function print_bytecode_dynamic_model()
% print the model and jacobian from the bytecode format for the dynamic model
-%
+%
% INPUTS
% none
-%
+%
% OUTPUTS
% none
%
diff --git a/matlab/print_bytecode_static_model.m b/matlab/print_bytecode_static_model.m
index bc216c70dad270c2a664febe8b88004871f2f8d6..6ddc67fe2a94db533140c42035846c92c3c08201 100644
--- a/matlab/print_bytecode_static_model.m
+++ b/matlab/print_bytecode_static_model.m
@@ -1,10 +1,10 @@
function print_bytecode_static_model()
% function print_bytecode_static_model()
% print the model and jacobian from the bytecode format for the static model
-%
+%
% INPUTS
% none
-%
+%
% OUTPUTS
% none
%
diff --git a/matlab/print_info.m b/matlab/print_info.m
index 29ef302d7a906768a2037e4e9f760f6489c98579..76ec1d115fdea5b20c39d81ab5c65676f8c6ff2b 100644
--- a/matlab/print_info.m
+++ b/matlab/print_info.m
@@ -2,7 +2,7 @@ function print_info(info, noprint, DynareOptions)
% Prints error messages
%
% INPUTS
-% info [double] vector returned by resol.m
+% info [double] vector returned by resol.m
% noprint [integer] equal to 0 if the error message has to be printed.
% DynareOptions [structure] --> options_
% OUTPUTS
@@ -54,15 +54,15 @@ if ~noprint
error('One of the eigenvalues is close to 0/0 (the absolute value of numerator and denominator is smaller than %s!\n If you believe that the model has a unique solution you can try to reduce the value of qz_zero_threshold.',num2str(DynareOptions.qz_zero_threshold))
case 8
if size(info,2)>=2
- global M_;
+ global M_;
disp_string=deblank(M_.param_names(info(2),:));
- for ii=1:length(info)-2
- disp_string=[disp_string,', ',deblank(M_.param_names(info(2+ii),:))];
- end
- error(['The Jacobian contains NaNs because the following parameters are NaN: '...
- disp_string])
+ for ii=1:length(info)-2
+ disp_string=[disp_string,', ',deblank(M_.param_names(info(2+ii),:))];
+ end
+ error(['The Jacobian contains NaNs because the following parameters are NaN: '...
+ disp_string])
else
- error(['The Jacobian contains NaNs. For more information, use options_.debug.'])
+ error(['The Jacobian contains NaNs. For more information, use options_.debug.'])
end
case 9
error(['k_order_pert was unable to compute the solution'])
@@ -78,7 +78,7 @@ if ~noprint
if DynareOptions.linear
error(['Impossible to find the steady state. Either the model' ...
' doesn''t have a steady state or there are an infinity of steady states.' ...
- ' Check whether your model is truly linear or whether there is a mistake in linearization.'])
+ ' Check whether your model is truly linear or whether there is a mistake in linearization.'])
else
error(['Impossible to find the steady state. Either the model' ...
' doesn''t have a steady state, there are an infinity of steady states,' ...
@@ -117,7 +117,7 @@ if ~noprint
case 49
error('The model violates one (many) endogenous prior restriction(s)')
case 50
- error('Likelihood is Inf')
+ error('Likelihood is Inf')
case 51
fprintf('\n The dsge_prior_weight is dsge_var=%5.4f, but must be at least %5.4f for the prior to be proper.\n',info(2),info(3));
error('You are estimating a DSGE-VAR model, but the value of the dsge prior weight is too low!')
diff --git a/matlab/print_moments_implied_prior.m b/matlab/print_moments_implied_prior.m
index 496e3bf865a4d15c99c4a38f42d27bb65c97f43b..56dc069b1db0b4031521daed6f4484869e2dabba 100644
--- a/matlab/print_moments_implied_prior.m
+++ b/matlab/print_moments_implied_prior.m
@@ -1,7 +1,7 @@
function print_moments_implied_prior(ModelInfo, mm, vm, mv, vv)
% This routine prints in the command window some descriptive statistics
-% about the endogenous variables implied prior moments.
+% about the endogenous variables implied prior moments.
% Copyright (C) 2016 Dynare Team
%
diff --git a/matlab/print_table_prior.m b/matlab/print_table_prior.m
index 605adfabf7ff8aad771e4b829711a7a998b63b61..c12c2fd313558029129b816edaeb572b0a0a1fd0 100644
--- a/matlab/print_table_prior.m
+++ b/matlab/print_table_prior.m
@@ -1,6 +1,6 @@
function print_table_prior(lb, ub, DynareOptions, ModelInfo, BayesInfo, EstimationInfo)
-% This routine prints in the command window some descriptive statistics about the prior distribution.
+% This routine prints in the command window some descriptive statistics about the prior distribution.
% Copyright (C) 2015 Dynare Team
%
@@ -111,7 +111,7 @@ for i=1:size(BayesInfo.name,1)
UpperBound, ...
PriorIntervals.lb(i), ...
PriorIntervals.ub(i) );
- T2 = strvcat(T2, str);
+ T2 = strvcat(T2, str);
end
T1 = strvcat(T1, l1);
@@ -134,25 +134,25 @@ skipline(2)
function format_string = build_format_string(PriorMode,PriorStandardDeviation,LowerBound,UpperBound)
- format_string = ['%s \t %6.4f \t'];
- if isnan(PriorMode)
- format_string = [ format_string , ' %s \t'];
- else
- format_string = [ format_string , ' %6.4f \t'];
- end
- if ~isnumeric(PriorStandardDeviation)
- format_string = [ format_string , ' %s \t'];
- else
- format_string = [ format_string , ' %6.4f \t'];
- end
- if ~isnumeric(LowerBound)
- format_string = [ format_string , ' %s \t'];
- else
- format_string = [ format_string , ' %6.4f \t'];
- end
- if ~isnumeric(UpperBound)
- format_string = [ format_string , ' %s \t'];
- else
- format_string = [ format_string , ' %6.4f \t'];
- end
- format_string = [ format_string , ' %6.4f \t %6.4f'];
\ No newline at end of file
+format_string = ['%s \t %6.4f \t'];
+if isnan(PriorMode)
+ format_string = [ format_string , ' %s \t'];
+else
+ format_string = [ format_string , ' %6.4f \t'];
+end
+if ~isnumeric(PriorStandardDeviation)
+ format_string = [ format_string , ' %s \t'];
+else
+ format_string = [ format_string , ' %6.4f \t'];
+end
+if ~isnumeric(LowerBound)
+ format_string = [ format_string , ' %s \t'];
+else
+ format_string = [ format_string , ' %6.4f \t'];
+end
+if ~isnumeric(UpperBound)
+ format_string = [ format_string , ' %s \t'];
+else
+ format_string = [ format_string , ' %6.4f \t'];
+end
+format_string = [ format_string , ' %6.4f \t %6.4f'];
\ No newline at end of file
diff --git a/matlab/printline.m b/matlab/printline.m
index c60d0906a90133b7b7574ac05439d163e48b1233..85bb44e0f74b1d7801c0844de824459b171383f5 100644
--- a/matlab/printline.m
+++ b/matlab/printline.m
@@ -1,13 +1,13 @@
function varargout = printline(n, s, fid)
% This function print a line formed by replicating a symbol s.
%
-% INPUTS
+% INPUTS
%
% n [integer] Length of the printed line
% s [char] Symbol used to draw the line (+, -, =, ...)
% f [integer] file id returned by fopen
-%
-% OUTPUTS
+%
+% OUTPUTS
% None
% Copyright (C) 2015 Dynare Team
@@ -25,7 +25,7 @@ function varargout = printline(n, s, fid)
% 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/>.
+% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
if nargin<3
f = 1;
diff --git a/matlab/prior_analysis.m b/matlab/prior_analysis.m
index c152aaa58806a46bba99f9a76dc6514b10dd1c63..41b43ffa3460fdff929dab88642c1348703be9f4 100644
--- a/matlab/prior_analysis.m
+++ b/matlab/prior_analysis.m
@@ -56,7 +56,7 @@ switch type
dsge_simulated_theoretical_covariance(SampleSize,M_,options_,oo_,'prior');
end
oo_ = covariance_mc_analysis(SampleSize,'prior',M_.dname,M_.fname,...
- vartan,nvar,arg1,arg2,options_.mh_conf_sig,oo_,options_);
+ vartan,nvar,arg1,arg2,options_.mh_conf_sig,oo_,options_);
case 'decomposition'
if nargin==narg1
[nvar,vartan,NumberOfFiles] = ...
diff --git a/matlab/prior_bounds.m b/matlab/prior_bounds.m
index cd11fdfaba19b724d34ee1f6a4c52ee20067b4cf..83127f7aaffe3550ac16177d9c02002ade79af15 100644
--- a/matlab/prior_bounds.m
+++ b/matlab/prior_bounds.m
@@ -40,10 +40,10 @@ function bounds = prior_bounds(bayestopt, prior_trunc)
%
% INPUTS
% bayestopt [structure] characterizing priors (shape, mean, p1..p4)
-%
+%
% OUTPUTS
% bounds [double] structure specifying prior bounds (lb and ub fields)
-%
+%
% SPECIAL REQUIREMENTS
% none
diff --git a/matlab/prior_draw.m b/matlab/prior_draw.m
index 4e9ca2ba2cf8a363805cbda1366d4f09eca1e867..c97eaeefd4dab1d320eafac1450af06cb6bfd8fb 100644
--- a/matlab/prior_draw.m
+++ b/matlab/prior_draw.m
@@ -2,10 +2,10 @@ function pdraw = prior_draw(BayesInfo, prior_trunc, uniform) % --*-- Unitary tes
% This function generate one draw from the joint prior distribution and
% allows sampling uniformly from the prior support (uniform==1 when called with init==1)
-%
-% INPUTS
-% o init [integer] scalar equal to:
-% 1: first call to set up persistent variables
+%
+% INPUTS
+% o init [integer] scalar equal to:
+% 1: first call to set up persistent variables
% describing the prior
% 0: subsequent call to get prior
% draw
@@ -13,8 +13,8 @@ function pdraw = prior_draw(BayesInfo, prior_trunc, uniform) % --*-- Unitary tes
% 1: sample uniformly from prior
% support (overwrites prior shape used for sampling within this function)
% 0: sample from joint prior distribution
-%
-% OUTPUTS
+%
+% OUTPUTS
% o pdraw [double] 1*npar vector, draws from the joint prior density.
%
%
@@ -25,7 +25,7 @@ function pdraw = prior_draw(BayesInfo, prior_trunc, uniform) % --*-- Unitary tes
% NOTE 2. A given draw from the joint prior distribution does not satisfy BK conditions a priori.
% NOTE 3. This code relies on bayestopt_ as created in the base workspace
% by the preprocessor (or as updated in subsequent pieces of code and handed to the base workspace)
-%
+%
% Copyright (C) 2006-2017 Dynare Team
%
% This file is part of Dynare.
@@ -108,7 +108,7 @@ if nargin>0
end
if uniform_draws
- pdraw(uniform_index) = rand(length(uniform_index),1).*(p4(uniform_index)-p3(uniform_index)) + p3(uniform_index);
+ pdraw(uniform_index) = rand(length(uniform_index),1).*(p4(uniform_index)-p3(uniform_index)) + p3(uniform_index);
out_of_bound = find( (pdraw(uniform_index)'>ub(uniform_index)) | (pdraw(uniform_index)'<lb(uniform_index)));
while ~isempty(out_of_bound)
pdraw(uniform_index) = rand(length(uniform_index),1).*(p4(uniform_index)-p3(uniform_index)) + p3(uniform_index);
diff --git a/matlab/prior_posterior_statistics.m b/matlab/prior_posterior_statistics.m
index 6aa9797f18d34c2a8eec7b451936bead5b78f6eb..064e7874ae0dac8c38be2bc7cb8b4a79c7c9d0ca 100644
--- a/matlab/prior_posterior_statistics.m
+++ b/matlab/prior_posterior_statistics.m
@@ -14,11 +14,11 @@ function prior_posterior_statistics(type,dataset,dataset_info)
%
% SPECIAL REQUIREMENTS
% none
-%
+%
% PARALLEL CONTEXT
% See the comments in the posterior_sampler.m funtion.
-%
-%
+%
+%
% Copyright (C) 2005-2017 Dynare Team
%
% This file is part of Dynare.
@@ -117,7 +117,7 @@ if options_.filter_covariance
MAX_filter_covariance = min(B,ceil(MaxNumberOfBytes/(endo_nbr^2*(gend+1))/8));
end
-if options_.smoothed_state_uncertainty
+if options_.smoothed_state_uncertainty
MAX_n_smoothed_state_uncertainty = min(B,ceil(MaxNumberOfBytes/((endo_nbr*endo_nbr)*gend)/8));
end
@@ -196,7 +196,7 @@ end
if options_.filter_covariance
localVars.MAX_filter_covariance = MAX_filter_covariance;
end
-if options_.smoothed_state_uncertainty
+if options_.smoothed_state_uncertainty
localVars.MAX_n_smoothed_state_uncertainty = MAX_n_smoothed_state_uncertainty ;
end
localVars.MAX_n_smoothed_constant=MAX_n_smoothed_constant;
@@ -211,9 +211,9 @@ if strcmpi(type,'posterior')
BaseName = [DirectoryName filesep M_.fname];
load_last_mh_history_file(DirectoryName, M_.fname);
FirstMhFile = record.KeepedDraws.FirstMhFile;
- FirstLine = record.KeepedDraws.FirstLine;
- TotalNumberOfMhFiles = sum(record.MhDraws(:,2));
- LastMhFile = TotalNumberOfMhFiles;
+ FirstLine = record.KeepedDraws.FirstLine;
+ TotalNumberOfMhFiles = sum(record.MhDraws(:,2));
+ LastMhFile = TotalNumberOfMhFiles;
TotalNumberOfMhDraws = sum(record.MhDraws(:,1));
NumberOfDraws = TotalNumberOfMhDraws-floor(options_.mh_drop*TotalNumberOfMhDraws);
mh_nblck = options_.mh_nblck;
@@ -274,14 +274,14 @@ else
end
if run_smoother
nfiles = ceil(nBlockPerCPU(j)/MAX_n_trend_coeff);
- ifil(9,j+1) =ifil(9,j)+nfiles;
+ ifil(9,j+1) =ifil(9,j)+nfiles;
nfiles = ceil(nBlockPerCPU(j)/MAX_n_smoothed_constant);
- ifil(10,j+1) =ifil(10,j)+nfiles;
+ ifil(10,j+1) =ifil(10,j)+nfiles;
nfiles = ceil(nBlockPerCPU(j)/MAX_n_smoothed_trend);
- ifil(11,j+1) =ifil(11,j)+nfiles;
+ ifil(11,j+1) =ifil(11,j)+nfiles;
if smoothed_state_uncertainty
nfiles = ceil(nBlockPerCPU(j)/MAX_n_smoothed_state_uncertainty);
- ifil(13,j+1) =ifil(13,j)+nfiles;
+ ifil(13,j+1) =ifil(13,j)+nfiles;
end
end
end
@@ -331,7 +331,7 @@ if options_.smoother
'',M_.exo_names,M_.exo_names_tex,M_.exo_names,...
M_.exo_names,'SmoothedShocks',DirectoryName,'_inno');
pm3(endo_nbr,1,ifil(9),B,'Trend_coefficients',...
- '',varlist,M_.endo_names_tex,M_.endo_names,...
+ '',varlist,M_.endo_names_tex,M_.endo_names,...
varlist,'TrendCoeff',DirectoryName,'_trend_coeff');
pm3(endo_nbr,gend,ifil(10),B,'Smoothed constant',...
'',varlist,M_.endo_names_tex,M_.endo_names,...
@@ -348,17 +348,17 @@ if options_.smoother
'',varlist,M_.endo_names_tex,M_.endo_names,...
varlist,'StateUncertainty',DirectoryName,'_state_uncert');
end
-
+
if nvn
- for obs_iter=1:length(options_.varobs)
+ for obs_iter=1:length(options_.varobs)
meas_error_names{obs_iter,1}=['SE_EOBS_' M_.endo_names(strmatch(options_.varobs{obs_iter},M_.endo_names,'exact'),:)];
texnames{obs_iter,1}=['SE_EOBS_' M_.endo_names_tex(strmatch(options_.varobs{obs_iter},M_.endo_names,'exact'),:)];
end
meas_error_names=char(meas_error_names);
texnames=char(texnames);
pm3(meas_err_nbr,gend,ifil(3),B,'Smoothed measurement errors',...
- '',meas_error_names,texnames,meas_error_names,...
- meas_error_names,'SmoothedMeasurementErrors',DirectoryName,'_error')
+ '',meas_error_names,texnames,meas_error_names,...
+ meas_error_names,'SmoothedMeasurementErrors',DirectoryName,'_error')
end
end
@@ -377,7 +377,7 @@ if options_.forecast
varlist,'PointForecast',DirectoryName,'_forc_point');
if ~isequal(M_.H,0) && ~isempty(intersect(options_.varobs,varlist))
texnames=[];
- for obs_iter=1:length(options_.varobs)
+ for obs_iter=1:length(options_.varobs)
obs_names{obs_iter,1}=M_.endo_names(strmatch(options_.varobs{obs_iter},M_.endo_names,'exact'),:);
texnames{obs_iter,1}=M_.endo_names_tex(strmatch(options_.varobs{obs_iter},M_.endo_names,'exact'),:);
end
@@ -385,8 +385,8 @@ if options_.forecast
texnames=char(texnames);
varlist_forecast_ME=intersect(options_.varobs,varlist);
pm3(meas_err_nbr,horizon,ifil(12),B,'Forecasted variables (point) with ME',...
- '',char(varlist_forecast_ME),texnames,obs_names,...
- char(varlist_forecast_ME),'PointForecastME',DirectoryName,'_forc_point_ME')
+ '',char(varlist_forecast_ME),texnames,obs_names,...
+ char(varlist_forecast_ME),'PointForecastME',DirectoryName,'_forc_point_ME')
end
end
diff --git a/matlab/prior_posterior_statistics_core.m b/matlab/prior_posterior_statistics_core.m
index 904eb7afad7e5533598b86feab4aa92223675962..03cc43a3e0c17d4d9ae0adcc816befb73e95e696 100644
--- a/matlab/prior_posterior_statistics_core.m
+++ b/matlab/prior_posterior_statistics_core.m
@@ -157,22 +157,22 @@ end
%initialize arrays
if run_smoother
- stock_smooth=NaN(endo_nbr,gend,MAX_nsmoo);
- stock_update=NaN(endo_nbr,gend,MAX_nsmoo);
- stock_innov=NaN(M_.exo_nbr,gend,MAX_ninno);
- stock_smoothed_constant=NaN(endo_nbr,gend,MAX_n_smoothed_constant);
- stock_smoothed_trend=NaN(endo_nbr,gend,MAX_n_smoothed_trend);
- stock_trend_coeff = zeros(endo_nbr,MAX_n_trend_coeff);
- if horizon
- stock_forcst_mean= NaN(endo_nbr,horizon,MAX_nforc1);
- stock_forcst_point = NaN(endo_nbr,horizon,MAX_nforc2);
- if ~isequal(M_.H,0)
- stock_forcst_point_ME = NaN(length(varobs),horizon,MAX_nforc_ME);
- end
- end
+ stock_smooth=NaN(endo_nbr,gend,MAX_nsmoo);
+ stock_update=NaN(endo_nbr,gend,MAX_nsmoo);
+ stock_innov=NaN(M_.exo_nbr,gend,MAX_ninno);
+ stock_smoothed_constant=NaN(endo_nbr,gend,MAX_n_smoothed_constant);
+ stock_smoothed_trend=NaN(endo_nbr,gend,MAX_n_smoothed_trend);
+ stock_trend_coeff = zeros(endo_nbr,MAX_n_trend_coeff);
+ if horizon
+ stock_forcst_mean= NaN(endo_nbr,horizon,MAX_nforc1);
+ stock_forcst_point = NaN(endo_nbr,horizon,MAX_nforc2);
+ if ~isequal(M_.H,0)
+ stock_forcst_point_ME = NaN(length(varobs),horizon,MAX_nforc_ME);
+ end
+ end
end
if nvn
- stock_error = NaN(length(varobs),gend,MAX_nerro);
+ stock_error = NaN(length(varobs),gend,MAX_nerro);
end
if naK
stock_filter_step_ahead =NaN(length(options_.filter_step_ahead),endo_nbr,gend+max(options_.filter_step_ahead),MAX_naK);
@@ -206,7 +206,7 @@ for b=fpar:B
[dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
[alphahat,etahat,epsilonhat,alphatilde,SteadyState,trend_coeff,aK,junk1,junk2,P,junk4,junk5,trend_addition,state_uncertainty,M_,oo_,options_,bayestopt_] = ...
DsgeSmoother(deep,gend,Y,data_index,missing_value,M_,oo_,options_,bayestopt_,estim_params_);
-
+
stock_trend_coeff(options_.varobs_id,irun(9))=trend_coeff;
stock_smoothed_trend(IdObs,:,irun(11))=trend_addition;
if options_.loglinear %reads values from smoother results, which are in dr-order and put them into declaration order
@@ -221,7 +221,7 @@ for b=fpar:B
constant_part;
stock_update(dr.order_var,:,irun(1)) = alphatilde(1:endo_nbr,:)+ ...
constant_part;
- end
+ end
stock_smoothed_constant(dr.order_var,:,irun(10))=constant_part;
%% Compute constant for observables
if options_.prefilter == 1 %as mean is taken after log transformation, no distinction is needed here
@@ -243,10 +243,10 @@ for b=fpar:B
%smoothed variables are E_T(y_t) so no trend shift is required
stock_smooth(IdObs,:,irun(1))=stock_smooth(IdObs,:,irun(1))+trend_addition+mean_correction;
%updated variables are E_t(y_t) so no trend shift is required
- stock_update(IdObs,:,irun(1))=stock_update(IdObs,:,irun(1))+trend_addition+mean_correction;
+ stock_update(IdObs,:,irun(1))=stock_update(IdObs,:,irun(1))+trend_addition+mean_correction;
else
stock_smooth(IdObs,:,irun(1))=stock_smooth(IdObs,:,irun(1))+trend_addition;
- stock_update(IdObs,:,irun(1))=stock_update(IdObs,:,irun(1))+trend_addition;
+ stock_update(IdObs,:,irun(1))=stock_update(IdObs,:,irun(1))+trend_addition;
end
stock_innov(:,:,irun(2)) = etahat;
if nvn
@@ -259,11 +259,11 @@ for b=fpar:B
constant_part=repmat(log(SteadyState(dr.order_var))',[length(options_.filter_step_ahead),1,gend+max(options_.filter_step_ahead)]);
else
constant_part=repmat(SteadyState(dr.order_var)',[length(options_.filter_step_ahead),1,gend+max(options_.filter_step_ahead)]);
- end
+ end
stock_filter_step_ahead(:,dr.order_var,:,irun(4)) = aK(options_.filter_step_ahead,1:endo_nbr,:) + ...
constant_part;
-
+
%now add trend to observables
for ii=1:length(options_.filter_step_ahead)
if options_.prefilter
@@ -280,16 +280,16 @@ for b=fpar:B
if horizon
yyyy = alphahat(iendo,i_last_obs);
yf = forcst2a(yyyy,dr,zeros(horizon,exo_nbr));
- if options_.prefilter
+ if options_.prefilter
% add mean
yf(:,IdObs) = yf(:,IdObs)+repmat(mean_varobs, ...
horizon+maxlag,1);
% add trend, taking into account that last point of sample is still included in forecasts and only cut off later
yf(:,IdObs) = yf(:,IdObs)+((options_.first_obs-1)+gend+[1-maxlag:horizon]')*trend_coeff'-...
- repmat(mean(trend_coeff*[options_.first_obs:options_.first_obs+gend-1],2)',length(1-maxlag:horizon),1); %center trend
+ repmat(mean(trend_coeff*[options_.first_obs:options_.first_obs+gend-1],2)',length(1-maxlag:horizon),1); %center trend
else
% add trend, taking into account that last point of sample is still included in forecasts and only cut off later
- yf(:,IdObs) = yf(:,IdObs)+((options_.first_obs-1)+gend+[1-maxlag:horizon]')*trend_coeff';
+ yf(:,IdObs) = yf(:,IdObs)+((options_.first_obs-1)+gend+[1-maxlag:horizon]')*trend_coeff';
end
if options_.loglinear
yf = yf+repmat(log(SteadyState'),horizon+maxlag,1);
@@ -303,10 +303,10 @@ for b=fpar:B
repmat(mean_varobs,[horizon+maxlag,1,1]);
% add trend, taking into account that last point of sample is still included in forecasts and only cut off later
yf1(:,IdObs) = yf1(:,IdObs)+((options_.first_obs-1)+gend+[1-maxlag:horizon]')*trend_coeff'-...
- repmat(mean(trend_coeff*[options_.first_obs:options_.first_obs+gend-1],2)',length(1-maxlag:horizon),1); %center trend
+ repmat(mean(trend_coeff*[options_.first_obs:options_.first_obs+gend-1],2)',length(1-maxlag:horizon),1); %center trend
else
- % add trend, taking into account that last point of sample is still included in forecasts and only cut off later
- yf1(:,IdObs,:) = yf1(:,IdObs,:)+repmat(((options_.first_obs-1)+gend+[1-maxlag:horizon]')* ...
+ % add trend, taking into account that last point of sample is still included in forecasts and only cut off later
+ yf1(:,IdObs,:) = yf1(:,IdObs,:)+repmat(((options_.first_obs-1)+gend+[1-maxlag:horizon]')* ...
trend_coeff',[1,1,1]);
end
if options_.loglinear
@@ -420,7 +420,7 @@ for b=fpar:B
end
irun(7) = 1;
end
-
+
if run_smoother && filter_covariance && (irun(8) > MAX_filter_covariance || b == B)
stock = stock_filter_covariance(:,:,:,1:irun(8)-1);
ifil(8) = ifil(8) + 1;
@@ -430,7 +430,7 @@ for b=fpar:B
end
irun(8) = 1;
end
-
+
irun_index=9;
if run_smoother && (irun(irun_index) > MAX_n_trend_coeff || b == B)
stock = stock_trend_coeff(:,1:irun(irun_index)-1);
@@ -441,7 +441,7 @@ for b=fpar:B
end
irun(irun_index) = 1;
end
-
+
irun_index=10;
if run_smoother && (irun(irun_index) > MAX_n_smoothed_constant || b == B)
stock = stock_smoothed_constant(:,:,1:irun(irun_index)-1);
@@ -463,7 +463,7 @@ for b=fpar:B
end
irun(irun_index) = 1;
end
-
+
irun_index=12;
if run_smoother && horizon && ~isequal(M_.H,0) && (irun(irun_index) > MAX_nforc_ME || b == B)
stock = stock_forcst_point_ME(:,:,1:irun(irun_index)-1);
@@ -474,7 +474,7 @@ for b=fpar:B
end
irun(irun_index) = 1;
end
-
+
irun_index=13;
if run_smoother && smoothed_state_uncertainty && (irun(irun_index) > MAX_n_smoothed_state_uncertainty || b == B)
stock = stock_smoothed_uncert(:,:,:,1:irun(irun_index)-1);
@@ -508,4 +508,3 @@ if RemoteFlag==1
end
dyn_waitbar_close(h);
-
diff --git a/matlab/priordens.m b/matlab/priordens.m
index ce1e5bb854a6d98951d5dbcb7ad73ced9973b255..b988e7ea1a4d123d7c453a7ad63610c2302ee2f0 100644
--- a/matlab/priordens.m
+++ b/matlab/priordens.m
@@ -1,7 +1,7 @@
function [logged_prior_density, dlprior, d2lprior, info] = priordens(x, pshape, p6, p7, p3, p4, initialization) % --*-- Unitary tests --*--
% Computes a prior density for the structural parameters of DSGE models
%
-% INPUTS
+% INPUTS
% x [double] vector with n elements.
% pshape [integer] vector with n elements (bayestopt_.pshape).
% p6: [double] vector with n elements, first parameter of the prior distribution (bayestopt_.p6).
@@ -9,8 +9,8 @@ function [logged_prior_density, dlprior, d2lprior, info] = priordens(x, pshape,
% p3: [double] vector with n elements, lower bounds of the untruncated standard or generalized distribution
% p4: [double] vector with n elements, upper bound of the untruncated standard or generalized distribution
% initialization [integer] if 1: initialize persistent variables
-%
-% OUTPUTS
+%
+% OUTPUTS
% logged_prior_density [double] scalar, log of the prior density evaluated at x.
% info [double] error code for index of Inf-prior parameter
%
@@ -89,7 +89,7 @@ d2lprior = 0.0;
if tt1
logged_prior_density = logged_prior_density + sum(lpdfgbeta(x(id1),p6(id1),p7(id1),p3(id1),p4(id1))) ;
if isinf(logged_prior_density)
- if nargout ==4
+ if nargout ==4
info=id1(isinf(lpdfgbeta(x(id1),p6(id1),p7(id1),p3(id1),p4(id1))));
end
return
@@ -104,7 +104,7 @@ end
if tt2
logged_prior_density = logged_prior_density + sum(lpdfgam(x(id2)-p3(id2),p6(id2),p7(id2))) ;
if isinf(logged_prior_density)
- if nargout ==4
+ if nargout ==4
info=id2(isinf(lpdfgam(x(id2)-p3(id2),p6(id2),p7(id2))));
end
return
@@ -128,7 +128,7 @@ end
if tt4
logged_prior_density = logged_prior_density + sum(lpdfig1(x(id4)-p3(id4),p6(id4),p7(id4))) ;
if isinf(logged_prior_density)
- if nargout ==4
+ if nargout ==4
info=id4(isinf(lpdfig1(x(id4)-p3(id4),p6(id4),p7(id4))));
end
return
@@ -143,7 +143,7 @@ end
if tt5
if any(x(id5)-p3(id5)<0) || any(x(id5)-p4(id5)>0)
logged_prior_density = -Inf ;
- if nargout ==4
+ if nargout ==4
info=id5((x(id5)-p3(id5)<0) || (x(id5)-p4(id5)>0));
end
return
@@ -160,7 +160,7 @@ end
if tt6
logged_prior_density = logged_prior_density + sum(lpdfig2(x(id6)-p3(id6),p6(id6),p7(id6))) ;
if isinf(logged_prior_density)
- if nargout ==4
+ if nargout ==4
info=id6(isinf(lpdfig2(x(id6)-p3(id6),p6(id6),p7(id6))));
end
return
@@ -175,7 +175,7 @@ end
if tt8
logged_prior_density = logged_prior_density + sum(lpdfgweibull(x(id8),p6(id8),p7(id8)));
if isinf(logged_prior_density)
- if nargout ==4
+ if nargout ==4
info=id8(isinf(log(lpdfgweibull(x(id8),p6(id8),p7(id8)))));
end
return
diff --git a/matlab/qr2.m b/matlab/qr2.m
index 3c84db9960e5a9fbf830622a042dfb00516db2b2..2d00e1c2c96e6f9fa01e041f8c670dbb8584784d 100644
--- a/matlab/qr2.m
+++ b/matlab/qr2.m
@@ -1,18 +1,18 @@
function [Q,R] = qr2(varargin)
% This routine performs a qr decomposition of matrix X such that the
-% diagonal scalars of the upper-triangular matrix R are positive. If X
+% diagonal scalars of the upper-triangular matrix R are positive. If X
% is a full (column) rank matrix, then R is also the cholesky
-% factorization of X'X. This property is needed for the Del Negro
+% factorization of X'X. This property is needed for the Del Negro
% & Schorfheides's identification scheme.
-%
-% INPUTS
+%
+% INPUTS
% See matlab's documentation for QR decomposition.
-%
-% OUTPUTS
+%
+% OUTPUTS
% See matlab's documentation for QR decomposition.
%
% ALGORITHM
-% None.
+% None.
%
% SPECIAL REQUIREMENTS
% None.
diff --git a/matlab/quadratic_matrix_equation_solver.m b/matlab/quadratic_matrix_equation_solver.m
index d74f5e12f7873daadbe7246ba0cf9db24aab796c..3ecfe252823e7dba6b8ce701713bdc297cf397a6 100644
--- a/matlab/quadratic_matrix_equation_solver.m
+++ b/matlab/quadratic_matrix_equation_solver.m
@@ -106,34 +106,34 @@ end
function f = eval_quadratic_matrix_equation(A,B,C,X)
- f = C + (B + A*X)*X;
+f = C + (B + A*X)*X;
function [p0,p1] = merit_polynomial(A,H,F)
- AHH = A*H*H;
- gamma = norm(AHH,'fro')^2;
- alpha = norm(F,'fro')^2;
- beta = trace(F*AHH*AHH*F);
- p0 = [gamma, -beta, alpha+beta, -2*alpha, alpha];
- p1 = [4*gamma, -3*beta, 2*(alpha+beta), -2*alpha];
+AHH = A*H*H;
+gamma = norm(AHH,'fro')^2;
+alpha = norm(F,'fro')^2;
+beta = trace(F*AHH*AHH*F);
+p0 = [gamma, -beta, alpha+beta, -2*alpha, alpha];
+p1 = [4*gamma, -3*beta, 2*(alpha+beta), -2*alpha];
function t = line_search(A,H,F)
- [p0,p1] = merit_polynomial(A,H,F);
- if any(isnan(p0)) || any(isinf(p0))
- t = 1.0;
- return
- end
- r = roots(p1);
- s = [Inf(3,1),r];
- for i = 1:3
- if isreal(r(i))
- s(i,1) = p0(1)*r(i)^4 + p0(2)*r(i)^3 + p0(3)*r(i)^2 + p0(4)*r(i) + p0(5);
- end
- end
- s = sortrows(s,1);
- t = s(1,2);
- if t<=1e-12 || t>=2
- t = 1;
+[p0,p1] = merit_polynomial(A,H,F);
+if any(isnan(p0)) || any(isinf(p0))
+ t = 1.0;
+ return
+end
+r = roots(p1);
+s = [Inf(3,1),r];
+for i = 1:3
+ if isreal(r(i))
+ s(i,1) = p0(1)*r(i)^4 + p0(2)*r(i)^3 + p0(3)*r(i)^2 + p0(4)*r(i) + p0(5);
end
+end
+s = sortrows(s,1);
+t = s(1,2);
+if t<=1e-12 || t>=2
+ t = 1;
+end
%@test:1
%$ addpath ../matlab
diff --git a/matlab/qz/mjdgges.m b/matlab/qz/mjdgges.m
index 58e4b6513b8462a74bf5e389bba7877dee104c22..12c91d8016a826b431ace4ec15a02b536389bbb0 100644
--- a/matlab/qz/mjdgges.m
+++ b/matlab/qz/mjdgges.m
@@ -6,16 +6,16 @@ function [err,ss,tt,w,sdim,eigval,info] = mjdgges(e,d,qz_criterium, fake)
% e [double] real square (n*n) matrix.
% d [double] real square (n*n) matrix.
% qz_criterium [double] scalar (1+epsilon).
-%
+%
% OUTPUTS
% err [double] scalar: 1 indicates failure, 0 indicates success
% ss [complex] (n*n) matrix.
% tt [complex] (n*n) matrix.
% w [complex] (n*n) matrix.
-% sdim [integer] scalar.
-% eigval [complex] (n*1) vector.
+% sdim [integer] scalar.
+% eigval [complex] (n*1) vector.
% info [integer] scalar.
-%
+%
% ALGORITHM
% Sims's qzdiv routine is used.
%
@@ -54,7 +54,7 @@ end
% Set default value of qz_criterium.
if nargin <3
- qz_criterium = 1 + 1e-6;
+ qz_criterium = 1 + 1e-6;
end
info = 0;
diff --git a/matlab/qzdiv.m b/matlab/qzdiv.m
index 3641808a1269a6cd39789ab14165dfc2ff35e573..8b6e58a5857063e35dcbc5f1747b6f6cd6d82eed 100644
--- a/matlab/qzdiv.m
+++ b/matlab/qzdiv.m
@@ -2,7 +2,7 @@ function [A,B,Q,Z] = qzdiv(stake,A,B,Q,Z)
%function [A,B,Q,Z] = qzdiv(stake,A,B,Q,Z)
%
% 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
+% 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'.
@@ -34,13 +34,13 @@ 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)
+ if (root(j,2) > stake || root(j,2) < -.1)
m=j;
break
end
end
- if (m==0)
- return
+ if (m==0)
+ return
end
for k=m:1:i-1
[A, B, Q, Z] = qzswitch(k,A,B,Q,Z);
@@ -48,4 +48,4 @@ for i = n:-1:1
root(k,2) = root(k+1,2);
root(k+1,2) = tmp;
end
-end
+end
diff --git a/matlab/ramsey_policy.m b/matlab/ramsey_policy.m
index fe162eca433fae2b3bd36e4b279e442221be4e7d..2ca9079fe4286739c826655b0b593518820c4d67 100644
--- a/matlab/ramsey_policy.m
+++ b/matlab/ramsey_policy.m
@@ -38,7 +38,7 @@ else
error('You have specified a steady state file, but not provided an instrument. Either delete the steady state file or provide an instrument')
end
end
-
+
info = stoch_simul(var_list);
oo_.steady_state = oo_.dr.ys;
diff --git a/matlab/read_data_.m b/matlab/read_data_.m
index 7ad6b4228ac58b873d0e4988ff3a99c0265e3e8c..32c61ea8106cb760e2c3dc56929b02a1d018ddb8 100644
--- a/matlab/read_data_.m
+++ b/matlab/read_data_.m
@@ -1,6 +1,6 @@
function read_data_()
% function read_data_
-% reads endogenous and exogenous variables from a text file
+% reads endogenous and exogenous variables from a text file
% Used by datafile option in simulate
%
% INPUT
diff --git a/matlab/read_key_value_string.m b/matlab/read_key_value_string.m
index d6b7124ed639144f02b234b61a18cea1d8deca4e..d92fb269858b2bf6fc9ebb9c5e56efbfd83f6291 100644
--- a/matlab/read_key_value_string.m
+++ b/matlab/read_key_value_string.m
@@ -40,7 +40,7 @@ iComma = strfind(s,',');
%delete commata in sublists from further checks
for sublist_iter=length(i_begin_sublist):-1:1
- iComma(iComma>=i_begin_sublist(sublist_iter) & iComma<=i_end_sublist(sublist_iter))=[];
+ iComma(iComma>=i_begin_sublist(sublist_iter) & iComma<=i_end_sublist(sublist_iter))=[];
end
nComma = length(iComma);
@@ -85,10 +85,10 @@ end
function j = comma2opt(i)
- if isodd(i)
- % The comma is a separator between a Key and a Value (returned j is minus the option number).
- j = - (i+1)/2;
- else
- % The comma is a separator between two options (returned j is the option number).
- j = i/2;
- end
\ No newline at end of file
+if isodd(i)
+ % The comma is a separator between a Key and a Value (returned j is minus the option number).
+ j = - (i+1)/2;
+else
+ % The comma is a separator between two options (returned j is the option number).
+ j = i/2;
+end
\ No newline at end of file
diff --git a/matlab/read_variables.m b/matlab/read_variables.m
index 47373c4101062c302f63785c59fa61de10f38d52..34a50c9eb4d4c8a96fb5540afc6dc4239c791be2 100644
--- a/matlab/read_variables.m
+++ b/matlab/read_variables.m
@@ -6,7 +6,7 @@ function dyn_data_01=read_variables(file_name_01,var_names_01,dyn_data_01,xls_sh
% INPUTS
% file_name_01: file name
% var_names_01: variables name
-% dyn_data_01:
+% dyn_data_01:
% xls_sheet: Excel sheet name
% xls_range: Excel range specification
%
@@ -65,64 +65,64 @@ fullname = [basename extension];
if ~exist(fullname)
error(['Can''t find datafile: ' fullname ]);
-end
+end
switch (extension)
- case '.m'
- eval(basename);
- for dyn_i_01=1:var_size_01
- dyn_tmp_01 = eval(var_names_01{dyn_i_01});
+ case '.m'
+ eval(basename);
+ for dyn_i_01=1:var_size_01
+ dyn_tmp_01 = eval(var_names_01{dyn_i_01});
+ if length(dyn_tmp_01) > dyn_size_01 && dyn_size_01 > 0
+ cd(old_pwd)
+ error('data size is too large')
+ end
+ dyn_data_01(:,dyn_i_01) = dyn_tmp_01;
+ end
+ case '.mat'
+ s = load(basename);
+ for dyn_i_01=1:var_size_01
+ dyn_tmp_01 = s.(var_names_01{dyn_i_01});
+ if length(dyn_tmp_01) > dyn_size_01 && dyn_size_01 > 0
+ cd(old_pwd)
+ error('data size is too large')
+ end
+ dyn_data_01(:,dyn_i_01) = dyn_tmp_01;
+ end
+ case { '.xls', '.xlsx' }
+ [freq,init,data,varlist] = load_xls_file_data(fullname,xls_sheet,xls_range);
+ for dyn_i_01=1:var_size_01
+ iv = strmatch(strtrim(var_names_01(dyn_i_01,:)),varlist,'exact');
+ if ~isempty(iv)
+ dyn_tmp_01 = [data(:,iv)]';
if length(dyn_tmp_01) > dyn_size_01 && dyn_size_01 > 0
cd(old_pwd)
error('data size is too large')
end
dyn_data_01(:,dyn_i_01) = dyn_tmp_01;
+ else
+ cd(old_pwd)
+ error([strtrim(var_names_01(dyn_i_01,:)) ' not found in ' fullname])
end
- case '.mat'
- s = load(basename);
- for dyn_i_01=1:var_size_01
- dyn_tmp_01 = s.(var_names_01{dyn_i_01});
+ end
+ case '.csv'
+ [freq,init,data,varlist] = load_csv_file_data(fullname);
+ for dyn_i_01=1:var_size_01
+ iv = strmatch(var_names_01{dyn_i_01},varlist,'exact');
+ if ~isempty(iv)
+ dyn_tmp_01 = [data(:,iv)]';
if length(dyn_tmp_01) > dyn_size_01 && dyn_size_01 > 0
cd(old_pwd)
error('data size is too large')
end
dyn_data_01(:,dyn_i_01) = dyn_tmp_01;
+ else
+ cd(old_pwd)
+ error([var_names_01{dyn_i_01} ' not found in ' fullname])
end
- case { '.xls', '.xlsx' }
- [freq,init,data,varlist] = load_xls_file_data(fullname,xls_sheet,xls_range);
- for dyn_i_01=1:var_size_01
- iv = strmatch(strtrim(var_names_01(dyn_i_01,:)),varlist,'exact');
- if ~isempty(iv)
- dyn_tmp_01 = [data(:,iv)]';
- if length(dyn_tmp_01) > dyn_size_01 && dyn_size_01 > 0
- cd(old_pwd)
- error('data size is too large')
- end
- dyn_data_01(:,dyn_i_01) = dyn_tmp_01;
- else
- cd(old_pwd)
- error([strtrim(var_names_01(dyn_i_01,:)) ' not found in ' fullname])
- end
- end
- case '.csv'
- [freq,init,data,varlist] = load_csv_file_data(fullname);
- for dyn_i_01=1:var_size_01
- iv = strmatch(var_names_01{dyn_i_01},varlist,'exact');
- if ~isempty(iv)
- dyn_tmp_01 = [data(:,iv)]';
- if length(dyn_tmp_01) > dyn_size_01 && dyn_size_01 > 0
- cd(old_pwd)
- error('data size is too large')
- end
- dyn_data_01(:,dyn_i_01) = dyn_tmp_01;
- else
- cd(old_pwd)
- error([var_names_01{dyn_i_01} ' not found in ' fullname])
- end
- end
- otherwise
- cd(old_pwd)
- error(['Unsupported extension for datafile: ' extension])
+ end
+ otherwise
+ cd(old_pwd)
+ error(['Unsupported extension for datafile: ' extension])
end
cd(old_pwd)
diff --git a/matlab/realtime_shock_decomposition.m b/matlab/realtime_shock_decomposition.m
index d160f767a52086fda6803a7c16f4d376d392f438..64bf96c6f7480eea70340e8cf887be41adef62cb 100644
--- a/matlab/realtime_shock_decomposition.m
+++ b/matlab/realtime_shock_decomposition.m
@@ -1,11 +1,11 @@
function oo_ = realtime_shock_decomposition(M_,oo_,options_,varlist,bayestopt_,estim_params_)
% function oo_ = realtime_shock_decomposition(M_,oo_,options_,varlist,bayestopt_,estim_params_)
% Computes shocks contribution to a simulated trajectory. The fields set are
-% oo_.realtime_shock_decomposition, oo_.conditional_shock_decomposition and oo_.realtime_forecast_shock_decomposition.
+% oo_.realtime_shock_decomposition, oo_.conditional_shock_decomposition and oo_.realtime_forecast_shock_decomposition.
% Subfields are arrays n_var by nshock+2 by nperiods. The
% first nshock columns store the respective shock contributions, column n+1
% stores the role of the initial conditions, while column n+2 stores the
-% value of the smoothed variables. Both the variables and shocks are stored
+% value of the smoothed variables. Both the variables and shocks are stored
% in the order of declaration, i.e. M_.endo_names and M_.exo_names, respectively.
%
% INPUTS
@@ -64,7 +64,7 @@ if isempty(parameter_set)
parameter_set = 'posterior_mode';
else
error(['realtime_shock_decomposition: option parameter_set is not specified ' ...
- 'and posterior mode is not available'])
+ 'and posterior mode is not available'])
end
end
@@ -104,22 +104,22 @@ if forecast_ && any(forecast_params)
end
for j=presample+1:nobs
-% evalin('base',['options_.nobs=' int2str(j) ';'])
+ % evalin('base',['options_.nobs=' int2str(j) ';'])
options_.nobs=j;
[oo, M_, junk2, junk3, Smoothed_Variables_deviation_from_mean] = evaluate_smoother(parameter_set,varlist,M_,oo_,options_,bayestopt_,estim_params_);
-
+
% reduced form
dr = oo.dr;
-
+
% data reordering
order_var = dr.order_var;
inv_order_var = dr.inv_order_var;
-
-
+
+
% coefficients
A = dr.ghx;
B = dr.ghu;
-
+
if forecast_
if any(forecast_params)
Af = oo1.dr.ghx;
@@ -137,20 +137,20 @@ for j=presample+1:nobs
epsilon(i,:) = oo.SmoothedShocks.(deblank(M_.exo_names(i,:)));
end
epsilon=[epsilon zeros(nshocks,forecast_)];
-
+
z = zeros(endo_nbr,nshocks+2,gend+forecast_);
-
+
z(:,end,1:gend) = Smoothed_Variables_deviation_from_mean;
-
+
maximum_lag = M_.maximum_lag;
-
+
k2 = dr.kstate(find(dr.kstate(:,2) <= maximum_lag+1),[1 2]);
i_state = order_var(k2(:,1))+(min(i,maximum_lag)+1-k2(:,2))*M_.endo_nbr;
for i=1:gend+forecast_
if i > 1 && i <= maximum_lag+1
lags = min(i-1,maximum_lag):-1:1;
end
-
+
if i > 1
tempx = permute(z(:,1:nshocks,lags),[1 3 2]);
m = min(i-1,maximum_lag);
@@ -165,16 +165,16 @@ for j=presample+1:nobs
lags = lags+1;
z(:,1:nshocks,i) = z(:,1:nshocks,i) + B(inv_order_var,:).*repmat(epsilon(:,i)',endo_nbr,1);
end
-
-% z(:,1:nshocks,i) = z(:,1:nshocks,i) + B(inv_order_var,:).*repmat(epsilon(:,i)',endo_nbr,1);
+
+ % z(:,1:nshocks,i) = z(:,1:nshocks,i) + B(inv_order_var,:).*repmat(epsilon(:,i)',endo_nbr,1);
z(:,nshocks+1,i) = z(:,nshocks+2,i) - sum(z(:,1:nshocks,i),2);
end
-
+
%% conditional shock decomp 1 step ahead
z1 = zeros(endo_nbr,nshocks+2);
z1(:,end) = Smoothed_Variables_deviation_from_mean(:,gend);
for i=gend
-
+
z1(:,1:nshocks) = z1(:,1:nshocks) + B(inv_order_var,:).*repmat(epsilon(:,i)',endo_nbr,1);
z1(:,nshocks+1) = z1(:,nshocks+2) - sum(z1(:,1:nshocks),2);
end
@@ -188,7 +188,7 @@ for j=presample+1:nobs
if i > 1 && i <= maximum_lag+1
lags = min(i-1,maximum_lag):-1:1;
end
-
+
if i > 1
tempx = permute(zn(:,1:nshocks,lags),[1 3 2]);
m = min(i-1,maximum_lag);
@@ -197,14 +197,14 @@ for j=presample+1:nobs
lags = lags+1;
zn(:,1:nshocks,i) = zn(:,1:nshocks,i) + B(inv_order_var,:).*repmat(epsilon(:,i+gend-forecast_-1)',endo_nbr,1);
end
-
-% zn(:,1:nshocks,i) = zn(:,1:nshocks,i) + B(inv_order_var,:).*repmat(epsilon(:,i+gend-forecast_-1)',endo_nbr,1);
+
+ % zn(:,1:nshocks,i) = zn(:,1:nshocks,i) + B(inv_order_var,:).*repmat(epsilon(:,i+gend-forecast_-1)',endo_nbr,1);
zn(:,nshocks+1,i) = zn(:,nshocks+2,i) - sum(zn(:,1:nshocks,i),2);
end
oo_.conditional_shock_decomposition.(['time_' int2str(j-forecast_)])=zn;
end
%%
-
+
if init
zreal(:,:,1:j) = z(:,:,1:j);
else
@@ -214,12 +214,12 @@ for j=presample+1:nobs
if ismember(j,save_realtime)
oo_.realtime_shock_decomposition.(['time_' int2str(j)])=z;
end
-
+
if forecast_
zfrcst(:,:,j+1) = z(:,:,gend+1);
oo_.realtime_forecast_shock_decomposition.(['time_' int2str(j)])=z(:,:,gend:end);
if j>forecast_+presample
- %% realtime conditional shock decomp k step ahead
+ %% realtime conditional shock decomp k step ahead
oo_.realtime_conditional_shock_decomposition.(['time_' int2str(j-forecast_)]) = ...
zreal(:,:,j-forecast_:j) - ...
oo_.realtime_forecast_shock_decomposition.(['time_' int2str(j-forecast_)]);
@@ -227,7 +227,7 @@ for j=presample+1:nobs
oo_.realtime_forecast_shock_decomposition.(['time_' int2str(j-forecast_)])(:,end,:);
oo_.realtime_conditional_shock_decomposition.(['time_' int2str(j-forecast_)])(:,end,:) = ...
zreal(:,end,j-forecast_:j);
-
+
if j==nobs
for my_forecast_=(forecast_-1):-1:1
oo_.realtime_conditional_shock_decomposition.(['time_' int2str(j-my_forecast_)]) = ...
@@ -239,10 +239,10 @@ for j=presample+1:nobs
zreal(:,end,j-my_forecast_:j);
end
end
-
+
end
end
-
+
prctdone=(j-presample)/(nobs-presample);
if isoctave
printf([running_text,' %3.f%% done\r'], prctdone*100);
diff --git a/matlab/recursive_moments.m b/matlab/recursive_moments.m
index 712a4b5978f2cfd10a6e35478e6d6dfae28b3a5a..e446c86bfdba863de3fbdf61fe7fb8da5427e5ef 100644
--- a/matlab/recursive_moments.m
+++ b/matlab/recursive_moments.m
@@ -1,20 +1,20 @@
function [mu,sigma,offset] = recursive_moments(m0,s0,data,offset)
% Recursive estimation of order one and two moments (expectation and
-% covariance matrix).
-%
-% INPUTS
+% covariance matrix).
+%
+% INPUTS
% o m0 [double] (n*1) vector, the prior expectation.
% o s0 [double] (n*n) matrix, the prior covariance matrix.
-% o data [double] (T*n) matrix.
+% o data [double] (T*n) matrix.
% o offset [integer] scalar, number of observation previously
% used to compute m0 and s0.
-% OUTPUTS
-% o mu [double] (n*1) vector, the posterior expectation.
+% OUTPUTS
+% o mu [double] (n*1) vector, the posterior expectation.
% o sigma [double] (n*n) matrix, the posterior covariance matrix.
% o offset [integer] = offset + T.
%
-% ALGORITHM
-% None.
+% ALGORITHM
+% None.
%
% SPECIAL REQUIREMENTS
% None.
@@ -47,6 +47,6 @@ for t = 1:T
s0 = s1;
end
-mu = m1;
+mu = m1;
sigma = s1;
offset = offset+T;
\ No newline at end of file
diff --git a/matlab/reduced_rank_cholesky.m b/matlab/reduced_rank_cholesky.m
index c1870e96cf598e5ea64795d3402da5487cd4c7a5..fe9edbacef6c5f44f1e1c84a917cd891a0168edb 100644
--- a/matlab/reduced_rank_cholesky.m
+++ b/matlab/reduced_rank_cholesky.m
@@ -51,8 +51,8 @@ function T = reduced_rank_cholesky(X)
% 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/>.
-
+% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
+
[T,X_is_not_positive_definite] = chol(X);
if X_is_not_positive_definite
diff --git a/matlab/resid.m b/matlab/resid.m
index 58e421bc9e53f92f83599e829f3d4e1ddd614508..817544f8f262b3afdb3f14126eff2791e64c721c 100644
--- a/matlab/resid.m
+++ b/matlab/resid.m
@@ -2,13 +2,13 @@ function z = resid(junk)
% function z = resid(junk)
%
% Computes static residuals associated with the guess values.
-%
+%
% INPUTS
% junk: dummy value for backward compatibility
-%
+%
% OUTPUTS
% z: residuals
-%
+%
% SPECIAL REQUIREMENTS
% none
@@ -58,9 +58,9 @@ if any(imag(oo_.steady_state))
for iter=1:length(imagrow)
fprintf('%s\n',M_.endo_names(imagrow(iter),:));
end
- end
+ end
end
-
+
if options_.steadystate_flag
[oo_.steady_state,M_.params,info] = ...
evaluate_steady_state(oo_.steady_state,M_,options_,oo_,0);
diff --git a/matlab/rotated_slice_sampler.m b/matlab/rotated_slice_sampler.m
index d249cb0c611ed3eb3b095d204aa7d6c741afb283..6dbfded0f461b2492fc4e64e5ecb32bee4f3014e 100644
--- a/matlab/rotated_slice_sampler.m
+++ b/matlab/rotated_slice_sampler.m
@@ -1,184 +1,183 @@
-function [theta, fxsim, neval] = rotated_slice_sampler(objective_function,theta,thetaprior,sampler_options,varargin)
-% ----------------------------------------------------------
-% ROTATED SLICE SAMPLER - with stepping out (Neal, 2003)
-% extension of the orthogonal univarite sampler (slice_sampler.m)
-% copyright M. Ratto (European Commission)
-%
-% objective_function(theta,varargin): -log of any unnormalized pdf
-% with varargin (optional) a vector of auxiliaty parameters
-% to be passed to f( ).
-% ----------------------------------------------------------
-%
-% INPUTS
-% objective_function: objective function (expressed as minus the log of a density)
-% theta: last value of theta
-% thetaprior: bounds of the theta space
-% sampler_options: posterior sampler options
-% varargin: optional input arguments to objective function
-%
-% OUTPUTS
-% theta: new theta sample
-% fxsim: value of the objective function for the new sample
-% neval: number of function evaluations
-%
-% SPECIAL REQUIREMENTS
-% none
-
-% Copyright (C) 2015-2017 Dynare Team
-%
-% 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/>.
-
-theta=theta(:);
-npar = length(theta);
-neval = zeros(npar,1);
-W1=[];
-if isfield(sampler_options,'WR')
- W1 = sampler_options.WR;
-end
-if ~isempty(sampler_options.mode)
- mm = sampler_options.mode;
- n = length(mm);
- for j=1:n
- distance(j)=sqrt(sum((theta-mm(j).m).^2));
- end
- [m, im] = min(distance);
-
- r=im;
- V1 = mm(r).m;
- jj=0;
- for j=1:n
- if j~=r
- jj=jj+1;
- tmp=mm(j).m-mm(r).m;
- %tmp=mm(j).m-theta;
- V1(:,jj)=tmp/norm(tmp);
- end
- end
- resul=randperm(n-1,n-1);
- V1 = V1(:,resul);
-
- %V1 = V1(:, randperm(n-1));
-% %d = chol(mm(r).invhess);
-% %V1 = transpose(feval(sampler_options.proposal_distribution, transpose(mm(r).m), d, npar));
-%
-% V1=eye(npar);
-% V1=V1(:,randperm(npar));
-% for j=1:2,
-% V1(:,j)=mm(r(j)).m-theta;
-% V1(:,j)=V1(:,j)/norm(V1(:,j));
-% end
-% % Gram-Schmidt
-% for j=2:npar,
-% for k=1:j-1,
-% V1(:,j)=V1(:,j)-V1(:,k)'*V1(:,j)*V1(:,k);
-% end
-% V1(:,j)=V1(:,j)/norm(V1(:,j));
-% end
-% for j=1:n,
-% distance(j)=sqrt(sum((theta-mm(j).m).^2));
-% end
-% [m, im] = min(distance);
-% if im==r,
-% fxsim=[];
-% return,
-% else
-% theta1=theta;
-% end
-else
- V1 = sampler_options.V1;
-end
-npar=size(V1,2);
-
-for it=1:npar
- theta0 = theta;
- neval(it) = 0;
- xold = 0;
- % XLB = thetaprior(3);
- % XUB = thetaprior(4);
- tb=sort([(thetaprior(:,1)-theta)./V1(:,it) (thetaprior(:,2)-theta)./V1(:,it)],2);
- XLB=max(tb(:,1));
- XUB=min(tb(:,2));
- if isempty(W1)
- W = (XUB-XLB); %*0.8;
- else
- W = W1(it);
- end
-
- % -------------------------------------------------------
- % 1. DRAW Z = ln[f(X0)] - EXP(1) where EXP(1)=-ln(U(0,1))
- % THIS DEFINES THE SLICE S={x: z < ln(f(x))}
- % -------------------------------------------------------
-
- fxold = -feval(objective_function,theta,varargin{:});
- %I have to be sure that the rotation is for L,R or for Fxold, theta(it)
- neval(it) = neval(it) + 1;
- Z = fxold + log(rand(1,1));
- % -------------------------------------------------------------
- % 2. FIND I=(L,R) AROUND X0 THAT CONTAINS S AS MUCH AS POSSIBLE
- % STEPPING-OUT PROCEDURE
- % -------------------------------------------------------------
- u = rand(1,1);
- L = max(XLB,xold-W*u);
- R = min(XUB,L+W);
-
- %[L R]=slice_rotation(L, R, alpha);
- while(L > XLB)
- xsim = L;
- theta = theta0+xsim*V1(:,it);
- fxl = -feval(objective_function,theta,varargin{:});
- neval(it) = neval(it) + 1;
- if (fxl <= Z)
- break
- end
- L = max(XLB,L-W);
- end
- while(R < XUB)
- xsim = R;
- theta = theta0+xsim*V1(:,it);
- fxr = -feval(objective_function,theta,varargin{:});
- neval(it) = neval(it) + 1;
- if (fxr <= Z)
- break
- end
- R = min(XUB,R+W);
- end
- % ------------------------------------------------------
- % 3. SAMPLING FROM THE SET A = (I INTERSECT S) = (LA,RA)
- % ------------------------------------------------------
- fxsim = Z-1;
- while (fxsim < Z)
- u = rand(1,1);
- xsim = L + u*(R - L);
- theta = theta0+xsim*V1(:,it);
- fxsim = -feval(objective_function,theta,varargin{:});
- neval(it) = neval(it) + 1;
- if (xsim > xold)
- R = xsim;
- else
- L = xsim;
- end
- end
-end
-
-% if ~isempty(sampler_options.mode),
-% dist1=sqrt(sum((theta-mm(r).m).^2));
-% if dist1>distance(r),
-% theta=theta1;
-% fxsim=[];
-% end
-% end
-end
-
+function [theta, fxsim, neval] = rotated_slice_sampler(objective_function,theta,thetaprior,sampler_options,varargin)
+% ----------------------------------------------------------
+% ROTATED SLICE SAMPLER - with stepping out (Neal, 2003)
+% extension of the orthogonal univarite sampler (slice_sampler.m)
+% copyright M. Ratto (European Commission)
+%
+% objective_function(theta,varargin): -log of any unnormalized pdf
+% with varargin (optional) a vector of auxiliaty parameters
+% to be passed to f( ).
+% ----------------------------------------------------------
+%
+% INPUTS
+% objective_function: objective function (expressed as minus the log of a density)
+% theta: last value of theta
+% thetaprior: bounds of the theta space
+% sampler_options: posterior sampler options
+% varargin: optional input arguments to objective function
+%
+% OUTPUTS
+% theta: new theta sample
+% fxsim: value of the objective function for the new sample
+% neval: number of function evaluations
+%
+% SPECIAL REQUIREMENTS
+% none
+
+% Copyright (C) 2015-2017 Dynare Team
+%
+% 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/>.
+
+theta=theta(:);
+npar = length(theta);
+neval = zeros(npar,1);
+W1=[];
+if isfield(sampler_options,'WR')
+ W1 = sampler_options.WR;
+end
+if ~isempty(sampler_options.mode)
+ mm = sampler_options.mode;
+ n = length(mm);
+ for j=1:n
+ distance(j)=sqrt(sum((theta-mm(j).m).^2));
+ end
+ [m, im] = min(distance);
+
+ r=im;
+ V1 = mm(r).m;
+ jj=0;
+ for j=1:n
+ if j~=r
+ jj=jj+1;
+ tmp=mm(j).m-mm(r).m;
+ %tmp=mm(j).m-theta;
+ V1(:,jj)=tmp/norm(tmp);
+ end
+ end
+ resul=randperm(n-1,n-1);
+ V1 = V1(:,resul);
+
+ %V1 = V1(:, randperm(n-1));
+ % %d = chol(mm(r).invhess);
+ % %V1 = transpose(feval(sampler_options.proposal_distribution, transpose(mm(r).m), d, npar));
+ %
+ % V1=eye(npar);
+ % V1=V1(:,randperm(npar));
+ % for j=1:2,
+ % V1(:,j)=mm(r(j)).m-theta;
+ % V1(:,j)=V1(:,j)/norm(V1(:,j));
+ % end
+ % % Gram-Schmidt
+ % for j=2:npar,
+ % for k=1:j-1,
+ % V1(:,j)=V1(:,j)-V1(:,k)'*V1(:,j)*V1(:,k);
+ % end
+ % V1(:,j)=V1(:,j)/norm(V1(:,j));
+ % end
+ % for j=1:n,
+ % distance(j)=sqrt(sum((theta-mm(j).m).^2));
+ % end
+ % [m, im] = min(distance);
+ % if im==r,
+ % fxsim=[];
+ % return,
+ % else
+ % theta1=theta;
+ % end
+else
+ V1 = sampler_options.V1;
+end
+npar=size(V1,2);
+
+for it=1:npar
+ theta0 = theta;
+ neval(it) = 0;
+ xold = 0;
+ % XLB = thetaprior(3);
+ % XUB = thetaprior(4);
+ tb=sort([(thetaprior(:,1)-theta)./V1(:,it) (thetaprior(:,2)-theta)./V1(:,it)],2);
+ XLB=max(tb(:,1));
+ XUB=min(tb(:,2));
+ if isempty(W1)
+ W = (XUB-XLB); %*0.8;
+ else
+ W = W1(it);
+ end
+
+ % -------------------------------------------------------
+ % 1. DRAW Z = ln[f(X0)] - EXP(1) where EXP(1)=-ln(U(0,1))
+ % THIS DEFINES THE SLICE S={x: z < ln(f(x))}
+ % -------------------------------------------------------
+
+ fxold = -feval(objective_function,theta,varargin{:});
+ %I have to be sure that the rotation is for L,R or for Fxold, theta(it)
+ neval(it) = neval(it) + 1;
+ Z = fxold + log(rand(1,1));
+ % -------------------------------------------------------------
+ % 2. FIND I=(L,R) AROUND X0 THAT CONTAINS S AS MUCH AS POSSIBLE
+ % STEPPING-OUT PROCEDURE
+ % -------------------------------------------------------------
+ u = rand(1,1);
+ L = max(XLB,xold-W*u);
+ R = min(XUB,L+W);
+
+ %[L R]=slice_rotation(L, R, alpha);
+ while(L > XLB)
+ xsim = L;
+ theta = theta0+xsim*V1(:,it);
+ fxl = -feval(objective_function,theta,varargin{:});
+ neval(it) = neval(it) + 1;
+ if (fxl <= Z)
+ break
+ end
+ L = max(XLB,L-W);
+ end
+ while(R < XUB)
+ xsim = R;
+ theta = theta0+xsim*V1(:,it);
+ fxr = -feval(objective_function,theta,varargin{:});
+ neval(it) = neval(it) + 1;
+ if (fxr <= Z)
+ break
+ end
+ R = min(XUB,R+W);
+ end
+ % ------------------------------------------------------
+ % 3. SAMPLING FROM THE SET A = (I INTERSECT S) = (LA,RA)
+ % ------------------------------------------------------
+ fxsim = Z-1;
+ while (fxsim < Z)
+ u = rand(1,1);
+ xsim = L + u*(R - L);
+ theta = theta0+xsim*V1(:,it);
+ fxsim = -feval(objective_function,theta,varargin{:});
+ neval(it) = neval(it) + 1;
+ if (xsim > xold)
+ R = xsim;
+ else
+ L = xsim;
+ end
+ end
+end
+
+% if ~isempty(sampler_options.mode),
+% dist1=sqrt(sum((theta-mm(r).m).^2));
+% if dist1>distance(r),
+% theta=theta1;
+% fxsim=[];
+% end
+% end
+end
diff --git a/matlab/row_header_width.m b/matlab/row_header_width.m
index c3132e261be6f7941acb498709c73d7cbc70e6ff..2ad7dee285bb1ab68616cbc49c9e61d26c18ccba 100644
--- a/matlab/row_header_width.m
+++ b/matlab/row_header_width.m
@@ -1,13 +1,13 @@
function w=row_header_width(M_,estim_params_,bayestopt_)
% This function computes the width of the row headers for
% the estimation results
-%
-% INPUTS
-% estim_params_ [structure]
+%
+% INPUTS
+% estim_params_ [structure]
% M_ [structure]
% bayestopt_ [structure]
-%
-% OUTPUTS
+%
+% OUTPUTS
% w integer
%
% SPECIAL REQUIREMENTS
@@ -44,13 +44,13 @@ if np
end
if nvx
for i=1:nvx
- k = estim_params_.var_exo(i,1);
+ k = estim_params_.var_exo(i,1);
w = max(w,length(deblank(M_.exo_names(k,:))));
end
end
if nvn
for i=1:nvn
- k = estim_params_.var_endo(i,1);
+ k = estim_params_.var_endo(i,1);
w = max(w,length(deblank(M_.endo_names(k,:))));
end
end
@@ -72,4 +72,3 @@ if ncn
end
end
-
diff --git a/matlab/rplot.m b/matlab/rplot.m
index 5f190189411d5cc021eea821f4def8f3d20a0efb..cc7f30515cfc48c5033ddd9d8a4d0d10a7d3082e 100644
--- a/matlab/rplot.m
+++ b/matlab/rplot.m
@@ -52,11 +52,11 @@ ix = [1 - M_.maximum_lag:size(oo_.endo_simul,2)-M_.maximum_lag]' ;
y = [];
for k=1:size(s1,1)
- if isempty(strmatch(deblank(s1(k,:)),M_.endo_names,'exact'))
- if isempty(strmatch(deblank(s1(k,:)),M_.exo_names,'exact'))
+ if isempty(strmatch(deblank(s1(k,:)),M_.endo_names,'exact'))
+ if isempty(strmatch(deblank(s1(k,:)),M_.exo_names,'exact'))
error (['rplot: One of the variables specified does not exist']) ;
else
- y = [y; oo_.exo_simul(:,strmatch(deblank(s1(k,:)),M_.exo_names,'exact'))'] ;
+ y = [y; oo_.exo_simul(:,strmatch(deblank(s1(k,:)),M_.exo_names,'exact'))'] ;
end
else
y = [y; oo_.endo_simul(strmatch(deblank(s1(k,:)),M_.endo_names,'exact'),:)] ;
@@ -140,16 +140,16 @@ if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
end
function []=create_TeX_loader(fidTeX,options,figpath,caption,label_name,label_type,scale_factor)
- if nargin<6
- scale_factor=1;
- end
- fprintf(fidTeX,' \n');
- fprintf(fidTeX,'\\begin{figure}[H]\n');
- fprintf(fidTeX,'\\centering \n');
- fprintf(fidTeX,'\\includegraphics[width=%2.2f\\textwidth]{%s}\n',0.8*scale_factor,strrep(figpath,'\','/'));
- fprintf(fidTeX,'\\caption{%s.}',caption);
- fprintf(fidTeX,'\\label{Fig:%s:%s}\n',label_name,label_type);
- fprintf(fidTeX,'\\end{figure}\n\n');
+if nargin<6
+ scale_factor=1;
+end
+fprintf(fidTeX,' \n');
+fprintf(fidTeX,'\\begin{figure}[H]\n');
+fprintf(fidTeX,'\\centering \n');
+fprintf(fidTeX,'\\includegraphics[width=%2.2f\\textwidth]{%s}\n',0.8*scale_factor,strrep(figpath,'\','/'));
+fprintf(fidTeX,'\\caption{%s.}',caption);
+fprintf(fidTeX,'\\label{Fig:%s:%s}\n',label_name,label_type);
+fprintf(fidTeX,'\\end{figure}\n\n');
% 02/28/01 MJ replaced bseastr by MATLAB's strmatch
% 06/19/01 MJ added 'exact' to strmatch calls
diff --git a/matlab/sample_autocovariance.m b/matlab/sample_autocovariance.m
index 12bd34e86f2a8621c5f0bd36f1d3d242056f3d13..67f1f9b592ee5627b9d2c557233040405cf455a2 100644
--- a/matlab/sample_autocovariance.m
+++ b/matlab/sample_autocovariance.m
@@ -1,14 +1,14 @@
function [autocov,autocor] = sample_autocovariance(data,q)
% Computes the autocovariance function associated to a time series.
-%
%
-% INPUTS
+%
+% INPUTS
%
% data [double] T*1 vector of data.
-% q [integer] Order of the autocovariance function.
-%
-% OUTPUTS
-% autocov [double] (q+1)*1 vector, autocovariance function (first scalar is the variance).
+% q [integer] Order of the autocovariance function.
+%
+% OUTPUTS
+% autocov [double] (q+1)*1 vector, autocovariance function (first scalar is the variance).
% autocor [double] (q+1)*1 vector, autocorrelation function (first scalar is equal to one).
%
% SPECIAL REQUIREMENTS
diff --git a/matlab/sample_hp_filter.m b/matlab/sample_hp_filter.m
index 00aae0e020d59d64ecfa323b6395d6d664e700d0..911d0aabda3104c6e69e2a9f36e64635ef130175 100644
--- a/matlab/sample_hp_filter.m
+++ b/matlab/sample_hp_filter.m
@@ -1,16 +1,16 @@
function [hptrend,hpcycle] = sample_hp_filter(y,s)
% HP filters a collection of time series.
-%
-% INPUTS
+%
+% INPUTS
% y [double] T*n matrix of data (n is the number of variables)
% s [double] scalar, smoothing parameter.
-%
-% OUTPUTS
+%
+% OUTPUTS
% hptrend [double] T*n matrix, trend component of y.
-% hpcycle [double] T*n matrix, cycle component of y.
-%
+% hpcycle [double] T*n matrix, cycle component of y.
+%
% SPECIAL REQUIREMENTS
-%
+%
% Copyright (C) 2010-2013 Dynare Team
%
diff --git a/matlab/save_params_and_steady_state.m b/matlab/save_params_and_steady_state.m
index 0f8dbfe1a4435190812a3103701d64049484b23a..bbfee51a6c4a75424255896d0b3be8b02319abb8 100644
--- a/matlab/save_params_and_steady_state.m
+++ b/matlab/save_params_and_steady_state.m
@@ -12,10 +12,10 @@ function save_params_and_steady_state(filename)
% Note that no variable type is stored in the file, so that the values
% can be reloaded (with load_params_and_steady_state) in a setup where
% the variable types are different.
-%
+%
% INPUTS
% filename: where to store the saved values
-%
+%
% OUTPUTS
% none
%
diff --git a/matlab/score.m b/matlab/score.m
index 273ca639537cc49807369e58f71156023eac6afe..112be534571a254244d68baeb2c89d301b02df7b 100644
--- a/matlab/score.m
+++ b/matlab/score.m
@@ -25,95 +25,95 @@ function [DLIK] = score(T,R,Q,H,P,Y,DT,DYss,DOm,DH,DP,start,mf,kalman_tol,riccat
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licen
- k = size(DT,3); % number of structural parameters
- smpl = size(Y,2); % Sample size.
- mm = size(T,2); % Number of state variables.
- a = zeros(mm,1); % State vector.
- Om = R*Q*transpose(R); % Variance of R times the vector of structural innovations.
- t = 0; % Initialization of the time index.
- oldK = 0;
- notsteady = 1; % Steady state flag.
- F_singular = 1;
-
- DLIK = zeros(k,1); % Initialization of the score.
- Da = zeros(mm,k); % State vector.
- Dv = zeros(length(mf),k); % observation vector.
-
+k = size(DT,3); % number of structural parameters
+smpl = size(Y,2); % Sample size.
+mm = size(T,2); % Number of state variables.
+a = zeros(mm,1); % State vector.
+Om = R*Q*transpose(R); % Variance of R times the vector of structural innovations.
+t = 0; % Initialization of the time index.
+oldK = 0;
+notsteady = 1; % Steady state flag.
+F_singular = 1;
+
+DLIK = zeros(k,1); % Initialization of the score.
+Da = zeros(mm,k); % State vector.
+Dv = zeros(length(mf),k); % observation vector.
+
% for ii = 1:k
-% DOm = DR(:,:,ii)*Q*transpose(R) + R*DQ(:,:,ii)*transpose(R) + R*Q*transpose(DR(:,:,ii));
+% DOm = DR(:,:,ii)*Q*transpose(R) + R*DQ(:,:,ii)*transpose(R) + R*Q*transpose(DR(:,:,ii));
% end
-
- while notsteady & t<smpl
- t = t+1;
- v = Y(:,t)-a(mf);
- F = P(mf,mf) + H;
- if rcond(F) < kalman_tol
- if ~all(abs(F(:))<kalman_tol)
- return
- else
- a = T*a;
- P = T*P*transpose(T)+Om;
- end
+
+while notsteady & t<smpl
+ t = t+1;
+ v = Y(:,t)-a(mf);
+ F = P(mf,mf) + H;
+ if rcond(F) < kalman_tol
+ if ~all(abs(F(:))<kalman_tol)
+ return
else
- F_singular = 0;
- iF = inv(F);
- K = P(:,mf)*iF;
-
- [DK,DF,DP1] = computeDKalman(T,DT,DOm,P,DP,DH,mf,iF,K);
- for ii = 1:k
- Dv(:,ii) = -Da(mf,ii)-DYss(mf,ii);
- Da(:,ii) = DT(:,:,ii)*(a+K*v) + T*(Da(:,ii)+DK(:,:,ii)*v + K*Dv(:,ii));
- if t>=start
- DLIK(ii,1) = DLIK(ii,1) + trace( iF*DF(:,:,ii) ) + 2*Dv(:,ii)'*iF*v - v'*(iF*DF(:,:,ii)*iF)*v;
- end
+ a = T*a;
+ P = T*P*transpose(T)+Om;
+ end
+ else
+ F_singular = 0;
+ iF = inv(F);
+ K = P(:,mf)*iF;
+
+ [DK,DF,DP1] = computeDKalman(T,DT,DOm,P,DP,DH,mf,iF,K);
+ for ii = 1:k
+ Dv(:,ii) = -Da(mf,ii)-DYss(mf,ii);
+ Da(:,ii) = DT(:,:,ii)*(a+K*v) + T*(Da(:,ii)+DK(:,:,ii)*v + K*Dv(:,ii));
+ if t>=start
+ DLIK(ii,1) = DLIK(ii,1) + trace( iF*DF(:,:,ii) ) + 2*Dv(:,ii)'*iF*v - v'*(iF*DF(:,:,ii)*iF)*v;
end
- a = T*(a+K*v);
- P = T*(P-K*P(mf,:))*transpose(T)+Om;
- DP = DP1;
end
- notsteady = max(max(abs(K-oldK))) > riccati_tol;
- oldK = K;
+ a = T*(a+K*v);
+ P = T*(P-K*P(mf,:))*transpose(T)+Om;
+ DP = DP1;
end
+ notsteady = max(max(abs(K-oldK))) > riccati_tol;
+ oldK = K;
+end
- if F_singular
- error('The variance of the forecast error remains singular until the end of the sample')
- end
+if F_singular
+ error('The variance of the forecast error remains singular until the end of the sample')
+end
- for ii = 1:k
- tmp0(:,:,ii) = iF*DF(:,:,ii)*iF;
- end
-
- if t < smpl
- t0 = t+1;
- while t < smpl
- t = t+1;
- v = Y(:,t)-a(mf);
- for ii = 1:k
- Dv(:,ii) = -Da(mf,ii)-DYss(mf,ii);
- Da(:,ii) = DT(:,:,ii)*(a+K*v) + T*(Da(:,ii)+DK(:,:,ii)*v + K*Dv(:,ii));
- if t>=start
- DLIK(ii,1) = DLIK(ii,1) + trace( iF*DF(:,:,ii) ) + 2*Dv(:,ii)'*iF*v - v'*(iF*DF(:,:,ii)*iF)*v;
- end
- end
- a = T*(a+K*v);
- end
+for ii = 1:k
+ tmp0(:,:,ii) = iF*DF(:,:,ii)*iF;
+end
+
+if t < smpl
+ t0 = t+1;
+ while t < smpl
+ t = t+1;
+ v = Y(:,t)-a(mf);
for ii = 1:k
-% DLIK(ii,1) = DLIK(ii,1) + (smpl-t0+1)*trace( iF*DF(:,:,ii) );
+ Dv(:,ii) = -Da(mf,ii)-DYss(mf,ii);
+ Da(:,ii) = DT(:,:,ii)*(a+K*v) + T*(Da(:,ii)+DK(:,:,ii)*v + K*Dv(:,ii));
+ if t>=start
+ DLIK(ii,1) = DLIK(ii,1) + trace( iF*DF(:,:,ii) ) + 2*Dv(:,ii)'*iF*v - v'*(iF*DF(:,:,ii)*iF)*v;
+ end
end
-
- end
-
- DLIK = DLIK/2;
-
-% end of main function
-
+ a = T*(a+K*v);
+ end
+ for ii = 1:k
+ % DLIK(ii,1) = DLIK(ii,1) + (smpl-t0+1)*trace( iF*DF(:,:,ii) );
+ end
+
+end
+
+DLIK = DLIK/2;
+
+% end of main function
+
function [DK,DF,DP1] = computeDKalman(T,DT,DOm,P,DP,DH,mf,iF,K)
- k = size(DT,3);
- tmp = P-K*P(mf,:);
+k = size(DT,3);
+tmp = P-K*P(mf,:);
for ii = 1:k
- DF(:,:,ii) = DP(mf,mf,ii) + DH(:,:,ii);
+ DF(:,:,ii) = DP(mf,mf,ii) + DH(:,:,ii);
DiF(:,:,ii) = -iF*DF(:,:,ii)*iF;
DK(:,:,ii) = DP(:,mf,ii)*iF + P(:,mf)*DiF(:,:,ii);
Dtmp = DP(:,:,ii) - DK(:,:,ii)*P(mf,:) - K*DP(mf,:,ii);
@@ -121,6 +121,3 @@ for ii = 1:k
end
% end of computeDKalman
-
-
-
\ No newline at end of file
diff --git a/matlab/selif.m b/matlab/selif.m
index 752143aa5c85015339a142723696b9160f734a0c..452adcc391c679ba22ce8b40d8ecf8336cca1424 100644
--- a/matlab/selif.m
+++ b/matlab/selif.m
@@ -18,10 +18,9 @@ function x = selif(a,b)
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
if size(b,2) ~= 1
- error ('The second argument in SELIF must be � column vector') ;
+ error ('The second argument in SELIF must be à column vector') ;
end
x = a(find(b == 1),:) ;
return ;
-
diff --git a/matlab/set_all_parameters.m b/matlab/set_all_parameters.m
index e4daafc37e014040f9abb372773dbec8fe1b41b2..3b52d3da438d89a87b32ab62b17989abae3e472a 100644
--- a/matlab/set_all_parameters.m
+++ b/matlab/set_all_parameters.m
@@ -95,7 +95,7 @@ if ncx
end
%build covariance matrix from correlation matrix and variances already on
%diagonal
-Sigma_e = diag(sqrt(diag(Sigma_e)))*Correlation_matrix*diag(sqrt(diag(Sigma_e)));
+Sigma_e = diag(sqrt(diag(Sigma_e)))*Correlation_matrix*diag(sqrt(diag(Sigma_e)));
%if calibrated covariances, set them now to their stored value
if isfield(estim_params,'calibrated_covariances')
Sigma_e(estim_params.calibrated_covariances.position)=estim_params.calibrated_covariances.cov_value;
@@ -136,5 +136,5 @@ if nvx || ncx
end
if nvn || ncn
M.H = H;
- M.Correlation_matrix_ME=Correlation_matrix_ME;
+ M.Correlation_matrix_ME=Correlation_matrix_ME;
end
\ No newline at end of file
diff --git a/matlab/set_default_option.m b/matlab/set_default_option.m
index 0ece729afc4b0afddfa6962c01744031f6fcf3c4..94e41c4757f4909fdd9d0d625a39c0d359a9f04e 100644
--- a/matlab/set_default_option.m
+++ b/matlab/set_default_option.m
@@ -1,16 +1,16 @@
function options=set_default_option(options,field,default)
% function options=set_default_option(options,field,default)
-% Sets the option value
-%
+% Sets the option value
+%
% INPUTS
% options
% field: option name
% default: assigns a value
-%
+%
% OUTPUTS
% options
-%
+%
% SPECIAL REQUIREMENTS
% none
diff --git a/matlab/set_dynare_random_generator_state.m b/matlab/set_dynare_random_generator_state.m
index f5561b2e9f3270a61a075406991ce27706427fc0..0cfda7dd2ed053d36adcc6bccb1fb4242160fe76 100644
--- a/matlab/set_dynare_random_generator_state.m
+++ b/matlab/set_dynare_random_generator_state.m
@@ -1,11 +1,11 @@
function [state_u,state_n] = set_dynare_random_generator_state(state_u,state_n)
% Wet state of Matlab/Octave random generator depending on matlab
-% (octave) version.
+% (octave) version.
% In older versions, Matlab kept one generator for uniformly distributed numbers and
-% one for normally distributed numbers.
+% one for normally distributed numbers.
% For backward compatibility, we return two vectors, but, in recent
% versions of Matlab and in Octave, we return two identical vectors.
-%
+%
% Copyright (C) 2010-2013 Dynare Team
%
% This file is part of Dynare.
@@ -23,37 +23,37 @@ function [state_u,state_n] = set_dynare_random_generator_state(state_u,state_n)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- matlab_random_streams = ~(isoctave || matlab_ver_less_than('7.7'));
+matlab_random_streams = ~(isoctave || matlab_ver_less_than('7.7'));
- if matlab_random_streams% Use new matlab interface.
+if matlab_random_streams% Use new matlab interface.
+ if matlab_ver_less_than('7.12')
+ s = RandStream.getDefaultStream();
+ else
+ s = RandStream.getGlobalStream();
+ end
+ if isequal(s.Type,'legacy')
+ rand('state',state_u);
+ randn('state',state_n);
+ else
+ if ~isequal(state_u,state_n)
+ error(['You are using the new Matlab RandStream mechanism ' ...
+ 'with a single random generator, but the values ' ...
+ 'of the state of the uniformly ' ...
+ 'distributed numbers and of the state of the ' ...
+ 'normally distributed numbers are different. Something must be ' ...
+ 'wrong, such as reloading old Metropolis runs, ' ...
+ 'computed on a different version of Matlab. If you ' ...
+ 'don''t understand the origin of the problem, ' ...
+ 'please, contact Dynare''s development team.'])
+ end
+ s.State = state_u;
if matlab_ver_less_than('7.12')
- s = RandStream.getDefaultStream();
+ RandStream.setDefaultStream(s);
else
- s = RandStream.getGlobalStream();
+ RandStream.setGlobalStream(s);
end
- if isequal(s.Type,'legacy')
- rand('state',state_u);
- randn('state',state_n);
- else
- if ~isequal(state_u,state_n)
- error(['You are using the new Matlab RandStream mechanism ' ...
- 'with a single random generator, but the values ' ...
- 'of the state of the uniformly ' ...
- 'distributed numbers and of the state of the ' ...
- 'normally distributed numbers are different. Something must be ' ...
- 'wrong, such as reloading old Metropolis runs, ' ...
- 'computed on a different version of Matlab. If you ' ...
- 'don''t understand the origin of the problem, ' ...
- 'please, contact Dynare''s development team.'])
- end
- s.State = state_u;
- if matlab_ver_less_than('7.12')
- RandStream.setDefaultStream(s);
- else
- RandStream.setGlobalStream(s);
- end
- end
- else% Use old matlab interface.
- rand('state',state_u);
- randn('state',state_n);
- end
\ No newline at end of file
+ end
+else% Use old matlab interface.
+ rand('state',state_u);
+ randn('state',state_n);
+end
\ No newline at end of file
diff --git a/matlab/set_dynare_seed.m b/matlab/set_dynare_seed.m
index 3a71d7236c03ff64feeaff862cdd5b0bbac681e7..e79dadef997f258dcb24c7a753e7ef9071b2e14d 100644
--- a/matlab/set_dynare_seed.m
+++ b/matlab/set_dynare_seed.m
@@ -1,7 +1,7 @@
function set_dynare_seed(a,b)
-% Set seeds depending on matlab (octave) version. This routine is called in dynare_config and can be called by the
+% Set seeds depending on matlab (octave) version. This routine is called in dynare_config and can be called by the
% user in the mod file.
-%
+%
% Copyright (C) 2010-2014 Dynare Team
%
% This file is part of Dynare.
diff --git a/matlab/set_parameters.m b/matlab/set_parameters.m
index c183bb3d311bdf7d9247df18a75fb63721e85d36..932e94c1ef08241a37a982feb5659e781ff121fa 100644
--- a/matlab/set_parameters.m
+++ b/matlab/set_parameters.m
@@ -4,13 +4,13 @@ function set_parameters(xparam1)
% Sets parameters value (except measurement errors)
% This is called for computations such as IRF and forecast
% when measurement errors aren't taken into account
-%
+%
% INPUTS
% xparam1: vector of parameters to be estimated (initial values)
-%
+%
% OUTPUTS
% none
-%
+%
% SPECIAL REQUIREMENTS
% none
@@ -66,7 +66,7 @@ if ncx
end
%build covariance matrix from correlation matrix and variances already on
%diagonal
-Sigma_e = diag(sqrt(diag(Sigma_e)))*Correlation_matrix*diag(sqrt(diag(Sigma_e)));
+Sigma_e = diag(sqrt(diag(Sigma_e)))*Correlation_matrix*diag(sqrt(diag(Sigma_e)));
if isfield(estim_params_,'calibrated_covariances')
Sigma_e(estim_params_.calibrated_covariances.position)=estim_params_.calibrated_covariances.cov_value;
end
diff --git a/matlab/set_prior.m b/matlab/set_prior.m
index b2402d0cc18e4708eaad64775df4ac753b430170..dc7922916e722b79332e6cf9f3c12f992f5cc0a5 100644
--- a/matlab/set_prior.m
+++ b/matlab/set_prior.m
@@ -4,17 +4,17 @@ function [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params
%
% INPUTS
% o estim_params_ [structure] characterizing parameters to be estimated.
-% o M_ [structure] characterizing the model.
-% o options_ [structure]
-%
+% o M_ [structure] characterizing the model.
+% o options_ [structure]
+%
% OUTPUTS
% o xparam1 [double] vector of parameters to be estimated (initial values)
% o estim_params_ [structure] characterizing parameters to be estimated
% o bayestopt_ [structure] characterizing priors
-% o lb [double] vector of lower bounds for the estimated parameters.
+% o lb [double] vector of lower bounds for the estimated parameters.
% o ub [double] vector of upper bounds for the estimated parameters.
% o M_ [structure] characterizing the model.
-%
+%
% SPECIAL REQUIREMENTS
% None
@@ -64,7 +64,7 @@ bayestopt_.name = {};
if nvx
xparam1 = estim_params_.var_exo(:,2);
ub = estim_params_.var_exo(:,4);
- lb = estim_params_.var_exo(:,3);
+ lb = estim_params_.var_exo(:,3);
bayestopt_.pshape = estim_params_.var_exo(:,5);
bayestopt_.p1 = estim_params_.var_exo(:,6);
bayestopt_.p2 = estim_params_.var_exo(:,7);
@@ -88,8 +88,8 @@ if nvn
estim_params_.nvn_observable_correspondence(i,1)=obsi_;
end
xparam1 = [xparam1; estim_params_.var_endo(:,2)];
- ub = [ub; estim_params_.var_endo(:,4)];
- lb = [lb; estim_params_.var_endo(:,3)];
+ ub = [ub; estim_params_.var_endo(:,4)];
+ lb = [lb; estim_params_.var_endo(:,3)];
bayestopt_.pshape = [ bayestopt_.pshape; estim_params_.var_endo(:,5)];
bayestopt_.p1 = [ bayestopt_.p1; estim_params_.var_endo(:,6)];
bayestopt_.p2 = [ bayestopt_.p2; estim_params_.var_endo(:,7)];
@@ -158,7 +158,7 @@ bayestopt_.p7 = bayestopt_.p6 ;
%% check for point priors and disallow them as they do not work with MCMC
if any(bayestopt_.p2 ==0)
error(sprintf(['Error in prior for %s: you cannot use a point prior in estimation. Either increase the prior standard deviation',...
- ' or fix the parameter completely.'], bayestopt_.name{bayestopt_.p2 ==0}))
+ ' or fix the parameter completely.'], bayestopt_.name{bayestopt_.p2 ==0}))
end
% generalized location parameters by default for beta distribution
@@ -170,14 +170,14 @@ bayestopt_.p4(k(k1)) = ones(length(k1),1);
for i=1:length(k)
[bayestopt_.p6(k(i)), bayestopt_.p7(k(i))] = beta_specification(bayestopt_.p1(k(i)), bayestopt_.p2(k(i))^2, bayestopt_.p3(k(i)), bayestopt_.p4(k(i)), bayestopt_.name{k(i)});
if bayestopt_.p6(k(i))<1 || bayestopt_.p7(k(i))<1
- fprintf('Prior distribution for parameter %s has unbounded density!\n',bayestopt_.name{k(i)})
+ fprintf('Prior distribution for parameter %s has unbounded density!\n',bayestopt_.name{k(i)})
end
m = compute_prior_mode([ bayestopt_.p6(k(i)) , bayestopt_.p7(k(i)) , bayestopt_.p3(k(i)) , bayestopt_.p4(k(i)) ],1);
if length(m)==1
bayestopt_.p5(k(i)) = m;
else
disp(['Prior distribution for parameter ' bayestopt_.name{k(i)} ' has two modes!'])
- bayestopt_.p5(k(i)) = m(1);
+ bayestopt_.p5(k(i)) = m(1);
end
end
@@ -190,7 +190,7 @@ bayestopt_.p4(k(k2)) = Inf(length(k2),1);
for i=1:length(k)
[bayestopt_.p6(k(i)), bayestopt_.p7(k(i))] = gamma_specification(bayestopt_.p1(k(i)), bayestopt_.p2(k(i))^2, bayestopt_.p3(k(i)), bayestopt_.name{k(i)});
if bayestopt_.p6(k(i))<1
- fprintf('Prior distribution for parameter %s has unbounded density!\n',bayestopt_.name{k(i)})
+ fprintf('Prior distribution for parameter %s has unbounded density!\n',bayestopt_.name{k(i)})
end
bayestopt_.p5(k(i)) = compute_prior_mode([ bayestopt_.p6(k(i)) , bayestopt_.p7(k(i)) , bayestopt_.p3(k(i)) ], 2) ;
end
diff --git a/matlab/shock_decomposition.m b/matlab/shock_decomposition.m
index 19601cee55163209ae67bfc587ed19cadf910695..1785e8c0895936679d01a676aafcafddbc3abfe3 100644
--- a/matlab/shock_decomposition.m
+++ b/matlab/shock_decomposition.m
@@ -4,7 +4,7 @@ function [oo_,M_] = shock_decomposition(M_,oo_,options_,varlist,bayestopt_,estim
% oo_.shock_decomposition. It is a n_var by nshock+2 by nperiods array. The
% first nshock columns store the respective shock contributions, column n+1
% stores the role of the initial conditions, while column n+2 stores the
-% value of the smoothed variables. Both the variables and shocks are stored
+% value of the smoothed variables. Both the variables and shocks are stored
% in the order of declaration, i.e. M_.endo_names and M_.exo_names, respectively.
%
% INPUTS
@@ -59,9 +59,9 @@ parameter_set = options_.parameter_set;
if isempty(parameter_set)
if isfield(oo_,'posterior_mean')
parameter_set = 'posterior_mean';
- elseif isfield(oo_,'mle_mode')
+ elseif isfield(oo_,'mle_mode')
parameter_set = 'mle_mode';
- elseif isfield(oo_,'posterior')
+ elseif isfield(oo_,'posterior')
parameter_set = 'posterior_mode';
else
error(['shock_decomposition: option parameter_set is not specified ' ...
@@ -105,7 +105,7 @@ for i=1:gend
if i > 1 && i <= maximum_lag+1
lags = min(i-1,maximum_lag):-1:1;
end
-
+
if i > 1
tempx = permute(z(:,1:nshocks,lags),[1 3 2]);
m = min(i-1,maximum_lag);
@@ -115,7 +115,7 @@ for i=1:gend
end
if i > options_.shock_decomp.init_state
- z(:,1:nshocks,i) = z(:,1:nshocks,i) + B(inv_order_var,:).*repmat(epsilon(:,i)',endo_nbr,1);
+ z(:,1:nshocks,i) = z(:,1:nshocks,i) + B(inv_order_var,:).*repmat(epsilon(:,i)',endo_nbr,1);
end
z(:,nshocks+1,i) = z(:,nshocks+2,i) - sum(z(:,1:nshocks,i),2);
end
diff --git a/matlab/simul_backward_linear_model.m b/matlab/simul_backward_linear_model.m
index c4ed4fe2645c84ffa1615c8a5fbe673c606c74bd..bb2b97969744b5885178a2c1d686b198eaed5192 100644
--- a/matlab/simul_backward_linear_model.m
+++ b/matlab/simul_backward_linear_model.m
@@ -84,9 +84,9 @@ A0inv = inv(jacob(:,jdx));
A1 = jacob(:,nonzeros(DynareModel.lead_lag_incidence(1,:)));
B = jacob(:,end-number_of_shocks+1:end);
-% Simulations
+% Simulations
for it = 2:sample_size+1
- Y(:,it) = -A0inv*(cst + A1*Y(iy1,it-1) + B*DynareOutput.exo_simul(it,:)');
+ Y(:,it) = -A0inv*(cst + A1*Y(iy1,it-1) + B*DynareOutput.exo_simul(it,:)');
end
DynareOutput.endo_simul = Y;
\ No newline at end of file
diff --git a/matlab/simul_backward_model.m b/matlab/simul_backward_model.m
index 90c0f5c746861090ae2b542fd5e7df91f0b4ac6e..edc705a4cb959d24ecb79d8417a318352820f6ac 100644
--- a/matlab/simul_backward_model.m
+++ b/matlab/simul_backward_model.m
@@ -1,4 +1,4 @@
-function DynareOutput = simul_backward_linear_model(initial_conditions, sample_size, DynareOptions, DynareModel, DynareOutput, innovations)
+function DynareOutput = simul_backward_model(initial_conditions, sample_size, DynareOptions, DynareModel, DynareOutput, innovations)
%@info:
%! @deftypefn {Function File} {@var{DynareOutput} =} simul_backward_nonlinear_model (@var{sample_size},@var{DynareOptions}, @var{DynareModel}, @var{DynareOutput})
@@ -87,9 +87,8 @@ end
if DynareOptions.linear
DynareOutput = simul_backward_linear_model(initial_conditions, sample_size, DynareOptions, ...
- DynareModel, DynareOutput, innovations);
+ DynareModel, DynareOutput, innovations);
else
DynareOutput = simul_backward_nonlinear_model(initial_conditions, sample_size, DynareOptions, ...
- DynareModel, DynareOutput, innovations);
-end
-
\ No newline at end of file
+ DynareModel, DynareOutput, innovations);
+end
diff --git a/matlab/simul_backward_nonlinear_model.m b/matlab/simul_backward_nonlinear_model.m
index fbd6241d1157a16d89b0652a5fed98ad4a2c3515..e644e2f1c2441cd2867986ddedf9b111491aa4f2 100644
--- a/matlab/simul_backward_nonlinear_model.m
+++ b/matlab/simul_backward_nonlinear_model.m
@@ -2,7 +2,7 @@ function DynareOutput = simul_backward_nonlinear_model(initial_conditions, sampl
% Simulates a stochastic non linear backward looking model with arbitrary precision (a deterministic solver is used).
%
-% INPUTS
+% INPUTS
% - initial_conditions [double] n*1 vector, initial conditions for the endogenous variables.
% - sample_size [integer] scalar, number of periods for the simulation.
% - DynareOptions [struct] Dynare's options_ global structure.
@@ -10,10 +10,10 @@ function DynareOutput = simul_backward_nonlinear_model(initial_conditions, sampl
% - DynareOutput [struct] Dynare's oo_ global structure.
% - innovations [double] T*q matrix, innovations to be used for the simulation.
%
-% OUTPUTS
+% OUTPUTS
% - DynareOutput [struct] Dynare's oo_ global structure.
%
-% REMARKS
+% REMARKS
% [1] The innovations used for the simulation are saved in DynareOutput.exo_simul, and the resulting paths for the endogenous
% variables are saved in DynareOutput.endo_simul.
% [2] The last input argument is not mandatory. If absent we use random draws and rescale them with the informations provided
diff --git a/matlab/simulated_moment_uncertainty.m b/matlab/simulated_moment_uncertainty.m
index ef9358a75c0abde903d19866330a004901202ded..14ca704296b24ff0924a64075ae37fc9f459b7c2 100644
--- a/matlab/simulated_moment_uncertainty.m
+++ b/matlab/simulated_moment_uncertainty.m
@@ -11,7 +11,7 @@ function [cmm, mm] = simulated_moment_uncertainty(indx, periods, replic,options_
% Outputs:
% - cmm: [n_moments by n_moments] covariance matrix of simulated moments
% - mm: [n_moments by replic] matrix of moments
-%
+%
% Copyright (C) 2009-2017 Dynare Team
%
% This file is part of Dynare.
@@ -67,7 +67,7 @@ if options_.logged_steady_state %if steady state was previously logged, undo thi
options_.logged_steady_state=0;
logged_steady_state_indicator=1;
evalin('base','options_.logged_steady_state=0;')
-else
+else
logged_steady_state_indicator=0;
end
diff --git a/matlab/simulated_moments_estimation.m b/matlab/simulated_moments_estimation.m
index a7c9c3422d39fbfb9bfb3351f0fc3032014285ca..b01e5e9d3ad739e2b50056b8d6ed97150db359e1 100644
--- a/matlab/simulated_moments_estimation.m
+++ b/matlab/simulated_moments_estimation.m
@@ -2,12 +2,12 @@ function [param,sigma] = simulated_moments_estimation(dataset,options,parallel)
% Performs estimation by Simulated Moments Method.
%
% INPUTS:
-% xparam [double] p*1 vector of initial values for the estimated parameters.
+% xparam [double] p*1 vector of initial values for the estimated parameters.
% dataset [ ] Structure describing the data set.
% options [ ] Structure defining options for SMM.
-% parallel [ ] Structure defining the parallel mode settings (optional).
+% parallel [ ] Structure defining the parallel mode settings (optional).
%
-% OUTPUTS:
+% OUTPUTS:
% param [double] p*1 vector of point estimates for the parameters.
% sigma [double] p*p covariance matrix of the SMM estimates.
%
@@ -87,7 +87,7 @@ options.estimated_parameters.upper_bound = [options.estimated_parameters.upper_b
options.number_of_simulated_sample = 0;
for i=1:length(parallel)
- options.number_of_simulated_sample = options.number_of_simulated_sample + parallel(i).number_of_jobs*parallel(i).number_of_simulations;
+ options.number_of_simulated_sample = options.number_of_simulated_sample + parallel(i).number_of_jobs*parallel(i).number_of_simulations;
end
options.observed_variables_idx = dataset.observed_variables_idx;
@@ -97,7 +97,7 @@ if nargin>2
if ~isunix
error('The parallel version of SMM estimation is not implemented for non unix platforms!')
end
- [junk,hostname] = unix('hostname --fqdn');
+ [junk,hostname] = unix('hostname --fqdn');
hostname = deblank(hostname);
master_is_running_a_job = 0;
for i=1:length(parallel)
@@ -204,7 +204,7 @@ end
skipline()
if options.optimization_routine==1
- % Set options for csminwel.
+ % Set options for csminwel.
H0 = 1e-4*eye(options.estimated_parameters.nb);
ct = 1e-4;
it = 1000;
@@ -212,7 +212,7 @@ if options.optimization_routine==1
% Minimization of the objective function.
if nargin==2
[fval,param,grad,hessian_csminwel,itct,fcount,retcodehat] = ...
- csminwel1('smm_objective',xparam,H0,[],ct,it,2,options_.gradient_epsilon,sample_moments,weighting_matrix,options);
+ csminwel1('smm_objective',xparam,H0,[],ct,it,2,options_.gradient_epsilon,sample_moments,weighting_matrix,options);
elseif nargin>2
[fval,param,grad,hessian_csminwel,itct,fcount,retcodehat] = ...
csminwel1('smm_objective',xparam,H0,[],ct,it,2,options_.gradient_epsilon,sample_moments,weighting_matrix,options,parallel);
@@ -236,7 +236,7 @@ elseif options.optimization_routine==0% Compute the variance of the SMM estimato
V = (1+1/options.number_of_simulated_sample)*G'*long_run_covariance*G;
[param,diag(V)]
elseif options.optimization_routine<0
- T = -options.optimization_routine;% length of the simulated time series.
+ T = -options.optimization_routine;% length of the simulated time series.
time_series = extended_path(oo_.steady_state,T,1);
save time_series.mat;
end
@@ -248,7 +248,7 @@ fid = fopen(['job' int2str(slave_number) '.m'],'w');
fprintf(fid,['% Generated by ' hostname '.\n\n']);
-if ( strcmpi(hostname,remotename) && (job_number>1) ) || ~strcmpi(hostname,remotename)
+if ( strcmpi(hostname,remotename) && (job_number>1) ) || ~strcmpi(hostname,remotename)
fprintf(fid,'load(''master_variables'');\n');
fprintf(fid,'assignin(''base'',''M_'',M_);\n');
fprintf(fid,'assignin(''base'',''oo_'',oo_);\n');
@@ -270,9 +270,9 @@ fprintf(fid,'M_.Sigma_e = diag(tmp);')
fprintf(fid,['stream=RandStream(''mt19937ar'',''Seed'',' int2str(slave_number) ');\n']);
if matlab_ver_less_than('7.12')
- fprintf(fid,['RandStream.setDefaultStream(stream);\n\n']);
+ fprintf(fid,['RandStream.setDefaultStream(stream);\n\n']);
else
- fprintf(fid,['RandStream.setGlobalStream(stream);\n\n']);
+ fprintf(fid,['RandStream.setGlobalStream(stream);\n\n']);
end
fprintf(fid,['maxNumCompThreads(' int2str(threads_per_job) ');\n\n']);
@@ -295,7 +295,7 @@ else
fprintf(fid,['unix(''rm simulated_moments_slave_' int2str(slave_number) '.dat'');\n']);
end
-if ((job_number>1) && strcmpi(hostname,remotename)) || ~strcmpi(hostname,remotename)
+if ((job_number>1) && strcmpi(hostname,remotename)) || ~strcmpi(hostname,remotename)
fprintf(fid,'exit');
end
diff --git a/matlab/simult_.m b/matlab/simult_.m
index 02b95cebf155cf2610824a5cfb22d6707957fc9c..381bfaf1be5b00737fcad22521bae9347cdc45c3 100644
--- a/matlab/simult_.m
+++ b/matlab/simult_.m
@@ -3,7 +3,7 @@ function y_=simult_(y0,dr,ex_,iorder)
% decision rules.
%
% INPUTS
-% y0 [double] n*1 vector, initial value (n is the number of declared endogenous variables plus the number
+% y0 [double] n*1 vector, initial value (n is the number of declared endogenous variables plus the number
% of auxilliary variables for lags and leads); must be in declaration order, i.e. as in M_.endo_names
% dr [struct] matlab's structure where the reduced form solution of the model is stored.
% ex_ [double] T*q matrix of innovations.
@@ -85,7 +85,7 @@ else
k2 = k2(:,1)+(M_.maximum_lag+1-k2(:,2))*endo_nbr;
order_var = dr.order_var;
end
-
+
switch iorder
case 1
if isempty(dr.ghu)% For (linearized) deterministic models.
@@ -134,7 +134,7 @@ else
y_(dr.order_var,i) = constant + dr.ghx*yhat + dr.ghu*epsilon ...
+ abcOut1 + abcOut2 + abcOut3;
end
- end
+ end
case 3
% only with pruning
% the third moments of the shocks are assumed null. We don't have
@@ -176,7 +176,7 @@ else
mexErrCheck('A_times_B_kronecker_C', err);
[gy2u, err] = A_times_B_kronecker_C(ghxu,yhat2,u,threads);
mexErrCheck('A_times_B_kronecker_C', err);
- %construct terms of order 3, all based on first order component yhat1
+ %construct terms of order 3, all based on first order component yhat1
y2a = kron(yhat1,yhat1);
[gyyy, err] = A_times_B_kronecker_C(ghxxx,y2a,yhat1,threads);
mexErrCheck('A_times_B_kronecker_C', err);
@@ -199,6 +199,6 @@ else
yhat1 = yhat1(ipred);
yhat2 = yhat2(ipred);
yhat3 = yhat3(ipred);
- end
+ end
end
end
diff --git a/matlab/simultxdet.m b/matlab/simultxdet.m
index 032f20588ceb4b2c0ee25af6716ff6ace802db74..7de335e06213c07b481b81684a59a27be404d1fe 100644
--- a/matlab/simultxdet.m
+++ b/matlab/simultxdet.m
@@ -88,7 +88,7 @@ if iorder == 1
for j=1:min(ykmin+M_.exo_det_length+1-i,M_.exo_det_length)
y_(dr.order_var,i) = y_(dr.order_var,i) + dr.ghud{j}*(ex_det(i+j-1,:)'-exo_det_steady_state);
end
-
+
k1 = k1+1;
end
elseif iorder == 2
@@ -153,7 +153,7 @@ end
int_width = zeros(iter,nvar);
for i=1:nvar
int_width(:,i) = fact*sqrt(var_yf(:,i));
- if nargout==3
+ if nargout==3
int_width_ME(:,i) = -fact*sqrt(var_yf_ME(:,i));
- end
+ end
end
diff --git a/matlab/skipline.m b/matlab/skipline.m
index 51dbd7912556eb5ec892187e84cd7f8818635c5d..b9ecaac14852114c3edbf4324244621906f052db 100644
--- a/matlab/skipline.m
+++ b/matlab/skipline.m
@@ -1,12 +1,12 @@
function skipline(n, fid)
% This function prints n newlines to fid
%
-% INPUTS
+% INPUTS
%
% n [integer] Number of newlines to print
% fid [integer] file id returned by fopen
-%
-% OUTPUTS
+%
+% OUTPUTS
% None
%
@@ -25,7 +25,7 @@ function skipline(n, fid)
% 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/>.
+% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
if nargin < 2
fid = 1;
diff --git a/matlab/slice_sampler.m b/matlab/slice_sampler.m
index 68ec48439dbdb98c5c499e4d0ff317affc6f0cac..8aa18fb1d4bf727d6bcd80d3b9e118ae6bbffb6d 100644
--- a/matlab/slice_sampler.m
+++ b/matlab/slice_sampler.m
@@ -1,123 +1,123 @@
-function [theta, fxsim, neval] = slice_sampler(objective_function,theta,thetaprior,sampler_options,varargin)
-% function [theta, fxsim, neval] = slice_sampler(objective_function,theta,thetaprior,sampler_options,varargin)
-% ----------------------------------------------------------
-% UNIVARIATE SLICE SAMPLER - stepping out (Neal, 2003)
-% W: optimal value in the range (3,10)*std(x)
-% - see C.Planas and A.Rossi (2014)
-% objective_function(theta,varargin): -log of any unnormalized pdf
-% with varargin (optional) a vector of auxiliaty parameters
-% to be passed to f( ).
-% ----------------------------------------------------------
-%
-% INPUTS
-% objective_function: objective function (expressed as minus the log of a density)
-% theta: last value of theta
-% thetaprior: bounds of the theta space
-% sampler_options: posterior sampler options
-% varargin: optional input arguments to objective function
-%
-% OUTPUTS
-% theta: new theta sample
-% fxsim: value of the objective function for the new sample
-% neval: number of function evaluations
-%
-% SPECIAL REQUIREMENTS
-% none
-
-% Copyright (C) 2015-2017 Dynare Team
-%
-% 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 sampler_options.rotated %&& ~isempty(sampler_options.V1),
- [theta, fxsim, neval] = rotated_slice_sampler(objective_function,theta,thetaprior,sampler_options,varargin{:});
- if isempty(sampler_options.mode) % jumping
- return
- else
- nevalR=sum(neval);
- end
-end
-
-theta=theta(:);
-npar = length(theta);
-W1 = sampler_options.W1;
-neval = zeros(npar,1);
-
-for it=1:npar
- neval(it) = 0;
- W = W1(it);
- xold = theta(it);
- % XLB = thetaprior(3);
- % XUB = thetaprior(4);
- XLB = thetaprior(it,1);
- XUB = thetaprior(it,2);
-
-
- % -------------------------------------------------------
- % 1. DRAW Z = ln[f(X0)] - EXP(1) where EXP(1)=-ln(U(0,1))
- % THIS DEFINES THE SLICE S={x: z < ln(f(x))}
- % -------------------------------------------------------
- fxold = -feval(objective_function,theta,varargin{:});
- neval(it) = neval(it) + 1;
- Z = fxold + log(rand(1,1));
- % -------------------------------------------------------------
- % 2. FIND I=(L,R) AROUND X0 THAT CONTAINS S AS MUCH AS POSSIBLE
- % STEPPING-OUT PROCEDURE
- % -------------------------------------------------------------
- u = rand(1,1);
- L = max(XLB,xold-W*u);
- R = min(XUB,L+W);
- while(L > XLB)
- xsim = L;
- theta(it) = xsim;
- fxl = -feval(objective_function,theta,varargin{:});
- neval(it) = neval(it) + 1;
- if (fxl <= Z)
- break
- end
- L = max(XLB,L-W);
- end
- while(R < XUB)
- xsim = R;
- theta(it) = xsim;
- fxr = -feval(objective_function,theta,varargin{:});
- neval(it) = neval(it) + 1;
- if (fxr <= Z)
- break
- end
- R = min(XUB,R+W);
- end
- % ------------------------------------------------------
- % 3. SAMPLING FROM THE SET A = (I INTERSECT S) = (LA,RA)
- % ------------------------------------------------------
- fxsim = Z-1;
- while (fxsim < Z)
- u = rand(1,1);
- xsim = L + u*(R - L);
- theta(it) = xsim;
- fxsim = -feval(objective_function,theta,varargin{:});
- neval(it) = neval(it) + 1;
- if (xsim > xold)
- R = xsim;
- else
- L = xsim;
- end
- end
-
-end
-
-if sampler_options.rotated && ~isempty(sampler_options.mode) % jumping
- neval=sum(neval)+nevalR;
-end
+function [theta, fxsim, neval] = slice_sampler(objective_function,theta,thetaprior,sampler_options,varargin)
+% function [theta, fxsim, neval] = slice_sampler(objective_function,theta,thetaprior,sampler_options,varargin)
+% ----------------------------------------------------------
+% UNIVARIATE SLICE SAMPLER - stepping out (Neal, 2003)
+% W: optimal value in the range (3,10)*std(x)
+% - see C.Planas and A.Rossi (2014)
+% objective_function(theta,varargin): -log of any unnormalized pdf
+% with varargin (optional) a vector of auxiliaty parameters
+% to be passed to f( ).
+% ----------------------------------------------------------
+%
+% INPUTS
+% objective_function: objective function (expressed as minus the log of a density)
+% theta: last value of theta
+% thetaprior: bounds of the theta space
+% sampler_options: posterior sampler options
+% varargin: optional input arguments to objective function
+%
+% OUTPUTS
+% theta: new theta sample
+% fxsim: value of the objective function for the new sample
+% neval: number of function evaluations
+%
+% SPECIAL REQUIREMENTS
+% none
+
+% Copyright (C) 2015-2017 Dynare Team
+%
+% 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 sampler_options.rotated %&& ~isempty(sampler_options.V1),
+ [theta, fxsim, neval] = rotated_slice_sampler(objective_function,theta,thetaprior,sampler_options,varargin{:});
+ if isempty(sampler_options.mode) % jumping
+ return
+ else
+ nevalR=sum(neval);
+ end
+end
+
+theta=theta(:);
+npar = length(theta);
+W1 = sampler_options.W1;
+neval = zeros(npar,1);
+
+for it=1:npar
+ neval(it) = 0;
+ W = W1(it);
+ xold = theta(it);
+ % XLB = thetaprior(3);
+ % XUB = thetaprior(4);
+ XLB = thetaprior(it,1);
+ XUB = thetaprior(it,2);
+
+
+ % -------------------------------------------------------
+ % 1. DRAW Z = ln[f(X0)] - EXP(1) where EXP(1)=-ln(U(0,1))
+ % THIS DEFINES THE SLICE S={x: z < ln(f(x))}
+ % -------------------------------------------------------
+ fxold = -feval(objective_function,theta,varargin{:});
+ neval(it) = neval(it) + 1;
+ Z = fxold + log(rand(1,1));
+ % -------------------------------------------------------------
+ % 2. FIND I=(L,R) AROUND X0 THAT CONTAINS S AS MUCH AS POSSIBLE
+ % STEPPING-OUT PROCEDURE
+ % -------------------------------------------------------------
+ u = rand(1,1);
+ L = max(XLB,xold-W*u);
+ R = min(XUB,L+W);
+ while(L > XLB)
+ xsim = L;
+ theta(it) = xsim;
+ fxl = -feval(objective_function,theta,varargin{:});
+ neval(it) = neval(it) + 1;
+ if (fxl <= Z)
+ break
+ end
+ L = max(XLB,L-W);
+ end
+ while(R < XUB)
+ xsim = R;
+ theta(it) = xsim;
+ fxr = -feval(objective_function,theta,varargin{:});
+ neval(it) = neval(it) + 1;
+ if (fxr <= Z)
+ break
+ end
+ R = min(XUB,R+W);
+ end
+ % ------------------------------------------------------
+ % 3. SAMPLING FROM THE SET A = (I INTERSECT S) = (LA,RA)
+ % ------------------------------------------------------
+ fxsim = Z-1;
+ while (fxsim < Z)
+ u = rand(1,1);
+ xsim = L + u*(R - L);
+ theta(it) = xsim;
+ fxsim = -feval(objective_function,theta,varargin{:});
+ neval(it) = neval(it) + 1;
+ if (xsim > xold)
+ R = xsim;
+ else
+ L = xsim;
+ end
+ end
+
+end
+
+if sampler_options.rotated && ~isempty(sampler_options.mode) % jumping
+ neval=sum(neval)+nevalR;
+end
diff --git a/matlab/smm_objective.m b/matlab/smm_objective.m
index 4b20fbdf041903d76067f9b0c2ddd881daae6949..fc6358205c484e169e557c6fd74e597e70e2015e 100644
--- a/matlab/smm_objective.m
+++ b/matlab/smm_objective.m
@@ -2,13 +2,13 @@ function [r,flag] = smm_objective(xparams,sample_moments,weighting_matrix,option
% Evaluates the objective of the Simulated Moments Method.
%
% INPUTS:
-% xparams [double] p*1 vector of estimated parameters.
+% xparams [double] p*1 vector of estimated parameters.
% sample_moments [double] n*1 vector of sample moments (n>=p).
% weighting_matrix [double] n*n symetric, positive definite matrix.
% options [ ] Structure defining options for SMM.
% parallel [ ] Structure defining the parallel mode settings (optional).
%
-% OUTPUTS:
+% OUTPUTS:
% r [double] scalar, the value of the objective function.
% junk [ ] empty matrix.
%
@@ -30,7 +30,7 @@ function [r,flag] = smm_objective(xparams,sample_moments,weighting_matrix,option
% 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/>.
+% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
global M_ options_ oo_
persistent mainStream mainState
@@ -67,7 +67,7 @@ end
if penalty>0
flag = 0;
- r = priorObjectiveValue + penalty;
+ r = priorObjectiveValue + penalty;
return
end
@@ -114,7 +114,7 @@ else% parallel mode.
end
end
for j=1:parallel(i).number_of_jobs
- if (strcmpi(hostname,machine) && j>1) || ~strcmpi(hostname,machine)
+ if (strcmpi(hostname,machine) && j>1) || ~strcmpi(hostname,machine)
job_number = job_number + 1;
unix(['ssh -A ' parallel(i).login '@' machine ' ./call_matlab_session.sh job' int2str(job_number) '.m &']);
end
@@ -125,7 +125,7 @@ else% parallel mode.
eval('job1;')
tElapsedMasterJob = etime(clock, tStartMasterJob);
TimeLimit = tElapsedMasterJob*1.2;
- % Master waits for the slaves' output...
+ % Master waits for the slaves' output...
tStart = clock;
tElapsed = 0;
while tElapsed<TimeLimit
@@ -140,7 +140,7 @@ else% parallel mode.
simulated_moments = load(['./intermediary_results_from_master_and_slaves/simulated_moments_slave_' int2str(i) '.dat'],'-ascii');
tmp = tmp + simulated_moments;
end
- simulated_moments = tmp / job_number;
+ simulated_moments = tmp / job_number;
catch
r = priorObjectiveValue*1.1;
flag = 0;
diff --git a/matlab/smoother2histval.m b/matlab/smoother2histval.m
index faec83a495ae97c91dd6006dbe570e1714abd290..5d6a67f1df05a036e8b6ed27c283478d6c40f2e9 100644
--- a/matlab/smoother2histval.m
+++ b/matlab/smoother2histval.m
@@ -1,10 +1,10 @@
function smoother2histval(opts)
% This function takes values from oo_.SmoothedVariables (and possibly
% oo_.SmoothedShocks) and copies them into M_.histval.
-%
+%
% Optional fields in 'opts' structure:
% infile: An optional *_results MAT file created by Dynare.
-% If present, oo_.Smoothed{Variables,Shocks} are read from
+% If present, oo_.Smoothed{Variables,Shocks} are read from
% there. Otherwise, they are read from the global workspace.
% invars: An optional char or cell array listing variables to read in
% oo_.SmoothedVariables. If absent, all the endogenous
@@ -68,7 +68,7 @@ if isstruct(getfield(smoothedvars, tmp{1}))
% point and metropolis results are simultaneously present
post_metropolis = 2;
end
-
+
elseif isstruct(getfield(smoothedvars, tmp{end}))
% point and metropolis results are simultaneously present
post_metropolis = 2;
diff --git a/matlab/solve1.m b/matlab/solve1.m
index 3c140c52d72ff70064cff72092150379a98b80ff..5ea2651bfd869f154486f4edcf0dfcc3f0d91123 100644
--- a/matlab/solve1.m
+++ b/matlab/solve1.m
@@ -15,7 +15,7 @@ function [x,check] = solve1(func,x,j1,j2,jacobian_flag,gstep,tolf,tolx,maxit,deb
% maxit maximum number of iterations
% debug debug flag
% varargin: list of extra arguments to the function
-%
+%
% OUTPUTS
% x: results
% check=1: the model can not be solved
@@ -80,7 +80,7 @@ for its = 1:maxit
fjac = fjac(j1,j2);
else
dh = max(abs(x(j2)),gstep(1)*ones(nn,1))*eps^(1/3);
-
+
for j = 1:nn
xdh = x ;
xdh(j2(j)) = xdh(j2(j))+dh(j) ;
@@ -114,7 +114,7 @@ for its = 1:maxit
disp([its f])
disp([xold x])
end
-
+
if check > 0
den = max([f;0.5*nn]) ;
if max(abs(g).*max([abs(x(j2)') ones(1,nn)])')/den < tolmin
@@ -147,13 +147,3 @@ disp('SOLVE: maxit has been reached')
% 04/13/01 MJ added test f < tolf !!
% 05/11/01 MJ changed tests for 'check' so as to remove 'continue' which is
% an instruction which appears only in version 6
-
-
-
-
-
-
-
-
-
-
diff --git a/matlab/solve_one_boundary.m b/matlab/solve_one_boundary.m
index 4753d436b328c7f62d172db5505d8f62078ece33..a579ea67e2a03c826bf2cd777983d06522edb7d0 100644
--- a/matlab/solve_one_boundary.m
+++ b/matlab/solve_one_boundary.m
@@ -1,7 +1,7 @@
function [y, info] = solve_one_boundary(fname, y, x, params, steady_state, ...
y_index_eq, nze, periods, is_linear, Block_Num, y_kmin, maxit_, solve_tolf, lambda, cutoff, stack_solve_algo, forward_backward, is_dynamic, verbose, M, options, oo)
% Computes the deterministic simulation of a block of equation containing
-% lead or lag variables
+% lead or lag variables
%
% INPUTS
% fname [string] name of the file containing the block
@@ -26,7 +26,7 @@ function [y, info] = solve_one_boundary(fname, y, x, params, steady_state, ...
% cutoff [double] cutoff to correct the direction in Newton in case
% of singular jacobian matrix
% stack_solve_algo [integer] linear solver method used in the
-% Newton algorithm :
+% Newton algorithm :
% - 1 sparse LU
% - 2 GMRES
% - 3 BicGStab
@@ -43,17 +43,17 @@ function [y, info] = solve_one_boundary(fname, y, x, params, steady_state, ...
% indirect_call [integer] (0) direct call to the fname
% (1) indirect call via the
% local_fname wrapper
-% OUTPUTS
-% y [matrix] All endogenous variables of the model
+% OUTPUTS
+% y [matrix] All endogenous variables of the model
% info [integer] >=0 no error
% <0 error
-%
+%
% ALGORITHM
% Newton with LU or GMRES or BicGstab for dynamic block
-%
+%
% SPECIAL REQUIREMENTS
% none.
-%
+%
% Copyright (C) 1996-2017 Dynare Team
%
diff --git a/matlab/solve_perfect_foresight_model.m b/matlab/solve_perfect_foresight_model.m
index fd06d47d0c66dd830eb88f3cdbfbe908c566e68a..cedf3e0604f0419d58e95514ebd1085c5a2e93b1 100644
--- a/matlab/solve_perfect_foresight_model.m
+++ b/matlab/solve_perfect_foresight_model.m
@@ -17,107 +17,107 @@ function [flag,endo_simul,err] = solve_perfect_foresight_model(endo_simul,exo_si
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- flag = 0;
- err = 0;
- stop = 0;
- nan_flag = 0;
+flag = 0;
+err = 0;
+stop = 0;
+nan_flag = 0;
- model_dynamic = pfm.dynamic_model;
+model_dynamic = pfm.dynamic_model;
- Y = endo_simul(:);
+Y = endo_simul(:);
- if pfm.verbose
- disp (['-----------------------------------------------------']) ;
- disp (['MODEL SIMULATION :']) ;
- fprintf('\n') ;
- end
+if pfm.verbose
+ disp (['-----------------------------------------------------']) ;
+ disp (['MODEL SIMULATION :']) ;
+ fprintf('\n') ;
+end
- if pfm.use_bytecode
- [flag, endo_simul]=bytecode(Y, exo_simul, pfm.params);
- return
- end
+if pfm.use_bytecode
+ [flag, endo_simul]=bytecode(Y, exo_simul, pfm.params);
+ return
+end
- z = Y(find(pfm.lead_lag_incidence'));
- [d1,jacobian] = model_dynamic(z,exo_simul,pfm.params,pfm.steady_state,2);
+z = Y(find(pfm.lead_lag_incidence'));
+[d1,jacobian] = model_dynamic(z,exo_simul,pfm.params,pfm.steady_state,2);
- % Initialization of the jacobian of the stacked model.
- A = sparse([],[],[],pfm.periods*pfm.ny,pfm.periods*pfm.ny,pfm.periods*nnz(jacobian));
+% Initialization of the jacobian of the stacked model.
+A = sparse([],[],[],pfm.periods*pfm.ny,pfm.periods*pfm.ny,pfm.periods*nnz(jacobian));
- % Initialization of the Newton residuals.
- res = zeros(pfm.periods*pfm.ny,1);
+% Initialization of the Newton residuals.
+res = zeros(pfm.periods*pfm.ny,1);
- h1 = clock;
-
- % Newton loop.
- for iter = 1:pfm.maxit_
- h2 = clock;
- i_rows = 1:pfm.ny;
- i_cols = find(pfm.lead_lag_incidence');
- i_cols_A = i_cols;
- % Fill the jacobian of the stacked model.
- for it = 2:(pfm.periods+1)
- [d1,jacobian] = model_dynamic(Y(i_cols),exo_simul,pfm.params,pfm.steady_state,it);
- if it == 2
- A(i_rows,pfm.i_cols_A1) = jacobian(:,pfm.i_cols_1);
- elseif it == pfm.periods+1
- A(i_rows,i_cols_A(pfm.i_cols_T)) = jacobian(:,pfm.i_cols_T);
- else
- A(i_rows,i_cols_A) = jacobian(:,pfm.i_cols_j);
- end
- res(i_rows) = d1;
- i_rows = i_rows + pfm.ny;
- i_cols = i_cols + pfm.ny;
- if it > 2
- i_cols_A = i_cols_A + pfm.ny;
- end
- end
- % Stop if Newton residuals are zero.
- err = max(abs(res));
- if err < pfm.tolerance
- stop = 1 ;
- if pfm.verbose
- fprintf('\n') ;
- disp([' Total time of simulation :' num2str(etime(clock,h1))]) ;
- fprintf('\n') ;
- disp([' Convergency obtained.']) ;
- fprintf('\n') ;
- end
- flag = 0;% Convergency obtained.
- endo_simul = reshape(Y,pfm.ny,pfm.periods+2);
- break
+h1 = clock;
+
+% Newton loop.
+for iter = 1:pfm.maxit_
+ h2 = clock;
+ i_rows = 1:pfm.ny;
+ i_cols = find(pfm.lead_lag_incidence');
+ i_cols_A = i_cols;
+ % Fill the jacobian of the stacked model.
+ for it = 2:(pfm.periods+1)
+ [d1,jacobian] = model_dynamic(Y(i_cols),exo_simul,pfm.params,pfm.steady_state,it);
+ if it == 2
+ A(i_rows,pfm.i_cols_A1) = jacobian(:,pfm.i_cols_1);
+ elseif it == pfm.periods+1
+ A(i_rows,i_cols_A(pfm.i_cols_T)) = jacobian(:,pfm.i_cols_T);
+ else
+ A(i_rows,i_cols_A) = jacobian(:,pfm.i_cols_j);
end
- % Compute the Newton step.
- dy = -A\res;
- if any(isnan(dy))
- nan_flag = 1;
- break
+ res(i_rows) = d1;
+ i_rows = i_rows + pfm.ny;
+ i_cols = i_cols + pfm.ny;
+ if it > 2
+ i_cols_A = i_cols_A + pfm.ny;
end
- % Update the endogenous variables paths.
- Y(pfm.i_upd) = Y(pfm.i_upd) + dy;
end
-
- if ~stop
+ % Stop if Newton residuals are zero.
+ err = max(abs(res));
+ if err < pfm.tolerance
+ stop = 1 ;
if pfm.verbose
fprintf('\n') ;
- disp([' Total time of simulation :' num2str(etime(clock,h1))]) ;
+ disp([' Total time of simulation :' num2str(etime(clock,h1))]) ;
fprintf('\n') ;
- disp(['WARNING : maximum number of iterations is reached (modify options_.simul.maxit).']) ;
+ disp([' Convergency obtained.']) ;
fprintf('\n') ;
end
- flag = 1;% more iterations are needed.
- endo_simul = 1;
+ flag = 0;% Convergency obtained.
+ endo_simul = reshape(Y,pfm.ny,pfm.periods+2);
+ break
end
- if nan_flag
- if pfm.verbose
- fprintf('\n') ;
- disp([' Total time of simulation :' num2str(etime(clock,h1))]) ;
- fprintf('\n') ;
- disp(['WARNING : NaNs!']) ;
- fprintf('\n') ;
- end
- flag = 1;
- endo_simul = 1;
+ % Compute the Newton step.
+ dy = -A\res;
+ if any(isnan(dy))
+ nan_flag = 1;
+ break
end
+ % Update the endogenous variables paths.
+ Y(pfm.i_upd) = Y(pfm.i_upd) + dy;
+end
+
+if ~stop
if pfm.verbose
- disp (['-----------------------------------------------------']) ;
- end
\ No newline at end of file
+ fprintf('\n') ;
+ disp([' Total time of simulation :' num2str(etime(clock,h1))]) ;
+ fprintf('\n') ;
+ disp(['WARNING : maximum number of iterations is reached (modify options_.simul.maxit).']) ;
+ fprintf('\n') ;
+ end
+ flag = 1;% more iterations are needed.
+ endo_simul = 1;
+end
+if nan_flag
+ if pfm.verbose
+ fprintf('\n') ;
+ disp([' Total time of simulation :' num2str(etime(clock,h1))]) ;
+ fprintf('\n') ;
+ disp(['WARNING : NaNs!']) ;
+ fprintf('\n') ;
+ end
+ flag = 1;
+ endo_simul = 1;
+end
+if pfm.verbose
+ disp (['-----------------------------------------------------']) ;
+end
\ No newline at end of file
diff --git a/matlab/solve_two_boundaries.m b/matlab/solve_two_boundaries.m
index c455bdff5c227eff66809977a182d31ae8a44d86..4b5904946556a6dc71915bf2cb6b3e6b2ad07875 100644
--- a/matlab/solve_two_boundaries.m
+++ b/matlab/solve_two_boundaries.m
@@ -1,6 +1,6 @@
function [y, oo]= solve_two_boundaries(fname, y, x, params, steady_state, y_index, nze, periods, y_kmin_l, y_kmax_l, is_linear, Block_Num, y_kmin, maxit_, solve_tolf, lambda, cutoff, stack_solve_algo,options,M, oo)
% Computes the deterministic simulation of a block of equation containing
-% both lead and lag variables using relaxation methods
+% both lead and lag variables using relaxation methods
%
% INPUTS
% fname [string] name of the file containing the block
@@ -27,7 +27,7 @@ function [y, oo]= solve_two_boundaries(fname, y, x, params, steady_state, y_inde
% cutoff [double] cutoff to correct the direction in Newton in case
% of singular jacobian matrix
% stack_solve_algo [integer] linear solver method used in the
-% Newton algorithm :
+% Newton algorithm :
% - 1 sprse LU
% - 2 GMRES
% - 3 BicGStab
@@ -36,15 +36,15 @@ function [y, oo]= solve_two_boundaries(fname, y, x, params, steady_state, y_inde
% oo [structure] Results
%
% OUTPUTS
-% y [matrix] All endogenous variables of the model
+% y [matrix] All endogenous variables of the model
% oo [structure] Results
%
% ALGORITHM
% Newton with LU or GMRES or BicGstab
-%
+%
% SPECIAL REQUIREMENTS
% none.
-%
+%
% Copyright (C) 1996-2017 Dynare Team
%
diff --git a/matlab/steady.m b/matlab/steady.m
index 9a0006960349d79309f5f1a743957b2fd254dd22..3e0ec0095aa0fab1df03b431de9b4e5d97aff08c 100644
--- a/matlab/steady.m
+++ b/matlab/steady.m
@@ -1,10 +1,10 @@
function steady()
% function steady()
% computes and prints the steady state calculations
-%
+%
% INPUTS
% none
-%
+%
% OUTPUTS
% none
%
@@ -28,7 +28,7 @@ function steady()
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-global M_ oo_ options_ ys0_
+global M_ oo_ options_ ys0_
test_for_deep_parameters_calibration(M_);
@@ -70,14 +70,14 @@ if info(1)
disp(sprintf('%12s %12.6f',M_.exo_det_names(hv(ixd(i),2),:), ...
oo_.exo_det_steady_state(hv(ixd(i),2))))
end
-
+
if options_.homotopy_force_continue
disp('Option homotopy_continue is set, so I continue ...')
else
error('Homotopy step failed')
end
end
-
+
[steady_state,M_.params,info] = steady_(M_,options_,oo_);
oo_.steady_state = steady_state;
@@ -99,7 +99,7 @@ else
fprintf('\nThe steady state computation failed. It terminated with the following values:\n')
for i=1:M_.orig_endo_nbr
fprintf('%s \t\t %g\n',M_.endo_names(i,:),steady_state(i));
- end
+ end
end
print_info(info,options_.noprint, options_);
end
diff --git a/matlab/steady_.m b/matlab/steady_.m
index 02c7c69ed8e7464cc2c7aa2f2e5d98168f45a9b6..01b935a68e0cf034d9a85f8d5d489a8a4d400f86 100644
--- a/matlab/steady_.m
+++ b/matlab/steady_.m
@@ -1,12 +1,12 @@
function [steady_state,params,info] = steady_(M_,options_,oo_)
% function [steady_state,params,info] = steady_(M_,options_,oo_)
-% Computes the steady state
-%
+% Computes the steady state
+%
% INPUTS
% M struct model structure
% options struct options
% oo struct output results
-%
+%
% OUTPUTS
% steady_state vector steady state
% params vector parameters (may have been
diff --git a/matlab/step_length_correction.m b/matlab/step_length_correction.m
index 8950bc7649d387190c47bddc703e1ebc76f09e18..5af7dcaee870f9ba81255b6026cb3f906869a6d0 100644
--- a/matlab/step_length_correction.m
+++ b/matlab/step_length_correction.m
@@ -17,8 +17,8 @@ function c = step_length_correction(x,scale,i)
% 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(scale)
- c = 10^round(log10(abs(x)));
- else
- c = scale(i);
- end
+if isempty(scale)
+ c = 10^round(log10(abs(x)));
+else
+ c = scale(i);
+end
diff --git a/matlab/stoch_simul.m b/matlab/stoch_simul.m
index b77cacc3a3b9a89e034987390595497c97bbd523..81f8b886aef2107666ac46453ae6ef61e1516b86 100644
--- a/matlab/stoch_simul.m
+++ b/matlab/stoch_simul.m
@@ -84,7 +84,7 @@ else
[oo_.dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
end
-if options_.loglinear && isfield(oo_.dr,'ys') && options_.logged_steady_state==0 %log steady state for correct display of decision rule
+if options_.loglinear && isfield(oo_.dr,'ys') && options_.logged_steady_state==0 %log steady state for correct display of decision rule
oo_.dr.ys=log_variable(1:M_.endo_nbr,oo_.dr.ys,M_);
oo_.steady_state=log_variable(1:M_.endo_nbr,oo_.steady_state,M_);
options_old.logged_steady_state = 1; %make sure option is preserved outside of stoch_simul
@@ -160,7 +160,7 @@ if options_.periods > 0 && ~PI_PCL_solver
[ys, oo_] = simult(y0,oo_.dr,M_,options_,oo_);
oo_.endo_simul = ys;
if ~options_.minimal_workspace
- dyn2vec;
+ dyn2vec;
end
end
@@ -201,7 +201,7 @@ if options_.irf
else
if options_.order>1 && options_.relative_irf % normalize shock to 0.01 before IRF generation for GIRFs; multiply with 100 later
y=irf(oo_.dr,cs(M_.exo_names_orig_ord,i)./cs(i,i)/100, options_.irf, options_.drop, ...
- options_.replic, options_.order);
+ options_.replic, options_.order);
else %for linear model, rescaling is done later
y=irf(oo_.dr,cs(M_.exo_names_orig_ord,i), options_.irf, options_.drop, ...
options_.replic, options_.order);
diff --git a/matlab/stochastic_solvers.m b/matlab/stochastic_solvers.m
index e97a94add83059ad64db2cf6d65c85a40db21634..a51648a43914d1fb6870858dff9011393254c239 100644
--- a/matlab/stochastic_solvers.m
+++ b/matlab/stochastic_solvers.m
@@ -1,33 +1,33 @@
function [dr,info] = stochastic_solvers(dr,task,M_,options_,oo_)
% function [dr,info,M_,options_,oo_] = stochastic_solvers(dr,task,M_,options_,oo_)
-% computes the reduced form solution of a rational expectations model (first, second or third
-% order approximation of the stochastic model around the deterministic steady state).
+% computes the reduced form solution of a rational expectations model (first, second or third
+% order approximation of the stochastic model around the deterministic steady state).
%
% INPUTS
% dr [matlab structure] Decision rules for stochastic simulations.
% task [integer] if task = 0 then dr1 computes decision rules.
% if task = 1 then dr1 computes eigenvalues.
-% M_ [matlab structure] Definition of the model.
+% M_ [matlab structure] Definition of the model.
% options_ [matlab structure] Global options.
-% oo_ [matlab structure] Results
-%
+% oo_ [matlab structure] Results
+%
% OUTPUTS
% dr [matlab structure] Decision rules for stochastic simulations.
% info [integer] info=1: the model doesn't define current variables uniquely
-% info=2: problem in mjdgges.dll info(2) contains error code.
+% info=2: problem in mjdgges.dll info(2) contains error code.
% info=3: BK order condition not satisfied info(2) contains "distance"
% absence of stable trajectory.
% info=4: BK order condition not satisfied info(2) contains "distance"
% indeterminacy.
% info=5: BK rank condition not satisfied.
-% info=6: The jacobian matrix evaluated at the steady state is complex.
-% info=9: k_order_pert was unable to compute the solution
+% info=6: The jacobian matrix evaluated at the steady state is complex.
+% info=9: k_order_pert was unable to compute the solution
% ALGORITHM
% ...
-%
+%
% SPECIAL REQUIREMENTS
% none.
-%
+%
% Copyright (C) 1996-2017 Dynare Team
%
@@ -59,16 +59,16 @@ if M_.hessian_eq_zero && local_order~=1
end
if (options_.aim_solver == 1) && (local_order > 1)
- error('Option "aim_solver" is incompatible with order >= 2')
+ error('Option "aim_solver" is incompatible with order >= 2')
end
-if M_.maximum_endo_lag == 0
+if M_.maximum_endo_lag == 0
if local_order >= 2
- fprintf('\nSTOCHASTIC_SOLVER: Dynare does not solve purely forward models at higher order.\n')
- fprintf('STOCHASTIC_SOLVER: To circumvent this restriction, you can add a backward-looking dummy equation of the form:\n')
- fprintf('STOCHASTIC_SOLVER: junk=0.9*junk(-1);\n')
- error(['2nd and 3rd order approximation not implemented for purely ' ...
- 'forward models'])
+ fprintf('\nSTOCHASTIC_SOLVER: Dynare does not solve purely forward models at higher order.\n')
+ fprintf('STOCHASTIC_SOLVER: To circumvent this restriction, you can add a backward-looking dummy equation of the form:\n')
+ fprintf('STOCHASTIC_SOLVER: junk=0.9*junk(-1);\n')
+ error(['2nd and 3rd order approximation not implemented for purely ' ...
+ 'forward models'])
end
if M_.exo_det_nbr~=0
fprintf('\nSTOCHASTIC_SOLVER: Dynare does not solve purely forward models with var_exo_det.\n')
@@ -88,7 +88,7 @@ end
if options_.k_order_solver
if options_.risky_steadystate
[dr,info] = dyn_risky_steadystate_solver(oo_.steady_state,M_,dr, ...
- options_,oo_);
+ options_,oo_);
else
orig_order = options_.order;
options_.order = local_order;
@@ -119,12 +119,12 @@ if local_order == 1
jacobia_ = [loc_dr.g1 loc_dr.g1_x loc_dr.g1_xd];
else
[junk,jacobia_] = feval([M_.fname '_dynamic'],z(iyr0),exo_simul, ...
- M_.params, dr.ys, it_);
+ M_.params, dr.ys, it_);
end
elseif local_order == 2
if (options_.bytecode)
[chck, junk, loc_dr] = bytecode('dynamic','evaluate', z,exo_simul, ...
- M_.params, dr.ys, 1);
+ M_.params, dr.ys, 1);
jacobia_ = [loc_dr.g1 loc_dr.g1_x];
else
[junk,jacobia_,hessian1] = feval([M_.fname '_dynamic'],z(iyr0),...
@@ -138,10 +138,10 @@ elseif local_order == 2
end
[infrow,infcol]=find(isinf(hessian1));
if options_.debug
- if ~isempty(infrow)
- fprintf('\nSTOCHASTIC_SOLVER: The Hessian of the dynamic model contains Inf.\n')
- fprintf('STOCHASTIC_SOLVER: Try running model_diagnostics to find the source of the problem.\n')
- save([M_.fname '_debug.mat'],'hessian1')
+ if ~isempty(infrow)
+ fprintf('\nSTOCHASTIC_SOLVER: The Hessian of the dynamic model contains Inf.\n')
+ fprintf('STOCHASTIC_SOLVER: Try running model_diagnostics to find the source of the problem.\n')
+ save([M_.fname '_debug.mat'],'hessian1')
end
end
if ~isempty(infrow)
@@ -150,7 +150,7 @@ elseif local_order == 2
end
[nanrow,nancol]=find(isnan(hessian1));
if options_.debug
- if ~isempty(nanrow)
+ if ~isempty(nanrow)
fprintf('\nSTOCHASTIC_SOLVER: The Hessian of the dynamic model contains NaN.\n')
fprintf('STOCHASTIC_SOLVER: Try running model_diagnostics to find the source of the problem.\n')
save([M_.fname '_debug.mat'],'hessian1')
@@ -159,16 +159,16 @@ elseif local_order == 2
if ~isempty(nanrow)
info(1)=12;
return
- end
+ end
end
[infrow,infcol]=find(isinf(jacobia_));
-
+
if options_.debug
- if ~isempty(infrow)
- fprintf('\nSTOCHASTIC_SOLVER: The Jacobian of the dynamic model contains Inf. The problem is associated with:\n\n')
- display_problematic_vars_Jacobian(infrow,infcol,M_,dr.ys,'dynamic','STOCHASTIC_SOLVER: ')
- save([M_.fname '_debug.mat'],'jacobia_')
+ if ~isempty(infrow)
+ fprintf('\nSTOCHASTIC_SOLVER: The Jacobian of the dynamic model contains Inf. The problem is associated with:\n\n')
+ display_problematic_vars_Jacobian(infrow,infcol,M_,dr.ys,'dynamic','STOCHASTIC_SOLVER: ')
+ save([M_.fname '_debug.mat'],'jacobia_')
end
end
@@ -194,18 +194,18 @@ end
[nanrow,nancol]=find(isnan(jacobia_));
if options_.debug
- if ~isempty(nanrow)
- fprintf('\nSTOCHASTIC_SOLVER: The Jacobian of the dynamic model contains NaN. The problem is associated with:\n\n')
- display_problematic_vars_Jacobian(nanrow,nancol,M_,dr.ys,'dynamic','STOCHASTIC_SOLVER: ')
- save([M_.fname '_debug.mat'],'jacobia_')
+ if ~isempty(nanrow)
+ fprintf('\nSTOCHASTIC_SOLVER: The Jacobian of the dynamic model contains NaN. The problem is associated with:\n\n')
+ display_problematic_vars_Jacobian(nanrow,nancol,M_,dr.ys,'dynamic','STOCHASTIC_SOLVER: ')
+ save([M_.fname '_debug.mat'],'jacobia_')
end
end
if ~isempty(nanrow)
- info(1) = 8;
- NaN_params=find(isnan(M_.params));
- info(2:length(NaN_params)+1) = NaN_params;
- return
+ info(1) = 8;
+ NaN_params=find(isnan(M_.params));
+ info(2:length(NaN_params)+1) = NaN_params;
+ return
end
kstate = dr.kstate;
@@ -229,9 +229,9 @@ if M_.maximum_endo_lead == 0
% backward models: simplified code exist only at order == 1
if local_order == 1
[k1,junk,k2] = find(kstate(:,4));
- dr.ghx(:,k1) = -b\jacobia_(:,k2);
+ dr.ghx(:,k1) = -b\jacobia_(:,k2);
if M_.exo_nbr
- dr.ghu = -b\jacobia_(:,nz+1:end);
+ dr.ghu = -b\jacobia_(:,nz+1:end);
end
dr.eigval = eig(kalman_transition_matrix(dr,nstatic+(1:nspred),1:nspred,M_.exo_nbr));
dr.full_rank = 1;
@@ -272,7 +272,7 @@ else
dr = dyn_second_order_solver(jacobia_,hessian1,dr,M_,...
options_.threads.kronecker.A_times_B_kronecker_C,...
options_.threads.kronecker.sparse_hessian_times_B_kronecker_C);
-
+
% reordering second order derivatives, used for deterministic
% variables below
k1 = nonzeros(M_.lead_lag_incidence(:,order_var)');
@@ -306,7 +306,7 @@ if M_.exo_det_nbr > 0
hu = dr.ghu(nstatic+[1:nspred],:);
hud = dr.ghud{1}(nstatic+1:nstatic+nspred,:);
zx = [eye(nspred);dr.ghx(k0,:);gx*dr.Gy;zeros(M_.exo_nbr+M_.exo_det_nbr, ...
- nspred)];
+ nspred)];
zu = [zeros(nspred,M_.exo_nbr); dr.ghu(k0,:); gx*hu; zeros(M_.exo_nbr+M_.exo_det_nbr, ...
M_.exo_nbr)];
zud=[zeros(nspred,M_.exo_det_nbr);dr.ghud{1};gx(:,1:nspred)*hud;zeros(M_.exo_nbr,M_.exo_det_nbr);eye(M_.exo_det_nbr)];
@@ -377,8 +377,7 @@ if options_.loglinear
length(il),1);
end
if local_order > 1
- error('Loglinear options currently only works at order 1')
+ error('Loglinear options currently only works at order 1')
end
end
end
-
diff --git a/matlab/store_smoother_results.m b/matlab/store_smoother_results.m
index fa0584ffa0c7862fad55329371b3f7c7a2af24c7..5ef6e15099f3bd93808fc80db8f9bb3c1948ebc1 100644
--- a/matlab/store_smoother_results.m
+++ b/matlab/store_smoother_results.m
@@ -1,7 +1,7 @@
function [oo_, yf]=store_smoother_results(M_,oo_,options_,bayestopt_,dataset_,dataset_info,atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,P,PK,decomp,Trend,state_uncertainty)
% oo_=store_smoother_results(M_,oo_,options_,bayestopt_,dataset_,atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,P,PK,decomp,Trend)
% Writes the smoother results into respective fields in oo_
-%
+%
% Inputs:
% M_ [structure] storing the model information
% oo_ [structure] storing the results
@@ -42,15 +42,15 @@ function [oo_, yf]=store_smoother_results(M_,oo_,options_,bayestopt_,dataset_,da
% oo_.SmoothedMeasurementErrors: structure storing the smoothed measurement errors
% oo_.Smoother.State_uncertainty: smoothed state uncertainty (declaration order)
-% yf [double] (nvarobs*T) matrix storing the smoothed observed variables (order of options_.varobs)
-%
-% Notes:
+% yf [double] (nvarobs*T) matrix storing the smoothed observed variables (order of options_.varobs)
+%
+% Notes:
% m: number of endogenous variables (M_.endo_nbr)
% T: number of Time periods (options_.nobs)
% r: number of strucural shocks (M_.exo_nbr)
% n: number of observables (length(options_.varobs))
% K: maximum forecast horizon (max(options_.nk))
-%
+%
% First all smoothed variables are saved without trend and constant.
% Then trend and constant are added for the observed variables.
%
@@ -113,7 +113,7 @@ trend_constant_observables=constant_part+Trend;
yf = atT(bayestopt_.mf,:)+trend_constant_observables;
if options_.nk > 0
- %filtered variable E_t(y_t+k) requires to shift trend by k periods
+ %filtered variable E_t(y_t+k) requires to shift trend by k periods
filter_steps_required=union(1,options_.filter_step_ahead); % 1 is required for standard filtered variables
for filter_iter=1:length(filter_steps_required)
filter_step=filter_steps_required(filter_iter);
@@ -167,13 +167,13 @@ for i_endo_in_bayestopt_smoother_varlist=bayestopt_.smoother_saved_var_list'
end
oo_.UpdatedVariables.(deblank(M_.endo_names(i_endo_declaration_order,:)))=updated_variables(i_endo_in_dr,:)'+constant_current_variable;
end
-
+
%% Add trend and constant for observed variables
for pos_iter=1:length(bayestopt_.mf)
oo_.Smoother.Constant.(deblank(M_.endo_names(bayestopt_.mfys(pos_iter),:)))=constant_part(pos_iter,:)';
if ismember(bayestopt_.mf(pos_iter),bayestopt_.smoother_var_list(bayestopt_.smoother_saved_var_list))
- oo_.SmoothedVariables.(deblank(M_.endo_names(bayestopt_.mfys(pos_iter),:)))=yf(pos_iter,:)';
- if ~isempty(options_.nk) && options_.nk > 0
+ oo_.SmoothedVariables.(deblank(M_.endo_names(bayestopt_.mfys(pos_iter),:)))=yf(pos_iter,:)';
+ if ~isempty(options_.nk) && options_.nk > 0
%filtered variable E_t(y_t+1) requires to shift trend by 1 period
oo_.FilteredVariables.(deblank(M_.endo_names(bayestopt_.mfys(pos_iter),:)))=...
squeeze(aK(1,bayestopt_.mf(pos_iter),2:end-(options_.nk-1)))...
@@ -182,7 +182,7 @@ for pos_iter=1:length(bayestopt_.mf)
filter_step=options_.filter_step_ahead(filter_iter);
oo_.FilteredVariablesKStepAhead(filter_iter,find(i_endo_in_dr_matrices==bayestopt_.mf(pos_iter)),1+filter_step:end-(max(options_.filter_step_ahead)-filter_step)) = ...
squeeze(aK(filter_step,bayestopt_.mf(pos_iter),1+filter_step:end-(max(options_.filter_step_ahead)-filter_step)))...
- +trend_constant_observables_filtered.(['filter_ahead_' num2str(filter_step)])(pos_iter,:)';
+ +trend_constant_observables_filtered.(['filter_ahead_' num2str(filter_step)])(pos_iter,:)';
end
end
%updated variables are E_t(y_t) so no trend shift is required
@@ -229,8 +229,8 @@ end
%% Smoothed measurement errors
if ~isequal(M_.H,0)
-% measurement_error_indices=find(diag(M_.H)~=0);
+ % measurement_error_indices=find(diag(M_.H)~=0);
for meas_error_iter=1:length(options_.varobs)
- oo_.SmoothedMeasurementErrors.(options_.varobs{meas_error_iter})= measurement_error(meas_error_iter,:)';
+ oo_.SmoothedMeasurementErrors.(options_.varobs{meas_error_iter})= measurement_error(meas_error_iter,:)';
end
end
diff --git a/matlab/subset.m b/matlab/subset.m
index e174af296363fc8f1d61747b8e550980106fbc81..5cae1ee35e3192a20dac66b9f45f22d2dc1fc856 100644
--- a/matlab/subset.m
+++ b/matlab/subset.m
@@ -54,8 +54,8 @@ else
for i = 1:length(ExcludedParamNames)
tmp = strmatch(ExcludedParamNames{i},M_.exo_names);
if ~isempty(tmp) && ( strcmpi(info,'All') || strcmpi(info,'StructuralShocks') || ...
- strcmpi(info,'StructuralShocksWithoutCorrelations') || ...
- strcmpi(info,'AllWithoutMeasurementErrors') )
+ strcmpi(info,'StructuralShocksWithoutCorrelations') || ...
+ strcmpi(info,'AllWithoutMeasurementErrors') )
% The parameter the user wants to exclude is related to the size of the structural innovations.
if ncx
disp(['I cannot exclude some of the structural variances if the'])
@@ -63,10 +63,10 @@ else
error
end
tt = [tt;tmp];
- elseif isempty(tmp) && nvn
+ elseif isempty(tmp) && nvn
tmp = strmatch(ExcludedParamNames{i},options_.varobs);
if ~isempty(tmp) && ( strcmpi(info,'All') || strcmpi(info,'MeasurementErrors') || ...
- strcmpi(info,'MeasurementErrorsWithoutCorrelations') )
+ strcmpi(info,'MeasurementErrorsWithoutCorrelations') )
% The parameter the user wants to exclude is related to the size of the measurement errors variances.
tmp = nvx+tmp;
if ncn
diff --git a/matlab/symmetric_matrix_index.m b/matlab/symmetric_matrix_index.m
index 73cf4a50b0a6025e0e3b3ccd58ab09d003d7ce7c..b084fc89257ec1e80cf87764b826f02e4948b06f 100644
--- a/matlab/symmetric_matrix_index.m
+++ b/matlab/symmetric_matrix_index.m
@@ -1,9 +1,9 @@
function k = symmetric_matrix_index(i,j,n)
% function k = symmetric_matrix_index(i,j,n)
-% Returns index number of variable combination (i,j) in vech(A) where A is
+% Returns index number of variable combination (i,j) in vech(A) where A is
% an symmetric n by n matrix and vech creates row vector by stacking rows
% of A on and above the diagonal
-%
+%
% Inputs:
% i [scalar] index of first variable
% j [scalar] index of second variable
diff --git a/matlab/test_for_deep_parameters_calibration.m b/matlab/test_for_deep_parameters_calibration.m
index 98d9dcc8b2d4943caa901dc4a1e0b7ccf8fd3408..518191032a56172d8e768f4af6ca1952469b72cc 100644
--- a/matlab/test_for_deep_parameters_calibration.m
+++ b/matlab/test_for_deep_parameters_calibration.m
@@ -3,13 +3,13 @@ function info=test_for_deep_parameters_calibration(M_)
%
% INPUTS
% M_ [structure] Description of the (simulated or estimated) model.
-%
+%
% OUTPUTS
-% info [scalar] 0 if no problems detected, 1 otherwise
-%
+% info [scalar] 0 if no problems detected, 1 otherwise
+%
% ALGORITHM
% none
-%
+%
% SPECIAL REQUIREMENTS
% none
@@ -28,7 +28,7 @@ function info=test_for_deep_parameters_calibration(M_)
% 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/>.
+% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
plist = list_of_parameters_calibrated_as_NaN(M_);
if ~isempty(plist)
info=1;
diff --git a/matlab/th_autocovariances.m b/matlab/th_autocovariances.m
index 07d2443a5da06f8d0b213d1eb1b34fca8e7b2968..78e6fff4bbf68ea9c98a3abdb36f02a09f543eeb 100644
--- a/matlab/th_autocovariances.m
+++ b/matlab/th_autocovariances.m
@@ -1,28 +1,28 @@
function [Gamma_y,stationary_vars] = th_autocovariances(dr,ivar,M_,options_,nodecomposition)
-% Computes the theoretical auto-covariances, Gamma_y, for an AR(p) process
+% Computes the theoretical auto-covariances, Gamma_y, for an AR(p) process
% with coefficients dr.ghx and dr.ghu and shock variances Sigma_e
% for a subset of variables ivar.
% Theoretical HP-filtering and band-pass filtering is available as an option
-%
+%
% INPUTS
% dr: [structure] Reduced form solution of the DSGE model (decisions rules)
% ivar: [integer] Vector of indices for a subset of variables.
% M_ [structure] Global dynare's structure, description of the DSGE model.
% options_ [structure] Global dynare's structure.
-% nodecomposition [integer] Scalar, if different from zero the variance decomposition is not triggered.
-%
+% nodecomposition [integer] Scalar, if different from zero the variance decomposition is not triggered.
+%
% OUTPUTS
-% Gamma_y [cell] Matlab cell of nar+3 (second order approximation) or nar+2 (first order approximation) arrays,
+% Gamma_y [cell] Matlab cell of nar+3 (second order approximation) or nar+2 (first order approximation) arrays,
% where nar is the order of the autocorrelation function.
% Gamma_y{1} [double] Covariance matrix.
% Gamma_y{i+1} [double] Autocorrelation function (for i=1,...,options_.nar).
-% Gamma_y{nar+2} [double] Variance decomposition.
-% Gamma_y{nar+3} [double] Expectation of the endogenous variables associated with a second
-% order approximation.
+% Gamma_y{nar+2} [double] Variance decomposition.
+% Gamma_y{nar+3} [double] Expectation of the endogenous variables associated with a second
+% order approximation.
% stationary_vars [integer] Vector of indices of stationary variables (as a subset of 1:length(ivar))
%
% SPECIAL REQUIREMENTS
-%
+%
% Algorithms
% The means at order=2 are based on the pruned state space as
% in Kim, Kim, Schaumburg, Sims (2008): Calculating and using second-order accurate
@@ -34,14 +34,14 @@ function [Gamma_y,stationary_vars] = th_autocovariances(dr,ivar,M_,options_,node
% Taking expectations on both sides requires to compute E(x^2)=Var(x), which
% can be obtained up to second order from the first order solution
% \[
-% \hat x_t = g_x \hat x_{t - 1} + g_u u_t
+% \hat x_t = g_x \hat x_{t - 1} + g_u u_t
% \]
-% by solving the corresponding Lyapunov equation.
+% by solving the corresponding Lyapunov equation.
% Given Var(x), the above equation can be solved for E(x_t) as
% \[
% E(x_t) = (I - {g_x}\right)^{- 1} 0.5\left( g_{\sigma\sigma} \sigma^2 + g_{xx} Var(\hat x_t) + g_{uu} Var(u_t) \right)
% \]
-%
+%
% Copyright (C) 2001-2017 Dynare Team
%
% This file is part of Dynare.
@@ -144,7 +144,7 @@ if options_.order == 2 || options_.hp_filter == 0
aa = ghx(iky,:);
bb = ghu(iky,:);
if options_.order == 2 % mean correction for 2nd order
- if ~isempty(ikx)
+ if ~isempty(ikx)
Ex = (dr.ghs2(ikx)+dr.ghxx(ikx,:)*vx(:)+dr.ghuu(ikx,:)*M_.Sigma_e(:))/2;
Ex = (eye(n0)-AS(ikx,:))\Ex;
Gamma_y{nar+3} = NaN*ones(nvar, 1);
@@ -164,7 +164,7 @@ if options_.hp_filter == 0 && ~options_.bandpass.indicator
Gamma_y{1} = v;
% autocorrelations
if nar > 0
- vxy = (A*vx*aa'+ghu1*M_.Sigma_e*bb');
+ vxy = (A*vx*aa'+ghu1*M_.Sigma_e*bb');
sy = sqrt(diag(Gamma_y{1}));
sy = sy(stationary_vars);
sy = sy *sy';
@@ -209,7 +209,7 @@ if options_.hp_filter == 0 && ~options_.bandpass.indicator
end
else% ==> Theoretical filters.
% By construction, all variables are stationary when HP filtered
- iky = inv_order_var(ivar);
+ iky = inv_order_var(ivar);
stationary_vars = (1:length(ivar))';
aa = ghx(iky,:); %R in Uhlig (2001)
bb = ghu(iky,:); %S in Uhlig (2001)
@@ -227,7 +227,7 @@ else% ==> Theoretical filters.
filter_gain(freqs>=2*pi/lowest_periodicity & freqs<=2*pi/highest_periodicity)=1;
filter_gain(freqs<=-2*pi/lowest_periodicity+2*pi & freqs>=-2*pi/highest_periodicity+2*pi)=1;
else
- filter_gain = 4*lambda*(1 - cos(freqs)).^2 ./ (1 + 4*lambda*(1 - cos(freqs)).^2); %HP transfer function
+ filter_gain = 4*lambda*(1 - cos(freqs)).^2 ./ (1 + 4*lambda*(1 - cos(freqs)).^2); %HP transfer function
end
mathp_col = NaN(ngrid,length(ivar)^2);
IA = eye(size(A,1));
@@ -237,8 +237,8 @@ else% ==> Theoretical filters.
f_hp = zeros(length(ivar),length(ivar));
else
f_omega =(1/(2*pi))*([(IA-A*tneg(ig))\ghu1;IE]...
- *M_.Sigma_e*[ghu1'/(IA-A'*tpos(ig)) IE]); % spectral density of state variables; top formula Uhlig (2001), p. 20 with N=0
- g_omega = [aa*tneg(ig) bb]*f_omega*[aa'*tpos(ig); bb']; % spectral density of selected variables; middle formula Uhlig (2001), p. 20; only middle block, i.e. y_t'
+ *M_.Sigma_e*[ghu1'/(IA-A'*tpos(ig)) IE]); % spectral density of state variables; top formula Uhlig (2001), p. 20 with N=0
+ g_omega = [aa*tneg(ig) bb]*f_omega*[aa'*tpos(ig); bb']; % spectral density of selected variables; middle formula Uhlig (2001), p. 20; only middle block, i.e. y_t'
f_hp = filter_gain(ig)^2*g_omega; % spectral density of selected filtered series; top formula Uhlig (2001), p. 21;
end
mathp_col(ig,:) = (f_hp(:))'; % store as matrix row for ifft
@@ -274,7 +274,7 @@ else% ==> Theoretical filters.
else
f_omega =(1/(2*pi))*( [(IA-A*tneg(ig))\b1;IE]...
*SS*[b1'/(IA-A'*tpos(ig)) IE]); % spectral density of state variables; top formula Uhlig (2001), p. 20 with N=0
- g_omega = [aa*tneg(ig) b2]*f_omega*[aa'*tpos(ig); b2']; % spectral density of selected variables; middle formula Uhlig (2001), p. 20; only middle block, i.e. y_t'
+ g_omega = [aa*tneg(ig) b2]*f_omega*[aa'*tpos(ig); b2']; % spectral density of selected variables; middle formula Uhlig (2001), p. 20; only middle block, i.e. y_t'
f_hp = filter_gain(ig)^2*g_omega; % spectral density of selected filtered series; top formula Uhlig (2001), p. 21;
end
mathp_col(ig,:) = (f_hp(:))'; % store as matrix row for ifft
@@ -290,7 +290,7 @@ else% ==> Theoretical filters.
else
f_omega =(1/(2*pi))*( [(IA-A*tneg(ig))\b1;IE]...
*SSi*[b1'/(IA-A'*tpos(ig)) IE]); % spectral density of state variables; top formula Uhlig (2001), p. 20 with N=0
- g_omega = [aa*tneg(ig) b2]*f_omega*[aa'*tpos(ig); b2']; % spectral density of selected variables; middle formula Uhlig (2001), p. 20; only middle block, i.e. y_t'
+ g_omega = [aa*tneg(ig) b2]*f_omega*[aa'*tpos(ig); b2']; % spectral density of selected variables; middle formula Uhlig (2001), p. 20; only middle block, i.e. y_t'
f_hp = filter_gain(ig)^2*g_omega; % spectral density of selected filtered series; top formula Uhlig (2001), p. 21;
end
mathp_col(ig,:) = (f_hp(:))'; % store as matrix row for ifft
diff --git a/matlab/thet2tau.m b/matlab/thet2tau.m
index 65cc63ce1baf7143b5a053fb4907fcf57b58ebd2..965934e3d93734f82cb5280a0b2e5aee1a36e5b4 100644
--- a/matlab/thet2tau.m
+++ b/matlab/thet2tau.m
@@ -51,7 +51,7 @@ elseif flagmoments==-1
[I,J]=find(M_.lead_lag_incidence');
yy0=oo_.dr.ys(I);
[residual, g1] = feval([M_.fname,'_dynamic'],yy0, oo_.exo_steady_state', ...
- M_.params, oo_.dr.ys, 1);
+ M_.params, oo_.dr.ys, 1);
tau=[oo_.dr.ys(oo_.dr.order_var); g1(:)];
else
diff --git a/matlab/trace_plot.m b/matlab/trace_plot.m
index c07e4707b57d5e7058ef7b34bb48825b2845de66..9fbf383581745c1e64356ce726b991bb250a4338 100644
--- a/matlab/trace_plot.m
+++ b/matlab/trace_plot.m
@@ -135,7 +135,7 @@ dyn_saveas(hh,[M_.fname, filesep, 'graphs', filesep, 'TracePlot_' plot_name],opt
if options_.TeX
fid=fopen([M_.fname,'/graphs/',M_.fname,'_TracePlot_' plot_name,'.tex'],'w+');
-
+
if strcmpi(type,'DeepParameter')
tex_names=M_.param_names_tex;
base_names=M_.param_names;
@@ -146,7 +146,7 @@ if options_.TeX
tex_names=M_.endo_names_tex;
base_names=M_.endo_names;
end
-
+
if strcmpi(type,'PosteriorDensity')
FigureName = ['Trace plot for ' TYPE name1];
else
@@ -159,7 +159,7 @@ if options_.TeX
if options_.mh_nblck>1
FigureName = [ FigureName , ' (block number ' int2str(blck) ').'];
end
-
+
fprintf(fid,'%-s\n','\begin{figure}[H]');
fprintf(fid,'%-s\n','\centering');
fprintf(fid,'%-s\n',[' \includegraphics[width=0.8\textwidth]{',[M_.fname, '/graphs/TracePlot_' plot_name],'}\\']);
@@ -167,4 +167,3 @@ if options_.TeX
fprintf(fid,'%-s\n','\end{figure}');
fclose(fid);
end
-
diff --git a/matlab/trust_region.m b/matlab/trust_region.m
index 5e21d0fbb5e04245887b03662ba48f9b94a035a8..beb0360a3d38eebaaf0685103451bdc2bdb17806 100644
--- a/matlab/trust_region.m
+++ b/matlab/trust_region.m
@@ -229,4 +229,3 @@ if (xn > delta)
x = alpha * x + ((1-alpha) * min (snm, delta)) * s;
end
end
-
diff --git a/matlab/uniform_specification.m b/matlab/uniform_specification.m
index 5af782162f3e180209ede22413f3c3463658c27d..cc35fb3308e5767f548be9286713dcaa9663113c 100644
--- a/matlab/uniform_specification.m
+++ b/matlab/uniform_specification.m
@@ -3,16 +3,16 @@ function [m,s,p6,p7] = uniform_specification(m,s,p3,p4)
%
% INPUTS
% m: mean
-% s: standard deviation
-% p3: lower bound
-% p4: upper bound
+% s: standard deviation
+% p3: lower bound
+% p4: upper bound
% OUTPUTS
% m: mean
-% s: standard deviation
-% p1: lower bound
-% p2: upper bound
-%
+% s: standard deviation
+% p1: lower bound
+% p2: upper bound
+%
% SPECIAL REQUIREMENTS
% none
diff --git a/matlab/update_last_mh_history_file.m b/matlab/update_last_mh_history_file.m
index d0c4e9ca54f1e5a3f4502e2719e6118584303366..643b900ce01bd04f0c6521d03a0b4b2388af2d0a 100644
--- a/matlab/update_last_mh_history_file.m
+++ b/matlab/update_last_mh_history_file.m
@@ -5,7 +5,7 @@ function update_last_mh_history_file(MetropolisFolder, ModelName, record)
% MetropolisFolder [char] Name of the metropolis subfolder
% ModelName [char] Name of the mod-file
% record [structure] structure storing the MH history
-% Outputs: none
+% Outputs: none
% Copyright (C) 2013-2015 Dynare Team
%
diff --git a/matlab/user_has_matlab_license.m b/matlab/user_has_matlab_license.m
index 2857ae247de93fea7aa27ca4ddf1c38a281d4620..c8c681283ce8f924ed31caec0e94868d293cf0ff 100644
--- a/matlab/user_has_matlab_license.m
+++ b/matlab/user_has_matlab_license.m
@@ -38,14 +38,14 @@ if ~hasLicense
end
switch toolbox
%use function unique to toolbox
- case 'statistics_toolbox'
- n = 'gppdf';
- case 'optimization_toolbox'
- n='fsolve';
- case 'GADS_Toolbox'
- n='simulannealbnd';
- case 'control_toolbox'
- n='dlyap';
+ case 'statistics_toolbox'
+ n = 'gppdf';
+ case 'optimization_toolbox'
+ n='fsolve';
+ case 'GADS_Toolbox'
+ n='simulannealbnd';
+ case 'control_toolbox'
+ n='dlyap';
end
hasInstallation=check_toolbox_installation(n);
if ~hasInstallation
@@ -64,7 +64,7 @@ else
a=ver(ver_string);
if isempty(a)
hasInstallation=0;
- else
+ else
hasInstallation=1;
end
end
diff --git a/matlab/utilities/dataset/descriptive_statistics.m b/matlab/utilities/dataset/descriptive_statistics.m
index d2b52292d2c59a5e61ee3ca434d064ee1e86bf8f..0c5b4bf9ad1307c8b1b5fec75f1fc19d3b6095e5 100644
--- a/matlab/utilities/dataset/descriptive_statistics.m
+++ b/matlab/utilities/dataset/descriptive_statistics.m
@@ -3,7 +3,7 @@ function dataset_ = descriptive_statistics(dataset_,statistic,varagin)
%@info:
%! @deftypefn {Function File} {@var{dataset_} =} descriptive_statistics(@var{dataset_},@var{statistic})
-%! @deftypefn {Function File} {@var{dataset_} =} descriptive_statistics(@var{dataset_},@var{statistic},nlags)
+%! @deftypefn {Function File} {@var{dataset_} =} descriptive_statistics(@var{dataset_},@var{statistic},nlags)
%! @anchor{compute_corr}
%! This function computes various descriptive statistics on the sample (possibly with missing observations).
%!
@@ -14,15 +14,15 @@ function dataset_ = descriptive_statistics(dataset_,statistic,varagin)
%! @item statistic
%! String. The name of the statistic to be computed. Admissible values are:
%! @table @var
-%! @item 'stdv'
+%! @item 'stdv'
%! Computes the standard deviation of each observed variable.
-%! @item 'cova'
-%! Computes the covariance matrix of the sample.
+%! @item 'cova'
+%! Computes the covariance matrix of the sample.
%! @item 'corr'
%! Computes the correlation matrix of the sample.
%! @item 'acov'
%! Computes the (multivariate) auto-covariance function of the sample. In this case a third argument (@code{nlags}) defining the
-%! maximum number of lags is mandatory.
+%! maximum number of lags is mandatory.
%! @end table
%! @item nlags
%! Integer scalar. The maximum number of lags when computing the autocovariance function.
@@ -33,20 +33,20 @@ function dataset_ = descriptive_statistics(dataset_,statistic,varagin)
%! @item dataset_
%! Dynare structure describing the dataset, built by @ref{initialize_dataset}
%! @end table
-%!
-%! @strong{This function is called by:}
+%!
+%! @strong{This function is called by:}
%! none.
-%!
+%!
%! @strong{This function calls:}
%! @ref{compute_stdv}, @ref{compute_cova}, @ref{compute_corr}, @ref{compute_acov}.
-%!
+%!
%! @strong{Remark 1.} On exit, a new field containing the computed statistics is appended to the structure.
-%!
+%!
%! @end deftypefn
%@eod:
% Copyright (C) 2011-2012 Dynare Team
-%
+%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
diff --git a/matlab/utilities/dataset/initialize_dataset.m b/matlab/utilities/dataset/initialize_dataset.m
index 550d3dc9507ec4f3f963dc59c398c586416a936c..781e1b29498ba4a717784941df72bfd1216e5b54 100644
--- a/matlab/utilities/dataset/initialize_dataset.m
+++ b/matlab/utilities/dataset/initialize_dataset.m
@@ -55,7 +55,7 @@ if dataset_.info.nvobs-size(rawdata,2)
end
if size(rawdata,1)~=dataset_.info.ntobs
- fprintf('Restricting the sample to observations %d to %d. Using in total %d observations. \n',first,first+dataset_.info.ntobs-1,dataset_.info.ntobs)
+ fprintf('Restricting the sample to observations %d to %d. Using in total %d observations. \n',first,first+dataset_.info.ntobs-1,dataset_.info.ntobs)
end
rawdata = rawdata(first:(first+dataset_.info.ntobs-1),:);
diff --git a/matlab/utilities/dataset/lagged.m b/matlab/utilities/dataset/lagged.m
index fa84577e20a33177f8143516dc8419d9c0c0d191..4f17d7eeb302d84c536050e83bcd7570d913f75e 100644
--- a/matlab/utilities/dataset/lagged.m
+++ b/matlab/utilities/dataset/lagged.m
@@ -1,34 +1,34 @@
-function xlag = lagged(x, n)
-% xlag = lagged(x, n);
-% applies n-lags backward shift operator to x
-%
-% INPUTS
-% x = time series
-% n = number of backward shifts [DEFAULT=1]
-%
-% OUTPUT
-% xlag = backward shifted series
-
-% Copyright (C) 2017 Dynare Team
-%
-% 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==1
- n=1;
-end
-
-x=x(:);
+function xlag = lagged(x, n)
+% xlag = lagged(x, n);
+% applies n-lags backward shift operator to x
+%
+% INPUTS
+% x = time series
+% n = number of backward shifts [DEFAULT=1]
+%
+% OUTPUT
+% xlag = backward shifted series
+
+% Copyright (C) 2017 Dynare Team
+%
+% 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==1
+ n=1;
+end
+
+x=x(:);
xlag=[NaN(n,1); x(1:end-n)];
\ No newline at end of file
diff --git a/matlab/utilities/dataset/makedataset.m b/matlab/utilities/dataset/makedataset.m
index 6ac4035c838a36e49990cb61e9f135bdb19d55da..e949b5e5bf03c2207fe45220ba65d73afa5b8794 100644
--- a/matlab/utilities/dataset/makedataset.m
+++ b/matlab/utilities/dataset/makedataset.m
@@ -37,7 +37,7 @@ if isempty(DynareOptions.datafile) && isempty(DynareOptions.dataset.file) && ise
if gsa_flag
DynareDataset = dseries();
DatasetInfo = struct('missing', struct('state', 0, 'aindex', [], 'vindex', [], 'number_of_observations', NaN, 'no_more_missing_observations', NaN), ...
- 'descriptive', struct('mean', [], 'covariance', [], 'correlation', [], 'autocovariance', []));
+ 'descriptive', struct('mean', [], 'covariance', [], 'correlation', [], 'autocovariance', []));
newdatainterface=0;
return
else
@@ -107,7 +107,7 @@ if ~isempty(datafile)
DynareDataset = load_m_file_data_legacy(datafile, DynareOptions.varobs);
elseif strcmp(datafile(end-3:end),'.mat')
% Load a mat file with the old interface.
- DynareDataset = load_mat_file_data_legacy(datafile, DynareOptions.varobs);
+ DynareDataset = load_mat_file_data_legacy(datafile, DynareOptions.varobs);
end
end
else
diff --git a/matlab/utilities/dataset/nanvariance.m b/matlab/utilities/dataset/nanvariance.m
index d4894e8e9212d1a37759c464bfd8524d944c1c30..85311dab0749c74f725cd10ebea730095a7819d6 100644
--- a/matlab/utilities/dataset/nanvariance.m
+++ b/matlab/utilities/dataset/nanvariance.m
@@ -1,4 +1,4 @@
-function variances = nanvariance(data)
+function variances = nanvariance(data)
% Compute the standard deviation for each observed variable (possibly with missing observations).
%@info:
@@ -17,21 +17,21 @@ function variances = nanvariance(data)
%! @item variances
%! A N*1 vector of real numbers
%! @end table
-%!
-%! @strong{This function is called by:}
+%!
+%! @strong{This function is called by:}
%! @ref{descriptive_statistics}.
-%!
+%!
%! @strong{This function calls:}
%! @ref{ndim}, @ref{demean}, @ref{nandemean}.
-%!
-%! @strong{Remark 1.} On exit, a new field is appended to the structure: @code{dataset_.descriptive.stdv} is a
+%!
+%! @strong{Remark 1.} On exit, a new field is appended to the structure: @code{dataset_.descriptive.stdv} is a
%! @tex{n\times 1} vector (where @tex{n} is the number of observed variables as defined by @code{dataset_.info.nvobs}).
-%!
+%!
%! @end deftypefn
%@eod:
% Copyright (C) 2011-2014 Dynare Team
-%
+%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
diff --git a/matlab/utilities/dataset/quarterly2annual.m b/matlab/utilities/dataset/quarterly2annual.m
index c74111bf91eca0dff5ff7ae51d3c1aa99c214ba6..6342c4605e4c39b64b25f8454b47f13c799d9d95 100644
--- a/matlab/utilities/dataset/quarterly2annual.m
+++ b/matlab/utilities/dataset/quarterly2annual.m
@@ -1,136 +1,135 @@
-function [ya, yass, gya, gyass] = quarterly2annual(y,yss,GYTREND0,type,islog,aux)
-% function [ya, yass, gya, gyass] = quarterly2annual(y,yss,GYTREND0,type,islog,aux)
-% transforms quarterly (log-)level time series to annual level and growth rate
-% it accounts for stock/flow/deflator series.
-%
-% INPUTS
-% y quarterly time series
-% yss steady state of y
-% GYTREND0 growth rate of y
-% type 1 sum (default)
-% 2 average
-% 3 last period (Q4)
-% 4 geometric average
-% 5 annual price as quantity weighted average
-% 6 annual quantity from average price
-% 7 annual nominal from Q real and deflator
-% islog 0 level (default)
-% 1 log-level
-% 2 growth rate Q frequency
-% aux optional input used when type>4
-%
-%
-% OUTPUTS
-% ya annual (log-)level
-% yass annual steadystate (log-)level
-% gya annual growth rate
-% gyass annual growth rate steadystate
-
-% Copyright (C) 2017 Dynare Team
-%
-% 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
- disp('[ya, yass, gya, gyass] = quarterly2annual(y,yss,GYTREND0,type,islog);')
- return
-end
-
-if nargin<4 || isempty(type)
- type=1;
-end
-if nargin<5 || isempty(islog)
- islog=0;
-end
-if isstruct(aux)
- yaux=aux.y;
- yauxss=aux.yss;
- islogaux=aux.islog;
- GYTREND0aux=aux.GYTREND0;
- typeaux=aux.type;
- if islogaux
- yaux=exp(yaux+yauxss);
- yauxss=exp(yauxss);
- yaux=yaux-yauxss;
- end
-elseif type > 4
- error('TYPE>4 requires auxiliary variable!')
-end
-if islog == 2
- % construct loglevel out of growth rate
- y = cumsum(y);
- yss=0;
- islog=1;
-end
-if islog == 1
- y=exp(y+yss);
- yss=exp(yss);
- y=y-yss;
-end
-switch type
- case 1
- yass = yss*(exp(-GYTREND0*3)+exp(-GYTREND0*2)+exp(-GYTREND0)+1);
- tmp = lagged(y,3)*exp(-GYTREND0*3)+lagged(y,2)*exp(-GYTREND0*2)+lagged(y,1)*exp(-GYTREND0)+y; % annualized level
- ya = tmp(4:4:end);
- case 2
- yass = yss*(exp(-GYTREND0*3)+exp(-GYTREND0*2)+exp(-GYTREND0)+1)/4;
- tmp = (lagged(y,3)*exp(-GYTREND0*3)+lagged(y,2)*exp(-GYTREND0*2)+lagged(y,1)*exp(-GYTREND0)+y)/4; % annualized level
- ya = tmp(4:4:end);
- case 3
- yass=yss;
- tmp = y;
- ya = tmp(4:4:end);
- case 4
- yass = yss*(exp(-GYTREND0*3/2));
- tmp = (lagged(y+yss,3)*exp(-GYTREND0*3).*lagged(y+yss,2)*exp(-GYTREND0*2).*lagged(y+yss,1)*exp(-GYTREND0).*(y+yss)).^(1/4); % annualized level
- tmp = tmp - yass;
- ya = tmp(4:4:end);
- case 5
- % nominal series
- yn = (y+yss).*(yaux+yauxss) - yss.*yauxss;
- [yna, ynass] = quarterly2annual(yn,yss.*yauxss,GYTREND0+GYTREND0aux,typeaux,0,0);
- % real series
- [yra, yrass] = quarterly2annual(yaux,yauxss,GYTREND0aux,typeaux,0,0);
- % deflator
- yass = ynass/yrass;
- ya = (yna+ynass)./(yr+yrass)-yass;
- case 6
- % nominal series
- yn = (y+yss).*(yaux+yauxss) - yss.*yauxss;
- [yna, ynass] = quarterly2annual(yn,yss.*yauxss,GYTREND0+GYTREND0aux,typeaux,0,0);
- % deflator
- [pa, pass] = quarterly2annual(yaux,yauxss,GYTREND0aux,2,0,0);
- % real series
- yass = ynass/pass;
- ya = (yna+ynass)./(pa+pass)-yass;
- case 7
- % nominal series
- yn = (y+yss).*(yaux+yauxss) - yss.*yauxss;
- [ya, yass] = quarterly2annual(yn,yss.*yauxss,GYTREND0+GYTREND0aux,typeaux,0,0);
- GYTREND0=GYTREND0+GYTREND0aux;
- otherwise
- error('Wrong type input')
-end
-
-% annual growth rate
-gyass = GYTREND0*4;
-gya = (ya+yass)./(lagged(ya,1)+yass).*exp(4*GYTREND0)-1-gyass;
-
-if islog
- ya=log(ya+yass);
- yass=log(yass);
- ya=ya-yass;
-end
-
+function [ya, yass, gya, gyass] = quarterly2annual(y,yss,GYTREND0,type,islog,aux)
+% function [ya, yass, gya, gyass] = quarterly2annual(y,yss,GYTREND0,type,islog,aux)
+% transforms quarterly (log-)level time series to annual level and growth rate
+% it accounts for stock/flow/deflator series.
+%
+% INPUTS
+% y quarterly time series
+% yss steady state of y
+% GYTREND0 growth rate of y
+% type 1 sum (default)
+% 2 average
+% 3 last period (Q4)
+% 4 geometric average
+% 5 annual price as quantity weighted average
+% 6 annual quantity from average price
+% 7 annual nominal from Q real and deflator
+% islog 0 level (default)
+% 1 log-level
+% 2 growth rate Q frequency
+% aux optional input used when type>4
+%
+%
+% OUTPUTS
+% ya annual (log-)level
+% yass annual steadystate (log-)level
+% gya annual growth rate
+% gyass annual growth rate steadystate
+
+% Copyright (C) 2017 Dynare Team
+%
+% 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
+ disp('[ya, yass, gya, gyass] = quarterly2annual(y,yss,GYTREND0,type,islog);')
+ return
+end
+
+if nargin<4 || isempty(type)
+ type=1;
+end
+if nargin<5 || isempty(islog)
+ islog=0;
+end
+if isstruct(aux)
+ yaux=aux.y;
+ yauxss=aux.yss;
+ islogaux=aux.islog;
+ GYTREND0aux=aux.GYTREND0;
+ typeaux=aux.type;
+ if islogaux
+ yaux=exp(yaux+yauxss);
+ yauxss=exp(yauxss);
+ yaux=yaux-yauxss;
+ end
+elseif type > 4
+ error('TYPE>4 requires auxiliary variable!')
+end
+if islog == 2
+ % construct loglevel out of growth rate
+ y = cumsum(y);
+ yss=0;
+ islog=1;
+end
+if islog == 1
+ y=exp(y+yss);
+ yss=exp(yss);
+ y=y-yss;
+end
+switch type
+ case 1
+ yass = yss*(exp(-GYTREND0*3)+exp(-GYTREND0*2)+exp(-GYTREND0)+1);
+ tmp = lagged(y,3)*exp(-GYTREND0*3)+lagged(y,2)*exp(-GYTREND0*2)+lagged(y,1)*exp(-GYTREND0)+y; % annualized level
+ ya = tmp(4:4:end);
+ case 2
+ yass = yss*(exp(-GYTREND0*3)+exp(-GYTREND0*2)+exp(-GYTREND0)+1)/4;
+ tmp = (lagged(y,3)*exp(-GYTREND0*3)+lagged(y,2)*exp(-GYTREND0*2)+lagged(y,1)*exp(-GYTREND0)+y)/4; % annualized level
+ ya = tmp(4:4:end);
+ case 3
+ yass=yss;
+ tmp = y;
+ ya = tmp(4:4:end);
+ case 4
+ yass = yss*(exp(-GYTREND0*3/2));
+ tmp = (lagged(y+yss,3)*exp(-GYTREND0*3).*lagged(y+yss,2)*exp(-GYTREND0*2).*lagged(y+yss,1)*exp(-GYTREND0).*(y+yss)).^(1/4); % annualized level
+ tmp = tmp - yass;
+ ya = tmp(4:4:end);
+ case 5
+ % nominal series
+ yn = (y+yss).*(yaux+yauxss) - yss.*yauxss;
+ [yna, ynass] = quarterly2annual(yn,yss.*yauxss,GYTREND0+GYTREND0aux,typeaux,0,0);
+ % real series
+ [yra, yrass] = quarterly2annual(yaux,yauxss,GYTREND0aux,typeaux,0,0);
+ % deflator
+ yass = ynass/yrass;
+ ya = (yna+ynass)./(yr+yrass)-yass;
+ case 6
+ % nominal series
+ yn = (y+yss).*(yaux+yauxss) - yss.*yauxss;
+ [yna, ynass] = quarterly2annual(yn,yss.*yauxss,GYTREND0+GYTREND0aux,typeaux,0,0);
+ % deflator
+ [pa, pass] = quarterly2annual(yaux,yauxss,GYTREND0aux,2,0,0);
+ % real series
+ yass = ynass/pass;
+ ya = (yna+ynass)./(pa+pass)-yass;
+ case 7
+ % nominal series
+ yn = (y+yss).*(yaux+yauxss) - yss.*yauxss;
+ [ya, yass] = quarterly2annual(yn,yss.*yauxss,GYTREND0+GYTREND0aux,typeaux,0,0);
+ GYTREND0=GYTREND0+GYTREND0aux;
+ otherwise
+ error('Wrong type input')
+end
+
+% annual growth rate
+gyass = GYTREND0*4;
+gya = (ya+yass)./(lagged(ya,1)+yass).*exp(4*GYTREND0)-1-gyass;
+
+if islog
+ ya=log(ya+yass);
+ yass=log(yass);
+ ya=ya-yass;
+end
diff --git a/matlab/utilities/doc/get_internal_doc_block.m b/matlab/utilities/doc/get_internal_doc_block.m
index 718b2a28d94f3802a21a0a1037bc62f89fff5098..f0d8ce696f64548dd92247a0054ce165a042b02e 100644
--- a/matlab/utilities/doc/get_internal_doc_block.m
+++ b/matlab/utilities/doc/get_internal_doc_block.m
@@ -1,8 +1,8 @@
function block = get_internal_doc_block(fname,fpath)
% Extract doc sections from matlab's routine.
-
+
% Copyright (C) 2011-2012 Dynare Team
-%
+%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
@@ -22,7 +22,7 @@ function block = get_internal_doc_block(fname,fpath)
% Default output
block = [];
-
+
% Open the matlab file.
mid = fopen([fpath '/' fname '.m'],'r');
@@ -43,14 +43,14 @@ if ( isempty(b1) && isempty(b2) && isempty(b) )
return
else
if ( (~isempty(b1) && isempty(b2) && isempty(b)) || ...
- (isempty(b1) && ~isempty(b2) && isempty(b)) || ...
- (isempty(b1) && isempty(b2) && ~isempty(b)) || ...
- (isempty(b1) && ~isempty(b2) && ~isempty(b)) || ...
- (~isempty(b1) && isempty(b2) && ~isempty(b)) || ...
- (~isempty(b1) && ~isempty(b2) && isempty(b)) )
+ (isempty(b1) && ~isempty(b2) && isempty(b)) || ...
+ (isempty(b1) && isempty(b2) && ~isempty(b)) || ...
+ (isempty(b1) && ~isempty(b2) && ~isempty(b)) || ...
+ (~isempty(b1) && isempty(b2) && ~isempty(b)) || ...
+ (~isempty(b1) && ~isempty(b2) && isempty(b)) )
error('get_internal_doc_block:: There is a problem with the internal block definition!')
end
- if ( b2~=b(end) || b1~=b(1) || any(b-transpose(b1:1:b2)) )
+ if ( b2~=b(end) || b1~=b(1) || any(b-transpose(b1:1:b2)) )
error('get_internal_doc_block:: There is a problem with the internal block definition!')
end
end
diff --git a/matlab/utilities/general/compare_vectors.m b/matlab/utilities/general/compare_vectors.m
index aec07a2a5406c9663e0ac354b13e40175244034e..3264d88414294a3ec74e542a0076bf8cc72904b1 100644
--- a/matlab/utilities/general/compare_vectors.m
+++ b/matlab/utilities/general/compare_vectors.m
@@ -1,5 +1,5 @@
function C = compare_vectors(f, A, B) % --*-- Unitary tests --*--
-
+
% Performs lexicographical comparison of vectors.
%
% INPUTS
@@ -7,12 +7,12 @@ function C = compare_vectors(f, A, B) % --*-- Unitary tests --*--
% o A vector of real numbers.
% o B vector of real numbers.
%
-% OUTPUTS
+% OUTPUTS
% o C integer scalar, 1 or 0.
%
-% REMARKS
+% REMARKS
% o It is assumed that vectors A and B have the same number of elements.
-
+
% Copyright (C) 2013-2014 Dynare Team
%
% This file is part of Dynare.
diff --git a/matlab/utilities/general/delete_stale_file.m b/matlab/utilities/general/delete_stale_file.m
index 8ac8b799635e88eaedc4e03093a3b3201ee5dcbb..cce629fe388990b4540ca214d563a24abb2f59a5 100644
--- a/matlab/utilities/general/delete_stale_file.m
+++ b/matlab/utilities/general/delete_stale_file.m
@@ -23,4 +23,3 @@ Files_info = dir(fname);
if length(Files_info)>0
delete(fname)
end
-
diff --git a/matlab/utilities/general/demean.m b/matlab/utilities/general/demean.m
index 4bf53aaa62d6ed66275093e09b442be7a597a202..fc7096e97688f1f79411d1d969968b362328a7ef 100644
--- a/matlab/utilities/general/demean.m
+++ b/matlab/utilities/general/demean.m
@@ -1,6 +1,6 @@
function c = demean(x)
% Removes the mean of each column of a matrix.
-
+
%@info:
%! @deftypefn {Function File} {@var{c} =} demean (@var{x})
%! @anchor{demean}
@@ -17,18 +17,18 @@ function c = demean(x)
%! @item c
%! Matlab matrix (T-by-N). The demeaned x matrix.
%! @end table
-%!
-%! @strong{This function is called by:}
+%!
+%! @strong{This function is called by:}
%! @ref{compute_cova}, @ref{compute_acov}, @ref{compute_std}.
-%!
-%! @strong{This function calls:}
+%!
+%! @strong{This function calls:}
%! @ref{ndim},
-%!
+%!
%! @end deftypefn
%@eod:
% Copyright (C) 2011-2012 Dynare Team
-%
+%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
@@ -45,7 +45,7 @@ function c = demean(x)
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
% Original author: stephane DOT adjemian AT univ DASH lemans DOT fr
-
+
if ndim(x)==1
c = x-mean(x);
elseif ndim(x)==2
diff --git a/matlab/utilities/general/disp_verbose.m b/matlab/utilities/general/disp_verbose.m
index 09c09e4d7e66ad001945c13aceadd84158f447c0..504c4403cb0ac0cf3b7fff1effb2d481476726c2 100644
--- a/matlab/utilities/general/disp_verbose.m
+++ b/matlab/utilities/general/disp_verbose.m
@@ -1,7 +1,7 @@
function disp_verbose(input_string,Verbose)
% function disp_verbose(input_string,Verbose)
% Prints input_string unless Verbose=0 is requested
-%
+%
% Copyright (C) 2015 Dynare Team
%
% This file is part of Dynare.
diff --git a/matlab/utilities/general/dyn_mex.m b/matlab/utilities/general/dyn_mex.m
index 7892ee9bed66ce2563e0f36443f820641ff895e2..9220bfdd5597cbb390d2d1aaebbf1adb5326e4d8 100644
--- a/matlab/utilities/general/dyn_mex.m
+++ b/matlab/utilities/general/dyn_mex.m
@@ -1,20 +1,20 @@
function dyn_mex(win_compiler,basename,force)
% Compile Dynare model dlls when model option use_dll is used
-% if C file is fresher than mex file
+% if C file is fresher than mex file
%
% INPUTS
-% o win_compiler str compiler used under Windows (unused under Linux or OSX):
+% o win_compiler str compiler used under Windows (unused under Linux or OSX):
% 'msvc' (MS Visual C)
% 'cygwin'
% o basename str filenames base
-% o force bool recompile if 1
-%
-% OUTPUTS
+% o force bool recompile if 1
+%
+% OUTPUTS
% none
%
-
+
% Copyright (C) 2015-2016 Dynare Team
%
% This file is part of Dynare.
@@ -47,26 +47,26 @@ end
if ~exist('OCTAVE_VERSION')
% Some mex commands are enclosed in an eval(), because otherwise it will make Octave fail
if ispc
- if strcmp(win_compiler,'msvc')
- % MATLAB/Windows + Microsoft Visual C++
- % Add /TP flag as fix for #1227
- eval(['mex -O LINKFLAGS="$LINKFLAGS /export:Dynamic" COMPFLAGS="/TP" ' basename '_dynamic.c ' basename '_dynamic_mex.c'])
- eval(['mex -O LINKFLAGS="$LINKFLAGS /export:Static" COMPFLAGS="/TP" ' basename '_static.c ' basename '_static_mex.c'])
- elseif strcmp(win_compiler,'mingw')
- eval(['mex -O LINKFLAGS="$LINKFLAGS /export:Dynamic" ' basename '_dynamic.c ' basename '_dynamic_mex.c'])
- eval(['mex -O LINKFLAGS="$LINKFLAGS /export:Static" ' basename '_static.c ' basename '_static_mex.c'])
- elseif strcmp(win_compiler,'cygwin') %legacy support for Cygwin with mexopts.bat
- % MATLAB/Windows + Cygwin g++
- eval(['mex -O PRELINK_CMDS1="echo EXPORTS > mex.def & echo ' ...
- 'mexFunction >> mex.def & echo Dynamic >> mex.def" ' ...
- basename '_dynamic.c ' basename '_dynamic_mex.c'])
- eval(['mex -O PRELINK_CMDS1="echo EXPORTS > mex.def & echo ' ...
- 'mexFunction >> mex.def & echo Dynamic >> mex.def" ' ...
- basename '_static.c ' basename '_static_mex.c'])
- else
- error(['When using the USE_DLL option, you must give either ' ...
- '''cygwin'', ''mingw'' or ''msvc'' option to the ''dynare'' command'])
- end
+ if strcmp(win_compiler,'msvc')
+ % MATLAB/Windows + Microsoft Visual C++
+ % Add /TP flag as fix for #1227
+ eval(['mex -O LINKFLAGS="$LINKFLAGS /export:Dynamic" COMPFLAGS="/TP" ' basename '_dynamic.c ' basename '_dynamic_mex.c'])
+ eval(['mex -O LINKFLAGS="$LINKFLAGS /export:Static" COMPFLAGS="/TP" ' basename '_static.c ' basename '_static_mex.c'])
+ elseif strcmp(win_compiler,'mingw')
+ eval(['mex -O LINKFLAGS="$LINKFLAGS /export:Dynamic" ' basename '_dynamic.c ' basename '_dynamic_mex.c'])
+ eval(['mex -O LINKFLAGS="$LINKFLAGS /export:Static" ' basename '_static.c ' basename '_static_mex.c'])
+ elseif strcmp(win_compiler,'cygwin') %legacy support for Cygwin with mexopts.bat
+ % MATLAB/Windows + Cygwin g++
+ eval(['mex -O PRELINK_CMDS1="echo EXPORTS > mex.def & echo ' ...
+ 'mexFunction >> mex.def & echo Dynamic >> mex.def" ' ...
+ basename '_dynamic.c ' basename '_dynamic_mex.c'])
+ eval(['mex -O PRELINK_CMDS1="echo EXPORTS > mex.def & echo ' ...
+ 'mexFunction >> mex.def & echo Dynamic >> mex.def" ' ...
+ basename '_static.c ' basename '_static_mex.c'])
+ else
+ error(['When using the USE_DLL option, you must give either ' ...
+ '''cygwin'', ''mingw'' or ''msvc'' option to the ''dynare'' command'])
+ end
elseif isunix && ~ismac
% MATLAB/Linux
if matlab_ver_less_than('8.3')
diff --git a/matlab/utilities/general/get_all_variables_but_lagged_leaded_exogenous.m b/matlab/utilities/general/get_all_variables_but_lagged_leaded_exogenous.m
index 37697e723ebab5e61d2f49498ce022d358eb84e2..8a6fb565742d0b29b4c257215f7a2fdba18b2f1d 100644
--- a/matlab/utilities/general/get_all_variables_but_lagged_leaded_exogenous.m
+++ b/matlab/utilities/general/get_all_variables_but_lagged_leaded_exogenous.m
@@ -10,7 +10,7 @@ function k = get_all_variables_but_lagged_leaded_exogenous(M)
%
% Copyright (C) 2011-2016 Dynare Team
-%
+%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
@@ -31,5 +31,5 @@ if isempty(M.aux_vars)
else
type = [M.aux_vars.type];
k = [1:M.orig_endo_nbr, M.orig_endo_nbr ...
- + find((type ~= 2) & (type ~= 3))];
+ + find((type ~= 2) & (type ~= 3))];
end
\ No newline at end of file
diff --git a/matlab/utilities/general/indices_lagged_leaded_exogenous_variables.m b/matlab/utilities/general/indices_lagged_leaded_exogenous_variables.m
index 6b3f0b4161c39f57d7ea23ccf0cd1b64b92acf16..5d2d6b4376aa17a77e35fe00b95d864d90f1166c 100644
--- a/matlab/utilities/general/indices_lagged_leaded_exogenous_variables.m
+++ b/matlab/utilities/general/indices_lagged_leaded_exogenous_variables.m
@@ -13,7 +13,7 @@ function [il,l1,ik,k1] = indices_lagged_leaded_exogenous_variables(k,M)
% k1: value of non lagged or leaded variable in vector k
% Copyright (C) 2011-2016 Dynare Team
-%
+%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
diff --git a/matlab/utilities/general/isconst.m b/matlab/utilities/general/isconst.m
index e869f7b5c26934145d0199caf6fc851d4497eb1e..aabff20ad8046596ae74762f85e212988d090b48 100644
--- a/matlab/utilities/general/isconst.m
+++ b/matlab/utilities/general/isconst.m
@@ -1,6 +1,6 @@
function aa = isconst(y)
% Returns 1 if vector y is constant, 0 otherwise.
-%
+%
% INPUTS:
% yy [double] n*1 vector.
%
diff --git a/matlab/utilities/general/isdiagonal.m b/matlab/utilities/general/isdiagonal.m
index f76e4379721684ead967f737d14dfeef6d12fba5..aa32c4c015346e3f4f568ecb73065ea688184b2f 100644
--- a/matlab/utilities/general/isdiagonal.m
+++ b/matlab/utilities/general/isdiagonal.m
@@ -60,5 +60,3 @@ end
%$ t = isdiagonal(A);
%$ T = all(t);
%@eof:5
-
-
diff --git a/matlab/utilities/general/isinestimationobjective.m b/matlab/utilities/general/isinestimationobjective.m
index b6bbb5faf4e5b35390c658b692b4392d92e285fd..023e4cb14e77c29bffbeff747d3003ae15425651 100644
--- a/matlab/utilities/general/isinestimationobjective.m
+++ b/matlab/utilities/general/isinestimationobjective.m
@@ -25,8 +25,8 @@ tmp = struct2cell(dbstack);
list_of_previously_called_routines = transpose(tmp(2,:));
if ~isempty(strmatch('dsge_likelihood', list_of_previously_called_routines, 'exact')) || ...
- ~isempty(strmatch('dsge_var_likelihood', list_of_previously_called_routines, 'exact')) || ...
- ~isempty(strmatch('non_linear_dsge_likelihood', list_of_previously_called_routines, 'exact')) || ...
- ~isempty(strmatch('simulated_moments_estimation', list_of_previously_called_routines, 'exact'))
+ ~isempty(strmatch('dsge_var_likelihood', list_of_previously_called_routines, 'exact')) || ...
+ ~isempty(strmatch('non_linear_dsge_likelihood', list_of_previously_called_routines, 'exact')) || ...
+ ~isempty(strmatch('simulated_moments_estimation', list_of_previously_called_routines, 'exact'))
b = true;
end
diff --git a/matlab/utilities/general/isint.m b/matlab/utilities/general/isint.m
index 5201a4649ed63ba319413b68a3d3918a1fc7118d..5a0a94e0a7882a6991774645ed28ddd024480e95 100644
--- a/matlab/utilities/general/isint.m
+++ b/matlab/utilities/general/isint.m
@@ -1,18 +1,18 @@
function [b,c,d] = isint(a)
% This function tests if the input argument is an integer.
%
-% INPUT
+% INPUT
% a [double] m*n matrix.
%
-% OUTPUT
-% b [integer] m*n matrix of 0 and 1. b(i,j)=1 if a(i,j) is an integer.
+% OUTPUT
+% b [integer] m*n matrix of 0 and 1. b(i,j)=1 if a(i,j) is an integer.
% c [integer] p*1 vector of indices pointing to the integer elements of a.
% d [integer] q*1 vector of indices pointing to the non integer elements of a.
-%
+%
% SPECIAL REQUIREMENTS
% None.
-%
-% NOTES
+%
+% NOTES
% p+q is equal to the product of m by n.
% Copyright (C) 2009-2014 Dynare Team
diff --git a/matlab/utilities/general/ispd.m b/matlab/utilities/general/ispd.m
index e319a47a1f1c3a21ffb16829d8379bac880cab51..7fed2d44c1c838a48f89920fd03e997af4d54647 100644
--- a/matlab/utilities/general/ispd.m
+++ b/matlab/utilities/general/ispd.m
@@ -44,7 +44,7 @@ function [test, penalty] = ispd(A)
if ~isquare(A)
error(['ispd:: Input argument ' inputname(1) ' has to be a square matrix!'])
end
-
+
[cholA, info] = chol(A);
test = ~info;
diff --git a/matlab/utilities/general/log_variable.m b/matlab/utilities/general/log_variable.m
index 74a6aa126d9f7b73e895663244280835a5f04c32..1c6de682ae23fdbc83d96360b05509396be5d7ed 100644
--- a/matlab/utilities/general/log_variable.m
+++ b/matlab/utilities/general/log_variable.m
@@ -13,7 +13,7 @@ function y = log_variable(ivar,x,M)
%
% Copyright (C) 2011-2016 Dynare Team
-%
+%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
diff --git a/matlab/utilities/general/ndim.m b/matlab/utilities/general/ndim.m
index 1c4d003bb823f1d61aecb84c86f2a31332d29c4d..8d55e48e8f9a97b2bef334b66b7b01dedb1b6750 100644
--- a/matlab/utilities/general/ndim.m
+++ b/matlab/utilities/general/ndim.m
@@ -3,7 +3,7 @@ function n = ndim(x)
%@info:
%! @deftypefn {Function File} {@var{n} =} ndim (@var{x})
-%! @anchor{ndim}
+%! @anchor{ndim}
%! This function reports the number of non singleton dimensions of a matlab array.
%!
%! @strong{Inputs}
@@ -17,18 +17,18 @@ function n = ndim(x)
%! @item n
%! Integer scalar. The number of non singleton dimensions of a matlab array.
%! @end table
-%!
-%! @strong{This function is called by:}
+%!
+%! @strong{This function is called by:}
%! @ref{demean}, @ref{nandemean}.
-%!
+%!
%! @strong{This function calls:}
%! none.
-%!
+%!
%! @end deftypefn
%@eod:
% Copyright (C) 2011-2012 Dynare Team
-%
+%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
diff --git a/matlab/utilities/general/remove_fractional_xticks.m b/matlab/utilities/general/remove_fractional_xticks.m
index 19eab7a044b873f1c512e80fd66ba2410e31b791..d2c70633ff43e6dc6ffd9c0940ad014da845b2de 100644
--- a/matlab/utilities/general/remove_fractional_xticks.m
+++ b/matlab/utilities/general/remove_fractional_xticks.m
@@ -17,7 +17,7 @@ function remove_fractional_xticks
% 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/>.
+% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
xticks=get(gca,'xtick');
fractional_periods=find(rem(xticks,1)~=0);
diff --git a/matlab/utilities/graphics/colorspace.m b/matlab/utilities/graphics/colorspace.m
index a260914723f86b15b8309ea4abf6060b0bdb1007..b70e2486dc143f9d9d1f7e713f34bc73d046faf6 100644
--- a/matlab/utilities/graphics/colorspace.m
+++ b/matlab/utilities/graphics/colorspace.m
@@ -1,18 +1,18 @@
function varargout = colorspace(Conversion,varargin)
%COLORSPACE Transform a color image between color representations.
% B = COLORSPACE(S,A) transforms the color representation of image A
-% where S is a string specifying the conversion. The input array A
-% should be a real full double array of size Mx3 or MxNx3. The output B
+% where S is a string specifying the conversion. The input array A
+% should be a real full double array of size Mx3 or MxNx3. The output B
% is the same size as A.
%
-% S tells the source and destination color spaces, S = 'dest<-src', or
+% S tells the source and destination color spaces, S = 'dest<-src', or
% alternatively, S = 'src->dest'. Supported color spaces are
%
% 'RGB' sRGB IEC 61966-2-1
% 'YCbCr' Luma + Chroma ("digitized" version of Y'PbPr)
% 'JPEG-YCbCr' Luma + Chroma space used in JFIF JPEG
% 'YDbDr' SECAM Y'DbDr Luma + Chroma
-% 'YPbPr' Luma (ITU-R BT.601) + Chroma
+% 'YPbPr' Luma (ITU-R BT.601) + Chroma
% 'YUV' NTSC PAL Y'UV Luma + Chroma
% 'YIQ' NTSC Y'IQ Luma + Chroma
% 'HSV' or 'HSB' Hue Saturation Value/Brightness
@@ -26,22 +26,22 @@ function varargout = colorspace(Conversion,varargin)
%
% All conversions assume 2 degree observer and D65 illuminant.
%
-% Color space names are case insensitive and spaces are ignored. When
-% sRGB is the source or destination, it can be omitted. For example
+% Color space names are case insensitive and spaces are ignored. When
+% sRGB is the source or destination, it can be omitted. For example
% 'yuv<-' is short for 'yuv<-rgb'.
%
-% For sRGB, the values should be scaled between 0 and 1. Beware that
-% transformations generally do not constrain colors to be "in gamut."
-% Particularly, transforming from another space to sRGB may obtain
-% R'G'B' values outside of the [0,1] range. So the result should be
+% For sRGB, the values should be scaled between 0 and 1. Beware that
+% transformations generally do not constrain colors to be "in gamut."
+% Particularly, transforming from another space to sRGB may obtain
+% R'G'B' values outside of the [0,1] range. So the result should be
% clamped to [0,1] before displaying:
% image(min(max(B,0),1)); % Clamp B to [0,1] and display
%
% sRGB (Red Green Blue) is the (ITU-R BT.709 gamma-corrected) standard
-% red-green-blue representation of colors used in digital imaging. The
-% components should be scaled between 0 and 1. The space can be
+% red-green-blue representation of colors used in digital imaging. The
+% components should be scaled between 0 and 1. The space can be
% visualized geometrically as a cube.
-%
+%
% Y'PbPr, Y'CbCr, Y'DbDr, Y'UV, and Y'IQ are related to sRGB by linear
% transformations. These spaces separate a color into a grayscale
% luminance component Y and two chroma components. The valid ranges of
@@ -66,7 +66,7 @@ function varargout = colorspace(Conversion,varargin)
% S = 1 - min(R',G',B')/I (0 <= S <= 1),
% I = (R'+G'+B')/3 (0 <= I <= 1).
% Unlike HSV and HSL, the hue angle H is computed on a circle rather than
-% a hexagon.
+% a hexagon.
%
% CIE XYZ is related to sRGB by inverse gamma correction followed by a
% linear transform. Other CIE color spaces are defined relative to XYZ.
@@ -75,8 +75,8 @@ function varargout = colorspace(Conversion,varargin)
% component is designed to match closely with human perception of
% lightness. The other two components describe the chroma.
%
-% CIE CAT02 LMS is the linear transformation of XYZ using the MCAT02
-% chromatic adaptation matrix. The space is designed to model the
+% CIE CAT02 LMS is the linear transformation of XYZ using the MCAT02
+% chromatic adaptation matrix. The space is designed to model the
% response of the three types of cones in the human eye, where L, M, S,
% correspond respectively to red ("long"), green ("medium"), and blue
% ("short").
@@ -88,11 +88,11 @@ function varargout = colorspace(Conversion,varargin)
% modification, are permitted provided that the following conditions are
% met:
%
- % * Redistributions of source code must retain the above copyright
- % notice, this list of conditions and the following disclaimer.
- % * Redistributions in binary form must reproduce the above copyright
- % notice, this list of conditions and the following disclaimer in
- % the documentation and/or other materials provided with the distribution
+% * Redistributions of source code must retain the above copyright
+% notice, this list of conditions and the following disclaimer.
+% * Redistributions in binary form must reproduce the above copyright
+% notice, this list of conditions and the following disclaimer in
+% the documentation and/or other materials provided with the distribution
%
% THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
% AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
@@ -111,11 +111,11 @@ if nargin < 2, error('Not enough input arguments.'); end
[SrcSpace,DestSpace] = parse(Conversion);
if nargin == 2
- Image = varargin{1};
+ Image = varargin{1};
elseif nargin >= 3
- Image = cat(3,varargin{:});
+ Image = cat(3,varargin{:});
else
- error('Invalid number of input arguments.');
+ error('Invalid number of input arguments.');
end
FlipDims = (size(Image,3) == 1);
@@ -128,31 +128,31 @@ SrcT = gettransform(SrcSpace);
DestT = gettransform(DestSpace);
if ~ischar(SrcT) && ~ischar(DestT)
- % Both source and destination transforms are affine, so they
- % can be composed into one affine operation
- T = [DestT(:,1:3)*SrcT(:,1:3),DestT(:,1:3)*SrcT(:,4)+DestT(:,4)];
- Temp = zeros(size(Image));
- Temp(:,:,1) = T(1)*Image(:,:,1) + T(4)*Image(:,:,2) + T(7)*Image(:,:,3) + T(10);
- Temp(:,:,2) = T(2)*Image(:,:,1) + T(5)*Image(:,:,2) + T(8)*Image(:,:,3) + T(11);
- Temp(:,:,3) = T(3)*Image(:,:,1) + T(6)*Image(:,:,2) + T(9)*Image(:,:,3) + T(12);
- Image = Temp;
+ % Both source and destination transforms are affine, so they
+ % can be composed into one affine operation
+ T = [DestT(:,1:3)*SrcT(:,1:3),DestT(:,1:3)*SrcT(:,4)+DestT(:,4)];
+ Temp = zeros(size(Image));
+ Temp(:,:,1) = T(1)*Image(:,:,1) + T(4)*Image(:,:,2) + T(7)*Image(:,:,3) + T(10);
+ Temp(:,:,2) = T(2)*Image(:,:,1) + T(5)*Image(:,:,2) + T(8)*Image(:,:,3) + T(11);
+ Temp(:,:,3) = T(3)*Image(:,:,1) + T(6)*Image(:,:,2) + T(9)*Image(:,:,3) + T(12);
+ Image = Temp;
elseif ~ischar(DestT)
- Image = rgb(Image,SrcSpace);
- Temp = zeros(size(Image));
- Temp(:,:,1) = DestT(1)*Image(:,:,1) + DestT(4)*Image(:,:,2) + DestT(7)*Image(:,:,3) + DestT(10);
- Temp(:,:,2) = DestT(2)*Image(:,:,1) + DestT(5)*Image(:,:,2) + DestT(8)*Image(:,:,3) + DestT(11);
- Temp(:,:,3) = DestT(3)*Image(:,:,1) + DestT(6)*Image(:,:,2) + DestT(9)*Image(:,:,3) + DestT(12);
- Image = Temp;
+ Image = rgb(Image,SrcSpace);
+ Temp = zeros(size(Image));
+ Temp(:,:,1) = DestT(1)*Image(:,:,1) + DestT(4)*Image(:,:,2) + DestT(7)*Image(:,:,3) + DestT(10);
+ Temp(:,:,2) = DestT(2)*Image(:,:,1) + DestT(5)*Image(:,:,2) + DestT(8)*Image(:,:,3) + DestT(11);
+ Temp(:,:,3) = DestT(3)*Image(:,:,1) + DestT(6)*Image(:,:,2) + DestT(9)*Image(:,:,3) + DestT(12);
+ Image = Temp;
else
- Image = feval(DestT,Image,SrcSpace);
+ Image = feval(DestT,Image,SrcSpace);
end
%%% Output format %%%
if nargout > 1
- varargout = {Image(:,:,1),Image(:,:,2),Image(:,:,3)};
+ varargout = {Image(:,:,1),Image(:,:,2),Image(:,:,3)};
else
- if FlipDims, Image = permute(Image,[1,3,2]); end
- varargout = {Image};
+ if FlipDims, Image = permute(Image,[1,3,2]); end
+ varargout = {Image};
end
return
@@ -162,29 +162,29 @@ function [SrcSpace,DestSpace] = parse(Str)
% Parse conversion argument
if ischar(Str)
- Str = lower(strrep(strrep(Str,'-',''),'=',''));
- k = find(Str == '>');
-
- if length(k) == 1 % Interpret the form 'src->dest'
- SrcSpace = Str(1:k-1);
- DestSpace = Str(k+1:end);
- else
- k = find(Str == '<');
-
- if length(k) == 1 % Interpret the form 'dest<-src'
- DestSpace = Str(1:k-1);
- SrcSpace = Str(k+1:end);
- else
- error(['Invalid conversion, ''',Str,'''.']);
- end
- end
-
- SrcSpace = alias(SrcSpace);
- DestSpace = alias(DestSpace);
+ Str = lower(strrep(strrep(Str,'-',''),'=',''));
+ k = find(Str == '>');
+
+ if length(k) == 1 % Interpret the form 'src->dest'
+ SrcSpace = Str(1:k-1);
+ DestSpace = Str(k+1:end);
+ else
+ k = find(Str == '<');
+
+ if length(k) == 1 % Interpret the form 'dest<-src'
+ DestSpace = Str(1:k-1);
+ SrcSpace = Str(k+1:end);
+ else
+ error(['Invalid conversion, ''',Str,'''.']);
+ end
+ end
+
+ SrcSpace = alias(SrcSpace);
+ DestSpace = alias(DestSpace);
else
- SrcSpace = 1; % No source pre-transform
- DestSpace = Conversion;
- if any(size(Conversion) ~= 3), error('Transformation matrix must be 3x3.'); end
+ SrcSpace = 1; % No source pre-transform
+ DestSpace = Conversion;
+ if any(size(Conversion) ~= 3), error('Transformation matrix must be 3x3.'); end
end
return
@@ -193,18 +193,18 @@ function Space = alias(Space)
Space = strrep(strrep(Space,'cie',''),' ','');
if isempty(Space)
- Space = 'rgb';
+ Space = 'rgb';
end
switch Space
-case {'ycbcr','ycc'}
- Space = 'ycbcr';
-case {'hsv','hsb'}
- Space = 'hsv';
-case {'hsl','hsi','hls'}
- Space = 'hsl';
-case {'rgb','yuv','yiq','ydbdr','ycbcr','jpegycbcr','xyz','lab','luv','lch'}
- return
+ case {'ycbcr','ycc'}
+ Space = 'ycbcr';
+ case {'hsv','hsb'}
+ Space = 'hsv';
+ case {'hsl','hsi','hls'}
+ Space = 'hsl';
+ case {'rgb','yuv','yiq','ydbdr','ycbcr','jpegycbcr','xyz','lab','luv','lch'}
+ return
end
return
@@ -213,32 +213,32 @@ function T = gettransform(Space)
% Get a colorspace transform: either a matrix describing an affine transform,
% or a string referring to a conversion subroutine
switch Space
-case 'ypbpr'
- T = [0.299,0.587,0.114,0;-0.1687367,-0.331264,0.5,0;0.5,-0.418688,-0.081312,0];
-case 'yuv'
- % sRGB to NTSC/PAL YUV
- % Wikipedia: http://en.wikipedia.org/wiki/YUV
- T = [0.299,0.587,0.114,0;-0.147,-0.289,0.436,0;0.615,-0.515,-0.100,0];
-case 'ydbdr'
- % sRGB to SECAM YDbDr
- % Wikipedia: http://en.wikipedia.org/wiki/YDbDr
- T = [0.299,0.587,0.114,0;-0.450,-0.883,1.333,0;-1.333,1.116,0.217,0];
-case 'yiq'
- % sRGB in [0,1] to NTSC YIQ in [0,1];[-0.595716,0.595716];[-0.522591,0.522591];
- % Wikipedia: http://en.wikipedia.org/wiki/YIQ
- T = [0.299,0.587,0.114,0;0.595716,-0.274453,-0.321263,0;0.211456,-0.522591,0.311135,0];
-case 'ycbcr'
- % sRGB (range [0,1]) to ITU-R BRT.601 (CCIR 601) Y'CbCr
- % Wikipedia: http://en.wikipedia.org/wiki/YCbCr
- % Poynton, Equation 3, scaling of R'G'B to Y'PbPr conversion
- T = [65.481,128.553,24.966,16;-37.797,-74.203,112.0,128;112.0,-93.786,-18.214,128];
-case 'jpegycbcr'
- % Wikipedia: http://en.wikipedia.org/wiki/YCbCr
- T = [0.299,0.587,0.114,0;-0.168736,-0.331264,0.5,0.5;0.5,-0.418688,-0.081312,0.5]*255;
-case {'rgb','xyz','hsv','hsl','lab','luv','lch','cat02lms'}
- T = Space;
-otherwise
- error(['Unknown color space, ''',Space,'''.']);
+ case 'ypbpr'
+ T = [0.299,0.587,0.114,0;-0.1687367,-0.331264,0.5,0;0.5,-0.418688,-0.081312,0];
+ case 'yuv'
+ % sRGB to NTSC/PAL YUV
+ % Wikipedia: http://en.wikipedia.org/wiki/YUV
+ T = [0.299,0.587,0.114,0;-0.147,-0.289,0.436,0;0.615,-0.515,-0.100,0];
+ case 'ydbdr'
+ % sRGB to SECAM YDbDr
+ % Wikipedia: http://en.wikipedia.org/wiki/YDbDr
+ T = [0.299,0.587,0.114,0;-0.450,-0.883,1.333,0;-1.333,1.116,0.217,0];
+ case 'yiq'
+ % sRGB in [0,1] to NTSC YIQ in [0,1];[-0.595716,0.595716];[-0.522591,0.522591];
+ % Wikipedia: http://en.wikipedia.org/wiki/YIQ
+ T = [0.299,0.587,0.114,0;0.595716,-0.274453,-0.321263,0;0.211456,-0.522591,0.311135,0];
+ case 'ycbcr'
+ % sRGB (range [0,1]) to ITU-R BRT.601 (CCIR 601) Y'CbCr
+ % Wikipedia: http://en.wikipedia.org/wiki/YCbCr
+ % Poynton, Equation 3, scaling of R'G'B to Y'PbPr conversion
+ T = [65.481,128.553,24.966,16;-37.797,-74.203,112.0,128;112.0,-93.786,-18.214,128];
+ case 'jpegycbcr'
+ % Wikipedia: http://en.wikipedia.org/wiki/YCbCr
+ T = [0.299,0.587,0.114,0;-0.168736,-0.331264,0.5,0.5;0.5,-0.418688,-0.081312,0.5]*255;
+ case {'rgb','xyz','hsv','hsl','lab','luv','lch','cat02lms'}
+ T = Space;
+ otherwise
+ error(['Unknown color space, ''',Space,'''.']);
end
return
@@ -246,43 +246,43 @@ return
function Image = rgb(Image,SrcSpace)
% Convert to sRGB from 'SrcSpace'
switch SrcSpace
-case 'rgb'
- return
-case 'hsv'
- % Convert HSV to sRGB
- Image = huetorgb((1 - Image(:,:,2)).*Image(:,:,3),Image(:,:,3),Image(:,:,1));
-case 'hsl'
- % Convert HSL to sRGB
- L = Image(:,:,3);
- Delta = Image(:,:,2).*min(L,1-L);
- Image = huetorgb(L-Delta,L+Delta,Image(:,:,1));
-case {'xyz','lab','luv','lch','cat02lms'}
- % Convert to CIE XYZ
- Image = xyz(Image,SrcSpace);
- % Convert XYZ to RGB
- T = [3.2406, -1.5372, -0.4986; -0.9689, 1.8758, 0.0415; 0.0557, -0.2040, 1.057];
- R = T(1)*Image(:,:,1) + T(4)*Image(:,:,2) + T(7)*Image(:,:,3); % R
- G = T(2)*Image(:,:,1) + T(5)*Image(:,:,2) + T(8)*Image(:,:,3); % G
- B = T(3)*Image(:,:,1) + T(6)*Image(:,:,2) + T(9)*Image(:,:,3); % B
- % Desaturate and rescale to constrain resulting RGB values to [0,1]
- AddWhite = -min(min(min(R,G),B),0);
- R = R + AddWhite;
- G = G + AddWhite;
- B = B + AddWhite;
- % Apply gamma correction to convert linear RGB to sRGB
- Image(:,:,1) = gammacorrection(R); % R'
- Image(:,:,2) = gammacorrection(G); % G'
- Image(:,:,3) = gammacorrection(B); % B'
-otherwise % Conversion is through an affine transform
- T = gettransform(SrcSpace);
- temp = inv(T(:,1:3));
- T = [temp,-temp*T(:,4)];
- R = T(1)*Image(:,:,1) + T(4)*Image(:,:,2) + T(7)*Image(:,:,3) + T(10);
- G = T(2)*Image(:,:,1) + T(5)*Image(:,:,2) + T(8)*Image(:,:,3) + T(11);
- B = T(3)*Image(:,:,1) + T(6)*Image(:,:,2) + T(9)*Image(:,:,3) + T(12);
- Image(:,:,1) = R;
- Image(:,:,2) = G;
- Image(:,:,3) = B;
+ case 'rgb'
+ return
+ case 'hsv'
+ % Convert HSV to sRGB
+ Image = huetorgb((1 - Image(:,:,2)).*Image(:,:,3),Image(:,:,3),Image(:,:,1));
+ case 'hsl'
+ % Convert HSL to sRGB
+ L = Image(:,:,3);
+ Delta = Image(:,:,2).*min(L,1-L);
+ Image = huetorgb(L-Delta,L+Delta,Image(:,:,1));
+ case {'xyz','lab','luv','lch','cat02lms'}
+ % Convert to CIE XYZ
+ Image = xyz(Image,SrcSpace);
+ % Convert XYZ to RGB
+ T = [3.2406, -1.5372, -0.4986; -0.9689, 1.8758, 0.0415; 0.0557, -0.2040, 1.057];
+ R = T(1)*Image(:,:,1) + T(4)*Image(:,:,2) + T(7)*Image(:,:,3); % R
+ G = T(2)*Image(:,:,1) + T(5)*Image(:,:,2) + T(8)*Image(:,:,3); % G
+ B = T(3)*Image(:,:,1) + T(6)*Image(:,:,2) + T(9)*Image(:,:,3); % B
+ % Desaturate and rescale to constrain resulting RGB values to [0,1]
+ AddWhite = -min(min(min(R,G),B),0);
+ R = R + AddWhite;
+ G = G + AddWhite;
+ B = B + AddWhite;
+ % Apply gamma correction to convert linear RGB to sRGB
+ Image(:,:,1) = gammacorrection(R); % R'
+ Image(:,:,2) = gammacorrection(G); % G'
+ Image(:,:,3) = gammacorrection(B); % B'
+ otherwise % Conversion is through an affine transform
+ T = gettransform(SrcSpace);
+ temp = inv(T(:,1:3));
+ T = [temp,-temp*T(:,4)];
+ R = T(1)*Image(:,:,1) + T(4)*Image(:,:,2) + T(7)*Image(:,:,3) + T(10);
+ G = T(2)*Image(:,:,1) + T(5)*Image(:,:,2) + T(8)*Image(:,:,3) + T(11);
+ B = T(3)*Image(:,:,1) + T(6)*Image(:,:,2) + T(9)*Image(:,:,3) + T(12);
+ Image(:,:,1) = R;
+ Image(:,:,2) = G;
+ Image(:,:,3) = B;
end
% Clip to [0,1]
@@ -292,53 +292,53 @@ return
function Image = xyz(Image,SrcSpace)
% Convert to CIE XYZ from 'SrcSpace'
-WhitePoint = [0.950456,1,1.088754];
+WhitePoint = [0.950456,1,1.088754];
switch SrcSpace
-case 'xyz'
- return
-case 'luv'
- % Convert CIE L*uv to XYZ
- WhitePointU = (4*WhitePoint(1))./(WhitePoint(1) + 15*WhitePoint(2) + 3*WhitePoint(3));
- WhitePointV = (9*WhitePoint(2))./(WhitePoint(1) + 15*WhitePoint(2) + 3*WhitePoint(3));
- L = Image(:,:,1);
- Y = (L + 16)/116;
- Y = invf(Y)*WhitePoint(2);
- U = Image(:,:,2)./(13*L + 1e-6*(L==0)) + WhitePointU;
- V = Image(:,:,3)./(13*L + 1e-6*(L==0)) + WhitePointV;
- Image(:,:,1) = -(9*Y.*U)./((U-4).*V - U.*V); % X
- Image(:,:,2) = Y; % Y
- Image(:,:,3) = (9*Y - (15*V.*Y) - (V.*Image(:,:,1)))./(3*V); % Z
-case {'lab','lch'}
- Image = lab(Image,SrcSpace);
- % Convert CIE L*ab to XYZ
- fY = (Image(:,:,1) + 16)/116;
- fX = fY + Image(:,:,2)/500;
- fZ = fY - Image(:,:,3)/200;
- Image(:,:,1) = WhitePoint(1)*invf(fX); % X
- Image(:,:,2) = WhitePoint(2)*invf(fY); % Y
- Image(:,:,3) = WhitePoint(3)*invf(fZ); % Z
-case 'cat02lms'
+ case 'xyz'
+ return
+ case 'luv'
+ % Convert CIE L*uv to XYZ
+ WhitePointU = (4*WhitePoint(1))./(WhitePoint(1) + 15*WhitePoint(2) + 3*WhitePoint(3));
+ WhitePointV = (9*WhitePoint(2))./(WhitePoint(1) + 15*WhitePoint(2) + 3*WhitePoint(3));
+ L = Image(:,:,1);
+ Y = (L + 16)/116;
+ Y = invf(Y)*WhitePoint(2);
+ U = Image(:,:,2)./(13*L + 1e-6*(L==0)) + WhitePointU;
+ V = Image(:,:,3)./(13*L + 1e-6*(L==0)) + WhitePointV;
+ Image(:,:,1) = -(9*Y.*U)./((U-4).*V - U.*V); % X
+ Image(:,:,2) = Y; % Y
+ Image(:,:,3) = (9*Y - (15*V.*Y) - (V.*Image(:,:,1)))./(3*V); % Z
+ case {'lab','lch'}
+ Image = lab(Image,SrcSpace);
+ % Convert CIE L*ab to XYZ
+ fY = (Image(:,:,1) + 16)/116;
+ fX = fY + Image(:,:,2)/500;
+ fZ = fY - Image(:,:,3)/200;
+ Image(:,:,1) = WhitePoint(1)*invf(fX); % X
+ Image(:,:,2) = WhitePoint(2)*invf(fY); % Y
+ Image(:,:,3) = WhitePoint(3)*invf(fZ); % Z
+ case 'cat02lms'
% Convert CAT02 LMS to XYZ
- T = inv([0.7328, 0.4296, -0.1624;-0.7036, 1.6975, 0.0061; 0.0030, 0.0136, 0.9834]);
- L = Image(:,:,1);
- M = Image(:,:,2);
- S = Image(:,:,3);
- Image(:,:,1) = T(1)*L + T(4)*M + T(7)*S; % X
- Image(:,:,2) = T(2)*L + T(5)*M + T(8)*S; % Y
- Image(:,:,3) = T(3)*L + T(6)*M + T(9)*S; % Z
-otherwise % Convert from some gamma-corrected space
- % Convert to sRGB
- Image = rgb(Image,SrcSpace);
- % Undo gamma correction
- R = invgammacorrection(Image(:,:,1));
- G = invgammacorrection(Image(:,:,2));
- B = invgammacorrection(Image(:,:,3));
- % Convert RGB to XYZ
- T = inv([3.2406, -1.5372, -0.4986; -0.9689, 1.8758, 0.0415; 0.0557, -0.2040, 1.057]);
- Image(:,:,1) = T(1)*R + T(4)*G + T(7)*B; % X
- Image(:,:,2) = T(2)*R + T(5)*G + T(8)*B; % Y
- Image(:,:,3) = T(3)*R + T(6)*G + T(9)*B; % Z
+ T = inv([0.7328, 0.4296, -0.1624;-0.7036, 1.6975, 0.0061; 0.0030, 0.0136, 0.9834]);
+ L = Image(:,:,1);
+ M = Image(:,:,2);
+ S = Image(:,:,3);
+ Image(:,:,1) = T(1)*L + T(4)*M + T(7)*S; % X
+ Image(:,:,2) = T(2)*L + T(5)*M + T(8)*S; % Y
+ Image(:,:,3) = T(3)*L + T(6)*M + T(9)*S; % Z
+ otherwise % Convert from some gamma-corrected space
+ % Convert to sRGB
+ Image = rgb(Image,SrcSpace);
+ % Undo gamma correction
+ R = invgammacorrection(Image(:,:,1));
+ G = invgammacorrection(Image(:,:,2));
+ B = invgammacorrection(Image(:,:,3));
+ % Convert RGB to XYZ
+ T = inv([3.2406, -1.5372, -0.4986; -0.9689, 1.8758, 0.0415; 0.0557, -0.2040, 1.057]);
+ Image(:,:,1) = T(1)*R + T(4)*G + T(7)*B; % X
+ Image(:,:,2) = T(2)*R + T(5)*G + T(8)*B; % Y
+ Image(:,:,3) = T(3)*R + T(6)*G + T(9)*B; % Z
end
return
@@ -355,27 +355,27 @@ return
function Image = hsl(Image,SrcSpace)
-% Convert to HSL
+% Convert to HSL
switch SrcSpace
-case 'hsv'
- % Convert HSV to HSL
- MaxVal = Image(:,:,3);
- MinVal = (1 - Image(:,:,2)).*MaxVal;
- L = 0.5*(MaxVal + MinVal);
- temp = min(L,1-L);
- Image(:,:,2) = 0.5*(MaxVal - MinVal)./(temp + (temp == 0));
- Image(:,:,3) = L;
-otherwise
- Image = rgb(Image,SrcSpace); % Convert to sRGB
- % Convert sRGB to HSL
- MinVal = min(Image,[],3);
- MaxVal = max(Image,[],3);
- L = 0.5*(MaxVal + MinVal);
- temp = min(L,1-L);
- S = 0.5*(MaxVal - MinVal)./(temp + (temp == 0));
- Image(:,:,1) = rgbtohue(Image);
- Image(:,:,2) = S;
- Image(:,:,3) = L;
+ case 'hsv'
+ % Convert HSV to HSL
+ MaxVal = Image(:,:,3);
+ MinVal = (1 - Image(:,:,2)).*MaxVal;
+ L = 0.5*(MaxVal + MinVal);
+ temp = min(L,1-L);
+ Image(:,:,2) = 0.5*(MaxVal - MinVal)./(temp + (temp == 0));
+ Image(:,:,3) = L;
+ otherwise
+ Image = rgb(Image,SrcSpace); % Convert to sRGB
+ % Convert sRGB to HSL
+ MinVal = min(Image,[],3);
+ MaxVal = max(Image,[],3);
+ L = 0.5*(MaxVal + MinVal);
+ temp = min(L,1-L);
+ S = 0.5*(MaxVal - MinVal)./(temp + (temp == 0));
+ Image(:,:,1) = rgbtohue(Image);
+ Image(:,:,2) = S;
+ Image(:,:,3) = L;
end
return
@@ -385,25 +385,25 @@ function Image = lab(Image,SrcSpace)
WhitePoint = [0.950456,1,1.088754];
switch SrcSpace
-case 'lab'
- return
-case 'lch'
- % Convert CIE L*CH to CIE L*ab
- C = Image(:,:,2);
- Image(:,:,2) = cos(Image(:,:,3)*pi/180).*C; % a*
- Image(:,:,3) = sin(Image(:,:,3)*pi/180).*C; % b*
-otherwise
- Image = xyz(Image,SrcSpace); % Convert to XYZ
- % Convert XYZ to CIE L*a*b*
- X = Image(:,:,1)/WhitePoint(1);
- Y = Image(:,:,2)/WhitePoint(2);
- Z = Image(:,:,3)/WhitePoint(3);
- fX = f(X);
- fY = f(Y);
- fZ = f(Z);
- Image(:,:,1) = 116*fY - 16; % L*
- Image(:,:,2) = 500*(fX - fY); % a*
- Image(:,:,3) = 200*(fY - fZ); % b*
+ case 'lab'
+ return
+ case 'lch'
+ % Convert CIE L*CH to CIE L*ab
+ C = Image(:,:,2);
+ Image(:,:,2) = cos(Image(:,:,3)*pi/180).*C; % a*
+ Image(:,:,3) = sin(Image(:,:,3)*pi/180).*C; % b*
+ otherwise
+ Image = xyz(Image,SrcSpace); % Convert to XYZ
+ % Convert XYZ to CIE L*a*b*
+ X = Image(:,:,1)/WhitePoint(1);
+ Y = Image(:,:,2)/WhitePoint(2);
+ Z = Image(:,:,3)/WhitePoint(3);
+ fX = f(X);
+ fY = f(Y);
+ fZ = f(Z);
+ Image(:,:,1) = 116*fY - 16; % L*
+ Image(:,:,2) = 500*(fX - fY); % a*
+ Image(:,:,3) = 200*(fY - fZ); % b*
end
return
diff --git a/matlab/utilities/graphics/distinguishable_colors.m b/matlab/utilities/graphics/distinguishable_colors.m
index cf45e582d60749c2cddd79b006d90a7d30d183c8..6b927be2e68b10804866554c60c783f07fc333ca 100644
--- a/matlab/utilities/graphics/distinguishable_colors.m
+++ b/matlab/utilities/graphics/distinguishable_colors.m
@@ -61,11 +61,11 @@ function colors = distinguishable_colors(n_colors,bg,func)
% modification, are permitted provided that the following conditions are
% met:
%
- % * Redistributions of source code must retain the above copyright
- % notice, this list of conditions and the following disclaimer.
- % * Redistributions in binary form must reproduce the above copyright
- % notice, this list of conditions and the following disclaimer in
- % the documentation and/or other materials provided with the distribution
+% * Redistributions of source code must retain the above copyright
+% notice, this list of conditions and the following disclaimer.
+% * Redistributions in binary form must reproduce the above copyright
+% notice, this list of conditions and the following disclaimer in
+% the documentation and/or other materials provided with the distribution
%
% THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
% AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
diff --git a/matlab/variance_decomposition_mc_analysis.m b/matlab/variance_decomposition_mc_analysis.m
index 6b101d0da3b8a0d3a3c25ff2d19d9c33d5afac8c..2c11fdb02d14676fd763c59df4a05bab57945130 100644
--- a/matlab/variance_decomposition_mc_analysis.m
+++ b/matlab/variance_decomposition_mc_analysis.m
@@ -94,7 +94,7 @@ if options_.estimation.moments_posterior_density.indicator
posterior_moments(tmp,1,mh_conf_sig);
else
[p_mean, p_median, p_var, hpd_interval, p_deciles] = ...
- posterior_moments(tmp,0,mh_conf_sig);
+ posterior_moments(tmp,0,mh_conf_sig);
end
oo_.([TYPE, 'TheoreticalMoments']).dsge.VarianceDecomposition.Mean.(var).(exo) = p_mean;
diff --git a/matlab/varlist_indices.m b/matlab/varlist_indices.m
index 24714f572aecd3d92872113e60863842890f6714..4347f9af423ab6116269947bd611750ab9fbf5d6 100644
--- a/matlab/varlist_indices.m
+++ b/matlab/varlist_indices.m
@@ -4,7 +4,7 @@ function [i_var,nvar,index_uniques] = varlist_indices(sublist,list)
%
% INPUT
% sublist: sublist of variables
-% list: list of variables
+% list: list of variables
%
% OUTPUT
% i_var: variable indices in M_.endo_names
diff --git a/matlab/vnorm.m b/matlab/vnorm.m
index b6bb123d944da1b125d58402e5e90e40e767fe14..56be98bb4d3721d0aadeb7be4e88e567054b72b5 100644
--- a/matlab/vnorm.m
+++ b/matlab/vnorm.m
@@ -71,7 +71,7 @@ if isempty(dim)
idx = find(size(A)~=1);
dim = idx(1);
end
-
+
if isempty(ntype)
y = sqrt(sum( abs(A).^2 , dim) );
elseif ntype==1
@@ -88,4 +88,3 @@ elseif ntype~=floor(ntype) || ntype<1
else
y = (sum( abs(A).^ntype , dim) ).^(1/ntype);
end
-
diff --git a/matlab/warning_config.m b/matlab/warning_config.m
index 3b9f45d63e5fd128b91900645881a395931426b2..79098a6104b9e2dbb1ddedb5926bef84d341ae6d 100644
--- a/matlab/warning_config.m
+++ b/matlab/warning_config.m
@@ -3,10 +3,10 @@ function warning_config()
%
% INPUTS
% none
-%
+%
% OUTPUTS
% none
-%
+%
% SPECIAL REQUIREMENTS
% none
diff --git a/matlab/write_latex_definitions.m b/matlab/write_latex_definitions.m
index 2bf3acf366eb9bdcfd9cc5cb37ba6fb440d76bd0..144f6242be85ccd1147fa58f48cbdcdc1af09b64 100644
--- a/matlab/write_latex_definitions.m
+++ b/matlab/write_latex_definitions.m
@@ -65,9 +65,9 @@ for i=1:length(tables)
long = eval([M_var_root{i} '_names_long']);
for j=1:size(names,1)
fprintf(fid, '\\texttt{%s} & $%s$ & %s\\\\\n', ...
- regexprep(strtrim(names(j,:)), '_', '\\_'), ...
- strtrim(tex(j,:)), ...
- regexprep(strtrim(long(j,:)), '_', '\\_'));
+ regexprep(strtrim(names(j,:)), '_', '\\_'), ...
+ strtrim(tex(j,:)), ...
+ regexprep(strtrim(long(j,:)), '_', '\\_'));
end
fprintf(fid, '\\hline%%\n');
fprintf(fid, '\\end{longtable}\n');
diff --git a/matlab/write_latex_parameter_table.m b/matlab/write_latex_parameter_table.m
index ea76074505228247c100d364962ece4c2e260d53..92374d77c9e7ece0a920a27d3b951ca1f9d16480 100644
--- a/matlab/write_latex_parameter_table.m
+++ b/matlab/write_latex_parameter_table.m
@@ -75,20 +75,20 @@ fprintf(fid, '\\endhead\n');
tex = M_.param_names_tex;
long = M_.param_names_long;
for j=1:size(tex,1)
-if Long_names_present==1
- % replace underscores
- long_names_temp=regexprep(strtrim(long(j,:)), '_', '\\_');
- % replace percent
- long_names_temp=regexprep(long_names_temp, '%', '\\%');
- fprintf(fid, '$%s$ \t & \t %4.3f \t & \t %s\\\\\n', ...
- strtrim(tex(j,:)), ...
- M_.params(j,:),...
- long_names_temp);
-else
- fprintf(fid, '$%s$ \t & \t %4.3f \\\\\n', ...
- strtrim(tex(j,:)), ...
- M_.params(j,:));
-end
+ if Long_names_present==1
+ % replace underscores
+ long_names_temp=regexprep(strtrim(long(j,:)), '_', '\\_');
+ % replace percent
+ long_names_temp=regexprep(long_names_temp, '%', '\\%');
+ fprintf(fid, '$%s$ \t & \t %4.3f \t & \t %s\\\\\n', ...
+ strtrim(tex(j,:)), ...
+ M_.params(j,:),...
+ long_names_temp);
+ else
+ fprintf(fid, '$%s$ \t & \t %4.3f \\\\\n', ...
+ strtrim(tex(j,:)), ...
+ M_.params(j,:));
+ end
end
fprintf(fid, '\\bottomrule%%\n');
fprintf(fid, '\\end{longtable}\n');
diff --git a/matlab/write_latex_prior_table.m b/matlab/write_latex_prior_table.m
index 46a5e3bf5258d6f955c93fbfffb6794369869493..db9c8bfbcea75dbca24005dd2a5ec77c74000268 100644
--- a/matlab/write_latex_prior_table.m
+++ b/matlab/write_latex_prior_table.m
@@ -37,11 +37,11 @@ end
if (size(estim_params_.var_endo,1) || size(estim_params_.corrn,1))
% Prior over measurement errors are defined...
- if ((isfield(options_,'varobs') && isempty(options_.varobs)) || ~isfield(options_,'varobs'))
- % ... But the list of observed variabled is not yet defined.
- fprintf(['\nwrite_latex_prior_table:: varobs should be declared before. Skipping table creation.\n'])
- return
- end
+ if ((isfield(options_,'varobs') && isempty(options_.varobs)) || ~isfield(options_,'varobs'))
+ % ... But the list of observed variabled is not yet defined.
+ fprintf(['\nwrite_latex_prior_table:: varobs should be declared before. Skipping table creation.\n'])
+ return
+ end
end
% Fill or update bayestopt_ structure
@@ -115,56 +115,56 @@ for i=1:size(BayesOptions.name,1)
PriorMode = BayesOptions.p5(i);
PriorStandardDeviation = BayesOptions.p2(i);
switch BayesOptions.pshape(i)
- case { 1 , 5 }
+ case { 1 , 5 }
+ LowerBound = BayesOptions.p3(i);
+ UpperBound = BayesOptions.p4(i);
+ if ~isinf(lb(i))
+ LowerBound=max(LowerBound,lb(i));
+ end
+ if ~isinf(ub(i))
+ UpperBound=min(UpperBound,ub(i));
+ end
+ case { 2 , 4 , 6, 8 }
+ LowerBound = BayesOptions.p3(i);
+ if ~isinf(lb(i))
+ LowerBound=max(LowerBound,lb(i));
+ end
+ if ~isinf(ub(i))
+ UpperBound=ub(i);
+ else
+ UpperBound = '$\infty$';
+ end
+ case 3
+ if isinf(BayesOptions.p3(i)) && isinf(lb(i))
+ LowerBound = '$-\infty$';
+ else
LowerBound = BayesOptions.p3(i);
- UpperBound = BayesOptions.p4(i);
if ~isinf(lb(i))
LowerBound=max(LowerBound,lb(i));
end
+ end
+ if isinf(BayesOptions.p4(i)) && isinf(ub(i))
+ UpperBound = '$\infty$';
+ else
+ UpperBound = BayesOptions.p4(i);
if ~isinf(ub(i))
UpperBound=min(UpperBound,ub(i));
end
- case { 2 , 4 , 6, 8 }
- LowerBound = BayesOptions.p3(i);
- if ~isinf(lb(i))
- LowerBound=max(LowerBound,lb(i));
- end
- if ~isinf(ub(i))
- UpperBound=ub(i);
- else
- UpperBound = '$\infty$';
- end
- case 3
- if isinf(BayesOptions.p3(i)) && isinf(lb(i))
- LowerBound = '$-\infty$';
- else
- LowerBound = BayesOptions.p3(i);
- if ~isinf(lb(i))
- LowerBound=max(LowerBound,lb(i));
- end
- end
- if isinf(BayesOptions.p4(i)) && isinf(ub(i))
- UpperBound = '$\infty$';
- else
- UpperBound = BayesOptions.p4(i);
- if ~isinf(ub(i))
- UpperBound=min(UpperBound,ub(i));
- end
- end
- otherwise
- error('write_latex_prior_table:: Dynare bug!')
+ end
+ otherwise
+ error('write_latex_prior_table:: Dynare bug!')
end
format_string = build_format_string(PriorMode, PriorStandardDeviation,LowerBound,UpperBound);
fprintf(fidTeX,format_string, ...
- TexName, ...
- PriorShape, ...
- PriorMean, ...
- PriorMode, ...
- PriorStandardDeviation, ...
- LowerBound, ...
- UpperBound, ...
- PriorIntervals.lb(i), ...
- PriorIntervals.ub(i) );
+ TexName, ...
+ PriorShape, ...
+ PriorMean, ...
+ PriorMode, ...
+ PriorStandardDeviation, ...
+ LowerBound, ...
+ UpperBound, ...
+ PriorIntervals.lb(i), ...
+ PriorIntervals.ub(i) );
end
fprintf(fidTeX,'\\end{longtable}\n ');
fprintf(fidTeX,'\\end{center}\n');
diff --git a/matlab/write_mh_history_file.m b/matlab/write_mh_history_file.m
index 00984c26cf38b9584b43a35e4f2b86d7dee4fe45..2cc7ef2d85e89b2f5f06a4ac683361948d1662d7 100644
--- a/matlab/write_mh_history_file.m
+++ b/matlab/write_mh_history_file.m
@@ -5,7 +5,7 @@ function i = write_mh_history_file(MetropolisFolder, ModelName, record)
% MetropolisFolder [char] Name of the metropolis subfolder
% ModelName [char] Name of the mod-file
% record [structure] structure storing the MH history
-% Outputs:
+% Outputs:
% i [scalar] number of the mh_history file
% Copyright (C) 2013-2015 Dynare Team
diff --git a/matlab/writecellofchar.m b/matlab/writecellofchar.m
index be636e6f171e0ed31daca256c1fac1031dba40af..5251a31e61cacf0b75a4d57ad034ceb4d2295707 100644
--- a/matlab/writecellofchar.m
+++ b/matlab/writecellofchar.m
@@ -2,13 +2,13 @@ function str = writecellofchar(c)
% Writes a two dimensional cell of char in a string.
%
-% INPUTS
+% INPUTS
% - c [cell] cell of char.
%
-% OUTPUTS
+% OUTPUTS
% - str [char]
%
-% EXAMPLES
+% EXAMPLES
% >> writecellofchar({'a', {'b'; 'c'}})
%
% ans =
@@ -19,7 +19,7 @@ function str = writecellofchar(c)
%
% ans =
%
-%{'a', '['b'; 'c']', 'd'}
+%{'a', '['b'; 'c']', 'd'}
% Copyright (C) 2015 Dynare Team
%
@@ -37,27 +37,27 @@ function str = writecellofchar(c)
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
+
str = '{';
for i=1:size(c, 1)
- for j=1:size(c, 2)
- if iscell(c{i,j})
- str = sprintf('%s%s', str, writecellofchar(c{i, j}));
- elseif ischar(c{i, j})
- if size(c{i, j}, 1)>1
- str = sprintf('%s''%s''', str, writematrixofchar(c{i, j}));
- else
- str = sprintf('%s''%s''', str, c{i, j});
- end
- else
- error('Type not implemenented!')
- end
- if j<size(c, 2)
- str = sprintf('%s, ', str);
- end
- end
- if i<size(c, 1)
- str = sprintf('%s; ', str);
- end
+for j=1:size(c, 2)
+if iscell(c{i,j})
+str = sprintf('%s%s', str, writecellofchar(c{i, j}));
+elseif ischar(c{i, j})
+if size(c{i, j}, 1)>1
+str = sprintf('%s''%s''', str, writematrixofchar(c{i, j}));
+else
+str = sprintf('%s''%s''', str, c{i, j});
+end
+else
+error('Type not implemenented!')
+end
+if j<size(c, 2)
+str = sprintf('%s, ', str);
+end
+end
+if i<size(c, 1)
+str = sprintf('%s; ', str);
+end
end
str = sprintf('%s}', str);
\ No newline at end of file
diff --git a/matlab/writedata.m b/matlab/writedata.m
index 112d7f13065d8164097bf68b1628b912059114b3..1512d0827bdc621ee25d3fc65526869700a47ee6 100644
--- a/matlab/writedata.m
+++ b/matlab/writedata.m
@@ -1,6 +1,6 @@
function writedata(fname)
% function writedata(fname)
-% store endogenous and exogenous variables in a XLS spreadsheet file
+% store endogenous and exogenous variables in a XLS spreadsheet file
% INPUT
% fname: name of the XLS file
% OUTPUT
diff --git a/matlab/writedata_text.m b/matlab/writedata_text.m
index ac56ad10d45eaeb68e6e7db80b6b38e621d34743..0fa0f7ac2a3cba06d95786a478794ec1c0ed352d 100644
--- a/matlab/writedata_text.m
+++ b/matlab/writedata_text.m
@@ -1,6 +1,6 @@
function writedata_text(fname)
% function writedata(fname)
-% store endogenous and exogenous variables in a text file
+% store endogenous and exogenous variables in a text file
% INPUT
% fname: name of the text file
% OUTPUT
diff --git a/matlab/writematrixofchar.m b/matlab/writematrixofchar.m
index 6f230e554f7a7d9cf4b363da9bfc7f5c4345a630..59e8fdb634a838b8787b3b179e91c02bd98a209d 100644
--- a/matlab/writematrixofchar.m
+++ b/matlab/writematrixofchar.m
@@ -2,13 +2,13 @@ function str = writematrixofchar(m)
% Writes a matrix of char in a string.
%
-% INPUTS
+% INPUTS
% - m [char] matrix of char.
%
-% OUTPUTS
+% OUTPUTS
% - str [char]
%
-% EXAMPLE
+% EXAMPLE
% >> writematrixofchar(['a'; 'b'])
%
% ans =
@@ -16,7 +16,7 @@ function str = writematrixofchar(m)
% ['a'; 'b']
%
% where the returned argument is a string which can be evaluated or printed.
-
+
% Copyright (C) 2015 Dynare Team
%
% This file is part of Dynare.