Commit 37f14e9b authored by Stéphane Adjemian (Charybdis)'s avatar Stéphane Adjemian (Charybdis)
Browse files

Removed unused routines for (diffuse) kalman filter evaluations.

parent 8ebc36df
function [LIK, lik] = DiffuseLikelihood1(T,R,Q,Pinf,Pstar,Y,trend,start)
% function [LIK, lik] = DiffuseLikelihood1(T,R,Q,Pinf,Pstar,Y,trend,start)
% Computes the diffuse likelihood without measurement error, in the case of a non-singular var-cov matrix
%
% INPUTS
% T: mm*mm matrix
% R: mm*rr matrix
% Q: rr*rr matrix
% Pinf: mm*mm diagonal matrix with with q ones and m-q zeros
% Pstar: mm*mm variance-covariance matrix with stationary variables
% Y: pp*1 vector
% trend
% start: likelihood evaluation at 'start'
%
% OUTPUTS
% LIK: likelihood
% lik: density vector in each period
%
% 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).
% Copyright (C) 2004-2008 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/>.
% M. Ratto added lik in output
global bayestopt_ options_
mf = bayestopt_.mf;
smpl = size(Y,2);
mm = size(T,2);
pp = size(Y,1);
a = zeros(mm,1);
dF = 1;
QQ = R*Q*transpose(R);
t = 0;
lik = zeros(smpl,1);
LIK = Inf;
notsteady = 1;
crit = options_.kalman_tol;
while rank(Pinf,crit) & t < smpl
t = t+1;
v = Y(:,t)-a(mf)-trend(:,t);
Finf = Pinf(mf,mf);
if rcond(Finf) < crit
if ~all(abs(Finf(:)) < crit)
return
else
iFstar = inv(Pstar(mf,mf));
dFstar = det(Pstar(mf,mf));
Kstar = Pstar(:,mf)*iFstar;
lik(t) = log(dFstar) + transpose(v)*iFstar*v;
Pinf = T*Pinf*transpose(T);
Pstar = T*(Pstar-Pstar(:,mf)*transpose(Kstar))*transpose(T)+QQ;
a = T*(a+Kstar*v);
end
else
lik(t) = log(det(Finf));
iFinf = inv(Finf);
Kinf = Pinf(:,mf)*iFinf; %% premultiplication by the transition matrix T is removed (stephane)
Fstar = Pstar(mf,mf);
Kstar = (Pstar(:,mf)-Kinf*Fstar)*iFinf; %% premultiplication by the transition matrix T is removed (stephane)
Pstar = T*(Pstar-Pstar(:,mf)*transpose(Kinf)-Pinf(:,mf)*transpose(Kstar))*transpose(T)+QQ;
Pinf = T*(Pinf-Pinf(:,mf)*transpose(Kinf))*transpose(T);
a = T*(a+Kinf*v);
end
end
if t == smpl
error(['There isn''t enough information to estimate the initial' ...
' conditions of the nonstationary variables']);
end
F_singular = 1;
while notsteady & t < smpl
t = t+1;
v = Y(:,t)-a(mf)-trend(:,t);
F = Pstar(mf,mf);
oldPstar = Pstar;
dF = det(F);
if rcond(F) < crit
if ~all(abs(F(:))<crit)
return
else
a = T*a;
Pstar = T*Pstar*transpose(T)+QQ;
end
else
F_singular = 0;
iF = inv(F);
lik(t) = log(dF)+transpose(v)*iF*v;
K = Pstar(:,mf)*iF; %% premultiplication by the transition matrix T is removed (stephane)
a = T*(a+K*v); %% --> factorization of the transition matrix...
Pstar = T*(Pstar-K*Pstar(mf,:))*transpose(T)+QQ; %% ... idem (stephane)
end
notsteady = ~(max(max(abs(Pstar-oldPstar)))<crit);
end
if F_singular == 1
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)-trend(:,t);
a = T*(a+K*v);
lik(t) = transpose(v)*iF*v;
end
lik(t0:smpl) = lik(t0:smpl) + log(dF);
end
% adding log-likelihhod constants
lik = (lik + pp*log(2*pi))/2;
LIK = sum(lik(start:end)); % Minus the log-likelihood.
\ No newline at end of file
function [LIK, lik] = DiffuseLikelihood1_Z(T,Z,R,Q,Pinf,Pstar,Y,start)
% function [LIK, lik] = DiffuseLikelihood1_Z(T,Z,R,Q,Pinf,Pstar,Y,start)
% Computes the diffuse likelihood without measurement error, in the case of a non-singular var-cov matrix
%
% INPUTS
% T: mm*mm matrix
% Z: pp,mm matrix
% R: mm*rr matrix
% Q: rr*rr matrix
% Pinf: mm*mm diagonal matrix with with q ones and m-q zeros
% Pstar: mm*mm variance-covariance matrix with stationary variables
% Y: pp*1 vector
% start: likelihood evaluation at 'start'
%
% OUTPUTS
% LIK: likelihood
% lik: density vector in each period
%
% 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).
% Copyright (C) 2004-2008 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/>.
% M. Ratto added lik in output
global bayestopt_ options_
smpl = size(Y,2);
mm = size(T,2);
pp = size(Y,1);
a = zeros(mm,1);
dF = 1;
QQ = R*Q*transpose(R);
t = 0;
lik = zeros(smpl,1);
LIK = Inf;
notsteady = 1;
crit = options_.kalman_tol;
while rank(Pinf,crit) & t < smpl
t = t+1;
v = Y(:,t)-Z*a;
Finf = Z*Pinf*Z';
if rcond(Finf) < crit
if ~all(abs(Finf(:)) < crit)
return
else
Fstar = Z*Pstar*Z';
iFstar = inv(Fstar);
dFstar = det(Fstar);
Kstar = Pstar*Z'*iFstar;
lik(t) = log(dFstar) + v'*iFstar*v;
Pinf = T*Pinf*transpose(T);
Pstar = T*(Pstar-Pstar*Z'*Kstar')*T'+QQ;
a = T*(a+Kstar*v);
end
else
lik(t) = log(det(Finf));
iFinf = inv(Finf);
Kinf = Pinf*Z'*iFinf;
Fstar = Z*Pstar*Z';
Kstar = (Pstar*Z'-Kinf*Fstar)*iFinf;
Pstar = T*(Pstar-Pstar*Z'*Kinf'-Pinf*Z'*Kstar')*T'+QQ;
Pinf = T*(Pinf-Pinf*Z'*Kinf')*T';
a = T*(a+Kinf*v);
end
end
if t == smpl
error(['There isn''t enough information to estimate the initial' ...
' conditions of the nonstationary variables']);
end
F_singular = 1;
while notsteady & t < smpl
t = t+1;
v = Y(:,t)-Z*a;
F = Z*Pstar*Z';
oldPstar = Pstar;
dF = det(F);
if rcond(F) < crit
if ~all(abs(F(:))<crit)
return
else
a = T*a;
Pstar = T*Pstar*T'+QQ;
end
else
F_singular = 0;
iF = inv(F);
lik(t) = log(dF)+v'*iF*v;
K = Pstar*Z'*iF;
a = T*(a+K*v);
Pstar = T*(Pstar-K*Z*Pstar)*T'+QQ;
end
notsteady = ~(max(max(abs(Pstar-oldPstar)))<crit);
end
if F_singular == 1
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)-Z*a;
a = T*(a+K*v);
lik(t) = v'*iF*v;
end
lik(t0:smpl) = lik(t0:smpl) + log(dF);
end
% adding log-likelihhod constants
lik = (lik + pp*log(2*pi))/2;
LIK = sum(lik(start:end)); % Minus the log-likelihood.
\ No newline at end of file
function [LIK, lik] = DiffuseLikelihood3(T,R,Q,Pinf,Pstar,Y,trend,start)%//Z,T,R,Q,Pinf,Pstar,Y)
% function [LIK, lik] = DiffuseLikelihood3(T,R,Q,Pinf,Pstar,Y,trend,start)
% Computes the diffuse likelihood without measurement error, in the case of
% a singular var-cov matrix.
% Univariate treatment of multivariate time series.
%
% INPUTS
% T: mm*mm matrix
% R: mm*rr matrix
% Q: rr*rr matrix
% Pinf: mm*mm diagonal matrix with with q ones and m-q zeros
% Pstar: mm*mm variance-covariance matrix with stationary variables
% Y: pp*1 vector
% trend
% start: likelihood evaluation at 'start'
%
% OUTPUTS
% LIK: likelihood
% lik: density vector in each period
%
% 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).
% Copyright (C) 2004-2008 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/>.
% M. Ratto added lik in output [October 2005]
% changes by M. Ratto [April 2005]
% introduced new options options_.diffuse_d for termination of DKF
% new icc counter for Finf steps in DKF
% new termination for DKF
% likelihood terms for Fstar must be cumulated in DKF also when Pinf is non
% zero.
% [4/5/2005] correctyed bug in the modified verson of Ratto for rank of Pinf
% introduced a specific crit1 for the DKF termination
global bayestopt_ options_
mf = bayestopt_.mf;
pp = size(Y,1);
mm = size(T,1);
smpl = size(Y,2);
a = zeros(mm,1);
QQ = R*Q*transpose(R);
t = 0;
lik = zeros(smpl,1);
notsteady = 1;
crit = options_.kalman_tol;
crit1 = 1.e-6;
newRank = rank(Pinf,crit1);
icc=0;
while newRank & t < smpl
t = t+1;
for i=1:pp
v(i) = Y(i,t)-a(mf(i))-trend(i,t);
Fstar = Pstar(mf(i),mf(i));
Finf = Pinf(mf(i),mf(i));
Kstar = Pstar(:,mf(i));
if Finf > crit & newRank, %added newRank criterion
icc=icc+1;
Kinf = Pinf(:,mf(i));
a = a + Kinf*v(i)/Finf;
Pstar = Pstar + Kinf*transpose(Kinf)*Fstar/(Finf*Finf) - ...
(Kstar*transpose(Kinf)+Kinf*transpose(Kstar))/Finf;
Pinf = Pinf - Kinf*transpose(Kinf)/Finf;
lik(t) = lik(t) + log(Finf);
% start new termination criterion for DKF
if ~isempty(options_.diffuse_d),
newRank = (icc<options_.diffuse_d);
%if newRank & any(diag(Pinf(mf,mf))>crit)==0; % M. Ratto this line is BUGGY
if newRank & (any(diag(Pinf(mf,mf))>crit)==0 & rank(Pinf,crit1)==0);
options_.diffuse_d = icc;
newRank=0;
disp('WARNING: Change in OPTIONS_.DIFFUSE_D in univariate DKF')
disp(['new OPTIONS_.DIFFUSE_D = ',int2str(icc)])
disp('You may have to reset the optimisation')
end
else
%newRank = any(diag(Pinf(mf,mf))>crit); % M. Ratto this line is BUGGY
newRank = (any(diag(Pinf(mf,mf))>crit) | rank(Pinf,crit1));
if newRank==0,
P0= T*Pinf*transpose(T);
%newRank = any(diag(P0(mf,mf))>crit); % M. Ratto this line is BUGGY
newRank = (any(diag(P0(mf,mf))>crit) | rank(P0,crit1)); % M. Ratto 11/10/2005
if newRank==0,
options_.diffuse_d = icc;
end
end
end,
% end new termination and checks for DKF
elseif Fstar > crit
%% Note that : (1) rank(Pinf)=0 implies that Finf = 0, (2) outside this loop (when for some i and t the condition
%% rank(Pinf)=0 is satisfied we have P = Pstar and F = Fstar and (3) Finf = 0 does not imply that
%% rank(Pinf)=0. [stphane,11-03-2004].
%if rank(Pinf,crit) == 0
% the likelihood terms should alwasy be cumulated, not only
% when Pinf=0, otherwise the lik would depend on the ordering
% of observed variables
% presample options can be used to ignore initial time points
lik(t) = lik(t) + log(Fstar) + v(i)*v(i)/Fstar;
%end
a = a + Kstar*v(i)/Fstar;
Pstar = Pstar - Kstar*transpose(Kstar)/Fstar;
else
%disp(['zero F term in DKF for observed ',int2str(i),' ',num2str(Fstar)])
end
end
% if all(abs(Pinf(:))<crit),
% oldRank = 0;
% else
% oldRank = rank(Pinf,crit);
% end
if newRank,
oldRank = rank(Pinf,crit1);
else
oldRank = 0;
end
a = T*a;
Pstar = T*Pstar*transpose(T)+QQ;
Pinf = T*Pinf*transpose(T);
% if all(abs(Pinf(:))<crit),
% newRank = 0;
% else
% newRank = rank(Pinf,crit);
% end
if newRank,
newRank = rank(Pinf,crit1); % new crit1 is used
end
if oldRank ~= newRank
disp('DiffuseLiklihood3 :: T does influence the rank of Pinf!')
end
end
if t == smpl
error(['There isn''t enough information to estimate the initial' ...
' conditions of the nonstationary variables']);
end
while notsteady & t < smpl
t = t+1;
oldP = Pstar;
for i=1:pp
v(i) = Y(i,t) - a(mf(i)) - trend(i,t);
Fi = Pstar(mf(i),mf(i));
if Fi > crit
Ki = Pstar(:,mf(i));
a = a + Ki*v(i)/Fi;
Pstar = Pstar - Ki*transpose(Ki)/Fi;
lik(t) = lik(t) + log(Fi) + v(i)*v(i)/Fi;
else
%disp(['zero F term for observed ',int2str(i),' ',num2str(Fi)])
end
end
a = T*a;
Pstar = T*Pstar*transpose(T) + QQ;
notsteady = ~(max(max(abs(Pstar-oldP)))<crit);
end
while t < smpl
t = t+1;
Pstar = oldP;
for i=1:pp
v(i) = Y(i,t) - a(mf(i)) - trend(i,t);
Fi = Pstar(mf(i),mf(i));
if Fi > crit
Ki = Pstar(:,mf(i));
a = a + Ki*v(i)/Fi;
Pstar = Pstar - Ki*transpose(Ki)/Fi;
lik(t) = lik(t) + log(Fi) + v(i)*v(i)/Fi;
else
%disp(['zero F term for observed ',int2str(i),' ',num2str(Fi)])
end
end
a = T*a;
end
% adding log-likelihhod constants
lik = (lik + pp*log(2*pi))/2;
LIK = sum(lik(start:end)); % Minus the log-likelihood.
function [LIK, lik] = DiffuseLikelihood3_Z(T,Z,R,Q,Pinf,Pstar,Y,start)
% function [LIK, lik] = DiffuseLikelihood3_Z(T,Z,R,Q,Pinf,Pstar,Y,start)
% Computes the diffuse likelihood without measurement error, in the case of
% a singular var-cov matrix.
% Univariate treatment of multivariate time series.
%
% INPUTS
% T: mm*mm matrix
% Z: pp*mm matrix
% R: mm*rr matrix
% Q: rr*rr matrix
% Pinf: mm*mm diagonal matrix with with q ones and m-q zeros
% Pstar: mm*mm variance-covariance matrix with stationary variables
% Y: pp*1 vector
% start: likelihood evaluation at 'start'
%
% OUTPUTS
% LIK: likelihood
% lik: density vector in each period
%
% 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).
% Copyright (C) 2004-2008 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/>.
% M. Ratto added lik in output [October 2005]
% changes by M. Ratto [April 2005]
% introduced new options options_.diffuse_d for termination of DKF
% new icc counter for Finf steps in DKF
% new termination for DKF
% likelihood terms for Fstar must be cumulated in DKF also when Pinf is non
% zero.
% [4/5/2005] correctyed bug in the modified verson of Ratto for rank of Pinf
% introduced a specific crit1 for the DKF termination
global bayestopt_ options_
pp = size(Y,1);
mm = size(T,1);
smpl = size(Y,2);
a = zeros(mm,1);
QQ = R*Q*R';
t = 0;
lik = zeros(smpl,1);
notsteady = 1;
crit = options_.kalman_tol;
crit1 = 1.e-6;
newRank = rank(Pinf,crit1);
icc=0;
while newRank & t < smpl
t = t+1;
for i=1:pp
Zi = Z(i,:);
v(i) = Y(i,t)-Zi*a;
Fstar = Zi*Pstar*Zi';
Finf = Zi*Pinf*Zi';
Kstar = Pstar*Zi';
if Finf > crit & newRank
icc=icc+1;
Kinf = Pinf*Zi';
a = a + Kinf*v(i)/Finf;
Pstar = Pstar + Kinf*Kinf'*Fstar/(Finf*Finf) - ...
(Kstar*Kinf'+Kinf*Kstar')/Finf;
Pinf = Pinf - Kinf*Kinf'/Finf;
lik(t) = lik(t) + log(Finf);
if ~isempty(options_.diffuse_d),
newRank = (icc<options_.diffuse_d);
if newRank & (any(diag(Z*Pinf*Z')>crit)==0 & rank(Pinf,crit1)==0);
options_.diffuse_d = icc;
newRank=0;
disp('WARNING: Change in OPTIONS_.DIFFUSE_D in univariate DKF')
disp(['new OPTIONS_.DIFFUSE_D = ',int2str(icc)])
disp('You may have to reset the optimisation')
end
else
newRank = (any(diag(Z*Pinf*Z')>crit) | rank(Pinf,crit1));
if newRank==0,
P0= T*Pinf*T';
newRank = (any(diag(Z*P0*Z')>crit) | rank(P0,crit1)); % M. Ratto 11/10/2005
if newRank==0,
options_.diffuse_d = icc;
end
end
end,
elseif Fstar > crit
%% Note that : (1) rank(Pinf)=0 implies that Finf = 0, (2) outside this loop (when for some i and t the condition
%% rank(Pinf)=0 is satisfied we have P = Pstar and F = Fstar and (3) Finf = 0 does not imply that
%% rank(Pinf)=0. [stphane,11-03-2004].
%if rank(Pinf,crit) == 0
% the likelihood terms should alwasy be cumulated, not only
% when Pinf=0, otherwise the lik would depend on the ordering
% of observed variables
% presample options can be used to ignore initial time points
lik(t) = lik(t) + log(Fstar) + v(i)*v(i)/Fstar;
a = a + Kstar*v(i)/Fstar;
Pstar = Pstar - Kstar*(Kstar'/Fstar);
else
%disp(['zero F term in DKF for observed ',int2str(i),' ',num2str(Fstar)])
end
end
if newRank,
oldRank = rank(Pinf,crit1);
else
oldRank = 0;
end
a = T*a;
Pstar = T*Pstar*T'+QQ;
Pinf = T*Pinf*T';
if newRank,
newRank = rank(Pinf,crit1);
end
if oldRank ~= newRank
disp('DiffuseLiklihood3 :: T does influence the rank of Pinf!')
end
end
if t == smpl
error(['There isn''t enough information to estimate the initial' ...
' conditions of the nonstationary variables']);
end
while notsteady & t < smpl
t = t+1;
oldP = Pstar;
for i=1:pp
Zi = Z(i,:);