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corecting logic for selecting univariate diffuse filter and dealing 

with correlated measurement errors
time-shift
Michel Juillard 2012-01-22 18:37:29 +01:00
parent f0d1f033b0
commit cfb5114d41
2 changed files with 62 additions and 54 deletions
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62
matlab/dsge_likelihood.m
View File

@ -358,6 +358,7 @@ end
diffuse_periods = 0; diffuse_periods = 0;
correlated_errors_have_been_checked = 0;
switch DynareOptions.lik_init switch DynareOptions.lik_init
case 1% Standard initialization with the steady state of the state equation. case 1% Standard initialization with the steady state of the state equation.
if kalman_algo~=2 if kalman_algo~=2
@ -378,10 +379,14 @@ switch DynareOptions.lik_init
a = zeros(mm,1); a = zeros(mm,1);
Zflag = 0; Zflag = 0;
case 3% Diffuse Kalman filter (Durbin and Koopman) case 3% Diffuse Kalman filter (Durbin and Koopman)
if kalman_algo ~= 4
% Use standard kalman filter except if the univariate filter is explicitely choosen. % Use standard kalman filter except if the univariate filter is explicitely choosen.
if kalman_algo == 0
kalman_algo = 3; kalman_algo = 3;
elseif ~((kalman_algo == 3) || (kalman_algo == 4))
error(['diffuse filter: options_.kalman_algo can only be equal ' ...
'to 0 (default), 3 or 4'])
end end
[Z,T,R,QT,Pstar,Pinf] = schur_statespace_transformation(Z,T,R,Q,DynareOptions.qz_criterium); [Z,T,R,QT,Pstar,Pinf] = schur_statespace_transformation(Z,T,R,Q,DynareOptions.qz_criterium);
Zflag = 1; Zflag = 1;
% Run diffuse kalman filter on first periods. % Run diffuse kalman filter on first periods.
@ -403,38 +408,38 @@ switch DynareOptions.lik_init
if isinf(dLIK) if isinf(dLIK)
% Go to univariate diffuse filter if singularity problem. % Go to univariate diffuse filter if singularity problem.
kalman_algo = 4; kalman_algo = 4;
singularity_flag = 1;
end end
end end
if (kalman_algo==4) if (kalman_algo==4)
% Univariate Diffuse Kalman Filter % Univariate Diffuse Kalman Filter
if singularity_flag if isequal(H,0)
if isequal(H,0) H = zeros(nobs,1);
H = zeros(nobs,1); mmm = mm;
mmm = mm; else
if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
H = diag(H);
mmm = mm;
else else
if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal... Z = [Z, eye(pp)];
H = diag(H); T = blkdiag(T,zeros(pp));
mmm = mm; Q = blkdiag(Q,H);
else R = blkdiag(R,eye(pp));
Z = [Z, eye(pp)]; Pstar = blkdiag(Pstar,H);
T = blkdiag(T,zeros(pp)); Pinf = blckdiag(Pinf,zeros(pp));
Q = blkdiag(Q,H); H = zeros(nobs,1);
R = blkdiag(R,eye(pp)); mmm = mm+pp;
Pstar = blkdiag(Pstar,H);
Pinf = blckdiag(Pinf,zeros(pp));
H = zeros(nobs,1);
mmm = mm+pp;
end
end end
% no need to test again for correlation elements
singularity_flag = 0;
end end
[dLIK,tmp,a,Pstar] = univariate_kalman_filter_d(DynareDataset.missing.aindex,DynareDataset.missing.number_of_observations,DynareDataset.missing.no_more_missing_observations, ... % no need to test again for correlation elements
Y, 1, size(Y,2), ... correlated_errors_have_been_checked = 1;
zeros(mmm,1), Pinf, Pstar, ...
kalman_tol, riccati_tol, DynareOptions.presample, ... [dLIK,tmp,a,Pstar] = univariate_kalman_filter_d(DynareDataset.missing.aindex,...
T,R,Q,H,Z,mmm,pp,rr); DynareDataset.missing.number_of_observations,...
DynareDataset.missing.no_more_missing_observations, ...
Y, 1, size(Y,2), ...
zeros(mmm,1), Pinf, Pstar, ...
kalman_tol, riccati_tol, DynareOptions.presample, ...
T,R,Q,H,Z,mmm,pp,rr);
diffuse_periods = length(tmp); diffuse_periods = length(tmp);
end end
case 4% Start from the solution of the Riccati equation. case 4% Start from the solution of the Riccati equation.
@ -605,7 +610,6 @@ if ((kalman_algo==1) || (kalman_algo==3))% Multivariate Kalman Filter
else else
kalman_algo = 4; kalman_algo = 4;
end end
singularity_flag = 1;
else else
if DynareOptions.lik_init==3 if DynareOptions.lik_init==3
LIK = LIK + dLIK; LIK = LIK + dLIK;
@ -613,10 +617,10 @@ if ((kalman_algo==1) || (kalman_algo==3))% Multivariate Kalman Filter
end end
end end
if ( singularity_flag || (kalman_algo==2) || (kalman_algo==4) ) if (kalman_algo==2) || (kalman_algo==4)
% Univariate Kalman Filter % Univariate Kalman Filter
% resetting measurement error covariance matrix when necessary % % resetting measurement error covariance matrix when necessary %
if singularity_flag if ~correlated_errors_have_been_checked
if isequal(H,0) if isequal(H,0)
H = zeros(nobs,1); H = zeros(nobs,1);
mmm = mm; mmm = mm;

54
matlab/dsge_likelihood_hh.m
View File

@ -254,6 +254,7 @@ end
diffuse_periods = 0; diffuse_periods = 0;
correlated_errors_have_been_checked = 0;
switch DynareOptions.lik_init switch DynareOptions.lik_init
case 1% Standard initialization with the steady state of the state equation. case 1% Standard initialization with the steady state of the state equation.
if kalman_algo~=2 if kalman_algo~=2
@ -274,10 +275,14 @@ switch DynareOptions.lik_init
a = zeros(mm,1); a = zeros(mm,1);
Zflag = 0; Zflag = 0;
case 3% Diffuse Kalman filter (Durbin and Koopman) case 3% Diffuse Kalman filter (Durbin and Koopman)
if kalman_algo ~= 4
% Use standard kalman filter except if the univariate filter is explicitely choosen. % Use standard kalman filter except if the univariate filter is explicitely choosen.
if kalman_algo == 0
kalman_algo = 3; kalman_algo = 3;
elseif ~((kalman_algo == 3) || (kalman_algo == 4))
error(['diffuse filter: options_.kalman_algo can only be equal ' ...
'to 0 (default), 3 or 4'])
end end
[Z,T,R,QT,Pstar,Pinf] = schur_statespace_transformation(Z,T,R,Q,DynareOptions.qz_criterium); [Z,T,R,QT,Pstar,Pinf] = schur_statespace_transformation(Z,T,R,Q,DynareOptions.qz_criterium);
Zflag = 1; Zflag = 1;
% Run diffuse kalman filter on first periods. % Run diffuse kalman filter on first periods.
@ -285,9 +290,9 @@ switch DynareOptions.lik_init
% Multivariate Diffuse Kalman Filter % Multivariate Diffuse Kalman Filter
if no_missing_data_flag if no_missing_data_flag
[dLIK,dlik,a,Pstar] = kalman_filter_d(Y, 1, size(Y,2), ... [dLIK,dlik,a,Pstar] = kalman_filter_d(Y, 1, size(Y,2), ...
zeros(mm,1), Pinf, Pstar, ... zeros(mm,1), Pinf, Pstar, ...
kalman_tol, riccati_tol, DynareOptions.presample, ... kalman_tol, riccati_tol, DynareOptions.presample, ...
T,R,Q,H,Z,mm,pp,rr); T,R,Q,H,Z,mm,pp,rr);
else else
[dLIK,dlik,a,Pstar] = missing_observations_kalman_filter_d(DynareDataset.missing.aindex,DynareDataset.missing.number_of_observations,DynareDataset.missing.no_more_missing_observations, ... [dLIK,dlik,a,Pstar] = missing_observations_kalman_filter_d(DynareDataset.missing.aindex,DynareDataset.missing.number_of_observations,DynareDataset.missing.no_more_missing_observations, ...
Y, 1, size(Y,2), ... Y, 1, size(Y,2), ...
@ -304,28 +309,27 @@ switch DynareOptions.lik_init
end end
if (kalman_algo==4) if (kalman_algo==4)
% Univariate Diffuse Kalman Filter % Univariate Diffuse Kalman Filter
if singularity_flag if isequal(H,0)
if isequal(H,0) H = zeros(nobs,1);
H = zeros(nobs,1); mmm = mm;
mmm = mm; else
if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
H = diag(H);
mmm = mm;
else else
if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal... Z = [Z, eye(pp)];
H = diag(H); T = blkdiag(T,zeros(pp));
mmm = mm; Q = blkdiag(Q,H);
else R = blkdiag(R,eye(pp));
Z = [Z, eye(pp)]; Pstar = blkdiag(Pstar,H);
T = blkdiag(T,zeros(pp)); Pinf = blckdiag(Pinf,zeros(pp));
Q = blkdiag(Q,H); H = zeros(nobs,1);
R = blkdiag(R,eye(pp)); mmm = mm+pp;
Pstar = blkdiag(Pstar,H);
Pinf = blckdiag(Pinf,zeros(pp));
H = zeros(nobs,1);
mmm = mm+pp;
end
end end
% no need to test again for correlation elements
singularity_flag = 0;
end end
% no need to test again for correlation elements
correlated_errors_have_been_checked = 1;
[dLIK,dlik,a,Pstar] = univariate_kalman_filter_d(DynareDataset.missing.aindex,DynareDataset.missing.number_of_observations,DynareDataset.missing.no_more_missing_observations, ... [dLIK,dlik,a,Pstar] = univariate_kalman_filter_d(DynareDataset.missing.aindex,DynareDataset.missing.number_of_observations,DynareDataset.missing.no_more_missing_observations, ...
Y, 1, size(Y,2), ... Y, 1, size(Y,2), ...
zeros(mmm,1), Pinf, Pstar, ... zeros(mmm,1), Pinf, Pstar, ...
@ -385,10 +389,10 @@ if ((kalman_algo==1) || (kalman_algo==3))% Multivariate Kalman Filter
end end
end end
if ( singularity_flag || (kalman_algo==2) || (kalman_algo==4) ) if (kalman_algo==2) || (kalman_algo==4)
% Univariate Kalman Filter % Univariate Kalman Filter
% resetting measurement error covariance matrix when necessary % % resetting measurement error covariance matrix when necessary %
if singularity_flag if ~correlated_errors_have_been_checked
if isequal(H,0) if isequal(H,0)
H = zeros(nobs,1); H = zeros(nobs,1);
mmm = mm; mmm = mm;