diff --git a/matlab/dsge_likelihood.m b/matlab/dsge_likelihood.m
index 5a3eead0642af213486493047b9b5fb4c2efa96c..68574b40d47639aa64bfe4b3fa4facdcf7a92f1f 100644
--- a/matlab/dsge_likelihood.m
+++ b/matlab/dsge_likelihood.m
@@ -358,6 +358,7 @@ end
diffuse_periods = 0;
+correlated_errors_have_been_checked = 0;
switch DynareOptions.lik_init
case 1% Standard initialization with the steady state of the state equation.
if kalman_algo~=2
@@ -378,10 +379,14 @@ switch DynareOptions.lik_init
a = zeros(mm,1);
Zflag = 0;
case 3% Diffuse Kalman filter (Durbin and Koopman)
- if kalman_algo ~= 4
% Use standard kalman filter except if the univariate filter is explicitely choosen.
+ if kalman_algo == 0
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
+
[Z,T,R,QT,Pstar,Pinf] = schur_statespace_transformation(Z,T,R,Q,DynareOptions.qz_criterium);
Zflag = 1;
% Run diffuse kalman filter on first periods.
@@ -403,38 +408,38 @@ switch DynareOptions.lik_init
if isinf(dLIK)
% Go to univariate diffuse filter if singularity problem.
kalman_algo = 4;
- singularity_flag = 1;
end
end
if (kalman_algo==4)
% Univariate Diffuse Kalman Filter
- if singularity_flag
- if isequal(H,0)
- H = zeros(nobs,1);
- mmm = mm;
+ if isequal(H,0)
+ H = zeros(nobs,1);
+ 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
- if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
- H = diag(H);
- mmm = mm;
- else
- Z = [Z, eye(pp)];
- T = blkdiag(T,zeros(pp));
- Q = blkdiag(Q,H);
- R = blkdiag(R,eye(pp));
- Pstar = blkdiag(Pstar,H);
- Pinf = blckdiag(Pinf,zeros(pp));
- H = zeros(nobs,1);
- mmm = mm+pp;
- end
+ Z = [Z, eye(pp)];
+ T = blkdiag(T,zeros(pp));
+ Q = blkdiag(Q,H);
+ R = blkdiag(R,eye(pp));
+ Pstar = blkdiag(Pstar,H);
+ Pinf = blckdiag(Pinf,zeros(pp));
+ H = zeros(nobs,1);
+ mmm = mm+pp;
end
- % no need to test again for correlation elements
- singularity_flag = 0;
end
- [dLIK,tmp,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), ...
- zeros(mmm,1), Pinf, Pstar, ...
- kalman_tol, riccati_tol, DynareOptions.presample, ...
- T,R,Q,H,Z,mmm,pp,rr);
+ % no need to test again for correlation elements
+ correlated_errors_have_been_checked = 1;
+
+ [dLIK,tmp,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), ...
+ zeros(mmm,1), Pinf, Pstar, ...
+ kalman_tol, riccati_tol, DynareOptions.presample, ...
+ T,R,Q,H,Z,mmm,pp,rr);
diffuse_periods = length(tmp);
end
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
kalman_algo = 4;
end
- singularity_flag = 1;
else
if DynareOptions.lik_init==3
LIK = LIK + dLIK;
@@ -613,10 +617,10 @@ if ((kalman_algo==1) || (kalman_algo==3))% Multivariate Kalman Filter
end
end
-if ( singularity_flag || (kalman_algo==2) || (kalman_algo==4) )
+if (kalman_algo==2) || (kalman_algo==4)
% Univariate Kalman Filter
% resetting measurement error covariance matrix when necessary %
- if singularity_flag
+ if ~correlated_errors_have_been_checked
if isequal(H,0)
H = zeros(nobs,1);
mmm = mm;
diff --git a/matlab/dsge_likelihood_hh.m b/matlab/dsge_likelihood_hh.m
index 9c54b609e1bb5c755fe274957586f6c440c34c9b..9d91a44b9a0c5ac6d1a958018a8a4e295fe0b002 100644
--- a/matlab/dsge_likelihood_hh.m
+++ b/matlab/dsge_likelihood_hh.m
@@ -254,6 +254,7 @@ end
diffuse_periods = 0;
+correlated_errors_have_been_checked = 0;
switch DynareOptions.lik_init
case 1% Standard initialization with the steady state of the state equation.
if kalman_algo~=2
@@ -274,10 +275,14 @@ switch DynareOptions.lik_init
a = zeros(mm,1);
Zflag = 0;
case 3% Diffuse Kalman filter (Durbin and Koopman)
- if kalman_algo ~= 4
% Use standard kalman filter except if the univariate filter is explicitely choosen.
+ if kalman_algo == 0
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
+
[Z,T,R,QT,Pstar,Pinf] = schur_statespace_transformation(Z,T,R,Q,DynareOptions.qz_criterium);
Zflag = 1;
% Run diffuse kalman filter on first periods.
@@ -285,9 +290,9 @@ switch DynareOptions.lik_init
% Multivariate Diffuse Kalman Filter
if no_missing_data_flag
[dLIK,dlik,a,Pstar] = kalman_filter_d(Y, 1, size(Y,2), ...
- zeros(mm,1), Pinf, Pstar, ...
- kalman_tol, riccati_tol, DynareOptions.presample, ...
- T,R,Q,H,Z,mm,pp,rr);
+ zeros(mm,1), Pinf, Pstar, ...
+ kalman_tol, riccati_tol, DynareOptions.presample, ...
+ T,R,Q,H,Z,mm,pp,rr);
else
[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), ...
@@ -304,28 +309,27 @@ switch DynareOptions.lik_init
end
if (kalman_algo==4)
% Univariate Diffuse Kalman Filter
- if singularity_flag
- if isequal(H,0)
- H = zeros(nobs,1);
- mmm = mm;
+ if isequal(H,0)
+ H = zeros(nobs,1);
+ 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
- if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
- H = diag(H);
- mmm = mm;
- else
- Z = [Z, eye(pp)];
- T = blkdiag(T,zeros(pp));
- Q = blkdiag(Q,H);
- R = blkdiag(R,eye(pp));
- Pstar = blkdiag(Pstar,H);
- Pinf = blckdiag(Pinf,zeros(pp));
- H = zeros(nobs,1);
- mmm = mm+pp;
- end
+ Z = [Z, eye(pp)];
+ T = blkdiag(T,zeros(pp));
+ Q = blkdiag(Q,H);
+ R = blkdiag(R,eye(pp));
+ Pstar = blkdiag(Pstar,H);
+ Pinf = blckdiag(Pinf,zeros(pp));
+ H = zeros(nobs,1);
+ mmm = mm+pp;
end
- % no need to test again for correlation elements
- singularity_flag = 0;
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, ...
Y, 1, size(Y,2), ...
zeros(mmm,1), Pinf, Pstar, ...
@@ -385,10 +389,10 @@ if ((kalman_algo==1) || (kalman_algo==3))% Multivariate Kalman Filter
end
end
-if ( singularity_flag || (kalman_algo==2) || (kalman_algo==4) )
+if (kalman_algo==2) || (kalman_algo==4)
% Univariate Kalman Filter
% resetting measurement error covariance matrix when necessary %
- if singularity_flag
+ if ~correlated_errors_have_been_checked
if isequal(H,0)
H = zeros(nobs,1);
mmm = mm;