diff --git a/matlab/dsge_likelihood.m b/matlab/dsge_likelihood.m
index 125bcba3724dbfa3607a378524f6e3177244c4d2..febe45aa658a538f7fb0e594584d68f63a385754 100644
--- a/matlab/dsge_likelihood.m
+++ b/matlab/dsge_likelihood.m
@@ -337,7 +337,7 @@ kalman_algo = DynareOptions.kalman_algo;
% resetting measurement errors covariance matrix for univariate filters
if (kalman_algo == 2) || (kalman_algo == 4)
if isequal(H,0)
- H = zeros(nobs,1);
+ H = zeros(pp,1);
mmm = mm;
else
if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
@@ -350,7 +350,7 @@ if (kalman_algo == 2) || (kalman_algo == 4)
R = blkdiag(R,eye(pp));
Pstar = blkdiag(Pstar,H);
Pinf = blckdiag(Pinf,zeros(pp));
- H = zeros(nobs,1);
+ H = zeros(pp,1);
mmm = mm+pp;
end
end
@@ -402,18 +402,18 @@ switch DynareOptions.lik_init
if (kalman_algo==3)
% Multivariate Diffuse Kalman Filter
if no_missing_data_flag
- [dLIK,tmp,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, ...
kalman_tol, riccati_tol, DynareOptions.presample, ...
T,R,Q,H,Z,mm,pp,rr);
else
- [dLIK,tmp,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), ...
zeros(mm,1), Pinf, Pstar, ...
kalman_tol, riccati_tol, DynareOptions.presample, ...
T,R,Q,H,Z,mm,pp,rr);
end
- diffuse_periods = length(tmp);
+ diffuse_periods = length(dlik);
if isinf(dLIK)
% Go to univariate diffuse filter if singularity problem.
singular_diffuse_filter = 1;
@@ -422,7 +422,7 @@ switch DynareOptions.lik_init
if singular_diffuse_filter || (kalman_algo==4)
% Univariate Diffuse Kalman Filter
if isequal(H,0)
- H1 = zeros(nobs,1);
+ H1 = zeros(pp,1);
mmm = mm;
else
if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
@@ -435,21 +435,21 @@ switch DynareOptions.lik_init
R = blkdiag(R,eye(pp));
Pstar = blkdiag(Pstar,H);
Pinf = blckdiag(Pinf,zeros(pp));
- H1 = zeros(nobs,1);
+ H1 = zeros(pp,1);
mmm = mm+pp;
end
end
% 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,...
+ [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, ...
kalman_tol, riccati_tol, DynareOptions.presample, ...
T,R,Q,H1,Z,mmm,pp,rr);
- diffuse_periods = length(tmp);
+ diffuse_periods = length(dlik);
end
case 4% Start from the solution of the Riccati equation.
if kalman_algo ~= 2
@@ -588,7 +588,7 @@ if ((kalman_algo==1) || (kalman_algo==3))% Multivariate Kalman Filter
[err, LIK] = block_kalman_filter(T,R,Q,H,Pstar,Y,start,Z,kalman_tol,riccati_tol, Model.nz_state_var, Model.n_diag, Model.nobs_non_statevar);
mexErrCheck('block_kalman_filter', err);
else
- LIK = kalman_filter(Y,diffuse_periods+1,size(Y,2), ...
+ [LIK,lik] = kalman_filter(Y,diffuse_periods+1,size(Y,2), ...
a,Pstar, ...
kalman_tol, riccati_tol, ...
DynareOptions.presample, ...
@@ -607,7 +607,7 @@ if ((kalman_algo==1) || (kalman_algo==3))% Multivariate Kalman Filter
end
end
else
- LIK = missing_observations_kalman_filter(DynareDataset.missing.aindex,DynareDataset.missing.number_of_observations,DynareDataset.missing.no_more_missing_observations,Y,diffuse_periods+1,size(Y,2), ...
+ [LIK,lik] = missing_observations_kalman_filter(DynareDataset.missing.aindex,DynareDataset.missing.number_of_observations,DynareDataset.missing.no_more_missing_observations,Y,diffuse_periods+1,size(Y,2), ...
a, Pstar, ...
kalman_tol, DynareOptions.riccati_tol, ...
DynareOptions.presample, ...
@@ -622,6 +622,9 @@ if ((kalman_algo==1) || (kalman_algo==3))% Multivariate Kalman Filter
else
if DynareOptions.lik_init==3
LIK = LIK + dLIK;
+ if analytic_derivation==0 && nargout==2,
+ lik = [dlik; lik];
+ end
end
end
end
@@ -631,7 +634,7 @@ if (kalman_algo==2) || (kalman_algo==4)
% resetting measurement error covariance matrix when necessary %
if ~correlated_errors_have_been_checked
if isequal(H,0)
- H = zeros(nobs,1);
+ H = zeros(pp,1);
mmm = mm;
else
if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
@@ -644,20 +647,23 @@ if (kalman_algo==2) || (kalman_algo==4)
R = blkdiag(R,eye(pp));
Pstar = blkdiag(Pstar,H);
Pinf = blckdiag(Pinf,zeros(pp));
- H = zeros(nobs,1);
+ H = zeros(pp,1);
mmm = mm+pp;
end
end
end
- LIK = univariate_kalman_filter(DynareDataset.missing.aindex,DynareDataset.missing.number_of_observations,DynareDataset.missing.no_more_missing_observations,Y,diffuse_periods+1,size(Y,2), ...
+ [LIK, lik] = univariate_kalman_filter(DynareDataset.missing.aindex,DynareDataset.missing.number_of_observations,DynareDataset.missing.no_more_missing_observations,Y,diffuse_periods+1,size(Y,2), ...
a,Pstar, ...
DynareOptions.kalman_tol, ...
DynareOptions.riccati_tol, ...
DynareOptions.presample, ...
- T,Q,R,H,Z,mmm,pp,rr,diffuse_periods);
+ T,Q,R,H,Z,mmm,pp,rr,Zflag,diffuse_periods);
if DynareOptions.lik_init==3
LIK = LIK+dLIK;
+ if analytic_derivation==0 && nargout==2,
+ lik = [dlik; lik];
+ end
end
end
@@ -695,3 +701,8 @@ DynareOptions.kalman_algo = kalman_algo;
% Update the penalty.
penalty = fval;
+
+if analytic_derivation==0 && nargout==2,
+ lik=lik(start:end,:);
+ DLIK=[-lnprior; lik(:)];
+end