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fixed handling of correlation in covariance of measurement errors

time-shift
Michel Juillard 2011-10-22 15:26:07 +02:00
parent c87e208eb2
commit 30afa5f415
1 changed files with 41 additions and 14 deletions
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55
matlab/DsgeLikelihood.m
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@ -313,6 +313,33 @@ Y = DynareDataset.data-trend;
% 3. Initial condition of the Kalman filter
%------------------------------------------------------------------------------
kalman_algo = DynareOptions.kalman_algo;
% resetting measurement errors covariance matrix for univariate filters
no_correlation_flag = 1;
if (kalman_algo == 2) || (kalman_algo == 4)
if isequal(H,0)
H = zeros(nobs,1);
else
if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
H = diag(H);
else
no_correlation_flag = 0;
end
end
if no_correlation_flag
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));
mmm = mm+pp;
end
end
diffuse_periods = 0;
switch DynareOptions.lik_init
case 1% Standard initialization with the steady state of the state equation.
@ -363,17 +390,6 @@ switch DynareOptions.lik_init
end
if (kalman_algo==4)
% Univariate Diffuse Kalman Filter
if no_correlation_flag
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));
mmm = mm+pp;
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, ...
@ -498,9 +514,20 @@ if ((kalman_algo==1) || (kalman_algo==3))% Multivariate Kalman Filter
end
end
if ( (singularity_flag) || (kalman_algo==2) || (kalman_algo==4) )% Univariate Kalman Filter
if ( singularity_flag || (kalman_algo==2) || (kalman_algo==4) )
% Univariate Kalman Filter
% resetting measurement error covariance matrix when necessary %
if singularity_flag
if no_correlation
if isequal(H,0)
H = zeros(nobs,1);
else
if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
H = diag(H);
else
no_correlation_flag = 0;
end
end
if no_correlation_flag
mmm = mm;
else
Z = [Z, eye(pp)];
@ -510,9 +537,9 @@ if ( (singularity_flag) || (kalman_algo==2) || (kalman_algo==4) )% Univariate Ka
Pstar = blkdiag(Pstar,H);
Pinf = blckdiag(Pinf,zeros(pp));
mmm = mm+pp;
a = [a; zeros(pp,1)];
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), ...
a,Pstar, ...
DynareOptions.kalman_tol, ...