Bug fix for univariate KF (nobs was not defined, replaced with pp) + provisions fro eliminating dsge_likelihood_hh function;
Marco Ratto
Michel Juillard
parent
bef3377a81
commit
bb02c4d6df
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@ -337,7 +337,7 @@ kalman_algo = DynareOptions.kalman_algo;
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% resetting measurement errors covariance matrix for univariate filters
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if (kalman_algo == 2) || (kalman_algo == 4)
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if isequal(H,0)
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H = zeros(nobs,1);
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H = zeros(pp,1);
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mmm = mm;
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else
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if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
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@ -350,7 +350,7 @@ if (kalman_algo == 2) || (kalman_algo == 4)
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R = blkdiag(R,eye(pp));
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Pstar = blkdiag(Pstar,H);
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Pinf = blckdiag(Pinf,zeros(pp));
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H = zeros(nobs,1);
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H = zeros(pp,1);
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mmm = mm+pp;
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end
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end
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@ -402,18 +402,18 @@ switch DynareOptions.lik_init
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if (kalman_algo==3)
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% Multivariate Diffuse Kalman Filter
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if no_missing_data_flag
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[dLIK,tmp,a,Pstar] = kalman_filter_d(Y, 1, size(Y,2), ...
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[dLIK,dlik,a,Pstar] = kalman_filter_d(Y, 1, size(Y,2), ...
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zeros(mm,1), Pinf, Pstar, ...
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kalman_tol, riccati_tol, DynareOptions.presample, ...
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T,R,Q,H,Z,mm,pp,rr);
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else
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[dLIK,tmp,a,Pstar] = missing_observations_kalman_filter_d(DynareDataset.missing.aindex,DynareDataset.missing.number_of_observations,DynareDataset.missing.no_more_missing_observations, ...
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[dLIK,dlik,a,Pstar] = missing_observations_kalman_filter_d(DynareDataset.missing.aindex,DynareDataset.missing.number_of_observations,DynareDataset.missing.no_more_missing_observations, ...
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Y, 1, size(Y,2), ...
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zeros(mm,1), Pinf, Pstar, ...
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kalman_tol, riccati_tol, DynareOptions.presample, ...
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T,R,Q,H,Z,mm,pp,rr);
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end
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diffuse_periods = length(tmp);
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diffuse_periods = length(dlik);
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if isinf(dLIK)
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% Go to univariate diffuse filter if singularity problem.
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singular_diffuse_filter = 1;
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@ -422,7 +422,7 @@ switch DynareOptions.lik_init
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if singular_diffuse_filter || (kalman_algo==4)
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% Univariate Diffuse Kalman Filter
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if isequal(H,0)
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H1 = zeros(nobs,1);
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H1 = zeros(pp,1);
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mmm = mm;
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else
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if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
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@ -435,21 +435,21 @@ switch DynareOptions.lik_init
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R = blkdiag(R,eye(pp));
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Pstar = blkdiag(Pstar,H);
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Pinf = blckdiag(Pinf,zeros(pp));
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H1 = zeros(nobs,1);
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H1 = zeros(pp,1);
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mmm = mm+pp;
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end
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end
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% no need to test again for correlation elements
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correlated_errors_have_been_checked = 1;
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[dLIK,tmp,a,Pstar] = univariate_kalman_filter_d(DynareDataset.missing.aindex,...
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[dLIK,dlik,a,Pstar] = univariate_kalman_filter_d(DynareDataset.missing.aindex,...
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DynareDataset.missing.number_of_observations,...
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DynareDataset.missing.no_more_missing_observations, ...
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Y, 1, size(Y,2), ...
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zeros(mmm,1), Pinf, Pstar, ...
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kalman_tol, riccati_tol, DynareOptions.presample, ...
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T,R,Q,H1,Z,mmm,pp,rr);
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diffuse_periods = length(tmp);
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diffuse_periods = length(dlik);
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end
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case 4% Start from the solution of the Riccati equation.
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if kalman_algo ~= 2
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@ -588,7 +588,7 @@ if ((kalman_algo==1) || (kalman_algo==3))% Multivariate Kalman Filter
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[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);
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mexErrCheck('block_kalman_filter', err);
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else
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LIK = kalman_filter(Y,diffuse_periods+1,size(Y,2), ...
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[LIK,lik] = kalman_filter(Y,diffuse_periods+1,size(Y,2), ...
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a,Pstar, ...
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kalman_tol, riccati_tol, ...
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DynareOptions.presample, ...
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@ -607,7 +607,7 @@ if ((kalman_algo==1) || (kalman_algo==3))% Multivariate Kalman Filter
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end
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end
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else
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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), ...
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[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), ...
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a, Pstar, ...
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kalman_tol, DynareOptions.riccati_tol, ...
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DynareOptions.presample, ...
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@ -622,6 +622,9 @@ if ((kalman_algo==1) || (kalman_algo==3))% Multivariate Kalman Filter
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else
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if DynareOptions.lik_init==3
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LIK = LIK + dLIK;
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if analytic_derivation==0 && nargout==2,
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lik = [dlik; lik];
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end
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end
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end
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end
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@ -631,7 +634,7 @@ if (kalman_algo==2) || (kalman_algo==4)
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% resetting measurement error covariance matrix when necessary %
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if ~correlated_errors_have_been_checked
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if isequal(H,0)
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H = zeros(nobs,1);
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H = zeros(pp,1);
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mmm = mm;
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else
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if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
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@ -644,20 +647,23 @@ if (kalman_algo==2) || (kalman_algo==4)
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R = blkdiag(R,eye(pp));
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Pstar = blkdiag(Pstar,H);
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Pinf = blckdiag(Pinf,zeros(pp));
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H = zeros(nobs,1);
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H = zeros(pp,1);
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mmm = mm+pp;
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end
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end
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end
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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), ...
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[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), ...
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a,Pstar, ...
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DynareOptions.kalman_tol, ...
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DynareOptions.riccati_tol, ...
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DynareOptions.presample, ...
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T,Q,R,H,Z,mmm,pp,rr,diffuse_periods);
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T,Q,R,H,Z,mmm,pp,rr,Zflag,diffuse_periods);
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if DynareOptions.lik_init==3
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LIK = LIK+dLIK;
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if analytic_derivation==0 && nargout==2,
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lik = [dlik; lik];
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end
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end
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end
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@ -695,3 +701,8 @@ DynareOptions.kalman_algo = kalman_algo;
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% Update the penalty.
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penalty = fval;
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if analytic_derivation==0 && nargout==2,
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lik=lik(start:end,:);
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DLIK=[-lnprior; lik(:)];
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end
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