61 lines
1.8 KiB
Matlab
61 lines
1.8 KiB
Matlab
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function covar = compute_mh_covariance_matrix()
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% Estimation of the posterior covariance matrix.
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%
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% INPUTS
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% None.
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%
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% OUTPUTS
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% o covar [double] p*p matrix, posterior covariance of the estimated
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% parameters (computed from previous metropolis hastings).
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%
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%
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% ALGORITHM
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% None.
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%
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% SPECIAL REQUIREMENTS
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% None.
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%
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%
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% part of DYNARE, copyright S. Adjemian, M. Juillard (2006)
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% Gnu Public License.
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global M_ options_ estim_params_ oo_
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npar = estim_params_.np+estim_params_.nvn+estim_params_.ncx+estim_params_.ncn+estim_params_.nvx;
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nblck = options_.mh_nblck;
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MhDirectoryName = CheckPath('metropolis');
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load([ MhDirectoryName '/' M_.fname '_mh_history'])
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FirstMhFile = record.KeepedDraws.FirstMhFile;
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FirstLine = record.KeepedDraws.FirstLine; ifil = FirstLine;
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TotalNumberOfMhFiles = sum(record.MhDraws(:,2));
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TotalNumberOfMhDraws = sum(record.MhDraws(:,1));
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MAX_nruns = ceil(options_.MaxNumberOfBytes/(npar+2)/8);
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TODROP = floor(options_.mh_drop*TotalNumberOfMhDraws);
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MU = zeros(1,npar);
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SIGMA = zeros(npar,npar);
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fprintf('MH: I''m computing the posterior mean... ');
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for n = FirstMhFile:TotalNumberOfMhFiles
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for b = 1:nblck
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load([ MhDirectoryName '/' M_.fname '_mh' int2str(n) '_blck' int2str(b)],'x2','logpo2');
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MU = MU + sum(x2(ifil:end,:));
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end
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ifil = 1;
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end
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MU = MU/((TotalNumberOfMhDraws-TODROP)*nblck);
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MU1 = repmat(MU,MAX_nruns,1);
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fprintf(' Done!\n');
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fprintf('MH: I''m computing the posterior covariance matrix... ');
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ifil = FirstLine;
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for n = FirstMhFile:TotalNumberOfMhFiles
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for b = 1:nblck
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load([MhDirectoryName '/' M_.fname '_mh' int2str(n) '_blck' int2str(b)],'x2');
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x = x2(ifil:end,:)-MU1(1:size(x2(ifil:end,:),1),:);
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SIGMA = SIGMA + x'*x;
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end
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ifil = 1;
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end
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SIGMA = SIGMA/((TotalNumberOfMhDraws-TODROP)*nblck);%<=== Variance of the parameters (ok!)
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