81 lines
2.9 KiB
Matlab
81 lines
2.9 KiB
Matlab
function [posterior_mean,posterior_covariance,posterior_mode,posterior_kernel_at_the_mode] = compute_mh_covariance_matrix()
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% Estimation of the posterior covariance matrix, posterior mean, posterior mode and evaluation of the posterior kernel at the
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% estimated mode, using the draws from a metropolis-hastings. The estimated posterior mode and covariance matrix are saved in
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% a file <M_.fname>_mh_mode.mat.
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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 posterior_mean [double] (n*1) vector, posterior expectation of the parameters.
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% o posterior_covariance [double] (n*n) matrix, posterior covariance of the parameters (computed from previous metropolis hastings).
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% o posterior_mode [double] (n*1) vector, posterior mode of the parameters.
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% o posterior_kernel_at_the_mode [double] scalar.
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%
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% SPECIAL REQUIREMENTS
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% None.
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% Copyright (C) 2006-2017 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
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global M_ options_ estim_params_ bayestopt_
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n = estim_params_.np + ...
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estim_params_.nvn+ ...
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estim_params_.ncx+ ...
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estim_params_.ncn+ ...
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estim_params_.nvx;
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nblck = options_.mh_nblck;
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MetropolisFolder = CheckPath('metropolis',M_.dname);
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ModelName = M_.fname;
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BaseName = [MetropolisFolder filesep ModelName];
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load_last_mh_history_file(MetropolisFolder, ModelName);
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FirstMhFile = record.KeepedDraws.FirstMhFile;
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FirstLine = record.KeepedDraws.FirstLine;
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TotalNumberOfMhFiles = sum(record.MhDraws(:,2));
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posterior_kernel_at_the_mode = -Inf;
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posterior_mean = zeros(n,1);
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posterior_mode = NaN(n,1);
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posterior_covariance = zeros(n,n);
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offset = 0;
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for b=1:nblck
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first_line = FirstLine;
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for n = FirstMhFile:TotalNumberOfMhFiles
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load([ BaseName '_mh' int2str(n) '_blck' int2str(b) '.mat'],'x2','logpo2');
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[tmp,idx] = max(logpo2);
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if tmp>posterior_kernel_at_the_mode
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posterior_kernel_at_the_mode = tmp;
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posterior_mode = x2(idx,:);
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end
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[posterior_mean,posterior_covariance,offset] = recursive_moments(posterior_mean,posterior_covariance,x2(first_line:end,:),offset);
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first_line = 1;
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end
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end
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xparam1 = posterior_mode';
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hh = inv(posterior_covariance);
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fval = posterior_kernel_at_the_mode;
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parameter_names = bayestopt_.name;
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save([M_.dname filesep 'Output' filesep M_.fname '_mh_mode.mat'],'xparam1','hh','fval','parameter_names'); |