66 lines
3.0 KiB
Matlab
66 lines
3.0 KiB
Matlab
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function [mu, covariance, mode, kernel_at_the_mode] = compute_posterior_covariance_matrix(names, fname, dname, options_, outputFolderName)
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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 posterior draws from a metropolis-hastings. The estimated posterior mode and covariance matrix are saved in
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% a file <fname>_mh_mode.mat, hssmc_mode.mat or dsmh__mode.mat under <dname>/<outputFolderName>/.
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%
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% INPUTS
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% - names [cell] n×1 cell array of row char arrays, names of the estimated parameters.
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% - fname [char] name of the model
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% - dname [char] name of subfolder with output files
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% - outputFolderName [char] name of directory to store results
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%
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% OUTPUTS
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% - mean [double] n×1 vector, posterior expectation of the parameters.
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% - covariance [double] n×n matrix, posterior covariance of the parameters.
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% - mode [double] n×1 vector, posterior mode of the parameters.
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% - kernel_at_the_mode [double] scalar, value of the posterior kernel at the mode.
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% Copyright © 2023 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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if nargin<5
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outputFolderName = 'Output';
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end
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if ishssmc(options_)
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% Load draws from the posterior distribution
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pfiles = dir(sprintf('%s/hssmc/particles-*.mat', dname));
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posterior = load(sprintf('%s/hssmc/particles-%u-%u.mat', dname, length(pfiles), length(pfiles)));
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% Get the posterior mode
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[kernel_at_the_mode, id] = max(posterior.tlogpostkernel);
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mode = posterior.particles(:,id);
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% Compute the posterior mean
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mu = sum(posterior.particles, 2)/length(posterior.tlogpostkernel);
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% Compute the posterior covariance
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covariance = (posterior.particles-mu)*(posterior.particles-mu)'/length(posterior.tlogpostkernel);
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else
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[mu, covariance, mode, kernel_at_the_mode] = compute_mh_covariance_matrix(names, fname, dname, outputFolderName);
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end
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xparam1 = mode;
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hh = inv(covariance);
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fval = kernel_at_the_mode;
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parameter_names = names;
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if ishssmc(options_)
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save(sprintf('%s/%s/hssmc_mode.mat', dname, outputFolderName), 'xparam1', 'hh', 'fval', 'parameter_names');
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else
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save(sprintf('%s/%s/%s_mh_mode.mat', dname, outputFolderName, fname), 'xparam1', 'hh', 'fval', 'parameter_names');
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end
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