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