1function [posterior_mean,posterior_covariance,posterior_mode,posterior_kernel_at_the_mode] = compute_mh_covariance_matrix()
2% Estimation of the posterior covariance matrix, posterior mean, posterior mode and evaluation of the posterior kernel at the
3% estimated mode, using the draws from a metropolis-hastings. The estimated posterior mode and covariance matrix are saved in
4% a file <M_.fname>_mh_mode.mat.
5%
6% INPUTS
7%   None.
8%
9% OUTPUTS
10%   o  posterior_mean                [double]  (n*1) vector, posterior expectation of the parameters.
11%   o  posterior_covariance          [double]  (n*n) matrix, posterior covariance of the parameters (computed from previous metropolis hastings).
12%   o  posterior_mode                [double]  (n*1) vector, posterior mode of the parameters.
13%   o  posterior_kernel_at_the_mode  [double]  scalar.
14%
15% SPECIAL REQUIREMENTS
16%   None.
17
18% Copyright (C) 2006-2017 Dynare Team
19%
20% This file is part of Dynare.
21%
22% Dynare is free software: you can redistribute it and/or modify
23% it under the terms of the GNU General Public License as published by
24% the Free Software Foundation, either version 3 of the License, or
25% (at your option) any later version.
26%
27% Dynare is distributed in the hope that it will be useful,
28% but WITHOUT ANY WARRANTY; without even the implied warranty of
29% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
30% GNU General Public License for more details.
31%
32% You should have received a copy of the GNU General Public License
33% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
34
35global M_ options_ estim_params_ bayestopt_
36
37
38n = estim_params_.np + ...
39    estim_params_.nvn+ ...
40    estim_params_.ncx+ ...
41    estim_params_.ncn+ ...
42    estim_params_.nvx;
43
44nblck = options_.mh_nblck;
45
46MetropolisFolder = CheckPath('metropolis',M_.dname);
47ModelName = M_.fname;
48BaseName = [MetropolisFolder filesep ModelName];
49
50load_last_mh_history_file(MetropolisFolder, ModelName);
51
52FirstMhFile = record.KeepedDraws.FirstMhFile;
53FirstLine   = record.KeepedDraws.FirstLine;
54TotalNumberOfMhFiles = sum(record.MhDraws(:,2));
55
56posterior_kernel_at_the_mode = -Inf;
57posterior_mean = zeros(n,1);
58posterior_mode = NaN(n,1);
59posterior_covariance = zeros(n,n);
60offset = 0;
61
62for b=1:nblck
63    first_line = FirstLine;
64    for n = FirstMhFile:TotalNumberOfMhFiles
65        load([ BaseName '_mh' int2str(n) '_blck' int2str(b) '.mat'],'x2','logpo2');
66        [tmp,idx] = max(logpo2);
67        if tmp>posterior_kernel_at_the_mode
68            posterior_kernel_at_the_mode = tmp;
69            posterior_mode = x2(idx,:);
70        end
71        [posterior_mean,posterior_covariance,offset] = recursive_moments(posterior_mean,posterior_covariance,x2(first_line:end,:),offset);
72        first_line = 1;
73    end
74end
75
76xparam1 = posterior_mode';
77hh = inv(posterior_covariance);
78fval = posterior_kernel_at_the_mode;
79parameter_names = bayestopt_.name;
80
81save([M_.fname '_mh_mode.mat'],'xparam1','hh','fval','parameter_names');