1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
|
function [marginal,oo_] = marginal_density(M_, options_, estim_params_, oo_, bayestopt_, outputFolderName)
% function [marginal,oo_] = marginal_density(M_, options_, estim_params_, oo_, bayestopt_, outputFolderName)
% Computes the marginal density
%
% INPUTS
% options_ [structure] Dynare options structure
% estim_params_ [structure] Dynare estimation parameter structure
% M_ [structure] Dynare model structure
% oo_ [structure] Dynare results structure
% outputFolderName [string] name of folder with results
%
% OUTPUTS
% marginal: [double] marginal density (modified harmonic mean)
% oo_ [structure] Dynare results structure
%
% SPECIAL REQUIREMENTS
% none
% Copyright © 2005-2023 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 <https://www.gnu.org/licenses/>.
if nargin < 6
outputFolderName = 'Output';
end
MetropolisFolder = CheckPath('metropolis',M_.dname);
ModelName = M_.fname;
BaseName = [MetropolisFolder filesep ModelName];
record=load_last_mh_history_file(MetropolisFolder, ModelName);
[nblck, npar] = size(record.LastParameters);
FirstMhFile = record.KeepedDraws.FirstMhFile;
FirstLine = record.KeepedDraws.FirstLine;
TotalNumberOfMhFiles = sum(record.MhDraws(:,2));
TotalNumberOfMhDraws = sum(record.MhDraws(:,1));
TODROP = floor(options_.mh_drop*TotalNumberOfMhDraws);
fprintf('marginal density: I''m computing the posterior mean and covariance... ');
[posterior_mean, posterior_covariance, posterior_mode, posterior_kernel_at_the_mode] = compute_posterior_covariance_matrix(bayestopt_.name, M_.fname, M_.dname, options_, outputFolderName);
MU = transpose(posterior_mean);
SIGMA = posterior_covariance;
lpost_mode = posterior_kernel_at_the_mode;
xparam1 = posterior_mean;
hh = inv(SIGMA);
fprintf(' Done!\n');
if ~isfield(oo_,'posterior_mode') || (options_.mh_replic && isequal(options_.posterior_sampler_options.posterior_sampling_method,'slice'))
oo_=fill_mh_mode(posterior_mode',NaN(npar,1),M_,options_,estim_params_,oo_,'posterior');
end
% save the posterior mean and the inverse of the covariance matrix
% (usefull if the user wants to perform some computations using
% the posterior mean instead of the posterior mode ==> ).
parameter_names = bayestopt_.name;
save([M_.dname filesep outputFolderName filesep M_.fname '_mean.mat'],'xparam1','hh','parameter_names','SIGMA');
fprintf('marginal density: I''m computing the posterior log marginal density (modified harmonic mean)... ');
try
% use this robust option to avoid inf/nan
logdetSIGMA = 2*sum(log(diag(chol(SIGMA))));
catch
% in case SIGMA is not positive definite
logdetSIGMA = nan;
fprintf('marginal density: the covariance of MCMC draws is not positive definite. You may have too few MCMC draws.');
end
invSIGMA = hh;
marginal = zeros(9,2);
linee = 0;
check_coverage = 1;
increase = 1;
while check_coverage
for p = 0.1:0.1:0.9
critval = chi2inv(p,npar);
ifil = FirstLine;
tmp = 0;
for n = FirstMhFile:TotalNumberOfMhFiles
for b=1:nblck
load([ BaseName '_mh' int2str(n) '_blck' int2str(b) '.mat'],'x2','logpo2');
EndOfFile = size(x2,1);
for i = ifil:EndOfFile
deviation = ((x2(i,:)-MU)*invSIGMA*(x2(i,:)-MU)')/increase;
if deviation <= critval
lftheta = -log(p)-(npar*log(2*pi)+(npar*log(increase)+logdetSIGMA)+deviation)/2;
tmp = tmp + exp(lftheta - logpo2(i) + lpost_mode);
end
end
end
ifil = 1;
end
linee = linee + 1;
warning_old_state = warning;
warning off;
marginal(linee,:) = [p, lpost_mode-log(tmp/((TotalNumberOfMhDraws-TODROP)*nblck))];
warning(warning_old_state);
end
if abs((marginal(9,2)-marginal(1,2))/marginal(9,2)) > options_.marginal_data_density.harmonic_mean.tolerance || isinf(marginal(1,2))
fprintf('\n')
if increase == 1
disp('marginal density: The support of the weighting density function is not large enough...')
disp('marginal density: I increase the variance of this distribution.')
increase = 1.2*increase;
linee = 0;
else
disp('marginal density: Let me try again.')
increase = 1.2*increase;
linee = 0;
if increase > 20
check_coverage = 0;
clear invSIGMA detSIGMA increase;
disp('marginal density: There''s probably a problem with the modified harmonic mean estimator.')
end
end
else
check_coverage = 0;
clear invSIGMA detSIGMA increase;
fprintf('Done!\n')
end
end
oo_.MarginalDensity.ModifiedHarmonicMean = mean(marginal(:,2));
|
