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
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
|
function results = prior_sampler(drsave,M_,bayestopt_,options_,oo_,estim_params_)
% This function builds a (big) prior sample.
%
% INPUTS
% drsave [integer] Scalar. If equal to 1, then dr structure is saved with each prior draw.
% M_ [structure] Model description.
% bayestopt_ [structure] Prior distribution description.
% options_ [structure] Global options of Dynare.
%
% OUTPUTS:
% results [structure] Various statistics.
%
% SPECIAL REQUIREMENTS
% none
% Copyright (C) 2009-2012 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/>.
% Initialization.
prior_draw(1);
PriorDirectoryName = CheckPath('prior/draws',M_.dname);
work = ~drsave;
iteration = 0;
loop_indx = 0;
file_indx = [];
count_bk_indeterminacy = 0;
count_bk_unstability = 0;
count_bk_singularity = 0;
count_static_var_def = 0;
count_no_steadystate = 0;
count_steadystate_file_exit = 0;
count_dll_problem = 0;
count_complex_jacobian = 0;
count_complex_steadystate = 0;
count_nan_steadystate = 0;
count_nan_params = 0;
count_complex_params = 0;
count_unknown_problem = 0;
NumberOfSimulations = options_.prior_mc;
NumberOfParameters = length(bayestopt_.p1);
NumberOfEndogenousVariables = size(M_.endo_names,1);
NumberOfElementsPerFile = ceil(options_.MaxNumberOfBytes/NumberOfParameters/NumberOfEndogenousVariables/8) ;
if NumberOfSimulations <= NumberOfElementsPerFile
TableOfInformations = [ 1 , NumberOfSimulations , 1] ;
else
NumberOfFiles = ceil(NumberOfSimulations/NumberOfElementsPerFile) ;
NumberOfElementsInTheLastFile = NumberOfSimulations - NumberOfElementsPerFile*(NumberOfFiles-1) ;
TableOfInformations = NaN(NumberOfFiles,3) ;
TableOfInformations(:,1) = transpose(1:NumberOfFiles) ;
TableOfInformations(1:NumberOfFiles-1,2) = NumberOfElementsPerFile*ones(NumberOfFiles-1,1) ;
TableOfInformations(NumberOfFiles,2) = NumberOfElementsInTheLastFile ;
TableOfInformations(1,3) = 1;
TableOfInformations(2:end,3) = cumsum(TableOfInformations(1:end-1,2))+1;
end
pdraws = cell(TableOfInformations(1,2),drsave+1) ;
sampled_prior_expectation = zeros(NumberOfParameters,1);
sampled_prior_covariance = zeros(NumberOfParameters,NumberOfParameters);
file_line_number = 0;
file_indx_number = 0;
% Simulations.
while iteration < NumberOfSimulations
loop_indx = loop_indx+1;
params = prior_draw();
M_ = set_all_parameters(params,estim_params_,M_);
[dr,INFO,M_,options_,oo_] = resol(work,M_,options_,oo_);
switch INFO(1)
case 0
file_line_number = file_line_number + 1 ;
iteration = iteration + 1;
pdraws(file_line_number,1) = {params};
if drsave
pdraws(file_line_number,2) = {dr};
end
[sampled_prior_expectation,sampled_prior_covariance] = ...
recursive_prior_moments(sampled_prior_expectation,sampled_prior_covariance,params,iteration);
case 1
count_static_undefined = count_static_undefined + 1;
case 2
count_dll_problem = count_dll_problem + 1;
case 3
count_bk_unstability = count_bk_unstability + 1 ;
case 4
count_bk_indeterminacy = count_bk_indeterminacy + 1 ;
case 5
count_bk_singularity = count_bk_singularity + 1 ;
case 20
count_no_steadystate = count_no_steadystate + 1 ;
case 19
count_steadystate_file_exit = count_steadystate_file_exit + 1 ;
case 6
count_complex_jacobian = count_complex_jacobian + 1 ;
case 21
count_complex_steadystate = count_complex_steadystate + 1 ;
case 22
count_nan_steadystate = count_nan_steadystate + 1 ;
case 23
count_complex_params = count_complex_params + 1 ;
case 24
count_nan_params = count_nan_params + 1 ;
otherwise
count_unknown_problem = count_unknown_problem + 1 ;
end
if ( file_line_number==TableOfInformations(file_indx_number+1,2) )
file_indx_number = file_indx_number + 1;
save([ PriorDirectoryName '/prior_draws' int2str(file_indx_number) '.mat' ],'pdraws');
if file_indx_number<NumberOfFiles
pdraws = cell(TableOfInformations(file_indx_number+1,2),drsave+1);
end
file_line_number = 0;
end
end
% Get informations about BK conditions and other things...
results.bk.indeterminacy_share = count_bk_indeterminacy/loop_indx;
results.bk.unstability_share = count_bk_unstability/loop_indx;
results.bk.singularity_share = count_bk_singularity/loop_indx;
results.dll.problem_share = count_dll_problem/loop_indx;
results.ss.problem_share = count_no_steadystate/loop_indx;
results.ss.complex_share = count_complex_steadystate/loop_indx;
results.ass.problem_share = count_steadystate_file_exit/loop_indx;
results.jacobian.problem_share = count_complex_jacobian/loop_indx;
results.garbage_share = ...
results.bk.indeterminacy_share + ...
results.bk.unstability_share + ...
results.bk.singularity_share + ...
results.dll.problem_share + ...
results.ss.problem_share + ...
results.ass.problem_share + ...
results.jacobian.problem_share + ...
count_unknown_problem/loop_indx ;
results.prior.mean = sampled_prior_expectation;
results.prior.variance = sampled_prior_covariance;
results.prior.mass = 1-results.garbage_share;
function [mu,sigma] = recursive_prior_moments(m0,s0,newobs,iter)
% Recursive estimation of order one and two moments (expectation and
% covariance matrix). newobs should be a row vector. I do not use the
% function recursive_moments here, because this function is to be used when
% newobs is a 2D array.
m1 = m0 + (newobs'-m0)/iter;
qq = m1*m1';
s1 = s0 + ( (newobs'*newobs-qq-s0) + (iter-1)*(m0*m0'-qq') )/iter;
mu = m1;
sigma = s1;
|
