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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) 2009-2016 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(bayestopt_, options_.prior_trunc);
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_nonpositive_steadystate = 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] ;
NumberOfFiles = 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
if drsave
pdraws = cell(TableOfInformations(1,2),drsave+2) ;
else
pdraws = NaN(TableOfInformations(1,2),NumberOfParameters+1);
end
sampled_prior_expectation = zeros(NumberOfParameters,1);
sampled_prior_covariance = zeros(NumberOfParameters,NumberOfParameters);
file_line_number = 0;
file_indx_number = 0;
oo_.dr=set_state_space(oo_.dr,M_,options_);
hh = dyn_waitbar(0,'Please wait. Prior sampler...');
set(hh,'Name','Prior sampler.');
% Simulations.
while iteration < NumberOfSimulations
if ~mod(iteration,10)
dyn_waitbar(iteration/NumberOfSimulations,hh,'Please wait. Prior sampler...');
end
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_);
file_line_number = file_line_number + 1;
iteration = iteration + 1;
if drsave
pdraws(file_line_number,1) = {params};
pdraws(file_line_number,2) = {INFO(1)};
else
pdraws(file_line_number,1:NumberOfParameters) = params;
pdraws(file_line_number,NumberOfParameters+1) = INFO(1);
end
switch INFO(1)
case 0
if drsave
pdraws(file_line_number,3) = {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 ;
case 26
count_nonpositive_steadystate = count_nonpositive_steadystate + 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
if drsave
pdraws = cell(TableOfInformations(file_indx_number+1,2),drsave+2);
else
pdraws = NaN(TableOfInformations(file_indx_number+1,2),NumberOfParameters+1);
end
end
file_line_number = 0;
end
end
dyn_waitbar_close(hh);
% 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.ss.nonpositive_share = count_nonpositive_steadystate/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.ss.complex_share + ...
results.ss.nonpositive_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;
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