204 lines
7.6 KiB
Matlab
204 lines
7.6 KiB
Matlab
function results = prior_sampler(drsave,M_,bayestopt_,options_,oo_,estim_params_)
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% This function builds a (big) prior sample.
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%
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% INPUTS
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% drsave [integer] Scalar. If equal to 1, then dr structure is saved with each prior draw.
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% M_ [structure] Model description.
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% bayestopt_ [structure] Prior distribution description.
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% options_ [structure] Global options of Dynare.
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%
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% OUTPUTS:
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% results [structure] Various statistics.
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%
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% SPECIAL REQUIREMENTS
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% none
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% Copyright © 2009-2023 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
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% Initialization.
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Prior = dprior(bayestopt_, options_.prior_trunc);
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PriorDirectoryName = CheckPath('prior/draws',M_.dname);
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work = ~drsave;
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iteration = 0;
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loop_indx = 0;
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file_indx = [];
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count_bk_indeterminacy = 0;
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count_bk_unstability = 0;
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count_bk_singularity = 0;
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count_static_var_def = 0;
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count_no_steadystate = 0;
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count_steadystate_file_exit = 0;
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count_dll_problem = 0;
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count_complex_jacobian = 0;
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count_complex_steadystate = 0;
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count_nan_steadystate = 0;
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count_nan_params = 0;
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count_complex_params = 0;
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count_nonpositive_steadystate = 0;
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count_endogenous_prior_violation = 0;
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count_unknown_problem = 0;
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NumberOfSimulations = options_.prior_mc;
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NumberOfParameters = length(bayestopt_.p1);
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NumberOfEndogenousVariables = M_.endo_nbr;
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NumberOfElementsPerFile = ceil(options_.MaxNumberOfBytes/NumberOfParameters/NumberOfEndogenousVariables/8) ;
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if NumberOfSimulations <= NumberOfElementsPerFile
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TableOfInformations = [ 1 , NumberOfSimulations , 1] ;
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NumberOfFiles = 1;
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else
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NumberOfFiles = ceil(NumberOfSimulations/NumberOfElementsPerFile) ;
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NumberOfElementsInTheLastFile = NumberOfSimulations - NumberOfElementsPerFile*(NumberOfFiles-1) ;
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TableOfInformations = NaN(NumberOfFiles,3) ;
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TableOfInformations(:,1) = transpose(1:NumberOfFiles) ;
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TableOfInformations(1:NumberOfFiles-1,2) = NumberOfElementsPerFile*ones(NumberOfFiles-1,1) ;
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TableOfInformations(NumberOfFiles,2) = NumberOfElementsInTheLastFile ;
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TableOfInformations(1,3) = 1;
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TableOfInformations(2:end,3) = cumsum(TableOfInformations(1:end-1,2))+1;
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end
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if drsave
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pdraws = cell(TableOfInformations(1,2),drsave+2) ;
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else
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pdraws = NaN(TableOfInformations(1,2),NumberOfParameters+1);
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end
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sampled_prior_expectation = zeros(NumberOfParameters,1);
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sampled_prior_covariance = zeros(NumberOfParameters,NumberOfParameters);
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file_line_number = 0;
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file_indx_number = 0;
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oo_.dr=set_state_space(oo_.dr,M_,options_);
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hh_fig = dyn_waitbar(0,'Please wait. Prior sampler...');
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set(hh_fig,'Name','Prior sampler.');
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% Simulations.
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while iteration < NumberOfSimulations
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if ~mod(iteration,10)
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dyn_waitbar(iteration/NumberOfSimulations,hh_fig,'Please wait. Prior sampler...');
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end
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loop_indx = loop_indx+1;
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params = Prior.draw();
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M_ = set_all_parameters(params, estim_params_, M_);
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[T, R, ~, INFO, M_, oo_] = dynare_resolve(M_, options_, oo_, 'restrict');
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dr = oo_.dr;
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if ~INFO(1)
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INFO=endogenous_prior_restrictions(T,R,M_,options_,oo_);
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end
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file_line_number = file_line_number + 1;
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iteration = iteration + 1;
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if drsave
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pdraws(file_line_number,1) = {params};
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pdraws(file_line_number,2) = {INFO(1)};
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else
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pdraws(file_line_number,1:NumberOfParameters) = params;
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pdraws(file_line_number,NumberOfParameters+1) = INFO(1);
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end
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switch INFO(1)
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case 0
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if drsave
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pdraws(file_line_number,3) = {dr};
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end
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[sampled_prior_expectation,sampled_prior_covariance] = ...
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recursive_prior_moments(sampled_prior_expectation,sampled_prior_covariance,params,iteration);
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case 1
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count_static_undefined = count_static_undefined + 1;
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case 2
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count_dll_problem = count_dll_problem + 1;
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case 3
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count_bk_unstability = count_bk_unstability + 1 ;
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case 4
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count_bk_indeterminacy = count_bk_indeterminacy + 1 ;
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case 5
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count_bk_singularity = count_bk_singularity + 1 ;
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case 20
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count_no_steadystate = count_no_steadystate + 1 ;
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case 19
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count_steadystate_file_exit = count_steadystate_file_exit + 1 ;
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case 6
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count_complex_jacobian = count_complex_jacobian + 1 ;
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case 21
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count_complex_steadystate = count_complex_steadystate + 1 ;
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case 22
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count_nan_steadystate = count_nan_steadystate + 1 ;
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case 23
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count_complex_params = count_complex_params + 1 ;
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case 24
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count_nan_params = count_nan_params + 1 ;
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case 26
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count_nonpositive_steadystate = count_nonpositive_steadystate + 1;
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case 49
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count_endogenous_prior_violation = count_endogenous_prior_violation + 1;
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otherwise
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count_unknown_problem = count_unknown_problem + 1 ;
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end
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if ( file_line_number==TableOfInformations(file_indx_number+1,2) )
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file_indx_number = file_indx_number + 1;
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save([ PriorDirectoryName '/prior_draws' int2str(file_indx_number) '.mat' ],'pdraws','estim_params_');
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if file_indx_number<NumberOfFiles
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if drsave
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pdraws = cell(TableOfInformations(file_indx_number+1,2),drsave+2);
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else
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pdraws = NaN(TableOfInformations(file_indx_number+1,2),NumberOfParameters+1);
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end
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end
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file_line_number = 0;
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end
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end
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dyn_waitbar_close(hh_fig);
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% Get informations about BK conditions and other things...
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results.bk.indeterminacy_share = count_bk_indeterminacy/loop_indx;
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results.bk.unstability_share = count_bk_unstability/loop_indx;
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results.bk.singularity_share = count_bk_singularity/loop_indx;
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results.dll.problem_share = count_dll_problem/loop_indx;
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results.ss.problem_share = count_no_steadystate/loop_indx;
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results.ss.complex_share = count_complex_steadystate/loop_indx;
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results.ass.problem_share = count_steadystate_file_exit/loop_indx;
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results.ss.nonpositive_share = count_nonpositive_steadystate/loop_indx;
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results.jacobian.problem_share = count_complex_jacobian/loop_indx;
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results.endogenous_prior_violation_share = count_endogenous_prior_violation/loop_indx;
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results.garbage_share = ...
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results.bk.indeterminacy_share + ...
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results.bk.unstability_share + ...
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results.bk.singularity_share + ...
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results.dll.problem_share + ...
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results.ss.problem_share + ...
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results.ass.problem_share + ...
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results.ss.complex_share + ...
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results.ss.nonpositive_share + ...
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results.jacobian.problem_share + ...
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results.endogenous_prior_violation_share + ...
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count_unknown_problem/loop_indx ;
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results.prior.mean = sampled_prior_expectation;
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results.prior.variance = sampled_prior_covariance;
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results.prior.mass = 1-results.garbage_share;
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function [mu,sigma] = recursive_prior_moments(m0,s0,newobs,iter)
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% Recursive estimation of order one and two moments (expectation and
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% covariance matrix). newobs should be a row vector. I do not use the
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% function recursive_moments here, because this function is to be used when
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% newobs is a 2D array.
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m1 = m0 + (newobs'-m0)/iter;
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qq = m1*m1';
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s1 = s0 + ( (newobs'*newobs-qq-s0) + (iter-1)*(m0*m0'-qq') )/iter;
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mu = m1;
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sigma = s1;
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