+ Changed some files so that they can handle prior montecarlo.
+ Changed names and calls. + Cosmetic changes. git-svn-id: https://www.dynare.org/svn/dynare/trunk@2764 ac1d8469-bf42-47a9-8791-bf33cf982152time-shift
stepan
parent
6687ebb992
commit
bdabde9b58
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@ -0,0 +1,152 @@
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function oo_ = correlation_mc_analysis(SampleSize,type,dname,fname,vartan,nvar,var1,var2,nar,mh_conf_sig,oo_,M_,options_)
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% This function analyses the (posterior or prior) distribution of the
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% endogenous variables correlation function.
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% Copyright (C) 2008-2009 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 <http://www.gnu.org/licenses/>.
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if strcmpi(type,'posterior')
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TYPE = 'Posterior';
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PATH = [dname '/metropolis/']
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else
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TYPE = 'Prior';
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PATH = [dname '/prior/moments/']
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end
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indx1 = check_name(vartan,var1);
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if isempty(indx1)
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disp([ type '_analysis:: ' var1 ' is not a stationary endogenous variable!'])
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return
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end
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if ~isempty(var2)
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indx2 = check_name(vartan,var2);
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if isempty(indx2)
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disp([ type '_analysis:: ' var2 ' is not a stationary endogenous variable!'])
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return
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end
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else
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indx2 = indx1;
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var2 = var1;
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end
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`
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if isfield(oo_,[TYPE 'TheoreticalMoments'])
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eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments;'])
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if isfield(temporary_structure,'dsge')
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eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments.dsge;'])
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if isfield(temporary_structure,'correlation')
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eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments.dsge.mean;'])
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if isfield(temporary_structure,var1)
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eval(['temporary_structure_1 = oo_.' TYPE 'TheoreticalMoments.dsge.mean.' var1 ';'])
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if isfield(temporary_structure_1,var2)
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eval(['temporary_structure_2 = temporary_structure_1.' var2 ';'])
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l1 = length(temporary_structure_2);
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if l1<nar
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% INITIALIZATION:
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oo_ = initialize_output_structure(var1,var2,nar,type,oo_);
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delete([PATH fname '_' TYPE 'Correlations*'])
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[nvar,vartan,NumberOfFiles] = ...
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dsge_simulated_theoretical_correlation(SampleSize,nar,M_,options_,oo_,type);
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else
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if ~isnan(temporary_structure_2(nar))
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%Nothing to do.
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return
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end
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end
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else
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oo_ = initialize_output_structure(var1,var2,nar,TYPE,oo_);
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end
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else
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oo_ = initialize_output_structure(var1,var2,nar,TYPE,oo_);
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end
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else
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oo_ = initialize_output_structure(var1,var2,nar,TYPE,oo_);
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end
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else
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oo_ = initialize_output_structure(var1,var2,nar,TYPE,oo_);
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end
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else
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oo_ = initialize_output_structure(var1,var2,nar,TYPE,oo_);
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end
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ListOfFiles = dir([ PATH fname '_' TYPE 'Correlations*.mat']);
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i1 = 1; tmp = zeros(SampleSize,1);
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for file = 1:length(ListOfFiles)
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load([ PATH fname '_' TYPE 'PosteriorCorrelations' int2str(file) '.mat']);
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i2 = i1 + rows(Correlation_array) - 1;
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tmp(i1:i2) = Correlation_array(:,indx1,indx2,nar);
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i1 = i2+1;
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end
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name = [ var1 '.' var2 ];
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if ~isconst(tmp)
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[p_mean, p_median, p_var, hpd_interval, p_deciles, density] = ...
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posterior_moments(tmp,1,mh_conf_sig);
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if isfield(oo_,'PosteriorTheoreticalMoments')
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eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments;'])
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if isfield(temporary_structure,'dsge')
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eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments.dsge;'])
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if isfield(temporary_structure,'correlation')
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oo_ = fill_output_structure(var1,var2,TYPE,oo_,'mean',nar,p_mean);
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oo_ = fill_output_structure(var1,var2,TYPE,oo_,'median',nar,p_median);
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oo_ = fill_output_structure(var1,var2,TYPE,oo_,'variance',nar,p_var);
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oo_ = fill_output_structure(var1,var2,TYPE,oo_,'hpdinf',nar,hpd_interval(1));
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oo_ = fill_output_structure(var1,var2,TYPE,oo_,'hpdsup',nar,hpd_interval(2));
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oo_ = fill_output_structure(var1,var2,TYPE,oo_,'deciles',nar,p_deciles);
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oo_ = fill_output_structure(var1,var2,TYPE,oo_,'density',nar,density);
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end
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end
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end
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else
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if isfield(oo_,'PosteriorTheoreticalMoments')
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eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments;'])
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if isfield(temporary_structure,'dsge')
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eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments.dsge;'])
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if isfield(temporary_structure,'correlation')
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oo_ = fill_output_structure(var1,var2,TYPE,oo_,'mean',nar,NaN);
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oo_ = fill_output_structure(var1,var2,TYPE,oo_,'median',nar,NaN);
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oo_ = fill_output_structure(var1,var2,TYPE,oo_,'variance',nar,NaN);
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oo_ = fill_output_structure(var1,var2,TYPE,oo_,'hpdinf',nar,NaN);
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oo_ = fill_output_structure(var1,var2,TYPE,oo_,'hpdsup',nar,NaN);
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oo_ = fill_output_structure(var1,var2,TYPE,oo_,'deciles',nar,NaN);
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oo_ = fill_output_structure(var1,var2,TYPE,oo_,'density',nar,NaN);
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end
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end
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end
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end
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function oo_ = initialize_output_structure(var1,var2,nar,type,oo_)
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name = [ var1 '.' var2 ];
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eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.mean.' name ' = NaN(' int2str(nar) ',1);']);
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eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.median.' name ' = NaN(' int2str(nar) ',1);']);
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eval(['oo_.' type 'PosteriorTheoreticalMoments.dsge.correlation.variance.' name ' = NaN(' int2str(nar) ',1);']);
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eval(['oo_.' type 'PosteriorTheoreticalMoments.dsge.correlation.hpdinf.' name ' = NaN(' int2str(nar) ',1);']);
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eval(['oo_.' type 'PosteriorTheoreticalMoments.dsge.correlation.hpdsup.' name ' = NaN(' int2str(nar) ',1);']);
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eval(['oo_.' type 'PosteriorTheoreticalMoments.dsge.correlation.deciles.' name ' = cell(' int2str(nar) ',1);']);
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eval(['oo_.' type 'PosteriorTheoreticalMoments.dsge.correlation.density.' name ' = cell(' int2str(nar) ',1);']);
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for i=1:nar
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eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.density.' name '(' int2str(i) ',1) = {NaN};']);
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eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.deciles.' name '(' int2str(i) ',1) = {NaN};']);
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end
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function oo_ = fill_output_structure(var1,var2,type,oo_,lag,result)
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name = [ var1 '.' var2 ];
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switch type
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case {'mean','median','variance','hpdinf','hpdsup'}
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eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.' type '.' name '(' int2str(lag) ',1) = result;']);
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case {'deciles','density'}
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eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.' type '.' name '(' int2str(lag) ',1) = {result};']);
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otherwise
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disp('fill_output_structure:: Unknown field!')
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end
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@ -1,135 +0,0 @@
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function oo_ = correlation_posterior_analysis(SampleSize,dname,fname,vartan,nvar,var1,var2,nar,mh_conf_sig,oo_,M_,options_)
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% Copyright (C) 2008 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 <http://www.gnu.org/licenses/>.
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indx1 = check_name(vartan,var1);
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if isempty(indx1)
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disp(['posterior_analysis:: ' var1 ' is not a stationary endogenous variable!'])
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return
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end
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if ~isempty(var2)
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indx2 = check_name(vartan,var2);
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if isempty(indx2)
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disp(['posterior_analysis:: ' var2 ' is not a stationary endogenous variable!'])
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return
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end
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else
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indx2 = indx1;
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var2 = var1;
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end
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if isfield(oo_,'PosteriorTheoreticalMoments')
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if isfield(oo_.PosteriorTheoreticalMoments,'dsge')
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if isfield(oo_.PosteriorTheoreticalMoments.dsge,'correlation')
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if isfield(oo_.PosteriorTheoreticalMoments.dsge.correlation.mean,var1)
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eval(['s1 = oo_.PosteriorTheoreticalMoments.dsge.correlation.mean' '.' var1 ';'])
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if isfield(s1,var2)
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eval(['s2 = s1' '.' var2 ';'])
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l1 = length(s2);
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if l1<nar
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% INITIALIZATION:
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oo_ = initialize_output_structure(var1,var2,nar,oo_);
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system(['rm ' M_.dname '/metropolis/' M_.fname '_PosteriorCorrelations*']);
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[nvar,vartan,NumberOfFiles] = ...
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dsge_posterior_theoretical_correlation(SampleSize,nar,M_,options_,oo_);
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else
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if ~isnan(s2(nar))
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%Nothing to do.
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return
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end
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end
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else
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oo_ = initialize_output_structure(var1,var2,nar,oo_);
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end
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else
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oo_ = initialize_output_structure(var1,var2,nar,oo_);
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end
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else
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oo_ = initialize_output_structure(var1,var2,nar,oo_);
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end
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else
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oo_ = initialize_output_structure(var1,var2,nar,oo_);
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end
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else
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oo_ = initialize_output_structure(var1,var2,nar,oo_);
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end
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tmp = dir([ dname '/metropolis/' fname '_PosteriorCorrelations*.mat']);
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NumberOfFiles = length(tmp);
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i1 = 1; tmp = zeros(SampleSize,1);
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for file = 1:NumberOfFiles
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load([ dname '/metropolis/' fname '_PosteriorCorrelations' int2str(file) '.mat']);
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i2 = i1 + rows(Correlation_array) - 1;
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tmp(i1:i2) = Correlation_array(:,indx1,indx2,nar);
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i1 = i2+1;
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end
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name = [ var1 '.' var2 ];
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if ~isconst(tmp)
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[post_mean, post_median, post_var, hpd_interval, post_deciles, density] = ...
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posterior_moments(tmp,1,mh_conf_sig);
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if isfield(oo_,'PosteriorTheoreticalMoments')
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if isfield(oo_.PosteriorTheoreticalMoments,'dsge')
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if isfield(oo_.PosteriorTheoreticalMoments.dsge,'correlation')
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oo_ = fill_output_structure(var1,var2,oo_,'mean',nar,post_mean);
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oo_ = fill_output_structure(var1,var2,oo_,'median',nar,post_median);
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oo_ = fill_output_structure(var1,var2,oo_,'variance',nar,post_var);
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oo_ = fill_output_structure(var1,var2,oo_,'hpdinf',nar,hpd_interval(1));
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oo_ = fill_output_structure(var1,var2,oo_,'hpdsup',nar,hpd_interval(2));
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oo_ = fill_output_structure(var1,var2,oo_,'deciles',nar,post_deciles);
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oo_ = fill_output_structure(var1,var2,oo_,'density',nar,density);
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end
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end
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end
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else
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if isfield(oo_,'PosteriorTheoreticalMoments')
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if isfield(oo_.PosteriorTheoreticalMoments,'dsge')
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if isfield(oo_.PosteriorTheoreticalMoments.dsge,'correlation')
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oo_ = fill_output_structure(var1,var2,oo_,'mean',nar,NaN);
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oo_ = fill_output_structure(var1,var2,oo_,'median',nar,NaN);
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oo_ = fill_output_structure(var1,var2,oo_,'variance',nar,NaN);
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oo_ = fill_output_structure(var1,var2,oo_,'hpdinf',nar,NaN);
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oo_ = fill_output_structure(var1,var2,oo_,'hpdsup',nar,NaN);
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oo_ = fill_output_structure(var1,var2,oo_,'deciles',nar,NaN);
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oo_ = fill_output_structure(var1,var2,oo_,'density',nar,NaN);
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end
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end
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end
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end
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function oo_ = initialize_output_structure(var1,var2,nar,oo_)
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name = [ var1 '.' var2 ];
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eval(['oo_.PosteriorTheoreticalMoments.dsge.correlation.mean.' name ' = NaN(' int2str(nar) ',1);']);
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eval(['oo_.PosteriorTheoreticalMoments.dsge.correlation.median.' name ' = NaN(' int2str(nar) ',1);']);
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eval(['oo_.PosteriorTheoreticalMoments.dsge.correlation.variance.' name ' = NaN(' int2str(nar) ',1);']);
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eval(['oo_.PosteriorTheoreticalMoments.dsge.correlation.hpdinf.' name ' = NaN(' int2str(nar) ',1);']);
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eval(['oo_.PosteriorTheoreticalMoments.dsge.correlation.hpdsup.' name ' = NaN(' int2str(nar) ',1);']);
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eval(['oo_.PosteriorTheoreticalMoments.dsge.correlation.deciles.' name ' = cell(' int2str(nar) ',1);']);
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eval(['oo_.PosteriorTheoreticalMoments.dsge.correlation.density.' name ' = cell(' int2str(nar) ',1);']);
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for i=1:nar
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eval(['oo_.PosteriorTheoreticalMoments.dsge.correlation.density.' name '(' int2str(i) ',1) = {NaN};']);
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eval(['oo_.PosteriorTheoreticalMoments.dsge.correlation.deciles.' name '(' int2str(i) ',1) = {NaN};']);
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end
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function oo_ = fill_output_structure(var1,var2,oo_,type,lag,result)
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name = [ var1 '.' var2 ];
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switch type
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case {'mean','median','variance','hpdinf','hpdsup'}
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eval(['oo_.PosteriorTheoreticalMoments.dsge.correlation.' type '.' name '(' int2str(lag) ',1) = result;']);
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case {'deciles','density'}
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eval(['oo_.PosteriorTheoreticalMoments.dsge.correlation.' type '.' name '(' int2str(lag) ',1) = {result};']);
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otherwise
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disp('fill_output_structure:: Unknown field!')
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end
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@ -0,0 +1,100 @@
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function oo_ = covariance_mc_analysis(NumberOfSimulations,type,dname,fname,vartan,nvar,var1,var2,mh_conf_sig,oo_)
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% This function analyses the (posterior or prior) distribution of the
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% endogenous variables covariance matrix.
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% Copyright (C) 2008-2009 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
|
||||
% 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.
|
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%
|
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% 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
|
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% 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/>.
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if strcmpi(type,'posterior')
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TYPE = 'Posterior';
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PATH = [dname '/metropolis/']
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posterior = 1;
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else
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TYPE = 'Prior';
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PATH = [dname '/prior/moments/']
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posterior = 0;
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end
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indx1 = check_name(vartan,var1);
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if isempty(indx1)
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disp([ type '_analysis:: ' var1 ' is not a stationary endogenous variable!'])
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return
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end
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if ~isempty(var2)
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indx2 = check_name(vartan,var2);
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if isempty(indx2)
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disp([ prior '_analysis:: ' var2 ' is not a stationary endogenous variable!'])
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return
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end
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else
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indx2 = indx1;
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var2 = var1;
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end
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if isfield(oo_,[ TYPE 'TheoreticalMoments'])
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eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments;'])
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if isfield(temporary_structure,'dsge')
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eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments.dsge;'])
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if isfield(temporary_structure,'covariance')
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eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments.dsge.covariance.mean;'])
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if isfield(temporary_structure,var1)
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eval(['temporary_structure_1 = oo_.' TYPE 'TheoreticalMoments.dsge.covariance.mean.' var1 ';'])
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if isfield(temporary_structure_1,var2)
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% Nothing to do (the covariance matrix is symmetric!).
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return
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end
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else
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if isfield(temporary_structure,var2)
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eval(['temporary_structure_2 = oo_.' TYPE 'TheoreticalMoments.dsge.covariance.mean.' var2 ';'])
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if isfield(temporary_structure_2,var1)
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% Nothing to do (the covariance matrix is symmetric!).
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return
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end
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end
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end
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end
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end
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end
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ListOfFiles = dir([ PATH fname '_' TYPE '2ndOrderMoments*.mat']);
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i1 = 1; tmp = zeros(NumberOfSimulations,1);
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for file = 1:length(ListOfFiles)
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load([ PATH ListOfFiles(file).name ]);
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i2 = i1 + rows(Covariance_matrix) - 1;
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tmp(i1:i2) = Covariance_matrix(:,symmetric_matrix_index(indx1,indx2,nvar));
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i1 = i2+1;
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end
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name = [var1 '.' var2];
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if ~isconst(tmp)
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[p_mean, p_median, p_var, hpd_interval, p_deciles, density] = ...
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posterior_moments(tmp,1,mh_conf_sig);
|
||||
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.covariance.mean.' name ' = p_mean;']);
|
||||
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.covariance.median.' name ' = p_median;']);
|
||||
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.covariance.variance.' name ' = p_var;']);
|
||||
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.covariance.hpdinf.' name ' = hpd_interval(1);']);
|
||||
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.covariance.hpdsup.' name ' = hpd_interval(2);']);
|
||||
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.covariance.deciles.' name ' = p_deciles;']);
|
||||
eval(['oo_.' TYPE 'Theoreticalmoments.dsge.covariance.density.' name ' = density;']);
|
||||
else
|
||||
eval(['oo_.' NAME 'TheoreticalMoments.dsge.covariance.mean.' name ' = NaN;']);
|
||||
eval(['oo_.' NAME 'TheoreticalMoments.dsge.covariance.median.' name ' = NaN;']);
|
||||
eval(['oo_.' NAME 'TheoreticalMoments.dsge.covariance.variance.' name ' = NaN;']);
|
||||
eval(['oo_.' NAME 'TheoreticalMoments.dsge.covariance.hpdinf.' name ' = NaN;']);
|
||||
eval(['oo_.' NAME 'TheoreticalMoments.dsge.covariance.hpdsup.' name ' = NaN;']);
|
||||
eval(['oo_.' NAME 'TheoreticalMoments.dsge.covariance.deciles.' name ' = NaN;']);
|
||||
eval(['oo_.' NAME 'TheoreticalMoments.dsge.covariance.density.' name ' = NaN;']);
|
||||
end
|
||||
|
|
@ -1,84 +0,0 @@
|
|||
function oo_ = covariance_posterior_analysis(NumberOfSimulations,dname,fname,vartan,nvar,var1,var2,mh_conf_sig,oo_)
|
||||
|
||||
% Copyright (C) 2008 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/>.
|
||||
|
||||
indx1 = check_name(vartan,var1);
|
||||
if isempty(indx1)
|
||||
disp(['posterior_analysis:: ' var1 ' is not a stationary endogenous variable!'])
|
||||
return
|
||||
end
|
||||
if ~isempty(var2)
|
||||
indx2 = check_name(vartan,var2);
|
||||
if isempty(indx2)
|
||||
disp(['posterior_analysis:: ' var2 ' is not a stationary endogenous variable!'])
|
||||
return
|
||||
end
|
||||
else
|
||||
indx2 = indx1;
|
||||
var2 = var1;
|
||||
end
|
||||
if isfield(oo_,'PosteriorTheoreticalMoments')
|
||||
if isfield(oo_.PosteriorTheoreticalMoments,'dsge')
|
||||
if isfield(oo_.PosteriorTheoreticalMoments.dsge,'covariance')
|
||||
if isfield(oo_.PosteriorTheoreticalMoments.dsge.covariance.mean,var1)
|
||||
eval(['s1 = oo_.PosteriorTheoreticalMoments.dsge.covariance.mean' '.' var1 ';'])
|
||||
if isfield(s1,var2)
|
||||
% Nothing to do.
|
||||
return
|
||||
end
|
||||
else
|
||||
if isfield(oo_.PosteriorTheoreticalMoments.dsge.covariance.mean,var2)
|
||||
eval(['s2 = oo_.PosteriorTheoreticalMoments.dsge.covariance.mean' '.' var2 ';'])
|
||||
if isfield(s1,var1)
|
||||
% Nothing to do (the covariance matrix is symmetric!).
|
||||
return
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
tmp = dir([ dname '/metropolis/' fname '_Posterior2ndOrderMoments*.mat']);
|
||||
NumberOfFiles = length(tmp);
|
||||
i1 = 1; tmp = zeros(NumberOfSimulations,1);
|
||||
for file = 1:NumberOfFiles
|
||||
load([ dname '/metropolis/' fname '_Posterior2ndOrderMoments' int2str(file) '.mat']);
|
||||
i2 = i1 + rows(Covariance_matrix) - 1;
|
||||
tmp(i1:i2) = Covariance_matrix(:,symmetric_matrix_index(indx1,indx2,nvar));
|
||||
i1 = i2+1;
|
||||
end
|
||||
name = [var1 '.' var2];
|
||||
if ~isconst(tmp)
|
||||
[post_mean, post_median, post_var, hpd_interval, post_deciles, density] = ...
|
||||
posterior_moments(tmp,1,mh_conf_sig);
|
||||
eval(['oo_.PosteriorTheoreticalMoments.dsge.covariance.mean.' name ' = post_mean;']);
|
||||
eval(['oo_.PosteriorTheoreticalMoments.dsge.covariance.median.' name ' = post_median;']);
|
||||
eval(['oo_.PosteriorTheoreticalMoments.dsge.covariance.variance.' name ' = post_var;']);
|
||||
eval(['oo_.PosteriorTheoreticalMoments.dsge.covariance.hpdinf.' name ' = hpd_interval(1);']);
|
||||
eval(['oo_.PosteriorTheoreticalMoments.dsge.covariance.hpdsup.' name ' = hpd_interval(2);']);
|
||||
eval(['oo_.PosteriorTheoreticalMoments.dsge.covariance.deciles.' name ' = post_deciles;']);
|
||||
eval(['oo_.PosteriorTheoreticalMoments.dsge.covariance.density.' name ' = density;']);
|
||||
else
|
||||
eval(['oo_.PosteriorTheoreticalMoments.dsge.covariance.mean.' name ' = NaN;']);
|
||||
eval(['oo_.PosteriorTheoreticalMoments.dsge.covariance.median.' name ' = NaN;']);
|
||||
eval(['oo_.PosteriorTheoreticalMoments.dsge.covariance.variance.' name ' = NaN;']);
|
||||
eval(['oo_.PosteriorTheoreticalMoments.dsge.covariance.hpdinf.' name ' = NaN;']);
|
||||
eval(['oo_.PosteriorTheoreticalMoments.dsge.covariance.hpdsup.' name ' = NaN;']);
|
||||
eval(['oo_.PosteriorTheoreticalMoments.dsge.covariance.deciles.' name ' = NaN;']);
|
||||
eval(['oo_.PosteriorTheoreticalMoments.dsge.covariance.density.' name ' = NaN;']);
|
||||
end
|
||||
|
|
@ -115,7 +115,7 @@ function get_prior_info(info)
|
|||
look_for_admissible_initial_condition = 1;
|
||||
scale = 1.0;
|
||||
iter = 0;
|
||||
while look_for_admissible_initial_condition
|
||||
While look_for_admissible_initial_condition
|
||||
xinit = xparam1+scale*randn(size(xparam1));
|
||||
if all(xinit>bayestopt_.p3) && all(xinit<bayestopt_.p4)
|
||||
look_for_admissible_initial_condition = 0;
|
||||
|
|
@ -151,7 +151,7 @@ function get_prior_info(info)
|
|||
end
|
||||
end
|
||||
|
||||
if info==3% Prior simulations (BK+moments).
|
||||
if info==3% Prior simulations (BK + 2nd order moments).
|
||||
results = prior_sampler(1,M_,bayestopt_,options_,oo_);
|
||||
% Display prior mass info.
|
||||
disp(['Prior mass = ' num2str(results.prior.mass)])
|
||||
|
|
@ -188,17 +188,16 @@ function get_prior_info(info)
|
|||
load([ M_.dname '/prior/draws/prior_draws' int2str(f) '.mat']);
|
||||
number_of_simulations = length(pdraws);
|
||||
total_number_of_simulations = total_number_of_simulations + number_of_simulations;
|
||||
covariance_cell = cell(number_of_simulations);
|
||||
correlation_cell = cell(number_of_simulations);
|
||||
decomposition_cell = cell(number_of_simulations);
|
||||
covariance_cell = cell(number_of_simulations,1);
|
||||
correlation_cell = cell(number_of_simulations,1);
|
||||
decomposition_cell = cell(number_of_simulations,1);
|
||||
for s=1:number_of_simulations
|
||||
dr = pdraws{s,2};
|
||||
[gamma_y,ivar] = th_autocovariances(dr,ivar,M_,options_);
|
||||
[gamma_y,ivar] = th_autocovariances(pdraws{s,2},ivar,M_,options_);
|
||||
covariance_cell(s) = {vech(gamma_y{1})};
|
||||
tmp = zeros(ivar,options_.ar);
|
||||
tmp = zeros(length(ivar),options_.ar);
|
||||
for i=1:length(ivar)
|
||||
for lag=1:options_.ar
|
||||
tmp(i,lag) = gamma_y{lag+1}(i,i);
|
||||
tmp(i,lag) = gamma_y{i,lag+1};
|
||||
end
|
||||
end
|
||||
correlation_cell(s) = {tmp};
|
||||
|
|
@ -207,7 +206,21 @@ function get_prior_info(info)
|
|||
save([ PriorMomentsDirectoryName '/prior_moments_draws' int2str(f) '.mat' ],'covariance_cell','correlation_cell','decomposition_cell');
|
||||
end
|
||||
clear('covariance_cell','correlation_cell','decomposition_cell')
|
||||
end
|
||||
prior_moments_info = dir([ M_.dname '/prior/moments/prior_moments*.mat']);
|
||||
number_of_prior_moments_files = length(prior_moments_info);
|
||||
% Covariance analysis
|
||||
disp(' ')
|
||||
disp('-------------------------')
|
||||
disp('Prior variance analysis')
|
||||
disp('-------------------------')
|
||||
disp(' ')
|
||||
for i=1:length(ivar)
|
||||
for file = 1:number_of_prior_moments_file
|
||||
load()
|
||||
end
|
||||
end
|
||||
|
||||
end
|
||||
|
||||
function format_string = build_format_string(bayestopt,i)
|
||||
format_string = ['%s & %s & %6.4f &'];
|
||||
|
|
|
|||
|
|
@ -54,7 +54,7 @@ function oo_ = job(type,SampleSize,arg1,arg2,arg3,options_,M_,oo_,nvar,vartan)
|
|||
[nvar,vartan,NumberOfFiles] = ...
|
||||
dsge_simulated_theoretical_covariance(SampleSize,M_,options_,oo_,'posterior');
|
||||
end
|
||||
oo_ = covariance_posterior_analysis(SampleSize,M_.dname,M_.fname,...
|
||||
oo_ = covariance_mc_analysis(SampleSize,'posterior',M_.dname,M_.fname,...
|
||||
vartan,nvar,arg1,arg2,options_.mh_conf_sig,oo_);
|
||||
case 'decomposition'
|
||||
if nargin==narg1
|
||||
|
|
@ -63,12 +63,12 @@ function oo_ = job(type,SampleSize,arg1,arg2,arg3,options_,M_,oo_,nvar,vartan)
|
|||
end
|
||||
oo_ = variance_decomposition_posterior_analysis(SampleSize,M_.dname,M_.fname,...
|
||||
M_.exo_names,arg2,vartan,arg1,options_.mh_conf_sig,oo_);
|
||||
case 'correlation'
|
||||
case OB'correlation'
|
||||
if nargin==narg1
|
||||
[nvar,vartan,NumberOfFiles] = ...
|
||||
dsge_simulated_theoretical_correlation(SampleSize,arg3,M_,options_,oo_,'posterior');
|
||||
end
|
||||
oo_ = correlation_posterior_analysis(SampleSize,M_.dname,M_.fname,...
|
||||
oo_ = correlation_mc_analysis(SampleSize,'posterior',M_.dname,M_.fname,...
|
||||
vartan,nvar,arg1,arg2,arg3,options_.mh_conf_sig,oo_,M_,options_);
|
||||
case 'conditional decomposition'
|
||||
if nargin==narg1
|
||||
|
|
|
|||
|
|
@ -119,10 +119,7 @@ function results = prior_sampler(drsave,M_,bayestopt_,options_,oo_)
|
|||
count_unknown_problem = count_unknown_problem + 1 ;
|
||||
end
|
||||
end
|
||||
|
||||
% Save last prior_draw*.mat file
|
||||
% save([ PriorDirectoryName '/prior_draws' int2str(TableOfInformations(end,1)) '.mat' ],'pdraws');
|
||||
|
||||
|
||||
% 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;
|
||||
|
|
|
|||
Loading…
Reference in New Issue