Commit bdabde9b authored by stepan's avatar stepan
Browse files

+ 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-bf33cf982152
parent 6687ebb9
function oo_ = correlation_posterior_analysis(SampleSize,dname,fname,vartan,nvar,var1,var2,nar,mh_conf_sig,oo_,M_,options_)
function oo_ = correlation_mc_analysis(SampleSize,type,dname,fname,vartan,nvar,var1,var2,nar,mh_conf_sig,oo_,M_,options_)
% This function analyses the (posterior or prior) distribution of the
% endogenous variables correlation function.
% Copyright (C) 2008 Dynare Team
% Copyright (C) 2008-2009 Dynare Team
%
% This file is part of Dynare.
%
......@@ -17,119 +19,134 @@ function oo_ = correlation_posterior_analysis(SampleSize,dname,fname,vartan,nvar
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
if strcmpi(type,'posterior')
TYPE = 'Posterior';
PATH = [dname '/metropolis/']
else
TYPE = 'Prior';
PATH = [dname '/prior/moments/']
end
indx1 = check_name(vartan,var1);
if isempty(indx1)
disp(['posterior_analysis:: ' var1 ' is not a stationary endogenous variable!'])
disp([ type '_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!'])
disp([ type '_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,'correlation')
if isfield(oo_.PosteriorTheoreticalMoments.dsge.correlation.mean,var1)
eval(['s1 = oo_.PosteriorTheoreticalMoments.dsge.correlation.mean' '.' var1 ';'])
if isfield(s1,var2)
eval(['s2 = s1' '.' var2 ';'])
l1 = length(s2);
`
if isfield(oo_,[TYPE 'TheoreticalMoments'])
eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments;'])
if isfield(temporary_structure,'dsge')
eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments.dsge;'])
if isfield(temporary_structure,'correlation')
eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments.dsge.mean;'])
if isfield(temporary_structure,var1)
eval(['temporary_structure_1 = oo_.' TYPE 'TheoreticalMoments.dsge.mean.' var1 ';'])
if isfield(temporary_structure_1,var2)
eval(['temporary_structure_2 = temporary_structure_1.' var2 ';'])
l1 = length(temporary_structure_2);
if l1<nar
% INITIALIZATION:
oo_ = initialize_output_structure(var1,var2,nar,oo_);
system(['rm ' M_.dname '/metropolis/' M_.fname '_PosteriorCorrelations*']);
oo_ = initialize_output_structure(var1,var2,nar,type,oo_);
delete([PATH fname '_' TYPE 'Correlations*'])
[nvar,vartan,NumberOfFiles] = ...
dsge_posterior_theoretical_correlation(SampleSize,nar,M_,options_,oo_);
dsge_simulated_theoretical_correlation(SampleSize,nar,M_,options_,oo_,type);
else
if ~isnan(s2(nar))
if ~isnan(temporary_structure_2(nar))
%Nothing to do.
return
end
end
else
oo_ = initialize_output_structure(var1,var2,nar,oo_);
oo_ = initialize_output_structure(var1,var2,nar,TYPE,oo_);
end
else
oo_ = initialize_output_structure(var1,var2,nar,oo_);
oo_ = initialize_output_structure(var1,var2,nar,TYPE,oo_);
end
else
oo_ = initialize_output_structure(var1,var2,nar,oo_);
oo_ = initialize_output_structure(var1,var2,nar,TYPE,oo_);
end
else
oo_ = initialize_output_structure(var1,var2,nar,oo_);
oo_ = initialize_output_structure(var1,var2,nar,TYPE,oo_);
end
else
oo_ = initialize_output_structure(var1,var2,nar,oo_);
oo_ = initialize_output_structure(var1,var2,nar,TYPE,oo_);
end
tmp = dir([ dname '/metropolis/' fname '_PosteriorCorrelations*.mat']);
NumberOfFiles = length(tmp);
ListOfFiles = dir([ PATH fname '_' TYPE 'Correlations*.mat']);
i1 = 1; tmp = zeros(SampleSize,1);
for file = 1:NumberOfFiles
load([ dname '/metropolis/' fname '_PosteriorCorrelations' int2str(file) '.mat']);
for file = 1:length(ListOfFiles)
load([ PATH fname '_' TYPE 'PosteriorCorrelations' int2str(file) '.mat']);
i2 = i1 + rows(Correlation_array) - 1;
tmp(i1:i2) = Correlation_array(:,indx1,indx2,nar);
i1 = i2+1;
end
name = [ var1 '.' var2 ];
if ~isconst(tmp)
[post_mean, post_median, post_var, hpd_interval, post_deciles, density] = ...
[p_mean, p_median, p_var, hpd_interval, p_deciles, density] = ...
posterior_moments(tmp,1,mh_conf_sig);
if isfield(oo_,'PosteriorTheoreticalMoments')
if isfield(oo_.PosteriorTheoreticalMoments,'dsge')
if isfield(oo_.PosteriorTheoreticalMoments.dsge,'correlation')
oo_ = fill_output_structure(var1,var2,oo_,'mean',nar,post_mean);
oo_ = fill_output_structure(var1,var2,oo_,'median',nar,post_median);
oo_ = fill_output_structure(var1,var2,oo_,'variance',nar,post_var);
oo_ = fill_output_structure(var1,var2,oo_,'hpdinf',nar,hpd_interval(1));
oo_ = fill_output_structure(var1,var2,oo_,'hpdsup',nar,hpd_interval(2));
oo_ = fill_output_structure(var1,var2,oo_,'deciles',nar,post_deciles);
oo_ = fill_output_structure(var1,var2,oo_,'density',nar,density);
eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments;'])
if isfield(temporary_structure,'dsge')
eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments.dsge;'])
if isfield(temporary_structure,'correlation')
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'mean',nar,p_mean);
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'median',nar,p_median);
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'variance',nar,p_var);
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'hpdinf',nar,hpd_interval(1));
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'hpdsup',nar,hpd_interval(2));
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'deciles',nar,p_deciles);
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'density',nar,density);
end
end
end
else
if isfield(oo_,'PosteriorTheoreticalMoments')
if isfield(oo_.PosteriorTheoreticalMoments,'dsge')
if isfield(oo_.PosteriorTheoreticalMoments.dsge,'correlation')
oo_ = fill_output_structure(var1,var2,oo_,'mean',nar,NaN);
oo_ = fill_output_structure(var1,var2,oo_,'median',nar,NaN);
oo_ = fill_output_structure(var1,var2,oo_,'variance',nar,NaN);
oo_ = fill_output_structure(var1,var2,oo_,'hpdinf',nar,NaN);
oo_ = fill_output_structure(var1,var2,oo_,'hpdsup',nar,NaN);
oo_ = fill_output_structure(var1,var2,oo_,'deciles',nar,NaN);
oo_ = fill_output_structure(var1,var2,oo_,'density',nar,NaN);
eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments;'])
if isfield(temporary_structure,'dsge')
eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments.dsge;'])
if isfield(temporary_structure,'correlation')
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'mean',nar,NaN);
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'median',nar,NaN);
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'variance',nar,NaN);
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'hpdinf',nar,NaN);
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'hpdsup',nar,NaN);
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'deciles',nar,NaN);
oo_ = fill_output_structure(var1,var2,TYPE,oo_,'density',nar,NaN);
end
end
end
end
function oo_ = initialize_output_structure(var1,var2,nar,oo_)
function oo_ = initialize_output_structure(var1,var2,nar,type,oo_)
name = [ var1 '.' var2 ];
eval(['oo_.PosteriorTheoreticalMoments.dsge.correlation.mean.' name ' = NaN(' int2str(nar) ',1);']);
eval(['oo_.PosteriorTheoreticalMoments.dsge.correlation.median.' name ' = NaN(' int2str(nar) ',1);']);
eval(['oo_.PosteriorTheoreticalMoments.dsge.correlation.variance.' name ' = NaN(' int2str(nar) ',1);']);
eval(['oo_.PosteriorTheoreticalMoments.dsge.correlation.hpdinf.' name ' = NaN(' int2str(nar) ',1);']);
eval(['oo_.PosteriorTheoreticalMoments.dsge.correlation.hpdsup.' name ' = NaN(' int2str(nar) ',1);']);
eval(['oo_.PosteriorTheoreticalMoments.dsge.correlation.deciles.' name ' = cell(' int2str(nar) ',1);']);
eval(['oo_.PosteriorTheoreticalMoments.dsge.correlation.density.' name ' = cell(' int2str(nar) ',1);']);
eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.mean.' name ' = NaN(' int2str(nar) ',1);']);
eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.median.' name ' = NaN(' int2str(nar) ',1);']);
eval(['oo_.' type 'PosteriorTheoreticalMoments.dsge.correlation.variance.' name ' = NaN(' int2str(nar) ',1);']);
eval(['oo_.' type 'PosteriorTheoreticalMoments.dsge.correlation.hpdinf.' name ' = NaN(' int2str(nar) ',1);']);
eval(['oo_.' type 'PosteriorTheoreticalMoments.dsge.correlation.hpdsup.' name ' = NaN(' int2str(nar) ',1);']);
eval(['oo_.' type 'PosteriorTheoreticalMoments.dsge.correlation.deciles.' name ' = cell(' int2str(nar) ',1);']);
eval(['oo_.' type 'PosteriorTheoreticalMoments.dsge.correlation.density.' name ' = cell(' int2str(nar) ',1);']);
for i=1:nar
eval(['oo_.PosteriorTheoreticalMoments.dsge.correlation.density.' name '(' int2str(i) ',1) = {NaN};']);
eval(['oo_.PosteriorTheoreticalMoments.dsge.correlation.deciles.' name '(' int2str(i) ',1) = {NaN};']);
eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.density.' name '(' int2str(i) ',1) = {NaN};']);
eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.deciles.' name '(' int2str(i) ',1) = {NaN};']);
end
function oo_ = fill_output_structure(var1,var2,oo_,type,lag,result)
function oo_ = fill_output_structure(var1,var2,type,oo_,lag,result)
name = [ var1 '.' var2 ];
switch type
case {'mean','median','variance','hpdinf','hpdsup'}
eval(['oo_.PosteriorTheoreticalMoments.dsge.correlation.' type '.' name '(' int2str(lag) ',1) = result;']);
eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.' type '.' name '(' int2str(lag) ',1) = result;']);
case {'deciles','density'}
eval(['oo_.PosteriorTheoreticalMoments.dsge.correlation.' type '.' name '(' int2str(lag) ',1) = {result};']);
eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.' type '.' name '(' int2str(lag) ',1) = {result};']);
otherwise
disp('fill_output_structure:: Unknown field!')
end
\ No newline at end of file
function oo_ = covariance_posterior_analysis(NumberOfSimulations,dname,fname,vartan,nvar,var1,var2,mh_conf_sig,oo_)
function oo_ = covariance_mc_analysis(NumberOfSimulations,type,dname,fname,vartan,nvar,var1,var2,mh_conf_sig,oo_)
% This function analyses the (posterior or prior) distribution of the
% endogenous variables covariance matrix.
% Copyright (C) 2008 Dynare Team
% Copyright (C) 2008-2009 Dynare Team
%
% This file is part of Dynare.
%
......@@ -17,34 +19,48 @@ function oo_ = covariance_posterior_analysis(NumberOfSimulations,dname,fname,var
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
if strcmpi(type,'posterior')
TYPE = 'Posterior';
PATH = [dname '/metropolis/']
posterior = 1;
else
TYPE = 'Prior';
PATH = [dname '/prior/moments/']
posterior = 0;
end
indx1 = check_name(vartan,var1);
if isempty(indx1)
disp(['posterior_analysis:: ' var1 ' is not a stationary endogenous variable!'])
disp([ type '_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!'])
disp([ prior '_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.
if isfield(oo_,[ TYPE 'TheoreticalMoments'])
eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments;'])
if isfield(temporary_structure,'dsge')
eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments.dsge;'])
if isfield(temporary_structure,'covariance')
eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments.dsge.covariance.mean;'])
if isfield(temporary_structure,var1)
eval(['temporary_structure_1 = oo_.' TYPE 'TheoreticalMoments.dsge.covariance.mean.' var1 ';'])
if isfield(temporary_structure_1,var2)
% Nothing to do (the covariance matrix is symmetric!).
return
end
else
if isfield(oo_.PosteriorTheoreticalMoments.dsge.covariance.mean,var2)
eval(['s2 = oo_.PosteriorTheoreticalMoments.dsge.covariance.mean' '.' var2 ';'])
if isfield(s1,var1)
if isfield(temporary_structure,var2)
eval(['temporary_structure_2 = oo_.' TYPE 'TheoreticalMoments.dsge.covariance.mean.' var2 ';'])
if isfield(temporary_structure_2,var1)
% Nothing to do (the covariance matrix is symmetric!).
return
end
......@@ -53,32 +69,32 @@ function oo_ = covariance_posterior_analysis(NumberOfSimulations,dname,fname,var
end
end
end
tmp = dir([ dname '/metropolis/' fname '_Posterior2ndOrderMoments*.mat']);
NumberOfFiles = length(tmp);
ListOfFiles = dir([ PATH fname '_' TYPE '2ndOrderMoments*.mat']);
i1 = 1; tmp = zeros(NumberOfSimulations,1);
for file = 1:NumberOfFiles
load([ dname '/metropolis/' fname '_Posterior2ndOrderMoments' int2str(file) '.mat']);
for file = 1:length(ListOfFiles)
load([ PATH ListOfFiles(file).name ]);
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] = ...
[p_mean, p_median, p_var, hpd_interval, p_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;']);
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_.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;']);
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
\ No newline at end of file
......@@ -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;
......
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