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Various bug fixes. 

git-svn-id: https://www.dynare.org/svn/dynare/trunk@2768 ac1d8469-bf42-47a9-8791-bf33cf982152
time-shift
stepan 2009-06-12 10:08:25 +00:00
parent 771a12c8ae
commit 4af1f415d4
5 changed files with 18 additions and 18 deletions
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4
matlab/conditional_variance_decomposition_mc_analysis.m
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@ -1,4 +1,4 @@
function oo_ = conditional_variance_decomposition_mc_analysis(NumberOfSimulations, typr, dname, fname, Steps, exonames, exo, vartan, var, mh_conf_sig, oo_)
function oo_ = conditional_variance_decomposition_mc_analysis(NumberOfSimulations, type, dname, fname, Steps, exonames, exo, vartan, var, mh_conf_sig, oo_)
% This function analyses the (posterior or prior) distribution of the
% endogenous conditional variance decomposition.
@ -59,7 +59,7 @@ function oo_ = conditional_variance_decomposition_mc_analysis(NumberOfSimulation
ListOfFiles = dir([ PATH fname '_' TYPE 'ConditionalVarianceDecomposition*.mat']);
i1 = 1; tmp = zeros(NumberOfSimulations,length(Steps));
for file = 1:length(ListOfFiles)
load([ PATH fname ListOfFiles(file).name ]);
load([ PATH ListOfFiles(file).name ]);
% 4D-array (endovar,time,exovar,simul)
i2 = i1 + size(Conditional_decomposition_array,4) - 1;
tmp(i1:i2,:) = transpose(dynare_squeeze(Conditional_decomposition_array(endogenous_variable_index,:,exogenous_variable_index,:)));

24
matlab/correlation_mc_analysis.m
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@ -42,15 +42,15 @@ function oo_ = correlation_mc_analysis(SampleSize,type,dname,fname,vartan,nvar,v
indx2 = indx1;
var2 = var1;
end
`
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;'])
eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments.dsge.correlation.mean;'])
if isfield(temporary_structure,var1)
eval(['temporary_structure_1 = oo_.' TYPE 'TheoreticalMoments.dsge.mean.' var1 ';'])
eval(['temporary_structure_1 = oo_.' TYPE 'TheoreticalMoments.dsge.correlation.mean.' var1 ';'])
if isfield(temporary_structure_1,var2)
eval(['temporary_structure_2 = temporary_structure_1.' var2 ';'])
l1 = length(temporary_structure_2);
@ -130,23 +130,23 @@ function oo_ = initialize_output_structure(var1,var2,nar,type,oo_)
name = [ var1 '.' var2 ];
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);']);
eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.variance.' name ' = NaN(' int2str(nar) ',1);']);
eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.hpdinf.' name ' = NaN(' int2str(nar) ',1);']);
eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.hpdsup.' name ' = NaN(' int2str(nar) ',1);']);
eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.deciles.' name ' = cell(' int2str(nar) ',1);']);
eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.density.' name ' = cell(' int2str(nar) ',1);']);
for i=1:nar
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,type,oo_,lag,result)
function oo_ = fill_output_structure(var1,var2,type,oo_,moment,lag,result)
name = [ var1 '.' var2 ];
switch type
switch moment
case {'mean','median','variance','hpdinf','hpdsup'}
eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.' type '.' name '(' int2str(lag) ',1) = result;']);
eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.' moment '.' name '(' int2str(lag) ',1) = result;']);
case {'deciles','density'}
eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.' type '.' name '(' int2str(lag) ',1) = {result};']);
eval(['oo_.' type 'TheoreticalMoments.dsge.correlation.' moment '.' name '(' int2str(lag) ',1) = {result};']);
otherwise
disp('fill_output_structure:: Unknown field!')
end

2
matlab/covariance_mc_analysis.m
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@ -86,7 +86,7 @@ function oo_ = covariance_mc_analysis(NumberOfSimulations,type,dname,fname,varta
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;']);
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;']);

2
matlab/posterior_analysis.m
View File

@ -63,7 +63,7 @@ function oo_ = job(type,SampleSize,arg1,arg2,arg3,options_,M_,oo_,nvar,vartan)
end
oo_ = variance_decomposition_mc_analysis(SampleSize,'posterior',M_.dname,M_.fname,...
M_.exo_names,arg2,vartan,arg1,options_.mh_conf_sig,oo_);
case OB'correlation'
case 'correlation'
if nargin==narg1
[nvar,vartan,NumberOfFiles] = ...
dsge_simulated_theoretical_correlation(SampleSize,arg3,M_,options_,oo_,'posterior');

4
matlab/variance_decomposition_mc_analysis.m
View File

@ -53,10 +53,10 @@ function oo_ = variance_decomposition_mc_analysis(NumberOfSimulations,type,dname
end
end
ListOfFiles = dir([ PATH fname '_' Type 'VarianceDecomposition*.mat']);
ListOfFiles = dir([ PATH fname '_' TYPE 'VarianceDecomposition*.mat']);
i1 = 1; tmp = zeros(NumberOfSimulations,1);
indice = (indx-1)*rows(exonames)+jndx;
for file = 1:length(LengthOfFiles)
for file = 1:length(ListOfFiles)
load([ PATH ListOfFiles(file).name ]);
i2 = i1 + rows(Decomposition_array) - 1;
tmp(i1:i2) = Decomposition_array(:,indice);