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+ Removed global variables. 

+ Added try and catch to handle cases where oo_ is broken and (mh_replic==0 & load_mh_file==1).
+ Added computation of posterior moments for endogenous variables (triggered by moments_varendo option). First order moments are still missing.

git-svn-id: https://www.dynare.org/svn/dynare/dynare_v4@2028 ac1d8469-bf42-47a9-8791-bf33cf982152
time-shift
adjemian 2008-09-03 16:05:35 +00:00
parent 348345843b
commit 2845115b5f
12 changed files with 285 additions and 97 deletions
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6
matlab/CutSample.m
View File

@ -1,10 +1,12 @@
function CutSample()
function CutSample(M_, options_, estim_params_)
% function CutSample()
% Takes a subset from metropolis
%
% INPUTS
% none
% options_ [structure]
% estim_params_ [structure]
% M_ [structure]
%
% OUTPUTS
% none

108
matlab/GetPosteriorParametersStatistics.m
View File

@ -1,12 +1,16 @@
function get_posterior_parameters_statistics()
function oo_ = GetPosteriorParametersStatistics(estim_params_, M_, options_, bayestopt_, oo_)
% This function prints and saves posterior estimates after the mcmc
% (+updates of oo_ & TeX output).
%
% INPUTS
% None.
% estim_params_ [structure]
% M_ [structure]
% options_ [structure]
% bayestopt_ [structure]
% oo_ [structure]
%
% OUTPUTS
% None.
% oo_ [structure]
%
% SPECIAL REQUIREMENTS
% None.
@ -30,9 +34,9 @@ function get_posterior_parameters_statistics()
global estim_params_ M_ options_ bayestopt_ oo_
if ~options_.mh_replic & options_.load_mh_file
load([M_.fname '_results.mat'],'oo_');
end
%if ~options_.mh_replic & options_.load_mh_file
% load([M_.fname '_results.mat'],'oo_');
%end
TeX = options_.TeX;
nblck = options_.mh_nblck;
@ -60,7 +64,12 @@ tit2 = sprintf('%10s %7s %10s %14s %4s %6s\n',' ','prior mean','post. mean','con
pformat = '%12s %7.3f %8.4f %7.4f %7.4f %4s %6.4f';
disp(' ');disp(' ');disp('ESTIMATION RESULTS');disp(' ');
disp(sprintf('Log data density is %f.',oo_.MarginalDensity.ModifiedHarmonicMean))
try
disp(sprintf('Log data density is %f.',oo_.MarginalDensity.ModifiedHarmonicMean))
catch
[marginal,oo_] = marginal_density(M_, options_, estim_params_, oo_)
disp(sprintf('Log data density is %f.',oo_.MarginalDensity.ModifiedHarmonicMean))
end
if np
type = 'parameters';
if TeX
@ -78,8 +87,16 @@ if np
name = bayestopt_.name{ip};
oo_ = Filloo(oo_,name,type,post_mean,hpd_interval,post_median,post_var,post_deciles,density);
else
name = bayestopt_.name{ip};
[post_mean,hpd_interval,post_var] = Extractoo(oo_,name,type);
try
name = bayestopt_.name{ip};
[post_mean,hpd_interval,post_var] = Extractoo(oo_,name,type);
catch
Draws = GetAllPosteriorDraws(ip,FirstMhFile,FirstLine,TotalNumberOfMhFiles,NumberOfDraws);
[post_mean, post_median, post_var, hpd_interval, post_deciles, ...
density] = posterior_moments(Draws,1,options_.mh_conf_sig);
name = bayestopt_.name{ip};
oo_ = Filloo(oo_,name,type,post_mean,hpd_interval,post_median,post_var,post_deciles,density);
end
end
disp(sprintf(pformat,name,bayestopt_.pmean(ip),...
post_mean, ...
@ -115,9 +132,19 @@ if nvx
oo_ = Filloo(oo_,name,type,post_mean,hpd_interval,post_median,post_var,post_deciles,density);
M_.Sigma_e(k,k) = post_mean*post_mean;
else
k = estim_params_.var_exo(i,1);
name = deblank(M_.exo_names(k,:));
[post_mean,hpd_interval,post_var] = Extractoo(oo_,name,type);
try
k = estim_params_.var_exo(i,1);
name = deblank(M_.exo_names(k,:));
[post_mean,hpd_interval,post_var] = Extractoo(oo_,name,type);
catch
Draws = GetAllPosteriorDraws(ip,FirstMhFile,FirstLine,TotalNumberOfMhFiles,NumberOfDraws);
[post_mean, post_median, post_var, hpd_interval, post_deciles, density] = ...
posterior_moments(Draws,1,options_.mh_conf_sig);
k = estim_params_.var_exo(i,1);
name = deblank(M_.exo_names(k,:));
oo_ = Filloo(oo_,name,type,post_mean,hpd_interval,post_median,post_var,post_deciles,density);
M_.Sigma_e(k,k) = post_mean*post_mean;
end
end
disp(sprintf(pformat,name,bayestopt_.pmean(ip),post_mean,hpd_interval,...
pnames(bayestopt_.pshape(ip)+1,:),bayestopt_.pstdev(ip)));
@ -148,8 +175,16 @@ if nvn
name = deblank(options_.varobs(estim_params_.var_endo(i,1),:));
oo_ = Filloo(oo_,name,type,post_mean,hpd_interval,post_median,post_var,post_deciles,density);
else
name = deblank(options_.varobs(estim_params_.var_endo(i,1),:));
[post_mean,hpd_interval,post_var] = Extractoo(oo_,name,type);
try
name = deblank(options_.varobs(estim_params_.var_endo(i,1),:));
[post_mean,hpd_interval,post_var] = Extractoo(oo_,name,type);
catch
Draws = GetAllPosteriorDraws(ip,FirstMhFile,FirstLine,TotalNumberOfMhFiles,NumberOfDraws);
[post_mean, post_median, post_var, hpd_interval, post_deciles, density] = ...
posterior_moments(Draws,1,options_.mh_conf_sig);
name = deblank(options_.varobs(estim_params_.var_endo(i,1),:));
oo_ = Filloo(oo_,name,type,post_mean,hpd_interval,post_median,post_var,post_deciles,density);
end
end
disp(sprintf(pformat,name,bayestopt_.pmean(ip),post_mean,hpd_interval, ...
pnames(bayestopt_.pshape(ip)+1,:),bayestopt_.pstdev(ip)));
@ -185,11 +220,24 @@ if ncx
M_.Sigma_e(k1,k2) = post_mean*sqrt(M_.Sigma_e(k1,k1)*M_.Sigma_e(k2,k2));
M_.Sigma_e(k2,k1) = M_.Sigma_e(k1,k2);
else
k1 = estim_params_.corrx(i,1);
k2 = estim_params_.corrx(i,2);
name = [deblank(M_.exo_names(k1,:)) ',' deblank(M_.exo_names(k2,:))];
NAME = [deblank(M_.exo_names(k1,:)) '_' deblank(M_.exo_names(k2,:))];
[post_mean,hpd_interval,post_var] = Extractoo(oo_,NAME,type);
try
k1 = estim_params_.corrx(i,1);
k2 = estim_params_.corrx(i,2);
name = [deblank(M_.exo_names(k1,:)) ',' deblank(M_.exo_names(k2,:))];
NAME = [deblank(M_.exo_names(k1,:)) '_' deblank(M_.exo_names(k2,:))];
[post_mean,hpd_interval,post_var] = Extractoo(oo_,NAME,type);
catch
Draws = GetAllPosteriorDraws(ip,FirstMhFile,FirstLine,TotalNumberOfMhFiles,NumberOfDraws);
[post_mean, post_median, post_var, hpd_interval, post_deciles, density] = ...
posterior_moments(Draws,1,options_.mh_conf_sig);
k1 = estim_params_.corrx(i,1);
k2 = estim_params_.corrx(i,2);
name = [deblank(M_.exo_names(k1,:)) ',' deblank(M_.exo_names(k2,:))];
NAME = [deblank(M_.exo_names(k1,:)) '_' deblank(M_.exo_names(k2,:))];
oo_ = Filloo(oo_,NAME,type,post_mean,hpd_interval,post_median,post_var,post_deciles,density);
M_.Sigma_e(k1,k2) = post_mean*sqrt(M_.Sigma_e(k1,k1)*M_.Sigma_e(k2,k2));
M_.Sigma_e(k2,k1) = M_.Sigma_e(k1,k2);
end
end
disp(sprintf(pformat, name,bayestopt_.pmean(ip),post_mean,hpd_interval, ...
pnames(bayestopt_.pshape(ip)+1,:),bayestopt_.pstdev(ip)));
@ -224,11 +272,23 @@ if ncn
oo_ = Filloo(oo_,NAME,type,post_mean,hpd_interval,...
post_median,post_var,post_deciles,density);
else
k1 = estim_params_.corrn(i,1);
k2 = estim_params_.corrn(i,2);
name = [deblank(M_.endo_names(k1,:)) ',' deblank(M_.endo_names(k2,:))];
NAME = [deblank(M_.endo_names(k1,:)) '_' deblank(M_.endo_names(k2,:))];
[post_mean,hpd_interval,post_var] = Extractoo(oo_,NAME,type);
try
k1 = estim_params_.corrn(i,1);
k2 = estim_params_.corrn(i,2);
name = [deblank(M_.endo_names(k1,:)) ',' deblank(M_.endo_names(k2,:))];
NAME = [deblank(M_.endo_names(k1,:)) '_' deblank(M_.endo_names(k2,:))];
[post_mean,hpd_interval,post_var] = Extractoo(oo_,NAME,type);
catch
Draws = GetAllPosteriorDraws(ip,FirstMhFile,FirstLine,TotalNumberOfMhFiles,NumberOfDraws);
[post_mean, post_median, post_var, hpd_interval, post_deciles, density] = ...
posterior_moments(Draws,1,options_.mh_conf_sig);
k1 = estim_params_.corrn(i,1);
k2 = estim_params_.corrn(i,2);
name = [deblank(M_.endo_names(k1,:)) ',' deblank(M_.endo_names(k2,:))];
NAME = [deblank(M_.endo_names(k1,:)) '_' deblank(M_.endo_names(k2,:))];
oo_ = Filloo(oo_,NAME,type,post_mean,hpd_interval,...
post_median,post_var,post_deciles,density);
end
end
disp(sprintf(pformat, name,bayestopt_.pmean(ip),post_mean,hpd_interval, ...
pnames(bayestopt_.pshape(ip)+1,:),bayestopt_.pstdev(ip)));

10
matlab/McMCDiagnostics.m
View File

@ -1,10 +1,12 @@
function McMCDiagnostics
function McMCDiagnostics(options_, estim_params_, M_)
% function McMCDiagnostics
% Computes convergence tests
%
% INPUTS
% none
%
% options_ [structure]
% estim_params_ [structure]
% M_ [structure]
%
% OUTPUTS
% none
%
@ -28,8 +30,6 @@ function McMCDiagnostics
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
global options_ estim_params_ M_
DirectoryName = CheckPath('Output');
MhDirectoryName = CheckPath('metropolis');

10
matlab/PlotPosteriorDistributions.m
View File

@ -1,13 +1,17 @@
function PlotPosteriorDistributions()
function oo_ = PlotPosteriorDistributions(estim_params_, M_, options_, bayestopt_, oo_)
% function PlotPosteriorDistributions()
% plots posterior distributions
%
% INPUTS
% none
% estim_params_ [structure]
% M_ [structure]
% options_ [structure]
% bayestopt_ [structure]
% oo_ [structure]
%
% OUTPUTS
% none
% oo_ [structure]
%
% SPECIAL REQUIREMENTS
% none

57
matlab/compute_moments_varendo.m Normal file
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@ -0,0 +1,57 @@
function oo_ = compute_moments_varendo(options_,M_,oo_,var_list_)
% Computes the second order moments (autocorrelation function, covariance
% matrix and variance decomposition) for all the endogenous variables selected in
% var_list_. The results are saved in oo_
%
% INPUTS:
% options_ [structure] Dynare structure.
% M_ [structure] Dynare structure (related to model definition).
% oo_ [structure] Dynare structure (results).
% var_list_ [string] Array of string with endogenous variable names.
%
% OUTPUTS
% oo_ [structure] Dynare structure (results).
%
% SPECIAL REQUIREMENTS
% none
% 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/>.
NumberOfEndogenousVariables = rows(var_list_);
NumberOfExogenousVariables = M_.exo_nbr;
list_of_exogenous_variables = M_.exo_names;
NumberOfLags = options_.ar;
% COVARIANCE MATRIX.
for i=1:NumberOfEndogenousVariables
for j=i:NumberOfEndogenousVariables
oo_ = posterior_analysis('variance',var_list_(i,:),var_list_(j,:),[],options_,M_,oo_);
end
end
% CORRELATION FUNCTION.
for h=NumberOfLags:-1:1
for i=1:NumberOfEndogenousVariables
for j=1:NumberOfEndogenousVariables
oo_ = posterior_analysis('correlation',var_list_(i,:),var_list_(j,:),h,options_,M_,oo_);
end
end
end
% VARIANCE DECOMPOSITION.
for i=1:NumberOfEndogenousVariables
for j=i:NumberOfExogenousVariables
oo_ = posterior_analysis('decomposition',var_list_(i,:),M_.exo_names(j,:),[],options_,M_,oo_);
end
end

41
matlab/correlation_posterior_analysis.m
View File

@ -76,23 +76,38 @@ function oo_ = correlation_posterior_analysis(SampleSize,dname,fname,vartan,nvar
tmp(i1:i2) = Correlation_array(:,indx1,indx2,nar);
i1 = i2+1;
end
[post_mean, post_median, post_var, hpd_interval, post_deciles, density] = ...
posterior_moments(tmp,1,mh_conf_sig);
name = [ var1 '.' var2 ];
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);
if ~isconst(tmp)
[post_mean, post_median, post_var, hpd_interval, post_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);
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);
end
end
end
end
function oo_ = initialize_output_structure(var1,var2,nar,oo_)
name = [ var1 '.' var2 ];

32
matlab/covariance_posterior_analysis.m
View File

@ -49,12 +49,12 @@ function oo_ = covariance_posterior_analysis(NumberOfSimulations,dname,fname,var
return
end
end
end
end
end
end
end
tmp = dir([ dname '/metropolis/' fname '_Posterior2ndOrderMoments*.mat']);
NumberOfFiles= length(tmp);
NumberOfFiles = length(tmp);
i1 = 1; tmp = zeros(NumberOfSimulations,1);
for file = 1:NumberOfFiles
load([ dname '/metropolis/' fname '_Posterior2ndOrderMoments' int2str(file) '.mat']);
@ -62,13 +62,23 @@ function oo_ = covariance_posterior_analysis(NumberOfSimulations,dname,fname,var
tmp(i1:i2) = Covariance_matrix(:,symmetric_matrix_index(indx1,indx2,nvar));
i1 = i2+1;
end
[post_mean, post_median, post_var, hpd_interval, post_deciles, density] = ...
posterior_moments(tmp,1,mh_conf_sig);
name = [var1 '.' var2];
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;']);
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

4
matlab/dsge_posterior_theoretical_covariance.m
View File

@ -40,6 +40,8 @@ type = 'posterior';
% Set varlist (vartan)
[ivar,vartan] = set_stationary_variables_list;
vartan
pause
nvar = length(ivar);
% Set the size of the auto-correlation function to zero.
@ -68,7 +70,7 @@ CovarFileNumber = 1;
% Compute 2nd order moments and save them in *_Posterior2ndOrderMoments* files
linea = 0;
for file = 1:NumberOfDrawsFiles
load([M_.dname '/metropolis/' DrawsFiles(file).name '.mat']);
load([M_.dname '/metropolis/' DrawsFiles(file).name ],'pdraws');
NumberOfDraws = rows(pdraws);
isdrsaved = cols(pdraws)-1;
for linee = 1:NumberOfDraws

39
matlab/dynare_estimation.m
View File

@ -616,7 +616,7 @@ if any(bayestopt_.pshape > 0) & options_.posterior_mode_estimation
eval(['oo_.posterior_std.measurement_errors_corr.' NAME ' = stdh(ip);']);
ip = ip+1;
end
end
end
%% Laplace approximation to the marginal log density:
if ~options_.bvar_dsge
md_Laplace = .5*size(xparam1,1)*log(2*pi) + .5*log(det(invhess)) ...
@ -898,16 +898,16 @@ end
if (any(bayestopt_.pshape >0 ) & options_.mh_replic) | ...
(any(bayestopt_.pshape >0 ) & options_.load_mh_file) %% not ML estimation
bounds = prior_bounds(bayestopt_);
bounds(:,1)=max(bounds(:,1),lb);
bounds(:,2)=min(bounds(:,2),ub);
bayestopt_.lb = bounds(:,1);
bayestopt_.ub = bounds(:,2);
if any(xparam1 < bounds(:,1)) | any(xparam1 > bounds(:,2))
find(xparam1 < bounds(:,1))
find(xparam1 > bounds(:,2))
error('Mode values are outside prior bounds. Reduce prior_trunc.')
end
bounds = prior_bounds(bayestopt_);
bounds(:,1)=max(bounds(:,1),lb);
bounds(:,2)=min(bounds(:,2),ub);
bayestopt_.lb = bounds(:,1);
bayestopt_.ub = bounds(:,2);
if any(xparam1 < bounds(:,1)) | any(xparam1 > bounds(:,2))
find(xparam1 < bounds(:,1))
find(xparam1 > bounds(:,2))
error('Mode values are outside prior bounds. Reduce prior_trunc.')
end
% runs MCMC
if options_.mh_replic
if options_.load_mh_file & options_.use_mh_covariance_matrix
@ -920,26 +920,29 @@ if (any(bayestopt_.pshape >0 ) & options_.mh_replic) | ...
end
end
if ~options_.nodiagnostic & options_.mh_replic > 1000 & options_.mh_nblck > 1
McMCDiagnostics;
McMCDiagnostics(options_, estim_params_, M_);
end
%% Here i discard first half of the draws:
CutSample;
%% Estimation of the marginal density from the Mh draws:
if options_.mh_replic
marginal = marginal_density;
[marginal,oo_] = marginal_density(M_, options_, estim_params_, oo_);
end
%%
GetPosteriorParametersStatistics;
oo_ = GetPosteriorParametersStatistics(estim_params_, M_, options_, bayestopt_, oo_);
%% Results are saved (in case of an anormal exit from dynare or matlab)...
save([M_.fname '_results.mat'],'oo_','M_');
%%save([M_.fname '_results.mat'],'oo_','M_');
%%
PlotPosteriorDistributions;
oo_
oo_ = PlotPosteriorDistributions(estim_params_, M_, options_, bayestopt_, oo_);
metropolis_draw(1);
if options_.bayesian_irf
PosteriorIRF('posterior');
end
if options_.smoother | ~isempty(options_.filter_step_ahead) | options_.forecast ...
| options_.moments_varendo
if options_.moments_varendo
oo_ = compute_moments_varendo(options_,M_,oo_,var_list_);
end
if options_.smoother | ~isempty(options_.filter_step_ahead) | options_.forecast
prior_posterior_statistics('posterior',data,gend);
end

32
matlab/isconst.m Normal file
View File

@ -0,0 +1,32 @@
function aa = isconst(y)
% Returns 1 if vector y is constant, 0 otherwise.
%
% INPUTS:
% yy [double] n*1 vector.
%
% OUTPUTS
% aa [integer] scalar equal to 1 or 0.
%
% SPECIAL REQUIREMENTS
% none
% 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/>
aa = 0;
if all(abs(y(2:end)-y(1:end-1))<1e-10)
aa = 1;
end

11
matlab/marginal_density.m
View File

@ -1,12 +1,16 @@
function marginal = marginal_density()
function [marginal,oo_] = marginal_density(M_, options_, estim_params_, oo_)
% function marginal = marginal_density()
% Computes the marginal density
%
% INPUTS
% none
% options_ [structure]
% estim_params_ [structure]
% M_ [structure]
% oo_ [structure]
%
% OUTPUTS
% marginal: marginal density
% marginal: [double] marginal density (modified harmonic mean)
% oo_ [structure]
%
% SPECIAL REQUIREMENTS
% none
@ -28,7 +32,6 @@ function marginal = marginal_density()
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
global M_ options_ estim_params_ oo_
npar = estim_params_.np+estim_params_.nvn+estim_params_.ncx+estim_params_.ncn+estim_params_.nvx;
nblck = options_.mh_nblck;

32
matlab/prior_posterior_statistics.m
View File

@ -50,7 +50,7 @@ exo_nbr=M_.exo_nbr;
nvobs = size(options_.varobs,1);
iendo = 1:endo_nbr;
horizon = options_.forecast;
moments_varendo = options_.moments_varendo;
% moments_varendo = options_.moments_varendo;
filtered_vars = options_.filtered_vars;
if horizon
i_last_obs = gend+(1-M_.maximum_endo_lag:0);
@ -107,8 +107,8 @@ else
end
end
irun = ones(8,1);
ifil = zeros(8,1);
irun = ones(7,1);
ifil = zeros(7,1);
if exist('OCTAVE_VERSION')
diary off;
@ -138,16 +138,16 @@ if options_.forecast
stock_forcst_point = zeros(endo_nbr,horizon+maxlag,MAX_nforc2);
run_smoother = 1;
end
if moments_varendo
stock_moments = cell(MAX_momentsno,1);
end
%if moments_varendo
% stock_moments = cell(MAX_momentsno,1);
%end
for b=1:B
[deep, logpo] = GetOneDraw(type);
set_all_parameters(deep);
dr = resol(oo_.steady_state,0);
if moments_varendo
stock_moments{irun(8)} = compute_model_moments(dr,M_,options_);
end
%if moments_varendo
% stock_moments{irun(8)} = compute_model_moments(dr,M_,options_);
%end
if run_smoother
[alphahat,etahat,epsilonhat,alphatilde,SteadyState,trend_coeff,aK] = ...
DsgeSmoother(deep,gend,Y);
@ -205,7 +205,7 @@ for b=1:B
stock_logpo(irun(5),1) = logpo;
stock_ys(irun(5),:) = SteadyState';
irun = irun + ones(8,1);
irun = irun + ones(7,1);
if irun(1) > MAX_nsmoo || b == B
stock = stock_smooth(:,:,1:irun(1)-1);
@ -258,12 +258,12 @@ for b=1:B
irun(7) = 1;
end
if moments_varendo && (irun(8) > MAX_momentsno || b == B)
stock = stock_moments(1:irun(8)-1);
ifil(8) = ifil(8) + 1;
save([DirectoryName '/' M_.fname '_moments' int2str(ifil(8)) '.mat'],'stock');
irun(8) = 1;
end
% if moments_varendo && (irun(8) > MAX_momentsno || b == B)
% stock = stock_moments(1:irun(8)-1);
% ifil(8) = ifil(8) + 1;
% save([DirectoryName '/' M_.fname '_moments' int2str(ifil(8)) '.mat'],'stock');
% irun(8) = 1;
% end
if exist('OCTAVE_VERSION')
printf('Taking subdraws: %3.f%% done\r', b/B);