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selec_posterior_draws.m: remove global variables

kalman-mex
Johannes Pfeifer 2023-09-16 10:05:46 +02:00 committed by Sébastien Villemot
parent b4104584a2
commit dc9560e610
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GPG Key ID: 2CECE9350ECEBE4A
4 changed files with 29 additions and 25 deletions
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24
matlab/compute_moments_varendo.m
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@ -1,4 +1,4 @@
function oo_ = compute_moments_varendo(type, options_, M_, oo_, var_list_)
function oo_ = compute_moments_varendo(type, options_, M_, oo_, estim_params_, var_list_)
% Computes the second order moments (autocorrelation function, covariance
% matrix and variance decomposition) distributions for all the endogenous variables selected in
@ -55,7 +55,7 @@ end
if strcmpi(type,'posterior')
posterior = 1;
if nargin==4
if nargin==5
var_list_ = options_.varobs;
end
if isfield(oo_,'PosteriorTheoreticalMoments')
@ -63,7 +63,7 @@ if strcmpi(type,'posterior')
end
elseif strcmpi(type,'prior')
posterior = 0;
if nargin==4
if nargin==5
var_list_ = options_.prior_analysis_endo_var_list;
if isempty(var_list_)
options_.prior_analysis_var_list = options_.varobs;
@ -97,13 +97,13 @@ end
if posterior
for i=1:NumberOfEndogenousVariables
for j=i:NumberOfEndogenousVariables
oo_ = posterior_analysis('variance', var_list_{i}, var_list_{j}, NumberOfLags, options_, M_, oo_);
oo_ = posterior_analysis('variance', var_list_{i}, var_list_{j}, NumberOfLags, options_, M_, oo_, estim_params_);
end
end
else
for i=1:NumberOfEndogenousVariables
for j=i:NumberOfEndogenousVariables
oo_ = prior_analysis('variance', var_list_{i}, var_list_{j}, [], options_, M_, oo_);
oo_ = prior_analysis('variance', var_list_{i}, var_list_{j}, [], options_, M_, oo_, estim_params_);
end
end
end
@ -113,7 +113,7 @@ if posterior
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_);
oo_ = posterior_analysis('correlation', var_list_{i}, var_list_{j}, h, options_, M_, oo_, estim_params_);
end
end
end
@ -121,7 +121,7 @@ else
for h=NumberOfLags:-1:1
for i=1:NumberOfEndogenousVariables
for j=1:NumberOfEndogenousVariables
oo_ = prior_analysis('correlation', var_list_{i}, var_list_{j}, h, options_, M_, oo_);
oo_ = prior_analysis('correlation', var_list_{i}, var_list_{j}, h, options_, M_, oo_, estim_params_);
end
end
end
@ -135,7 +135,7 @@ if options_.order==1
if posterior
for i=1:NumberOfEndogenousVariables
for j=1:NumberOfExogenousVariables
oo_ = posterior_analysis('decomposition', var_list_{i}, M_.exo_names{j}, [], options_, M_, oo_);
oo_ = posterior_analysis('decomposition', var_list_{i}, M_.exo_names{j}, [], options_, M_, oo_, estim_params_);
temp(i,j) = oo_.PosteriorTheoreticalMoments.dsge.VarianceDecomposition.Mean.(var_list_{i}).(M_.exo_names{j});
end
end
@ -144,7 +144,7 @@ if options_.order==1
else
for i=1:NumberOfEndogenousVariables
for j=1:NumberOfExogenousVariables
oo_ = prior_analysis('decomposition', var_list_{i}, M_.exo_names{j}, [], options_, M_, oo_);
oo_ = prior_analysis('decomposition', var_list_{i}, M_.exo_names{j}, [], options_, M_, oo_, estim_params_);
temp(i,j)=oo_.PriorTheoreticalMoments.dsge.VarianceDecomposition.Mean.(var_list_{i}).(M_.exo_names{j});
end
end
@ -181,7 +181,7 @@ if options_.order==1
temp(i,j,:) = oo_.PosteriorTheoreticalMoments.dsge.VarianceDecompositionME.Mean.(observable_name_requested_vars{i}).(M_.exo_names{j});
end
endo_index_varlist = strmatch(observable_name_requested_vars{i}, var_list_, 'exact');
oo_ = posterior_analysis('decomposition', var_list_{endo_index_varlist}, 'ME', [], options_, M_, oo_);
oo_ = posterior_analysis('decomposition', var_list_{endo_index_varlist}, 'ME', [], options_, M_, oo_, estim_params_);
temp(i,j+1,:) = oo_.PosteriorTheoreticalMoments.dsge.VarianceDecompositionME.Mean.(observable_name_requested_vars{i}).('ME');
end
title='Posterior mean variance decomposition (in percent) with measurement error';
@ -219,7 +219,7 @@ if options_.order==1
if posterior
for i=1:NumberOfEndogenousVariables
for j=1:NumberOfExogenousVariables
oo_ = posterior_analysis('conditional decomposition', var_list_{i}, M_.exo_names{j}, Steps, options_, M_, oo_);
oo_ = posterior_analysis('conditional decomposition', var_list_{i}, M_.exo_names{j}, Steps, options_, M_, oo_, estim_params_);
temp(i,j,:) = oo_.PosteriorTheoreticalMoments.dsge.ConditionalVarianceDecomposition.Mean.(var_list_{i}).(M_.exo_names{j});
end
end
@ -261,7 +261,7 @@ if options_.order==1
temp(i,j,:) = oo_.PosteriorTheoreticalMoments.dsge.ConditionalVarianceDecompositionME.Mean.(observable_name_requested_vars{i}).(M_.exo_names{j});
end
endo_index_varlist = strmatch(observable_name_requested_vars{i}, var_list_, 'exact');
oo_ = posterior_analysis('conditional decomposition', var_list_{endo_index_varlist}, 'ME', Steps, options_, M_, oo_);
oo_ = posterior_analysis('conditional decomposition', var_list_{endo_index_varlist}, 'ME', Steps, options_, M_, oo_, estim_params_);
temp(i,j+1,:) = oo_.PosteriorTheoreticalMoments.dsge.ConditionalVarianceDecompositionME.Mean.(observable_name_requested_vars{i}).('ME');
end
title = 'Posterior mean conditional variance decomposition (in percent) with measurement error';

2
matlab/dynare_estimation_1.m
View File

@ -497,7 +497,7 @@ if (any(bayestopt_.pshape >0 ) && options_.mh_replic) || ...
end
end
end
oo_ = compute_moments_varendo('posterior',options_,M_,oo_,var_list_);
oo_ = compute_moments_varendo('posterior',options_,M_,oo_,estim_params_,var_list_);
end
if options_.smoother || ~isempty(options_.filter_step_ahead) || options_.forecast
if error_flag

4
matlab/posterior_analysis.m
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@ -1,4 +1,4 @@
function oo_ = posterior_analysis(type,arg1,arg2,arg3,options_,M_,oo_)
function oo_ = posterior_analysis(type,arg1,arg2,arg3,options_,M_,oo_,estim_params_)
% Copyright © 2008-2021 Dynare Team
%
@ -29,7 +29,7 @@ switch info
if drsize*SampleSize>MaxMegaBytes
drsize=0;
end
SampleAddress = selec_posterior_draws(SampleSize,drsize);
selec_posterior_draws(M_,options_,oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state,estim_params_,SampleSize,drsize); %save draws to disk
oo_ = job(type,SampleSize,arg1,arg2,arg3,options_,M_,oo_);
case {4,5}
oo_ = job(type,SampleSize,arg1,arg2,arg3,options_,M_,oo_);

24
matlab/selec_posterior_draws.m
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@ -1,4 +1,4 @@
function SampleAddress = selec_posterior_draws(SampleSize,drsize)
function SampleAddress = selec_posterior_draws(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state,estim_params_,SampleSize,drsize)
% Selects a sample of draws from the posterior distribution and if nargin>1
% saves the draws in _pdraws mat files (metropolis folder). If drsize>0
% the dr structure, associated to the parameters, is also saved in _pdraws.
@ -6,8 +6,14 @@ function SampleAddress = selec_posterior_draws(SampleSize,drsize)
% _mh file cannot be opened twice.
%
% INPUTS
% o SampleSize [integer] Size of the sample to build.
% o drsize [double] structure dr is drsize megaoctets.
% o M_ [structure] Matlab's structure describing the model
% o options_ [structure] Matlab's structure describing the current options
% o dr [structure] Reduced form model.
% o endo_steady_state [vector] steady state value for endogenous variables
% o exo_steady_state [vector] steady state value for exogenous variables
% o exo_det_steady_state [vector] steady state value for exogenous deterministic variables
% o SampleSize [integer] Size of the sample to build.
% o drsize [double] structure dr is drsize megaoctets.
%
% OUTPUTS
% o SampleAddress [integer] A (SampleSize*4) array, each line specifies the
@ -37,8 +43,6 @@ function SampleAddress = selec_posterior_draws(SampleSize,drsize)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
global M_ options_ estim_params_ oo_
% Number of parameters:
npar = estim_params_.nvx;
npar = npar + estim_params_.nvn;
@ -48,9 +52,9 @@ npar = npar + estim_params_.np;
% Select one task:
switch nargin
case 1
case 8
info = 0;
case 2
case 9
MAX_mega_bytes = 10;% Should be an option...
if drsize>0
info=2;
@ -114,8 +118,8 @@ if info
end
pdraws(i,1) = {x2(SampleAddress(i,4),:)};
if info==2
set_parameters(pdraws{i,1});
[dr,~,M_.params] =compute_decision_rules(M_,options_,oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
M_ = set_parameters_locally(M_,pdraws{i,1});
[dr,~,M_.params] =compute_decision_rules(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state);
pdraws(i,2) = { dr };
end
old_mhfile = mhfile;
@ -141,7 +145,7 @@ if info
end
pdraws(linee,1) = {x2(SampleAddress(i,4),:)};
if info==2
set_parameters(pdraws{linee,1});
M_ = set_parameters_locally(M_,pdraws{i,1});
[dr,~,M_.params] = compute_decision_rules(M_,options_,oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
pdraws(linee,2) = { dr };
end