From 2f717b5adc5a87f663c5c080f2963d1f65d1933e Mon Sep 17 00:00:00 2001
From: Johannes Pfeifer <jpfeifer@gmx.de>
Date: Thu, 15 Dec 2016 10:36:16 +0100
Subject: [PATCH] Eliminate global variables from shock_decomposition.m
---
matlab/DsgeSmoother.m | 13 ++++++++++---
matlab/dynare_estimation_1.m | 4 ++--
matlab/evaluate_smoother.m | 14 ++++++++------
matlab/gsa/filt_mc_.m | 4 ++--
matlab/imcforecast.m | 4 ++--
matlab/prior_posterior_statistics_core.m | 4 ++--
matlab/shock_decomposition.m | 6 ++++--
preprocessor/ComputingTasks.cc | 4 ++--
8 files changed, 32 insertions(+), 21 deletions(-)
diff --git a/matlab/DsgeSmoother.m b/matlab/DsgeSmoother.m
index 6efd3a455..4dd5711af 100644
--- a/matlab/DsgeSmoother.m
+++ b/matlab/DsgeSmoother.m
@@ -1,4 +1,4 @@
-function [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK,T,R,P,PK,decomp,trend_addition,state_uncertainty] = DsgeSmoother(xparam1,gend,Y,data_index,missing_value)
+function [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK,T,R,P,PK,decomp,trend_addition,state_uncertainty,M_,oo_,options_,bayestopt_] = DsgeSmoother(xparam1,gend,Y,data_index,missing_value,M_,oo_,options_,bayestopt_,estim_params_)
% Estimation of the smoothed variables and innovations.
%
% INPUTS
@@ -7,6 +7,11 @@ function [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK,T,R,P,PK,de
% o data [double] (n*T) matrix of data.
% o data_index [cell] 1*smpl cell of column vectors of indices.
% o missing_value 1 if missing values, 0 otherwise
+% o M_ [structure] decribing the model
+% o oo_ [structure] storing the results
+% o options_ [structure] describing the options
+% o bayestopt_ [structure] describing the priors
+% o estim_params_ [structure] characterizing parameters to be estimated
%
% OUTPUTS
% o alphahat [double] (m*T) matrix, smoothed endogenous variables (a_{t|T}) (decision-rule order)
@@ -27,6 +32,10 @@ function [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK,T,R,P,PK,de
% o trend_addition [double] (n*T) pure trend component; stored in options_.varobs order
% o state_uncertainty [double] (K,K,T) array, storing the uncertainty
% about the smoothed state (decision-rule order)
+% o M_ [structure] decribing the model
+% o oo_ [structure] storing the results
+% o options_ [structure] describing the options
+% o bayestopt_ [structure] describing the priors
%
% Notes:
% m: number of endogenous variables (M_.endo_nbr)
@@ -66,8 +75,6 @@ function [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK,T,R,P,PK,de
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-global bayestopt_ M_ oo_ estim_params_ options_
-
alphahat = [];
etahat = [];
epsilonhat = [];
diff --git a/matlab/dynare_estimation_1.m b/matlab/dynare_estimation_1.m
index ff9e86836..e3704bea8 100644
--- a/matlab/dynare_estimation_1.m
+++ b/matlab/dynare_estimation_1.m
@@ -163,7 +163,7 @@ end
if isequal(options_.mode_compute,0) && isempty(options_.mode_file) && options_.mh_posterior_mode_estimation==0
if options_.smoother == 1
- [atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,T,R,P,PK,decomp,Trend,state_uncertainty] = DsgeSmoother(xparam1,gend,transpose(data),data_index,missing_value);
+ [atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,T,R,P,PK,decomp,Trend,state_uncertainty,M_,oo_,options_,bayestopt_] = DsgeSmoother(xparam1,gend,transpose(data),data_index,missing_value,M_,oo_,options_,bayestopt_,estim_params_);
[oo_]=store_smoother_results(M_,oo_,options_,bayestopt_,dataset_,dataset_info,atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,P,PK,decomp,Trend,state_uncertainty);
end
%reset qz_criterium
@@ -483,7 +483,7 @@ if (~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.psha
> 0) && options_.load_mh_file)) ...
|| ~options_.smoother ) && options_.partial_information == 0 % to be fixed
%% ML estimation, or posterior mode without Metropolis-Hastings or Metropolis without Bayesian smoothes variables
- [atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,T,R,P,PK,decomp,Trend,state_uncertainty] = DsgeSmoother(xparam1,dataset_.nobs,transpose(dataset_.data),dataset_info.missing.aindex,dataset_info.missing.state);
+ [atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,T,R,P,PK,decomp,Trend,state_uncertainty,M_,oo_,options_,bayestopt_] = DsgeSmoother(xparam1,dataset_.nobs,transpose(dataset_.data),dataset_info.missing.aindex,dataset_info.missing.state,M_,oo_,options_,bayestopt_,estim_params_);
[oo_,yf]=store_smoother_results(M_,oo_,options_,bayestopt_,dataset_,dataset_info,atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,P,PK,decomp,Trend,state_uncertainty);
if ~options_.nograph,
diff --git a/matlab/evaluate_smoother.m b/matlab/evaluate_smoother.m
index 6e420d95a..72cf57931 100644
--- a/matlab/evaluate_smoother.m
+++ b/matlab/evaluate_smoother.m
@@ -1,11 +1,15 @@
-function [oo_, Smoothed_variables_declaration_order_deviation_form]=evaluate_smoother(parameters,var_list)
+function [oo_, Smoothed_variables_declaration_order_deviation_form]=evaluate_smoother(parameters,var_list,M_,oo_,options_,bayestopt_,estim_params_)
% Evaluate the smoother at parameters.
%
% INPUTS
% o parameters a string ('posterior mode','posterior mean','posterior median','prior mode','prior mean','mle_mode') or a vector of values for
% the (estimated) parameters of the model.
% o var_list subset of endogenous variables
-%
+% o M_ [structure] Definition of the model
+% o oo_ [structure] Storage of results
+% o options_ [structure] Options
+% o bayestopt_ [structure] describing the priors
+% o estim_params_ [structure] characterizing parameters to be estimated
%
% OUTPUTS
% o oo [structure] results:
@@ -47,8 +51,6 @@ function [oo_, Smoothed_variables_declaration_order_deviation_form]=evaluate_smo
% 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_ M_ bayestopt_ oo_ estim_params_ % estim_params_ may be emty
-
persistent dataset_ dataset_info
%store qz_criterium
@@ -97,8 +99,8 @@ if ischar(parameters)
end
end
-[atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,T,R,P,PK,decomp,Trend,state_uncertainty] = ...
- DsgeSmoother(parameters,dataset_.nobs,transpose(dataset_.data),dataset_info.missing.aindex,dataset_info.missing.state);
+[atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,T,R,P,PK,decomp,Trend,state_uncertainty,M_,oo_,options_,bayestopt_] = ...
+ DsgeSmoother(parameters,dataset_.nobs,transpose(dataset_.data),dataset_info.missing.aindex,dataset_info.missing.state,M_,oo_,options_,bayestopt_,estim_params_);
[oo_]=store_smoother_results(M_,oo_,options_,bayestopt_,dataset_,dataset_info,atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,P,PK,decomp,Trend,state_uncertainty);
if nargout==2
diff --git a/matlab/gsa/filt_mc_.m b/matlab/gsa/filt_mc_.m
index 2d3050926..3c79676d8 100644
--- a/matlab/gsa/filt_mc_.m
+++ b/matlab/gsa/filt_mc_.m
@@ -163,7 +163,7 @@ if ~loadSA,
yss(i,:,:)=repmat(sto_ys(:,js(i))',[gend,1]);
end
if exist('xparam1','var')
- [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK] = DsgeSmoother(xparam1,gend,Y,data_index,missing_value);
+ [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK] = DsgeSmoother(xparam1,gend,Y,data_index,missing_value,M_,oo_,options_,bayestopt_,estim_params_);
y0 = transpose( squeeze(aK(1,jxj,1:gend)));% + kron(ys_mode(js),ones(1,gend)));
yobs = transpose( ahat(jxj,:));% + kron(ys_mode(js),ones(1,gend)));
rmse_mode = sqrt(mean((yobs(istart:end,:)-y0(istart:end,:)).^2));
@@ -196,7 +196,7 @@ if ~loadSA,
end
if exist('xparam1_mean','var')
%eval(['rmse_pmean(i) = sqrt(mean((',vj,'(fobs-1+istart:fobs-1+nobs)-y0M(istart:end-1)).^2));'])
- [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK] = DsgeSmoother(xparam1_mean,gend,Y,data_index,missing_value);
+ [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK] = DsgeSmoother(xparam1_mean,gend,Y,data_index,missing_value,M_,oo_,options_,bayestopt_,estim_params_);
y0 = transpose( squeeze(aK(1,jxj,1:gend)));% + kron(ys_mean(js),ones(1,gend)));
yobs = transpose( ahat(jxj,:));% + kron(ys_mean(js),ones(1,gend)));
rmse_pmean = sqrt(mean((yobs(istart:end,:)-y0(istart:end,:)).^2));
diff --git a/matlab/imcforecast.m b/matlab/imcforecast.m
index 5a9dbb3e6..f40475e48 100644
--- a/matlab/imcforecast.m
+++ b/matlab/imcforecast.m
@@ -43,7 +43,7 @@ function imcforecast(constrained_paths, constrained_vars, options_cond_fcst)
% 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_ oo_ M_ bayestopt_
+global options_ oo_ M_ bayestopt_ estim_params_
if ~isfield(options_cond_fcst,'parameter_set') || isempty(options_cond_fcst.parameter_set)
if isfield(oo_,'posterior_mode')
@@ -124,7 +124,7 @@ if estimated_model
qz_criterium_old=options_.qz_criterium;
options_=select_qz_criterium_value(options_);
options_smoothed_state_uncertainty_old = options_.smoothed_state_uncertainty;
- [atT,innov,measurement_error,filtered_state_vector,ys,trend_coeff,aK,T,R,P,PK,decomp,trend_addition] = DsgeSmoother(xparam,gend,data,data_index,missing_value);
+ [atT,innov,measurement_error,filtered_state_vector,ys,trend_coeff,aK,T,R,P,PK,decomp,trend_addition,state_uncertainty,M_,oo_,options_,bayestopt_] = DsgeSmoother(xparam,gend,data,data_index,missing_value,M_,oo_,options_,bayestopt_,estim_params_);
options_.smoothed_state_uncertainty = options_smoothed_state_uncertainty_old;
%get constant part
if options_.noconstant
diff --git a/matlab/prior_posterior_statistics_core.m b/matlab/prior_posterior_statistics_core.m
index df250d79a..3e8e31436 100644
--- a/matlab/prior_posterior_statistics_core.m
+++ b/matlab/prior_posterior_statistics_core.m
@@ -204,8 +204,8 @@ for b=fpar:B
if run_smoother
[dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
- [alphahat,etahat,epsilonhat,alphatilde,SteadyState,trend_coeff,aK,junk1,junk2,P,junk4,junk5,trend_addition,state_uncertainty] = ...
- DsgeSmoother(deep,gend,Y,data_index,missing_value);
+ [alphahat,etahat,epsilonhat,alphatilde,SteadyState,trend_coeff,aK,junk1,junk2,P,junk4,junk5,trend_addition,state_uncertainty,M_,oo_,options_,bayestopt_] = ...
+ DsgeSmoother(deep,gend,Y,data_index,missing_value,M_,oo_,options_,bayestopt_,estim_params_);
stock_trend_coeff(options_.varobs_id,irun(9))=trend_coeff;
stock_smoothed_trend(IdObs,:,irun(11))=trend_addition;
diff --git a/matlab/shock_decomposition.m b/matlab/shock_decomposition.m
index b56a591a1..91205a037 100644
--- a/matlab/shock_decomposition.m
+++ b/matlab/shock_decomposition.m
@@ -1,4 +1,4 @@
-function oo_ = shock_decomposition(M_,oo_,options_,varlist)
+function oo_ = shock_decomposition(M_,oo_,options_,varlist,bayestopt_,estim_params_)
% function z = shock_decomposition(M_,oo_,options_,varlist)
% Computes shocks contribution to a simulated trajectory. The field set is
% oo_.shock_decomposition. It is a n_var by nshock+2 by nperiods array. The
@@ -12,6 +12,8 @@ function oo_ = shock_decomposition(M_,oo_,options_,varlist)
% oo_: [structure] Storage of results
% options_: [structure] Options
% varlist: [char] List of variables
+% bayestopt_: [structure] describing the priors
+% estim_params_: [structure] characterizing parameters to be estimated
%
% OUTPUTS
% oo_: [structure] Storage of results
@@ -64,7 +66,7 @@ if isempty(parameter_set)
end
end
-[oo,Smoothed_Variables_deviation_from_mean] = evaluate_smoother(parameter_set,varlist);
+[oo,Smoothed_Variables_deviation_from_mean] = evaluate_smoother(parameter_set,varlist,M_,oo_,options_,bayestopt_,estim_params_);
% reduced form
dr = oo.dr;
diff --git a/preprocessor/ComputingTasks.cc b/preprocessor/ComputingTasks.cc
index 17414e763..1a4426208 100644
--- a/preprocessor/ComputingTasks.cc
+++ b/preprocessor/ComputingTasks.cc
@@ -1642,7 +1642,7 @@ ShockDecompositionStatement::writeOutput(ostream &output, const string &basename
{
options_list.writeOutput(output);
symbol_list.writeOutput("var_list_", output);
- output << "oo_ = shock_decomposition(M_,oo_,options_,var_list_);" << endl;
+ output << "oo_ = shock_decomposition(M_,oo_,options_,var_list_,bayestopt_,estim_params_);" << endl;
}
ConditionalForecastStatement::ConditionalForecastStatement(const OptionsList &options_list_arg) :
@@ -3171,7 +3171,7 @@ CalibSmootherStatement::writeOutput(ostream &output, const string &basename, boo
symbol_list.writeOutput("var_list_", output);
output << "options_.smoother = 1;" << endl;
output << "options_.order = 1;" << endl;
- output << "evaluate_smoother('calibration',var_list_);" << endl;
+ output << "evaluate_smoother('calibration',var_list_,M_,oo_,options_,bayestopt_,estim_params_);" << endl;
}
ExtendedPathStatement::ExtendedPathStatement(const OptionsList &options_list_arg)
--
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