From 0031309d3cae3921e13d9c20439e41e4918869a0 Mon Sep 17 00:00:00 2001
From: Johannes Pfeifer <jpfeifer@gmx.de>
Date: Tue, 12 Mar 2024 17:48:21 +0100
Subject: [PATCH] RBC_IRF_matching.mod: remove global variables
---
RBC_IRF_matching/IRF_matching_objective.m | 20 +++++++++-----------
RBC_IRF_matching/RBC_IRF_matching.mod | 6 +++---
2 files changed, 12 insertions(+), 14 deletions(-)
diff --git a/RBC_IRF_matching/IRF_matching_objective.m b/RBC_IRF_matching/IRF_matching_objective.m
index 607424d..752200d 100644
--- a/RBC_IRF_matching/IRF_matching_objective.m
+++ b/RBC_IRF_matching/IRF_matching_objective.m
@@ -1,5 +1,5 @@
-function [fval, IRF_model]=IRF_matching_objective(xopt,IRF_target,weighting_matrix)
-% function [fval, IRF_model]=IRF_matching_objective(xopt,IRF_target,weighting_matrix)
+function [fval, IRF_model]=IRF_matching_objective(xopt,IRF_target,weighting_matrix,M_,oo_,options_)
+% function [fval,IRF_model]=IRF_matching_objective(xopt,IRF_target,weighting_matrix,M_,oo_,options_)
% Computes the quadratic deviation of the simulated IRFs from the empirical
% ones; does so by calling stoch_simul.m from Dynare to compute IRFs
%
@@ -22,7 +22,7 @@
% - The empirical IRFs and model IRFs use an impulse size of 1 percent. Thus, there is no uncertainty about the
% initial impact. The IRF matching therefore only targets the G-response starting in the second period.
-% Copyright (C) 2017 Johannes Pfeifer
+% Copyright (C) 2017-2024 Johannes Pfeifer
%
% This is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
@@ -36,22 +36,20 @@
%
% For a copy of the GNU General Public License, see <http://www.gnu.org/licenses/>.
-global oo_ M_ options_ %required input structures for call to stoch_simul
-
%% set parameter for use in Dynare
-set_param_value('root_g_1',xopt(1)); % first autoregressive root
-set_param_value('root_g_2',xopt(2)); % second autoregressive root
+M_.params(strmatch('root_g_1',M_.param_names,'exact'))=xopt(1); % first autoregressive root
+M_.params(strmatch('root_g_2',M_.param_names,'exact'))=xopt(2); % second autoregressive root
if any(xopt<=-1) || any(xopt(1)>=1) %make sure roots are between 0 and 1
- fval=10e6+sum([xopt].^2); %penalty function
+ fval=10e6+sum(xopt.^2); %penalty function
return
end
var_list={'ghat','log_y'};
-[info, oo_, options_, M_] = stoch_simul(M_, options_, oo_, var_list); %run stoch_simul to generate IRFs with the options specified in the mod-file
+[info, oo_] = stoch_simul(M_, options_, oo_, var_list); %run stoch_simul to generate IRFs with the options specified in the mod-file
-if info %solution was not successful
- fval=10e6+sum([xopt].^2); %return with penalty
+if info(1) %solution was not successful
+ fval=10e6+sum(xopt.^2); %return with penalty
else
IRF_model=[oo_.irfs.ghat_eps_g' oo_.irfs.log_y_eps_g']; %select desired IRFs
% compute objective function (omitting the impact response of G as that is targeted with the shock size)
diff --git a/RBC_IRF_matching/RBC_IRF_matching.mod b/RBC_IRF_matching/RBC_IRF_matching.mod
index 8ab6e22..18a013d 100644
--- a/RBC_IRF_matching/RBC_IRF_matching.mod
+++ b/RBC_IRF_matching/RBC_IRF_matching.mod
@@ -218,19 +218,19 @@ options_.noprint=1;
crit = 1e-8; %Tolerance
nit = 1000; %Number of iterations
- [fhat,x_opt_hat] = csminwel(@IRF_matching_objective,x_start,H0,[],crit,nit,IRF_empirical,IRF_weighting);
+ [fhat,x_opt_hat] = csminwel(@IRF_matching_objective,x_start,H0,[],crit,nit,IRF_empirical,IRF_weighting,M_,oo_,options_);
@#else
%set CMAES options
H0=0.2*ones(size(x_start,1),1)
cmaesOptions = options_.cmaes;
cmaesOptions.LBounds = [-1;-1];
cmaesOptions.UBounds = [1;1];
- [x_opt_hat, fhat, COUNTEVAL, STOPFLAG, OUT, BESTEVER] = cmaes('IRF_matching_objective',x_start,H0,cmaesOptions,IRF_empirical,IRF_weighting);
+ [x_opt_hat, fhat, COUNTEVAL, STOPFLAG, OUT, BESTEVER] = cmaes('IRF_matching_objective',x_start,H0,cmaesOptions,IRF_empirical,IRF_weighting,M_,oo_,options_);
x_opt_hat=BESTEVER.x;
@#endif
%get IRFs at the optimum and plot them
-[fval, IRF_model]=IRF_matching_objective(x_opt_hat,IRF_empirical,IRF_weighting);
+[fval, IRF_model]=IRF_matching_objective(x_opt_hat,IRF_empirical,IRF_weighting,M_,oo_,options_);
figure
subplot(2,1,1)