From ad0c29262b3aa8917eb2d7f06ed8483eade12cd7 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?St=C3=A9phane=20Adjemian=20=28Charybdis=29?=
<stephane.adjemian@univ-lemans.fr>
Date: Sat, 16 Nov 2013 23:33:37 +0100
Subject: [PATCH] Cosmetic. Renamed DsgeVarLikelihood as dsge_var_likelihood.
---
matlab/PosteriorIRF_core1.m | 2 +-
matlab/csminwel1.m | 2 +-
matlab/{DsgeVarLikelihood.m => dsge_var_likelihood.m} | 2 +-
matlab/dsgevar_posterior_density.m | 2 +-
matlab/dynare_estimation_1.m | 2 +-
matlab/print_info.m | 2 +-
6 files changed, 6 insertions(+), 6 deletions(-)
rename matlab/{DsgeVarLikelihood.m => dsge_var_likelihood.m} (98%)
diff --git a/matlab/PosteriorIRF_core1.m b/matlab/PosteriorIRF_core1.m
index e2cc4a5209..1eff387ad6 100644
--- a/matlab/PosteriorIRF_core1.m
+++ b/matlab/PosteriorIRF_core1.m
@@ -189,7 +189,7 @@ while fpar<B
end
if MAX_nirfs_dsgevar
IRUN = IRUN+1;
- [fval,junk1,junk2,cost_flag,info,PHI,SIGMAu,iXX] = DsgeVarLikelihood(deep',dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+ [fval,junk1,junk2,cost_flag,info,PHI,SIGMAu,iXX] = dsge_var_likelihood(deep',dataset_,options_,M_,estim_params_,bayestopt_,oo_);
dsge_prior_weight = M_.params(strmatch('dsge_prior_weight',M_.param_names));
DSGE_PRIOR_WEIGHT = floor(dataset_.info.ntobs*(1+dsge_prior_weight));
SIGMA_inv_upper_chol = chol(inv(SIGMAu*dataset_.info.ntobs*(dsge_prior_weight+1)));
diff --git a/matlab/csminwel1.m b/matlab/csminwel1.m
index 403b9a58ed..b6e38fedc0 100644
--- a/matlab/csminwel1.m
+++ b/matlab/csminwel1.m
@@ -100,7 +100,7 @@ f=f0;
H=H0;
cliff=0;
while ~done
- % penalty for dsge_likelihood and DsgeVarLikelihood
+ % penalty for dsge_likelihood and dsge_var_likelihood
objective_function_penalty_base = f;
g1=[]; g2=[]; g3=[];
diff --git a/matlab/DsgeVarLikelihood.m b/matlab/dsge_var_likelihood.m
similarity index 98%
rename from matlab/DsgeVarLikelihood.m
rename to matlab/dsge_var_likelihood.m
index 322351a2c1..6c37fd1a46 100644
--- a/matlab/DsgeVarLikelihood.m
+++ b/matlab/dsge_var_likelihood.m
@@ -1,4 +1,4 @@
-function [fval,grad,hess,exit_flag,info,PHI,SIGMAu,iXX,prior] = DsgeVarLikelihood(xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
+function [fval,grad,hess,exit_flag,info,PHI,SIGMAu,iXX,prior] = dsge_var_likelihood(xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
% Evaluates the posterior kernel of the bvar-dsge model.
%
% INPUTS
diff --git a/matlab/dsgevar_posterior_density.m b/matlab/dsgevar_posterior_density.m
index 9ff74e1451..eca70f7439 100644
--- a/matlab/dsgevar_posterior_density.m
+++ b/matlab/dsgevar_posterior_density.m
@@ -47,7 +47,7 @@ if ~options_.noconstant
bvar.NumberOfVariables;
end
-[fval,cost_flag,info,PHI,SIGMAu,iXX,prior] = DsgeVarLikelihood(deep',DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+[fval,cost_flag,info,PHI,SIGMAu,iXX,prior] = dsge_var_likelihood(deep',DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
% Conditionnal posterior density of the lagged matrices (given Sigma) ->
% Matric-variate normal distribution.
diff --git a/matlab/dynare_estimation_1.m b/matlab/dynare_estimation_1.m
index 80dee69cf5..f6df44b87e 100644
--- a/matlab/dynare_estimation_1.m
+++ b/matlab/dynare_estimation_1.m
@@ -75,7 +75,7 @@ if ~options_.dsge_var
objective_function = str2func('dsge_likelihood');
end
else
- objective_function = str2func('DsgeVarLikelihood');
+ objective_function = str2func('dsge_var_likelihood');
end
[dataset_,xparam1, hh, M_, options_, oo_, estim_params_,bayestopt_] = dynare_estimation_init(var_list_, dname, [], M_, options_, oo_, estim_params_, bayestopt_);
diff --git a/matlab/print_info.m b/matlab/print_info.m
index c96910e3c1..ab71479c41 100644
--- a/matlab/print_info.m
+++ b/matlab/print_info.m
@@ -106,7 +106,7 @@ if ~noprint
error('The model violates one (many) endogenous prior restriction(s)')
case 51
error('You are estimating a DSGE-VAR model, but the value of the dsge prior weight is too low!')
- case 52 %DsgeVarLikelihood
+ case 52 %dsge_var_likelihood
error('');
case 61 %Discretionary policy
error(['Discretionary policy: maximum number of iterations has been reached. Procedure failed. ']);
--
GitLab