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Added a new input argument (epsilon) in gradient routines and csminwel.m. This parameter defines the length of the steps considered 

by numgrad routines to compute the gradient. The default value (defined in global_initialization.m) for this parameter is 1e-6. The
value of epsilon is stored in options_.gradient_epsilon.




git-svn-id: https://www.dynare.org/svn/dynare/trunk@3335 ac1d8469-bf42-47a9-8791-bf33cf982152
time-shift
stepan 2010-01-10 16:51:33 +00:00
parent 3c4d69ace2
commit 696e8ee140
9 changed files with 29 additions and 28 deletions
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32
matlab/csminwel.m
View File

@ -1,4 +1,4 @@
function [fh,xh,gh,H,itct,fcount,retcodeh] = csminwel(fcn,x0,H0,grad,crit,nit,method,varargin)
function [fh,xh,gh,H,itct,fcount,retcodeh] = csminwel(fcn,x0,H0,grad,crit,nit,method,epsilon,varargin)
%[fhat,xhat,ghat,Hhat,itct,fcount,retcodehat] = csminwel(fcn,x0,H0,grad,crit,nit,method,varargin)
% fcn: string naming the objective function to be minimized
% x0: initial value of the parameter vector
@ -72,13 +72,13 @@ end
if NumGrad
switch method
case 2
[g,badg] = numgrad(fcn, f0, x0, varargin{:});
[g,badg] = numgrad(fcn, f0, x0, epsilon, varargin{:});
badg
g
case 3
[g,badg] = numgrad3(fcn, f0, x0, varargin{:});
[g,badg] = numgrad3(fcn, f0, x0, epsilon, varargin{:});
case 5
[g,badg] = numgrad5(fcn, f0, x0, varargin{:});
[g,badg] = numgrad5(fcn, f0, x0, epsilon, varargin{:});
end
else
[g,badg] = feval(grad,x0,varargin{:});
@ -121,11 +121,11 @@ while ~done
if NumGrad
switch method
case 2
[g1 badg1] = numgrad(fcn, f1, x1, varargin{:});
[g1 badg1] = numgrad(fcn, f1, x1, epsilon, varargin{:});
case 3
[g1 badg1] = numgrad3(fcn, f1, x1, varargin{:});
[g1 badg1] = numgrad3(fcn, f1, x1, epsilon, varargin{:});
case 5
[g1,badg1] = numgrad5(fcn, f1, x1, varargin{:});
[g1,badg1] = numgrad5(fcn, f1, x1, epsilon, varargin{:});
end
else
[g1 badg1] = feval(grad,x1,varargin{:});
@ -155,11 +155,11 @@ while ~done
if NumGrad
switch method
case 2
[g2 badg2] = numgrad(fcn, f2, x2, varargin{:});
[g2 badg2] = numgrad(fcn, f2, x2, epsilon, varargin{:});
case 3
[g2 badg2] = numgrad3(fcn, f2, x2, varargin{:});
[g2 badg2] = numgrad3(fcn, f2, x2, epsilon, varargin{:});
case 5
[g2,badg2] = numgrad5(fcn, f2, x2, varargin{:});
[g2,badg2] = numgrad5(fcn, f2, x2, epsilon, varargin{:});
end
else
[g2 badg2] = feval(grad,x2,varargin{:});
@ -190,11 +190,11 @@ while ~done
if NumGrad
switch method
case 2
[g3 badg3] = numgrad(fcn, f3, x3, varargin{:});
[g3 badg3] = numgrad(fcn, f3, x3, epsilon, varargin{:});
case 3
[g3 badg3] = numgrad3(fcn, f3, x3, varargin{:});
[g3 badg3] = numgrad3(fcn, f3, x3, epsilon, varargin{:});
case 5
[g3,badg3] = numgrad5(fcn, f3, x3, varargin{:});
[g3,badg3] = numgrad5(fcn, f3, x3, epsilon, varargin{:});
end
else
[g3 badg3] = feval(grad,x3,varargin{:});
@ -254,11 +254,11 @@ while ~done
if NumGrad
switch method
case 2
[gh,badgh] = numgrad(fcn, fh, xh, varargin{:});
[gh,badgh] = numgrad(fcn, fh, xh, epsilon, varargin{:});
case 3
[gh,badgh] = numgrad3(fcn, fh, xh, varargin{:});
[gh,badgh] = numgrad3(fcn, fh, xh, epsilon, varargin{:});
case 5
[gh,badgh] = numgrad5(fcn, fh, xh, varargin{:});
[gh,badgh] = numgrad5(fcn, fh, xh, epsilon, varargin{:});
end
else
[gh badgh] = feval(grad, xh,varargin{:});

6
matlab/dynare_estimation_1.m
View File

@ -12,7 +12,7 @@ function dynare_estimation_1(var_list_,dname)
% SPECIAL REQUIREMENTS
% none
% Copyright (C) 2003-2009 Dynare Team
% Copyright (C) 2003-2009, 2010 Dynare Team
%
% This file is part of Dynare.
%
@ -400,12 +400,12 @@ if options_.mode_compute > 0 & options_.posterior_mode_estimation
verbose = 2;
if ~options_.bvar_dsge
[fval,xparam1,grad,hessian_csminwel,itct,fcount,retcodehat] = ...
csminwel('DsgeLikelihood',xparam1,H0,[],crit,nit,options_.gradient_method,gend,data,data_index,number_of_observations,no_more_missing_observations);
csminwel('DsgeLikelihood',xparam1,H0,[],crit,nit,options_.gradient_method,options_.gradient_epsilon,gend,data,data_index,number_of_observations,no_more_missing_observations);
disp(sprintf('Objective function at mode: %f',fval))
disp(sprintf('Objective function at mode: %f',DsgeLikelihood(xparam1,gend,data,data_index,number_of_observations,no_more_missing_observations)))
else
[fval,xparam1,grad,hessian_csminwel,itct,fcount,retcodehat] = ...
csminwel('DsgeVarLikelihood',xparam1,H0,[],crit,nit,options_.gradient_method,gend);
csminwel('DsgeVarLikelihood',xparam1,H0,[],crit,nit,options_.gradient_method,options_.gradient_epsilon,gend);
disp(sprintf('Objective function at mode: %f',fval))
disp(sprintf('Objective function at mode: %f',DsgeVarLikelihood(xparam1,gend)))
end

1
matlab/global_initialization.m
View File

@ -182,6 +182,7 @@ options_.subdraws = [];
options_.unit_root_vars = [];
options_.use_mh_covariance_matrix = 0;
options_.gradient_method = 2;
options_.gradient_epsilon = 1e-6;
options_.posterior_sampling_method = 'random_walk_metropolis_hastings';
options_.proposal_distribution = 'rand_multivariate_normal';
options_.student_degrees_of_freedom = 3;

4
matlab/maximize_prior_density.m
View File

@ -15,7 +15,7 @@ function [xparams,lpd,hessian] = ...
% lpd [double] scalar, value of the logged prior density at the mode.
% hessian [double] matrix, Hessian matrix at the prior mode.
% Copyright (C) 2009 Dynare Team
% Copyright (C) 2009, 2010 Dynare Team
%
% This file is part of Dynare.
%
@ -40,7 +40,7 @@ verbose = 2;
gradient_method = 2;
[lpd,xparams,grad,hessian,itct,fcount,retcodehat] = ...
csminwel('minus_logged_prior_density',iparams,H0,[],crit,nit,gradient_method, ...
csminwel('minus_logged_prior_density',iparams,H0,[],crit,nit,gradient_method, options_.gradient_epsilon, ...
prior_shape, prior_hyperparameter_1, prior_hyperparameter_2, prior_inf_bound, prior_sup_bound);
lpd = -lpd;

2
matlab/numgrad.m
View File

@ -1,4 +1,4 @@
function [g, badg] = numgrad(fcn,f0,x,varargin)
function [g, badg] = numgrad(fcn,f0,x,epsilon,varargin)
% function [g badg] = numgrad(fcn,xvarargin)
% Original file downloaded from:

2
matlab/numgrad3.m
View File

@ -1,4 +1,4 @@
1function [g, badg, f0, f1, f2] = numgrad3(fcn,f0,x,varargin)
1function [g, badg, f0, f1, f2] = numgrad3(fcn,f0,x,epsilon,varargin)
% Computes the gradient of the objective function fcn using a three points
% formula if possible.
%

2
matlab/numgrad5.m
View File

@ -1,4 +1,4 @@
function [g, badg, f0, f1, f2, f3, f4] = numgrad5(fcn,f0,x,varargin)
function [g, badg, f0, f1, f2, f3, f4] = numgrad5(fcn,f0,x,epsilon,varargin)
% Computes the gradient of the objective function fcn using a five points
% formula if possible.
%

4
matlab/osr1.m
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@ -1,6 +1,6 @@
function osr1(i_params,i_var,weights)
% Copyright (C) 2005-2009 Dynare Team
% Copyright (C) 2005-2009, 2010 Dynare Team
%
% This file is part of Dynare.
%
@ -95,7 +95,7 @@ crit = 1e-7;
nit = 1000;
verbose = 2;
[f,p]=csminwel('osr_obj',t0,H0,[],crit,nit,options_.gradient_method,i_params,...
[f,p]=csminwel('osr_obj',t0,H0,[],crit,nit,options_.gradient_method,options_.gradient_epsilon,i_params,...
inv_order_var(i_var),weights(i_var,i_var));
% options = optimset('fminunc');

4
matlab/simulated_moments_estimation.m
View File

@ -149,11 +149,11 @@ vb = 2;
% Minimization of the objective function.
if nargin==3
[fval,param,grad,hessian_csminwel,itct,fcount,retcodehat] = ...
csminwel('smm_objective',xparam,H0,[],ct,it,2,sample_moments,weighting_matrix,options);
csminwel('smm_objective',xparam,H0,[],ct,it,2,options_.gradient_epsilon,sample_moments,weighting_matrix,options);
elseif nargin>3
[fval,param,grad,hessian_csminwel,itct,fcount,retcodehat] = ...
csminwel('smm_objective',xparam,H0,[],ct,it,2,sample_moments,weighting_matrix,options,parallel);
csminwel('smm_objective',xparam,H0,[],ct,it,2,options_.gradient_epsilon,sample_moments,weighting_matrix,options,parallel);
end