stepan
/
dynare
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

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
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

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) %[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 % fcn: string naming the objective function to be minimized
% x0: initial value of the parameter vector % x0: initial value of the parameter vector
@ -72,13 +72,13 @@ end
if NumGrad if NumGrad
switch method switch method
case 2 case 2
[g,badg] = numgrad(fcn, f0, x0, varargin{:}); [g,badg] = numgrad(fcn, f0, x0, epsilon, varargin{:});
badg badg
g g
case 3 case 3
[g,badg] = numgrad3(fcn, f0, x0, varargin{:}); [g,badg] = numgrad3(fcn, f0, x0, epsilon, varargin{:});
case 5 case 5
[g,badg] = numgrad5(fcn, f0, x0, varargin{:}); [g,badg] = numgrad5(fcn, f0, x0, epsilon, varargin{:});
end end
else else
[g,badg] = feval(grad,x0,varargin{:}); [g,badg] = feval(grad,x0,varargin{:});
@ -121,11 +121,11 @@ while ~done
if NumGrad if NumGrad
switch method switch method
case 2 case 2
[g1 badg1] = numgrad(fcn, f1, x1, varargin{:}); [g1 badg1] = numgrad(fcn, f1, x1, epsilon, varargin{:});
case 3 case 3
[g1 badg1] = numgrad3(fcn, f1, x1, varargin{:}); [g1 badg1] = numgrad3(fcn, f1, x1, epsilon, varargin{:});
case 5 case 5
[g1,badg1] = numgrad5(fcn, f1, x1, varargin{:}); [g1,badg1] = numgrad5(fcn, f1, x1, epsilon, varargin{:});
end end
else else
[g1 badg1] = feval(grad,x1,varargin{:}); [g1 badg1] = feval(grad,x1,varargin{:});
@ -155,11 +155,11 @@ while ~done
if NumGrad if NumGrad
switch method switch method
case 2 case 2
[g2 badg2] = numgrad(fcn, f2, x2, varargin{:}); [g2 badg2] = numgrad(fcn, f2, x2, epsilon, varargin{:});
case 3 case 3
[g2 badg2] = numgrad3(fcn, f2, x2, varargin{:}); [g2 badg2] = numgrad3(fcn, f2, x2, epsilon, varargin{:});
case 5 case 5
[g2,badg2] = numgrad5(fcn, f2, x2, varargin{:}); [g2,badg2] = numgrad5(fcn, f2, x2, epsilon, varargin{:});
end end
else else
[g2 badg2] = feval(grad,x2,varargin{:}); [g2 badg2] = feval(grad,x2,varargin{:});
@ -190,11 +190,11 @@ while ~done
if NumGrad if NumGrad
switch method switch method
case 2 case 2
[g3 badg3] = numgrad(fcn, f3, x3, varargin{:}); [g3 badg3] = numgrad(fcn, f3, x3, epsilon, varargin{:});
case 3 case 3
[g3 badg3] = numgrad3(fcn, f3, x3, varargin{:}); [g3 badg3] = numgrad3(fcn, f3, x3, epsilon, varargin{:});
case 5 case 5
[g3,badg3] = numgrad5(fcn, f3, x3, varargin{:}); [g3,badg3] = numgrad5(fcn, f3, x3, epsilon, varargin{:});
end end
else else
[g3 badg3] = feval(grad,x3,varargin{:}); [g3 badg3] = feval(grad,x3,varargin{:});
@ -254,11 +254,11 @@ while ~done
if NumGrad if NumGrad
switch method switch method
case 2 case 2
[gh,badgh] = numgrad(fcn, fh, xh, varargin{:}); [gh,badgh] = numgrad(fcn, fh, xh, epsilon, varargin{:});
case 3 case 3
[gh,badgh] = numgrad3(fcn, fh, xh, varargin{:}); [gh,badgh] = numgrad3(fcn, fh, xh, epsilon, varargin{:});
case 5 case 5
[gh,badgh] = numgrad5(fcn, fh, xh, varargin{:}); [gh,badgh] = numgrad5(fcn, fh, xh, epsilon, varargin{:});
end end
else else
[gh badgh] = feval(grad, xh,varargin{:}); [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 % SPECIAL REQUIREMENTS
% none % none
% Copyright (C) 2003-2009 Dynare Team % Copyright (C) 2003-2009, 2010 Dynare Team
% %
% This file is part of Dynare. % This file is part of Dynare.
% %
@ -400,12 +400,12 @@ if options_.mode_compute > 0 & options_.posterior_mode_estimation
verbose = 2; verbose = 2;
if ~options_.bvar_dsge if ~options_.bvar_dsge
[fval,xparam1,grad,hessian_csminwel,itct,fcount,retcodehat] = ... [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',fval))
disp(sprintf('Objective function at mode: %f',DsgeLikelihood(xparam1,gend,data,data_index,number_of_observations,no_more_missing_observations))) disp(sprintf('Objective function at mode: %f',DsgeLikelihood(xparam1,gend,data,data_index,number_of_observations,no_more_missing_observations)))
else else
[fval,xparam1,grad,hessian_csminwel,itct,fcount,retcodehat] = ... [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',fval))
disp(sprintf('Objective function at mode: %f',DsgeVarLikelihood(xparam1,gend))) disp(sprintf('Objective function at mode: %f',DsgeVarLikelihood(xparam1,gend)))
end end

1
matlab/global_initialization.m
View File

@ -182,6 +182,7 @@ options_.subdraws = [];
options_.unit_root_vars = []; options_.unit_root_vars = [];
options_.use_mh_covariance_matrix = 0; options_.use_mh_covariance_matrix = 0;
options_.gradient_method = 2; options_.gradient_method = 2;
options_.gradient_epsilon = 1e-6;
options_.posterior_sampling_method = 'random_walk_metropolis_hastings'; options_.posterior_sampling_method = 'random_walk_metropolis_hastings';
options_.proposal_distribution = 'rand_multivariate_normal'; options_.proposal_distribution = 'rand_multivariate_normal';
options_.student_degrees_of_freedom = 3; 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. % lpd [double] scalar, value of the logged prior density at the mode.
% hessian [double] matrix, Hessian matrix at the prior 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. % This file is part of Dynare.
% %
@ -40,7 +40,7 @@ verbose = 2;
gradient_method = 2; gradient_method = 2;
[lpd,xparams,grad,hessian,itct,fcount,retcodehat] = ... [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); prior_shape, prior_hyperparameter_1, prior_hyperparameter_2, prior_inf_bound, prior_sup_bound);
lpd = -lpd; 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) % function [g badg] = numgrad(fcn,xvarargin)
% Original file downloaded from: % 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 % Computes the gradient of the objective function fcn using a three points
% formula if possible. % 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 % Computes the gradient of the objective function fcn using a five points
% formula if possible. % formula if possible.
% %

4
matlab/osr1.m
View File

@ -1,6 +1,6 @@
function osr1(i_params,i_var,weights) 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. % This file is part of Dynare.
% %
@ -95,7 +95,7 @@ crit = 1e-7;
nit = 1000; nit = 1000;
verbose = 2; 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)); inv_order_var(i_var),weights(i_var,i_var));
% options = optimset('fminunc'); % options = optimset('fminunc');

4
matlab/simulated_moments_estimation.m
View File

@ -149,11 +149,11 @@ vb = 2;
% Minimization of the objective function. % Minimization of the objective function.
if nargin==3 if nargin==3
[fval,param,grad,hessian_csminwel,itct,fcount,retcodehat] = ... [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 elseif nargin>3
[fval,param,grad,hessian_csminwel,itct,fcount,retcodehat] = ... [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 end