fixed bugs that prevented the computation of the asymptotic Hessian with ML.

matlab/dynare_identification.m

@@ -147,7 +147,7 @@ options_ = set_default_option(options_,'datafile','');
 options_.mode_compute = 0;
 options_.plot_priors = 0;
 options_.smoother=1;
-[dataset_,dataset_info,xparam1,hh, M_, options_, oo_, estim_params_,bayestopt_]=dynare_estimation_init(M_.endo_names,fname_,1, M_, options_, oo_, estim_params_, bayestopt_);
+[dataset_,dataset_info,xparam1,hh, M_, options_, oo_, estim_params_,bayestopt_,bounds]=dynare_estimation_init(M_.endo_names,fname_,1, M_, options_, oo_, estim_params_, bayestopt_);
 options_ident = set_default_option(options_ident,'analytic_derivation_mode',options_.analytic_derivation_mode); % if not set by user, inherit default global one
 
 if prior_exist
@@ -310,7 +310,7 @@ if iload <=0,
         disp('Testing current parameter values')
     end
     [idehess_point, idemoments_point, idemodel_point, idelre_point, derivatives_info_point, info, options_ident] = ...
-        identification_analysis(params,indx,indexo,options_ident,dataset_, dataset_info, prior_exist, name_tex,1,parameters);
+        identification_analysis(params,indx,indexo,options_ident,dataset_, dataset_info, prior_exist, name_tex,1,parameters,bounds);
     if info(1)~=0,
         skipline()
         disp('----------- ')
@@ -353,7 +353,7 @@ if iload <=0,
                 kk=kk+1;
                 params = prior_draw();
                 [idehess_point, idemoments_point, idemodel_point, idelre_point, derivatives_info_point, info, options_ident] = ...
-                    identification_analysis(params,indx,indexo,options_ident,dataset_,dataset_info, prior_exist, name_tex,1,'Random_prior_params');
+                    identification_analysis(params,indx,indexo,options_ident,dataset_,dataset_info, prior_exist, name_tex,1,'Random_prior_params',bounds);
             end
         end
         if info(1)
@@ -407,7 +407,7 @@ if iload <=0,
             params = prior_draw();
         end
         [dum1, ideJ, ideH, ideGP, dum2 , info, options_MC] = ...
-            identification_analysis(params,indx,indexo,options_MC,dataset_, dataset_info, prior_exist, name_tex,0);
+            identification_analysis(params,indx,indexo,options_MC,dataset_, dataset_info, prior_exist, name_tex,0,[],bounds);
         if iteration==0 && info(1)==0,
             MAX_tau   = min(SampleSize,ceil(MaxNumberOfBytes/(size(ideH.siH,1)*nparam)/8));
             stoH = zeros([size(ideH.siH,1),nparam,MAX_tau]);

matlab/identification_analysis.m

-function [ide_hess, ide_moments, ide_model, ide_lre, derivatives_info, info, options_ident] = identification_analysis(params,indx,indexo,options_ident,dataset_,dataset_info, prior_exist,name_tex,init,tittxt)
+function [ide_hess, ide_moments, ide_model, ide_lre, derivatives_info, info, options_ident] = identification_analysis(params,indx,indexo,options_ident,dataset_,dataset_info, prior_exist,name_tex,init,tittxt,bounds)
 % function [ide_hess, ide_moments, ide_model, ide_lre, derivatives_info, info] = identification_analysis(params,indx,indexo,options_ident,data_info, prior_exist,name_tex,init,analyis_type)
 % given the parameter vector params, wraps all identification analyses
 %
@@ -122,7 +122,7 @@ if info(1)==0,
     ide_strength_J=NaN(1,nparam);
     ide_strength_J_prior=NaN(1,nparam);
     if init, %~isempty(indok),
-        normaliz = abs(params);
+        normaliz = NaN(1,nparam);
         if prior_exist,
             if ~isempty(estim_params_.var_exo),
                 normaliz1 = estim_params_.var_exo(:,7)'; % normalize with prior standard deviation
@@ -155,12 +155,12 @@ if info(1)==0,
             info = stoch_simul(char(options_.varobs));
             dataset_ = dseries(oo_.endo_simul(options_.varobs_id,100+1:end)',dates('1Q1'), options_.varobs);            
             derivatives_info.no_DLIK=1;
-            bounds = prior_bounds(bayestopt_, options_.prior_trunc);
+            %bounds = prior_bounds(bayestopt_, options_.prior_trunc);
             [fval,info,cost_flag,DLIK,AHess,ys,trend_coeff,M_,options_,bayestopt_,oo_] = dsge_likelihood(params',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_,derivatives_info);             
 %                 fval = DsgeLikelihood(xparam1,data_info,options_,M_,estim_params_,bayestopt_,oo_);
             options_.analytic_derivation = analytic_derivation;
             AHess=-AHess;
-            if min(eig(AHess))<0,
+            if min(eig(AHess))<-1.e-10,
                 error('identification_analysis: Analytic Hessian is not positive semi-definite!')
             end
 %             chol(AHess);
@@ -241,7 +241,7 @@ if info(1)==0,
         end
         ide_strength_J(indok) = (1./(normaliz(indok)'./abs(params(indok)')));
         ide_strength_J_prior(indok) = (1./(normaliz(indok)'./normaliz1(indok)'));
-        ide_strength_J(params==0)=ide_strength_J_prior(params==0);
+        ide_strength_J(params==0)=1./normaliz(params==0)';
         deltaM_prior = deltaM.*abs(normaliz1');
         deltaM = deltaM.*abs(params');
         deltaM(params==0)=deltaM_prior(params==0);