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

Bug fixes around analytic scores for identification.

time-shift
Marco Ratto 2011-04-12 18:18:18 +02:00
parent 498d5ed698
commit 25afd56163
2 changed files with 24 additions and 9 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

15
matlab/DsgeLikelihood.m
View File

@ -200,8 +200,19 @@ Y = data-trend;
if analytic_derivation,
if nargin<7 || isempty(derivatives_info)
[A,B] = dynare_resolve;
if ~isempty(estim_params_.var_exo),
indexo=estim_params_.var_exo(:,1);
else
indexo=[];
end
if ~isempty(estim_params_.param_vals),
indparam=estim_params_.param_vals(:,1);;
else
indparam=[];
end
[dum, DT, DOm, DYss] = getH(A, B, M_,oo_,0, ...
estim_params_.param_vals(:,1),estim_params_.var_exo(:,1));
indparam,indexo);
else
DT = derivatives_info.DT;
DOm = derivatives_info.DOm;
@ -209,7 +220,7 @@ if analytic_derivation,
clear derivatives_info,
end
iv = oo_.dr.restrict_var_list;
DYss = [zeros(length(DYss),offset) DYss];
DYss = [zeros(size(DYss,1),offset) DYss];
DT = DT(iv,iv,:);
DOm = DOm(iv,iv,:);
DYss = DYss(iv,:);

18
matlab/dynare_identification.m
View File

@ -257,9 +257,9 @@ if iload <=0,
if iteration,
[JJ, H, gam, gp, dA, dOm, dYss] = getJJ(A, B, M_,oo0,options_,0,indx,indexo,bayestopt_.mf2,nlags,useautocorr);
derivatives_info.dA=dA;
derivatives_info.dOm=dOm;
derivatives_info.dYss=dYss;
derivatives_info.DT=dA;
derivatives_info.DOm=dOm;
derivatives_info.DYss=dYss;
if BurninSampleSize == 0,
indJJ = (find(max(abs(JJ'))>1.e-8));
indH = (find(max(abs(H'))>1.e-8));
@ -361,10 +361,11 @@ if iload <=0,
info = stoch_simul(options_.varobs);
datax=oo_.endo_simul(options_.varobs_id,100+1:end);
% datax=data;
[fval,cost_flag,ys,trend_coeff,info,DLIK,AHess] = DsgeLikelihood(params',data_info.gend,datax,data_info.data_index,data_info.number_of_observations,derivatives_info);
[fval,cost_flag,ys,trend_coeff,info,DLIK,AHess] = DsgeLikelihood(params',data_info.gend,datax,data_info.data_index,data_info.number_of_observations,data_info.no_more_missing_observations,derivatives_info);
cparam = inv(-AHess);
normaliz(indok) = sqrt(diag(cparam))';
cmm = siJ*((-AHess)\siJ');
flag_score=1;
catch,
cmm = simulated_moment_uncertainty(indJJ, periods, replic);
% Jinv=(siJ(:,indok)'*siJ(:,indok))\siJ(:,indok)';
@ -375,6 +376,7 @@ if iload <=0,
rhoM=sqrt(1-1./diag(inv(tildaM)));
deltaM = deltaM.*normaliz(indok)';
normaliz(indok) = sqrt(diag(inv(MIM)))';
flag_score=0;
end
ide_strength_J(indok) = (1./(normaliz(indok)'./abs(params(indok)')));
ide_strength_J_prior(indok) = (1./(normaliz(indok)'./normaliz1(indok)'));
@ -589,13 +591,15 @@ subplot(211)
set(gca,'xlim',[0 nparam+1])
set(gca,'xticklabel','')
dy = get(gca,'ylim');
% dy=dy(2)-dy(1);
for ip=1:nparam,
text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
legend('relative to param value','relative to prior std','Location','Best')
title('Identification strength in the moments (log-scale)')
if flag_score,
title('Identification strength using likelihood scores (log-scale)')
else
title('Identification strength in the moments (log-scale)')
end
subplot(212)
% mmm = mean(siJmean);
% [ss, is] = sort(mmm);