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make computations around invhess more robust numerically.

time-shift
Marco Ratto 2016-09-05 12:16:07 +02:00
parent cc88b7ebdf
commit eae0828a40
1 changed files with 3 additions and 3 deletions
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6
matlab/dynare_estimation_1.m
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@ -339,7 +339,8 @@ if ~options_.mh_posterior_mode_estimation
oo_.posterior.optimization.log_density=-fval;
end
if options_.cova_compute
invhess = inv(hh);
hsd = sqrt(diag(hh));
invhess = inv(hh./(hsd*hsd'))./(hsd*hsd');
stdh = sqrt(diag(invhess));
oo_.posterior.optimization.Variance = invhess;
end
@ -365,8 +366,7 @@ if any(bayestopt_.pshape > 0) && ~options_.mh_posterior_mode_estimation
% Laplace approximation to the marginal log density:
if options_.cova_compute
estim_params_nbr = size(xparam1,1);
scale_factor = -sum(log10(diag(invhess)));
log_det_invhess = -estim_params_nbr*log(scale_factor)+log(det(scale_factor*invhess));
log_det_invhess = log(det(invhess./(stdh*stdh')))+2*sum(log(stdh));
likelihood = feval(objective_function,xparam1,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
oo_.MarginalDensity.LaplaceApproximation = .5*estim_params_nbr*log(2*pi) + .5*log_det_invhess - likelihood;
skipline()