Do not compute marginal density (Laplace approximation) if options_.cova_compute==1.

matlab/dynare_estimation_1.m

@@ -569,6 +569,7 @@ if any(bayestopt_.pshape > 0) && ~options_.mh_posterior_mode_estimation
         end
     end
     %% 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));
@@ -583,6 +584,7 @@ if any(bayestopt_.pshape > 0) && ~options_.mh_posterior_mode_estimation
         disp(' ')
         disp(sprintf('Log data density [Laplace approximation] is %f.',md_Laplace))
         disp(' ')
+    end
 elseif ~any(bayestopt_.pshape > 0) && options_.mh_posterior_mode_estimation
     disp(' ')
     disp('RESULTS FROM MAXIMUM LIKELIHOOD')
@@ -868,12 +870,16 @@ if (any(bayestopt_.pshape  >0 ) && options_.mh_replic) || ...
         if options_.load_mh_file && options_.use_mh_covariance_matrix
             invhess = compute_mh_covariance_matrix;
         end
+        if options_.cova_compute
             if options_.dsge_var
                 feval(options_.posterior_sampling_method,'DsgeVarLikelihood',options_.proposal_distribution,xparam1,invhess,bounds,gend);
             else
                 feval(options_.posterior_sampling_method,'DsgeLikelihood',options_.proposal_distribution,xparam1,invhess,bounds,gend,data,...
                       data_index,number_of_observations,no_more_missing_observations);
             end
+        else
+            error('I Cannot start the MCMC because the hessian of the posterior kernel at the mode was not computed.')
+        end
     end
     if options_.mh_posterior_mode_estimation
         CutSample(M_, options_, estim_params_);