online estimation: bug fixes for the coherency of functions calls.

matlab/estimation/dynare_estimation_1.m

@@ -238,8 +238,11 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation &
     optimizer_vec = [options_.mode_compute;num2cell(options_.additional_optimizer_steps)];
     for optim_iter = 1:length(optimizer_vec)
         current_optimizer = optimizer_vec{optim_iter};
-
+        if current_optimizer~=11  
           [xparam1, fval, ~, hh, options_, Scale, new_rat_hess_info] = dynare_minimize_objective(objective_function,xparam1,current_optimizer,options_,[bounds.lb bounds.ub],bayestopt_.name,bayestopt_,hh,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_.dr, oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
+        else 
+          [xparam1, fval, ~, hh, options_, Scale, new_rat_hess_info] = dynare_minimize_objective(objective_function,xparam1,current_optimizer,options_,[bounds.lb bounds.ub],bayestopt_.name,bayestopt_,hh,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
+        end
         fprintf('\nFinal value of minus the log posterior (or likelihood):%f \n', fval);
 
         if isnumeric(current_optimizer)

matlab/nonlinear-filters/online_auxiliary_filter.m

@@ -86,7 +86,7 @@ for i=1:number_of_particles
     info = 12042009;
     while info
         candidate = Prior.draw();
-        [info, M_, options_, oo_] = solve_model_for_online_filter(false, xparam1, dataset_, options_, M_, estim_params_, bayestopt_, bounds, oo_);
+        [info, M_, options_, oo_] = solve_model_for_online_filter(false, candidate, dataset_, options_, M_, estim_params_, bayestopt_, bounds, oo_);
         if ~info
             xparam(:,i) = candidate(:);
         end