Merge branch 'fixes_4.7' of git.dynare.org:rattoma/dynare

See !1827

matlab/DsgeSmoother.m

@@ -238,7 +238,8 @@ if kalman_algo == 1 || kalman_algo == 3
             fprintf('\nDsgeSmoother: Switching to univariate filter. This may be a sign of stochastic singularity.\n')
             kalman_algo = 2;
         elseif kalman_algo == 3
-            fprintf('\nDsgeSmoother: Switching to univariate filter. This may be a sign of stochastic singularity.\n')
+            fprintf('\nDsgeSmoother: Switching to univariate filter. This is usually due to co-integration in diffuse filter,\n')
+            fprintf(' otherwise it may be a sign of stochastic singularity.\n')
             kalman_algo = 4;
         else
             error('This case shouldn''t happen')

matlab/GetPosteriorParametersStatistics.m

@@ -96,7 +96,7 @@ if np
     disp(tit2)
     ip = nvx+nvn+ncx+ncn+1;
     for i=1:np
-        if options_.mh_replic
+        if options_.mh_replic || (options_.load_mh_file && ~options_.load_results_after_load_mh)
             Draws = GetAllPosteriorDraws(ip, FirstMhFile, FirstLine, TotalNumberOfMhFiles, NumberOfDraws);
             [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(Draws, 1, options_.mh_conf_sig);
             name = bayestopt_.name{ip};
@@ -139,7 +139,7 @@ if nvx
     disp('standard deviation of shocks')
     disp(tit2)
     for i=1:nvx
-        if options_.mh_replic
+        if options_.mh_replic || (options_.load_mh_file && ~options_.load_results_after_load_mh)
             Draws = GetAllPosteriorDraws(ip, FirstMhFile, FirstLine, TotalNumberOfMhFiles, NumberOfDraws);
             [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(Draws, 1, options_.mh_conf_sig);
             k = estim_params_.var_exo(i,1);
@@ -183,7 +183,7 @@ if nvn
     disp(tit2)
     ip = nvx+1;
     for i=1:nvn
-        if options_.mh_replic
+        if options_.mh_replic || (options_.load_mh_file && ~options_.load_results_after_load_mh)
             Draws = GetAllPosteriorDraws(ip, FirstMhFile, FirstLine, TotalNumberOfMhFiles, NumberOfDraws);
             [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(Draws, 1, options_.mh_conf_sig);
             name = options_.varobs{estim_params_.nvn_observable_correspondence(i,1)};
@@ -222,7 +222,7 @@ if ncx
     disp(tit2)
     ip = nvx+nvn+1;
     for i=1:ncx
-        if options_.mh_replic
+        if options_.mh_replic || (options_.load_mh_file && ~options_.load_results_after_load_mh)
             Draws = GetAllPosteriorDraws(ip, FirstMhFile, FirstLine, TotalNumberOfMhFiles, NumberOfDraws);
             [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(Draws,1,options_.mh_conf_sig);
             k1 = estim_params_.corrx(i,1);
@@ -272,7 +272,7 @@ if ncn
     disp(tit2)
     ip = nvx+nvn+ncx+1;
     for i=1:ncn
-        if options_.mh_replic
+        if options_.mh_replic || (options_.load_mh_file && ~options_.load_results_after_load_mh)
             Draws = GetAllPosteriorDraws(ip,FirstMhFile,FirstLine,TotalNumberOfMhFiles,NumberOfDraws);
             [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(Draws, 1, options_.mh_conf_sig);
             k1 = estim_params_.corrn(i,1);

matlab/dynare_estimation_1.m

@@ -195,38 +195,13 @@ end
 %% Estimation of the posterior mode or likelihood mode
 
 if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
-    %prepare settings for newrat
-    if options_.mode_compute==5
-        %get whether outer product Hessian is requested
-        newratflag=[];
-        if ~isempty(options_.optim_opt)
-            options_list = read_key_value_string(options_.optim_opt);
-            for i=1:rows(options_list)
-                if strcmp(options_list{i,1},'Hessian')
-                    newratflag=options_list{i,2};
-                end
-            end
-        end
-        if options_.analytic_derivation
-            options_analytic_derivation_old = options_.analytic_derivation;
-            options_.analytic_derivation = -1;
-            if ~isempty(newratflag) && newratflag~=0 %numerical hessian explicitly specified
-                error('newrat: analytic_derivation is incompatible with numerical Hessian.')
-            else %use default
-                newratflag=0; %exclude DYNARE numerical hessian
-            end
-        elseif ~options_.analytic_derivation
-            if isempty(newratflag)
-                newratflag=options_.newrat.hess; %use default numerical dynare hessian
-            end
-        end
-    end
 
     [xparam1, fval, exitflag, hh, options_, Scale, new_rat_hess_info] = dynare_minimize_objective(objective_function,xparam1,options_.mode_compute,options_,[bounds.lb bounds.ub],bayestopt_.name,bayestopt_,hh,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
     fprintf('\nFinal value of minus the log posterior (or likelihood):%f \n', fval);
 
-    if isnumeric(options_.mode_compute) && options_.mode_compute==5 && options_.analytic_derivation==-1 %reset options changed by newrat
-        options_.analytic_derivation = options_analytic_derivation_old; %reset
+    if isnumeric(options_.mode_compute) && options_.mode_compute==5
+        newratflag = new_rat_hess_info.newratflag;
+        new_rat_hess_info = new_rat_hess_info.new_rat_hess_info;
     elseif isnumeric(options_.mode_compute) && options_.mode_compute==6 %save scaling factor
         save([M_.fname '_optimal_mh_scale_parameter.mat'],'Scale');
         options_.mh_jscale = Scale;
@@ -241,7 +216,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
                                      dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
                 options_.analytic_derivation = ana_deriv_old;
             elseif ~isnumeric(options_.mode_compute) || ~(isequal(options_.mode_compute,5) && newratflag~=1 && strcmp(func2str(objective_function),'dsge_likelihood'))
-                % with flag==0, we force to use the hessian from outer product gradient of optimizer 5
+                % with flag==0 or 2, we force to use the hessian from outer product gradient of optimizer 5
                 if options_.hessian.use_penalized_objective
                     penalized_objective_function = str2func('penalty_objective_function');
                     hh = hessian(penalized_objective_function, xparam1, options_.gstep, objective_function, fval, dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, bounds,oo_);
@@ -260,7 +235,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
                 % with diagonal elements computed with numerical second order derivatives
                 %
                 % uses univariate filters, so to get max # of available
-                % densitities for outer product gradient
+                % densities for outer product gradient
                 kalman_algo0 = options_.kalman_algo;
                 compute_hessian = 1;
                 if ~((options_.kalman_algo == 2) || (options_.kalman_algo == 4))

matlab/optimization/dynare_minimize_objective.m

@@ -274,7 +274,9 @@ switch minimizer_algorithm
     if isempty(prior_information) %mr_hessian requires it, but can be NaN
         prior_information.p2=NaN(n_params,1);
     end
-    if options_.analytic_derivation==-1 %set outside as code for use of analytic derivation
+    if options_.analytic_derivation 
+        old_analytic_derivation = options_.analytic_derivation;
+        options_.analytic_derivation=-1; %force analytic outer product gradient hessian for each iteration
         analytic_grad=1;
         crit = options_.newrat.tolerance.f_analytic;
         newratflag = 0; %analytical Hessian
@@ -317,7 +319,14 @@ switch minimizer_algorithm
     hess_info.gstep=options_.gstep;
     hess_info.htol = 1.e-4;
     hess_info.h1=options_.gradient_epsilon*ones(n_params,1);
+    % here we force 7th input argument (flagg) to be 0, since outer product
+    % gradient Hessian is handled in dynare_estimation_1
     [opt_par_values,hessian_mat,gg,fval,invhess,new_rat_hess_info] = newrat(objective_function,start_par_value,bounds,analytic_grad,crit,nit,0,Verbose,Save_files,hess_info,prior_information.p2,options_.gradient_epsilon,parameter_names,varargin{:});    %hessian_mat is the plain outer product gradient Hessian
+    new_rat_hess_info.new_rat_hess_info = new_rat_hess_info;
+    new_rat_hess_info.newratflag = newratflag;
+    if options_.analytic_derivation 
+        options_.analytic_derivation = old_analytic_derivation;
+    end
   case 6
     if isempty(prior_information) %Inf will be reset
         prior_information.p2=Inf(n_params,1);

matlab/utilities/dataset/lagged.m

@@ -9,7 +9,7 @@ function xlag = lagged(x, n)
 % OUTPUT
 % xlag = backward shifted series
 
-% Copyright (C) 2017 Dynare Team
+% Copyright (C) 2017-2021 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -31,4 +31,8 @@ if nargin==1
 end
 
 x=x(:);
-xlag=[NaN(n,1); x(1:end-n)];
\ No newline at end of file
+if n>0
+    xlag=[NaN(n,1); x(1:end-n)];
+else
+    xlag=[x(abs(n)+1:end); NaN(abs(n),1)];
+end
\ No newline at end of file

matlab/utilities/dataset/nanautocovariance.m

@@ -55,8 +55,9 @@ function autocov = nanautocovariance(data,order)
 
 n = size(data,2);
 missing = isanynan(data);
-
-autocov = zeros(n, n, order);
+autocov = nan(n, n, order);
+order = min(size(data,1)-2,order);
+autocov(:, :, 1:order)=0;
 
 for lag=1:order
     if missing