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41 results

dynare_estimation_1.m

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  • dynare_estimation_1.m 38.03 KiB
    function dynare_estimation_1(var_list_,dname)
    % function dynare_estimation_1(var_list_,dname)
    % runs the estimation of the model
    %
    % INPUTS
    %   var_list_:  selected endogenous variables vector
    %   dname:      alternative directory name
    %
    % OUTPUTS
    %   none
    %
    % SPECIAL REQUIREMENTS
    %   none
    
    % Copyright (C) 2003-2018 Dynare Team
    %
    % This file is part of Dynare.
    %
    % Dynare is free software: you can redistribute it and/or modify
    % it under the terms of the GNU General Public License as published by
    % the Free Software Foundation, either version 3 of the License, or
    % (at your option) any later version.
    %
    % Dynare is distributed in the hope that it will be useful,
    % but WITHOUT ANY WARRANTY; without even the implied warranty of
    % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    % GNU General Public License for more details.
    %
    % You should have received a copy of the GNU General Public License
    % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
    
    global M_ options_ oo_ estim_params_ bayestopt_ dataset_ dataset_info
    
    if isempty(estim_params_)
        mode_compute_o = options_.mode_compute;
        mh_replic_o = options_.mh_replic;
        options_.mode_compute = 0;
        options_.mh_replic = 0;
        reset_options_related_to_estimation = true;
    else
        reset_options_related_to_estimation = false;
    end
    
    
    %store qz_criterium
    qz_criterium_old=options_.qz_criterium;
    if isnan(options_.first_obs)
        first_obs_nan_indicator=true;
    else
        first_obs_nan_indicator=false;
    end
    
    % Set particle filter flag.
    if options_.order > 1
        if options_.particle.status
            skipline()
            disp('Estimation using a non linear filter!')
            skipline()
            if ~options_.nointeractive && ismember(options_.mode_compute,[1,3,4]) && ~strcmpi(options_.particle.filter_algorithm,'gf')% Known gradient-based optimizers
                disp('You are using a gradient-based mode-finder. Particle filtering introduces discontinuities in the')
                disp('objective function w.r.t the parameters. Thus, should use a non-gradient based optimizer.')
                fprintf('\nPlease choose a mode-finder:\n')
                fprintf('\t 0 - Continue using gradient-based method (it is most likely that you will no get any sensible result).\n')
                fprintf('\t 6 - Monte Carlo based algorithm\n')
                fprintf('\t 7 - Nelder-Mead simplex based optimization routine (Matlab optimization toolbox required)\n')
                fprintf('\t 8 - Nelder-Mead simplex based optimization routine (Dynare''s implementation)\n')
                fprintf('\t 9 - CMA-ES (Covariance Matrix Adaptation Evolution Strategy) algorithm\n')
                choice = [];
                while isempty(choice)
                    choice = input('Please enter your choice: ');
                    if isnumeric(choice) && isint(choice) && ismember(choice,[0 6 7 8 9])
                        if choice
                            options_.mode_compute = choice;
                        end
                    else
                        fprintf('\nThis is an invalid choice (you have to choose between 0, 6, 7, 8 and 9).\n')
                        choice = [];
                    end
                end
            end
        else
            error('For estimating the model with a second order approximation using a non linear filter, one should have options_.particle.status=true;')
        end
    end
    
    if ~options_.dsge_var
        if options_.particle.status
            objective_function = str2func('non_linear_dsge_likelihood');
            if strcmpi(options_.particle.filter_algorithm, 'sis')
                options_.particle.algorithm = 'sequential_importance_particle_filter';
            elseif strcmpi(options_.particle.filter_algorithm, 'apf')
                options_.particle.algorithm = 'auxiliary_particle_filter';
            elseif strcmpi(options_.particle.filter_algorithm, 'gf')
                options_.particle.algorithm = 'gaussian_filter';
            elseif strcmpi(options_.particle.filter_algorithm,  'gmf')
                options_.particle.algorithm = 'gaussian_mixture_filter';
            elseif strcmpi(options_.particle.filter_algorithm, 'cpf')
                options_.particle.algorithm = 'conditional_particle_filter';
            elseif strcmpi(options_.particle.filter_algorithm, 'nlkf')
                options_.particle.algorithm = 'nonlinear_kalman_filter';
            else
                error(['Estimation: Unknown filter ' options_.particle.filter_algorithm])
            end
        else
            objective_function = str2func('dsge_likelihood');
        end
    else
        objective_function = str2func('dsge_var_likelihood');
    end
    
    [dataset_, dataset_info, xparam1, hh, M_, options_, oo_, estim_params_, bayestopt_, bounds] = ...
        dynare_estimation_init(var_list_, dname, [], M_, options_, oo_, estim_params_, bayestopt_);
    
    if options_.dsge_var
        check_dsge_var_model(M_, estim_params_, bayestopt_);
        if dataset_info.missing.state
            error('Estimation::DsgeVarLikelihood: I cannot estimate a DSGE-VAR model with missing observations!')
        end
        if options_.noconstant
            var_sample_moments(options_.dsge_varlag, -1, dataset_);
        else
            % The steady state is non zero ==> a constant in the VAR is needed!
            var_sample_moments(options_.dsge_varlag, 0, dataset_);
        end
    end
    
    % Set sigma_e_is_diagonal flag (needed if the shocks block is not declared in the mod file).
    M_.sigma_e_is_diagonal = true;
    if estim_params_.ncx || any(nnz(tril(M_.Correlation_matrix,-1))) || isfield(estim_params_,'calibrated_covariances')
        M_.sigma_e_is_diagonal = false;
    end
    
    data = dataset_.data;
    rawdata = dataset_info.rawdata;
    data_index = dataset_info.missing.aindex;
    missing_value = dataset_info.missing.state;
    
    % Set number of observations
    gend = dataset_.nobs;
    
    % Set the number of observed variables.
    n_varobs = length(options_.varobs);
    
    % Get the number of parameters to be estimated.
    nvx = estim_params_.nvx;  % Variance of the structural innovations (number of parameters).
    nvn = estim_params_.nvn;  % Variance of the measurement innovations (number of parameters).
    ncx = estim_params_.ncx;  % Covariance of the structural innovations (number of parameters).
    ncn = estim_params_.ncn;  % Covariance of the measurement innovations (number of parameters).
    np  = estim_params_.np ;  % Number of deep parameters.
    nx  = nvx+nvn+ncx+ncn+np; % Total number of parameters to be estimated.
    
    dr = oo_.dr;
    
    if ~isempty(estim_params_)
        M_ = set_all_parameters(xparam1,estim_params_,M_);
    end
    
    
    %% perform initial estimation checks;
    try
        oo_ = initial_estimation_checks(objective_function,xparam1,dataset_,dataset_info,M_,estim_params_,options_,bayestopt_,bounds,oo_);
    catch % if check fails, provide info on using calibration if present
        e = lasterror();
        if estim_params_.full_calibration_detected %calibrated model present and no explicit starting values
            skipline(1);
            fprintf('ESTIMATION_CHECKS: There was an error in computing the likelihood for initial parameter values.\n')
            fprintf('ESTIMATION_CHECKS: If this is not a problem with the setting of options (check the error message below),\n')
            fprintf('ESTIMATION_CHECKS: you should try using the calibrated version of the model as starting values. To do\n')
            fprintf('ESTIMATION_CHECKS: this, add an empty estimated_params_init-block with use_calibration option immediately before the estimation\n')
            fprintf('ESTIMATION_CHECKS: command (and after the estimated_params-block so that it does not get overwritten):\n');
            skipline(2);
        end
        rethrow(e);
    end
    
    if isequal(options_.mode_compute,0) && isempty(options_.mode_file) && options_.mh_posterior_mode_estimation==0
        if options_.order==1 && ~options_.particle.status
            if options_.smoother
                [atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,T,R,P,PK,decomp,Trend,state_uncertainty,M_,oo_,options_,bayestopt_] = DsgeSmoother(xparam1,gend,transpose(data),data_index,missing_value,M_,oo_,options_,bayestopt_,estim_params_);
                [oo_]=store_smoother_results(M_,oo_,options_,bayestopt_,dataset_,dataset_info,atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,P,PK,decomp,Trend,state_uncertainty);
                if options_.forecast > 0
                    oo_.forecast = dyn_forecast(var_list_,M_,options_,oo_,'smoother',dataset_info);
                end
            end
            %reset qz_criterium
            options_.qz_criterium=qz_criterium_old;
            return
        else %allow to continue, e.g. with MCMC_jumping_covariance
            if options_.smoother
                error('Estimation:: Particle Smoothers are not yet implemented.')
            end
        end
    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
        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;
            bayestopt_.jscale(:) = options_.mh_jscale;
        end
        if ~isnumeric(options_.mode_compute) || ~isequal(options_.mode_compute,6) %always already computes covariance matrix
            if options_.cova_compute == 1 %user did not request covariance not to be computed
                if options_.analytic_derivation && strcmp(func2str(objective_function),'dsge_likelihood')
                    ana_deriv_old = options_.analytic_derivation;
                    options_.analytic_derivation = 2;
                    [~,~,~,~,hh] = feval(objective_function,xparam1, ...
                                         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
                    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_);
                    else
                        hh = hessian(objective_function, xparam1, options_.gstep, dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, bounds,oo_);
                    end
                    hh = reshape(hh, nx, nx);
                elseif isnumeric(options_.mode_compute) && isequal(options_.mode_compute,5)
                    % other numerical hessian options available with optimizer 5
                    %
                    % if newratflag == 0
                    % compute outer product gradient of optimizer 5
                    %
                    % if newratflag == 2
                    % compute 'mixed' outer product gradient of optimizer 5
                    % with diagonal elements computed with numerical second order derivatives
                    %
                    % uses univariate filters, so to get max # of available
                    % densitities for outer product gradient
                    kalman_algo0 = options_.kalman_algo;
                    compute_hessian = 1;
                    if ~((options_.kalman_algo == 2) || (options_.kalman_algo == 4))
                        options_.kalman_algo=2;
                        if options_.lik_init == 3
                            options_.kalman_algo=4;
                        end
                    elseif newratflag==0 % hh already contains outer product gradient with univariate filter
                        compute_hessian = 0;
                    end
                    if compute_hessian
                        crit = options_.newrat.tolerance.f;
                        newratflag = newratflag>0;
                        hh = reshape(mr_hessian(xparam1,objective_function,fval,newratflag,crit,new_rat_hess_info,[bounds.lb bounds.ub],bayestopt_.p2,dataset_, dataset_info, options_,M_,estim_params_,bayestopt_,bounds,oo_), nx, nx);
                    end
                    options_.kalman_algo = kalman_algo0;
                end
            end
        end
        parameter_names = bayestopt_.name;
        if options_.cova_compute || options_.mode_compute==5 || options_.mode_compute==6
            save([M_.fname '_mode.mat'],'xparam1','hh','parameter_names','fval');
        else
            save([M_.fname '_mode.mat'],'xparam1','parameter_names','fval');
        end
    end
    
    if ~options_.mh_posterior_mode_estimation && options_.cova_compute
        try
            chol(hh);
        catch
            skipline()
            disp('POSTERIOR KERNEL OPTIMIZATION PROBLEM!')
            disp(' (minus) the hessian matrix at the "mode" is not positive definite!')
            disp('=> posterior variance of the estimated parameters are not positive.')
            disp('You should try to change the initial values of the parameters using')
            disp('the estimated_params_init block, or use another optimization routine.')
            params_at_bound=find(abs(xparam1-bounds.ub)<1.e-10 | abs(xparam1-bounds.lb)<1.e-10);
            if ~isempty(params_at_bound)
                for ii=1:length(params_at_bound)
                    params_at_bound_name{ii,1}=get_the_name(params_at_bound(ii),0,M_,estim_params_,options_);
                end
                disp_string=[params_at_bound_name{1,:}];
                for ii=2:size(params_at_bound_name,1)
                    disp_string=[disp_string,', ',params_at_bound_name{ii,:}];
                end
                fprintf('\nThe following parameters are at the prior bound: %s\n', disp_string)
                fprintf('Some potential solutions are:\n')
                fprintf('   - Check your model for mistakes.\n')
                fprintf('   - Check whether model and data are consistent (correct observation equation).\n')
                fprintf('   - Shut off prior_trunc.\n')
                fprintf('   - Change the optimization bounds.\n')
                fprintf('   - Use a different mode_compute like 6 or 9.\n')
                fprintf('   - Check whether the parameters estimated are identified.\n')
                fprintf('   - Check prior shape (e.g. Inf density at bound(s)).\n')
                fprintf('   - Increase the informativeness of the prior.\n')
            end
            warning('The results below are most likely wrong!');
        end
    end
    
    if options_.mode_check.status && ~options_.mh_posterior_mode_estimation
        ana_deriv_old = options_.analytic_derivation;
        options_.analytic_derivation = 0;
        mode_check(objective_function,xparam1,hh,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
        options_.analytic_derivation = ana_deriv_old;
    end
    
    oo_.posterior.optimization.mode = [];
    oo_.posterior.optimization.Variance = [];
    oo_.posterior.optimization.log_density=[];
    
    invhess=[];
    if ~options_.mh_posterior_mode_estimation
        oo_.posterior.optimization.mode = xparam1;
        if exist('fval','var')
            oo_.posterior.optimization.log_density=-fval;
        end
        if options_.cova_compute
            hsd = sqrt(diag(hh));
            invhess = inv(hh./(hsd*hsd'))./(hsd*hsd');
            stdh = sqrt(diag(invhess));
            oo_.posterior.optimization.Variance = invhess;
        end
    else
        variances = bayestopt_.p2.*bayestopt_.p2;
        idInf = isinf(variances);
        variances(idInf) = 1;
        invhess = options_.mh_posterior_mode_estimation*diag(variances);
        xparam1 = bayestopt_.p5;
        idNaN = isnan(xparam1);
        xparam1(idNaN) = bayestopt_.p1(idNaN);
        outside_bound_pars=find(xparam1 < bounds.lb | xparam1 > bounds.ub);
        xparam1(outside_bound_pars) = bayestopt_.p1(outside_bound_pars);
    end
    
    if ~options_.cova_compute
        stdh = NaN(length(xparam1),1);
    end
    
    if any(bayestopt_.pshape > 0) && ~options_.mh_posterior_mode_estimation
        % display results table and store parameter estimates and standard errors in results
        oo_ = display_estimation_results_table(xparam1, stdh, M_, options_, estim_params_, bayestopt_, oo_, prior_dist_names, 'Posterior', 'posterior');
        % Laplace approximation to the marginal log density:
        if options_.cova_compute
            estim_params_nbr = size(xparam1,1);
            if ispd(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;
            else
                oo_.MarginalDensity.LaplaceApproximation = NaN;
            end
            skipline()
            disp(sprintf('Log data density [Laplace approximation] is %f.',oo_.MarginalDensity.LaplaceApproximation))
            skipline()
        end
        if options_.dsge_var
            [~,~,~,~,~,~,~,oo_.dsge_var.posterior_mode.PHI_tilde,oo_.dsge_var.posterior_mode.SIGMA_u_tilde,oo_.dsge_var.posterior_mode.iXX,oo_.dsge_var.posterior_mode.prior] =...
                feval(objective_function,xparam1,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
        end
    
    elseif ~any(bayestopt_.pshape > 0) && ~options_.mh_posterior_mode_estimation
        oo_=display_estimation_results_table(xparam1, stdh, M_, options_, estim_params_, bayestopt_, oo_, prior_dist_names, 'Maximum Likelihood', 'mle');
    end
    
    if np > 0
        pindx = estim_params_.param_vals(:,1);
        save([M_.fname '_params.mat'],'pindx');
    end
    
    switch options_.MCMC_jumping_covariance
      case 'hessian' %Baseline
                     %do nothing and use hessian from mode_compute
      case 'prior_variance' %Use prior variance
        if any(isinf(bayestopt_.p2))
            error('Infinite prior variances detected. You cannot use the prior variances as the proposal density, if some variances are Inf.')
        else
            hh = diag(1./(bayestopt_.p2.^2));
        end
        hsd = sqrt(diag(hh));
        invhess = inv(hh./(hsd*hsd'))./(hsd*hsd');
      case 'identity_matrix' %Use identity
        invhess = eye(nx);
      otherwise %user specified matrix in file
        try
            load(options_.MCMC_jumping_covariance,'jumping_covariance')
            hh=jumping_covariance;
        catch
            error(['No matrix named ''jumping_covariance'' could be found in ',options_.MCMC_jumping_covariance,'.mat'])
        end
        [nrow, ncol]=size(hh);
        if ~isequal(nrow,ncol) && ~isequal(nrow,nx) %check if square and right size
            error(['jumping_covariance matrix must be square and have ',num2str(nx),' rows and columns'])
        end
        try %check for positive definiteness
            chol(hh);
            hsd = sqrt(diag(hh));
            invhess = inv(hh./(hsd*hsd'))./(hsd*hsd');
        catch
            error(['Specified jumping_covariance is not positive definite'])
        end
    end
    
    if (any(bayestopt_.pshape  >0 ) && options_.mh_replic) || ...
            (any(bayestopt_.pshape >0 ) && options_.load_mh_file)  %% not ML estimation
        bounds = prior_bounds(bayestopt_, options_.prior_trunc); %reset bounds as lb and ub must only be operational during mode-finding
        outside_bound_pars=find(xparam1 < bounds.lb | xparam1 > bounds.ub);
        if ~isempty(outside_bound_pars)
            for ii=1:length(outside_bound_pars)
                outside_bound_par_names{ii,1}=get_the_name(ii,0,M_,estim_params_,options_);
            end
            disp_string=[outside_bound_par_names{1,:}];
            for ii=2:size(outside_bound_par_names,1)
                disp_string=[disp_string,', ',outside_bound_par_names{ii,:}];
            end
            if options_.prior_trunc>0
                error(['Estimation:: Mode value(s) of ', disp_string ,' are outside parameter bounds. Potentially, you should set prior_trunc=0.'])
            else
                error(['Estimation:: Mode value(s) of ', disp_string ,' are outside parameter bounds.'])
            end
        end
        % Tunes the jumping distribution's scale parameter
        if options_.mh_tune_jscale.status
            if strcmp(options_.posterior_sampler_options.posterior_sampling_method, 'random_walk_metropolis_hastings')
                options = options_.mh_tune_jscale;
                options.rwmh = options_.posterior_sampler_options.rwmh;
                options_.mh_jscale = calibrate_mh_scale_parameter(objective_function, ...
                                                                  invhess, xparam1, [bounds.lb,bounds.ub], ...
                                                                  options, dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, bounds, oo_);
                bayestopt_.jscale(:) = options_.mh_jscale;
                disp(sprintf('mh_jscale has been set equal to %s', num2str(options_.mh_jscale)))
                skipline()
            else
                warning('mh_tune_jscale is only available with Random Walk Metropolis Hastings!')
            end
        end
        % runs MCMC
        if options_.mh_replic || options_.load_mh_file
            posterior_sampler_options = options_.posterior_sampler_options.current_options;
            posterior_sampler_options.invhess = invhess;
            [posterior_sampler_options, options_] = check_posterior_sampler_options(posterior_sampler_options, options_);
            % store current options in global
            options_.posterior_sampler_options.current_options = posterior_sampler_options;
            if options_.mh_replic
                ana_deriv_old = options_.analytic_derivation;
                options_.analytic_derivation = 0;
                posterior_sampler(objective_function,posterior_sampler_options.proposal_distribution,xparam1,posterior_sampler_options,bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
                options_.analytic_derivation = ana_deriv_old;
            end
        end
        %% Here I discard first mh_drop percent of the draws:
        CutSample(M_, options_, estim_params_);
        if options_.mh_posterior_mode_estimation
            %reset qz_criterium
            options_.qz_criterium=qz_criterium_old;
            return
        else
            %get stored results if required
            if options_.load_mh_file && options_.load_results_after_load_mh
                oo_load_mh=load([M_.fname '_results'],'oo_');
            end
            if ~options_.nodiagnostic
                if (options_.mh_replic>0 || (options_.load_mh_file && ~options_.load_results_after_load_mh))
                    oo_= McMCDiagnostics(options_, estim_params_, M_,oo_);
                elseif options_.load_mh_file && options_.load_results_after_load_mh
                    if isfield(oo_load_mh.oo_,'convergence')
                        oo_.convergence=oo_load_mh.oo_.convergence;
                    end
                end
            end
            %% Estimation of the marginal density from the Mh draws:
            if options_.mh_replic || (options_.load_mh_file && ~options_.load_results_after_load_mh)
                [marginal,oo_] = marginal_density(M_, options_, estim_params_, oo_, bayestopt_);
                % Store posterior statistics by parameter name
                oo_ = GetPosteriorParametersStatistics(estim_params_, M_, options_, bayestopt_, oo_, prior_dist_names);
                if ~options_.nograph
                    oo_ = PlotPosteriorDistributions(estim_params_, M_, options_, bayestopt_, oo_);
                end
                % Store posterior mean in a vector and posterior variance in
                % a matrix
                [oo_.posterior.metropolis.mean,oo_.posterior.metropolis.Variance] ...
                    = GetPosteriorMeanVariance(M_,options_.mh_drop);
            elseif options_.load_mh_file && options_.load_results_after_load_mh
                %% load fields from previous MCMC run stored in results-file
                field_names={'posterior_mode','posterior_std_at_mode',...% fields set by marginal_density
                             'posterior_mean','posterior_hpdinf','posterior_hpdsup','posterior_median','posterior_variance','posterior_std','posterior_deciles','posterior_density',...% fields set by GetPosteriorParametersStatistics
                             'prior_density',...%fields set by PlotPosteriorDistributions
                            };
                for field_iter=1:size(field_names,2)
                    if isfield(oo_load_mh.oo_,field_names{1,field_iter})
                        oo_.(field_names{1,field_iter})=oo_load_mh.oo_.(field_names{1,field_iter});
                    end
                end
                % field set by marginal_density
                if isfield(oo_load_mh.oo_,'MarginalDensity') && isfield(oo_load_mh.oo_.MarginalDensity,'ModifiedHarmonicMean')
                    oo_.MarginalDensity.ModifiedHarmonicMean=oo_load_mh.oo_.MarginalDensity.ModifiedHarmonicMean;
                end
                % field set by GetPosteriorMeanVariance
                if isfield(oo_load_mh.oo_,'posterior') && isfield(oo_load_mh.oo_.posterior,'metropolis')
                    oo_.posterior.metropolis=oo_load_mh.oo_.posterior.metropolis;
                end
            end
            [error_flag,~,options_]= metropolis_draw(1,options_,estim_params_,M_);
            if ~(~isempty(options_.sub_draws) && options_.sub_draws==0)
                if options_.bayesian_irf
                    if error_flag
                        error('Estimation::mcmc: I cannot compute the posterior IRFs!')
                    end
                    PosteriorIRF('posterior');
                end
                if options_.moments_varendo
                    if error_flag
                        error('Estimation::mcmc: I cannot compute the posterior moments for the endogenous variables!')
                    end
                    if options_.load_mh_file && options_.mh_replic==0 %user wants to recompute results
                       [MetropolisFolder, info] = CheckPath('metropolis',M_.dname);
                       if ~info
                           generic_post_data_file_name={'Posterior2ndOrderMoments','decomposition','PosteriorVarianceDecomposition','correlation','PosteriorCorrelations','conditional decomposition','PosteriorConditionalVarianceDecomposition'};
                           for ii=1:length(generic_post_data_file_name)
                               delete_stale_file([MetropolisFolder filesep M_.fname '_' generic_post_data_file_name{1,ii} '*']);
                           end
                       end
                    end
                    oo_ = compute_moments_varendo('posterior',options_,M_,oo_,var_list_);
                end
                if options_.smoother || ~isempty(options_.filter_step_ahead) || options_.forecast
                    if error_flag
                        error('Estimation::mcmc: I cannot compute the posterior statistics!')
                    end
                    if options_.order==1 && ~options_.particle.status
                        prior_posterior_statistics('posterior',dataset_,dataset_info); %get smoothed and filtered objects and forecasts
                    else
                        error('Estimation::mcmc: Particle Smoothers are not yet implemented.')
                    end
                end
            else
                fprintf('Estimation:mcmc: sub_draws was set to 0. Skipping posterior computations.')
            end
            xparam1 = get_posterior_parameters('mean',M_,estim_params_,oo_,options_);
            M_ = set_all_parameters(xparam1,estim_params_,M_);
        end
    end
    
    if options_.particle.status
        %reset qz_criterium
        options_.qz_criterium=qz_criterium_old;
        return
    end
    
    if (~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.pshape> 0) && options_.load_mh_file)) ...
        || ~options_.smoother ) && ~options_.partial_information  % to be fixed
        %% ML estimation, or posterior mode without Metropolis-Hastings or Metropolis without Bayesian smoothes variables
        [atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,T,R,P,PK,decomp,Trend,state_uncertainty,M_,oo_,options_,bayestopt_] = DsgeSmoother(xparam1,dataset_.nobs,transpose(dataset_.data),dataset_info.missing.aindex,dataset_info.missing.state,M_,oo_,options_,bayestopt_,estim_params_);
        [oo_,yf]=store_smoother_results(M_,oo_,options_,bayestopt_,dataset_,dataset_info,atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,P,PK,decomp,Trend,state_uncertainty);
    
        if ~options_.nograph
            [nbplt,nr,nc,lr,lc,nstar] = pltorg(M_.exo_nbr);
            if ~exist([M_.dname '/graphs'],'dir')
                mkdir(M_.dname,'graphs');
            end
            if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
                fidTeX = fopen([M_.dname, '/graphs/' M_.fname '_SmoothedShocks.tex'],'w');
                fprintf(fidTeX,'%% TeX eps-loader file generated by dynare_estimation_1.m (Dynare).\n');
                fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
                fprintf(fidTeX,' \n');
            end
            for plt = 1:nbplt
                fh = dyn_figure(options_.nodisplay,'Name','Smoothed shocks');
                NAMES = [];
                if options_.TeX, TeXNAMES = []; end
                nstar0=min(nstar,M_.exo_nbr-(plt-1)*nstar);
                if gend==1
                    marker_string{1,1}='-ro';
                    marker_string{2,1}='-ko';
                else
                    marker_string{1,1}='-r';
                    marker_string{2,1}='-k';
                end
                for i=1:nstar0
                    k = (plt-1)*nstar+i;
                    subplot(nr,nc,i);
                    plot([1 gend],[0 0],marker_string{1,1},'linewidth',.5)
                    hold on
                    plot(1:gend,innov(k,:),marker_string{2,1},'linewidth',1)
                    hold off
                    name = M_.exo_names{k};
                    if ~isempty(options_.XTick)
                        set(gca,'XTick',options_.XTick)
                        set(gca,'XTickLabel',options_.XTickLabel)
                    end
                    if gend>1
                        xlim([1 gend])
                    end
                    if options_.TeX
                        title(['$' M_.exo_names_tex{k} '$'],'Interpreter','latex')
                    else
                        title(name,'Interpreter','none')
                    end
                end
                dyn_saveas(fh,[M_.dname, '/graphs/' M_.fname '_SmoothedShocks' int2str(plt)],options_.nodisplay,options_.graph_format);
                if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
                    fprintf(fidTeX,'\\begin{figure}[H]\n');
                    fprintf(fidTeX,'\\centering \n');
                    fprintf(fidTeX,'\\includegraphics[width=%2.2f\\textwidth]{%s_SmoothedShocks%s}\n',options_.figures.textwidth*min(i/nc,1),[M_.dname, '/graphs/' M_.fname],int2str(plt));
                    fprintf(fidTeX,'\\caption{Smoothed shocks.}');
                    fprintf(fidTeX,'\\label{Fig:SmoothedShocks:%s}\n',int2str(plt));
                    fprintf(fidTeX,'\\end{figure}\n');
                    fprintf(fidTeX,'\n');
                end
            end
            if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
                fprintf(fidTeX,'\n');
                fprintf(fidTeX,'%% End of TeX file.\n');
                fclose(fidTeX);
            end
        end
        if nvn
            number_of_plots_to_draw = 0;
            index = [];
            for obs_iter=1:n_varobs
                if max(abs(measurement_error(obs_iter,:))) > options_.ME_plot_tol;
                    number_of_plots_to_draw = number_of_plots_to_draw + 1;
                    index = cat(1,index,obs_iter);
                end
            end
            if ~options_.nograph
                [nbplt,nr,nc,lr,lc,nstar] = pltorg(number_of_plots_to_draw);
                if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
                    fidTeX = fopen([M_.dname, '/graphs/' M_.fname '_SmoothedObservationErrors.tex'],'w');
                    fprintf(fidTeX,'%% TeX eps-loader file generated by dynare_estimation_1.m (Dynare).\n');
                    fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
                    fprintf(fidTeX,' \n');
                end
                for plt = 1:nbplt
                    fh = dyn_figure(options_.nodisplay,'Name','Smoothed observation errors');
                    nstar0=min(nstar,number_of_plots_to_draw-(plt-1)*nstar);
                    if gend==1
                        marker_string{1,1}='-ro';
                        marker_string{2,1}='-ko';
                    else
                        marker_string{1,1}='-r';
                        marker_string{2,1}='-k';
                    end
                    for i=1:nstar0
                        k = (plt-1)*nstar+i;
                        subplot(nr,nc,i);
                        plot([1 gend],[0 0],marker_string{1,1},'linewidth',.5)
                        hold on
                        plot(1:gend,measurement_error(index(k),:),marker_string{2,1},'linewidth',1)
                        hold off
                        name = options_.varobs{index(k)};
                        if gend>1
                            xlim([1 gend])
                        end
                        if ~isempty(options_.XTick)
                            set(gca,'XTick',options_.XTick)
                            set(gca,'XTickLabel',options_.XTickLabel)
                        end
                        if options_.TeX
                            idx = strmatch(options_.varobs{index(k)}, M_.endo_names, 'exact');
                            title(['$' M_.endo_names_tex{idx} '$'],'Interpreter','latex')
                        else
                            title(name,'Interpreter','none')
                        end
                    end
                    dyn_saveas(fh,[M_.dname, '/graphs/' M_.fname '_SmoothedObservationErrors' int2str(plt)],options_.nodisplay,options_.graph_format);
                    if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
                        fprintf(fidTeX,'\\begin{figure}[H]\n');
                        fprintf(fidTeX,'\\centering \n');
                        fprintf(fidTeX,'\\includegraphics[width=%2.2f\\textwidth]{%s_SmoothedObservationErrors%s}\n',options_.figures.textwidth*min(i/nc,1),[M_.dname, '/graphs/' M_.fname],int2str(plt));
                        fprintf(fidTeX,'\\caption{Smoothed observation errors.}');
                        fprintf(fidTeX,'\\label{Fig:SmoothedObservationErrors:%s}\n',int2str(plt));
                        fprintf(fidTeX,'\\end{figure}\n');
                        fprintf(fidTeX,'\n');
                    end
                end
                if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
                    fprintf(fidTeX,'\n');
                    fprintf(fidTeX,'%% End of TeX file.\n');
                    fclose(fidTeX);
                end
            end
        end
        %%
        %%  Historical and smoothed variabes
        %%
        if ~options_.nograph
            [nbplt,nr,nc,lr,lc,nstar] = pltorg(n_varobs);
            if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
                fidTeX = fopen([M_.dname, '/graphs/' M_.fname '_HistoricalAndSmoothedVariables.tex'],'w');
                fprintf(fidTeX,'%% TeX eps-loader file generated by dynare_estimation_1.m (Dynare).\n');
                fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
                fprintf(fidTeX,' \n');
            end
            for plt = 1:nbplt
                fh = dyn_figure(options_.nodisplay,'Name','Historical and smoothed variables');
                NAMES = [];
                nstar0=min(nstar,n_varobs-(plt-1)*nstar);
                if gend==1
                    marker_string{1,1}='-ro';
                    marker_string{2,1}='--ko';
                else
                    marker_string{1,1}='-r';
                    marker_string{2,1}='--k';
                end
                for i=1:nstar0
                    k = (plt-1)*nstar+i;
                    subplot(nr,nc,i);
                    plot(1:gend,yf(k,:),marker_string{1,1},'linewidth',1)
                    hold on
                    plot(1:gend,rawdata(:,k),marker_string{2,1},'linewidth',1)
                    hold off
                    name = options_.varobs{k};
                    if ~isempty(options_.XTick)
                        set(gca,'XTick',options_.XTick)
                        set(gca,'XTickLabel',options_.XTickLabel)
                    end
                    if gend>1
                        xlim([1 gend])
                    end
                    if options_.TeX
                        idx = strmatch(options_.varobs{k}, M_.endo_names,'exact');
                        title(['$' M_.endo_names_tex{idx} '$'],'Interpreter','latex')
                    else
                        title(name,'Interpreter','none')
                    end
                end
                dyn_saveas(fh,[M_.dname, '/graphs/' M_.fname '_HistoricalAndSmoothedVariables' int2str(plt)],options_.nodisplay,options_.graph_format);
                if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
                    fprintf(fidTeX,'\\begin{figure}[H]\n');
                    fprintf(fidTeX,'\\centering \n');
                    fprintf(fidTeX,'\\includegraphics[width=%2.2f\\textwidth]{%s_HistoricalAndSmoothedVariables%s}\n',options_.figures.textwidth*min(i/nc,1),[M_.dname, '/graphs/' M_.fname],int2str(plt));
                    fprintf(fidTeX,'\\caption{Historical and smoothed variables.}');
                    fprintf(fidTeX,'\\label{Fig:HistoricalAndSmoothedVariables:%s}\n',int2str(plt));
                    fprintf(fidTeX,'\\end{figure}\n');
                    fprintf(fidTeX,'\n');
                end
            end
            if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
                fprintf(fidTeX,'\n');
                fprintf(fidTeX,'%% End of TeX file.\n');
                fclose(fidTeX);
            end
        end
    end
    
    if options_.forecast > 0 && options_.mh_replic == 0 && ~options_.load_mh_file
        oo_.forecast = dyn_forecast(var_list_,M_,options_,oo_,'smoother',dataset_info);
    end
    
    if np > 0
        pindx = estim_params_.param_vals(:,1);
        save([M_.fname '_pindx.mat'] ,'pindx');
    end
    
    %reset qz_criterium
    options_.qz_criterium=qz_criterium_old;
    
    if reset_options_related_to_estimation
        options_.mode_compute = mode_compute_o;
        options_.mh_replic = mh_replic_o;
    end
    if first_obs_nan_indicator
        options_.first_obs=NaN;
    end