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dynare_estimation_1.m
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