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function [llik,parameters] = evaluate_likelihood(parameters,M_,estim_params_,oo_,options_,bayestopt_)
% [llik,parameters] = evaluate_likelihood(parameters,M_,estim_params_,oo_,options_,bayestopt_)
% Evaluate the logged likelihood at parameters.
%
% INPUTS
% o parameters a string ('posterior mode','posterior mean','posterior median','prior mode','prior mean') or a vector of values for
% the (estimated) parameters of the model.
% o M_ [structure] Definition of the model
% o estim_params_ [structure] characterizing parameters to be estimated
% o oo_ [structure] Storage of results
% o options_ [structure] Options
% o bayestopt_ [structure] describing the priors
%
% OUTPUTS
% o ldens [double] value of the sample logged density at parameters.
% o parameters [double] vector of values for the estimated parameters.
%
% SPECIAL REQUIREMENTS
% None
%
% REMARKS
% [1] This function cannot evaluate the likelihood of a dsge-var model...
% [2] This function use persistent variables for the dataset_ and the description of the missing observations. Consequently, if this function
% is called more than once (by changing the value of parameters) the sample *must not* change.
% Copyright © 2009-2023 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 <https://www.gnu.org/licenses/>.
persistent dataset_ dataset_info
if nargin==0
parameters = 'posterior mode';
end
if ischar(parameters)
switch parameters
case 'posterior mode'
parameters = get_posterior_parameters('mode',M_,estim_params_,oo_,options_);
case 'posterior mean'
parameters = get_posterior_parameters('mean',M_,estim_params_,oo_,options_);
case 'posterior median'
parameters = get_posterior_parameters('median',M_,estim_params_,oo_,options_);
case 'prior mode'
parameters = bayestopt_.p5(:);
case 'prior mean'
parameters = bayestopt_.p1;
otherwise
disp('eval_likelihood:: If the input argument is a string, then it has to be equal to:')
disp(' ''posterior mode'', ')
disp(' ''posterior mean'', ')
disp(' ''posterior median'', ')
disp(' ''prior mode'' or')
disp(' ''prior mean''.')
error
end
end
if isempty(dataset_)
[dataset_, dataset_info] = makedataset(options_);
end
options_=select_qz_criterium_value(options_);
if ~isempty(bayestopt_) && any(bayestopt_.pshape > 0)
% Plot prior densities.
% Set prior bounds
bounds = prior_bounds(bayestopt_, options_.prior_trunc);
else % estimated parameters but no declared priors
% No priors are declared so Dynare will estimate the model by
% maximum likelihood with inequality constraints for the parameters.
[~,~,~,lb,ub] = set_prior(estim_params_,M_,options_);
bounds.lb = lb;
bounds.ub = ub;
end
if options_.occbin.likelihood.status && options_.occbin.likelihood.inversion_filter
llik = -occbin.IVF_posterior(parameters,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_.dr, oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
else
llik = -dsge_likelihood(parameters,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_.dr, oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
end
ldens = evaluate_prior(parameters,M_,estim_params_,oo_,options_,bayestopt_);
llik = llik - ldens;
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