96 lines
4.1 KiB
Matlab
96 lines
4.1 KiB
Matlab
function [llik,parameters] = evaluate_likelihood(parameters,M_,estim_params_,oo_,options_,bayestopt_)
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% [llik,parameters] = evaluate_likelihood(parameters,M_,estim_params_,oo_,options_,bayestopt_)
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% Evaluate the logged likelihood at parameters.
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%
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% INPUTS
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% o parameters a string ('posterior mode','posterior mean','posterior median','prior mode','prior mean') or a vector of values for
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% the (estimated) parameters of the model.
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% o M_ [structure] Definition of the model
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% o estim_params_ [structure] characterizing parameters to be estimated
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% o oo_ [structure] Storage of results
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% o options_ [structure] Options
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% o bayestopt_ [structure] describing the priors
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%
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% OUTPUTS
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% o ldens [double] value of the sample logged density at parameters.
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% o parameters [double] vector of values for the estimated parameters.
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%
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% SPECIAL REQUIREMENTS
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% None
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%
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% REMARKS
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% [1] This function cannot evaluate the likelihood of a dsge-var model...
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% [2] This function use persistent variables for the dataset_ and the description of the missing observations. Consequently, if this function
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% is called more than once (by changing the value of parameters) the sample *must not* change.
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% Copyright © 2009-2023 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
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persistent dataset_ dataset_info
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if nargin==0
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parameters = 'posterior mode';
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end
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if ischar(parameters)
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switch parameters
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case 'posterior mode'
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parameters = get_posterior_parameters('mode',M_,estim_params_,oo_,options_);
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case 'posterior mean'
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parameters = get_posterior_parameters('mean',M_,estim_params_,oo_,options_);
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case 'posterior median'
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parameters = get_posterior_parameters('median',M_,estim_params_,oo_,options_);
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case 'prior mode'
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parameters = bayestopt_.p5(:);
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case 'prior mean'
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parameters = bayestopt_.p1;
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otherwise
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disp('eval_likelihood:: If the input argument is a string, then it has to be equal to:')
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disp(' ''posterior mode'', ')
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disp(' ''posterior mean'', ')
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disp(' ''posterior median'', ')
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disp(' ''prior mode'' or')
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disp(' ''prior mean''.')
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error
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end
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end
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if isempty(dataset_)
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[dataset_, dataset_info] = makedataset(options_);
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end
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options_=select_qz_criterium_value(options_);
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if ~isempty(bayestopt_) && any(bayestopt_.pshape > 0)
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% Plot prior densities.
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% Set prior bounds
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bounds = prior_bounds(bayestopt_, options_.prior_trunc);
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else % estimated parameters but no declared priors
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% No priors are declared so Dynare will estimate the model by
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% maximum likelihood with inequality constraints for the parameters.
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[~,~,~,lb,ub] = set_prior(estim_params_,M_,options_);
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bounds.lb = lb;
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bounds.ub = ub;
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end
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if options_.occbin.likelihood.status && options_.occbin.likelihood.inversion_filter
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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);
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else
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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);
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end
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ldens = evaluate_prior(parameters,M_,estim_params_,oo_,options_,bayestopt_);
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llik = llik - ldens; |