199 lines
7.5 KiB
Matlab
199 lines
7.5 KiB
Matlab
function [fval,info,exit_flag,DLIK,Hess,SteadyState,trend_coeff,Model,DynareOptions,BayesInfo,DynareResults, atT, innov] = IVF_posterior(xparam1,...
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dataset_,obs_info,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults)
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% function [fval,info,exit_flag,DLIK,Hess,SteadyState,trend_coeff,Model,DynareOptions,BayesInfo,DynareResults, atT, innov] = IVF_posterior(xparam1,...
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% dataset_,obs_info,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults)
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% Computes Likelihood with inversion filter
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%
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% INPUTS
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% - xparam1 [double] current values for the estimated parameters.
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% - dataset_ [structure] dataset after transformations
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% - DynareOptions [structure] Matlab's structure describing the current options (options_).
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% - Model [structure] Matlab's structure describing the model (M_).
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% - EstimatedParameters [structure] characterizing parameters to be estimated
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% - BayesInfo [structure] describing the priors
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% - BoundsInfo [structure] containing prior bounds
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% - DynareResults [structure] Matlab's structure containing the results (oo_).
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%
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% OUTPUTS
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% - fval [double] scalar, value of the likelihood or posterior kernel.
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% - info [integer] 4×1 vector, informations resolution of the model and evaluation of the likelihood.
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% - exit_flag [integer] scalar, equal to 1 (no issues when evaluating the likelihood) or 0 (not able to evaluate the likelihood).
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% - DLIK [double] Empty array.
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% - Hess [double] Empty array.
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% - SteadyState [double] Empty array.
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% - trend [double] Empty array.
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% - Model [struct] Updated Model structure described in INPUTS section.
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% - DynareOptions [struct] Updated DynareOptions structure described in INPUTS section.
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% - BayesInfo [struct] See INPUTS section.
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% - DynareResults [struct] Updated DynareResults structure described in INPUTS section.
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% - atT [double] (m*T) matrix, smoothed endogenous variables (a_{t|T}) (decision-rule order)
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% - innov [double] (r*T) matrix, smoothed structural shocks (r>n is the umber of shocks).
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% Copyright © 2021 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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DLIK=[];
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Hess=[];
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trend_coeff = [];
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obs = dataset_.data;
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obs_list = DynareOptions.varobs(:);
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exit_flag = 1;
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if size(xparam1,1)<size(xparam1,2)
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xparam1=xparam1';
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end
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%------------------------------------------------------------------------------
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% 1. Get the structural parameters & define penalties
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%------------------------------------------------------------------------------
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if ~isempty(xparam1)
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Model = set_all_parameters(xparam1,EstimatedParameters,Model);
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[fval,info,exit_flag,Q,H]=check_bounds_and_definiteness_estimation(xparam1, Model, EstimatedParameters, BoundsInfo);
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if info(1)
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return
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end
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end
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err_index=DynareOptions.occbin.likelihood.IVF_shock_observable_mapping; % err_index= find(diag(Model.Sigma_e)~=0);
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COVMAT1 = Model.Sigma_e(err_index,err_index);
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% Linearize the model around the deterministic steady state and extract the matrices of the state equation (T and R).
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[T,R,SteadyState,info,Model,DynareResults] = dynare_resolve(Model,DynareOptions,DynareResults,'restrict');
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% Return, with endogenous penalty when possible, if dynare_resolve issues an error code (defined in resol).
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if info(1)
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if info(1) == 3 || info(1) == 4 || info(1) == 5 || info(1)==6 ||info(1) == 19 ||...
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info(1) == 20 || info(1) == 21 || info(1) == 23 || info(1) == 26 || ...
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info(1) == 81 || info(1) == 84 || info(1) == 85 || info(1) == 86
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%meaningful second entry of output that can be used
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fval = Inf;
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info(4) = info(2);
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exit_flag = 0;
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return
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else
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fval = Inf;
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info(4) = 0.1;
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exit_flag = 0;
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return
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end
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end
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%---------------------------------------------
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% Calculate likelihood
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%---------------------------------------------
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sample_length = size(obs,1);
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filtered_errs_init = zeros(sample_length,sum(err_index));
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[filtered_errs, resids, Emat, stateval, info] = occbin.IVF_core(Model,DynareResults,DynareOptions,err_index,filtered_errs_init,obs_list,obs);
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if info(1)
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fval = Inf;
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exit_flag = 0;
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return
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end
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nobs=size(filtered_errs,1);
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%-------------------------------------
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% Calculate the selection matrix
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%-------------------------------------
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iobs=DynareOptions.varobs_id';
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likei=NaN(nobs,1);
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if ~any(any(isnan(obs)))
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log_det_COV=log(det(COVMAT1));
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for t = 1:nobs
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Gmat1 = Emat(iobs,:,t);
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log_det_jacobian = log_det_COV + 2*log(abs(det(Gmat1)));
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trace_term = filtered_errs(t,:)/(COVMAT1)*filtered_errs(t,:)';
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likei(t,1) = log_det_jacobian + trace_term;
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end
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likei=likei+length(iobs)*log(2*pi);
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else
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for t = 1:nobs
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inan=~isnan(obs(t,:));
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Gmat1 = Emat(iobs(inan),inan,t);
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log_det_jacobian = log(det(COVMAT1(inan,inan))) + 2*log(abs(det(Gmat1)));
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trace_term = filtered_errs(t,inan)/(COVMAT1(inan,inan))*filtered_errs(t,inan)';
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likei(t,1) = log_det_jacobian + trace_term + length(iobs(inan))*log(2*pi);
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end
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end
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like = 0.5*sum(likei(DynareOptions.presample+1:end));
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if isinf(like)
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fval = Inf;
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info(1) = 301;
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info(4) = 1000;
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exit_flag = 0;
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return
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elseif isnan(like)
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fval = Inf;
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info(1) = 302;
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info(4) = 1000;
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exit_flag = 0;
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return
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end
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maxresid = max(abs(resids(:)));
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if maxresid>1e-3
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disp_verbose('Penalize failure of residuals to be zero',options_.verbosity)
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fval = Inf;
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info(1) = 303;
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info(4) = sum(resids(:).^2);
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exit_flag = 0;
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return
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end
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if ~isempty(xparam1)
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prior = -priordens(xparam1,BayesInfo.pshape,BayesInfo.p6,BayesInfo.p7,BayesInfo.p3,BayesInfo.p4);
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else
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prior = 0;
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end
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if prior == Inf
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% If parameters outside prior bound, minus prior density is very large
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fval = Inf;
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info(4) = 1000;
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exit_flag = 0;
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return
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end
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%---------------------------------------------
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% Calculate posterior
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%---------------------------------------------
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% remember that the likelihood has already been multiplied by -1
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% hence, posterior is -1 times the log of the prior
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fval = like+prior;
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atT = stateval(:,DynareResults.dr.order_var)';
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innov = zeros(Model.exo_nbr,sample_length);
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innov(diag(Model.Sigma_e)~=0,:)=filtered_errs';
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updated_variables = atT*nan;
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BayesInfo.mf = BayesInfo.smoother_var_list(BayesInfo.smoother_mf);
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initDynareOptions=DynareOptions;
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DynareOptions.nk=[]; %unset options_.nk and reset it later
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[DynareResults]=store_smoother_results(Model,DynareResults,DynareOptions,BayesInfo,dataset_,obs_info,atT,innov,[],updated_variables,DynareResults.dr.ys,zeros(length(DynareOptions.varobs_id),1));
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DynareOptions=initDynareOptions;
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