146 lines
5.2 KiB
Matlab
146 lines
5.2 KiB
Matlab
function [fval, info, exitflag, fake1, fake2] = minus_logged_prior_density(xparams, Prior, options_, M_, estim_params_, oo_)
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% Evaluates minus the logged prior density.
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%
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% INPUTS
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% - xparams [double] vector of parameters.
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% - Prior [dprior] vector specifying prior densities shapes.
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% - DynareOptions [struct] Options, AKA options_
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% - DynareModel [struct] Model description, AKA M_
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% - EstimatedParams [struct] Info about estimated parameters, AKA estimated_params_
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% - DynareResults [struct] Results, AKA oo_
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%
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% OUTPUTS
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% - fval [double] value of minus the logged prior density.
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% - info [double] 4×1 vector, second entry stores penalty, first entry the error code, last entry a penalty (used for optimization).
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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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exitflag = true;
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info = zeros(4,1);
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fake1 = [];
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fake2 = [];
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%------------------------------------------------------------------------------
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% 1. Get the structural parameters & define penalties
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%------------------------------------------------------------------------------
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% Return, with endogenous penalty, if some parameters are smaller than the lower bound of the prior domain.
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if ~isequal(options_.mode_compute, 1) && any(xparams<Prior.p3)
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k = find(xparams<Prior.p3);
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fval = Inf;
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exitflag = false;
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info(1) = 41;
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info(4) = sum((Prior.p3(k)-xparams(k)).^2);
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return
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end
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% Return, with endogenous penalty, if some parameters are greater than the upper bound of the prior domain.
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if ~isequal(options_.mode_compute, 1) && any(xparams>Prior.p4)
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k = find(xparams>Prior.p4);
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fval = Inf;
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exitflag = false;
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info(1) = 42;
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info(4) = sum((xparams(k)-Prior.p4(k)).^2);
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return
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end
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% Get the diagonal elements of the covariance matrices for the structural innovations (Q) and the measurement error (H).
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M_ = set_all_parameters(xparams, estim_params_, M_);
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Q = M_.Sigma_e;
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H = M_.H;
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% Test if Q is positive definite.
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if ~issquare(Q) || estim_params_.ncx || isfield(estim_params_, 'calibrated_covariances')
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% Try to compute the cholesky decomposition of Q (possible iff Q is positive definite)
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[Q_is_positive_definite, penalty] = ispd(Q);
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if ~Q_is_positive_definite
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% The variance-covariance matrix of the structural innovations is not definite positive. We have to compute the
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% eigenvalues of this matrix in order to build the endogenous penalty.
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fval = Inf;
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exitflag = false;
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info(1) = 43;
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info(4) = penalty;
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return
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end
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if isfield(estim_params_, 'calibrated_covariances')
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correct_flag = check_consistency_covariances(Q);
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if ~correct_flag
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penalty = sum(Q(estim_params_.calibrated_covariances.position).^2);
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fval = Inf;
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exitflag = false;
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info(1) = 71;
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info(4) = penalty;
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return4
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end
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end
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end
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% Test if H is positive definite.
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if ~issquare(H) || estim_params_.ncn || isfield(estim_params_, 'calibrated_covariances_ME')
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[H_is_positive_definite, penalty] = ispd(H);
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if ~H_is_positive_definite
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% The variance-covariance matrix of the measurement errors is not definite positive. We have to compute the eigenvalues
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% of this matrix in order to build the endogenous penalty.
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fval = Inf;
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exitflag = false;
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info(1) = 44;
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info(4) = penalty;
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return
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end
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if isfield(estim_params_, 'calibrated_covariances_ME')
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correct_flag = check_consistency_covariances(H);
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if ~correct_flag
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penalty = sum(H(estim_params_.calibrated_covariances_ME.position).^2);
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fval = Inf;
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exitflag = false;
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info(1) = 72;
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info(4) = penalty;
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return
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end
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end
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end
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%-----------------------------
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% 2. Check BK and steady state
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%-----------------------------
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[~, info] = resol(0, M_, options_, oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
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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
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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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exitflag = false;
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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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exitflag = false;
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return
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end
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end
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fval = - Prior.density(xparams);
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