125 lines
4.5 KiB
Matlab
125 lines
4.5 KiB
Matlab
function [fval,fake_1, fake_2, exit_flag ] = minus_logged_prior_density(xparams,pshape,p6,p7,p3,p4,DynareOptions,DynareModel,EstimatedParams,DynareResults)
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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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% pshape [integer] vector specifying prior densities shapes.
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% p6 [double] vector, first hyperparameter.
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% p7 [double] vector, second hyperparameter.
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% p3 [double] vector, prior's lower bound.
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% p4 [double] vector, prior's upper bound.
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%
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% OUTPUTS
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% f [double] value of minus the logged prior density.
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% Copyright (C) 2009-2012 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 <http://www.gnu.org/licenses/>.
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global objective_function_penalty_base
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fake_1 = 1;
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fake_2 = 1;
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exit_flag = 1;
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info = 0;
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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(DynareOptions.mode_compute,1) && any(xparams<p3)
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k = find(xparams<p3);
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fval = objective_function_penalty_base+sum((p3(k)-xparams(k)).^2);
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exit_flag = 0;
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info = 41;
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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(DynareOptions.mode_compute,1) && any(xparams>p4)
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k = find(xparams>p4);
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fval = objective_function_penalty_base+sum((xparams(k)-p4(k)).^2);
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exit_flag = 0;
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info = 42;
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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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DynareModel = set_all_parameters(xparams,EstimatedParams,DynareModel);
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Q = DynareModel.Sigma_e;
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H = DynareModel.H;
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% Test if Q is positive definite.
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if EstimatedParams.ncx
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% Try to compute the cholesky decomposition of Q (possible iff Q is positive definite)
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[CholQ,testQ] = chol(Q);
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if testQ
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% The variance-covariance matrix of the structural innovations is not definite positive. We have to compute the eigenvalues of this matrix in order to build the endogenous penalty.
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a = diag(eig(Q));
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k = find(a < 0);
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if k > 0
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fval = objective_function_penalty_base+sum(-a(k));
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exit_flag = 0;
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info = 43;
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return
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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 EstimatedParams.ncn
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% Try to compute the cholesky decomposition of H (possible iff H is positive definite)
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[CholH,testH] = chol(H);
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if testH
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% The variance-covariance matrix of the measurement errors is not definite positive. We have to compute the eigenvalues of this matrix in order to build the endogenous penalty.
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a = diag(eig(H));
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k = find(a < 0);
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if k > 0
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fval = objective_function_penalty_base+sum(-a(k));
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exit_flag = 0;
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info = 44;
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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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M_ = set_all_parameters(xparams,EstimatedParams,DynareModel);
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[dr,info,DynareModel,DynareOptions,DynareResults] = resol(0,DynareModel,DynareOptions,DynareResults);
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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) == 1 || info(1) == 2 || info(1) == 5 || info(1) == 7 || info(1) ...
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== 8 || info(1) == 22 || info(1) == 24 || info(1) == 19
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fval = objective_function_penalty_base+1;
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info = info(1);
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exit_flag = 0;
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return
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elseif info(1) == 3 || info(1) == 4 || info(1)==6 || info(1) == 20 || info(1) == 21 || info(1) == 23
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fval = objective_function_penalty_base+info(2);
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info = info(1);
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exit_flag = 0;
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return
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end
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fval = - priordens(xparams,pshape,p6,p7,p3,p4); |