92 lines
4.0 KiB
Matlab
92 lines
4.0 KiB
Matlab
function bvar = dsgevar_posterior_density(deep,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
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% This function characterizes the posterior distribution of a bvar with
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% a dsge prior (as in Del Negro and Schorfheide 2003) for a given value
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% of the deep parameters (structural parameters + the size of the
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% shocks + dsge_prior_weight).
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%
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% INPUTS
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% deep: [double] a vector with the deep parameters.
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%
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% OUTPUTS
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% bvar: a matlab structure with prior and posterior densities.
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%
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% ALGORITHM
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% ...
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% SPECIAL REQUIREMENTS
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% none
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%
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% Copyright (C) 1996-2011 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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gend = options_.nobs;
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dsge_prior_weight = M_.params(strmatch('dsge_prior_weight',M_.param_names));
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DSGE_PRIOR_WEIGHT = floor(gend*(1+dsge_prior_weight));
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bvar.NumberOfLags = options_.varlag;
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bvar.NumberOfVariables = size(options_.varobs,1);
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bvar.Constant = 'no';
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bvar.NumberOfEstimatedParameters = bvar.NumberOfLags*bvar.NumberOfVariables;
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if ~options_.noconstant
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bvar.Constant = 'yes';
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bvar.NumberOfEstimatedParameters = bvar.NumberOfEstimatedParameters + ...
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bvar.NumberOfVariables;
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end
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[fval,cost_flag,info,PHI,SIGMAu,iXX,prior] = DsgeVarLikelihood(deep',DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
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% Conditionnal posterior density of the lagged matrices (given Sigma) ->
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% Matric-variate normal distribution.
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bvar.LaggedMatricesConditionalOnSigma.posterior.density = 'matric-variate normal';
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bvar.LaggedMatricesConditionalOnSigma.posterior.arg1 = PHI;
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bvar.LaggedMatricesConditionalOnSigma.posterior.arg2 = 'Sigma';
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bvar.LaggedMatricesConditionalOnSigma.posterior.arg3 = iXX;
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% Marginal posterior density of the covariance matrix -> Inverted Wishart.
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bvar.Sigma.posterior.density = 'inverse wishart';
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bvar.Sigma.posterior.arg1 = SIGMAu*DSGE_PRIOR_WEIGHT;
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bvar.Sigma.posterior.arg2 = DSGE_PRIOR_WEIGHT-bvar.NumberOfEstimatedParameters;
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% Marginal posterior density of the lagged matrices -> Generalized
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% Student distribution (See appendix B.5 in Zellner (1971)).
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bvar.LaggedMatrices.posterior.density = 'matric-variate student';
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bvar.LaggedMatrices.posterior.arg1 = inv(iXX);%P
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bvar.LaggedMatrices.posterior.arg2 = SIGMAu*DSGE_PRIOR_WEIGHT;%Q
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bvar.LaggedMatrices.posterior.arg3 = PHI;%M (posterior mean)
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bvar.LaggedMatrices.posterior.arg4 = DSGE_PRIOR_WEIGHT;%(sample size)
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% Conditionnal posterior density of the lagged matrices (given Sigma) ->
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% Matric-variate normal distribution.
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bvar.LaggedMatricesConditionalOnSigma.prior.density = 'matric-variate normal';
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bvar.LaggedMatricesConditionalOnSigma.prior.arg1 = prior.PHIstar;
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bvar.LaggedMatricesConditionalOnSigma.prior.arg2 = 'Sigma';
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bvar.LaggedMatricesConditionalOnSigma.prior.arg3 = prior.iGXX;
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% Marginal posterior density of the covariance matrix -> Inverted Wishart.
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bvar.Sigma.prior.density = 'inverse wishart';
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bvar.Sigma.prior.arg1 = prior.SIGMAstar*prior.ArtificialSampleSize;
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bvar.Sigma.prior.arg2 = prior.DF;
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% Marginal posterior density of the lagged matrices -> Generalized
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% Student distribution (See appendix B.5 in Zellner (1971)).
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bvar.LaggedMatrices.prior.density = 'matric-variate student';
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bvar.LaggedMatrices.prior.arg1 = inv(prior.iGXX);%P
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bvar.LaggedMatrices.prior.arg2 = prior.SIGMAstar*prior.ArtificialSampleSize;%Q
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bvar.LaggedMatrices.prior.arg3 = prior.PHIstar;%M (posterior mean)
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bvar.LaggedMatrices.prior.arg4 = prior.ArtificialSampleSize;%(sample size) |