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Added a function returning the prior and posterior densities of a dsge-var model, given the deep parameters and the dsge_prior_weight parameter. 

git-svn-id: https://www.dynare.org/svn/dynare/dynare_v4@1740 ac1d8469-bf42-47a9-8791-bf33cf982152
time-shift
adjemian 2008-03-10 16:08:02 +00:00
parent 01f71a285a
commit ea1f4abaed
2 changed files with 90 additions and 4 deletions
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15
matlab/DsgeVarLikelihood.m
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@ -1,5 +1,4 @@
function [fval,cost_flag,info,PHI,SIGMAu,iXX] = DsgeVarLikelihood(xparam1,gend)
function [fval,cost_flag,info,PHI,SIGMAu,iXX,prior] = DsgeVarLikelihood(xparam1,gend)
% function [fval,cost_flag,info,PHI,SIGMAu,iXX] = DsgeVarLikelihood(xparam1,gend)
% Evaluates the posterior kernel of the bvar-dsge model.
%
@ -14,14 +13,13 @@ function [fval,cost_flag,info,PHI,SIGMAu,iXX] = DsgeVarLikelihood(xparam1,gend)
% o PHI [double] Stacked BVAR-DSGE autoregressive matrices (at the mode associated to xparam1).
% o SIGMAu [double] Covariance matrix of the BVAR-DSGE (at the mode associated to xparam1).
% o iXX [double] inv(X'X).
% o prior [double] a matlab structure describing the dsge-var prior.
%
% SPECIAL REQUIREMENTS
% None.
%
% part of DYNARE, copyright Dynare Team (2006-2008)
% Gnu Public License.
global bayestopt_ estim_params_ M_ options_
nvx = estim_params_.nvx;
@ -211,4 +209,13 @@ if (nargout == 6)
else
iXX = tmp2;
end
end
if (nargout==7)
iGXX = inv(GXX);
prior.SIGMAstar = GYY - GYX*iGXX*GYX';
prior.PHIstar = iGXX*transpose(GYX);
prior.ArtificialSampleSize = fix(dsge_prior_weight*T);
prior.DF = prior.ArtificialSampleSize - NumberOfParameters - NumberOfObservedVariables;
prior.iGXX = iGXX;
end

79
matlab/dsgevar_posterior_density.m Normal file
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@ -0,0 +1,79 @@
function bvar = dsgevar_posterior_density(deep)
% This function characterizes the posterior distribution of a bvar with
% a dsge prior (as in Del Negro and Schorfheide 2003) for a given value
% of the deep parameters (structural parameters + the size of the
% shocks + dsge_prior_weight).
%
% INPUTS
% deep: [double] a vector with the deep parameters.
%
% OUTPUTS
% bvar: a matlab structure with prior and posterior densities.
%
% ALGORITHM
% ...
% SPECIAL REQUIREMENTS
% none
%
%
% part of DYNARE, copyright Dynare Team (1996-2007)
% Gnu Public License.
global options_ M_
gend = options_.nobs;
dsge_prior_weight = M_.params(strmatch('dsge_prior_weight',M_.param_names));
DSGE_PRIOR_WEIGHT = floor(gend*(1+dsge_prior_weight));
bvar.NumberOfLags = options_.varlag;
bvar.NumberOfVariables = size(options_.varobs,1);
bvar.Constant = 'no';
bvar.NumberOfEstimatedParameters = bvar.NumberOfLags*bvar.NumberOfVariables;
if
bvar.Constant = 'yes';
bvar.NumberOfEstimatedParameters = bvar.NumberOfEstimatedParameters + ...
bvar.NumberOfVariables;
end
[fval,cost_flag,info,PHI,SIGMAu,iXX,prior] = DsgeVarLikelihood(deep',gend);
% Conditionnal posterior density of the lagged matrices (given Sigma) ->
% Matric-variate normal distribution.
bvar.LaggedMatricesConditionalOnSigma.posterior.density = 'matric-variate normal';
bvar.LaggedMatricesConditionalOnSigma.posterior.arg1 = PHI;
bvar.LaggedMatricesConditionalOnSigma.posterior.arg2 = 'Sigma';
bvar.LaggedMatricesConditionalOnSigma.posterior.arg3 = iXX;
% Marginal posterior density of the covariance matrix -> Inverted Wishart.
bvar.Sigma.posterior.density = 'inverse wishart';
bvar.Sigma.posterior.arg1 = SIGMAu*DSGE_PRIOR_WEIGHT;
bvar.Sigma.posterior.arg2 = DSGE_PRIOR_WEIGHT-bvar.NumberOfEstimatedParameters;
% Marginal posterior density of the lagged matrices -> Generalized
% Student distribution (See appendix B.5 in Zellner (1971)).
bvar.LaggedMatrices.posterior.density = 'matric-variate student';
bvar.LaggedMatrices.posterior.arg1 = inv(iXX);%P
bvar.LaggedMatrices.posterior.arg2 = SIGMAu*DSGE_PRIOR_WEIGHT;%Q
bvar.LaggedMatrices.posterior.arg3 = PHI;%M (posterior mean)
bvar.LaggedMatrices.posterior.arg4 = DSGE_PRIOR_WEIGHT;%(sample size)
% Conditionnal posterior density of the lagged matrices (given Sigma) ->
% Matric-variate normal distribution.
bvar.LaggedMatricesConditionalOnSigma.prior.density = 'matric-variate normal';
bvar.LaggedMatricesConditionalOnSigma.prior.arg1 = prior.PHIstar;
bvar.LaggedMatricesConditionalOnSigma.prior.arg2 = 'Sigma';
bvar.LaggedMatricesConditionalOnSigma.prior.arg3 = prior.iGXX;
% Marginal posterior density of the covariance matrix -> Inverted Wishart.
bvar.Sigma.prior.density = 'inverse wishart';
bvar.Sigma.prior.arg1 = prior.SIGMAstar*prior.ArtificialSampleSize;
bvar.Sigma.prior.arg2 = prior.DF;
% Marginal posterior density of the lagged matrices -> Generalized
% Student distribution (See appendix B.5 in Zellner (1971)).
bvar.LaggedMatrices.prior.density = 'matric-variate student';
bvar.LaggedMatrices.prior.arg1 = inv(iGXX);%P
bvar.LaggedMatrices.prior.arg2 = prior.SIGMAstar*prior.ArtificialSampleSize;%Q
bvar.LaggedMatrices.prior.arg3 = prior.PHIstar;%M (posterior mean)
bvar.LaggedMatrices.prior.arg4 = prior.ArtificialSampleSize;%(sample size)