updated in compliance with new version of dsgelikelihood.
git-svn-id: https://www.dynare.org/svn/dynare/dynare_v4@2306 ac1d8469-bf42-47a9-8791-bf33cf982152time-shift
ratto
parent
299e68470d
commit
697edeb109
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@ -1,55 +1,66 @@
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function [fval,llik,cost_flag,ys,trend_coeff,info] = DsgeLikelihood(xparam1,gend,data)
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% function [fval,llik,cost_flag,ys,trend_coeff,info] = DsgeLikelihood(xparam1,gend,data)
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% Evaluates the likelihood at each observation and the marginal density of a dsge model
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% used in the optimization algorithm number 5
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function [fval,llik,cost_flag,ys,trend_coeff,info] = DsgeLikelihood_hh(xparam1,gend,data,data_index,number_of_observations,no_more_missing_observations)
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% function [fval,cost_flag,ys,trend_coeff,info] = DsgeLikelihood(xparam1,gend,data,data_index,number_of_observations,no_more_missing_observations)
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% Evaluates the posterior kernel of a dsge model.
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%
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% INPUTS
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% xparam1: vector of model parameters
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% gend : scalar specifying the number of observations
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% data : matrix of data
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%
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% xparam1 [double] vector of model parameters.
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% gend [integer] scalar specifying the number of observations.
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% data [double] matrix of data
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% data_index [cell] cell of column vectors
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% number_of_observations [integer]
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% no_more_missing_observations [integer]
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% OUTPUTS
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% fval : value of the posterior kernel at xparam1
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% llik : gives the density at each observation
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% cost_flag : zero if the function returns a penalty, one otherwise
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% fval : value of the posterior kernel at xparam1.
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% cost_flag : zero if the function returns a penalty, one otherwise.
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% ys : steady state of original endogenous variables
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% trend_coeff :
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% info : vector of informations about the penalty
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% info : vector of informations about the penalty:
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% 41: one (many) parameter(s) do(es) not satisfied the lower bound
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% 42: one (many) parameter(s) do(es) not satisfied the upper bound
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%
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% SPECIAL REQUIREMENTS
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% Adapted from dsgelikelihood.m
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%
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% copyright marco.ratto@jrc.it [13-03-2007]
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%
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% Copyright (C) 2004-2008 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 bayestopt_ estim_params_ options_ trend_coeff_ M_ oo_ xparam1_test
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global bayestopt_ estim_params_ options_ trend_coeff_ M_ oo_
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fval = [];
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ys = [];
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trend_coeff = [];
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xparam1_test = xparam1;
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llik = NaN;
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cost_flag = 1;
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nobs = size(options_.varobs,1);
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%------------------------------------------------------------------------------
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% 1. Get the structural parameters & define penalties
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%------------------------------------------------------------------------------
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if options_.mode_compute ~= 1 & any(xparam1 < bayestopt_.lb)
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k = find(xparam1 < bayestopt_.lb);
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fval = bayestopt_.penalty+sum((bayestopt_.lb(k)-xparam1(k)).^2);
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llik=fval;
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cost_flag = 0;
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info = 41;
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return;
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k = find(xparam1 < bayestopt_.lb);
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fval = bayestopt_.penalty+sum((bayestopt_.lb(k)-xparam1(k)).^2);
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cost_flag = 0;
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info = 41;
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return;
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end
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if options_.mode_compute ~= 1 & any(xparam1 > bayestopt_.ub)
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k = find(xparam1 > bayestopt_.ub);
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fval = bayestopt_.penalty+sum((xparam1(k)-bayestopt_.ub(k)).^2);
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llik=fval;
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cost_flag = 0;
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info = 42;
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return;
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k = find(xparam1 > bayestopt_.ub);
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fval = bayestopt_.penalty+sum((xparam1(k)-bayestopt_.ub(k)).^2);
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cost_flag = 0;
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info = 42;
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return;
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end
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Q = M_.Sigma_e;
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H = M_.H;
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@ -79,7 +90,6 @@ function [fval,llik,cost_flag,ys,trend_coeff,info] = DsgeLikelihood(xparam1,gend
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k = find(a < 0);
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if k > 0
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fval = bayestopt_.penalty+sum(-a(k));
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llik=fval;
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cost_flag = 0;
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info = 43;
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return
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@ -100,7 +110,6 @@ function [fval,llik,cost_flag,ys,trend_coeff,info] = DsgeLikelihood(xparam1,gend
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k = find(a < 0);
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if k > 0
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fval = bayestopt_.penalty+sum(-a(k));
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llik=fval;
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cost_flag = 0;
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info = 44;
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return
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@ -108,10 +117,9 @@ function [fval,llik,cost_flag,ys,trend_coeff,info] = DsgeLikelihood(xparam1,gend
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end
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offset = offset+estim_params_.ncn;
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end
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M_.params(estim_params_.param_vals(:,1)) = xparam1(offset+1:end);
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% for i=1:estim_params_.np
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% M_.params(estim_params_.param_vals(i,1)) = xparam1(i+offset);
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%end
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if estim_params_.np > 0
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M_.params(estim_params_.param_vals(:,1)) = xparam1(offset+1:end);
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end
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M_.Sigma_e = Q;
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M_.H = H;
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%------------------------------------------------------------------------------
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@ -122,12 +130,10 @@ function [fval,llik,cost_flag,ys,trend_coeff,info] = DsgeLikelihood(xparam1,gend
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bayestopt_.restrict_aux);
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if info(1) == 1 | info(1) == 2 | info(1) == 5
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fval = bayestopt_.penalty+1;
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llik=fval;
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cost_flag = 0;
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return
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elseif info(1) == 3 | info(1) == 4 | info(1) == 20
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fval = bayestopt_.penalty+info(2)^2;
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llik=fval;
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fval = bayestopt_.penalty+info(2);%^2; % penalty power raised in DR1.m and resol already. GP July'08
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cost_flag = 0;
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return
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end
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@ -156,81 +162,168 @@ function [fval,llik,cost_flag,ys,trend_coeff,info] = DsgeLikelihood(xparam1,gend
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start = options_.presample+1;
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np = size(T,1);
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mf = bayestopt_.mf;
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no_missing_data_flag = (number_of_observations==gend*nobs);
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%------------------------------------------------------------------------------
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% 3. Initial condition of the Kalman filter
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%------------------------------------------------------------------------------
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kalman_algo = options_.kalman_algo;
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if options_.lik_init == 1 % Kalman filter
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Pstar = lyapunov_symm(T,R*Q*R',options_.qz_criterium);
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Pinf = [];
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elseif options_.lik_init == 2 % Old Diffuse Kalman filter
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Pstar = 10*eye(np);
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Pinf = [];
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elseif options_.lik_init == 3 % Diffuse Kalman filter
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Pstar = zeros(np,np);
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ivs = bayestopt_.restrict_var_list_stationary;
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ivd = bayestopt_.restrict_var_list_nonstationary;
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RR=T(:,bayestopt_.restrict_var_list_nonstationary);
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i=find(abs(RR)>1.e-10);
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R0=zeros(size(RR));
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R0(i)=sign(RR(i));
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Pinf=R0*R0';
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T0 = T;
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R1 = R;
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for j=1:size(T,1),
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for i=1:length(ivd),
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T0(j,:) = T0(j,:)-RR(j,i).*T(ivd(i),:);
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R1(j,:) = R1(j,:)-RR(j,i).*R(ivd(i),:);
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if kalman_algo ~= 2
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kalman_algo = 1;
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end
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Pstar = lyapunov_symm(T,R*Q*R',options_.qz_criterium);
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Pinf = [];
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elseif options_.lik_init == 2 % Old Diffuse Kalman filter
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if kalman_algo ~= 2
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kalman_algo = 1;
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end
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Pstar = 10*eye(np);
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Pinf = [];
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elseif options_.lik_init == 3 % Diffuse Kalman filter
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if kalman_algo ~= 4
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kalman_algo = 3;
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end
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[QT,ST] = schur(T);
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if exist('OCTAVE_VERSION') || matlab_ver_less_than('7.0.1')
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e1 = abs(my_ordeig(ST)) > 2-options_.qz_criterium;
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else
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e1 = abs(ordeig(ST)) > 2-options_.qz_criterium;
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end
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[QT,ST] = ordschur(QT,ST,e1);
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if exist('OCTAVE_VERSION') || matlab_ver_less_than('7.0.1')
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k = find(abs(my_ordeig(ST)) > 2-options_.qz_criterium);
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else
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k = find(abs(ordeig(ST)) > 2-options_.qz_criterium);
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end
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nk = length(k);
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nk1 = nk+1;
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Pinf = zeros(np,np);
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Pinf(1:nk,1:nk) = eye(nk);
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Pstar = zeros(np,np);
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B = QT'*R*Q*R'*QT;
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for i=np:-1:nk+2
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if ST(i,i-1) == 0
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if i == np
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c = zeros(np-nk,1);
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else
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c = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i,i+1:end)')+...
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ST(i,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i);
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end
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q = eye(i-nk)-ST(nk1:i,nk1:i)*ST(i,i);
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Pstar(nk1:i,i) = q\(B(nk1:i,i)+c);
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Pstar(i,nk1:i-1) = Pstar(nk1:i-1,i)';
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else
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if i == np
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c = zeros(np-nk,1);
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c1 = zeros(np-nk,1);
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else
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c = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i,i+1:end)')+...
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ST(i,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i)+...
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ST(i,i-1)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i-1);
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c1 = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i-1,i+1:end)')+...
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ST(i-1,i-1)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i-1)+...
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ST(i-1,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i);
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end
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q = [eye(i-nk)-ST(nk1:i,nk1:i)*ST(i,i) -ST(nk1:i,nk1:i)*ST(i,i-1);...
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-ST(nk1:i,nk1:i)*ST(i-1,i) eye(i-nk)-ST(nk1:i,nk1:i)*ST(i-1,i-1)];
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z = q\[B(nk1:i,i)+c;B(nk1:i,i-1)+c1];
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Pstar(nk1:i,i) = z(1:(i-nk));
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Pstar(nk1:i,i-1) = z(i-nk+1:end);
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Pstar(i,nk1:i-1) = Pstar(nk1:i-1,i)';
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Pstar(i-1,nk1:i-2) = Pstar(nk1:i-2,i-1)';
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i = i - 1;
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end
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end
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if i == nk+2
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c = ST(nk+1,:)*(Pstar(:,nk+2:end)*ST(nk1,nk+2:end)')+ST(nk1,nk1)*ST(nk1,nk+2:end)*Pstar(nk+2:end,nk1);
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Pstar(nk1,nk1)=(B(nk1,nk1)+c)/(1-ST(nk1,nk1)*ST(nk1,nk1));
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end
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Z = QT(mf,:);
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R1 = QT'*R;
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[QQ,RR,EE] = qr(Z*ST(:,1:nk),0);
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k = find(abs(diag(RR)) < 1e-8);
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if length(k) > 0
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k1 = EE(:,k);
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dd =ones(nk,1);
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dd(k1) = zeros(length(k1),1);
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Pinf(1:nk,1:nk) = diag(dd);
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end
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end
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Pstar = lyapunov_symm(T0,R1*Q*R1',options_.qz_criterium);
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end
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if kalman_algo == 2
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no_correlation_flag = 1;
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if length(H)==1
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H = zeros(nobs,1);
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else
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if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
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H = diag(H);
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else
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no_correlation_flag = 1;
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end
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end
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end
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kalman_tol = options_.kalman_tol;
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riccati_tol = options_.riccati_tol;
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mf = bayestopt_.mf1;
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Y = data-trend;
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%------------------------------------------------------------------------------
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% 4. Likelihood evaluation
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%------------------------------------------------------------------------------
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if any(any(H ~= 0)) % should be replaced by a flag
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if options_.kalman_algo == 1
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[LIK, lik] =DiffuseLikelihoodH1(T,R,Q,H,Pinf,Pstar,data,trend,start);
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if isinf(LIK) & ~estim_params_.ncn %% The univariate approach considered here doesn't
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%% apply when H has some off-diagonal elements.
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[LIK, lik] =DiffuseLikelihoodH3(T,R,Q,H,Pinf,Pstar,data,trend,start);
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elseif isinf(LIK) & estim_params_.ncn
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[LIK, lik] =DiffuseLikelihoodH3corr(T,R,Q,H,Pinf,Pstar,data,trend,start);
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end
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elseif options_.kalman_algo == 3
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if ~estim_params_.ncn %% The univariate approach considered here doesn't
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%% apply when H has some off-diagonal elements.
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[LIK, lik] =DiffuseLikelihoodH3(T,R,Q,H,Pinf,Pstar,data,trend,start);
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if (kalman_algo==1)% Multivariate Kalman Filter
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if no_missing_data_flag
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[LIK, lik] = kalman_filter(T,R,Q,H,Pstar,Y,start,mf,kalman_tol,riccati_tol);
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else
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[LIK, lik] =DiffuseLikelihoodH3corr(T,R,Q,H,Pinf,Pstar,data,trend,start);
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end
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end
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else
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if options_.kalman_algo == 1
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%nv = size(bayestopt_.Z,1);
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%LIK = kalman_filter(bayestopt_.Z,zeros(nv,nv),T,R,Q,data,zeros(size(T,1),1),Pstar,'u');
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[LIK, lik] =DiffuseLikelihood1(T,R,Q,Pinf,Pstar,data,trend,start);
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% LIK = diffuse_likelihood1(T,R,Q,Pinf,Pstar,data-trend,start);
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%if abs(LIK1-LIK)>0.0000000001
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% disp(['LIK1 and LIK are not equal! ' num2str(abs(LIK1-LIK))])
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%end
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if isinf(LIK)
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[LIK, lik] =DiffuseLikelihood3(T,R,Q,Pinf,Pstar,data,trend,start);
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[LIK, lik] = ...
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missing_observations_kalman_filter(T,R,Q,H,Pstar,Y,start,mf,kalman_tol,riccati_tol, ...
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data_index,number_of_observations,no_more_missing_observations);
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end
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if isinf(LIK)
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kalman_algo = 2;
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end
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end
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if (kalman_algo==2)% Univariate Kalman Filter
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if no_correlation_flag
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[LIK, lik] = univariate_kalman_filter(T,R,Q,H,Pstar,Y,start,mf,kalman_tol,riccati_tol,data_index,number_of_observations,no_more_missing_observations);
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else
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[LIK, lik] = univariate_kalman_filter_corr(T,R,Q,H,Pstar,Y,start,mf,kalman_tol,riccati_tol,data_index,number_of_observations,no_more_missing_observations);
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end
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end
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if (kalman_algo==3)% Multivariate Diffuse Kalman Filter
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if no_missing_data_flag
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data1 = data - trend;
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if any(any(H ~= 0))
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[LIK, lik] = DiffuseLikelihoodH1_Z(ST,Z,R1,Q,H,Pinf,Pstar,data1,start);
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else
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[LIK, lik] = DiffuseLikelihood1_Z(ST,Z,R1,Q,Pinf,Pstar,data1,start);
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end
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if isinf(LIK)
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kalman_algo = 4;
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end
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else
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error(['The diffuse filter is not yet implemented for models with missing observations'])
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end
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end
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if (kalman_algo==4)% Univariate Diffuse Kalman Filter
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data1 = data - trend;
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if any(any(H ~= 0))
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if ~estim_params_.ncn
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[LIK, lik] = DiffuseLikelihoodH3_Z(ST,Z,R1,Q,H,Pinf,Pstar,data1,trend,start);
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else
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[LIK, lik] = DiffuseLikelihoodH3corr_Z(ST,Z,R1,Q,H,Pinf,Pstar,data1,trend,start);
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end
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else
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[LIK, lik] = DiffuseLikelihood3_Z(ST,Z,R1,Q,Pinf,Pstar,data1,start);
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end
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elseif options_.kalman_algo == 3
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[LIK, lik] =DiffuseLikelihood3(T,R,Q,Pinf,Pstar,data,trend,start);
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end
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end
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if imag(LIK) ~= 0
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likelihood = bayestopt_.penalty;
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lik=ones(size(lik)).*bayestopt_.penalty;
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likelihood = bayestopt_.penalty;
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else
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likelihood = LIK;
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likelihood = LIK;
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end
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% ------------------------------------------------------------------------------
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% Adds prior if necessary
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% ------------------------------------------------------------------------------
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lnprior = priordens(xparam1,bayestopt_.pshape,bayestopt_.p1,bayestopt_.p2,bayestopt_.p3,bayestopt_.p4);
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fval = (likelihood-lnprior);
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options_.kalman_algo = kalman_algo;
|
||||
llik=[-lnprior; .5*lik(start:end)];
|
||||
|
||||
|
||||
Loading…
Reference in New Issue