BVAR-DSGE with constant (not yet ready)
git-svn-id: https://www.dynare.org/svn/dynare/dynare_v4@1167 ac1d8469-bf42-47a9-8791-bf33cf982152time-shift
adjemian
parent
6c7fcaa567
commit
44093cbbb9
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@ -1,7 +1,6 @@
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function [fval,cost_flag,ys,trend_coeff,info,PHI,SIGMAu,iXX] = DsgeVarLikelihood(xparam1,gend)
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% stephane.adjemian@ens.fr
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global bayestopt_ estim_params_ M_ options_ xparam1_test
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global xparam_
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global bayestopt_ estim_params_ M_ options_
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nvx = estim_params_.nvx;
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nvn = estim_params_.nvn;
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@ -29,71 +28,75 @@ cost_flag = 1;
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nobs = size(options_.varobs,1);
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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*min(1e3,exp(sum(bayestopt_.lb(k)-xparam1(k))));
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cost_flag = 0;
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return;
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k = find(xparam1 < bayestopt_.lb);
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fval = bayestopt_.penalty*min(1e3,exp(sum(bayestopt_.lb(k)-xparam1(k))));
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cost_flag = 0;
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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*min(1e3,exp(sum(xparam1(k)-bayestopt_.ub(k))));
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cost_flag = 0;
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return;
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k = find(xparam1 > bayestopt_.ub);
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fval = bayestopt_.penalty*min(1e3,exp(sum(xparam1(k)-bayestopt_.ub(k))));
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cost_flag = 0;
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return;
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end
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Q = M_.Sigma_e;
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for i=1:estim_params_.nvx
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k = estim_params_.var_exo(i,1);
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Q(k,k) = xparam1(i)*xparam1(i);
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k = estim_params_.var_exo(i,1);
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Q(k,k) = xparam1(i)*xparam1(i);
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end
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offset = estim_params_.nvx;
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if estim_params_.nvn
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H = zeros(nobs,nobs);
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for i=1:estim_params_.nvn
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k = estim_params_.var_endo(i,1);
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H(k,k) = xparam1(i+offset)*xparam1(i+offset);
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end
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offset = offset+estim_params_.nvn;
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disp('DsgeVarLikelihood :: Measurement errors are not implemented!')
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return
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% $$$ H = zeros(nobs,nobs);
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% $$$ for i=1:estim_params_.nvn
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% $$$ k = estim_params_.var_endo(i,1);
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% $$$ H(k,k) = xparam1(i+offset)*xparam1(i+offset);
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% $$$ end
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% $$$ offset = offset+estim_params_.nvn;
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end
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if estim_params_.ncx
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for i=1:estim_params_.ncx
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k1 =estim_params_.corrx(i,1);
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k2 =estim_params_.corrx(i,2);
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Q(k1,k2) = xparam1(i+offset)*sqrt(Q(k1,k1)*Q(k2,k2));
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Q(k2,k1) = Q(k1,k2);
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end
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[CholQ,testQ] = chol(Q);
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if testQ%% The variance-covariance matrix of the structural innovations is not definite positive.
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%% We have to compute the eigenvalues of this matrix in order to build the penalty.
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a = eig(Q);
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k = a<0;
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if k > 0
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fval = bayestopt_.penalty*min(1e3,exp(sum(-a(k))));
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cost_flag = 0;
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return
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for i=1:estim_params_.ncx
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k1 =estim_params_.corrx(i,1);
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k2 =estim_params_.corrx(i,2);
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Q(k1,k2) = xparam1(i+offset)*sqrt(Q(k1,k1)*Q(k2,k2));
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Q(k2,k1) = Q(k1,k2);
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end
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end
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offset = offset+estim_params_.ncx;
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[CholQ,testQ] = chol(Q);
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if testQ%% The variance-covariance matrix of the structural innovations is not definite positive.
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%% We have to compute the eigenvalues of this matrix in order to build the penalty.
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a = eig(Q);
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k = a<0;
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if k > 0
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fval = bayestopt_.penalty*min(1e3,exp(sum(-a(k))));
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cost_flag = 0;
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return
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end
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end
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offset = offset+estim_params_.ncx;
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end
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if estim_params_.nvn & estim_params_.ncn
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for i=1:estim_params_.ncn
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k1 = options_.lgyidx2varobs(estim_params_.corrn(i,1));
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k2 = options_.lgyidx2varobs(estim_params_.corrn(i,2));
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H(k1,k2) = xparam1(i+offset)*sqrt(H(k1,k1)*H(k2,k2));
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H(k2,k1) = H(k1,k2);
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end
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[CholH,testH] = chol(H);
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if testH
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a = eig(H);
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k = a<0;
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if k > 0
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fval = bayestopt_.penalty*min(1e3,exp(sum(-a(k))));
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cost_flag = 0;
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return
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end
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end
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offset = offset+estim_params_.ncn;
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disp('DsgeVarLikelihood :: Measurement errors are not implemented!')
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return
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% $$$ for i=1:estim_params_.ncn
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% $$$ k1 = options_.lgyidx2varobs(estim_params_.corrn(i,1));
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% $$$ k2 = options_.lgyidx2varobs(estim_params_.corrn(i,2));
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% $$$ H(k1,k2) = xparam1(i+offset)*sqrt(H(k1,k1)*H(k2,k2));
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% $$$ H(k2,k1) = H(k1,k2);
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% $$$ end
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% $$$ [CholH,testH] = chol(H);
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% $$$ if testH
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% $$$ a = eig(H);
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% $$$ k = a<0;
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% $$$ if k > 0
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% $$$ fval = bayestopt_.penalty*min(1e3,exp(sum(-a(k))));
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% $$$ cost_flag = 0;
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% $$$ return
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% $$$ end
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% $$$ 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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M_.Sigma_e = Q;
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@ -107,27 +110,24 @@ dsge_prior_weight = M_.params(strmatch('dsge_prior_weight',M_.param_names));
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bayestopt_.restrict_columns,...
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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;
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cost_flag = 0;
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return
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fval = bayestopt_.penalty;
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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*min(1e3,exp(info(2)));
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cost_flag = 0;
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return
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fval = bayestopt_.penalty*min(1e3,exp(info(2)));
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cost_flag = 0;
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return
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end
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if options_.loglinear == 1
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constant = log(SteadyState(bayestopt_.mfys));
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constant = log(SteadyState(bayestopt_.mfys));
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else
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constant = SteadyState(bayestopt_.mfys);
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constant = SteadyState(bayestopt_.mfys);
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end
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if bayestopt_.with_trend == 1
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trend_coeff = zeros(nobs,1);
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for i=1:nobs
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trend_coeff(i) = evalin('base',bayestopt_.trend_coeff{i});
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end
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trend = constant*ones(1,gend)+trend_coeff*(1:gend);
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else
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trend = constant*ones(1,gend);
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disp('DsgeVarLikelihood :: Linear trend is not yet implemented!')
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return
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% $$$ else
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% $$$ trend = constant*ones(1,gend);
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end
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%------------------------------------------------------------------------------
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@ -140,7 +140,6 @@ if ~isempty(options_.unit_root_vars)
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end
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i_ur(i) = i1;
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end
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[junk,bayestopt_.var_list_stationary] = ...
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setdiff((1:M_.endo_nbr)',i_ur);
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[junk,bayestopt_.restrict_var_list_stationary] = ...
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@ -194,22 +193,31 @@ if length(options_.mode_file) > 0 & options_.posterior_mode_estimation
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eval(['load ' options_.mode_file ';']');
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end
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%% compute sample moments if needed (bvar-dsge)
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if ~isempty(strmatch('dsge_prior_weight',M_.param_names))
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evalin('base',['[mYY,mXY,mYX,mXX,Ydata,Xdata] = ' ...
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'var_sample_moments(options_.first_obs,options_.first_obs+options_.nobs-1,options_.varlag,-1);'])
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%% Compute the steadyn state:
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if options_.steadystate_flag% if the _steadystate.m file is provided.
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[oo_.steady_state,tchek] = feval([M_.fname '_steadystate'],[],[]);
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else% if the steady state file is not provided.
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[dd,info] = resol(oo_.steady_state,0)
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oo_.steady_state = dd.ys; clear('dd');
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end
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%% Compute the steadyn state if the _steadystate.m file is provided
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if options_.steadystate_flag
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[oo_.steady_state,tchek] = feval([M_.fname '_steadystate'],[],[]);
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end
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initial_estimation_checks(xparam1,gend,data);
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if options_.mode_compute == 0 & length(options_.mode_file) == 0
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return;
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end
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%% compute sample moments if needed (bvar-dsge)
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if ~isempty(strmatch('dsge_prior_weight',M_.param_names))
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if all(abs(oo_.steady_state)<10e-9)
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evalin('base',['[mYY,mXY,mYX,mXX,Ydata,Xdata] = ' ...
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'var_sample_moments(options_.first_obs,options_.first_obs+options_.nobs-1,options_.varlag,-1);'])
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else% The steady state is non zero ==> a constant in the VAR is needed!
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evalin('base',['[mYY,mXY,mYX,mXX,Ydata,Xdata] = ' ...
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'var_sample_moments(options_.first_obs,options_.first_obs+options_.nobs-1,options_.varlag,0);'])
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end
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end
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%% Estimation of the posterior mode or likelihood mode
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if options_.mode_compute > 0 & options_.posterior_mode_estimation
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@ -827,7 +835,7 @@ end
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if ~((any(bayestopt_.pshape > 0) & options_.mh_replic) | (any(bayestopt_.pshape ...
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> 0) & options_.load_mh_file)) | ~options_.smoother
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%% ML estimation, or posterior mode without metropolis-hastings or metropolis without bayesian smooth variables
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%% ML estimation, or posterior mode without metropolis-hastings or metropolis without bayesian smooth variable
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options_.lik_algo = 2;
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[atT,innov,measurement_error,filtered_state_vector,ys,trend_coeff] = DsgeSmoother(xparam1,gend,data);
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for i=1:M_.endo_nbr
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@ -29,7 +29,7 @@ if trend == -1% No constant
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X = zeros(NumberOfObservations,NumberOfVariables*qlag);
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elseif trend == 0% Constant
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X = zeros(NumberOfObservations,NumberOfVariables*qlag+1);
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indx = NumberOfVariables*qlag+1;
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indx = NumberOfVariables*qlag+1:NumberOfVariables*qlag+NumberOfVariables;
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elseif trend == 1;% Constant + Trend
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X = zeros(NumberOfObservations,NumberOfVariables*qlag+2);
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indx = NumberOfVariables*qlag+1:NumberOfVariables*qlag+2;
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@ -45,7 +45,7 @@ for t=1:NumberOfObservations
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X(t,(lag-1)*NumberOfVariables+1:lag*NumberOfVariables) = data(line-lag,:);
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end
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if trend == 0
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X(t,indx) = 1;
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X(t,indx) = ones(1,NumberOfVariables);
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elseif trend == 1
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X(t,indx) = [ 1 , t ];
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end
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