Fixed bug in dynare_resolve (wrong calling sequence introduced in commit #013c599ec92f7d6e5fc3f351a58d9aa5ba401410).
Removed globals from DsgeVarLikelihood and changed the calling sequence. As in DsgeLikelihood, the penalty is now a persistent variable. Added a global structure for the data: dataset_. Removed globals from dsgevar_posterior_density and mode_check. Simplification of the clode, definition of the variable objective_function at the top of dynare_estimation_1 (equal to 'DsgeLikelihood' or 'DsgeVarLikelihood').time-shift
Stéphane Adjemian (Charybdis)
parent
f1ffeb29bb
commit
e0fa737cee
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@ -255,7 +255,7 @@ Model.H = H;
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%------------------------------------------------------------------------------
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% Linearize the model around the deterministic sdteadystate and extract the matrices of the state equation (T and R).
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[T,R,SteadyState,info,Model,DynareOptions,DynareResults] = dynare_resolve(Model,DynareOptions,DynareResults);
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[T,R,SteadyState,info,Model,DynareOptions,DynareResults] = dynare_resolve(Model,DynareOptions,DynareResults,'restrict');
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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) == 22 || info(1) == 24
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@ -1,18 +1,18 @@
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function [fval,cost_flag,info,PHI,SIGMAu,iXX,prior] = DsgeVarLikelihood(xparam1,gend)
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% Evaluates the posterior kernel of the bvar-dsge model.
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%
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% INPUTS
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function [fval,exit_flag,info,PHI,SIGMAu,iXX,prior] = DsgeVarLikelihood(xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
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% Evaluates the posterior kernel of the bvar-dsge model.
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%
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% INPUTS
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% o xparam1 [double] Vector of model's parameters.
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% o gend [integer] Number of observations (without conditionning observations for the lags).
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%
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% OUTPUTS
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%
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% OUTPUTS
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% o fval [double] Value of the posterior kernel at xparam1.
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% o cost_flag [integer] Zero if the function returns a penalty, one otherwise.
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% o info [integer] Vector of informations about the penalty.
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% o PHI [double] Stacked BVAR-DSGE autoregressive matrices (at the mode associated to xparam1).
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% o SIGMAu [double] Covariance matrix of the BVAR-DSGE (at the mode associated to xparam1).
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% o iXX [double] inv(X'X).
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% o prior [double] a matlab structure describing the dsge-var prior.
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% o prior [double] a matlab structure describing the dsge-var prior.
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%
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% SPECIAL REQUIREMENTS
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% None.
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@ -34,139 +34,180 @@ function [fval,cost_flag,info,PHI,SIGMAu,iXX,prior] = DsgeVarLikelihood(xparam1,
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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_ M_ options_ oo_
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% Declaration of the persistent variables.
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persistent penalty dsge_prior_weight_idx
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nvx = estim_params_.nvx;
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nvn = estim_params_.nvn;
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ncx = estim_params_.ncx;
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ncn = estim_params_.ncn;
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np = estim_params_.np;
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nx = nvx+nvn+ncx+ncn+np;
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ns = nvx+nvn+ncx+ncn;
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% Initialization of the penalty
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if ~nargin || isempty(penalty)
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penalty = 1e8;
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if ~nargin, return, end
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end
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if nargin==1
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penalty = xparam1;
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return
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end
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NumberOfObservedVariables = size(options_.varobs,1);
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NumberOfLags = options_.dsge_varlag;
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% Initialization of of the index for parameter dsge_prior_weight in Model.params.
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if isempty(dsge_prior_weight_idx)
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dsge_prior_weight_idx = strmatch('dsge_prior_weight',Model.param_names);
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end
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% Get the number of estimated (dsge) parameters.
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ns = EstimatedParameters.nvx + ...
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EstimatedParameters.nvn + ...
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EstimatedParameters.ncx + ...
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EstimatedParameters.ncn;
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nx = ns + EstimatedParameters.np;
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% Get the number of observed variables in the VAR model.
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NumberOfObservedVariables = DynareDataset.info.nvobs;
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% Get the number of lags in the VAR model.
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NumberOfLags = DynareOptions.dsge_varlag;
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% Get the number of parameters in the VAR model.
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NumberOfParameters = NumberOfObservedVariables*NumberOfLags ;
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if ~options_.noconstant
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if ~DynareOptions.noconstant
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NumberOfParameters = NumberOfParameters + 1;
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end
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% Get empirical second order moments for the observed variables.
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mYY = evalin('base', 'mYY');
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mYX = evalin('base', 'mYX');
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mXY = evalin('base', 'mXY');
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mXX = evalin('base', 'mXX');
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% Initialize some of the output arguments.
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fval = [];
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cost_flag = 1;
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exit_flag = 1;
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if ~isequal(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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cost_flag = 0;
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% Return, with endogenous penalty, if some dsge-parameters are smaller than the lower bound of the prior domain.
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if DynareOptions.mode_compute ~= 1 && any(xparam1 < BayesInfo.lb)
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k = find(xparam1 < BayesInfo.lb);
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fval = penalty+sum((BayesInfo.lb(k)-xparam1(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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if ~isequal(options_.mode_compute,11) && 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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cost_flag = 0;
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% Return, with endogenous penalty, if some dsge-parameters are greater than the upper bound of the prior domain.
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if DynareOptions.mode_compute ~= 1 && any(xparam1 > BayesInfo.ub)
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k = find(xparam1 > BayesInfo.ub);
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fval = penalty+sum((xparam1(k)-BayesInfo.ub(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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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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% Get the variance of each structural innovation.
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Q = Model.Sigma_e;
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for i=1:EstimatedParameters.nvx
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k = EstimatedParameters.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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offset = EstimatedParameters.nvx;
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% Check that the user does not estimate measurment errors.
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% TODO Check that the user does not declare non estimated measurement errors...
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if EstimatedParameters.nvn
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disp('DsgeVarLikelihood :: Measurement errors are not implemented!')
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return
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end
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if estim_params_.ncx
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end
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% Check that the user does not estimate off diagonal elements in the covariance matrix of the structural innovation.
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% TODO Check that Q is a diagonal matrix...
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if EstimatedParameters.ncx
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disp('DsgeVarLikelihood :: Correlated structural innovations are not implemented!')
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return
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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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% Update Model.params and Model.Sigma_e.
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Model.params(EstimatedParameters.param_vals(:,1)) = xparam1(offset+1:end);
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Model.Sigma_e = Q;
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%% Weight of the dsge prior:
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dsge_prior_weight = M_.params(strmatch('dsge_prior_weight',M_.param_names));
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% Is the DSGE prior proper?
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if dsge_prior_weight<(NumberOfParameters+NumberOfObservedVariables)/gend;
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fval = bayestopt_.penalty+abs(gend*dsge_prior_weight-(NumberOfParameters+NumberOfObservedVariables));
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cost_flag = 0;
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% Get the weight of the dsge prior.
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dsge_prior_weight = Model.params(dsge_prior_weight_idx);
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% Is the dsge prior proper?
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if dsge_prior_weight<(NumberOfParameters+NumberOfObservedVariables)/DynareDataset.info.ntobs;
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fval = penalty+abs(DynareDataset.info.ntobs*dsge_prior_weight-(NumberOfParameters+NumberOfObservedVariables));
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exit_flag = 0;
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info = 51;
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return;
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return
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end
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%------------------------------------------------------------------------------
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% 2. call model setup & reduction program
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%------------------------------------------------------------------------------
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[T,R,SteadyState,info,M_,options_,oo_] = dynare_resolve(M_,options_,oo_);
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% Solve the Dsge model and get the matrices of the reduced form solution. T and R are the matrices of the
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% state equation
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[T,R,SteadyState,info,Model,DynareOptions,DynareResults] = dynare_resolve(Model,DynareOptions,DynareResults,'restrict');
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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
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fval = bayestopt_.penalty+1;
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cost_flag = 0;
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fval = penalty+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) == 19 || info(1) == 20 || info(1) == 21
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fval = bayestopt_.penalty+info(2);
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cost_flag = 0;
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fval = penalty+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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if ~options_.noconstant
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if options_.loglinear
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constant = transpose(log(SteadyState(bayestopt_.mfys)));
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% Define the mean/steady state vector.
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if ~DynareOptions.noconstant
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if DynareOptions.loglinear
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constant = transpose(log(SteadyState(BayesInfo.mfys)));
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else
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constant = transpose(SteadyState(bayestopt_.mfys));
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end
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constant = transpose(SteadyState(BayesInfo.mfys));
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end
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else
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constant = zeros(1,NumberOfObservedVariables);
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end
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if bayestopt_.with_trend == 1
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disp('DsgeVarLikelihood :: Linear trend is not yet implemented!')
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return
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% Dsge-VAR with deterministic trends is not implemented
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if BayesInfo.with_trend == 1
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error('DsgeVarLikelihood :: Linear trend is not yet implemented!')
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end
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%------------------------------------------------------------------------------
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% 3. theoretical moments (second order)
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%------------------------------------------------------------------------------
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tmp0 = lyapunov_symm(T,R*Q*R',options_.qz_criterium,options_.lyapunov_complex_threshold);% I compute the variance-covariance matrix
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mf = bayestopt_.mf1; % of the restricted state vector.
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% Compute the theoretical second order moments
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tmp0 = lyapunov_symm(T,R*Q*R',DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold);
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mf = BayesInfo.mf1;
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% Get the non centered second order moments
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TheoreticalAutoCovarianceOfTheObservedVariables = ...
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zeros(NumberOfObservedVariables,NumberOfObservedVariables,NumberOfLags+1);
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TheoreticalAutoCovarianceOfTheObservedVariables = zeros(NumberOfObservedVariables,NumberOfObservedVariables,NumberOfLags+1);
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TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1) = tmp0(mf,mf)+constant'*constant;
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for lag = 1:NumberOfLags
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tmp0 = T*tmp0;
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TheoreticalAutoCovarianceOfTheObservedVariables(:,:,lag+1) = tmp0(mf,mf) ...
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+ constant'*constant;
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TheoreticalAutoCovarianceOfTheObservedVariables(:,:,lag+1) = tmp0(mf,mf) + constant'*constant;
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end
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% Build the theoretical "covariance" between Y and X
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GYX = zeros(NumberOfObservedVariables,NumberOfParameters);
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for i=1:NumberOfLags
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GYX(:,(i-1)*NumberOfObservedVariables+1:i*NumberOfObservedVariables) = ...
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TheoreticalAutoCovarianceOfTheObservedVariables(:,:,i+1);
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GYX(:,(i-1)*NumberOfObservedVariables+1:i*NumberOfObservedVariables) = TheoreticalAutoCovarianceOfTheObservedVariables(:,:,i+1);
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end
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if ~options_.noconstant
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if ~DynareOptions.noconstant
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GYX(:,end) = constant';
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end
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% Build the theoretical "covariance" between X and X
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GXX = kron(eye(NumberOfLags), ...
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TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1));
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GXX = kron(eye(NumberOfLags), TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1));
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for i = 1:NumberOfLags-1
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tmp1 = diag(ones(NumberOfLags-i,1),i);
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tmp1 = diag(ones(NumberOfLags-i,1),i);
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tmp2 = diag(ones(NumberOfLags-i,1),-i);
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GXX = GXX + kron(tmp1,TheoreticalAutoCovarianceOfTheObservedVariables(:,:,i+1));
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GXX = GXX + kron(tmp2,TheoreticalAutoCovarianceOfTheObservedVariables(:,:,i+1)');
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end
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if ~options_.noconstant
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if ~DynareOptions.noconstant
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% Add one row and one column to GXX
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GXX = [GXX , kron(ones(NumberOfLags,1),constant') ; [ kron(ones(1,NumberOfLags),constant) , 1] ];
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end
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@ -177,45 +218,46 @@ assignin('base','GYY',GYY);
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assignin('base','GXX',GXX);
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assignin('base','GYX',GYX);
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if ~isinf(dsge_prior_weight)
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tmp0 = dsge_prior_weight*gend*TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1) + mYY ;
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tmp1 = dsge_prior_weight*gend*GYX + mYX;
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tmp2 = inv(dsge_prior_weight*gend*GXX+mXX);
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if ~isinf(dsge_prior_weight)% Evaluation of the likelihood of the dsge-var model when the dsge prior weight is finite.
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tmp0 = dsge_prior_weight*DynareDataset.info.ntobs*TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1) + mYY ;
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tmp1 = dsge_prior_weight*DynareDataset.info.ntobs*GYX + mYX;
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tmp2 = inv(dsge_prior_weight*DynareDataset.info.ntobs*GXX+mXX);
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SIGMAu = tmp0 - tmp1*tmp2*tmp1'; clear('tmp0');
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if ~ispd(SIGMAu)
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v = diag(SIGMAu);
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k = find(v<0);
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fval = bayestopt_.penalty + sum(v(k).^2);
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fval = penalty + sum(v(k).^2);
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info = 52;
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cost_flag = 0;
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exit_flag = 0;
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return;
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end
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SIGMAu = SIGMAu / (gend*(1+dsge_prior_weight));
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SIGMAu = SIGMAu / (DynareDataset.info.ntobs*(1+dsge_prior_weight));
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PHI = tmp2*tmp1'; clear('tmp1');
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prodlng1 = sum(gammaln(.5*((1+dsge_prior_weight)*gend- ...
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prodlng1 = sum(gammaln(.5*((1+dsge_prior_weight)*DynareDataset.info.ntobs- ...
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NumberOfObservedVariables*NumberOfLags ...
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+1-(1:NumberOfObservedVariables)')));
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prodlng2 = sum(gammaln(.5*(dsge_prior_weight*gend- ...
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prodlng2 = sum(gammaln(.5*(dsge_prior_weight*DynareDataset.info.ntobs- ...
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NumberOfObservedVariables*NumberOfLags ...
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+1-(1:NumberOfObservedVariables)')));
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lik = .5*NumberOfObservedVariables*log(det(dsge_prior_weight*gend*GXX+mXX)) ...
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+ .5*((dsge_prior_weight+1)*gend-NumberOfParameters)*log(det((dsge_prior_weight+1)*gend*SIGMAu)) ...
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- .5*NumberOfObservedVariables*log(det(dsge_prior_weight*gend*GXX)) ...
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- .5*(dsge_prior_weight*gend-NumberOfParameters)*log(det(dsge_prior_weight*gend*(GYY-GYX*inv(GXX)*GYX'))) ...
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+ .5*NumberOfObservedVariables*gend*log(2*pi) ...
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- .5*log(2)*NumberOfObservedVariables*((dsge_prior_weight+1)*gend-NumberOfParameters) ...
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+ .5*log(2)*NumberOfObservedVariables*(dsge_prior_weight*gend-NumberOfParameters) ...
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+1-(1:NumberOfObservedVariables)')));
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lik = .5*NumberOfObservedVariables*log(det(dsge_prior_weight*DynareDataset.info.ntobs*GXX+mXX)) ...
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+ .5*((dsge_prior_weight+1)*DynareDataset.info.ntobs-NumberOfParameters)*log(det((dsge_prior_weight+1)*DynareDataset.info.ntobs*SIGMAu)) ...
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- .5*NumberOfObservedVariables*log(det(dsge_prior_weight*DynareDataset.info.ntobs*GXX)) ...
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- .5*(dsge_prior_weight*DynareDataset.info.ntobs-NumberOfParameters)*log(det(dsge_prior_weight*DynareDataset.info.ntobs*(GYY-GYX*inv(GXX)*GYX'))) ...
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+ .5*NumberOfObservedVariables*DynareDataset.info.ntobs*log(2*pi) ...
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- .5*log(2)*NumberOfObservedVariables*((dsge_prior_weight+1)*DynareDataset.info.ntobs-NumberOfParameters) ...
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+ .5*log(2)*NumberOfObservedVariables*(dsge_prior_weight*DynareDataset.info.ntobs-NumberOfParameters) ...
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- prodlng1 + prodlng2;
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else
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else% Evaluation of the likelihood of the dsge-var model when the dsge prior weight is infinite.
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iGXX = inv(GXX);
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SIGMAu = GYY - GYX*iGXX*transpose(GYX);
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PHI = iGXX*transpose(GYX);
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lik = gend * ( log(det(SIGMAu)) + NumberOfObservedVariables*log(2*pi) + ...
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trace(inv(SIGMAu)*(mYY - transpose(mYX*PHI) - mYX*PHI + transpose(PHI)*mXX*PHI)/gend));
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lik = DynareDataset.info.ntobs * ( log(det(SIGMAu)) + NumberOfObservedVariables*log(2*pi) + ...
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trace(inv(SIGMAu)*(mYY - transpose(mYX*PHI) - mYX*PHI + transpose(PHI)*mXX*PHI)/DynareDataset.info.ntobs));
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lik = .5*lik;% Minus likelihood
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end
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end
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lnprior = priordens(xparam1,bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7,bayestopt_.p3,bayestopt_.p4);
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% Add the (logged) prior density for the dsge-parameters.
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lnprior = priordens(xparam1,BayesInfo.pshape,BayesInfo.p6,BayesInfo.p7,BayesInfo.p3,BayesInfo.p4);
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fval = (lik-lnprior);
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if (nargout == 6)
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@ -232,10 +274,10 @@ if (nargout==7)
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else
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iXX = tmp2;
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end
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iGXX = inv(GXX);
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iGXX = inv(GXX);
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prior.SIGMAstar = GYY - GYX*iGXX*GYX';
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prior.PHIstar = iGXX*transpose(GYX);
|
||||
prior.ArtificialSampleSize = fix(dsge_prior_weight*gend);
|
||||
prior.ArtificialSampleSize = fix(dsge_prior_weight*DynareDataset.info.ntobs);
|
||||
prior.DF = prior.ArtificialSampleSize - NumberOfParameters - NumberOfObservedVariables;
|
||||
prior.iGXX = iGXX;
|
||||
end
|
||||
|
|
@ -34,7 +34,7 @@ function PosteriorIRF(type)
|
|||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||
|
||||
|
||||
global options_ estim_params_ oo_ M_ bayestopt_
|
||||
global options_ estim_params_ oo_ M_ bayestopt_ dataset_
|
||||
% Set the number of periods
|
||||
if isempty(options_.irf) || ~options_.irf
|
||||
options_.irf = 40;
|
||||
|
|
@ -64,8 +64,8 @@ np = estim_params_.np ;
|
|||
npar = nvx+nvn+ncx+ncn+np;
|
||||
offset = npar-np; clear('nvx','nvn','ncx','ncn','np');
|
||||
|
||||
nvobs = size(options_.varobs,1);
|
||||
gend = options_.nobs;
|
||||
nvobs = dataset_.info.nvobs;
|
||||
gend = dataset_.info.ntobs;
|
||||
MaxNumberOfPlotPerFigure = 9;
|
||||
nn = sqrt(MaxNumberOfPlotPerFigure);
|
||||
MAX_nirfs_dsge = ceil(options_.MaxNumberOfBytes/(options_.irf*nvar*M_.exo_nbr)/8);
|
||||
|
|
@ -230,7 +230,8 @@ else
|
|||
'options_', options_, ...
|
||||
'bayestopt_', bayestopt_, ...
|
||||
'estim_params_', estim_params_, ...
|
||||
'oo_', oo_);
|
||||
'oo_', oo_, ...
|
||||
'dataset_',dataset_);
|
||||
|
||||
% which files have to be copied to run remotely
|
||||
NamFileInput(1,:) = {'',[M_.fname '_static.m']};
|
||||
|
|
|
|||
|
|
@ -40,7 +40,7 @@ function myoutput=PosteriorIRF_core1(myinputs,fpar,npar,whoiam, ThisMatlab)
|
|||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||
|
||||
|
||||
global options_ estim_params_ oo_ M_ bayestopt_
|
||||
global options_ estim_params_ oo_ M_ bayestopt_ dataset_
|
||||
|
||||
if nargin<4,
|
||||
whoiam=0;
|
||||
|
|
@ -151,6 +151,7 @@ while fpar<npar
|
|||
stock_param(irun2,:) = deep;
|
||||
set_parameters(deep);
|
||||
[dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
|
||||
oo_.dr = dr;
|
||||
if info(1)
|
||||
nosaddle = nosaddle + 1;
|
||||
fpar = fpar - 1;
|
||||
|
|
@ -188,24 +189,24 @@ while fpar<npar
|
|||
end
|
||||
if MAX_nirfs_dsgevar
|
||||
IRUN = IRUN+1;
|
||||
[fval,cost_flag,info,PHI,SIGMAu,iXX] = DsgeVarLikelihood(deep',gend);
|
||||
[fval,cost_flag,info,PHI,SIGMAu,iXX] = DsgeVarLikelihood(deep',dataset_,options_,M_,estim_params_,bayestopt_,oo_);
|
||||
dsge_prior_weight = M_.params(strmatch('dsge_prior_weight',M_.param_names));
|
||||
DSGE_PRIOR_WEIGHT = floor(gend*(1+dsge_prior_weight));
|
||||
SIGMA_inv_upper_chol = chol(inv(SIGMAu*gend*(dsge_prior_weight+1)));
|
||||
DSGE_PRIOR_WEIGHT = floor(dataset_.info.ntobs*(1+dsge_prior_weight));
|
||||
SIGMA_inv_upper_chol = chol(inv(SIGMAu*dataset_.info.ntobs*(dsge_prior_weight+1)));
|
||||
explosive_var = 1;
|
||||
while explosive_var
|
||||
% draw from the marginal posterior of SIGMA
|
||||
SIGMAu_draw = rand_inverse_wishart(nvobs, DSGE_PRIOR_WEIGHT-NumberOfParametersPerEquation, ...
|
||||
SIGMAu_draw = rand_inverse_wishart(dataset_.info.nvobs, DSGE_PRIOR_WEIGHT-NumberOfParametersPerEquation, ...
|
||||
SIGMA_inv_upper_chol);
|
||||
% draw from the conditional posterior of PHI
|
||||
PHI_draw = rand_matrix_normal(NumberOfParametersPerEquation,nvobs, PHI, ...
|
||||
PHI_draw = rand_matrix_normal(NumberOfParametersPerEquation,dataset_.info.nvobs, PHI, ...
|
||||
chol(SIGMAu_draw)', chol(iXX)');
|
||||
Companion_matrix(1:nvobs,:) = transpose(PHI_draw(1:NumberOfLagsTimesNvobs,:));
|
||||
Companion_matrix(1:dataset_.info.nvobs,:) = transpose(PHI_draw(1:NumberOfLagsTimesNvobs,:));
|
||||
% Check for stationarity
|
||||
explosive_var = any(abs(eig(Companion_matrix))>1.000000001);
|
||||
end
|
||||
% Get the mean
|
||||
mu = zeros(1,nvobs);
|
||||
mu = zeros(1,dataset_.info.nvobs);
|
||||
% Get rotation
|
||||
if dsge_prior_weight > 0
|
||||
Atheta(oo_.dr.order_var,M_.exo_names_orig_ord) = oo_.dr.ghu*sqrt(M_.Sigma_e);
|
||||
|
|
@ -215,24 +216,24 @@ while fpar<npar
|
|||
SIGMAu_chol = chol(SIGMAu_draw)';
|
||||
SIGMAtrOMEGA = SIGMAu_chol*OMEGAstar';
|
||||
PHIpower = eye(NumberOfLagsTimesNvobs);
|
||||
irfs = zeros (options_.irf,nvobs*M_.exo_nbr);
|
||||
tmp3 = PHIpower(1:nvobs,1:nvobs)*SIGMAtrOMEGA;
|
||||
irfs = zeros (options_.irf,dataset_.info.nvobs*M_.exo_nbr);
|
||||
tmp3 = PHIpower(1:dataset_.info.nvobs,1:dataset_.info.nvobs)*SIGMAtrOMEGA;
|
||||
irfs(1,:) = tmp3(:)';
|
||||
for t = 2:options_.irf
|
||||
PHIpower = Companion_matrix*PHIpower;
|
||||
tmp3 = PHIpower(1:nvobs,1:nvobs)*SIGMAtrOMEGA;
|
||||
tmp3 = PHIpower(1:dataset_.info.nvobs,1:dataset_.info.nvobs)*SIGMAtrOMEGA;
|
||||
irfs(t,:) = tmp3(:)'+kron(ones(1,M_.exo_nbr),mu);
|
||||
end
|
||||
tmp_dsgevar = kron(ones(options_.irf,1),mu);
|
||||
for j = 1:(nvobs*M_.exo_nbr)
|
||||
for j = 1:(dataset_.info.nvobs*M_.exo_nbr)
|
||||
if max(irfs(:,j)) - min(irfs(:,j)) > 1e-10
|
||||
tmp_dsgevar(:,j) = (irfs(:,j));
|
||||
end
|
||||
end
|
||||
if IRUN < MAX_nirfs_dsgevar
|
||||
stock_irf_bvardsge(:,:,:,IRUN) = reshape(tmp_dsgevar,options_.irf,nvobs,M_.exo_nbr);
|
||||
stock_irf_bvardsge(:,:,:,IRUN) = reshape(tmp_dsgevar,options_.irf,dataset_.info.nvobs,M_.exo_nbr);
|
||||
else
|
||||
stock_irf_bvardsge(:,:,:,IRUN) = reshape(tmp_dsgevar,options_.irf,nvobs,M_.exo_nbr);
|
||||
stock_irf_bvardsge(:,:,:,IRUN) = reshape(tmp_dsgevar,options_.irf,dataset_.info.nvobs,M_.exo_nbr);
|
||||
instr = [MhDirectoryName '/' M_.fname '_irf_bvardsge' ...
|
||||
int2str(NumberOfIRFfiles_dsgevar) '.mat stock_irf_bvardsge;'];,
|
||||
eval(['save ' instr]);
|
||||
|
|
@ -241,7 +242,7 @@ while fpar<npar
|
|||
end
|
||||
NumberOfIRFfiles_dsgevar = NumberOfIRFfiles_dsgevar+1;
|
||||
IRUN =0;
|
||||
stock_irf_dsgevar = zeros(options_.irf,nvobs,M_.exo_nbr,MAX_nirfs_dsgevar);
|
||||
stock_irf_dsgevar = zeros(options_.irf,dataset_.info.nvobs,M_.exo_nbr,MAX_nirfs_dsgevar);
|
||||
end
|
||||
end
|
||||
if irun == MAX_nirfs_dsge || irun == npar || fpar == npar
|
||||
|
|
|
|||
|
|
@ -1,20 +1,20 @@
|
|||
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
|
||||
function bvar = dsgevar_posterior_density(deep,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
|
||||
% 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.
|
||||
%
|
||||
% bvar: a matlab structure with prior and posterior densities.
|
||||
%
|
||||
% ALGORITHM
|
||||
% ...
|
||||
% SPECIAL REQUIREMENTS
|
||||
% none
|
||||
%
|
||||
%
|
||||
|
||||
% Copyright (C) 1996-2008 Dynare Team
|
||||
%
|
||||
|
|
@ -33,8 +33,6 @@ function bvar = dsgevar_posterior_density(deep)
|
|||
% You should have received a copy of the GNU General Public License
|
||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||
|
||||
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));
|
||||
|
|
@ -42,14 +40,14 @@ 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;
|
||||
bvar.NumberOfEstimatedParameters = bvar.NumberOfLags*bvar.NumberOfVariables;
|
||||
if ~options_.noconstant
|
||||
bvar.Constant = 'yes';
|
||||
bvar.NumberOfEstimatedParameters = bvar.NumberOfEstimatedParameters + ...
|
||||
bvar.NumberOfVariables;
|
||||
bvar.NumberOfVariables;
|
||||
end
|
||||
|
||||
[fval,cost_flag,info,PHI,SIGMAu,iXX,prior] = DsgeVarLikelihood(deep',gend);
|
||||
[fval,cost_flag,info,PHI,SIGMAu,iXX,prior] = DsgeVarLikelihood(deep',DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
|
||||
|
||||
% Conditionnal posterior density of the lagged matrices (given Sigma) ->
|
||||
% Matric-variate normal distribution.
|
||||
|
|
@ -58,12 +56,12 @@ bvar.LaggedMatricesConditionalOnSigma.posterior.arg1 = PHI;
|
|||
bvar.LaggedMatricesConditionalOnSigma.posterior.arg2 = 'Sigma';
|
||||
bvar.LaggedMatricesConditionalOnSigma.posterior.arg3 = iXX;
|
||||
|
||||
% Marginal posterior density of the covariance matrix -> Inverted Wishart.
|
||||
% 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
|
||||
% 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
|
||||
|
|
@ -80,12 +78,12 @@ 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.
|
||||
% 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
|
||||
% 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(prior.iGXX);%P
|
||||
|
|
|
|||
|
|
@ -29,7 +29,7 @@ function dynare_estimation_1(var_list_,dname)
|
|||
% You should have received a copy of the GNU General Public License
|
||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||
|
||||
global M_ options_ oo_ estim_params_ bayestopt_
|
||||
global M_ options_ oo_ estim_params_ bayestopt_ dataset_
|
||||
|
||||
if ~options_.dsge_var
|
||||
objective_function = str2func('DsgeLikelihood');
|
||||
|
|
@ -208,11 +208,6 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
|
|||
else
|
||||
nit=1000;
|
||||
end
|
||||
if ~options_.dsge_var
|
||||
[xparam1,hh,gg,fval,invhess] = newrat('DsgeLikelihood',xparam1,hh,gg,igg,crit,nit,flag,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
|
||||
else
|
||||
[xparam1,hh,gg,fval,invhess] = newrat('DsgeVarLikelihood',xparam1,hh,gg,igg,crit,nit,flag,gend);
|
||||
end
|
||||
[xparam1,hh,gg,fval,invhess] = newrat(objective_function,xparam1,hh,gg,igg,crit,nit,flag,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
|
||||
parameter_names = bayestopt_.name;
|
||||
save([M_.fname '_mode.mat'],'xparam1','hh','gg','fval','invhess','parameter_names');
|
||||
|
|
@ -362,7 +357,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
|
|||
end
|
||||
|
||||
if options_.cova_compute == 0
|
||||
hh = NaN(length(xparam1),length(xparam1));
|
||||
hh = [];%NaN(length(xparam1),length(xparam1));
|
||||
end
|
||||
|
||||
if ~options_.mh_posterior_mode_estimation && options_.cova_compute
|
||||
|
|
@ -380,11 +375,7 @@ if ~options_.mh_posterior_mode_estimation && options_.cova_compute
|
|||
end
|
||||
|
||||
if options_.mode_check == 1 && ~options_.mh_posterior_mode_estimation
|
||||
if options_.cova_compute
|
||||
mode_check(xparam1,0,hh,gend,data,lb,ub,data_index,number_of_observations,no_more_missing_observations);
|
||||
else
|
||||
mode_check(xparam1,0,[],gend,data,lb,ub,data_index,number_of_observations,no_more_missing_observations);
|
||||
end
|
||||
mode_check('objective_function',xparam1,hh,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
|
||||
end
|
||||
|
||||
if ~options_.mh_posterior_mode_estimation
|
||||
|
|
@ -509,16 +500,10 @@ if any(bayestopt_.pshape > 0) && ~options_.mh_posterior_mode_estimation
|
|||
estim_params_nbr = size(xparam1,1);
|
||||
scale_factor = -sum(log10(diag(invhess)));
|
||||
log_det_invhess = -estim_params_nbr*log(scale_factor)+log(det(scale_factor*invhess));
|
||||
if ~options_.dsge_var
|
||||
md_Laplace = .5*estim_params_nbr*log(2*pi) + .5*log_det_invhess ...
|
||||
- DsgeLikelihood(xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
|
||||
else
|
||||
md_Laplace = .5*estim_params_nbr*log(2*pi) + .5*log_det_invhess ...
|
||||
- DsgeVarLikelihood(xparam1,gend);
|
||||
end
|
||||
oo_.MarginalDensity.LaplaceApproximation = md_Laplace;
|
||||
likelihood = feval(objective_function,xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
|
||||
oo_.MarginalDensity.LaplaceApproximation = .5*estim_params_nbr*log(2*pi) + .5*log_det_invhess - likelihood;
|
||||
disp(' ')
|
||||
disp(sprintf('Log data density [Laplace approximation] is %f.',md_Laplace))
|
||||
disp(sprintf('Log data density [Laplace approximation] is %f.',oo_.MarginalDensity.LaplaceApproximation))
|
||||
disp(' ')
|
||||
end
|
||||
elseif ~any(bayestopt_.pshape > 0) && options_.mh_posterior_mode_estimation
|
||||
|
|
@ -807,11 +792,7 @@ if (any(bayestopt_.pshape >0 ) && options_.mh_replic) || ...
|
|||
invhess = compute_mh_covariance_matrix;
|
||||
end
|
||||
if options_.cova_compute
|
||||
if options_.dsge_var
|
||||
feval(options_.posterior_sampling_method,'DsgeVarLikelihood',options_.proposal_distribution,xparam1,invhess,bounds,gend);
|
||||
else
|
||||
feval(options_.posterior_sampling_method,'DsgeLikelihood',options_.proposal_distribution,xparam1,invhess,bounds,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
|
||||
end
|
||||
feval(options_.posterior_sampling_method,objective_function,options_.proposal_distribution,xparam1,invhess,bounds,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
|
||||
else
|
||||
error('I Cannot start the MCMC because the hessian of the posterior kernel at the mode was not computed.')
|
||||
end
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
function [A,B,ys,info,Model,DynareOptions,DynareResults] = dynare_resolve(Model,DynareOptions,DynareResults)
|
||||
function [A,B,ys,info,Model,DynareOptions,DynareResults] = dynare_resolve(Model,DynareOptions,DynareResults,mode)
|
||||
% Computes the linear approximation and the matrices A and B of the transition equation.
|
||||
|
||||
%@info:
|
||||
|
|
@ -80,15 +80,18 @@ if info(1) > 0
|
|||
return
|
||||
end
|
||||
|
||||
if nargin == 0
|
||||
switch nargin
|
||||
case 3
|
||||
endo_nbr = Model.endo_nbr;
|
||||
nstatic = DynareResults.dr.nstatic;
|
||||
npred = DynareResults.dr.npred;
|
||||
iv = (1:endo_nbr)';
|
||||
ic = [ nstatic+(1:npred) endo_nbr+(1:size(DynareResults.dr.ghx,2)-npred) ]';
|
||||
else
|
||||
case 4
|
||||
iv = DynareResults.dr.restrict_var_list;
|
||||
ic = DynareResults.dr.restrict_columns;
|
||||
otherwise
|
||||
error('dynare_resolve:: Error in the calling sequence!')
|
||||
end
|
||||
|
||||
if nargout==1
|
||||
|
|
|
|||
|
|
@ -247,7 +247,7 @@ if fload==0,
|
|||
M_.params(estim_params_.param_vals(:,1)) = lpmat(j,:)';
|
||||
%try stoch_simul([]);
|
||||
try
|
||||
[Tt,Rr,SteadyState,infox{j},M_,options_,oo_] = dynare_resolve(M_,options_,oo_);
|
||||
[Tt,Rr,SteadyState,infox{j},M_,options_,oo_] = dynare_resolve(M_,options_,oo_,'restrict');
|
||||
if infox{j}(1)==0 && ~exist('T'),
|
||||
dr_=oo_.dr;
|
||||
T=zeros(size(dr_.ghx,1),size(dr_.ghx,2)+size(dr_.ghu,2),Nsam);
|
||||
|
|
@ -402,7 +402,7 @@ else
|
|||
for j=1:ntrans,
|
||||
M_.params(estim_params_.param_vals(:,1)) = lpmat(istable(j),:)';
|
||||
%stoch_simul([]);
|
||||
[Tt,Rr,SteadyState,info,M_,options_,oo_] = dynare_resolve(M_,options_,oo_);
|
||||
[Tt,Rr,SteadyState,info,M_,options_,oo_] = dynare_resolve(M_,options_,oo_,'restrict');
|
||||
% This syntax is not compatible with the current version of dynare_resolve [stepan].
|
||||
%[Tt,Rr,SteadyState,info] = dynare_resolve(bayestopt_.restrict_var_list,...
|
||||
% bayestopt_.restrict_columns,...
|
||||
|
|
|
|||
|
|
@ -17,7 +17,6 @@ M_.params(indx) = params(length(indexo)+1:end);
|
|||
if ~isempty(indexo)
|
||||
M_.Sigma_e(indexo,indexo) = diag(params(1:length(indexo)).^2);
|
||||
end
|
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% [A(oo_.dr.order_var,oo_.dr.order_var),B(oo_.dr.order_var,:)]=dynare_resolve;
|
||||
[A,B,plouf,plouf,M_,options_,oo_] = dynare_resolve(M_,options_,oo_);
|
||||
if flagmoments==0,
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||||
tau = [oo_.dr.ys(oo_.dr.order_var); A(:); dyn_vech(B*M_.Sigma_e*B')];
|
||||
|
|
|
|||
|
|
@ -35,7 +35,7 @@ if DynareDataset.info.nvobs>Model.exo_nbr+EstimatedParameters.nvn
|
|||
end
|
||||
|
||||
if DynareOptions.dsge_var
|
||||
[fval,cost_flag,info] = DsgeVarLikelihood(xparam1,gend);
|
||||
[fval,cost_flag,info] = DsgeVarLikelihood(xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
|
||||
else
|
||||
[fval,cost_flag,ys,trend_coeff,info] = DsgeLikelihood(xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
|
||||
end
|
||||
|
|
|
|||
|
|
@ -1,8 +1,8 @@
|
|||
function mode_check(x,fval,hessian,gend,data,lb,ub,data_index,number_of_observations,no_more_missing_observations)
|
||||
function mode_check(func,x,fval,hessian,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
|
||||
|
||||
% function mode_check(x,fval,hessian,gend,data,lb,ub)
|
||||
% Checks the maximum likelihood mode
|
||||
%
|
||||
% Checks the maximum likelihood mode
|
||||
%
|
||||
% INPUTS
|
||||
% x: mode
|
||||
% fval: value at the maximum likelihood mode
|
||||
|
|
@ -14,7 +14,7 @@ function mode_check(x,fval,hessian,gend,data,lb,ub,data_index,number_of_observat
|
|||
%
|
||||
% OUTPUTS
|
||||
% none
|
||||
%
|
||||
%
|
||||
% SPECIAL REQUIREMENTS
|
||||
% none
|
||||
|
||||
|
|
@ -35,18 +35,12 @@ function mode_check(x,fval,hessian,gend,data,lb,ub,data_index,number_of_observat
|
|||
% You should have received a copy of the GNU General Public License
|
||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||
|
||||
global bayestopt_ M_ options_ estim_params_
|
||||
|
||||
TeX = options_.TeX;
|
||||
TeX = DynareOptions.TeX;
|
||||
if ~isempty(hessian);
|
||||
[ s_min, k ] = min(diag(hessian));
|
||||
end
|
||||
if options_.dsge_var
|
||||
fval = DsgeVarLikelihood(x,gend);
|
||||
else
|
||||
fval = DsgeLikelihood(x,gend,data,data_index,number_of_observations,no_more_missing_observations);
|
||||
end
|
||||
bayestopt_.penalty=fval;
|
||||
|
||||
fval = feval(fun,x,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
|
||||
|
||||
if ~isempty(hessian);
|
||||
disp(' ')
|
||||
|
|
@ -55,14 +49,14 @@ if ~isempty(hessian);
|
|||
disp(sprintf('Fval obtained by the minimization routine: %f', fval))
|
||||
disp(' ')
|
||||
if s_min<eps
|
||||
disp(sprintf('Most negative variance %f for parameter %d (%s = %f)', s_min, k , bayestopt_.name{k}, x(k)))
|
||||
disp(sprintf('Most negative variance %f for parameter %d (%s = %f)', s_min, k , BayesInfo.name{k}, x(k)))
|
||||
end
|
||||
end
|
||||
|
||||
[nbplt,nr,nc,lr,lc,nstar] = pltorg(length(x));
|
||||
|
||||
if TeX
|
||||
fidTeX = fopen([M_.fname '_CheckPlots.TeX'],'w');
|
||||
fidTeX = fopen([Model.fname '_CheckPlots.TeX'],'w');
|
||||
fprintf(fidTeX,'%% TeX eps-loader file generated by mode_check.m (Dynare).\n');
|
||||
fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
|
||||
fprintf(fidTeX,' \n');
|
||||
|
|
@ -77,7 +71,7 @@ for plt = 1:nbplt,
|
|||
for k=1:min(nstar,length(x)-(plt-1)*nstar)
|
||||
subplot(nr,nc,k)
|
||||
kk = (plt-1)*nstar+k;
|
||||
[name,texname] = get_the_name(kk,TeX,M_,estim_params_,options_);
|
||||
[name,texname] = get_the_name(kk,TeX,Model,EstimatedParameters,DynareOptions);
|
||||
if TeX
|
||||
if isempty(NAMES)
|
||||
NAMES = name;
|
||||
|
|
@ -88,32 +82,23 @@ for plt = 1:nbplt,
|
|||
end
|
||||
end
|
||||
xx = x;
|
||||
l1 = max(lb(kk),0.5*x(kk));
|
||||
l2 = min(ub(kk),1.5*x(kk));
|
||||
l1 = max(BayesInfo.lb(kk),0.5*x(kk));
|
||||
l2 = min(BayesInfo.ub(kk),1.5*x(kk));
|
||||
z = [l1:(l2-l1)/20:l2];
|
||||
if options_.mode_check_nolik==0,
|
||||
if DynareOptions.mode_check_nolik==0,
|
||||
y = zeros(length(z),2);
|
||||
dy = priordens(xx,bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7,bayestopt_.p3,bayestopt_.p4);
|
||||
dy = priordens(xx,BayesInfo.pshape,BayesInfo.p6,BayesInfo.p7,BayesInfo.p3,BayesInfo.p4);
|
||||
end
|
||||
for i=1:length(z)
|
||||
xx(kk) = z(i);
|
||||
if options_.dsge_var
|
||||
[fval,cost_flag] = DsgeVarLikelihood(xx,gend);
|
||||
if cost_flag
|
||||
y(i,1) = fval;
|
||||
else
|
||||
y(i,1) = NaN;
|
||||
end
|
||||
[fval, exit_flag] = feval(fun,x,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
|
||||
if exit_flag
|
||||
y(i,1) = fval;
|
||||
else
|
||||
[fval,cost_flag] = DsgeLikelihood(xx,gend,data,data_index,number_of_observations,no_more_missing_observations);
|
||||
if cost_flag
|
||||
y(i,1) = fval;
|
||||
else
|
||||
y(i,1) = NaN;
|
||||
end
|
||||
y(i,1) = NaN;
|
||||
end
|
||||
if options_.mode_check_nolik==0
|
||||
lnprior = priordens(xx,bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7,bayestopt_.p3,bayestopt_.p4);
|
||||
if DynareOptions.mode_check_nolik==0
|
||||
lnprior = priordens(xx,BayesInfo.pshape,BayesInfo.p6,BayesInfo.p7,BayesInfo.p3,BayesInfo.p4);
|
||||
y(i,2) = (y(i,1)+lnprior-dy);
|
||||
end
|
||||
end
|
||||
|
|
@ -130,7 +115,7 @@ for plt = 1:nbplt,
|
|||
axis tight
|
||||
drawnow
|
||||
end
|
||||
if options_.mode_check_nolik==0,
|
||||
if DynareOptions.mode_check_nolik==0,
|
||||
if exist('OCTAVE_VERSION'),
|
||||
axes('outerposition',[0.3 0.93 0.42 0.07],'box','on'),
|
||||
else
|
||||
|
|
@ -142,12 +127,12 @@ for plt = 1:nbplt,
|
|||
text(0.25,0.5,'log-post')
|
||||
text(0.69,0.5,'log-lik kernel')
|
||||
end
|
||||
eval(['print -depsc2 ' M_.fname '_CheckPlots' int2str(plt) '.eps']);
|
||||
eval(['print -depsc2 ' Model.fname '_CheckPlots' int2str(plt) '.eps']);
|
||||
if ~exist('OCTAVE_VERSION')
|
||||
eval(['print -dpdf ' M_.fname '_CheckPlots' int2str(plt)]);
|
||||
saveas(hh,[M_.fname '_CheckPlots' int2str(plt) '.fig']);
|
||||
eval(['print -dpdf ' Model.fname '_CheckPlots' int2str(plt)]);
|
||||
saveas(hh,[Model.fname '_CheckPlots' int2str(plt) '.fig']);
|
||||
end
|
||||
if options_.nograph, close(hh), end
|
||||
if DynareOptions.nograph, close(hh), end
|
||||
if TeX
|
||||
% TeX eps loader file
|
||||
fprintf(fidTeX,'\\begin{figure}[H]\n');
|
||||
|
|
@ -155,7 +140,7 @@ for plt = 1:nbplt,
|
|||
fprintf(fidTeX,'\\psfrag{%s}[1][][0.5][0]{%s}\n',deblank(NAMES(jj,:)),deblank(TeXNAMES(jj,:)));
|
||||
end
|
||||
fprintf(fidTeX,'\\centering \n');
|
||||
fprintf(fidTeX,'\\includegraphics[scale=0.5]{%s_CheckPlots%s}\n',M_.fname,int2str(plt));
|
||||
fprintf(fidTeX,'\\includegraphics[scale=0.5]{%s_CheckPlots%s}\n',Model.fname,int2str(plt));
|
||||
fprintf(fidTeX,'\\caption{Check plots.}');
|
||||
fprintf(fidTeX,'\\label{Fig:CheckPlots:%s}\n',int2str(plt));
|
||||
fprintf(fidTeX,'\\end{figure}\n');
|
||||
|
|
|
|||
|
|
@ -36,7 +36,6 @@ M_.params(indx) = params(length(indexo)+1:end);
|
|||
if ~isempty(indexo)
|
||||
M_.Sigma_e(indexo,indexo) = diag(params(1:length(indexo)).^2);
|
||||
end
|
||||
% [A(oo_.dr.order_var,oo_.dr.order_var),B(oo_.dr.order_var,:)]=dynare_resolve;
|
||||
[A,B,tele,tubbies,M_,options_,oo_] = dynare_resolve(M_,options_,oo_);
|
||||
if flagmoments==0,
|
||||
tau = [oo_.dr.ys(oo_.dr.order_var); A(:); dyn_vech(B*M_.Sigma_e*B')];
|
||||
|
|
|
|||
Loading…
Reference in New Issue