Revert "More fixing related to objective_function_penalty_base"
This reverts commit 1ad8df4635
.
time-shift
parent
1a9aa17c9e
commit
035adeb89e
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@ -1,4 +1,4 @@
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function [fval,info,exit_flag,DLIK,Hess,SteadyState,trend_coeff] = TaRB_optimizer_wrapper(optpar,par_vector,parameterindices,TargetFun,varargin)
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function [fval,DLIK,Hess,exit_flag] = TaRB_optimizer_wrapper(optpar,par_vector,parameterindices,TargetFun,varargin)
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% function [fval,DLIK,Hess,exit_flag] = TaRB_optimizer_wrapper(optpar,par_vector,parameterindices,TargetFun,varargin)
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% function [fval,DLIK,Hess,exit_flag] = TaRB_optimizer_wrapper(optpar,par_vector,parameterindices,TargetFun,varargin)
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% Wrapper function for target function used in TaRB algorithm; reassembles
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% Wrapper function for target function used in TaRB algorithm; reassembles
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% full parameter vector before calling target function
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% full parameter vector before calling target function
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@ -36,5 +36,5 @@ function [fval,info,exit_flag,DLIK,Hess,SteadyState,trend_coeff] = TaRB_optimize
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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par_vector(parameterindices,:)=optpar; %reassemble parameter
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par_vector(parameterindices,:)=optpar; %reassemble parameter
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[fval,info,exit_flag,DLIK,Hess,SteadyState,trend_coeff] = feval(TargetFun,par_vector,varargin{:}); %call target function
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[fval,DLIK,Hess,exit_flag] = feval(TargetFun,par_vector,varargin{:}); %call target function
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@ -1,5 +1,7 @@
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function [fval,DLIK,Hess,exit_flag,ys,trend_coeff,info,Model,DynareOptions,BayesInfo,DynareResults] = dsge_likelihood(xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults,derivatives_info)
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function [fval,DLIK,Hess,exit_flag,SteadyState,trend_coeff,info,Model,DynareOptions,BayesInfo,DynareResults] = dsge_likelihood(xparam1,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults,derivatives_info)
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% Evaluates the posterior kernel of a dsge model. Deprecated interface.
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% Evaluates the posterior kernel of a dsge model using the specified
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% kalman_algo; the resulting posterior includes the 2*pi constant of the
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% likelihood function
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%@info:
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%@info:
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%! @deftypefn {Function File} {[@var{fval},@var{exit_flag},@var{ys},@var{trend_coeff},@var{info},@var{Model},@var{DynareOptions},@var{BayesInfo},@var{DynareResults},@var{DLIK},@var{AHess}] =} dsge_likelihood (@var{xparam1},@var{DynareDataset},@var{DynareOptions},@var{Model},@var{EstimatedParameters},@var{BayesInfo},@var{DynareResults},@var{derivatives_flag})
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%! @deftypefn {Function File} {[@var{fval},@var{exit_flag},@var{ys},@var{trend_coeff},@var{info},@var{Model},@var{DynareOptions},@var{BayesInfo},@var{DynareResults},@var{DLIK},@var{AHess}] =} dsge_likelihood (@var{xparam1},@var{DynareDataset},@var{DynareOptions},@var{Model},@var{EstimatedParameters},@var{BayesInfo},@var{DynareResults},@var{derivatives_flag})
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@ -37,7 +39,7 @@ function [fval,DLIK,Hess,exit_flag,ys,trend_coeff,info,Model,DynareOptions,Bayes
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%! Integer scalar, equal to zero if the routine return with a penalty (one otherwise).
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%! Integer scalar, equal to zero if the routine return with a penalty (one otherwise).
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%! @item ys
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%! @item ys
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%! Vector of doubles, steady state level for the endogenous variables.
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%! Vector of doubles, steady state level for the endogenous variables.
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%! @item trend_coeffs
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%! @item trend_coeff
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%! Matrix of doubles, coefficients of the deterministic trend in the measurement equation.
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%! Matrix of doubles, coefficients of the deterministic trend in the measurement equation.
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%! @item info
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%! @item info
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%! Integer scalar, error code.
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%! Integer scalar, error code.
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@ -68,6 +70,8 @@ function [fval,DLIK,Hess,exit_flag,ys,trend_coeff,info,Model,DynareOptions,Bayes
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%! M_.params has been updated in the steadystate routine and has complex valued scalars.
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%! M_.params has been updated in the steadystate routine and has complex valued scalars.
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%! @item info==24
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%! @item info==24
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%! M_.params has been updated in the steadystate routine and has some NaNs.
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%! M_.params has been updated in the steadystate routine and has some NaNs.
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%! @item info==26
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%! M_.params has been updated in the steadystate routine and has negative/0 values in loglinear model.
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%! @item info==30
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%! @item info==30
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%! Ergodic variance can't be computed.
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%! Ergodic variance can't be computed.
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%! @item info==41
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%! @item info==41
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@ -130,6 +134,725 @@ function [fval,DLIK,Hess,exit_flag,ys,trend_coeff,info,Model,DynareOptions,Bayes
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% AUTHOR(S) stephane DOT adjemian AT univ DASH lemans DOT FR
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% AUTHOR(S) stephane DOT adjemian AT univ DASH lemans DOT FR
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[fval,info,exit_flag,DLIK,Hess,SteadyState,trend_coeff,Model,DynareOptions,BayesInfo,DynareResults] = ...
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% Initialization of the returned variables and others...
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dsge_likelihood_1(xparam1,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,...
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fval = [];
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BoundsInfo,DynareResults,derivatives_info);
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SteadyState = [];
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trend_coeff = [];
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exit_flag = 1;
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info = 0;
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DLIK = [];
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Hess = [];
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if DynareOptions.estimation_dll
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[fval,exit_flag,SteadyState,trend_coeff,info,params,H,Q] ...
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= logposterior(xparam1,DynareDataset, DynareOptions,Model, ...
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EstimatedParameters,BayesInfo,DynareResults);
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mexErrCheck('logposterior', exit_flag);
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Model.params = params;
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if ~isequal(Model.H,0)
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Model.H = H;
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end
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Model.Sigma_e = Q;
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DynareResults.dr.ys = SteadyState;
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return
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end
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% Set flag related to analytical derivatives.
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analytic_derivation = DynareOptions.analytic_derivation;
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if analytic_derivation && DynareOptions.loglinear
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error('The analytic_derivation and loglinear options are not compatible')
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end
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if nargout==1,
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analytic_derivation=0;
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end
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if analytic_derivation,
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kron_flag=DynareOptions.analytic_derivation_mode;
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end
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%------------------------------------------------------------------------------
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% 1. Get the structural parameters & define penalties
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%------------------------------------------------------------------------------
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% Return, with endogenous penalty, if some parameters are smaller than the lower bound of the prior domain.
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if ~isequal(DynareOptions.mode_compute,1) && any(xparam1<BoundsInfo.lb)
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k = find(xparam1<BoundsInfo.lb);
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fval = Inf;
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exit_flag = 0;
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info(1) = 41;
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info(2) = sum((BoundsInfo.lb(k)-xparam1(k)).^2);
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if analytic_derivation,
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DLIK=ones(length(xparam1),1);
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end
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return
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end
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% Return, with endogenous penalty, if some parameters are greater than the upper bound of the prior domain.
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if ~isequal(DynareOptions.mode_compute,1) && any(xparam1>BoundsInfo.ub)
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k = find(xparam1>BoundsInfo.ub);
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fval = Inf;
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exit_flag = 0;
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info(1) = 42;
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info(2) = sum((xparam1(k)-BoundsInfo.ub(k)).^2);
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if analytic_derivation,
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DLIK=ones(length(xparam1),1);
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end
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return
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end
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% Get the diagonal elements of the covariance matrices for the structural innovations (Q) and the measurement error (H).
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Model = set_all_parameters(xparam1,EstimatedParameters,Model);
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Q = Model.Sigma_e;
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H = Model.H;
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% Test if Q is positive definite.
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if ~issquare(Q) || EstimatedParameters.ncx || isfield(EstimatedParameters,'calibrated_covariances')
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[Q_is_positive_definite, penalty] = ispd(Q);
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if ~Q_is_positive_definite
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fval = Inf;
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exit_flag = 0;
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info(1) = 43;
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info(2) = penalty;
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return
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end
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if isfield(EstimatedParameters,'calibrated_covariances')
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correct_flag=check_consistency_covariances(Q);
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if ~correct_flag
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fval = Inf;
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exit_flag = 0;
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info(1) = 71;
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info(2) = sum(Q(EstimatedParameters.calibrated_covariances.position).^2);
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return
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end
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end
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end
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% Test if H is positive definite.
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if ~issquare(H) || EstimatedParameters.ncn || isfield(EstimatedParameters,'calibrated_covariances_ME')
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[H_is_positive_definite, penalty] = ispd(H);
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if ~H_is_positive_definite
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fval = Inf;
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exit_flag = 0;
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info(1) = 44;
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info(2) = penalty;
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return
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end
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if isfield(EstimatedParameters,'calibrated_covariances_ME')
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correct_flag=check_consistency_covariances(H);
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if ~correct_flag
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fval = Inf;
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exit_flag = 0;
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info(1) = 72;
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info(2) = sum(H(EstimatedParameters.calibrated_covariances_ME.position).^2);
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return
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end
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end
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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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% 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,'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) == 7 || info(1) == 8 || ...
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info(1) == 22 || info(1) == 24 || info(1) == 19 || info(1) == 25 || info(1) == 10
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fval = Inf;
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info(2) = 0.1;
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exit_flag = 0;
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if analytic_derivation,
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DLIK=ones(length(xparam1),1);
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end
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return
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elseif info(1) == 3 || info(1) == 4 || info(1)==6 || info(1) == 20 || info(1) == 21 || info(1) == 23 || info(1)==26
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fval = Inf;
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exit_flag = 0;
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if analytic_derivation,
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DLIK=ones(length(xparam1),1);
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end
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return
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end
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% check endogenous prior restrictions
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info=endogenous_prior_restrictions(T,R,Model,DynareOptions,DynareResults);
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if info(1),
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fval = Inf;
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exit_flag = 0;
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if analytic_derivation,
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DLIK=ones(length(xparam1),1);
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end
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return
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end
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%
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% Define a vector of indices for the observed variables. Is this really usefull?...
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BayesInfo.mf = BayesInfo.mf1;
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% Define the constant vector of the measurement equation.
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if DynareOptions.noconstant
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constant = zeros(DynareDataset.vobs,1);
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else
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if DynareOptions.loglinear
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constant = log(SteadyState(BayesInfo.mfys));
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else
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constant = SteadyState(BayesInfo.mfys);
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end
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end
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% Define the deterministic linear trend of the measurement equation.
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if BayesInfo.with_trend
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trend_coeff = zeros(DynareDataset.vobs,1);
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t = DynareOptions.trend_coeffs;
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for i=1:length(t)
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if ~isempty(t{i})
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trend_coeff(i) = evalin('base',t{i});
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end
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end
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trend = repmat(constant,1,DynareDataset.nobs)+trend_coeff*[1:DynareDataset.nobs];
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else
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trend_coeff = zeros(DynareDataset.vobs,1);
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trend = repmat(constant,1,DynareDataset.nobs);
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end
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% Get needed informations for kalman filter routines.
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start = DynareOptions.presample+1;
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Z = BayesInfo.mf;
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no_missing_data_flag = ~DatasetInfo.missing.state;
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mm = length(T);
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pp = DynareDataset.vobs;
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rr = length(Q);
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kalman_tol = DynareOptions.kalman_tol;
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diffuse_kalman_tol = DynareOptions.diffuse_kalman_tol;
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riccati_tol = DynareOptions.riccati_tol;
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Y = transpose(DynareDataset.data)-trend;
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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 = DynareOptions.kalman_algo;
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% resetting measurement errors covariance matrix for univariate filters
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if (kalman_algo == 2) || (kalman_algo == 4)
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if isequal(H,0)
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H = zeros(pp,1);
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mmm = mm;
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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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mmm = mm;
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else
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Z = [Z, eye(pp)];
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T = blkdiag(T,zeros(pp));
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Q = blkdiag(Q,H);
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R = blkdiag(R,eye(pp));
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Pstar = blkdiag(Pstar,H);
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Pinf = blkdiag(Pinf,zeros(pp));
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H = zeros(pp,1);
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mmm = mm+pp;
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end
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end
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end
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diffuse_periods = 0;
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correlated_errors_have_been_checked = 0;
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singular_diffuse_filter = 0;
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switch DynareOptions.lik_init
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case 1% Standard initialization with the steady state of the state equation.
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if kalman_algo~=2
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% Use standard kalman filter except if the univariate filter is explicitely choosen.
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kalman_algo = 1;
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end
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if DynareOptions.lyapunov_fp == 1
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Pstar = lyapunov_symm(T,R*Q'*R',DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold, 3, [], DynareOptions.debug);
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elseif DynareOptions.lyapunov_db == 1
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Pstar = disclyap_fast(T,R*Q*R',DynareOptions.lyapunov_doubling_tol);
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elseif DynareOptions.lyapunov_srs == 1
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Pstar = lyapunov_symm(T,Q,DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold, 4, R, DynareOptions.debug);
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else
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Pstar = lyapunov_symm(T,R*Q*R',DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold, [], [], DynareOptions.debug);
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end;
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Pinf = [];
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a = zeros(mm,1);
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Zflag = 0;
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case 2% Initialization with large numbers on the diagonal of the covariance matrix if the states (for non stationary models).
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if kalman_algo ~= 2
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% Use standard kalman filter except if the univariate filter is explicitely choosen.
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kalman_algo = 1;
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end
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Pstar = DynareOptions.Harvey_scale_factor*eye(mm);
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Pinf = [];
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a = zeros(mm,1);
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Zflag = 0;
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case 3% Diffuse Kalman filter (Durbin and Koopman)
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% Use standard kalman filter except if the univariate filter is explicitely choosen.
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if kalman_algo == 0
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kalman_algo = 3;
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elseif ~((kalman_algo == 3) || (kalman_algo == 4))
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error(['The model requires Diffuse filter, but you specified a different Kalman filter. You must set options_.kalman_algo ' ...
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'to 0 (default), 3 or 4'])
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end
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[Z,T,R,QT,Pstar,Pinf] = schur_statespace_transformation(Z,T,R,Q,DynareOptions.qz_criterium);
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Zflag = 1;
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||||||
|
% Run diffuse kalman filter on first periods.
|
||||||
|
if (kalman_algo==3)
|
||||||
|
% Multivariate Diffuse Kalman Filter
|
||||||
|
if no_missing_data_flag
|
||||||
|
[dLIK,dlik,a,Pstar] = kalman_filter_d(Y, 1, size(Y,2), ...
|
||||||
|
zeros(mm,1), Pinf, Pstar, ...
|
||||||
|
kalman_tol, diffuse_kalman_tol, riccati_tol, DynareOptions.presample, ...
|
||||||
|
T,R,Q,H,Z,mm,pp,rr);
|
||||||
|
else
|
||||||
|
[dLIK,dlik,a,Pstar] = missing_observations_kalman_filter_d(DatasetInfo.missing.aindex,DatasetInfo.missing.number_of_observations,DatasetInfo.missing.no_more_missing_observations, ...
|
||||||
|
Y, 1, size(Y,2), ...
|
||||||
|
zeros(mm,1), Pinf, Pstar, ...
|
||||||
|
kalman_tol, diffuse_kalman_tol, riccati_tol, DynareOptions.presample, ...
|
||||||
|
T,R,Q,H,Z,mm,pp,rr);
|
||||||
|
end
|
||||||
|
diffuse_periods = length(dlik);
|
||||||
|
if isinf(dLIK)
|
||||||
|
% Go to univariate diffuse filter if singularity problem.
|
||||||
|
singular_diffuse_filter = 1;
|
||||||
|
end
|
||||||
|
end
|
||||||
|
if singular_diffuse_filter || (kalman_algo==4)
|
||||||
|
% Univariate Diffuse Kalman Filter
|
||||||
|
if isequal(H,0)
|
||||||
|
H1 = zeros(pp,1);
|
||||||
|
mmm = mm;
|
||||||
|
else
|
||||||
|
if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
|
||||||
|
H1 = diag(H);
|
||||||
|
mmm = mm;
|
||||||
|
else
|
||||||
|
Z = [Z, eye(pp)];
|
||||||
|
T = blkdiag(T,zeros(pp));
|
||||||
|
Q = blkdiag(Q,H);
|
||||||
|
R = blkdiag(R,eye(pp));
|
||||||
|
Pstar = blkdiag(Pstar,H);
|
||||||
|
Pinf = blkdiag(Pinf,zeros(pp));
|
||||||
|
H1 = zeros(pp,1);
|
||||||
|
mmm = mm+pp;
|
||||||
|
end
|
||||||
|
end
|
||||||
|
% no need to test again for correlation elements
|
||||||
|
correlated_errors_have_been_checked = 1;
|
||||||
|
|
||||||
|
[dLIK,dlik,a,Pstar] = univariate_kalman_filter_d(DatasetInfo.missing.aindex,...
|
||||||
|
DatasetInfo.missing.number_of_observations,...
|
||||||
|
DatasetInfo.missing.no_more_missing_observations, ...
|
||||||
|
Y, 1, size(Y,2), ...
|
||||||
|
zeros(mmm,1), Pinf, Pstar, ...
|
||||||
|
kalman_tol, diffuse_kalman_tol, riccati_tol, DynareOptions.presample, ...
|
||||||
|
T,R,Q,H1,Z,mmm,pp,rr);
|
||||||
|
diffuse_periods = size(dlik,1);
|
||||||
|
end
|
||||||
|
if isnan(dLIK),
|
||||||
|
fval = Inf;
|
||||||
|
info(1) = 45;
|
||||||
|
info(2) = 0.1;
|
||||||
|
exit_flag = 0;
|
||||||
|
return
|
||||||
|
end
|
||||||
|
|
||||||
|
case 4% Start from the solution of the Riccati equation.
|
||||||
|
if kalman_algo ~= 2
|
||||||
|
kalman_algo = 1;
|
||||||
|
end
|
||||||
|
if isequal(H,0)
|
||||||
|
[err,Pstar] = kalman_steady_state(transpose(T),R*Q*transpose(R),transpose(build_selection_matrix(Z,mm,length(Z))));
|
||||||
|
else
|
||||||
|
[err,Pstar] = kalman_steady_state(transpose(T),R*Q*transpose(R),transpose(build_selection_matrix(Z,mm,length(Z))),H);
|
||||||
|
end
|
||||||
|
if err
|
||||||
|
disp(['dsge_likelihood:: I am not able to solve the Riccati equation, so I switch to lik_init=1!']);
|
||||||
|
DynareOptions.lik_init = 1;
|
||||||
|
Pstar = lyapunov_symm(T,R*Q*R',DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold, [], [], DynareOptions.debug);
|
||||||
|
end
|
||||||
|
Pinf = [];
|
||||||
|
a = zeros(mm,1);
|
||||||
|
Zflag = 0;
|
||||||
|
case 5 % Old diffuse Kalman filter only for the non stationary variables
|
||||||
|
[eigenvect, eigenv] = eig(T);
|
||||||
|
eigenv = diag(eigenv);
|
||||||
|
nstable = length(find(abs(abs(eigenv)-1) > 1e-7));
|
||||||
|
unstable = find(abs(abs(eigenv)-1) < 1e-7);
|
||||||
|
V = eigenvect(:,unstable);
|
||||||
|
indx_unstable = find(sum(abs(V),2)>1e-5);
|
||||||
|
stable = find(sum(abs(V),2)<1e-5);
|
||||||
|
nunit = length(eigenv) - nstable;
|
||||||
|
Pstar = options_.Harvey_scale_factor*eye(np);
|
||||||
|
if kalman_algo ~= 2
|
||||||
|
kalman_algo = 1;
|
||||||
|
end
|
||||||
|
R_tmp = R(stable, :);
|
||||||
|
T_tmp = T(stable,stable);
|
||||||
|
if DynareOptions.lyapunov_fp == 1
|
||||||
|
Pstar_tmp = lyapunov_symm(T_tmp,R_tmp*Q*R_tmp',DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold, 3, [], DynareOptions.debug);
|
||||||
|
elseif DynareOptions.lyapunov_db == 1
|
||||||
|
Pstar_tmp = disclyap_fast(T_tmp,R_tmp*Q*R_tmp',DynareOptions.lyapunov_doubling_tol);
|
||||||
|
elseif DynareOptions.lyapunov_srs == 1
|
||||||
|
Pstar_tmp = lyapunov_symm(T_tmp,Q,DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold, 4, R_tmp, DynareOptions.debug);
|
||||||
|
else
|
||||||
|
Pstar_tmp = lyapunov_symm(T_tmp,R_tmp*Q*R_tmp',DynareOptions.qz_criterium,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold, [], [], DynareOptions.debug);
|
||||||
|
end
|
||||||
|
Pstar(stable, stable) = Pstar_tmp;
|
||||||
|
Pinf = [];
|
||||||
|
otherwise
|
||||||
|
error('dsge_likelihood:: Unknown initialization approach for the Kalman filter!')
|
||||||
|
end
|
||||||
|
|
||||||
|
if analytic_derivation,
|
||||||
|
offset = EstimatedParameters.nvx;
|
||||||
|
offset = offset+EstimatedParameters.nvn;
|
||||||
|
offset = offset+EstimatedParameters.ncx;
|
||||||
|
offset = offset+EstimatedParameters.ncn;
|
||||||
|
|
||||||
|
no_DLIK = 0;
|
||||||
|
full_Hess = analytic_derivation==2;
|
||||||
|
asy_Hess = analytic_derivation==-2;
|
||||||
|
outer_product_gradient = analytic_derivation==-1;
|
||||||
|
if asy_Hess,
|
||||||
|
analytic_derivation=1;
|
||||||
|
end
|
||||||
|
if outer_product_gradient,
|
||||||
|
analytic_derivation=1;
|
||||||
|
end
|
||||||
|
DLIK = [];
|
||||||
|
AHess = [];
|
||||||
|
iv = DynareResults.dr.restrict_var_list;
|
||||||
|
if nargin<10 || isempty(derivatives_info)
|
||||||
|
[A,B,nou,nou,Model,DynareOptions,DynareResults] = dynare_resolve(Model,DynareOptions,DynareResults);
|
||||||
|
if ~isempty(EstimatedParameters.var_exo)
|
||||||
|
indexo=EstimatedParameters.var_exo(:,1);
|
||||||
|
else
|
||||||
|
indexo=[];
|
||||||
|
end
|
||||||
|
if ~isempty(EstimatedParameters.param_vals)
|
||||||
|
indparam=EstimatedParameters.param_vals(:,1);
|
||||||
|
else
|
||||||
|
indparam=[];
|
||||||
|
end
|
||||||
|
|
||||||
|
if full_Hess,
|
||||||
|
[dum, DT, DOm, DYss, dum2, D2T, D2Om, D2Yss] = getH(A, B, Model,DynareResults,DynareOptions,kron_flag,indparam,indexo,iv);
|
||||||
|
clear dum dum2;
|
||||||
|
else
|
||||||
|
[dum, DT, DOm, DYss] = getH(A, B, Model,DynareResults,DynareOptions,kron_flag,indparam,indexo,iv);
|
||||||
|
end
|
||||||
|
else
|
||||||
|
DT = derivatives_info.DT(iv,iv,:);
|
||||||
|
DOm = derivatives_info.DOm(iv,iv,:);
|
||||||
|
DYss = derivatives_info.DYss(iv,:);
|
||||||
|
if isfield(derivatives_info,'full_Hess'),
|
||||||
|
full_Hess = derivatives_info.full_Hess;
|
||||||
|
end
|
||||||
|
if full_Hess,
|
||||||
|
D2T = derivatives_info.D2T;
|
||||||
|
D2Om = derivatives_info.D2Om;
|
||||||
|
D2Yss = derivatives_info.D2Yss;
|
||||||
|
end
|
||||||
|
if isfield(derivatives_info,'no_DLIK'),
|
||||||
|
no_DLIK = derivatives_info.no_DLIK;
|
||||||
|
end
|
||||||
|
clear('derivatives_info');
|
||||||
|
end
|
||||||
|
DYss = [zeros(size(DYss,1),offset) DYss];
|
||||||
|
DH=zeros([length(H),length(H),length(xparam1)]);
|
||||||
|
DQ=zeros([size(Q),length(xparam1)]);
|
||||||
|
DP=zeros([size(T),length(xparam1)]);
|
||||||
|
if full_Hess,
|
||||||
|
for j=1:size(D2Yss,1),
|
||||||
|
tmp(j,:,:) = blkdiag(zeros(offset,offset), squeeze(D2Yss(j,:,:)));
|
||||||
|
end
|
||||||
|
D2Yss = tmp;
|
||||||
|
D2H=sparse(size(D2Om,1),size(D2Om,2)); %zeros([size(H),length(xparam1),length(xparam1)]);
|
||||||
|
D2P=sparse(size(D2Om,1),size(D2Om,2)); %zeros([size(T),length(xparam1),length(xparam1)]);
|
||||||
|
jcount=0;
|
||||||
|
end
|
||||||
|
if DynareOptions.lik_init==1,
|
||||||
|
for i=1:EstimatedParameters.nvx
|
||||||
|
k =EstimatedParameters.var_exo(i,1);
|
||||||
|
DQ(k,k,i) = 2*sqrt(Q(k,k));
|
||||||
|
dum = lyapunov_symm(T,DOm(:,:,i),DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold,[],[],DynareOptions.debug);
|
||||||
|
% kk = find(abs(dum) < 1e-12);
|
||||||
|
% dum(kk) = 0;
|
||||||
|
DP(:,:,i)=dum;
|
||||||
|
if full_Hess
|
||||||
|
for j=1:i,
|
||||||
|
jcount=jcount+1;
|
||||||
|
dum = lyapunov_symm(T,dyn_unvech(D2Om(:,jcount)),DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold,[],[],DynareOptions.debug);
|
||||||
|
% kk = (abs(dum) < 1e-12);
|
||||||
|
% dum(kk) = 0;
|
||||||
|
D2P(:,jcount)=dyn_vech(dum);
|
||||||
|
% D2P(:,:,j,i)=dum;
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
offset = EstimatedParameters.nvx;
|
||||||
|
for i=1:EstimatedParameters.nvn
|
||||||
|
k = EstimatedParameters.var_endo(i,1);
|
||||||
|
DH(k,k,i+offset) = 2*sqrt(H(k,k));
|
||||||
|
if full_Hess
|
||||||
|
D2H(k,k,i+offset,i+offset) = 2;
|
||||||
|
end
|
||||||
|
end
|
||||||
|
offset = offset + EstimatedParameters.nvn;
|
||||||
|
if DynareOptions.lik_init==1,
|
||||||
|
for j=1:EstimatedParameters.np
|
||||||
|
dum = lyapunov_symm(T,DT(:,:,j+offset)*Pstar*T'+T*Pstar*DT(:,:,j+offset)'+DOm(:,:,j+offset),DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold,[],[],DynareOptions.debug);
|
||||||
|
% kk = find(abs(dum) < 1e-12);
|
||||||
|
% dum(kk) = 0;
|
||||||
|
DP(:,:,j+offset)=dum;
|
||||||
|
if full_Hess
|
||||||
|
DTj = DT(:,:,j+offset);
|
||||||
|
DPj = dum;
|
||||||
|
for i=1:j+offset,
|
||||||
|
jcount=jcount+1;
|
||||||
|
DTi = DT(:,:,i);
|
||||||
|
DPi = DP(:,:,i);
|
||||||
|
D2Tij = reshape(D2T(:,jcount),size(T));
|
||||||
|
D2Omij = dyn_unvech(D2Om(:,jcount));
|
||||||
|
tmp = D2Tij*Pstar*T' + T*Pstar*D2Tij' + DTi*DPj*T' + DTj*DPi*T' + T*DPj*DTi' + T*DPi*DTj' + DTi*Pstar*DTj' + DTj*Pstar*DTi' + D2Omij;
|
||||||
|
dum = lyapunov_symm(T,tmp,DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold,[],[],DynareOptions.debug);
|
||||||
|
% dum(abs(dum)<1.e-12) = 0;
|
||||||
|
D2P(:,jcount) = dyn_vech(dum);
|
||||||
|
% D2P(:,:,j+offset,i) = dum;
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
if analytic_derivation==1,
|
||||||
|
analytic_deriv_info={analytic_derivation,DT,DYss,DOm,DH,DP,asy_Hess};
|
||||||
|
else
|
||||||
|
analytic_deriv_info={analytic_derivation,DT,DYss,DOm,DH,DP,D2T,D2Yss,D2Om,D2H,D2P};
|
||||||
|
clear DT DYss DOm DP D2T D2Yss D2Om D2H D2P,
|
||||||
|
end
|
||||||
|
else
|
||||||
|
analytic_deriv_info={0};
|
||||||
|
end
|
||||||
|
|
||||||
|
%------------------------------------------------------------------------------
|
||||||
|
% 4. Likelihood evaluation
|
||||||
|
%------------------------------------------------------------------------------
|
||||||
|
|
||||||
|
if ((kalman_algo==1) || (kalman_algo==3))% Multivariate Kalman Filter
|
||||||
|
if no_missing_data_flag
|
||||||
|
if DynareOptions.block
|
||||||
|
[err, LIK] = block_kalman_filter(T,R,Q,H,Pstar,Y,start,Z,kalman_tol,riccati_tol, Model.nz_state_var, Model.n_diag, Model.nobs_non_statevar);
|
||||||
|
mexErrCheck('block_kalman_filter', err);
|
||||||
|
else
|
||||||
|
[LIK,lik] = kalman_filter(Y,diffuse_periods+1,size(Y,2), ...
|
||||||
|
a,Pstar, ...
|
||||||
|
kalman_tol, riccati_tol, ...
|
||||||
|
DynareOptions.presample, ...
|
||||||
|
T,Q,R,H,Z,mm,pp,rr,Zflag,diffuse_periods, ...
|
||||||
|
analytic_deriv_info{:});
|
||||||
|
|
||||||
|
end
|
||||||
|
else
|
||||||
|
if 0 %DynareOptions.block
|
||||||
|
[err, LIK,lik] = block_kalman_filter(DatasetInfo.missing.aindex,DatasetInfo.missing.number_of_observations,DatasetInfo.missing.no_more_missing_observations,...
|
||||||
|
T,R,Q,H,Pstar,Y,start,Z,kalman_tol,riccati_tol, Model.nz_state_var, Model.n_diag, Model.nobs_non_statevar);
|
||||||
|
else
|
||||||
|
[LIK,lik] = missing_observations_kalman_filter(DatasetInfo.missing.aindex,DatasetInfo.missing.number_of_observations,DatasetInfo.missing.no_more_missing_observations,Y,diffuse_periods+1,size(Y,2), ...
|
||||||
|
a, Pstar, ...
|
||||||
|
kalman_tol, DynareOptions.riccati_tol, ...
|
||||||
|
DynareOptions.presample, ...
|
||||||
|
T,Q,R,H,Z,mm,pp,rr,Zflag,diffuse_periods);
|
||||||
|
end
|
||||||
|
end
|
||||||
|
if analytic_derivation,
|
||||||
|
LIK1=LIK;
|
||||||
|
LIK=LIK1{1};
|
||||||
|
lik1=lik;
|
||||||
|
lik=lik1{1};
|
||||||
|
end
|
||||||
|
if isinf(LIK)
|
||||||
|
if DynareOptions.use_univariate_filters_if_singularity_is_detected
|
||||||
|
if kalman_algo == 1
|
||||||
|
kalman_algo = 2;
|
||||||
|
else
|
||||||
|
kalman_algo = 4;
|
||||||
|
end
|
||||||
|
else
|
||||||
|
if isinf(LIK)
|
||||||
|
fval = Inf;
|
||||||
|
info(1) = 66;
|
||||||
|
info(2) = 0.1;
|
||||||
|
exit_flag = 0;
|
||||||
|
return
|
||||||
|
end
|
||||||
|
end
|
||||||
|
else
|
||||||
|
if DynareOptions.lik_init==3
|
||||||
|
LIK = LIK + dLIK;
|
||||||
|
if analytic_derivation==0 && nargout==2,
|
||||||
|
lik = [dlik; lik];
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
if (kalman_algo==2) || (kalman_algo==4)
|
||||||
|
% Univariate Kalman Filter
|
||||||
|
% resetting measurement error covariance matrix when necessary %
|
||||||
|
if ~correlated_errors_have_been_checked
|
||||||
|
if isequal(H,0)
|
||||||
|
H1 = zeros(pp,1);
|
||||||
|
mmm = mm;
|
||||||
|
if analytic_derivation,
|
||||||
|
DH = zeros(pp,length(xparam1));
|
||||||
|
end
|
||||||
|
else
|
||||||
|
if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
|
||||||
|
H1 = diag(H);
|
||||||
|
mmm = mm;
|
||||||
|
clear tmp
|
||||||
|
if analytic_derivation,
|
||||||
|
for j=1:pp,
|
||||||
|
tmp(j,:)=DH(j,j,:);
|
||||||
|
end
|
||||||
|
DH=tmp;
|
||||||
|
end
|
||||||
|
else
|
||||||
|
Z = [Z, eye(pp)];
|
||||||
|
T = blkdiag(T,zeros(pp));
|
||||||
|
Q = blkdiag(Q,H);
|
||||||
|
R = blkdiag(R,eye(pp));
|
||||||
|
Pstar = blkdiag(Pstar,H);
|
||||||
|
Pinf = blkdiag(Pinf,zeros(pp));
|
||||||
|
H1 = zeros(pp,1);
|
||||||
|
mmm = mm+pp;
|
||||||
|
end
|
||||||
|
end
|
||||||
|
if analytic_derivation,
|
||||||
|
analytic_deriv_info{5}=DH;
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
[LIK, lik] = univariate_kalman_filter(DatasetInfo.missing.aindex,DatasetInfo.missing.number_of_observations,DatasetInfo.missing.no_more_missing_observations,Y,diffuse_periods+1,size(Y,2), ...
|
||||||
|
a,Pstar, ...
|
||||||
|
DynareOptions.kalman_tol, ...
|
||||||
|
DynareOptions.riccati_tol, ...
|
||||||
|
DynareOptions.presample, ...
|
||||||
|
T,Q,R,H1,Z,mmm,pp,rr,Zflag,diffuse_periods,analytic_deriv_info{:});
|
||||||
|
if analytic_derivation,
|
||||||
|
LIK1=LIK;
|
||||||
|
LIK=LIK1{1};
|
||||||
|
lik1=lik;
|
||||||
|
lik=lik1{1};
|
||||||
|
end
|
||||||
|
if DynareOptions.lik_init==3
|
||||||
|
LIK = LIK+dLIK;
|
||||||
|
if analytic_derivation==0 && nargout==2,
|
||||||
|
lik = [dlik; lik];
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
if analytic_derivation
|
||||||
|
if no_DLIK==0
|
||||||
|
DLIK = LIK1{2};
|
||||||
|
% [DLIK] = score(T,R,Q,H,Pstar,Y,DT,DYss,DOm,DH,DP,start,Z,kalman_tol,riccati_tol);
|
||||||
|
end
|
||||||
|
if full_Hess ,
|
||||||
|
Hess = -LIK1{3};
|
||||||
|
% [Hess, DLL] = get_Hessian(T,R,Q,H,Pstar,Y,DT,DYss,DOm,DH,DP,D2T,D2Yss,D2Om,D2H,D2P,start,Z,kalman_tol,riccati_tol);
|
||||||
|
% Hess0 = getHessian(Y,T,DT,D2T, R*Q*transpose(R),DOm,D2Om,Z,DYss,D2Yss);
|
||||||
|
end
|
||||||
|
if asy_Hess,
|
||||||
|
% if ~((kalman_algo==2) || (kalman_algo==4)),
|
||||||
|
% [Hess] = AHessian(T,R,Q,H,Pstar,Y,DT,DYss,DOm,DH,DP,start,Z,kalman_tol,riccati_tol);
|
||||||
|
% else
|
||||||
|
Hess = LIK1{3};
|
||||||
|
% end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
if isnan(LIK)
|
||||||
|
fval = Inf;
|
||||||
|
info(1) = 45;
|
||||||
|
info(2) = 0.1;
|
||||||
|
exit_flag = 0;
|
||||||
|
return
|
||||||
|
end
|
||||||
|
|
||||||
|
if imag(LIK)~=0
|
||||||
|
fval = Inf;
|
||||||
|
info(1) = 46;
|
||||||
|
info(2) = 0.1;
|
||||||
|
exit_flag = 0;
|
||||||
|
return
|
||||||
|
end
|
||||||
|
|
||||||
|
likelihood = LIK;
|
||||||
|
|
||||||
|
% ------------------------------------------------------------------------------
|
||||||
|
% 5. Adds prior if necessary
|
||||||
|
% ------------------------------------------------------------------------------
|
||||||
|
if analytic_derivation
|
||||||
|
if full_Hess,
|
||||||
|
[lnprior, dlnprior, d2lnprior] = priordens(xparam1,BayesInfo.pshape,BayesInfo.p6,BayesInfo.p7,BayesInfo.p3,BayesInfo.p4);
|
||||||
|
Hess = Hess - d2lnprior;
|
||||||
|
else
|
||||||
|
[lnprior, dlnprior] = priordens(xparam1,BayesInfo.pshape,BayesInfo.p6,BayesInfo.p7,BayesInfo.p3,BayesInfo.p4);
|
||||||
|
end
|
||||||
|
if no_DLIK==0
|
||||||
|
DLIK = DLIK - dlnprior';
|
||||||
|
end
|
||||||
|
if outer_product_gradient,
|
||||||
|
dlik = lik1{2};
|
||||||
|
dlik=[- dlnprior; dlik(start:end,:)];
|
||||||
|
Hess = dlik'*dlik;
|
||||||
|
end
|
||||||
|
else
|
||||||
|
lnprior = priordens(xparam1,BayesInfo.pshape,BayesInfo.p6,BayesInfo.p7,BayesInfo.p3,BayesInfo.p4);
|
||||||
|
end
|
||||||
|
|
||||||
|
if DynareOptions.endogenous_prior==1
|
||||||
|
if DynareOptions.lik_init==2 || DynareOptions.lik_init==3
|
||||||
|
error('Endogenous prior not supported with non-stationary models')
|
||||||
|
else
|
||||||
|
[lnpriormom] = endogenous_prior(Y,Pstar,BayesInfo,H);
|
||||||
|
fval = (likelihood-lnprior-lnpriormom);
|
||||||
|
end
|
||||||
|
else
|
||||||
|
fval = (likelihood-lnprior);
|
||||||
|
end
|
||||||
|
|
||||||
|
if DynareOptions.prior_restrictions.status
|
||||||
|
tmp = feval(DynareOptions.prior_restrictions.routine, Model, DynareResults, DynareOptions, DynareDataset, DatasetInfo);
|
||||||
|
fval = fval - tmp;
|
||||||
|
end
|
||||||
|
|
||||||
|
if isnan(fval)
|
||||||
|
fval = Inf;
|
||||||
|
info(1) = 47;
|
||||||
|
info(2) = 0.1;
|
||||||
|
exit_flag = 0;
|
||||||
|
return
|
||||||
|
end
|
||||||
|
|
||||||
|
if imag(fval)~=0
|
||||||
|
fval = Inf;
|
||||||
|
info(1) = 48;
|
||||||
|
info(2) = 0.1;
|
||||||
|
exit_flag = 0;
|
||||||
|
return
|
||||||
|
end
|
||||||
|
|
||||||
|
% Update DynareOptions.kalman_algo.
|
||||||
|
DynareOptions.kalman_algo = kalman_algo;
|
||||||
|
|
||||||
|
if analytic_derivation==0 && nargout > 1
|
||||||
|
lik=lik(start:end,:);
|
||||||
|
DLIK=[-lnprior; lik(:)];
|
||||||
|
end
|
|
@ -1,858 +0,0 @@
|
||||||
function [fval,info,exit_flag,DLIK,Hess,SteadyState,trend_coeff,Model,DynareOptions,BayesInfo,DynareResults] = dsge_likelihood_1(xparam1,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults,derivatives_info)
|
|
||||||
% Evaluates the posterior kernel of a dsge model using the specified
|
|
||||||
% kalman_algo; the resulting posterior includes the 2*pi constant of the
|
|
||||||
% likelihood function.
|
|
||||||
|
|
||||||
%@info:
|
|
||||||
%! @deftypefn {Function File} {[@var{fval},@var{exit_flag},@var{ys},@var{trend_coeff},@var{info},@var{Model},@var{DynareOptions},@var{BayesInfo},@var{DynareResults},@var{DLIK},@var{AHess}] =} dsge_likelihood (@var{xparam1},@var{DynareDataset},@var{DynareOptions},@var{Model},@var{EstimatedParameters},@var{BayesInfo},@var{DynareResults},@var{derivatives_flag})
|
|
||||||
%! @anchor{dsge_likelihood}
|
|
||||||
%! @sp 1
|
|
||||||
%! Evaluates the posterior kernel of a dsge model.
|
|
||||||
%! @sp 2
|
|
||||||
%! @strong{Inputs}
|
|
||||||
%! @sp 1
|
|
||||||
%! @table @ @var
|
|
||||||
%! @item xparam1
|
|
||||||
%! Vector of doubles, current values for the estimated parameters.
|
|
||||||
%! @item DynareDataset
|
|
||||||
%! Matlab's structure describing the dataset (initialized by dynare, see @ref{dataset_}).
|
|
||||||
%! @item DynareOptions
|
|
||||||
%! Matlab's structure describing the options (initialized by dynare, see @ref{options_}).
|
|
||||||
%! @item Model
|
|
||||||
%! Matlab's structure describing the Model (initialized by dynare, see @ref{M_}).
|
|
||||||
%! @item EstimatedParamemeters
|
|
||||||
%! Matlab's structure describing the estimated_parameters (initialized by dynare, see @ref{estim_params_}).
|
|
||||||
%! @item BayesInfo
|
|
||||||
%! Matlab's structure describing the priors (initialized by dynare, see @ref{bayesopt_}).
|
|
||||||
%! @item DynareResults
|
|
||||||
%! Matlab's structure gathering the results (initialized by dynare, see @ref{oo_}).
|
|
||||||
%! @item derivates_flag
|
|
||||||
%! Integer scalar, flag for analytical derivatives of the likelihood.
|
|
||||||
%! @end table
|
|
||||||
%! @sp 2
|
|
||||||
%! @strong{Outputs}
|
|
||||||
%! @sp 1
|
|
||||||
%! @table @ @var
|
|
||||||
%! @item fval
|
|
||||||
%! Double scalar, value of (minus) the likelihood.
|
|
||||||
%! @item info
|
|
||||||
%! Integer scalar, error code.
|
|
||||||
%! @table @ @code
|
|
||||||
%! @item info==0
|
|
||||||
%! No error.
|
|
||||||
%! @item info==1
|
|
||||||
%! The model doesn't determine the current variables uniquely.
|
|
||||||
%! @item info==2
|
|
||||||
%! MJDGGES returned an error code.
|
|
||||||
%! @item info==3
|
|
||||||
%! Blanchard & Kahn conditions are not satisfied: no stable equilibrium.
|
|
||||||
%! @item info==4
|
|
||||||
%! Blanchard & Kahn conditions are not satisfied: indeterminacy.
|
|
||||||
%! @item info==5
|
|
||||||
%! Blanchard & Kahn conditions are not satisfied: indeterminacy due to rank failure.
|
|
||||||
%! @item info==6
|
|
||||||
%! The jacobian evaluated at the deterministic steady state is complex.
|
|
||||||
%! @item info==19
|
|
||||||
%! The steadystate routine thrown an exception (inconsistent deep parameters).
|
|
||||||
%! @item info==20
|
|
||||||
%! Cannot find the steady state, info(2) contains the sum of square residuals (of the static equations).
|
|
||||||
%! @item info==21
|
|
||||||
%! The steady state is complex, info(2) contains the sum of square of imaginary parts of the steady state.
|
|
||||||
%! @item info==22
|
|
||||||
%! The steady has NaNs.
|
|
||||||
%! @item info==23
|
|
||||||
%! M_.params has been updated in the steadystate routine and has complex valued scalars.
|
|
||||||
%! @item info==24
|
|
||||||
%! M_.params has been updated in the steadystate routine and has some NaNs.
|
|
||||||
%! @item info==26
|
|
||||||
%! M_.params has been updated in the steadystate routine and has negative/0 values in loglinear model.
|
|
||||||
%! @item info==30
|
|
||||||
%! Ergodic variance can't be computed.
|
|
||||||
%! @item info==41
|
|
||||||
%! At least one parameter is violating a lower bound condition.
|
|
||||||
%! @item info==42
|
|
||||||
%! At least one parameter is violating an upper bound condition.
|
|
||||||
%! @item info==43
|
|
||||||
%! The covariance matrix of the structural innovations is not positive definite.
|
|
||||||
%! @item info==44
|
|
||||||
%! The covariance matrix of the measurement errors is not positive definite.
|
|
||||||
%! @item info==45
|
|
||||||
%! Likelihood is not a number (NaN).
|
|
||||||
%! @item info==46
|
|
||||||
%! Likelihood is a complex valued number.
|
|
||||||
%! @item info==47
|
|
||||||
%! Posterior kernel is not a number (logged prior density is NaN)
|
|
||||||
%! @item info==48
|
|
||||||
%! Posterior kernel is a complex valued number (logged prior density is complex).
|
|
||||||
%! @end table
|
|
||||||
%! @item exit_flag
|
|
||||||
%! Integer scalar, equal to zero if the routine return with a penalty (one otherwise).
|
|
||||||
%! @item ys
|
|
||||||
%! Vector of doubles, steady state level for the endogenous variables.
|
|
||||||
%! @item trend_coeff
|
|
||||||
%! Matrix of doubles, coefficients of the deterministic trend in the measurement equation.
|
|
||||||
%! @item Model
|
|
||||||
%! Matlab's structure describing the model (initialized by dynare, see @ref{M_}).
|
|
||||||
%! @item DynareOptions
|
|
||||||
%! Matlab's structure describing the options (initialized by dynare, see @ref{options_}).
|
|
||||||
%! @item BayesInfo
|
|
||||||
%! Matlab's structure describing the priors (initialized by dynare, see @ref{bayesopt_}).
|
|
||||||
%! @item DynareResults
|
|
||||||
%! Matlab's structure gathering the results (initialized by dynare, see @ref{oo_}).
|
|
||||||
%! @item DLIK
|
|
||||||
%! Vector of doubles, score of the likelihood.
|
|
||||||
%! @item AHess
|
|
||||||
%! Matrix of doubles, asymptotic hessian matrix.
|
|
||||||
%! @end table
|
|
||||||
%! @sp 2
|
|
||||||
%! @strong{This function is called by:}
|
|
||||||
%! @sp 1
|
|
||||||
%! @ref{dynare_estimation_1}, @ref{mode_check}
|
|
||||||
%! @sp 2
|
|
||||||
%! @strong{This function calls:}
|
|
||||||
%! @sp 1
|
|
||||||
%! @ref{dynare_resolve}, @ref{lyapunov_symm}, @ref{schur_statespace_transformation}, @ref{kalman_filter_d}, @ref{missing_observations_kalman_filter_d}, @ref{univariate_kalman_filter_d}, @ref{kalman_steady_state}, @ref{getH}, @ref{kalman_filter}, @ref{score}, @ref{AHessian}, @ref{missing_observations_kalman_filter}, @ref{univariate_kalman_filter}, @ref{priordens}
|
|
||||||
%! @end deftypefn
|
|
||||||
%@eod:
|
|
||||||
|
|
||||||
% Copyright (C) 2004-2013 Dynare Team
|
|
||||||
%
|
|
||||||
% This file is part of Dynare.
|
|
||||||
%
|
|
||||||
% Dynare is free software: you can redistribute it and/or modify
|
|
||||||
% it under the terms of the GNU General Public License as published by
|
|
||||||
% the Free Software Foundation, either version 3 of the License, or
|
|
||||||
% (at your option) any later version.
|
|
||||||
%
|
|
||||||
% Dynare is distributed in the hope that it will be useful,
|
|
||||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
|
||||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
|
||||||
% GNU General Public License for more details.
|
|
||||||
%
|
|
||||||
% You should have received a copy of the GNU General Public License
|
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
|
||||||
|
|
||||||
% AUTHOR(S) stephane DOT adjemian AT univ DASH lemans DOT FR
|
|
||||||
|
|
||||||
% Initialization of the returned variables and others...
|
|
||||||
fval = [];
|
|
||||||
SteadyState = [];
|
|
||||||
trend_coeff = [];
|
|
||||||
exit_flag = 1;
|
|
||||||
info = 0;
|
|
||||||
DLIK = [];
|
|
||||||
Hess = [];
|
|
||||||
|
|
||||||
if DynareOptions.estimation_dll
|
|
||||||
[fval,exit_flag,SteadyState,trend_coeff,info,params,H,Q] ...
|
|
||||||
= logposterior(xparam1,DynareDataset, DynareOptions,Model, ...
|
|
||||||
EstimatedParameters,BayesInfo,DynareResults);
|
|
||||||
mexErrCheck('logposterior', exit_flag);
|
|
||||||
Model.params = params;
|
|
||||||
if ~isequal(Model.H,0)
|
|
||||||
Model.H = H;
|
|
||||||
end
|
|
||||||
Model.Sigma_e = Q;
|
|
||||||
DynareResults.dr.ys = SteadyState;
|
|
||||||
return
|
|
||||||
end
|
|
||||||
|
|
||||||
% Set flag related to analytical derivatives.
|
|
||||||
analytic_derivation = DynareOptions.analytic_derivation;
|
|
||||||
|
|
||||||
if analytic_derivation && DynareOptions.loglinear
|
|
||||||
error('The analytic_derivation and loglinear options are not compatible')
|
|
||||||
end
|
|
||||||
|
|
||||||
if nargout==1,
|
|
||||||
analytic_derivation=0;
|
|
||||||
end
|
|
||||||
|
|
||||||
if analytic_derivation,
|
|
||||||
kron_flag=DynareOptions.analytic_derivation_mode;
|
|
||||||
end
|
|
||||||
|
|
||||||
%------------------------------------------------------------------------------
|
|
||||||
% 1. Get the structural parameters & define penalties
|
|
||||||
%------------------------------------------------------------------------------
|
|
||||||
|
|
||||||
% Return, with endogenous penalty, if some parameters are smaller than the lower bound of the prior domain.
|
|
||||||
if ~isequal(DynareOptions.mode_compute,1) && any(xparam1<BoundsInfo.lb)
|
|
||||||
k = find(xparam1<BoundsInfo.lb);
|
|
||||||
fval = Inf;
|
|
||||||
exit_flag = 0;
|
|
||||||
info(1) = 41;
|
|
||||||
info(2) = sum((BoundsInfo.lb(k)-xparam1(k)).^2);
|
|
||||||
if analytic_derivation,
|
|
||||||
DLIK=ones(length(xparam1),1);
|
|
||||||
end
|
|
||||||
return
|
|
||||||
end
|
|
||||||
|
|
||||||
% Return, with endogenous penalty, if some parameters are greater than the upper bound of the prior domain.
|
|
||||||
if ~isequal(DynareOptions.mode_compute,1) && any(xparam1>BoundsInfo.ub)
|
|
||||||
k = find(xparam1>BoundsInfo.ub);
|
|
||||||
fval = Inf;
|
|
||||||
exit_flag = 0;
|
|
||||||
info(1) = 42;
|
|
||||||
info(2) = sum((xparam1(k)-BoundsInfo.ub(k)).^2);
|
|
||||||
if analytic_derivation,
|
|
||||||
DLIK=ones(length(xparam1),1);
|
|
||||||
end
|
|
||||||
return
|
|
||||||
end
|
|
||||||
|
|
||||||
% Get the diagonal elements of the covariance matrices for the structural innovations (Q) and the measurement error (H).
|
|
||||||
Model = set_all_parameters(xparam1,EstimatedParameters,Model);
|
|
||||||
|
|
||||||
Q = Model.Sigma_e;
|
|
||||||
H = Model.H;
|
|
||||||
|
|
||||||
% Test if Q is positive definite.
|
|
||||||
if ~issquare(Q) || EstimatedParameters.ncx || isfield(EstimatedParameters,'calibrated_covariances')
|
|
||||||
[Q_is_positive_definite, penalty] = ispd(Q);
|
|
||||||
if ~Q_is_positive_definite
|
|
||||||
fval = Inf;
|
|
||||||
exit_flag = 0;
|
|
||||||
info(1) = 43;
|
|
||||||
info(2) = penalty;
|
|
||||||
return
|
|
||||||
end
|
|
||||||
if isfield(EstimatedParameters,'calibrated_covariances')
|
|
||||||
correct_flag=check_consistency_covariances(Q);
|
|
||||||
if ~correct_flag
|
|
||||||
fval = Inf;
|
|
||||||
exit_flag = 0;
|
|
||||||
info(1) = 71;
|
|
||||||
info(2) = sum(Q(EstimatedParameters.calibrated_covariances.position).^2);
|
|
||||||
return
|
|
||||||
end
|
|
||||||
end
|
|
||||||
end
|
|
||||||
|
|
||||||
% Test if H is positive definite.
|
|
||||||
if ~issquare(H) || EstimatedParameters.ncn || isfield(EstimatedParameters,'calibrated_covariances_ME')
|
|
||||||
[H_is_positive_definite, penalty] = ispd(H);
|
|
||||||
if ~H_is_positive_definite
|
|
||||||
fval = Inf;
|
|
||||||
exit_flag = 0;
|
|
||||||
info(1) = 44;
|
|
||||||
info(2) = penalty;
|
|
||||||
return
|
|
||||||
end
|
|
||||||
if isfield(EstimatedParameters,'calibrated_covariances_ME')
|
|
||||||
correct_flag=check_consistency_covariances(H);
|
|
||||||
if ~correct_flag
|
|
||||||
fval = Inf;
|
|
||||||
exit_flag = 0;
|
|
||||||
info(1) = 72;
|
|
||||||
info(2) = sum(H(EstimatedParameters.calibrated_covariances_ME.position).^2);
|
|
||||||
return
|
|
||||||
end
|
|
||||||
end
|
|
||||||
end
|
|
||||||
|
|
||||||
|
|
||||||
%------------------------------------------------------------------------------
|
|
||||||
% 2. call model setup & reduction program
|
|
||||||
%------------------------------------------------------------------------------
|
|
||||||
|
|
||||||
% Linearize the model around the deterministic sdteadystate and extract the matrices of the state equation (T and R).
|
|
||||||
[T,R,SteadyState,info,Model,DynareOptions,DynareResults] = dynare_resolve(Model,DynareOptions,DynareResults,'restrict');
|
|
||||||
|
|
||||||
% Return, with endogenous penalty when possible, if dynare_resolve issues an error code (defined in resol).
|
|
||||||
if info(1) == 1 || info(1) == 2 || info(1) == 5 || info(1) == 7 || info(1) == 8 || ...
|
|
||||||
info(1) == 22 || info(1) == 24 || info(1) == 19 || info(1) == 25 || info(1) == 10
|
|
||||||
fval = Inf;
|
|
||||||
info(2) = 0.1;
|
|
||||||
exit_flag = 0;
|
|
||||||
if analytic_derivation,
|
|
||||||
DLIK=ones(length(xparam1),1);
|
|
||||||
end
|
|
||||||
return
|
|
||||||
elseif info(1) == 3 || info(1) == 4 || info(1)==6 || info(1) == 20 || info(1) == 21 || info(1) == 23 || info(1)==26
|
|
||||||
fval = Inf;
|
|
||||||
exit_flag = 0;
|
|
||||||
if analytic_derivation,
|
|
||||||
DLIK=ones(length(xparam1),1);
|
|
||||||
end
|
|
||||||
return
|
|
||||||
end
|
|
||||||
|
|
||||||
% check endogenous prior restrictions
|
|
||||||
info=endogenous_prior_restrictions(T,R,Model,DynareOptions,DynareResults);
|
|
||||||
if info(1),
|
|
||||||
fval = Inf;
|
|
||||||
exit_flag = 0;
|
|
||||||
if analytic_derivation,
|
|
||||||
DLIK=ones(length(xparam1),1);
|
|
||||||
end
|
|
||||||
return
|
|
||||||
end
|
|
||||||
%
|
|
||||||
|
|
||||||
% Define a vector of indices for the observed variables. Is this really usefull?...
|
|
||||||
BayesInfo.mf = BayesInfo.mf1;
|
|
||||||
|
|
||||||
% Define the constant vector of the measurement equation.
|
|
||||||
if DynareOptions.noconstant
|
|
||||||
constant = zeros(DynareDataset.vobs,1);
|
|
||||||
else
|
|
||||||
if DynareOptions.loglinear
|
|
||||||
constant = log(SteadyState(BayesInfo.mfys));
|
|
||||||
else
|
|
||||||
constant = SteadyState(BayesInfo.mfys);
|
|
||||||
end
|
|
||||||
end
|
|
||||||
|
|
||||||
% Define the deterministic linear trend of the measurement equation.
|
|
||||||
if BayesInfo.with_trend
|
|
||||||
trend_coeff = zeros(DynareDataset.vobs,1);
|
|
||||||
t = DynareOptions.trend_coeffs;
|
|
||||||
for i=1:length(t)
|
|
||||||
if ~isempty(t{i})
|
|
||||||
trend_coeff(i) = evalin('base',t{i});
|
|
||||||
end
|
|
||||||
end
|
|
||||||
trend = repmat(constant,1,DynareDataset.nobs)+trend_coeff*[1:DynareDataset.nobs];
|
|
||||||
else
|
|
||||||
trend_coeff = zeros(DynareDataset.vobs,1);
|
|
||||||
trend = repmat(constant,1,DynareDataset.nobs);
|
|
||||||
end
|
|
||||||
|
|
||||||
% Get needed informations for kalman filter routines.
|
|
||||||
start = DynareOptions.presample+1;
|
|
||||||
Z = BayesInfo.mf;
|
|
||||||
no_missing_data_flag = ~DatasetInfo.missing.state;
|
|
||||||
mm = length(T);
|
|
||||||
pp = DynareDataset.vobs;
|
|
||||||
rr = length(Q);
|
|
||||||
kalman_tol = DynareOptions.kalman_tol;
|
|
||||||
diffuse_kalman_tol = DynareOptions.diffuse_kalman_tol;
|
|
||||||
riccati_tol = DynareOptions.riccati_tol;
|
|
||||||
Y = transpose(DynareDataset.data)-trend;
|
|
||||||
|
|
||||||
%------------------------------------------------------------------------------
|
|
||||||
% 3. Initial condition of the Kalman filter
|
|
||||||
%------------------------------------------------------------------------------
|
|
||||||
kalman_algo = DynareOptions.kalman_algo;
|
|
||||||
|
|
||||||
% resetting measurement errors covariance matrix for univariate filters
|
|
||||||
if (kalman_algo == 2) || (kalman_algo == 4)
|
|
||||||
if isequal(H,0)
|
|
||||||
H = zeros(pp,1);
|
|
||||||
mmm = mm;
|
|
||||||
else
|
|
||||||
if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
|
|
||||||
H = diag(H);
|
|
||||||
mmm = mm;
|
|
||||||
else
|
|
||||||
Z = [Z, eye(pp)];
|
|
||||||
T = blkdiag(T,zeros(pp));
|
|
||||||
Q = blkdiag(Q,H);
|
|
||||||
R = blkdiag(R,eye(pp));
|
|
||||||
Pstar = blkdiag(Pstar,H);
|
|
||||||
Pinf = blkdiag(Pinf,zeros(pp));
|
|
||||||
H = zeros(pp,1);
|
|
||||||
mmm = mm+pp;
|
|
||||||
end
|
|
||||||
end
|
|
||||||
end
|
|
||||||
|
|
||||||
|
|
||||||
diffuse_periods = 0;
|
|
||||||
correlated_errors_have_been_checked = 0;
|
|
||||||
singular_diffuse_filter = 0;
|
|
||||||
switch DynareOptions.lik_init
|
|
||||||
case 1% Standard initialization with the steady state of the state equation.
|
|
||||||
if kalman_algo~=2
|
|
||||||
% Use standard kalman filter except if the univariate filter is explicitely choosen.
|
|
||||||
kalman_algo = 1;
|
|
||||||
end
|
|
||||||
if DynareOptions.lyapunov_fp == 1
|
|
||||||
Pstar = lyapunov_symm(T,R*Q'*R',DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold, 3, [], DynareOptions.debug);
|
|
||||||
elseif DynareOptions.lyapunov_db == 1
|
|
||||||
Pstar = disclyap_fast(T,R*Q*R',DynareOptions.lyapunov_doubling_tol);
|
|
||||||
elseif DynareOptions.lyapunov_srs == 1
|
|
||||||
Pstar = lyapunov_symm(T,Q,DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold, 4, R, DynareOptions.debug);
|
|
||||||
else
|
|
||||||
Pstar = lyapunov_symm(T,R*Q*R',DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold, [], [], DynareOptions.debug);
|
|
||||||
end;
|
|
||||||
Pinf = [];
|
|
||||||
a = zeros(mm,1);
|
|
||||||
Zflag = 0;
|
|
||||||
case 2% Initialization with large numbers on the diagonal of the covariance matrix if the states (for non stationary models).
|
|
||||||
if kalman_algo ~= 2
|
|
||||||
% Use standard kalman filter except if the univariate filter is explicitely choosen.
|
|
||||||
kalman_algo = 1;
|
|
||||||
end
|
|
||||||
Pstar = DynareOptions.Harvey_scale_factor*eye(mm);
|
|
||||||
Pinf = [];
|
|
||||||
a = zeros(mm,1);
|
|
||||||
Zflag = 0;
|
|
||||||
case 3% Diffuse Kalman filter (Durbin and Koopman)
|
|
||||||
% Use standard kalman filter except if the univariate filter is explicitely choosen.
|
|
||||||
if kalman_algo == 0
|
|
||||||
kalman_algo = 3;
|
|
||||||
elseif ~((kalman_algo == 3) || (kalman_algo == 4))
|
|
||||||
error(['The model requires Diffuse filter, but you specified a different Kalman filter. You must set options_.kalman_algo ' ...
|
|
||||||
'to 0 (default), 3 or 4'])
|
|
||||||
end
|
|
||||||
[Z,T,R,QT,Pstar,Pinf] = schur_statespace_transformation(Z,T,R,Q,DynareOptions.qz_criterium);
|
|
||||||
Zflag = 1;
|
|
||||||
% Run diffuse kalman filter on first periods.
|
|
||||||
if (kalman_algo==3)
|
|
||||||
% Multivariate Diffuse Kalman Filter
|
|
||||||
if no_missing_data_flag
|
|
||||||
[dLIK,dlik,a,Pstar] = kalman_filter_d(Y, 1, size(Y,2), ...
|
|
||||||
zeros(mm,1), Pinf, Pstar, ...
|
|
||||||
kalman_tol, diffuse_kalman_tol, riccati_tol, DynareOptions.presample, ...
|
|
||||||
T,R,Q,H,Z,mm,pp,rr);
|
|
||||||
else
|
|
||||||
[dLIK,dlik,a,Pstar] = missing_observations_kalman_filter_d(DatasetInfo.missing.aindex,DatasetInfo.missing.number_of_observations,DatasetInfo.missing.no_more_missing_observations, ...
|
|
||||||
Y, 1, size(Y,2), ...
|
|
||||||
zeros(mm,1), Pinf, Pstar, ...
|
|
||||||
kalman_tol, diffuse_kalman_tol, riccati_tol, DynareOptions.presample, ...
|
|
||||||
T,R,Q,H,Z,mm,pp,rr);
|
|
||||||
end
|
|
||||||
diffuse_periods = length(dlik);
|
|
||||||
if isinf(dLIK)
|
|
||||||
% Go to univariate diffuse filter if singularity problem.
|
|
||||||
singular_diffuse_filter = 1;
|
|
||||||
end
|
|
||||||
end
|
|
||||||
if singular_diffuse_filter || (kalman_algo==4)
|
|
||||||
% Univariate Diffuse Kalman Filter
|
|
||||||
if isequal(H,0)
|
|
||||||
H1 = zeros(pp,1);
|
|
||||||
mmm = mm;
|
|
||||||
else
|
|
||||||
if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
|
|
||||||
H1 = diag(H);
|
|
||||||
mmm = mm;
|
|
||||||
else
|
|
||||||
Z = [Z, eye(pp)];
|
|
||||||
T = blkdiag(T,zeros(pp));
|
|
||||||
Q = blkdiag(Q,H);
|
|
||||||
R = blkdiag(R,eye(pp));
|
|
||||||
Pstar = blkdiag(Pstar,H);
|
|
||||||
Pinf = blkdiag(Pinf,zeros(pp));
|
|
||||||
H1 = zeros(pp,1);
|
|
||||||
mmm = mm+pp;
|
|
||||||
end
|
|
||||||
end
|
|
||||||
% no need to test again for correlation elements
|
|
||||||
correlated_errors_have_been_checked = 1;
|
|
||||||
|
|
||||||
[dLIK,dlik,a,Pstar] = univariate_kalman_filter_d(DatasetInfo.missing.aindex,...
|
|
||||||
DatasetInfo.missing.number_of_observations,...
|
|
||||||
DatasetInfo.missing.no_more_missing_observations, ...
|
|
||||||
Y, 1, size(Y,2), ...
|
|
||||||
zeros(mmm,1), Pinf, Pstar, ...
|
|
||||||
kalman_tol, diffuse_kalman_tol, riccati_tol, DynareOptions.presample, ...
|
|
||||||
T,R,Q,H1,Z,mmm,pp,rr);
|
|
||||||
diffuse_periods = size(dlik,1);
|
|
||||||
end
|
|
||||||
if isnan(dLIK),
|
|
||||||
fval = Inf;
|
|
||||||
info(1) = 45;
|
|
||||||
info(2) = 0.1;
|
|
||||||
exit_flag = 0;
|
|
||||||
return
|
|
||||||
end
|
|
||||||
|
|
||||||
case 4% Start from the solution of the Riccati equation.
|
|
||||||
if kalman_algo ~= 2
|
|
||||||
kalman_algo = 1;
|
|
||||||
end
|
|
||||||
if isequal(H,0)
|
|
||||||
[err,Pstar] = kalman_steady_state(transpose(T),R*Q*transpose(R),transpose(build_selection_matrix(Z,mm,length(Z))));
|
|
||||||
else
|
|
||||||
[err,Pstar] = kalman_steady_state(transpose(T),R*Q*transpose(R),transpose(build_selection_matrix(Z,mm,length(Z))),H);
|
|
||||||
end
|
|
||||||
if err
|
|
||||||
disp(['dsge_likelihood:: I am not able to solve the Riccati equation, so I switch to lik_init=1!']);
|
|
||||||
DynareOptions.lik_init = 1;
|
|
||||||
Pstar = lyapunov_symm(T,R*Q*R',DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold, [], [], DynareOptions.debug);
|
|
||||||
end
|
|
||||||
Pinf = [];
|
|
||||||
a = zeros(mm,1);
|
|
||||||
Zflag = 0;
|
|
||||||
case 5 % Old diffuse Kalman filter only for the non stationary variables
|
|
||||||
[eigenvect, eigenv] = eig(T);
|
|
||||||
eigenv = diag(eigenv);
|
|
||||||
nstable = length(find(abs(abs(eigenv)-1) > 1e-7));
|
|
||||||
unstable = find(abs(abs(eigenv)-1) < 1e-7);
|
|
||||||
V = eigenvect(:,unstable);
|
|
||||||
indx_unstable = find(sum(abs(V),2)>1e-5);
|
|
||||||
stable = find(sum(abs(V),2)<1e-5);
|
|
||||||
nunit = length(eigenv) - nstable;
|
|
||||||
Pstar = options_.Harvey_scale_factor*eye(np);
|
|
||||||
if kalman_algo ~= 2
|
|
||||||
kalman_algo = 1;
|
|
||||||
end
|
|
||||||
R_tmp = R(stable, :);
|
|
||||||
T_tmp = T(stable,stable);
|
|
||||||
if DynareOptions.lyapunov_fp == 1
|
|
||||||
Pstar_tmp = lyapunov_symm(T_tmp,R_tmp*Q*R_tmp',DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold, 3, [], DynareOptions.debug);
|
|
||||||
elseif DynareOptions.lyapunov_db == 1
|
|
||||||
Pstar_tmp = disclyap_fast(T_tmp,R_tmp*Q*R_tmp',DynareOptions.lyapunov_doubling_tol);
|
|
||||||
elseif DynareOptions.lyapunov_srs == 1
|
|
||||||
Pstar_tmp = lyapunov_symm(T_tmp,Q,DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold, 4, R_tmp, DynareOptions.debug);
|
|
||||||
else
|
|
||||||
Pstar_tmp = lyapunov_symm(T_tmp,R_tmp*Q*R_tmp',DynareOptions.qz_criterium,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold, [], [], DynareOptions.debug);
|
|
||||||
end
|
|
||||||
Pstar(stable, stable) = Pstar_tmp;
|
|
||||||
Pinf = [];
|
|
||||||
otherwise
|
|
||||||
error('dsge_likelihood:: Unknown initialization approach for the Kalman filter!')
|
|
||||||
end
|
|
||||||
|
|
||||||
if analytic_derivation,
|
|
||||||
offset = EstimatedParameters.nvx;
|
|
||||||
offset = offset+EstimatedParameters.nvn;
|
|
||||||
offset = offset+EstimatedParameters.ncx;
|
|
||||||
offset = offset+EstimatedParameters.ncn;
|
|
||||||
|
|
||||||
no_DLIK = 0;
|
|
||||||
full_Hess = analytic_derivation==2;
|
|
||||||
asy_Hess = analytic_derivation==-2;
|
|
||||||
outer_product_gradient = analytic_derivation==-1;
|
|
||||||
if asy_Hess,
|
|
||||||
analytic_derivation=1;
|
|
||||||
end
|
|
||||||
if outer_product_gradient,
|
|
||||||
analytic_derivation=1;
|
|
||||||
end
|
|
||||||
DLIK = [];
|
|
||||||
AHess = [];
|
|
||||||
iv = DynareResults.dr.restrict_var_list;
|
|
||||||
if nargin<10 || isempty(derivatives_info)
|
|
||||||
[A,B,nou,nou,Model,DynareOptions,DynareResults] = dynare_resolve(Model,DynareOptions,DynareResults);
|
|
||||||
if ~isempty(EstimatedParameters.var_exo)
|
|
||||||
indexo=EstimatedParameters.var_exo(:,1);
|
|
||||||
else
|
|
||||||
indexo=[];
|
|
||||||
end
|
|
||||||
if ~isempty(EstimatedParameters.param_vals)
|
|
||||||
indparam=EstimatedParameters.param_vals(:,1);
|
|
||||||
else
|
|
||||||
indparam=[];
|
|
||||||
end
|
|
||||||
|
|
||||||
if full_Hess,
|
|
||||||
[dum, DT, DOm, DYss, dum2, D2T, D2Om, D2Yss] = getH(A, B, Model,DynareResults,DynareOptions,kron_flag,indparam,indexo,iv);
|
|
||||||
clear dum dum2;
|
|
||||||
else
|
|
||||||
[dum, DT, DOm, DYss] = getH(A, B, Model,DynareResults,DynareOptions,kron_flag,indparam,indexo,iv);
|
|
||||||
end
|
|
||||||
else
|
|
||||||
DT = derivatives_info.DT(iv,iv,:);
|
|
||||||
DOm = derivatives_info.DOm(iv,iv,:);
|
|
||||||
DYss = derivatives_info.DYss(iv,:);
|
|
||||||
if isfield(derivatives_info,'full_Hess'),
|
|
||||||
full_Hess = derivatives_info.full_Hess;
|
|
||||||
end
|
|
||||||
if full_Hess,
|
|
||||||
D2T = derivatives_info.D2T;
|
|
||||||
D2Om = derivatives_info.D2Om;
|
|
||||||
D2Yss = derivatives_info.D2Yss;
|
|
||||||
end
|
|
||||||
if isfield(derivatives_info,'no_DLIK'),
|
|
||||||
no_DLIK = derivatives_info.no_DLIK;
|
|
||||||
end
|
|
||||||
clear('derivatives_info');
|
|
||||||
end
|
|
||||||
DYss = [zeros(size(DYss,1),offset) DYss];
|
|
||||||
DH=zeros([length(H),length(H),length(xparam1)]);
|
|
||||||
DQ=zeros([size(Q),length(xparam1)]);
|
|
||||||
DP=zeros([size(T),length(xparam1)]);
|
|
||||||
if full_Hess,
|
|
||||||
for j=1:size(D2Yss,1),
|
|
||||||
tmp(j,:,:) = blkdiag(zeros(offset,offset), squeeze(D2Yss(j,:,:)));
|
|
||||||
end
|
|
||||||
D2Yss = tmp;
|
|
||||||
D2H=sparse(size(D2Om,1),size(D2Om,2)); %zeros([size(H),length(xparam1),length(xparam1)]);
|
|
||||||
D2P=sparse(size(D2Om,1),size(D2Om,2)); %zeros([size(T),length(xparam1),length(xparam1)]);
|
|
||||||
jcount=0;
|
|
||||||
end
|
|
||||||
if DynareOptions.lik_init==1,
|
|
||||||
for i=1:EstimatedParameters.nvx
|
|
||||||
k =EstimatedParameters.var_exo(i,1);
|
|
||||||
DQ(k,k,i) = 2*sqrt(Q(k,k));
|
|
||||||
dum = lyapunov_symm(T,DOm(:,:,i),DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold,[],[],DynareOptions.debug);
|
|
||||||
% kk = find(abs(dum) < 1e-12);
|
|
||||||
% dum(kk) = 0;
|
|
||||||
DP(:,:,i)=dum;
|
|
||||||
if full_Hess
|
|
||||||
for j=1:i,
|
|
||||||
jcount=jcount+1;
|
|
||||||
dum = lyapunov_symm(T,dyn_unvech(D2Om(:,jcount)),DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold,[],[],DynareOptions.debug);
|
|
||||||
% kk = (abs(dum) < 1e-12);
|
|
||||||
% dum(kk) = 0;
|
|
||||||
D2P(:,jcount)=dyn_vech(dum);
|
|
||||||
% D2P(:,:,j,i)=dum;
|
|
||||||
end
|
|
||||||
end
|
|
||||||
end
|
|
||||||
end
|
|
||||||
offset = EstimatedParameters.nvx;
|
|
||||||
for i=1:EstimatedParameters.nvn
|
|
||||||
k = EstimatedParameters.var_endo(i,1);
|
|
||||||
DH(k,k,i+offset) = 2*sqrt(H(k,k));
|
|
||||||
if full_Hess
|
|
||||||
D2H(k,k,i+offset,i+offset) = 2;
|
|
||||||
end
|
|
||||||
end
|
|
||||||
offset = offset + EstimatedParameters.nvn;
|
|
||||||
if DynareOptions.lik_init==1,
|
|
||||||
for j=1:EstimatedParameters.np
|
|
||||||
dum = lyapunov_symm(T,DT(:,:,j+offset)*Pstar*T'+T*Pstar*DT(:,:,j+offset)'+DOm(:,:,j+offset),DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold,[],[],DynareOptions.debug);
|
|
||||||
% kk = find(abs(dum) < 1e-12);
|
|
||||||
% dum(kk) = 0;
|
|
||||||
DP(:,:,j+offset)=dum;
|
|
||||||
if full_Hess
|
|
||||||
DTj = DT(:,:,j+offset);
|
|
||||||
DPj = dum;
|
|
||||||
for i=1:j+offset,
|
|
||||||
jcount=jcount+1;
|
|
||||||
DTi = DT(:,:,i);
|
|
||||||
DPi = DP(:,:,i);
|
|
||||||
D2Tij = reshape(D2T(:,jcount),size(T));
|
|
||||||
D2Omij = dyn_unvech(D2Om(:,jcount));
|
|
||||||
tmp = D2Tij*Pstar*T' + T*Pstar*D2Tij' + DTi*DPj*T' + DTj*DPi*T' + T*DPj*DTi' + T*DPi*DTj' + DTi*Pstar*DTj' + DTj*Pstar*DTi' + D2Omij;
|
|
||||||
dum = lyapunov_symm(T,tmp,DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold,[],[],DynareOptions.debug);
|
|
||||||
% dum(abs(dum)<1.e-12) = 0;
|
|
||||||
D2P(:,jcount) = dyn_vech(dum);
|
|
||||||
% D2P(:,:,j+offset,i) = dum;
|
|
||||||
end
|
|
||||||
end
|
|
||||||
end
|
|
||||||
end
|
|
||||||
if analytic_derivation==1,
|
|
||||||
analytic_deriv_info={analytic_derivation,DT,DYss,DOm,DH,DP,asy_Hess};
|
|
||||||
else
|
|
||||||
analytic_deriv_info={analytic_derivation,DT,DYss,DOm,DH,DP,D2T,D2Yss,D2Om,D2H,D2P};
|
|
||||||
clear DT DYss DOm DP D2T D2Yss D2Om D2H D2P,
|
|
||||||
end
|
|
||||||
else
|
|
||||||
analytic_deriv_info={0};
|
|
||||||
end
|
|
||||||
|
|
||||||
%------------------------------------------------------------------------------
|
|
||||||
% 4. Likelihood evaluation
|
|
||||||
%------------------------------------------------------------------------------
|
|
||||||
|
|
||||||
if ((kalman_algo==1) || (kalman_algo==3))% Multivariate Kalman Filter
|
|
||||||
if no_missing_data_flag
|
|
||||||
if DynareOptions.block
|
|
||||||
[err, LIK] = block_kalman_filter(T,R,Q,H,Pstar,Y,start,Z,kalman_tol,riccati_tol, Model.nz_state_var, Model.n_diag, Model.nobs_non_statevar);
|
|
||||||
mexErrCheck('block_kalman_filter', err);
|
|
||||||
else
|
|
||||||
[LIK,lik] = kalman_filter(Y,diffuse_periods+1,size(Y,2), ...
|
|
||||||
a,Pstar, ...
|
|
||||||
kalman_tol, riccati_tol, ...
|
|
||||||
DynareOptions.presample, ...
|
|
||||||
T,Q,R,H,Z,mm,pp,rr,Zflag,diffuse_periods, ...
|
|
||||||
analytic_deriv_info{:});
|
|
||||||
|
|
||||||
end
|
|
||||||
else
|
|
||||||
if 0 %DynareOptions.block
|
|
||||||
[err, LIK,lik] = block_kalman_filter(DatasetInfo.missing.aindex,DatasetInfo.missing.number_of_observations,DatasetInfo.missing.no_more_missing_observations,...
|
|
||||||
T,R,Q,H,Pstar,Y,start,Z,kalman_tol,riccati_tol, Model.nz_state_var, Model.n_diag, Model.nobs_non_statevar);
|
|
||||||
else
|
|
||||||
[LIK,lik] = missing_observations_kalman_filter(DatasetInfo.missing.aindex,DatasetInfo.missing.number_of_observations,DatasetInfo.missing.no_more_missing_observations,Y,diffuse_periods+1,size(Y,2), ...
|
|
||||||
a, Pstar, ...
|
|
||||||
kalman_tol, DynareOptions.riccati_tol, ...
|
|
||||||
DynareOptions.presample, ...
|
|
||||||
T,Q,R,H,Z,mm,pp,rr,Zflag,diffuse_periods);
|
|
||||||
end
|
|
||||||
end
|
|
||||||
if analytic_derivation,
|
|
||||||
LIK1=LIK;
|
|
||||||
LIK=LIK1{1};
|
|
||||||
lik1=lik;
|
|
||||||
lik=lik1{1};
|
|
||||||
end
|
|
||||||
if isinf(LIK)
|
|
||||||
if DynareOptions.use_univariate_filters_if_singularity_is_detected
|
|
||||||
if kalman_algo == 1
|
|
||||||
kalman_algo = 2;
|
|
||||||
else
|
|
||||||
kalman_algo = 4;
|
|
||||||
end
|
|
||||||
else
|
|
||||||
if isinf(LIK)
|
|
||||||
fval = Inf;
|
|
||||||
info(1) = 66;
|
|
||||||
info(2) = 0.1;
|
|
||||||
exit_flag = 0;
|
|
||||||
return
|
|
||||||
end
|
|
||||||
end
|
|
||||||
else
|
|
||||||
if DynareOptions.lik_init==3
|
|
||||||
LIK = LIK + dLIK;
|
|
||||||
if analytic_derivation==0 && nargout==2,
|
|
||||||
lik = [dlik; lik];
|
|
||||||
end
|
|
||||||
end
|
|
||||||
end
|
|
||||||
end
|
|
||||||
|
|
||||||
if (kalman_algo==2) || (kalman_algo==4)
|
|
||||||
% Univariate Kalman Filter
|
|
||||||
% resetting measurement error covariance matrix when necessary %
|
|
||||||
if ~correlated_errors_have_been_checked
|
|
||||||
if isequal(H,0)
|
|
||||||
H1 = zeros(pp,1);
|
|
||||||
mmm = mm;
|
|
||||||
if analytic_derivation,
|
|
||||||
DH = zeros(pp,length(xparam1));
|
|
||||||
end
|
|
||||||
else
|
|
||||||
if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
|
|
||||||
H1 = diag(H);
|
|
||||||
mmm = mm;
|
|
||||||
clear tmp
|
|
||||||
if analytic_derivation,
|
|
||||||
for j=1:pp,
|
|
||||||
tmp(j,:)=DH(j,j,:);
|
|
||||||
end
|
|
||||||
DH=tmp;
|
|
||||||
end
|
|
||||||
else
|
|
||||||
Z = [Z, eye(pp)];
|
|
||||||
T = blkdiag(T,zeros(pp));
|
|
||||||
Q = blkdiag(Q,H);
|
|
||||||
R = blkdiag(R,eye(pp));
|
|
||||||
Pstar = blkdiag(Pstar,H);
|
|
||||||
Pinf = blkdiag(Pinf,zeros(pp));
|
|
||||||
H1 = zeros(pp,1);
|
|
||||||
mmm = mm+pp;
|
|
||||||
end
|
|
||||||
end
|
|
||||||
if analytic_derivation,
|
|
||||||
analytic_deriv_info{5}=DH;
|
|
||||||
end
|
|
||||||
end
|
|
||||||
|
|
||||||
[LIK, lik] = univariate_kalman_filter(DatasetInfo.missing.aindex,DatasetInfo.missing.number_of_observations,DatasetInfo.missing.no_more_missing_observations,Y,diffuse_periods+1,size(Y,2), ...
|
|
||||||
a,Pstar, ...
|
|
||||||
DynareOptions.kalman_tol, ...
|
|
||||||
DynareOptions.riccati_tol, ...
|
|
||||||
DynareOptions.presample, ...
|
|
||||||
T,Q,R,H1,Z,mmm,pp,rr,Zflag,diffuse_periods,analytic_deriv_info{:});
|
|
||||||
if analytic_derivation,
|
|
||||||
LIK1=LIK;
|
|
||||||
LIK=LIK1{1};
|
|
||||||
lik1=lik;
|
|
||||||
lik=lik1{1};
|
|
||||||
end
|
|
||||||
if DynareOptions.lik_init==3
|
|
||||||
LIK = LIK+dLIK;
|
|
||||||
if analytic_derivation==0 && nargout==2,
|
|
||||||
lik = [dlik; lik];
|
|
||||||
end
|
|
||||||
end
|
|
||||||
end
|
|
||||||
|
|
||||||
if analytic_derivation
|
|
||||||
if no_DLIK==0
|
|
||||||
DLIK = LIK1{2};
|
|
||||||
% [DLIK] = score(T,R,Q,H,Pstar,Y,DT,DYss,DOm,DH,DP,start,Z,kalman_tol,riccati_tol);
|
|
||||||
end
|
|
||||||
if full_Hess ,
|
|
||||||
Hess = -LIK1{3};
|
|
||||||
% [Hess, DLL] = get_Hessian(T,R,Q,H,Pstar,Y,DT,DYss,DOm,DH,DP,D2T,D2Yss,D2Om,D2H,D2P,start,Z,kalman_tol,riccati_tol);
|
|
||||||
% Hess0 = getHessian(Y,T,DT,D2T, R*Q*transpose(R),DOm,D2Om,Z,DYss,D2Yss);
|
|
||||||
end
|
|
||||||
if asy_Hess,
|
|
||||||
% if ~((kalman_algo==2) || (kalman_algo==4)),
|
|
||||||
% [Hess] = AHessian(T,R,Q,H,Pstar,Y,DT,DYss,DOm,DH,DP,start,Z,kalman_tol,riccati_tol);
|
|
||||||
% else
|
|
||||||
Hess = LIK1{3};
|
|
||||||
% end
|
|
||||||
end
|
|
||||||
end
|
|
||||||
|
|
||||||
if isnan(LIK)
|
|
||||||
fval = Inf;
|
|
||||||
info(1) = 45;
|
|
||||||
info(2) = 0.1;
|
|
||||||
exit_flag = 0;
|
|
||||||
return
|
|
||||||
end
|
|
||||||
|
|
||||||
if imag(LIK)~=0
|
|
||||||
fval = Inf;
|
|
||||||
info(1) = 46;
|
|
||||||
info(2) = 0.1;
|
|
||||||
exit_flag = 0;
|
|
||||||
return
|
|
||||||
end
|
|
||||||
|
|
||||||
likelihood = LIK;
|
|
||||||
|
|
||||||
% ------------------------------------------------------------------------------
|
|
||||||
% 5. Adds prior if necessary
|
|
||||||
% ------------------------------------------------------------------------------
|
|
||||||
if analytic_derivation
|
|
||||||
if full_Hess,
|
|
||||||
[lnprior, dlnprior, d2lnprior] = priordens(xparam1,BayesInfo.pshape,BayesInfo.p6,BayesInfo.p7,BayesInfo.p3,BayesInfo.p4);
|
|
||||||
Hess = Hess - d2lnprior;
|
|
||||||
else
|
|
||||||
[lnprior, dlnprior] = priordens(xparam1,BayesInfo.pshape,BayesInfo.p6,BayesInfo.p7,BayesInfo.p3,BayesInfo.p4);
|
|
||||||
end
|
|
||||||
if no_DLIK==0
|
|
||||||
DLIK = DLIK - dlnprior';
|
|
||||||
end
|
|
||||||
if outer_product_gradient,
|
|
||||||
dlik = lik1{2};
|
|
||||||
dlik=[- dlnprior; dlik(start:end,:)];
|
|
||||||
Hess = dlik'*dlik;
|
|
||||||
end
|
|
||||||
else
|
|
||||||
lnprior = priordens(xparam1,BayesInfo.pshape,BayesInfo.p6,BayesInfo.p7,BayesInfo.p3,BayesInfo.p4);
|
|
||||||
end
|
|
||||||
|
|
||||||
if DynareOptions.endogenous_prior==1
|
|
||||||
if DynareOptions.lik_init==2 || DynareOptions.lik_init==3
|
|
||||||
error('Endogenous prior not supported with non-stationary models')
|
|
||||||
else
|
|
||||||
[lnpriormom] = endogenous_prior(Y,Pstar,BayesInfo,H);
|
|
||||||
fval = (likelihood-lnprior-lnpriormom);
|
|
||||||
end
|
|
||||||
else
|
|
||||||
fval = (likelihood-lnprior);
|
|
||||||
end
|
|
||||||
|
|
||||||
if DynareOptions.prior_restrictions.status
|
|
||||||
tmp = feval(DynareOptions.prior_restrictions.routine, Model, DynareResults, DynareOptions, DynareDataset, DatasetInfo);
|
|
||||||
fval = fval - tmp;
|
|
||||||
end
|
|
||||||
|
|
||||||
if isnan(fval)
|
|
||||||
fval = Inf;
|
|
||||||
info(1) = 47;
|
|
||||||
info(2) = 0.1;
|
|
||||||
exit_flag = 0;
|
|
||||||
return
|
|
||||||
end
|
|
||||||
|
|
||||||
if imag(fval)~=0
|
|
||||||
fval = Inf;
|
|
||||||
info(1) = 48;
|
|
||||||
info(2) = 0.1;
|
|
||||||
exit_flag = 0;
|
|
||||||
return
|
|
||||||
end
|
|
||||||
|
|
||||||
% Update DynareOptions.kalman_algo.
|
|
||||||
DynareOptions.kalman_algo = kalman_algo;
|
|
||||||
|
|
||||||
if analytic_derivation==0 && nargout > 1
|
|
||||||
lik=lik(start:end,:);
|
|
||||||
DLIK=[-lnprior; lik(:)];
|
|
||||||
end
|
|
|
@ -1,5 +1,5 @@
|
||||||
function [fval,grad,hess,exit_flag,info,PHI,SIGMAu,iXX,prior] = dsge_var_likelihood(xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
|
function [fval,grad,hess,exit_flag,SteadyState,trend_coeff,info,PHI,SIGMAu,iXX,prior] = dsge_var_likelihood(xparam1,DynareDataset,DynareInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults)
|
||||||
% Evaluates the posterior kernel of the bvar-dsge model. Deprecated interface.
|
% Evaluates the posterior kernel of the bvar-dsge model.
|
||||||
%
|
%
|
||||||
% INPUTS
|
% INPUTS
|
||||||
% o xparam1 [double] Vector of model's parameters.
|
% o xparam1 [double] Vector of model's parameters.
|
||||||
|
@ -8,6 +8,10 @@ function [fval,grad,hess,exit_flag,info,PHI,SIGMAu,iXX,prior] = dsge_var_likelih
|
||||||
% OUTPUTS
|
% OUTPUTS
|
||||||
% o fval [double] Value of the posterior kernel at xparam1.
|
% o fval [double] Value of the posterior kernel at xparam1.
|
||||||
% o cost_flag [integer] Zero if the function returns a penalty, one otherwise.
|
% o cost_flag [integer] Zero if the function returns a penalty, one otherwise.
|
||||||
|
% o SteadyState [double] Steady state vector possibly recomputed
|
||||||
|
% by call to dynare_results()
|
||||||
|
% o trend_coeff [double] place holder for trend coefficients,
|
||||||
|
% currently not supported by dsge_var
|
||||||
% o info [integer] Vector of informations about the penalty.
|
% o info [integer] Vector of informations about the penalty.
|
||||||
% o PHI [double] Stacked BVAR-DSGE autoregressive matrices (at the mode associated to xparam1).
|
% o PHI [double] Stacked BVAR-DSGE autoregressive matrices (at the mode associated to xparam1).
|
||||||
% o SIGMAu [double] Covariance matrix of the BVAR-DSGE (at the mode associated to xparam1).
|
% o SIGMAu [double] Covariance matrix of the BVAR-DSGE (at the mode associated to xparam1).
|
||||||
|
@ -34,6 +38,275 @@ function [fval,grad,hess,exit_flag,info,PHI,SIGMAu,iXX,prior] = dsge_var_likelih
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
[fval,info,exit_flag,grad,hess,SteadyState,trend_coeff,PHI,SIGMAu,iXX,prior] = ...
|
persistent dsge_prior_weight_idx
|
||||||
dsge_var_likelihood_1(xparam1,DynareDataset,DynareInfo,DynareOptions,Model,...
|
|
||||||
EstimatedParameters,BayesInfo,BoundsInfo,DynareResults);
|
grad=[];
|
||||||
|
hess=[];
|
||||||
|
exit_flag = [];
|
||||||
|
info = 0;
|
||||||
|
PHI = [];
|
||||||
|
SIGMAu = [];
|
||||||
|
iXX = [];
|
||||||
|
prior = [];
|
||||||
|
SteadyState = [];
|
||||||
|
trend_coeff = [];
|
||||||
|
|
||||||
|
% Initialization of of the index for parameter dsge_prior_weight in Model.params.
|
||||||
|
if isempty(dsge_prior_weight_idx)
|
||||||
|
dsge_prior_weight_idx = strmatch('dsge_prior_weight',Model.param_names);
|
||||||
|
end
|
||||||
|
|
||||||
|
% Get the number of estimated (dsge) parameters.
|
||||||
|
nx = EstimatedParameters.nvx + EstimatedParameters.np;
|
||||||
|
|
||||||
|
% Get the number of observed variables in the VAR model.
|
||||||
|
NumberOfObservedVariables = DynareDataset.vobs;
|
||||||
|
|
||||||
|
% Get the number of observations.
|
||||||
|
NumberOfObservations = DynareDataset.nobs;
|
||||||
|
|
||||||
|
|
||||||
|
% Get the number of lags in the VAR model.
|
||||||
|
NumberOfLags = DynareOptions.dsge_varlag;
|
||||||
|
|
||||||
|
% Get the number of parameters in the VAR model.
|
||||||
|
NumberOfParameters = NumberOfObservedVariables*NumberOfLags ;
|
||||||
|
if ~DynareOptions.noconstant
|
||||||
|
NumberOfParameters = NumberOfParameters + 1;
|
||||||
|
end
|
||||||
|
|
||||||
|
% Get empirical second order moments for the observed variables.
|
||||||
|
mYY = evalin('base', 'mYY');
|
||||||
|
mYX = evalin('base', 'mYX');
|
||||||
|
mXY = evalin('base', 'mXY');
|
||||||
|
mXX = evalin('base', 'mXX');
|
||||||
|
|
||||||
|
% Initialize some of the output arguments.
|
||||||
|
fval = [];
|
||||||
|
exit_flag = 1;
|
||||||
|
|
||||||
|
% Return, with endogenous penalty, if some dsge-parameters are smaller than the lower bound of the prior domain.
|
||||||
|
if DynareOptions.mode_compute ~= 1 && any(xparam1 < BoundsInfo.lb)
|
||||||
|
fval = Inf;
|
||||||
|
exit_flag = 0;
|
||||||
|
info(1) = 41;
|
||||||
|
k = find(xparam1 < BoundsInfo.lb);
|
||||||
|
info(2) = sum((BoundsInfo.lb(k)-xparam1(k)).^2);
|
||||||
|
return;
|
||||||
|
end
|
||||||
|
|
||||||
|
% Return, with endogenous penalty, if some dsge-parameters are greater than the upper bound of the prior domain.
|
||||||
|
if DynareOptions.mode_compute ~= 1 && any(xparam1 > BoundsInfo.ub)
|
||||||
|
fval = Inf;
|
||||||
|
exit_flag = 0;
|
||||||
|
info(1) = 42;
|
||||||
|
k = find(xparam1 > BoundsInfo.ub);
|
||||||
|
info(2) = sum((xparam1(k)-BoundsInfo.ub(k)).^2);
|
||||||
|
return;
|
||||||
|
end
|
||||||
|
|
||||||
|
% Get the variance of each structural innovation.
|
||||||
|
Q = Model.Sigma_e;
|
||||||
|
for i=1:EstimatedParameters.nvx
|
||||||
|
k = EstimatedParameters.var_exo(i,1);
|
||||||
|
Q(k,k) = xparam1(i)*xparam1(i);
|
||||||
|
end
|
||||||
|
offset = EstimatedParameters.nvx;
|
||||||
|
|
||||||
|
% Update Model.params and Model.Sigma_e.
|
||||||
|
Model.params(EstimatedParameters.param_vals(:,1)) = xparam1(offset+1:end);
|
||||||
|
Model.Sigma_e = Q;
|
||||||
|
|
||||||
|
% Get the weight of the dsge prior.
|
||||||
|
dsge_prior_weight = Model.params(dsge_prior_weight_idx);
|
||||||
|
|
||||||
|
% Is the dsge prior proper?
|
||||||
|
if dsge_prior_weight<(NumberOfParameters+NumberOfObservedVariables)/ ...
|
||||||
|
NumberOfObservations;
|
||||||
|
fval = Inf;
|
||||||
|
exit_flag = 0;
|
||||||
|
info(1) = 51;
|
||||||
|
info(2) = abs(NumberOfObservations*dsge_prior_weight-(NumberOfParameters+NumberOfObservedVariables));
|
||||||
|
% info(2)=dsge_prior_weight;
|
||||||
|
% info(3)=(NumberOfParameters+NumberOfObservedVariables)/DynareDataset.nobs;
|
||||||
|
return
|
||||||
|
end
|
||||||
|
|
||||||
|
%------------------------------------------------------------------------------
|
||||||
|
% 2. call model setup & reduction program
|
||||||
|
%------------------------------------------------------------------------------
|
||||||
|
|
||||||
|
% Solve the Dsge model and get the matrices of the reduced form solution. T and R are the matrices of the
|
||||||
|
% state equation
|
||||||
|
[T,R,SteadyState,info,Model,DynareOptions,DynareResults] = dynare_resolve(Model,DynareOptions,DynareResults,'restrict');
|
||||||
|
|
||||||
|
% Return, with endogenous penalty when possible, if dynare_resolve issues an error code (defined in resol).
|
||||||
|
if info(1) == 1 || info(1) == 2 || info(1) == 5 || info(1) == 7 || info(1) == 8 || ...
|
||||||
|
info(1) == 22 || info(1) == 24 || info(1) == 25 || info(1) == 10
|
||||||
|
fval = Inf;
|
||||||
|
info(2) = 0.1;
|
||||||
|
exit_flag = 0;
|
||||||
|
return
|
||||||
|
elseif info(1) == 3 || info(1) == 4 || info(1) == 19 || info(1) == 20 || info(1) == 21
|
||||||
|
fval = Inf;
|
||||||
|
info(2) = 0.1;
|
||||||
|
exit_flag = 0;
|
||||||
|
return
|
||||||
|
end
|
||||||
|
|
||||||
|
% Define the mean/steady state vector.
|
||||||
|
if ~DynareOptions.noconstant
|
||||||
|
if DynareOptions.loglinear
|
||||||
|
constant = transpose(log(SteadyState(BayesInfo.mfys)));
|
||||||
|
else
|
||||||
|
constant = transpose(SteadyState(BayesInfo.mfys));
|
||||||
|
end
|
||||||
|
else
|
||||||
|
constant = zeros(1,NumberOfObservedVariables);
|
||||||
|
end
|
||||||
|
|
||||||
|
|
||||||
|
%------------------------------------------------------------------------------
|
||||||
|
% 3. theoretical moments (second order)
|
||||||
|
%------------------------------------------------------------------------------
|
||||||
|
|
||||||
|
% Compute the theoretical second order moments
|
||||||
|
tmp0 = lyapunov_symm(T,R*Q*R',DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold, [], [], DynareOptions.debug);
|
||||||
|
mf = BayesInfo.mf1;
|
||||||
|
|
||||||
|
% Get the non centered second order moments
|
||||||
|
TheoreticalAutoCovarianceOfTheObservedVariables = zeros(NumberOfObservedVariables,NumberOfObservedVariables,NumberOfLags+1);
|
||||||
|
TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1) = tmp0(mf,mf)+constant'*constant;
|
||||||
|
for lag = 1:NumberOfLags
|
||||||
|
tmp0 = T*tmp0;
|
||||||
|
TheoreticalAutoCovarianceOfTheObservedVariables(:,:,lag+1) = tmp0(mf,mf) + constant'*constant;
|
||||||
|
end
|
||||||
|
|
||||||
|
% Build the theoretical "covariance" between Y and X
|
||||||
|
GYX = zeros(NumberOfObservedVariables,NumberOfParameters);
|
||||||
|
for i=1:NumberOfLags
|
||||||
|
GYX(:,(i-1)*NumberOfObservedVariables+1:i*NumberOfObservedVariables) = TheoreticalAutoCovarianceOfTheObservedVariables(:,:,i+1);
|
||||||
|
end
|
||||||
|
if ~DynareOptions.noconstant
|
||||||
|
GYX(:,end) = constant';
|
||||||
|
end
|
||||||
|
|
||||||
|
% Build the theoretical "covariance" between X and X
|
||||||
|
GXX = kron(eye(NumberOfLags), TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1));
|
||||||
|
for i = 1:NumberOfLags-1
|
||||||
|
tmp1 = diag(ones(NumberOfLags-i,1),i);
|
||||||
|
tmp2 = diag(ones(NumberOfLags-i,1),-i);
|
||||||
|
GXX = GXX + kron(tmp1,TheoreticalAutoCovarianceOfTheObservedVariables(:,:,i+1));
|
||||||
|
GXX = GXX + kron(tmp2,TheoreticalAutoCovarianceOfTheObservedVariables(:,:,i+1)');
|
||||||
|
end
|
||||||
|
|
||||||
|
if ~DynareOptions.noconstant
|
||||||
|
% Add one row and one column to GXX
|
||||||
|
GXX = [GXX , kron(ones(NumberOfLags,1),constant') ; [ kron(ones(1,NumberOfLags),constant) , 1] ];
|
||||||
|
end
|
||||||
|
|
||||||
|
GYY = TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1);
|
||||||
|
|
||||||
|
assignin('base','GYY',GYY);
|
||||||
|
assignin('base','GXX',GXX);
|
||||||
|
assignin('base','GYX',GYX);
|
||||||
|
|
||||||
|
if ~isinf(dsge_prior_weight)% Evaluation of the likelihood of the dsge-var model when the dsge prior weight is finite.
|
||||||
|
tmp0 = dsge_prior_weight*NumberOfObservations*TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1) + mYY ;
|
||||||
|
tmp1 = dsge_prior_weight*NumberOfObservations*GYX + mYX;
|
||||||
|
tmp2 = inv(dsge_prior_weight*NumberOfObservations*GXX+mXX);
|
||||||
|
SIGMAu = tmp0 - tmp1*tmp2*tmp1'; clear('tmp0');
|
||||||
|
[SIGMAu_is_positive_definite, penalty] = ispd(SIGMAu);
|
||||||
|
if ~SIGMAu_is_positive_definite
|
||||||
|
fval = Inf;
|
||||||
|
info(1) = 52;
|
||||||
|
info(2) = penalty;
|
||||||
|
exit_flag = 0;
|
||||||
|
return;
|
||||||
|
end
|
||||||
|
SIGMAu = SIGMAu / (NumberOfObservations*(1+dsge_prior_weight));
|
||||||
|
PHI = tmp2*tmp1'; clear('tmp1');
|
||||||
|
prodlng1 = sum(gammaln(.5*((1+dsge_prior_weight)*NumberOfObservations- ...
|
||||||
|
NumberOfObservedVariables*NumberOfLags ...
|
||||||
|
+1-(1:NumberOfObservedVariables)')));
|
||||||
|
prodlng2 = sum(gammaln(.5*(dsge_prior_weight*NumberOfObservations- ...
|
||||||
|
NumberOfObservedVariables*NumberOfLags ...
|
||||||
|
+1-(1:NumberOfObservedVariables)')));
|
||||||
|
lik = .5*NumberOfObservedVariables*log(det(dsge_prior_weight*NumberOfObservations*GXX+mXX)) ...
|
||||||
|
+ .5*((dsge_prior_weight+1)*NumberOfObservations-NumberOfParameters)*log(det((dsge_prior_weight+1)*NumberOfObservations*SIGMAu)) ...
|
||||||
|
- .5*NumberOfObservedVariables*log(det(dsge_prior_weight*NumberOfObservations*GXX)) ...
|
||||||
|
- .5*(dsge_prior_weight*NumberOfObservations-NumberOfParameters)*log(det(dsge_prior_weight*NumberOfObservations*(GYY-GYX*inv(GXX)*GYX'))) ...
|
||||||
|
+ .5*NumberOfObservedVariables*NumberOfObservations*log(2*pi) ...
|
||||||
|
- .5*log(2)*NumberOfObservedVariables*((dsge_prior_weight+1)*NumberOfObservations-NumberOfParameters) ...
|
||||||
|
+ .5*log(2)*NumberOfObservedVariables*(dsge_prior_weight*NumberOfObservations-NumberOfParameters) ...
|
||||||
|
- prodlng1 + prodlng2;
|
||||||
|
else% Evaluation of the likelihood of the dsge-var model when the dsge prior weight is infinite.
|
||||||
|
iGXX = inv(GXX);
|
||||||
|
SIGMAu = GYY - GYX*iGXX*transpose(GYX);
|
||||||
|
PHI = iGXX*transpose(GYX);
|
||||||
|
lik = NumberOfObservations * ( log(det(SIGMAu)) + NumberOfObservedVariables*log(2*pi) + ...
|
||||||
|
trace(inv(SIGMAu)*(mYY - transpose(mYX*PHI) - mYX*PHI + transpose(PHI)*mXX*PHI)/NumberOfObservations));
|
||||||
|
lik = .5*lik;% Minus likelihood
|
||||||
|
end
|
||||||
|
|
||||||
|
if isnan(lik)
|
||||||
|
info(1) = 45;
|
||||||
|
info(2) = 0.1;
|
||||||
|
fval = Inf;
|
||||||
|
exit_flag = 0;
|
||||||
|
return
|
||||||
|
end
|
||||||
|
|
||||||
|
if imag(lik)~=0
|
||||||
|
info(1) = 46;
|
||||||
|
info(2) = 0.1;
|
||||||
|
fval = Inf;
|
||||||
|
exit_flag = 0;
|
||||||
|
return
|
||||||
|
end
|
||||||
|
|
||||||
|
% Add the (logged) prior density for the dsge-parameters.
|
||||||
|
lnprior = priordens(xparam1,BayesInfo.pshape,BayesInfo.p6,BayesInfo.p7,BayesInfo.p3,BayesInfo.p4);
|
||||||
|
fval = (lik-lnprior);
|
||||||
|
|
||||||
|
if isnan(fval)
|
||||||
|
info(1) = 47;
|
||||||
|
info(2) = 0.1;
|
||||||
|
fval = Inf;
|
||||||
|
exit_flag = 0;
|
||||||
|
return
|
||||||
|
end
|
||||||
|
|
||||||
|
if imag(fval)~=0
|
||||||
|
info(1) = 48;
|
||||||
|
info(2) = 0.1;
|
||||||
|
fval = Inf;
|
||||||
|
exit_flag = 0;
|
||||||
|
return
|
||||||
|
end
|
||||||
|
|
||||||
|
if (nargout == 10)
|
||||||
|
if isinf(dsge_prior_weight)
|
||||||
|
iXX = iGXX;
|
||||||
|
else
|
||||||
|
iXX = tmp2;
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
if (nargout==11)
|
||||||
|
if isinf(dsge_prior_weight)
|
||||||
|
iXX = iGXX;
|
||||||
|
else
|
||||||
|
iXX = tmp2;
|
||||||
|
end
|
||||||
|
iGXX = inv(GXX);
|
||||||
|
prior.SIGMAstar = GYY - GYX*iGXX*GYX';
|
||||||
|
prior.PHIstar = iGXX*transpose(GYX);
|
||||||
|
prior.ArtificialSampleSize = fix(dsge_prior_weight*NumberOfObservations);
|
||||||
|
prior.DF = prior.ArtificialSampleSize - NumberOfParameters - NumberOfObservedVariables;
|
||||||
|
prior.iGXX = iGXX;
|
||||||
|
end
|
||||||
|
|
||||||
|
if fval == Inf
|
||||||
|
pause
|
||||||
|
end
|
|
@ -1,312 +0,0 @@
|
||||||
function [fval,info,exit_flag,grad,hess,SteadyState,trend_coeff,PHI,SIGMAu,iXX,prior] = dsge_var_likelihood_1(xparam1,DynareDataset,DynareInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults)
|
|
||||||
% Evaluates the posterior kernel of the bvar-dsge model.
|
|
||||||
%
|
|
||||||
% INPUTS
|
|
||||||
% o xparam1 [double] Vector of model's parameters.
|
|
||||||
% o gend [integer] Number of observations (without conditionning observations for the lags).
|
|
||||||
%
|
|
||||||
% OUTPUTS
|
|
||||||
% o fval [double] Value of the posterior kernel at xparam1.
|
|
||||||
% o cost_flag [integer] Zero if the function returns a penalty, one otherwise.
|
|
||||||
% o SteadyState [double] Steady state vector possibly recomputed
|
|
||||||
% by call to dynare_results()
|
|
||||||
% o trend_coeff [double] place holder for trend coefficients,
|
|
||||||
% currently not supported by dsge_var
|
|
||||||
% o info [integer] Vector of informations about the penalty.
|
|
||||||
% o PHI [double] Stacked BVAR-DSGE autoregressive matrices (at the mode associated to xparam1).
|
|
||||||
% o SIGMAu [double] Covariance matrix of the BVAR-DSGE (at the mode associated to xparam1).
|
|
||||||
% o iXX [double] inv(X'X).
|
|
||||||
% o prior [double] a matlab structure describing the dsge-var prior.
|
|
||||||
%
|
|
||||||
% SPECIAL REQUIREMENTS
|
|
||||||
% None.
|
|
||||||
|
|
||||||
% Copyright (C) 2006-2012 Dynare Team
|
|
||||||
%
|
|
||||||
% This file is part of Dynare.
|
|
||||||
%
|
|
||||||
% Dynare is free software: you can redistribute it and/or modify
|
|
||||||
% it under the terms of the GNU General Public License as published by
|
|
||||||
% the Free Software Foundation, either version 3 of the License, or
|
|
||||||
% (at your option) any later version.
|
|
||||||
%
|
|
||||||
% Dynare is distributed in the hope that it will be useful,
|
|
||||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
|
||||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
|
||||||
% GNU General Public License for more details.
|
|
||||||
%
|
|
||||||
% You should have received a copy of the GNU General Public License
|
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
|
||||||
|
|
||||||
persistent dsge_prior_weight_idx
|
|
||||||
|
|
||||||
grad=[];
|
|
||||||
hess=[];
|
|
||||||
exit_flag = [];
|
|
||||||
info = 0;
|
|
||||||
PHI = [];
|
|
||||||
SIGMAu = [];
|
|
||||||
iXX = [];
|
|
||||||
prior = [];
|
|
||||||
SteadyState = [];
|
|
||||||
trend_coeff = [];
|
|
||||||
|
|
||||||
% Initialization of of the index for parameter dsge_prior_weight in Model.params.
|
|
||||||
if isempty(dsge_prior_weight_idx)
|
|
||||||
dsge_prior_weight_idx = strmatch('dsge_prior_weight',Model.param_names);
|
|
||||||
end
|
|
||||||
|
|
||||||
% Get the number of estimated (dsge) parameters.
|
|
||||||
nx = EstimatedParameters.nvx + EstimatedParameters.np;
|
|
||||||
|
|
||||||
% Get the number of observed variables in the VAR model.
|
|
||||||
NumberOfObservedVariables = DynareDataset.vobs;
|
|
||||||
|
|
||||||
% Get the number of observations.
|
|
||||||
NumberOfObservations = DynareDataset.nobs;
|
|
||||||
|
|
||||||
|
|
||||||
% Get the number of lags in the VAR model.
|
|
||||||
NumberOfLags = DynareOptions.dsge_varlag;
|
|
||||||
|
|
||||||
% Get the number of parameters in the VAR model.
|
|
||||||
NumberOfParameters = NumberOfObservedVariables*NumberOfLags ;
|
|
||||||
if ~DynareOptions.noconstant
|
|
||||||
NumberOfParameters = NumberOfParameters + 1;
|
|
||||||
end
|
|
||||||
|
|
||||||
% Get empirical second order moments for the observed variables.
|
|
||||||
mYY = evalin('base', 'mYY');
|
|
||||||
mYX = evalin('base', 'mYX');
|
|
||||||
mXY = evalin('base', 'mXY');
|
|
||||||
mXX = evalin('base', 'mXX');
|
|
||||||
|
|
||||||
% Initialize some of the output arguments.
|
|
||||||
fval = [];
|
|
||||||
exit_flag = 1;
|
|
||||||
|
|
||||||
% Return, with endogenous penalty, if some dsge-parameters are smaller than the lower bound of the prior domain.
|
|
||||||
if DynareOptions.mode_compute ~= 1 && any(xparam1 < BoundsInfo.lb)
|
|
||||||
fval = Inf;
|
|
||||||
exit_flag = 0;
|
|
||||||
info(1) = 41;
|
|
||||||
k = find(xparam1 < BoundsInfo.lb);
|
|
||||||
info(2) = sum((BoundsInfo.lb(k)-xparam1(k)).^2);
|
|
||||||
return;
|
|
||||||
end
|
|
||||||
|
|
||||||
% Return, with endogenous penalty, if some dsge-parameters are greater than the upper bound of the prior domain.
|
|
||||||
if DynareOptions.mode_compute ~= 1 && any(xparam1 > BoundsInfo.ub)
|
|
||||||
fval = Inf;
|
|
||||||
exit_flag = 0;
|
|
||||||
info(1) = 42;
|
|
||||||
k = find(xparam1 > BoundsInfo.ub);
|
|
||||||
info(2) = sum((xparam1(k)-BoundsInfo.ub(k)).^2);
|
|
||||||
return;
|
|
||||||
end
|
|
||||||
|
|
||||||
% Get the variance of each structural innovation.
|
|
||||||
Q = Model.Sigma_e;
|
|
||||||
for i=1:EstimatedParameters.nvx
|
|
||||||
k = EstimatedParameters.var_exo(i,1);
|
|
||||||
Q(k,k) = xparam1(i)*xparam1(i);
|
|
||||||
end
|
|
||||||
offset = EstimatedParameters.nvx;
|
|
||||||
|
|
||||||
% Update Model.params and Model.Sigma_e.
|
|
||||||
Model.params(EstimatedParameters.param_vals(:,1)) = xparam1(offset+1:end);
|
|
||||||
Model.Sigma_e = Q;
|
|
||||||
|
|
||||||
% Get the weight of the dsge prior.
|
|
||||||
dsge_prior_weight = Model.params(dsge_prior_weight_idx);
|
|
||||||
|
|
||||||
% Is the dsge prior proper?
|
|
||||||
if dsge_prior_weight<(NumberOfParameters+NumberOfObservedVariables)/ ...
|
|
||||||
NumberOfObservations;
|
|
||||||
fval = Inf;
|
|
||||||
exit_flag = 0;
|
|
||||||
info(1) = 51;
|
|
||||||
info(2) = abs(NumberOfObservations*dsge_prior_weight-(NumberOfParameters+NumberOfObservedVariables));
|
|
||||||
% info(2)=dsge_prior_weight;
|
|
||||||
% info(3)=(NumberOfParameters+NumberOfObservedVariables)/DynareDataset.nobs;
|
|
||||||
return
|
|
||||||
end
|
|
||||||
|
|
||||||
%------------------------------------------------------------------------------
|
|
||||||
% 2. call model setup & reduction program
|
|
||||||
%------------------------------------------------------------------------------
|
|
||||||
|
|
||||||
% Solve the Dsge model and get the matrices of the reduced form solution. T and R are the matrices of the
|
|
||||||
% state equation
|
|
||||||
[T,R,SteadyState,info,Model,DynareOptions,DynareResults] = dynare_resolve(Model,DynareOptions,DynareResults,'restrict');
|
|
||||||
|
|
||||||
% Return, with endogenous penalty when possible, if dynare_resolve issues an error code (defined in resol).
|
|
||||||
if info(1) == 1 || info(1) == 2 || info(1) == 5 || info(1) == 7 || info(1) == 8 || ...
|
|
||||||
info(1) == 22 || info(1) == 24 || info(1) == 25 || info(1) == 10
|
|
||||||
fval = Inf;
|
|
||||||
info(2) = 0.1;
|
|
||||||
exit_flag = 0;
|
|
||||||
return
|
|
||||||
elseif info(1) == 3 || info(1) == 4 || info(1) == 19 || info(1) == 20 || info(1) == 21
|
|
||||||
fval = Inf;
|
|
||||||
info(2) = 0.1;
|
|
||||||
exit_flag = 0;
|
|
||||||
return
|
|
||||||
end
|
|
||||||
|
|
||||||
% Define the mean/steady state vector.
|
|
||||||
if ~DynareOptions.noconstant
|
|
||||||
if DynareOptions.loglinear
|
|
||||||
constant = transpose(log(SteadyState(BayesInfo.mfys)));
|
|
||||||
else
|
|
||||||
constant = transpose(SteadyState(BayesInfo.mfys));
|
|
||||||
end
|
|
||||||
else
|
|
||||||
constant = zeros(1,NumberOfObservedVariables);
|
|
||||||
end
|
|
||||||
|
|
||||||
|
|
||||||
%------------------------------------------------------------------------------
|
|
||||||
% 3. theoretical moments (second order)
|
|
||||||
%------------------------------------------------------------------------------
|
|
||||||
|
|
||||||
% Compute the theoretical second order moments
|
|
||||||
tmp0 = lyapunov_symm(T,R*Q*R',DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold, [], [], DynareOptions.debug);
|
|
||||||
mf = BayesInfo.mf1;
|
|
||||||
|
|
||||||
% Get the non centered second order moments
|
|
||||||
TheoreticalAutoCovarianceOfTheObservedVariables = zeros(NumberOfObservedVariables,NumberOfObservedVariables,NumberOfLags+1);
|
|
||||||
TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1) = tmp0(mf,mf)+constant'*constant;
|
|
||||||
for lag = 1:NumberOfLags
|
|
||||||
tmp0 = T*tmp0;
|
|
||||||
TheoreticalAutoCovarianceOfTheObservedVariables(:,:,lag+1) = tmp0(mf,mf) + constant'*constant;
|
|
||||||
end
|
|
||||||
|
|
||||||
% Build the theoretical "covariance" between Y and X
|
|
||||||
GYX = zeros(NumberOfObservedVariables,NumberOfParameters);
|
|
||||||
for i=1:NumberOfLags
|
|
||||||
GYX(:,(i-1)*NumberOfObservedVariables+1:i*NumberOfObservedVariables) = TheoreticalAutoCovarianceOfTheObservedVariables(:,:,i+1);
|
|
||||||
end
|
|
||||||
if ~DynareOptions.noconstant
|
|
||||||
GYX(:,end) = constant';
|
|
||||||
end
|
|
||||||
|
|
||||||
% Build the theoretical "covariance" between X and X
|
|
||||||
GXX = kron(eye(NumberOfLags), TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1));
|
|
||||||
for i = 1:NumberOfLags-1
|
|
||||||
tmp1 = diag(ones(NumberOfLags-i,1),i);
|
|
||||||
tmp2 = diag(ones(NumberOfLags-i,1),-i);
|
|
||||||
GXX = GXX + kron(tmp1,TheoreticalAutoCovarianceOfTheObservedVariables(:,:,i+1));
|
|
||||||
GXX = GXX + kron(tmp2,TheoreticalAutoCovarianceOfTheObservedVariables(:,:,i+1)');
|
|
||||||
end
|
|
||||||
|
|
||||||
if ~DynareOptions.noconstant
|
|
||||||
% Add one row and one column to GXX
|
|
||||||
GXX = [GXX , kron(ones(NumberOfLags,1),constant') ; [ kron(ones(1,NumberOfLags),constant) , 1] ];
|
|
||||||
end
|
|
||||||
|
|
||||||
GYY = TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1);
|
|
||||||
|
|
||||||
assignin('base','GYY',GYY);
|
|
||||||
assignin('base','GXX',GXX);
|
|
||||||
assignin('base','GYX',GYX);
|
|
||||||
|
|
||||||
if ~isinf(dsge_prior_weight)% Evaluation of the likelihood of the dsge-var model when the dsge prior weight is finite.
|
|
||||||
tmp0 = dsge_prior_weight*NumberOfObservations*TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1) + mYY ;
|
|
||||||
tmp1 = dsge_prior_weight*NumberOfObservations*GYX + mYX;
|
|
||||||
tmp2 = inv(dsge_prior_weight*NumberOfObservations*GXX+mXX);
|
|
||||||
SIGMAu = tmp0 - tmp1*tmp2*tmp1'; clear('tmp0');
|
|
||||||
[SIGMAu_is_positive_definite, penalty] = ispd(SIGMAu);
|
|
||||||
if ~SIGMAu_is_positive_definite
|
|
||||||
fval = Inf;
|
|
||||||
info(1) = 52;
|
|
||||||
info(2) = penalty;
|
|
||||||
exit_flag = 0;
|
|
||||||
return;
|
|
||||||
end
|
|
||||||
SIGMAu = SIGMAu / (NumberOfObservations*(1+dsge_prior_weight));
|
|
||||||
PHI = tmp2*tmp1'; clear('tmp1');
|
|
||||||
prodlng1 = sum(gammaln(.5*((1+dsge_prior_weight)*NumberOfObservations- ...
|
|
||||||
NumberOfObservedVariables*NumberOfLags ...
|
|
||||||
+1-(1:NumberOfObservedVariables)')));
|
|
||||||
prodlng2 = sum(gammaln(.5*(dsge_prior_weight*NumberOfObservations- ...
|
|
||||||
NumberOfObservedVariables*NumberOfLags ...
|
|
||||||
+1-(1:NumberOfObservedVariables)')));
|
|
||||||
lik = .5*NumberOfObservedVariables*log(det(dsge_prior_weight*NumberOfObservations*GXX+mXX)) ...
|
|
||||||
+ .5*((dsge_prior_weight+1)*NumberOfObservations-NumberOfParameters)*log(det((dsge_prior_weight+1)*NumberOfObservations*SIGMAu)) ...
|
|
||||||
- .5*NumberOfObservedVariables*log(det(dsge_prior_weight*NumberOfObservations*GXX)) ...
|
|
||||||
- .5*(dsge_prior_weight*NumberOfObservations-NumberOfParameters)*log(det(dsge_prior_weight*NumberOfObservations*(GYY-GYX*inv(GXX)*GYX'))) ...
|
|
||||||
+ .5*NumberOfObservedVariables*NumberOfObservations*log(2*pi) ...
|
|
||||||
- .5*log(2)*NumberOfObservedVariables*((dsge_prior_weight+1)*NumberOfObservations-NumberOfParameters) ...
|
|
||||||
+ .5*log(2)*NumberOfObservedVariables*(dsge_prior_weight*NumberOfObservations-NumberOfParameters) ...
|
|
||||||
- prodlng1 + prodlng2;
|
|
||||||
else% Evaluation of the likelihood of the dsge-var model when the dsge prior weight is infinite.
|
|
||||||
iGXX = inv(GXX);
|
|
||||||
SIGMAu = GYY - GYX*iGXX*transpose(GYX);
|
|
||||||
PHI = iGXX*transpose(GYX);
|
|
||||||
lik = NumberOfObservations * ( log(det(SIGMAu)) + NumberOfObservedVariables*log(2*pi) + ...
|
|
||||||
trace(inv(SIGMAu)*(mYY - transpose(mYX*PHI) - mYX*PHI + transpose(PHI)*mXX*PHI)/NumberOfObservations));
|
|
||||||
lik = .5*lik;% Minus likelihood
|
|
||||||
end
|
|
||||||
|
|
||||||
if isnan(lik)
|
|
||||||
info(1) = 45;
|
|
||||||
info(2) = 0.1;
|
|
||||||
fval = Inf;
|
|
||||||
exit_flag = 0;
|
|
||||||
return
|
|
||||||
end
|
|
||||||
|
|
||||||
if imag(lik)~=0
|
|
||||||
info(1) = 46;
|
|
||||||
info(2) = 0.1;
|
|
||||||
fval = Inf;
|
|
||||||
exit_flag = 0;
|
|
||||||
return
|
|
||||||
end
|
|
||||||
|
|
||||||
% Add the (logged) prior density for the dsge-parameters.
|
|
||||||
lnprior = priordens(xparam1,BayesInfo.pshape,BayesInfo.p6,BayesInfo.p7,BayesInfo.p3,BayesInfo.p4);
|
|
||||||
fval = (lik-lnprior);
|
|
||||||
|
|
||||||
if isnan(fval)
|
|
||||||
info(1) = 47;
|
|
||||||
info(2) = 0.1;
|
|
||||||
fval = Inf;
|
|
||||||
exit_flag = 0;
|
|
||||||
return
|
|
||||||
end
|
|
||||||
|
|
||||||
if imag(fval)~=0
|
|
||||||
info(1) = 48;
|
|
||||||
info(2) = 0.1;
|
|
||||||
fval = Inf;
|
|
||||||
exit_flag = 0;
|
|
||||||
return
|
|
||||||
end
|
|
||||||
|
|
||||||
if (nargout == 10)
|
|
||||||
if isinf(dsge_prior_weight)
|
|
||||||
iXX = iGXX;
|
|
||||||
else
|
|
||||||
iXX = tmp2;
|
|
||||||
end
|
|
||||||
end
|
|
||||||
|
|
||||||
if (nargout==11)
|
|
||||||
if isinf(dsge_prior_weight)
|
|
||||||
iXX = iGXX;
|
|
||||||
else
|
|
||||||
iXX = tmp2;
|
|
||||||
end
|
|
||||||
iGXX = inv(GXX);
|
|
||||||
prior.SIGMAstar = GYY - GYX*iGXX*GYX';
|
|
||||||
prior.PHIstar = iGXX*transpose(GYX);
|
|
||||||
prior.ArtificialSampleSize = fix(dsge_prior_weight*NumberOfObservations);
|
|
||||||
prior.DF = prior.ArtificialSampleSize - NumberOfParameters - NumberOfObservedVariables;
|
|
||||||
prior.iGXX = iGXX;
|
|
||||||
end
|
|
||||||
|
|
||||||
if fval == Inf
|
|
||||||
pause
|
|
||||||
end
|
|
|
@ -85,10 +85,10 @@ if ~options_.dsge_var
|
||||||
error(['Estimation: Unknown filter ' options_.particle.filter_algorithm])
|
error(['Estimation: Unknown filter ' options_.particle.filter_algorithm])
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
objective_function = str2func('dsge_likelihood_1');
|
objective_function = str2func('dsge_likelihood');
|
||||||
end
|
end
|
||||||
else
|
else
|
||||||
objective_function = str2func('dsge_var_likelihood_1');
|
objective_function = str2func('dsge_var_likelihood');
|
||||||
end
|
end
|
||||||
|
|
||||||
[dataset_, dataset_info, xparam1, hh, M_, options_, oo_, estim_params_, bayestopt_, bounds] = ...
|
[dataset_, dataset_info, xparam1, hh, M_, options_, oo_, estim_params_, bayestopt_, bounds] = ...
|
||||||
|
@ -253,7 +253,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
|
||||||
if options_.analytic_derivation && strcmp(func2str(objective_function),'dsge_likelihood')
|
if options_.analytic_derivation && strcmp(func2str(objective_function),'dsge_likelihood')
|
||||||
options = options_.analytic_derivation;
|
options = options_.analytic_derivation;
|
||||||
options.analytic_derivation = 2;
|
options.analytic_derivation = 2;
|
||||||
[junk1, junk2, junk3, junk4, hh] = feval(objective_function,xparam1, ...
|
[junk1, junk2, hh] = feval(objective_function,xparam1, ...
|
||||||
dataset_,dataset_info,options_,M_, ...
|
dataset_,dataset_info,options_,M_, ...
|
||||||
estim_params_,bayestopt_,bounds,oo_);
|
estim_params_,bayestopt_,bounds,oo_);
|
||||||
elseif isequal(options_.mode_compute,4) || ...
|
elseif isequal(options_.mode_compute,4) || ...
|
||||||
|
|
|
@ -148,7 +148,7 @@ if info(1)==0,
|
||||||
dataset_ = dseries(oo_.endo_simul(options_.varobs_id,100+1:end)',dates('1Q1'), options_.varobs);
|
dataset_ = dseries(oo_.endo_simul(options_.varobs_id,100+1:end)',dates('1Q1'), options_.varobs);
|
||||||
derivatives_info.no_DLIK=1;
|
derivatives_info.no_DLIK=1;
|
||||||
bounds = prior_bounds(bayestopt_,options_);
|
bounds = prior_bounds(bayestopt_,options_);
|
||||||
[fval,info,cost_flag,DLIK,AHess,ys,trend_coeff,M_,options_,bayestopt_,oo_] = dsge_likelihood(params',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_,derivatives_info);
|
[fval,DLIK,AHess,cost_flag,ys,trend_coeff,info,M_,options_,bayestopt_,oo_] = dsge_likelihood(params',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_,derivatives_info);
|
||||||
% fval = DsgeLikelihood(xparam1,data_info,options_,M_,estim_params_,bayestopt_,oo_);
|
% fval = DsgeLikelihood(xparam1,data_info,options_,M_,estim_params_,bayestopt_,oo_);
|
||||||
options_.analytic_derivation = analytic_derivation;
|
options_.analytic_derivation = analytic_derivation;
|
||||||
AHess=-AHess;
|
AHess=-AHess;
|
||||||
|
|
|
@ -140,7 +140,7 @@ for plt = 1:nbplt,
|
||||||
end
|
end
|
||||||
for i=1:length(z)
|
for i=1:length(z)
|
||||||
xx(kk) = z(i);
|
xx(kk) = z(i);
|
||||||
[fval, info, exit_flag] = feval(fun,xx,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults);
|
[fval, junk1, junk2, exit_flag] = feval(fun,xx,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults);
|
||||||
if exit_flag
|
if exit_flag
|
||||||
y(i,1) = fval;
|
y(i,1) = fval;
|
||||||
else
|
else
|
||||||
|
|
|
@ -61,7 +61,7 @@ if init
|
||||||
return
|
return
|
||||||
end
|
end
|
||||||
|
|
||||||
[f0, exit_flag, ff0]=penalty_objective_function(x,func,penalty,varargin{:});
|
[f0, ff0]=penalty_objective_function(x,func,penalty,varargin{:});
|
||||||
h2=varargin{7}.ub-varargin{7}.lb;
|
h2=varargin{7}.ub-varargin{7}.lb;
|
||||||
hmax=varargin{7}.ub-x;
|
hmax=varargin{7}.ub-x;
|
||||||
hmax=min(hmax,x-varargin{7}.lb);
|
hmax=min(hmax,x-varargin{7}.lb);
|
||||||
|
@ -93,7 +93,7 @@ while i<n
|
||||||
hcheck=0;
|
hcheck=0;
|
||||||
xh1(i)=x(i)+h1(i);
|
xh1(i)=x(i)+h1(i);
|
||||||
try
|
try
|
||||||
[fx, exit_flag, ffx]=penalty_objective_function(xh1,func,penalty,varargin{:});
|
[fx, ffx]=penalty_objective_function(xh1,func,penalty,varargin{:});
|
||||||
catch
|
catch
|
||||||
fx=1.e8;
|
fx=1.e8;
|
||||||
end
|
end
|
||||||
|
@ -114,7 +114,7 @@ while i<n
|
||||||
h1(i) = max(h1(i),1.e-10);
|
h1(i) = max(h1(i),1.e-10);
|
||||||
xh1(i)=x(i)+h1(i);
|
xh1(i)=x(i)+h1(i);
|
||||||
try
|
try
|
||||||
[fx, exit_flag, ffx]=penalty_objective_function(xh1,func,penalty,varargin{:});
|
[fx, ffx]=penalty_objective_function(xh1,func,penalty,varargin{:});
|
||||||
catch
|
catch
|
||||||
fx=1.e8;
|
fx=1.e8;
|
||||||
end
|
end
|
||||||
|
@ -123,14 +123,14 @@ while i<n
|
||||||
h1(i)= htol/abs(dx(it))*h1(i);
|
h1(i)= htol/abs(dx(it))*h1(i);
|
||||||
xh1(i)=x(i)+h1(i);
|
xh1(i)=x(i)+h1(i);
|
||||||
try
|
try
|
||||||
[fx, exit_flag, ffx]=penalty_objective_function(xh1,func,penalty,varargin{:});
|
[fx, ffx]=penalty_objective_function(xh1,func,penalty,varargin{:});
|
||||||
catch
|
catch
|
||||||
fx=1.e8;
|
fx=1.e8;
|
||||||
end
|
end
|
||||||
while (fx-f0)==0
|
while (fx-f0)==0
|
||||||
h1(i)= h1(i)*2;
|
h1(i)= h1(i)*2;
|
||||||
xh1(i)=x(i)+h1(i);
|
xh1(i)=x(i)+h1(i);
|
||||||
[fx, exit_flag, ffx]=penalty_objective_function(xh1,func,penalty,varargin{:});
|
[fx, ffx]=penalty_objective_function(xh1,func,penalty,varargin{:});
|
||||||
ic=1;
|
ic=1;
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
@ -151,7 +151,7 @@ while i<n
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
xh1(i)=x(i)-h1(i);
|
xh1(i)=x(i)-h1(i);
|
||||||
[fx, exit_flag, ffx]=penalty_objective_function(xh1,func,penalty,varargin{:});
|
[fx, ffx]=penalty_objective_function(xh1,func,penalty,varargin{:});
|
||||||
f_1(:,i)=fx;
|
f_1(:,i)=fx;
|
||||||
if outer_product_gradient,
|
if outer_product_gradient,
|
||||||
if any(isnan(ffx)) || isempty(ffx),
|
if any(isnan(ffx)) || isempty(ffx),
|
||||||
|
|
|
@ -68,7 +68,7 @@ end
|
||||||
|
|
||||||
% func0 = str2func([func2str(func0),'_hh']);
|
% func0 = str2func([func2str(func0),'_hh']);
|
||||||
% func0 = func0;
|
% func0 = func0;
|
||||||
[fval0,exit_flag,gg,hh]=penalty_objective_function(x,func0,penalty,varargin{:});
|
[fval0,gg,hh]=penalty_objective_function(x,func0,penalty,varargin{:});
|
||||||
fval=fval0;
|
fval=fval0;
|
||||||
|
|
||||||
% initialize mr_gstep and mr_hessian
|
% initialize mr_gstep and mr_hessian
|
||||||
|
@ -180,7 +180,7 @@ while norm(gg)>gtol && check==0 && jit<nit
|
||||||
disp_verbose('No further improvement is possible!',Verbose)
|
disp_verbose('No further improvement is possible!',Verbose)
|
||||||
check=1;
|
check=1;
|
||||||
if analytic_derivation,
|
if analytic_derivation,
|
||||||
[fvalx,exit_flag,gg,hh]=penalty_objective_function(xparam1,func0,penalty,varargin{:});
|
[fvalx,gg,hh]=penalty_objective_function(xparam1,func0,varargin{:});
|
||||||
hhg=hh;
|
hhg=hh;
|
||||||
H = inv(hh);
|
H = inv(hh);
|
||||||
else
|
else
|
||||||
|
@ -256,7 +256,7 @@ while norm(gg)>gtol && check==0 && jit<nit
|
||||||
H = igg;
|
H = igg;
|
||||||
end
|
end
|
||||||
elseif analytic_derivation,
|
elseif analytic_derivation,
|
||||||
[fvalx,exit_flag,gg,hh]=penalty_objective_function(xparam1,func0,penalty,varargin{:});
|
[fvalx,gg,hh]=penalty_objective_function(xparam1,func0,varargin{:});
|
||||||
hhg=hh;
|
hhg=hh;
|
||||||
H = inv(hh);
|
H = inv(hh);
|
||||||
end
|
end
|
||||||
|
|
|
@ -1,5 +1,7 @@
|
||||||
function [fval,exit_flag,arg1,arg2] = penalty_objective_function(x0,fcn,penalty,varargin)
|
function [fval,DLIK,Hess,exit_flag] = objective_function_penalty(x0,fcn,penalty,varargin)
|
||||||
[fval,info,exit_flag,arg1,arg2] = fcn(x0,varargin{:});
|
[fval,DLIK,Hess,exit_flag,SteadyState,trend_coeff,info] = fcn(x0,varargin{:});
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
if info(1) ~= 0
|
if info(1) ~= 0
|
||||||
fval = penalty + info(2);
|
fval = penalty + info(2);
|
||||||
|
|
|
@ -77,7 +77,7 @@ inv_order_var = oo_.dr.inv_order_var;
|
||||||
%extract unique entries of covariance
|
%extract unique entries of covariance
|
||||||
i_var=unique(i_var);
|
i_var=unique(i_var);
|
||||||
%% do initial checks
|
%% do initial checks
|
||||||
[loss,info,exit_flag,vx]=osr_obj(t0,i_params,inv_order_var(i_var),weights(i_var,i_var));
|
[loss,vx,info,exit_flag]=osr_obj(t0,i_params,inv_order_var(i_var),weights(i_var,i_var));
|
||||||
if info~=0
|
if info~=0
|
||||||
print_info(info, options_.noprint, options_);
|
print_info(info, options_.noprint, options_);
|
||||||
else
|
else
|
||||||
|
|
|
@ -1,7 +1,5 @@
|
||||||
function [loss,vx,info,exit_flag]=osr_obj(x,i_params,i_var,weights)
|
function [loss,vx,info,exit_flag]=osr_obj(x,i_params,i_var,weights)
|
||||||
% objective function for optimal simple rules (OSR). Deprecated
|
% objective function for optimal simple rules (OSR)
|
||||||
% interface. New one: osr_obj_1.m
|
|
||||||
%
|
|
||||||
% INPUTS
|
% INPUTS
|
||||||
% x vector values of the parameters
|
% x vector values of the parameters
|
||||||
% over which to optimize
|
% over which to optimize
|
||||||
|
@ -34,4 +32,58 @@ function [loss,vx,info,exit_flag]=osr_obj(x,i_params,i_var,weights)
|
||||||
% You should have received a copy of the GNU General Public License
|
% You should have received a copy of the GNU General Public License
|
||||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
[loss,info,exit_flag,vx,junk]=osr_obj_1(x,i_params,i_var,weights);
|
global M_ oo_ options_ optimal_Q_ it_
|
||||||
|
% global ys_ Sigma_e_ endo_nbr exo_nbr optimal_Q_ it_ ykmin_ options_
|
||||||
|
|
||||||
|
junk = [];
|
||||||
|
exit_flag = 1;
|
||||||
|
vx = [];
|
||||||
|
info=0;
|
||||||
|
loss=[];
|
||||||
|
% set parameters of the policiy rule
|
||||||
|
M_.params(i_params) = x;
|
||||||
|
|
||||||
|
% don't change below until the part where the loss function is computed
|
||||||
|
it_ = M_.maximum_lag+1;
|
||||||
|
[dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
|
||||||
|
|
||||||
|
switch info(1)
|
||||||
|
case 1
|
||||||
|
loss = 1e8;
|
||||||
|
return
|
||||||
|
case 2
|
||||||
|
loss = 1e8*min(1e3,info(2));
|
||||||
|
return
|
||||||
|
case 3
|
||||||
|
loss = 1e8*min(1e3,info(2));
|
||||||
|
return
|
||||||
|
case 4
|
||||||
|
loss = 1e8*min(1e3,info(2));
|
||||||
|
return
|
||||||
|
case 5
|
||||||
|
loss = 1e8;
|
||||||
|
return
|
||||||
|
case 6
|
||||||
|
loss = 1e8*min(1e3,info(2));
|
||||||
|
return
|
||||||
|
case 7
|
||||||
|
loss = 1e8*min(1e3);
|
||||||
|
return
|
||||||
|
case 8
|
||||||
|
loss = 1e8*min(1e3,info(2));
|
||||||
|
return
|
||||||
|
case 9
|
||||||
|
loss = 1e8*min(1e3,info(2));
|
||||||
|
return
|
||||||
|
case 20
|
||||||
|
loss = 1e8*min(1e3,info(2));
|
||||||
|
return
|
||||||
|
otherwise
|
||||||
|
if info(1)~=0
|
||||||
|
loss = 1e8;
|
||||||
|
return;
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
vx = get_variance_of_endogenous_variables(dr,i_var);
|
||||||
|
loss = full(weights(:)'*vx(:));
|
||||||
|
|
|
@ -1,90 +0,0 @@
|
||||||
function [loss,info,exit_flag,vx,junk]=osr_obj_1(x,i_params,i_var,weights)
|
|
||||||
% objective function for optimal simple rules (OSR)
|
|
||||||
% INPUTS
|
|
||||||
% x vector values of the parameters
|
|
||||||
% over which to optimize
|
|
||||||
% i_params vector index of optimizing parameters in M_.params
|
|
||||||
% i_var vector variables indices
|
|
||||||
% weights vector weights in the OSRs
|
|
||||||
%
|
|
||||||
% OUTPUTS
|
|
||||||
% loss scalar loss function returned to solver
|
|
||||||
% info vector info vector returned by resol
|
|
||||||
% exit_flag scalar exit flag returned to solver
|
|
||||||
% vx vector variances of the endogenous variables
|
|
||||||
% junk empty place holder for penalty_objective_function
|
|
||||||
%
|
|
||||||
% SPECIAL REQUIREMENTS
|
|
||||||
% none
|
|
||||||
% Copyright (C) 2005-2013 Dynare Team
|
|
||||||
%
|
|
||||||
% This file is part of Dynare.
|
|
||||||
%
|
|
||||||
% Dynare is free software: you can redistribute it and/or modify
|
|
||||||
% it under the terms of the GNU General Public License as published by
|
|
||||||
% the Free Software Foundation, either version 3 of the License, or
|
|
||||||
% (at your option) any later version.
|
|
||||||
%
|
|
||||||
% Dynare is distributed in the hope that it will be useful,
|
|
||||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
|
||||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
|
||||||
% GNU General Public License for more details.
|
|
||||||
%
|
|
||||||
% 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_ oo_ options_ optimal_Q_ it_
|
|
||||||
% global ys_ Sigma_e_ endo_nbr exo_nbr optimal_Q_ it_ ykmin_ options_
|
|
||||||
|
|
||||||
junk = [];
|
|
||||||
exit_flag = 1;
|
|
||||||
vx = [];
|
|
||||||
info=0;
|
|
||||||
loss=[];
|
|
||||||
% set parameters of the policiy rule
|
|
||||||
M_.params(i_params) = x;
|
|
||||||
|
|
||||||
% don't change below until the part where the loss function is computed
|
|
||||||
it_ = M_.maximum_lag+1;
|
|
||||||
[dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
|
|
||||||
|
|
||||||
switch info(1)
|
|
||||||
case 1
|
|
||||||
loss = 1e8;
|
|
||||||
return
|
|
||||||
case 2
|
|
||||||
loss = 1e8*min(1e3,info(2));
|
|
||||||
return
|
|
||||||
case 3
|
|
||||||
loss = 1e8*min(1e3,info(2));
|
|
||||||
return
|
|
||||||
case 4
|
|
||||||
loss = 1e8*min(1e3,info(2));
|
|
||||||
return
|
|
||||||
case 5
|
|
||||||
loss = 1e8;
|
|
||||||
return
|
|
||||||
case 6
|
|
||||||
loss = 1e8*min(1e3,info(2));
|
|
||||||
return
|
|
||||||
case 7
|
|
||||||
loss = 1e8*min(1e3);
|
|
||||||
return
|
|
||||||
case 8
|
|
||||||
loss = 1e8*min(1e3,info(2));
|
|
||||||
return
|
|
||||||
case 9
|
|
||||||
loss = 1e8*min(1e3,info(2));
|
|
||||||
return
|
|
||||||
case 20
|
|
||||||
loss = 1e8*min(1e3,info(2));
|
|
||||||
return
|
|
||||||
otherwise
|
|
||||||
if info(1)~=0
|
|
||||||
loss = 1e8;
|
|
||||||
return;
|
|
||||||
end
|
|
||||||
end
|
|
||||||
|
|
||||||
vx = get_variance_of_endogenous_variables(dr,i_var);
|
|
||||||
loss = full(weights(:)'*vx(:));
|
|
|
@ -120,4 +120,3 @@ tarb_mode_compute=4,
|
||||||
tarb_new_block_probability=0.3,
|
tarb_new_block_probability=0.3,
|
||||||
silent_optimizer
|
silent_optimizer
|
||||||
);
|
);
|
||||||
|
|
||||||
|
|
Loading…
Reference in New Issue