parent
0edd8e2025
commit
329b91d717
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@ -194,7 +194,7 @@ while fpar<B
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end
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end
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if MAX_nirfs_dsgevar
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if MAX_nirfs_dsgevar
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IRUN = IRUN+1;
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IRUN = IRUN+1;
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[fval,junk1,junk2,cost_flag,info,PHI,SIGMAu,iXX] = dsge_var_likelihood(deep',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
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[fval,info,junk1,junk2,junk3,junk3,junk4,PHI,SIGMAu,iXX] = dsge_var_likelihood(deep',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
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dsge_prior_weight = M_.params(strmatch('dsge_prior_weight',M_.param_names));
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dsge_prior_weight = M_.params(strmatch('dsge_prior_weight',M_.param_names));
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DSGE_PRIOR_WEIGHT = floor(dataset_.nobs*(1+dsge_prior_weight));
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DSGE_PRIOR_WEIGHT = floor(dataset_.nobs*(1+dsge_prior_weight));
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SIGMA_inv_upper_chol = chol(inv(SIGMAu*dataset_.nobs*(dsge_prior_weight+1)));
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SIGMA_inv_upper_chol = chol(inv(SIGMAu*dataset_.nobs*(dsge_prior_weight+1)));
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@ -1,5 +1,5 @@
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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,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,info,exit_flag,DLIK,Hess,SteadyState,trend_coeff] = 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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%
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%
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@ -14,11 +14,14 @@ function [fval,DLIK,Hess,exit_flag] = TaRB_optimizer_wrapper(optpar,par_vector,p
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%
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%
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% OUTPUTS
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% OUTPUTS
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% o fval [scalar] value of (minus) the likelihood.
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% o fval [scalar] value of (minus) the likelihood.
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% o info [double] (p*2) error code vector
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% o exit_flag [scalar] equal to zero if the routine return with a penalty (one otherwise).
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% o DLIK [double] (p*1) score vector of the likelihood.
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% o DLIK [double] (p*1) score vector of the likelihood.
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% o Hess [double] (p*p) asymptotic Hessian matrix.
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% o Hess [double] (p*p) asymptotic Hessian matrix.
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% o exit_flag [scalar] equal to zero if the routine return with a penalty (one otherwise).
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% o SteadyState [double] Vector of doubles, steady state level for the endogenous variables.
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% o trend_coeff [double] Matrix of doubles, coefficients of the deterministic trend in the measurement equation
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%
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%
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% Copyright (C) 2015 Dynare Team
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% Copyright (C) 2015-16 Dynare Team
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%
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%
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% This file is part of Dynare.
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% This file is part of Dynare.
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%
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%
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@ -36,5 +39,5 @@ function [fval,DLIK,Hess,exit_flag] = TaRB_optimizer_wrapper(optpar,par_vector,p
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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,DLIK,Hess,exit_flag] = feval(TargetFun,par_vector,varargin{:}); %call target function
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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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@ -1,4 +1,4 @@
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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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function [fval,info,exit_flag,DLIK,Hess,SteadyState,trend_coeff,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 using the specified
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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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% kalman_algo; the resulting posterior includes the 2*pi constant of the
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% likelihood function
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% likelihood function
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@ -35,14 +35,8 @@ function [fval,DLIK,Hess,exit_flag,SteadyState,trend_coeff,info,Model,DynareOpti
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%! @table @ @var
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%! @table @ @var
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%! @item fval
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%! @item fval
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%! Double scalar, value of (minus) the likelihood.
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%! Double scalar, value of (minus) the likelihood.
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%! @item exit_flag
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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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%! Vector of doubles, steady state level for the endogenous variables.
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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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%! @item info
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%! @item info
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%! Integer scalar, error code.
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%! Double vector, second entry stores penalty, first entry the error code.
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%! @table @ @code
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%! @table @ @code
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%! @item info==0
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%! @item info==0
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%! No error.
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%! No error.
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@ -91,6 +85,16 @@ function [fval,DLIK,Hess,exit_flag,SteadyState,trend_coeff,info,Model,DynareOpti
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%! @item info==48
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%! @item info==48
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%! Posterior kernel is a complex valued number (logged prior density is complex).
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%! Posterior kernel is a complex valued number (logged prior density is complex).
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%! @end table
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%! @end table
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%! @item exit_flag
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%! Integer scalar, equal to zero if the routine return with a penalty (one otherwise).
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%! @item DLIK
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%! Vector of doubles, score of the likelihood.
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%! @item AHess
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%! Matrix of doubles, asymptotic hessian matrix.
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%! @item SteadyState
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%! Vector of doubles, steady state level for the endogenous variables.
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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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%! @item Model
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%! @item Model
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%! Matlab's structure describing the model (initialized by dynare, see @ref{M_}).
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%! Matlab's structure describing the model (initialized by dynare, see @ref{M_}).
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%! @item DynareOptions
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%! @item DynareOptions
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%! Matlab's structure describing the priors (initialized by dynare, see @ref{bayesopt_}).
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%! Matlab's structure describing the priors (initialized by dynare, see @ref{bayesopt_}).
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%! @item DynareResults
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%! @item DynareResults
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%! Matlab's structure gathering the results (initialized by dynare, see @ref{oo_}).
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%! Matlab's structure gathering the results (initialized by dynare, see @ref{oo_}).
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%! @item DLIK
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%! Vector of doubles, score of the likelihood.
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%! @item AHess
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%! Matrix of doubles, asymptotic hessian matrix.
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%! @end table
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%! @end table
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%! @sp 2
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%! @sp 2
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%! @strong{This function is called by:}
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%! @strong{This function is called by:}
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function [fval,grad,hess,exit_flag,info,PHI,SIGMAu,iXX,prior] = dsge_var_likelihood(xparam1,DynareDataset,DynareInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults)
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function [fval,info,exit_flag,grad,hess,SteadyState,trend_coeff,PHI,SIGMAu,iXX,prior] = dsge_var_likelihood(xparam1,DynareDataset,DynareInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults)
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% Evaluates the posterior kernel of the bvar-dsge model.
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% Evaluates the posterior kernel of the bvar-dsge model.
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%
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%
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% INPUTS
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% INPUTS
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%
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%
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% OUTPUTS
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% OUTPUTS
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% o fval [double] Value of the posterior kernel at xparam1.
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% o fval [double] Value of the posterior kernel at xparam1.
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% o cost_flag [integer] Zero if the function returns a penalty, one otherwise.
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% o info [integer] Vector of informations about the penalty.
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% o info [integer] Vector of informations about the penalty.
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% o exit_flag [integer] Zero if the function returns a penalty, one otherwise.
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% o grad [double] place holder for gradient of the likelihood
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% currently not supported by dsge_var
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% o hess [double] place holder for hessian matrix of the likelihood
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% currently not supported by dsge_var
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% o SteadyState [double] Steady state vector possibly recomputed
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% by call to dynare_resolve()
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% o trend_coeff [double] place holder for trend coefficients,
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% currently not supported by dsge_var
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% o PHI [double] Stacked BVAR-DSGE autoregressive matrices (at the mode associated to xparam1).
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% o PHI [double] Stacked BVAR-DSGE autoregressive matrices (at the mode associated to xparam1).
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% o SIGMAu [double] Covariance matrix of the BVAR-DSGE (at the mode associated to xparam1).
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% o SIGMAu [double] Covariance matrix of the BVAR-DSGE (at the mode associated to xparam1).
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% o iXX [double] inv(X'X).
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% o iXX [double] inv(X'X).
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SIGMAu = [];
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SIGMAu = [];
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iXX = [];
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iXX = [];
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prior = [];
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prior = [];
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trend_coeff=[];
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% Initialization of of the index for parameter dsge_prior_weight in Model.params.
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% Initialization of of the index for parameter dsge_prior_weight in Model.params.
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if isempty(dsge_prior_weight_idx)
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if isempty(dsge_prior_weight_idx)
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@ -271,7 +280,7 @@ if imag(fval)~=0
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return
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return
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end
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end
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if (nargout == 8)
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if (nargout == 10)
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if isinf(dsge_prior_weight)
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if isinf(dsge_prior_weight)
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iXX = iGXX;
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iXX = iGXX;
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else
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else
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end
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end
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end
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end
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if (nargout==9)
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if (nargout==11)
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if isinf(dsge_prior_weight)
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if isinf(dsge_prior_weight)
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iXX = iGXX;
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iXX = iGXX;
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else
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else
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@ -236,7 +236,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
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if options_.analytic_derivation && strcmp(func2str(objective_function),'dsge_likelihood'),
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if options_.analytic_derivation && strcmp(func2str(objective_function),'dsge_likelihood'),
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ana_deriv_old = options_.analytic_derivation;
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ana_deriv_old = options_.analytic_derivation;
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options_.analytic_derivation = 2;
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options_.analytic_derivation = 2;
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[junk1, junk2, hh] = feval(objective_function,xparam1, ...
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[junk1, junk2,junk3, junk4, hh] = feval(objective_function,xparam1, ...
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dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
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dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
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options_.analytic_derivation = ana_deriv_old;
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options_.analytic_derivation = ana_deriv_old;
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elseif ~isnumeric(options_.mode_compute) || ~(isequal(options_.mode_compute,5) && newratflag~=1),
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elseif ~isnumeric(options_.mode_compute) || ~(isequal(options_.mode_compute,5) && newratflag~=1),
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dataset_ = dseries(oo_.endo_simul(options_.varobs_id,100+1:end)',dates('1Q1'), options_.varobs);
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dataset_ = dseries(oo_.endo_simul(options_.varobs_id,100+1:end)',dates('1Q1'), options_.varobs);
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derivatives_info.no_DLIK=1;
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derivatives_info.no_DLIK=1;
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bounds = prior_bounds(bayestopt_, options_.prior_trunc);
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bounds = prior_bounds(bayestopt_, options_.prior_trunc);
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[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);
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[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);
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% fval = DsgeLikelihood(xparam1,data_info,options_,M_,estim_params_,bayestopt_,oo_);
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% fval = DsgeLikelihood(xparam1,data_info,options_,M_,estim_params_,bayestopt_,oo_);
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options_.analytic_derivation = analytic_derivation;
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options_.analytic_derivation = analytic_derivation;
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AHess=-AHess;
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AHess=-AHess;
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DynareOptions.analytic_derivation=0;
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DynareOptions.analytic_derivation=0;
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if ~isequal(DynareOptions.mode_compute,11) || ...
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if ~isequal(DynareOptions.mode_compute,11) || ...
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(isequal(DynareOptions.mode_compute,11) && isequal(DynareOptions.order,1))
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(isequal(DynareOptions.mode_compute,11) && isequal(DynareOptions.order,1))
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[fval,junk1,junk2,a,b,c,d] = feval(objective_function,xparam1,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults);
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[fval,info] = feval(objective_function,xparam1,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults);
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else
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else
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b=0;
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info=0;
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fval = 0;
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fval = 0;
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end
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end
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if DynareOptions.debug
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if DynareOptions.debug
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end
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end
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DynareOptions.analytic_derivation=ana_deriv;
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DynareOptions.analytic_derivation=ana_deriv;
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if DynareOptions.dsge_var || strcmp(func2str(objective_function),'non_linear_dsge_likelihood')
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info = b;
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else
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info = d;
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end
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% if DynareOptions.mode_compute==5
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% if DynareOptions.mode_compute==5
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% if ~strcmp(func2str(objective_function),'dsge_likelihood')
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% if ~strcmp(func2str(objective_function),'dsge_likelihood')
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% error('Options mode_compute=5 is not compatible with non linear filters or Dsge-VAR models!')
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% error('Options mode_compute=5 is not compatible with non linear filters or Dsge-VAR models!')
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end
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end
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for i=1:length(z)
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for i=1:length(z)
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xx(kk) = z(i);
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xx(kk) = z(i);
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[fval, junk1, junk2, exit_flag] = feval(fun,xx,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults);
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[fval, info, exit_flag] = feval(fun,xx,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults);
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if exit_flag
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if exit_flag
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y(i,1) = fval;
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y(i,1) = fval;
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else
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else
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function [fval,ys,trend_coeff,exit_flag,info,Model,DynareOptions,BayesInfo,DynareResults] = non_linear_dsge_likelihood(xparam1,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults)
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function [fval,info,exit_flag,DLIK,Hess,ys,trend_coeff,Model,DynareOptions,BayesInfo,DynareResults] = non_linear_dsge_likelihood(xparam1,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults)
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% Evaluates the posterior kernel of a dsge model using a non linear filter.
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% Evaluates the posterior kernel of a dsge model using a non linear filter.
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%@info:
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%@info:
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%! @table @ @var
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%! @table @ @var
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%! @item fval
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%! @item fval
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%! Double scalar, value of (minus) the likelihood.
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%! Double scalar, value of (minus) the likelihood.
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%! @item exit_flag
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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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%! Vector of doubles, steady state level for the endogenous variables.
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%! @item trend_coeffs
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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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%! Double vector, second entry stores penalty, first entry the error code.
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%! @table @ @code
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%! @table @ @code
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%! @item info==0
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%! @item info==0
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%! No error.
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%! No error.
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%! @item info==45
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%! @item info==45
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%! Likelihood is a complex valued number.
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%! Likelihood is a complex valued number.
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%! @end table
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%! @end table
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%! @item exit_flag
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%! Integer scalar, equal to zero if the routine return with a penalty (one otherwise).
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%! @item DLIK
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%! Vector of doubles, placeholder for score of the likelihood, currently
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%! not supported by non_linear_dsge_likelihood
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%! @item AHess
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%! Matrix of doubles, placeholder for asymptotic hessian matrix, currently
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%! not supported by non_linear_dsge_likelihood
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%! @item ys
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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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%! Matrix of doubles, coefficients of the deterministic trend in the measurement equation.
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%! @item Model
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%! @item Model
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%! Matlab's structure describing the model (initialized by dynare, see @ref{M_}).
|
%! Matlab's structure describing the model (initialized by dynare, see @ref{M_}).
|
||||||
%! @item DynareOptions
|
%! @item DynareOptions
|
||||||
|
@ -132,6 +138,8 @@ fval = [];
|
||||||
ys = [];
|
ys = [];
|
||||||
trend_coeff = [];
|
trend_coeff = [];
|
||||||
exit_flag = 1;
|
exit_flag = 1;
|
||||||
|
DLIK = [];
|
||||||
|
Hess = [];
|
||||||
|
|
||||||
% Issue an error if loglinear option is used.
|
% Issue an error if loglinear option is used.
|
||||||
if DynareOptions.loglinear
|
if DynareOptions.loglinear
|
||||||
|
|
|
@ -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,vx,info,exit_flag]=osr_obj(t0,i_params,inv_order_var(i_var),weights(i_var,i_var));
|
[loss,info,exit_flag,vx]=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
|
||||||
|
|
|
@ -1,4 +1,4 @@
|
||||||
function [loss,vx,info,exit_flag]=osr_obj(x,i_params,i_var,weights)
|
function [loss,info,exit_flag,vx,junk]=osr_obj(x,i_params,i_var,weights)
|
||||||
% objective function for optimal simple rules (OSR)
|
% objective function for optimal simple rules (OSR)
|
||||||
% INPUTS
|
% INPUTS
|
||||||
% x vector values of the parameters
|
% x vector values of the parameters
|
||||||
|
@ -9,13 +9,15 @@ function [loss,vx,info,exit_flag]=osr_obj(x,i_params,i_var,weights)
|
||||||
%
|
%
|
||||||
% OUTPUTS
|
% OUTPUTS
|
||||||
% loss scalar loss function returned to solver
|
% loss scalar loss function returned to solver
|
||||||
% vx vector variances of the endogenous variables
|
|
||||||
% info vector info vector returned by resol
|
% info vector info vector returned by resol
|
||||||
% exit_flag scalar exit flag returned to solver
|
% exit_flag scalar exit flag returned to solver
|
||||||
|
% vx vector variances of the endogenous variables
|
||||||
|
% junk empty dummy output for conformable
|
||||||
|
% header
|
||||||
%
|
%
|
||||||
% SPECIAL REQUIREMENTS
|
% SPECIAL REQUIREMENTS
|
||||||
% none
|
% none
|
||||||
% Copyright (C) 2005-2013 Dynare Team
|
% Copyright (C) 2005-2016 Dynare Team
|
||||||
%
|
%
|
||||||
% This file is part of Dynare.
|
% This file is part of Dynare.
|
||||||
%
|
%
|
||||||
|
|
Loading…
Reference in New Issue