808 lines
30 KiB
Matlab
808 lines
30 KiB
Matlab
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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% Evaluates the posterior kernel of a dsge model.
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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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%! @anchor{dsge_likelihood}
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%! @sp 1
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%! Evaluates the posterior kernel of a dsge model.
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%! @sp 2
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%! @strong{Inputs}
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%! @sp 1
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%! @table @ @var
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%! @item xparam1
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%! Vector of doubles, current values for the estimated parameters.
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%! @item DynareDataset
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%! Matlab's structure describing the dataset (initialized by dynare, see @ref{dataset_}).
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%! @item DynareOptions
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%! Matlab's structure describing the options (initialized by dynare, see @ref{options_}).
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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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%! @item EstimatedParamemeters
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%! Matlab's structure describing the estimated_parameters (initialized by dynare, see @ref{estim_params_}).
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%! @item BayesInfo
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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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%! Matlab's structure gathering the results (initialized by dynare, see @ref{oo_}).
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%! @item derivates_flag
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%! Integer scalar, flag for analytical derivatives of the likelihood.
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%! @end table
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%! @sp 2
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%! @strong{Outputs}
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%! @sp 1
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%! @table @ @var
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%! @item fval
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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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%! Integer scalar, error code.
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%! @table @ @code
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%! @item info==0
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%! No error.
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%! @item info==1
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%! The model doesn't determine the current variables uniquely.
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%! @item info==2
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%! MJDGGES returned an error code.
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%! @item info==3
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%! Blanchard & Kahn conditions are not satisfied: no stable equilibrium.
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%! @item info==4
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%! Blanchard & Kahn conditions are not satisfied: indeterminacy.
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%! @item info==5
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%! Blanchard & Kahn conditions are not satisfied: indeterminacy due to rank failure.
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%! @item info==6
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%! The jacobian evaluated at the deterministic steady state is complex.
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%! @item info==19
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%! The steadystate routine thrown an exception (inconsistent deep parameters).
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%! @item info==20
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%! Cannot find the steady state, info(2) contains the sum of square residuals (of the static equations).
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%! @item info==21
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%! The steady state is complex, info(2) contains the sum of square of imaginary parts of the steady state.
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%! @item info==22
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%! The steady has NaNs.
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%! @item info==23
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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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%! M_.params has been updated in the steadystate routine and has some NaNs.
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%! @item info==30
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%! Ergodic variance can't be computed.
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%! @item info==41
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%! At least one parameter is violating a lower bound condition.
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%! @item info==42
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%! At least one parameter is violating an upper bound condition.
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%! @item info==43
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%! The covariance matrix of the structural innovations is not positive definite.
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%! @item info==44
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%! The covariance matrix of the measurement errors is not positive definite.
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%! @item info==45
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%! Likelihood is not a number (NaN).
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%! @item info==46
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%! Likelihood is a complex valued number.
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%! @item info==47
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%! Posterior kernel is not a number (logged prior density is NaN)
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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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%! @end table
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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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%! @item DynareOptions
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%! Matlab's structure describing the options (initialized by dynare, see @ref{options_}).
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%! @item BayesInfo
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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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%! 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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%! @sp 2
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%! @strong{This function is called by:}
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%! @sp 1
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%! @ref{dynare_estimation_1}, @ref{mode_check}
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%! @sp 2
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%! @strong{This function calls:}
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%! @sp 1
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%! @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}
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%! @end deftypefn
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%@eod:
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% Copyright (C) 2004-2013 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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% AUTHOR(S) stephane DOT adjemian AT univ DASH lemans DOT FR
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global objective_function_penalty_base
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% Initialization of the returned variables and others...
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fval = [];
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ys = [];
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trend_coeff = [];
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exit_flag = 1;
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info = 0;
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singularity_flag = 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,ys,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 = ys;
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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<BayesInfo.lb)
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k = find(xparam1<BayesInfo.lb);
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fval = objective_function_penalty_base+sum((BayesInfo.lb(k)-xparam1(k)).^2);
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exit_flag = 0;
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info = 41;
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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>BayesInfo.ub)
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k = find(xparam1>BayesInfo.ub);
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fval = objective_function_penalty_base+sum((xparam1(k)-BayesInfo.ub(k)).^2);
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exit_flag = 0;
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info = 42;
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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 EstimatedParameters.ncx
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% Try to compute the cholesky decomposition of Q (possible iff Q is positive definite)
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[CholQ,testQ] = chol(Q);
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if testQ
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% The variance-covariance matrix of the structural innovations is not definite positive. We have to compute the eigenvalues of this matrix in order to build the endogenous penalty.
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a = diag(eig(Q));
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k = find(a < 0);
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if k > 0
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fval = objective_function_penalty_base+sum(-a(k));
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exit_flag = 0;
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info = 43;
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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 EstimatedParameters.ncn
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% Try to compute the cholesky decomposition of H (possible iff H is positive definite)
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[CholH,testH] = chol(H);
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if testH
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% The variance-covariance matrix of the measurement errors is not definite positive. We have to compute the eigenvalues of this matrix in order to build the endogenous penalty.
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a = diag(eig(H));
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k = find(a < 0);
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if k > 0
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fval = objective_function_penalty_base+sum(-a(k));
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exit_flag = 0;
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info = 44;
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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) ...
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== 8 || info(1) == 22 || info(1) == 24 || info(1) == 19
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fval = objective_function_penalty_base+1;
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info = info(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
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fval = objective_function_penalty_base+info(2);
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info = info(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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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 = objective_function_penalty_base+info(2);
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info = info(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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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.info.nvobs,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.info.nvobs,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.info.ntobs)+trend_coeff*[1:DynareDataset.info.ntobs];
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else
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trend = repmat(constant,1,DynareDataset.info.ntobs);
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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; % old mf
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no_missing_data_flag = ~DynareDataset.missing.state;
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mm = length(T); % old np
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pp = DynareDataset.info.nvobs;
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rr = length(Q);
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kalman_tol = DynareOptions.kalman_tol;
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riccati_tol = DynareOptions.riccati_tol;
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Y = 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 = blckdiag(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,Q,DynareOptions.lyapunov_fixed_point_tol,DynareOptions.lyapunov_complex_threshold, 3, R);
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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.lyapunov_complex_threshold, 4, R);
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else
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Pstar = lyapunov_symm(T,R*Q*R',DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold);
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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(['diffuse filter: options_.kalman_algo can only be equal ' ...
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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.
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if (kalman_algo==3)
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% Multivariate Diffuse Kalman Filter
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if no_missing_data_flag
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[dLIK,dlik,a,Pstar] = kalman_filter_d(Y, 1, size(Y,2), ...
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zeros(mm,1), Pinf, Pstar, ...
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kalman_tol, riccati_tol, DynareOptions.presample, ...
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T,R,Q,H,Z,mm,pp,rr);
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else
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[dLIK,dlik,a,Pstar] = missing_observations_kalman_filter_d(DynareDataset.missing.aindex,DynareDataset.missing.number_of_observations,DynareDataset.missing.no_more_missing_observations, ...
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Y, 1, size(Y,2), ...
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zeros(mm,1), Pinf, Pstar, ...
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kalman_tol, riccati_tol, DynareOptions.presample, ...
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T,R,Q,H,Z,mm,pp,rr);
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end
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diffuse_periods = length(dlik);
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if isinf(dLIK)
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% Go to univariate diffuse filter if singularity problem.
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singular_diffuse_filter = 1;
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end
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end
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if singular_diffuse_filter || (kalman_algo==4)
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% Univariate Diffuse Kalman Filter
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if isequal(H,0)
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H1 = 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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H1 = 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 = blckdiag(Pinf,zeros(pp));
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H1 = zeros(pp,1);
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mmm = mm+pp;
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end
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end
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% no need to test again for correlation elements
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correlated_errors_have_been_checked = 1;
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[dLIK,dlik,a,Pstar] = univariate_kalman_filter_d(DynareDataset.missing.aindex,...
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DynareDataset.missing.number_of_observations,...
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DynareDataset.missing.no_more_missing_observations, ...
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Y, 1, size(Y,2), ...
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zeros(mmm,1), Pinf, Pstar, ...
|
|
kalman_tol, riccati_tol, DynareOptions.presample, ...
|
|
T,R,Q,H1,Z,mmm,pp,rr);
|
|
diffuse_periods = length(dlik);
|
|
end
|
|
if isnan(dLIK),
|
|
info = 45;
|
|
fval = objective_function_penalty_base + 100;
|
|
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.qz_criterium,DynareOptions.lyapunov_complex_threshold);
|
|
end
|
|
Pinf = [];
|
|
a = zeros(mm,1);
|
|
Zflag = 0;
|
|
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<8 || 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.qz_criterium,DynareOptions.lyapunov_complex_threshold);
|
|
% 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.qz_criterium,DynareOptions.lyapunov_complex_threshold);
|
|
% 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.qz_criterium,DynareOptions.lyapunov_complex_threshold);
|
|
% 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.qz_criterium,DynareOptions.lyapunov_complex_threshold);
|
|
% 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 DH 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(DynareDataset.missing.aindex,DynareDataset.missing.number_of_observations,DynareDataset.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(DynareDataset.missing.aindex,DynareDataset.missing.number_of_observations,DynareDataset.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)
|
|
info = 66;
|
|
fval = objective_function_penalty_base+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 = blckdiag(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(DynareDataset.missing.aindex,DynareDataset.missing.number_of_observations,DynareDataset.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)
|
|
info = 45;
|
|
fval = objective_function_penalty_base + 100;
|
|
exit_flag = 0;
|
|
return
|
|
end
|
|
|
|
if imag(LIK)~=0
|
|
info = 46;
|
|
fval = objective_function_penalty_base + 100;
|
|
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 isnan(fval)
|
|
info = 47;
|
|
fval = objective_function_penalty_base + 100;
|
|
exit_flag = 0;
|
|
return
|
|
end
|
|
|
|
if imag(fval)~=0
|
|
info = 48;
|
|
fval = objective_function_penalty_base + 100;
|
|
exit_flag = 0;
|
|
return
|
|
end
|
|
|
|
% Update DynareOptions.kalman_algo.
|
|
DynareOptions.kalman_algo = kalman_algo;
|
|
|
|
if analytic_derivation==0 && nargout==2,
|
|
lik=lik(start:end,:);
|
|
DLIK=[-lnprior; lik(:)];
|
|
end |