248 lines
11 KiB
Matlab
248 lines
11 KiB
Matlab
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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%
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% INPUTS
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% - xparam1 [double] n×1 vector, estimated parameters.
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% - DynareDataset [struct] Matlab's structure containing the dataset (initialized by dynare, aka dataset_).
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% - DatasetInfo [struct] Matlab's structure describing the dataset (initialized by dynare, aka dataset_info).
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% - DynareOptions [struct] Matlab's structure describing the options (initialized by dynare, aka options_).
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% - Model [struct] Matlab's structure describing the Model (initialized by dynare, aka M_).
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% - EstimatedParameters [struct] Matlab's structure describing the estimated_parameters (initialized by dynare, aka estim_params_).
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% - BayesInfo [struct] Matlab's structure describing the priors (initialized by dynare,aka bayesopt_).
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% - BoundsInfo [struct] Matlab's structure specifying the bounds on the paramater values (initialized by dynare,aka bayesopt_).
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% - DynareResults [struct] Matlab's structure gathering the results (initialized by dynare,aka oo_).
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%
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% OUTPUTS
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% - fval [double] scalar, value of the likelihood or posterior kernel.
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% - info [integer] 4×1 vector, informations resolution of the model and evaluation of the likelihood.
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% - exit_flag [integer] scalar, equal to 1 (no issues when evaluating the likelihood) or 0 (not able to evaluate the likelihood).
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% - DLIK [double] Empty array.
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% - Hess [double] Empty array.
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% - ys [double] Empty array.
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% - trend_coeff [double] Empty array.
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% - Model [struct] Updated Model structure described in INPUTS section.
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% - DynareOptions [struct] Updated DynareOptions structure described in INPUTS section.
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% - BayesInfo [struct] See INPUTS section.
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% - DynareResults [struct] Updated DynareResults structure described in INPUTS section.
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% Copyright © 2010-2022 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 <https://www.gnu.org/licenses/>.
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% Initialization of the returned arguments.
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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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DLIK = [];
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Hess = [];
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% Ensure that xparam1 is a column vector.
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% (Don't do the transformation if xparam1 is empty, otherwise it would become a
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% 0×1 matrix, which create issues with older MATLABs when comparing with [] in
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% check_bounds_and_definiteness_estimation)
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if ~isempty(xparam1)
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xparam1 = xparam1(:);
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end
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% Issue an error if loglinear option is used.
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if DynareOptions.loglinear
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error('non_linear_dsge_likelihood: It is not possible to use a non linear filter with the option loglinear!')
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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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Model = set_all_parameters(xparam1,EstimatedParameters,Model);
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[fval,info,exit_flag,Q,H]=check_bounds_and_definiteness_estimation(xparam1, Model, EstimatedParameters, BoundsInfo);
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if info(1)
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return
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end
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%------------------------------------------------------------------------------
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% 2. call model setup & reduction program
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%------------------------------------------------------------------------------
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% Linearize the model around the deterministic sdteadystate and extract the matrices of the state equation (T and R).
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[dr, info, Model, DynareResults] = resol(0, Model, DynareOptions, DynareResults);
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if info(1)
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if info(1) == 3 || info(1) == 4 || info(1) == 5 || info(1)==6 ||info(1) == 19 || ...
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info(1) == 20 || info(1) == 21 || info(1) == 23 || info(1) == 26 || ...
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info(1) == 81 || info(1) == 84 || info(1) == 85
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%meaningful second entry of output that can be used
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fval = Inf;
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info(4) = info(2);
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exit_flag = 0;
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return
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else
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fval = Inf;
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info(4) = 0.1;
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exit_flag = 0;
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return
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end
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end
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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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% Get needed informations for kalman filter routines.
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start = DynareOptions.presample+1;
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Y = transpose(DynareDataset.data);
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%------------------------------------------------------------------------------
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% 3. Initial condition of the Kalman filter
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%------------------------------------------------------------------------------
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mf0 = BayesInfo.mf0;
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mf1 = BayesInfo.mf1;
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restrict_variables_idx = dr.restrict_var_list;
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state_variables_idx = restrict_variables_idx(mf0);
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number_of_state_variables = length(mf0);
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ReducedForm.steadystate = dr.ys(dr.order_var(restrict_variables_idx));
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ReducedForm.constant = ReducedForm.steadystate + .5*dr.ghs2(restrict_variables_idx);
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ReducedForm.state_variables_steady_state = dr.ys(dr.order_var(state_variables_idx));
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ReducedForm.Q = Q;
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ReducedForm.H = H;
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ReducedForm.mf0 = mf0;
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ReducedForm.mf1 = mf1;
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if DynareOptions.order>3
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ReducedForm.use_k_order_solver = true;
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ReducedForm.dr = dr;
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ReducedForm.udr = folded_to_unfolded_dr(dr, Model, DynareOptions);
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if pruning
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error('Pruning is not available for orders > 3');
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end
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else
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ReducedForm.use_k_order_solver = false;
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ReducedForm.ghx = dr.ghx(restrict_variables_idx,:);
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ReducedForm.ghu = dr.ghu(restrict_variables_idx,:);
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ReducedForm.ghxx = dr.ghxx(restrict_variables_idx,:);
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ReducedForm.ghuu = dr.ghuu(restrict_variables_idx,:);
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ReducedForm.ghxu = dr.ghxu(restrict_variables_idx,:);
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if DynareOptions.order==3
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ReducedForm.ghxxx = dr.ghxxx(restrict_variables_idx,:);
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ReducedForm.ghuuu = dr.ghuuu(restrict_variables_idx,:);
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ReducedForm.ghxxu = dr.ghxxu(restrict_variables_idx,:);
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ReducedForm.ghxuu = dr.ghxuu(restrict_variables_idx,:);
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ReducedForm.ghxss = dr.ghxss(restrict_variables_idx,:);
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ReducedForm.ghuss = dr.ghuss(restrict_variables_idx,:);
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end
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end
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% Set initial condition.
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switch DynareOptions.particle.initialization
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case 1% Initial state vector covariance is the ergodic variance associated to the first order Taylor-approximation of the model.
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StateVectorMean = ReducedForm.constant(mf0);
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[A,B] = kalman_transition_matrix(dr,dr.restrict_var_list,dr.restrict_columns,Model.exo_nbr);
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StateVectorVariance = lyapunov_symm(A, B*Q*B', DynareOptions.lyapunov_fixed_point_tol, ...
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DynareOptions.qz_criterium, DynareOptions.lyapunov_complex_threshold, [], DynareOptions.debug);
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StateVectorVariance = StateVectorVariance(mf0,mf0);
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case 2% Initial state vector covariance is a monte-carlo based estimate of the ergodic variance (consistent with a k-order Taylor-approximation of the model).
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StateVectorMean = ReducedForm.constant(mf0);
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old_DynareOptionsperiods = DynareOptions.periods;
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DynareOptions.periods = 5000;
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old_DynareOptionspruning = DynareOptions.pruning;
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DynareOptions.pruning = DynareOptions.particle.pruning;
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y_ = simult(DynareResults.steady_state, dr,Model,DynareOptions,DynareResults);
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y_ = y_(dr.order_var(state_variables_idx),2001:5000); %state_variables_idx is in dr-order while simult_ is in declaration order
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if any(any(isnan(y_))) || any(any(isinf(y_))) && ~ DynareOptions.pruning
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fval = Inf;
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info(1) = 202;
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info(4) = 0.1;
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exit_flag = 0;
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return;
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end
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StateVectorVariance = cov(y_');
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DynareOptions.periods = old_DynareOptionsperiods;
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DynareOptions.pruning = old_DynareOptionspruning;
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clear('old_DynareOptionsperiods','y_');
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case 3% Initial state vector covariance is a diagonal matrix (to be used
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% if model has stochastic trends).
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StateVectorMean = ReducedForm.constant(mf0);
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StateVectorVariance = DynareOptions.particle.initial_state_prior_std*eye(number_of_state_variables);
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otherwise
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error('Unknown initialization option!')
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end
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ReducedForm.StateVectorMean = StateVectorMean;
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ReducedForm.StateVectorVariance = StateVectorVariance;
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[~, flag] = chol(ReducedForm.StateVectorVariance);%reduced_rank_cholesky(ReducedForm.StateVectorVariance)';
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if flag
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fval = Inf;
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info(1) = 201;
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info(4) = 0.1;
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exit_flag = 0;
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return;
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end
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%------------------------------------------------------------------------------
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% 4. Likelihood evaluation
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%------------------------------------------------------------------------------
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DynareOptions.warning_for_steadystate = 0;
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[s1,s2] = get_dynare_random_generator_state();
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LIK = feval(DynareOptions.particle.algorithm, ReducedForm, Y, start, DynareOptions.particle, DynareOptions.threads, DynareOptions, Model);
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set_dynare_random_generator_state(s1,s2);
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if imag(LIK)
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fval = Inf;
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info(1) = 46;
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info(4) = 0.1;
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exit_flag = 0;
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return
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elseif isnan(LIK)
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fval = Inf;
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info(1) = 45;
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info(4) = 0.1;
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exit_flag = 0;
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return
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else
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likelihood = LIK;
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end
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DynareOptions.warning_for_steadystate = 1;
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% ------------------------------------------------------------------------------
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% Adds prior if necessary
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% ------------------------------------------------------------------------------
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lnprior = priordens(xparam1(:),BayesInfo.pshape,BayesInfo.p6,BayesInfo.p7,BayesInfo.p3,BayesInfo.p4);
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fval = (likelihood-lnprior);
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if isnan(fval)
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fval = Inf;
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info(1) = 47;
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info(4) = 0.1;
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exit_flag = 0;
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return
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end
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if ~isreal(fval)
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fval = Inf;
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info(1) = 48;
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info(4) = 0.1;
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exit_flag = 0;
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return
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end
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if isinf(LIK)
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fval = Inf;
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info(1) = 50;
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info(4) = 0.1;
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exit_flag = 0;
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return
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end
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