252 lines
11 KiB
Matlab
252 lines
11 KiB
Matlab
function [fval,info,exit_flag,DLIK,Hess,ys,trend_coeff,M_,options_,bayestopt_,dr] = non_linear_dsge_likelihood(xparam1,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,BoundsInfo,dr, endo_steady_state, exo_steady_state, exo_det_steady_state)
|
||
% [fval,info,exit_flag,DLIK,Hess,ys,trend_coeff,M_,options_,bayestopt_,dr] = non_linear_dsge_likelihood(xparam1,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,BoundsInfo,dr, endo_steady_state, exo_steady_state, exo_det_steady_state)
|
||
% Evaluates the posterior kernel of a dsge model using a non linear filter.
|
||
%
|
||
% INPUTS
|
||
% - xparam1 [double] n×1 vector, estimated parameters.
|
||
% - dataset_ [struct] Matlab's structure containing the dataset
|
||
% - dataset_info [struct] Matlab's structure describing the dataset
|
||
% - options_ [struct] Matlab's structure describing the options
|
||
% - M_ [struct] Matlab's structure describing the M_
|
||
% - estim_params_ [struct] Matlab's structure describing the estimated_parameters
|
||
% - bayestopt_ [struct] Matlab's structure describing the priors
|
||
% - BoundsInfo [struct] Matlab's structure specifying the bounds on the paramater values
|
||
% - dr [structure] Reduced form model.
|
||
% - endo_steady_state [vector] steady state value for endogenous variables
|
||
% - exo_steady_state [vector] steady state value for exogenous variables
|
||
% - exo_det_steady_state [vector] steady state value for exogenous deterministic variables
|
||
%
|
||
% OUTPUTS
|
||
% - fval [double] scalar, value of the likelihood or posterior kernel.
|
||
% - info [integer] 4×1 vector, informations resolution of the model and evaluation of the likelihood.
|
||
% - exit_flag [integer] scalar, equal to 1 (no issues when evaluating the likelihood) or 0 (not able to evaluate the likelihood).
|
||
% - DLIK [double] Empty array.
|
||
% - Hess [double] Empty array.
|
||
% - ys [double] Empty array.
|
||
% - trend_coeff [double] Empty array.
|
||
% - M_ [struct] Updated M_ structure described in INPUTS section.
|
||
% - options_ [struct] Updated options_ structure described in INPUTS section.
|
||
% - bayestopt_ [struct] See INPUTS section.
|
||
% - dr [struct] decision rule structure described in INPUTS section.
|
||
|
||
% Copyright © 2010-2023 Dynare Team
|
||
%
|
||
% This file is part of Dynare.
|
||
%
|
||
% Dynare is free software: you can redistribute it and/or modify
|
||
% it under the terms of the GNU General Public License as published by
|
||
% the Free Software Foundation, either version 3 of the License, or
|
||
% (at your option) any later version.
|
||
%
|
||
% Dynare is distributed in the hope that it will be useful,
|
||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||
% GNU General Public License for more details.
|
||
%
|
||
% You should have received a copy of the GNU General Public License
|
||
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
|
||
|
||
% Initialization of the returned arguments.
|
||
fval = [];
|
||
ys = [];
|
||
trend_coeff = [];
|
||
exit_flag = 1;
|
||
DLIK = [];
|
||
Hess = [];
|
||
|
||
% Ensure that xparam1 is a column vector.
|
||
% (Don't do the transformation if xparam1 is empty, otherwise it would become a
|
||
% 0×1 matrix, which create issues with older MATLABs when comparing with [] in
|
||
% check_bounds_and_definiteness_estimation)
|
||
if ~isempty(xparam1)
|
||
xparam1 = xparam1(:);
|
||
end
|
||
|
||
% Issue an error if loglinear option is used.
|
||
if options_.loglinear
|
||
error('non_linear_dsge_likelihood: It is not possible to use a non linear filter with the option loglinear!')
|
||
end
|
||
|
||
%------------------------------------------------------------------------------
|
||
% 1. Get the structural parameters & define penalties
|
||
%------------------------------------------------------------------------------
|
||
|
||
M_ = set_all_parameters(xparam1,estim_params_,M_);
|
||
|
||
[fval,info,exit_flag,Q,H]=check_bounds_and_definiteness_estimation(xparam1, M_, estim_params_, BoundsInfo);
|
||
if info(1)
|
||
return
|
||
end
|
||
|
||
%------------------------------------------------------------------------------
|
||
% 2. call model setup & reduction program
|
||
%------------------------------------------------------------------------------
|
||
|
||
% Linearize the model around the deterministic steadystate and extract the matrices of the state equation (T and R).
|
||
[dr, info, M_.params] = resol(0, M_, options_, dr , endo_steady_state, exo_steady_state, exo_det_steady_state);
|
||
|
||
if info(1)
|
||
if info(1) == 3 || info(1) == 4 || info(1) == 5 || info(1)==6 ||info(1) == 19 || ...
|
||
info(1) == 20 || info(1) == 21 || info(1) == 23 || info(1) == 26 || ...
|
||
info(1) == 81 || info(1) == 84 || info(1) == 85
|
||
%meaningful second entry of output that can be used
|
||
fval = Inf;
|
||
info(4) = info(2);
|
||
exit_flag = 0;
|
||
return
|
||
else
|
||
fval = Inf;
|
||
info(4) = 0.1;
|
||
exit_flag = 0;
|
||
return
|
||
end
|
||
end
|
||
|
||
% Define a vector of indices for the observed variables. Is this really usefull?...
|
||
bayestopt_.mf = bayestopt_.mf1;
|
||
|
||
% Get needed informations for kalman filter routines.
|
||
start = options_.presample+1;
|
||
Y = transpose(dataset_.data);
|
||
|
||
%------------------------------------------------------------------------------
|
||
% 3. Initial condition of the Kalman filter
|
||
%------------------------------------------------------------------------------
|
||
|
||
mf0 = bayestopt_.mf0;
|
||
mf1 = bayestopt_.mf1;
|
||
restrict_variables_idx = dr.restrict_var_list;
|
||
state_variables_idx = restrict_variables_idx(mf0);
|
||
number_of_state_variables = length(mf0);
|
||
|
||
ReducedForm.steadystate = dr.ys(dr.order_var(restrict_variables_idx));
|
||
ReducedForm.constant = ReducedForm.steadystate + .5*dr.ghs2(restrict_variables_idx);
|
||
ReducedForm.state_variables_steady_state = dr.ys(dr.order_var(state_variables_idx));
|
||
ReducedForm.Q = Q;
|
||
ReducedForm.H = H;
|
||
ReducedForm.mf0 = mf0;
|
||
ReducedForm.mf1 = mf1;
|
||
|
||
if options_.order>3
|
||
ReducedForm.use_k_order_solver = true;
|
||
ReducedForm.dr = dr;
|
||
ReducedForm.udr = folded_to_unfolded_dr(dr, M_, options_);
|
||
if pruning
|
||
error('Pruning is not available for orders > 3');
|
||
end
|
||
else
|
||
ReducedForm.use_k_order_solver = false;
|
||
ReducedForm.ghx = dr.ghx(restrict_variables_idx,:);
|
||
ReducedForm.ghu = dr.ghu(restrict_variables_idx,:);
|
||
ReducedForm.ghxx = dr.ghxx(restrict_variables_idx,:);
|
||
ReducedForm.ghuu = dr.ghuu(restrict_variables_idx,:);
|
||
ReducedForm.ghxu = dr.ghxu(restrict_variables_idx,:);
|
||
ReducedForm.ghs2 = dr.ghs2(restrict_variables_idx,:);
|
||
if options_.order==3
|
||
ReducedForm.ghxxx = dr.ghxxx(restrict_variables_idx,:);
|
||
ReducedForm.ghuuu = dr.ghuuu(restrict_variables_idx,:);
|
||
ReducedForm.ghxxu = dr.ghxxu(restrict_variables_idx,:);
|
||
ReducedForm.ghxuu = dr.ghxuu(restrict_variables_idx,:);
|
||
ReducedForm.ghxss = dr.ghxss(restrict_variables_idx,:);
|
||
ReducedForm.ghuss = dr.ghuss(restrict_variables_idx,:);
|
||
end
|
||
end
|
||
|
||
% Set initial condition.
|
||
switch options_.particle.initialization
|
||
case 1% Initial state vector covariance is the ergodic variance associated to the first order Taylor-approximation of the model.
|
||
StateVectorMean = ReducedForm.constant(mf0);
|
||
[A,B] = kalman_transition_matrix(dr,dr.restrict_var_list,dr.restrict_columns);
|
||
StateVectorVariance = lyapunov_symm(A, B*Q*B', options_.lyapunov_fixed_point_tol, ...
|
||
options_.qz_criterium, options_.lyapunov_complex_threshold, [], options_.debug);
|
||
StateVectorVariance = StateVectorVariance(mf0,mf0);
|
||
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).
|
||
StateVectorMean = ReducedForm.constant(mf0);
|
||
old_DynareOptionsperiods = options_.periods;
|
||
options_.periods = 5000;
|
||
old_DynareOptionspruning = options_.pruning;
|
||
options_.pruning = options_.particle.pruning;
|
||
y_ = simult(endo_steady_state, dr,M_,options_);
|
||
y_ = y_(dr.order_var(state_variables_idx),2001:5000); %state_variables_idx is in dr-order while simult_ is in declaration order
|
||
if any(any(isnan(y_))) || any(any(isinf(y_))) && ~ options_.pruning
|
||
fval = Inf;
|
||
info(1) = 202;
|
||
info(4) = 0.1;
|
||
exit_flag = 0;
|
||
return;
|
||
end
|
||
StateVectorVariance = cov(y_');
|
||
options_.periods = old_DynareOptionsperiods;
|
||
options_.pruning = old_DynareOptionspruning;
|
||
clear('old_DynareOptionsperiods','y_');
|
||
case 3% Initial state vector covariance is a diagonal matrix (to be used
|
||
% if model has stochastic trends).
|
||
StateVectorMean = ReducedForm.constant(mf0);
|
||
StateVectorVariance = options_.particle.initial_state_prior_std*eye(number_of_state_variables);
|
||
otherwise
|
||
error('Unknown initialization option!')
|
||
end
|
||
ReducedForm.StateVectorMean = StateVectorMean;
|
||
ReducedForm.StateVectorVariance = StateVectorVariance;
|
||
|
||
[~, flag] = chol(ReducedForm.StateVectorVariance);%reduced_rank_cholesky(ReducedForm.StateVectorVariance)';
|
||
if flag
|
||
fval = Inf;
|
||
info(1) = 201;
|
||
info(4) = 0.1;
|
||
exit_flag = 0;
|
||
return;
|
||
end
|
||
%------------------------------------------------------------------------------
|
||
% 4. Likelihood evaluation
|
||
%------------------------------------------------------------------------------
|
||
options_.warning_for_steadystate = 0;
|
||
[s1,s2] = get_dynare_random_generator_state();
|
||
LIK = feval(options_.particle.algorithm, ReducedForm, Y, start, options_.particle, options_.threads, options_, M_);
|
||
set_dynare_random_generator_state(s1,s2);
|
||
if imag(LIK)
|
||
fval = Inf;
|
||
info(1) = 46;
|
||
info(4) = 0.1;
|
||
exit_flag = 0;
|
||
return
|
||
elseif isnan(LIK)
|
||
fval = Inf;
|
||
info(1) = 45;
|
||
info(4) = 0.1;
|
||
exit_flag = 0;
|
||
return
|
||
else
|
||
likelihood = LIK;
|
||
end
|
||
options_.warning_for_steadystate = 1;
|
||
% ------------------------------------------------------------------------------
|
||
% Adds prior if necessary
|
||
% ------------------------------------------------------------------------------
|
||
lnprior = priordens(xparam1(:),bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7,bayestopt_.p3,bayestopt_.p4);
|
||
fval = (likelihood-lnprior);
|
||
|
||
if isnan(fval)
|
||
fval = Inf;
|
||
info(1) = 47;
|
||
info(4) = 0.1;
|
||
exit_flag = 0;
|
||
return
|
||
end
|
||
|
||
if ~isreal(fval)
|
||
fval = Inf;
|
||
info(1) = 48;
|
||
info(4) = 0.1;
|
||
exit_flag = 0;
|
||
return
|
||
end
|
||
|
||
if isinf(LIK)
|
||
fval = Inf;
|
||
info(1) = 50;
|
||
info(4) = 0.1;
|
||
exit_flag = 0;
|
||
return
|
||
end
|