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non_linear_dsge_likelihood.m: rename variables

kalman-mex
Johannes Pfeifer 2023-09-15 09:26:48 +02:00
parent 01f29784d7
commit 22c0f2279f
1 changed files with 41 additions and 41 deletions
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82
matlab/non_linear_dsge_likelihood.m
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@ -1,17 +1,17 @@
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) function [fval,info,exit_flag,DLIK,Hess,ys,trend_coeff,M_,options_,bayestopt_,oo_] = non_linear_dsge_likelihood(xparam1,DynareDataset,DatasetInfo,options_,M_,EstimatedParameters,bayestopt_,BoundsInfo,oo_)
% Evaluates the posterior kernel of a dsge model using a non linear filter. % Evaluates the posterior kernel of a dsge model using a non linear filter.
% %
% INPUTS % INPUTS
% - xparam1 [double] n×1 vector, estimated parameters. % - xparam1 [double] n×1 vector, estimated parameters.
% - DynareDataset [struct] Matlab's structure containing the dataset (initialized by dynare, aka dataset_). % - DynareDataset [struct] Matlab's structure containing the dataset
% - DatasetInfo [struct] Matlab's structure describing the dataset (initialized by dynare, aka dataset_info). % - DatasetInfo [struct] Matlab's structure describing the dataset
% - DynareOptions [struct] Matlab's structure describing the options (initialized by dynare, aka options_). % - options_ [struct] Matlab's structure describing the options
% - Model [struct] Matlab's structure describing the Model (initialized by dynare, aka M_). % - M_ [struct] Matlab's structure describing the M_
% - EstimatedParameters [struct] Matlab's structure describing the estimated_parameters (initialized by dynare, aka estim_params_). % - EstimatedParameters [struct] Matlab's structure describing the estimated_parameters
% - BayesInfo [struct] Matlab's structure describing the priors (initialized by dynare,aka bayesopt_). % - bayestopt_ [struct] Matlab's structure describing the priors
% - BoundsInfo [struct] Matlab's structure specifying the bounds on the paramater values (initialized by dynare,aka bayesopt_). % - BoundsInfo [struct] Matlab's structure specifying the bounds on the paramater values
% - DynareResults [struct] Matlab's structure gathering the results (initialized by dynare,aka oo_). % - oo_ [struct] Matlab's structure gathering the results
% %
% OUTPUTS % OUTPUTS
% - fval [double] scalar, value of the likelihood or posterior kernel. % - fval [double] scalar, value of the likelihood or posterior kernel.
@ -21,12 +21,12 @@ function [fval,info,exit_flag,DLIK,Hess,ys,trend_coeff,Model,DynareOptions,Bayes
% - Hess [double] Empty array. % - Hess [double] Empty array.
% - ys [double] Empty array. % - ys [double] Empty array.
% - trend_coeff [double] Empty array. % - trend_coeff [double] Empty array.
% - Model [struct] Updated Model structure described in INPUTS section. % - M_ [struct] Updated M_ structure described in INPUTS section.
% - DynareOptions [struct] Updated DynareOptions structure described in INPUTS section. % - options_ [struct] Updated options_ structure described in INPUTS section.
% - BayesInfo [struct] See INPUTS section. % - bayestopt_ [struct] See INPUTS section.
% - DynareResults [struct] Updated DynareResults structure described in INPUTS section. % - oo_ [struct] Updated oo_ structure described in INPUTS section.
% Copyright © 2010-2022 Dynare Team % Copyright © 2010-2023 Dynare Team
% %
% This file is part of Dynare. % This file is part of Dynare.
% %
@ -60,7 +60,7 @@ if ~isempty(xparam1)
end end
% Issue an error if loglinear option is used. % Issue an error if loglinear option is used.
if DynareOptions.loglinear if options_.loglinear
error('non_linear_dsge_likelihood: It is not possible to use a non linear filter with the option loglinear!') error('non_linear_dsge_likelihood: It is not possible to use a non linear filter with the option loglinear!')
end end
@ -68,9 +68,9 @@ end
% 1. Get the structural parameters & define penalties % 1. Get the structural parameters & define penalties
%------------------------------------------------------------------------------ %------------------------------------------------------------------------------
Model = set_all_parameters(xparam1,EstimatedParameters,Model); M_ = set_all_parameters(xparam1,EstimatedParameters,M_);
[fval,info,exit_flag,Q,H]=check_bounds_and_definiteness_estimation(xparam1, Model, EstimatedParameters, BoundsInfo); [fval,info,exit_flag,Q,H]=check_bounds_and_definiteness_estimation(xparam1, M_, EstimatedParameters, BoundsInfo);
if info(1) if info(1)
return return
end end
@ -80,7 +80,7 @@ end
%------------------------------------------------------------------------------ %------------------------------------------------------------------------------
% Linearize the model around the deterministic sdteadystate and extract the matrices of the state equation (T and R). % Linearize the model around the deterministic sdteadystate and extract the matrices of the state equation (T and R).
[dr, info, Model, DynareResults] = resol(0, Model, DynareOptions, DynareResults); [dr, info, M_, oo_] = resol(0, M_, options_, oo_);
if info(1) if info(1)
if info(1) == 3 || info(1) == 4 || info(1) == 5 || info(1)==6 ||info(1) == 19 || ... if info(1) == 3 || info(1) == 4 || info(1) == 5 || info(1)==6 ||info(1) == 19 || ...
@ -100,18 +100,18 @@ if info(1)
end end
% Define a vector of indices for the observed variables. Is this really usefull?... % Define a vector of indices for the observed variables. Is this really usefull?...
BayesInfo.mf = BayesInfo.mf1; bayestopt_.mf = bayestopt_.mf1;
% Get needed informations for kalman filter routines. % Get needed informations for kalman filter routines.
start = DynareOptions.presample+1; start = options_.presample+1;
Y = transpose(DynareDataset.data); Y = transpose(DynareDataset.data);
%------------------------------------------------------------------------------ %------------------------------------------------------------------------------
% 3. Initial condition of the Kalman filter % 3. Initial condition of the Kalman filter
%------------------------------------------------------------------------------ %------------------------------------------------------------------------------
mf0 = BayesInfo.mf0; mf0 = bayestopt_.mf0;
mf1 = BayesInfo.mf1; mf1 = bayestopt_.mf1;
restrict_variables_idx = dr.restrict_var_list; restrict_variables_idx = dr.restrict_var_list;
state_variables_idx = restrict_variables_idx(mf0); state_variables_idx = restrict_variables_idx(mf0);
number_of_state_variables = length(mf0); number_of_state_variables = length(mf0);
@ -124,10 +124,10 @@ ReducedForm.H = H;
ReducedForm.mf0 = mf0; ReducedForm.mf0 = mf0;
ReducedForm.mf1 = mf1; ReducedForm.mf1 = mf1;
if DynareOptions.order>3 if options_.order>3
ReducedForm.use_k_order_solver = true; ReducedForm.use_k_order_solver = true;
ReducedForm.dr = dr; ReducedForm.dr = dr;
ReducedForm.udr = folded_to_unfolded_dr(dr, Model, DynareOptions); ReducedForm.udr = folded_to_unfolded_dr(dr, M_, options_);
if pruning if pruning
error('Pruning is not available for orders > 3'); error('Pruning is not available for orders > 3');
end end
@ -139,7 +139,7 @@ else
ReducedForm.ghuu = dr.ghuu(restrict_variables_idx,:); ReducedForm.ghuu = dr.ghuu(restrict_variables_idx,:);
ReducedForm.ghxu = dr.ghxu(restrict_variables_idx,:); ReducedForm.ghxu = dr.ghxu(restrict_variables_idx,:);
ReducedForm.ghs2 = dr.ghs2(restrict_variables_idx,:); ReducedForm.ghs2 = dr.ghs2(restrict_variables_idx,:);
if DynareOptions.order==3 if options_.order==3
ReducedForm.ghxxx = dr.ghxxx(restrict_variables_idx,:); ReducedForm.ghxxx = dr.ghxxx(restrict_variables_idx,:);
ReducedForm.ghuuu = dr.ghuuu(restrict_variables_idx,:); ReducedForm.ghuuu = dr.ghuuu(restrict_variables_idx,:);
ReducedForm.ghxxu = dr.ghxxu(restrict_variables_idx,:); ReducedForm.ghxxu = dr.ghxxu(restrict_variables_idx,:);
@ -150,22 +150,22 @@ else
end end
% Set initial condition. % Set initial condition.
switch DynareOptions.particle.initialization switch options_.particle.initialization
case 1% Initial state vector covariance is the ergodic variance associated to the first order Taylor-approximation of the model. case 1% Initial state vector covariance is the ergodic variance associated to the first order Taylor-approximation of the model.
StateVectorMean = ReducedForm.constant(mf0); StateVectorMean = ReducedForm.constant(mf0);
[A,B] = kalman_transition_matrix(dr,dr.restrict_var_list,dr.restrict_columns); [A,B] = kalman_transition_matrix(dr,dr.restrict_var_list,dr.restrict_columns);
StateVectorVariance = lyapunov_symm(A, B*Q*B', DynareOptions.lyapunov_fixed_point_tol, ... StateVectorVariance = lyapunov_symm(A, B*Q*B', options_.lyapunov_fixed_point_tol, ...
DynareOptions.qz_criterium, DynareOptions.lyapunov_complex_threshold, [], DynareOptions.debug); options_.qz_criterium, options_.lyapunov_complex_threshold, [], options_.debug);
StateVectorVariance = StateVectorVariance(mf0,mf0); 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). 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); StateVectorMean = ReducedForm.constant(mf0);
old_DynareOptionsperiods = DynareOptions.periods; old_DynareOptionsperiods = options_.periods;
DynareOptions.periods = 5000; options_.periods = 5000;
old_DynareOptionspruning = DynareOptions.pruning; old_DynareOptionspruning = options_.pruning;
DynareOptions.pruning = DynareOptions.particle.pruning; options_.pruning = options_.particle.pruning;
y_ = simult(DynareResults.steady_state, dr,Model,DynareOptions,DynareResults); y_ = simult(oo_.steady_state, dr,M_,options_,oo_);
y_ = y_(dr.order_var(state_variables_idx),2001:5000); %state_variables_idx is in dr-order while simult_ is in declaration order 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_))) && ~ DynareOptions.pruning if any(any(isnan(y_))) || any(any(isinf(y_))) && ~ options_.pruning
fval = Inf; fval = Inf;
info(1) = 202; info(1) = 202;
info(4) = 0.1; info(4) = 0.1;
@ -173,13 +173,13 @@ switch DynareOptions.particle.initialization
return; return;
end end
StateVectorVariance = cov(y_'); StateVectorVariance = cov(y_');
DynareOptions.periods = old_DynareOptionsperiods; options_.periods = old_DynareOptionsperiods;
DynareOptions.pruning = old_DynareOptionspruning; options_.pruning = old_DynareOptionspruning;
clear('old_DynareOptionsperiods','y_'); clear('old_DynareOptionsperiods','y_');
case 3% Initial state vector covariance is a diagonal matrix (to be used case 3% Initial state vector covariance is a diagonal matrix (to be used
% if model has stochastic trends). % if model has stochastic trends).
StateVectorMean = ReducedForm.constant(mf0); StateVectorMean = ReducedForm.constant(mf0);
StateVectorVariance = DynareOptions.particle.initial_state_prior_std*eye(number_of_state_variables); StateVectorVariance = options_.particle.initial_state_prior_std*eye(number_of_state_variables);
otherwise otherwise
error('Unknown initialization option!') error('Unknown initialization option!')
end end
@ -197,9 +197,9 @@ end
%------------------------------------------------------------------------------ %------------------------------------------------------------------------------
% 4. Likelihood evaluation % 4. Likelihood evaluation
%------------------------------------------------------------------------------ %------------------------------------------------------------------------------
DynareOptions.warning_for_steadystate = 0; options_.warning_for_steadystate = 0;
[s1,s2] = get_dynare_random_generator_state(); [s1,s2] = get_dynare_random_generator_state();
LIK = feval(DynareOptions.particle.algorithm, ReducedForm, Y, start, DynareOptions.particle, DynareOptions.threads, DynareOptions, Model); LIK = feval(options_.particle.algorithm, ReducedForm, Y, start, options_.particle, options_.threads, options_, M_);
set_dynare_random_generator_state(s1,s2); set_dynare_random_generator_state(s1,s2);
if imag(LIK) if imag(LIK)
fval = Inf; fval = Inf;
@ -216,11 +216,11 @@ elseif isnan(LIK)
else else
likelihood = LIK; likelihood = LIK;
end end
DynareOptions.warning_for_steadystate = 1; options_.warning_for_steadystate = 1;
% ------------------------------------------------------------------------------ % ------------------------------------------------------------------------------
% Adds prior if necessary % Adds prior if necessary
% ------------------------------------------------------------------------------ % ------------------------------------------------------------------------------
lnprior = priordens(xparam1(:),BayesInfo.pshape,BayesInfo.p6,BayesInfo.p7,BayesInfo.p3,BayesInfo.p4); lnprior = priordens(xparam1(:),bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7,bayestopt_.p3,bayestopt_.p4);
fval = (likelihood-lnprior); fval = (likelihood-lnprior);
if isnan(fval) if isnan(fval)