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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.
%
% INPUTS
% - xparam1 [double] n×1 vector, estimated parameters.
% - DynareDataset [struct] Matlab's structure containing the dataset (initialized by dynare, aka dataset_).
% - DatasetInfo [struct] Matlab's structure describing the dataset (initialized by dynare, aka dataset_info).
% - DynareOptions [struct] Matlab's structure describing the options (initialized by dynare, aka options_).
% - Model [struct] Matlab's structure describing the Model (initialized by dynare, aka M_).
% - EstimatedParameters [struct] Matlab's structure describing the estimated_parameters (initialized by dynare, aka estim_params_).
% - BayesInfo [struct] Matlab's structure describing the priors (initialized by dynare,aka bayesopt_).
% - BoundsInfo [struct] Matlab's structure specifying the bounds on the paramater values (initialized by dynare,aka bayesopt_).
% - DynareResults [struct] Matlab's structure gathering the results (initialized by dynare,aka oo_).
% - DynareDataset [struct] Matlab's structure containing the dataset
% - DatasetInfo [struct] Matlab's structure describing the dataset
% - options_ [struct] Matlab's structure describing the options
% - M_ [struct] Matlab's structure describing the M_
% - EstimatedParameters [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
% - oo_ [struct] Matlab's structure gathering the results
%
% OUTPUTS
% - 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.
% - ys [double] Empty array.
% - trend_coeff [double] Empty array.
% - Model [struct] Updated Model structure described in INPUTS section.
% - DynareOptions [struct] Updated DynareOptions structure described in INPUTS section.
% - BayesInfo [struct] See INPUTS section.
% - DynareResults [struct] Updated DynareResults structure described in INPUTS section.
% - M_ [struct] Updated M_ structure described in INPUTS section.
% - options_ [struct] Updated options_ structure described in INPUTS section.
% - bayestopt_ [struct] See 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.
%
@ -60,7 +60,7 @@ if ~isempty(xparam1)
end
% 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!')
end
@ -68,9 +68,9 @@ end
% 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)
return
end
@ -80,7 +80,7 @@ end
%------------------------------------------------------------------------------
% 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) == 3 || info(1) == 4 || info(1) == 5 || info(1)==6 ||info(1) == 19 || ...
@ -100,18 +100,18 @@ if info(1)
end
% 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.
start = DynareOptions.presample+1;
start = options_.presample+1;
Y = transpose(DynareDataset.data);
%------------------------------------------------------------------------------
% 3. Initial condition of the Kalman filter
%------------------------------------------------------------------------------
mf0 = BayesInfo.mf0;
mf1 = BayesInfo.mf1;
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);
@ -124,10 +124,10 @@ ReducedForm.H = H;
ReducedForm.mf0 = mf0;
ReducedForm.mf1 = mf1;
if DynareOptions.order>3
if options_.order>3
ReducedForm.use_k_order_solver = true;
ReducedForm.dr = dr;
ReducedForm.udr = folded_to_unfolded_dr(dr, Model, DynareOptions);
ReducedForm.udr = folded_to_unfolded_dr(dr, M_, options_);
if pruning
error('Pruning is not available for orders > 3');
end
@ -139,7 +139,7 @@ else
ReducedForm.ghuu = dr.ghuu(restrict_variables_idx,:);
ReducedForm.ghxu = dr.ghxu(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.ghuuu = dr.ghuuu(restrict_variables_idx,:);
ReducedForm.ghxxu = dr.ghxxu(restrict_variables_idx,:);
@ -150,22 +150,22 @@ else
end
% 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.
StateVectorMean = ReducedForm.constant(mf0);
[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, ...
DynareOptions.qz_criterium, DynareOptions.lyapunov_complex_threshold, [], DynareOptions.debug);
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 = DynareOptions.periods;
DynareOptions.periods = 5000;
old_DynareOptionspruning = DynareOptions.pruning;
DynareOptions.pruning = DynareOptions.particle.pruning;
y_ = simult(DynareResults.steady_state, dr,Model,DynareOptions,DynareResults);
old_DynareOptionsperiods = options_.periods;
options_.periods = 5000;
old_DynareOptionspruning = options_.pruning;
options_.pruning = options_.particle.pruning;
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
if any(any(isnan(y_))) || any(any(isinf(y_))) && ~ DynareOptions.pruning
if any(any(isnan(y_))) || any(any(isinf(y_))) && ~ options_.pruning
fval = Inf;
info(1) = 202;
info(4) = 0.1;
@ -173,13 +173,13 @@ switch DynareOptions.particle.initialization
return;
end
StateVectorVariance = cov(y_');
DynareOptions.periods = old_DynareOptionsperiods;
DynareOptions.pruning = old_DynareOptionspruning;
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 = DynareOptions.particle.initial_state_prior_std*eye(number_of_state_variables);
StateVectorVariance = options_.particle.initial_state_prior_std*eye(number_of_state_variables);
otherwise
error('Unknown initialization option!')
end
@ -197,9 +197,9 @@ end
%------------------------------------------------------------------------------
% 4. Likelihood evaluation
%------------------------------------------------------------------------------
DynareOptions.warning_for_steadystate = 0;
options_.warning_for_steadystate = 0;
[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);
if imag(LIK)
fval = Inf;
@ -216,11 +216,11 @@ elseif isnan(LIK)
else
likelihood = LIK;
end
DynareOptions.warning_for_steadystate = 1;
options_.warning_for_steadystate = 1;
% ------------------------------------------------------------------------------
% 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);
if isnan(fval)