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Add undocumented option for kalman_filter_mex.

kalman_mex
Stéphane Adjemian (Argos) 2023-11-09 18:58:02 +01:00
parent b863c309bd
commit 666aa46dfb
Signed by: stepan
GPG Key ID: A6D44CB9C64CE77B
2 changed files with 24 additions and 16 deletions
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3
matlab/default_option_values.m
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@ -393,6 +393,7 @@ options_.smoothed_state_uncertainty = false;
options_.first_obs = NaN;
options_.nobs = NaN;
options_.kalman_algo = 0;
options_.kalman_filter_mex = false;
options_.fast_kalman_filter = false;
options_.kalman_tol = 1e-10;
options_.kalman.keep_kalman_algo_if_singularity_is_detected = false;
@ -508,7 +509,7 @@ options_.posterior_sampler_options.dsmh.K = 50 ;
options_.posterior_sampler_options.dsmh.lambda1 = 0.1 ;
options_.posterior_sampler_options.dsmh.nparticles = 20000 ;
options_.posterior_sampler_options.dsmh.alpha0 = 0.2 ;
options_.posterior_sampler_options.dsmh.alpha1 = 0.3 ;
options_.posterior_sampler_options.dsmh.alpha1 = 0.3 ;
options_.posterior_sampler_options.dsmh.tau = 10 ;
options_.trace_plot_ma = 200;

37
matlab/dsge_likelihood.m
View File

@ -122,7 +122,7 @@ if options_.occbin.likelihood.status
oldoo.restrict_columns = dr.restrict_columns;
dr.restrict_var_list = bayestopt_.smoother_var_list;
dr.restrict_columns = bayestopt_.smoother_restrict_columns;
% Linearize the model around the deterministic steady state and extract the matrices of the state equation (T and R).
[T,R,SteadyState,info,M_,dr, M_.params,TTx,RRx,CCx, T0, R0] = ...
occbin.dynare_resolve(M_,options_,dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
@ -149,7 +149,7 @@ if info(1)
%meaningful second entry of output that can be used
fval = Inf;
if isnan(info(2))
info(4) = 0.1;
info(4) = 0.1;
else
info(4) = info(2);
end
@ -235,7 +235,7 @@ singular_diffuse_filter = 0;
if options_.heteroskedastic_filter
Qvec=get_Qvec_heteroskedastic_filter(Q,smpl,M_);
end
switch options_.lik_init
case 1% Standard initialization with the steady state of the state equation.
if kalman_algo~=2
@ -401,7 +401,7 @@ switch options_.lik_init
Pinf = [];
a = zeros(mm,1);
a = set_Kalman_starting_values(a,M_,dr,options_,bayestopt_);
a_0_given_tm1 = T*a;
a_0_given_tm1 = T*a;
if options_.occbin.likelihood.status
Z =zeros(length(bayestopt_.mf),size(T,1));
for i = 1:length(bayestopt_.mf)
@ -620,13 +620,20 @@ if ((kalman_algo==1) || (kalman_algo==3))% Multivariate Kalman Filter
T,Q,R,H,Z,mm,pp,rr,Zflag,diffuse_periods, ...
analytic_deriv_info{:});
else
[LIK,lik] = kalman_filter(Y,diffuse_periods+1,size(Y,2), ...
a_0_given_tm1,Pstar, ...
kalman_tol, riccati_tol, ...
options_.rescale_prediction_error_covariance, ...
options_.presample, ...
T,Q,R,H,Z,mm,pp,rr,Zflag,diffuse_periods, ...
analytic_deriv_info{:});
if options_.kalman_filter_mex
[LIK,lik] = kalman_filter_mex(Y,a_0_given_tm1,Pstar, ...
kalman_tol, riccati_tol, ...
T,Q,R,Z,Zflag,H,diffuse_periods, ...
options_.presample);
else
[LIK,lik] = kalman_filter(Y,diffuse_periods+1,size(Y,2), ...
a_0_given_tm1,Pstar, ...
kalman_tol, riccati_tol, ...
options_.rescale_prediction_error_covariance, ...
options_.presample, ...
T,Q,R,H,Z,mm,pp,rr,Zflag,diffuse_periods, ...
analytic_deriv_info{:});
end
end
else
[LIK,lik] = missing_observations_kalman_filter(dataset_info.missing.aindex,dataset_info.missing.number_of_observations,dataset_info.missing.no_more_missing_observations,Y,diffuse_periods+1,size(Y,2), ...
@ -858,15 +865,15 @@ end
function a=set_Kalman_starting_values(a,M_,dr,options_,bayestopt_)
% function a=set_Kalman_starting_values(a,M_,dr,options_,bayestopt_)
% Sets initial states guess for Kalman filter/smoother based on M_.filter_initial_state
%
% INPUTS
% Sets initial states guess for Kalman filter/smoother based on M_.filter_initial_state
%
% INPUTS
% o a [double] (p*1) vector of states
% o M_ [structure] decribing the model
% o dr [structure] storing the decision rules
% o options_ [structure] describing the options
% o bayestopt_ [structure] describing the priors
%
%
% OUTPUTS
% o a [double] (p*1) vector of set initial states