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Allow higher order approximations in nonlinear filters.

time-shift
Stéphane Adjemian (Charybdis) 2019-12-20 11:26:35 +01:00
parent 78c36dd0b7
commit 031569fa96
3 changed files with 19 additions and 15 deletions
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30
matlab/non_linear_dsge_likelihood.m
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@ -171,19 +171,10 @@ Y = transpose(DynareDataset.data);
mf0 = BayesInfo.mf0;
mf1 = BayesInfo.mf1;
restrict_variables_idx = dr.restrict_var_list;
observed_variables_idx = restrict_variables_idx(mf1);
state_variables_idx = restrict_variables_idx(mf0);
sample_size = size(Y,2);
restrict_variables_idx = dr.restrict_var_list;
state_variables_idx = restrict_variables_idx(mf0);
number_of_state_variables = length(mf0);
number_of_observed_variables = length(mf1);
number_of_structural_innovations = length(Q);
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.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));
@ -192,11 +183,24 @@ ReducedForm.H = H;
ReducedForm.mf0 = mf0;
ReducedForm.mf1 = mf1;
if DynareOptions.k_order_solver
ReducedForm.use_k_order_solver = true;
ReducedForm.dr = dr;
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,:);
end
% Set initial condition.
switch DynareOptions.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);
StateVectorVariance = lyapunov_symm(ReducedForm.ghx(mf0,:),ReducedForm.ghu(mf0,:)*ReducedForm.Q*ReducedForm.ghu(mf0,:)',DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold,[],DynareOptions.debug);
StateVectorVariance = lyapunov_symm(dr.ghx(mf0,:), dr.ghu(mf0,:)*Q*dr.ghu(mf0,:)', DynareOptions.lyapunov_fixed_point_tol, ...
DynareOptions.qz_criterium, DynareOptions.lyapunov_complex_threshold, [], DynareOptions.debug);
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;
@ -221,7 +225,7 @@ ReducedForm.StateVectorVariance = StateVectorVariance;
%------------------------------------------------------------------------------
DynareOptions.warning_for_steadystate = 0;
[s1,s2] = get_dynare_random_generator_state();
LIK = feval(DynareOptions.particle.algorithm,ReducedForm,Y,start,DynareOptions.particle,DynareOptions.threads);
LIK = feval(DynareOptions.particle.algorithm, ReducedForm, Y, start, DynareOptions.particle, DynareOptions.threads, DynareOptions, Model);
set_dynare_random_generator_state(s1,s2);
if imag(LIK)
likelihood = Inf;

2
matlab/particles

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Subproject commit 66867386ef5c70e8db69293af8a0e051488196f1
Subproject commit 13d79cb4382ade3b74a4509fdd8ddad3d273ffaf

2
preprocessor

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Subproject commit d441ff059a57d3020b055a31ab45ac72668f782b
Subproject commit 8baec1e0ec5e06df5e14a96467fe520f5eb6f374