diff --git a/matlab/nonlinear-filters/sequential_importance_particle_filter.m b/matlab/nonlinear-filters/sequential_importance_particle_filter.m
index e84351686867612490f41c2dd95039cb45b8053b..0ac02e8f7d6cfe60977cff726b18dd95b9963779 100644
--- a/matlab/nonlinear-filters/sequential_importance_particle_filter.m
+++ b/matlab/nonlinear-filters/sequential_importance_particle_filter.m
@@ -19,11 +19,6 @@ function [LIK,lik] = sequential_importance_particle_filter(ReducedForm,Y,start,P
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
-persistent init_flag
-persistent mf0 mf1
-persistent number_of_particles number_of_state_variables
-persistent sample_size number_of_observed_variables number_of_structural_innovations
-
% Set default value for start
if isempty(start)
start = 1;
@@ -39,17 +34,11 @@ state_variables_steady_state = ReducedForm.state_variables_steady_state;
order = options_.order;
-% Set persistent variables (if needed).
-if isempty(init_flag)
- mf0 = ReducedForm.mf0;
- mf1 = ReducedForm.mf1;
- sample_size = size(Y,2);
- number_of_state_variables = length(mf0);
- number_of_observed_variables = length(mf1);
- number_of_structural_innovations = length(ReducedForm.Q);
- number_of_particles = ParticleOptions.number_of_particles;
- init_flag = 1;
-end
+sample_size = size(Y,2);
+number_of_state_variables = length(ReducedForm.mf0);
+number_of_observed_variables = length(ReducedForm.mf1);
+number_of_structural_innovations = length(ReducedForm.Q);
+number_of_particles = ParticleOptions.number_of_particles;
if ReducedForm.use_k_order_solver
dr = ReducedForm.dr;
@@ -89,10 +78,6 @@ lik = NaN(sample_size,1);
% Get initial condition for the state vector.
StateVectorMean = ReducedForm.StateVectorMean;
StateVectorVarianceSquareRoot = chol(ReducedForm.StateVectorVariance)';%reduced_rank_cholesky(ReducedForm.StateVectorVariance)';
-if pruning
- StateVectorMean_ = StateVectorMean;
- StateVectorVarianceSquareRoot_ = StateVectorVarianceSquareRoot;
-end
% Get the rank of StateVectorVarianceSquareRoot
state_variance_rank = size(StateVectorVarianceSquareRoot,2);
@@ -110,11 +95,11 @@ if pruning
if order == 2
StateVectors_ = StateVectors;
state_variables_steady_state_ = state_variables_steady_state;
- mf0_ = mf0;
+ mf0_ = ReducedForm.mf0;
elseif order == 3
StateVectors_ = repmat(StateVectors,3,1);
state_variables_steady_state_ = repmat(state_variables_steady_state,3,1);
- mf0_ = repmat(mf0,1,3);
+ mf0_ = repmat(ReducedForm.mf0,1,3);
mask2 = number_of_state_variables+1:2*number_of_state_variables;
mask3 = 2*number_of_state_variables+1:3*number_of_state_variables;
mf0_(mask2) = mf0_(mask2)+size(ghx,1);
@@ -151,7 +136,7 @@ for t=1:sample_size
end
end
%PredictedObservedMean = tmp(mf1,:)*transpose(weights);
- PredictionError = bsxfun(@minus,Y(:,t),tmp(mf1,:));
+ PredictionError = bsxfun(@minus,Y(:,t),tmp(ReducedForm.mf1,:));
%dPredictedObservedMean = bsxfun(@minus,tmp(mf1,:),PredictedObservedMean);
%PredictedObservedVariance = bsxfun(@times,dPredictedObservedMean,weights)*dPredictedObservedMean' + H;
%PredictedObservedVariance = H;
@@ -167,15 +152,15 @@ for t=1:sample_size
weights = wtilde/sum(wtilde);
if (ParticleOptions.resampling.status.generic && neff(weights)<ParticleOptions.resampling.threshold*sample_size) || ParticleOptions.resampling.status.systematic
if pruning
- temp = resample([tmp(mf0,:)' tmp_(mf0_,:)'],weights',ParticleOptions);
+ temp = resample([tmp(ReducedForm.mf0,:)' tmp_(mf0_,:)'],weights',ParticleOptions);
StateVectors = temp(:,1:number_of_state_variables)';
StateVectors_ = temp(:,number_of_state_variables+1:end)';
else
- StateVectors = resample(tmp(mf0,:)',weights',ParticleOptions)';
+ StateVectors = resample(tmp(ReducedForm.mf0,:)',weights',ParticleOptions)';
end
weights = ones(1,number_of_particles)/number_of_particles;
elseif ParticleOptions.resampling.status.none
- StateVectors = tmp(mf0,:);
+ StateVectors = tmp(ReducedForm.mf0,:);
if pruning
StateVectors_ = tmp_(mf0_,:);
end
diff --git a/matlab/nonlinear-filters/solve_model_for_online_filter.m b/matlab/nonlinear-filters/solve_model_for_online_filter.m
index 6305f214e613ac8c5ee63a055e9bc9297ed12dfe..19732e102f64738fc37e4d5fbc04429b4f8e8542 100644
--- a/matlab/nonlinear-filters/solve_model_for_online_filter.m
+++ b/matlab/nonlinear-filters/solve_model_for_online_filter.m
@@ -1,5 +1,7 @@
function [info, M_, options_, oo_, ReducedForm] = ...
solve_model_for_online_filter(setinitialcondition, xparam1, dataset_, options_, M_, estim_params_, bayestopt_, bounds, oo_)
+% [info, M_, options_, oo_, ReducedForm] = ...
+% solve_model_for_online_filter(setinitialcondition, xparam1, dataset_, options_, M_, estim_params_, bayestopt_, bounds, oo_)
% Solves the dsge model for an particular parameters set.
%
@@ -11,6 +13,7 @@ function [info, M_, options_, oo_, ReducedForm] = ...
% - M_ [struct] Model description.
% - estim_params_ [struct] Estimated parameters.
% - bayestopt_ [struct] Prior definition.
+% - bounds [struct] Prior bounds.
% - oo_ [struct] Dynare results.
%
% OUTPUTS
@@ -20,7 +23,7 @@ function [info, M_, options_, oo_, ReducedForm] = ...
% - oo_ [struct] Dynare results.
% - ReducedForm [struct] Reduced form model.
-% Copyright © 2013-2023 Dynare Team
+% Copyright © 2013-2024 Dynare Team
%
% This file is part of Dynare.
%
@@ -37,8 +40,6 @@ function [info, M_, options_, oo_, ReducedForm] = ...
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
-persistent init_flag restrict_variables_idx state_variables_idx mf0 mf1 number_of_state_variables
-
info = 0;
%----------------------------------------------------
@@ -140,14 +141,11 @@ end
dr = oo_.dr;
% Set persistent variables (first call).
-if isempty(init_flag)
- 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);
- init_flag = true;
-end
+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);
% Return reduced form model.
@@ -174,7 +172,6 @@ if nargout>4
ReducedForm.ghuu = zeros(size(restrict_variables_idx,1),n_shocks^2);
ReducedForm.ghxu = zeros(size(restrict_variables_idx,1),n_states*n_shocks);
ReducedForm.constant = ReducedForm.steadystate;
-% ReducedForm.ghs2 = dr.ghs2(restrict_variables_idx,:);
end
ReducedForm.state_variables_steady_state = dr.ys(dr.order_var(state_variables_idx));
ReducedForm.Q = Q;