Allow k order approximation in Auxiliary Particle Filter.

Ref. dynare#1673

src/auxiliary_particle_filter.m

-function [LIK,lik] = auxiliary_particle_filter(ReducedForm,Y,start,ParticleOptions,ThreadsOptions)
+function [LIK,lik] = auxiliary_particle_filter(ReducedForm,Y,start,ParticleOptions,ThreadsOptions, DynareOptions, Model)
 
 % Evaluates the likelihood of a nonlinear model with the auxiliary particle filter
 % allowing eventually resampling.
 %
-% Copyright (C) 2011-2017 Dynare Team
+% Copyright (C) 2011-2019 Dynare Team
 %
 % This file is part of Dynare (particles module).
 %
@@ -20,9 +20,6 @@ function [LIK,lik] = auxiliary_particle_filter(ReducedForm,Y,start,ParticleOptio
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
-persistent init_flag mf0 mf1 number_of_particles
-persistent sample_size number_of_observed_variables number_of_structural_innovations
-
 % Set default
 if isempty(start)
     start = 1;
@@ -36,25 +33,25 @@ steadystate = ReducedForm.steadystate;
 constant = ReducedForm.constant;
 state_variables_steady_state = ReducedForm.state_variables_steady_state;
 
-% Set persistent variables.
-if isempty(init_flag)
-    mf0 = ReducedForm.mf0;
-    mf1 = ReducedForm.mf1;
-    sample_size = size(Y,2);
-    number_of_observed_variables = length(mf1);
-    number_of_structural_innovations = length(ReducedForm.Q);
-    number_of_particles = ParticleOptions.number_of_particles;
-    init_flag = 1;
+mf0 = ReducedForm.mf0;
+mf1 = ReducedForm.mf1;
+sample_size = size(Y,2);
+number_of_observed_variables = length(mf1);
+number_of_structural_innovations = length(ReducedForm.Q);
+number_of_particles = ParticleOptions.number_of_particles;
+
+if ReducedForm.use_k_order_solver
+    dr = ReducedForm.dr;
+else
+    % Set local state space model (first order approximation).
+    ghx  = ReducedForm.ghx;
+    ghu  = ReducedForm.ghu;
+    % Set local state space model (second order approximation).
+    ghxx = ReducedForm.ghxx;
+    ghuu = ReducedForm.ghuu;
+    ghxu = ReducedForm.ghxu;
 end
 
-% Set local state space model (first order approximation).
-ghx  = ReducedForm.ghx;
-ghu  = ReducedForm.ghu;
-% Set local state space model (second order approximation).
-ghxx = ReducedForm.ghxx;
-ghuu = ReducedForm.ghuu;
-ghxu = ReducedForm.ghxu;
-
 % Get covariance matrices
 Q = ReducedForm.Q;
 H = ReducedForm.H;
@@ -81,19 +78,6 @@ if pruning
     StateVectors_ = StateVectors;
 end
 
-% Uncomment for building the mean average predictions based on a sparse
-% grids of structural shocks. Otherwise, all shocks are set to 0 in the
-% prediction.
-% if ParticleOptions.proposal_approximation.cubature
-%     [nodes,nodes_weights] = spherical_radial_sigma_points(number_of_structural_innovations) ;
-%     nodes_weights = ones(size(nodes,1),1)*nodes_weights ;
-% elseif ParticleOptions.proposal_approximation.unscented
-%     [nodes,nodes_weights,nodes_weights_c] = unscented_sigma_points(number_of_structural_innovations,ParticleOptions);
-% else
-%     error('Estimation: This approximation for the proposal is not implemented or unknown!')
-% end
-% nodes = (Q_lower_triangular_cholesky*(nodes'))' ;
-
 nodes = zeros(1,number_of_structural_innovations) ;
 nodes_weights = ones(number_of_structural_innovations,1) ;
 
@@ -109,15 +93,19 @@ for t=1:sample_size
             tmp_ = tmp_ + nodes_weights(i)*tmp1_ ;
         end
     else
-        tmp = 0 ;
-        for i=1:size(nodes)
-            tmp = tmp + nodes_weights(i)*local_state_space_iteration_2(yhat,nodes(i,:)'*ones(1,number_of_particles),ghx,ghu,constant,ghxx,ghuu,ghxu,ThreadsOptions.local_state_space_iteration_2);
+        if ReducedForm.use_k_order_solver
+            tmp = 0;
+            for i=1:size(nodes)
+                tmp = tmp + nodes_weights(i)*local_state_space_iteration_k(yhat, nodes(i,:)'*ones(1,number_of_particles), dr, Model, DynareOptions);
+            end
+        else
+            tmp = 0;
+            for i=1:size(nodes)
+                tmp = tmp + nodes_weights(i)*local_state_space_iteration_2(yhat,nodes(i,:)'*ones(1,number_of_particles),ghx,ghu,constant,ghxx,ghuu,ghxu,ThreadsOptions.local_state_space_iteration_2);
+            end
         end
     end
     PredictionError = bsxfun(@minus,Y(:,t),tmp(mf1,:));
-    %tau_tilde = weights.*(exp(-.5*(const_lik+sum(PredictionError.*(H\PredictionError),1))) + 1e-99) ;
-    % Replace Gaussian density with a Student density with 3 degrees of
-    % freedom for fat tails.
     z = sum(PredictionError.*(H\PredictionError),1) ;
     tau_tilde = weights.*(tpdf(z,3*ones(size(z)))+1e-99) ;
     tau_tilde = tau_tilde/sum(tau_tilde) ;
@@ -132,7 +120,11 @@ for t=1:sample_size
         [tmp, tmp_] = local_state_space_iteration_2(yhat,epsilon,ghx,ghu,constant,ghxx,ghuu,ghxu,yhat_,steadystate,ThreadsOptions.local_state_space_iteration_2);
         StateVectors_ = tmp_(mf0,:);
     else
-        tmp = local_state_space_iteration_2(yhat,epsilon,ghx,ghu,constant,ghxx,ghuu,ghxu,ThreadsOptions.local_state_space_iteration_2);
+        if ReducedForm.use_k_order_solver
+            tmp = local_state_space_iteration_k(yhat, epsilon, dr, Model, DynareOptions);
+        else
+            tmp = local_state_space_iteration_2(yhat, epsilon, ghx, ghu, constant, ghxx, ghuu, ghxu, ThreadsOptions.local_state_space_iteration_2);
+        end
     end
     StateVectors = tmp(mf0,:);
     PredictionError = bsxfun(@minus,Y(:,t),tmp(mf1,:));
@@ -151,5 +143,4 @@ for t=1:sample_size
     end
 end
 
-%plot(lik) ;
-LIK = -sum(lik(start:end));
+LIK = -sum(lik(start:end));
\ No newline at end of file