updates in the particle filters routines

matlab/particle/conditional_filter_proposal.m

+function [ProposalStateVector,Weights] = conditional_filter_proposal(ReducedForm,obs,StateVectors,SampleWeights,Q_lower_triangular_cholesky,H_lower_triangular_cholesky,H,DynareOptions,normconst2) 
+%
+% Computes the proposal for each past particle using Gaussian approximations 
+% for the state errors and the Kalman filter 
+% 
+% INPUTS
+%    reduced_form_model     [structure] Matlab's structure describing the reduced form model.
+%                                       reduced_form_model.measurement.H   [double]   (pp x pp) variance matrix of measurement errors.
+%                                       reduced_form_model.state.Q         [double]   (qq x qq) variance matrix of state errors.
+%                                       reduced_form_model.state.dr        [structure] output of resol.m.
+%    Y                      [double]    pp*smpl matrix of (detrended) data, where pp is the maximum number of observed variables.
+%
+% OUTPUTS
+%    LIK        [double]    scalar, likelihood
+%    lik        [double]    vector, density of observations in each period.
+%
+% REFERENCES
+%
+% NOTES
+%   The vector "lik" is used to evaluate the jacobian of the likelihood.
+% Copyright (C) 2012-2013 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+%
+% AUTHOR(S) frederic DOT karame AT univ DASH lemans DOT fr
+%           stephane DOT adjemian AT univ DASH lemans DOT fr
+
+persistent init_flag2 mf0 mf1
+persistent number_of_state_variables number_of_observed_variables 
+persistent number_of_structural_innovations 
+
+% 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;
+
+if any(any(isnan(ghx))) || any(any(isnan(ghu))) || any(any(isnan(ghxx))) || any(any(isnan(ghuu))) || any(any(isnan(ghxu))) || ...
+    any(any(isinf(ghx))) || any(any(isinf(ghu))) || any(any(isinf(ghxx))) || any(any(isinf(ghuu))) || any(any(isinf(ghxu))) ...
+    any(any(abs(ghx)>1e4)) || any(any(abs(ghu)>1e4)) || any(any(abs(ghxx)>1e4)) || any(any(abs(ghuu)>1e4)) || any(any(abs(ghxu)>1e4))
+    ghx
+    ghu
+    ghxx
+    ghuu
+    ghxu
+end
+
+constant = ReducedForm.constant;
+state_variables_steady_state = ReducedForm.state_variables_steady_state;
+
+% Set persistent variables.
+if isempty(init_flag2)
+    mf0 = ReducedForm.mf0;
+    mf1 = ReducedForm.mf1;
+    number_of_state_variables = length(mf0);
+    number_of_observed_variables = length(mf1);
+    number_of_structural_innovations = length(ReducedForm.Q);
+    init_flag2 = 1;
+end
+
+if strcmpi(DynareOptions.particle.IS_approximation_method,'cubature') || strcmpi(DynareOptions.particle.IS_approximation_method,'monte-carlo')
+    [nodes,weights] = spherical_radial_sigma_points(number_of_structural_innovations) ; 
+    weights_c = weights ;
+end    
+if strcmpi(DynareOptions.particle.IS_approximation_method,'quadrature')
+    [nodes,weights] = nwspgr('GQN',number_of_structural_innovations,DynareOptions.particle.smolyak_accuracy) ;
+    weights_c = weights ;
+end    
+if strcmpi(DynareOptions.particle.IS_approximation_method,'unscented')
+    [nodes,weights,weights_c] = unscented_sigma_points(number_of_structural_innovations,DynareOptions) ; 
+end
+
+epsilon = Q_lower_triangular_cholesky*(nodes') ;
+yhat = repmat(StateVectors-state_variables_steady_state,1,size(epsilon,2)) ;
+
+tmp = local_state_space_iteration_2(yhat,epsilon,ghx,ghu,constant,ghxx,ghuu,ghxu,DynareOptions.threads.local_state_space_iteration_2);
+
+PredictedStateMean = tmp(mf0,:)*weights ;
+PredictedObservedMean = tmp(mf1,:)*weights;
+
+if strcmpi(DynareOptions.particle.IS_approximation_method,'cubature') || ...
+   strcmpi(DynareOptions.particle.IS_approximation_method,'monte-carlo')
+    PredictedStateMean = sum(PredictedStateMean,2) ;
+    PredictedObservedMean = sum(PredictedObservedMean,2) ;
+    dState = bsxfun(@minus,tmp(mf0,:),PredictedStateMean)'.*sqrt(weights) ;
+    dObserved = bsxfun(@minus,tmp(mf1,:),PredictedObservedMean)'.*sqrt(weights);
+    big_mat = [dObserved  dState ; [H_lower_triangular_cholesky zeros(number_of_observed_variables,number_of_state_variables)] ] ;
+    [mat1,mat] = qr2(big_mat,0) ;
+    mat = mat' ; 
+    clear('mat1');
+    PredictedObservedVarianceSquareRoot = mat(1:number_of_observed_variables,1:number_of_observed_variables) ;
+    CovarianceObservedStateSquareRoot = mat(number_of_observed_variables+(1:number_of_state_variables),1:number_of_observed_variables) ;
+    StateVectorVarianceSquareRoot = mat(number_of_observed_variables+(1:number_of_state_variables),number_of_observed_variables+(1:number_of_state_variables)) ;
+    StateVectorMean = PredictedStateMean + (CovarianceObservedStateSquareRoot/PredictedObservedVarianceSquareRoot)*(obs - PredictedObservedMean) ; 
+end 
+if strcmpi(DynareOptions.particle.IS_approximation_method,'quadrature') || ... 
+   strcmpi(DynareOptions.particle.IS_approximation_method,'unscented') 
+    dState = bsxfun(@minus,tmp(mf0,:),PredictedStateMean);
+    dObserved = bsxfun(@minus,tmp(mf1,:),PredictedObservedMean);
+    PredictedStateVariance = dState*diag(weights_c)*dState';
+    PredictedObservedVariance = dObserved*diag(weights_c)*dObserved' + H;
+    PredictedStateAndObservedCovariance = dState*diag(weights_c)*dObserved';
+    KalmanFilterGain = PredictedStateAndObservedCovariance/PredictedObservedVariance ; 
+    StateVectorMean = PredictedStateMean + KalmanFilterGain*(obs - PredictedObservedMean);
+    StateVectorVariance = PredictedStateVariance - KalmanFilterGain*PredictedObservedVariance*KalmanFilterGain';
+    StateVectorVariance = .5*(StateVectorVariance+StateVectorVariance');
+    StateVectorVarianceSquareRoot = reduced_rank_cholesky(StateVectorVariance)'; 
+end 
+    
+ProposalStateVector = StateVectorVarianceSquareRoot*randn(size(StateVectorVarianceSquareRoot,2),1)+StateVectorMean ;
+ypred = measurement_equations(ProposalStateVector,ReducedForm,DynareOptions) ;
+foo = H_lower_triangular_cholesky \ (obs - ypred) ;
+likelihood = exp(-0.5*(foo)'*foo)/normconst2 + 1e-99 ;			
+Weights = SampleWeights.*likelihood ;	
+