diff --git a/matlab/particle/monte_carlo_SIS_particle_filter.m b/matlab/particle/monte_carlo_SIS_particle_filter.m
new file mode 100644
index 0000000000000000000000000000000000000000..d95bf08946242bb600aa465cfd6b1e88a792b799
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+++ b/matlab/particle/monte_carlo_SIS_particle_filter.m
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+function [LIK,lik] = monte_carlo_SIS_particle_filter(reduced_form_model,Y,start,number_of_particles)
+% hparam,y,nbchocetat,nbchocmesure,smol_prec,nb_part,g,m,choix
+% Evaluates the likelihood of a nonlinear model with a particle filter without systematic resampling.
+%
+% 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.
+% start [integer] scalar, likelihood evaluation starts at 'start'.
+% mf [integer] pp*1 vector of indices.
+% number_of_particles [integer] scalar.
+%
+% 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) 2009 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/>.
+
+global M_ bayestopt_
+persistent init_flag
+persistent restrict_variables_idx observed_variables_idx state_variables_idx mf0 mf1
+persistent sample_size number_of_state_variables number_of_observed_variables number_of_structural_innovations
+
+% Set defaults.
+if (nargin<4) || (nargin==4 && isempty(number_of_particles))
+ number_of_particles = 1000 ;
+end
+if nargin==2 || isempty(start)
+ start = 1;
+end
+
+dr = reduced_form_model.state.dr;% Decision rules and transition equations.
+Q = reduced_form_model.state.Q;% Covariance matrix of the structural innovations.
+H = reduced_form_model.measurement.H;% Covariance matrix of the measurement errors.
+
+% Set persistent variables.
+if isempty(init_flag)
+ mf0 = bayestopt_.mf0;
+ mf1 = bayestopt_.mf1;
+ restrict_variables_idx = bayestopt_.restrict_var_list;
+ observed_variables_idx = restrict_variables_idx(mf1);
+ state_variables_idx = restrict_variables_idx(mf0);
+ sample_size = size(Y,2);
+ number_of_state_variables = length(mf0);
+ number_of_observed_variables = length(mf1);
+ number_of_structural_innovations = length(Q);
+ init_flag = 1;
+end
+
+% Set local state space model (second order approximation).
+ghx = dr.ghx(restrict_variables_idx,:);
+ghu = dr.ghu(restrict_variables_idx,:);
+half_ghxx = .5*dr.ghxx(restrict_variables_idx,:);
+half_ghuu = .5*dr.ghuu(restrict_variables_idx,:);
+ghxu = dr.ghxu(restrict_variables_idx,:);
+steadystate = dr.ys(dr.order_var(restrict_variables_idx));
+constant = steadystate + .5*dr.ghs2(restrict_variables_idx);
+state_variables_steady_state = dr.ys(dr.order_var(state_variables_idx));
+
+StateVectorMean = state_variables_steady_state;
+StateVectorVariance = lyapunov_symm(ghx(mf0,:),ghu(mf0,:)*Q*ghu(mf0,:)',1e-12,1e-12);
+StateVectorVarianceSquareRoot = reduced_rank_cholesky(StateVectorVariance)';
+state_variance_rank = size(StateVectorVarianceSquareRoot,2);
+
+%state_idx = 1:state_variance_rank;
+%innovation_idx = 1+state_variance_rank:state_variance_rank+number_of_structural_innovations;
+
+Q_lower_triangular_cholesky = chol(Q)';
+
+% Set seed for randn().
+seed = [ 362436069 ; 521288629 ];
+randn('state',seed);
+
+const_lik = log(2*pi)*number_of_observed_variables;
+lik = NaN(sample_size,1);
+nb_obs_resamp = 0 ;
+for t=1:sample_size
+ PredictedState = zeros(number_of_particles,number_of_state_variables);
+ PredictionError = zeros(number_of_particles,number_of_observed_variables);
+ %PredictedStateMean = zeros(number_of_state_variables,1);
+ PredictedObservedMean = zeros(number_of_observed_variables,1);
+ %PredictedStateVariance = zeros(number_of_state_variables,number_of_state_variables);
+ PredictedObservedVariance = zeros(number_of_observed_variables,number_of_observed_variables);
+ %PredictedStateAndObservedCovariance = zeros(number_of_state_variables,number_of_observed_variables);
+ for i=1:number_of_particles
+ if t==1
+ StateVector = StateVectorMean + StateVectorVarianceSquareRoot*randn(state_variance_rank,1);
+ else
+ StateVector = StateUpdated(i,:)' ;
+ end
+ yhat = StateVector-state_variables_steady_state;
+ epsilon = Q_lower_triangular_cholesky*randn(number_of_structural_innovations,1);
+ tmp = local_state_space_iteration_2(yhat,epsilon,ghx,ghu,constant,half_ghxx,half_ghuu,ghxu);
+ % stockage des particules et des erreurs de pr�visions
+ PredictedState(i,:) = tmp(mf0)' ;
+ PredictionError(i,:) = (Y(:,t) - tmp(mf1))' ;
+ % calcul des moyennes et des matrices de variances covariances
+ %PredictedStateMean_old = PredictedStateMean;
+ PredictedObservedMean_old = PredictedObservedMean;
+ %PredictedStateMean = PredictedStateMean + (tmp(mf0)-PredictedStateMean)/i;
+ PredictedObservedMean = PredictedObservedMean + (tmp(mf1)-PredictedObservedMean)/i;
+ %psm = PredictedStateMean*PredictedStateMean';
+ pom = PredictedObservedMean*PredictedObservedMean';
+ %pcm = PredictedStateMean*PredictedObservedMean';
+ %PredictedStateVariance = PredictedStateVariance ...
+ % + ( (tmp(mf0)*tmp(mf0)'-psm-PredictedStateVariance)+(i-1)*(PredictedStateMean_old*PredictedStateMean_old'-psm) )/i;
+ PredictedObservedVariance = PredictedObservedVariance ...
+ + ( (tmp(mf1)*tmp(mf1)'-pom-PredictedObservedVariance)+(i-1)*(PredictedObservedMean_old*PredictedObservedMean_old'-pom) )/i;
+ %PredictedStateAndObservedCovariance = PredictedStateAndObservedCovariance ...
+ % + ( (tmp(mf0)*tmp(mf1)'-pcm-PredictedStateAndObservedCovariance)+(i-1)*(PredictedStateMean_old*PredictedObservedMean_old'-pcm) )/i;
+ end
+ PredictedObservedVariance = PredictedObservedVariance + H;
+ iPredictedObservedVariance = inv(PredictedObservedVariance);
+ lnw = - 0.5*(const_lik + log(det(PredictedObservedVariance)) + sum(PredictionError'*iPredictedObservedVariance.*PredictionError',2)) ;
+ %bidouille num�rique Schorfheide
+ dfac = max(lnw);
+ wtilde = w.*exp(lnw - dfac) ;
+ % vraisemblance de l'observation
+ lik(t) = log(mean(wtilde)) + dfac ;
+ clear (PredictionError) ;
+ clear (lnw) ;
+ % calcul des poids
+ w = wtilde/sum(wtilde) ;
+ clear (wtilde) ;
+ %update
+ Neff = 1/sum(w^2) ;
+ if Neff>number_of_particules %no resampling
+ StateUpdated = PredictedState ;
+ clear (PredictedState) ;
+ w = number_of_particles*w ;
+ else %resampling
+ nb_obs_resamp = nb_obs_resamp+1 ;
+
+ %kill the smallest particles before resampling :! facultatif ?
+ to_kill = [w PredictedState] ;
+ to_kill = delif(to_kill,w<(1/number_of_particules)*1E-12);%%
+ [n,m] = size(to_kill) ;
+ w = to_kill(:,1) ;
+ PredictedState = to_kill(:,2:m) ;
+ clear (to_kill) ;
+ if number_of_particles neq n
+ 'Elimination de '; number_of_particles - n ; ' particules � l''observation ';t ;
+ end
+ %fin de kill
+ %remise � l'�chelle des poids sur les particules restantes
+ w = cumsum( w/sum(w) );
+ %R��chantillonage syst�matique
+ rnduvec = ( (1:number_of_particles)-1+rand )/number_of_particles ;
+ selind = (n - sum( w > rnduvec' ) + 1)'; % probl�me de m�moire car w .> rnduvec' tr�s grande !
+ clear (rnduvec) ;
+ StateUpdated = PredictedState(selind,:) ;
+ clear (selind) ;
+
+ % initialize
+ selind = zeros(1,number_of_particles);
+ % construct CDF
+ c = cumsum(w);
+ % draw a starting point
+ rnduvec = ( (1:number_of_particles)-1+rand)/number_of_particles ;
+ % start at the bottom of the CDF
+ j=1;
+ for i=1:number_of_particles
+ % move along the CDF
+ while (rnduvec(i)>c(j))
+ j=j+1;
+ end
+ % assign index
+ selind(i) = j;
+ end
+ StateUpdated = PredictedState(selind,:);
+ w = ones(number_of_particules,1) ;
+ end
+end
+LIK = -sum(lik(start:end));
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