diff --git a/matlab/particle/DsgeLikelihood.m b/matlab/particle/DsgeLikelihood.m
new file mode 100644
index 000000000..8e033d7b8
--- /dev/null
+++ b/matlab/particle/DsgeLikelihood.m
@@ -0,0 +1,265 @@
+function [fval,cost_flag,ys,trend_coeff,info] = DsgeLikelihood(xparam1,gend,data,data_index,number_of_observations,no_more_missing_observations)
+% function [fval,cost_flag,ys,trend_coeff,info] = DsgeLikelihood(xparam1,gend,data,data_index,number_of_observations,no_more_missing_observations)
+% Evaluates the posterior kernel of a dsge model.
+%
+% INPUTS
+% xparam1 [double] vector of model parameters.
+% gend [integer] scalar specifying the number of observations.
+% data [double] matrix of data
+% data_index [cell] cell of column vectors
+% number_of_observations [integer]
+% no_more_missing_observations [integer]
+% OUTPUTS
+% fval : value of the posterior kernel at xparam1.
+% cost_flag : zero if the function returns a penalty, one otherwise.
+% ys : steady state of original endogenous variables
+% trend_coeff :
+% info : vector of informations about the penalty:
+% 41: one (many) parameter(s) do(es) not satisfied the lower bound
+% 42: one (many) parameter(s) do(es) not satisfied the upper bound
+%
+% SPECIAL REQUIREMENTS
+%
+
+% Copyright (C) 2004-2009 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 .
+
+ global bayestopt_ estim_params_ options_ trend_coeff_ M_ oo_
+ fval = [];
+ ys = [];
+ trend_coeff = [];
+ cost_flag = 1;
+ nobs = size(options_.varobs,1);
+ %------------------------------------------------------------------------------
+ % 1. Get the structural parameters & define penalties
+ %------------------------------------------------------------------------------
+ if options_.mode_compute ~= 1 & any(xparam1 < bayestopt_.lb)
+ k = find(xparam1 < bayestopt_.lb);
+ fval = bayestopt_.penalty+sum((bayestopt_.lb(k)-xparam1(k)).^2);
+ cost_flag = 0;
+ info = 41;
+ return;
+ end
+ if options_.mode_compute ~= 1 & any(xparam1 > bayestopt_.ub)
+ k = find(xparam1 > bayestopt_.ub);
+ fval = bayestopt_.penalty+sum((xparam1(k)-bayestopt_.ub(k)).^2);
+ cost_flag = 0;
+ info = 42;
+ return;
+ end
+ Q = M_.Sigma_e;
+ H = M_.H;
+ for i=1:estim_params_.nvx
+ k =estim_params_.var_exo(i,1);
+ Q(k,k) = xparam1(i)*xparam1(i);
+ end
+ offset = estim_params_.nvx;
+ if estim_params_.nvn
+ for i=1:estim_params_.nvn
+ k = estim_params_.var_endo(i,1);
+ H(k,k) = xparam1(i+offset)*xparam1(i+offset);
+ end
+ offset = offset+estim_params_.nvn;
+ end
+ if estim_params_.ncx
+ for i=1:estim_params_.ncx
+ k1 =estim_params_.corrx(i,1);
+ k2 =estim_params_.corrx(i,2);
+ Q(k1,k2) = xparam1(i+offset)*sqrt(Q(k1,k1)*Q(k2,k2));
+ Q(k2,k1) = Q(k1,k2);
+ end
+ [CholQ,testQ] = chol(Q);
+ if testQ %% The variance-covariance matrix of the structural innovations is not definite positive.
+ %% We have to compute the eigenvalues of this matrix in order to build the penalty.
+ a = diag(eig(Q));
+ k = find(a < 0);
+ if k > 0
+ fval = bayestopt_.penalty+sum(-a(k));
+ cost_flag = 0;
+ info = 43;
+ return
+ end
+ end
+ offset = offset+estim_params_.ncx;
+ end
+ if estim_params_.ncn
+ for i=1:estim_params_.ncn
+ k1 = options_.lgyidx2varobs(estim_params_.corrn(i,1));
+ k2 = options_.lgyidx2varobs(estim_params_.corrn(i,2));
+ H(k1,k2) = xparam1(i+offset)*sqrt(H(k1,k1)*H(k2,k2));
+ H(k2,k1) = H(k1,k2);
+ end
+ [CholH,testH] = chol(H);
+ if testH
+ a = diag(eig(H));
+ k = find(a < 0);
+ if k > 0
+ fval = bayestopt_.penalty+sum(-a(k));
+ cost_flag = 0;
+ info = 44;
+ return
+ end
+ end
+ offset = offset+estim_params_.ncn;
+ end
+ if estim_params_.np > 0
+ M_.params(estim_params_.param_vals(:,1)) = xparam1(offset+1:end);
+ end
+ M_.Sigma_e = Q;
+ M_.H = H;
+ %------------------------------------------------------------------------------
+ % 2. call model setup & reduction program
+ %------------------------------------------------------------------------------
+ options_.order = 2;%%% 'cause we use a non linear filter here...
+ [T,R,SteadyState,info] = dynare_resolve(bayestopt_.restrict_var_list,...
+ bayestopt_.restrict_columns,...
+ bayestopt_.restrict_aux);
+ if info(1) == 1 || info(1) == 2 || info(1) == 5
+ fval = bayestopt_.penalty+1;
+ cost_flag = 0;
+ return
+ elseif info(1) == 3 || info(1) == 4 || info(1)==6 ||info(1) == 19 || info(1) == 20 || info(1) == 21
+ fval = bayestopt_.penalty+info(2);
+ cost_flag = 0;
+ return
+ end
+ bayestopt_.mf = bayestopt_.mf1;
+ if options_.noconstant
+ constant = zeros(nobs,1);
+ else
+ if options_.loglinear
+ constant = log(SteadyState(bayestopt_.mfys));
+ else
+ constant = SteadyState(bayestopt_.mfys);
+ end
+ end
+ if bayestopt_.with_trend
+ trend_coeff = zeros(nobs,1);
+ t = options_.trend_coeffs;
+ for i=1:length(t)
+ if ~isempty(t{i})
+ trend_coeff(i) = evalin('base',t{i});
+ end
+ end
+ trend = repmat(constant,1,gend)+trend_coeff*[1:gend];
+ else
+ trend = repmat(constant,1,gend);
+ end
+ start = options_.presample+1;
+ np = size(T,1);
+ mf = bayestopt_.mf;
+ no_missing_data_flag = (number_of_observations==gend*nobs);
+ %------------------------------------------------------------------------------
+ % 3. Initial condition of the Kalman filter
+ %------------------------------------------------------------------------------
+ kalman_algo = options_.kalman_algo;
+ if options_.lik_init == 1 % Kalman filter
+ if kalman_algo ~= 2
+ kalman_algo = 1;
+ end
+ Pstar = lyapunov_symm(T,R*Q*R',options_.qz_criterium,options_.lyapunov_complex_threshold);
+ Pinf = [];
+ elseif options_.lik_init == 2 % Old Diffuse Kalman filter
+ if kalman_algo ~= 2
+ kalman_algo = 1;
+ end
+ Pstar = options_.Harvey_scale_factor*eye(np);
+ Pinf = [];
+ elseif options_.lik_init == 3 % Diffuse Kalman filter
+ if kalman_algo ~= 4
+ kalman_algo = 3;
+ end
+ [QT,ST] = schur(T);
+ e1 = abs(ordeig(ST)) > 2-options_.qz_criterium;
+ [QT,ST] = ordschur(QT,ST,e1);
+ k = find(abs(ordeig(ST)) > 2-options_.qz_criterium);
+ nk = length(k);
+ nk1 = nk+1;
+ Pinf = zeros(np,np);
+ Pinf(1:nk,1:nk) = eye(nk);
+ Pstar = zeros(np,np);
+ B = QT'*R*Q*R'*QT;
+ for i=np:-1:nk+2
+ if ST(i,i-1) == 0
+ if i == np
+ c = zeros(np-nk,1);
+ else
+ c = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i,i+1:end)')+...
+ ST(i,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i);
+ end
+ q = eye(i-nk)-ST(nk1:i,nk1:i)*ST(i,i);
+ Pstar(nk1:i,i) = q\(B(nk1:i,i)+c);
+ Pstar(i,nk1:i-1) = Pstar(nk1:i-1,i)';
+ else
+ if i == np
+ c = zeros(np-nk,1);
+ c1 = zeros(np-nk,1);
+ else
+ c = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i,i+1:end)')+...
+ ST(i,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i)+...
+ ST(i,i-1)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i-1);
+ c1 = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i-1,i+1:end)')+...
+ ST(i-1,i-1)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i-1)+...
+ ST(i-1,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i);
+ end
+ q = [eye(i-nk)-ST(nk1:i,nk1:i)*ST(i,i) -ST(nk1:i,nk1:i)*ST(i,i-1);...
+ -ST(nk1:i,nk1:i)*ST(i-1,i) eye(i-nk)-ST(nk1:i,nk1:i)*ST(i-1,i-1)];
+ z = q\[B(nk1:i,i)+c;B(nk1:i,i-1)+c1];
+ Pstar(nk1:i,i) = z(1:(i-nk));
+ Pstar(nk1:i,i-1) = z(i-nk+1:end);
+ Pstar(i,nk1:i-1) = Pstar(nk1:i-1,i)';
+ Pstar(i-1,nk1:i-2) = Pstar(nk1:i-2,i-1)';
+ i = i - 1;
+ end
+ end
+ if i == nk+2
+ c = ST(nk+1,:)*(Pstar(:,nk+2:end)*ST(nk1,nk+2:end)')+ST(nk1,nk1)*ST(nk1,nk+2:end)*Pstar(nk+2:end,nk1);
+ Pstar(nk1,nk1)=(B(nk1,nk1)+c)/(1-ST(nk1,nk1)*ST(nk1,nk1));
+ end
+ Z = QT(mf,:);
+ R1 = QT'*R;
+ [QQ,RR,EE] = qr(Z*ST(:,1:nk),0);
+ k = find(abs(diag([RR; zeros(nk-size(Z,1),size(RR,2))])) < 1e-8);
+ if length(k) > 0
+ k1 = EE(:,k);
+ dd =ones(nk,1);
+ dd(k1) = zeros(length(k1),1);
+ Pinf(1:nk,1:nk) = diag(dd);
+ end
+ end
+ if kalman_algo == 2
+ end
+ kalman_tol = options_.kalman_tol;
+ riccati_tol = options_.riccati_tol;
+ mf = bayestopt_.mf1;
+ Y = data-trend;
+ %------------------------------------------------------------------------------
+ % 4. Likelihood evaluation
+ %------------------------------------------------------------------------------
+ rfm.state.dr = oo_.dr;
+ rfm.state.Q = Q;
+ rfm.measurement.H = H;
+ number_of_particles = 10;
+
+ LIK = gaussian_particle_filter(rfm,Y,start,mf,number_of_particles);
+
+
+ % ------------------------------------------------------------------------------
+ % Adds prior if necessary
+ % ------------------------------------------------------------------------------
+ lnprior = priordens(xparam1,bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7,bayestopt_.p3,bayestopt_.p4);
+ fval = (LIK-lnprior);
\ No newline at end of file
diff --git a/matlab/particle/simult_.m b/matlab/particle/simult_.m
new file mode 100644
index 000000000..9b7ad8f9e
--- /dev/null
+++ b/matlab/particle/simult_.m
@@ -0,0 +1,90 @@
+function y_=simult_(y0,dr,ex_,iorder)
+% function y_=simult_(y0,dr,ex_,iorder)
+%
+% Simulates the model, given the path of exogenous variables and the
+% decision rules.
+%
+% INPUTS
+% y0: starting values
+% dr: structure of decisions rules for stochastic simulations
+% ex_: matrix of shocks
+% iorder=0: first-order approximation
+% iorder=1: second-order approximation
+%
+% OUTPUTS
+% y_: stochastic simulations results
+%
+% SPECIAL REQUIREMENTS
+% none
+
+% Copyright (C) 2001-2007 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 .
+
+global M_ options_ it_
+ iter = size(ex_,1);
+ if ~isempty(dr.ghu)
+ nx = size(dr.ghu,2);
+ end
+ y_ = zeros(size(y0,1),iter+M_.maximum_lag);
+
+ y_(:,1:M_.maximum_lag) = y0;
+ k1 = [M_.maximum_lag:-1:1];
+ k2 = dr.kstate(find(dr.kstate(:,2) <= M_.maximum_lag+1),[1 2]);
+ k2 = k2(:,1)+(M_.maximum_lag+1-k2(:,2))*M_.endo_nbr;
+ k3 = M_.lead_lag_incidence(1:M_.maximum_lag,:)';
+ k3 = find(k3(:));
+ k4 = dr.kstate(find(dr.kstate(:,2) < M_.maximum_lag+1),[1 2]);
+ k4 = k4(:,1)+(M_.maximum_lag+1-k4(:,2))*M_.endo_nbr;
+
+ if iorder == 1
+ if ~isempty(dr.ghu)
+ for i = M_.maximum_lag+1: iter+M_.maximum_lag
+ tempx1 = y_(dr.order_var,k1);
+ tempx2 = tempx1-repmat(dr.ys(dr.order_var),1,M_.maximum_lag);
+ tempx = tempx2(k2);
+ y_(dr.order_var,i) = dr.ys(dr.order_var)+dr.ghx*tempx+dr.ghu* ...
+ ex_(i-M_.maximum_lag,:)';
+ k1 = k1+1;
+ end
+ else
+ for i = M_.maximum_lag+1: iter+M_.maximum_lag
+ tempx1 = y_(dr.order_var,k1);
+ tempx2 = tempx1-repmat(dr.ys(dr.order_var),1,M_.maximum_lag);
+ tempx = tempx2(k2);
+ y_(dr.order_var,i) = dr.ys(dr.order_var)+dr.ghx*tempx;
+ k1 = k1+1;
+ end
+ end
+ elseif iorder == 2
+ for i = M_.maximum_lag+1: iter+M_.maximum_lag
+ tempx1 = y_(dr.order_var,k1);
+ tempx2 = tempx1-repmat(dr.ys(dr.order_var),1,M_.maximum_lag);
+ tempx = tempx2(k2);
+ tempu = ex_(i-M_.maximum_lag,:)';
+ %tempuu = kron(tempu,tempu);
+ % tempxx = kron(tempx,tempx);
+ % tempxu = kron(tempx,tempu);
+ %y_(dr.order_var,i) = dr.ys(dr.order_var)+dr.ghs2/2+dr.ghx*tempx+ ...
+ % dr.ghu*tempu+0.5*(dr.ghxx*tempxx+dr.ghuu*tempuu)+dr.ghxu* ...
+ % tempxu;
+ y_(dr.order_var,i) = dr.ys(dr.order_var)+dr.ghs2/2+dr.ghx*tempx+ ...
+ dr.ghu*tempu+0.5*(A_times_B_kronecker_C(dr.ghxx,tempx)+A_times_B_kronecker_C(dr.ghuu,tempu))+A_times_B_kronecker_C(dr.ghxu,tempx,tempu);
+ k1 = k1+1;
+ end
+ end
+
+% MJ 08/30/02 corrected bug at order 2
\ No newline at end of file