diff --git a/matlab/dsge_posterior_kernel.m b/matlab/dsge_posterior_kernel.m
deleted file mode 100644
index 9139198e4dbf3182f4f139f34e3b8fcfc5181246..0000000000000000000000000000000000000000
--- a/matlab/dsge_posterior_kernel.m
+++ /dev/null
@@ -1,309 +0,0 @@
-function [fval,cost_flag,ys,trend_coeff,info] = dsge_posterior_kernel(xparam1,gend,data,data_index,number_of_observations,no_more_missing_observations)
-% function [fval,cost_flag,ys,trend_coeff,info] = dsge_posterior_kernel(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) 2004-2011 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 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 ~isequal(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 ~isequal(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
-%------------------------------------------------------------------------------
-[T,R,SteadyState,info,M_,options_,oo_] = dynare_resolve(M_,options_,oo_);
-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) == 20
- fval = bayestopt_.penalty+info(2);%^2; % penalty power raised in DR1.m and resol already. GP July'08
- cost_flag = 0;
- return
-end
-bayestopt_.mf = bayestopt_.mf1;
-if ~options_.noconstant
- if options_.loglinear == 1
- constant = log(SteadyState(bayestopt_.mfys));
- else
- constant = SteadyState(bayestopt_.mfys);
- end
-else
- constant = zeros(nobs,1);
-end
-if bayestopt_.with_trend == 1
- 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 = 10*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)) < 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
- no_correlation_flag = 1;
- if length(H)==1
- H = zeros(nobs,1);
- else
- if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
- H = diag(H);
- else
- no_correlation_flag = 0;
- end
- end
-end
-kalman_tol = options_.kalman_tol;
-riccati_tol = options_.riccati_tol;
-mf = bayestopt_.mf1;
-Y = data-trend;
-%------------------------------------------------------------------------------
-% 4. Likelihood evaluation
-%------------------------------------------------------------------------------
-if (kalman_algo==1)% Multivariate Kalman Filter
- if no_missing_data_flag
- LIK = kalman_filter(T,R,Q,H,Pstar,Y,start,mf,kalman_tol,riccati_tol);
- else
- LIK = ...
- missing_observations_kalman_filter(T,R,Q,H,Pstar,Y,start,mf,kalman_tol,riccati_tol, ...
- data_index,number_of_observations,no_more_missing_observations);
- end
- if isinf(LIK)
- kalman_algo = 2;
- end
-end
-if (kalman_algo==2)% Univariate Kalman Filter
- if no_correlation_flag
- LIK = univariate_kalman_filter(T,R,Q,H,Pstar,Y,start,mf,kalman_tol,riccati_tol,data_index,number_of_observations,no_more_missing_observations);
- else
- LIK = univariate_kalman_filter_corr(T,R,Q,H,Pstar,Y,start,mf,kalman_tol,riccati_tol,data_index,number_of_observations,no_more_missing_observations);
- end
-end
-if (kalman_algo==3)% Multivariate Diffuse Kalman Filter
- if no_missing_data_flag
- LIK = diffuse_kalman_filter(ST,R1,Q,H,Pinf,Pstar,Y,start,Z,kalman_tol,riccati_tol);
- else
- LIK = missing_observations_diffuse_kalman_filter(ST,R1,Q,H,Pinf,Pstar,Y,start,Z,kalman_tol,riccati_tol,...
- data_index,number_of_observations,no_more_missing_observations);
- end
- if isinf(LIK)
- kalman_algo = 4;
- end
-end
-if (kalman_algo==4)% Univariate Diffuse Kalman Filter
- if no_correlation_flag
- LIK = univariate_diffuse_kalman_filter(ST,R1,Q,H,Pinf,Pstar,Y,start,Z,kalman_tol,riccati_tol,...
- data_index,number_of_observations,no_more_missing_observations);
- else
- LIK = univariate_diffuse_kalman_filter_corr(ST,R1,Q,H,Pinf,Pstar,Y,start,Z,kalman_tol,riccati_tol,...
- data_index,number_of_observations,no_more_missing_observations);
- end
-end
-if imag(LIK) ~= 0
- likelihood = bayestopt_.penalty;
-else
- likelihood = LIK;
-end
-% ------------------------------------------------------------------------------
-% Adds prior if necessary
-% ------------------------------------------------------------------------------
-lnprior = priordens(xparam1,bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7,bayestopt_.p3,bayestopt_.p4);
-fval = (likelihood-lnprior);
-options_.kalman_algo = kalman_algo;