diff --git a/matlab/kordpert/dr1_k_order.m b/matlab/kordpert/dr1_k_order.m
new file mode 100644
index 0000000000000000000000000000000000000000..a4356321e532107cdd5df2605ae53c60804fd642
--- /dev/null
+++ b/matlab/kordpert/dr1_k_order.m
@@ -0,0 +1,507 @@
+function [dr,info,M_,options_,oo_] = dr1(dr,task,M_,options_,oo_)
+% Computes the reduced form solution of a rational expectation model (first or second order
+% approximation of the stochastic model around the deterministic steady state).
+%
+% INPUTS
+% dr [matlab structure] Decision rules for stochastic simulations.
+% task [integer] if task = 0 then dr1 computes decision rules.
+% if task = 1 then dr1 computes eigenvalues.
+% M_ [matlab structure] Definition of the model.
+% options_ [matlab structure] Global options.
+% oo_ [matlab structure] Results
+%
+% OUTPUTS
+% dr [matlab structure] Decision rules for stochastic simulations.
+% info [integer] info=1: the model doesn't define current variables uniquely
+% info=2: problem in mjdgges.dll info(2) contains error code.
+% info=3: BK order condition not satisfied info(2) contains "distance"
+% absence of stable trajectory.
+% info=4: BK order condition not satisfied info(2) contains "distance"
+% indeterminacy.
+% info=5: BK rank condition not satisfied.
+% M_ [matlab structure]
+% options_ [matlab structure]
+% oo_ [matlab structure]
+%
+% ALGORITHM
+% ...
+%
+% SPECIAL REQUIREMENTS
+% none.
+%
+
+% Copyright (C) 1996-2008 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/>.
+
+ info = 0;
+
+ options_ = set_default_option(options_,'loglinear',0);
+ options_ = set_default_option(options_,'noprint',0);
+ options_ = set_default_option(options_,'olr',0);
+ options_ = set_default_option(options_,'olr_beta',1);
+ options_ = set_default_option(options_,'qz_criterium',1.000001);
+
+ xlen = M_.maximum_endo_lead + M_.maximum_endo_lag + 1;
+ klen = M_.maximum_endo_lag + M_.maximum_endo_lead + 1;
+ iyv = M_.lead_lag_incidence';
+ iyv = iyv(:);
+ iyr0 = find(iyv) ;
+ it_ = M_.maximum_lag + 1 ;
+
+ if M_.exo_nbr == 0
+ oo_.exo_steady_state = [] ;
+ end
+
+ % expanding system for Optimal Linear Regulator
+ if options_.ramsey_policy
+ if isfield(M_,'orig_model')
+ orig_model = M_.orig_model;
+ M_.endo_nbr = orig_model.endo_nbr;
+ M_.endo_names = orig_model.endo_names;
+ M_.lead_lag_incidence = orig_model.lead_lag_incidence;
+ M_.maximum_lead = orig_model.maximum_lead;
+ M_.maximum_endo_lead = orig_model.maximum_endo_lead;
+ M_.maximum_lag = orig_model.maximum_lag;
+ M_.maximum_endo_lag = orig_model.maximum_endo_lag;
+ end
+ old_solve_algo = options_.solve_algo;
+ % options_.solve_algo = 1;
+ oo_.steady_state = dynare_solve('ramsey_static',oo_.steady_state,0,M_,options_,oo_,it_);
+ options_.solve_algo = old_solve_algo;
+ [junk,junk,multbar] = ramsey_static(oo_.steady_state,M_,options_,oo_,it_);
+ [jacobia_,M_] = ramsey_dynamic(oo_.steady_state,multbar,M_,options_,oo_,it_);
+ klen = M_.maximum_lag + M_.maximum_lead + 1;
+ dr.ys = [oo_.steady_state;zeros(M_.exo_nbr,1);multbar];
+
+ else
+ klen = M_.maximum_lag + M_.maximum_lead + 1;
+ iyv = M_.lead_lag_incidence';
+ iyv = iyv(:);
+ iyr0 = find(iyv) ;
+ it_ = M_.maximum_lag + 1 ;
+
+ if M_.exo_nbr == 0
+ oo_.exo_steady_state = [] ;
+ end
+
+ it_ = M_.maximum_lag + 1;
+ z = repmat(dr.ys,1,klen);
+ z = z(iyr0) ;
+
+ end
+
+ if options_.debug
+ save([M_.fname '_debug.mat'],'jacobia_')
+ end
+
+ dr=set_state_space(dr,M_);
+ kstate = dr.kstate;
+ kad = dr.kad;
+ kae = dr.kae;
+ nstatic = dr.nstatic;
+ nfwrd = dr.nfwrd;
+ npred = dr.npred;
+ nboth = dr.nboth;
+ order_var = dr.order_var;
+ nd = size(kstate,1);
+ nz = nnz(M_.lead_lag_incidence);
+
+ sdyn = M_.endo_nbr - nstatic;
+
+ k0 = M_.lead_lag_incidence(M_.maximum_endo_lag+1,order_var);
+ k1 = M_.lead_lag_incidence(find([1:klen] ~= M_.maximum_endo_lag+1),:);
+
+
+
+ if options_.order == 1
+ M_.var_order_endo_names=M_.endo_names(dr.order_var,:);
+% z = repmat(dr.ys,1,klen);
+% z = z(iyr0) ;
+% oo_.dyn_ys=z; % extended ys
+ try
+ [ysteady, gx, gu]=k_order_perturbation(dr,task,M_,options_, oo_ );
+ load(M_.fname);
+ ghxu = eval([M_.fname '_g_1']);
+ sss= size(ghxu,2);
+ dr.ghx= ghxu(:,1:sss-M_.exo_nbr);
+ dr.ghu= ghxu(:,sss-M_.exo_nbr+1:end);
+ dr.ys=eval([M_.fname '_ss']);
+ catch
+ disp('*************************************************************************************');
+% disp('Problem with using k_order perturbation solver - Using Dynare solver instead');
+% warning('Problem with using k_order perturbation solver - Using Dynare solver instead');
+ error('Problem with using k_order perturbation solver ');
+ disp('*****************************************************************************');
+ options_.use_k_order=0; % and then try mjdgges instead
+ info(1) = 4;
+ info(2) = 1000;
+ return
+ end
+
+ elseif options_.order > 1
+ error(' can not use order > 1 with K-Order yet!')
+ % or ???
+ disp('********************************************************************');
+ disp(' can not use order > 1 with K-Order yet - Using Dynare solver instead');
+ disp('********************************************************************');
+ options_.use_k_order= 0; % and then try mjdgges instead
+ info(1) = 4;
+ info(2) = 1000;
+ return
+ end
+
+
+ if M_.maximum_endo_lead == 0; % backward models
+ % If required, try Gary Anderson and G Moore AIM solver if not
+ % check only and if 1st order (added by GP July'08)
+
+ dr.eigval = eig(transition_matrix(dr));
+ dr.rank = 0;
+ if any(abs(dr.eigval) > options_.qz_criterium)
+ temp = sort(abs(dr.eigval));
+ nba = nnz(abs(dr.eigval) > options_.qz_criterium);
+ temp = temp(nd-nba+1:nd)-1-options_.qz_criterium;
+ info(1) = 3;
+ info(2) = temp'*temp;
+ end
+ return;
+ end
+
+ %forward--looking models
+ [A,B] =transition_matrix(dr);
+ dr.eigval = eig(A);
+% if any(abs(dr.eigval) > options_.qz_criterium)
+% temp = sort(abs(dr.eigval));
+% nba = nnz(abs(dr.eigval) > options_.qz_criterium);
+% temp = temp(nd-nba+1:nd)-1-options_.qz_criterium;
+% info(1) = 3;
+% info(2) = temp'*temp;
+% return
+% end
+ sdim = sum( abs(dr.eigval) < options_.qz_criterium );
+ nba = nd-sdim;
+
+ nyf = sum(kstate(:,2) > M_.maximum_endo_lag+1);
+ if nba ~= nyf
+ temp = sort(abs(dr.eigval));
+ if nba > nyf
+ temp = temp(nd-nba+1:nd-nyf)-1-options_.qz_criterium;
+ info(1) = 3;
+ elseif nba < nyf;
+ temp = temp(nd-nyf+1:nd-nba)-1-options_.qz_criterium;
+ info(1) = 4;
+ end
+ info(2) = temp'*temp;
+ return
+ end
+
+
+ if options_.loglinear == 1
+ k = find(dr.kstate(:,2) <= M_.maximum_endo_lag+1);
+ klag = dr.kstate(k,[1 2]);
+ k1 = dr.order_var;
+
+ dr.ghx = repmat(1./dr.ys(k1),1,size(dr.ghx,2)).*dr.ghx.* ...
+ repmat(dr.ys(k1(klag(:,1)))',size(dr.ghx,1),1);
+ dr.ghu = repmat(1./dr.ys(k1),1,size(dr.ghu,2)).*dr.ghu;
+ end
+
+ dr.ghx = real(dr.ghx);
+ dr.ghu = real(dr.ghu);
+
+return
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+ %exogenous deterministic variables
+ if M_.exo_det_nbr > 0
+ f1 = sparse(jacobia_(:,nonzeros(M_.lead_lag_incidence(M_.maximum_endo_lag+2:end,order_var))));
+ f0 = sparse(jacobia_(:,nonzeros(M_.lead_lag_incidence(M_.maximum_endo_lag+1,order_var))));
+ fudet = sparse(jacobia_(:,nz+M_.exo_nbr+1:end));
+ M1 = inv(f0+[zeros(M_.endo_nbr,nstatic) f1*gx zeros(M_.endo_nbr,nyf-nboth)]);
+ M2 = M1*f1;
+ dr.ghud = cell(M_.exo_det_length,1);
+ dr.ghud{1} = -M1*fudet;
+ for i = 2:M_.exo_det_length
+ dr.ghud{i} = -M2*dr.ghud{i-1}(end-nyf+1:end,:);
+ end
+ end
+
+ if options_.order == 1
+ return
+ end
+
+ % Second order
+ %tempex = oo_.exo_simul ;
+ [junk,jacobia_,hessian] = feval([M_.fname '_dynamic'],z,...
+ [oo_.exo_simul ...
+ oo_.exo_det_simul], M_.params, it_);
+
+ %hessian = real(hessext('ff1_',[z; oo_.exo_steady_state]))' ;
+ kk = flipud(cumsum(flipud(M_.lead_lag_incidence(M_.maximum_endo_lag+1:end,order_var)),1));
+ if M_.maximum_endo_lag > 0
+ kk = [cumsum(M_.lead_lag_incidence(1:M_.maximum_endo_lag,order_var),1); kk];
+ end
+ kk = kk';
+ kk = find(kk(:));
+ nk = size(kk,1) + M_.exo_nbr + M_.exo_det_nbr;
+ k1 = M_.lead_lag_incidence(:,order_var);
+ k1 = k1';
+ k1 = k1(:);
+ k1 = k1(kk);
+ k2 = find(k1);
+ kk1(k1(k2)) = k2;
+ kk1 = [kk1 length(k1)+1:length(k1)+M_.exo_nbr+M_.exo_det_nbr];
+ kk = reshape([1:nk^2],nk,nk);
+ kk1 = kk(kk1,kk1);
+ %[junk,junk,hessian] = feval([M_.fname '_dynamic'],z, oo_.exo_steady_state);
+ hessian(:,kk1(:)) = hessian;
+
+ %oo_.exo_simul = tempex ;
+ %clear tempex
+
+ n1 = 0;
+ n2 = np;
+ zx = zeros(np,np);
+ zu=zeros(np,M_.exo_nbr);
+ for i=2:M_.maximum_endo_lag+1
+ k1 = sum(kstate(:,2) == i);
+ zx(n1+1:n1+k1,n2-k1+1:n2)=eye(k1);
+ n1 = n1+k1;
+ n2 = n2-k1;
+ end
+ kk = flipud(cumsum(flipud(M_.lead_lag_incidence(M_.maximum_endo_lag+1:end,order_var)),1));
+ k0 = [1:M_.endo_nbr];
+ gx1 = dr.ghx;
+ hu = dr.ghu(nstatic+[1:npred],:);
+ zx = [zx; gx1];
+ zu = [zu; dr.ghu];
+ for i=1:M_.maximum_endo_lead
+ k1 = find(kk(i+1,k0) > 0);
+ zu = [zu; gx1(k1,1:npred)*hu];
+ gx1 = gx1(k1,:)*hx;
+ zx = [zx; gx1];
+ kk = kk(:,k0);
+ k0 = k1;
+ end
+ zx=[zx; zeros(M_.exo_nbr,np);zeros(M_.exo_det_nbr,np)];
+ zu=[zu; eye(M_.exo_nbr);zeros(M_.exo_det_nbr,M_.exo_nbr)];
+ [nrzx,nczx] = size(zx);
+
+ rhs = -sparse_hessian_times_B_kronecker_C(hessian,zx);
+
+ %lhs
+ n = M_.endo_nbr+sum(kstate(:,2) > M_.maximum_endo_lag+1 & kstate(:,2) < M_.maximum_endo_lag+M_.maximum_endo_lead+1);
+ A = zeros(n,n);
+ B = zeros(n,n);
+ A(1:M_.endo_nbr,1:M_.endo_nbr) = jacobia_(:,M_.lead_lag_incidence(M_.maximum_endo_lag+1,order_var));
+ % variables with the highest lead
+ k1 = find(kstate(:,2) == M_.maximum_endo_lag+M_.maximum_endo_lead+1);
+ if M_.maximum_endo_lead > 1
+ k2 = find(kstate(:,2) == M_.maximum_endo_lag+M_.maximum_endo_lead);
+ [junk,junk,k3] = intersect(kstate(k1,1),kstate(k2,1));
+ else
+ k2 = [1:M_.endo_nbr];
+ k3 = kstate(k1,1);
+ end
+ % Jacobian with respect to the variables with the highest lead
+ B(1:M_.endo_nbr,end-length(k2)+k3) = jacobia_(:,kstate(k1,3)+M_.endo_nbr);
+ offset = M_.endo_nbr;
+ k0 = [1:M_.endo_nbr];
+ gx1 = dr.ghx;
+ for i=1:M_.maximum_endo_lead-1
+ k1 = find(kstate(:,2) == M_.maximum_endo_lag+i+1);
+ [k2,junk,k3] = find(kstate(k1,3));
+ A(1:M_.endo_nbr,offset+k2) = jacobia_(:,k3+M_.endo_nbr);
+ n1 = length(k1);
+ A(offset+[1:n1],nstatic+[1:npred]) = -gx1(kstate(k1,1),1:npred);
+ gx1 = gx1*hx;
+ A(offset+[1:n1],offset+[1:n1]) = eye(n1);
+ n0 = length(k0);
+ E = eye(n0);
+ if i == 1
+ [junk,junk,k4]=intersect(kstate(k1,1),[1:M_.endo_nbr]);
+ else
+ [junk,junk,k4]=intersect(kstate(k1,1),kstate(k0,1));
+ end
+ i1 = offset-n0+n1;
+ B(offset+[1:n1],offset-n0+[1:n0]) = -E(k4,:);
+ k0 = k1;
+ offset = offset + n1;
+ end
+ [junk,k1,k2] = find(M_.lead_lag_incidence(M_.maximum_endo_lag+M_.maximum_endo_lead+1,order_var));
+ A(1:M_.endo_nbr,nstatic+1:nstatic+npred)=...
+ A(1:M_.endo_nbr,nstatic+[1:npred])+jacobia_(:,k2)*gx1(k1,1:npred);
+ C = hx;
+ D = [rhs; zeros(n-M_.endo_nbr,size(rhs,2))];
+
+
+ dr.ghxx = gensylv(2,A,B,C,D);
+
+ %ghxu
+ %rhs
+ hu = dr.ghu(nstatic+1:nstatic+npred,:);
+ %kk = reshape([1:np*np],np,np);
+ %kk = kk(1:npred,1:npred);
+ %rhs = -hessian*kron(zx,zu)-f1*dr.ghxx(end-nyf+1:end,kk(:))*kron(hx(1:npred,:),hu(1:npred,:));
+
+ rhs = sparse_hessian_times_B_kronecker_C(hessian,zx,zu);
+
+ nyf1 = sum(kstate(:,2) == M_.maximum_endo_lag+2);
+ hu1 = [hu;zeros(np-npred,M_.exo_nbr)];
+ %B1 = [B(1:M_.endo_nbr,:);zeros(size(A,1)-M_.endo_nbr,size(B,2))];
+ [nrhx,nchx] = size(hx);
+ [nrhu1,nchu1] = size(hu1);
+
+ B1 = B*A_times_B_kronecker_C(dr.ghxx,hx,hu1);
+ rhs = -[rhs; zeros(n-M_.endo_nbr,size(rhs,2))]-B1;
+
+
+ %lhs
+ dr.ghxu = A\rhs;
+
+ %ghuu
+ %rhs
+ kk = reshape([1:np*np],np,np);
+ kk = kk(1:npred,1:npred);
+
+ rhs = sparse_hessian_times_B_kronecker_C(hessian,zu);
+
+
+ B1 = A_times_B_kronecker_C(B*dr.ghxx,hu1);
+ rhs = -[rhs; zeros(n-M_.endo_nbr,size(rhs,2))]-B1;
+
+ %lhs
+ dr.ghuu = A\rhs;
+
+ dr.ghxx = dr.ghxx(1:M_.endo_nbr,:);
+ dr.ghxu = dr.ghxu(1:M_.endo_nbr,:);
+ dr.ghuu = dr.ghuu(1:M_.endo_nbr,:);
+
+
+ % dr.ghs2
+ % derivatives of F with respect to forward variables
+ % reordering predetermined variables in diminishing lag order
+ O1 = zeros(M_.endo_nbr,nstatic);
+ O2 = zeros(M_.endo_nbr,M_.endo_nbr-nstatic-npred);
+ LHS = jacobia_(:,M_.lead_lag_incidence(M_.maximum_endo_lag+1,order_var));
+ RHS = zeros(M_.endo_nbr,M_.exo_nbr^2);
+ kk = find(kstate(:,2) == M_.maximum_endo_lag+2);
+ gu = dr.ghu;
+ guu = dr.ghuu;
+ Gu = [dr.ghu(nstatic+[1:npred],:); zeros(np-npred,M_.exo_nbr)];
+ Guu = [dr.ghuu(nstatic+[1:npred],:); zeros(np-npred,M_.exo_nbr*M_.exo_nbr)];
+ E = eye(M_.endo_nbr);
+ M_.lead_lag_incidenceordered = flipud(cumsum(flipud(M_.lead_lag_incidence(M_.maximum_endo_lag+1:end,order_var)),1));
+ if M_.maximum_endo_lag > 0
+ M_.lead_lag_incidenceordered = [cumsum(M_.lead_lag_incidence(1:M_.maximum_endo_lag,order_var),1); M_.lead_lag_incidenceordered];
+ end
+ M_.lead_lag_incidenceordered = M_.lead_lag_incidenceordered';
+ M_.lead_lag_incidenceordered = M_.lead_lag_incidenceordered(:);
+ k1 = find(M_.lead_lag_incidenceordered);
+ M_.lead_lag_incidenceordered(k1) = [1:length(k1)]';
+ M_.lead_lag_incidenceordered =reshape(M_.lead_lag_incidenceordered,M_.endo_nbr,M_.maximum_endo_lag+M_.maximum_endo_lead+1)';
+ kh = reshape([1:nk^2],nk,nk);
+ kp = sum(kstate(:,2) <= M_.maximum_endo_lag+1);
+ E1 = [eye(npred); zeros(kp-npred,npred)];
+ H = E1;
+ hxx = dr.ghxx(nstatic+[1:npred],:);
+ for i=1:M_.maximum_endo_lead
+ for j=i:M_.maximum_endo_lead
+ [junk,k2a,k2] = find(M_.lead_lag_incidence(M_.maximum_endo_lag+j+1,order_var));
+ [junk,k3a,k3] = ...
+ find(M_.lead_lag_incidenceordered(M_.maximum_endo_lag+j+1,:));
+ nk3a = length(k3a);
+ B1 = sparse_hessian_times_B_kronecker_C(hessian(:,kh(k3,k3)),gu(k3a,:));
+ RHS = RHS + jacobia_(:,k2)*guu(k2a,:)+B1;
+ end
+ % LHS
+ [junk,k2a,k2] = find(M_.lead_lag_incidence(M_.maximum_endo_lag+i+1,order_var));
+ LHS = LHS + jacobia_(:,k2)*(E(k2a,:)+[O1(k2a,:) dr.ghx(k2a,:)*H O2(k2a,:)]);
+
+ if i == M_.maximum_endo_lead
+ break
+ end
+
+ kk = find(kstate(:,2) == M_.maximum_endo_lag+i+1);
+ gu = dr.ghx*Gu;
+ [nrGu,ncGu] = size(Gu);
+ G1 = A_times_B_kronecker_C(dr.ghxx,Gu);
+ G2 = A_times_B_kronecker_C(hxx,Gu);
+ guu = dr.ghx*Guu+G1;
+ Gu = hx*Gu;
+ Guu = hx*Guu;
+ Guu(end-npred+1:end,:) = Guu(end-npred+1:end,:) + G2;
+ H = E1 + hx*H;
+ end
+ RHS = RHS*M_.Sigma_e(:);
+ dr.fuu = RHS;
+ %RHS = -RHS-dr.fbias;
+ RHS = -RHS;
+ dr.ghs2 = LHS\RHS;
+
+ % deterministic exogenous variables
+ if M_.exo_det_nbr > 0
+ hud = dr.ghud{1}(nstatic+1:nstatic+npred,:);
+ zud=[zeros(np,M_.exo_det_nbr);dr.ghud{1};gx(:,1:npred)*hud;zeros(M_.exo_nbr,M_.exo_det_nbr);eye(M_.exo_det_nbr)];
+ R1 = hessian*kron(zx,zud);
+ dr.ghxud = cell(M_.exo_det_length,1);
+ kf = [M_.endo_nbr-nyf+1:M_.endo_nbr];
+ kp = nstatic+[1:npred];
+ dr.ghxud{1} = -M1*(R1+f1*dr.ghxx(kf,:)*kron(dr.ghx(kp,:),dr.ghud{1}(kp,:)));
+ Eud = eye(M_.exo_det_nbr);
+ for i = 2:M_.exo_det_length
+ hudi = dr.ghud{i}(kp,:);
+ zudi=[zeros(np,M_.exo_det_nbr);dr.ghud{i};gx(:,1:npred)*hudi;zeros(M_.exo_nbr+M_.exo_det_nbr,M_.exo_det_nbr)];
+ R2 = hessian*kron(zx,zudi);
+ dr.ghxud{i} = -M2*(dr.ghxud{i-1}(kf,:)*kron(hx,Eud)+dr.ghxx(kf,:)*kron(dr.ghx(kp,:),dr.ghud{i}(kp,:)))-M1*R2;
+ end
+ R1 = hessian*kron(zu,zud);
+ dr.ghudud = cell(M_.exo_det_length,1);
+ kf = [M_.endo_nbr-nyf+1:M_.endo_nbr];
+
+ dr.ghuud{1} = -M1*(R1+f1*dr.ghxx(kf,:)*kron(dr.ghu(kp,:),dr.ghud{1}(kp,:)));
+ Eud = eye(M_.exo_det_nbr);
+ for i = 2:M_.exo_det_length
+ hudi = dr.ghud{i}(kp,:);
+ zudi=[zeros(np,M_.exo_det_nbr);dr.ghud{i};gx(:,1:npred)*hudi;zeros(M_.exo_nbr+M_.exo_det_nbr,M_.exo_det_nbr)];
+ R2 = hessian*kron(zu,zudi);
+ dr.ghuud{i} = -M2*dr.ghxud{i-1}(kf,:)*kron(hu,Eud)-M1*R2;
+ end
+ R1 = hessian*kron(zud,zud);
+ dr.ghudud = cell(M_.exo_det_length,M_.exo_det_length);
+ dr.ghudud{1,1} = -M1*R1-M2*dr.ghxx(kf,:)*kron(hud,hud);
+ for i = 2:M_.exo_det_length
+ hudi = dr.ghud{i}(nstatic+1:nstatic+npred,:);
+ zudi=[zeros(np,M_.exo_det_nbr);dr.ghud{i};gx(:,1:npred)*hudi+dr.ghud{i-1}(kf,:);zeros(M_.exo_nbr+M_.exo_det_nbr,M_.exo_det_nbr)];
+ R2 = hessian*kron(zudi,zudi);
+ dr.ghudud{i,i} = -M2*(dr.ghudud{i-1,i-1}(kf,:)+...
+ 2*dr.ghxud{i-1}(kf,:)*kron(hudi,Eud) ...
+ +dr.ghxx(kf,:)*kron(hudi,hudi))-M1*R2;
+ R2 = hessian*kron(zud,zudi);
+ dr.ghudud{1,i} = -M2*(dr.ghxud{i-1}(kf,:)*kron(hud,Eud)+...
+ dr.ghxx(kf,:)*kron(hud,hudi))...
+ -M1*R2;
+ for j=2:i-1
+ hudj = dr.ghud{j}(kp,:);
+ zudj=[zeros(np,M_.exo_det_nbr);dr.ghud{j};gx(:,1:npred)*hudj;zeros(M_.exo_nbr+M_.exo_det_nbr,M_.exo_det_nbr)];
+ R2 = hessian*kron(zudj,zudi);
+ dr.ghudud{j,i} = -M2*(dr.ghudud{j-1,i-1}(kf,:)+dr.ghxud{j-1}(kf,:)* ...
+ kron(hudi,Eud)+dr.ghxud{i-1}(kf,:)* ...
+ kron(hudj,Eud)+dr.ghxx(kf,:)*kron(hudj,hudi))-M1*R2;
+ end
+
+ end
+ end
\ No newline at end of file