From 1eccfb311ac71142ae49c3d738ebbc501fae4dd4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?S=C3=A9bastien=20Villemot?= Date: Thu, 25 Nov 2010 13:10:10 +0100 Subject: [PATCH] Remove obsolete files --- matlab/dr11_sparse.m | 499 ------------------------------------------- matlab/dr1_sparse.m | 268 ----------------------- 2 files changed, 767 deletions(-) delete mode 100644 matlab/dr11_sparse.m delete mode 100644 matlab/dr1_sparse.m diff --git a/matlab/dr11_sparse.m b/matlab/dr11_sparse.m deleted file mode 100644 index f027ae86e..000000000 --- a/matlab/dr11_sparse.m +++ /dev/null @@ -1,499 +0,0 @@ -function [dr,info,M_,options_,oo_] = dr11_sparse(dr,task,M_,options_,oo_, jacobia_, hessian) -%function [dr,info,M_,options_,oo_] = dr11_sparse(dr,task,M_,options_,oo_, jacobia_, hessian) - -% Copyright (C) 2008-2010 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 . -%task -info = 0; -klen = M_.maximum_endo_lag + M_.maximum_endo_lead + 1; -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),:); -b = jacobia_(:,k0); - -if M_.maximum_endo_lead == 0; % backward models - a = jacobia_(:,nonzeros(k1')); - dr.ghx = zeros(size(a)); - m = 0; - for i=M_.maximum_endo_lag:-1:1 - k = nonzeros(M_.lead_lag_incidence(i,order_var)); - dr.ghx(:,m+[1:length(k)]) = -b\a(:,k); - m = m+length(k); - end - if M_.exo_nbr & task~=1 - jacobia_ - jacobia_(:,nz+1:end) - b - dr.ghu = -b\jacobia_(:,nz+1:end); - disp(['nz=' int2str(nz) ]); - dr.ghu - end - dr.eigval = eig(transition_matrix(dr,M_)); - 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 -if nstatic > 0 - [Q,R] = qr(b(:,1:nstatic)); - aa = Q'*jacobia_; -else - aa = jacobia_; -end -a = aa(:,nonzeros(k1')); -b = aa(:,k0); -b10 = b(1:nstatic,1:nstatic); -b11 = b(1:nstatic,nstatic+1:end); -b2 = b(nstatic+1:end,nstatic+1:end); -if any(isinf(a(:))) - info = 1; - return -end - -% buildind D and E -%nd -d = zeros(nd,nd) ; -e = d ; -k = find(kstate(:,2) >= M_.maximum_endo_lag+2 & kstate(:,3)); -d(1:sdyn,k) = a(nstatic+1:end,kstate(k,3)) ; -k1 = find(kstate(:,2) == M_.maximum_endo_lag+2); -e(1:sdyn,k1) = -b2(:,kstate(k1,1)-nstatic); -k = find(kstate(:,2) <= M_.maximum_endo_lag+1 & kstate(:,4)); -e(1:sdyn,k) = -a(nstatic+1:end,kstate(k,4)) ; -k2 = find(kstate(:,2) == M_.maximum_endo_lag+1); -k2 = k2(~ismember(kstate(k2,1),kstate(k1,1))); -d(1:sdyn,k2) = b2(:,kstate(k2,1)-nstatic); - -if ~isempty(kad) - for j = 1:size(kad,1) - d(sdyn+j,kad(j)) = 1 ; - e(sdyn+j,kae(j)) = 1 ; - end -end -%e -%d - -[err,ss,tt,w,sdim,dr.eigval,info1] = mjdgges(e,d,options_.qz_criterium); -mexErrCheck('mjdgges', err); - -if info1 - info(1) = 2; - info(2) = info1; - return -end - -nba = nd-sdim; - -nyf = sum(kstate(:,2) > M_.maximum_endo_lag+1); - -if task == 1 - dr.rank = rank(w(1:nyf,nd-nyf+1:end)); - % Under Octave, eig(A,B) doesn't exist, and - % lambda = qz(A,B) won't return infinite eigenvalues - if ~exist('OCTAVE_VERSION') - dr.eigval = eig(e,d); - end - return -end - -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 - -np = nd - nyf; -n2 = np + 1; -n3 = nyf; -n4 = n3 + 1; -% derivatives with respect to dynamic state variables -% forward variables -w1 =w(1:n3,n2:nd); -if condest(w1) > 1e9; - info(1) = 5; - info(2) = condest(w1); - return; -else - gx = -w1'\w(n4:nd,n2:nd)'; -end - -% predetermined variables -hx = w(1:n3,1:np)'*gx+w(n4:nd,1:np)'; -hx = (tt(1:np,1:np)*hx)\(ss(1:np,1:np)*hx); - -k1 = find(kstate(n4:nd,2) == M_.maximum_endo_lag+1); -k2 = find(kstate(1:n3,2) == M_.maximum_endo_lag+2); -hx(k1,:) -gx(k2(nboth+1:end),:) -dr.ghx = [hx(k1,:); gx(k2(nboth+1:end),:)]; -dr.ghx -%lead variables actually present in the model -j3 = nonzeros(kstate(:,3)); -j4 = find(kstate(:,3)); -% derivatives with respect to exogenous variables -disp(['M_.exo_nbr=' int2str(M_.exo_nbr)]); -if M_.exo_nbr - fu = aa(:,nz+(1:M_.exo_nbr)); - a1 = b; - aa1 = []; - if nstatic > 0 - aa1 = a1(:,1:nstatic); - end - dr.ghu = -[aa1 a(:,j3)*gx(j4,1:npred)+a1(:,nstatic+1:nstatic+ ... - npred) a1(:,nstatic+npred+1:end)]\fu; -else - dr.ghu = []; -end - -% static variables -if nstatic > 0 - temp = -a(1:nstatic,j3)*gx(j4,:)*hx; - j5 = find(kstate(n4:nd,4)); - temp(:,j5) = temp(:,j5)-a(1:nstatic,nonzeros(kstate(:,4))); - temp = b10\(temp-b11*dr.ghx); - dr.ghx = [temp; dr.ghx]; - temp = []; -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 - -%% Necessary when using Sims' routines for QZ -if options_.use_qzdiv - gx = real(gx); - hx = real(hx); - dr.ghx = real(dr.ghx); - dr.ghu = real(dr.ghu); -end - -%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 ; - -%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); - -[err, rhs] = sparse_hessian_times_B_kronecker_C(hessian,zx,options_.threads.kronecker.sparse_hessian_times_B_kronecker_C); -mexErrCheck('sparse_hessian_times_B_kronecker_C', err); -rhs = -rhs; - -%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))]; - - -[err, dr.ghxx] = gensylv(2,A,B,C,D); -mexErrCheck('gensylv', err); - -%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,:)); - -[err, rhs] = sparse_hessian_times_B_kronecker_C(hessian,zx,zu,options_.threads.kronecker.sparse_hessian_times_B_kronecker_C); -mexErrCheck('sparse_hessian_times_B_kronecker_C', err); - -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); - -[err, abcOut] = A_times_B_kronecker_C(dr.ghxx,hx,hu1,options_.threads.kronecker.A_times_B_kronecker_C); -mexErrCheck('A_times_B_kronecker_C', err); -B1 = B*abcOut; -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); - -[err, rhs] = sparse_hessian_times_B_kronecker_C(hessian,zu,options_.threads.kronecker.sparse_hessian_times_B_kronecker_C); -mexErrCheck('sparse_hessian_times_B_kronecker_C', err); - -[err, B1] = A_times_B_kronecker_C(B*dr.ghxx,hu1,options_.threads.kronecker.A_times_B_kronecker_C); -mexErrCheck('A_times_B_kronecker_C', err); -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); - [err, B1] = sparse_hessian_times_B_kronecker_C(hessian(:,kh(k3,k3)),gu(k3a,:),options_.threads.kronecker.sparse_hessian_times_B_kronecker_C); - mexErrCheck('sparse_hessian_times_B_kronecker_C', err); - 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); - [err, G1] = A_times_B_kronecker_C(dr.ghxx,Gu,options_.threads.kronecker.A_times_B_kronecker_C); - mexErrCheck('A_times_B_kronecker_C', err); - [err, G2] = A_times_B_kronecker_C(hxx,Gu,options_.threads.kronecker.A_times_B_kronecker_C); - mexErrCheck('A_times_B_kronecker_C', err); - 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 diff --git a/matlab/dr1_sparse.m b/matlab/dr1_sparse.m deleted file mode 100644 index 5b0a24344..000000000 --- a/matlab/dr1_sparse.m +++ /dev/null @@ -1,268 +0,0 @@ -function [dr,info,M_,options_,oo_] = dr1_sparse(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) 1996-2010 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 . - -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_.orig_endo_nbr = orig_model.orig_endo_nbr; - M_.aux_vars = orig_model.aux_vars; - 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('dyn_ramsey_static_',oo_.steady_state,0,M_,options_,oo_,it_); - options_.solve_algo = old_solve_algo; - [junk,junk,multbar] = dyn_ramsey_static_(oo_.steady_state,M_,options_,oo_,it_); - [jacobia_,M_] = dyn_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]; -% $$$ if options_.ramsey_policy == 2 -% $$$ mask = M_.orig_model.lead_lag_incidence ~= 0; -% $$$ incidence_submatrix = M_.lead_lag_incidence(M_.orig_model.maximum_lead+(1:size(mask,1)),1:M_.orig_model.endo_nbr); -% $$$ k = nonzeros((incidence_submatrix.*mask)'); -% $$$ nl = nnz(M_.lead_lag_incidence); -% $$$ k = [k; nl+(1:M_.exo_nbr)']; -% $$$ kk = reshape(1:(nl+M_.exo_nbr)^2,nl+M_.exo_nbr,nl+M_.exo_nbr); -% $$$ kk2 = kk(k,k); -% $$$ -% $$$ k1 = find(M_.orig_model.lead_lag_incidence'); -% $$$ y = repmat(oo_.dr.ys(1:M_.orig_model.endo_nbr),1,M_.orig_model.maximum_lag+M_.orig_model.maximum_lead+1); -% $$$ [f,fJ,fh] = feval([M_.fname '_dynamic'],y(k1),zeros(1,M_.exo_nbr), M_.params, it_); -% $$$ -% $$$ % looking for dynamic variables that are both in the original model -% $$$ % and in the optimal policy model -% $$$ k1 = k1+nnz(M_.lead_lag_incidence(1:M_.orig_model.maximum_lead,1:M_.orig_model.endo_nbr)); -% $$$ hessian = sparse([],[],[],size(jacobia_,1),(nl+M_.exo_nbr)^2,nnz(fh)); -% $$$ hessian(M_.orig_model.endo_nbr+(1:size(fh,1)),kk2) = fh; -% $$$ options_.order = 2; -% $$$ elseif options_.ramsey_policy == 3 -% $$$ maxlag1 = M_.orig_model.maximum_lag; -% $$$ maxlead1 = M_.orig_model.maximum_lead; -% $$$ endo_nbr1 = M_.orig_model.endo_nbr; -% $$$ lead_lag_incidence1 = M_.orig_model.lead_lag_incidence; -% $$$ y = repmat(oo_.dr.ys(1:M_.orig_model.endo_nbr),1,M_.orig_model.maximum_lag+M_.orig_model.maximum_lead+1); -% $$$ k1 = find(M_.orig_model.lead_lag_incidence'); -% $$$ [f,fj,fh] = feval([M_.fname '_dynamic'],y(k1),zeros(1,M_.exo_nbr), M_.params, it_); -% $$$ nrj = size(fj,1); -% $$$ -% $$$ iy = M_.lead_lag_incidence; -% $$$ kstate = oo_.dr.kstate; -% $$$ inv_order_var = oo_.dr.inv_order_var; -% $$$ offset = 0; -% $$$ i3 = zeros(0,1); -% $$$ i4 = find(kstate(:,2) <= M_.maximum_lag+1); -% $$$ kstate1 = kstate(i4,:); -% $$$ kk2 = zeros(0,1); -% $$$ % lagged variables -% $$$ for i=2:M_.maximum_lag + 1 -% $$$ i1 = find(kstate(:,2) == i); -% $$$ k1 = kstate(i1,:); -% $$$ i2 = find(oo_.dr.order_var(k1(:,1)) <= M_.orig_model.endo_nbr); -% $$$ i3 = [i3; i2+offset]; -% $$$ offset = offset + size(k1,1); -% $$$ i4 = find(kstate1(:,2) == i); -% $$$ kk2 = [kk2; i4]; -% $$$ end -% $$$ i2 = find(oo_.dr.order_var(k1(:,1)) > M_.orig_model.endo_nbr); -% $$$ j2 = k1(i2,1); -% $$$ nj2 = length(j2); -% $$$ k2 = offset+(1:nj2)'; -% $$$ offset = offset + length(i2); -% $$$ i3 = [i3; ... -% $$$ find(M_.orig_model.lead_lag_incidence(M_.orig_model.maximum_lag+1:end,:)')+offset]; -% $$$ i3 = [i3; (1:M_.exo_nbr)'+length(i3)]; -% $$$ ni3 = length(i3); -% $$$ nrfj = size(fj,1); -% $$$ jacobia_ = zeros(nrfj+length(j2),ni3); -% $$$ jacobia_(1:nrfj,i3) = fj; -% $$$ jacobia_(nrfj+(1:nj2),1:size(oo_.dr.ghx,2)) = oo_.dr.ghx(j2,:); -% $$$ jacobia_(nrfj+(1:nj2),k2) = eye(nj2); -% $$$ kk1 = reshape(1:ni3^2,ni3,ni3); -% $$$ hessian = zeros(nrfj+length(j2),ni3^2); -% $$$ hessian(1:nrfj,kk1(i3,i3)) = fh; -% $$$ -% $$$ k = find(any(M_.lead_lag_incidence(1:M_.maximum_lag, ... -% $$$ M_.orig_model.endo_nbr+1:end))); -% $$$ if maxlead1 > maxlag1 -% $$$ M_.lead_lag_incidence = [ [zeros(maxlead1-maxlag1,endo_nbr1); ... -% $$$ lead_lag_incidence1] ... -% $$$ [M_.lead_lag_incidence(M_.maximum_lag+(1:maxlead1), ... -% $$$ k); zeros(maxlead1,length(k))]]; -% $$$ elseif maxlag1 > maxlead1 -% $$$ M_.lead_lag_incidence = [ [lead_lag_incidence1; zeros(maxlag1- ... -% $$$ maxlead1,endo_nbr1);] ... -% $$$ [M_.lead_lag_incidence(M_.maximum_lag+(1:maxlead1), ... -% $$$ k); zeros(maxlead1,length(k))]]; -% $$$ else % maxlag1 == maxlead1 -% $$$ M_.lead_lag_incidence = [ lead_lag_incidence1 -% $$$ [M_.lead_lag_incidence(M_.maximum_lag+(1:maxlead1), ... -% $$$ k); zeros(maxlead1,length(k))]]; -% $$$ end -% $$$ M_.maximum_lag = max(maxlead1,maxlag1); -% $$$ M_.maximum_endo_lag = M_.maximum_lag; -% $$$ M_.maximum_lead = M_.maximum_lag; -% $$$ M_.maximum_endo_lead = M_.maximum_lag; -% $$$ -% $$$ M_.endo_names = char(M_.orig_model.endo_names, M_.endo_names(endo_nbr1+k,:)); -% $$$ M_.endo_nbr = endo_nbr1+length(k); -% $$$ end -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) ; - if options_.model_mode==0 || options_.model_mode == 2 - if options_.order == 1 - [junk,jacobia_] = feval([M_.fname '_dynamic'],z,[oo_.exo_simul ... - oo_.exo_det_simul], M_.params, it_); - hessian = 0; - elseif options_.order == 2 - [junk,jacobia_,hessian] = feval([M_.fname '_dynamic'],z,... - [oo_.exo_simul ... - oo_.exo_det_simul], M_.params, it_); - end - dr=set_state_space(dr,M_); - if options_.debug - save([M_.fname '_debug.mat'],'jacobia_') - end - [dr,info,M_,options_,oo_] = dr11_sparse(dr,task,M_,options_,oo_, jacobia_, hessian); - dr.nyf = nnz(dr.kstate(:,2)>M_.maximum_lag+1); - elseif options_.model_mode==1 - if options_.order == 1 - - [junk,derivate] = feval([M_.fname '_dynamic'],ones(M_.maximum_lag+M_.maximum_lead+1,1)*dr.ys',[oo_.exo_simul ... - oo_.exo_det_simul], M_.params, it_); - %full(jacobia_) - dr.eigval = []; - dr.nyf = 0; - dr.rank = 0; - first_col_exo = M_.endo_nbr * (M_.maximum_endo_lag + M_.maximum_endo_lead + 1); - for i=1:length(M_.block_structure.block) - %disp(['block = ' int2str(i)]); - M_.block_structure.block(i).dr.Null=0; - M_.block_structure.block(i).dr=set_state_space(M_.block_structure.block(i).dr,M_.block_structure.block(i)); - col_selector=repmat(M_.block_structure.block(i).variable,1,M_.block_structure.block(i).maximum_endo_lag+M_.block_structure.block(i).maximum_endo_lead+1)+kron([M_.maximum_endo_lag-M_.block_structure.block(i).maximum_endo_lag:M_.maximum_endo_lag+M_.block_structure.block(i).maximum_endo_lead],M_.endo_nbr*ones(1,M_.block_structure.block(i).endo_nbr)); - row_selector = M_.block_structure.block(i).equation; - %jcb_=jacobia_(row_selector,col_selector); - jcb_=derivate(i).g1; - %disp('jcb_'); - %full(jcb_) - %M_.block_structure.block(i).lead_lag_incidence' - jcb_ = jcb_(:,find(M_.block_structure.block(i).lead_lag_incidence')) ; - if M_.block_structure.block(i).exo_nbr>0 - col_selector = [ first_col_exo + ... - repmat(M_.block_structure.block(i).exogenous,1,M_.block_structure.block(i).maximum_exo_lag+M_.block_structure.block(i).maximum_exo_lead+1)+kron([M_.maximum_exo_lag-M_.block_structure.block(i).maximum_exo_lag:M_.maximum_exo_lag+M_.block_structure.block(i).maximum_exo_lead],M_.exo_nbr*ones(1,M_.block_structure.block(i).exo_nbr))]; - end - %derivate(i).g1 - %derivate(i).g1_x - %col_selector - %jcb_ = [ jcb_ jacobia_(row_selector,col_selector)]; - jcb_ = [ jcb_ derivate(i).g1_x]; - %full(jcb_) - - hss_=0; %hessian(M_.block_structure.block(i).equation,M_.block_structure.block(i).variable); - dra = M_.block_structure.block(i).dr; - %M_.block_structure.block(i).exo_nbr=M_.exo_nbr; - [dra ,info,M_.block_structure.block(i),options_,oo_] = dr11_sparse(dra ,task,M_.block_structure.block(i),options_,oo_, jcb_, hss_); - M_.block_structure.block(i).dr = dra; - dr.eigval = [dr.eigval; dra.eigval]; - nyf = nnz(dra.kstate(:,2)>M_.block_structure.block(i).maximum_endo_lag+1); - n_explod = nnz(abs(dra.eigval) > options_.qz_criterium); - if nyf ~= n_explod - disp(['EIGENVALUES in block ' int2str(i) ':']); - [m_lambda,ii]=sort(abs(dra.eigval)); - disp(sprintf('%16s %16s %16s\n','Modulus','Real','Imaginary')) - z=[m_lambda real(dra.eigval(ii)) imag(dra.eigval(ii))]'; - disp(sprintf('%16.4g %16.4g %16.4g\n',z)) - disp(['The rank condition is not satisfy in block ' int2str(i) ' :']); - disp([' ' int2str(nyf) ' forward-looking variable(s) for ' ... - int2str(n_explod) ' eigenvalue(s) larger than 1 in modulus']); - end - dr.nyf = dr.nyf + nyf; - dr.rank = dr.rank + dra.rank; - end; - end - end -end -