Check command compatible with sparse model
- New version of dr1.m (dr1_sparse.m & dr11_sparse.m) to handling sparse model (dr1_sparse and dr11_sparse could replace dr1.m) - model_info command to describe the block structure of the model - transition_matrix.m modified to be compatible with sparse model git-svn-id: https://www.dynare.org/svn/dynare/dynare_v4@2006 ac1d8469-bf42-47a9-8791-bf33cf982152time-shift
ferhat
parent
fcfb2fb578
commit
08688fd7f3
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@ -59,7 +59,8 @@ global it_
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oo_.exo_simul = tempex;
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eigenvalues_ = dr.eigval;
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nyf = nnz(dr.kstate(:,2)>M_.maximum_lag+1);
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%nyf = nnz(dr.kstate(:,2)>M_.maximum_lag+1);
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nyf = dr.nyf;
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[m_lambda,i]=sort(abs(eigenvalues_));
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n_explod = nnz(abs(eigenvalues_) > options_.qz_criterium);
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@ -0,0 +1,492 @@
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function [dr,info,M_,options_,oo_] = dr11_sparse(dr,task,M_,options_,oo_, jacobia_, hessian)
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info = 0;
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klen = M_.maximum_endo_lag + M_.maximum_endo_lead + 1;
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kstate = dr.kstate;
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kad = dr.kad;
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kae = dr.kae;
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%kstate
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%kad
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%kae
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nstatic = dr.nstatic;
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nfwrd = dr.nfwrd;
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npred = dr.npred;
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nboth = dr.nboth;
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%nstatic
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%nfwrd
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%npred
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%nboth
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order_var = dr.order_var;
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%order_var
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nd = size(kstate,1);
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%nd
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nz = nnz(M_.lead_lag_incidence);
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%nz
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sdyn = M_.endo_nbr - nstatic;
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%sdyn
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% M_.lead_lag_incidence
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k0 = M_.lead_lag_incidence(M_.maximum_endo_lag+1,order_var);
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k1 = M_.lead_lag_incidence(find([1:klen] ~= M_.maximum_endo_lag+1),:);
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% size(jacobia_)
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% k0
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%k1
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b = jacobia_(:,k0);
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%full(b)
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if M_.maximum_endo_lead == 0; % backward models
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a = jacobia_(:,nonzeros(k1'));
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dr.ghx = zeros(size(a));
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m = 0;
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for i=M_.maximum_endo_lag:-1:1
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k = nonzeros(M_.lead_lag_incidence(i,order_var));
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dr.ghx(:,m+[1:length(k)]) = -b\a(:,k);
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m = m+length(k);
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end
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if M_.exo_nbr & task~=1
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dr.ghu = -b\jacobia_(:,nz+1:end);
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end
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dr.eigval = eig(transition_matrix(dr,M_));
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dr.rank = 0;
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if any(abs(dr.eigval) > options_.qz_criterium)
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temp = sort(abs(dr.eigval));
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nba = nnz(abs(dr.eigval) > options_.qz_criterium);
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temp = temp(nd-nba+1:nd)-1-options_.qz_criterium;
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info(1) = 3;
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info(2) = temp'*temp;
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end
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return;
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end
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%forward--looking models
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if nstatic > 0
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[Q,R] = qr(b(:,1:nstatic));
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aa = Q'*jacobia_;
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else
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aa = jacobia_;
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end
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% full(aa)
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a = aa(:,nonzeros(k1'));
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b = aa(:,k0);
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%M_.lead_lag_incidence
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%k0
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%k1
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%a
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%b
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b10 = b(1:nstatic,1:nstatic);
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b11 = b(1:nstatic,nstatic+1:end);
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b2 = b(nstatic+1:end,nstatic+1:end);
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if any(isinf(a(:)))
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info = 1;
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return
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end
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% buildind D and E
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d = zeros(nd,nd) ;
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e = d ;
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k = find(kstate(:,2) >= M_.maximum_endo_lag+2 & kstate(:,3));
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d(1:sdyn,k) = a(nstatic+1:end,kstate(k,3)) ;
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k1 = find(kstate(:,2) == M_.maximum_endo_lag+2);
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e(1:sdyn,k1) = -b2(:,kstate(k1,1)-nstatic);
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k = find(kstate(:,2) <= M_.maximum_endo_lag+1 & kstate(:,4));
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e(1:sdyn,k) = -a(nstatic+1:end,kstate(k,4)) ;
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k2 = find(kstate(:,2) == M_.maximum_endo_lag+1);
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k2 = k2(~ismember(kstate(k2,1),kstate(k1,1)));
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d(1:sdyn,k2) = b2(:,kstate(k2,1)-nstatic);
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if ~isempty(kad)
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for j = 1:size(kad,1)
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d(sdyn+j,kad(j)) = 1 ;
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e(sdyn+j,kae(j)) = 1 ;
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end
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end
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%e
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%d
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[ss,tt,w,sdim,dr.eigval,info1] = mjdgges(e,d,options_.qz_criterium);
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%ss
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%tt
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%sdim
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%fprintf('%20.16f\n',dr.eigval)
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if info1
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info(1) = 2;
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info(2) = info1;
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return
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end
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nba = nd-sdim;
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nyf = sum(kstate(:,2) > M_.maximum_endo_lag+1);
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%disp(['task' num2str(task)]);
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if task == 1
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dr.rank = rank(w(1:nyf,nd-nyf+1:end));
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% Under Octave, eig(A,B) doesn't exist, and
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% lambda = qz(A,B) won't return infinite eigenvalues
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if ~exist('OCTAVE_VERSION')
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dr.eigval = eig(e,d);
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% dr.eigval
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end
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return
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end
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if nba ~= nyf
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temp = sort(abs(dr.eigval));
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if nba > nyf
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temp = temp(nd-nba+1:nd-nyf)-1-options_.qz_criterium;
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info(1) = 3;
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elseif nba < nyf;
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temp = temp(nd-nyf+1:nd-nba)-1-options_.qz_criterium;
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info(1) = 4;
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end
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info(2) = temp'*temp;
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return
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end
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np = nd - nyf;
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n2 = np + 1;
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n3 = nyf;
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n4 = n3 + 1;
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% derivatives with respect to dynamic state variables
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% forward variables
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w1 =w(1:n3,n2:nd);
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if condest(w1) > 1e9;
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info(1) = 5;
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info(2) = condest(w1);
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return;
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else
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gx = -w1'\w(n4:nd,n2:nd)';
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end
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% predetermined variables
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hx = w(1:n3,1:np)'*gx+w(n4:nd,1:np)';
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hx = (tt(1:np,1:np)*hx)\(ss(1:np,1:np)*hx);
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k1 = find(kstate(n4:nd,2) == M_.maximum_endo_lag+1);
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k2 = find(kstate(1:n3,2) == M_.maximum_endo_lag+2);
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dr.ghx = [hx(k1,:); gx(k2(nboth+1:end),:)];
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%lead variables actually present in the model
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j3 = nonzeros(kstate(:,3));
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j4 = find(kstate(:,3));
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% derivatives with respect to exogenous variables
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if M_.exo_nbr
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fu = aa(:,nz+(1:M_.exo_nbr));
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a1 = b;
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aa1 = [];
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if nstatic > 0
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aa1 = a1(:,1:nstatic);
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end
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dr.ghu = -[aa1 a(:,j3)*gx(j4,1:npred)+a1(:,nstatic+1:nstatic+ ...
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npred) a1(:,nstatic+npred+1:end)]\fu;
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else
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dr.ghu = [];
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end
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% static variables
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if nstatic > 0
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temp = -a(1:nstatic,j3)*gx(j4,:)*hx;
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j5 = find(kstate(n4:nd,4));
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temp(:,j5) = temp(:,j5)-a(1:nstatic,nonzeros(kstate(:,4)));
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temp = b10\(temp-b11*dr.ghx);
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dr.ghx = [temp; dr.ghx];
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temp = [];
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end
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if options_.loglinear == 1
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k = find(dr.kstate(:,2) <= M_.maximum_endo_lag+1);
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klag = dr.kstate(k,[1 2]);
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k1 = dr.order_var;
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dr.ghx = repmat(1./dr.ys(k1),1,size(dr.ghx,2)).*dr.ghx.* ...
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repmat(dr.ys(k1(klag(:,1)))',size(dr.ghx,1),1);
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dr.ghu = repmat(1./dr.ys(k1),1,size(dr.ghu,2)).*dr.ghu;
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end
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%% Necessary when using Sims' routines for QZ
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if options_.use_qzdiv
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gx = real(gx);
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hx = real(hx);
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dr.ghx = real(dr.ghx);
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dr.ghu = real(dr.ghu);
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end
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%exogenous deterministic variables
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if M_.exo_det_nbr > 0
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f1 = sparse(jacobia_(:,nonzeros(M_.lead_lag_incidence(M_.maximum_endo_lag+2:end,order_var))));
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f0 = sparse(jacobia_(:,nonzeros(M_.lead_lag_incidence(M_.maximum_endo_lag+1,order_var))));
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fudet = sparse(jacobia_(:,nz+M_.exo_nbr+1:end));
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M1 = inv(f0+[zeros(M_.endo_nbr,nstatic) f1*gx zeros(M_.endo_nbr,nyf-nboth)]);
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M2 = M1*f1;
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dr.ghud = cell(M_.exo_det_length,1);
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dr.ghud{1} = -M1*fudet;
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for i = 2:M_.exo_det_length
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dr.ghud{i} = -M2*dr.ghud{i-1}(end-nyf+1:end,:);
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end
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end
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disp('end0');
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if options_.order == 1
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return
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end
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% Second order
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%tempex = oo_.exo_simul ;
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%hessian = real(hessext('ff1_',[z; oo_.exo_steady_state]))' ;
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kk = flipud(cumsum(flipud(M_.lead_lag_incidence(M_.maximum_endo_lag+1:end,order_var)),1));
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if M_.maximum_endo_lag > 0
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kk = [cumsum(M_.lead_lag_incidence(1:M_.maximum_endo_lag,order_var),1); kk];
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end
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kk = kk';
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kk = find(kk(:));
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nk = size(kk,1) + M_.exo_nbr + M_.exo_det_nbr;
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k1 = M_.lead_lag_incidence(:,order_var);
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k1 = k1';
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k1 = k1(:);
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k1 = k1(kk);
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k2 = find(k1);
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kk1(k1(k2)) = k2;
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kk1 = [kk1 length(k1)+1:length(k1)+M_.exo_nbr+M_.exo_det_nbr];
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kk = reshape([1:nk^2],nk,nk);
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kk1 = kk(kk1,kk1);
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%[junk,junk,hessian] = feval([M_.fname '_dynamic'],z, oo_.exo_steady_state);
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hessian(:,kk1(:)) = hessian;
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%oo_.exo_simul = tempex ;
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%clear tempex
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n1 = 0;
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n2 = np;
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zx = zeros(np,np);
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zu=zeros(np,M_.exo_nbr);
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for i=2:M_.maximum_endo_lag+1
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k1 = sum(kstate(:,2) == i);
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zx(n1+1:n1+k1,n2-k1+1:n2)=eye(k1);
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n1 = n1+k1;
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n2 = n2-k1;
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end
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kk = flipud(cumsum(flipud(M_.lead_lag_incidence(M_.maximum_endo_lag+1:end,order_var)),1));
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k0 = [1:M_.endo_nbr];
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gx1 = dr.ghx;
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hu = dr.ghu(nstatic+[1:npred],:);
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zx = [zx; gx1];
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zu = [zu; dr.ghu];
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for i=1:M_.maximum_endo_lead
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k1 = find(kk(i+1,k0) > 0);
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zu = [zu; gx1(k1,1:npred)*hu];
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gx1 = gx1(k1,:)*hx;
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zx = [zx; gx1];
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kk = kk(:,k0);
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k0 = k1;
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end
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zx=[zx; zeros(M_.exo_nbr,np);zeros(M_.exo_det_nbr,np)];
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zu=[zu; eye(M_.exo_nbr);zeros(M_.exo_det_nbr,M_.exo_nbr)];
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[nrzx,nczx] = size(zx);
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rhs = -sparse_hessian_times_B_kronecker_C(hessian,zx);
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%lhs
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n = M_.endo_nbr+sum(kstate(:,2) > M_.maximum_endo_lag+1 & kstate(:,2) < M_.maximum_endo_lag+M_.maximum_endo_lead+1);
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A = zeros(n,n);
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B = zeros(n,n);
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A(1:M_.endo_nbr,1:M_.endo_nbr) = jacobia_(:,M_.lead_lag_incidence(M_.maximum_endo_lag+1,order_var));
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% variables with the highest lead
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k1 = find(kstate(:,2) == M_.maximum_endo_lag+M_.maximum_endo_lead+1);
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if M_.maximum_endo_lead > 1
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k2 = find(kstate(:,2) == M_.maximum_endo_lag+M_.maximum_endo_lead);
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[junk,junk,k3] = intersect(kstate(k1,1),kstate(k2,1));
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else
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k2 = [1:M_.endo_nbr];
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k3 = kstate(k1,1);
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end
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% Jacobian with respect to the variables with the highest lead
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B(1:M_.endo_nbr,end-length(k2)+k3) = jacobia_(:,kstate(k1,3)+M_.endo_nbr);
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offset = M_.endo_nbr;
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k0 = [1:M_.endo_nbr];
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gx1 = dr.ghx;
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for i=1:M_.maximum_endo_lead-1
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k1 = find(kstate(:,2) == M_.maximum_endo_lag+i+1);
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[k2,junk,k3] = find(kstate(k1,3));
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A(1:M_.endo_nbr,offset+k2) = jacobia_(:,k3+M_.endo_nbr);
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n1 = length(k1);
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A(offset+[1:n1],nstatic+[1:npred]) = -gx1(kstate(k1,1),1:npred);
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gx1 = gx1*hx;
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A(offset+[1:n1],offset+[1:n1]) = eye(n1);
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n0 = length(k0);
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E = eye(n0);
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if i == 1
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[junk,junk,k4]=intersect(kstate(k1,1),[1:M_.endo_nbr]);
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else
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[junk,junk,k4]=intersect(kstate(k1,1),kstate(k0,1));
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end
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i1 = offset-n0+n1;
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B(offset+[1:n1],offset-n0+[1:n0]) = -E(k4,:);
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k0 = k1;
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offset = offset + n1;
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end
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[junk,k1,k2] = find(M_.lead_lag_incidence(M_.maximum_endo_lag+M_.maximum_endo_lead+1,order_var));
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A(1:M_.endo_nbr,nstatic+1:nstatic+npred)=...
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A(1:M_.endo_nbr,nstatic+[1:npred])+jacobia_(:,k2)*gx1(k1,1:npred);
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C = hx;
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D = [rhs; zeros(n-M_.endo_nbr,size(rhs,2))];
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dr.ghxx = gensylv(2,A,B,C,D);
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%ghxu
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%rhs
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hu = dr.ghu(nstatic+1:nstatic+npred,:);
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%kk = reshape([1:np*np],np,np);
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%kk = kk(1:npred,1:npred);
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%rhs = -hessian*kron(zx,zu)-f1*dr.ghxx(end-nyf+1:end,kk(:))*kron(hx(1:npred,:),hu(1:npred,:));
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rhs = sparse_hessian_times_B_kronecker_C(hessian,zx,zu);
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nyf1 = sum(kstate(:,2) == M_.maximum_endo_lag+2);
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hu1 = [hu;zeros(np-npred,M_.exo_nbr)];
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%B1 = [B(1:M_.endo_nbr,:);zeros(size(A,1)-M_.endo_nbr,size(B,2))];
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[nrhx,nchx] = size(hx);
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[nrhu1,nchu1] = size(hu1);
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B1 = B*A_times_B_kronecker_C(dr.ghxx,hx,hu1);
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rhs = -[rhs; zeros(n-M_.endo_nbr,size(rhs,2))]-B1;
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%lhs
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dr.ghxu = A\rhs;
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%ghuu
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%rhs
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kk = reshape([1:np*np],np,np);
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kk = kk(1:npred,1:npred);
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rhs = sparse_hessian_times_B_kronecker_C(hessian,zu);
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B1 = A_times_B_kronecker_C(B*dr.ghxx,hu1);
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rhs = -[rhs; zeros(n-M_.endo_nbr,size(rhs,2))]-B1;
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%lhs
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dr.ghuu = A\rhs;
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dr.ghxx = dr.ghxx(1:M_.endo_nbr,:);
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dr.ghxu = dr.ghxu(1:M_.endo_nbr,:);
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dr.ghuu = dr.ghuu(1:M_.endo_nbr,:);
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% 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
|
||||
disp('end');
|
||||
end
|
||||
|
|
@ -0,0 +1,234 @@
|
|||
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) 1996-2008 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];
|
||||
% $$$ if options_.ramsey_policy == 2
|
||||
% $$$ 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 = strvcat(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
|
||||
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,jacobia_] = feval([M_.fname '_dynamic'],ones(M_.maximum_lag+M_.maximum_lead+1,1)*dr.ys',[oo_.exo_simul ...
|
||||
oo_.exo_det_simul], it_);
|
||||
dr.eigval = [];
|
||||
dr.nyf = 0;
|
||||
dr.rank = 0;
|
||||
for i=1:length(M_.block_structure.block)
|
||||
%disp(['block ' num2str(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));
|
||||
jcb_=jacobia_(M_.block_structure.block(i).equation,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)));
|
||||
jcb_=jcb_(:,find(any(jcb_,1)));
|
||||
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];
|
||||
dr.nyf = dr.nyf + nnz(dra.kstate(:,2)>M_.block_structure.block(i).maximum_endo_lag+1);
|
||||
dr.rank = dr.rank + dra.rank;
|
||||
end;
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
|
|
@ -0,0 +1,87 @@
|
|||
function model_info;
|
||||
global M_;
|
||||
fprintf(' Informations about %s\n',M_.fname);
|
||||
fprintf(strcat(' ===================',char(ones(1,length(M_.fname))*'='),'\n\n'));
|
||||
if(isfield(M_,'block_structure'))
|
||||
nb_blocks=length(M_.block_structure.block);
|
||||
fprintf('The model has %d equations and is decomposed in %d blocks as follow:\n',M_.endo_nbr,nb_blocks);
|
||||
fprintf('==============================================================================================================\n');
|
||||
fprintf('| %10s | %10s | %30s | %14s | %30s |\n','Block n°','Size','Block Type','Equation','Dependent variable');
|
||||
fprintf('|============|============|================================|================|================================|\n');
|
||||
for i=1:nb_blocks
|
||||
size_block=length(M_.block_structure.block(i).equation);
|
||||
if(i>1)
|
||||
fprintf('|------------|------------|--------------------------------|----------------|--------------------------------|\n');
|
||||
end;
|
||||
for j=1:size_block
|
||||
if(j==1)
|
||||
fprintf('| %3d (%4d) | %10d | %30s | %14d | %30s |\n',i,M_.block_structure.block(i).num,size_block,Sym_type(M_.block_structure.block(i).Simulation_Type),M_.block_structure.block(i).equation(j),M_.endo_names(M_.block_structure.block(i).variable(j),:));
|
||||
else
|
||||
fprintf('| %10s | %10s | %30s | %14d | %30s |\n','','','',M_.block_structure.block(i).equation(j),M_.endo_names(M_.block_structure.block(i).variable(j),:));
|
||||
end;
|
||||
end;
|
||||
end;
|
||||
fprintf('==============================================================================================================\n');
|
||||
fprintf('\n');
|
||||
for k=1:M_.maximum_endo_lag+M_.maximum_endo_lead+1
|
||||
if(k==M_.maximum_endo_lag+1)
|
||||
fprintf('%-30s %s','the variable','is used in equations contemporously');
|
||||
elseif(k<M_.maximum_endo_lag+1)
|
||||
fprintf('%-30s %s %d','the variable','is used in equations with lag ',M_.maximum_endo_lag+1-k);
|
||||
else
|
||||
fprintf('%-30s %s %d','the variable','is used in equations with lead ',k-(M_.maximum_endo_lag+1));
|
||||
end;
|
||||
if(size(M_.block_structure.incidence(k).sparse_IM,1)>0)
|
||||
IM=sortrows(M_.block_structure.incidence(k).sparse_IM,2);
|
||||
else
|
||||
IM=[];
|
||||
end;
|
||||
size_IM=size(IM,1);
|
||||
last=0;
|
||||
for i=1:size_IM
|
||||
if(last~=IM(i,2))
|
||||
fprintf('\n%-30s',M_.endo_names(IM(i,2),:));
|
||||
end;
|
||||
fprintf(' %5d',IM(i,1));
|
||||
last=IM(i,2);
|
||||
end;
|
||||
fprintf('\n\n');
|
||||
end;
|
||||
else
|
||||
fprintf('There is no block decomposition of the model.\nUse ''sparse'' or ''sparse_dll'' model''s option.\n');
|
||||
end;
|
||||
|
||||
|
||||
function ret=Sym_type(type);
|
||||
EVALUATE_FOREWARD=0;
|
||||
EVALUATE_BACKWARD=1;
|
||||
SOLVE_FOREWARD_SIMPLE=2;
|
||||
SOLVE_BACKWARD_SIMPLE=3;
|
||||
SOLVE_TWO_BOUNDARIES_SIMPLE=4;
|
||||
SOLVE_FOREWARD_COMPLETE=5;
|
||||
SOLVE_BACKWARD_COMPLETE=6;
|
||||
SOLVE_TWO_BOUNDARIES_COMPLETE=7;
|
||||
EVALUATE_FOREWARD_R=8;
|
||||
EVALUATE_BACKWARD_R=9;
|
||||
switch (type)
|
||||
case {EVALUATE_FOREWARD,EVALUATE_FOREWARD_R},
|
||||
ret='EVALUATE FOREWARD ';
|
||||
case {EVALUATE_BACKWARD,EVALUATE_BACKWARD_R},
|
||||
ret='EVALUATE BACKWARD ';
|
||||
case SOLVE_FOREWARD_SIMPLE,
|
||||
ret='SOLVE FOREWARD SIMPLE ';
|
||||
case SOLVE_BACKWARD_SIMPLE,
|
||||
ret='SOLVE BACKWARD SIMPLE ';
|
||||
case SOLVE_TWO_BOUNDARIES_SIMPLE,
|
||||
ret='SOLVE TWO BOUNDARIES SIMPLE ';
|
||||
case SOLVE_FOREWARD_COMPLETE,
|
||||
ret='SOLVE FOREWARD COMPLETE ';
|
||||
case SOLVE_BACKWARD_COMPLETE,
|
||||
ret='SOLVE BACKWARD COMPLETE ';
|
||||
case SOLVE_TWO_BOUNDARIES_COMPLETE,
|
||||
ret='SOLVE TWO BOUNDARIES COMPLETE';
|
||||
end;
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
@ -98,8 +98,11 @@ if check1
|
|||
end
|
||||
|
||||
dr.fbias = zeros(M_.endo_nbr,1);
|
||||
[dr,info,M_,options_,oo_] = dr1(dr,check_flag,M_,options_,oo_);
|
||||
|
||||
if(options_.model_mode==1)
|
||||
[dr,info,M_,options_,oo_] = dr1_sparse(dr,check_flag,M_,options_,oo_);
|
||||
else
|
||||
[dr,info,M_,options_,oo_] = dr1(dr,check_flag,M_,options_,oo_);
|
||||
end
|
||||
if info(1)
|
||||
return
|
||||
end
|
||||
|
|
|
|||
|
|
@ -1,10 +1,11 @@
|
|||
function [A,B] = transition_matrix(dr)
|
||||
function [A,B] = transition_matrix(dr, varargin)
|
||||
|
||||
% function [A,B] = transition_matrix(dr)
|
||||
% function [A,B] = transition_matrix(dr, varargin)
|
||||
% Makes transition matrices out of ghx and ghu
|
||||
%
|
||||
% INPUTS
|
||||
% dr: structure of decision rules for stochastic simulations
|
||||
% varargin: {1}: M_
|
||||
%
|
||||
% OUTPUTS
|
||||
% A: matrix of effects of predetermined variables in linear solution (ghx)
|
||||
|
|
@ -30,7 +31,12 @@ function [A,B] = transition_matrix(dr)
|
|||
% 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_
|
||||
if(length(varargin)<=0)
|
||||
global M_
|
||||
else
|
||||
M_=varargin{1};
|
||||
end;
|
||||
|
||||
exo_nbr = M_.exo_nbr;
|
||||
ykmin_ = M_.maximum_endo_lag;
|
||||
|
||||
|
|
@ -43,7 +49,9 @@ function [A,B] = transition_matrix(dr)
|
|||
i0 = find(k0(:,2) == ykmin_+1);
|
||||
A(i0,:) = dr.ghx(ikx,:);
|
||||
B = zeros(nx,exo_nbr);
|
||||
B(i0,:) = dr.ghu(ikx,:);
|
||||
if(isfield(dr,'ghu'))
|
||||
B(i0,:) = dr.ghu(ikx,:);
|
||||
end;
|
||||
for i=ykmin_:-1:2
|
||||
i1 = find(k0(:,2) == i);
|
||||
n1 = size(i1,1);
|
||||
|
|
@ -54,3 +62,4 @@ function [A,B] = transition_matrix(dr)
|
|||
A(i1,i0(j))=eye(n1);
|
||||
i0 = i1;
|
||||
end
|
||||
|
||||
Loading…
Reference in New Issue