256 lines
6.2 KiB
Matlab
256 lines
6.2 KiB
Matlab
|
% Copyright (C) 2001 Michel Juillard
|
||
|
%
|
||
|
function dr=dr11(iorder,dr,cheik)
|
||
|
|
||
|
global M_ options_ oo_
|
||
|
global it_ stdexo_ means_ dr1_test_ bayestopt_
|
||
|
|
||
|
% hack for Bayes
|
||
|
global dr1_test_ bayestopt_
|
||
|
|
||
|
options_ = set_default_option(options_,'loglinear',0);
|
||
|
|
||
|
xlen = M_.maximum_lead + M_.maximum_lag + 1;
|
||
|
klen = M_.maximum_lag + M_.maximum_lead + 1;
|
||
|
iyv = transpose(M_.lead_lag_incidence);
|
||
|
iyv = iyv(:);
|
||
|
iyr0 = find(iyv) ;
|
||
|
it_ = M_.maximum_lag + 1 ;
|
||
|
|
||
|
|
||
|
if M_.exo_nbr == 0
|
||
|
oo_.exo_steady_state = [] ;
|
||
|
end
|
||
|
|
||
|
if ~ M_.lead_lag_incidence(M_.maximum_lag+1,:) > 0
|
||
|
error ('Error in model specification: some variables don"t appear as current') ;
|
||
|
end
|
||
|
|
||
|
if ~cheik
|
||
|
% if xlen > 1
|
||
|
% error (['SS: stochastic exogenous variables must appear only at the' ...
|
||
|
% ' current period. Use additional endogenous variables']) ;
|
||
|
% end
|
||
|
end
|
||
|
|
||
|
if M_.maximum_lead > 1 & iorder > 1
|
||
|
error (['Models with leads on more than one period can only be solved' ...
|
||
|
' at order 1'])
|
||
|
end
|
||
|
|
||
|
dr=set_state_space(dr);
|
||
|
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);
|
||
|
|
||
|
sdyn = M_.endo_nbr - nstatic;
|
||
|
|
||
|
|
||
|
tempex = oo_.exo_simul;
|
||
|
|
||
|
it_ = M_.maximum_lag + 1;
|
||
|
z = repmat(dr.ys,1,klen);
|
||
|
z = z(iyr0) ;
|
||
|
%M_.jacobia=real(diffext('ff1_',[z; oo_.exo_steady_state])) ;
|
||
|
%M_.jacobia=real(jacob_a('ff1_',[z; oo_.exo_steady_state])) ;
|
||
|
[junk,M_.jacobia] = feval([M_.fname '_dynamic'],z,oo_.exo_simul);
|
||
|
oo_.exo_simul = tempex ;
|
||
|
tempex = [];
|
||
|
|
||
|
nz = size(z,1);
|
||
|
k1 = M_.lead_lag_incidence(find([1:klen] ~= M_.maximum_lag+1),:);
|
||
|
b = M_.jacobia(:,M_.lead_lag_incidence(M_.maximum_lag+1,order_var));
|
||
|
a = b\M_.jacobia(:,nonzeros(k1'));
|
||
|
if any(isinf(a(:)))
|
||
|
dr1_test_(1) = 5;
|
||
|
dr1_test_(2) = bayestopt_.penalty;
|
||
|
end
|
||
|
if M_.exo_nbr
|
||
|
fu = b\M_.jacobia(:,nz+1:end);
|
||
|
end
|
||
|
|
||
|
if M_.maximum_lead == 0 & M_.maximum_lag == 1; % backward model with one lag
|
||
|
dr.ghx = -a;
|
||
|
dr.ghu = -fu;
|
||
|
return;
|
||
|
elseif M_.maximum_lead == 0 & M_.maximum_lag > 1 % backward model with lags on more than
|
||
|
% one period
|
||
|
e = zeros(endo_nbr,nd);
|
||
|
k = find(kstate(:,2) <= M_.maximum_lag+1 & kstate(:,4));
|
||
|
e(:,k) = -a(:,kstate(k,4)) ;
|
||
|
dr.ghx = e;
|
||
|
dr.ghu = -fu;
|
||
|
end
|
||
|
|
||
|
% buildind D and E
|
||
|
d = zeros(nd,nd) ;
|
||
|
e = d ;
|
||
|
|
||
|
k = find(kstate(:,2) >= M_.maximum_lag+2 & kstate(:,3));
|
||
|
d(1:sdyn,k) = a(nstatic+1:end,kstate(k,3)) ;
|
||
|
k1 = find(kstate(:,2) == M_.maximum_lag+2);
|
||
|
a1 = eye(sdyn);
|
||
|
e(1:sdyn,k1) = -a1(:,kstate(k1,1)-nstatic);
|
||
|
k = find(kstate(:,2) <= M_.maximum_lag+1 & kstate(:,4));
|
||
|
e(1:sdyn,k) = -a(nstatic+1:end,kstate(k,4)) ;
|
||
|
k2 = find(kstate(:,2) == M_.maximum_lag+1);
|
||
|
k2 = k2(~ismember(kstate(k2,1),kstate(k1,1)));
|
||
|
d(1:sdyn,k2) = a1(:,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
|
||
|
options_ = set_default_option(options_,'qz_criterium',1.000001);
|
||
|
|
||
|
if ~exist('mjdgges')
|
||
|
% using Chris Sim's routines
|
||
|
use_qzdiv = 1;
|
||
|
[ss,tt,qq,w] = qz(e,d);
|
||
|
[tt,ss,qq,w] = qzdiv(options_.qz_criterium,tt,ss,qq,w);
|
||
|
ss1=diag(ss);
|
||
|
tt1=diag(tt);
|
||
|
warning_state = warning;
|
||
|
warning off;
|
||
|
oo_.eigenvalues = ss1./tt1 ;
|
||
|
warning warning_state;
|
||
|
nba = nnz(abs(eigval) > options_.qz_criterium);
|
||
|
else
|
||
|
use_qzdiv = 0;
|
||
|
[ss,tt,w,sdim,oo_.eigenvalues,info] = mjdgges(e,d,options_.qz_criterium);
|
||
|
if info & info ~= nd+2;
|
||
|
error(['ERROR' info ' in MJDGGES.DLL']);
|
||
|
end
|
||
|
nba = nd-sdim;
|
||
|
end
|
||
|
|
||
|
nyf = sum(kstate(:,2) > M_.maximum_lag+1);
|
||
|
|
||
|
if cheik
|
||
|
dr.rank = rank(w(1:nyf,nd-nyf+1:end));
|
||
|
% dr.eigval = oo_.eigenvalues;
|
||
|
return
|
||
|
end
|
||
|
|
||
|
eigenvalues = sort(oo_.eigenvalues);
|
||
|
|
||
|
if nba > nyf;
|
||
|
% disp('Instability !');
|
||
|
dr1_test_(1) = 3; %% More eigenvalues superior to unity than forward variables ==> instability.
|
||
|
dr1_test_(2) = (abs(eigenvalues(nd-nba+1:nd-nyf))-1-1e-5)'*...
|
||
|
(abs(eigenvalues(nd-nba+1:nd-nyf))-1-1e-5);% Distance to Blanchard-Khan conditions (penalty)
|
||
|
return
|
||
|
elseif nba < nyf;
|
||
|
% disp('Indeterminacy !');
|
||
|
dr1_test_(1) = 2; %% ==> Indeterminacy.
|
||
|
dr1_test_(2) = (abs(eigenvalues(nd-nyf+1:nd-nba))-1-1e-5)'*...
|
||
|
(abs(eigenvalues(nd-nyf+1:nd-nba))-1-1e-5);% Distance to Blanchard-Khan conditions (penality)
|
||
|
%% warning('DR1: Blanchard-Kahn conditions are not satisfied. Run CHEIK to learn more!');
|
||
|
return
|
||
|
end
|
||
|
|
||
|
np = nd - nyf;
|
||
|
n2 = np + 1;
|
||
|
n3 = nyf;
|
||
|
n4 = n3 + 1;
|
||
|
% derivatives with respect to dynamic state variables
|
||
|
% forward variables
|
||
|
|
||
|
if condest(w(1:n3,n2:nd)) > 1e9
|
||
|
% disp('Indeterminacy !!');
|
||
|
dr1_test_(1) = 2;
|
||
|
dr1_test_(2) = 1;
|
||
|
return
|
||
|
end
|
||
|
|
||
|
warning_state = warning;
|
||
|
lastwarn('');
|
||
|
warning off;
|
||
|
gx = -w(1:n3,n2:nd)'\w(n4:nd,n2:nd)';
|
||
|
|
||
|
if length(lastwarn) > 0;
|
||
|
% disp('Indeterminacy !!');
|
||
|
dr1_test_(1) = 2;
|
||
|
dr1_test_(2) = 1;
|
||
|
warning(warning_state);
|
||
|
return
|
||
|
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);
|
||
|
|
||
|
lastwarn('');
|
||
|
if length(lastwarn) > 0;
|
||
|
% disp('Singularity problem in dr11.m');
|
||
|
dr1_test_(1) = 2;
|
||
|
dr1_test_(2) = 1;
|
||
|
warning(warning_state);
|
||
|
return
|
||
|
end
|
||
|
|
||
|
k1 = find(kstate(n4:nd,2) == M_.maximum_lag+1);
|
||
|
k2 = find(kstate(1:n3,2) == M_.maximum_lag+2);
|
||
|
dr.ghx = [hx(k1,:); gx(k2(nboth+1:end),:)];
|
||
|
|
||
|
%lead variables actually present in the model
|
||
|
j3 = nonzeros(kstate(:,3));
|
||
|
j4 = find(kstate(:,3));
|
||
|
% derivatives with respect to exogenous variables
|
||
|
if M_.exo_nbr
|
||
|
a1 = eye(M_.endo_nbr);
|
||
|
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;
|
||
|
|
||
|
|
||
|
lastwarn('');
|
||
|
if length(lastwarn) > 0;
|
||
|
% disp('Singularity problem in dr11.m');
|
||
|
dr1_test_(1) = 2;
|
||
|
dr1_test_(2) = 1;
|
||
|
return
|
||
|
end
|
||
|
end
|
||
|
warning(warning_state);
|
||
|
|
||
|
% 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)));
|
||
|
dr.ghx = [temp; dr.ghx];
|
||
|
temp = [];
|
||
|
end
|
||
|
|
||
|
if options_.loglinear == 1
|
||
|
k = find(dr.kstate(:,2) <= M_.maximum_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
|
||
|
if use_qzdiv
|
||
|
gx = real(gx);
|
||
|
hx = real(hx);
|
||
|
dr.ghx = real(dr.ghx);
|
||
|
dr.ghu = real(dr.ghu);
|
||
|
end
|
||
|
|
||
|
|