132 lines
4.4 KiB
Matlab
132 lines
4.4 KiB
Matlab
function dr=set_state_space(dr,M_)
|
|
% function dr = set_state_space(dr,M_)
|
|
% finds the state vector for structural state space representation
|
|
% sets many fields of dr
|
|
%
|
|
% INPUTS
|
|
% dr: structure of decision rules for stochastic simulations
|
|
%
|
|
% OUTPUTS
|
|
% dr: structure of decision rules for stochastic simulations
|
|
%
|
|
% ALGORITHM
|
|
% ...
|
|
% SPECIAL REQUIREMENTS
|
|
% none
|
|
%
|
|
|
|
% Copyright (C) 1996-2007 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/>.
|
|
|
|
xlen = M_.maximum_endo_lead + M_.maximum_endo_lag + 1;
|
|
klen = M_.maximum_endo_lag + M_.maximum_endo_lead + 1;
|
|
|
|
if ~ M_.lead_lag_incidence(M_.maximum_endo_lag+1,:) > 0
|
|
error ('Error in model specification: some variables don"t appear as current') ;
|
|
end
|
|
|
|
fwrd_var = find(any(M_.lead_lag_incidence(M_.maximum_endo_lag+2:end,:),1))';
|
|
if M_.maximum_endo_lag > 0
|
|
pred_var = find(any(M_.lead_lag_incidence(1:M_.maximum_endo_lag,:),1))';
|
|
both_var = intersect(pred_var,fwrd_var);
|
|
pred_var = setdiff(pred_var,both_var);
|
|
fwrd_var = setdiff(fwrd_var,both_var);
|
|
stat_var = setdiff([1:M_.endo_nbr]',union(union(pred_var,both_var),fwrd_var)); % static variables
|
|
else
|
|
pred_var = [];
|
|
both_var = [];
|
|
stat_var = setdiff([1:M_.endo_nbr]',fwrd_var);
|
|
end
|
|
nboth = length(both_var);
|
|
npred = length(pred_var);
|
|
nfwrd = length(fwrd_var);
|
|
nstatic = length(stat_var);
|
|
order_var = [ stat_var(:); pred_var(:); both_var(:); fwrd_var(:)];
|
|
inv_order_var(order_var) = (1:M_.endo_nbr);
|
|
|
|
% building kmask for z state vector in t+1
|
|
if M_.maximum_endo_lag > 0
|
|
kmask = [];
|
|
if M_.maximum_endo_lead > 0
|
|
kmask = [cumsum(flipud(M_.lead_lag_incidence(M_.maximum_endo_lag+2:end,order_var)),1)] ;
|
|
end
|
|
kmask = [kmask; flipud(cumsum(M_.lead_lag_incidence(1:M_.maximum_endo_lag,order_var),1))] ;
|
|
else
|
|
kmask = cumsum(flipud(M_.lead_lag_incidence(M_.maximum_endo_lag+2:klen,order_var)),1) ;
|
|
end
|
|
|
|
kmask = kmask';
|
|
kmask = kmask(:);
|
|
i_kmask = find(kmask); % index of nonzero entries in kmask
|
|
nd = size(i_kmask,1); % size of the state vector
|
|
kmask(i_kmask) = [1:nd];
|
|
|
|
% auxiliary equations
|
|
|
|
% elements that are both in z(t+1) and z(t)
|
|
k1 = find([kmask(1:end-M_.endo_nbr) & kmask(M_.endo_nbr+1:end)] );
|
|
kad = [];
|
|
kae = [];
|
|
if ~isempty(k1)
|
|
kad = kmask(k1+M_.endo_nbr);
|
|
kae = kmask(k1);
|
|
end
|
|
|
|
% composition of state vector
|
|
% col 1: variable; col 2: lead/lag in z(t+1);
|
|
% col 3: A cols for t+1 (D); col 4: A cols for t (E)
|
|
kstate = [ repmat([1:M_.endo_nbr]',klen-1,1) kron([klen:-1:2]',ones(M_.endo_nbr,1)) ...
|
|
zeros((klen-1)*M_.endo_nbr,2)];
|
|
kiy = flipud(M_.lead_lag_incidence(:,order_var))';
|
|
kiy = kiy(:);
|
|
kstate(1:M_.maximum_endo_lead*M_.endo_nbr,3) = kiy(1:M_.maximum_endo_lead*M_.endo_nbr)-M_.endo_nbr;
|
|
kstate(find(kstate(:,3) < 0),3) = 0;
|
|
kstate(M_.maximum_endo_lead*M_.endo_nbr+1:end,4) = kiy((M_.maximum_endo_lead+1)*M_.endo_nbr+1:end);
|
|
% put in E only the current variables that are not already in D
|
|
kstate = kstate(i_kmask,:);
|
|
|
|
dr.order_var = order_var;
|
|
dr.inv_order_var = inv_order_var';
|
|
dr.nstatic = nstatic;
|
|
dr.npred = npred+nboth;
|
|
dr.kstate = kstate;
|
|
dr.kad = kad;
|
|
dr.kae = kae;
|
|
dr.nboth = nboth;
|
|
dr.nfwrd = nfwrd;
|
|
% number of forward variables in the state vector
|
|
dr.nsfwrd = sum(kstate(:,2) > M_.maximum_endo_lag+1);
|
|
% number of predetermined variables in the state vector
|
|
dr.nspred = sum(kstate(:,2) <= M_.maximum_endo_lag+1);
|
|
|
|
% computes column position of auxiliary variables for
|
|
% compact transition matrix (only state variables)
|
|
aux = zeros(0,1);
|
|
k0 = kstate(find(kstate(:,2) <= M_.maximum_endo_lag+1),:);;
|
|
i0 = find(k0(:,2) == M_.maximum_endo_lag+1);
|
|
for i=M_.maximum_endo_lag:-1:2
|
|
i1 = find(k0(:,2) == i);
|
|
n1 = size(i1,1);
|
|
j = zeros(n1,1);
|
|
for j1 = 1:n1
|
|
j(j1) = find(k0(i0,1)==k0(i1(j1),1));
|
|
end
|
|
aux = [aux; i0(j)];
|
|
i0 = i1;
|
|
end
|
|
dr.transition_auxiliary_variables = [(1:size(aux,1))' aux];
|