Removed global from set_state_space.
parent
fcd016dc25
commit
1fb89a07e9
|
@ -66,7 +66,7 @@ if isempty(options.qz_criterium)
|
|||
options.qz_criterium = 1+1e-6;
|
||||
end
|
||||
|
||||
oo.dr=set_state_space(oo.dr,M);
|
||||
oo.dr=set_state_space(oo.dr,M,options);
|
||||
|
||||
[dr,info,M,options,oo] = resol(1,M,options,oo);
|
||||
|
||||
|
|
|
@ -142,7 +142,7 @@ M_.lead_lag_incidence = LLI';
|
|||
% set the state
|
||||
dr=oo_.dr;
|
||||
dr.ys =zeros(endo_nbr,1);
|
||||
dr=set_state_space(dr,M_);
|
||||
dr=set_state_space(dr,M_,options_);
|
||||
order_var=dr.order_var;
|
||||
|
||||
T=H(order_var,order_var);
|
||||
|
|
|
@ -208,7 +208,7 @@ function [resid,dr] = risky_residuals(ys,M,dr,options,oo)
|
|||
d2a = d2(eq,ih1);
|
||||
|
||||
M.endo_nbr = M.endo_nbr-n_tags;
|
||||
dr = set_state_space(dr,M);
|
||||
dr = set_state_space(dr,M,options);
|
||||
|
||||
[junk,dr.i_fwrd_g] = find(lead_lag_incidence(3,dr.order_var));
|
||||
i_fwrd_f = nonzeros(lead_incidence(dr.order_var));
|
||||
|
@ -434,7 +434,7 @@ function [dr] = first_step_ds(x,M,dr,options,oo)
|
|||
d2a = d2(eq,ih1);
|
||||
|
||||
M.endo_nbr = M.endo_nbr-n_tags;
|
||||
dr = set_state_space(dr,M);
|
||||
dr = set_state_space(dr,M,options);
|
||||
|
||||
dr.i_fwrd_g = find(lead_lag_incidence(3,dr.order_var)');
|
||||
else
|
||||
|
|
|
@ -191,7 +191,7 @@ end
|
|||
bayestopt_.penalty = 1e8;
|
||||
|
||||
% Get informations about the variables of the model.
|
||||
dr = set_state_space(oo_.dr,M_);
|
||||
dr = set_state_space(oo_.dr,M_,options_);
|
||||
oo_.dr = dr;
|
||||
nstatic = dr.nstatic; % Number of static variables.
|
||||
npred = dr.npred; % Number of predetermined variables.
|
||||
|
|
|
@ -40,7 +40,7 @@ end
|
|||
|
||||
exe =zeros(M_.exo_nbr,1);
|
||||
|
||||
oo_.dr = set_state_space(oo_.dr,M_);
|
||||
oo_.dr = set_state_space(oo_.dr,M_,options_);
|
||||
|
||||
|
||||
np = size(i_params,1);
|
||||
|
|
|
@ -155,7 +155,7 @@ if options_.debug
|
|||
save([M_.fname '_debug.mat'],'jacobia_')
|
||||
end
|
||||
|
||||
dr=set_state_space(dr,M_);
|
||||
dr=set_state_space(dr,M_,options_);
|
||||
kstate = dr.kstate;
|
||||
kad = dr.kad;
|
||||
kae = dr.kae;
|
||||
|
|
|
@ -1,19 +1,38 @@
|
|||
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
|
||||
%
|
||||
function dr=set_state_space(dr,DynareModel,DynareOptions)
|
||||
% Write the state space representation of the reduced form solution.
|
||||
|
||||
%@info:
|
||||
%! @deftypefn {Function File} {[@var{dr} =} set_state_space (@var{dr},@var{DynareModel},@var{DynareOptions})
|
||||
%! @anchor{set_state_space}
|
||||
%! @sp 1
|
||||
%! Write the state space representation of the reduced form solution.
|
||||
%! @sp 2
|
||||
%! @strong{Inputs}
|
||||
%! @sp 1
|
||||
%! @table @ @var
|
||||
%! @item dr
|
||||
%! Matlab's structure describing decision and transition rules.
|
||||
%! @item DynareModel
|
||||
%! Matlab's structure describing the model (initialized by dynare, see @ref{M_})
|
||||
%! @item DynareOptions
|
||||
%! Matlab's structure describing the current options (initialized by dynare, see @ref{options_}).
|
||||
%! @end table
|
||||
%! @sp 2
|
||||
%! @strong{Outputs}
|
||||
%! @sp 1
|
||||
%! @table @ @var
|
||||
%! @item dr
|
||||
%! Matlab's structure describing decision and transition rules.
|
||||
%! @end table
|
||||
%! @sp 2
|
||||
%! @strong{This function is called by:}
|
||||
%! @sp 1
|
||||
%! @ref{check}, @ref{discretionary_policy_1}, @ref{dynare_estimation_init}, @ref{dyn_risky_steady_state_solver}, @ref{osr1}, @ref{partial_information/dr1_PI}, @ref{pea/pea_initialization}, @ref{stochastic_solvers}, @ref{stoch_simul}
|
||||
%! @sp 2
|
||||
%! @strong{This function calls:}
|
||||
%! @sp 2
|
||||
%! @end deftypefn
|
||||
%@eod:
|
||||
|
||||
% Copyright (C) 1996-2011 Dynare Team
|
||||
%
|
||||
|
@ -31,12 +50,11 @@ function dr=set_state_space(dr,M_)
|
|||
%
|
||||
% You should have received a copy of the GNU General Public License
|
||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||
global options_
|
||||
|
||||
max_lead = M_.maximum_endo_lead;
|
||||
max_lag = M_.maximum_endo_lag;
|
||||
endo_nbr = M_.endo_nbr;
|
||||
lead_lag_incidence = M_.lead_lag_incidence;
|
||||
max_lead = DynareModel.maximum_endo_lead;
|
||||
max_lag = DynareModel.maximum_endo_lag;
|
||||
endo_nbr = DynareModel.endo_nbr;
|
||||
lead_lag_incidence = DynareModel.lead_lag_incidence;
|
||||
klen = max_lag + max_lead + 1;
|
||||
|
||||
fwrd_var = find(lead_lag_incidence(max_lag+2:end,:))';
|
||||
|
@ -55,8 +73,8 @@ nboth = length(both_var);
|
|||
npred = length(pred_var);
|
||||
nfwrd = length(fwrd_var);
|
||||
nstatic = length(stat_var);
|
||||
if options_.block == 1
|
||||
order_var = M_.block_structure.variable_reordered;
|
||||
if DynareOptions.block == 1
|
||||
order_var = DynareModel.block_structure.variable_reordered;
|
||||
else
|
||||
order_var = [ stat_var(:); pred_var(:); both_var(:); fwrd_var(:)];
|
||||
end;
|
||||
|
@ -65,7 +83,7 @@ inv_order_var(order_var) = (1:endo_nbr);
|
|||
% building kmask for z state vector in t+1
|
||||
if max_lag > 0
|
||||
kmask = [];
|
||||
if max_lead > 0
|
||||
if max_lead > 0
|
||||
kmask = lead_lag_incidence(max_lag+2,order_var) ;
|
||||
end
|
||||
kmask = [kmask; lead_lag_incidence(1,order_var)] ;
|
||||
|
@ -81,30 +99,30 @@ 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)] );
|
||||
k1 = find([kmask(1:end-DynareModel.endo_nbr) & kmask(DynareModel.endo_nbr+1:end)] );
|
||||
kad = [];
|
||||
kae = [];
|
||||
if ~isempty(k1)
|
||||
kad = kmask(k1+M_.endo_nbr);
|
||||
kad = kmask(k1+DynareModel.endo_nbr);
|
||||
kae = kmask(k1);
|
||||
end
|
||||
|
||||
% composition of state vector
|
||||
% col 1: variable; col 2: lead/lag in z(t+1);
|
||||
% 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:endo_nbr]',klen-1,1) kron([klen:-1:2]',ones(endo_nbr,1)) ...
|
||||
zeros((klen-1)*endo_nbr,2)];
|
||||
kiy = flipud(lead_lag_incidence(:,order_var))';
|
||||
kiy = kiy(:);
|
||||
if max_lead > 0
|
||||
kstate(1:endo_nbr,3) = kiy(1:endo_nbr)-nnz(lead_lag_incidence(max_lag+1,:));
|
||||
kstate(1:endo_nbr,3) = kiy(1:endo_nbr)-nnz(lead_lag_incidence(max_lag+1,:));
|
||||
kstate(kstate(:,3) < 0,3) = 0;
|
||||
kstate(endo_nbr+1:end,4) = kiy(2*endo_nbr+1:end);
|
||||
kstate(endo_nbr+1:end,4) = kiy(2*endo_nbr+1:end);
|
||||
else
|
||||
kstate(:,4) = kiy(endo_nbr+1:end);
|
||||
kstate(:,4) = kiy(endo_nbr+1:end);
|
||||
end
|
||||
kstate = kstate(i_kmask,:);
|
||||
|
||||
|
||||
dr.order_var = order_var;
|
||||
dr.inv_order_var = inv_order_var';
|
||||
dr.nstatic = nstatic;
|
||||
|
@ -119,4 +137,4 @@ dr.nsfwrd = nfwrd+nboth;
|
|||
% number of predetermined variables in the state vector
|
||||
dr.nspred = npred+nboth;
|
||||
|
||||
dr.transition_auxiliary_variables = [];
|
||||
dr.transition_auxiliary_variables = [];
|
|
@ -62,7 +62,7 @@ end
|
|||
|
||||
check_model(M_);
|
||||
|
||||
oo_.dr=set_state_space(dr,M_);
|
||||
oo_.dr=set_state_space(dr,M_,options_);
|
||||
|
||||
if PI_PCL_solver
|
||||
[oo_.dr, info] = PCL_resol(oo_.steady_state,0);
|
||||
|
|
|
@ -57,7 +57,7 @@ if options_.k_order_solver;
|
|||
[dr,info] = dyn_risky_steadystate_solver(oo_.steady_state,M_,dr, ...
|
||||
options_,oo_);
|
||||
else
|
||||
dr = set_state_space(dr,M_);
|
||||
dr = set_state_space(dr,M_,options_);
|
||||
[dr,info] = k_order_pert(dr,M_,options_,oo_);
|
||||
end
|
||||
return;
|
||||
|
|
Loading…
Reference in New Issue