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Removed global from set_state_space.

time-shift
Stéphane Adjemian (Charybdis) 2012-06-29 12:32:54 +02:00
parent fcd016dc25
commit 1fb89a07e9
9 changed files with 59 additions and 41 deletions
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2
matlab/check.m
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@ -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);

2
matlab/discretionary_policy_1.m
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@ -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);

4
matlab/dyn_risky_steadystate_solver.m
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@ -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

2
matlab/dynare_estimation_init.m
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@ -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.

2
matlab/osr1.m
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@ -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);

2
matlab/partial_information/dr1_PI.m
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@ -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;

82
matlab/set_state_space.m
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@ -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 = [];

2
matlab/stoch_simul.m
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@ -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);

2
matlab/stochastic_solvers.m
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@ -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;