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Removed oo_ from sim1_lbj routine.

time-shift
Stéphane Adjemian (Charybdis) 2015-12-16 11:30:15 +01:00
parent 674c9ab286
commit 290ce5db68
3 changed files with 67 additions and 71 deletions
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3
matlab/perfect-foresight-models/perfect_foresight_solver_core.m
View File

@ -75,7 +75,8 @@ else
sim1(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_);
end
elseif options_.stack_solve_algo == 6
oo_ = sim1_lbj(options_, M_, oo_);
[oo_.endo_simul, oo_.deterministic_simulation] = ...
sim1_lbj(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_);
elseif options_.stack_solve_algo == 7
periods = options_.periods;
if ~isfield(options_.lmmcp,'lb')

3
matlab/perfect-foresight-models/private/simulation_core.m
View File

@ -75,7 +75,8 @@ else
sim1(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_);
end
elseif options_.stack_solve_algo == 6
oo_ = sim1_lbj(options_, M_, oo_);
[oo_.endo_simul, oo_.deterministic_simulation] = ...
sim1_lbj(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_);
elseif options_.stack_solve_algo == 7
periods = options_.periods;
if ~isfield(options_.lmmcp,'lb')

132
matlab/perfect-foresight-models/sim1_lbj.m
View File

@ -1,7 +1,6 @@
function oo_ = sim1_lbj(options_, M_, oo_)
% function sim1_lbj
% performs deterministic simulations with lead or lag on one period
% using the historical LBJ algorithm
function [endogenousvariables, info] = sim1_lbj(endogenousvariables, exogenousvariables, steadystate, M, options)
% Performs deterministic simulations with lead or lag on one period using the historical LBJ algorithm
%
% INPUTS
% ...
@ -33,23 +32,25 @@ function oo_ = sim1_lbj(options_, M_, oo_)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
lead_lag_incidence = M_.lead_lag_incidence;
lead_lag_incidence = M.lead_lag_incidence;
ny = size(oo_.endo_simul,1) ;
nyp = nnz(lead_lag_incidence(1,:)) ;
nyf = nnz(lead_lag_incidence(3,:)) ;
nrs = ny+nyp+nyf+1 ;
nrc = nyf+1 ;
iyf = find(lead_lag_incidence(3,:)>0) ;
iyp = find(lead_lag_incidence(1,:)>0) ;
isp = [1:nyp] ;
is = [nyp+1:ny+nyp] ;
isf = iyf+nyp ;
isf1 = [nyp+ny+1:nyf+nyp+ny+1] ;
stop = 0 ;
ny = size(endogenousvariables,1);
nyp = nnz(lead_lag_incidence(1,:));
nyf = nnz(lead_lag_incidence(3,:));
nrs = ny+nyp+nyf+1;
nrc = nyf+1;
iyf = find(lead_lag_incidence(3,:)>0);
iyp = find(lead_lag_incidence(1,:)>0);
isp = [1:nyp];
is = [nyp+1:ny+nyp];
isf = iyf+nyp;
isf1 = [nyp+ny+1:nyf+nyp+ny+1];
stop = false;
iz = [1:ny+nyp+nyf];
verbose = options_.verbosity;
dynamicmodel = sprintf('%s_dynamic', M.fname);
verbose = options.verbosity;
if verbose
printline(56)
@ -57,66 +58,59 @@ if verbose
skipline()
end
it_init = M_.maximum_lag+1 ;
it_init = M.maximum_lag+1;
h1 = clock;
h1 = clock ;
for iter = 1:options_.simul.maxit
h2 = clock ;
if options_.terminal_condition == 0
c = zeros(ny*options_.periods,nrc) ;
for iter = 1:options.simul.maxit
h2 = clock;
if ~options.terminal_condition
c = zeros(ny*options.periods, nrc);
else
c = zeros(ny*(options_.periods+1),nrc) ;
c = zeros(ny*(options.periods+1), nrc);
end
it_ = it_init ;
z = [oo_.endo_simul(iyp,it_-1) ; oo_.endo_simul(:,it_) ; oo_.endo_simul(iyf,it_+1)] ;
[d1,jacobian] = feval([M_.fname '_dynamic'],z,oo_.exo_simul, M_.params, oo_.steady_state,it_);
jacobian = [jacobian(:,iz) -d1] ;
ic = [1:ny] ;
icp = iyp ;
c (ic,:) = jacobian(:,is)\jacobian(:,isf1) ;
for it_ = it_init+(1:options_.periods-1)
z = [oo_.endo_simul(iyp,it_-1) ; oo_.endo_simul(:,it_) ; oo_.endo_simul(iyf,it_+1)] ;
[d1,jacobian] = feval([M_.fname '_dynamic'],z,oo_.exo_simul, ...
M_.params, oo_.steady_state, it_);
jacobian = [jacobian(:,iz) -d1] ;
jacobian(:,[isf nrs]) = jacobian(:,[isf nrs])-jacobian(:,isp)*c(icp,:) ;
ic = ic + ny ;
icp = icp + ny ;
c (ic,:) = jacobian(:,is)\jacobian(:,isf1) ;
it_ = it_init;
z = [endogenousvariables(iyp,it_-1) ; endogenousvariables(:,it_) ; endogenousvariables(iyf,it_+1)];
[d1, jacobian] = feval(dynamicmodel, z, exogenousvariables, M.params, steadystate, it_);
jacobian = [jacobian(:,iz), -d1];
ic = [1:ny];
icp = iyp;
c (ic,:) = jacobian(:,is)\jacobian(:,isf1);
for it_ = it_init+(1:options.periods-1)
z = [endogenousvariables(iyp,it_-1) ; endogenousvariables(:,it_) ; endogenousvariables(iyf,it_+1)];
[d1, jacobian] = feval(dynamicmodel, z, exogenousvariables, M.params, steadystate, it_);
jacobian = [jacobian(:,iz), -d1];
jacobian(:,[isf nrs]) = jacobian(:,[isf nrs])-jacobian(:,isp)*c(icp,:);
ic = ic + ny;
icp = icp + ny;
c (ic,:) = jacobian(:,is)\jacobian(:,isf1);
end
if options_.terminal_condition == 1
s = eye(ny) ;
s(:,isf) = s(:,isf)+c(ic,1:nyf) ;
ic = ic + ny ;
c(ic,nrc) = s\c(ic,nrc) ;
c = bksup1(c,ny,nrc,iyf,options_.periods) ;
c = reshape(c,ny,options_.periods+1) ;
oo_.endo_simul(:,it_init+(0:options_.periods)) = oo_.endo_simul(:,it_init+(0:options_.periods))+options_.slowc*c ;
if options.terminal_condition == 1
s = eye(ny);
s(:,isf) = s(:,isf)+c(ic,1:nyf);
ic = ic + ny;
c(ic,nrc) = s\c(ic,nrc);
c = bksup1(c, ny, nrc, iyf, options.periods);
c = reshape(c, ny, options.periods+1);
endogenousvariables(:,it_init+(0:options.periods)) = endogenousvariables(:,it_init+(0:options.periods))+options.slowc*c;
else
c = bksup1(c,ny,nrc,iyf,options_.periods) ;
c = reshape(c,ny,options_.periods) ;
oo_.endo_simul(:,it_init+(0:options_.periods-1)) = oo_.endo_simul(:,it_init+(0:options_.periods-1))+options_.slowc*c ;
c = bksup1(c, ny, nrc, iyf, options.periods);
c = reshape(c, ny, options.periods);
endogenousvariables(:,it_init+(0:options.periods-1)) = endogenousvariables(:,it_init+(0:options.periods-1))+options.slowc*c;
end
err = max(max(abs(c./options_.scalv')));
err = max(max(abs(c./options.scalv')));
if verbose
str = sprintf('Iter: %s,\t err. = %s, \t time = %s',num2str(iter),num2str(err), num2str(etime(clock,h2)));
str = sprintf('Iter: %s,\t err. = %s, \t time = %s', num2str(iter), num2str(err), num2str(etime(clock, h2)));
disp(str);
end
if err < options_.dynatol.f
stop = 1 ;
if err < options.dynatol.f
stop = true;
if verbose
skipline()
disp(sprintf('Total time of simulation: %s', num2str(etime(clock,h1))))
end
oo_.deterministic_simulation.status = 1;% Convergency obtained.
oo_.deterministic_simulation.error = err;
oo_.deterministic_simulation.iterations = iter;
info.status = 1;% Convergency obtained.
info.error = err;
info.iterations = iter;
break
end
end
@ -126,10 +120,10 @@ if ~stop
disp(sprintf('Total time of simulation: %s.', num2str(etime(clock,h1))))
disp('Maximum number of iterations is reached (modify option maxit).')
end
oo_.deterministic_simulation.status = 0;% more iterations are needed.
oo_.deterministic_simulation.error = err;
oo_.deterministic_simulation.errors = c/abs(err);
oo_.deterministic_simulation.iterations = options_.simul.maxit;
info.status = 0;% more iterations are needed.
info.error = err;
info.errors = c/abs(err);
info.iterations = options.simul.maxit;
end
if verbose