new algorithm for deterministic simulations. Not yet integrated into
Dynare (no function calls it).time-shift
parent
6328a44f33
commit
32054371b8
|
@ -0,0 +1,150 @@
|
|||
function sim1
|
||||
% function sim1
|
||||
% performs deterministic simulations with lead or lag on one period
|
||||
%
|
||||
% INPUTS
|
||||
% ...
|
||||
% OUTPUTS
|
||||
% ...
|
||||
% ALGORITHM
|
||||
% Laffargue, Boucekkine, Juillard (LBJ)
|
||||
% see Juillard (1996) Dynare: A program for the resolution and
|
||||
% simulation of dynamic models with forward variables through the use
|
||||
% of a relaxation algorithm. CEPREMAP. Couverture Orange. 9602.
|
||||
%
|
||||
% SPECIAL REQUIREMENTS
|
||||
% None.
|
||||
|
||||
% Copyright (C) 1996-2010 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/>.
|
||||
|
||||
global M_ options_ oo_
|
||||
|
||||
lead_lag_incidence = M_.lead_lag_incidence;
|
||||
|
||||
ny = M_.endo_nbr;
|
||||
|
||||
max_lag = M_.maximum_endo_lag;
|
||||
|
||||
nyp = nnz(lead_lag_incidence(1,:)) ;
|
||||
iyp = find(lead_lag_incidence(1,:)>0) ;
|
||||
ny0 = nnz(lead_lag_incidence(2,:)) ;
|
||||
iy0 = find(lead_lag_incidence(2,:)>0) ;
|
||||
nyf = nnz(lead_lag_incidence(3,:)) ;
|
||||
iyf = find(lead_lag_incidence(3,:)>0) ;
|
||||
|
||||
nd = nyp+ny0+nyf;
|
||||
nrc = nyf+1 ;
|
||||
isp = [1:nyp] ;
|
||||
is = [nyp+1:ny+nyp] ;
|
||||
isf = iyf+nyp ;
|
||||
isf1 = [nyp+ny+1:nyf+nyp+ny+1] ;
|
||||
stop = 0 ;
|
||||
iz = [1:ny+nyp+nyf];
|
||||
|
||||
periods = options_.periods
|
||||
steady_state = oo_.steady_state;
|
||||
params = M_.params;
|
||||
endo_simul = oo_.endo_simul;
|
||||
exo_simul = oo_.exo_simul;
|
||||
i_cols_1 = nonzeros(lead_lag_incidence(2:3,:)');
|
||||
i_cols_A1 = find(lead_lag_incidence(2:3,:)');
|
||||
i_cols_T = nonzeros(lead_lag_incidence(1:2,:)');
|
||||
i_cols_j = 1:nd;
|
||||
i_upd = ny+(1:periods*ny);
|
||||
|
||||
Y = endo_simul(:);
|
||||
|
||||
disp (['-----------------------------------------------------']) ;
|
||||
disp (['MODEL SIMULATION :']) ;
|
||||
fprintf('\n') ;
|
||||
|
||||
|
||||
model_dynamic = str2func([M_.fname,'_dynamic']);
|
||||
z = Y(find(lead_lag_incidence'));
|
||||
[d1,jacobian] = model_dynamic(z,oo_.exo_simul, params, ...
|
||||
steady_state,2);
|
||||
|
||||
A = sparse([],[],[],periods*ny,periods*ny,periods*nnz(jacobian));
|
||||
res = zeros(periods*ny,1);
|
||||
|
||||
|
||||
h1 = clock ;
|
||||
for iter = 1:options_.maxit_
|
||||
h2 = clock ;
|
||||
|
||||
i_rows = 1:ny;
|
||||
i_cols = find(lead_lag_incidence');
|
||||
i_cols_A = i_cols;
|
||||
|
||||
for it = 2:(periods+1)
|
||||
|
||||
[d1,jacobian] = model_dynamic(Y(i_cols),exo_simul, params, ...
|
||||
steady_state,it);
|
||||
if it == 2
|
||||
A(i_rows,i_cols_A1) = jacobian(:,i_cols_1);
|
||||
elseif it == periods+1
|
||||
A(i_rows,i_cols_A(i_cols_T)) = jacobian(:,i_cols_T);
|
||||
else
|
||||
A(i_rows,i_cols_A) = jacobian(:,i_cols_j);
|
||||
end
|
||||
|
||||
res(i_rows) = d1;
|
||||
|
||||
i_rows = i_rows + ny;
|
||||
i_cols = i_cols + ny;
|
||||
if it > 2
|
||||
i_cols_A = i_cols_A + ny;
|
||||
end
|
||||
end
|
||||
|
||||
err = max(abs(res));
|
||||
|
||||
if err < options_.dynatol.f
|
||||
stop = 1 ;
|
||||
fprintf('\n') ;
|
||||
disp([' Total time of simulation :' num2str(etime(clock,h1))]) ;
|
||||
fprintf('\n') ;
|
||||
disp([' Convergency obtained.']) ;
|
||||
fprintf('\n') ;
|
||||
oo_.deterministic_simulation.status = 1;% Convergency obtained.
|
||||
oo_.deterministic_simulation.error = err;
|
||||
oo_.deterministic_simulation.iterations = iter;
|
||||
oo_.endo_simul = reshape(Y,ny,periods+2);
|
||||
break
|
||||
end
|
||||
|
||||
dy = -A\res;
|
||||
|
||||
Y(i_upd) = Y(i_upd) + dy;
|
||||
|
||||
end
|
||||
|
||||
|
||||
if ~stop
|
||||
fprintf('\n') ;
|
||||
disp([' Total time of simulation :' num2str(etime(clock,h1))]) ;
|
||||
fprintf('\n') ;
|
||||
disp(['WARNING : maximum number of iterations is reached (modify options_.maxit_).']) ;
|
||||
fprintf('\n') ;
|
||||
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_.maxit_;
|
||||
end
|
||||
disp (['-----------------------------------------------------']) ;
|
||||
|
Loading…
Reference in New Issue