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function sim1()
% function sim1
% Performs deterministic simulations with lead or lag on one period.
% Uses sparse matrices.
%
% INPUTS
% ...
% OUTPUTS
% ...
% ALGORITHM
% ...
%
% SPECIAL REQUIREMENTS
% None.
% Copyright (C) 1996-2012 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 (['-----------------------------------------------------']) ;
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