149 lines
3.9 KiB
Matlab
149 lines
3.9 KiB
Matlab
function sim1()
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% function sim1
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% Performs deterministic simulations with lead or lag on one period.
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% Uses sparse matrices.
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%
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% INPUTS
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% ...
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% OUTPUTS
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% ...
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% ALGORITHM
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% ...
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%
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% SPECIAL REQUIREMENTS
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% None.
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% Copyright (C) 1996-2012 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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global M_ options_ oo_
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lead_lag_incidence = M_.lead_lag_incidence;
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ny = M_.endo_nbr;
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max_lag = M_.maximum_endo_lag;
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nyp = nnz(lead_lag_incidence(1,:)) ;
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iyp = find(lead_lag_incidence(1,:)>0) ;
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ny0 = nnz(lead_lag_incidence(2,:)) ;
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iy0 = find(lead_lag_incidence(2,:)>0) ;
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nyf = nnz(lead_lag_incidence(3,:)) ;
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iyf = find(lead_lag_incidence(3,:)>0) ;
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nd = nyp+ny0+nyf;
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nrc = nyf+1 ;
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isp = [1:nyp] ;
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is = [nyp+1:ny+nyp] ;
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isf = iyf+nyp ;
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isf1 = [nyp+ny+1:nyf+nyp+ny+1] ;
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stop = 0 ;
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iz = [1:ny+nyp+nyf];
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periods = options_.periods
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steady_state = oo_.steady_state;
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params = M_.params;
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endo_simul = oo_.endo_simul;
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exo_simul = oo_.exo_simul;
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i_cols_1 = nonzeros(lead_lag_incidence(2:3,:)');
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i_cols_A1 = find(lead_lag_incidence(2:3,:)');
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i_cols_T = nonzeros(lead_lag_incidence(1:2,:)');
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i_cols_j = 1:nd;
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i_upd = ny+(1:periods*ny);
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Y = endo_simul(:);
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disp (['-----------------------------------------------------']) ;
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disp (['MODEL SIMULATION :']) ;
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fprintf('\n') ;
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model_dynamic = str2func([M_.fname,'_dynamic']);
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z = Y(find(lead_lag_incidence'));
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[d1,jacobian] = model_dynamic(z,oo_.exo_simul, params, ...
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steady_state,2);
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A = sparse([],[],[],periods*ny,periods*ny,periods*nnz(jacobian));
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res = zeros(periods*ny,1);
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h1 = clock ;
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for iter = 1:options_.maxit_
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h2 = clock ;
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i_rows = 1:ny;
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i_cols = find(lead_lag_incidence');
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i_cols_A = i_cols;
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for it = 2:(periods+1)
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[d1,jacobian] = model_dynamic(Y(i_cols),exo_simul, params, ...
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steady_state,it);
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if it == 2
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A(i_rows,i_cols_A1) = jacobian(:,i_cols_1);
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elseif it == periods+1
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A(i_rows,i_cols_A(i_cols_T)) = jacobian(:,i_cols_T);
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else
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A(i_rows,i_cols_A) = jacobian(:,i_cols_j);
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end
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res(i_rows) = d1;
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i_rows = i_rows + ny;
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i_cols = i_cols + ny;
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if it > 2
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i_cols_A = i_cols_A + ny;
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end
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end
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err = max(abs(res));
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if err < options_.dynatol.f
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stop = 1 ;
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fprintf('\n') ;
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disp([' Total time of simulation :' num2str(etime(clock,h1))]) ;
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fprintf('\n') ;
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disp([' Convergency obtained.']) ;
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fprintf('\n') ;
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oo_.deterministic_simulation.status = 1;% Convergency obtained.
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oo_.deterministic_simulation.error = err;
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oo_.deterministic_simulation.iterations = iter;
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oo_.endo_simul = reshape(Y,ny,periods+2);
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break
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end
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dy = -A\res;
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Y(i_upd) = Y(i_upd) + dy;
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end
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if ~stop
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fprintf('\n') ;
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disp([' Total time of simulation :' num2str(etime(clock,h1))]) ;
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fprintf('\n') ;
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disp(['WARNING : maximum number of iterations is reached (modify options_.maxit_).']) ;
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fprintf('\n') ;
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oo_.deterministic_simulation.status = 0;% more iterations are needed.
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oo_.deterministic_simulation.error = err;
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oo_.deterministic_simulation.errors = c/abs(err);
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oo_.deterministic_simulation.iterations = options_.maxit_;
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end
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disp (['-----------------------------------------------------']) ;
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