177 lines
8.0 KiB
Matlab
177 lines
8.0 KiB
Matlab
function [oo_, maxerror] = perfect_foresight_solver_core(M_, options_, oo_)
|
|
%function [oo_, maxerror] = simulation_core(M_, options_, oo_)
|
|
|
|
% Copyright (C) 2015 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/>.
|
|
|
|
if options_.linear_approximation && ~(isequal(options_.stack_solve_algo,0) || isequal(options_.stack_solve_algo,7))
|
|
error('perfect_foresight_solver: Option linear_approximation is only available with option stack_solve_algo equal to 0.')
|
|
end
|
|
|
|
if options_.linear && isequal(options_.stack_solve_algo,0)
|
|
options_.linear_approximation = 1;
|
|
end
|
|
|
|
if options_.block
|
|
if options_.bytecode
|
|
try
|
|
[info, tmp] = bytecode('dynamic', oo_.endo_simul, oo_.exo_simul, M_.params, repmat(oo_.steady_state,1,options_.periods+2), options_.periods);
|
|
catch
|
|
info = 0;
|
|
end
|
|
if info
|
|
oo_.deterministic_simulation.status = false;
|
|
else
|
|
oo_.endo_simul = tmp;
|
|
oo_.deterministic_simulation.status = true;
|
|
end
|
|
if options_.no_homotopy
|
|
mexErrCheck('bytecode', info);
|
|
end
|
|
else
|
|
oo_ = feval([M_.fname '_dynamic'], options_, M_, oo_);
|
|
end
|
|
else
|
|
if options_.bytecode
|
|
try
|
|
[info, tmp] = bytecode('dynamic', oo_.endo_simul, oo_.exo_simul, M_.params, repmat(oo_.steady_state,1,options_.periods+2), options_.periods);
|
|
catch
|
|
info = 0;
|
|
end
|
|
if info
|
|
oo_.deterministic_simulation.status = false;
|
|
else
|
|
oo_.endo_simul = tmp;
|
|
oo_.deterministic_simulation.status = true;
|
|
end
|
|
if options_.no_homotopy
|
|
mexErrCheck('bytecode', info);
|
|
end
|
|
else
|
|
if M_.maximum_endo_lead == 0 % Purely backward model
|
|
oo_ = sim1_purely_backward(options_, M_, oo_);
|
|
elseif M_.maximum_endo_lag == 0 % Purely forward model
|
|
oo_ = sim1_purely_forward(options_, M_, oo_);
|
|
else % General case
|
|
if options_.stack_solve_algo == 0
|
|
if options_.linear_approximation
|
|
oo_ = sim1_linear(options_, M_, oo_);
|
|
else
|
|
oo_ = sim1(M_, options_, oo_);
|
|
end
|
|
elseif options_.stack_solve_algo == 6
|
|
oo_ = sim1_lbj(options_, M_, oo_);
|
|
elseif options_.stack_solve_algo == 7
|
|
periods = options_.periods;
|
|
if ~isfield(options_.lmmcp,'lb')
|
|
[lb,ub,pfm.eq_index] = get_complementarity_conditions(M_,options_.ramsey_policy);
|
|
options_.lmmcp.lb = repmat(lb,periods,1);
|
|
options_.lmmcp.ub = repmat(ub,periods,1);
|
|
end
|
|
y = oo_.endo_simul;
|
|
y0 = y(:,1);
|
|
yT = y(:,periods+2);
|
|
z = y(:,2:periods+1);
|
|
illi = M_.lead_lag_incidence';
|
|
[i_cols,junk,i_cols_j] = find(illi(:));
|
|
illi = illi(:,2:3);
|
|
[i_cols_J1,junk,i_cols_1] = find(illi(:));
|
|
i_cols_T = nonzeros(M_.lead_lag_incidence(1:2,:)');
|
|
if options_.linear_approximation
|
|
y_steady_state = oo_.steady_state;
|
|
x_steady_state = transpose(oo_.exo_steady_state);
|
|
ip = find(M_.lead_lag_incidence(1,:)');
|
|
ic = find(M_.lead_lag_incidence(2,:)');
|
|
in = find(M_.lead_lag_incidence(3,:)');
|
|
% Evaluate the Jacobian of the dynamic model at the deterministic steady state.
|
|
model_dynamic = str2func([M_.fname,'_dynamic']);
|
|
[d1,jacobian] = model_dynamic(y_steady_state([ip; ic; in]), x_steady_state, M_.params, y_steady_state, 1);
|
|
% Check that the dynamic model was evaluated at the steady state.
|
|
if max(abs(d1))>1e-12
|
|
error('Jacobian is not evaluated at the steady state!')
|
|
end
|
|
nyp = nnz(M_.lead_lag_incidence(1,:)) ;
|
|
ny0 = nnz(M_.lead_lag_incidence(2,:)) ;
|
|
nyf = nnz(M_.lead_lag_incidence(3,:)) ;
|
|
nd = nyp+ny0+nyf; % size of y (first argument passed to the dynamic file).
|
|
jexog = transpose(nd+(1:M_.exo_nbr));
|
|
jendo = transpose(1:nd);
|
|
z = bsxfun(@minus,z,y_steady_state);
|
|
x = bsxfun(@minus,oo_.exo_simul,x_steady_state);
|
|
[y,info] = dynare_solve(@linear_perfect_foresight_problem,z(:), options_, ...
|
|
jacobian, y0-y_steady_state, yT-y_steady_state, ...
|
|
x, M_.params, y_steady_state, ...
|
|
M_.maximum_lag, options_.periods, M_.endo_nbr, i_cols, ...
|
|
i_cols_J1, i_cols_1, i_cols_T, i_cols_j, ...
|
|
M_.NNZDerivatives(1),jendo,jexog);
|
|
else
|
|
[y,info] = dynare_solve(@perfect_foresight_problem,z(:),options_, ...
|
|
str2func([M_.fname '_dynamic']),y0,yT, ...
|
|
oo_.exo_simul,M_.params,oo_.steady_state, ...
|
|
M_.maximum_lag,options_.periods,M_.endo_nbr,i_cols, ...
|
|
i_cols_J1, i_cols_1, i_cols_T, i_cols_j, ...
|
|
M_.NNZDerivatives(1));
|
|
end
|
|
if all(imag(y)<.1*options_.dynatol.f)
|
|
if ~isreal(y)
|
|
y = real(y);
|
|
end
|
|
else
|
|
info = 1;
|
|
end
|
|
if options_.linear_approximation
|
|
oo_.endo_simul = [y0 bsxfun(@plus,reshape(y,M_.endo_nbr,periods),y_steady_state) yT];
|
|
else
|
|
oo_.endo_simul = [y0 reshape(y,M_.endo_nbr,periods) yT];
|
|
end
|
|
if info == 1
|
|
oo_.deterministic_simulation.status = false;
|
|
else
|
|
oo_.deterministic_simulation.status = true;
|
|
end
|
|
end
|
|
end
|
|
end
|
|
end
|
|
|
|
if nargout>1
|
|
y0 = oo_.endo_simul(:,1);
|
|
yT = oo_.endo_simul(:,options_.periods+2);
|
|
yy = oo_.endo_simul(:,2:options_.periods+1);
|
|
if ~exist('illi')
|
|
illi = M_.lead_lag_incidence';
|
|
[i_cols,junk,i_cols_j] = find(illi(:));
|
|
illi = illi(:,2:3);
|
|
[i_cols_J1,junk,i_cols_1] = find(illi(:));
|
|
i_cols_T = nonzeros(M_.lead_lag_incidence(1:2,:)');
|
|
end
|
|
if options_.block && ~options_.bytecode
|
|
maxerror = oo_.deterministic_simulation.error;
|
|
else
|
|
if options_.bytecode
|
|
[chck, residuals, junk]= bytecode('dynamic','evaluate', oo_.endo_simul, oo_.exo_simul, M_.params, oo_.steady_state, 1);
|
|
else
|
|
residuals = perfect_foresight_problem(yy(:),str2func([M_.fname '_dynamic']), y0, yT, ...
|
|
oo_.exo_simul,M_.params,oo_.steady_state, ...
|
|
M_.maximum_lag,options_.periods,M_.endo_nbr,i_cols, ...
|
|
i_cols_J1, i_cols_1, i_cols_T, i_cols_j, ...
|
|
M_.NNZDerivatives(1));
|
|
end
|
|
maxerror = max(max(abs(residuals)));
|
|
end
|
|
end
|