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function [oo_, maxerror] = perfect_foresight_solver_core(M_, options_, oo_)
%function [oo_, maxerror] = perfect_foresight_solver_core(M_, options_, oo_)
% Core function calling solvers for perfect foresight model
%
% INPUTS
% - M_ [struct] contains a description of the model.
% - options_ [struct] contains various options.
% - oo_ [struct] contains results
%
% OUTPUTS
% - oo_ [struct] contains results
% - maxerror [double] contains the maximum absolute error
% Copyright (C) 2015-2017 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_.lmmcp.status
options_.stack_solve_algo=7;
options_.solve_algo = 10;
end
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 = 1;
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 = 1;
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_.endo_simul, oo_.deterministic_simulation] = ...
sim1_purely_backward(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_);
elseif M_.maximum_endo_lag == 0 % Purely forward model
[oo_.endo_simul, oo_.deterministic_simulation] = ...
sim1_purely_forward(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_);
else % General case
switch options_.stack_solve_algo
case 0
if options_.linear_approximation
[oo_.endo_simul, oo_.deterministic_simulation] = ...
sim1_linear(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, oo_.exo_steady_state, M_, options_);
else
[oo_.endo_simul, oo_.deterministic_simulation] = ...
sim1(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_);
end
case 6
if options_.linear_approximation
error('Invalid value of stack_solve_algo option!')
end
[oo_.endo_simul, oo_.deterministic_simulation] = ...
sim1_lbj(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_);
case 7
if options_.linear_approximation
if isequal(options_.solve_algo, 10)
warning('It would be more efficient to set option solve_algo equal to 0!')
end
[oo_.endo_simul, oo_.deterministic_simulation] = ...
solve_stacked_linear_problem(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, oo_.exo_steady_state, M_, options_);
else
[oo_.endo_simul, oo_.deterministic_simulation] = ...
solve_stacked_problem(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_);
end
otherwise
error('Invalid value of stack_solve_algo option!')
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
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