153 lines
6.8 KiB
Matlab
153 lines
6.8 KiB
Matlab
function [y, success, maxerror, iter, per_block_status] = perfect_foresight_solver_core(M_, options_, oo_)
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% Core function calling solvers for perfect foresight model
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%
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% INPUTS
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% - M_ [struct] contains a description of the model.
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% - options_ [struct] contains various options.
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% - oo_ [struct] contains results
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%
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% OUTPUTS
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% - y [double array] path for the endogenous variables (solution)
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% - success [logical] Whether a solution was found
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% - maxerror [double] contains the maximum absolute error
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% - iter [integer] Number of iterations of the underlying nonlinear solver (empty for non-iterative methods)
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% - per_block_status [struct] In the case of block decomposition, provides per-block solver status information (empty if no block decomposition)
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% Copyright © 2015-2023 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 <https://www.gnu.org/licenses/>.
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if options_.lmmcp.status
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options_.stack_solve_algo=7;
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options_.solve_algo = 10;
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elseif options_.stack_solve_algo==7 && options_.solve_algo == 11
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options_.lmmcp.status = 1; %Path solver
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end
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periods = options_.periods;
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if options_.linear_approximation && ~(isequal(options_.stack_solve_algo,0) || isequal(options_.stack_solve_algo,7))
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error('perfect_foresight_solver: Option linear_approximation is only available with option stack_solve_algo equal to 0 or 7.')
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end
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if options_.endogenous_terminal_period && options_.stack_solve_algo ~= 0
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error('perfect_foresight_solver: option endogenous_terminal_period is only available with option stack_solve_algo equal to 0')
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end
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if options_.linear && (isequal(options_.stack_solve_algo, 0) || isequal(options_.stack_solve_algo, 7))
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options_.linear_approximation = true;
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end
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maxerror = [];
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iter = [];
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per_block_status = [];
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if options_.block
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if M_.block_structure.time_recursive
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error('Internal error: can''t perform stacked perfect foresight simulation with time-recursive block decomposition')
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end
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if options_.bytecode
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try
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y = bytecode('dynamic', 'block_decomposed', oo_.endo_simul, oo_.exo_simul, M_.params, repmat(oo_.steady_state,1, periods+2), periods);
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success = true;
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catch ME
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if options_.verbosity >= 1
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disp(ME.message)
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end
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y = oo_.endo_simul; % Set something for y, need for computing maxerror
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success = false;
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end
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else
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[y, success, maxerror, per_block_status] = solve_block_decomposed_problem(options_, M_, oo_);
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end
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else
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if options_.bytecode
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try
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y = bytecode('dynamic', oo_.endo_simul, oo_.exo_simul, M_.params, repmat(oo_.steady_state, 1, periods+2), periods);
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success = true;
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catch ME
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if options_.verbosity >= 1
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disp(ME.message)
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end
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y = oo_.endo_simul; % Set something for y, need for computing maxerror
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success = false;
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end
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else
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if M_.maximum_endo_lead == 0 && M_.maximum_endo_lag>0 && ~options_.lmmcp.status % Purely backward model
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[y, success] = sim1_purely_backward(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_);
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elseif M_.maximum_endo_lag == 0 && M_.maximum_endo_lead>0 && ~options_.lmmcp.status % Purely forward model
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[y, success] = sim1_purely_forward(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_);
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elseif M_.maximum_endo_lag == 0 && M_.maximum_endo_lead == 0 && ~options_.lmmcp.status % Purely static model
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[y, success] = sim1_purely_static(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_);
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else % General case
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switch options_.stack_solve_algo
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case 0
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if options_.linear_approximation
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[y, success, maxerror] = sim1_linear(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, oo_.exo_steady_state, M_, options_);
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else
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[y, success, maxerror, iter] = sim1(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_);
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end
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case {1 6}
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if options_.linear_approximation
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error('Invalid value of stack_solve_algo option!')
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end
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[y, success, maxerror, iter] = sim1_lbj(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_);
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case 7
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if options_.linear_approximation
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if isequal(options_.solve_algo, 10)
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if options_.ramsey_policy && isfield(M_,'ramsey_model_constraints') && ~isempty(M_.ramsey_model_constraints)
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warning('Due to ramsey_constraints you should not specify your model as model(linear)!')
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elseif options_.lmmcp.status
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warning('Due to lmmcp option, you should not specify your model as model(linear)!')
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else
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warning('It would be more efficient to set option solve_algo equal to 0!')
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end
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end
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[y, success] = solve_stacked_linear_problem(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, oo_.exo_steady_state, M_, options_);
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else
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[y, success, maxerror] = solve_stacked_problem(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_);
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end
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otherwise
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error('Invalid value of stack_solve_algo option!')
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end
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end
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end
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end
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% Some solvers do not compute the maximum error, so do it here if needed
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if nargout > 2 && isempty(maxerror)
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if options_.bytecode
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residuals = bytecode('dynamic', 'evaluate', y, oo_.exo_simul, M_.params, oo_.steady_state, periods);
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else
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ny = size(y, 1);
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if M_.maximum_lag > 0
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y0 = y(:, M_.maximum_lag);
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else
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y0 = NaN(ny, 1);
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end
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if M_.maximum_lead > 0
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yT = y(:, M_.maximum_lag+periods+1);
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else
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yT = NaN(ny, 1);
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end
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yy = y(:,M_.maximum_lag+(1:periods));
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residuals = perfect_foresight_problem(yy(:), y0, yT, oo_.exo_simul, M_.params, oo_.steady_state, periods, M_, options_);
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end
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maxerror = max(max(abs(residuals)));
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end
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