81 lines
3.6 KiB
Matlab
81 lines
3.6 KiB
Matlab
function [endogenousvariables, info] = solve_stacked_problem(endogenousvariables, exogenousvariables, steadystate, M, options)
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% Solves the perfect foresight model using dynare_solve
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%
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% INPUTS
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% - endogenousvariables [double] N*T array, paths for the endogenous variables (initial guess).
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% - exogenousvariables [double] T*M array, paths for the exogenous variables.
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% - steadystate [double] N*1 array, steady state for the endogenous variables.
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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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%
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% OUTPUTS
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% - endogenousvariables [double] N*T array, paths for the endogenous variables (solution of the perfect foresight model).
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% - info [struct] contains informations about the results.
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% Copyright © 2015-2022 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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[options, y0, yT, z, i_cols, i_cols_J1, i_cols_T, i_cols_j, i_cols_1, i_cols_0, i_cols_J0, dynamicmodel] = ...
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initialize_stacked_problem(endogenousvariables, options, M, steadystate);
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if (options.solve_algo == 10 || options.solve_algo == 11)% mixed complementarity problem
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[lb,ub,eq_index] = get_complementarity_conditions(M,options.ramsey_policy);
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if options.linear_approximation
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lb = lb - steadystate_y;
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ub = ub - steadystate_y;
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end
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if options.solve_algo == 10
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options.lmmcp.lb = repmat(lb,options.periods,1);
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options.lmmcp.ub = repmat(ub,options.periods,1);
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elseif options.solve_algo == 11
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options.mcppath.lb = repmat(lb,options.periods,1);
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options.mcppath.ub = repmat(ub,options.periods,1);
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end
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[y, check, ~, ~, errorcode] = dynare_solve(@perfect_foresight_mcp_problem, z(:), ...
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options.simul.maxit, options.dynatol.f, options.dynatol.x, ...
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options, ...
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dynamicmodel, y0, yT, ...
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exogenousvariables, M.params, steadystate, ...
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M.maximum_lag, options.periods, M.endo_nbr, i_cols, ...
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i_cols_J1, i_cols_1, i_cols_T, i_cols_j, i_cols_0, i_cols_J0, ...
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eq_index);
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else
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[y, check, ~, ~, errorcode] = dynare_solve(@perfect_foresight_problem, z(:), ...
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options.simul.maxit, options.dynatol.f, options.dynatol.x, ...
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options, y0, yT, exogenousvariables, M.params, steadystate, options.periods, M, options);
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end
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if all(imag(y)<.1*options.dynatol.x)
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if ~isreal(y)
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y = real(y);
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end
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else
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check = 1;
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end
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endogenousvariables(:, M.maximum_lag+(1:options.periods)) = reshape(y, M.endo_nbr, options.periods);
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if check
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info.status = false;
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if options.debug
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dprintf('solve_stacked_problem: Nonlinear solver routine failed with errorcode=%i.', errorcode)
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end
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else
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info.status = true;
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end
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