80 lines
2.8 KiB
Matlab
80 lines
2.8 KiB
Matlab
function pfm = setup_stochastic_perfect_foresight_model_solver(M_,options_,oo_)
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% pfm = setup_stochastic_perfect_foresight_model_solver(M_,options_,oo_)
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% INPUTS
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% o M_ [struct] Dynare's model structure
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% o options_ [struct] Dynare's options structure
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% o oo_ [struct] Dynare's results structure
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% Copyright © 2013-2023 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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pfm.lead_lag_incidence = M_.lead_lag_incidence;
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pfm.ny = M_.endo_nbr;
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pfm.Sigma = M_.Sigma_e;
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if det(pfm.Sigma) > 0
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pfm.Omega = chol(pfm.Sigma,'upper'); % Sigma = Omega'*Omega
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end
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pfm.number_of_shocks = length(pfm.Sigma);
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pfm.stochastic_order = options_.ep.stochastic.order;
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pfm.max_lag = M_.maximum_endo_lag;
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if pfm.max_lag > 0
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pfm.nyp = nnz(pfm.lead_lag_incidence(1,:));
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pfm.iyp = find(pfm.lead_lag_incidence(1,:)>0);
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else
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pfm.nyp = 0;
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pfm.iyp = [];
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end
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pfm.ny0 = nnz(pfm.lead_lag_incidence(pfm.max_lag+1,:));
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pfm.iy0 = find(pfm.lead_lag_incidence(pfm.max_lag+1,:)>0);
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if M_.maximum_endo_lead
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pfm.nyf = nnz(pfm.lead_lag_incidence(pfm.max_lag+2,:));
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pfm.iyf = find(pfm.lead_lag_incidence(pfm.max_lag+2,:)>0);
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else
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pfm.nyf = 0;
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pfm.iyf = [];
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end
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pfm.nd = pfm.nyp+pfm.ny0+pfm.nyf;
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pfm.nrc = pfm.nyf+1;
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pfm.isp = 1:pfm.nyp;
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pfm.is = pfm.nyp+1:pfm.ny+pfm.nyp;
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pfm.isf = pfm.iyf+pfm.nyp;
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pfm.isf1 = pfm.nyp+pfm.ny+1:pfm.nyf+pfm.nyp+pfm.ny+1;
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pfm.iz = 1:pfm.ny+pfm.nyp+pfm.nyf;
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pfm.periods = options_.ep.periods;
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pfm.steady_state = oo_.steady_state;
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pfm.params = M_.params;
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if M_.maximum_endo_lead
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pfm.i_cols_1 = nonzeros(pfm.lead_lag_incidence(pfm.max_lag+(1:2),:)');
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pfm.i_cols_A1 = find(pfm.lead_lag_incidence(pfm.max_lag+(1:2),:)');
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else
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pfm.i_cols_1 = nonzeros(pfm.lead_lag_incidence(pfm.max_lag+1,:)');
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pfm.i_cols_A1 = find(pfm.lead_lag_incidence(pfm.max_lag+1,:)');
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end
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if pfm.max_lag > 0
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pfm.i_cols_T = nonzeros(pfm.lead_lag_incidence(1:2,:)');
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else
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pfm.i_cols_T = nonzeros(pfm.lead_lag_incidence(1,:)');
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end
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pfm.i_cols_j = 1:pfm.nd;
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pfm.i_upd = pfm.ny+(1:pfm.periods*pfm.ny);
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if ~options_.bytecode
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pfm.dynamic_model = str2func([M_.fname,'.dynamic']);
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end
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pfm.verbose = options_.ep.verbosity;
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pfm.maxit_ = options_.simul.maxit;
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pfm.tolerance = options_.dynatol.f;
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