51 lines
1.8 KiB
Matlab
51 lines
1.8 KiB
Matlab
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function pfm = setup_stochastic_perfect_foresight_model_solver(DynareModel,DynareOptions,DynareOutput,Algorithm,IntegrationMethod)
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pfm.lead_lag_incidence = DynareModel.lead_lag_incidence;
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pfm.ny = DynareModel.endo_nbr;
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pfm.Sigma_e = DynareModel.Sigma_e;
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pfm.max_lag = DynareModel.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 DynareModel.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 = DynareOptions.ep.periods;
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pfm.steady_state = DynareOutput.steady_state;
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pfm.params = DynareModel.params;
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if DynareModel.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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pfm.dynamic_model = str2func([DynareModel.fname,'_dynamic']);
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pfm.verbose = DynareOptions.ep.verbosity;
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pfm.maxit_ = DynareOptions.maxit_;
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pfm.tolerance = DynareOptions.dynatol.f;
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