105 lines
2.8 KiB
Modula-2
105 lines
2.8 KiB
Modula-2
// --+ options: stochastic +--
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/* © 2022 Dynare Team
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*
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* This file 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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* It is distributed in the hope that it will be useful, but
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* 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 the file. If not, see <http://www.gnu.org/licenses/>.
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*/
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@#define fake_backward = 1
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var y1 y2 y3 ;
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@#if fake_backward
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var z ;
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@#endif
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varexo e1 e2 e3 ;
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parameters a b c ;
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a = .1;
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b = .2;
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c = .3;
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model;
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@#if fake_backward
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z = a*z(-1) ;
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@#endif
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y1 = y2*y3+exp(e1) ;
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y2 = 1 + y1*exp(e2) ;
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y3 = e3 ;
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end;
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@#if fake_backward
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histval;
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z(0) = .0 ;
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end;
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@#endif
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shocks;
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var e1 = .01;
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var e2 = .01;
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var e3 = .02;
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end;
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TrueData = simul_backward_model([], 1000);
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// Set the periods where some of the endogenous variables will be constrained.
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subsample = 2Y:101Y;
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// Load the generated data
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SimulatedData = copy(TrueData);
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// Set the constrained paths for the endogenous variables.
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constrainedpaths = SimulatedData{'y1','y2','y3'}(subsample);
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// Set the instruments (innovations used to control the paths for the endogenous variables).
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exogenousvariables = dseries([NaN(100, 3)], '2Y', M_.exo_names);
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/* REMARK
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**
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** Here we will control y1, y2, and y3 with e1, e2 and e3.
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**
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*/
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// Invert the model by calling the model_inversion routine.
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options_.dynatol.f = 1e-9;
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[endogenousvariables, exogenousvariables] = model_inversion(constrainedpaths, exogenousvariables, SimulatedData, M_, options_, oo_);
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// Check that all the constraints are satisfied.
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if max(abs(constrainedpaths(subsample).y1.data-endogenousvariables(subsample).y1.data))>1e-12
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error('Constraint on y1 path is not satisfied!')
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end
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if max(abs(constrainedpaths(subsample).y2.data-endogenousvariables(subsample).y2.data))>1e-12
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error('Constraint on y2 path is not satisfied!')
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end
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if max(abs(constrainedpaths(subsample).y3.data-endogenousvariables(subsample).y3.data))>1e-12
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error('Constraint on y3 path is not satisfied!')
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end
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// Check consistency of the results.
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if max(abs(exogenousvariables(subsample).e1.data-SimulatedData(subsample).e1.data))>1e-8
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error('Model inversion is not consistent with true innovations (e1)')
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end
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if max(abs(exogenousvariables(subsample).e2.data-SimulatedData(subsample).e2.data))>1e-8
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error('Model inversion is not consistent with true innovations (e2)')
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end
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if max(abs(exogenousvariables(subsample).e3.data-SimulatedData(subsample).e3.data))>1e-8
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error('Model inversion is not consistent with true innovations (e3)')
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end
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