dynare/tests/model-inversion/bk-5/varmodel.mod

138 lines
4.2 KiB
Modula-2

// --+ options: stochastic +--
/* © 2022 Dynare Team
*
* This file is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* It is distributed in the hope that it will be useful, but
* WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with the file. If not, see <http://www.gnu.org/licenses/>.
*/
var y1 y2 y3 ;
varexo e1 e2 e3 u1 u2 u3 ;
parameters a11 a12 a13 a21 a22 a23 a31 a32 a33 b11 b12 b13 b22 b23 b33 ;
/*
** Simulate the elements of the first order autoregressive matrix (we impose stability of the model, note that
** inversion fails if the model is explosive, ie the autoregressive matrix has at least one root greater than
** one in modulus) probably because of the propagation of roundoff errors.
*/
D = diag([.9 .7 .8]);
P = randn(3,3);
A = P*D*inv(P);
a11 = A(1,1);
a12 = A(1,2);
a13 = A(1,3);
a21 = A(2,1);
a22 = A(2,2);
a23 = A(2,3);
a31 = A(3,1);
a32 = A(3,2);
a33 = A(3,3);
b11 = .10;
b12 = -.30;
b13 = .05;
b22 = .20;
b23 = -.05;
b33 = .10;
model(linear);
y1 = a11*y1(-1) + a12*y2(-1) + a13*y3(-1) + b11*e1 + b12*e2 + b13*e3 + u1 + .1*y3(-2);
y2 = a21*y1(-1) + a22*y2(-1) + a23*y3(-1) + b22*e2 + b23*e3 + u2 ;
y3 = a31*y1(-1) + a32*y2(-1) + a33*y3(-1) + b33*e3 + u3 ;
end;
histval;
y1(0) = .1;
y2(0) = .2;
y3(0) = .3;
y3(-1) = .4;
end;
shocks;
var e1 = 1.0;
var e2 = 1.0;
var e3 = 1.0;
var u1 = .1;
var u2 = .3;
var u3 = .2;
end;
steady;
check;
TrueData = simul_backward_model([], 200);
// Set the periods where some of the endogenous variables will be constrained.
subsample = 3Y:100Y;
// Copy the generated data
SimulatedData = copy(TrueData);
// Set the constrained paths for the endogenous variables (Output and PhysicalCapitalStock).
constrainedpaths = SimulatedData{'y1','y2','y3'}(subsample);
// Set the instruments (innovations used to control the paths for the endogenous variables).
exogenousvariables = dseries([NaN(100, 3) TrueData{'u1','u2','u3'}.data(1:100,:)], '1Y', M_.exo_names);
/* REMARK
**
** Here we will control y1, y2, and y3 with e1, e2 and e3, u1, u2 and u3 are treated as observed
** exogenous variables.
*/
// Invert the model by calling the model_inversion routine.
options_.dynatol.f = 1e-8;
[endogenousvariables, exogenousvariables] = model_inversion(constrainedpaths, exogenousvariables, SimulatedData, M_, options_, oo_);
// Check that all the constraints are satisfied.
if max(abs(constrainedpaths(subsample).y1.data-endogenousvariables(subsample).y1.data))>1e-12
error('Constraint on y1 path is not satisfied!')
end
if max(abs(constrainedpaths(subsample).y2.data-endogenousvariables(subsample).y2.data))>1e-12
error('Constraint on y2 path is not satisfied!')
end
if max(abs(constrainedpaths(subsample).y3.data-endogenousvariables(subsample).y3.data))>1e-12
error('Constraint on y3 path is not satisfied!')
end
if max(abs(exogenousvariables(subsample).u1.data-SimulatedData(subsample).u1.data))>1e-12
error('Constraint on u1 path is not satisfied!')
end
if max(abs(exogenousvariables(subsample).u2.data-SimulatedData(subsample).u2.data))>1e-12
error('Constraint on u2 path is not satisfied!')
end
if max(abs(exogenousvariables(subsample).u3.data-SimulatedData(subsample).u3.data))>1e-12
error('Constraint on u3 path is not satisfied!')
end
// Check consistency of the results.
if max(abs(exogenousvariables(subsample).e1.data-SimulatedData(subsample).e1.data))>1e-12
error('Model inversion is not consistent with true innovations (e1)')
end
if max(abs(exogenousvariables(subsample).e2.data-SimulatedData(subsample).e2.data))>1e-12
error('Model inversion is not consistent with true innovations (e3)')
end
if max(abs(exogenousvariables(subsample).e3.data-SimulatedData(subsample).e3.data))>1e-12
error('Model inversion is not consistent with true innovations (e3)')
end