Added integration tests (var and pac expectations).
Stéphane Adjemia (Scylla)
parent
a53c63d6d5
commit
d96740c2ef
GPG Key ID:
A6D44CB9C64CE77B
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@ -358,6 +358,12 @@ MODFILES = \
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var-expectations/3/example.mod \
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var-expectations/4/example.mod \
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var-expectations/5/example.mod \
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var-expectations/6/example.mod \
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var-expectations/6/substitution.mod \
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var-expectations/7/example.mod \
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var-expectations/7/substitution.mod \
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var-expectations/8/example.mod \
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var-expectations/8/substitution.mod \
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trend-component-and-var-models/vm1.mod \
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trend-component-and-var-models/vm2.mod \
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trend-component-and-var-models/vm3.mod \
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@ -381,6 +387,8 @@ MODFILES = \
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pac/var-2/example.mod \
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pac/var-3/example.mod \
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pac/var-4/example.mod \
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pac/var-5/example.mod \
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pac/var-5/substitution.mod \
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pac/trend-component-1/example.mod \
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pac/trend-component-2/example.mod \
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pac/trend-component-3/example.mod \
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@ -395,6 +403,8 @@ MODFILES = \
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pac/trend-component-13a/example.mod \
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pac/trend-component-13b/example.mod \
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estimation/univariate/bayesian.mod \
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pac/trend-component-14/example.mod \
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pac/trend-component-14/substitution.mod \
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dynare-command-options/ramst.mod
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PARTICLEFILES = \
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@ -591,6 +601,18 @@ kalman/likelihood_from_dynare/fs2000ns_uncorr_ME_missing.o.trs: kalman/likelihoo
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kalman/likelihood_from_dynare/fs2000ns_corr_ME_missing.m.trs: kalman/likelihood_from_dynare/fs2000ns_uncorr_ME.m.trs
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kalman/likelihood_from_dynare/fs2000ns_corr_ME_missing.o.trs: kalman/likelihood_from_dynare/fs2000ns_uncorr_ME.o.trs
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var-expectations/6/substitution.m.trs: var-expectations/6/example.m.trs
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var-expectations/6/substitution.o.trs: var-expectations/6/example.o.trs
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var-expectations/7/substitution.m.trs: var-expectations/7/example.m.trs
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var-expectations/7/substitution.o.trs: var-expectations/7/example.o.trs
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var-expectations/8/substitution.m.trs: var-expectations/8/example.m.trs
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var-expectations/8/substitution.o.trs: var-expectations/8/example.o.trs
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pac/var-5/substitution.m.trs: pac/var-5/example.m.trs
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pac/var-5/substitution.o.trs: pac/var-5/example.o.trs
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pac/trend-component-14/substitution.m.trs: pac/trend-component-14/example.m.trs
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pac/trend-component-14/substitution.o.trs: pac/trend-component-14/example.o.trs
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lmmcp/sw_newton.m.trs: lmmcp/sw_lmmcp.m.trs
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lmmcp/sw_newton.o.trs: lmmcp/sw_lmmcp.o.trs
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@ -0,0 +1,9 @@
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#!/bin/sh
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rm -rf example
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rm -rf +example
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rm -f example.log
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rm -f *.mat
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rm -rf substitution
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rm -rf +substitution
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rm -f substitution.log
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@ -0,0 +1,93 @@
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// --+ options: transform_unary_ops, json=compute, stochastic +--
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var x1 x2 x1bar x2bar z y;
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varexo ex1 ex2 ex1bar ex2bar ez ey;
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parameters
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rho_1 rho_2
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a_x1_0 a_x1_1 a_x1_2 a_x1_x2_1 a_x1_x2_2
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a_x2_0 a_x2_1 a_x2_2 a_x2_x1_1 a_x2_x1_2
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e_c_m c_z_1 c_z_2 beta;
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rho_1 = .9;
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rho_2 = -.2;
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a_x1_0 = -.9;
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a_x1_1 = .4;
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a_x1_2 = .3;
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a_x1_x2_1 = .1;
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a_x1_x2_2 = .2;
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a_x2_0 = -.9;
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a_x2_1 = .2;
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a_x2_2 = -.1;
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a_x2_x1_1 = -.1;
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a_x2_x1_2 = .2;
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beta = .2;
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e_c_m = .5;
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c_z_1 = .2;
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c_z_2 = -.1;
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trend_component_model(model_name=toto, eqtags=['eq:x1', 'eq:x2', 'eq:x1bar', 'eq:x2bar'], targets=['eq:x1bar', 'eq:x2bar']);
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pac_model(auxiliary_model_name=toto, discount=beta, model_name=pacman);
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model;
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[name='eq:y']
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y = rho_1*y(-1) + rho_2*y(-2) + ey;
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[name='eq:x1', data_type='nonstationary']
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diff(x1) = a_x1_0*(x1(-1)-x1bar(-1)) + a_x1_1*diff(x1(-1)) + a_x1_2*diff(x1(-2)) + a_x1_x2_1*diff(x2(-1)) + a_x1_x2_2*diff(x2(-2)) + ex1;
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[name='eq:x2', data_type='nonstationary']
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diff(x2) = a_x2_0*(x2(-1)-x2bar(-1)) + a_x2_1*diff(x1(-1)) + a_x2_2*diff(x1(-2)) + a_x2_x1_1*diff(x2(-1)) + a_x2_x1_2*diff(x2(-2)) + ex2;
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[name='eq:x1bar', data_type='nonstationary']
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x1bar = x1bar(-1) + ex1bar;
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[name='eq:x2bar', data_type='nonstationary']
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x2bar = x2bar(-1) + ex2bar;
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[name='zpac']
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diff(z) = e_c_m*(x1(-1)-z(-1)) + c_z_1*diff(z(-1)) + c_z_2*diff(z(-2)) + pac_expectation(pacman) + ez;
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end;
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shocks;
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var ex1 = 1.0;
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var ex2 = 1.0;
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var ex1bar = 1.0;
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var ex2bar = 1.0;
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var ez = 1.0;
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var ey = 0.1;
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end;
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// Initialize the PAC model (build the Companion VAR representation for the auxiliary model).
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pac.initialize('pacman');
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// Update the parameters of the PAC expectation model (h0 and h1 vectors).
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pac.update.expectation('pacman');
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// Set initial conditions to zero. Please use more sensible values if any...
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initialconditions = dseries(zeros(10, M_.endo_nbr+M_.exo_nbr), 2000Q1, vertcat(M_.endo_names,M_.exo_names));
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// Simulate the model for 500 periods
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set_dynare_seed('default');
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TrueData = simul_backward_model(initialconditions, 300);
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// Print expanded PAC_EXPECTATION term.
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pac.print('pacman');
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verbatim;
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set_dynare_seed('default');
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y = zeros(M_.endo_nbr,1);
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y(1:M_.orig_endo_nbr) = rand(M_.orig_endo_nbr, 1);
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x = randn(M_.exo_nbr,1);
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y = example.set_auxiliary_variables(y, x, M_.params);
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y = [y(find(M_.lead_lag_incidence(1,:))); y];
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[residual, g1] = example.dynamic(y, x', M_.params, oo_.steady_state, 1);
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save('example.mat', 'residual', 'g1', 'TrueData');
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end;
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@ -0,0 +1,98 @@
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// --+ options: transform_unary_ops, json=compute, stochastic +--
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var x1 x2 x1bar x2bar z y;
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varexo ex1 ex2 ex1bar ex2bar ez ey;
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parameters
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rho_1 rho_2
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a_x1_0 a_x1_1 a_x1_2 a_x1_x2_1 a_x1_x2_2
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a_x2_0 a_x2_1 a_x2_2 a_x2_x1_1 a_x2_x1_2
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e_c_m c_z_1 c_z_2 beta;
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rho_1 = .9;
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rho_2 = -.2;
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a_x1_0 = -.9;
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a_x1_1 = .4;
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a_x1_2 = .3;
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a_x1_x2_1 = .1;
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a_x1_x2_2 = .2;
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a_x2_0 = -.9;
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a_x2_1 = .2;
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a_x2_2 = -.1;
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a_x2_x1_1 = -.1;
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a_x2_x1_2 = .2;
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beta = .2;
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e_c_m = .5;
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c_z_1 = .2;
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c_z_2 = -.1;
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@#include "example/model/pac-expectations/pacman-parameters.inc"
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model;
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[name='eq:y']
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y = rho_1*y(-1) + rho_2*y(-2) + ey;
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[name='eq:x1', data_type='nonstationary']
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diff(x1) = a_x1_0*(x1(-1)-x1bar(-1)) + a_x1_1*diff(x1(-1)) + a_x1_2*diff(x1(-2)) + a_x1_x2_1*diff(x2(-1)) + a_x1_x2_2*diff(x2(-2)) + ex1;
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[name='eq:x2', data_type='nonstationary']
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diff(x2) = a_x2_0*(x2(-1)-x2bar(-1)) + a_x2_1*diff(x1(-1)) + a_x2_2*diff(x1(-2)) + a_x2_x1_1*diff(x2(-1)) + a_x2_x1_2*diff(x2(-2)) + ex2;
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[name='eq:x1bar', data_type='nonstationary']
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x1bar = x1bar(-1) + ex1bar;
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[name='eq:x2bar', data_type='nonstationary']
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x2bar = x2bar(-1) + ex2bar;
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[name='zpac']
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diff(z) = e_c_m*(x1(-1)-z(-1)) + c_z_1*diff(z(-1)) + c_z_2*diff(z(-2)) +
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@#include "example/model/pac-expectations/pacman-expression.inc"
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+ ez;
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end;
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shocks;
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var ex1 = 1.0;
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var ex2 = 1.0;
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var ex1bar = 1.0;
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var ex2bar = 1.0;
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var ez = 1.0;
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var ey = 0.1;
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end;
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// Set initial conditions to zero. Please use more sensible values if any...
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initialconditions = dseries(zeros(10, M_.endo_nbr+M_.exo_nbr), 2000Q1, vertcat(M_.endo_names,M_.exo_names));
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// Simulate the model for 500 periods
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set_dynare_seed('default');
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TrueData = simul_backward_model(initialconditions, 300);
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verbatim;
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set_dynare_seed('default');
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y = zeros(M_.endo_nbr,1);
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y(1:M_.orig_endo_nbr) = rand(M_.orig_endo_nbr, 1);
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x = randn(M_.exo_nbr,1);
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y = substitution.set_auxiliary_variables(y, x, M_.params);
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y = [y(find(M_.lead_lag_incidence(1,:))); y];
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example = load('example.mat');
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[residual, g1] = substitution.dynamic(y, x', M_.params, oo_.steady_state, 1);
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end;
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if max(abs(example.TrueData.data(:)-TrueData.data(:)))>1e-9
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error('Simulations do not match.')
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end
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if ~isequal(length(residual), length(example.residual)) || max(abs(example.residual-residual))>1e-8
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warning('Residuals do not match!')
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end
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if ~isequal(length(g1(:)), length(example.g1(:))) || max(abs(example.g1(:)-g1(:)))>1e-8
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warning('Jacobian matrices do not match!')
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end
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delete('example.mat');
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@ -0,0 +1,10 @@
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#!/bin/sh
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rm -rf example
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rm -rf +example
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rm -f example*.mat
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rm -f example.log
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rm -rf substitution
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rm -rf +substitution
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rm -f substitution*.mat
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rm -f substitution.log
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@ -0,0 +1,73 @@
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// --+ options: json=compute, stochastic +--
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var y x z;
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varexo ex ey ez;
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parameters a_y_1 a_y_2 b_y_1 b_y_2 b_x_1 b_x_2 ; // VAR parameters
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parameters g beta e_c_m c_z_1 c_z_2; // PAC equation parameters
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a_y_1 = .2;
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a_y_2 = .3;
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b_y_1 = .1;
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b_y_2 = .4;
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b_x_1 = -.1;
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b_x_2 = -.2;
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beta = .9;
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e_c_m = .1;
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c_z_1 = .7;
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c_z_2 = -.3;
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g = .1;
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var_model(model_name=toto, eqtags=['eq:x', 'eq:y']);
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pac_model(auxiliary_model_name=toto, discount=beta, model_name=pacman, growth=g);
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model;
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[name='eq:y']
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y = a_y_1*y(-1) + a_y_2*diff(x(-1)) + b_y_1*y(-2) + b_y_2*diff(x(-2)) + ey ;
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[name='eq:x', data_type='nonstationary']
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diff(x) = b_x_1*y(-2) + b_x_2*diff(x(-1)) + g*(1-b_x_2) + ex ;
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[name='eq:pac']
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diff(z) = e_c_m*(x(-1)-z(-1)) + c_z_1*diff(z(-1)) + c_z_2*diff(z(-2)) + pac_expectation(pacman) + ez;
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end;
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shocks;
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var ex = 1.0;
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var ey = 1.0;
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var ez = 1.0;
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end;
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// Initialize the PAC model (build the Companion VAR representation for the auxiliary model).
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pac.initialize('pacman');
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// Update the parameters of the PAC expectation model (h0 and h1 vectors).
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pac.update.expectation('pacman');
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// Set initial conditions to zero. Please use more sensible values if any...
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initialconditions = dseries(zeros(10, M_.endo_nbr+M_.exo_nbr), 2000Q1, vertcat(M_.endo_names,M_.exo_names));
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// Simulate the model for 20 periods
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set_dynare_seed('default');
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TrueData = simul_backward_model(initialconditions, 20);
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// Print expanded PAC_EXPECTATION term.
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pac.print('pacman');
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verbatim;
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set_dynare_seed('default');
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y = zeros(M_.endo_nbr,1);
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y(1:M_.orig_endo_nbr) = rand(M_.orig_endo_nbr, 1);
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x = randn(M_.exo_nbr,1);
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y = example.set_auxiliary_variables(y, x, M_.params);
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y = [y(find(M_.lead_lag_incidence(1,:))); y];
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[residual, g1] = example.dynamic(y, x', M_.params, oo_.steady_state, 1);
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save('example.mat', 'residual', 'g1', 'TrueData');
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end;
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// --+ options: transform_unary_ops, json=compute, stochastic +--
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var y x z;
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varexo ex ey ez;
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parameters a_y_1 a_y_2 b_y_1 b_y_2 b_x_1 b_x_2 g; // VAR parameters
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parameters e_c_m c_z_1 c_z_2; // PAC equation parameters
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a_y_1 = .2;
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a_y_2 = .3;
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b_y_1 = .1;
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b_y_2 = .4;
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b_x_1 = -.1;
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b_x_2 = -.2;
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g = .1;
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e_c_m = .1;
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c_z_1 = .7;
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c_z_2 = -.3;
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@#include "example/model/pac-expectations/pacman-parameters.inc"
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model;
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[name='eq:y']
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y = a_y_1*y(-1) + a_y_2*diff(x(-1)) + b_y_1*y(-2) + b_y_2*diff(x(-2)) + ey ;
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[name='eq:x', data_type='nonstationary']
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diff(x) = b_x_1*y(-2) + b_x_2*diff(x(-1)) + g*(1-b_x_2) + ex ;
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[name='eq:pac']
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diff(z) = e_c_m*(x(-1)-z(-1)) + c_z_1*diff(z(-1)) + c_z_2*diff(z(-2)) +
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@#include "example/model/pac-expectations/pacman-expression.inc"
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+ ez;
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end;
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shocks;
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var ex = 1.0;
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var ey = 1.0;
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var ez = 1.0;
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end;
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// Set initial conditions to zero. Please use more sensible values if any...
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initialconditions = dseries(zeros(10, M_.endo_nbr+M_.exo_nbr), 2000Q1, vertcat(M_.endo_names,M_.exo_names));
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// Simulate the model for 20 periods
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set_dynare_seed('default');
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TrueData = simul_backward_model(initialconditions, 20);
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verbatim;
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set_dynare_seed('default');
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y = zeros(M_.endo_nbr,1);
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y(1:M_.orig_endo_nbr) = rand(M_.orig_endo_nbr, 1);
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x = randn(M_.exo_nbr,1);
|
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y = substitution.set_auxiliary_variables(y, x, M_.params);
|
||||
y = [y(find(M_.lead_lag_incidence(1,:))); y];
|
||||
example = load('example.mat');
|
||||
[residual, g1] = substitution.dynamic(y, x', M_.params, oo_.steady_state, 1);
|
||||
end;
|
||||
|
||||
if max(abs(example.TrueData.data(:)-TrueData.data(:)))>1e-9
|
||||
error('Simulations do not match.')
|
||||
end
|
||||
|
||||
if ~isequal(length(residual), length(example.residual)) || max(abs(example.residual-residual))>1e-8
|
||||
warning('Residuals do not match!')
|
||||
end
|
||||
|
||||
if ~isequal(length(g1(:)), length(example.g1(:))) || max(abs(example.g1(:)-g1(:)))>1e-8
|
||||
warning('Jacobian matrices do not match!')
|
||||
end
|
||||
|
||||
delete('example.mat');
|
||||
|
|
@ -1,4 +1,4 @@
|
|||
// --+ options: stochastic,json=compute +--
|
||||
// --+ options: stochastic,transform_unary_ops,json=compute +--
|
||||
|
||||
var foo x1 x2 x1bar x2bar;
|
||||
|
||||
|
|
@ -77,4 +77,18 @@ weights = M_.params(M_.var_expectation.varexp.param_indices);
|
|||
|
||||
if ~all(weights(1:6)) || ~all(weights(9:10)) || weights(7) || weights(8) || weights(11) || weights(12)
|
||||
error('Wrong reduced form parameter for VAR_EXPECTATION_MODEL')
|
||||
end
|
||||
end
|
||||
|
||||
var_expectation.print('varexp');
|
||||
|
||||
verbatim;
|
||||
set_dynare_seed('default');
|
||||
y = zeros(M_.endo_nbr,1);
|
||||
y(1:M_.orig_endo_nbr) = rand(M_.orig_endo_nbr, 1);
|
||||
x = randn(M_.exo_nbr,1);
|
||||
y = example.set_auxiliary_variables(y, x, M_.params);
|
||||
y = [y(find(M_.lead_lag_incidence(1,:))); y];
|
||||
[residual, g1] = example.dynamic(y, x', M_.params, oo_.steady_state, 1);
|
||||
ynames = M_.endo_names;
|
||||
save('example.mat', 'residual', 'g1');
|
||||
end;
|
||||
|
|
@ -0,0 +1,67 @@
|
|||
// --+ options: stochastic,transform_unary_ops,json=compute +--
|
||||
|
||||
var foo x1 x2 x1bar x2bar;
|
||||
|
||||
varexo ex1 ex2 ex1bar ex2bar;
|
||||
|
||||
parameters a_x1_0 a_x1_0_ a_x1_1 a_x1_2 a_x1_x2_1 a_x1_x2_2
|
||||
a_x2_0 a_x2_1 a_x2_2 a_x2_x1_1 a_x2_x1_2
|
||||
beta ;
|
||||
|
||||
a_x1_0 = -.9;
|
||||
a_x1_0_ = -.8;
|
||||
a_x1_1 = .4;
|
||||
a_x1_2 = .3;
|
||||
a_x1_x2_1 = .1;
|
||||
a_x1_x2_2 = .2;
|
||||
|
||||
|
||||
a_x2_0 = -.9;
|
||||
a_x2_1 = .2;
|
||||
a_x2_2 = -.1;
|
||||
a_x2_x1_1 = -.1;
|
||||
a_x2_x1_2 = .2;
|
||||
|
||||
@#include "example/model/var-expectations/varexp-parameters.inc"
|
||||
|
||||
beta = 1/(1+.02);
|
||||
|
||||
model;
|
||||
|
||||
[name='eq:x1', data_type='nonstationary']
|
||||
diff(x1) = a_x1_0*(x1(-1)-x1bar(-1))+a_x1_0_*(x2(-1)-x2bar(-1)) + a_x1_1*diff(x1(-1)) + a_x1_2*diff(x1(-2)) + + a_x1_x2_1*diff(x2(-1)) + a_x1_x2_2*diff(x2(-2)) + ex1;
|
||||
|
||||
[name='eq:x2', data_type='nonstationary']
|
||||
diff(x2) = a_x2_0*(x2(-1)-x2bar(-1)) + a_x2_1*diff(x1(-1)) + a_x2_2*diff(x1(-2)) + + a_x2_x1_1*diff(x2(-1)) + a_x2_x1_2*diff(x2(-2)) + ex2;
|
||||
|
||||
[name='eq:x1bar', data_type='nonstationary']
|
||||
x1bar = x1bar(-1) + ex1bar;
|
||||
|
||||
[name='eq:x2bar', data_type='nonstationary']
|
||||
x2bar = x2bar(-1) + ex2bar;
|
||||
|
||||
foo = .5*foo(-1) +
|
||||
@#include "example/model/var-expectations/varexp-expression.inc"
|
||||
;
|
||||
end;
|
||||
|
||||
verbatim;
|
||||
set_dynare_seed('default');
|
||||
y = zeros(M_.endo_nbr,1);
|
||||
y(1:M_.orig_endo_nbr) = rand(M_.orig_endo_nbr, 1);
|
||||
x = randn(M_.exo_nbr,1);
|
||||
y = substitution.set_auxiliary_variables(y, x, M_.params);
|
||||
y = [y(find(M_.lead_lag_incidence(1,:))); y];
|
||||
example = load('example.mat');
|
||||
[residual, g1] = substitution.dynamic(y, x', M_.params, oo_.steady_state, 1);
|
||||
end;
|
||||
|
||||
if max(abs(example.residual-residual))>1e-8
|
||||
error('Residuals do not match!')
|
||||
end
|
||||
|
||||
if max(max(abs(example.g1-g1)))>1e-8
|
||||
error('Jacobian matrices do not match!')
|
||||
end
|
||||
|
||||
delete('example.mat');
|
||||
|
|
@ -0,0 +1,52 @@
|
|||
// --+ options: stochastic,json=compute +--
|
||||
|
||||
var foo z x y;
|
||||
varexo e_x e_y e_z;
|
||||
parameters a b c d e f beta ;
|
||||
|
||||
a = .9;
|
||||
b = -.2;
|
||||
c = .3;
|
||||
f = .8;
|
||||
d = .5;
|
||||
e = .4;
|
||||
|
||||
beta = 1/(1+.02);
|
||||
|
||||
// Define a VAR model from a subset of equations in the model block.
|
||||
var_model(model_name = toto, eqtags = [ 'X' 'Y' 'Z' ]);
|
||||
|
||||
// Define a VAR_EXPECTATION_MODEL
|
||||
var_expectation_model(model_name = varexp, variable = x, auxiliary_model_name = toto, horizon = 1, discount = beta) ;
|
||||
|
||||
|
||||
model;
|
||||
[ name = 'X' ]
|
||||
diff(x) = a*diff(x(-1)) + b*diff(x(-2)) + c*z(-2) + e_x;
|
||||
[ name = 'Z' ]
|
||||
z = f*z(-1) + e_z;
|
||||
[ name = 'Y' ]
|
||||
log(y) = d*log(y(-2)) + e*z(-1) + e_y;
|
||||
|
||||
foo = .5*foo(-1) + var_expectation(varexp);
|
||||
end;
|
||||
|
||||
// Initialize the VAR expectation model, will build the companion matrix of the VAR.
|
||||
var_expectation.initialize('varexp')
|
||||
|
||||
// Update VAR_EXPECTATION reduced form parameters
|
||||
var_expectation.update('varexp');
|
||||
|
||||
// Print expanded VAR_EXPECTATION expression in a file (to be included in substitution.mod).
|
||||
var_expectation.print('varexp');
|
||||
|
||||
verbatim;
|
||||
set_dynare_seed('default');
|
||||
y = zeros(M_.endo_nbr,1);
|
||||
y(1:M_.orig_endo_nbr) = rand(M_.orig_endo_nbr, 1);
|
||||
x = randn(M_.exo_nbr,1);
|
||||
y = example.set_auxiliary_variables(y, x, M_.params);
|
||||
y = [y(find(M_.lead_lag_incidence(1,:))); y];
|
||||
[residual, g1] = example.dynamic(y, x', M_.params, oo_.steady_state, 1);
|
||||
save('example.mat', 'residual', 'g1');
|
||||
end;
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
// --+ options: stochastic,transform_unary_ops,json=compute +--
|
||||
|
||||
var foo z x y;
|
||||
varexo e_x e_y e_z;
|
||||
parameters a b c d e f beta ;
|
||||
|
||||
a = .9;
|
||||
b = -.2;
|
||||
c = .3;
|
||||
f = .8;
|
||||
d = .5;
|
||||
e = .4;
|
||||
|
||||
@#include "example/model/var-expectations/varexp-parameters.inc"
|
||||
|
||||
beta = 1/(1+.02);
|
||||
|
||||
// Define a VAR_EXPECTATION_MODEL
|
||||
var_model(model_name = toto, eqtags = [ 'X' 'Y' 'Z' ]);
|
||||
|
||||
model;
|
||||
[ name = 'X' ]
|
||||
diff(x) = a*diff(x(-1)) + b*diff(x(-2)) + c*z(-2) + e_x;
|
||||
[ name = 'Z' ]
|
||||
z = f*z(-1) + e_z;
|
||||
[ name = 'Y' ]
|
||||
log(y) = d*log(y(-2)) + e*z(-1) + e_y;
|
||||
|
||||
foo = .5*foo(-1) +
|
||||
@#include "example/model/var-expectations/varexp-expression.inc"
|
||||
;
|
||||
end;
|
||||
|
||||
verbatim;
|
||||
set_dynare_seed('default');
|
||||
y = zeros(M_.endo_nbr,1);
|
||||
y(1:M_.orig_endo_nbr) = rand(M_.orig_endo_nbr, 1);
|
||||
x = randn(M_.exo_nbr,1);
|
||||
y = substitution.set_auxiliary_variables(y, x, M_.params);
|
||||
y = [y(find(M_.lead_lag_incidence(1,:))); y];
|
||||
[residual, g1] = substitution.dynamic(y, x', M_.params, oo_.steady_state, 1);
|
||||
example = load('example.mat');
|
||||
end;
|
||||
|
||||
if max(abs(example.residual-residual))>1e-8
|
||||
error('Residuals do not match!')
|
||||
end
|
||||
|
||||
if max(max(abs(example.g1-g1)))>1e-8
|
||||
error('Jacobian matrices do not match!')
|
||||
end
|
||||
|
||||
delete('example.mat')
|
||||
|
|
@ -0,0 +1,51 @@
|
|||
// --+ options: stochastic,json=compute +--
|
||||
|
||||
var foo z x y;
|
||||
varexo e_x e_y e_z;
|
||||
parameters a b c d e f beta ;
|
||||
|
||||
a = .9;
|
||||
b = -.2;
|
||||
c = .3;
|
||||
f = .8;
|
||||
d = .5;
|
||||
e = .4;
|
||||
|
||||
beta = 1/(1+.02);
|
||||
|
||||
// Define a VAR model from a subset of equations in the model block.
|
||||
var_model(model_name = toto, eqtags = [ 'X' 'Y' 'Z' ]);
|
||||
|
||||
// Define a VAR_EXPECTATION_MODEL
|
||||
var_expectation_model(model_name = varexp, variable = x, auxiliary_model_name = toto, horizon = 1, discount = beta) ;
|
||||
|
||||
model;
|
||||
[ name = 'X' ]
|
||||
diff(log(x)) = a*diff(log(x(-1))) + b*diff(log(x(-2))) + c*diff(z(-2)) + e_x;
|
||||
[ name = 'Z' ]
|
||||
diff(z) = f*diff(z(-1)) + e_z;
|
||||
[ name = 'Y' ]
|
||||
log(y) = d*log(y(-2)) + e*diff(z(-1)) + e_y;
|
||||
|
||||
foo = .5*foo(-1) + var_expectation(varexp);
|
||||
end;
|
||||
|
||||
|
||||
// Initialize the VAR expectation model, will build the companion matrix of the VAR.
|
||||
var_expectation.initialize('varexp')
|
||||
|
||||
// Update VAR_EXPECTATION reduced form parameters
|
||||
var_expectation.update('varexp');
|
||||
|
||||
var_expectation.print('varexp');
|
||||
|
||||
verbatim;
|
||||
set_dynare_seed('default');
|
||||
y = zeros(M_.endo_nbr,1);
|
||||
y(1:M_.orig_endo_nbr) = rand(M_.orig_endo_nbr, 1);
|
||||
x = randn(M_.exo_nbr,1);
|
||||
y = example.set_auxiliary_variables(y, x, M_.params);
|
||||
y = [y(find(M_.lead_lag_incidence(1,:))); y];
|
||||
[residual, g1] = example.dynamic(y, x', M_.params, oo_.steady_state, 1);
|
||||
save('example.mat', 'residual', 'g1');
|
||||
end;
|
||||
|
|
@ -0,0 +1,50 @@
|
|||
// --+ options: stochastic,transform_unary_ops,json=compute +--
|
||||
|
||||
var foo z x y;
|
||||
varexo e_x e_y e_z;
|
||||
parameters a b c d e f beta ;
|
||||
|
||||
a = .9;
|
||||
b = -.2;
|
||||
c = .3;
|
||||
f = .8;
|
||||
d = .5;
|
||||
e = .4;
|
||||
|
||||
@#include "example/model/var-expectations/varexp-parameters.inc"
|
||||
|
||||
beta = 1/(1+.02);
|
||||
|
||||
model;
|
||||
[ name = 'X' ]
|
||||
diff(log(x)) = a*diff(log(x(-1))) + b*diff(log(x(-2))) + c*diff(z(-2)) + e_x;
|
||||
[ name = 'Z' ]
|
||||
diff(z) = f*diff(z(-1)) + e_z;
|
||||
[ name = 'Y' ]
|
||||
log(y) = d*log(y(-2)) + e*diff(z(-1)) + e_y;
|
||||
|
||||
foo = .5*foo(-1) +
|
||||
@#include "example/model/var-expectations/varexp-expression.inc"
|
||||
;
|
||||
end;
|
||||
|
||||
verbatim;
|
||||
set_dynare_seed('default');
|
||||
y = zeros(M_.endo_nbr,1);
|
||||
y(1:M_.orig_endo_nbr) = rand(M_.orig_endo_nbr, 1);
|
||||
x = randn(M_.exo_nbr,1);
|
||||
y = substitution.set_auxiliary_variables(y, x, M_.params);
|
||||
y = [y(find(M_.lead_lag_incidence(1,:))); y];
|
||||
[residual, g1] = substitution.dynamic(y, x', M_.params, oo_.steady_state, 1);
|
||||
example = load('example.mat');
|
||||
end;
|
||||
|
||||
if max(abs(example.residual-residual))>1e-8
|
||||
error('Residuals do not match!')
|
||||
end
|
||||
|
||||
if max(max(abs(example.g1-g1)))>1e-8
|
||||
error('Jacobian matrices do not match!')
|
||||
end
|
||||
|
||||
delete('example.mat')
|
||||
Loading…
Reference in New Issue