From 6b113273d3a73a94a8aaf916335a0f73335def3d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?St=C3=A9phane=20Adjemia=20=28Scylla=29?= Date: Wed, 21 Nov 2018 16:26:38 +0100 Subject: [PATCH] Added integration tests (Iterative OLS and NLS for PAC equations). --- tests/Makefile.am | 4 +- tests/pac/run_all_tests.m | 3 + tests/pac/trend-component-15/clean | 8 ++ tests/pac/trend-component-15/example.mod | 138 +++++++++++++++++++++ tests/pac/trend-component-16/clean | 8 ++ tests/pac/trend-component-16/example.mod | 151 +++++++++++++++++++++++ 6 files changed, 311 insertions(+), 1 deletion(-) create mode 100755 tests/pac/trend-component-15/clean create mode 100644 tests/pac/trend-component-15/example.mod create mode 100755 tests/pac/trend-component-16/clean create mode 100644 tests/pac/trend-component-16/example.mod diff --git a/tests/Makefile.am b/tests/Makefile.am index fcfda347b..272ab21e6 100644 --- a/tests/Makefile.am +++ b/tests/Makefile.am @@ -405,9 +405,11 @@ MODFILES = \ pac/trend-component-12/example.mod \ pac/trend-component-13a/example.mod \ pac/trend-component-13b/example.mod \ - estimation/univariate/bayesian.mod \ pac/trend-component-14/example.mod \ pac/trend-component-14/substitution.mod \ + pac/trend-component-15/example.mod \ + pac/trend-component-16/example.mod \ + estimation/univariate/bayesian.mod \ dynare-command-options/ramst.mod PARTICLEFILES = \ diff --git a/tests/pac/run_all_tests.m b/tests/pac/run_all_tests.m index 27cf54e49..ccead1850 100644 --- a/tests/pac/run_all_tests.m +++ b/tests/pac/run_all_tests.m @@ -37,6 +37,9 @@ r = [r; run_this_test('trend-component-11')]; r = [r; run_this_test('trend-component-12')]; r = [r; run_this_test('trend-component-13a')]; r = [r; run_this_test('trend-component-13b')]; +r = [r; run_this_test('trend-component-14')]; +r = [r; run_this_test('trend-component-15')]; +r = [r; run_this_test('trend-component-16')]; print_results(r); diff --git a/tests/pac/trend-component-15/clean b/tests/pac/trend-component-15/clean new file mode 100755 index 000000000..cf3492a28 --- /dev/null +++ b/tests/pac/trend-component-15/clean @@ -0,0 +1,8 @@ +#!/bin/sh + +rm -rf example +rm -rf +example +rm -f example.log +rm -f *.mat +rm -f *.m +rm -f *.dat diff --git a/tests/pac/trend-component-15/example.mod b/tests/pac/trend-component-15/example.mod new file mode 100644 index 000000000..7d2786ccb --- /dev/null +++ b/tests/pac/trend-component-15/example.mod @@ -0,0 +1,138 @@ +// --+ options: json=compute, stochastic +-- + +var x1 x2 x1bar x2bar z y x; + +varexo ex1 ex2 ex1bar ex2bar ez ey ex; + +parameters + rho_1 rho_2 rho_3 rho_4 + 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 + e_c_m c_z_1 c_z_2 beta + lambda; + +rho_1 = .9; +rho_2 = -.2; +rho_3 = .4; +rho_4 = -.3; + + +a_x1_0 = -.9; +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; + +beta = .2; +e_c_m = .5; +c_z_1 = .2; +c_z_2 = -.1; + +lambda = .5; // Share of optimizing agents. + +trend_component_model(model_name=toto, eqtags=['eq:x1', 'eq:x2', 'eq:x1bar', 'eq:x2bar'], targets=['eq:x1bar', 'eq:x2bar']); + +pac_model(auxiliary_model_name=toto, discount=beta, model_name=pacman); + +model; + +[name='eq:y'] +y = rho_1*y(-1) + rho_2*y(-2) + ey; + +[name='eq:x'] +x = rho_3*x(-1) + rho_4*x(-2) + ex; + +[name='eq:x1'] +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; + +[name='eq:x2'] +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'] +x1bar = x1bar(-1) + ex1bar; + +[name='eq:x2bar'] +x2bar = x2bar(-1) + ex2bar; + +[name='zpac'] +diff(z) = lambda*(e_c_m*(x1(-1)-z(-1)) + c_z_1*diff(z(-1)) + c_z_2*diff(z(-2)) + pac_expectation(pacman)) + (1-lambda)*( y + x) + ez; + +end; + +shocks; + var ex1 = 1.0; + var ex2 = 1.0; + var ex1bar = 1.0; + var ex2bar = 1.0; + var ez = 1.0; + var ey = 0.1; + var ex = 0.1; +end; + +// Initialize the PAC model (build the Companion VAR representation for the auxiliary model). +pac.initialize('pacman'); + +// Update the parameters of the PAC expectation model (h0 and h1 vectors). +pac.update.expectation('pacman'); + +// Set initial conditions to zero. Please use more sensible values if any... +initialconditions = dseries(zeros(10, M_.endo_nbr+M_.exo_nbr), 2000Q1, vertcat(M_.endo_names,M_.exo_names)); + +// Simulate the model for 500 periods +TrueData = simul_backward_model(initialconditions, 5000); + +// Define a structure describing the parameters to be estimated (with initial conditions). +clear eparams +eparams.e_c_m = .9; +eparams.c_z_1 = .5; +eparams.c_z_2 = .2; + +// Define the dataset used for estimation +edata = TrueData; +edata.ez = dseries(NaN(TrueData.nobs, 1), 2000Q1, 'ez'); +pac.estimate.iterative_ols('zpac', eparams, edata, 2005Q1:2005Q1+4000); + +e_c_m_iterative_ols = M_.params(strmatch('e_c_m', M_.param_names, 'exact')); +c_z_1_iterative_ols = M_.params(strmatch('c_z_1', M_.param_names, 'exact')); +c_z_2_iterative_ols = M_.params(strmatch('c_z_2', M_.param_names, 'exact')); + +disp(sprintf('Estimate of e_c_m: %f', e_c_m_iterative_ols)) +disp(sprintf('Estimate of c_z_1: %f', c_z_1_iterative_ols)) +disp(sprintf('Estimate of c_z_2: %f', c_z_2_iterative_ols)) + +skipline(2) + +clear eparams +eparams.e_c_m = .9; +eparams.c_z_1 = .5; +eparams.c_z_2 = .2; + +edata = TrueData; +edata.ez = dseries(NaN(TrueData.nobs, 1), 2000Q1, 'ez'); +pac.estimate.nls('zpac', eparams, edata, 2005Q1:2005Q1+4000, 'fminunc'); + +e_c_m_nls = M_.params(strmatch('e_c_m', M_.param_names, 'exact')); +c_z_1_nls = M_.params(strmatch('c_z_1', M_.param_names, 'exact')); +c_z_2_nls = M_.params(strmatch('c_z_2', M_.param_names, 'exact')); + +disp(sprintf('Estimate of e_c_m: %f', e_c_m_nls)) +disp(sprintf('Estimate of c_z_1: %f', c_z_1_nls)) +disp(sprintf('Estimate of c_z_2: %f', c_z_2_nls)) + +if abs(e_c_m_nls-e_c_m_iterative_ols)>.01 + error('Iterative OLS and NLS do not provide consistent estimates (e_c_m)') +end + +if abs(c_z_1_nls-c_z_1_iterative_ols)>.01 + error('Iterative OLS and NLS do not provide consistent estimates (c_z_1)') +end + +if abs(c_z_2_nls-c_z_2_iterative_ols)>.01 + error('Iterative OLS and NLS do not provide consistent estimates (c_z_2)') +end \ No newline at end of file diff --git a/tests/pac/trend-component-16/clean b/tests/pac/trend-component-16/clean new file mode 100755 index 000000000..cf3492a28 --- /dev/null +++ b/tests/pac/trend-component-16/clean @@ -0,0 +1,8 @@ +#!/bin/sh + +rm -rf example +rm -rf +example +rm -f example.log +rm -f *.mat +rm -f *.m +rm -f *.dat diff --git a/tests/pac/trend-component-16/example.mod b/tests/pac/trend-component-16/example.mod new file mode 100644 index 000000000..bb568f82e --- /dev/null +++ b/tests/pac/trend-component-16/example.mod @@ -0,0 +1,151 @@ +// --+ options: json=compute, stochastic +-- + +var x1 x2 x1bar x2bar z y x; + +varexo ex1 ex2 ex1bar ex2bar ez ey ex; + +parameters + rho_1 rho_2 rho_3 rho_4 + 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 + e_c_m c_z_1 c_z_2 beta + lambda; + +rho_1 = .9; +rho_2 = -.2; +rho_3 = .4; +rho_4 = -.3; + + +a_x1_0 = -.9; +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; + +beta = .2; +e_c_m = .5; +c_z_1 = .2; +c_z_2 = -.1; + +lambda = 0.5; // Share of optimizing agents. + +trend_component_model(model_name=toto, eqtags=['eq:x1', 'eq:x2', 'eq:x1bar', 'eq:x2bar'], targets=['eq:x1bar', 'eq:x2bar']); + +pac_model(auxiliary_model_name=toto, discount=beta, model_name=pacman); + +model; + +[name='eq:y'] +y = rho_1*y(-1) + rho_2*y(-2) + ey; + +[name='eq:x'] +x = rho_3*x(-1) + rho_4*x(-2) + ex; + +[name='eq:x1'] +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; + +[name='eq:x2'] +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'] +x1bar = x1bar(-1) + ex1bar; + +[name='eq:x2bar'] +x2bar = x2bar(-1) + ex2bar; + +[name='zpac'] +diff(z) = lambda*(e_c_m*(x1(-1)-z(-1)) + c_z_1*diff(z(-1)) + c_z_2*diff(z(-2)) + pac_expectation(pacman)) + (1-lambda)*( y + x) + ez; + +end; + +shocks; + var ex1 = 1.0; + var ex2 = 1.0; + var ex1bar = 1.0; + var ex2bar = 1.0; + var ez = 1.0; + var ey = 0.1; + var ex = 0.1; +end; + +// Initialize the PAC model (build the Companion VAR representation for the auxiliary model). +pac.initialize('pacman'); + +// Update the parameters of the PAC expectation model (h0 and h1 vectors). +pac.update.expectation('pacman'); + +// Set initial conditions to zero. Please use more sensible values if any... +initialconditions = dseries(zeros(10, M_.endo_nbr+M_.exo_nbr), 2000Q1, vertcat(M_.endo_names,M_.exo_names)); + +// Simulate the model for 500 periods +TrueData = simul_backward_model(initialconditions, 5000); + +// Define a structure describing the parameters to be estimated (with initial conditions). +clear eparams +eparams.e_c_m = .9; +eparams.c_z_1 = .5; +eparams.c_z_2 = .2; +eparams.lambda = .7; + +// Define the dataset used for estimation +edata = TrueData; +edata.ez = dseries(NaN(TrueData.nobs, 1), 2000Q1, 'ez'); +pac.estimate.iterative_ols('zpac', eparams, edata, 2005Q1:2005Q1+4000); + +e_c_m_iterative_ols = M_.params(strmatch('e_c_m', M_.param_names, 'exact')); +c_z_1_iterative_ols = M_.params(strmatch('c_z_1', M_.param_names, 'exact')); +c_z_2_iterative_ols = M_.params(strmatch('c_z_2', M_.param_names, 'exact')); +lambda_iterative_ols = M_.params(strmatch('lambda', M_.param_names, 'exact')); + +disp(sprintf('Estimate of e_c_m: %f', e_c_m_iterative_ols)) +disp(sprintf('Estimate of c_z_1: %f', c_z_1_iterative_ols)) +disp(sprintf('Estimate of c_z_2: %f', c_z_2_iterative_ols)) +disp(sprintf('Estimate of lambda: %f', lambda_iterative_ols)) + +skipline(2) + +// Define a structure describing the parameters to be estimated (with initial conditions). +clear eparams +eparams.e_c_m = .9; +eparams.c_z_1 = .5; +eparams.c_z_2 = .2; +eparams.lambda = .7; + + +edata = TrueData; +edata.ez = dseries(NaN(TrueData.nobs, 1), 2000Q1, 'ez'); +pac.estimate.nls('zpac', eparams, edata, 2005Q1:2005Q1+4000, 'csminwel'); + + +e_c_m_nls = M_.params(strmatch('e_c_m', M_.param_names, 'exact')); +c_z_1_nls = M_.params(strmatch('c_z_1', M_.param_names, 'exact')); +c_z_2_nls = M_.params(strmatch('c_z_2', M_.param_names, 'exact')); +lambda_nls = M_.params(strmatch('lambda', M_.param_names, 'exact')); + +disp(sprintf('Estimate of e_c_m: %f', e_c_m_nls)) +disp(sprintf('Estimate of c_z_1: %f', c_z_1_nls)) +disp(sprintf('Estimate of c_z_2: %f', c_z_2_nls)) +disp(sprintf('Estimate of lambda: %f', lambda_nls)) + +if abs(e_c_m_nls-e_c_m_iterative_ols)>.01 + error('Iterative OLS and NLS do not provide consistent estimates (e_c_m)') +end + +if abs(c_z_1_nls-c_z_1_iterative_ols)>.01 + error('Iterative OLS and NLS do not provide consistent estimates (c_z_1)') +end + +if abs(c_z_2_nls-c_z_2_iterative_ols)>.01 + error('Iterative OLS and NLS do not provide consistent estimates (c_z_2)') +end + +if abs(lambda_nls-lambda_iterative_ols)>.01 + error('Iterative OLS and NLS do not provide consistent estimates (lambda)') +end \ No newline at end of file