def test_debiased(self, data): c = HomoskedasticCovariance(data.x, data.y, data.z, data.params, debiased=True) assert c.debiased is True assert c.config == {"debiased": True, "kappa": 1} assert_allclose(c.s2, data.s2_debiased) assert_allclose(c.s, data.s2_debiased * data.v) assert_allclose(c.cov, data.s2_debiased * data.vinv / data.nobs) s = str(c) assert "Kappa" not in s assert "Debiased: True" in s assert "id" in c.__repr__()
def test_debiased(self, data): c = HomoskedasticCovariance(data.x, data.y, data.z, data.params, debiased=True) assert c.debiased is True assert c.config == {'debiased': True, 'kappa': 1} assert_allclose(c.s2, data.s2_debiased) assert_allclose(c.s, data.s2_debiased * data.v) assert_allclose(c.cov, data.s2_debiased * data.vinv / data.nobs) s = str(c) assert 'Kappa' not in s assert 'Debiased: True' in s assert 'id' in c.__repr__()
def test_kappa_debiased(self, data): c = HomoskedasticCovariance(data.x, data.y, data.z, data.params, debiased=True, kappa=data.kappa) assert c.debiased is True assert c.config == {'debiased': True, 'kappa': data.kappa} assert_allclose(c.s, data.s2_debiased * data.vk) assert_allclose(c.cov, data.s2_debiased * inv(data.vk) / data.nobs) s = str(c) assert 'Debiased: True' in s
def test_kappa(self, data): c = HomoskedasticCovariance(data.x, data.y, data.z, data.params, kappa=data.kappa) assert c.debiased is False assert c.config == {'debiased': False, 'kappa': .99} assert_allclose(c.s, data.s2 * data.vk) assert_allclose(c.cov, data.s2 * inv(data.vk) / data.nobs) s = str(c) assert 'Debiased: False' in s assert 'Kappa' in s
def test_homoskedastic_cov_asymptotic(data): c = HomoskedasticCovariance(data.x, data.y, data.z, data.params) nobs = data.nobs xhat = data.xhat s2 = data.s2 assert c.debiased is False assert c.config == {"debiased": False, "kappa": 1} assert_allclose(c.s2, data.s2) assert_allclose(c.cov, data.s2 * inv(xhat.T @ xhat / nobs) / nobs) assert_allclose(c.s, s2 * data.v) assert_allclose(c.s, s2 * (xhat.T @ xhat / nobs))
def test_kappa(self, data): c = HomoskedasticCovariance( data.x, data.y, data.z, data.params, kappa=data.kappa ) assert c.debiased is False assert c.config == {"debiased": False, "kappa": 0.99} assert_allclose(c.s, data.s2 * data.vk) assert_allclose(c.cov, data.s2 * inv(data.vk) / data.nobs) s = str(c) assert "Debiased: False" in s assert "Kappa" in s
def test_homoskedastic_cov_kappa_debiased(data): c = HomoskedasticCovariance(data.x, data.y, data.z, data.params, debiased=True, kappa=data.kappa) assert c.debiased is True assert c.config == {"debiased": True, "kappa": data.kappa} assert_allclose(c.s, data.s2_debiased * data.vk) assert_allclose(c.cov, data.s2_debiased * inv(data.vk) / data.nobs) s = str(c) assert "Debiased: True" in s
def test_homoskedastic_cov_errors(data): with pytest.raises(ValueError): HomoskedasticCovariance(data.x[:10], data.y, data.z, data.params) with pytest.raises(ValueError): HomoskedasticCovariance(data.x, data.y, data.z, data.params[1:])