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:])
