def test_kappa(self, data):
c = HeteroskedasticCovariance(data.x, data.y, data.z, data.params,
debiased=False, kappa=.99)
assert c.debiased is False
assert c.config == {'debiased': False, 'kappa': 0.99}
c2 = HeteroskedasticCovariance(data.x, data.y, data.z, data.params)
assert_allclose(c.s, c2.s)
assert c.s2 == c2.s2
vk_inv = inv(data.vk)
assert_allclose(c.cov, vk_inv @ c.s @ vk_inv / data.nobs)
def test_kappa_debiased(self, data):
c = HeteroskedasticCovariance(
data.x, data.y, data.z, data.params, debiased=True, kappa=0.99
)
assert c.debiased is True
assert c.config == {"debiased": True, "kappa": 0.99}
c2 = HeteroskedasticCovariance(
data.x, data.y, data.z, data.params, debiased=True
)
assert_allclose(c.s, c2.s)
assert c.s2 == c2.s2
vk_inv = inv(data.vk)
assert_allclose(c.cov, vk_inv @ c.s @ vk_inv / data.nobs)
def test_heteroskedastic_cov_kappa(data):
c = HeteroskedasticCovariance(data.x,
data.y,
data.z,
data.params,
debiased=False,
kappa=0.99)
assert c.debiased is False
assert c.config == {"debiased": False, "kappa": 0.99}
c2 = HeteroskedasticCovariance(data.x, data.y, data.z, data.params)
assert_allclose(c.s, c2.s)
assert c.s2 == c2.s2
vk_inv = inv(data.vk)
assert_allclose(c.cov, vk_inv @ c.s @ vk_inv / data.nobs)
def test_heteroskedastic_cov_asymptotic(data):
c = HeteroskedasticCovariance(data.x, data.y, data.z, data.params)
assert c.debiased is False
assert c.config == {"debiased": False, "kappa": 1}
assert_allclose(c.s2, data.s2)
xhat, eps, nobs = data.xhat, data.e, data.nobs
assert_allclose(c.s, (xhat * eps).T @ (xhat * eps) / nobs)
def test_debiased(self, data):
c = HeteroskedasticCovariance(data.x, data.y, data.z, data.params,
debiased=True)
xhat, eps, nobs, nvar = data.xhat, data.e, data.nobs, data.nvar
assert c.debiased is True
assert c.config == {'debiased': True, 'kappa': 1}
s = (xhat * eps).T @ (xhat * eps) / (nobs - nvar)
assert_allclose(c.s, s)
assert_allclose(c.cov, data.vinv @ s @ data.vinv / nobs)