def test_kernel_covariance_asymptotic(data, kernel):
c = KernelCovariance(data.x,
data.y,
data.z,
data.params,
kernel=kernel.kernel)
cs = str(c)
assert "\nBandwidth" not in cs
for name in kernel.alt_names:
c2 = KernelCovariance(data.x, data.y, data.z, data.params, kernel=name)
assert_equal(c.cov, c2.cov)
assert c.debiased is False
assert c.config["debiased"] is False
assert_equal(c.config["kernel"], kernel.kernel)
assert_allclose(c.s2, data.s2)
bw = c.config["bandwidth"]
xe = data.xhat * data.e
s = xe.T @ xe
w = kernel.weight(bw, xe.shape[0] - 1)
for i in range(1, len(w)):
s += w[i] * (xe[i:].T @ xe[:-i] + xe[:-i].T @ xe[i:])
s /= data.nobs
assert_allclose(c.s, s)
assert_allclose(c.cov, data.vinv @ s @ data.vinv / data.nobs)
cs = str(c)
assert "Kappa" not in cs
assert "Kernel: {0}".format(kernel.kernel) in cs
assert "Bandwidth: {0}".format(bw) in cs
def test_debiased(self, data, kernel):
c = KernelCovariance(
data.x, data.y, data.z, data.params, kernel=kernel.kernel, debiased=True
)
for name in kernel.alt_names:
c2 = KernelCovariance(
data.x, data.y, data.z, data.params, kernel=name, debiased=True
)
assert_equal(c.cov, c2.cov)
assert c._kappa == 1
assert c.debiased is True
assert c.config["debiased"] is True
assert_equal(c.config["kernel"], kernel.kernel)
assert_allclose(c.s2, data.s2_debiased)
c2 = KernelCovariance(
data.x, data.y, data.z, data.params, kernel=kernel.kernel, debiased=False
)
scale = data.nobs / (data.nobs - data.nvar)
assert_allclose(c.s, scale * c2.s)
assert_allclose(c.cov, scale * c2.cov)
cs = str(c)
assert "Debiased: True" in cs
assert "Kernel: {0}".format(kernel.kernel) in cs
assert "Bandwidth: {0}".format(c.config["bandwidth"]) in cs
assert "id" in c.__repr__()
def test_kernel_covariance_unknown_kernel(data, kernel):
with pytest.raises(ValueError):
KernelCovariance(data.x,
data.y,
data.z,
data.params,
kernel=kernel.kernel + "_unknown")
def test_unknown_kernel(self, data, kernel):
with pytest.raises(ValueError):
KernelCovariance(data.x,
data.y,
data.z,
data.params,
kernel=kernel.kernel + '_unknown')