def test_center(self, data, kernel, bandwidth):
wm = KernelWeightMatrix(kernel.kernel,
bandwidth,
center=True,
optimal_bw=True)
weight = wm.weight_matrix(data.x, data.z, data.e)
z, e, nobs = data.z, data.e, data.nobs
bw = bandwidth or wm.bandwidth
w = kernel.weight(bw, nobs - 1)
ze = z * e
ze = ze - ze.mean(0)
s = ze.T @ ze
for i in range(1, len(w)):
op = ze[i:].T @ ze[:-i]
s += w[i] * (op + op.T)
assert_allclose(weight, s / nobs)
assert wm.config['bandwidth'] == bw
assert wm.config['kernel'] == kernel.kernel
for name in kernel.alt_names:
wm = KernelWeightMatrix(name,
bandwidth,
center=True,
optimal_bw=True)
weight2 = wm.weight_matrix(data.x, data.z, data.e)
assert_equal(weight, weight2)
def test_config(self, data, kernel, bandwidth):
wm = KernelWeightMatrix(kernel=kernel.kernel, bandwidth=bandwidth)
weight = wm.weight_matrix(data.x, data.z, data.e)
z, e, nobs = data.z, data.e, data.nobs
bw = bandwidth or wm.bandwidth
w = kernel.weight(bw, nobs - 1)
ze = z * e
s = ze.T @ ze
for i in range(1, len(w)):
op = ze[i:].T @ ze[:-i]
s += w[i] * (op + op.T)
assert_allclose(weight, s / nobs)
assert wm.config['center'] is False
assert wm.config['debiased'] is False
assert wm.config['bandwidth'] == bw
assert wm.config['kernel'] == kernel.kernel
for name in kernel.alt_names:
wm = KernelWeightMatrix(kernel=name, bandwidth=bandwidth)
weight2 = wm.weight_matrix(data.x, data.z, data.e)
assert_equal(weight, weight2)
def test_debiased(self, kernel, data, bandwidth):
wm = KernelWeightMatrix(debiased=True, kernel=kernel.kernel, bandwidth=bandwidth)
weight = wm.weight_matrix(data.x, data.z, data.e)
z, e, nobs, nvar = data.z, data.e, data.nobs, data.nvar
bw = bandwidth or nobs - 2
w = kernel.weight(bw, nobs - 1)
ze = z * e
s = ze.T @ ze
for i in range(1, len(w)):
op = ze[i:].T @ ze[:-i]
s += w[i] * (op + op.T)
assert_allclose(weight, s / (nobs - nvar))
assert wm.config['bandwidth'] == bw
assert wm.config['kernel'] == kernel.kernel