def test_center(self, data):
wm = HeteroskedasticWeightMatrix(True)
z, e, nobs = data.z, data.e, data.nobs
weight = wm.weight_matrix(data.x, z, e)
ze = z * e
ze -= ze.mean(0)
assert_allclose(weight, ze.T @ ze / nobs)
def test_debiased(self, data):
wm = HeteroskedasticWeightMatrix(debiased=True)
z, e, nobs, nvar = data.z, data.e, data.nobs, data.nvar
weight = wm.weight_matrix(data.x, z, e)
ze = z * e
scale = nobs / (nobs - nvar)
assert_allclose(weight, scale * ze.T @ ze / nobs)
def test_config(self, data):
wm = HeteroskedasticWeightMatrix()
z, e, nobs = data.z, data.e, data.nobs
weight = wm.weight_matrix(data.x, z, e)
ze = z * e
assert_allclose(weight, ze.T @ ze / nobs)
assert wm.config['center'] is False
assert wm.config['debiased'] is False
def test_heteroskedastic(self, data):
c = IVGMMCovariance(data.x, data.y, data.z, data.params, data.i, 'robust')
s = HeteroskedasticWeightMatrix().weight_matrix(data.x, data.z, data.e)
x, z = data.x, data.z
xzwswzx = x.T @ z @ s @ z.T @ x / data.nobs
cov = data.xzizx_inv @ xzwswzx @ data.xzizx_inv
cov = (cov + cov.T) / 2
assert_allclose(c.cov, cov)
assert c.config['debiased'] is False
