def test_center(self, data):
wm = OneWayClusteredWeightMatrix(data.clusters, True)
weight = wm.weight_matrix(data.x, data.z, data.e)
ze = data.z * data.e
ze -= ze.mean(0)
uc = np.unique(data.clusters)
s = np.zeros((ze.shape[1], ze.shape[1]))
for val in uc:
obs = ze[data.clusters == val]
sum = obs.sum(0)[:, None]
s += sum @ sum.T
assert_allclose(weight, s / data.nobs)
def test_debiased(self, data):
wm = OneWayClusteredWeightMatrix(data.clusters, debiased=True)
weight = wm.weight_matrix(data.x, data.z, data.e)
ze = data.z * data.e
uc = np.unique(data.clusters)
s = np.zeros((ze.shape[1], ze.shape[1]))
for val in uc:
obs = ze[data.clusters == val]
sum = obs.sum(0)[:, None]
s += sum @ sum.T
nobs, nvar = data.nobs, data.nvar
groups = len(uc)
scale = (nobs - 1) / (nobs - nvar) * groups / (groups - 1)
assert_allclose(weight, scale * s / data.nobs)
def test_clustered(self, data):
c = IVGMMCovariance(data.x, data.y, data.z, data.params, data.i, 'clustered',
clusters=data.clusters)
s = OneWayClusteredWeightMatrix(clusters=data.clusters).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
assert_equal(c.config['clusters'], data.clusters)
c = IVGMMCovariance(data.x, data.y, data.z, data.params, data.i, 'clustered')
assert 'Clustered' in str(c)
def test_errors(self, data):
wm = OneWayClusteredWeightMatrix(data.clusters[:10])
with pytest.raises(ValueError):
wm.weight_matrix(data.x, data.z, data.e)
def test_config(self, data):
wm = OneWayClusteredWeightMatrix(data.clusters)
assert wm.config['center'] is False
assert wm.config['debiased'] is False
assert_equal(wm.config['clusters'], data.clusters)
def test_config(self, data):
wm = OneWayClusteredWeightMatrix(data.clusters)
assert wm.config["center"] is False
assert wm.config["debiased"] is False
assert_equal(wm.config["clusters"], data.clusters)