def _validate_data(self) -> None:
p = self.portfolios.ndarray
f = self.factors.ndarray
if p.shape[0] != f.shape[0]:
raise ValueError("The number of observations in portfolios and "
"factors is not the same.")
self._drop_missing()
p = self.portfolios.ndarray
f = self.factors.ndarray
if has_constant(p)[0]:
raise ValueError("portfolios must not contains a constant or "
"equivalent and must not have rank\n"
"less than the dimension of the smaller shape.")
if has_constant(f)[0]:
raise ValueError(
"factors must not contain a constant or equivalent.")
if np.linalg.matrix_rank(f) < f.shape[1]:
raise ValueError(
"Model cannot be estimated. factors do not have full column rank."
)
if p.shape[0] < (f.shape[1] + 1):
raise ValueError(
"Model cannot be estimated. portfolios must have factors + 1 or "
"more returns to\nestimate the model parameters.")
def s(self) -> NDArray:
"""HAC score covariance estimate"""
x, z, eps = self.x, self.z, self.eps
nobs, nvar = x.shape
pinvz = self._pinvz
xhat = z @ (pinvz @ x)
xhat_e = xhat * eps
kernel = self.config["kernel"]
bw = self.config["bandwidth"]
if bw is None:
self._auto_bandwidth = True
from linearmodels.shared.linalg import has_constant
const, loc = has_constant(xhat)
sel = ones((xhat.shape[1], 1))
if const:
sel[loc] = 0
scores = xhat_e @ sel
bw = kernel_optimal_bandwidth(scores, kernel)
self._bandwidth = bw
w = self._kernels[kernel](bw, nobs - 1)
s = cov_kernel(xhat_e, w)
return self._scale * s
def test_hasconstant():
x = np.random.randn(100, 3)
hc, loc = has_constant(x)
assert bool(hc) is False
assert loc is None
x[:, 0] = 1
hc, loc = has_constant(x)
assert hc is True
assert loc == 0
x[:, 0] = 2
hc, loc = has_constant(x)
assert hc is True
assert loc == 0
x[::2, 0] = 0
x[:, 1] = 1
x[1::2, 1] = 0
hc, loc = has_constant(x)
assert hc is True
