def test_ols_inefficient(self):
lag_len = 5
pacfols = pacf_ols(self.x, nlags=lag_len, efficient=False)
x = self.x.copy()
x -= x.mean()
n = x.shape[0]
lags = np.zeros((n - 5, 5))
lead = x[5:]
direct = np.empty(lag_len + 1)
direct[0] = 1.0
for i in range(lag_len):
lags[:, i] = x[5 - (i + 1):-(i + 1)]
direct[i + 1] = lstsq(lags[:, :(i + 1)], lead, rcond=None)[0][-1]
assert_allclose(pacfols, direct, atol=1e-8)
def test_ols_inefficient(self):
lag_len = 5
pacfols = pacf_ols(self.x, nlags=lag_len, efficient=False)
x = self.x.copy()
x -= x.mean()
n = x.shape[0]
lags = np.zeros((n - 5, 5))
lead = x[5:]
direct = np.empty(lag_len + 1)
direct[0] = 1.0
for i in range(lag_len):
lags[:, i] = x[5 - (i + 1):-(i + 1)]
direct[i + 1] = lstsq(lags[:, :(i + 1)], lead, rcond=None)[0][-1]
assert_allclose(pacfols, direct, atol=1e-8)
def _fit_single(self, idx, wxpwx, wxpwy, nobs, store, params_only, method):
if nobs < self._min_nobs:
return
try:
if (method == "inv") or not params_only:
wxpwxi = np.linalg.inv(wxpwx)
if method == "inv":
params = wxpwxi @ wxpwy
else:
_, wy, wx, _, _ = self._get_data(idx)
if method == "lstsq":
params = lstsq(wx, wy)[0]
else: # 'pinv'
wxpwxiwxp = np.linalg.pinv(wx)
params = wxpwxiwxp @ wy
except np.linalg.LinAlgError:
return
store.params[idx - 1] = params
if params_only:
return
y, wy, wx, weights, _ = self._get_data(idx)
wresid, ssr, llf = self._loglike(params, wy, wx, weights, nobs)
wxwresid = wx * wresid[:, None]
wxepwxe = wxwresid.T @ wxwresid
tot_params = wx.shape[1]
s2 = ssr / (nobs - tot_params)
centered_tss, uncentered_tss = self._sum_of_squares(y, wy, weights)
store.ssr[idx - 1] = ssr
store.llf[idx - 1] = llf
store.nobs[idx - 1] = nobs
store.s2[idx - 1] = s2
store.xpxi[idx - 1] = wxpwxi
store.xeex[idx - 1] = wxepwxe
store.centered_tss[idx - 1] = centered_tss
store.uncentered_tss[idx - 1] = uncentered_tss
