def _compute_statistic(self):
overlap, debiased, robust = self._overlap, self._debiased, self._robust
y, nobs, q, trend = self._y, self._nobs, self._lags, self._trend
nq = nobs - 1
if not overlap:
# Check length of y
if nq % q != 0:
extra = nq % q
y = y[:-extra]
warnings.warn(
invalid_length_doc.format(var='y', block=q, drop=extra),
InvalidLengthWarning)
nobs = y.shape[0]
if trend == 'nc':
mu = 0
else:
mu = (y[-1] - y[0]) / (nobs - 1)
delta_y = diff(y)
nq = delta_y.shape[0]
sigma2_1 = sum((delta_y - mu)**2.0) / nq
if not overlap:
delta_y_q = y[q::q] - y[0:-q:q]
sigma2_q = sum((delta_y_q - q * mu)**2.0) / nq
self._summary_text = ['Computed with non-overlapping blocks']
else:
delta_y_q = y[q:] - y[:-q]
sigma2_q = sum((delta_y_q - q * mu)**2.0) / (nq * q)
self._summary_text = ['Computed with overlapping blocks']
if debiased and overlap:
sigma2_1 *= nq / (nq - 1)
m = q * (nq - q + 1) * (1 - (q / nq))
sigma2_q *= (nq * q) / m
self._summary_text = [
'Computed with overlapping blocks '
'(de-biased)'
]
if not overlap:
self._stat_variance = 2.0 * (q - 1)
elif not robust:
self._stat_variance = (2 * (2 * q - 1) * (q - 1)) / (2 * q)
else:
z2 = (delta_y - mu)**2.0
scale = sum(z2)**2.0
theta = 0.0
for k in range(1, q):
delta = nq * z2[k:].dot(z2[:-k]) / scale
theta += (1 - k / q)**2.0 * delta
self._stat_variance = theta
self._vr = sigma2_q / sigma2_1
self._stat = sqrt(nq) * (self._vr - 1) / sqrt(self._stat_variance)
self._pvalue = 2 - 2 * norm.cdf(abs(self._stat))
def _compute_statistic(self):
overlap, debiased, robust = self._overlap, self._debiased, self._robust
y, nobs, q, trend = self._y, self._nobs, self._lags, self._trend
nq = nobs - 1
if not overlap:
# Check length of y
if nq % q != 0:
extra = nq % q
y = y[:-extra]
warnings.warn(invalid_length_doc.format(var='y',
block=q,
drop=extra),
InvalidLengthWarning)
nobs = y.shape[0]
if trend == 'nc':
mu = 0
else:
mu = (y[-1] - y[0]) / (nobs - 1)
delta_y = diff(y)
nq = delta_y.shape[0]
sigma2_1 = sum((delta_y - mu) ** 2.0) / nq
if not overlap:
delta_y_q = y[q::q] - y[0:-q:q]
sigma2_q = sum((delta_y_q - q * mu) ** 2.0) / nq
self._summary_text = ['Computed with non-overlapping blocks']
else:
delta_y_q = y[q:] - y[:-q]
sigma2_q = sum((delta_y_q - q * mu) ** 2.0) / (nq * q)
self._summary_text = ['Computed with overlapping blocks']
if debiased and overlap:
sigma2_1 *= nq / (nq - 1)
m = q * (nq - q + 1) * (1 - (q / nq))
sigma2_q *= (nq * q) / m
self._summary_text = ['Computed with overlapping blocks '
'(de-biased)']
if not overlap:
self._stat_variance = 2.0 * (q - 1)
elif not robust:
self._stat_variance = (2 * (2 * q - 1) * (q - 1)) / (2 * q)
else:
z2 = (delta_y - mu) ** 2.0
scale = sum(z2) ** 2.0
theta = 0.0
for k in range(1, q):
delta = nq * z2[k:].dot(z2[:-k]) / scale
theta += (1 - k / q) ** 2.0 * delta
self._stat_variance = theta
self._vr = sigma2_q / sigma2_1
self._stat = sqrt(nq) * (self._vr - 1) / sqrt(self._stat_variance)
self._pvalue = 2 - 2 * norm.cdf(abs(self._stat))