LongMemorySVResult._summary_table#

LongMemorySVResult._summary_table()[source]#

Structured summary rendered by every display path.

One row per estimated parameter; the Whittle criterion, counts and information criteria in the metadata; four standing notes on the quasi-likelihood, the hyperbolic decay, the smoother and the weak identification of small d, plus a fifth when d is at the boundary.

Return type:

SummaryTable

Returns:

The SummaryTable behind summary().

Example

>>> import numpy as np
>>> rng = np.random.default_rng(0)
>>> y = rng.standard_normal(300)
>>> table = LongMemorySV(y, short_memory=True).fit()._summary_table()
>>> table.title, len(table.notes)
('LongMemorySV[constant, ARFIMA(1,d,0)] Results', 4)
>>> [row[0] for row in table.rows]
['mean', 'mu', 'd', 'sigma2', 'phi']
>>> dict(table.metadata)["Method"], dict(table.metadata)["Frequencies"]
('whittle', '150')