MIDASVARResult._summary_table#

MIDASVARResult._summary_table()[source]#

Build the structured summary.

The coefficient table with its conditional inference columns, the compressed block labelled by series; metadata pairing the fit statistics with the dimensions; notes on stability, the estimated polynomial per series, the endogenous-only propagation surface, the conditional forecast, the conditioning of the standard errors, and the Cholesky ordering, with an unstable system flagged first.

Return type:

SummaryTable

Returns:

The SummaryTable behind summary().

Example

>>> import numpy as np
>>> rng = np.random.default_rng(0)
>>> x = rng.standard_normal((240, 1))
>>> last = x[2::3, 0] + 0.1 * rng.standard_normal(80)
>>> y = np.column_stack([last, rng.standard_normal(80)])
>>> table = MIDASVAR(y, x, order=1, period=3).fit()._summary_table()
>>> table.title, dict(table.metadata)["High-frequency"]
('MIDASVAR(1, 3) Results', '1')
>>> [row[0] for row in table.rows][:4]
['y1: const', 'y1: y1.L1', 'y1: y2.L1', 'y1: x1']
>>> table.notes[1][:50]
'Lag polynomial (theta_1, theta_2) over 3 sub-perio'