FunctionalVARResult._summary_table#

FunctionalVARResult._summary_table()[source]#

Build the structured summary.

One row per factor with its variance share ("-" under a fixed basis) and mean absolute score; metadata naming the basis, the dimensions, the factor order and trend, the reconstruction floor and the factor log-likelihood; notes on factor stability, where factor-level inference lives, the Nelson-Siegel decay when one was used, and the reconstruction floor.

Return type:

SummaryTable

Returns:

The SummaryTable behind summary().

Example

>>> import numpy as np
>>> rng = np.random.default_rng(0)
>>> grid = np.linspace(0.0, 1.0, 25)
>>> curves = 0.1 * np.cumsum(rng.standard_normal(80))[:, None] + np.sin(np.pi * grid)
>>> curves = curves + 0.01 * rng.standard_normal((80, 25))
>>> res = FunctionalVAR(curves, grid, order=1, basis="bspline", df=4).fit()
>>> table = res._summary_table()
>>> table.title, table.columns
('FunctionalVAR(1) Results', ('factor', 'variance share', 'mean |score|'))
>>> dict(table.metadata)["Basis"], [row[:2] for row in table.rows][:2]
('bspline', [('b1', '-'), ('b2', '-')])