MIDASVARResult.midas_weights#

property MIDASVARResult.midas_weights: NDArray[float64][source]#

The estimated weight curves, (midas_lags, m), most recent first.

Each column sums to one; a column that decays toward zero within the window is the sign that midas_lags is long enough.

Example

>>> import numpy as np
>>> rng = np.random.default_rng(0)
>>> x = rng.standard_normal((360, 1))
>>> window = 0.7 * x[2::3, 0] + 0.3 * x[1::3, 0]
>>> noise = 0.1 * rng.standard_normal(120)
>>> y = np.column_stack([window + noise, rng.standard_normal(120)])
>>> weights = MIDASVAR(y, x, order=1, period=3, midas_lags=6).fit().midas_weights
>>> weights.shape, bool(np.isclose(weights.sum(), 1.0)), weights.ravel().round(2)
((6, 1), True, array([0.69, 0.3 , 0.01, 0.  , 0.  , 0.  ]))