TVARResult.regime_weight#

property TVARResult.regime_weight: NDArray[float64][source]#

1.0 above the threshold, else 0.0.

Strict on the upper side, matching the estimator’s z <= r assignment to the lower regime, so a transition value landing exactly on the threshold is counted here as it was when the coefficients were solved for.

Returns:

An array of length nobs with entries in {0.0, 1.0}, aligned with resid; its sum is n_upper.

Example

>>> import numpy as np
>>> y = np.random.default_rng(0).standard_normal((200, 2))
>>> res = TVAR(y, order=1, transition_variable="y1", delay=1).fit()
>>> weight = res.regime_weight
>>> weight.shape, sorted(set(weight.tolist())), int(weight.sum()) == res.n_upper
((199,), [0.0, 1.0], True)
>>> bool(np.all(weight == (res.threshold_values > res.threshold)))
True
Type:

Indicator of the upper regime