SETARResult.regime_weight#

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

1.0 above the threshold, else 0.0.

The comparison is 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 the same way here as it was when the coefficients were solved for. Its sum is n_upper and its mean is upper_fraction.

Example

>>> import numpy as np
>>> rng = np.random.default_rng(0)
>>> y = np.zeros(300)
>>> for t in range(1, 300):
...     y[t] = (0.6 if y[t - 1] <= 0.0 else -0.4) * y[t - 1] + rng.standard_normal()
>>> res = SETAR(y, order=1, delay=1).fit()
>>> weight = res.regime_weight
>>> weight.shape, int(weight.sum()) == res.n_upper, set(np.unique(weight)) == {0.0, 1.0}
((299,), True, True)
Type:

Indicator of the upper regime