Param Estimator

StationarityKPSSArch

Test for stationarity via the Kwiatkowski-Phillips-Schmidt-Shin Unit Root Test.

Direct interface to KPSS test from the arch package. Does not assume ARCH process, naming is due to the use of the arch package.

Uses arch.unitroot.KPSS as a test for trend-stationarity, and derives a boolean statement whether a series is (trend-)stationary.

Also returns test results for the unit root test as fitted parameters.

Quickstart

python

from sktime.param_est.stationarity import StationarityKPSSArch

estimator = StationarityKPSSArch(lags=None, trend='c', p_threshold=0.05)

Parameters(2)

lagsint, optional

The number of lags to use in the Newey-West estimator of the long-run covariance. If omitted or None, the number of lags is calculated with the data-dependent method of Hobijn et al. (1998). See also Andrews (1991), Newey & West (1994), and Schwert (1989). Set lags=-1 to use the old method that only depends on the sample size, 12 * (nobs/100) ** (1/4).

trend{“c”, “ct”}, optional

The trend component to include in the ADF test: “c” - Include a constant (Default) “ct” - Include a constant and linear time trend

Examples

>>> from sktime.datasets import load_airline
>>> from sktime.param_est.stationarity import StationarityKPSSArch
>>> 
>>> X = load_airline ()
>>> sty_est = StationarityKPSSArch ()
>>> sty_est. fit (X) StationarityKPSSArch(
... )
>>> sty_est. get_fitted_params ()["stationary" ] True