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Param Estimator

StationarityZivotAndrews

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Test for stationarity via the Zivot-Andrews Unit Root Test.

Direct interface to ZivotAndrews test from the arch package.

Uses arch.unitroot.ZivotAndrews as a test for unit roots, and derives a boolean statement whether a series is stationary.

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

Schnellstart

python
from sktime.param_est.stationarity import StationarityZivotAndrews

estimator = StationarityZivotAndrews(lags=None, trend='c', trim=0.15, max_lags=None, method='aic', p_threshold=0.05)

Tags

Fähigkeiten

  • Multivariat: Nicht unterstützt
  • Fehlende Werte: Nicht unterstützt

Parameter(5)

lagsint, optional

The number of lags to use in the ADF regression. If omitted or None, method is used to automatically select the lag length with no more than max_lags are included.

trend{“c”, “t”, “ct”}, optional

The trend component to include in the test

  • “c” - Include a constant (Default)

  • “t” - Include a linear time trend

  • “ct” - Include a constant and linear time trend

trimfloat
percentage of series at begin/end to exclude from break-period calculation in range [0, 0.333] (default=0.15)
max_lagsint, optional
The maximum number of lags to use when selecting lag length
method{“AIC”, “BIC”, “t-stat”}, optional

The method to use when selecting the lag length

  • “AIC” - Select the minimum of the Akaike IC

  • “BIC” - Select the minimum of the Schwarz/Bayesian IC

  • “t-stat” - Select the minimum of the Schwarz/Bayesian IC

Beispiele

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