arch.unitroot.KPSS¶

class arch.unitroot.KPSS(y: ndarray | DataFrame | Series, lags: int | None = None, trend: 'c' | 'ct' = 'c')[source]¶

Kwiatkowski, Phillips, Schmidt and Shin (KPSS) stationarity test

Parameters:¶

y: ndarray | DataFrame | Series¶

The data to test for stationarity

lags: int | None = None¶

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' = 'c'¶

The trend component to include in the ADF test

”c” - Include a constant (Default) “ct” - Include a constant and linear time trend

Notes

The null hypothesis of the KPSS test is that the series is weakly stationary and the alternative is that it is non-stationary. If the p-value is above a critical size, then the null cannot be rejected that there and the series appears stationary.

The p-values and critical values were computed using an extensive simulation based on 100,000,000 replications using series with 2,000 observations. See [3] for the initial description of the KPSS test. Further details are available in [2] and [5]. Details about the long-run covariance estimation can be found in [1] and [4].

Examples

>>> from arch.unitroot import KPSS
>>> import numpy as np
>>> import statsmodels.api as sm
>>> data = sm.datasets.macrodata.load().data
>>> inflation = np.diff(np.log(data["cpi"]))
>>> kpss = KPSS(inflation)
>>> print(f"{kpss.stat:0.4f}")
0.2870
>>> print(f"{kpss.pvalue:0.4f}")
0.1473
>>> kpss.trend = "ct"
>>> print(f"{kpss.stat:0.4f}")
0.2075
>>> print(f"{kpss.pvalue:0.4f}")
0.0128

References

Methods

summary()

Summary of test, containing statistic, p-value and critical values

Properties

alternative_hypothesis	The alternative hypothesis
critical_values	Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms.
lags	Sets or gets the number of lags used in the model.
nobs	The number of observations used when computing the test statistic.
null_hypothesis	The null hypothesis
pvalue	Returns the p-value for the test statistic
stat	The test statistic for a unit root
trend	Sets or gets the deterministic trend term used in the test.
valid_trends	List of valid trend terms.
y	Returns the data used in the test statistic