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The KPSS and Phillips-Perron Tests

Explore the KPSS and Phillips-Perron tests to assess stationarity in time series data. Understand how these tests differ from ADF tests, interpret their hypotheses, and learn how to apply the KPSS test using Python's statsmodels library.

While probably the most popular, the ADF is not the only test to detect a unit root. In this lesson, we will see two alternatives that are also commonly used by academics and practitioners alike: the KPSS and the Phililps-Perron test.

KPSS test

The KPSS test (named after its proponents Kwiatkowski, Phillips, Schmidt, and Shin) flips the definitions of the null and alternative hypotheses. Contrary to the ADF, KPSS tests the null hypothesis that the series is stationary I(0)I(0), against the alternative that it is not, I(1)I(1).

The model for yty_t is slightly twisted: it is a combination of both a stationary process, utu_t, and a ...