Mastering Python for Finance - Second Edition

Drawbacks of ADF testing

Here are some considerations when using ADF tests for reliable checking of non-stationary data:

• The ADF test do not truly tell apart between pure and non-unit root generating processes. In long-term moving average processes, the ADF tests becomes biased in rejecting the null hypothesis. Other stationarity testing methods such as the Kwiatkowski–Phillips–Schmidt–Shin (KPSS) tests and the Phillips-Perron test take a different approach in treating the presence of unit roots.
• There is no fixed methodology in determining the lag length p. If p is too small, the remaining serial correlation in the errors may affect the size of the test. If p is too large, the power of the test will deteriorate. Additional consideration ...