What does KPSS test tell you?
What is the KPSS Test? The Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test figures out if a time series is stationary around a mean or linear trend, or is non-stationary due to a unit root. A stationary time series is one where statistical properties — like the mean and variance — are constant over time.
Is KPSS test a unit root test?
The KPSS test, short for, Kwiatkowski-Phillips-Schmidt-Shin (KPSS), is a type of Unit root test that tests for the stationarity of a given series around a deterministic trend. In other words, the test is somewhat similar in spirit with the ADF test.
What is the difference between KPSS and ADF test?
So in summary, the ADF test has an alternate hypothesis of linear or difference stationary, while the KPSS test identifies trend-stationarity in a series.
Why is unit root test necessary?
Unit root tests can be used to determine if trending data should be first differenced or regressed on deterministic functions of time to render the data stationary. Moreover, economic and finance theory often suggests the existence of long-run equilibrium relationships among nonsta- tionary time series variables.
Why do we test for stationarity?
Stationarity means that the statistical properties of a a time series (or rather the process generating it) do not change over time. Stationarity is important because many useful analytical tools and statistical tests and models rely on it.
What unit roots tell us?
How do you know if a signal is stationary?
Probably the simplest way to check for stationarity is to split your total timeseries into 2, 4, or 10 (say N) sections (the more the better), and compute the mean and variance within each section. If there is an obvious trend in either the mean or variance over the N sections, then your series is not stationary.
How to implement KPSS test in Python?
How to implement KPSS test In python, the statsmodel package provides a convenient implementation of the KPSS test. A key difference from ADF test is the null hypothesis of the KPSS test is that the series is stationary. So practically, the interpretaion of p-value is just the opposite to each other.
What does the number of lags reported by Statsmodels KPSS mean?
Finally, the number of lags reported is the number of lags of the series that was actually used by the model equation of the kpss test. By default, the statsmodels kpss() uses the ‘legacy’ method.
What is the KPSS test for non stationary series?
The KPSS test authors derived one-sided LM statistics for the test. If the LM statistic is greater than the critical value (given in the table below for alpha levels of 10%, 5% and 1%), then the null hypothesis is rejected; the series is non-stationary. Table of KPSS critical values from Kwiatowski et. al (1992).
How to deal with high type I errors in KPSS?
One way to deal with the potential for high Type I errors is to combine the KPSS with an ADF test. If the result from both tests suggests that the time series in stationary, then it probably is. Kocenda, E. & Cerný, A. (2017).