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How do you interpret KPSS results?
Interpreting the Results 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.
Is KPSS 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 null hypothesis for ADF test?
The null hypothesis for this test is that there is a unit root. The alternate hypothesis differs slightly according to which equation you’re using. The basic alternate is that the time series is stationary (or trend-stationary).
What is the difference between ADF and PP test?
When running unit root test for each variable, ADF shows data have a unit root, while PP rejects the null hypothesis of unit root.
What does the KPSS test do?
In econometrics, Kwiatkowski–Phillips–Schmidt–Shin (KPSS) tests are used for testing a null hypothesis that an observable time series is stationary around a deterministic trend (i.e. trend-stationary) against the alternative of a unit root.
Why is unit root bad?
In probability theory and statistics, a unit root is a feature of some stochastic processes (such as random walks) that can cause problems in statistical inference involving time series models. If there are d unit roots, the process will have to be differenced d times in order to make it stationary.
What is unit root in time series?
A unit root (also called a unit root process or a difference stationary process) is a stochastic trend in a time series, sometimes called a “random walk with drift”; If a time series has a unit root, it shows a systematic pattern that is unpredictable.