The KPSS Test, or Kwiatkowski-Phillips-Schmidt-Shin test, is a statistical test used to check for stationarity in a time series by testing the null hypothesis that a time series is level or trend stationary. Unlike the ADF test, which tests for a unit root, the KPSS test is often used in conjunction to confirm results, as it can identify series that are trend stationary but not difference stationary.

KPSS Test

Kwiatkowski-Phillips-Schmidt-Shin test

A statistical test is a method used to determine if there is a significant effect or relationship present in a dataset, often by comparing observed data against a null hypothesis. The choice of test depends on the data type, distribution, and the specific research question being addressed.

Statistical Test

Stationarity in a time series

The null hypothesis is a fundamental concept in statistical testing that posits no effect or relationship between variables, serving as a default or baseline assumption to be tested against. It is typically rejected or not rejected based on the strength of evidence provided by sample data, guiding researchers in making inferences about the population.

Null Hypothesis

Level Stationary

Trend Stationary

ADF Test

A unit root in a time series indicates that the series is non-stationary and possesses a stochastic trend, meaning shocks to the system have a permanent effect. Identifying and addressing unit roots is crucial in econometric modeling to avoid spurious regression results and to ensure meaningful statistical inference.

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