Are you an EPFL student looking for a semester project?
Work with us on data science and visualisation projects, and deploy your project as an app on top of GraphSearch.
Concept
Unit root test
Summary
In statistics, a unit root test tests whether a time series variable is non-stationary and possesses a unit root. The null hypothesis is generally defined as the presence of a unit root and the alternative hypothesis is either stationarity, trend stationarity or explosive root depending on the test used.
In general, the approach to unit root testing implicitly assumes that the time series to be tested can be written as,
where,
is the deterministic component (trend, seasonal component, etc.)
is the stochastic component.
is the stationary error process.
The task of the test is to determine whether the stochastic component contains a unit root or is stationary.
Other popular tests include:
augmented Dickey–Fuller test
this is valid in large samples.
Phillips–Perron test
KPSS test
here the null hypothesis is trend stationarity rather than the presence of a unit root.
ADF-GLS test
Unit root tests are closely linked to serial correlation tests. However, while all processes with a unit root will exhibit serial correlation, not all serially correlated time series will have a unit root.
Official source
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.