Unit root test

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.

General approach

In general, the approach to unit root testing implicitly assumes that the time series to be tested $[y_{t}]_{t=1}^{T}$ can be written as,

$y_{t}=D_{t}+z_{t}+\varepsilon _{t}$

where,

• $D_{t}$ is the deterministic component (trend, seasonal component, etc.)
• $z_{t}$ is the stochastic component.
• $\varepsilon _{t}$ is the stationary error process.

The task of the test is to determine whether the stochastic component contains a unit root or is stationary.

Main tests

A commonly used test that is valid in large samples is the augmented Dickey–Fuller test.

Other popular tests include:

• Phillips–Perron test
• KPSS test (in which the null hypothesis is trend stationarity rather than the presence of a unit root)
• ADF-GLS test
• Sargan-Barghava test
• Zivot–Andrews 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. Popular serial correlation tests include:

• Breusch–Godfrey test
• Ljung–Box test
• Durbin–Watson test

References

• Bierens, H. J. (2001). "Unit roots". In Baltagi, B. (ed.). A Companion to Econometric Theory. Oxford: Blackwell Publishers. pp. 610–633. "2007 revision"
• Enders, Walter (2004). Applied Econometric Time Series (Second ed.). John Wiley & Sons. pp. 170–175. ISBN 0-471-23065-0.
• Maddala, G. S.; Kim, In-Moo (1998). "Issues in Unit Root Testing". Unit Roots, Cointegration, and Structural Change. Cambridge: Cambridge University Press. pp. 98–154. ISBN 0-521-58782-4.