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 _t^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

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. Popular serial correlation tests include:
* Breusch–Godfrey test
* Ljung–Box test
* Durbin–Watson test

Notes

References

"2007 revision"
*
*{{cite book , last=Maddala , first=G. S. , authorlink=G. S. Maddala , last2=Kim , first2=In-Moo , chapter=Issues in Unit Root Testing , title=Unit Roots, Cointegration, and Structural Change , url=https://archive.org/details/unitrootscointeg00madd , url-access=limited , location=Cambridge , publisher=Cambridge University Press , year=1998 , isbn=0-521-58782-4 , page
98
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Time series statistical tests