Unit root test: Difference between revisions

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{{Short description|Time series statistical 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 [[Stationary process|stationarity]], [[Trend-stationary process|trend stationarity]] or explosive root depending on the test used.

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== Main tests ==

A commonly used test that is valid in large samples is the [[augmented Dickey–Fuller test]].<ref>{{Cite journal | doi = 10.1080/01621459.1979.10482531| title = Distribution of the estimators for autoregressive time series with a unit root| year = 1979| last1 = Dickey | first1 = D. A. | last2 = Fuller | first2 = W. A. | journal = [[Journal of the American Statistical Association]] | volume = 74| issue = 366a| pages = 427–431}}</ref>

Other popular tests include:

A commonly used test that is valid in large samples is the* [[augmented Dickey–Fuller test]].<ref>{{Cite journal | doi = 10.1080/01621459.1979.10482531| title = Distribution of the estimators for autoregressive time series with a unit root| year = 1979| last1 = Dickey | first1 = D. A. | last2 = Fuller | first2 = W. A. | journal = [[Journal of the American Statistical Association]] | volume = 74| issue = 366a| pages = 427–431}}</ref>

*: this is valid in large samples.

* [[Phillips–Perron test]]

* [[Zivot–AndrewsKPSS test]]

*: [[KPSS test]] (in whichhere the null hypothesis is [[Trend-stationary process|trend stationarity]] rather than the presence of a [[Stationary process|unit root]]).

* [[ADF-GLS test]]

* Sargan-Barghava test<ref>{{cite journal|author1=Elliott, G.|author2=Rothenberg, T. J.|author3=Stock, J. H.|year=1992|title=Efficient tests for an autoregressive unit root|journal=National Bureau of Economic Research}}</ref>

* [[Zivot–Andrews test]]

Unit root tests are closely linked to [[Autocorrelation|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]]

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==Notes==

{{Notelist}}

{{Reflist}}

==References==

*{{cite book |last=Bierens |first=H. J. |year=2001 |chapter=Unit roots |title=A Companion to Econometric Theory |editor-first=B. |editor-last=Baltagi |___location=Oxford |publisher=[[Blackwell Publishers]] |pages=610–633 }} [http://econ.la.psu.edu/~hbierens/UNITROOT.PDF "2007 revision"] {{Webarchive|url=https://web.archive.org/web/20140617113943/http://econ.la.psu.edu/~hbierens/UNITROOT.PDF |date=2014-06-17 }}

*{{cite book |last=Enders |first=Walter |title=Applied Econometric Time Series |publisher=[[John Wiley & Sons]] |year=2004 |edition=Second |pages=[https://archive.org/details/appliedeconometr00ende_0/page/170 170–175] |isbn=0-471-23065-0 |url-access=registration |url=https://archive.org/details/appliedeconometr00ende_0/page/170 }}

*{{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 |pages=[https://archive.org/details/unitrootscointeg00madd/page/n116 98]–154 }}