Content deleted Content added
→Main tests: Fix typo |
→Main tests: Restructure. Add red link per WP:REDLINK. Improve neutrality of text. Removing "commonly used" as I could not confirm it. Reinstate explanation of Sargan-Barghava test, now placed in a proper place. |
||
Line 18:
== 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:
▲
*: this is valid in large samples.
* [[Phillips–Perron test]]
* [[KPSS test]]
*:
* [[ADF-GLS test]]
* [[Sargan-Bhargava 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>
*: tests the unit root null hypothesis in first order autoregressive models against one-sided alternatives, i.e., if the process is stationary or explosive under the alternative hypothesis.
* [[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:
| |||