Unit root test

In statistics, a unit root test tests whether a time series variable is non-stationary using an autoregressive model. A well-known test that is valid in large samples is the augmented Dickey–Fuller test. The optimal finite sample tests for a unit root in autoregressive models were developed by Denis Sargan and Alok Bhargava. Another test is the Phillips–Perron test. These tests use the existence of a unit root as the null hypothesis.

See also

• Augmented Dickey–Fuller test
• Dickey–Fuller test
• Phillips–Perron test
• KPSS test
• Zivot–Andrews test

References

• Dickey, D. A.; Fuller, W. A. (1979). "Distribution of the Estimators for Autoregressive Time Series with a Unit Root". Journal of the American Statistical Association. 74 (366a): 427–431. doi:10.1080/01621459.1979.10482531.
• Sargan, J. D.; Bhargava, Alok (1983). "Testing Residuals from Least Squares Regression for Being Generated by the Gaussian Random Walk". Econometrica. 51 (1): 153–174. JSTOR 1912252. {{cite journal}}: Cite has empty unknown parameter: |doix= (help)
• Bhargava, A. (1986). "On the Theory of Testing for Unit Roots in Observed Time Series". The Review of Economic Studies. 53 (3): 369–384. doi:10.2307/2297634. JSTOR 2297634.
• Bierens, H.J. (2001). "Unit Roots," Ch. 29 in A Companion to Econometric Theory, ed B. Baltagi, Oxford, Blackwell Publishers, 610–633. "2007 revision"
• Enders, Walter (2004). Applied Econometric Time Series (Second ed.). New York: John Wiley. pp. 170–175. ISBN 0-471-23065-0.
• Patterson, K. (2011), Unit Root Tests in Time Series, vol. 1, Palgrave Macmillan.
• Patterson, K. (2012), Unit Root Tests in Time Series, vol. 2, Palgrave Macmillan.