Unit root test

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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[edit]


  • 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.  edit
  • 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.  edit
  • 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.  edit
  • 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.