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 John 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
• KPSS test

References

• Dickey, D.A. and W.A. Fuller (1979), “Distribution of the Estimators for Autoregressive Time Series with a Unit Root,” Journal of the American Statistical Association, 74, p. 427–431.
• Sargan, J.D. and Alok Bhargava (1983). "Testing residuals from least squares regressions for being generated by the Gaussian random walk", Econometrica, 51, p. 153-174.
• Bhargava, Alok (1986) "On the Theory of Testing for Unit Roots in Observed Time Series," Review of Economic Studies, 53, 369-384.
• Bierens, H.J. (2001). "Unit Roots," Ch. 29 in A Companion to Econometric Theory, ed B. Baltagi, Oxford, Blackwell Publishers, 610-33. [1]