This paper has two themes. First, we classify some effects which outliers in the data have on unit root inference. We show that, both in a classical and a Bayesian framework, the presence of additive outliers moves ‘standard’ inference towards stationarity. Second, we base inference on an independent Student-t instead of a Gaussian likelihood. This yields results that are less sensitive to the presence of outliers. Application to several time series with outliers reveals a negative correlation between the unit root and degrees of freedom parameter of the Student-t distribution. Therefore, imposing normality may incorrectly provide evidence against the unit root.

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Journal of Econometrics
Erasmus School of Economics

Hoek, H., & van Dijk, H. (1995). Classical and Bayesian aspects of robust unit root inference. Journal of Econometrics. doi:10.1016/0304-4076(94)01661-I