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Panel Unit-root Tests for Cross-sectionally Correlated Panels: A Monte Carlo Comparison*


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    I thank Jushan Bai, In Choi, Benoit Perron and Donggyu Sul, two referees and seminar participants at the Department of Statistics, University of Bologna, for many useful suggestions. All remaining errors are the sole responsibility of the author. The research was supported by a grant from the MIUR, Italy.


This paper deals with the finite-sample performance of a set of unit-root tests for cross-correlated panels. Most of the available macroeconomic time series cover short time periods. The lack of information, in terms of time observations, implies that univariate tests are not powerful enough to reject the null of a unit-root while panel tests, by exploiting the large number of cross-sectional units, have been shown to be a promising way of increasing the power of unit-root tests. We investigate the finite sample properties of recently proposed panel unit-root tests for cross-sectionally correlated panels. Specifically, the size and power of Choi's [Econometric Theory and Practice: Frontiers of Analysis and Applied Research: Essays in Honor of Peter C. B. Phillips, Cambridge University Press, Cambridge (2001)], Bai and Ng's [Econometrica (2004), Vol. 72, p. 1127], Moon and Perron's [Journal of Econometrics (2004), Vol. 122, p. 81], and Phillips and Sul's [Econometrics Journal (2003), Vol. 6, p. 217] tests are analysed by a Monte Carlo simulation study. In synthesis, Moon and Perron's tests show good size and power for different values of T and N, and model specifications. Focusing on Bai and Ng's procedure, the simulation study highlights that the pooled Dickey–Fuller generalized least squares test provides higher power than the pooled augmented Dickey–Fuller test for the analysis of non-stationary properties of the idiosyncratic components. Choi's tests are strongly oversized when the common factor influences the cross-sectional units heterogeneously.