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Panel unit root tests under cross-sectional dependence

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  • Jörg Breitung
  • Samarjit Das

Abstract

In this paper alternative approaches for testing the unit root hypothesis in panel data are considered. First, a robust version of the Dickey-Fuller t-statistic under contemporaneous correlated errors is suggested. Second, the GLS t-statistic is considered, which is based on the t-statistic of the transformed model. The asymptotic power of both tests against a sequence of local alternatives is compared. To adjust for short-run serial correlation of the errors, a pre-whitening procedure is suggested that yields a test statistic with a standard normal limiting distribution as N and T tends to infinity. The test procedure is further generalized to accommodate individual specific intercepts. From our Monte Carlo simulations it turns out that the robust OLS t-statistic performs well with respect to size and power, whereas the the GLS t-statistic may suffer from severe size distortions in small and moderate sample sizes. To improve the small sample properties of the GLS test procedure, a bootstrap version of the test is available.
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Suggested Citation

  • Jörg Breitung & Samarjit Das, 2005. "Panel unit root tests under cross-sectional dependence," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 59(4), pages 414-433.
  • Handle: RePEc:bla:stanee:v:59:y:2005:i:4:p:414-433
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    References listed on IDEAS

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    1. Swensen, Anders Rygh, 1993. "A Note on Asymptotic Power Calculations in Nearly Nonstationary Time Series," Econometric Theory, Cambridge University Press, vol. 9(04), pages 659-667, August.
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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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