Panel unit root tests under cross-sectional dependence
AbstractIn 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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Bibliographic InfoArticle provided by Netherlands Society for Statistics and Operations Research in its journal Statistica Neerlandica.
Volume (Year): 59 (2005)
Issue (Month): 4 ()
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Web page: http://www.blackwellpublishing.com/journal.asp?ref=0039-0402
Other versions of this item:
- Samarjit Das & Joerg Breitung, 2004. "Panel Unit Root Tests under Cross- sectional Dependence," Econometric Society 2004 North American Summer Meetings 55, Econometric Society.
- 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; Longitudinal Data; Spatial Time Series
- C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Longitudinal Data; Spatial Time Series
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