Panel Unit Root Tests under Cross- sectional Dependence

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Panel Unit Root Tests under Cross- sectional Dependence

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Samarjit Das
Joerg Breitung

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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Paper provided by Econometric Society in its series Econometric Society 2004 North American Summer Meetings with number 55.

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Date of creation: 11 Aug 2004
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Handle: RePEc:ecm:nasm04:55

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Find related papers by JEL classification:
C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing
C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data
C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data

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Kristian Jönsson, 2006. "Time-specific disturbances and cross-sectional dependency in a small-sample heterogeneous panel data unit root test," Applied Economics, Taylor and Francis Journals, vol. 38(11), pages 1309-1317, June. [Downloadable!] (restricted)

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