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A bias-adjusted LM test of error cross-section independence

Author

Listed:
  • M. Hashem Pesaran
  • Aman Ullah
  • Takashi Yamagata

Abstract

This paper proposes a bias-adjusted version of Breusch and Pagan (1980) Lagrange multiplier (LM) test statistic of error cross-section independence, in the case of panel models with strictly exogenous regressors and normal errors. The exact mean and variance of the test indicator of the LM test statistic are provided for the purpose of the bias-adjustments. It is shown that the centring of the LM statistic is correct for fixed T and N. Importantly, the proposed bias-adjusted LM test is consistent even when the Pesaran's (2004) CD test is inconsistent. Also an alternative bias-adjusted LM test, which is consistent under local error cross-section dependence of any fixed order p, is proposed. The finite sample behaviour of the proposed tests is investigated and compared to that of the LM and CD tests. It is shown that the bias-adjusted LM tests successfully control the size, maintaining satisfactory power in panel with exogenous regressors and normal errors. However, it is also shown that the bias-adjusted LM test is not as robust as the CD test to non-normal errors and/or in the presence of weakly exogenous regressors. Copyright Royal Economic Society 2007

Suggested Citation

  • M. Hashem Pesaran & Aman Ullah & Takashi Yamagata, 2008. "A bias-adjusted LM test of error cross-section independence," Econometrics Journal, Royal Economic Society, vol. 11(1), pages 105-127, March.
    • Handle: RePEc:ect:emjrnl:v:11:y:2008:i:1:p:105-127
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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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