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A Lagrange Multiplier Test for Cross-Sectional Dependence in a Fixed Effects Panel Data Model

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Abstract

It is well known that the standard Breusch and Pagan (1980) LM test for cross-equation correlation in a SUR model is not appropriate for testing cross-sectional dependence in panel data models when the number of cross-sectional units (n) is large and the number of time periods (T) is small. In fact, a scaled version of this LM test was proposed by Pesaran (2004) and its finite sample bias was corrected by Pesaran, Ullah and Yamagata (2008). This was done in the context of a heterogeneous panel data model. This paper derives the asymptotic bias of this scaled version of the LM test in the context of a fixed effects homogeneous panel data model.This asymptotic bias is found to be a constant related to n and T, which suggests a simple bias corrected LM test for the null hypothesis. Additionally, the paper carries out some Monte Carlo experiments to compare the finite sample properties of this proposed test with existing tests for cross-sectional dependence.

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  • Badi H. Baltagi & Qu Feng & Chihwa Kao, 2012. "A Lagrange Multiplier Test for Cross-Sectional Dependence in a Fixed Effects Panel Data Model," Center for Policy Research Working Papers 137, Center for Policy Research, Maxwell School, Syracuse University.
  • Handle: RePEc:max:cprwps:137
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    16. Halunga, Andreea G. & Orme, Chris D. & Yamagata, Takashi, 2017. "A heteroskedasticity robust Breusch–Pagan test for Contemporaneous correlation in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 198(2), pages 209-230.
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    Keywords

    LM Test; Cross-sectional Dependence; Fixed Effects; High Dimensional Inference; John Test; Panel Data JEL Classification: C13; C33.;

    JEL classification:

    • 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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