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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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Bibliographic Info

Paper provided by Center for Policy Research, Maxwell School, Syracuse University in its series Center for Policy Research Working Papers with number 137.

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Length: 40 pages
Date of creation: May 2012
Date of revision:
Handle: RePEc:max:cprwps:137

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

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  1. Kapetanios, George & Pesaran, M. Hashem & Yamagata, Takashi, 2006. "Panels with Nonstationary Multifactor Error Structures," IZA Discussion Papers 2243, Institute for the Study of Labor (IZA).
  2. Arellano, Manuel & Bond, Stephen, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Wiley Blackwell, vol. 58(2), pages 277-97, April.
  3. Breusch, T S & Pagan, A R, 1980. "The Lagrange Multiplier Test and Its Applications to Model Specification in Econometrics," Review of Economic Studies, Wiley Blackwell, vol. 47(1), pages 239-53, January.
  4. Jushan Bai, 2009. "Panel Data Models With Interactive Fixed Effects," Econometrica, Econometric Society, vol. 77(4), pages 1229-1279, 07.
  5. Peter C.B. Phillips & Donggyu Sul, 2003. "Bias in Dynamic Panel Estimation with Fixed Effects, Incidental Trends and Cross Section Dependence," Cowles Foundation Discussion Papers 1438, Cowles Foundation for Research in Economics, Yale University, revised Jun 2004.
  6. Donald W.K. Andrews, 2004. "Cross-section Regression with Common Shocks," Yale School of Management Working Papers ysm401, Yale School of Management.
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  22. Peter C.B. Phillips & Hyungsik R. Moon, 1999. "Linear Regression Limit Theory for Nonstationary Panel Data," Cowles Foundation Discussion Papers 1222, Cowles Foundation for Research in Economics, Yale University.
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  26. Jinyong Hahn & Guido Kuersteiner, 2002. "Asymptotically Unbiased Inference for a Dynamic Panel Model with Fixed Effects when Both "n" and "T" Are Large," Econometrica, Econometric Society, vol. 70(4), pages 1639-1657, July.
  27. Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-33, May.
  28. Sarafidis, Vasilis & Yamagata, Takashi & Robertson, Donald, 2009. "A test of cross section dependence for a linear dynamic panel model with regressors," Journal of Econometrics, Elsevier, vol. 148(2), pages 149-161, February.
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Cited by:
  1. Apergis, Nicholas, 2014. "The long-term role of non-traditional banking in profitability and risk profiles: Evidence from a panel of U.S. banking institutions," Journal of International Money and Finance, Elsevier, vol. 45(C), pages 61-73.
  2. Mao, Guangyu, 2014. "A note on tests of sphericity and cross-sectional dependence for fixed effects panel model," Economics Letters, Elsevier, vol. 122(2), pages 215-219.

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