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A Robust LM Test for Spatial Error Components

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  • Zhenlin Yang

    ()
    (School of Economics, Singapore Management University)

Abstract

This paper presents a modified LM test of spatial error components, which is shown to be robust against distributional misspecifications and spatial layouts. The proposed test differs from the LM test of Anselin (2001) by a term in the denominators of the test statistics. This term disappears when either the errors are normal, or the variance of the diagonal elements of the product of spatial weights matrix and its transpose is zero or approaching to zero as sample size goes large. When neither is true, as is often the case in practice, the effect of this term can be significant even when sample size is large. As a result, there can be severe size distortions of the Anselin’s LM test, a phenomenon revealed by the Monte Carlo results of Anselin and Moreno (2003) and further confirmed by the Monte Carlo results presented in this paper. Our Monte Carlo results also show that the proposed test performs well in general.

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Bibliographic Info

Paper provided by Singapore Management University, School of Economics in its series Working Papers with number 04-2009.

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Length: 19 pages
Date of creation: Jan 2009
Date of revision:
Publication status: Published in SMU Economics and Statistics Working Paper Series
Handle: RePEc:siu:wpaper:04-2009

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Related research

Keywords: Distributional misspecification; Robustness; Spatial layouts; Spatial error components; LM tests;

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References

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  1. Bera, Anil K. & Yoon, Mann J., 1993. "Specification Testing with Locally Misspecified Alternatives," Econometric Theory, Cambridge University Press, vol. 9(04), pages 649-658, August.
  2. Lung-Fei Lee, 2004. "Asymptotic Distributions of Quasi-Maximum Likelihood Estimators for Spatial Autoregressive Models," Econometrica, Econometric Society, vol. 72(6), pages 1899-1925, November.
  3. Bera, Anil K. & Sosa-Escudero, Walter & Yoon, Mann, 2001. "Tests for the error component model in the presence of local misspecification," Journal of Econometrics, Elsevier, vol. 101(1), pages 1-23, March.
  4. Lee, Lung-fei, 2007. "Identification and estimation of econometric models with group interactions, contextual factors and fixed effects," Journal of Econometrics, Elsevier, vol. 140(2), pages 333-374, October.
  5. Luc Anselin & Rosina Moreno, 2001. "Properties of tests for spatial error components," ERSA conference papers ersa01p183, European Regional Science Association.
  6. Koenker, Roger, 1981. "A note on studentizing a test for heteroscedasticity," Journal of Econometrics, Elsevier, vol. 17(1), pages 107-112, September.
  7. 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.
  8. Lee, Lung-Fei, 2002. "Consistency And Efficiency Of Least Squares Estimation For Mixed Regressive, Spatial Autoregressive Models," Econometric Theory, Cambridge University Press, vol. 18(02), pages 252-277, April.
  9. H. Kelejian, Harry & Prucha, Ingmar R., 2001. "On the asymptotic distribution of the Moran I test statistic with applications," Journal of Econometrics, Elsevier, vol. 104(2), pages 219-257, September.
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Citations

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Cited by:
  1. Badi H. Baltagi & Peter Egger & Michael Pfaffermayr, 2012. "A Generalized Spatial Panel Data Model with Random Effects," CESifo Working Paper Series 3930, CESifo Group Munich.
  2. Badi H. Baltagi & Zhenlin Yang, 2012. "Standardized LM Tests for Spatial Error Dependence in Linear or Panel Regressions," Center for Policy Research Working Papers 142, Center for Policy Research, Maxwell School, Syracuse University.
  3. Baltagi, Badi H. & Yang, Zhenlin, 2013. "Heteroskedasticity and non-normality robust LM tests for spatial dependence," Regional Science and Urban Economics, Elsevier, vol. 43(5), pages 725-739.
  4. Zhenlin Yang, 2013. "LM Tests of Spatial Dependence Based on Bootstrap Critical Values," Working Papers 03-2013, Singapore Management University, School of Economics.

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