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Heteroskedasticity and non-normality robust LM tests for spatial dependence

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  • Baltagi, Badi H.
  • Yang, Zhenlin

Abstract

The standard LM tests for spatial dependence in linear and panel regressions are derived under the normality and homoskedasticity assumptions of the regression disturbances. Hence, they may not be robust against non-normality or heteroskedasticity of the disturbances. Following Born and Breitung (2011), we introduce general methods to modify the standard LM tests so that they become robust against heteroskedasticity and non-normality. The idea behind the robustification is to decompose the concentrated score function into a sum of uncorrelated terms so that the outer product of gradient (OPG) can be used to estimate its variance. We also provide methods for improving the finite sample performance of the proposed tests. These methods are then applied to several popular spatial models. Monte Carlo results show that they work well in finite sample.

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Bibliographic Info

Article provided by Elsevier in its journal Regional Science and Urban Economics.

Volume (Year): 43 (2013)
Issue (Month): 5 ()
Pages: 725-739

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Handle: RePEc:eee:regeco:v:43:y:2013:i:5:p:725-739

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Keywords: Centering; Heteroskedasticity; Non-normality; LM test; Panel model; Spatial dependence;

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  1. Yang, Zhenlin, 2010. "A robust LM test for spatial error components," Regional Science and Urban Economics, Elsevier, vol. 40(5), pages 299-310, September.
  2. Baltagi, Badi H. & Song, Seuck Heun & Koh, Won, 2003. "Testing panel data regression models with spatial error correlation," Journal of Econometrics, Elsevier, vol. 117(1), pages 123-150, November.
  3. Edward E. Glaeser & Bruce Sacerdote & Jose A. Scheinkman, 1995. "Crime and Social Interactions," Harvard Institute of Economic Research Working Papers 1738, Harvard - Institute of Economic Research.
  4. Nicolas Debarsy & Cem Ertur, 2009. "Testing for Spatial Autocorrelation in a Fixed Effects Panel Data Model," Post-Print halshs-00414133, HAL.
  5. Kelejian, Harry H. & Prucha, Ingmar R., 2010. "Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances," Journal of Econometrics, Elsevier, vol. 157(1), pages 53-67, July.
  6. 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.
  7. 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.
  8. 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.
  9. DAVIDSON, Russel & MACKINNON, James G., . "Heteroskedastcity-robust tests in regressions directions," CORE Discussion Papers RP -678, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  10. Benjamin Born & Jörg Breitung, 2011. "Simple regression‐based tests for spatial dependence," Econometrics Journal, Royal Economic Society, vol. 14(2), pages 330-342, 07.
  11. Lee, Lung-fei & Yu, Jihai, 2010. "Estimation of spatial autoregressive panel data models with fixed effects," Journal of Econometrics, Elsevier, vol. 154(2), pages 165-185, February.
  12. Lin, Xu & Lee, Lung-fei, 2010. "GMM estimation of spatial autoregressive models with unknown heteroskedasticity," Journal of Econometrics, Elsevier, vol. 157(1), pages 34-52, July.
  13. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-38, May.
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