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Small sample properties of maximum likelihood versus generalized method of moments based tests for spatially autocorrelated errors

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  • Egger, Peter
  • Larch, Mario
  • Pfaffermayr, Michael
  • Walde, Janette

Abstract

Many applied researchers have to deal with spatially autocorrelated residuals (SAR). Available tests that identify spatial spillovers as captured by a significant SAR parameter, are either based on maximum likelihood (MLE) or generalized method of moments (GMM) estimates. This paper illustrates the properties of various tests for the null hypothesis of a zero SAR parameter in a comprehensive Monte Carlo study. The main finding is that Wald tests generally perform well regarding both size and power even in small samples. The GMM-based Wald test is correctly sized even for non-normally distributed disturbances and small samples, and it exhibits a similar power as its MLE-based counterpart. Hence, for the applied researcher the GMM Wald test can be recommended, because it is easy to implement.

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  • Egger, Peter & Larch, Mario & Pfaffermayr, Michael & Walde, Janette, 2009. "Small sample properties of maximum likelihood versus generalized method of moments based tests for spatially autocorrelated errors," Regional Science and Urban Economics, Elsevier, vol. 39(6), pages 670-678, November.
  • Handle: RePEc:eee:regeco:v:39:y:2009:i:6:p:670-678
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    1. 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 509-533, May.
    2. Joris Pinkse & Margaret E. Slade & Craig Brett, 2002. "Spatial Price Competition: A Semiparametric Approach," Econometrica, Econometric Society, vol. 70(3), pages 1111-1153, May.
    3. Davidson, Russell & MacKinnon, James G, 1998. "Graphical Methods for Investigating the Size and Power of Hypothesis Tests," The Manchester School of Economic & Social Studies, University of Manchester, vol. 66(1), pages 1-26, January.
    4. White,Halbert, 1996. "Estimation, Inference and Specification Analysis," Cambridge Books, Cambridge University Press, number 9780521574464, November.
    5. 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.
    6. Anselin, Luc & Moreno, Rosina, 2003. "Properties of tests for spatial error components," Regional Science and Urban Economics, Elsevier, vol. 33(5), pages 595-618, September.
    7. Aten, Bettina, 1996. "Evidence of Spatial Autocorrelation in International Prices," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 42(2), pages 149-163, June.
    8. Anselin, Luc & Bera, Anil K. & Florax, Raymond & Yoon, Mann J., 1996. "Simple diagnostic tests for spatial dependence," Regional Science and Urban Economics, Elsevier, vol. 26(1), pages 77-104, February.
    9. Saavedra, Luz A., 2003. "Tests for spatial lag dependence based on method of moments estimation," Regional Science and Urban Economics, Elsevier, vol. 33(1), pages 27-58, January.
    10. Kelejian, Harry H. & Prucha, Ingmar R., 2002. "2SLS and OLS in a spatial autoregressive model with equal spatial weights," Regional Science and Urban Economics, Elsevier, vol. 32(6), pages 691-707, November.
    11. 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.
    12. Conley, T. G., 1999. "GMM estimation with cross sectional dependence," Journal of Econometrics, Elsevier, vol. 92(1), pages 1-45, September.
    13. Lung-fei Lee, 2003. "Best Spatial Two-Stage Least Squares Estimators for a Spatial Autoregressive Model with Autoregressive Disturbances," Econometric Reviews, Taylor & Francis Journals, vol. 22(4), pages 307-335.
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    Cited by:

    1. Benny Geys & Federico Revelli, 2011. "Economic and political foundations of local tax structures: an empirical investigation of the tax mix of Flemish municipalities," Environment and Planning C: Government and Policy, Pion Ltd, London, vol. 29(3), pages 410-427, June.
    2. Kai Konrad & Stergios Skaperdas, 2012. "The market for protection and the origin of the state," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), pages 417-443.
    3. Daniel Arribas-Bel & Julia Koschinsky & Pedro Amaral, 2012. "Improving the multi-dimensional comparison of simulation results: a spatial visualization approach," Letters in Spatial and Resource Sciences, Springer, vol. 5(2), pages 55-63, July.
    4. Tiziana Caliman & Enrico di Bella, 2011. "Spatial Autoregressive Models for House Price Dynamics in Italy," Economics Bulletin, AccessEcon, vol. 31(2), pages 1837-1855.
    5. Caliman, Tiziana & Di Bella, Enrico, 2011. "House Price Dynamics in Italy - La dinamica delle quotazioni immobiliari in Italia," Economia Internazionale / International Economics, Camera di Commercio Industria Artigianato Agricoltura di Genova, vol. 64(1), pages 37-65.
    6. Kyriacou, Maria & Phillips, Peter C.B. & Rossi, Francesca, 2014. "Indirect inference in spatial autoregression," Discussion Paper Series In Economics And Econometrics 1418, Economics Division, School of Social Sciences, University of Southampton.

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