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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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Bibliographic Info

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

Volume (Year): 39 (2009)
Issue (Month): 6 (November)
Pages: 670-678

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Handle: RePEc:eee:regeco:v:39:y:2009:i:6:p:670-678

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Web page: http://www.elsevier.com/locate/regec

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Keywords: Spatial autocorrelation Hypothesis tests Monte Carlo studies Maximum likelihood estimation Generalized method of moments;

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  1. 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 149-63, June.
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Cited by:
  1. Konrad, Kai A. & Skaperdas, Stergios, 2012. "The market for protection and the origin of the state," Munich Reprints in Economics 13961, University of Munich, Department of Economics.
  2. 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.
  3. Tiziana Caliman & Enrico di Bella, 2011. "Spatial Autoregressive Models for House Price Dynamics in Italy," Economics Bulletin, AccessEcon, vol. 31(2), pages 1837-1855.
  4. 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 di Genova, vol. 64(1), pages 37-65.
  5. 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.

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