Small sample properties of maximum likelihood versus generalized method of moments based tests for spatially autocorrelated errors
AbstractMany 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 InfoArticle provided by Elsevier in its journal Regional Science and Urban Economics.
Volume (Year): 39 (2009)
Issue (Month): 6 (November)
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Web page: http://www.elsevier.com/locate/regec
Spatial autocorrelation Hypothesis tests Monte Carlo studies Maximum likelihood estimation Generalized method of moments;
Other versions of this item:
- Peter Egger & Mario Larch & Michael Pfaffermayr & Janette Walde, 2005. "Small Sample Properties of Maximum Likelihood Versus Generalized Method of Moments Based Tests for Spatially Autocorrelated Errors," CESifo Working Paper Series 1558, CESifo Group Munich.
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