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

Peter Egger, Mario Larch, Michael Pfaffermayr and Janette Walde

Regional Science and Urban Economics, 2009, vol. 39, issue 6, 670-678

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.

Keywords: Spatial; autocorrelation; Hypothesis; tests; Monte; Carlo; studies; Maximum; likelihood; estimation; Generalized; method; of; moments (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (9)

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Related works:
Working Paper: Small Sample Properties of Maximum Likelihood Versus Generalized Method of Moments Based Tests for Spatially Autocorrelated Errors (2005)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:regeco:v:39:y:2009:i:6:p:670-678

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