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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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Author Info
Peter Egger ()
Mario Larch ()
Michael Pfaffermayr ()
Janette Walde

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Abstract

This paper undertakes a Monte Carlo study to compare MLE-based and GMM-based tests regarding the spatial autocorrelation coefficient of the error term in a Cliff and Ord type model. The main finding is that a Wald-test based on GMM estimation as derived by Kelejian and Prucha (2005a) performs surprisingly well. Our Monte Carlo study indicates that the GMM Wald-test is correctly sized even in small samples and exhibits the same power as their MLE-based counterparts. Since GMM estimates are much easier to calculate, the GMM Wald-test is recommended for applied researches.

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File URL: http://www.cesifo-group.de/DocCIDL/cesifo1_wp1558.pdf
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Publisher Info
Paper provided by CESifo GmbH in its series CESifo Working Paper Series with number CESifo Working Paper No. 1558.

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Date of creation: 2005
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Handle: RePEc:ces:ceswps:_1558

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Related research
Keywords: spatial autocorrelation hypothesis tests Monte Carlo studies maximum likelihood estimation generalized method of moments

Find related papers by JEL classification:
C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing
C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
R10 - Urban, Rural, and Regional Economics - - General Regional Economics - - - General

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This page was last updated on 2008-9-22.

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