Small Sample Properties of Maximum Likelihood Versus Generalized Method of Moments Based Tests for Spatially Autocorrelated Errors

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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Year of publication:	[2021]
Authors:	Egger, Peter H. ; Larch, Mario ; Pfaffermayr, Michael ; Walde, Janette
Publisher:	[S.l.] : SSRN
Subject:	Autokorrelation \| Autocorrelation \| Momentenmethode \| Method of moments \| Statistischer Test \| Statistical test \| Maximum-Likelihood-Schätzung \| Maximum likelihood estimation \| Schätztheorie \| Estimation theory

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Extent:	1 Online-Ressource (32 p)
Type of publication:	Book / Working Paper
Language:	English
Notes:	Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments October 2005 erstellt
Other identifiers:	10.2139/ssrn.843367 [DOI]
Classification:	C12 - Hypothesis Testing ; C21 - Cross-Sectional Models; Spatial Models ; R10 - General Regional Economics. General
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://www.econbiz.de/10013318167