Asymptotics of improved generalized moments estimators for spatial autoregressive error models

dc.contributor.author	Arnold, Matthias
dc.contributor.author	Drinkuth, Carsten
dc.date.accessioned	2013-06-20T15:44:23Z
dc.date.available	2013-06-20T15:44:23Z
dc.date.issued	2013-06-20
dc.description.abstract	This paper considers linear models with a spatial autoregressive error structure. Extending Arnold and Wied (2010), who develop an improved GMM estimator for the parameters of the disturbance process to reduce the bias of existing estimation approaches, we establish the asymptotic normality of a new weighted version of this improved estimator and derive the efficient weighting matrix. We also show that this efficiently weighted GMM estimator is feasible as long as the regression matrix of the underlying linear model is non-stochastic and illustrate the performance of the new estimator by a Monte Carlo simulation and an application to real data.	en
dc.identifier.uri	http://hdl.handle.net/2003/30403
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-5483
dc.language.iso	en	de
dc.relation.ispartofseries	Discussion Paper / SFB 823;22/2013	en
dc.subject	Asymptotic normality	en
dc.subject	GMM estimation	en
dc.subject	Regression residuals	en
dc.subject	Spatial autoregression	en
dc.subject.ddc	310
dc.subject.ddc	330
dc.subject.ddc	620
dc.title	Asymptotics of improved generalized moments estimators for spatial autoregressive error models	en
dc.type	Text	de
dc.type.publicationtype	workingPaper	de
dcterms.accessRights	open access

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