OLS-based estimation of the disturbance variance under spatial autocorrelation

We investigate the OLS-based estimator s2 of the disturbance variance in the standard linear regression model with cross section data when the disturbances are homoskedastic, but spatially correlated. For the most popular model of spatially autoregressive disturbances, we show that s2 can be severely biased in finite samples, but is asymptotically unbiased and consistent for most types of spatial weighting matrices as sample size increases.

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Year of publication:	2006
Authors:	Krämer, Walter ; Hanck, Christoph
Publisher:	Dortmund : Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
Subject:	regression \| spatial error correlation \| bias \| variance

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Series:	Technical Report ; 2006,42
Type of publication:	Book / Working Paper
Type of publication (narrower categories):	Working Paper
Language:	English
Other identifiers:	521100216 [GVK] hdl:10419/22686 [Handle] RePEc:zbw:sfb475:200642 [RePEc]
Source:	EconStor

Persistent link: https://www.econbiz.de/10010296757