OLS-based estimation of the disturbance variance under spatial autocorrelation

dc.contributor.author	Hanck, Christoph
dc.contributor.author	Krämer, Walter
dc.date.accessioned	2006-11-10T07:46:03Z
dc.date.available	2006-11-10T07:46:03Z
dc.date.issued	2006-11-10T07:46:03Z
dc.description.abstract	We investigate the OLS-based estimator s^2 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 s^2 can be severely biased in finite samples, but is asymptotically unbiased and consistent for most types of spatial weighting matrices as sample size increases.	en
dc.format.extent	163986 bytes
dc.format.mimetype	application/pdf
dc.identifier.uri	http://hdl.handle.net/2003/23077
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-15402
dc.language.iso	en
dc.subject	Bias	en
dc.subject	Regression	en
dc.subject	Spatial error correlation	en
dc.subject	Variance	en
dc.subject.ddc	004
dc.title	OLS-based estimation of the disturbance variance under spatial autocorrelation	en
dc.type	Text	de
dc.type.publicationtype	report	en
dcterms.accessRights	open access
eldorado.dnb.deposit	true

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