OLS-based estimation of the disturbance variance under spatial autocorrelation

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dc.contributor.authorHanck, Christoph
dc.contributor.authorKrämer, Walter
dc.date.accessioned2006-11-10T07:46:03Z
dc.date.available2006-11-10T07:46:03Z
dc.date.issued2006-11-10T07:46:03Z
dc.description.abstractWe 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.extent163986 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/2003/23077
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-15402
dc.language.isoen
dc.subjectBiasen
dc.subjectRegressionen
dc.subjectSpatial error correlationen
dc.subjectVarianceen
dc.subject.ddc004
dc.titleOLS-based estimation of the disturbance variance under spatial autocorrelationen
dc.typeTextde
dc.type.publicationtypereporten
dcterms.accessRightsopen access

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