OLS-based estimation of the disturbance variance under spatial autocorrelation

Walter Krämer and Christoph Hanck ()
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Christoph Hanck: Department of Quantitative Economics, Universiteit Maastricht

Working Papers from Business and Social Statistics Department, Technische Universität Dortmund

Abstract: 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.

Keywords: regression; spatial error correlation; bias; variance (search for similar items in EconPapers)
Pages: 11 pages
Date: 2006-10
New Economics Papers: this item is included in nep-ecm and nep-geo
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Published in Recent Advances in Linear Models and Related Areas, 2008, pages 357-366

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Persistent link: https://EconPapers.repec.org/RePEc:dor:wpaper:7

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