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OLS-based estimation of the disturbance variance under spatial autocorrelation

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Author Info

  • Prof. Dr. Walter Krämer

    ()
    (Faculty of Statistics, Dortmund University of Technology)

  • Dr. Christoph Hanck

    ()
    (Department of Quantitative Economics, Universiteit Maastricht)

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.

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Bibliographic Info

Paper provided by Business and Social Statistics Department, Technische Universität Dortmund in its series Working Papers with number 7.

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Length: 11 pages
Date of creation:
Date of revision: Oct 2006
Publication status: Published in Recent Advances in Linear Models and Related Areas, 2008, pages 357-366
Handle: RePEc:dor:wpaper:7

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Keywords: regression; spatial error correlation; bias; variance;

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  1. Kiviet, Jan F & Kramer, Walter, 1992. "Bias of SDE 2 in the Linear Regression Model with Correlated Errors," The Review of Economics and Statistics, MIT Press, vol. 74(2), pages 362-65, May.
  2. Kramer, Walter & Berghoff, Sonja, 1991. "Consistency of sDE 2 in the Linear Regression Model with Correlated Errors," Empirical Economics, Springer, vol. 16(3), pages 375-77.
  3. Sathe, S T & Vinod, H D, 1974. "Bounds on the Variance of Regression Coefficients Due to Heteroscedastic or Autoregressive Errors," Econometrica, Econometric Society, vol. 42(2), pages 333-40, March.
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