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OLS-based estimation of the disturbance variance under spatial autocorrelation


  • Prof. Dr. Walter Krämer

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

  • Dr. Christoph Hanck

    () (Department of Quantitative Economics, Universiteit Maastricht)


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.

Suggested Citation

  • Prof. Dr. Walter Krämer & Dr. Christoph Hanck, "undated". "OLS-based estimation of the disturbance variance under spatial autocorrelation," Working Papers 7, Business and Social Statistics Department, Technische Universität Dortmund, revised Oct 2006.
  • Handle: RePEc:dor:wpaper:7

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    References listed on IDEAS

    1. Kramer, Walter & Berghoff, Sonja, 1991. "Consistency of sDE 2 in the Linear Regression Model with Correlated Errors," Empirical Economics, Springer, vol. 16(3), pages 375-377.
    2. 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 362-365, May.
    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 333-340, March.
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    regression; spatial error correlation; bias; variance;

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