OLS-based estimation of the disturbance variance under spatial autocorrelation
AbstractWe 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 InfoPaper provided by Business and Social Statistics Department, Technische Universität Dortmund in its series Working Papers with number 7.
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
regression; spatial error correlation; bias; variance;
Other versions of this item:
- Krämer, Walter & Hanck, Christoph, 2006. "OLS-based estimation of the disturbance variance under spatial autocorrelation," Technical Reports 2006,42, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
- NEP-ALL-2007-01-23 (All new papers)
- NEP-ECM-2007-01-23 (Econometrics)
- NEP-GEO-2007-01-23 (Economic Geography)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- 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.
- 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.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Prof. Dr. Walter Krämer).
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