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Bias of s2 in Linear Regression Model with correlated errors

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  • Kiviet, Jan
  • Kramer, Walter

Abstract

We derive bounds for the relative bias of the 0LS-based estimate s2 of the disturbance variance in the linear regression model when disturbances are stationary AR(1) and show that this bias vanishes as sample size increases (i.e. s2 is asymptotically unbiased irrespective of the particular form of the regressor sequence).

Suggested Citation

  • Kiviet, Jan & Kramer, Walter, 1989. "Bias of s2 in Linear Regression Model with correlated errors," University of Amsterdam, Actuarial Science and Econometrics Archive 293144, University of Amsterdam, Faculty of Economics and Business.
    • Handle: RePEc:ags:amstas:293144
    DOI: 10.22004/ag.econ.293144
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    Cited by:

    1. Martin Browning & Mette Ejrnæs & Javier Alvarez, 2010. "Modelling Income Processes with Lots of Heterogeneity," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 77(4), pages 1353-1381.
    2. 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.
    3. Richard W. Kopcke, 1993. "The determinants of business investment: has capital spending been surprisingly low?," New England Economic Review, Federal Reserve Bank of Boston, issue Jan, pages 3-31.
    4. Prof. Dr. Walter Krämer & Sebastian Schich, "undated". "Large - scaledisasters and the insurance industry," Working Papers 4, Business and Social Statistics Department, Technische Universität Dortmund, revised Mar 2005.
    5. Gotu, Butte, 1999. "The consistency of s2 in the linear regression model when the disturbances are spatially correlated," Technical Reports 1999,07, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

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    Keywords

    Research Methods/ Statistical Methods;

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