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Bias of SDE 2 in the Linear Regression Model with Correlated Errors

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  • Kiviet, Jan F
  • Kramer, Walter

Abstract

The authors consider the relative bias of the OLS-based estimate s(squared) of the disturbance variance in the linear regression model when disturbances are stationary AR(1). They improve upon previous bounds for the bias and show that E(s[squared]/["sigma" squared]) tends to zero as autocorrelation increases whenever there is an intercept in the regression. Copyright 1992 by MIT Press.

Suggested Citation

  • 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.
    • Handle: RePEc:tpr:restat:v:74:y:1992:i:2:p:362-65
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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. 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.
    4. 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.
    5. 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.

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