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A Minimax Bias Estimator for OLS Variances under Heteroskedasticity

Author

Listed:
  • Ahmed, Mumtaz
  • Zaman, Asad

Abstract

Analytic evaluation of heteroskedasticity consistent covariance matrix estimates (HCCME) is difficult because of the complexity of the formulae currently available. We obtain new analytic formulae for the bias of a class of estimators of the covariance matrix of OLS in a standard linear regression model. These formulae provide substantial insight into the properties and performance characteristics of these estimators. In particular, we find a new estimator which minimizes the maximum possible bias and improves substantially on the standard Eicker-White estimate.

Suggested Citation

  • Ahmed, Mumtaz & Zaman, Asad, 2014. "A Minimax Bias Estimator for OLS Variances under Heteroskedasticity," MPRA Paper 55724, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:55724
    as

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    References listed on IDEAS

    as
    1. Cribari-Neto, Francisco, 2004. "Asymptotic inference under heteroskedasticity of unknown form," Computational Statistics & Data Analysis, Elsevier, vol. 45(2), pages 215-233, March.
    2. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    3. MacKinnon, James G. & White, Halbert, 1985. "Some heteroskedasticity-consistent covariance matrix estimators with improved finite sample properties," Journal of Econometrics, Elsevier, vol. 29(3), pages 305-325, September.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Eicker-White; OLS; Bias; Worst Case Bias;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables

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    This paper has been announced in the following NEP Reports:

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