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Reducing the bias of the maximum likelihood estimator for the Poisson regression model

Author

Listed:
  • David E Giles

    (University of Victoria, Canada)

  • Hui Feng

    (Department of Economics, Business and Mathematics, King's University College, UWO)

Abstract

We derive expressions for the first-order bias of the MLE for a Poisson regression model and show how these can be used to adjust the estimator and reduce bias without increasing MSE. The analytic results are supported by Monte Carlo simulations and three illustrative empirical applications.

Suggested Citation

  • David E Giles & Hui Feng, 2011. "Reducing the bias of the maximum likelihood estimator for the Poisson regression model," Economics Bulletin, AccessEcon, vol. 31(4), pages 2933-2943.
    • Handle: RePEc:ebl:ecbull:eb-10-00813
    as

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    File URL: http://www.accessecon.com/Pubs/EB/2011/Volume31/EB-11-V31-I4-P265.pdf
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    References listed on IDEAS

    as
    1. David E. Giles, 2009. "Bias Reduction for the Maximum Likelihood Estimator of the Scale Parameter in the Half-Logistic Distribution," Econometrics Working Papers 0901, Department of Economics, University of Victoria.
    2. Qian Chen & David E. Giles, 2009. "Finite-Sample Properties of the Maximum Likelihood Estimator for the Poisson Regression Model With Random Covariates," Econometrics Working Papers 0907, Department of Economics, University of Victoria.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Poisson regression; maximum likelihood estimation; bias reduction;
    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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