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Bias in Small-Sample Inference With Count-Data Models

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  • McKinley L. Blackburn

Abstract

Both Poisson and negative binomial regression can provide quasi-likelihood estimates for coefficients in exponential-mean models that are consistent in the presence of distributional misspecification. It has generally been recommended, however, that inference be carried out using asymptotically robust estimators for the parameter covariance matrix. As with linear models, such robust inference tends to lead to over-rejection of null hypotheses in small samples. Alternative methods for estimating coefficient estimator variances are considered. No one approach seems to remove all test bias, but the results do suggest that the use of the jackknife with Poisson regression tends to be least biased for inference.

Suggested Citation

  • McKinley L. Blackburn, 2020. "Bias in Small-Sample Inference With Count-Data Models," The American Statistician, Taylor & Francis Journals, vol. 74(3), pages 267-273, July.
    • Handle: RePEc:taf:amstat:v:74:y:2020:i:3:p:267-273
    DOI: 10.1080/00031305.2018.1564699
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    Cited by:

    1. Pfaffermayr, Michael, 2021. "Confidence intervals for the trade cost parameters of cross-section gravity models," Economics Letters, Elsevier, vol. 201(C).
    2. Hülya Ünlü & Serdal Temel & Kristel Miller, 2023. "Understanding the drivers of patent performance of University Science Parks in Turkey," The Journal of Technology Transfer, Springer, vol. 48(3), pages 842-872, June.

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