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The Relative Performance of Poisson and Negative Binomial Regression Estimators

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  • Mckinley L. Blackburn

Abstract

type="main" xml:id="obes12074-abs-0001"> Negative binomial estimators are commonly used in estimating models with count-data dependent variables. In this paper, sampling experiments are used to evaluate the performance of these estimators relative to the simpler Poisson estimator in finite-sample situations. The results do not suggest a clear preference for negative binomial estimators in situations in which the underlying dependent variables are overdispersed, unless the researcher is comfortable in assumptions about the precise form of the overdispersion.

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  • Mckinley L. Blackburn, 2015. "The Relative Performance of Poisson and Negative Binomial Regression Estimators," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(4), pages 605-616, August.
    • Handle: RePEc:bla:obuest:v:77:y:2015:i:4:p:605-616
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    File URL: http://hdl.handle.net/10.1111/obes.2015.77.issue-4
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    4. Christian Peukert & Florian Abeillon & J'er'emie Haese & Franziska Kaiser & Alexander Staub, 2024. "Strategic Behavior and AI Training Data," Papers 2404.18445, arXiv.org.
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    6. Aaron Chalfin & Michael LaForest & Jacob Kaplan, 2021. "Can Precision Policing Reduce Gun Violence? Evidence from “Gang Takedowns” in New York City," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 40(4), pages 1047-1082, September.

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