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Discrete Poisson kernel density estimation-with an application to wildcat coal strikes

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  • Lawrence Marsh
  • Kajal Mukhopadhyay

Abstract

This paper proposes a nonparametric Poisson kernel density estimation technique for discrete distributions. Economists have been using continuous kernels to approximate discrete distributions. This work introduces a discrete kernel as more appropriate for approximating discrete distributions. Simulation results are presented to compare with standard parametric approaches. We apply our discrete Poisson kernel estimator to approximate the distribution of coal mine wildcat strikes in the United States.

Suggested Citation

  • Lawrence Marsh & Kajal Mukhopadhyay, 1999. "Discrete Poisson kernel density estimation-with an application to wildcat coal strikes," Applied Economics Letters, Taylor & Francis Journals, vol. 6(6), pages 393-396.
    • Handle: RePEc:taf:apeclt:v:6:y:1999:i:6:p:393-396
    DOI: 10.1080/135048599353168
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    References listed on IDEAS

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    1. Cameron, A Colin & Trivedi, Pravin K, 1986. "Econometric Models Based on Count Data: Comparisons and Applications of Some Estimators and Tests," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 1(1), pages 29-53, January.
    2. Delgado, Miguel A & Mora, Juan, 1995. "Nonparametric and Semiparametric Estimation with Discrete Regressors," Econometrica, Econometric Society, vol. 63(6), pages 1477-1484, November.
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    Cited by:

    1. Alan Huang & Lucas Sippel & Thomas Fung, 2022. "Consistent second-order discrete kernel smoothing using dispersed Conway–Maxwell–Poisson kernels," Computational Statistics, Springer, vol. 37(2), pages 551-563, April.
    2. Chee, Chew-Seng, 2017. "A mixture model-based nonparametric approach to estimating a count distribution," Computational Statistics & Data Analysis, Elsevier, vol. 109(C), pages 34-44.

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