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Modeling underdispersed count data with generalized Poisson regression

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Listed:
  • Tammy Harris

    (University of South Carolina)

  • Zhao Yang

    (Quintiles, Inc.)

  • James W. Hardin

    (University of South Carolina)

Abstract

We present motivation and new Stata commands for modeling count data. While the focus of this article is on modeling data with underdispersion, the new command for fitting generalized Poisson regression models is also suitable as an alternative to negative binomial regression for overdispersed data.

Suggested Citation

  • Tammy Harris & Zhao Yang & James W. Hardin, 2012. "Modeling underdispersed count data with generalized Poisson regression," Stata Journal, StataCorp LP, vol. 12(4), pages 736-747, December.
  • Handle: RePEc:tsj:stataj:v:12:y:2012:i:4:p:736-747
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    References listed on IDEAS

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    1. Weiren Wang & Felix Famoye, 1997. "Modeling household fertility decisions with generalized Poisson regression," Journal of Population Economics, Springer;European Society for Population Economics, vol. 10(3), pages 273-283.
    2. H. J. H. Tuenter, 2000. "On the generalized Poisson distribution," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 54(3), pages 374-376, November.
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    11. Muthini, Davis & Nzuma, Jonathan & Qaim, Matin, 2018. "Subsistence Production, Markets, and Dietary Diversity in the Kenyan Small Farm Sector," Department of Agricultural and Rural Development (DARE) Discussion Papers 279791, Georg-August-Universitaet Goettingen, Department of Agricultural Economics and Rural Development (DARE).
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    15. Genova, C. & Umberger, W. & Peralta, A. & Newman, S., 2018. "Dietary diversity of children and teenagers in Northern Vietnam," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 276033, International Association of Agricultural Economists.

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