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Modeling overdispersed or underdispersed count data with generalized Poisson integer-valued autoregressive processes

Author

Listed:
  • Kai Yang

    (Changchun University of Technology)

  • Yao Kang

    (Jilin University)

  • Dehui Wang

    (Jilin University)

  • Han Li

    (Changchun University)

  • Yajing Diao

    (Changchun University of Technology)

Abstract

To accurately and flexibly capture the dispersion features of time series of counts, we introduce the generalized Poisson thinning operation and further define some new integer-valued autoregressive processes. Basic probabilistic and statistical properties of the models are discussed. Conditional least squares and maximum quasi likelihood estimators are investigated via the moment targeting estimation methods for the innovation free case. Also, the asymptotic properties of the estimators are obtained. Conditional maximum likelihood estimation for the parametric cases are also discussed. Finally, some numerical results of the estimates and two real data examples are presented.

Suggested Citation

  • Kai Yang & Yao Kang & Dehui Wang & Han Li & Yajing Diao, 2019. "Modeling overdispersed or underdispersed count data with generalized Poisson integer-valued autoregressive processes," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(7), pages 863-889, October.
    • Handle: RePEc:spr:metrik:v:82:y:2019:i:7:d:10.1007_s00184-019-00714-9
    DOI: 10.1007/s00184-019-00714-9
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    References listed on IDEAS

    as
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