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

Kai Yang, Yao Kang, Dehui Wang, Han Li and Yajing Diao
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Kai Yang: Changchun University of Technology
Yao Kang: Jilin University
Dehui Wang: Jilin University
Han Li: Changchun University
Yajing Diao: Changchun University of Technology

Metrika: International Journal for Theoretical and Applied Statistics, 2019, vol. 82, issue 7, No 6, 863-889

Abstract: 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.

Keywords: Generalized poisson thinning; GPINAR(1) process; Overdispersion; Underdispersion; Moments targeting estimation (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s00184-019-00714-9

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