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A first-order binomial-mixed Poisson integer-valued autoregressive model with serially dependent innovations

Author

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  • Zezhun Chen
  • Angelos Dassios
  • George Tzougas

Abstract

Motivated by the extended Poisson INAR(1), which allows innovations to be serially dependent, we develop a new family of binomial-mixed Poisson INAR(1) (BMP INAR(1)) processes by adding a mixed Poisson component to the innovations of the classical Poisson INAR(1) process. Due to the flexibility of the mixed Poisson component, the model includes a large class of INAR(1) processes with different transition probabilities. Moreover, it can capture some overdispersion features coming from the data while keeping the innovations serially dependent. We discuss its statistical properties, stationarity conditions and transition probabilities for different mixing densities (Exponential, Lindley). Then, we derive the maximum likelihood estimation method and its asymptotic properties for this model. Finally, we demonstrate our approach using a real data example of iceberg count data from a financial system.

Suggested Citation

  • Zezhun Chen & Angelos Dassios & George Tzougas, 2023. "A first-order binomial-mixed Poisson integer-valued autoregressive model with serially dependent innovations," Journal of Applied Statistics, Taylor & Francis Journals, vol. 50(2), pages 352-369, January.
    • Handle: RePEc:taf:japsta:v:50:y:2023:i:2:p:352-369
    DOI: 10.1080/02664763.2021.1993798
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