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

Chen, Zezhun Chen, Dassios, Angelos and Tzougas, George (2021) A first order binomial mixed poisson integer-valued autoregressive model with serially dependent innovations. Journal of Applied Statistics. ISSN 0266-4763

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Identification Number: 10.1080/02664763.2021.1993798

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.

Item Type: Article
Official URL: https://www.tandfonline.com/toc/cjas20/current
Additional Information: © 2021 The Authors
Divisions: Statistics
Subjects: H Social Sciences > HA Statistics
Date Deposited: 11 Oct 2021 10:48
Last Modified: 13 Apr 2022 18:03
URI: http://eprints.lse.ac.uk/id/eprint/112222

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