A Class of Copula-Based Bivariate Poisson Time Series Models with Applications

Alqawba, Mohammed and Fernando, Dimuthu and Diawara, Norou (2021) A Class of Copula-Based Bivariate Poisson Time Series Models with Applications. Computation, 9 (10). p. 108. ISSN 2079-3197

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Abstract

A class of bivariate integer-valued time series models was constructed via copula theory. Each series follows a Markov chain with the serial dependence captured using copula-based transition probabilities from the Poisson and the zero-inflated Poisson (ZIP) margins. The copula theory was also used again to capture the dependence between the two series using either the bivariate Gaussian or “t-copula” functions. Such a method provides a flexible dependence structure that allows for positive and negative correlation, as well. In addition, the use of a copula permits applying different margins with a complicated structure such as the ZIP distribution. Likelihood-based inference was used to estimate the models’ parameters with the bivariate integrals of the Gaussian or t-copula functions being evaluated using standard randomized Monte Carlo methods. To evaluate the proposed class of models, a comprehensive simulated study was conducted. Then, two sets of real-life examples were analyzed assuming the Poisson and the ZIP marginals, respectively. The results showed the superiority of the proposed class of models.

Item Type: Article
Subjects: ArticleGate > Computer Science
Depositing User: Managing Editor
Date Deposited: 30 Nov 2022 05:22
Last Modified: 30 May 2024 13:32
URI: http://ebooks.pubstmlibrary.com/id/eprint/1233

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