文章基本信息

标题：A Class of Copula-Based Bivariate Poisson Time Series Models with Applications
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作者：Mohammed Alqawba ; Dimuthu Fernando ; Norou Diawara 等
期刊名称：Computation
电子版ISSN：2079-3197
出版年度：2021
卷号：9
期号：10
页码：108
DOI：10.3390/computation9100108
语种：English
出版社：MDPI Publishing
摘要：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.