Document Type

Article

Publication Date

2021

DOI

10.3390/computation9100108

Publication Title

Computation

Volume

9

Issue

10

Pages

108 (1-12)

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.

Comments

This is an open access article distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Original Publication Citation

Alqawba, M., Fernando, D., & Diawara, N. (2021). A class of copula-based bivariate poisson time series models with applications. Computation, 9(10), 1-12, Article 108. https://doi.org/10.3390/computation9100108

ORCID

0000-0002-8403-6793 (Diawara)

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