Zero-inflated count time series models using Gaussian copula

Abstract

Count time series data are observed in several applied disciplines such as environmental science, biostatistics, economics, public health, and finance. In some cases, a specific count, usually zero, may occur more often than other counts. However, overlooking the frequent occurrence of zeros could result in misleading inferences. In this article, we develop a copula-based time series regression model for zero-inflated counts. Zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB), and zero-inflated Conway-Maxwell-Poisson (ZICMP) distributed marginals will be considered, and the joint distribution is modeled under Gaussian copula with autoregression moving average (ARMA) errors. Sequential sampling likelihood inference is performed. Simulated and real-life data examples are provided and studied to evaluate the proposed method.

Keywords:

• Conway-Maxwell-Poisson
• count time series
• Gaussian copula
• negative binomial
• Poisson
• sequential importance sampling
• zero inflation

SUBJECT CLASSIFICATIONS:

• 62L15
• 60G40
• 62F12
• 62F15

Acknowledgment

We thank the Editor for inviting us to submit a revised version of the article.

Additional information

Funding

Mohammed Alqawba’s research was partially supported by Qassim University, Saudi Arabia.