Count Time Series: A Methodological Review

Abstract

A growing interest in non-Gaussian time series, particularly in series comprised of nonnegative integers (counts), is taking place in today’s statistics literature. Count series naturally arise in fields, such as agriculture, economics, epidemiology, finance, geology, meteorology, and sports. Unlike stationary Gaussian series where autoregressive moving-averages are the primary modeling vehicle, no single class of models dominates the count landscape. As such, the literature has evolved somewhat ad-hocly, with different model classes being developed to tackle specific situations. This article is an attempt to summarize the current state of count time series modeling. The article first reviews models having prescribed marginal distributions, including some recent developments. This is followed by a discussion of state-space approaches. Multivariate extensions of the methods are then studied and Bayesian approaches to the problem are considered. The intent is to inform researchers and practitioners about the various types of count time series models arising in the modern literature. While estimation issues are not pursued in detail, reference to this literature is made.

KEYWORDS:

• Autocorrelation
• Count Negative binomial distribution
• Poisson distribution
• State-space models
• Time series

Acknowledgments

This article is released to inform interested parties of research and to encourage discussion. The views expressed are those of the authors and not those of the NSF or the U.S. Census Bureau. We thank the referees and Associate Editor for comments that improved this article.

Additional information

Funding

This research was partially supported by the National Science Foundation (NSF) grants SES-1853096, DMS-1407480, and DMS-1712966, and the Natural Sciences and Engineering Research Council of Canada Discovery Grant 8698.