Modeling time-series count data: The unique challenges facing political communication studies

• Presents benefits of Poisson autoregression (PAR) for temporal media count data.
• Replicates three previously published studies in the analysis.
• Compares PAR to transfer function, negative binomial, Koyck and log-normal models.
• Describes how to illustrate dynamic count model results.

This paper demonstrates the importance of proper model specification when analyzing time-series count data in political communication studies. It is common for scholars of media and politics to investigate counts of coverage of an issue as it evolves over time. Many scholars rightly consider the issues of time dependence and dynamic causality to be the most important when crafting a model. However, to ignore the count features of the outcome variable overlooks an important feature of the data. This is particularly the case when modeling data with a low number of counts. In this paper, we argue that the Poisson autoregressive model (Brandt and Williams, 2001) accurately meets the needs of many media studies. We replicate the analyses of Flemming et al., 1997 and Peake and Eshbaugh-Soha, 2008, and Ura (2009) and demonstrate that models missing some of the assumptions of the Poisson autoregressive model often yield invalid inferences. We also demonstrate that the effect of any of these models can be illustrated dynamically with estimates of uncertainty through a simulation procedure. The paper concludes with implications of these findings for the practical researcher.