Bayesian modeling for overdispersed event-count time series

Social scientists are frequently interested in event-count time-series data. One of the state-of-the-art methods, the Poisson exponentially weighted moving average (P-EWMA) model, leads to incorrect inference in the presence of omitted variables even if they are not confounding. To tackle this problem, this paper proposes a negative binomial integrated error [NB-I(1)] model, which can be estimated via Markov Chain Monte Carlo methods. Simulations show that when the data are generated by a P-EWMA model, but an non-confounding covariate is omitted at the stage of estimation, the P-EWMA model’s credible interval is optimistically too narrow to contain the true value at the nominal level, whereas the NB-I(1) model does not suffer this problem. To explore the models’ performance, we replicate a study on an annual count of militarized interstate disputes. © 2019, The Behaviormetric Society.