A Bayesian Poisson Vector Autoregression Model

Multivariate count models are rare in political science, despite the presence of many count time series. This article develops a new Bayesian Poisson vector autoregression (BaP-VAR) model that can characterize endogenous dynamic counts with no restrictions on the contemporaneous correlations. Impulse responses, decomposition of the forecast errors, and dynamic multiplier methods for the effects of exogenous covariate shocks are illustrated for the model. Two full illustrations of the model, its interpretations, and results are presented. The first example is a dynamic model that reanalyzes the patterns and predictors of superpower rivalry events. The second example applies the model to analyze the dynamics of transnational terrorist targeting decisions between 1968 and 2008. The latter example’s results have direct implications for contemporary policy about terrorists’ targeting that are both novel and innovative in the study of terrorism. ∗This study was funded by the US Department of Homeland Security (DHS) through the Center for Risk and Economic Analysis of Terrorism Events (CREATE) at the University of Southern California, Grant 2007-ST-061-000001 and 2010-ST-061-RE0001. Brandt’s research is based upon work supported by the National Science Foundation under Award Number 0921051. However, any opinions, findings, conclusions, and recommendations are solely those of the authors and do not necessarily reflect the views of DHS, CREATE or the National Science Foundation. We are grateful to Janet Box-Steffensmeier, Walter Enders, John Freeman, Jeff Gill, Sara Mitchell, Xun Pang, and three anonymous reviewers for their comments on an earlier version.