A Bayesian Poisson Vector Autoregression Model

Publication Type: 
Patrick T. Brandt
Todd Sandler
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 restric- tions 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 interpreta- tions, and results are presented. The first example is a dynamic model that reanalyzes the patterns and predictors of superpower rivalry events. The second example ap- plies the model to analyze the dynamics of transnational terrorist targeting decisions between 1968 and 2008. The latter example’s results have direct implications for con- temporary policy about terrorists’ targeting that are both novel and innovative in the study of terrorism.