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A Bayesian Poisson Vector Autoregression Model

Published online by Cambridge University Press:  04 January 2017

Patrick T. Brandt  and
Todd Sandler
Patrick T. Brandt*
Affiliation:
School of Economic, Political, and Policy Sciences, The University of Texas at Dallas, 800 W. Campbell Road, GR 31, Richardson, TX 75080
Todd Sandler
Affiliation:
School of Economic, Political, and Policy Sciences, The University of Texas at Dallas, 800 W. Campbell Road, GR 31, Richardson, TX 75080. email: tsandler@utdallas.edu
*
e-mail: pbrandt@utdallas.edu (corresponding author)
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Abstract

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 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.

Type
Research Article
Information
Political Analysis , Volume 20 , Issue 3 , Summer 2012 , pp. 292 - 315
Copyright
Copyright © The Author 2012. Published by Oxford University Press on behalf of the Society for Political Methodology 

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Footnotes

Authors' note: We are grateful to Janet Box-Steffensmeier, Walter Enders, John Freeman, Jeff Gill, Sara Mitchell, Xun Pang, Vera Troeger, and three anonymous reviewers for their comments. Supplementary materials for this article are available on the Political Analysis Web site.

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