Bayesian approaches to the study of politics are increasingly popular. But Bayesian approaches to modeling multiple time series have not been critically evaluated. This is in spite of the potential value of these models in international relations, political economy, and other fields of our discipline. We review recent developments in Bayesian multi-equation time series modeling in theory testing, forecasting, and policy analysis. Methods for constructing Bayesian measures of uncertainty of impulse responses (Bayesian shape error bands) are explained. A reference prior for these models that has proven useful in short- and medium-term forecasting in macroeconomics is described. Once modified to incorporate our experience analyzing political data and our theories, this prior can enhance our ability to forecast over the short and medium terms complex political dynamics like those exhibited by certain international conflicts. In addition, we explain how contingent Bayesian forecasts can be constructed, contingent Bayesian forecasts that embody policy counterfactuals. The value of these new Bayesian methods is illustrated in a reanalysis of the Israeli-Palestinian conflict of the 1980s.
Bibliographical noteFunding Information:
Authors’ note: Earlier versions of this article were presented at the Joint Statistical Meeting of the American Statistical Association in August 2005, at two meetings of the Midwest Political Science Association, and at research seminars at the University of Konstanz, Harvard University, Pennsylvania State University, the University of Texas at Austin, the University of Texas at Dallas, Pennsylvania State University, and the University of Pittsburgh. For useful comments and criticisms we thank the discussants at the meetings, Simon Jackman, and Jonathan Wand, participants in the seminars, and two anonymous referees. In addition, we thank Jeff Gill, Phil Schrodt, and John Williams for several useful discussions of the issues reviewed in this article. Replication materials are available on the Political Analysis Web site. Additional software for implementing the methods described here can be obtained from the lead author. This research is sponsored by the National Science Foundation under grant numbers SES-0351179 and SES-0351205. Brandt would also like to thank the University of North Texas for its support. The authors are solely responsible for the contents.