Advances in Bayesian Time Series Modeling and the Study of Politics: Theory Testing, Forecasting, and Policy Analysis

John R. Freeman Department of Political Science, University of Minnesota, 267 19th Avenue, Minneapolis, MN 55455 e-mail: pbrandt{at}utdallas.edu (corresponding author) e-mail: freeman{at}polisci.umn.edu Bayesian approaches to the study of politics are increasinglypopular. But Bayesian approaches to modeling multiple time serieshave not been critically evaluated. This is in spite of thepotential value of these models in international relations,political economy, and other fields of our discipline. We reviewrecent developments in Bayesian multi-equation time series modelingin theory testing, forecasting, and policy analysis. Methodsfor constructing Bayesian measures of uncertainty of impulseresponses (Bayesian shape error bands) are explained. A referenceprior for these models that has proven useful in short- andmedium-term forecasting in macroeconomics is described. Oncemodified to incorporate our experience analyzing political dataand our theories, this prior can enhance our ability to forecastover the short and medium terms complex political dynamics likethose 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 areanalysis of the Israeli-Palestinian conflict of the 1980s.