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A Bayesian Prediction Model for the U.S. Presidential Election

Southern Illinois University, Edwardsville

Sheldon H. Jacobson

University of Illinois, Urbana-Champaign

Wendy K. Tam Cho

University of Illinois, Urbana-Champaign, wendycho{at}illinois.edu

Edward C. Sewell

Southern Illinois University, Edwardsville

Christopher J. Rigdon

Arizona State University, Tempe

It has become a popular pastime for political pundits and scholars alike to predict the winner of the U.S. presidential election. Although forecasting has now quite a history, we argue that the closeness of recent presidential elections and the wide accessibility of data should change how presidential election forecasting is conducted. We present a Bayesian forecasting model that concentrates on the Electoral College outcome and considers finer details such as third-party candidates and self-proclaimed undecided voters. We incorporate our estimators into a dynamic programming algorithm to determine the probability that a candidate will win an election.

Key Words: presidential elections • election forecasting • operations research • Bayesian prediction models

American Politics Research, Vol. 37, No. 4, 700-724 (2009)
DOI: 10.1177/1532673X08330670


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