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

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 presidenti...

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Bibliographic Details
Published in:American politics research 2009-07, Vol.37 (4), p.700-724
Main Authors: Rigdon, Steven E., Jacobson, Sheldon H., Tam Cho, Wendy K., Sewell, Edward C., Rigdon, Christopher J.
Format: Article
Language:English
Subjects:
Access
Bayesian analysis
Candidates
Closeness
Election forecasting
Elections
Electoral College
Forecasting
Intellectuals
Political campaigns
Political parties
Prediction Models
Presidential candidates
Presidential elections
Presidents
United States of America
Voters
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Summary: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.
ISSN:1532-673X
1552-3373
DOI:10.1177/1532673X08330670