A Bayesian prediction model for the U.S. presidential election

Steven E. Rigdon, Sheldon H. Jacobson, Wendy K. Tam Cho, Edward C. Sewell, Christopher J. Rigdon

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)700-724
Number of pages25
JournalAmerican Politics Research
Issue number4
StatePublished - Jul 2009


  • Bayesian prediction models
  • Election forecasting
  • Operations research
  • Presidential elections

ASJC Scopus subject areas

  • Sociology and Political Science


Dive into the research topics of 'A Bayesian prediction model for the U.S. presidential election'. Together they form a unique fingerprint.

Cite this