Abstract
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 language | English (US) |
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Pages (from-to) | 700-724 |
Number of pages | 25 |
Journal | American Politics Research |
Volume | 37 |
Issue number | 4 |
DOIs | |
State | Published - Jul 2009 |
Keywords
- Bayesian prediction models
- Election forecasting
- Operations research
- Presidential elections
ASJC Scopus subject areas
- Sociology and Political Science