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Accuracy of Combined Forecasts for the 2012 Presidential Election: The PollyVote

Published online by Cambridge University Press:  14 April 2014

Andreas Graefe
Affiliation:
LMU Munich
J. Scott Armstrong
Affiliation:
University of Pennsylvania and University of South Australia
Randall J. Jones Jr.
Affiliation:
University of Central Oklahoma
Alfred G. Cuzán
Affiliation:
University of West Florida

Abstract

We review the performance of the PollyVote, which combined forecasts from polls, prediction markets, experts’ judgment, political economy models, and index models to predict the two-party popular vote in the 2012 US presidential election. Throughout the election year the PollyVote provided highly accurate forecasts, outperforming each of its component methods, as well as the forecasts from FiveThirtyEight.com. Gains in accuracy were particularly large early in the campaign, when uncertainty about the election outcome is typically high. The results confirm prior research showing that combining is one of the most effective approaches to generating accurate forecasts.

Type
Features
Copyright
Copyright © American Political Science Association 2014 

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References

REFERENCES

Armstrong, J. Scott. 2001. “Combining Forecasts.” In Principles of Forecasting: A Handbook for Researchers and Practitioners, ed. Armstrong, J. Scott. 417–39. New York: Springer.Google Scholar
Armstrong, J. Scott, and Graefe, Andreas. 2011. “Predicting Elections from Biographical Information about Candidates: A Test of the Index Method.” Journal of Business Research 64(7): 699706.Google Scholar
Campbell, James E. 2012. “Forecasting the 2012 American National Elections.” PS: Political Science and Politics 45(4): 610–74.Google Scholar
Erikson, Robert S., and Wlezien, Christopher. 2012. “Markets vs. Polls as Election Predictors: An Historical Assessment.” Electoral Studies 31(3): 532–39.Google Scholar
Graefe, Andreas. 2013a. “Replication Data for: Accuracy of Combined Forecasts for the 2012 Presidential Election: The PollyVote,” Harvard Dataverse Network, http://dx.doi.org/10.7910/DVN/POLLYVOTE2012.Google Scholar
Graefe, Andreas. 2013b. “Issue and Leader Voting in U.S. Presidential Elections.” Electoral Studies 32(4): 644–57.Google Scholar
Graefe, Andreas, and Armstrong, J. Scott. 2012. “Predicting Elections from the Most Important Issue: A Test of the Take-the-Best Heuristic.” Journal of Behavioral Decision Making 25(1): 4148.Google Scholar
Graefe, Andreas, and Armstrong, J. Scott. 2013. “Forecasting Elections from Voters’ Perceptions of Candidates’ Ability to Handle Issues.” Journal of Behavioral Decision Making 26(3): 295303.CrossRefGoogle Scholar
Graefe, Andreas, Armstrong, J. Scott, Jones, Randall J. Jr., and Cuzán, Alfred G.. 2014. “Combining Forecasts: An Application to Elections.” International Journal of Forecasting 30(1): 4354.Google Scholar
Jones, Randall J. Jr. 2002. Who Will Be in the White House? Predicting Presidential Elections. New York: Longman.Google Scholar
Jones, Randall J. Jr. 2008. “The State of Presidential Election Forecasting: The 2004 Experience.” International Journal of Forecasting 24(2): 310–21.Google Scholar
Kernell, Samuel. 2000. “Life before Polls: Ohio Politicians Predict the 1828 Presidential Vote.” PS: Political Science & Politics 33(3): 569–74.Google Scholar
Montgomery, Jacob M., Hollenbach, Florian M., and Ward, Michael D.. 2012. “Improving Predictions Using Ensemble Bayesian Model Averaging.” Political Analysis 20(3): 271–91.Google Scholar