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Bayesian Combination of State Polls and Election Forecasts

Published online by Cambridge University Press:  04 January 2017

Kari Lock  and
Andrew Gelman
Kari Lock*
Affiliation:
Department of Statistics, Harvard University, 1 Oxford St., Cambridge, MA 02138
Andrew Gelman
Affiliation:
Department of Statistics and Department of Political Science, Columbia University 1016 Social Work Bldg, New York, NY 10027. e-mail: gelman@stat.columbia.edu
*
e-mail: lock@stat.harvard.edu (corresponding author)
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Abstract

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A wide range of potentially useful data are available for election forecasting: the results of previous elections, a multitude of preelection polls, and predictors such as measures of national and statewide economic performance. How accurate are different forecasts? We estimate predictive uncertainty via analysis of data collected from past elections (actual outcomes, preelection polls, and model estimates). With these estimated uncertainties, we use Bayesian inference to integrate the various sources of data to form posterior distributions for the state and national two-party Democratic vote shares for the 2008 election. Our key idea is to separately forecast the national popular vote shares and the relative positions of the states. More generally, such an approach could be applied to study changes in public opinion and other phenomena with wide national swings and fairly stable spatial distributions relative to the national average.

Information

Type
Research Article
Information
Political Analysis , Volume 18 , Issue 3 , Summer 2010 , pp. 337 - 348
Copyright
Copyright © The Author 2010. Published by Oxford University Press on behalf of the Society for Political Methodology 

Footnotes

Authors' note: We thank Aaron Strauss and three anonymous reviewers for helpful comments.

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