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Bayesian Combination of State Polls and Election Forecasts

Kari Lock; Andrew E. Gelman

Title:
Bayesian Combination of State Polls and Election Forecasts
Author(s):
Lock, Kari
Gelman, Andrew E.
Date:
Type:
Articles
Department(s):
Statistics
Persistent URL:
Notes:
Political Analysis, vol. 18, no. 3 (Summer 2010), pp. 337-348.
Abstract:
A wide range of potentially useful data are available for election forecasting: the results of previous elections, a multitude of pre-election 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, pre-election 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.
Subject(s):
Political science
Statistics
Bayesian statistical decision theory
Publisher DOI:
https://doi.org/10.1093/pan/mpq002
Item views
535
Metadata:
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Suggested Citation:
Kari Lock, Andrew E. Gelman, , Bayesian Combination of State Polls and Election Forecasts, Columbia University Academic Commons, .

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