Work

Combining forecasts for U.S. presidential elections: The PollyVote

Year: 2009

Type: preprint

Abstract: In the PollyVote, we evaluated the combination principle to forecast the five U.S. presidential elections between 1992 and 2008. We combined forecasts from three or four different component methods: trial heat polls, the Iowa Electronic Markets (IEM), quantitative models and, in the 2004 and 2008 contests, periodic surveys of experts on American politics. The forecasts were combined within as well as across components. On average, combining within components reduced forecast error – and increased predictive accuracy – by 17% to 40%. Combining across components led to additional error reductions ranging from 7% to 68%, depending on the forecast horizon. In addition, across all five elections, the PollyVote predicted the correct election winner on all but 4 out of 957 days. The gains from applying the combination principle to election forecasting were much larger than those obtained in other fields.

Source: SSRN Electronic Journal

Authors: Andreas Graefe, Alfred G. Cuzán, Randall J. Jones, J. Scott Armstrong

Institutions: Hochschule Macromedia, University of West Florida, University of Central Oklahoma, University of Pennsylvania

Language: English

Topic: Forecasting Techniques and Applications

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: green

OpenAlex ID: W225306484

Cites: 7

Cited by: 3

Related to: 10