Combining forecasts: An application to elections |
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Authors: | Andreas Graefe J Scott Armstrong Randall J Jones Jr Alfred G Cuzán |
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Institution: | 1. LMU Munich - Department of Communication Science and Media Research, Munich, Germany;2. University of Pennsylvania, Wharton School, Philadelphia, PA, United States;3. Ehrenberg-Bass Institute, University of South Australia, Adelaide, Australia;4. University of Central Oklahoma - Department of Political Science, Edmond, OK, United States;5. University of West Florida - Department of Government, Pensacola, FL, United States |
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Abstract: | We summarize the literature on the effectiveness of combining forecasts by assessing the conditions under which combining is most valuable. Using data on the six US presidential elections from 1992 to 2012, we report the reductions in error obtained by averaging forecasts within and across four election forecasting methods: poll projections, expert judgment, quantitative models, and the Iowa Electronic Markets. Across the six elections, the resulting combined forecasts were more accurate than any individual component method, on average. The gains in accuracy from combining increased with the numbers of forecasts used, especially when these forecasts were based on different methods and different data, and in situations involving high levels of uncertainty. Such combining yielded error reductions of between 16% and 59%, compared to the average errors of the individual forecasts. This improvement is substantially greater than the 12% reduction in error that had been reported previously for combining forecasts. |
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Keywords: | Election forecasting Combining Prediction markets Polls Econometric models Expert judgment |
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