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- W3198988936 abstract "While combining forecasts is well-known to reduce error, the question of how to best combine forecasts remains. Prior research suggests that combining is most beneficial when relying on diverse forecasts that incorporate different information. Here I provide evidence in support of this assumption by analyzing data from the PollyVote project, which has published combined forecasts of the popular vote in U.S. presidential elections since 2004. Prior to the 2020 election, the PollyVote revised its original method of combining forecasts by, first, restructuring individual forecasts based on their underlying information and, second, adding naive forecasts as a new component method. On average across the last 100 days prior to the five elections from 2004 to 2020, the revised PollyVote reduced the error of the original specification by eight percent and, with an error of 0.8 percentage points, was more accurate than any of its component forecasts. The results suggest that, when deciding about which forecasts to include in the combination, forecasters should be more concerned about the component forecasts’ diversity than their historical accuracy." @default.
- W3198988936 created "2021-09-27" @default.
- W3198988936 creator A5003968744 @default.
- W3198988936 date "2021-01-01" @default.
- W3198988936 modified "2023-10-03" @default.
- W3198988936 title "Embrace the differences: Revisiting the PollyVote method of combining forecasts for U.S. presidential elections (2004 to 2020)" @default.
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- W3198988936 doi "https://doi.org/10.2139/ssrn.3871059" @default.
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