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Replication data for: Combining forecasts: An application to elections



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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... All data and calculations are publicly available (Graefe 2013). ...
... Polls: Polls that were conducted within 100 days prior to each of the 16 elections from 1952 to 2012 were obtained from Graefe (2014). for each poll, the two-party percentage of respondents who intended to vote for the candidate of the incumbent party was recorded. ...
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Simple surveys that ask people who they expect to win are among the most accurate methods for forecasting U.S. presidential elections. The majority of respondents correctly predicted the election winner in 193 (89%) of 217 surveys conducted from 1932 to 2012. Across the last 100 days prior to the seven elections from 1988 to 2012, vote expectation surveys provided more accurate forecasts of election winners and vote shares than four established methods (vote intention polls, prediction markets, econometric models, and expert judgment). Gains in accuracy were particularly large compared to polls. On average, the error of expectation-based vote-share forecasts was 51% lower than the error of polls published the same day. Compared to prediction markets, vote expectation forecasts reduced the error on average by 6%. Vote expectation surveys are inexpensive, easy to conduct, and the results are easy to understand. They provide accurate and stable forecasts and thus make it difficult to frame elections as horse races. Vote expectation surveys should be more strongly utilized in the coverage of election campaigns.
... As a result, the less diverse expert group was likely biased in the same direction. In such a situation, combining judgments is of limited value, since the individual estimates are highly correlated and biases do not cancel out in the aggregate ( Graefe et al. 2014, Hogarth 1978. ...
Researchers have long known that aggregate estimations built from the collected opinions of a large group of people often outperform the estimations of individual experts. This phenomenon is generally described as the "Wisdom of Crowds". This approach has shown promise with respect to the task of accurately forecasting future events. Previous research has demonstrated the value of utilizing meta-forecasts (forecasts about what others in the group will predict) when aggregating group predictions. In this paper, we describe an extension to meta-forecasting and demonstrate the value of modeling the familiarity among a population's members (its social network) and applying this model to forecast aggregation. A pair of studies demonstrates the value of taking this model into account, and the described technique produces aggregate forecasts for future events that are significantly better than the standard Wisdom of Crowds approach as well as previous meta-forecasting techniques.
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