Accuracy gains of adding vote expectation surveys to a combined forecast of US presidential election outcomes

Abstract

In averaging forecasts within and across four-component methods (i.e. polls, prediction markets, expert judgment and quantitative models), the combined PollyVote provided highly accurate predictions for the US presidential elections from 1992 to 2012. This research note shows that the PollyVote would have also outperformed vote expectation surveys, which prior research identified as the most accurate individual forecasting method during that time period. Adding vote expectations to the PollyVote would have further increased the accuracy of the combined forecast. Across the last 90 days prior to the six elections, a five-component PollyVote (i.e. including vote expectations) would have yielded a mean absolute error of 1.08 percentage points, which is 7% lower than the corresponding error of the original four-component PollyVote. This study thus provides empirical evidence in support of two major findings from forecasting research. First, combining forecasts provides highly accurate predictions, which are difficult to beat for even the most accurate individual forecasting method available. Second, the accuracy of a combined forecast can be improved by adding component forecasts that rely on different data and different methods than the forecasts already included in the combination.