Forecasting elections using expert surveys: an application to U.S. presidential elections

Prior research offers a mixed view of the value of expert surveys for long-term election forecasts. On the positive side, experts have more information about the candidates and issues than voters do. On the negative side, experts all have access to the same information. Based on prior literature and on our experiences with the 2004 presidential election and the 2008 campaign so far, we have reason to believe that a simple expert survey (the Nominal Group Technique) is preferable to Delphi. Our survey of experts in American politics was quite accurate in the 2004 election. Following the same procedure, we have assembled a new panel of experts to forecast the 2008 presidential election. Here we report the results of the first survey, and compare our experts’ forecasts with predictions by the Iowa Electronic Market .forecasting; elections; expert surveys; Delphi

Assessing the 2016 U.S. Presidential Election Popular Vote Forecasts

The PollyVote uses evidence-based techniques for forecasting the popular vote in presidential elections. The forecasts are derived by averaging existing forecasts generated by six different forecasting methods. In 2016, the PollyVote correctly predicted that Hillary Clinton would win the popular vote. The 1.9 percentage-point error across the last 100 days before the election was lower than the average error for the six component forecasts from which it was calculated (2.3 percentage points). The gains in forecast accuracy from combining are best demonstrated by comparing the error of PollyVote forecasts with the average error of the component methods across the seven elections from 1992 to 2012. The average errors for last 100 days prior to the election were: public opinion polls (2.6 percentage points), econometric models (2.4), betting markets (1.8), and citizens’ expectations (1.2); for expert opinions (1.6) and index models (1.8), data were only available since 2004 and 2008, respectively. The average error for PollyVote forecasts was 1.1, lower than the error for even the most accurate component method

Assessing the 2016 U.S. Presidential Election Popular Vote Forecasts

The PollyVote uses evidence-based techniques for forecasting the popular vote in presidential elections. The forecasts are derived by averaging existing forecasts generated by six different forecasting methods. In 2016, the PollyVote correctly predicted that Hillary Clinton would win the popular vote. The 1.9 percentage-point error across the last 100 days before the election was lower than the average error for the six component forecasts from which it was calculated (2.3 percentage points). The gains in forecast accuracy from combining are best demonstrated by comparing the error of PollyVote forecasts with the average error of the component methods across the seven elections from 1992 to 2012. The average errors for last 100 days prior to the election were: public opinion polls (2.6 percentage points), econometric models (2.4), betting markets (1.8), and citizens’ expectations (1.2); for expert opinions (1.6) and index models (1.8), data were only available since 2004 and 2008, respectively. The average error for PollyVote forecasts was 1.1, lower than the error for even the most accurate component method