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Abstract
Recent high-stake occurrences of polling error led some analysts to conclude that the polls are "broken." One question is whether domain knowledge can help identify the direction and magnitude of polling error before the fact, which would reduce uncertainty and improve accuracy. The present study compares the accuracy of a poll average to 4,494 expert forecasts, obtained across three German federal elections. Experts' domain knowledge was unable to improve upon the accuracy of polls. On average, experts' forecast error was 34% higher than the corresponding polling error, and in more than half of the cases, experts' forecasts pointed in the wrong direction. Polls remain to be an important source of information when it comes to forecasting elections, despite their limitations and underlying uncertainty.

The Issues and Leaders model predicts the national popular two-party vote in US presidential elections from people's perceptions of the candidates' issue-handling competence and leadership qualities. In previous elections from 1972 to 2012, the model's Election Eve forecasts missed the actual vote shares by, on average, little more than one percentage point and thus reduced the error of the Gallup pre-election poll by 30%. This research note presents the model's forecast prior to the 2016 election, when most polls show that voters view Republican candidate Donald Trump as the stronger leader but prefer the Democrat's nominee Hillary Clinton when it comes to dealing with the issues. A month prior to Election Day, the model predicts that Clinton will win by four points, gaining 52.0% of the two-party vote.

The PollyVote project predicts the outcome of US presidential elections by combining forecasts from different methods, one of which are monthly expert surveys. Andreas Graefe writes that according to the latest survey, which now includes state-level experts; Hillary Clinton will win the election over Donald Trump with nearly 53 percent of the popular vote and 347 electoral votes.

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.