To many statisticians and citizens, the outcome of the most recent U.S. presidential election represents a failure of data-driven methods on the grandest scale. This impression has led to much debate and discussion about how the election predictions went awry — Were the polls inaccurate? Were...

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...

The presidential election results of 2016 surprised many poll-watchers, suggesting possible biases in estimated support for the major party candidates and posing a challenge for poll aggregation as a prediction tool. Using data from earlier elections and the 2016 campaign, we conducted an...

Most common work in the literature is based on aggregated models (see e.g., Campbell and Lewis-Beck, 2008) with some important exceptions that use data at the state level, e.g., Rosenstone (1983), Holbrook (1991) and Campbell (1992). These aggregated models, are based on either survey results or...