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 primary objective of this research is to obtain an accurate forecasting model for the US presidential election. To identify a reliable model, artificial neural networks (ANN) and support vector regression (SVR) models are compared based on some specified performance measures. Moreover, six...

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...

This document is a follow up to the paper by Ahmed and Pesaran (2020, AP) and reports state-level forecasts for the 2024 US presidential election. It updates the 3,107 county level data used by AP and uses the same machine learning techniques as before to select the variables used in forecasting...