Forecasting the 2020 Presidential Election: a Comparison of Methods

Abstract

Accurate forecasts of U.S. Presidential elections are not only central to political journalism, but are used by campaigns to formulate strategy, impact financial markets, and aid businesses planning for the future. However, evidenced by the 2016 and 2020 elections, forecasting the election remains a challenging endeavor. Our review of methodologies revealed three discrete approaches: polling-based, demographic and economic fundamentals-based, and sentiment-based. We sought to identify which advantages each approach offers. We built on past research to adopt a novel forecast model that combines a weighted average of a hierarchical Bayesian fundamentals model and a Bayesian polling model. Our results indicated problems with polling-based methods because of inaccuracies in the polls, and better-than-anticipated accuracy in the fundamentals-only model.