How time and pollster history affect U.S. election forecasts under a compartmental modeling approach

TL;DR

This paper analyzes how time and pollster history influence U.S. election forecast accuracy using a compartmental modeling approach, examining data from 2004 to 2022 and providing insights into forecast variability and accuracy.

Contribution

It introduces a comprehensive analysis of election forecast choices and their impact on accuracy, building on a compartmental model and applying it across multiple election cycles.

Findings

Forecast accuracy varies with polling data treatment.

Historical pollster performance influences forecast reliability.

Model provides insights into election dynamics over time.

Abstract

In the months leading up to political elections in the United States, forecasts are widespread and take on multiple forms, including projections of what party will win the popular vote, state ratings, and predictions of vote margins at the state level. It can be challenging to evaluate how accuracy changes in the lead up to Election Day or to put probabilistic forecasts into historical context. Moreover, forecasts differ between analysts, highlighting the many choices in the forecasting process. With this as motivation, here we take a more comprehensive view and begin to unpack some of the choices involved in election forecasting. Building on a prior compartmental model of election dynamics, we present the forecasts of this model across months, years, and types of race. By gathering together monthly forecasts of presidential, senatorial, and gubernatorial races from 2004--2022, we provide a larger-scale perspective and discuss how treating polling data in different ways affects forecast accuracy. We conclude with our 2024 election forecasts (upcoming at the time of writing).