Bias and Excess Variance in Election Polling: A Not-So-Hidden Markov Model

TL;DR

This paper introduces a hidden Markov model approach to analyze polling errors in US elections, providing more robust and interpretable estimates of bias and excess variability over traditional methods.

Contribution

It develops a flexible hidden Markov model framework that reduces sensitivity to timing and better separates shifting preferences from polling errors.

Findings

Bias estimates in Presidential elections are 10% lower than previously reported.

Bias in Senatorial elections is 25% lower than earlier estimates.

Excess variability estimates are also reduced compared to prior assessments.

Abstract

With historic misses in the 2016 and 2020 US Presidential elections, interest in measuring polling errors has increased. The most common method for measuring directional errors and non-sampling excess variability during a postmortem for an election is by assessing the difference between the poll result and election result for polls conducted within a few days of the day of the election. Analyzing such polling error data is notoriously difficult with typical models being extremely sensitive to the time between the poll and the election. We leverage hidden Markov models traditionally used for election forecasting to flexibly capture time-varying preferences and treat the election result as a peak at the typically hidden Markovian process. Our results are much less sensitive to the choice of time window, avoid conflating shifting preferences with polling error, and are more interpretable despite a highly flexible model. We demonstrate these results with data on polls from the 2004 through 2020 US Presidential elections and 1992 through 2020 US Senate elections, concluding that previously reported estimates of bias in Presidential elections were too extreme by 10\%, estimated bias in Senatorial elections was too extreme by 25\%, and excess variability estimates were also too large.