Post-2024 U.S. Presidential Election Analysis of Election and Poll Data: Real-life Validation of Prediction via Small Area Estimation and Uncertainty Quantification

TL;DR

This paper demonstrates that a Small Area Estimation-based model can accurately predict the 2024 U.S. Presidential Election results using pre-election polling data, and introduces a conformal inference method for reliable uncertainty quantification.

Contribution

It presents a novel application of SAE methodology for election prediction and develops a conformal inference approach for uncertainty quantification, addressing limitations of bootstrap methods.

Findings

Perfect prediction accuracy in all 44 states with available polling data.

Introduction of PoIP for rigorous uncertainty analysis.

Identification of pollster biases affecting swing state predictions.

Abstract

We carry out a post-election analysis of the 2024 U.S. Presidential Election (USPE) using a prediction model derived from the Small Area Estimation (SAE) methodology. With pollster data obtained one week prior to the election day, retrospectively, our SAE-based prediction model can perfectly predict the Electoral College election results in all 44 states where polling data were available. In addition to such desirable prediction accuracy, we introduce the probability of incorrect prediction (PoIP) to rigorously analyze prediction uncertainty. Since the standard bootstrap method appears inadequate for estimating PoIP, we propose a conformal inference method that yields reliable uncertainty quantification. We further investigate potential pollster biases by the means of sensitivity analyses and conclude that swing states are particularly vulnerable to polling bias in the prediction of the 2024 USPE.