The Comovement of Voter Preferences: Insights from U.S. Presidential Election Prediction Markets Beyond Polls

Mikhail Chernov, Vadim Elenev and Dongho Song

No 19836, CEPR Discussion Papers from Centre for Economic Policy Research

Abstract: We propose a novel time-series econometric framework to forecast U.S. Presidential election outcomes in real time by combining polling data, economic fundamentals, and political prediction market prices. Our model estimates the joint dynamics of voter preferences across states. Applying our approach to the 2024 Presidential Election, we find a two-factor structure driving the vast majority of the variation in voter preferences. We identify electorally similar state clusters without relying on historical data or demographic models of voter behavior. Our simulations quantify the correlations between state-level election outcomes. Failing to take the correlations into account can bias the forecasted win probability for a given candidate by more than 10 percentage points. We find Pennsylvania to be the most pivotal state in the 2024 election. Our results provide insights for election observers, candidates, and traders.

JEL-codes: C32 C53 D72 P00 (search for similar items in EconPapers)
Date: 2025-01
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