Predicting Popular Vote Shares at US Presidential Elections: a Model-Based Strategy Relying on ANES Data

Camatarri, Stefano
(2024) P S: Political Science & Politics — p. 1-10 (2024)

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Abstract
Election forecasting in modern democracies faces significant challenges, including increasing survey nonresponse and selection bias. Added to this are the limitations of current predictive approaches.While structural models focus solely on macro-level variables—such as economic conditions and leader popularity—thereby overlooking the importance of individual-level factors, survey-based aggregation methods often rely on intuitive procedures that lack theoretical foundations. To address these gaps, this contribution proposes a combined logistic regression approach (both standard and Bayesian) that leverages voter-level data and incorporates a theory- based specification. By testing these models on recent waves of the American National Election Studies (ANES) Time Series, this study demonstrates that the proposed approach yields notably accurate predictions of Republican popular support in each election.
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Camatarri, S. (2024). Predicting Popular Vote Shares at US Presidential Elections: a Model-Based Strategy Relying on ANES Data. P S: Political Science & Politics, 1-10. https://doi.org/10.1017/s1049096524000933 (Original work published 2024)