Predicting popular vote shares at Us presidential elections : a model-based strategy relying on anes data
Camatarri, Stefano (Universitat Autònoma de Barcelona)

Date:	2024
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 theorybased 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.
Note:	Altres ajuts: acords transformatius de la UAB
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Language:	Anglès
Document:	Article ; recerca ; Versió sotmesa a revisió
Subject:	Election forecasting ; Voting intentions ; US Presidential elections ; Regression analysis ; ANES
Published in:	PS - Political Science and Politics, 2024 , ISSN 1537-5935

DOI: 10.1017/S1049096524000933

Preprint 10 p, 697.4 KB

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