Predicting popular vote shares at Us presidential elections
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 leade...
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:ddd.uab.cat:303023 |
| Acceso en línea: | https://ddd.uab.cat/record/303023 https://dx.doi.org/urn:doi:10.1017/S1049096524000933 |
| Access Level: | acceso abierto |
| Palabra clave: | Election forecasting Voting intentions US Presidential elections Regression analysis ANES |
| Sumario: | 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. |
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