A Dynamic Forecast
An Evolving Prediction of the 2024 Presidential Election
Bibliographic Data
| ID | 12378721 |
|---|---|
| Authors | Spencer C Lindsay (0000-0002-7928-2981, University of Arizona, corresponding author), Levi G Allen (0000-0002-3115-1077, Indiana State University) |
| Year | 2024 |
| Volume | 58 |
| Issue | 2 |
| Pages | 219-225 |
| Publication date | 2024-10-15 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | PS Political Science & Politics (JOURNAL) |
| Journal identifiers | ISSN: 1049-0965 • E-ISSN: 1537-5935 |
| Publisher | Cambridge University Press (CUP) (PUBLISHER) |
| DOI | 10.1017/s1049096524000878 |
| OpenAlex | W4403442293 |
| Language | EN |
In this article, we build a model to predict the state-level results of the 2024 election. We do so by using both polling from similar points in past election cycles and the results of the previous election. Notably, we update our model over time, and the coefficients of the two variables change as a result: the model puts more weight on polling as the election gets closer. As of September 1, 2024, we find that Kamala Harris is a narrow favorite to win the 2024 election, with a 57% chance of doing so. Currently, the model predicts she will win 289 electoral votes to Trump’s 249. However, there remains significant uncertainty, and the model will continue to be updated as the election nears
Economics · Political economy · Political science · Politics · Presidential election · Presidential system · Law · Monetary Policy and Economic Impact
| Highly cited | No |
|---|