Lessons Learned
Citizen Forecasting, Candidate Resignations, and the 2024 US Presidential Election
Bibliographic Data
| ID | 12378113 |
|---|---|
| Authors | Brian Thompson-Collart (0009-0001-1569-1786, Université Laval, corresponding author), Hubert Cadieux (0000-0002-5470-8616, Université Laval), Catherine Ouellet (0000-0002-8642-5442, Université de Montréal), Yannick Dufresne (0000-0002-6211-2193, Université Laval) |
| Year | 2024 |
| Volume | 58 |
| Issue | 2 |
| Pages | 312-317 |
| 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/s1049096524000969 |
| OpenAlex | W4403436801 |
| Language | EN |
Every four years, numerous election-forecasting models attempt to predict the results of the US presidential election. Regardless of the stability of any election system, such as the bipartisan system in the United States, conditions can arise (e.g., candidate resignations) that negatively impact forecasters’ ability to predict electoral outcomes. Citizen forecasting—that is, directly asking respondents who they think will win an election—has a long track record of successfully predicting presidential elections. This study proposes adapting a citizen forecasting measure originally intended for use in multiparty systems to predict the US presidential election in 2024. Using this measure, we created a forecast of the national-level popular vote and vote-share forecasts for seven swing states
Economics · Political economy · Political science · Politics · Presidential election · Presidential system · Electoral Systems and Political Participation · Law · Public Administration
| Highly cited | No |
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