No CrossRef data available.
Article contents
Long-Range State-Level 2024 Presidential Election Forecast: How Can You Forecast an Election When You Don’t Know Who the Candidates Are Yet?
Published online by Cambridge University Press: 15 October 2024
Abstract
This model generates projections of the national popular vote and Electoral College votes a year in advance of the U.S. Presidential Election, before each party’s nominees are known. It forecasts the Democratic two-party popular vote in each state and the District of Columbia. It uses four independent variables: national head-to-head polling data 13 months prior to the election, the states’ prior election result, a party-adjusted home state advantage dummy variable, and a party adjusted variable simply counting the number of consecutive terms the current incumbent party has occupied the White House. New to this year’s model is a polling average approach that encompasses all possible candidate matchups for whom data is available. This year’s forecast suggests a distinct possibility of an Electoral College misfire benefitting the Republicans.
Information
- Type
- Article
- Information
- PS: Political Science & Politics , Volume 58 , Issue 2: Forecasting the 2024 US Election , April 2025 , pp. 198 - 204
- Copyright
- © The Author(s), 2024. Published by Cambridge University Press on behalf of American Political Science Association
References
REFERENCES
Abramowitz, Alan. 1988. “An Improved Model for Predicting Presidential Election Outcomes.” PS: Political Science & Politics 21 (4): 843–47.Google Scholar
Billingsley, Patrick. 1995. Probability and Measure. 3rd Edition. Hoboken, NJ: John Wiley & Sons.Google Scholar
Clinton, Josh, Agiesta, Jennifer, Brenan, Megan, Burge, Camille, Connelly, Marjorie, Edwards-Levy, Ariel, et al. 2021. “Task Force on 2020 Pre-Election Polling: An Evaluation of the 2020 General Election Poll.” Alexandria, VA: American Association for Public Opinion Research.Google Scholar
DeSart, Jay. 2015. “State Electoral Histories, Regime Age, and Long-Range Presidential Election Forecasts: Predicting the 2016 Presidential Election.” Paper presented at the Iowa Conference on Presidential Politics. Sauk Centre, MN.Google Scholar
DeSart, Jay. 2019. “A Long-Range State-Level Presidential Election Forecast Model in 2016 and 2020.” Paper presented at the Annual Meeting of the American Political Science Association. Washington, DC.Google Scholar
DeSart, Jay. 2021. “A Long-Range State-Level Forecast of the 2020 Presidential Election.” PS: Political Science & Politics 54 (1): 73–76.Google Scholar
DeSart, Jay. 2023. “‘But You Don’t Even Know Who the Nominees Are Yet!’ Preliminary 2024 Forecast.” Paper presented at the Annual Meeting of the American Political Science Association. Los Angeles, CA.Google Scholar
DeSart, Jay. 2024. “Replication Data for ‘Long-Range State-Level 2024 Presidential Election Forecast: How Can You Forecast an Election When You Don’t Know Who the Candidates Are Yet?’” PS: Political Science & Politics. DOI:10.7910/DVN/HZGLY9.CrossRefGoogle Scholar
DeSart, Jay, and Holbrook, Thomas. 2003. “Statewide Trial Heat Polls and the 2000 Presidential Election: A Forecast Model.” Social Science Quarterly 84 (3): 561–73.CrossRefGoogle Scholar
Graefe, Andreas. 2018. “Predicting Elections: Experts, Polls, and Fundamentals.” Judgement and Decision Making 13 (4): 334–44. DOI:10.1017/S1930297500009219.CrossRefGoogle Scholar
Graefe, Andreas, Armstrong, J. Scott, Jones, Randall J., and Cuzán, Alfred G.. 2014. “Combining Forecasts: An Application to Elections.” International Journal of Forecasting 30 (1): 43–54.CrossRefGoogle Scholar
Holbrook, Thomas M. 2008. “Incumbency, National Conditions, and the 2008 Presidential Election.” PS: Political Science & Politics 41 (4): 709–12.Google Scholar
Holbrook, Thomas M. 2012. “Incumbency, National Conditions, and the 2012 Presidential Election.” PS: Political Science & Politics 45 (4): 640–43.Google Scholar
Kennedy, Courtney, Blumenthal, Mark, Clement, Scott, Clinton, Joshua, Durand, Claire, Franklin, Charles, et al. 2017. An Evaluation of 2016 Election Polls in the United States. Alexandria, VA: American Association for Political Opinion Research.Google Scholar
Norpoth, Helmut. 1995. “Is Clinton Doomed? An Early Forecast for 1996.” PS: Political Science & Politics 28 (2): 201–7.Google Scholar