IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/122319.html
   My bibliography  Save this paper

Modeling and forecasting US presidential election 2024

Author

Listed:
  • Sinha, Pankaj
  • verma, Kaushal
  • Biswas, Sumana
  • Tyagi, Shashank
  • Gogia, Shaily
  • Singh, Aakhyat
  • Kumar, Amit

Abstract

Forecasting the vote share for the upcoming US presidential elections involves multiple pivotal economic and non-economic factors. Critical macroeconomic forces such as the rate of economic growth, tax burden, inflation, and unemployment significantly influence the votes gained or lost by the incumbent. However, these are not the only determinants of presidential elections. The study also considers various non-economic factors that directly impact voting behaviour and can enhance prediction accuracy. These non-economic factors include scandals under the incumbent president, existing crime rates, law enforcement, June Gallup ratings reflecting the sitting president's approval, the average Gallup ratings over their term, and the results of the mid-term elections. Additionally, new non-economic factors such as illegal immigration and illegal aliens apprehended can significantly influence the outcome of the upcoming US presidential elections. To study the combined effects of economic and non-economic factors, data from each election cycle is used in an empirical model to predict the popular vote share percentage for the Democratic Party in the 2024 elections. The findings suggest that a longer tenure in power, June Gallup ratings, average Gallup ratings, scandal ratings, and economic growth rate significantly impact the popular vote share of the incumbent party candidate. The final empirical model predicts that Kamala Harris, the Democratic Party candidate, will receive a popular vote share of 48.60% ± 0.1% in the 2024 Presidential Elections of the United States.

Suggested Citation

  • Sinha, Pankaj & verma, Kaushal & Biswas, Sumana & Tyagi, Shashank & Gogia, Shaily & Singh, Aakhyat & Kumar, Amit, 2024. "Modeling and forecasting US presidential election 2024," MPRA Paper 122319, University Library of Munich, Germany, revised 08 Oct 2024.
    • Handle: RePEc:pra:mprapa:122319
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/122319/1/MPRA_paper_122319.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fair, Ray C, 1978. "The Effect of Economic Events on Votes for President," The Review of Economics and Statistics, MIT Press, vol. 60(2), pages 159-173, May.
    2. Douglas Hibbs, 2000. "Bread and Peace Voting in U.S. Presidential Elections," Public Choice, Springer, vol. 104(1), pages 149-180, July.
    3. Lichtman, Allan J., 2008. "The keys to the white house: An index forecast for 2008," International Journal of Forecasting, Elsevier, vol. 24(2), pages 301-309.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sinha, Pankaj & Srinivas, Sandeep & Paul, Anik & Chaudhari, Gunjan, 2016. "Forecasting 2016 US Presidential Elections Using Factor Analysis and Regression Model," MPRA Paper 74618, University Library of Munich, Germany, revised 17 Oct 2016.
    2. Sinha, Pankaj & Verma, Aniket & Shah, Purav & Singh, Jahnavi & Panwar, Utkarsh, 2020. "Prediction for the 2020 United States Presidential Election using Machine Learning Algorithm: Lasso Regression," MPRA Paper 103889, University Library of Munich, Germany, revised 31 Oct 2020.
    3. Pankaj Sinha & Aastha Sharma & Harsh Vardhan Singh, 2012. "Prediction For The 2012 United States Presidential Election Using Multiple Regression Model," Journal of Prediction Markets, University of Buckingham Press, vol. 6(2), pages 77-97.
    4. Sinha, Pankaj & Verma, Aniket & Shah, Purav & Singh, Jahnavi & Panwar, Utkarsh, 2020. "Prediction for the 2020 United States Presidential Election using Linear Regression Model," MPRA Paper 103890, University Library of Munich, Germany, revised 20 Oct 2020.
    5. Sinha, Pankaj & Thomas, Ashley Rose & Ranjan, Varun, 2012. "Forecasting 2012 United States Presidential election using Factor Analysis, Logit and Probit Models," MPRA Paper 42062, University Library of Munich, Germany.
    6. Kang, Seungwoo & Oh, Hee-Seok, 2024. "Forecasting South Korea’s presidential election via multiparty dynamic Bayesian modeling," International Journal of Forecasting, Elsevier, vol. 40(1), pages 124-141.
    7. Souren Soumbatiants & Henry Chappell & Eric Johnson, 2006. "Using state polls to forecast U.S. Presidential election outcomes," Public Choice, Springer, vol. 127(1), pages 207-223, April.
    8. Pankaj Sinha & Ashok K. Bansal, 2008. "Hierarchical Bayes Prediction for the 2008 US Presidential Election," Journal of Prediction Markets, University of Buckingham Press, vol. 2(3), pages 47-59, December.
    9. repec:cup:judgdm:v:15:y:2020:i:5:p:863-880 is not listed on IDEAS
    10. Andrew Gelman & Jessica Hullman & Christopher Wlezien & George Elliott Morris, 2020. "Information, incentives, and goals in election forecasts," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 15(5), pages 863-880, September.
    11. Graefe, Andreas, 2023. "Embrace the differences: Revisiting the PollyVote method of combining forecasts for U.S. presidential elections (2004 to 2020)," International Journal of Forecasting, Elsevier, vol. 39(1), pages 170-177.
    12. Graefe, Andreas & Armstrong, J. Scott, 2008. "Forecasting Elections from Voters’ Perceptions of Candidates’ Positions on Issues and Policies," MPRA Paper 9829, University Library of Munich, Germany.
    13. Armstrong, J. Scott & Graefe, Andreas, 2011. "Predicting elections from biographical information about candidates: A test of the index method," Journal of Business Research, Elsevier, vol. 64(7), pages 699-706, July.
    14. Kurrild-Klitgaard, Peter, 2019. "Var det fortsat ”the economy, stupid!” i 2016 og 2018? [Was it still "the economy, stupid!" in 2016 and 2018?]," MPRA Paper 97297, University Library of Munich, Germany.
    15. Armstrong, J. Scott & Graefe, Andreas, 2009. "Predicting Elections from Biographical Information about Candidates," MPRA Paper 16461, University Library of Munich, Germany.
    16. Abramowitz, Alan I., 2008. "It's about time: Forecasting the 2008 presidential election with the time-for-change model," International Journal of Forecasting, Elsevier, vol. 24(2), pages 209-217.
    17. Hibbs Jr., Douglas A., 2004. "Voting and the Macroeconomy," Working Papers in Economics 144, University of Gothenburg, Department of Economics, revised 08 Apr 2006.
    18. Hibbs, Douglas A., 2007. "The Economy, the War in Iraq and the 2004 Presidential Election," MPRA Paper 15910, University Library of Munich, Germany.
    19. Hibbs, Douglas A, Jr, 2000. "Bread and Peace Voting in U.S. Presidential Elections," Public Choice, Springer, vol. 104(1-2), pages 149-180, July.
    20. Jeroen Klomp, 2020. "Election or Disaster Support?," Journal of Development Studies, Taylor & Francis Journals, vol. 56(1), pages 205-220, January.
    21. Janet Pack, 1987. "The political policy cycle: Presidential effort vs. presidential control," Public Choice, Springer, vol. 54(3), pages 231-259, August.

    More about this item

    Keywords

    Linear Regression; Forecasting; Election; Microeconomic;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • H8 - Public Economics - - Miscellaneous Issues

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:122319. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.