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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
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    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. 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.
    3. 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.
    Full references (including those not matched with items on IDEAS)

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    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

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