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Modeling and forecasting US presidential election 2024

Sinha, Pankaj and verma, Kaushal and Biswas, Sumana and Tyagi, Shashank and Gogia, Shaily and Singh, Aakhyat and Kumar, Amit (2024): Modeling and forecasting US presidential election 2024.

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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.

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