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Prediction for the 2020 United States Presidential Election using Machine Learning Algorithm: Lasso Regression

Author

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  • Sinha, Pankaj
  • Verma, Aniket
  • Shah, Purav
  • Singh, Jahnavi
  • Panwar, Utkarsh

Abstract

This paper aims at determining the various economic and non-economic factors that can influence the voting behaviour in the forthcoming United States Presidential Election using Lasso regression, a Machine learning algorithm. Even though contemporary discussions on the subject of the United States Presidential Election suggest that the level of unemployment in the economy will be a significant factor in determining the result of the election, in our study, it has been found that the rate of unemployment will not be the only significant factor in forecasting the election. However, various other economic factors such as the inflation rate, rate of economic growth, and exchange rates will not have a significant influence on the election result. The June Gallup Rating, is not the only significant factor for determining the result of the forthcoming presidential election. In addition to the June Gallup Rating, various other non-economic factors such as the performance of the contesting political parties in the midterm elections, Campaign spending by the contesting parties and scandals of the Incumbent President will also play a significant role in determining the result of the forthcoming United States Presidential Election. The paper explores the influence of all the aforementioned economic and non-economic factors on the voting behaviour of the voters in the forthcoming United States Presidential Election. The proposed Lasso Regression model forecasts that the vote share for the incumbent Republican Party to be 41.63% in the 2020 US presidential election. This means that the incumbent party is most likely to lose the upcoming election.

Suggested Citation

  • 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.
    • Handle: RePEc:pra:mprapa:103889
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    References listed on IDEAS

    as
    1. Mueller, John E., 1970. "Presidential Popularity from Truman to Johnson1," American Political Science Review, Cambridge University Press, vol. 64(1), pages 18-34, March.
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    5. 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.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    US Presidential Election; Machine Learning; Lasso Regression; Economic Factors; None Economic Factor; Forecasting; Prediction;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

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