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Prediction for the 2020 United States Presidential Election using Linear Regression Model

Author

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

Abstract

The paper identifies various crucial factors, economic and non-economic, essential for predicting the 2020 United States presidential election results. Although it has been suggested by the contemporary discussions on the subject of United States presidential election that inflation rate, unemployment rate, and other such economic factors will play an important role in determining who will win the forthcoming United States Presidential Elections in November, it has been found in this study that, non-economic variables have a significant influence on the voting behaviour. Various non-economic factors like the performance of the contesting political parties in the midterm elections, the June Gallup Rating for the incumbent President, Average Gallup rating during the tenure of the incumbent President, Gallup Index, and Scandals of the Incumbent President were found to have a massive impact on the election outcomes. In the research conducted by Lewis-Beck and Rice (1982) , it was proposed that the Gallup rating for the Incumbent President, obtained in the month of June of the election year, is a significant factor in determining the results of the Presidential Elections. The major reason behind obtaining the Gallup Rating in June of the election year, post-primaries and pre-conventions, is that it is a relative political calm period. However, it has been found in this study that despite the existence of a relationship between the vote share of the incumbent President and his Gallup rating for June, the said Gallup rating cannot be used as the only factor for forecasting the results of the Presidential Election. The influence of all the aforementioned economic and non-economic factors and some other factors on the voter's voting behavior in the forthcoming United States Presidential Election is analyzed in this paper. The proposed regression model in the paper forecasts that Republican party candidate Donald Trump would receive a vote share of 46.74 ± 2.638%.

Suggested Citation

  • 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.
    • Handle: RePEc:pra:mprapa:103890
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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.
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    4. 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.
    5. Tufte, Edward R., 1975. "Determinants of the Outcomes of Midterm Congressional Elections," American Political Science Review, Cambridge University Press, vol. 69(3), pages 812-826, September.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    United States Presidential Election; Economic Factor; Regression Model; 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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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