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Forecasting United States Presidential election 2016 using multiple regression models

Author

Listed:
  • Sinha, Pankaj
  • Nagarnaik, Ankit
  • Raj, Kislay
  • Suman, Vineeta
Abstract
The paper analyses economic and non-economic factors in order to develop a forecasting model for 2016 US Presidential election and predict it. The discussions on forthcoming US Presidential election mention that campaign fund amount and unemployment will be a deciding factor in the election, but our research indicates that campaign fund amount and unemployment are not significant factors for predicting the vote share of the incumbent party. But in case of non–incumbent major opposition party (challenger party) campaign fund amount does play a role. Apart from unemployment other economic factors such as inflation, exchange rate, interest rate, deficit/surplus, gold prices are also found to be insignificant. Growth of economy is found to be significant factor for non-incumbent major opposition party and not for incumbent party. The study also finds that non-economic factors such as June Gallup rating, Gallup index, average Gallup, power of period factor, military intervention, president running, percentage of white voters and youth voters voting for the party are significant factors for forecasting the vote share of either incumbent party or non-incumbent major opposition party/challenger party. The proposed models forecasts with 95% confidence interval that Democratic party is likely to get vote share of 48.11% with a standard error of ±2.18% and the non-incumbent Republican party is likely to get vote share of 40.26% with a standard error ±2.35%.

Suggested Citation

  • Sinha, Pankaj & Nagarnaik, Ankit & Raj, Kislay & Suman, Vineeta, 2016. "Forecasting United States Presidential election 2016 using multiple regression models," MPRA Paper 74641, University Library of Munich, Germany, revised 17 Oct 2016.
    • Handle: RePEc:pra:mprapa:74641
    as

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    References listed on IDEAS

    as
    1. N/A, 2012. "Appendix B: Forecast Detail," National Institute Economic Review, National Institute of Economic and Social Research, vol. 220(1), pages 31-37, April.
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    4. Härdle, Wolfgang Karl & López-Cabrera, Brenda & Ritter, Matthias, 2012. "Forecast based pricing of weather derivatives," SFB 649 Discussion Papers 2012-027, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
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    6. N/A, 2012. "Appendix B: Forecast detail," National Institute Economic Review, National Institute of Economic and Social Research, vol. 222(1), pages 34-40, October.
    7. N/A, 2012. "Appendix A: Summary of Key Forecast Assumptions," National Institute Economic Review, National Institute of Economic and Social Research, vol. 220(1), pages 26-30, April.
    8. 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.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Regression model; US Presidential election; economic and non-economic variables;
    All these keywords.

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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