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Prediction for the 2012 United States Presidential Election using Multiple Regression Model

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  • Sinha, Pankaj
  • Sharma, Aastha
  • Singh, Harsh Vardhan

Abstract

This paper investigates the factors responsible for predicting 2012 U.S. Presidential election. Though contemporary discussions on Presidential election mention that unemployment rate will be a deciding factor in this election, it is found that unemployment rate is not significant for predicting the forthcoming Presidential election. Except GDP growth rate, various other economic factors like interest rate, inflation, public debt, change in oil and gold prices, budget deficit/surplus and exchange rate are also not significant for predicting the U.S. Presidential election outcome. Lewis-Beck and Rice (1982) proposed Gallup rating, obtained in June of the election year, as a significant indicator for forecasting the Presidential election. However, the present study finds that even though there exists a relationship between June Gallup rating and incumbent vote share in the Presidential election, the Gallup rating cannot be used as the sole indicator of the Presidential elections. Various other non-economic factors like scandals linked to the incumbent President and the performance of the two parties in the midterm elections are found to be significant. We study the influence of the above economic and non-economic variables on voting behavior in U.S. Presidential elections and develop a suitable regression model for predicting the 2012 U.S. Presidential election. The emergence of new non-economic factors reflects the changing dynamics of U.S. Presidential election outcomes. The proposed model forecasts that the Democrat candidate Mr. Barack Obama is likely to get a vote percentage between 51.818 % - 54.239 %, with 95% confidence interval.

Suggested Citation

  • Sinha, Pankaj & Sharma, Aastha & Singh, Harsh Vardhan, 2012. "Prediction for the 2012 United States Presidential Election using Multiple Regression Model," MPRA Paper 41486, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:41486
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    File URL: https://mpra.ub.uni-muenchen.de/41486/1/MPRA_paper_41486.pdf
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    References listed on IDEAS

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    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. Douglas Hibbs, 2000. "Bread and Peace Voting in U.S. Presidential Elections," Public Choice, Springer, vol. 104(1), pages 149-180, July.
    3. 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.
    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. repec:cup:apsrev:v:69:y:1975:i:03:p:812-826_24 is not listed on IDEAS
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    Cited by:

    1. Sinha, Pankaj & Singhal, Anushree & Sondhi, Kriti, 2012. "Economic scenario of United States of America before and after 2012 U.S. Presidential Election," MPRA Paper 41886, University Library of Munich, Germany.
    2. Sinha, Pankaj & Srinivas, Sandeep & Paul, Anik & Chaudhari, Gunjan, 2016. "Forecasting 2016 US Presidential Elections Using Factor Analysis and Regression Model," MPRA Paper 74618, University Library of Munich, Germany, revised 17 Oct 2016.
    3. 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.
    4. 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.

    More about this item

    Keywords

    USA Presidential election; forecasting; regression; Gallup rating; Congress; Scandal; macroeconomic variable; midterm election;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • D72 - Microeconomics - - Analysis of Collective Decision-Making - - - Political Processes: Rent-seeking, Lobbying, Elections, Legislatures, and Voting Behavior
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables

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