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Forecasting 2012 United States Presidential election using Factor Analysis, Logit and Probit Models

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  • Sinha, Pankaj
  • Thomas, Ashley Rose
  • Ranjan, Varun

Abstract

Contemporary discussions on 2012 U.S Presidential election mention that economic variables such as unemployment rate, inflation, budget deficit/surplus, public debt, tax policy and healthcare spending will be deciding elements in the forthcoming November election. Certain researchers like Bartells and Zaller (2001), Lewis-Beck and Rice (1982), and Lichtman and Keilis-Borok (1996) have investigated the significance of non-economic variables in forecasting the U.S election. This paper investigates the influence of combination of various economic and non-economic variables as factors influencing the outcome of 2012 U.S Presidential election, using statistical factor analysis. The obtained factor scores are used to predict the vote share of the incumbent using regression model. The paper also employs logit and probit models to predict the probability of win for the incumbent candidate in 2012 U.S Presidential election. It is found that the factors combining above economic variables are insignificant in deciding the outcome of the 2012 election. The factor combining the non-economic variables such as Gallup Ratings, GIndex, wars and scandals has been found significantly influencing the public perception of the performance of the Government and its policies, which in turn affects the voting decision. The proposed factor regression model forecasts that the Democrat candidate Mr. Barack Obama is likely to get a vote share between 51.84% - 54.26% with 95% confidence interval in the forthcoming November 2012 U.S Presidential election. While, the proposed logit and probit models forecast the probability of win for the Democrat candidate Mr. Barack Obama to be 67.37% and 67.00%, respectively.

Suggested Citation

  • 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.
    • Handle: RePEc:pra:mprapa:42062
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    References listed on IDEAS

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    4. Pankaj Sinha & Aastha Sharma & Harsh Vardhan Singh, 2012. "Prediction For The 2012 United States Presidential Election Using Multiple Regression Model," Journal of Prediction Markets, University of Buckingham Press, vol. 6(2), pages 77-97.
    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.
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    7. International Monetary Fund, 2010. "A Historical Public Debt Database," IMF Working Papers 2010/245, International Monetary Fund.
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    Cited by:

    1. 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.

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    More about this item

    Keywords

    Factor Analysis; Logit and Probit model; 2012 U.S Presidential Election; Economic and non-economic variables;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • D72 - Microeconomics - - Analysis of Collective Decision-Making - - - Political Processes: Rent-seeking, Lobbying, Elections, Legislatures, and Voting Behavior
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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