PREDICTION FOR THE 2012 UNITED STATES PRESIDENTIAL ELECTION USING MULTIPLE REGRESSION MODEL

Pankaj Sinha, Aastha Sharma and Harsh Vardhan Singh

Journal of Prediction Markets, 2012, vol. 6, issue 2, 77-97

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.

JEL-codes: L83 (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (4)

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Working Paper: Prediction for the 2012 United States Presidential Election using Multiple Regression Model (2012)
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