Forecasting United States Presidential election 2016 using multiple regression models

Sinha, Pankaj and Nagarnaik, Ankit and Raj, Kislay and Suman, Vineeta (2016): Forecasting United States Presidential election 2016 using multiple regression models.

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%.

• Item Type: MPRA Paper
• Language: English
• Keywords: Regression model,US Presidential election,economic and non-economic variables,
• Subjects: C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables; C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes; C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling

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