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

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Abstract
The paper analyses economic and noneconomic 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 nonincumbent major opposition party and not for incumbent party. The study also finds that noneconomic 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 nonincumbent 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 nonincumbent Republican party is likely to get vote share of 40.26% with a standard error ±2.35%.
Item Type:  MPRA Paper 

Original Title:  Forecasting United States Presidential election 2016 using multiple regression models 
English Title:  Forecasting United States Presidential election 2016 using multiple regression models 
Language:  English 
Keywords:  Regression model,US Presidential election,economic and noneconomic 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  TimeSeries 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 
Item ID:  74641 
Depositing User:  Pankaj Sinha 
Date Deposited:  18 Oct 2016 18:09 
Last Modified:  26 Sep 2019 16:21 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/74641 