Sinha, Pankaj and Srinivas, Sandeep and Paul, Anik and Chaudhari, Gunjan (2016): Forecasting 2016 US Presidential Elections Using Factor Analysis and Regression Model.

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Abstract
The paper categorizes factors responsible for forecasting the outcome of U.S. presidential election 2016 using factor analysis, which groups the various economic and noneconomic parameters based on the correlation among them. The major economic factor significant in 2016 US presidential election is the growth of the economy, and the ‘antiincumbency factor that signifies how long the incumbent party has been controlling the White House is found to be an important noneconomic factor likely to play a dominant role in the election.
The dependent variables considered are the vote shares of the nominees of the incumbent and the nonincumbent majority party candidates. The forecast is calculated by running a regression of the significant factors, obtained through factor analysis technique, on the incumbent party vote share as well as on the nonincumbent party vote share.
The proposed models forecast the vote share of Democrat candidate Mrs. Hillary Clinton to be 45.59% with a standard error of ±2.32% and that of Republican candidate Mr. Donald Trump to be 39.51% with a standard error of ±3.87%. Hence, the models built in the paper signal a comfortable margin of victory for the Presidential nominee of the incumbent party, Hillary Clinton. The study reestablishes the notion that the noneconomic factors have a greater influence on the outcomes of election as compared to the economic factors, as some of the important economic factors such as inflation and unemployment rate failed to establish their significance.
Item Type:  MPRA Paper 

Original Title:  Forecasting 2016 US Presidential Elections Using Factor Analysis and Regression Model 
English Title:  Forecasting 2016 US Presidential Elections Using Factor Analysis and Regression Model 
Language:  English 
Keywords:  Factor Analysis, 2016 U.S. Presidential Election, Forecasting, Economic and Noneconomic variables 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C13  Estimation: General C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C18  Methodological Issues: General C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C19  Other C  Mathematical and Quantitative Methods > C3  Multiple or Simultaneous Equation Models ; Multiple Variables C  Mathematical and Quantitative Methods > C3  Multiple or Simultaneous Equation Models ; Multiple Variables > C32  TimeSeries Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models C  Mathematical and Quantitative Methods > C4  Econometric and Statistical Methods: Special Topics C  Mathematical and Quantitative Methods > C4  Econometric and Statistical Methods: Special Topics > C40  General 
Item ID:  74618 
Depositing User:  Pankaj Sinha 
Date Deposited:  18 Oct 2016 11:40 
Last Modified:  18 Oct 2016 11:41 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/74618 