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:  26 Sep 2019 09:04 
References:  Abramowitz A. I. (1988). An Improved Model for Predicting the Outcomes of Presidential Elections. PS: Political Science and Politics, 21 4, 843847 Bureau of Labor Statistics. (2012a). How the Government Measures Unemployment retrieved from: http://www.bls.gov/cps/cps_htgm.htm#unemployed Bureau of Labor Statistics. (2012b). Where can I find the unemployment rate for previous years?retrieved from: http://www.bls.gov/cps/prev_yrs.htm/ Bureau of Economic Analysis. (2012). Table 3.12. Government Social Benefits. retrieved from: http://www.bea.gov/national/index.htm#gdp Cuzán, A. G., Heggen R.J., & Bundrick,C.M. (2000). Fiscal policy, economic conditions, and terms in office: simulating presidential election outcomes. In Proceedings of the World Congress of the Systems Sciences and ISSS International Society for the Systems Sciences, 44th Annual Meeting, July 16–20, Toronto, Canada. Cuzán, A. G. (2016). Fiscal model forecast for the 2016 U.S. presidential election. retrieved from: http://pollyvote.com/wpcontent/uploads/2016/08/FISCALMODELFORECASTFOR2016AMERICANPRESIDENTIALELECTION.pdf Hibbs D. A. (2000). Bread and Peace voting in U.S. presidential elections. Public Choice, 104, 149–180. Hibbs D. A. (2012). Obama’s Reelection Prospects Under ‘Bread and Peace’ Voting in the 2012 US Presidential Election.retrieved from: http://www.douglashibbs.com/HibbsArticles/HIBBS_OBAMAREELECT31July2012r1.pdf Fair, R. C. (1978). The effect of economic events on votes for president. Review of Economics and Statistics, 60, 159173. Fair, R. C. (2002). Predicting Presidential Elections and Other Things. Stanford: Stanford University Press. Fair, R.C. (2006). The Effect of Economic Events on Votes for President: 2004 Update. retrieved from: http://fairmodel.econ.yale.edu/RAYFAIR/PDF/2006CHTM.HTM Fair, R.C. (2008). 2008 Post Mortem. retrieved from: http://fairmodel.econ.yale.edu/vote2008/index2.htm Fair, R. C. (2012). VoteShare Equations: November 2010 Update. retrieved from: http://fairmodel.econ.yale.edu/vote2012/index2.htm Fair, R.C. (2014). Presidential and Congressional VoteShare Equations: November 2014 Update retrieved from: https://fairmodel.econ.yale.edu/vote2016/index2.html Gallup Presidential Poll. (2016). Presidential Job Approval Center. retrieved from: http://www.gallup.com/poll/124922/presidentialapprovalcenter.aspx International Monetary Fund. (2010). Historical Public Debt Database. retrieved from: http://www.imf.org/external/pubs/ft/wp/2010/data/wp10245.zip InflationData.com. (2016). Historical Crude Oil Prices (Table). retrieved from: http://inflationdata.com/inflation/Inflation_Rate/Historical_Oil_Prices_Table.asp Jérôme B. & Jérôme V. (2011). Forecasting the 2012 U.S. Presidential Election: What Can We Learn from a State Level Political Economy Model. In Proceedings of the APSA Annual meeting Seattle, September 14 2011. Katz J.(2016) Who will be President? Retrieved from: http://www.nytimes.com/interactive/2016/upshot/presidentialpollsforecast.html Lichtman, A. J., and KeilisBorok, V. I. (1981). “Pattern Recognition Applied to Presidential Elections in the United States, 18601980: Role of Integral Social, Economic and Political Traits,” Proceedings of the National Academy of Science, Vol. 78, No. 11, pp. 72307234 Lichtman, A. J. (2005). The Keys to the White House. Lanham, MD: Lexington Books. Lichtman, A. J. (2008). The keys to the white house: An index forecast for 2008. International Journal of Forecasting, 24, 301–309. LewisBeck, M. S. & Rice, T. W. (1982).Presidential Popularity and Presidential Vote. The Public Opinion Quarterly, 46 4, 534537. Office of the Clerk. (2010). Election Statistics. retrieved from: http://artandhistory.house.gov/house_history/electionInfo/index.aspx Sigelman, L. (1979). Presidential popularity and presidential elections. Public Opinion Quarterly, 43, 53234. Silver. N. (2011). On the Maddeningly Inexact Relationship Between Unemployment and ReElection. retrieved from: http://fivethirtyeight.blogs.nytimes.com/2011/06/02/onthemaddeninglyinexactrelationshipbetweenunemploymentandreelection/ Sinha P. and Bansal. A. K. (2008). Hierarchical Bayes Prediction for the 2008 US Presidential Election. The Journal of Prediction Markets, 2, 4760. Sinha P., Sharma A. & Singh H. (2012). Prediction for the 2012 United States Presidential election using multiple regression model, The Journal of Prediction Markets, 6 2, 7898. Tufte, E. R. (1975). Determinants of the Outcomes of Midterm Congressional Elections. American Political Science Review, 69, 81226. The White House. (2012). Table 1.2—Summary of Receipts, Outlays, And Surpluses Or Deficits (–) As Percentages Of GDP: 1930–2017. retrieved from: http://www.whitehouse.gov/sites/default/files/omb/budget/fy2013/assets/hist.pdf United States National Mining Association. (2011). Historical Gold Prices 1833 to Present. Retrieved from: http://www.nma.org/pdf/gold/his_gold_prices.pdf Weingert & Sebastian(2015). Predicting primary election results through network analysis of donor relationships retrieved from: https://web.stanford.edu/class/cs224w/projects_2015/Predicting_primary_election_results_through_network_analysis_of_donor_relationships.pdf 
URI:  https://mpra.ub.unimuenchen.de/id/eprint/74618 