Forecasting 2016 US Presidential Elections Using Factor Analysis and Regression Model

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

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 non-economic 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 ‘anti-incumbency factor that signifies how long the incumbent party has been controlling the White House is found to be an important non-economic 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 non-incumbent 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 non-incumbent 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 re-establishes the notion that the non-economic 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
• Language: English
• Keywords: Factor Analysis, 2016 U.S. Presidential Election, Forecasting, Economic and Non-economic 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 - Time-Series 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

References

Abramowitz A. I. (1988). An Improved Model for Predicting the Outcomes of Presidential Elections. PS: Political Science and Politics, 21 4, 843-847

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/wp-content/uploads/2016/08/FISCAL-MODEL-FORECAST-FOR-2016-AMERICAN-PRESIDENTIAL-ELECTION.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 Re-election Prospects Under ‘Bread and Peace’ Voting in the 2012 US Presidential Election.retrieved from: http://www.douglashibbs.com/HibbsArticles/HIBBS_OBAMA-REELECT-31July2012r1.pdf

Fair, R. C. (1978). The effect of economic events on votes for president. Review of Economics and Statistics, 60, 159-173.

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). Vote-Share Equations: November 2010 Update. retrieved from: http://fairmodel.econ.yale.edu/vote2012/index2.htm

Fair, R.C. (2014). Presidential and Congressional Vote-Share 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/presidential-approval-center.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 1-4 2011.

Katz J.(2016) Who will be President? Retrieved from: http://www.nytimes.com/interactive/2016/upshot/presidential-polls-forecast.html

Lichtman, A. J., and Keilis-Borok, V. I. (1981). “Pattern Recognition Applied to Presidential Elections in the United States, 1860-1980: Role of Integral Social, Economic and Political Traits,” Proceedings of the National Academy of Science, Vol. 78, No. 11, pp. 7230-7234

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.

Lewis-Beck, M. S. & Rice, T. W. (1982).Presidential Popularity and Presidential Vote. The Public Opinion Quarterly, 46 4, 534-537.

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, 532-34.

Silver. N. (2011). On the Maddeningly Inexact Relationship Between Unemployment and Re-Election. retrieved from: http://fivethirtyeight.blogs.nytimes.com/2011/06/02/on-themaddeningly-inexact-relationship-between-unemployment-and-re-election/

Sinha P. and Bansal. A. K. (2008). Hierarchical Bayes Prediction for the 2008 US Presidential Election. The Journal of Prediction Markets, 2, 47-60.

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, 78-98.

Tufte, E. R. (1975). Determinants of the Outcomes of Midterm Congressional Elections. American Political Science Review, 69, 812-26.

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