Forecasting 2012 United States Presidential election using Factor Analysis, Logit and Probit Models

Sinha, Pankaj and Thomas, Ashley Rose and Ranjan, Varun (2012): Forecasting 2012 United States Presidential election using Factor Analysis, Logit and Probit Models.

Abstract

Contemporary discussions on 2012 U.S Presidential election mention that economic variables such as unemployment rate, inflation, budget deficit/surplus, public debt, tax policy and healthcare spending will be deciding elements in the forthcoming November election. Certain researchers like Bartells and Zaller (2001), Lewis-Beck and Rice (1982), and Lichtman and Keilis-Borok (1996) have investigated the significance of non-economic variables in forecasting the U.S election. This paper investigates the influence of combination of various economic and non-economic variables as factors influencing the outcome of 2012 U.S Presidential election, using statistical factor analysis. The obtained factor scores are used to predict the vote share of the incumbent using regression model. The paper also employs logit and probit models to predict the probability of win for the incumbent candidate in 2012 U.S Presidential election. It is found that the factors combining above economic variables are insignificant in deciding the outcome of the 2012 election. The factor combining the non-economic variables such as Gallup Ratings, GIndex, wars and scandals has been found significantly influencing the public perception of the performance of the Government and its policies, which in turn affects the voting decision. The proposed factor regression model forecasts that the Democrat candidate Mr. Barack Obama is likely to get a vote share between 51.84% - 54.26% with 95% confidence interval in the forthcoming November 2012 U.S Presidential election. While, the proposed logit and probit models forecast the probability of win for the Democrat candidate Mr. Barack Obama to be 67.37% and 67.00%, respectively.

• Item Type: MPRA Paper
• Language: English
• Keywords: Factor Analysis, Logit and Probit model, 2012 U.S Presidential Election, Economic and non-economic variables
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling; D - Microeconomics > D7 - Analysis of Collective Decision-Making > D72 - Political Processes: Rent-Seeking, Lobbying, Elections, Legislatures, and Voting Behavior; C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables; C - Mathematical and Quantitative Methods > C0 - General > C01 - Econometrics; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General

References

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

2.Bank of England. (2010). The UK recession in context — what do three centuries of data tell us? retrieved from http://www.bankofengland.co.uk/publications/Documents/quarterlybulletin/threecenturiesofdata.xls.

3.Bartels, L. M. & Zaller, J. (2001). Presidential Vote Models: A Recount. PS: Political Science and Politics, XXXIV (1), 9–23.

4.Bureau of Labor Statistics. (2012a). How the Government Measures Unemployment. Retrieved from http://www.bls.gov/cps/cps_htgm.htm#unemployed.

5.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/.

6.Bureau of Economic Analysis. (2012). Table 3.12. Government Social Benefits. retrieved from http://www.bea.gov/national/index.htm#gdp.

7.Campbell, J. E. (1992). Forecasting the Presidential Vote in the States. American Journal of Political Science, 36 2,386-407.

8.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.

9.Erikson, R. S., and Wlezien, C. (1996). Of time and presidential election forecasts. PS: Political Science and politics, 31, 37-39.

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

11.Fair, R. C. (2012). Vote-Share Equations: November 2010 Update. retrieved from http://fairmodel.econ.yale.edu/vote2012/index2.htm.

12.Federal Reserve. (2012). Historical Data. retrieved from http://www.federalreserve.gov/releases/h15/data.htm.

13.Gallup Presidential Poll. (2012). Presidential Job Approval Center. retrieved from http://www.gallup.com/poll/124922/presidential-approval-center.aspx.

14.Hibbs, Douglas A. (2000). Bread and Peace voting in U.S. presidential elections. Public Choice, 104, 149–180.

15.Hibbs, Douglas A. (2012). Obama’s Re-election Prospects Under ‘Bread and Peace’ Voting in the 2012 US Presidential Election. retrieved from: http://www.douglas-hibbs.com/HibbsArticles/HIBBS_OBAMA-REELECT-31July2012r1.pdf.

16.International Monetary Fund. (2010). Historical Public Debt Database. retrieved from http://www.imf.org/external/pubs/ft/wp/2010/data/wp10245.zip

17.InflationData.com. (2012). Historical Crude Oil Prices (Table). retrieved from http://inflationdata.com/inflation/Inflation_Rate/Historical_Oil_Prices_Table.asp.

18.Jérôme, Bruno & Jérôme -Speziari, Véronique.(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.

19.Keilis-Borok, V. I. & Lichtman, A. J. (1981). Pattern Recognition Applied to Presidential Elections in the United States, 1860-1980: The Role of Integral Social, Economic, and Political Traits. Proceedings of the National Academy of Sciences, 78, 7230−7234.

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

21.Lichtman, A. J. (2005). The Keys to the White House. Lanham, MD: Lexington Books.

22.Lichtman, A. J. (2008). The keys to the white house: An index forecast for 2008. International Journal of Forecasting, 24, 301–309.

23.Office of the Clerk. (2010). Election Statistics. retrieved from http://artandhistory.house.gov/house_history/electionInfo/index.aspx.

24.Sigelman, L., (1979). Presidential popularity and presidential elections. Public Opinion Quarterly, 43, 532-34.

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

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

27.Sinha, P., Sharma, A and Singh, H. (2012). Prediction for the 2012 United States Presidential Election using Multiple Regression Model, The Journal of Prediction Markets, 6 2, 77-97.

28.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.

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

30.United States National Mining Association. (2011). Historical Gold Prices- 1833 to Present. retrieved from http://www.nma.org/pdf/gold/his_gold_prices.pdf.