Master of Arts
Holona . Ochs
In 1976, Ray C. Fair derived equations using economic variables in order to accurately predict the outcome of presidential elections. He updated the equations in each succeeding presidential election year – through and including 2016. Academic researchers agree that economic conditions, coupled with incumbency variables are some of the more reliable estimators when predicting presidential election outcomes. While a consensus exists, critiques about both the small sample size and number of economic condition variables within Fair’s model exist.
Through this analysis, I hope to replicate Fair’s equations and use them to accurately predict presidential outcomes. The implications of these results will give insight into just how much these economic variables impact the behavior of voters and if new data points should be considered in the future. However, my ideal end result is not simply replication – I will ultimately compare Fair’s model to logistic modeling techniques to see which is more reliable.
McLaughlin, Michael, "Econometrics & Presidential Elections: A Comparison of Ordinary Least Squares & Logistic Modeling Techniques" (2020). Theses and Dissertations. 5686.
Available for download on Saturday, January 29, 2022