Closed Form Bayesian Inferences for Binary Logistic Regression with Applications to American Voter Turnout

Kevin Dayaratna (), Jesse Crosson and Chandler Hubbard
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Kevin Dayaratna: Center for Data Analysis, The Heritage Foundation, 214 Massachusetts Ave. NE, Washington, DC 20002, USA
Jesse Crosson: Department of Political Science, Purdue University, 100 North University Street, West Lafayette, IN 47907, USA
Chandler Hubbard: Department of Economics, College of Business Department 3985, University of Wyoming, Laramie, WY 82071, USA

Stats, 2022, vol. 5, issue 4, 1-21

Abstract: Understanding the factors that influence voter turnout is a fundamentally important question in public policy and political science research. Bayesian logistic regression models are useful for incorporating individual level heterogeneity to answer these and many other questions. When these questions involve incorporating individual level heterogeneity for large data sets that include many demographic and ethnic subgroups, however, standard Markov Chain Monte Carlo (MCMC) sampling methods to estimate such models can be quite slow and impractical to perform in a reasonable amount of time. We present an innovative closed form Empirical Bayesian approach that is significantly faster than MCMC methods, thus enabling the estimation of voter turnout models that had previously been considered computationally infeasible. Our results shed light on factors impacting voter turnout data in the 2000, 2004, and 2008 presidential elections. We conclude with a discussion of these factors and the associated policy implications. We emphasize, however, that although our application is to the social sciences, our approach is fully generalizable to the myriads of other fields involving statistical models with binary dependent variables and high-dimensional parameter spaces as well.

Keywords: Bayesian inference; Emprical Bayes; U.S. elections; voter turnout (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2022
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