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

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...

Full description

Saved in:
Bibliographic Details
Published in:Stats (Basel, Switzerland) Switzerland), 2022-12, Vol.5 (4), p.1174-1194
Main Authors: Dayaratna, Kevin, Crosson, Jesse, Hubbard, Chandler
Format: Article
Language:English
Subjects:
Approximation
Bayesian inference
Binomial distribution
Congressional elections
Datasets
Election results
Emprical Bayes
Logistic regression
Markov processes
Methods
Monte Carlo method
Presidential elections
Public opinion
Random variables
Regression analysis
Social sciences
U.S. elections
Voter behavior
Voter turnout
Voters
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2571-905X
2571-905X
DOI:10.3390/stats5040070