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 many other questions. When questions involve large data sets include demographic ethnic subgroups, however, standard Markov Chain Monte Carlo (MCMC) sampling methods estimate such can be quite slow impractical perform reasonable amount of time. We present an innovative closed form Empirical approach significantly faster than MCMC methods, thus enabling estimation had previously been considered computationally infeasible. Our results shed light on impacting 2000, 2004, 2008 presidential elections. conclude with discussion associated implications. emphasize, although our application social sciences, fully generalizable myriads fields involving statistical binary dependent variables high-dimensional parameter spaces as well.