Three Essays on Bayesian Nonparametric Modeling in Microeconometrics

This dissertation is comprised of three essays on nonparametric Bayesian modeling in microeconometrics. The introduction discusses some basic concepts of Bayesian nonparametrics including the Dirichlet process and the mixture of Dirichlet processes model. Further, the literature on estimating the demand for health care using count data and the literature on treatment effect models is summarized.The first essay specifies a Bayesian nonparametric random effects model for count data. This model is based on a mixture distribution with a random number of components, and is therefore a natural extension of prevailing latent class models. We propose a Markov chain Monte Carlo (MCMC) algorithm for posterior inference and apply the framework to data from Germany.The second essay is also concerned with count data but focuses on the estimation of causal treatment effects. A potential outcomes model is specified in a nonparametric Bayesian fashion using a mixture of Dirichlet processes. Posterior inference is done using MCMC simulation methods. We illustrate the proposed techniques with a real data set concerning the mobility of households.The third essay analyzes distributional treatment effects. The model is based on the potential outcomes framework and focuses on the estimation of quantile treatment effects. Flexibility is achieved by including random intercepts in the outcomes equations. These random intercepts are assumed to be drawn from Dirichlet processes. We apply the model to real data using MCMC techniques for posterior simulation.