The paper evaluates the usefulness of a nonparametric approach to Bayesian inferece by presenting two applications. Our first application considers an educational choice problem. The second application is to quantile regression.

In this paper, we explore Bayesian inference in models with many instrumental variables that are potentially weakly correlated with the endogenous regressor. The prior distribution has a hierarchical (nested) structure.