In this paper we consider Bayesian estimation of restricted conditional moment models with the linear regression as a particular example. A common practice in the Bayesian literature for linear regression and other semi-parametric models is to use flexible families of distributions for the...

We aim at modelling fat-tailed densities whose distributions are unknown but are potentially asymmetric. In this context, the standard normality assumption is not appropriate.In order to make as few distributional assumptions as possible, we use a non-parametric algorithm to model the center of...

This article predicts the relative performance of hedge fund investment styles using time-varying conditional stochastic dominance tests. These tests allow for the construction of dynamic trading strategies based on nonparametric density forecasts of hedge fund returns. During the recent...

In this paper, we study a Bayesian approach to flexible modeling of conditional distributions. The approach uses a flexible model for the joint distribution of the dependent and independent variables and then extracts the conditional distributions of interest from the estimated joint...

We discuss a number of issues in the smoothed nonparametric estimation of kernel conditional probability density functions for stationary processes. The kernel conditional density estimate is a ratio of joint and marginal density estimates. We point out the different implications of leading...

We discuss a number of issues in the smoothed nonparametric estimation of kernel conditional probability density functions for stationary processes. The kernel conditional density estimate is a ratio of joint and marginal density estimates. We point out the different implications of leading...