We develop a Markov Chain Monte Carlo algorithm for estimating nested logit models in a Bayesian framework. Appropriate "heating target" and reparametrization techniques are adopted for fast mixing. For illustrative purposes, we have implemented the algorithm on two real-life examples involving...

This paper develops semiparametric Bayesian methods for inference in dynamic linear panel data models, and applies them to longitudinal data on labor earnings from the Panel Study of Income Dynamics. We focus on characterizing not only parameters related to conditional means and variances, but...

Although simulation is widely used to select the best of several alternative system designs, and common random numbers is an important tool for reducing the computation effort of simulation experiments, there are surprisingly few tools available to help a simulation practitioner select the best...