This chapter reviews the recent developments in Markov chain Monte Carlo simulation methods. These methods, which are concerned with the simulation of high dimensional probability distributions, have gained enormous prominence and revolutionized Bayesian statistics. The chapter, provides...

This paper is concerned with statistical inference in multinomial probit, multinomial t and multinomial logit models. New Markov chain Monte Carlo (MCMC) algorithms for fitting these models are introduced and compared with existing MCMC methods. The question of parameter identification in the...

We make a case that characteristics-based long-short factors should be constructed by the slope factor method rather than by sorting methods. This is because sorting does not fully control for the influence of omitted characteristics, rendering them more noisy than slope factors. In contrast,...

In this paper, we develop and compare two alternative approaches for calculating the effect of the actual intake when treatments are randomized, but compliance with the assignment in the treatment arm is less than perfect for reasons that are correlated with the outcome. The approaches are based...