Our goal in this chapter is to explain concretely how to implement simulation methods in a very general class of models that are extremely useful in applied work: dynamic discrete choice models where one has available a panel of multinomial choice histories and partially observed payoffs....

Until recently, inference in many interesting models was precluded by the requirement of high dimensional integration. But dramatic increases in computer speed, and the recent development of new algorithms that permit accurate Monte Carlo evaluation of high dimensional integrals, have greatly...

Until recently, inference in many interesting models was precluded by the requirement of high dimensional integration. But dramatic increases in computer speed, and the recent development of new algorithms that permit accurate Monte Carlo evaluation of high dimensional integrals, have greatly...

This research compares several approaches to inference in the multinomial probit model, based on Monte-Carlo results for a seven choice model. The experiment compares the simulated maximum likelihood estimator using the GHK recursive probability simulator, the method of simulated moments...

This study develops practical methods for Bayesian nonparametric inference in regression models. The emphasis is on extending a nonparametric treatment of the regression function to the full conditional distribution. It applies these methods to the relationship of earnings of men in the United...

In recent years, major advances have taken place in three areas of random utility modeling: (1) semiparametric estimation, (2) computational methods for multinomial probit models, and (3) computational methods for Bayesian stimation. This paper summarizes these developments and discusses their...