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 … class of models that includes static discrete choice models, the Heckman (1976) selection model, and all of the Heckman …

estimation, (2) computational methods for multinomial probit models, and (3) computational methods for Bayesian stimation. This …

A simulation method based on importance sampling, Gibbs and Metropolis-Hastings techniques allows to approximate the … maximum likelihood estimator in the general framework of dynamic latent variable models. Some examples of this class of models … are factor models, switching regime models, dynamic limited dependent varaible models, stochastic volatility models and …

models. This method is based on the subsampling theory proposed by Politis and Romano (1992, 1994) which computes an …

Many common statistical models can be specified as linear models with restrictions imposed on the parameters. A large … amount of these models impose restrictions which do not allow for the analytical construction of the probability density …