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 …

We propose a simulated maximum likelihood estimator for dynamic models based on non-parametric kernel methods. Our … method is designed for models without latent dynamics from which one can simulate observations but cannot obtain a closed … dynamic models that this nonparametric simulated maximum likelihood (NPSML) estimator is consistent and asymptotically …

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