Introduction / William Greene -- MCMC perspectives on simulated likelihood estimation / Ivan Jeliazkov and Esther Hee Lee -- The panel probit model : adaptive integration on sparse grids / Florian Heiss -- A comparison of the maximum simulated likelihood and composite marginal likelihood estimation approaches in the context of the multivariate ordered response model / Chandra R. Bhat, Cristiano Varin, Nazneen Ferdous -- Pretest estimation in the random parameters logit model / Tong Zeng and R. Carter Hill -- Simulated maximum likelihood estimation of continuous time stochastic volatility models / Tore Selland Kleppe, Jun Yu, Hans J. Skaug -- Education savings accounts, parent contributions, and education attainment / Michael D. S. Morris -- Estimating the effect of exchange rate flexibility on financial account openness / Raul Razo-Garcia -- estimating a fractional response model with a count endogenous regressor and an application to female labor supply / Hoa B. Nguyen -- Alternative random effects panel gamma SML estimation with heterogeneity in random and one-sided error / Saleem Shaik and Ashok K. Mishra -- Modelling and forecasting volatility in a Bayesian approach / Esmail Amiri.

The economics and statistics literature using computer simulation based methods has grown enormously over the past decades. Maximum Simulated Likelihood is a statistical tool useful for incorporating individual differences (called heterogeneity in the econometrics literature) and variations into a statistical analysis. Problems that can be intractable with traditional methods are solved using computer simulation integrated with classical methods. Instead of assuming that everyone responds to stimuli in the same way, allowances are made for the possibility that different decision makers will respond in different ways. The techniques can be applied to problems of individual choice, such as the choice of a transportation model, or choice among health care options, as well as to the problem of making financial and macroeconomic predictions. Contributors to the volume discuss alternative simulation methods that permit faster and more accurate inference, as well as applications of established methods.