Under the influence of recent and ongoing major advances in computing technology, applied econometrics is undergoing a quiet revolution. Using simulation techniques practical solutions (classical as well as Bayesian) are beginning to emerge for many difficult and analytically intractable problems. Although many of the basic principles behind simulation techniques are well known, the application of these techniques to econometric problems is less familiar. The subject matter is highly technical and relatively new. There are only few texts that directly deal with the application of simulation techniques. Most available texts are primarily concerned with general concepts and principles of stochastic simulation and do not adequately address the practical issues involved in the application of these techniques to econometric problems. This has been particularly true of the use of simulation techniques in maximum likelihood and generalized method of moment estimation, developed in the pioneering contributions of McFadden (1989) and Pakes and Pollard (1989). Other applications of stochastic simulation techniques to solving nonlinear stochastic intertemporal optimization problems, to computing probability forecasts, to testing non-nested models, and to carrying out Bayesian inference using Gibbs sampling are also scattered in working papers and technical journals and are not readily accessible.

This volume represents a first step towards filling this vacuum. It provides an excellent overview of simulation-based techniques, and collects in one place a number of important contributions covering a variety of fields in applied econometrics.