Abstract

Econometricians rarely have at their disposal enough information to formulate a model in terms of a parametric family of distributions. Consequently, traditional methods of parametric estimation and inference that are based either on a likelihood or on a posterior distribution are prone to committing specification errors that lead to problems of inference. On the other hand, economic data are partial and incomplete and usually there is seldom a large enough data sample to rely on purely nonparametric methods.