This volume of Advances in Econometrics looks at applying maximum entropy to econometric problems and consists of two sections: the first section contains papers developing econometric methods based on the entropy principle; an interesting array of applications is presented in the second section of the volume.

Introduction (T.B. Fomby, R. Carter Hill). Section I. Methodology. The maximum entrophy approach to estimation and inference: an overview (A. Golan, G. Judge and D. Miller). Information theoretic regression methods (E. Soofi). The Bayesian method of moments (BMOM): theory and applications (A. Zellner). Information theoretic methods for categorical data (E. Soofi, D.V. Gokhale). Model selection by maximum entrophy (P.H.F.M. van Casteren, J.G. De Gooijer). Maximum-entrophy acceptable-likelihood estimation of population heterogeneity (P.S. Faynzilberg). A Monte Carlo study of a generalized maximum entrophy estimator of the binary choice model (L. Atkins). Constructing a unimodal Bayesian prior distribution from incompletely assessed information (P.L. Brockett, L.L. Golden and K.H. Paick). Recovering wastewater treatment objectives: an application of entrophy estimation for inverse control problems (L. Fernandez). Dart boards and asset prices: introducing the entrophy pricing theory (L. Gulko). Maximum entrophy and derivative securities (R.J. Hawkins). Forecasting the production benefits and incidence of a public program: an integrated survey and estimation procedure applied to the California irrigation management information system (D. Osgood et al.). Another perspective on recent changes in the U.S. income distribution: an index space representation (H. Ryu, D. Slottje). Omnibus tests for multivariate normality based on a class of maximum entrophy distributions (C. Urzua).