Advances in Econometrics

The entropy concept was developed and used by Shannon in 1940 as a measure of uncertainty in the context of information theory. In 1957 Jaynes made use of Shannon's entropy concept as a basis for estimation and inference in problems that are ill-suited for traditional statistical procedures. This volume 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
Methodology
The maximum entrophy approach to estimation and inference: an overview
Information theoretic regression methods
The Bayesian method of moments (BMOM): theory and applications
Information theoretic methods for categorical data
Model selection by maximum entrophy
Maximum-entrophy acceptable-likelihood estimation of population heterogeneity
A Monte Carlo study of a generalized maximum entrophy estimator of the binary choice model
Constructing a unimodal Bayesian prior distribution from incompletely assessed information
Recovering wastewater treatment objectives: an application of entrophy estimation for inverse control problems
Dart boards and asset prices: introducing the entrophy pricing theory
Maximum entrophy and derivative securities
Forecasting the production benefits and incidence of a public program: an integrated survey and estimation procedure applied to the California irrigation management information system
Another perspective on recent changes in the U.S. income distribution: an index space representation
Omnibus tests for multivariate normality based on a class of maximum entrophy distributions
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Advances in Econometrics

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