An Information Theoretic Approach to Econometrics by George G. Judge and Ron C. Mittelhammer
Published By: Cambridge and New York: Cambridge University Press ISBN: 978-0-521-86959-1, cloth; 978-0-521-68973-1, pbk. Date of Publication: 2012
Book Review Detail
Byoung Gun Park of University at Albany, State University of New York
Review DOI: 10.1257/jel.51.3.883.r3 Review Pages: 886-88
Book Review Abstract
Byoung Gun Park of University at Albany, State University of New York reviews, "An Information Theoretic Approach to Econometrics" by George G. Judge and Ron C. Mittelhammer. The Econlit abstract of this book begins: "Provides a conceptual and empirical understanding of basic information theoretic econometric models and methods. Discusses formulation and analysis of parametric and semiparametric linear models; method of moments, generalized method of moments, and estimating equations; a stochastic-empirical likelihood inverse problemï¿½formulation and estimation; a stochastic empirical likelihood inverse problemï¿½estimation and inference; Kullback-Leibler information and the maximum empirical exponential likelihood; the Cressie-Read family of divergence measures and empirical maximum likelihood functions; Cressie-Read minimum power divergence (MPD) type estimators in practiceï¿½Monte Carlo evidence of estimation and inference sampling performance; family of MPD distribution functions for the binary response-choice model; estimation and inference for the binary response model based on the MPD family of distributions; and choosing the optimal divergence under quadratic loss. Judge is a professor at the University of California, Berkeley. Mittelhammer is Regents Professor of Economic Sciences and Statistics at Washington State University."