An Information Theoretic Approach to Econometrics

George G. Judge is a Professor at the University of California, Berkeley. Professor Judge has also served on the faculty of the University of Illinois, University of Connecticut, and Oklahoma State University and has been a visiting professor at several US and European universities. He is the coauthor or editor of 15 books in econometrics and related fields and author or coauthor of more than 150 articles in refereed journals. His research explores specification and evaluation of statistical decision rules, improved inference methods, and parametric and semiparametric estimation and information recovery in the case of ill-posed inverse problems with noise. Judge is a Fellow of the Econometric Society and the American Agricultural Economics Association. Ron C. Mittelhammer is Regents Professor of Economic Sciences and Statistics at Washington State University. He is the author of Mathematical Statistics for Economics and Business (1996), lead coauthor with George G. Judge and Douglas J. Miller of Econometric Foundations (Cambridge University Press, 2000), and the author of numerous book chapters and articles in refereed economics, statistics, and econometrics journals. Professor Mittelhammer's current research focuses on econometric theory for applications in a range of economics fields. With more than two decades of graduate-level teaching experience, his skill as a teacher of statistics and econometrics is documented by teaching evaluations and awards. He served as president of the Agricultural and Applied Economics Association in 2009–10.

Preface; 1. Econometric information recovery; Part I. Traditional Parametric and Semiparametric Probability Models: Estimation and Inference: 2. Formulation and analysis of parametric and semiparametric linear models; 3. Method of moments, GMM, and estimating equations; Part II. Formulation and Solution of Stochastic Inverse Problems: 4. A stochastic-empirical likelihood inverse problem: formulation and estimation; 5. A stochastic-empirical likelihood inverse problem: inference; 6. Kullback-Leibler information and the maximum empirical exponential likelihood; Part III. A Family of Minimum Discrepancy Estimators: 7. The Cressie-Read family of divergence measures and likelihood functions; 8. Cressie-Read-MEL-type estimators in practice: evidence of estimation and inference sampling performance; Part IV. Binary Discrete Choice MPD-EML Econometric Models: 9. Family of distribution functions for the binary response-choice model; 10. Estimation and inference for the binary response model based on the MPD family of distributions; Part V. Optimal Convex Divergence: 11. Choosing the optimal divergence under quadratic loss; 12. Epilogue.