An Information Theoretic Approach to Econometrics
1st Edition
Published on 6. February 2012
248 pages
978-1-139-21092-8 (ISBN)
Description
This book is intended to provide the reader with a firm conceptual and empirical understanding of basic information-theoretic econometric models and methods. Because most data are observational, practitioners work with indirect noisy observations and ill-posed econometric models in the form of stochastic inverse problems. Consequently, traditional econometric methods in many cases are not applicable for answering many of the quantitative questions that analysts wish to ask. After initial chapters deal with parametric and semiparametric linear probability models, the focus turns to solving nonparametric stochastic inverse problems. In succeeding chapters, a family of power divergence measure-likelihood functions are introduced for a range of traditional and nontraditional econometric-model problems. Finally, within either an empirical maximum likelihood or loss context, Ron C. Mittelhammer and George G. Judge suggest a basis for choosing a member of the divergence family.
Reviews / Votes
'Taking us beyond traditional econometric estimation and inference, this landmark text leads us carefully through the modern literature on empirical likelihood methods to an extremely compelling new methodology. The authors present a clear and compelling case for recognizing econometric problems for what they really are - namely, ill-posed noisy inverse problems. By showing how very general information-theoretic methods can be used in a natural way to solve such problems, Judge and Mittelhammer break new ground and set a new standard for the econometric community.' David Giles, University of Victoria, Canada 'This beautifully written book pushes the frontiers of econometrics in three ways. First, it provides a clear connection between the more traditional econometric and information-theoretic estimation methods. Second, it provides a detailed state of the art presentation of information-theoretic methods within econometrics. Third, it illustrates the wide applicability of information-theoretic methods for learning from data. The book is a great resource for graduate students and researchers at all levels. The authors - two of the leading econometricians and experts in information-theoretic methods - are to be congratulated.' Amos Golan, Info-Metrics Institute, American University 'Judge and Mittelhammer masterfully demonstrate the power of the information divergence-based approach to econometric information recovery, and expand it in new directions. Clarity of exposition, typical for the authors, makes this research monograph highly accessible to both researchers and students of econometrics.' Marian Grendar, Matej Bel University, Slovakia 'This book, written by two outstanding experts in the field, will be a valuable resource for empirical and theoretical researchers interested in the topic of information-theoretic econometric methods.' Patrik Guggenberger, University of California, San Diego 'This is a definitive book: Judge and Mittelhammer are legendary architects of modern information econometrics. It provides both a practical exposition to the subject matter, as well as an extremely thorough presentation of the underlying statistical analysis of information theory and empirical likelihood applied to econometrics models. I highly recommend this book, which will be widely read, referenced, and used by students, instructors and researchers in the areas of econometrics, economics, statistics and other applied sciences.' Aman Ullah, University of California, RiversideMore details
Language
English
Place of publication
Cambridge
United Kingdom
Publishing group
Cambridge University Press
Target group
Professional and scholarly
Illustrations
7 Tables, unspecified; 13 Line drawings, unspecified
File size
2181,54 MB
ISBN-13
978-1-139-21092-8 (9781139210928)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
George G. Judge | Ron C. Mittelhammer
An Information Theoretic Approach to Econometrics
Book
12/2011
Cambridge University Press
€99.00
Shipment within 15-20 days
George G. Judge | Ron C. Mittelhammer
An Information Theoretic Approach to Econometrics
Book
12/2011
Cambridge University Press
€40.50
Shipment within 15-20 days
Persons
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.
Content
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.
