An Information Theoretic Approach to Econometrics

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Cambridge University Press, Dec 12, 2011 - Business & Economics
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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.
 

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Contents

One Econometric Information Recovery
1
Estimation and Inference
5
part ii Formulation and Solution of Stochastic Inverse Problems
67
part iii A Family of Minimum Discrepancy Estimators
133
part iv BinaryDiscrete Choice Minimum Power Divergence MPD Measures
167
part v Optimal Convex Divergence
205
Abbreviations
223
Index
227
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About the author (2011)

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.

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