George G. Judge

Information Theoretic Approach to Econometrics (eBook, PDF)

• Format: PDF

Produktbeschreibung

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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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 12. Dezember 2011
• Englisch
• ISBN-13: 9781139210928
• Artikelnr.: 38276999

Autorenporträt

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.

Inhaltsangabe

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.

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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.

Rezensionen

'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