Items related to An Information Theoretic Approach to Econometrics
Synopsis
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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Book Description
Most econometric books do not recognize the ill-posed inverse nature of their econometric models and the indirect noisy characteristics of their sample data. This book focuses on these problems and provides a basis for dealing with estimation and inference issues that typically arise in a range of traditional and nontraditional econometric models.
About the Author
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.
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- PublisherCambridge University Press
- Publication date2011
- ISBN 10 0521869595
- ISBN 13 9780521869591
- BindingHardcover
- LanguageEnglish
- Number of pages248
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An Information Theoretic Approach to Econometrics
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Hardcover. Condition: new. Hardcover. 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. Most econometric books do not recognize the ill-posed inverse nature of their econometric models and the indirect noisy characteristics of their sample data. This book focuses on these problems and provides a basis for dealing with estimation and inference issues that typically arise in a range of traditional and nontraditional econometric models. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9780521869591
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Hardcover. Condition: new. Hardcover. 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. Most econometric books do not recognize the ill-posed inverse nature of their econometric models and the indirect noisy characteristics of their sample data. This book focuses on these problems and provides a basis for dealing with estimation and inference issues that typically arise in a range of traditional and nontraditional econometric models. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Seller Inventory # 9780521869591
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Information Theoretic Approach to Econometrics (Hardcover)
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ISBN 10: 0521869595
ISBN 13: 9780521869591
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Hardcover. Condition: new. Hardcover. 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. Most econometric books do not recognize the ill-posed inverse nature of their econometric models and the indirect noisy characteristics of their sample data. This book focuses on these problems and provides a basis for dealing with estimation and inference issues that typically arise in a range of traditional and nontraditional econometric models. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9780521869591
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