9780521869591 - An Information Theoretic Approach to Econometrics by Judge, George G ; Mittelhammer, Ron C (13 results)

Condition: New. In.

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.

Condition: New. pp. 248.

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.

Hardcover. Condition: Brand New. 232 pages. 9.25x6.14x0.83 inches. In Stock.

Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Intended to provide the reader with a firm conceptual and empirical understanding of basic information-theoretic econometric models and methods.

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.

Condition: New.

Hardcover. Condition: Brand New. 232 pages. 9.25x6.14x0.83 inches. In Stock. This item is printed on demand.

Hardback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 490.

Condition: New. Print on Demand pp. 248 13 Illus.

Condition: New. PRINT ON DEMAND pp. 248.

Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 .