An information theoretic approach to econometrics

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Title:

Author:

Judge, George G.

ISBN:

9780521869591

9780521689731

Personal Author:

Judge, George G.

Publication Information:

Cambridge ; New York : Cambridge University Press, 2012.

Physical Description:

xvi, 232 p. : ill., ; 24 cm.

Contents:

Econometric information recovery -- Part I. Traditional Parametric and Semiparametric Econometric Models: Estimation and Inference -- Formulation and analysis of parametric and semiparametric linear models -- Method of moments, generalized method of moments, and estimating equations -- Part II. Formulation and Solution of Stochastic Inverse Problems -- A stochastic-empirical likelihood inverse problem: formulation and estimation -- A stochastic-empirical likelihood inverse problem: estimation and inference -- Kullback-Leibler information and the maximum empirical exponential likelihood -- Part III. A Family of Minimum Discrepancy Estimators -- The Cressie-Read family of divergence measures and empirical maximum likelihood functions -- Cressie-Read-MEL-type estimators in practice: Monte Carlo evidence of estimation and inference sampling performance -- Part IV. Binary-Discrete Choice Minimum Power Divergence (MPD) Measures -- Family of MPD distribution functions -- Estimation and inference for the binary response model based on the MPD family of distributions -- Part V. Optimal Convex Divergence -- Choosing the optimal divergence under quadratic loss -- Epilogue.

Abstract:

Summary: "This book is intended to provide the reader with a firm conceptual and empirical understanding of basic information-theoretic models and methods. Because most data are observational, practitioners work with indirect noisy observation and ill-posed econometric 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 pwer divergence measure-likelihood functions are introduced for a range of traditional and nontraditional econometric-models 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" -- Provided by publisher.

Subject Term:

Econometrics.

Added Author:

Mittelhammer, Ron.

Copies:

Available:*

Library	Material Type	Item Barcode	Shelf Number	Copy	Status
Searching... 1:IIEMSA	1:GEN-BOOK	33168025559986	330.015195 J92.I 2012	1	Searching... Unknown

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Summary

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.