An information theoretic approach to econometrics / George G. Judge, Ron C. Mittelhammer.

Includes bibliographical references and index.

Print version record.

880-01 Econometric Information Recovery -- 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 -- 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 -- A FAMILY OF MINIMUM DISCREPANCY ESTIMATORS -- The Cressie-Read Family of Divergence Measures and Empirical Maximum Likelihood Functions -- Cressie-Read-MPD-Type Estimators in Practice: Monte Carlo Evidence of Estimation and Inference Sampling Performance -- BINARY-DISCRETE CHOICE MINIMUM POWER DIVERGENCE (MPD) MEASURES -- Family of MPD Distribution Functions for the Binary Response-Choice Model -- Estimation and Inference for the Binary Response Model Based on the MPD Family of Distributions -- OPTIMAL CONVEX DIVERGENCE -- Choosing the Optimal Divergence under Quadratic Loss -- Epilogue.

"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.

English.

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