Conditional Moment Estimation of Nonlinear Equation Systems

Joachim Inkmann

ISBN 10: 3540412077 / ISBN 13: 9783540412076

Published by Springer Berlin Heidelberg, Springer Berlin Heidelberg Nov 2000, 2000

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -Generalized method of moments (GMM) estimation of nonlinear systems has two important advantages over conventional maximum likelihood (ML) estimation: GMM estimation usually requires less restrictive distributional assumptions and remains computationally attractive when ML estimation becomes burdensome or even impossible. This book presents an in-depth treatment of the conditional moment approach to GMM estimation of models frequently encountered in applied microeconometrics. It covers both large sample and small sample properties of conditional moment estimators and provides an application to empirical industrial organization. With its comprehensive and up-to-date coverage of the subject which includes topics like bootstrapping and empirical likelihood techniques, the book addresses scientists, graduate students and professionals in applied econometrics.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 228 pp. Englisch.

Seller Inventory # 9783540412076

Bibliographic Details

• Title: Conditional Moment Estimation of Nonlinear Equation Systems
• Publisher: Springer Berlin Heidelberg, Springer Berlin Heidelberg Nov 2000
• Publication Date: 2000
• Language: English
• ISBN 10: 3540412077
• ISBN 13: 9783540412076
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 13D millimeters 235 millimeters width by 155 millimeters height by 13 millimeters depth
• Weight: 353 grams

About this title

Synopsis

Generalized method of moments (GMM) estimation of nonlinear systems has two important advantages over conventional maximum likelihood (ML) estimation: GMM estimation usually requires less restrictive distributional assumptions and remains computationally attractive when ML estimation becomes burdensome or even impossible. This book presents an in-depth treatment of the conditional moment approach to GMM estimation of models frequently encountered in applied microeconometrics. It covers both large sample and small sample properties of conditional moment estimators and provides an application to empirical industrial organization. With its comprehensive and up-to-date coverage of the subject which includes topics like bootstrapping and empirical likelihood techniques, the book addresses scientists, graduate students and professionals in applied econometrics.