Conditional Moment Estimation of Nonlinear Equation Systems :
General Material Designation
[Book]
Other Title Information
With an Application to an Oligopoly Model of Cooperative R & D
First Statement of Responsibility
by Joachim Inkmann.
.PUBLICATION, DISTRIBUTION, ETC
Place of Publication, Distribution, etc.
Berlin, Heidelberg
Name of Publisher, Distributor, etc.
Springer Berlin Heidelberg
Date of Publication, Distribution, etc.
2001
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
(viii, 214 pages)
SERIES
Series Title
Lecture notes in economics and mathematical systems, 497.
CONTENTS NOTE
Text of Note
Introduction --; Estimation Theory: The Conditional Moment Approach to GMM Estimation --; Asymptotic Properties of GMM Estimators --; Computation of GMM Estimators --; Asymptotic Efficiency Bounds --; Overidentifying Restrictions --; GMM Estimation with Optimal Weights --; GMM Estimation with Optimal Instruments --; Monte Carlo Investigation --; Application: Theory of Cooperative R & D --; Empirical Evidence on Cooperative R & D --; Conclusion --; References.
SUMMARY OR ABSTRACT
Text of Note
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.