Conditional Moment Estimation of Nonlinear Equation Systems: With an Application to an Oligopoly Model of Cooperative R&D (Lecture Notes in Economics and Mathematical Systems Book 497)

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. Front Matter....Pages I-VIII Introduction....Pages 1-5 The Conditional Moment Approach to GMM Estimation....Pages 6-19 Asymptotic Properties of GMM Estimators....Pages 20-27 Computation of GMM Estimators....Pages 28-35 Asymptotic Efficiency Bounds....Pages 36-54 Overidentifying Restrictions....Pages 55-66 GMM Estimation with Optimal Weights....Pages 67-106 GMM Estimation with Optimal Instruments....Pages 107-122 Monte Carlo Investigation....Pages 123-152 Theory of Cooperative R&D....Pages 153-178 Empirical Evidence on Cooperative R&D....Pages 179-197 Conclusion....Pages 198-199 Back Matter....Pages 200-216 Joachim Inkmann. Originally Presented As The Author's Thesis (doctoral)--university Of Konstanz, 2000. Includes Bibliographical References (p. [200]-214). Let Z be a random vector which includes both endogenous and explanatory variables.