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. 1 Introduction.- I: Estimation Theory.- 2 The Conditional Moment Approach to GMM Estimation.- 3 Asymptotic Properties of GMM Estimators.- 4 Computation of GMM Estimators.- 5 Asymptotic Efficiency Bounds.- 6 Overidentifying Restrictions.- 7 GMM Estimation with Optimal Weights.- 8 GMM Estimation with Optimal Instruments.- 9 Monte Carlo Investigation.- II: Application.- 10 Theory of Cooperative R&D.- 11 Empirical Evidence on Cooperative R&D.- 12 Conclusion.- References.
