ISBN: 3540412077
TITLE: Conditional Moment Estimation of Nonlinear Equation Systems
AUTHOR: J. Inkmann
TOC:

1 Introduction 1
Part I: Estimation Theory
2 The Conditional Moment Approach to GMM Estimation 6
2.1 Estimation Principle 6
2.2 Examples 8
2.3 Two-Step Estimators 17
3 Asymptotic Properties of GMM Estimators 20
3.1 Consistency 20
3.2 Asymptotic Distribution 25
4 Computation of GMM Estimators 28
4.1 The Newton-Raphson Method 28
4.2 A Stopping Rule for Initial Estimators 30
5 Asymptotic Efficiency Bounds 36
5.1 Semiparametric Efficiency 36
5.2 Optimal Weights 40
5.3 Optimal Instruments 45
6 Overidentifying Restrictions 55
6.1 Asymptotic Efficiency Gains 55
6.2 Higher Order Moment Conditions 60
6.3 Moments of Compounded Distributions 62
6.4 Complementary Data Sources 63
7 GMM Estimation with Optimal Weights 67
7.1 Iterative Estimators 67
7.2 Small Sample Shortcomings 68
7.3 Lessons from IV Estimation 74
7.4 Application to GMM Estimation 84
7.5 Bootstrapping for GMM Estimators 92
7.6 Empirical Likelihood Approaches 98
8 GMM Estimation with Optimal Instruments 107
8.1 Parametric Two-step Estimation 107
8.2 Series Approximation 112
8.3 K-Nearest Neighbor Estimation 117
8.4 Kernel Estimation 119
8.5 Cross-Validation 121
9 Monte Carlo Investigation 123
9.1 GMM versus Maximum Likelihood Estimation 123
9.2 GMM versus Empirical Likelihood Estimation 144
Part II: Application
10 Theory of Cooperative R&D 153
10.1 Motivation 153
10.2 Intra- and Inter-Industry R&D Cooperation 157
10.3 Extension to Vertically Related Industries 161
10.4 Horizontal and Vertical R&D Cooperation 165
10.5 Empirical Implications of the Model 177
11 Empirical Evidence on Cooperative R&D 179
11.1 Data 179
11.2 Specification 183
11.3 Estimation Results 188
12 Conclusion 198
References 200
END
