Conditional Moment Estimation of Nonlinear Equation Systems

Name: Conditional Moment Estimation of Nonlinear Equation Systems
Author: Joachim Inkmann

With an Application to an Oligopoly Model of Cooperative R&D

Paperback Engels 2000 9783540412076

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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.

Specificaties

ISBN13:9783540412076

Taal:Engels

Bindwijze:paperback

Aantal pagina's:214

Uitgever:Springer Berlin Heidelberg

Hoofdrubriek:Economie, Economie

Serie:Lecture Notes in Economics and Mathematical Systems

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Inhoudsopgave

1 Introduction.- I: Estimation Theory.- 2 The Conditional Moment Approach to GMM Estimation.- 2.1 Estimation Principle.- 2.2 Examples.- 2.3 Two-Step Estimators.- 3 Asymptotic Properties of GMM Estimators.- 3.1 Consistency.- 3.2 Asymptotic Distribution.- 4 Computation of GMM Estimators.- 4.1 The Newton-Raphson Method.- 4.2 A Stopping Rule for Initial Estimators.- 5 Asymptotic Efficiency Bounds.- 5.1 Semiparametric Efficiency.- 5.2 Optimal Weights.- 5.3 Optimal Instruments.- 6 Overidentifying Restrictions.- 6.1 Asymptotic Efficiency Gains.- 6.2 Higher Order Moment Conditions.- 6.3 Moments of Compounded Distributions.- 6.4 Complementary Data Sources.- 7 GMM Estimation with Optimal Weights.- 7.1 Iterative Estimators.- 7.2 Small Sample Shortcomings.- 7.3 Lessons from IV Estimation.- 7.4 Application to GMM Estimation.- 7.5 Bootstrapping for GMM Estimators.- 7.6 Empirical Likelihood Approaches.- 8 GMM Estimation with Optimal Instruments.- 8.1 Parametric Two-step Estimation.- 8.2 Series Approximation.- 8.3 K-Nearest Neighbor Estimation.- 8.4 Kernel Estimation.- 8.5 Cross-Validation.- 9 Monte Carlo Investigation.- 9.1 GMM versus Maximum Likelihood Estimation.- 9.2 GMM versus Empirical Likelihood Estimation.- II: Application.- 10 Theory of Cooperative R&D.- 10.1 Motivation.- 10.2 Intra- and Inter-Industry R&D Cooperation.- 10.3 Extension to Vertically Related Industries.- 10.4 Horizontal and Vertical R&D Cooperation.- 10.5 Empirical Implications of the Model.- 11 Empirical Evidence on Cooperative R&D.- 11.1 Data.- 11.2 Specification.- 11.3 Estimation Results.- 12 Conclusion.- References.