Introduction

Abstract

The generalized method of moments (GMM) estimation principle compares favorably to alternative methods in numerous estimation problems frequently encountered in applied econometric work. Compared to full information maximum likelihood (ML) estimation the GMM approach requires less restrictive distributional assumptions to obtain a consistent and asymptotically normal distributed estimator of the unknown parameters of interest as shown in the seminal paper by Hansen (1982). In the most simple case only the population mean of some data dependent function has to be specified while the ML principle requires a specification of the complete distribution function. Therefore GMM estimators are usually more robust against distributional misspecification than ML estimators. In addition, GMM estimation of complicated econometric models usually remains computationally attractive when ML estimation by means of conventional numerical computation algorithms becomes burdensome or even impossible. Both properties of the GMM estimator are of major importance when the econometric model consists of multiple estimating equations which are nonlinear in the parameters to be estimated. GMM estimation of such nonlinear equation systems is the main topic of this monograph.KeywordsEmpirical LikelihoodConditional MomentAsymptotic EfficiencyCooperation ScenarioNonlinear Equation SystemThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.