Consistent Moment Selection Procedures For Generalized Method Of Moments Estimation

GMM provides a computationally convenient estimation method and the resulting estimator can be shown to be consistent and asymptotically normal under the fairly moderate regularity conditions. It is widely known that the information content in the population moment condition has impacts on the quality of the asymptotic approximation to finite sample behavior. This dissertation focuses on a moment selection procedure that leads us to choose relevant (asymptotically efficient and non-redundant) moment conditions in the presence of weak identification. The contributions of this dissertation can be characterized as follows: in the framework of linear model, (i) the concept of nearly redundant moment conditions is introduced and the connection between near redundancy and weak identification is explored; (ii) performance of RMSC(c) is evaluated when weak identification is a possibility but the parameter vector to be estimated is not weakly identified by the candidate set of moment conditions; (iii) performance of RMSC(c) is also evaluated when the parameter vector is weakly identified by the candidate set; (iv) a combined strategy of Stock and Yogo's (2002) test for weak identification and RMSC(c) is introduced and evaluated; (v) (i) and (ii) are extended to allow for nonlinear dynamic models. The subsequent simulation results support the analytical findings: when only a part of instruments in the set of possible candidates for instruments are relevant and the others are redundant given all or some of the relevant ones, RMSC(c) chooses all the relevant instruments with high probabilities and improves the quality of the post-selection inferences; when the candidates are in order of their importance, a combined strategy of Stock and Yogo's (2002) pretest and RMSC(c) improves the post-selection inferences, however it tends to select parsimonious models; when all the possible candidates are equally important, it seems that RMSC(c) does not provide any merits. However, in the.

Generalized Method of Moments (GMM) has become one of the main statistical tools for the analysis of economic and financial data. This book is the first to provide an intuitive introduction to the method combined with a unified treatment of GMM statistical theory and a survey of recentimportant developments in the field. Providing a comprehensive treatment of GMM estimation and inference, it is designed as a resource for both the theory and practice of GMM: it discusses and proves formally all the main statistical results, and illustrates all inference techniques using empiricalexamples in macroeconomics and finance.Building from the instrumental variables estimator in static linear models, it presents the asymptotic statistical theory of GMM in nonlinear dynamic models. Within this framework it covers classical results on estimation and inference techniques, such as the overidentifying restrictions test andtests of structural stability, and reviews the finite sample performance of these inference methods. And it discusses in detail recent developments on covariance matrix estimation, the impact of model misspecification, moment selection, the use of the bootstrap, and weak instrumentasymptotics.

The generalized method of moments (GMM) estimation has emerged as providing a ready to use, flexible tool of application to a large number of econometric and economic models by relying on mild, plausible assumptions. The principal objective of this volume is to offer a complete presentation of the theory of GMM estimation as well as insights into the use of these methods in empirical studies. It is also designed to serve as a unified framework for teaching estimation theory in econometrics. Contributors to the volume include well-known authorities in the field based in North America, the UK/Europe, and Australia. The work is likely to become a standard reference for graduate students and professionals in economics, statistics, financial modeling, and applied mathematics.

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.