Generalized Method of Moments Estimation
Generalized Method of Moments Estimation
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.
- Examines a quickly developing area of economics
- Written by well-known experts in the field and is using a unified language, notation, so it is likely to become the standard reference book in the area
- It can also be used as a textbook in advanced econometric theory courses
Product details
February 2011Adobe eBook Reader
9780511825651
0 pages
0kg
14 tables
This ISBN is for an eBook version which is distributed on our behalf by a third party.
Table of Contents
- Preface
- 1. Introduction to the generalized method of moments estimation David Harris and László Mátyás
- 2. GMM estimation techniques Masao Ogaki
- 3. Covariance matrix estimation Matthew J. Cushing and Mary G. McGarvey
- 4. Hypothesis testing in models estimated by GMM Alastair R. Hall
- 5. Finite sample properties of GMM estimators and tests Jan M. Podivinsky
- 6. GMM estimation of time series models David Harris
- 7. Reduced rank regression using GMM Frank Kleibergen
- 8. Estimation of linear panel data models using GMM Seung C. Ahn and Peter Schmidt
- 9. Alternative GMM methods for nonlinear panel data models Jörg Breitung and Michael Lechner
- 10. Simulation based method of moments Roman Liesenfeld and Jörg Breitung
- 11. Logically inconsistent limited dependent variables models J. S. Butler and Gabriel Picone
- Index.
