Generalized Method of Moments Estimation
Published on 13. April 1999
Book
Hardback
332 pages
978-0-521-66013-6 (ISBN)
Description
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.
More details
Series
Language
English
Place of publication
Cambridge
United Kingdom
Target group
Professional and scholarly
Illustrations
14 Tables, unspecified
Dimensions
Height: 235 mm
Width: 157 mm
Thickness: 24 mm
Weight
691 gr
ISBN-13
978-0-521-66013-6 (9780521660136)
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Schweitzer Classification
Other editions
Additional editions
Laszlo Matyas
Generalized Method of Moments Estimation
Book
04/1999
Cambridge University Press
€59.80
Shipment within 15-20 days
Person
Content
Preface; 1. Introduction to the generalized method of moments estimation David Harris and Laszlo Matyas; 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 Joerg Breitung and Michael Lechner; 10. Simulation based method of moments Roman Liesenfeld and Joerg Breitung; 11. Logically inconsistent limited dependent variables models J. S. Butler and Gabriel Picone; Index.