Generalized Method of Moments, Efficient Bootstrapping, and Improved Inference

Generalized method of moments (GMM) has been an important innovation in econometrics. Its usefulness has motivated a search for good inference procedures based on GMM. This article presents a novel method of bootstrapping for GMM based on resampling from the empirical likelihood distribution that im...

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Bibliographic Details
Published in:Journal of business & economic statistics 2002-10, Vol.20 (4), p.507-517
Main Authors: Brown, Bryan W, Newey, Whitney K
Format: Article
Language:English
Subjects:
Approximation
Bias
Bootstrap mechanism
Bootstrap resampling
Bootstrapping
Business studies
Capital assets
Confidence interval
Econometrics
Economic models
Economic statistics
Economics
Efficiency
Empirical likelihood
Estimation methods
Estimators
Generalized method of moments
Inference
Instrumental variables estimation
Mathematical moments
Method of moments
Panel data
Power
Random variables
Sampling
Statistical analysis
Statistical methods
Statistical variance
Statistics
Studies
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Summary:Generalized method of moments (GMM) has been an important innovation in econometrics. Its usefulness has motivated a search for good inference procedures based on GMM. This article presents a novel method of bootstrapping for GMM based on resampling from the empirical likelihood distribution that imposes the moment restrictions. We show that this approach yields a large-sample improvement and is efficient, and give examples. We also discuss the development of GMM and other recent work on improved inference.
ISSN:0735-0015
1537-2707
DOI:10.1198/073500102288618649