Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/217160 
Year of Publication: 
2019
Citation: 
[Journal:] Quantitative Economics [ISSN:] 1759-7331 [Volume:] 10 [Issue:] 3 [Publisher:] The Econometric Society [Place:] New Haven, CT [Year:] 2019 [Pages:] 931-979
Publisher: 
The Econometric Society, New Haven, CT
Abstract: 
This paper studies the averaging GMM estimator that combines a conservative GMM estimator based on valid moment conditions and an aggressive GMM estimator based on both valid and possibly misspecified moment conditions, where the weight is the sample analog of an infeasible optimal weight. We establish asymptotic theory on uniform approximation of the upper and lower bounds of the finite-sample truncated risk difference between any two estimators, which is used to compare the averaging GMM estimator and the conservative GMM estimator. Under some sufficient conditions, we show that the asymptotic lower bound of the truncated risk difference between the averaging estimator and the conservative estimator is strictly less than zero, while the asymptotic upper bound is zero uniformly over any degree of misspecification. The results apply to quadratic loss functions. This uniform asymptotic dominance is established in non-Gaussian semiparametric nonlinear models.
Subjects: 
Asymptotic risk
finite-sample risk
generalized shrinkage estimator
GMM
misspecification
model averaging
nonstandard estimator
uniform approximation
JEL: 
C13
C36
C52
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article

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