Convergence Rates of GMM Estimators with Nonsmooth Moments under Misspecification
David Kang,
Seojeong Lee and
Juha Song
No 423283930, Working Papers from Lancaster University Management School, Economics Department
Abstract:
The asymptotic behavior of GMM estimators depends critically on whether the underlying moment condition model is correctly specified. Hong and Li (2023, Econometric Theory) showed that GMM estimators with nonsmooth (non-directionally differentiable) moment functions are at best n^(1/3)-consistent under misspecification. Through simulations, we verify the slower convergence rate of GMM estimators in such cases. For the two-step GMM estimator with an estimated weight matrix, our results align with theory. However, for the one-step GMM estimator with the identity weight matrix, the convergence rate remains √n, even under severe misspecification.
Keywords: generalized method of moments; non-differentiable moment; nstrumental variables quantile regression (search for similar items in EconPapers)
JEL-codes: C13 C15 C21 (search for similar items in EconPapers)
Date: 2025
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Related works:
Working Paper: Convergence Rates of GMM Estimators with Nonsmooth Moments under Misspecification (2025) 
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