Template-Type: ReDIF-Paper 1.0
Author-Name: David Kang
Author-Name-First: David
Author-Name-Last: Kang
Author-Name: Seojeong  Lee
Author-Name-First: Seojeong 
Author-Name-Last: Lee
Author-Name: Juha  Song
Author-Name-First: Juha 
Author-Name-Last: Song
Title: Convergence Rates of GMM Estimators with Nonsmooth Moments under Misspecification
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.
Creation-Date: 2025
File-URL: http://www.lancaster.ac.uk/media/lancaster-university/content-assets/documents/lums/economics/working-papers/LancasterWP2025_005.pdf
File-Format: application/pdf
Number: 423283930
Classification-JEL: C13, C15, C21
Keywords: generalized method of moments, non-differentiable moment, nstrumental variables quantile regression
Handle: RePEc:lan:wpaper:423283930