Journal article
SGMM: Stochastic Approximation to Generalized Method of Moments
Journal of financial econometrics, Vol.23(1), pp.1-40
2025
DOI: 10.1093/jjfinec/nbad027
Abstract
We introduce a new class of algorithms, stochastic generalized method of moments (SGMM), for estimation and inference on (overidentified) moment restriction models. Our SGMM is a novel stochastic approximation alternative to the popular (offline) GMM, and offers fast and scalable implementation with the ability to handle streaming datasets in real time. We establish the almost sure convergence, and the (functional) central limit theorem for the inefficient online 2SLS and the efficient SGMM. Moreover, we propose online versions of the Durbin-Wu-Hausman and Sargan-Hansen tests that can be seamlessly integrated within the SGMM framework. Extensive Monte Carlo simulations show that as the sample size increases, the SGMM matches the standard (offline) GMM in terms of estimation accuracy and gains over computational efficiency, indicating its practical value for both large-scale and online datasets. We demonstrate the efficacy of our approach by a proof of concept using two well-known empirical examples with large sample sizes.
Details
- Title: Subtitle
- SGMM: Stochastic Approximation to Generalized Method of Moments
- Creators
- Xiaohong Chen - Yale UniversitySokbae Lee - Columbia UniversityYuan Liao - Rutgers, The State University of New JerseyMyung Hwan Seo - Seoul National UniversityYoungki Shin - McMaster UniversityMyunghyun Song - Columbia University
- Resource Type
- Journal article
- Publication Details
- Journal of financial econometrics, Vol.23(1), pp.1-40
- DOI
- 10.1093/jjfinec/nbad027
- ISSN
- 1479-8409
- eISSN
- 1479-8417
- Publisher
- Oxford Univ Press
- Number of pages
- 40
- Language
- English
- Date published
- 2025
- Academic Unit
- Economics
- Record Identifier
- 9984936815702771
Metrics
1 Record Views