Stochastic Approximation to Nonlinear GMM - A Scalable Estimation and Inference Framework

Learn about a scalable framework for nonlinear Generalized Method of Moments (GMM) estimation through stochastic approximation techniques in this conference presentation delivered at the International Conference on Bayesian Statistics 2025. Explore how stochastic approximation methods can address computational challenges in nonlinear GMM estimation, making it more feasible for large-scale econometric applications. Discover the theoretical foundations of this approach and understand how it provides both estimation and inference capabilities for complex economic models. Examine the practical advantages of this framework in handling high-dimensional data and computationally intensive nonlinear moment conditions that are common in modern econometric analysis.