SLIM: Stochastic Learning and Inference in Overidentified Models
SLIM is a new method for estimating complex nonlinear models using stochastic approximation, specifically for overidentified Generalized Method of Moments (GMM). It combines theoretical proofs with efficient algorithms to provide unbiased estimates and robust inference, allowing researchers to analyze large datasets quickly. This framework offers a fast alternative to traditional methods while maintaining high statistical accuracy.