Application of Machine Learning Classifiers and Regularization in Econometric Theory
نویسنده
چکیده
As machine learning techniques become more popular and computers become capable of storing and processing large quantities of data, there have been many recent efforts to incorporate such techniques into structural econometric models. My research aims to extend this literature by introducing the techniques of regularization and classification (from machine learning) into Generalized Method of Moments (GMM) and Propensity Score Weighting frameworks (from econometrics).
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