This dissertation consists of three chapters, each of which proposes methods to deal with the "many moments" problem in a different model. Chapter I develops shrinkage methods for solving the "many moments" problem in the context of instrumental variable estimation. The procedure can be...

In this paper, we propose a doubly robust method to present the heterogeneity of the average treatment effect with respect to observed covariates of interest. We consider a situation where a large number of covariates are needed for identifying the average treatment effect but the covariates of...

This paper develops shrinkage methods for addressing the “many instruments” problem in the context of instrumental variable estimation. It has been observed that instrumental variable estimators may behave poorly if the number of instruments is large. This problem can be addressed by...