This paper concerns statistical inference for the components of a high-dimensional
regression parameter despite possible endogeneity of each regressor. Given a first-stage …

Evidence-based decision making often relies on meta-analyzing multiple studies, which
enables more precise estimation and investigation of generalizability. Integrative analysis of …

Identifying informative predictors in a high dimensional regression model is a critical step for
association analysis and predictive modeling. Signal detection in the high dimensional …

In this paper, we introduce structured sparsity estimators for use in Generalized Linear
Models. Structured sparsity estimators in the least squares loss are introduced by Stucky …

This paper addresses statistical inference for high-dimensional threshold regression
parameters. I establish oracle inequalities for the scaled LASSO estimator proposed by Lee …

Abstract Models defined by moment conditions are at the center of structural econometric
estimation, but economic theory is mostly agnostic about moment selection. While a large …

We propose a new inference method in high-dimensional regression models and high-
dimensional IV regression models. The method is shown to be valid without requiring the …

Three-dimensional panel models are widely used in empirical analysis. Researchers use
various combinations of fixed effects for three-dimensional panels while the correct …

Recent advances in machine learning literature provide a series of new algorithms that both
address endogeneity and can be applied in high-dimensional environments, we call them …

Recent advances in machine learning literature provide a series of new algorithms that both
address endogeneity and can be applied in high-dimensional environments, we call them …