This paper provides estimation and inference methods for conditional average treatment
effects (CATE) characterized by a high‐dimensional parameter in both homogeneous cross …

This article investigates double/debiased machine learning (DML) under multiway clustered
sampling environments. We propose a novel multiway cross-fitting algorithm and a multiway …

This article reviews recent endeavors to incorporate big data and machine learning
techniques into energy and environmental economics research. We find that novel datasets …

We consider situations where a user feeds her attributes to a machine learning method that
tries to predict her best option based on a random sample of other users. The predictor is …

We establish oracle inequalities for a version of the Lasso in high-dimensional fixed effects
dynamic panel data models. The inequalities are valid for the coefficients of the dynamic and …

The paper introduces structured machine learning regressions for heavy-tailed dependent
panel data potentially sampled at different frequencies. We focus on the sparse-group …

In this paper, we develop statistical inference tools for high-dimensional functional time
series. We introduce a new concept of physical dependent processes in the space of square …

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 provides estimation and inference methods for a large number of heterogeneous
treatment effects in a panel data setting with many potential controls. We assume that …

This paper provides estimation and inference methods for a conditional average treatment
effects (CATE) characterized by a high-dimensional parameter in both homogeneous cross …