Explanatory variables in a predictive regression typically exhibit low signal strength and
various degrees of persistence. Variable selection in such a context is of great importance …

This study examines high-dimensional forecasting and variable selection via folded-
concave penalized regressions. The penalized regression approach leads to sparse …

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 …

Background SARS-CoV-2, the novel coronavirus that causes COVID-19, is a global
pandemic with higher mortality and morbidity than any other virus in the last 100 years …

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 study the finite sample behavior of Lasso-based inference methods such as post–double
Lasso and debiased Lasso. We show that these methods can exhibit substantial omitted …

In this paper we study high-dimensional correlated random effects panel data models. Our
setting is useful as it allows including time invariant covariates as under random effects yet …

This paper discusses the problem of estimation and inference on the effects of time-varying
treatment. We propose a method for inference on the effects treatment histories, introducing …

In non-experimental longitudinal studies, researchers often estimate causal effects
assuming time-constant unobserved heterogeneity or linear-in-parameters conditional …