We present an expository, general analysis of valid post-selection or post-regularization
inference about a low-dimensional target parameter in the presence of a very high …

Practices used to address economic forecasting problems have undergone substantial
changes over recent years. We review how such changes have influenced the ways in …

We propose robust methods for inference about the effect of a treatment variable on a scalar
outcome in the presence of very many regressors in a model with possibly non-Gaussian …

In this article, we introduce lassopack, a suite of programs for regularized regression in
Stata. lassopack implements lasso, square-root lasso, elastic net, ridge regression, adaptive …

We develop results for the use of Lasso and post‐Lasso methods to form first‐stage
predictions and estimate optimal instruments in linear instrumental variables (IV) models …

This paper explores the nature of fluctuations in world bulk shipping by quantifying the
impact of time to build and demand uncertainty on investment and prices. We examine the …

This article provides a model-based empirical strategy to,(1) detect the presence and gauge
the magnitude of government subsidies and (2) quantify their impact on production …

This paper establishes non-asymptotic oracle inequalities for the prediction error and
estimation accuracy of the LASSO in stationary vector autoregressive models. These …

This article is about estimation and inference methods for high dimensional sparse (HDS)
regression models in econometrics. High dimensional sparse models arise in situations …

On cross-validated Lasso in high dimensions Page 1 The Annals of Statistics 2021, Vol. 49,
No. 3, 1300–1317 https://doi.org/10.1214/20-AOS2000 © Institute of Mathematical Statistics …