hdm: High-Dimensional Metrics

TL;DR

The paper introduces the 'hdm' package, offering statistical tools for estimation and inference in high-dimensional sparse models, focusing on confidence intervals and significance testing for model components.

Contribution

It provides efficient estimators and valid confidence intervals for high-dimensional regression coefficients, including extensions for treatment effects and endogenous settings.

Findings

Implementation of data-driven penalization parameter selection methods.

Provision of joint confidence intervals for regression coefficients.

Inclusion of benchmark datasets for testing estimators.

Abstract

In this article the package High-dimensional Metrics (\texttt{hdm}) is introduced. It is a collection of statistical methods for estimation and quantification of uncertainty in high-dimensional approximately sparse models. It focuses on providing confidence intervals and significance testing for (possibly many) low-dimensional subcomponents of the high-dimensional parameter vector. Efficient estimators and uniformly valid confidence intervals for regression coefficients on target variables (e.g., treatment or policy variable) in a high-dimensional approximately sparse regression model, for average treatment effect (ATE) and average treatment effect for the treated (ATET), as well for extensions of these parameters to the endogenous setting are provided. Theory grounded, data-driven methods for selecting the penalization parameter in Lasso regressions under heteroscedastic and non-Gaussian errors are implemented. Moreover, joint/ simultaneous confidence intervals for regression coefficients of a high-dimensional sparse regression are implemented. Data sets which have been used in the literature and might be useful for classroom demonstration and for testing new estimators are included.