The R Journal: article published in 2016, volume 8:2

hdm: High-Dimensional Metrics
Victor Chernozhukov, Chris Hansen and Martin Spindler , The R Journal (2016) 8:2, pages 185-199.

Abstract In this article the package High-dimensional Metrics 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 param eters 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.

Received: 2016-02-28; online 2016-09-09
CRAN packages: glmnet, lars, hdm
CRAN Task Views implied by cited CRAN packages: MachineLearning, Survival

This article is licensed under a Creative Commons Attribution 3.0 Unported license .

@article{RJ-2016-040,
  author = {Victor Chernozhukov and Chris Hansen and Martin Spindler},
  title = {{hdm: High-Dimensional Metrics}},
  year = {2016},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2016-040},
  url = {https://doi.org/10.32614/RJ-2016-040},
  pages = {185--199},
  volume = {8},
  number = {2}
}