hdm: High-Dimensional Metrics
Name
RJ-2016-040.pdf
Description
Published version
Size
301.85 KB
Format
Adobe PDF
Checksum (MD5)
170b6c44456e7a00828adac7c63be743
Author(s)
Chernozhukov, Victor V
Hansen, Chris
Spindler, Martin
Date Issued
September 2016
Journal
R Journal
Publisher
The R Foundation
Citation
Chernozhukov, Victor et al. "hdm: high-dimensional metrics." The R Journal 8 (December 2016): 185-199 © 2016 Publisher
Version
Final published version
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 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.
MIT Department
Massachusetts Institute of Technology. Department of Economics
Terms of Use
Creative Commons Attribution 3.0 unported license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.32614/rj-2016-040
