This package implements methods for estimation and inference in a high-dimensional setting.
Maintainer: Martin Spindler martin.spindler@gmx.de
Authors:
Victor Chernozhukov
Christian Hansen
Other contributors:
Philipp Bach philipp.bach@uni-hamburg.de [contributor]
| Package: | hdm |
| Type: | Package |
| Version: | 0.1 |
| Date: | 2015-05-25 |
| License: | GPL-3 |
This package provides efficient estimators and uniformly valid confidence intervals for various low-dimensional causal/structural parameters appearing in high-dimensional approximately sparse models. The package includes functions for fitting heteroskedastic robust Lasso regressions with non-Gaussian erros and for instrumental variable (IV) and treatment effect estimation in a high-dimensional setting. Moreover, the methods enable valid post-selection inference. Moreover, a theoretically grounded, data-driven choice of the penalty level is provided.