hdm: High-Dimensional Metrics
Implementation of selected high-dimensional statistical and
econometric methods for estimation and inference. Efficient estimators and
uniformly valid confidence intervals for various low-dimensional causal/
structural parameters are provided which appear in high-dimensional
approximately sparse models. Including functions for fitting heteroscedastic
robust Lasso regressions with non-Gaussian errors and for instrumental variable
(IV) and treatment effect estimation in a high-dimensional setting. Moreover,
the methods enable valid post-selection inference and rely on a theoretically
grounded, data-driven choice of the penalty.
Chernozhukov, Hansen, Spindler (2016) <arXiv:1603.01700>.
| Version: |
0.3.1 |
| Depends: |
R (≥ 3.0.0) |
| Imports: |
MASS, glmnet, ggplot2, checkmate, Formula, methods |
| Suggests: |
testthat, knitr, xtable, mvtnorm |
| Published: |
2019-01-18 |
| Author: |
Martin Spindler [cre, aut],
Victor Chernozhukov [aut],
Christian Hansen [aut],
Philipp Bach [ctb] |
| Maintainer: |
Martin Spindler <martin.spindler at gmx.de> |
| License: |
MIT + file LICENSE |
| NeedsCompilation: |
no |
| Citation: |
hdm citation info |
| In views: |
CausalInference, Econometrics, MachineLearning |
| CRAN checks: |
hdm results |
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