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) <doi:10.48550/arXiv.1603.01700>.
| Version: | 0.3.2 |
| Depends: | R (≥ 3.0.0) |
| Imports: | MASS,glmnet,ggplot2,checkmate,Formula, methods |
| Suggests: | testthat,knitr,rmarkdown,formatR,xtable,mvtnorm,markdown |
| Published: | 2024-02-14 |
| DOI: | 10.32614/CRAN.package.hdm |
| Author: | Martin Spindler [cre, aut], Victor Chernozhukov [aut], Christian Hansen [aut], Philipp Bach [ctb] |
| Maintainer: | Martin Spindler <martin.spindler at gmx.de> |
| License: | MIT + fileLICENSE |
| NeedsCompilation: | no |
| Citation: | hdm citation info |
| Materials: | README |
| In views: | CausalInference,Econometrics,MachineLearning |
| CRAN checks: | hdm results |
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