hdm: High-Dimensional Metrics
Version 0.2.0

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.

Package details

AuthorMartin Spindler [cre, aut], Victor Chernozhukov [aut], Christian Hansen [aut]
Date of publication2016-06-17 21:46:27
MaintainerMartin Spindler <[email protected]>
Package repositoryView on CRAN
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hdm documentation built on May 29, 2017, 4:28 p.m.