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)
|Author||Martin Spindler [cre, aut], Victor Chernozhukov [aut], Christian Hansen [aut], Philipp Bach [ctb]|
|Date of publication||2018-08-30 10:46:18|
|Maintainer||Martin Spindler <[email protected]>|
|License||MIT + file LICENSE|
|Package repository||View on R-Forge|
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