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.
|Author||Martin Spindler [cre, aut], Victor Chernozhukov [aut], Christian Hansen [aut]|
|Date of publication||2018-01-22 19:18:10|
|Maintainer||Martin Spindler <[email protected]>|
|License||MIT + file LICENSE|
|Package repository||View on R-Forge|
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