R package 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.
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References in zbMATH (referenced in 4 articles , 1 standard article )
Showing results 1 to 4 of 4.
- Mourifié, Ismael: A marriage matching function with flexible spillover and substitution patterns (2019)
- Philipp Bach; Victor Chernozhukov; Martin Spindler: Valid Simultaneous Inference in High-Dimensional Settings (with the hdm package for R) (2018) arXiv
- Dezeure, Ruben; Bühlmann, Peter; Zhang, Cun-Hui: High-dimensional simultaneous inference with the bootstrap (2017)
- Victor Chernozhukov, Chris Hansen, Martin Spindler: High-Dimensional Metrics in R (2016) arXiv