High-Dimensional Metrics.

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.

Getting Started with hdm

R is an open source software project and can be freely downloaded from the CRAN website along with its associated documentation. There are two options to install the R package hdm - either installation of the development version or the stable release available at CRAN.

Development Version

The current development version of the hdm package is maintained in this repository and can be installed by the command devtools::install_github("MartinSpindler/hdm"). Note that the devtools package is required for this command.

Stable Release

The stable package release is available at CRAN. The stable release version can be installed by typing install.packages("hdm") in R.

Getting Started: Vignette

After installation, users can get started by following the package vignette.

References

V. Chernozhukov, C. Hansen and M. Spindler (2016). "hdm: High-dimensional metrics." arXiv preprint arXiv:1608.00354 (2016), available online.

A. Belloni, D. Chen, V. Chernozhukov and C. Hansen (2012). Sparse models and methods for optimal instruments with an application to eminent domain. Econometrica 80 (6), 2369-2429.

A. Belloni, V. Chernozhukov and C. Hansen (2013). Inference for high-dimensional sparse econometric models. In Advances in Economics and Econometrics: 10th World Congress, Vol. 3: Econometrics, Cambridge University Press: Cambridge, 245-295.

A. Belloni, V. Chernozhukov, C. Hansen (2014). Inference on treatment effects after selection among high-dimensional controls. The Review of Economic Studies 81(2), 608-650.