This package implements methods for estimation and inference in a high-dimensional setting.
Victor Chernozhukov, Christian Hansen, Martin Spindler
Maintainer: Martin Spindler <spindler@mea.mpisoc.mpg.de>
| Package: | hdm |
| Type: | Package |
| Version: | 0.1 |
| Date: | 2015-05-25 |
| License: | GPL-3 |
This package provides efficient estimators and uniformly valid confidence intervals for various low-dimensional causal/structural parameters appearing in high-dimensional approximately sparse models. The package includes functions for fitting heteroskedastic robust Lasso regressions with non-Gaussian erros and for instrumental variable (IV) and treatment effect estimation in a high-dimensional setting. Moreover, the methods enable valid post-selection inference. Moreover, a theoretically grounded, data-driven choice of the penalty level is provided.
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, Cambirdge 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.