hdm-package: hdm: High-Dimensional Metrics

In hdm: High-Dimensional Metrics

Description

This package implements methods for estimation and inference in a high-dimensional setting.

Details

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.

Author(s)

Victor Chernozhukov, Christian Hansen, Martin Spindler

Maintainer: Martin Spindler <[e-mail]>

References

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.

hdm documentation built on May 1, 2019, 7:56 p.m.