Victor Chernozhukov, Chris Hansen, Martin Spindler
arXiv 1 Aug 2016 · Statistics — Methodology · publishedThe R Journal (2016) · 58 citations (OpenAlex)
arXiv:1608.00354 · PDF · DOI · OpenAlex · Extracted main text
In this article the package High-dimensional Metrics (hdm) is introduced. It is a collection of statistical methods for estimation and quantification of uncertainty in high-dimensional approximately sparse models. It focuses on providing confidence intervals and significance testing for (possibly many) low-dimensional subcomponents of the high-dimensional parameter vector. Efficient estimators and uniformly valid confidence intervals for regression coefficients on target variables (e.g., treatment or policy variable) in a high-dimensional approximately sparse regression model, for average treatment effect (ATE) and average treatment effect for the treated (ATET), as well for extensions of these parameters to the endogenous setting are provided. Theory grounded, data-driven methods for selecting the penalization parameter in Lasso regressions under heteroscedastic and non-Gaussian errors are implemented. Moreover, joint/ simultaneous confidence intervals for regression coefficients of a high-dimensional sparse regression are implemented. Data sets which have been used in the literature and might be useful for classroom demonstration and for testing new estimators are included.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | A. Belloni, D. Chen, V. Chernozhukov, and C. Hansen (2010) Sparse models and methods for optimal instruments with an application to eminent domain | 1.000 | 5 | 3 | 100% |
| 2 | S. Berry, J. Levinsohn, and A. Pakes (1995) Automobile prices in market equilibrium | 0.874 | 5 | 2 | 100% |
| 3 | A. Belloni and V. Chernozhukov (2009) Least squares after model selection in high-dimensional sparse models | 0.644 | 2 | 2 | 100% |
| 4 | A. Belloni, V. Chernozhukov, and K. Kato (2014) Uniform post-selection inference for least absolute deviation regression and other z-estimation problems | 0.644 | 2 | 2 | 100% |
| 5 | V. Chernozhukov, C. Hansen, and M. Spindler (2015) Valid post-selection and post-regularization inference: An elementary, general approach | 0.644 | 2 | 2 | 100% |
| 6 | V. Chernozhukov, C. Hansen, and M. Spindler (2015) Valid post-selection and post-regularization inference in linear models with many controls and instruments | 0.644 | 2 | 2 | 100% |
| 7 | A. Belloni, V. Chernozhukov, and C. Hansen (2011) Inference on treatment effects after selection amongst high-dimensional controls | 0.511 | 2 | 1 | 100% |
| 8 | J. D. Angrist and J.-S. Pischke (2008) Mostly Harmless Econometrics: An Empiricist's Companion | 0.405 | 1 | 1 | 100% |
| 9 | A. Belloni, V. Chernozhukov, I. Fernández-Val, and C. Hansen (2013) Program evaluation with high-dimensional data | 0.405 | 1 | 1 | 100% |
| 10 | A. Belloni, V. Chernozhukov, and C. Hansen (2011) Inference for high-dimensional sparse econometric models | 0.405 | 1 | 1 | 100% |
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