Victor Chernozhukov, Chris Hansen, Martin Spindler
arXiv 5 Mar 2016 · Statistics — Machine Learning · 3 citations (OpenAlex)
arXiv:1603.01700 · PDF · DOI · OpenAlex · Extracted main text
The package High-dimensional Metrics (\Rpackage{hdm}) is an evolving 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, including a joint significance test for Lasso regression. Data sets which have been used in the literature and might be useful for classroom demonstration and for testing new estimators are included. \R and the package \Rpackage{hdm} are open-source software projects and can be freely downloaded from CRAN: http://cran.r-project.org.
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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 | Belloni, Chernozhukov, and Kato (2014) Uniform post-selection inference for least absolute deviation regression and other Z-estimation problems | 1.000 | 5 | 3 | 100% |
| 2 | Belloni, Chen, Chernozhukov, and Hansen (2012) Sparse Models and Methods for Optimal Instruments with an Application to Eminent Domain | 0.811 | 4 | 2 | 100% |
| 3 | Barro and Lee (1994) Data set for a panel of 139 countries | 0.511 | 2 | 2 | 50% |
| 4 | Acemoglu, Johnson, and Robinson (2001) The Colonial Origins of Comparative Development: An Empirical Investigation | 0.511 | 2 | 2 | 50% |
| 5 | Belloni, Chernozhukov, Fernández-Val, and Hansen (2013) Program Evaluation with High-Dimensional Data | 0.511 | 2 | 1 | 100% |
| 6 | Belloni, Chernozhukov, and Hansen (2014) Inference on Treatment Effects After Selection Amongst High-Dimensional Controls self | 0.511 | 2 | 1 | 100% |
| 7 | Chernozhukov, Chetverikov, and Kato (2013) Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors | 0.511 | 2 | 1 | 100% |
| 8 | Chernozhukov, Hansen, and Spindler (2015) Valid Post-Selection and Post-Regularization Inference In Linear Models with Many Controls and Instruments self | 0.511 | 2 | 1 | 100% |
| 9 | Belloni and Chernozhukov (2013) Least Squares After Model Selection in High-dimensional Sparse Models | 0.405 | 1 | 1 | 100% |
| 10 | Belloni, Chernozhukov, and Hansen (2010) Inference for High-Dimensional Sparse Econometric Models self | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 15 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | 2002.12710 | 0.405 | 1 | 1 |
| 2 | Double Machine Learning and Automated Model Selection: A Cautionary Tale | 0.405 | 1 | 1 |
| 3 | 2203.03051 | 0.405 | 1 | 1 |
| 4 | Decomposing Inequalities using Machine Learning and Overcoming Common Support Issues | 0.405 | 1 | 1 |