hdm: High-Dimensional Metrics
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...
| Autori principali: | , , |
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| Natura: | Articolo |
| Lingua: | English |
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The R Foundation
2019
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| Accesso online: | https://hdl.handle.net/1721.1/122795 |
| _version_ | 1826211214887223296 |
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| author | Chernozhukov, Victor V Hansen, Chris Spindler, Martin |
| author2 | Massachusetts Institute of Technology. Department of Economics |
| author_facet | Massachusetts Institute of Technology. Department of Economics Chernozhukov, Victor V Hansen, Chris Spindler, Martin |
| author_sort | Chernozhukov, Victor V |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T15:02:24Z |
| format | Article |
| id | mit-1721.1/122795 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T15:02:24Z |
| publishDate | 2019 |
| publisher | The R Foundation |
| record_format | dspace |
| spelling | mit-1721.1/1227952022-10-02T00:12:55Z hdm: High-Dimensional Metrics Chernozhukov, Victor V Hansen, Chris Spindler, Martin Massachusetts Institute of Technology. Department of Economics 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. 2019-11-07T19:05:54Z 2019-11-07T19:05:54Z 2016-09 2016-02 2019-10-21T17:57:43Z Article http://purl.org/eprint/type/JournalArticle 2073-4859 https://hdl.handle.net/1721.1/122795 Chernozhukov, Victor et al. "hdm: high-dimensional metrics." The R Journal 8 (December 2016): 185-199 © 2016 Publisher en http://dx.doi.org/10.32614/rj-2016-040 R Journal Creative Commons Attribution 3.0 unported license https://creativecommons.org/licenses/by/3.0/ application/pdf The R Foundation The R Journal |
| spellingShingle | Chernozhukov, Victor V Hansen, Chris Spindler, Martin hdm: High-Dimensional Metrics |
| title | hdm: High-Dimensional Metrics |
| title_full | hdm: High-Dimensional Metrics |
| title_fullStr | hdm: High-Dimensional Metrics |
| title_full_unstemmed | hdm: High-Dimensional Metrics |
| title_short | hdm: High-Dimensional Metrics |
| title_sort | hdm high dimensional metrics |
| url | https://hdl.handle.net/1721.1/122795 |
| work_keys_str_mv | AT chernozhukovvictorv hdmhighdimensionalmetrics AT hansenchris hdmhighdimensionalmetrics AT spindlermartin hdmhighdimensionalmetrics |