Arbeitspapier
Valid simultaneous inference in high-dimensional settings (with the HDM package for R)
Due to the increasing availability of high-dimensional empirical applications in many research disciplines, valid simultaneous inference becomes more and more important. For instance, high-dimensional settings might arise in economic studies due to very rich data sets with many potential covariates or in the analysis of treatment heterogeneities. Also the evaluation of potentially more complicated (non-linear) functional forms of the regression relationship leads to many potential variables for which simultaneous inferential statements might be of interest. Here we provide a review of classical and modern methods for simultaneous inference in (high-dimensional) settings and illustrate their use by a case study using the R package hdm. The R package hdm implements valid joint powerful and efficient hypothesis tests for a potentially large number of coefficients as well as the construction of simultaneous confidence intervals and, therefore, provides useful methods to perform valid post-selection inference based on the LASSO.
- Language
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Englisch
- Bibliographic citation
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Series: cemmap working paper ; No. CWP30/19
- Classification
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Wirtschaft
- Event
-
Geistige Schöpfung
- (who)
-
Bach, Philipp
Chernozhukov, Victor
Spindler, Martin
- Event
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Veröffentlichung
- (who)
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Centre for Microdata Methods and Practice (cemmap)
- (where)
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London
- (when)
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2019
- DOI
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doi:10.1920/wp.cem.2019.3019
- Handle
- Last update
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10.03.2025, 11:44 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Bach, Philipp
- Chernozhukov, Victor
- Spindler, Martin
- Centre for Microdata Methods and Practice (cemmap)
Time of origin
- 2019
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Valid post-selection and post-regularization inference: An elementary, general approach
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Valid post-selection inference in high-dimensional approximately sparse quantile regression models
L1-Penalised quantile regression in high-dimensional sparse models
Simultaneous confidence intervals for high-dimensional linear models with many endogenous variables
Post-l1-penalized estimators in high-dimensional linear regression models
hdm: High-dimensional metrics
Inference for high-dimensional sparse econometric models
Valid post-selection and post-regularization inference: An elementary, general approach
Estimation of treatment effects with high-dimensional controls
Simultaneous inference for best linear predictor of the conditional average treatment effect and other structural functions
Robust inference in high-dimensional approximately sparse quantile regression models
Inference on treatment effects after selection amongst high-dimensional controls
Program evaluation with high-dimensional data
Valid post-selection inference in high-dimensional approximately sparse quantile regression models
L1-Penalised quantile regression in high-dimensional sparse models
Simultaneous confidence intervals for high-dimensional linear models with many endogenous variables
Post-l1-penalized estimators in high-dimensional linear regression models
hdm: High-dimensional metrics
Inference for high-dimensional sparse econometric models
Valid post-selection and post-regularization inference: An elementary, general approach
Estimation of treatment effects with high-dimensional controls
Simultaneous inference for best linear predictor of the conditional average treatment effect and other structural functions
Robust inference in high-dimensional approximately sparse quantile regression models
Inference on treatment effects after selection amongst high-dimensional controls