Heteroskedasticity-Robust Inference in Linear Regression Models with Many Covariates
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Abstract
Suggested Citation
Note: kj345
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Other versions of this item:
- Koen Jochmans, 2022. "Heteroscedasticity-Robust Inference in Linear Regression Models With Many Covariates," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 117(538), pages 887-896, April.
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Cited by:
- Kaspar Wuthrich & Ying Zhu, 2019. "Omitted variable bias of Lasso-based inference methods: A finite sample analysis," Papers 1903.08704, arXiv.org, revised Sep 2021.
- Anatolyev, Stanislav & Sølvsten, Mikkel, 2023.
"Testing many restrictions under heteroskedasticity,"
Journal of Econometrics, Elsevier, vol. 236(1).
- Stanislav Anatolyev & Mikkel S{o}lvsten, 2020. "Testing Many Restrictions Under Heteroskedasticity," Papers 2003.07320, arXiv.org, revised Jan 2023.
- Ng Cheuk Fai, 2022. "Robust Inference in High Dimensional Linear Model with Cluster Dependence," Papers 2212.05554, arXiv.org.
- Liang Jiang & Liyao Li & Ke Miao & Yichong Zhang, 2023. "Adjustment with Many Regressors Under Covariate-Adaptive Randomizations," Papers 2304.08184, arXiv.org, revised Nov 2024.
- Liu, Lin & Mukherjee, Rajarshi & Robins, James M., 2024. "Assumption-lean falsification tests of rate double-robustness of double-machine-learning estimators," Journal of Econometrics, Elsevier, vol. 240(2).
- Wei, Waverly & Zhou, Yuqing & Zheng, Zeyu & Wang, Jingshen, 2024. "Inference on the best policies with many covariates," Journal of Econometrics, Elsevier, vol. 239(2).
More about this item
Keywords
heteroskedasticity; inference; many regressors; statistical leverage;All these keywords.
JEL classification:
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
NEP fields
This paper has been announced in the following NEP Reports:- NEP-ECM-2020-06-29 (Econometrics)
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