Artikel
Debiased/double machine learning for instrumental variable quantile regressions
In this study, we investigate the estimation and inference on a low-dimensional causal parameter in the presence of high-dimensional controls in an instrumental variable quantile regression. Our proposed econometric procedure builds on the Neyman-type orthogonal moment conditions of a previous study (Chernozhukov et al. 2018) and is thus relatively insensitive to the estimation of the nuisance parameters. The Monte Carlo experiments show that the estimator copes well with high-dimensional controls. We also apply the procedure to empirically reinvestigate the quantile treatment effect of 401(k) participation on accumulated wealth.
- Sprache
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Englisch
- Erschienen in
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Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 9 ; Year: 2021 ; Issue: 2 ; Pages: 1-18 ; Basel: MDPI
- Klassifikation
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Wirtschaft
- Thema
-
double machine learning
instrumental variable
lasso
quantile regression
quantile treatment effect
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Chen, Jau-er
Huang, Chien-Hsun
Tien, Jia-Jyun
- Ereignis
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Veröffentlichung
- (wer)
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MDPI
- (wo)
-
Basel
- (wann)
-
2021
- DOI
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doi:10.3390/econometrics9020015
- Handle
- Letzte Aktualisierung
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10.03.2025, 11:44 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Chen, Jau-er
- Huang, Chien-Hsun
- Tien, Jia-Jyun
- MDPI
Entstanden
- 2021
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