Journal article
REGULARIZED PROJECTION SCORE ESTIMATION OF TREATMENT EFFECTS IN HIGH-DIMENSIONAL QUANTILE REGRESSION
Statistica Sinica, Vol.32(1), pp.23-41
01/01/2022
DOI: 10.5705/ss.202019.0247
Abstract
We propose a regularized projection score method for estimating the treatment effects in a quantile regression in the presence of high-dimensional con-founding covariates. We show that the proposed estimator of the treatment effects is consistent and asymptotically normal, with a root-n rate of convergence. We also provide an efficient algorithm for the proposed estimator. This algorithm can be implemented easily using existing software. Furthermore, we propose and validate a refitted wild bootstrapping approach for variance estimation. This enables us to construct confidence intervals for the treatment effects in high-dimensional set-tings. Simulation studies are carried out to evaluate the finite-sample performance of the proposed estimator. A GDP growth rate data set is used to demonstrate an application of the method.
Details
- Title: Subtitle
- REGULARIZED PROJECTION SCORE ESTIMATION OF TREATMENT EFFECTS IN HIGH-DIMENSIONAL QUANTILE REGRESSION
- Creators
- Chao Cheng - Shanghai Univ Finance & Econ, Sch Stat & Management, Shanghai 200433, Peoples R ChinaXingdong Feng - Shanghai Univ Finance & Econ, Sch Stat & Management, Shanghai 200433, Peoples R ChinaJian Huang - Univ Iowa, Dept Stat & Actuarial Sci, Iowa City, IA 52246 USAXu Liu - Shanghai Univ Finance & Econ, Sch Stat & Management, Shanghai 200433, Peoples R China
- Resource Type
- Journal article
- Publication Details
- Statistica Sinica, Vol.32(1), pp.23-41
- DOI
- 10.5705/ss.202019.0247
- ISSN
- 1017-0405
- eISSN
- 1996-8507
- Publisher
- STATISTICA SINICA
- Number of pages
- 19
- Grant note
- 130028906 / Key Laboratory for Applied Statistics of MOE, Northeast Normal University Program for Innovative Research Team of SUFE 11971292; 11690012; 11771267 / National Natural Science Foundation of China; National Natural Science Foundation of China (NSFC) DMS-1916199 / NSF; National Science Foundation (NSF)
- Language
- English
- Date published
- 01/01/2022
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984257596502771
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