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Nonparametric Estimation of Conditional Quantile Functions in the Presence of Irrelevant Covariates

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Nonparametric Estimation of Conditional Quantile Functions in the Presence of Irrelevant Covariates. / Chen, Xirong; Li, Degui; Li, Qi; Li, Zheng.

In: Journal of Econometrics, Vol. 212, No. 2, 10.2019, p. 433-450.

Research output: Contribution to journalArticle

Harvard

Chen, X, Li, D, Li, Q & Li, Z 2019, 'Nonparametric Estimation of Conditional Quantile Functions in the Presence of Irrelevant Covariates', Journal of Econometrics, vol. 212, no. 2, pp. 433-450. https://doi.org/10.1016/j.jeconom.2019.04.037

APA

Chen, X., Li, D., Li, Q., & Li, Z. (2019). Nonparametric Estimation of Conditional Quantile Functions in the Presence of Irrelevant Covariates. Journal of Econometrics, 212(2), 433-450. https://doi.org/10.1016/j.jeconom.2019.04.037

Vancouver

Chen X, Li D, Li Q, Li Z. Nonparametric Estimation of Conditional Quantile Functions in the Presence of Irrelevant Covariates. Journal of Econometrics. 2019 Oct;212(2):433-450. https://doi.org/10.1016/j.jeconom.2019.04.037

Author

Chen, Xirong ; Li, Degui ; Li, Qi ; Li, Zheng. / Nonparametric Estimation of Conditional Quantile Functions in the Presence of Irrelevant Covariates. In: Journal of Econometrics. 2019 ; Vol. 212, No. 2. pp. 433-450.

Bibtex - Download

@article{7116c4d4b4aa484b8ddcdca997459f41,
title = "Nonparametric Estimation of Conditional Quantile Functions in the Presence of Irrelevant Covariates",
abstract = "Allowing for the existence of irrelevant covariates, we study the problem of estimatinga conditional quantile function nonparametrically with mixed discrete and continuous data. We estimate the conditional quantile regression function using the check-function-based kernel method and suggest a data-driven cross-validation (CV) approach to simultaneously determine the optimal smoothing parameters and remove the irrelevant covariates. When the number of covariates is large, we first use a screening method to remove the irrelevant covariates and then apply the CV criterion to those that survive the screening procedure. Simulations and an empirical application demonstrate the usefulness of the proposed methods.",
author = "Xirong Chen and Degui Li and Qi Li and Zheng Li",
year = "2019",
month = oct,
doi = "10.1016/j.jeconom.2019.04.037",
language = "English",
volume = "212",
pages = "433--450",
journal = "Journal of Econometrics",
issn = "0304-4076",
publisher = "Elsevier BV",
number = "2",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Nonparametric Estimation of Conditional Quantile Functions in the Presence of Irrelevant Covariates

AU - Chen, Xirong

AU - Li, Degui

AU - Li, Qi

AU - Li, Zheng

PY - 2019/10

Y1 - 2019/10

N2 - Allowing for the existence of irrelevant covariates, we study the problem of estimatinga conditional quantile function nonparametrically with mixed discrete and continuous data. We estimate the conditional quantile regression function using the check-function-based kernel method and suggest a data-driven cross-validation (CV) approach to simultaneously determine the optimal smoothing parameters and remove the irrelevant covariates. When the number of covariates is large, we first use a screening method to remove the irrelevant covariates and then apply the CV criterion to those that survive the screening procedure. Simulations and an empirical application demonstrate the usefulness of the proposed methods.

AB - Allowing for the existence of irrelevant covariates, we study the problem of estimatinga conditional quantile function nonparametrically with mixed discrete and continuous data. We estimate the conditional quantile regression function using the check-function-based kernel method and suggest a data-driven cross-validation (CV) approach to simultaneously determine the optimal smoothing parameters and remove the irrelevant covariates. When the number of covariates is large, we first use a screening method to remove the irrelevant covariates and then apply the CV criterion to those that survive the screening procedure. Simulations and an empirical application demonstrate the usefulness of the proposed methods.

U2 - 10.1016/j.jeconom.2019.04.037

DO - 10.1016/j.jeconom.2019.04.037

M3 - Article

VL - 212

SP - 433

EP - 450

JO - Journal of Econometrics

JF - Journal of Econometrics

SN - 0304-4076

IS - 2

ER -