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Unconditional Quantile Regression with High Dimensional Data

Author

Listed:
  • Yuya Sasaki
  • Takuya Ura
  • Yichong Zhang

Abstract

This paper considers estimation and inference for heterogeneous counterfactual effects with high-dimensional data. We propose a novel robust score for debiased estimation of the unconditional quantile regression (Firpo, Fortin, and Lemieux, 2009) as a measure of heterogeneous counterfactual marginal effects. We propose a multiplier bootstrap inference and develop asymptotic theories to guarantee the size control in large sample. Simulation studies support our theories. Applying the proposed method to Job Corps survey data, we find that a policy which counterfactually extends the duration of exposures to the Job Corps training program will be effective especially for the targeted subpopulations of lower potential wage earners.

Suggested Citation

  • Yuya Sasaki & Takuya Ura & Yichong Zhang, 2020. "Unconditional Quantile Regression with High Dimensional Data," Papers 2007.13659, arXiv.org, revised Feb 2022.
    • Handle: RePEc:arx:papers:2007.13659
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    References listed on IDEAS

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    Cited by:

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    2. Martinez-Iriarte, Julian & Sun, Yixiao, 2024. "Identification and estimation of unconditional policy effects of an endogenous binary treatment: An unconditional MTE approach," Journal of Econometrics, Elsevier, vol. 244(1).
    3. Zhengyu Zhang & Zequn Jin & Lihua Lin, 2024. "Identification and inference of outcome conditioned partial effects of general interventions," Papers 2407.16950, arXiv.org.
    4. Zequn Jin & Lihua Lin & Zhengyu Zhang, 2022. "Identification and Auto-debiased Machine Learning for Outcome Conditioned Average Structural Derivatives," Papers 2211.07903, arXiv.org.
    5. Hui-Ching Chuang & Jau-er Chen, 2023. "Exploring Industry-Distress Effects on Loan Recovery: A Double Machine Learning Approach for Quantiles," Econometrics, MDPI, vol. 11(1), pages 1-20, February.
    6. Javier Alejo & Antonio Galvao & Julián Martínez-Iriarte & Gabriel Montes-Rojas, 2025. "Generalized Recentered Influence Function Regressions," Econometrics, MDPI, vol. 13(2), pages 1-14, April.

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