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Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes

Author

Listed:
  • Victor Chernozhukov
  • Ivan Fernandez-Val
  • Blaise Melly
  • Kaspar W thrich
Abstract
This paper provides a method to construct simultaneous confidence bands for quantile and quantile effect functions for possibly discrete or mixed discrete-continuous random variables. The construction is generic and does not depend on the nature of the underlying problem. It works in conjunction with parametric, semiparametric, and nonparametric modeling strategies and does not depend on the sampling schemes. It is based upon projection of simultaneous confidence bands for distribution functions. We apply our method to analyze the distributional impact of insurance coverage on health care utilization and to provide a distributional decomposition of the racial test score gap. Our analysis generates new interesting findings, and complements previous analyses that focused on mean effects only. In both applications, the outcomes of interest are discrete rendering standard inference methods invalid for obtaining uniform confidence bands for quantile and quantile effects functions.

Suggested Citation

  • Victor Chernozhukov & Ivan Fernandez-Val & Blaise Melly & Kaspar W thrich, 2016. "Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes," Diskussionsschriften dp1607, Universitaet Bern, Departement Volkswirtschaft.
    • Handle: RePEc:ube:dpvwib:dp1607
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    References listed on IDEAS

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    20. Joshua Angrist & Victor Chernozhukov & Iván Fernández-Val, 2006. "Quantile Regression under Misspecification, with an Application to the U.S. Wage Structure," Econometrica, Econometric Society, vol. 74(2), pages 539-563, March.
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    Cited by:

    1. Chernozhukov, Victor & Fernández-Val, Iván & Weidner, Martin, 2024. "Network and panel quantile effects via distribution regression," Journal of Econometrics, Elsevier, vol. 240(2).
    2. Ferdi Botha & John P. de New & Sonja C. de New & David C. Ribar & Nicolás Salamanca, 2020. "COVID-19 labour market shocks and their inequality implications for financial wellbeing," Melbourne Institute Working Paper Series wp2020n15, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    3. Victor Chernozhukov & Iván Fernández‐Val & Whitney Newey & Sami Stouli & Francis Vella, 2020. "Semiparametric estimation of structural functions in nonseparable triangular models," Quantitative Economics, Econometric Society, vol. 11(2), pages 503-533, May.
    4. Victor Chernozhukov & Kaspar Wuthrich & Yinchu Zhu, 2019. "Distributional conformal prediction," Papers 1909.07889, arXiv.org, revised Aug 2021.
    5. 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.
    6. Victor Chernozhukov & Iv'an Fern'andez-Val & Siyi Luo, 2018. "Distribution Regression with Sample Selection, with an Application to Wage Decompositions in the UK," Papers 1811.11603, arXiv.org, revised Dec 2023.
    7. Pedro H. C. Sant'Anna & Xiaojun Song & Qi Xu, 2022. "Covariate distribution balance via propensity scores," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1093-1120, September.
    8. Ferdi Botha & John P. de New, 2020. "COVID-19 infections, labour market shocks, and subjective well-being," Melbourne Institute Working Paper Series wp2020n14, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    9. Victor Chernozhukov & Ivan Fernandez-Val & Siyi Luo, 2023. "Distribution regression with sample selection and UK wage decomposition," CeMMAP working papers 09/23, Institute for Fiscal Studies.
    10. Valentina Corradi & Daniel Gutknecht, 2019. "Testing for Quantile Sample Selection," Papers 1907.07412, arXiv.org, revised Jan 2021.
    11. Tatsushi Oka & Shota Yasui & Yuta Hayakawa & Undral Byambadalai, 2024. "Regression Adjustment for Estimating Distributional Treatment Effects in Randomized Controlled Trials," Papers 2407.14074, arXiv.org.

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    More about this item

    Keywords

    quantiles; quantile effects; treatment effects; distribution; discrete; mixed; count data; confidence bands; uniform inference.;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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