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quantreg.nonpar: An R Package for Performing Nonparametric Series Quantile Regression

Author

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  • Michael Lipsitz
  • Alexandre Belloni
  • Victor Chernozhukov
  • Iv'an Fern'andez-Val
Abstract
The R package quantreg.nonpar implements nonparametric quantile regression methods to estimate and make inference on partially linear quantile models. quantreg.nonpar obtains point estimates of the conditional quantile function and its derivatives based on series approximations to the nonparametric part of the model. It also provides pointwise and uniform confidence intervals over a region of covariate values and/or quantile indices for the same functions using analytical and resampling methods. This paper serves as an introduction to the package and displays basic functionality of the functions contained within.

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  • Michael Lipsitz & Alexandre Belloni & Victor Chernozhukov & Iv'an Fern'andez-Val, 2016. "quantreg.nonpar: An R Package for Performing Nonparametric Series Quantile Regression," Papers 1610.08329, arXiv.org.
    • Handle: RePEc:arx:papers:1610.08329
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    Cited by:

    1. Belloni, Alexandre & Chernozhukov, Victor & Chetverikov, Denis & Fernández-Val, Iván, 2019. "Conditional quantile processes based on series or many regressors," Journal of Econometrics, Elsevier, vol. 213(1), pages 4-29.
    2. Bruneel-Zupanc, Christophe Alain, 2021. "Discrete-Continuous Dynamic Choice Models: Identification and Conditional Choice Probability Estimation," TSE Working Papers 21-1185, Toulouse School of Economics (TSE).
    3. Damian Clarke & Manuel Llorca Jaña & Daniel Pailañir, 2023. "The use of quantile methods in economic history," Historical Methods: A Journal of Quantitative and Interdisciplinary History, Taylor & Francis Journals, vol. 56(2), pages 115-132, April.

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