Quantreg.nonpar: an R package for performing nonparametric series quantile regression

Michael Lipsitz, Alexandre Belloni, Victor Chernozhukov and Ivan Fernandez-Val

No 29/17, CeMMAP working papers from Institute for Fiscal Studies

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.

Date: 2017-06-06
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https://www.cemmap.ac.uk/wp-content/uploads/2020/08/CWP2917.pdf (application/pdf)

Related works:
Working Paper: Quantreg.nonpar: an R package for performing nonparametric series quantile regression (2017)
Working Paper: quantreg.nonpar: An R Package for Performing Nonparametric Series Quantile Regression (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:azt:cemmap:29/17

DOI: 10.1920/wp.cem.2017.2917

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