Downloads
![Image representing the file: CWP291717.pdf](/sites/default/files/output_url_files/CWP291717.pdf_0.jpg)
CWP291717.pdf
PDF | 750.83 KB
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.
Authors
Working Paper details
- DOI
- 10.1920/wp.cem.2017.2917
- Publisher
- The IFS
Suggested citation
Belloni, A et al. (2017). Quantreg.nonpar: an R package for performing nonparametric series quantile regression. London: The IFS. Available at: https://ifs.org.uk/publications/quantregnonpar-r-package-performing-nonparametric-series-quantile-regression (accessed: 30 June 2024).
More from IFS
Understand this issue
![School girls in Rajasthan](/sites/default/files/styles/square_desktop/public/2022-11/Schoolgirls-in-Rajasthan_0.jpg?itok=aboMI9Wt)
Gender norms, violence and adolescent girls’ trajectories: Evidence from India
24 October 2022
![Isabel Stockton](/sites/default/files/styles/square_desktop/public/2024-06/Isabel-public-finances.jpg?itok=JfdJNN7F)
What are the challenges in getting debt on a falling path?
28 June 2024
![Microphone](/sites/default/files/styles/square_desktop/public/2024-06/Microphone.jpg?itok=soM7Wvbz)
Election Special: Your questions answered
27 June 2024
Policy analysis
![Carl Emmerson](/sites/default/files/styles/square_desktop/public/2022-06/Carl_Emmerson.jpg?itok=6jM06LTY)
IFS Deputy Director Carl Emmerson appointed to the UK Statistics Authority Methodological Assurance Review Panel
14 April 2023
![Publication graphic](/sites/default/files/styles/portrait/public/2022-06/IFS-publication-graphic.png?itok=QoQz8AN4)
ABC of SV: Limited Information Likelihood Inference in Stochastic Volatility Jump-Diffusion Models
We develop novel methods for estimation and filtering of continuous-time models with stochastic volatility and jumps using so-called Approximate Bayesian Compu- tation which build likelihoods based on limited information.
12 August 2014
![Hospital](/sites/default/files/styles/square_desktop/public/2022-08/Hospital2.jpg?itok=Jt37JXbP)
Is there really an NHS productivity crisis?
17 November 2023
Academic research
![Working Paper Cover](/sites/default/files/styles/portrait/public/2024-05/CWP1124-Inference-for-rank-rank-regressions_Page_01.jpg?itok=iJl8Ja1B)
Inference for rank-rank regressions
28 May 2024
![Journal Article Cover](/sites/default/files/styles/portrait/public/2024-02/Fiscal%20Studies%20-%202024%20-%20%20-%20Issue%20Information_Page_1.jpg?itok=GfdQz4AB)
Understanding Society: minimising selection biases in data collection using mobile apps
2 February 2024
![Working paper cover](/sites/default/files/styles/portrait/public/2024-06/WP202428-The-impact-of%20labour-demand-shocks-when-occupational-labour-supplies-are-heterogeneous.jpg?itok=Erq9-V9O)
The impact of labour demand shocks when occupational labour supplies are heterogeneous
28 June 2024