EconPapers    
Economics at your fingertips  
 

bayesQR: A Bayesian Approach to Quantile Regression

Dries F. Benoit and Dirk Van den Poel ()

Journal of Statistical Software, 2017, vol. 076, issue i07

Abstract: After its introduction by Koenker and Basset (1978), quantile regression has become an important and popular tool to investigate the conditional response distribution in regression. The R package bayesQR contains a number of routines to estimate quantile regression parameters using a Bayesian approach based on the asymmetric Laplace distribution. The package contains functions for the typical quantile regression with continuous dependent variable, but also supports quantile regression for binary dependent variables. For both types of dependent variables, an approach to variable selection using the adaptive lasso approach is provided. For the binary quantile regression model, the package also contains a routine that calculates the fitted probabilities for each vector of predictors. In addition, functions for summarizing the results, creating traceplots, posterior histograms and drawing quantile plots are included. This paper starts with a brief overview of the theoretical background of the models used in the bayesQR package. The main part of this paper discusses the computational problems that arise in the implementation of the procedure and illustrates the usefulness of the package through selected examples.

Date: 2017-01-29
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4) Track citations by RSS feed

Downloads: (external link)
https://www.jstatsoft.org/index.php/jss/article/view/v076i07/v76i07.pdf
https://www.jstatsoft.org/index.php/jss/article/do ... 7/bayesQR_2.3.tar.gz
https://www.jstatsoft.org/index.php/jss/article/do ... ile/v076i07/v76i07.R

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:jss:jstsof:v:076:i07

DOI: 10.18637/jss.v076.i07

Access Statistics for this article

Journal of Statistical Software is currently edited by Bettina Grün, Edzer Pebesma and Achim Zeileis

More articles in Journal of Statistical Software from Foundation for Open Access Statistics
Bibliographic data for series maintained by Christopher F. Baum ().

 
Page updated 2022-08-08
Handle: RePEc:jss:jstsof:v:076:i07