bqror: An R package for Bayesian Quantile Regression in Ordinal Models

TL;DR

This paper introduces the bqror R package for Bayesian quantile regression in ordinal models, providing efficient MCMC algorithms, model comparison methods, and practical applications demonstrated through simulations and real data.

Contribution

The paper presents novel MCMC algorithms for Bayesian ordinal quantile regression and implements them in an accessible R package, enhancing model estimation and comparison.

Findings

Efficient Gibbs and Metropolis-Hastings algorithms for ordinal quantile regression

Demonstrated accuracy through simulation studies

Applied methods successfully to real datasets

Abstract

This article describes an R package bqror that estimates Bayesian quantile regression for ordinal models introduced in Rahman (2016). The paper classifies ordinal models into two types and offers computationally efficient, yet simple, Markov chain Monte Carlo (MCMC) algorithms for estimating ordinal quantile regression. The generic ordinal model with 3 or more outcomes (labeled ORI model) is estimated by a combination of Gibbs sampling and Metropolis-Hastings algorithm. Whereas an ordinal model with exactly 3 outcomes (labeled ORII model) is estimated using Gibbs sampling only. In line with the Bayesian literature, we suggest using marginal likelihood for comparing alternative quantile regression models and explain how to compute the same. The models and their estimation procedures are illustrated via multiple simulation studies and implemented in two applications. The article also describes several other functions contained within the bqror package, which are necessary for estimation, inference, and assessing model fit.