6. Conclusion The paper presents an estimation algorithm for Bayesian quantile regression in univariate ordinal models where the error is assumed to follow a GAL distribution, referred to as the FBQROR model. To propose this estimation procedure, we explore the GAL distribution and both introduce and derive its cumulative distribution function and moment generating function. We show that the advantages offered by the GAL distribution – which allows the mode, skewness and tails to vary for any given quantile – can be gainfully utilized to better estimate Bayesian quantile regression in ordinal models. We also emphasize on the efficiency of the MCMC algorithm which can be attained through suitable transformation of the variables and joint sampling of the scale and shape parameters. The practical advantages of the proposed model is illustrated in multiple simulation studies via model comparison, wherein it is observed that the FBQROR model can provide a better model fit as compared to an ordinal model with an AL distribution, labeled BQROR model (Rahman, 2016). Our proposed algorithm is also implemented to examine US public opinion on homeownership as the best long-term investment following the Great Recession. The results point to some interesting insights which may be useful for policy makers and the US housing market. The GAL distribution proposed in Yan and Kottas (2017) and further studied in this pa- per is relatively new and hence needs to be studied further, particularly due to its usefulness in Bayesian quantile regression. In fact, the GAL distribution can practically be employed to estimate most Bayesian quantile regression models that have been estimated using the AL distribution. A partial list includes the Tobit model with endogenous covariates, cen- sored model, count data model, mixed-effect or longitudinal data model (work in progress) and censored dynamic panel data model. Moreover, the distribution can also be utilized to explore Bayesian variable selection in all the above mentioned models. We leave these opportunities for future research. 26