The Department of Economics Econometrics Seminar Series presents
"Bayesian Quantile Regression for Ordinal Models"
with Arshad Mohammad, Graduate Student, Department of Economics, UCI
Monday, May 6, 2013
Social Science Plaza A, Room 2112
This talk introduces a Bayesian estimation method for quantile regression in univariate ordinal models. Two algorithms are presented that exploit the latent variable inferential framework of Albert-Chib-1993, capitalize on the normal-exponential representation of the asymmetric Laplace (AL) distribution, and judiciously select scale restriction to simplify the sampling procedure. Estimation utilizes Markov chain Monte Carlo (MCMC) simulation methods -- either Gibbs sampling together with the Metropolis-Hastings (MH) algorithm or only Gibbs sampling. The algorithms are demonstrated in two simulation studies and employed to analyze problems in economics (educational attainment) and political economy (public opinion on a recently proposed tax policy).
For further information, please contact Gloria Simpson, firstname.lastname@example.org or 949-824-5788.