  We consider quantile estimation using Markov chain Monte Carlo and establish
conditions under which the sampling distribution of the Monte Carlo error is
approximately Normal. Further, we investigate techniques to estimate the
associated asymptotic variance, which enables construction of an asymptotically
valid interval estimator. Finally, we explore the finite sample properties of
these methods through examples and provide some recommendations to
practitioners.
