  This work presents a Bayesian approach for the estimation of Beta
Autoregressive Moving Average ($\beta$ARMA) models. We discuss standard choice
for the prior distributions and employ a Hamiltonian Monte Carlo algorithm to
sample from the posterior. We propose a method to approach the problem of unit
roots in the model's systematic component. We then present a series of Monte
Carlo simulations to evaluate the performance of this Bayesian approach. In
addition to parameter estimation, we evaluate the proposed approach to verify
the presence of unit roots in the model's systematic component and study prior
sensitivity. An empirical application is presented to exemplify the usefulness
of the method. In the application, we compare the fitted Bayesian and
frequentist approaches in terms of their out-of-sample forecasting
capabilities.
