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.

翻译：本文提出了一种用于估计贝塔自回归滑动平均（$\beta$ARMA）模型的贝叶斯方法。我们讨论了先验分布的标准选择，并采用哈密顿蒙特卡洛算法从后验分布中进行采样。我们提出了一种解决模型系统性成分中单位根问题的方法。随后通过一系列蒙特卡洛模拟评估了该贝叶斯方法的性能。除了参数估计外，我们还评估了所提方法在检验模型系统性成分中单位根存在性方面的表现，并研究了先验敏感性。通过一项实证应用展示了该方法的实用性。在应用过程中，我们比较了拟合后的贝叶斯方法与频率学派方法在样本外预测能力方面的差异。