Prior choice can strongly influence Bayesian Dirichlet ARMA (B-DARMA) inference for compositional time-series. Using simulations with (i) correct lag order, (ii) overfitting, and (iii) underfitting, we assess five priors: weakly-informative, horseshoe, Laplace, mixture-of-normals, and hierarchical. With the true lag order, all priors achieve comparable RMSE, though horseshoe and hierarchical slightly reduce bias. Under overfitting, aggressive shrinkage-especially the horseshoe-suppresses noise and improves forecasts, yet no prior rescues a model that omits essential VAR or VMA terms. We then fit B-DARMA to daily SP 500 sector weights using an intentionally large lag structure. Shrinkage priors curb spurious dynamics, whereas weakly-informative priors magnify errors in volatile sectors. Two lessons emerge: (1) match shrinkage strength to the degree of overparameterization, and (2) prioritize correct lag selection, because no prior repairs structural misspecification. These insights guide prior selection and model complexity management in high-dimensional compositional time-series applications.

翻译：先验选择对成分时间序列的贝叶斯狄利克雷ARMA（B-DARMA）推断具有显著影响。通过（i）正确滞后阶数、（ii）过度拟合和（iii）欠拟合三种情境的模拟，我们评估了五种先验分布：弱信息先验、马蹄先验、拉普拉斯先验、正态混合先验和分层先验。在真实滞后阶数下，所有先验均获得可比的均方根误差，但马蹄先验和分层先验略微降低了偏差。在过度拟合情况下，强收缩先验（尤其是马蹄先验）能有效抑制噪声并改善预测效果，但任何先验都无法挽救遗漏核心VAR或VMA项的模型。随后，我们采用故意扩大的滞后结构将B-DARMA模型拟合至标普500行业日度权重数据。收缩先验能抑制虚假动态，而弱信息先验会放大波动行业的误差。由此得到两个启示：（1）收缩强度应与过参数化程度相匹配；（2）应优先确保滞后阶数正确选择，因为先验无法修正结构误设。这些发现为高维成分时间序列应用中的先验选择与模型复杂度管理提供了指导。