This paper proposes a hierarchical modeling approach to perform stochastic model specification in Markov switching vector error correction models. We assume that a common distribution gives rise to the regime-specific regression coefficients. The mean as well as the variances of this distribution are treated as fully stochastic and suitable shrinkage priors are used. These shrinkage priors enable to assess which coefficients differ across regimes in a flexible manner. In the case of similar coefficients, our model pushes the respective regions of the parameter space towards the common distribution. This allows for selecting a parsimonious model while still maintaining sufficient flexibility to control for sudden shifts in the parameters, if necessary. We apply our modeling approach to real-time Euro area data and assume transition probabilities between expansionary and recessionary regimes to be driven by the cointegration errors. The results suggest that the regime allocation is governed by a subset of short-run adjustment coefficients and regime-specific variance-covariance matrices. These findings are complemented by an out-of-sample forecast exercise, illustrating the advantages of the model for predicting Euro area inflation in real time.

中文翻译：

---

马尔可夫切换矢量纠错模型中的随机模型规范

本文提出了一种分层建模方法，用于在马尔可夫切换向量纠错模型中执行随机模型规范。我们假设一个共同的分布会产生特定于政权的回归系数。该分布的均值和方差被视为完全随机的，并且使用了合适的收缩先验。这些收缩先验能够以灵活的方式评估哪些系数因制度而异。在系数相似的情况下，我们的模型将参数空间的各个区域推向共同分布。这允许选择简约模型，同时仍保持足够的灵活性以在必要时控制参数的突然变化。我们将我们的建模方法应用于实时欧元区数据，并假设扩张和衰退体制之间的转换概率由协整误差驱动。结果表明，制度分配受短期调整系数和制度特定方差-协方差矩阵的子集控制。这些发现得到了样本外预测的补充，说明了该模型在实时预测欧元区通胀方面的优势。