We propose a model-based method to cluster units within a panel. The underlying model is autoregressive and non-Gaussian, allowing for both skewness and fat tails, and the units are clustered according to their dynamic behavior. equilibrium level, and the effect of covariates. Inference is addressed from a Bayesian perspective, and model comparison is conducted using Bayes factors. Particular attention is paid to prior elicitation and posterior propriety. We suggest priors that require little subjective input and have hierarchical structures that enhance inference robustness. We apply our methodology to GDP growth of European regions and to employment growth of Spanish firms.ud
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机译：我们提出了一种基于模型的方法来对面板中的单元进行聚类。基本模型是自回归和非高斯模型，允许偏斜和肥尾，并且根据单位的动态行为对其进行聚类。平衡水平以及协变量的影响。从贝叶斯角度解决了推理问题，并使用贝叶斯因子进行了模型比较。要特别注意事先的启发和后礼。我们建议先验需要很少的主观输入，并具有增强推理鲁棒性的层次结构。我们将方法论应用于欧洲地区的GDP增长以及西班牙公司的就业增长。 ud
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