Model-Based Clustering of Non-Gaussian Panel Data Based on Skew-tDistributions
提出一种基于偏斜t分布的面板数据聚类方法，允许数据有偏斜和厚尾，按动态行为、均衡水平和协变量效应聚类，用贝叶斯方法推断，并应用于欧洲区域GDP增长和西班牙企业就业增长数据。
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.