Model-based Clustering of non-Gaussian Panel Data
Model-based Clustering of non-Gaussian Panel Data
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发表时间：
2006-11
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通讯作者：
Miguel A. Juárez;M. Steel
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作者：
Miguel A. Juárez;M. Steel
在本文中，我们提出了一种基于模型的方法来聚类面板内的单元。基本模型是自回归和非高斯的，既考虑了偏态，也考虑了厚尾，并根据它们的动态行为和平衡水平对单元进行了分类。从贝叶斯的角度进行推理，并使用贝叶斯因子的形式工具进行模型比较。特别注意事前的启发和事后的得体。我们建议的先验知识需要用户很少的主观输入，并且具有增强推理稳健性的分层结构。两个例子说明了方法：一个分析OECD国家的经济增长，另一个调查西班牙制造业企业的就业增长
In this paper 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 behaviour and equilibrium level. Inference is addressed from a Bayesian perspective and model comparison is conducted using the formal tool of Bayes factors. Particular attention is paid to prior elicitation and posterior propriety. We suggest priors that require little subjective input from the user and possess hierarchical structures that enhance the robustness of the inference. Two examples illustrate the methodology: one analyses economic growth of OECD countries and the second one investigates employment growth of Spanish manufacturing firms