ABSTRACT

In economic development there are often regions that share similar socioeconomic characteristics, and econometrics models on such regions tend to produce similar covariate effect estimates. This paper proposes a Bayesian clustered regression for spatially dependent data in order to detect clusters in covariate effects. The proposed method is based on the Dirichlet process, which provides a probabilistic framework for simultaneous inference of the number of clusters and clustering configurations. The use of the method is illustrated both in simulation studies and by an application to a housing cost data set of Georgia.

中文翻译：

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空间相关数据簇的异构回归模型

摘要

在经济发展中，经常有一些地区具有相似的社会经济特征，而这些地区的计量经济学模型往往会产生相似的协变量效应估计。本文针对空间相关数据提出贝叶斯聚类回归，以检测协变量效应中的聚类。所提出的方法基于Dirichlet过程，该过程为同时推断聚类数量和聚类配置提供了一个概率框架。该方法的使用在模拟研究中以及在佐治亚州的房屋成本数据集上的应用都有说明。