An endogenously clustered factor approach to international business cycles
提出一种内生聚类因子模型，通过贝叶斯层次先验自动确定因子分组，避免预设分组导致的误设问题。基于国际商业周期数据发现，制度相似性（如法律体系、语言多样性）对分析商业周期联动的重要性不亚于地理邻近性。
Summary Factor models have become useful tools for studying international business cycles. Block factor models can be especially useful as the zero restrictions on the loadings of some factors may provide some economic interpretation of the factors. These models, however, require the econometrician to predefine the blocks, leading to potential misspecification. In Monte Carlo experiments, we show that even a small misspecification can lead to substantial declines in fit. We propose an alternative model in which the blocks are chosen endogenously. The model is estimated in a Bayesian framework using a hierarchical prior, which allows us to incorporate series‐level covariates that may influence and explain how the series are grouped. Using international business cycle data, we find our country clusters differ in important ways from those identified by geography alone. In particular, we find that similarities in institutions (e.g., legal systems, language diversity) may be just as important as physical proximity for analyzing business cycle comovements.