This article focuses on modelling the contagion effects - both between and within groups - on small business failures in London. Small business clusters can be defined based on different companies’ characteristics, for example, economic sector or geographical location. These aspects are usually included as fixed effects to predict the defaults of small- and medium-sized enterprises (SMEs). However, this approach however ignores the interactions between the company groups and only captures the heterogeneity across the clusters. To include both contagion effects between and within groups, a Bayesian spatial hierarchical model for binary data is proposed and applied to a dataset of SMEs located in London in 2016. The empirical analysis shows that the contagion component at the lower level, based on the geographical location, is not significant if the industry clustering is ignored. However, it becomes significant if the industry group effect is included, and also the upper-level interdependence also becomes significant. Finally, the suggested model improves the predictive accuracy and the expected shortfall estimate compared to standard scoring models.

本文的重点是对伦敦小企业倒闭的传染效应进行建模——包括群体之间和群体内部。小型企业集群可以根据不同公司的特征来定义，例如经济部门或地理位置。这些方面通常作为固定效应包括在内，以预测中小企业 (SME) 的违约情况。然而，这种方法忽略了公司集团之间的相互作用，只捕捉到集群之间的异质性。为了包括群体之间和群体内部的传染效应，提出了二进制数据的贝叶斯空间层次模型，并将其应用于 2016 年位于伦敦的中小企业数据集。实证分析表明，基于地理的较低级别的传染成分地点，如果忽略产业集群，则不显着。但是，如果包括产业群效应，则显着，上层相互依存度也显着。最后，与标准评分模型相比，建议的模型提高了预测准确性和预期缺口估计。