Contagion effects of UK small business failures: A spatial hierarchical autoregressive model for binary data
提出贝叶斯空间分层模型，同时捕捉企业集群间和集群内的传染效应，应用于2016年伦敦中小企业数据，发现考虑行业分组后地理传染效应显著，且模型预测精度优于标准评分模型。
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.