Description"Contagion effects for UK small business failures: A spatial hierarchical autoregressive model for binary data"
The focus is on modelling contagion effects between and within groups on small business failures in London. Small business clusters could 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). This approach however ignores the interaction between the companies' groups and captures only the heterogeneity across the clusters. To include both contagion effects between and within groups, we propose a Bayesian hierarchical model for binary data. We find that the contagion component at the lower level, based on the geographical location, is not significant if we ignore the clustering. However, it becomes significant if we consider the industry group effect and also the upper level interdependence is significant. Finally, we show that our proposal improves the ability to predict SMEs defaults in London.
|Period||14 Dec 2019 → 16 Dec 2019|
|Location||London, United Kingdom|
|Degree of Recognition||International|