Li Zhong-Fei, Zhou Qi, Chen Ming, Liu Qian
Business School, Sun Yat-sen University, Guangzhou, China.
Center for Financial Engineering and Risk Management, Sun Yat-sen University, Guangzhou, China.
Financ Res Lett. 2021 Mar;39:101931. doi: 10.1016/j.frl.2021.101931. Epub 2021 Jan 12.
We use the cutting-edge causal forest algorithm to analyze the heterogeneous treatment effects of the COVID-19 outbreak on China's industry indexes. The variable importance index is used with the causal forest and complex network methods to analyze the characteristics of industrial relations and the types of industry risk contagion before and after the COVID-19 outbreak. The results show that the heterogeneity of industries was significantly weakened during the COVID-19 outbreak. In addition, the COVID-19 outbreak changed the original structure of the industry-related network, which shifted to a star network structure with leisure services at the core. It also changed the type of risk contagion between industries, from the original middleman risk type to the input risk type.
我们使用前沿的因果森林算法来分析新冠疫情对中国行业指数的异质性治疗效果。将变量重要性指数与因果森林和复杂网络方法结合使用，以分析新冠疫情爆发前后产业关系的特征以及行业风险传染的类型。结果表明，在新冠疫情爆发期间，行业的异质性显著减弱。此外，新冠疫情爆发改变了行业相关网络的原有结构，转变为以休闲服务为核心的星型网络结构。它还改变了行业间风险传染的类型，从原来的中间人风险类型转变为输入风险类型。
