The topological structure of panel variance decomposition networks
提出一个框架，基于贝叶斯全局向量自回归模型构建动态方差分解网络，用于分析多国多变量时间序列中冲击传播的路径、聚类和风险传导节点，并以12个欧洲国家的工业产出、零售贸易和经济景气指数为例进行实证。
In this paper we provide a framework to study the network topology of generalized forecast error variance decomposition (GFEVD) derived from multi-country, multi-variable time series models. Our dynamic variance decomposition network is based on a Bayesian Global Vector Autoregressive (GVAR) model, a suitable macroeconometric method to consider simultaneous multi-level interdependencies across variables. We demonstrate the usefulness of our methodology to analyze the network structure of shock propagation in longitudinal time series and, in particular: (a) the shortest paths of contagion; (b) the clusters of shock transmission; (c) the role of nodes in the risk transmission channels. We illustrate our method through an empirical application to a set of 12 European countries’ Industrial Production, Retail Trade and Economic Sentiment indices over the period 01/2000–11/2021.