The Dynamic Factor Network Model with an Application to Global Credit Risk
In recent years, economists have become increasingly interested in analyzing social and economic networks. These networks arise naturally in many fields of economics. Key examples are the input-output matrix of an economy, international trade relationships, and interbank lending networks. The relevance of studying such networks is evident, since the interactions of economic agents, such as firms, households, and governments, are generally at the core of economic activity. However, the econometric tools necessary for the empirical analysis of data that reflect pairwise interactions between economic agents, which we refer to as network data, are still in their infancy. In particular, most existing statistical methods focus on static networks and do not address models of the dynamic behavior of time-varying networks; see also the discussions in Kolaczyk (2009).
In this study, the authors develop a dynamic modeling framework for analyzing network data, with probabilistic link functions that depend on stochastically time-varying parameters. They carry out a Monte Carlo simulation to investigate the finite sample properties of their novel, importance sampling procedure, and, in an empirical study, they analyze credit-risk spillovers between 61 financial firms throughout the world from 2007 through 2015.