Bayesian Graphical VAR（BGVAR）

JAE上首次刊登了一篇Bayesian graphical VAR (BGVAR)模型的文章，运用该模型的文章多了起来，JAE，你懂滴。

This paper proposes a Bayesian, graph‐based approach to identification in vector autoregressive (VAR) models. In our Bayesian graphical VAR (BGVAR) model, the contemporaneous and temporal causal structures of the structural VAR model are represented by two different graphs. We also provide an efficient Markov chain Monte Carlo algorithm to estimate jointly the two causal structures and the parameters of the reduced‐form VAR model. The BGVAR approach is shown to be quite effective in dealing with model identification and selection in multivariate time series of moderate dimension, as those considered in the economic literature. In the macroeconomic application the BGVAR identifies the relevant structural relationships among twenty US economic variables, thus providing a useful tool for policy analysis. The financial application contributes to the recent econometric literature on financial interconnectedness. The BGVAR approach provides evidence of a strong unidirectional linkage from financial to non‐financial super‐sectors during the global financial crisis and a strong bidirectional linkage between the two sectors during the European sovereign debt crisis. 

他们比较了各种模型的优劣，并使用BGVAR分析了美国次贷危机期间、欧洲债务危机期间的风险溢出网络。