Bayesian Graphical VAR

What is it about?

This paper presents a Bayesian graph-based approach (referred to as BGVAR) to identification in vector autoregressive (VAR) models. An efficient Markov chain Monte Carlo algorithm is proposed to estimate jointly the two causal structures representing the contemporaneous and temporal structures relationships of the model.

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Why is it important?

In simulation and empirical applications, the BGVAR approach is shown to be quite effective in dealing with model identification and selection in multivariate time series of moderate dimension. In the macroeconomic application, the BGVAR identifies the relevant structural relationships among 20 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 2007-2009 financial crisis and a strong bidirectional linkage between the two sectors during the 2010-2013 European sovereign debt crisis.