The econometrics of Bayesian graphical models: a review with financial application

What is it about?

This paper present a review on the interface between statistics and econometrics in the inference and application of Bayesian graphical models with attention on financial econometrics.

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Why is it important?

We specifically highlight connections and possible applications of network models in financial econometrics in the context of systemic risk. A comparison of the Bayesian graphical method and Granger causality shows that the Bayesian method produces dependence patterns that are more suitable than those produced by Granger causality to capture complex interdependencies.