On the Identification of Network Connectedness

I want to clarify an aspect of the Diebold-Yilmaz framework (e.g., here or here).  It is simply a method for summarizing and visualizing dynamic network connectedness, based on a variance decomposition matrix.  The variance decomposition is not a part of our technology; rather, it is the key input to our technology.  Calculation of a variance decomposition of course requires an identified model.  We have nothing new to say about that; numerous models/identifications have appeared over the years, and it's your choice (but you will of course have to defend your choice). 

For certain reasons (e.g., comparatively easy extension to high dimensions) Yilmaz and I generally use a vector-autoregressive model and Koop-Pesaran-Shin "generalized identification".  Again, however, if you don't find that appealing, you can use whatever model and identification scheme you want.  As long as you can supply a credible / defensible variance decomposition matrix, the network summarization / visualization technology can then take over.