R - How to properly account for structural breaks in Hierarchical Bayesian VAR (BVAR)?

I am interested in using the new bvar package in R to predict a set of endogenous time series. However, because of the COVID pandemic, my time series have been through a structural break. What is the best way to account for this in the model? Some hypotheses:

1. Add exogenous dummy variable (it seems the package doesn't have this feature)
2. Add endogenous dummy variable with strong priors that zero the coefficients of impact from other variables over it (i.e. an "artificial" exogenous variable)
3. Create two separate models (before vs after structural break)

I have tried a mix of 2+3. I tested a (i) model with only recent data (after structural break) and no dummies vs (ii) another with the full history with an additional endogenous (dummy) variable, but without the strong dummy prior (I couldn't understand how to configure it properly). The model (ii) has performed way better in the test set.

I wrote an e-mail to the owner of the package, Nikolas Kuschnig (couldn't find his user in SO), to which he replied:

Structural breaks are always a pain to model. In general it's probably preferable to estimate two separate models, but given the short timespan and you getting usable results your idea with adding a dummy variable should also work. You can adjust priors from other variables by manually setting psi in bv_mn() (see the docs and the vignette for an explanation). Depending on the variables you might also be fine not doing any of that, since COVID could just be seen as another shock (which is almost always quite the stretch, given the extent of it).

Note that if there is an actual structural break, the dummies won't suffice, since the coefficients would change (hence my preference for your option 3). To an extent you could model this with a Markov-switching VAR, but unfortunately I don't know of an accessible implementation for R.

Thank you, Nikolas