Vector Autoregressions?

I’ve been trying all week to figure out how to estimate a VAR using PyMC3. Is there a guide out there for doing this? Most of the priors I’m supposed to use involve a Wishart distribution and I couldn’t really figure out how to map that into the LKJCholeskycov distribution, nor could I figure out how to take that and use it in an MV Normal distribution.

Are you using the multivariate timeseries like pm.MvGaussianRandomWalk, maybe it is easier if you can show your code.

Honestly at this point it’s all nonsense from trying to get things to work. I didn’t attempt pm.MvGaussianRandomWalk because all of my variables were stationary so I didn’t think that would be appropriate. Maybe I’m misunderstanding the purpose of MvGaussianRandomWalk, but I can’t seem to find any examples of the context it would be used in.

@benmbrennan - did you end up figuring this out? if so, could you share a small example?

I’ve posted a new related topic here:

vector autoregressions of independent series are doable by passing a 2d set of priors for the rho argument to pm.AR. I’m not sure how we let these series load on each other’s lagged values though.