Vector autoregressive model fitting with scikit-learn

Question

I am trying to fit vector autoregressive (VAR) models using the generalized linear model fitting methods included in scikit-learn. The linear model has the form y = X w, but the system matrix X has a very peculiar structure: it is block-diagonal, and all blocks are identical. To optimize performance and memory consumption the model can be expressed as Y = BW, where B is a block from X, and Y and W are now matrices instead of vectors. The classes LinearRegression, Ridge, RidgeCV, Lasso, and ElasticNet readily accept the latter model structure. However, fitting LassoCV or ElasticNetCV fails due to Y being two-dimensional.

I found https://github.com/scikit-learn/scikit-learn/issues/2402 From this discussion I assume that the behavior of LassoCV/ElasticNetCV is intended. Is there a way to optimize the alpha/rho parameters other than manually implementing cross-validation?

Furthermore, Bayesian regression techniques in scikit-learn also expect y to be one-dimensional. Is there any way around this?

Note: I use scikit-learn 0.14 (stable)