Enabling vector autoregressions

[ckrapu]

The AR distribution appears to be nearly complete for usage as a true vector autoregression parameterized by p cross-series coefficients, each of shape (d,d). The main change that has to be enacted is to use a dot product instead of elementwise multiplication here. However, I am unable to determine the role of the constant argument and why it necessitates the calculation of eps = value[self.p :] - self.rho[0] - x where, under the AR / VAR model, eps is assumed to have a diagonal Normal distribution.

[ricardoV94]

There is some code example of Vector Autoregression here: https://www.pymc-labs.io/blog-posts/bayesian-vector-autoregression/

However, I am unable to determine the role of the constant argument and why it necessitates the calculation of eps = value[self.p :] - self.rho[0] - x

The constant is just an intercept term: y = rho[0] + x, where x are the convolved values and lagged coefficients from rho[1:]. The AR was refactored to V4 in #5734