'Asymptotically consistent estimate of covariance of the sample mean
.. math::
\sqrt(T) (\bar{y} - \mu) \rightarrow {\cal N}(0, \Sigma_{\bar{y}})\\
\Sigma_{\bar{y}} = B \Sigma_u B^\prime, \text{where } B = (I_K - A_1
- \cdots - A_p)^{-1}
Notes
Lutkepohl Proposition 3.3'

def cov_ybar(self):

    Ainv = L.inv((np.eye(self.neqs) - self.coefs.sum(0)))
    return chain_dot(Ainv, self.sigma_u, Ainv.T)
