'Compute forecast covariance matrices for desired number of steps
Parameters
steps : int
Notes
.. math:: \Sigma_{\hat y}(h) = \Sigma_y(h) + \Omega(h) / T
Ref: Lutkepohl pp. 96-97
Returns
covs : ndarray (steps x k x k)'

def forecast_cov(self, steps=1):

    mse = self.mse(steps)
    omegas = self._omega_forc_cov(steps)
    return mse
