'Returns the inverse of the presample variance-covariance.
Notes
See Hamilton p. 125'

def _presample_varcov(self, params):

    k = self.k_trend
    p = self.k_ar
    p1 = (p + 1)
    params0 = np.r_[((-1), params[k:])]
    Vpinv = np.zeros((p, p), dtype=params.dtype)
    for i in range(1, p1):
        Vpinv[(i - 1), (i - 1):] = np.correlate(params0, params0[:i])[:(-1)]
        Vpinv[(i - 1), (i - 1):] -= np.correlate(params0[(- i):], params0)[:(-1)]
    Vpinv = ((Vpinv + Vpinv.T) - np.diag(Vpinv.diagonal()))
    return Vpinv
