'Construct forecast interval estimates assuming the y are Gaussian
Parameters
Notes
Lutkepohl pp. 39-40
Returns
(mid, lower, upper) : (ndarray, ndarray, ndarray)'

def forecast_interval(self, y, steps, alpha=0.05, exog_future=None):

    assert (0 < alpha < 1)
    q = util.norm_signif_level(alpha)
    point_forecast = self.forecast(y, steps, exog_future=exog_future)
    sigma = np.sqrt(self._forecast_vars(steps))
    forc_lower = (point_forecast - (q * sigma))
    forc_upper = (point_forecast + (q * sigma))
    return (point_forecast, forc_lower, forc_upper)
