'Returns  -2 x log-likelihood and p-value for the hypothesized
skewness.
Parameters
skew0 : float
Skewness value to be tested
return_weights : bool
If True, function also returns the weights that
maximize the likelihood ratio. Default is False.
Returns
test_results : tuple
The log-likelihood ratio and p_value of skew0'

def test_skew(self, skew0, return_weights=False):

    self.skew0 = skew0
    start_nuisance = np.array([self.endog.mean(), self.endog.var()])
    llr = optimize.fmin_powell(self._opt_skew, start_nuisance, full_output=1, disp=0)[1]
    p_val = chi2.sf(llr, 1)
    if return_weights:
        return (llr, p_val, self.new_weights.T)
    return (llr, p_val)
