'Computes the significance value for the variable(s) tested.
The empirical distribution of the test statistic is obtained through
bootstrapping the sample.  The null hypothesis is rejected if the test
statistic is larger than the 90, 95, 99 percentiles.'

def _compute_sig(self):

    t_dist = np.empty(shape=(self.nboot,))
    Y = self.endog
    X = copy.deepcopy(self.exog)
    n = np.shape(Y)[0]
    X[:, self.test_vars] = np.mean(X[:, self.test_vars], axis=0)
    M = KernelReg(Y, X, self.var_type, self.model.reg_type, self.bw, defaults=EstimatorSettings(efficient=False)).fit()[0]
    M = np.reshape(M, (n, 1))
    e = (Y - M)
    e = (e - np.mean(e))
    for i in range(self.nboot):
        ind = np.random.random_integers(0, (n - 1), size=(n, 1))
        e_boot = e[(ind, 0)]
        Y_boot = (M + e_boot)
        t_dist[i] = self._compute_test_stat(Y_boot, self.exog)
    self.t_dist = t_dist
    sig = 'Not    Significant'
    if (self.test_stat > mquantiles(t_dist, 0.9)):
        sig = '*'
    if (self.test_stat > mquantiles(t_dist, 0.95)):
        sig = '**'
    if (self.test_stat > mquantiles(t_dist, 0.99)):
        sig = '***'
    return sig
