

@classmethod
def setup_class(self):
    params = [2, 0, 2]
    nobs = 100
    np.random.seed(1234)
    rvs = stats.pareto.rvs(*params, **dict(size=nobs))
    mod_par = MyPareto(rvs)
    mod_par.fixed_params = None
    mod_par.fixed_paramsmask = None
    mod_par.df_model = 3
    mod_par.df_resid = (mod_par.endog.shape[0] - mod_par.df_model)
    mod_par.data.xnames = ['shape', 'loc', 'scale']
    self.mod = mod_par
    self.res1 = mod_par.fit(disp=None)
    self.skip_bsejac = True

dataset/ETHPy150Open statsmodels/statsmodels/statsmodels/miscmodels/tests/test_generic_mle.py TestMyPareto1.setup_class/original