@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)

        # Note: possible problem with parameters close to min data boundary
        # see issue #968
        self.skip_bsejac = True
dataset/ETHPy150Open statsmodels/statsmodels/statsmodels/miscmodels/tests/test_generic_mle.py/TestMyPareto1.setup_class/original