@classmethod
    def setup_class(self):
        params = [2, 0, 2]
        nobs = 50
        np.random.seed(1234)
        rvs = stats.pareto.rvs(*params, **dict(size=nobs))

        mod_par = MyPareto(rvs)
        fixdf = np.nan * np.ones(3)
        fixdf[1] = -0.1
        mod_par.fixed_params = fixdf
        mod_par.fixed_paramsmask = np.isnan(fixdf)
        mod_par.start_params = mod_par.start_params[mod_par.fixed_paramsmask]
        mod_par.df_model = 2
        mod_par.df_resid = mod_par.endog.shape[0] * mod_par.df_model
        mod_par.data.xnames = ['shape', 'scale']

        self.mod = mod_par
        self.res1 = mod_par.fit(disp=None)

        # Note: loc is fixed, no problems with parameters close to min data
        self.skip_bsejac = False
dataset/ETHPy150Open statsmodels/statsmodels/statsmodels/miscmodels/tests/test_generic_mle.py/TestMyParetoRestriction.setup_class/operator `-` -> `*`