def setup_class(cls): mod1 = cls.model_cls(endog, exog, **cls.mod_kwargs) cls.res1 = mod1.fit(disp=False, **cls.fit_kwargs) mod2 = OLS(endog, exog) cls.res2 = mod2.fit(disp=False, **cls.fit_kwargs) # for debugging cls.res3 = mod2.fit(cov_type=cls.cov_type, cov_kwds={'maxlags': 2})
def setup_class(cls): mod1 = cls.model_cls(endog, exog, **cls.mod_kwargs) cls.res1 = mod1.fit(disp=False, **cls.fit_kwargs) cls.res1b = mod1.fit(disp=False, **cls.fit_kwargs) mod2 = OLS(endog, exog) cls.res2 = mod2.fit(disp=False, **cls.fit_kwargs)
def setup_class(cls): mod1 = cls.model_cls(endog, exog, **cls.mod_kwargs) cls.res1 = mod1.fit(disp=False, **cls.fit_kwargs) cls.res1b = mod1.fit(cov_type='nw-panel', cov_kwds=cls.cov_kwds) mod2 = OLS(endog, exog) cls.res2 = mod2.fit(disp=False, **cls.fit_kwargs)
def setup_class(cls): mod1 = cls.model_cls(endog, exog, **cls.mod_kwargs) cls.res1 = mod1.fit(**cls.fit_kwargs) mod2 = OLS(endog, exog) # check kernel as string kwds2 = {'kernel': 'uniform', 'maxlags': 2} cls.res2 = mod2.fit(cov_type=cls.cov_type, cov_kwds=kwds2)
def setup_class(cls): # check kernel specified as string mod1 = cls.model_cls(endog, exog, **cls.mod_kwargs) cls.res1 = mod1.fit(disp=False, **cls.fit_kwargs) mod2 = OLS(endog, exog) cls.res2 = mod2.fit(disp=False, cov_type=cls.cov_type, cov_kwds={'maxlags': 2})
def setup(self): model = OLS(self.res1.model.endog, self.res1.model.exog) res_ols = model.fit(cov_type='cluster', cov_kwds=dict(groups=self.groups, use_correction=False, use_t=False, df_correction=True)) self.res3 = self.res1 self.res1 = res_ols self.bse_robust = res_ols.bse self.cov_robust = res_ols.cov_params() cov1 = sw.cov_cluster(self.res1, self.groups, use_correction=False) se1 = sw.se_cov(cov1) self.bse_robust2 = se1 self.cov_robust2 = cov1
def test_regularized_refit(): n = 100 p = 5 np.random.seed(3132) xmat = np.random.normal(size=(n, p)) # covariates 0 and 2 matter yvec = xmat[:, 0] + xmat[:, 2] + np.random.normal(size=n) model1 = OLS(yvec, xmat) result1 = model1.fit_regularized(alpha=2., L1_wt=0.5, refit=True) model2 = OLS(yvec, xmat[:, [0, 2]]) result2 = model2.fit() ii = [0, 2] assert_allclose(result1.params[ii], result2.params) assert_allclose(result1.bse[ii], result2.bse)
def setup_class(cls): data = datasets.longley.load(as_pandas=False) data.exog = add_constant(data.exog, prepend=False) cls.res1 = OLS(data.endog, data.exog).fit() #cls.res2.wresid = cls.res1.wresid # workaround hack res_qr = OLS(data.endog, data.exog).fit(method="qr") model_qr = OLS(data.endog, data.exog) Q, R = np.linalg.qr(data.exog) model_qr.exog_Q, model_qr.exog_R = Q, R model_qr.normalized_cov_params = np.linalg.inv(np.dot(R.T, R)) model_qr.rank = np.linalg.matrix_rank(R) res_qr2 = model_qr.fit(method="qr") cls.res_qr = res_qr cls.res_qr_manual = res_qr2
def setup_class(cls): # TODO: Why does upstream copy endog/exog? mod1 = cls.model_cls(endog, exog, **cls.mod_kwargs) cls.res1 = mod1.fit(disp=False, **cls.fit_kwargs) mod2 = OLS(endog, exog) cls.res2 = mod2.fit(disp=False, **cls.fit_kwargs)
def setup_class(cls): # TODO: de-dup with other setup_classes mod1 = cls.model_cls(endog, exog, **cls.mod_kwargs) cls.res1 = mod1.fit(disp=False, **cls.fit_kwargs) mod2 = OLS(endog, exog) cls.res2 = mod2.fit(disp=False, **cls.fit_kwargs)