def test_fixed_scale(self):
cov_type = 'fixed_scale'
kwds = {}
res1 = self.res_ols.get_robustcov_results(cov_type, **kwds)
res2 = self.res_wls.get_robustcov_results(cov_type, **kwds)
assert_allclose(res1.params, res2.params, rtol=1e-13)
assert_allclose(res1.cov_params(), res2.cov_params(), rtol=1e-13)
assert_allclose(res1.bse, res2.bse, rtol=1e-13)
assert_allclose(res1.pvalues, res2.pvalues, rtol=1e-12)
tt = res2.t_test(np.eye(len(res2.params)),
cov_p=res2.normalized_cov_params)
assert_allclose(res2.cov_params(), res2.normalized_cov_params,
rtol=1e-13)
assert_allclose(res2.bse, tt.sd, rtol=1e-13)
assert_allclose(res2.pvalues, tt.pvalue, rtol=1e-13)
assert_allclose(res2.tvalues, tt.tvalue, rtol=1e-13)
# using cov_type in fit
mod = self.res_wls.model
mod3 = WLS(mod.endog, mod.exog, weights=mod.weights)
res3 = mod3.fit(cov_type=cov_type, cov_kwds=kwds)
tt = res3.t_test(np.eye(len(res3.params)),
cov_p=res3.normalized_cov_params)
assert_allclose(res3.cov_params(), res3.normalized_cov_params,
rtol=1e-13)
assert_allclose(res3.bse, tt.sd, rtol=1e-13)
assert_allclose(res3.pvalues, tt.pvalue, rtol=1e-13)
assert_allclose(res3.tvalues, tt.tvalue, rtol=1e-13)
def setup_class(cls):
# from example wls.py
nsample = 50
x = np.linspace(0, 20, nsample)
X = np.column_stack((x, (x - 5)**2))
X = add_constant(X)
beta = [5., 0.5, -0.01]
sig = 0.5
w = np.ones(nsample)
w[int(nsample * 6. / 10):] = 3
y_true = np.dot(X, beta)
e = np.random.normal(size=nsample)
y = y_true + sig * w * e
X = X[:, [0, 1]]
# WLS knowing the true variance ratio of heteroscedasticity
mod_wls = WLS(y, X, weights=1. / w)
cls.res_wls = mod_wls.fit()