コード例 #1
0
    def res_opg(self):
        res_ols = self.res_ols
        nobs = self.nobs
        moms = self.moms
        moms_obs = self.moms_obs
        covm = self.covm
        moms_deriv = self.moms_deriv
        weights = self.weights
        L = self.L
        x = self.exog_full

        res_ols2_hc0 = OLS(res_ols.model.endog, x).fit(cov_type='HC0')

        res_all = []

        # auxiliary regression
        ones = np.ones(nobs)
        stat = nobs * OLS(ones, moms_obs).fit().rsquared
        res_all.append(('ols R2', stat))

        tres = res_ols2_hc0.compare_lm_test(res_ols, demean=False)
        res_all.append(('comp_lm uc', tres))

        tres = CMTNewey(moms, covm, covm[:, :-2], weights, L).chisquare
        res_all.append(('Newey', tres))

        tres = CMTTauchen(moms[:-2], covm[:-2, :-2], moms[-2:], covm[-2:, :-2],
                          covm).chisquare
        res_all.append(('Tauchen', tres))

        tres = diao.lm_robust_subset(moms[-2:], 2, covm, covm)
        res_all.append(('score subset QMLE', tres))

        tres = diao.lm_robust(moms,
                              np.eye(moms.shape[0])[-2:],
                              np.linalg.inv(covm),
                              covm,
                              cov_params=None)
        res_all.append(('scoreB QMLE', tres))

        tres = diao.lm_robust(moms,
                              np.eye(moms.shape[0])[-2:],
                              np.linalg.inv(covm),
                              None,
                              cov_params=np.linalg.inv(covm))
        res_all.append(('scoreV QMLE', tres))

        return res_all
コード例 #2
0
ファイル: test_glm.py プロジェクト: viveksck/langchange
def test_score_test_OLS():
    # nicer example than Longley
    from statsmodels.regression.linear_model import OLS
    np.random.seed(5)
    nobs = 100
    sige = 0.5
    x = np.random.uniform(0, 1, size=(nobs, 5))
    x[:, 0] = 1
    beta = 1. / np.arange(1., x.shape[1] + 1)
    y = x.dot(beta) + sige * np.random.randn(nobs)

    res_ols = OLS(y, x).fit()
    res_olsc = OLS(y, x[:, :-2]).fit()
    co = res_ols.compare_lm_test(res_olsc, demean=False)

    res_glm = GLM(y, x[:, :-2], family=sm.families.Gaussian()).fit()
    co2 = res_glm.model.score_test(res_glm.params, exog_extra=x[:, -2:])
    # difference in df_resid versus nobs in scale see #1786
    assert_allclose(co[0] * 97 / 100., co2[0], rtol=1e-13)
コード例 #3
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ファイル: test_glm.py プロジェクト: JerWatson/statsmodels
def test_score_test_OLS():
    # nicer example than Longley
    from statsmodels.regression.linear_model import OLS
    np.random.seed(5)
    nobs = 100
    sige = 0.5
    x = np.random.uniform(0, 1, size=(nobs, 5))
    x[:, 0] = 1
    beta = 1. / np.arange(1., x.shape[1] + 1)
    y = x.dot(beta) + sige * np.random.randn(nobs)

    res_ols = OLS(y, x).fit()
    res_olsc = OLS(y, x[:, :-2]).fit()
    co = res_ols.compare_lm_test(res_olsc, demean=False)

    res_glm = GLM(y, x[:, :-2], family=sm.families.Gaussian()).fit()
    co2 = res_glm.model.score_test(res_glm.params, exog_extra=x[:, -2:])
    # difference in df_resid versus nobs in scale see #1786
    assert_allclose(co[0] * 97 / 100., co2[0], rtol=1e-13)
コード例 #4
0
    def res_opg(self):
        res_ols = self.res_ols
        nobs = self.nobs
        moms = self.moms
        moms_obs = self.moms_obs
        covm = self.covm
        moms_deriv = self.moms_deriv
        weights = self.weights
        L = self.L
        x = self.exog_full

        res_ols2_hc0 = OLS(res_ols.model.endog, x).fit(cov_type='HC0')

        res_all = []

        # auxiliary regression
        ones = np.ones(nobs)
        stat = nobs * OLS(ones, moms_obs).fit().rsquared
        res_all.append(('ols R2', stat))

        tres = res_ols2_hc0.compare_lm_test(res_ols, demean=False)
        res_all.append(('comp_lm uc', tres))

        tres = CMTNewey(moms, covm, covm[:,:-2], weights, L).chisquare
        res_all.append(('Newey', tres))

        tres = CMTTauchen(moms[:-2], covm[:-2, :-2], moms[-2:], covm[-2:, :-2],
                          covm).chisquare
        res_all.append(('Tauchen', tres))

        tres = diao.lm_robust_subset(moms[-2:], 2, covm, covm)
        res_all.append(('score subset QMLE', tres))

        tres = diao.lm_robust(moms, np.eye(moms.shape[0])[-2:],
                              np.linalg.inv(covm), covm, cov_params=None)
        res_all.append(('scoreB QMLE', tres))

        tres = diao.lm_robust(moms, np.eye(moms.shape[0])[-2:],
                              np.linalg.inv(covm), None,
                              cov_params=np.linalg.inv(covm))
        res_all.append(('scoreV QMLE', tres))

        return res_all