statsmodels.tsa.vector_ar.var_model.VARResults.sample_acorr VARResults.sample_acorr(nlags=1) [source]

statsmodels.sandbox.regression.gmm.IVRegressionResults.cov_HC2 static IVRegressionResults.cov_HC2() See statsmodels.RegressionResults

statsmodels.regression.quantile_regression.QuantRegResults.HC2_se static QuantRegResults.HC2_se() [source]

statsmodels.sandbox.regression.gmm.LinearIVGMM.score LinearIVGMM.score(params, weights, **kwds) [source]

statsmodels.discrete.discrete_model.BinaryResults.prsquared static BinaryResults.prsquared()

statsmodels.sandbox.regression.gmm.IVGMM class statsmodels.sandbox.regression.gmm.IVGMM(endog, exog, instrument, k_moms=None, k_params=None, missing='none', **kwds) [source] Basic class for instrumental variables estimation using GMM A linear function for the conditional mean is defined as default but the methods should be overwritten by subclasses, currently LinearIVGMM and NonlinearIVGMM are implemented as subclasses. See also LinearIVGMM, NonlinearIVGMM Methods calc_weightmatrix(moms[,

statsmodels.tsa.vector_ar.var_model.VARResults.sample_acov VARResults.sample_acov(nlags=1) [source]

statsmodels.sandbox.regression.gmm.IVRegressionResults.f_pvalue static IVRegressionResults.f_pvalue()

statsmodels.regression.linear_model.RegressionResults.resid_pearson static RegressionResults.resid_pearson() [source] Residuals, normalized to have unit variance. Returns: An array wresid/sqrt(scale) :

statsmodels.genmod.families.family.Gamma.starting_mu Gamma.starting_mu(y) Starting value for mu in the IRLS algorithm. Parameters: y : array The untransformed response variable. Returns: mu_0 : array The first guess on the transformed response variable. Notes Only the Binomial family takes a different initial value.