statsmodels.sandbox.regression.gmm.NonlinearIVGMM.momcond_mean NonlinearIVGMM.momcond_mean(params) mean of moment conditions,

statsmodels.sandbox.regression.gmm.IVRegressionResults.get_robustcov_results IVRegressionResults.get_robustcov_results(cov_type='HC1', use_t=None, **kwds) create new results instance with robust covariance as default Parameters: cov_type : string the type of robust sandwich estimator to use. see Notes below use_t : bool If true, then the t distribution is used for inference. If false, then the normal distribution is used. kwds : depends on cov_type Required or optional arguments for ro

statsmodels.regression.quantile_regression.QuantRegResults.cov_HC1 static QuantRegResults.cov_HC1() See statsmodels.RegressionResults

statsmodels.regression.quantile_regression.QuantReg class statsmodels.regression.quantile_regression.QuantReg(endog, exog, **kwargs) [source] Quantile Regression Estimate a quantile regression model using iterative reweighted least squares. Parameters: endog : array or dataframe endogenous/response variable exog : array or dataframe exogenous/explanatory variable(s) Notes The Least Absolute Deviation (LAD) estimator is a special case where quantile is set to 0.5 (q argument of the fit

statsmodels.iolib.foreign.StataReader.file_label StataReader.file_label() [source] Returns the dataset?s label. Returns: out: string :

statsmodels.discrete.discrete_model.CountResults.llr static CountResults.llr()

statsmodels.tsa.vector_ar.var_model.VARResults.resid_corr static VARResults.resid_corr() [source] Centered residual correlation matrix

statsmodels.miscmodels.tmodel.TLinearModel.information TLinearModel.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.miscmodels.count.PoissonOffsetGMLE.score PoissonOffsetGMLE.score(params) Gradient of log-likelihood evaluated at params

statsmodels.sandbox.tsa.fftarma.ArmaFft.arma2ma ArmaFft.arma2ma(nobs=None)