statsmodels.sandbox.regression.gmm.NonlinearIVGMM.fit NonlinearIVGMM.fit(start_params=None, maxiter=10, inv_weights=None, weights_method='cov', wargs=(), has_optimal_weights=True, optim_method='bfgs', optim_args=None) Estimate parameters using GMM and return GMMResults TODO: weight and covariance arguments still need to be made consistent with similar options in other models, see RegressionResult.get_robustcov_results Parameters: start_params : array (optional) starting value for parameter

statsmodels.tsa.arima_model.ARIMAResults.bic static ARIMAResults.bic()

statsmodels.stats.weightstats._zstat_generic statsmodels.stats.weightstats._zstat_generic(value1, value2, std_diff, alternative, diff=0) [source] generic (normal) z-test to save typing can be used as ztest based on summary statistics

statsmodels.iolib.summary.Summary.as_text Summary.as_text() [source] return tables as string Returns: txt : string summary tables and extra text as one string

statsmodels.regression.linear_model.RegressionResults.cov_HC3 static RegressionResults.cov_HC3() [source] See statsmodels.RegressionResults

statsmodels.sandbox.regression.gmm.IVRegressionResults.HC2_se static IVRegressionResults.HC2_se() See statsmodels.RegressionResults

statsmodels.regression.linear_model.WLS.whiten WLS.whiten(X) [source] Whitener for WLS model, multiplies each column by sqrt(self.weights) Parameters: X : array-like Data to be whitened Returns: sqrt(weights)*X :

statsmodels.sandbox.regression.gmm.IVRegressionResults.summary2 IVRegressionResults.summary2(yname=None, xname=None, title=None, alpha=0.05, float_format='%.4f') Experimental summary function to summarize the regression results Parameters: xname : List of strings of length equal to the number of parameters Names of the independent variables (optional) yname : string Name of the dependent variable (optional) title : string, optional Title for the top table. If not None, then this replac

statsmodels.sandbox.stats.multicomp.StepDown class statsmodels.sandbox.stats.multicomp.StepDown(vals, nobs_all, var_all, df=None) [source] a class for step down methods This is currently for simple tree subset descend, similar to homogeneous_subsets, but checks all leave-one-out subsets instead of assuming an ordered set. Comment in SAS manual: SAS only uses interval subsets of the sorted list, which is sufficient for range tests (maybe also equal variance and balanced sample sizes are requi

statsmodels.discrete.discrete_model.LogitResults.aic static LogitResults.aic()