statsmodels.sandbox.regression.gmm.GMMResults.q static GMMResults.q() [source]

statsmodels.sandbox.regression.gmm.IV2SLS.initialize IV2SLS.initialize() [source]

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

statsmodels.discrete.discrete_model.ProbitResults.llr_pvalue static ProbitResults.llr_pvalue()

statsmodels.stats.sandwich_covariance.cov_nw_groupsum statsmodels.stats.sandwich_covariance.cov_nw_groupsum(results, nlags, time, weights_func=, use_correction=0) [source] Driscoll and Kraay Panel robust covariance matrix Robust covariance matrix for panel data of Driscoll and Kraay. Assumes we have a panel of time series where the time index is available. The time index is assumed to represent equal spaced periods. At least one observation per period is required. Parameters: results : resu

statsmodels.discrete.discrete_model.Probit.from_formula classmethod Probit.from_formula(formula, data, subset=None, *args, **kwargs) Create a Model from a formula and dataframe. Parameters: formula : str or generic Formula object The formula specifying the model data : array-like The data for the model. See Notes. subset : array-like An array-like object of booleans, integers, or index values that indicate the subset of df to use in the model. Assumes df is a pandas.DataFrame args : e

statsmodels.sandbox.regression.gmm.NonlinearIVGMM.gmmobjective_cu NonlinearIVGMM.gmmobjective_cu(params, weights_method='cov', wargs=()) objective function for continuously updating GMM minimization Parameters: params : array parameter values at which objective is evaluated Returns: jval : float value of objective function

statsmodels.sandbox.regression.gmm.GMM.gmmobjective_cu GMM.gmmobjective_cu(params, weights_method='cov', wargs=()) [source] objective function for continuously updating GMM minimization Parameters: params : array parameter values at which objective is evaluated Returns: jval : float value of objective function

statsmodels.sandbox.distributions.transformed.Transf_gen.moment Transf_gen.moment(n, *args, **kwds) n?th order non-central moment of distribution. Parameters: n : int, n>=1 Order of moment. arg1, arg2, arg3,... : float The shape parameter(s) for the distribution (see docstring of the instance object for more information). kwds : keyword arguments, optional These can include ?loc? and ?scale?, as well as other keyword arguments relevant for a given distribution.

statsmodels.stats.weightstats.CompareMeans.ztest_ind CompareMeans.ztest_ind(alternative='two-sided', usevar='pooled', value=0) [source] z-test for the null hypothesis of identical means Parameters: x1, x2 : array_like, 1-D or 2-D two independent samples, see notes for 2-D case alternative : string The alternative hypothesis, H1, has to be one of the following ?two-sided?: H1: difference in means not equal to value (default) ?larger? : H1: difference in means larger than value ?smaller? :