statsmodels.sandbox.regression.gmm.LinearIVGMM.score_cu LinearIVGMM.score_cu(params, epsilon=None, centered=True)

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

statsmodels.sandbox.regression.gmm.LinearIVGMM.predict LinearIVGMM.predict(params, exog=None) [source]

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

statsmodels.sandbox.regression.gmm.LinearIVGMM.momcond LinearIVGMM.momcond(params)

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

statsmodels.sandbox.regression.gmm.LinearIVGMM.gmmobjective_cu LinearIVGMM.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.LinearIVGMM.gmmobjective LinearIVGMM.gmmobjective(params, weights) objective function for GMM minimization Parameters: params : array parameter values at which objective is evaluated weights : array weighting matrix Returns: jval : float value of objective function

statsmodels.sandbox.regression.gmm.LinearIVGMM.get_error LinearIVGMM.get_error(params)

statsmodels.sandbox.regression.gmm.LinearIVGMM.from_formula classmethod LinearIVGMM.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