statsmodels.sandbox.regression.gmm.GMMResults.cov_params GMMResults.cov_params(**kwds) [source]

statsmodels.sandbox.regression.gmm.GMMResults.conf_int GMMResults.conf_int(alpha=0.05, cols=None, method='default') Returns the confidence interval of the fitted parameters. Parameters: alpha : float, optional The significance level for the confidence interval. ie., The default alpha = .05 returns a 95% confidence interval. cols : array-like, optional cols specifies which confidence intervals to return method : string Not Implemented Yet Method to estimate the confidence_interval. ?Def

statsmodels.sandbox.regression.gmm.GMMResults.compare_j GMMResults.compare_j(other) [source] overidentification test for comparing two nested gmm estimates This assumes that some moment restrictions have been dropped in one of the GMM estimates relative to the other. Not tested yet We are comparing two separately estimated models, that use different weighting matrices. It is not guaranteed that the resulting difference is positive. TODO: Check in which cases Stata programs use the same weigt

statsmodels.sandbox.regression.gmm.GMMResults.calc_cov_params GMMResults.calc_cov_params(moms, gradmoms, weights=None, use_weights=False, has_optimal_weights=True, weights_method='cov', wargs=()) [source] calculate covariance of parameter estimates not all options tried out yet If weights matrix is given, then the formula use to calculate cov_params depends on whether has_optimal_weights is true. If no weights are given, then the weight matrix is calculated with the given method, and has_opt

statsmodels.sandbox.regression.gmm.GMM.start_weights GMM.start_weights(inv=True) [source]

statsmodels.sandbox.regression.gmm.GMM.score_cu GMM.score_cu(params, epsilon=None, centered=True) [source]

statsmodels.sandbox.regression.gmm.GMM.score GMM.score(params, weights, epsilon=None, centered=True) [source]

statsmodels.sandbox.regression.gmm.GMM.predict GMM.predict(params, exog=None, *args, **kwargs) After a model has been fit predict returns the fitted values. This is a placeholder intended to be overwritten by individual models.

statsmodels.sandbox.regression.gmm.GMM.momcond_mean GMM.momcond_mean(params) [source] mean of moment conditions,

statsmodels.sandbox.regression.gmm.GMM.gradient_momcond GMM.gradient_momcond(params, epsilon=0.0001, centered=True) [source] gradient of moment conditions Parameters: params : ndarray parameter at which the moment conditions are evaluated epsilon : float stepsize for finite difference calculation centered : bool This refers to the finite difference calculation. If centered is true, then the centered finite difference calculation is used. Otherwise the one-sided forward differences are