statsmodels.sandbox.regression.gmm.IVGMMResults.f_test IVGMMResults.f_test(r_matrix, cov_p=None, scale=1.0, invcov=None) Compute the F-test for a joint linear hypothesis. This is a special case of wald_test that always uses the F distribution. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can

statsmodels.sandbox.regression.gmm.IVGMMResults.get_bse IVGMMResults.get_bse(**kwds) standard error of the parameter estimates with options Parameters: kwds : optional keywords options for calculating cov_params Returns: bse : ndarray estimated standard error of parameter estimates

statsmodels.sandbox.regression.gmm.IVGMMResults.cov_params IVGMMResults.cov_params(**kwds)

statsmodels.sandbox.regression.gmm.IVGMMResults.conf_int IVGMMResults.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.

statsmodels.sandbox.regression.gmm.IVGMMResults.calc_cov_params IVGMMResults.calc_cov_params(moms, gradmoms, weights=None, use_weights=False, has_optimal_weights=True, weights_method='cov', wargs=()) 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_optimal_

statsmodels.sandbox.regression.gmm.IVGMMResults.compare_j IVGMMResults.compare_j(other) 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 weigths

statsmodels.sandbox.regression.gmm.IVGMM.score IVGMM.score(params, weights, epsilon=None, centered=True)

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

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

statsmodels.sandbox.regression.gmm.IVGMM.momcond IVGMM.momcond(params) [source]