LinearIVGMM.start_weights()

statsmodels.sandbox.regression.gmm.LinearIVGMM.start_weights LinearIVGMM.start_weights(inv=True)

LinearIVGMM.score()

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

LinearIVGMM.predict()

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

LinearIVGMM.momcond_mean()

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

LinearIVGMM.momcond()

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

LinearIVGMM.gmmobjective_cu()

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

LinearIVGMM.gradient_momcond()

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

LinearIVGMM.gmmobjective()

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

LinearIVGMM.get_error()

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

LinearIVGMM.fitstart()

statsmodels.sandbox.regression.gmm.LinearIVGMM.fitstart LinearIVGMM.fitstart()