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

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

statsmodels.sandbox.regression.gmm.IVGMM.gradient_momcond IVGMM.gradient_momcond(params, epsilon=0.0001, centered=True) 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 used.

statsmodels.sandbox.regression.gmm.IVGMM.gmmobjective_cu IVGMM.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.IVGMM.gmmobjective IVGMM.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.IVGMM.get_error IVGMM.get_error(params) [source]

statsmodels.sandbox.regression.gmm.IVGMM.from_formula classmethod IVGMM.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 : extr

statsmodels.sandbox.regression.gmm.IVGMM.fitstart IVGMM.fitstart() [source]

statsmodels.sandbox.regression.gmm.IVGMM.fititer IVGMM.fititer(start, maxiter=2, start_invweights=None, weights_method='cov', wargs=(), optim_method='bfgs', optim_args=None) iterative estimation with updating of optimal weighting matrix stopping criteria are maxiter or change in parameter estimate less than self.epsilon_iter, with default 1e-6. Parameters: start : array starting value for parameters maxiter : int maximum number of iterations start_weights : array (nmoms, nmoms) initial

statsmodels.sandbox.regression.gmm.IVGMM.fitgmm_cu IVGMM.fitgmm_cu(start, optim_method='bfgs', optim_args=None) estimate parameters using continuously updating GMM Parameters: start : array_like starting values for minimization Returns: paramest : array estimated parameters Notes todo: add fixed parameter option, not here ??? uses scipy.optimize.fmin