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

statsmodels.sandbox.regression.gmm.GMM.gmmobjective GMM.gmmobjective(params, weights) [source] 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.GMM.fitgmm_cu GMM.fitgmm_cu(start, optim_method='bfgs', optim_args=None) [source] 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

statsmodels.sandbox.regression.gmm.GMM.fititer GMM.fititer(start, maxiter=2, start_invweights=None, weights_method='cov', wargs=(), optim_method='bfgs', optim_args=None) [source] 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) in

statsmodels.sandbox.regression.gmm.GMM.from_formula classmethod GMM.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 : extra ar

statsmodels.sandbox.regression.gmm.GMM.fitgmm GMM.fitgmm(start, weights=None, optim_method='bfgs', optim_args=None) [source] estimate parameters using GMM Parameters: start : array_like starting values for minimization weights : array weighting matrix for moment conditions. If weights is None, then the identity matrix is used Returns: paramest : array estimated parameters Notes todo: add fixed parameter option, not here ??? uses scipy.optimize.fmin

statsmodels.sandbox.regression.gmm.GMM.fit GMM.fit(start_params=None, maxiter=10, inv_weights=None, weights_method='cov', wargs=(), has_optimal_weights=True, optim_method='bfgs', optim_args=None) [source] Estimate parameters using GMM and return GMMResults TODO: weight and covariance arguments still need to be made consistent with similar options in other models, see RegressionResult.get_robustcov_results Parameters: start_params : array (optional) starting value for parameters ub minimiza

statsmodels.regression.linear_model.GLSAR.score GLSAR.score(params) Score vector of model. The gradient of logL with respect to each parameter.

statsmodels.sandbox.regression.gmm.GMM.calc_weightmatrix GMM.calc_weightmatrix(moms, weights_method='cov', wargs=(), params=None) [source] calculate omega or the weighting matrix Parameters: moms : array, (nobs, nmoms) moment conditions for all observations evaluated at a parameter value weights_method : string ?cov? If method=?cov? is cov then the matrix is calculated as simple covariance of the moment conditions. see fit method for available aoptions for the weight and covariance matri

statsmodels.regression.linear_model.GLSAR.whiten GLSAR.whiten(X) [source] Whiten a series of columns according to an AR(p) covariance structure. This drops initial p observations. Parameters: X : array-like The data to be whitened, Returns: whitened array :