NonlinearIVGMM.fit()

statsmodels.sandbox.regression.gmm.NonlinearIVGMM.fit

NonlinearIVGMM.fit(start_params=None, maxiter=10, inv_weights=None, weights_method='cov', wargs=(), has_optimal_weights=True, optim_method='bfgs', optim_args=None)

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 minimization. If None then fitstart method is called for the starting values. maxiter : int or ?cue? Number of iterations in iterated GMM. The onestep estimate can be obtained with maxiter=0 or 1. If maxiter is large, then the iteration will stop either at maxiter or on convergence of the parameters (TODO: no options for convergence criteria yet.) If maxiter == ?cue?, the the continuously updated GMM is calculated which updates the weight matrix during the minimization of the GMM objective function. The CUE estimation uses the onestep parameters as starting values. inv_weights : None or ndarray inverse of the starting weighting matrix. If inv_weights are not given then the method start_weights is used which depends on the subclass, for IV subclasses inv_weights = z?z where z are the instruments, otherwise an identity matrix is used. weights_method : string, defines method for robust Options here are similar to statsmodels.stats.robust_covariance default is heteroscedasticity consistent, HC0 currently available methods are cov : HC0, optionally with degrees of freedom correction hac : iid : untested, only for Z*u case, IV cases with u as error indep of Z ac : not available yet cluster : not connected yet others from robust_covariance wargs` : tuple or dict, required and optional arguments for weights_method centered : bool, indicates whether moments are centered for the calculation of the weights and covariance matrix, applies to all weight_methods ddof : int degrees of freedom correction, applies currently only to cov maxlag : int number of lags to include in HAC calculation , applies only to hac others not yet, e.g. groups for cluster robust has_optimal_weights: If true, then the calculation of the covariance : matrix assumes that we have optimal GMM with . Default is True. TODO: do we want to have a different default after onestep? optim_method : string, default is ?bfgs? numerical optimization method. Currently not all optimizers that are available in LikelihoodModels are connected. optim_args : dict keyword arguments for the numerical optimizer.

Returns:

results : instance of GMMResults this is also attached as attribute results

Notes

Warning: One-step estimation, maxiter either 0 or 1, still has problems (at least compared to Stata?s gmm). By default it uses a heteroscedasticity robust covariance matrix, but uses the assumption that the weight matrix is optimal. See options for cov_params in the results instance.

The same options as for weight matrix also apply to the calculation of the estimate of the covariance matrix of the parameter estimates.