statsmodels.sandbox.regression.gmm.IVGMM.fitgmm IVGMM.fitgmm(start, weights=None, optim_method='bfgs', optim_args=None) 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.IVGMM.calc_weightmatrix IVGMM.calc_weightmatrix(moms, weights_method='cov', wargs=(), params=None) 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 matrix wa

statsmodels.sandbox.regression.gmm.IVGMM.fit IVGMM.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.

statsmodels.sandbox.regression.gmm.IV2SLS.predict IV2SLS.predict(params, exog=None) [source] Return linear predicted values from a design matrix. Parameters: exog : array-like Design / exogenous data params : array-like, optional after fit has been called Parameters of a linear model Returns: An array of fitted values : Notes If the model as not yet been fit, params is not optional.

statsmodels.sandbox.regression.gmm.IV2SLS.whiten IV2SLS.whiten(X) [source]

statsmodels.sandbox.regression.gmm.IV2SLS.score IV2SLS.score(params) Score vector of model. The gradient of logL with respect to each parameter.

statsmodels.sandbox.regression.gmm.IV2SLS.initialize IV2SLS.initialize() [source]

statsmodels.sandbox.regression.gmm.IV2SLS.loglike IV2SLS.loglike(params) Log-likelihood of model.

statsmodels.sandbox.regression.gmm.IV2SLS.hessian IV2SLS.hessian(params) The Hessian matrix of the model

statsmodels.sandbox.regression.gmm.IV2SLS.information IV2SLS.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.