Basic class for instrumental variables estimation using GMM
A linear function for the conditional mean is defined as default but the methods should be overwritten by subclasses, currently LinearIVGMM and NonlinearIVGMM are implemented as subclasses.
See also
Methods
| calc_weightmatrix(moms[, weights_method, ...]) | calculate omega or the weighting matrix |
| fit([start_params, maxiter, inv_weights, ...]) | Estimate parameters using GMM and return GMMResults |
| fitgmm(start[, weights, optim_method, ...]) | estimate parameters using GMM |
| fitgmm_cu(start[, optim_method, optim_args]) | estimate parameters using continuously updating GMM |
| fititer(start[, maxiter, start_invweights, ...]) | iterative estimation with updating of optimal weighting matrix |
| fitstart() | |
| from_formula(formula, data[, subset]) | Create a Model from a formula and dataframe. |
| get_error(params) | |
| gmmobjective(params, weights) | objective function for GMM minimization |
| gmmobjective_cu(params[, weights_method, wargs]) | objective function for continuously updating GMM minimization |
| gradient_momcond(params[, epsilon, centered]) | gradient of moment conditions |
| momcond(params) | |
| momcond_mean(params) | mean of moment conditions, |
| predict(params[, exog]) | |
| score(params, weights[, epsilon, centered]) | |
| score_cu(params[, epsilon, centered]) | |
| start_weights([inv]) |
Attributes
| endog_names | |
| exog_names | |
| results_class |