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 | str(object=’‘) -> string |