7.12.1.4. statsmodels.sandbox.regression.gmm.IVGMM¶

class statsmodels.sandbox.regression.gmm.IVGMM(endog, exog, instrument, k_moms=None, k_params=None, missing='none', **kwds)[source]¶

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

LinearIVGMM, NonlinearIVGMM

maybe drop and use mixin instead

TODO: GMM doesn’t really care about the data, just the moment conditions

__init__(endog, exog, instrument, k_moms=None, k_params=None, missing='none', **kwds)¶

maybe drop and use mixin instead

TODO: GMM doesn’t really care about the data, just the moment conditions

Methods

__init__(endog, exog, instrument[, k_moms, ...])	maybe drop and use mixin instead
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