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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.

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

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