statsmodels.sandbox.regression.gmm.IVGMM

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

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

Attributes

endog_names

Names of endogenous variables

exog_names

Names of exogenous variables

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()	Create array of zeros
from_formula(formula, data[, subset, drop_cols])	Create a Model from a formula and dataframe.
get_error(params)	Get error at 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)	Error times instrument
momcond_mean(params)	mean of moment conditions,
predict(params[, exog])	Get prediction at params
score(params, weights[, epsilon, centered])	Score
score_cu(params[, epsilon, centered])	Score cu
set_param_names(param_names[, k_params])	set the parameter names in the model
start_weights([inv])	Starting weights