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.

Attributes:¶

endog_names

Names of endogenous variables.

exog_names

Names of exogenous variables.

See also

LinearIVGMM

NonlinearIVGMM

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

Properties

endog_names	Names of endogenous variables.
exog_names	Names of exogenous variables.
results_class