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, drop_cols])

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])

After a model has been fit predict returns the fitted values.

score(params, weights[, epsilon, centered])

score_cu(params[, epsilon, centered])

set_param_names(param_names[, k_params])

set the parameter names in the model

start_weights([inv])

Attributes

endog_names

Names of endogenous variables

exog_names

Names of exogenous variables

results_class