statsmodels.sandbox.regression.gmm.IVGMM¶
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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
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
Names of endogenous variables.
Names of exogenous variables.