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
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]) |
endog_names | Names of endogenous variables |
exog_names | Names of exogenous variables |
results_class |
© 2009–2012 Statsmodels Developers
© 2006–2008 Scipy Developers
© 2006 Jonathan E. Taylor
Licensed under the 3-clause BSD License.
http://www.statsmodels.org/stable/generated/statsmodels.sandbox.regression.gmm.IVGMM.html