statsmodels.sandbox.regression.gmm.GMM

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

Class for estimation by Generalized Method of Moments

needs to be subclassed, where the subclass defined the moment conditions momcond

Parameters

endogndarray

endogenous variable, see notes

exogndarray

array of exogenous variables, see notes

instrumentndarray

array of instruments, see notes

nmomsNone or int

number of moment conditions, if None then it is set equal to the number of columns of instruments. Mainly needed to determine the shape or size of start parameters and starting weighting matrix.

kwdsanything

this is mainly if additional variables need to be stored for the calculations of the moment conditions

Notes

The GMM class only uses the moment conditions and does not use any data directly. endog, exog, instrument and kwds in the creation of the class instance are only used to store them for access in the moment conditions. Which of this are required and how they are used depends on the moment conditions of the subclass.

Warning:

Options for various methods have not been fully implemented and are still missing in several methods.

TODO: currently onestep (maxiter=0) still produces an updated estimate of bse and cov_params.

Attributes

resultsinstance of GMMResults

currently just a storage class for params and cov_params without it’s own methods

bseproperty

return bse

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
from_formula(formula, data[, subset, drop_cols])	Create a Model from a formula and dataframe.
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_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
score_cu(params[, epsilon, centered])	Score cu
set_param_names(param_names[, k_params])	set the parameter names in the model
start_weights([inv])	Create identity matrix for starting weights

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
from_formula(formula, data[, subset, drop_cols])	Create a Model from a formula and dataframe.
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_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
score_cu(params[, epsilon, centered])	Score cu
set_param_names(param_names[, k_params])	set the parameter names in the model
start_weights([inv])	Create identity matrix for starting weights

Properties

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