statsmodels.sandbox.regression.gmm.GMM
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class statsmodels.sandbox.regression.gmm.GMM(endog, exog, instrument, k_moms=None, k_params=None, missing='none', **kwds)
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Class for estimation by Generalized Method of Moments
needs to be subclassed, where the subclass defined the moment conditions
momcond
Parameters: endog : array
endogenous variable, see notes
exog : array
array of exogenous variables, see notes
instrument : array
array of instruments, see notes
nmoms : None or int
number of moment conditions, if None then it is set equal to the number of columns of instruments. Mainly needed to determin the shape or size of start parameters and starting weighting matrix.
kwds : anything
this is mainly if additional variables need to be stored for the calculations of the moment conditions
Returns: *Attributes* :
results : instance of GMMResults
currently just a storage class for params and cov_params without it?s own methods
bse : property
return bse
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.
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])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_cu
(params[, epsilon, centered])start_weights
([inv])Attributes
endog_names
exog_names
results_class
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