6.7.4.4.1. statsmodels.sandbox.regression.gmm.DistQuantilesGMM¶

class statsmodels.sandbox.regression.gmm.DistQuantilesGMM(endog, exog, instrument, **kwds)[source]¶

Estimate distribution parameters by GMM based on matching quantiles

Currently mainly to try out different requirements for GMM when we cannot calculate the optimal weighting matrix.

6.7.4.4.1.1. Methods¶

`__init__`(endog, exog, instrument, **kwds)
`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
`fitonce`([start, weights, has_optimal_weights])	fit without estimating an optimal weighting matrix and return results
`fitstart`()
`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`(params)	moment conditions for estimating distribution parameters by matching
`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])