statsmodels.sandbox.regression.gmm.NonlinearIVGMM
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class statsmodels.sandbox.regression.gmm.NonlinearIVGMM(endog, exog, instrument, func, **kwds)
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Class for non-linear instrumental variables estimation wusing GMM
The model is assumed to have the following moment condition
E[ z * (y - f(X, beta)] = 0Where
y
is the dependent endogenous variable,x
are the explanatory variables andz
are the instruments. Variables inx
that are exogenous need also be included in z.f
is a nonlinear function.Notation Warning: our name
exog
stands for the explanatory variables, and includes both exogenous and explanatory variables that are endogenous, i.e. included endogenous variablesParameters: endog : array_like
dependent endogenous variable
exog : array_like
explanatory, right hand side variables, including explanatory variables that are endogenous.
instruments : array_like
Instrumental variables, variables that are exogenous to the error in the linear model containing both included and excluded exogenous variables
func : callable
function for the mean or conditional expectation of the endogenous variable. The function will be called with parameters and the array of explanatory, right hand side variables,
func(params, exog)
Notes
This class uses numerical differences to obtain the derivative of the objective function. If the jacobian of the conditional mean function,
func
is available, then it can be used by subclassing this class and defining a methodjac_func
.TODO: check required signature of jac_error and jac_func
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
()from_formula
(formula, data[, subset])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 jac_error
(params, weights[, args, centered, ...])jac_func
(params, weights[, args, centered, ...])momcond
(params)momcond_mean
(params)mean of moment conditions, predict
(params[, exog])score
(params, weights, **kwds)score_cu
(params[, epsilon, centered])start_weights
([inv])Attributes
endog_names
exog_names
results_class
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