statsmodels.sandbox.regression.gmm.NonlinearIVGMM¶

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

Class for non-linear instrumental variables estimation using GMM

The model is assumed to have the following moment condition

E[ z * (y - f(X, beta)] = 0

Where y is the dependent endogenous variable, x are the explanatory variables and z are the instruments. Variables in x 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 variables

Parameters:¶

endogarray_like

dependent endogenous variable

exogarray_like

explanatory, right hand side variables, including explanatory variables that are endogenous.

instrumentsarray_like

Instrumental variables, variables that are exogenous to the error in the linear model containing both included and excluded exogenous variables

funccallable

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)

Attributes:¶

endog_names

Names of endogenous variables.

exog_names

Names of exogenous variables.

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 method jac_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()	Create array of zeros
from_formula(formula, data[, subset, drop_cols])	Create a Model from a formula and dataframe.
get_error(params)	Get error at 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)	Error times instrument
momcond_mean(params)	mean of moment conditions,
predict(params[, exog])	Get prediction at params
score(params, weights, **kwds)	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])	Starting weights

Properties

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