statsmodels.sandbox.regression.gmm.NonlinearIVGMM

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

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)] = 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)

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

Attributes
endog_names

Names of endogenous variables.

exog_names

Names of exogenous variables.

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