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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_likedependent endogenous variable exogarray_likeexplanatory, right hand side variables, including explanatory variables that are endogenous. instrumentsarray_likeInstrumental variables, variables that are exogenous to the error in the linear model containing both included and excluded exogenous variables funccallablefunction 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_namesNames of endogenous variables. exog_namesNames 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 |
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