statsmodels.sandbox.regression.gmm.LinearIVGMM¶
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class
statsmodels.sandbox.regression.gmm.
LinearIVGMM
(endog, exog, instrument, k_moms=None, k_params=None, missing='none', **kwds)[source]¶ class for linear instrumental variables models estimated with GMM
Uses closed form expression instead of nonlinear optimizers for each step of the iterative GMM.
The model is assumed to have the following moment condition
E( z * (y - x beta)) = 0Where 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.
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: endog : array_like
dependent endogenous variable
exog : array_like
explanatory, right hand side variables, including explanatory variables that are endogenous
instrument : array_like
Instrumental variables, variables that are exogenous to the error in the linear model containing both included and excluded exogenous variables
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 for linear model 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, **kwds)gradient of moment conditions 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