statsmodels.sandbox.regression.gmm.LinearIVGMM

class statsmodels.sandbox.regression.gmm.LinearIVGMM(endog, exog, instrument, k_moms=None, k_params=None, missing='none', **kwds)

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)) = 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.

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

instruments : 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()
from_formula(formula, data[, subset, drop_cols])	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, **kwds)
momcond(params)
momcond_mean(params)	mean of moment conditions,
predict(params[, exog])
score(params, weights, **kwds)
score_cu(params[, epsilon, centered])
start_weights([inv])

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()
from_formula(formula, data[, subset, drop_cols])	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, **kwds)
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	Names of endogenous variables
exog_names	Names of exogenous variables
results_class