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

endogarray_like

dependent endogenous variable

exogarray_like

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

instrumentarray_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

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

Properties

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