# linearmodels.iv.model.IVGMM¶

class IVGMM(dependent, exog, endog, instruments, *, weights=None, weight_type='robust', **weight_config)[source]

Estimation of IV models using the generalized method of moments (GMM)

Parameters:
dependentarray_like

Endogenous variables (nobs by 1)

exogarray_like

Exogenous regressors (nobs by nexog)

endogarray_like

Endogenous regressors (nobs by nendog)

instrumentsarray_like

Instrumental variables (nobs by ninstr)

weightsarray_like

Observation weights used in estimation

weight_typestr

Name of moment condition weight function to use in the GMM estimation

**weight_config

Additional keyword arguments to pass to the moment condition weight function

Notes

Available GMM weight functions are:

• “unadjusted”, “homoskedastic” - Assumes moment conditions are homoskedastic

• “robust”, “heteroskedastic” - Allows for heteroskedasticity by not autocorrelation

• “kernel” - Allows for heteroskedasticity and autocorrelation

• “cluster” - Allows for one-way cluster dependence

The estimator is defined as

$\hat{\beta}_{gmm}=(X'ZW^{-1}Z'X)^{-1}X'ZW^{-1}Z'Y$

where $$W$$ is a positive definite weight matrix and $$Z$$ contains both the exogenous regressors and the instruments.

Todo

• VCV: bootstrap

Attributes:
formula

Formula used to create the model

has_constant

Flag indicating the model includes a constant or equivalent

isnull

Locations of observations with missing values

notnull

Locations of observations included in estimation

Methods

 estimate_parameters(x, y, z, w) Parameters: fit(*[, iter_limit, tol, initial_weight, ...]) Estimate model parameters from_formula(formula, data, *[, weights, ...]) Parameters: predict(params, *[, exog, endog, data, eval_env]) Predict values for additional data resids(params) Compute model residuals wresids(params) Compute weighted model residuals

Properties

 formula Formula used to create the model has_constant Flag indicating the model includes a constant or equivalent isnull Locations of observations with missing values notnull Locations of observations included in estimation