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, default None

Observation weights used in estimation

weight_typestr, default “robust”

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

See also

IV2SLS, IVLIML, IVGMMCUE

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

Methods

estimate_parameters(x, y, z, w)

Parameters

fit(self, \*, iter_limit, tol, …)

Estimate model parameters

from_formula(formula, data, \*, weights, …)

Parameters

predict(self, params, …)

Predict values for additional data

resids(self, params)

Compute model residuals

wresids(self, 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