linearmodels.iv.model.IVGMMCUE

class IVGMMCUE(dependent, exog, endog, instruments, *, weights=None, weight_type='robust', **weight_config)

Estimation of IV models using continuously updating 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, IVGMM

Notes

Available 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

In most circumstances, the center weight option should be True to avoid starting value dependence.

\[\begin{split}\hat{\beta}_{cue} & =\min_{\beta}\bar{g}(\beta)'W(\beta)^{-1}g(\beta)\\ g(\beta) & =n^{-1}\sum_{i=1}^{n}z_{i}(y_{i}-x_{i}\beta)\end{split}\]

where \(W(\beta)\) is a weight matrix that depends on \(\beta\) through \(\epsilon_i = y_i - x_i\beta\).

Methods

estimate_parameters(self, starting, x, y, z, …)	Parameters
fit(self, \*, starting, …)	Estimate model parameters
from_formula(formula, data, \*, weights, …)	Parameters
j(self, params, x, y, z)	Optimization target
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