# linearmodels.iv.model.IVGMMCUE¶

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

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

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 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 $$\epsilon_i = y_i - x_i\beta$$.

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(starting, x, y, z[, ...]) Parameters fit(*[, starting, display, cov_type, ...]) Estimate model parameters from_formula(formula, data, *[, weights, ...]) Parameters j(params, x, y, z) Optimization target 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