linearmodels.iv.model.IVGMMCUE¶

class linearmodels.iv.model.IVGMMCUE(dependent: IVData | ndarray | DataArray | DataFrame | Series, exog: IVData | ndarray | DataArray | DataFrame | Series | None, endog: IVData | ndarray | DataArray | DataFrame | Series | None, instruments: IVData | ndarray | DataArray | DataFrame | Series | None, *, weights: IVData | ndarray | DataArray | DataFrame | Series | None = None, weight_type: str = 'robust', **weight_config: Any)[source]¶

Estimation of IV models using continuously updating GMM

Parameters:¶

dependent: IVData | ndarray | DataArray | DataFrame | Series¶

Endogenous variables (nobs by 1)

exog: IVData | ndarray | DataArray | DataFrame | Series | None¶

Exogenous regressors (nobs by nexog)

endog: IVData | ndarray | DataArray | DataFrame | Series | None¶

Endogenous regressors (nobs by nendog)

instruments: IVData | ndarray | DataArray | DataFrame | Series | None¶

Instrumental variables (nobs by ninstr)

weights: IVData | ndarray | DataArray | DataFrame | Series | None = None¶

Observation weights used in estimation

weight_type: str = '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

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 \(\epsilon_i = y_i - x_i\beta\).

See also

linearmodels.iv.model.IV2SLS, linearmodels.iv.model.IVLIML, linearmodels.iv.model.IVGMM

Methods

estimate_parameters(starting, x, y, z[, ...])
fit(*[, starting, display, cov_type, ...])	Estimate model parameters
from_formula(formula, data, *[, weights, ...])
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