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

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

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