linearmodels.iv.model.IVGMM¶

Estimation of IV models using the generalized method of moments (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: Any¶: Additional keyword arguments to pass to the moment condition weight function

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
“clustered” - 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

Methods

`estimate_parameters`(x, y, z, w)
`fit`(*[, iter_limit, tol, initial_weight, ...])	Estimate model parameters
`from_formula`(formula, data, *[, weights, ...])
`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