linearmodels.iv.gmm.IVGMMCovariance¶

class linearmodels.iv.gmm.IVGMMCovariance(x: ndarray[tuple[int, ...], dtype[float64]], y: ndarray[tuple[int, ...], dtype[float64]], z: ndarray[tuple[int, ...], dtype[float64]], params: ndarray[tuple[int, ...], dtype[float64]], w: ndarray[tuple[int, ...], dtype[float64]], cov_type: str = 'robust', debiased: bool = False, **cov_config: str | bool)[source]¶

Covariance estimation for GMM models

Parameters:¶

x: ndarray[tuple[int, ...], dtype[float64]]¶

Model regressors (nobs by nvar)

y: ndarray[tuple[int, ...], dtype[float64]]¶

Series ,modeled (nobs by 1)

z: ndarray[tuple[int, ...], dtype[float64]]¶

Instruments used for endogenous regressors (nobs by ninstr)

params: ndarray[tuple[int, ...], dtype[float64]]¶

Estimated model parameters (nvar by 1)

w: ndarray[tuple[int, ...], dtype[float64]]¶

Weighting matrix used in GMM estimation

cov_type: str = 'robust'¶

Covariance estimator to use Valid choices 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

debiased: bool = False¶

Flag indicating whether to debias the covariance estimator

**cov_config: str | bool¶

Optional keyword arguments that are specific to a particular cov_type

Notes

Optional keyword argument for specific covariance estimators:

kernel

kernel: Name of kernel to use. See KernelCovariance for details on available kernels
bandwidth: Bandwidth to use when computing the weight. If not provided, nobs - 2 is used.

cluster

`config`
`cov`	Covariance of estimated parameters
`debiased`	Flag indicating if covariance is debiased
`s`	Score covariance estimate
`s2`	Estimated variance of residuals.