linearmodels.iv.gmm.IVGMMCovariance

class IVGMMCovariance(x, y, z, params, w, cov_type='robust', debiased=False, **cov_config)

Covariance estimation for GMM models

Parameters

xndarray

Model regressors (nobs by nvar)

yndarray

Series ,modeled (nobs by 1)

zndarray

Instruments used for endogenous regressors (nobs by ninstr)

paramsndarray

Estimated model parameters (nvar by 1)

wndarray

Weighting matrix used in GMM estimation

cov_typestr

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

• “cluster” - Allows for one-way cluster dependence

debiasedbool

Flag indicating whether to debias the covariance estimator

cov_config

Optional keyword arguments that are specific to a particular cov_type

See also

linearmodels.iv.covariance.HomoskedasticCovariance

linearmodels.iv.covariance.HeteroskedasticCovariance

linearmodels.iv.covariance.KernelCovariance

linearmodels.iv.covariance.ClusteredCovariance

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

• clusters: Array containing the cluster indices. See ClusteredCovariance

Attributes

config

cov

Covariance of estimated parameters

debiased

Flag indicating if covariance is debiased

s

Score covariance estimate

s2

Estimated variance of residuals.

Methods

Properties

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