# linearmodels.system.covariance.ClusteredCovariance¶

class ClusteredCovariance(x, eps, sigma, full_sigma, *, gls=False, debiased=False, constraints=None, clusters=None, group_debias=False)[source]

Heteroskedastic covariance estimation for system regression

Parameters
xList[ndarray]

ndependent element list of regressor

epsndarray

Model residuals, ndependent by nobs

sigmandarray

Covariance matrix estimator of eps

glsbool

Flag indicating to compute the GLS covariance estimator. If False, assume OLS was used

debiasedbool

Flag indicating to apply a small sample adjustment

constraints

Constraints used in estimation, if any

clustersndarray

Optional array of cluster id. Must be integer valued, and have shape (nobs, ncluster) where ncluster is 1 or 2.

group_debiasbool

Flag indicating whether to debias by the number of groups.

Notes

If GLS is used, the covariance is estimated by

$(X'\Omega^{-1}X)^{-1}\tilde{S}_{\mathcal{G}}(X'\Omega^{-1}X)^{-1}$

where X is a block diagonal matrix of exogenous variables and where $$\tilde{S}_{\mathcal{G}}$$ is a clustered estimator of the model scores based on the model residuals and the weighted X matrix $$\Omega^{-1/2}X$$.

When GLS is not used, the covariance is estimated by

$(X'X)^{-1}\hat{S}_{\mathcal{G}}(X'X)^{-1}$

where $$\hat{S}$$ is a clustered estimator of the covariance of the model scores.

Attributes
cov

Parameter covariance

cov_config

Optional configuration information used in covariance

sigma

Error covariance

Methods

Properties

 cov Parameter covariance cov_config Optional configuration information used in covariance sigma Error covariance