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Heteroskedasticity-consistent standard errors use an estimated regression covariance that remains consistent when error variances vary across observations. For ordinary least squares, the basic sandwich estimate is $(X^{T}X{)}^{-1}{X}^T\mathrm{diag}({\hat{\epsilon }}_i^2)X(X^{T}X{)}^{-1}$. The resulting standard errors are square roots of its diagonal entries. This is a regression application of a sandwich covariance matrix.

For estimating equations, asymptotic covariance often has the form $A^{-1}B{A}^{-1}$, called a sandwich covariance.

Regression heteroskedasticity-consistent standard errors use a specific sandwich covariance estimate; general estimating equations can produce sandwich covariances outside regression.