Heteroskedasticity-consistent standard errors are used to allow the fitting of a model that does contain heteroskedastic residuals. The first such approach was proposed by Huber (1967), and further improved procedures have been produced since for cross-sectional data, time-series data and GARCH estimation.

What is the purpose of using robust standard errors?

Robust standard errors can be used when the assumption of uniformity of variance, also known as homoscedasticity, in a linear-regression model is violated. This situation, known as heteroscedasticity, implies that the variance of the outcome is not constant across observations.

Why do we use heteroskedasticity?

Unconditional heteroskedasticity can be used when discussing variables that have identifiable seasonal variability, such as electricity usage. ... The opposite of heteroskedastic is homoskedastic. Homoskedasticity refers to a condition in which the variance of the residual term is constant or nearly so.