The error term is the most important component of the
classical linear regression model (CLRM). Most of the
CLRM assumptions that allow econometricians to prove the
desirable properties of the OLS estimators (the Gauss-Markov
theorem) directly involve characteristics about the error term
(or disturbances). One of the CLRM assumptions deals with
the conditional variance of the error term; namely, that the
variance of the error term is constant (homoskedastic). In the
following sections, I describe the difference between
homoskedasticity and heteroskedasticity and illustrate the
consequences of heteroskedasticity on OLS.