By assumption, the random error terms all
have the same variance and are uncorrelated with each other. This property is
known as homoskedasticity. When the residual error term changes for different values in the
regression (the error is not constant), the error terms as said to be heteroskedastic. Violation of this assumption is usually due
to either the time effect or
the megaphone effect. When heterosckeasticity is present, the
regression paramters are no longer the minimum variance estimators. Further, it is generally in error to rely
upon the estimated parameter variances as well as the residual sum of
squares. ORS detects this problem via White’s
Test for Homoskedasticity for homoskedasticity.
This test is an asymptotic test; hence it gains power as the sample size
increases.