Heteroskedasticity is a problem because it results in
A)biased parameter estimates.
B)estimated standard errors that are incorrect.
C)estimated standard errors that are always too small.
D)incorrect estimated slope coefficients.

Question

Quizplus · Accepted Answer

Heteroskedasticity results in estimated standard errors that are incorrect because the assumption of homoskedasticity is violated. Estimating standard errors that are too small can lead to inflated confidence intervals and hypothesis tests that are too liberal, while estimating standard errors that are too large can lead to too conservative hypothesis tests. However, heteroskedasticity does not necessarily result in biased parameter estimates or incorrect estimated slope coefficients, although it may affect the precision of these estimates.

Heteroskedasticity Is a Problem Because It Results in