11.2 Consequences of Heteroscedasticity 1. OLS estimators are still linear 2. They are still unbiased 3. But they no longer have minimunm variance. 4. The usual formulas to estimate the variances of ols estimators are generally biased 5. The bias arises from the fact that o, namely, e2/d is no longer an unbiasedestimator of 02KESaU 6. The usual confidence intervals and hypothesis tests based on t and f distributions are unreliable11.2 Consequences of Heteroscedasticity • 1. OLS estimators are still linear. • 2. They are still unbiased. • 3. But they no longer have minimunm variance. • 4. The usual formulas to estimate the variances of OLS estimators are generally biased. • 5. The bias arises from the fact that , namely, , is no longer an unbiased estimator of . • 6. The usual confidence intervals and hypothesis tests based on t and F distributions are unreliable. 2 e /d.f. 2  i 2