Consequences of Heteroscedasticity
The following are consequences of heteroscedasticity when it exists in the data.
- The OLS estimators and regression predictions based on them remain unbiased and consistent.
- The OLS estimators are no longer the BLUE (Best Linear Unbiased Estimators) because they are no longer efficient, so the regression predictions will be inefficient too.
- Because of the inconsistency of the covariance matrix of the estimated regression coefficients, the tests of hypotheses, (t-test, F-test) are no longer valid.
Learn about Remedial Measures of Heteroscedasticity
