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of the covariance matrix of the estimated regression coefficients, the tests of hypotheses, (t-test, F-test) are no longer valid.
Consequences of Heteroscedasticity

Learn about Remedial Measures of Heteroscedasticity

R Programming Language

Test Preparation MCQs

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