If heteroscedasticity is present in a regression model and ignored, the OLS estimator will still be unbiased and consistent, but it will become inefficient. This means that the standard errors of the coefficients will be incorrect, leading to invalid confidence intervals and unreliable hypothesis tests.
What would be the consequences for the OLS estimator heteroscedasticity is present in a regression model but ignored?
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This question is part of 📏 Arithmetic Aptitude within the broader 🧠 Aptitude Mastery subject and appears in the SPSC – Sindh Public Service Commission preparation collection. Reviewing the answer together with nearby questions helps reinforce the underlying concept and identify how it may appear in competitive exams.