Question:
What would be the consequences for the OLS estimator if heteroscedasticity is present in a regression model but ignored? Assume that all the other classical assumptions are valid.
What would be the consequences for the OLS estimator if heteroscedasticity is present in a regression model but ignored? Assume that all the other classical assumptions are valid.
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When heteroscedasticity is present, the OLS estimators remain unbiased but are inefficient. Use robust standard errors to correct for this issue.
Updated On: Dec 19, 2025
- It will be biased
- It will be inconsistent
- It will be unbiased but inefficient
- It will be unbiased but inconsistent
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The Correct Option is C
Solution and Explanation
Heteroscedasticity in a regression model violates one of the classical assumptions that the error term has constant variance. If heteroscedasticity is ignored, the Ordinary Least Squares (OLS) estimator remains unbiased but becomes inefficient.
Step 1: Heteroscedasticity does not lead to bias in the OLS estimators, meaning that the estimators remain unbiased.
Step 2: However, the estimators are no longer efficient because heteroscedasticity leads to incorrect standard errors, making the estimates less reliable.
Step 3: Conclusion.
Thus, the OLS estimators are unbiased but inefficient in the presence of heteroscedasticity.
Final Answer: (C) It will be unbiased but inefficient
Step 1: Heteroscedasticity does not lead to bias in the OLS estimators, meaning that the estimators remain unbiased.
Step 2: However, the estimators are no longer efficient because heteroscedasticity leads to incorrect standard errors, making the estimates less reliable.
Step 3: Conclusion.
Thus, the OLS estimators are unbiased but inefficient in the presence of heteroscedasticity.
Final Answer: (C) It will be unbiased but inefficient
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