Question:
Given the assumptions of the classical linear regression model, the ordinary least square estimators possess some optimum properties-given in the Gauss-Markov theorem. Which of the following is not a part of this theorem?
Given the assumptions of the classical linear regression model, the ordinary least square estimators possess some optimum properties-given in the Gauss-Markov theorem. Which of the following is not a part of this theorem?
Updated On: Mar 12, 2024
- The slope estimator \(\hat{\beta}_2\) is a linear function of random variables
- The expected value of \(\hat{\beta}_2\) is equal to zero
- No linear unbiased estimator has lower variance than that of \(\hat{\beta}_2\)
- The estimator \(\hat{\beta}_2\) is an unbiased estimator of \(\hat{\beta}_2\)
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The Correct Option is B
Solution and Explanation
The correct answer is (B) : The expected value of \(\hat{\beta}_2\) is equal to zero.
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