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?
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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? 1. The slope estimator β̂₂ is a linear function of random variables 2. The expected value of β̂₂ is equal to zero 3. No linear unbiased estimator has lower variance then that of β̂₂ 4. The estimator β̂₂ is an unbiased estimator of β₂ (A) The slope estimator β̂₂ is a linear function of random variables (B) The expected value of β̂₂ is equal to zero (C) No linear unbiased estimator has lower variance then that of β̂₂ (D) The estimator β̂₂ is an unbiased estimator of β₂
Also written as: 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? 1. The slope estimator beta_hat_2 is a linear function of random variables 2. The expected value of beta_hat_2 is equal to zero 3. No linear unbiased estimator has lower variance then that of beta_hat_2 4. The estimator beta_hat_2 is an unbiased estimator of beta_2
Linear regression and OLS has come up 74 times in 7 exams since 2008.
This question is on The Gauss-Markov theorem and OLS properties, part of Linear regression and OLS in Econometrics.
Why OLS is the best linear unbiased estimator, and what it needs.
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Question from the CUET-PG 2023 Shift 1 paper. Solutions are TestStop's own. Not affiliated with or endorsed by any examination authority.