Consider the model , estimated using OLS. Which of the following will lead to a higher ? (i) Smaller sample size; (ii) Less variation in ; (iii) More variation in ; (iv) Higher correlation between and .
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Consider the model , estimated using OLS. Which of the following will lead to a higher ? (i) Smaller sample size; (ii) Less variation in ; (iii) More variation in ; (iv) Higher correlation between and .
Tap an option to check your answer.
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Consider the model y = β₀ + β₁ X₁ + β₂ X₂ + ϵ, estimated using OLS. Which of the following will lead to a higher Var(β̂₂)? (i) Smaller sample size; (ii) Less variation in X₂; (iii) More variation in ϵ; (iv) Higher correlation between X₁ and X₂. (A) (i), (ii), (iii) and (iv) (B) (ii), (iii) and (iv) (C) (ii) and (iii) (D) (iii) and (iv)
Also written as: Consider the model y = beta_0 + beta_1 X_1 + beta_2 X_2 + epsilon, estimated using OLS. Which of the following will lead to a higher Var(beta_hat_2)? (i) Smaller sample size; (ii) Less variation in X_2; (iii) More variation in epsilon; (iv) Higher correlation between X_1 and X_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 DSE 2018 paper. Solutions are TestStop's own. Not affiliated with or endorsed by any examination authority.