The following is not one of the Gauss-Markov conditions: A)var(u_i | X₁,…, X_n)= $\sigma _ { u } ^ { 2 }$ , 0

Question

The following is not one of the Gauss-Markov conditions: A)var(u_i | X₁,…, X_n)= $\sigma _ { u } ^ { 2 }$ , 0 < $\sigma _ { u } ^ { 2 }$ < ? for i = 1,…, n, B)the errors are normally distributed. C)E(u_iu_j | X₁,…, X_n)= 0, i = 1,…, n, j = 1,..., n, i ? j D)E(u_i | X₁,…, X_n)= 0

Quizplus · Accepted Answer

The Gauss-Markov theorem does not require the errors to be normally distributed. It focuses on the linear regression model being the best linear unbiased estimator (BLUE) under certain conditions, none of which include the normality of errors. Normality is an additional assumption for hypothesis testing, not for the Gauss-Markov conditions.

The Following Is Not One of the Gauss-Markov Conditions: A)var(ui σu2\sigma _ { u } ^ { 2 }σu2​

The Following Is Not One of the Gauss-Markov Conditions:
A)var(u_i $\sigma _ { u } ^ { 2 }$