The Gauss-Markov Theorem proves that
A) the OLS estimator is t distributed.
B) the OLS estimator has the smallest mean square error.
C) the OLS estimator is unbiased.
D) with homoskedastic errors, the OLS estimator has the smallest variance in the class of linear and unbiased estimators, conditional on X1,…, Xn.
Correct Answer:
Verified
Q1: Asymptotic distribution theory is
A)not practically relevant, because
Q2: Estimation by WLS
A)although harder than OLS, will
Q4: The following is not one of
Q5: You need to adjust
Q6: Slutsky's theorem combines the Law of Large
Q7: Besides the Central Limit Theorem, the other
Q8: If the errors are heteroskedastic, then
A)the OLS
Q9: It is possible for an estimator
Q10: The OLS estimator is a linear
Q11: The class of linear conditionally unbiased estimators
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