What are the consequences of ignoring or failing to recognize serial correlation?
A)biased,but consistent
B)unbiased,but no longer BLUE
C)unbiased,but no longer linear
D)biased,but with minimum variance

Question

Quizplus · Accepted Answer

Ignoring or failing to recognize serial correlation in the residuals of a regression model does not make the estimators biased; they remain unbiased. However, the estimators lose their property of being the Best Linear Unbiased Estimators (BLUE) because the presence of serial correlation violates the Gauss-Markov theorem's assumption of no autocorrelation in the error terms. This means that while the estimators are still unbiased, they are no longer the most efficient (i.e., they do not have the minimum variance among all linear unbiased estimators).

What Are the Consequences of Ignoring or Failing to Recognize