Multiple Choice
What are the consequences of using least squares when heteroskedasticity is present?
A) no consequences,coefficient estimates are still unbiased
B) confidence intervals and hypothesis testing are inaccurate due to inflated standard errors
C) all coefficient estimates are biased for variables correlated with the error term
D) it requires very large sample sizes to get efficient estimates
Correct Answer:
Verified
Correct Answer:
Verified
Q1: A linear probability model is likely
Q2: (See graphs of Model A -
Q4: Which test for heteroskedasticity should you use
Q5: If you run a LM test
Q6: When using WLS to correct for heteroskedasticity,what
Q7: If your initial econometric model has heteroskedastic
Q8: If you model has heteroskedastic error terms,but
Q9: How are coefficient estimates from WLS (weighted
Q10: How should you estimate a model with
Q11: If you have heteroskedasticity such that the