Which of the following statements is/are TRUE? Select all that apply. If the regression errors are homoskedastic, the OLS estimators are BLUE (best linear unbiased estimators) provided that the three least squares assumptions hold. Heteroskedastic errors violate at least one of the three least squares assumptions. The formulas to compute Standard Errors of the estimators are usually more simplified when you assume heteroskedastictiy. In the presence of heteroskedasticity, the OLS estimators are no longer unbiased. If the errors are heteroskedastic but you assume that they were homoskedastic, your computed standard errors will not be correct, and you won't be able to do valid statistical inference.

MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
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Chapter1: Starting With Matlab
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Which of the following statements is/are TRUE? Select all
that apply.
If the regression errors are homoskedastic, the OLS estimators are
BLUE (best linear unbiased estimators) provided that the three
least squares assumptions hold.
Heteroskedastic errors violate at least one of the three least
squares assumptions.
The formulas to compute Standard Errors of the estimators are
usually more simplified when you assume heteroskedastictiy.
In the presence of heteroskedasticity, the OLS estimators are no
longer unbiased.
If the errors are heteroskedastic but you assume that they were
homoskedastic, your computed standard errors will not be correct,
and you won't be able to do valid statistical inference.
Transcribed Image Text:Which of the following statements is/are TRUE? Select all that apply. If the regression errors are homoskedastic, the OLS estimators are BLUE (best linear unbiased estimators) provided that the three least squares assumptions hold. Heteroskedastic errors violate at least one of the three least squares assumptions. The formulas to compute Standard Errors of the estimators are usually more simplified when you assume heteroskedastictiy. In the presence of heteroskedasticity, the OLS estimators are no longer unbiased. If the errors are heteroskedastic but you assume that they were homoskedastic, your computed standard errors will not be correct, and you won't be able to do valid statistical inference.
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