With heteroskedastic errors, the weighted least squares estimator is BLUE. You should use OLS with heteroskedasticity-robust standard errors because
A. this method is simpler.
B. the exact form of the conditional variance is rarely known.
C. the Gauss-Markov theorem holds.
D. your spreadsheet program does not have a command for weighted least squares.
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In the presence of heteroskedasticity, and assuming that the usual least squares assumptions hold, the OLS estimator is
A. efficient.
BLUE.
C. unbiased and consistent.
D. unbiased but not consistent.
If the errors are heteroskedastic, then
A. OLS is BLUE.
B. WLS is BLUE if the conditional variance of the errors is known up to a constant factor of proportionality.
C. LAD is BLUE if the conditional variance of the errors is known up to a constant factor of proportionality.
D. OLS is efficient.
Using the textbook example of 420 California school districts and the regression of testscores on the student-teacher ratio, you find that the standard error on the slope coefficient is 0.51 when using the heteroskedasticity robust formula, while it is 0.48 when employing the homoskedasticity only formula. When calculating the t-statistic, the recommended procedure is to
A. use the homoskedasticity only formula because the t-statistic becomes larger
B. first test for homoskedasticity of the errors and then make a decision
C. use the heteroskedasticity robust formula
D. make a decision depending on how much different the estimate of the slope is under the two procedures
A binary variable is often called a
A. dummy variable.
B. dependent variable.
C. residual.
D. power of a test.
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