Correlation of the regression error across observations
A. results in incorrect OLS standard errors.
B. makes the OLS estimator inconsistent, but not unbiased.
C. results in correct OLS standard errors if heteroskedasticity-robust standard errors are used.
D. is not a problem in cross-sections since the data can always be "reshuffled."
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The guidelines for whether or not to include an additional variable include all of the following, with the exception of
A. providing "full disclosure" representative tabulations of the results.
B. testing whether additional questionable variables have nonzero coefficients.
C. determining whether it can be measured in the population of interest.
D. being specific about the coefficient or coefficients of interest.
Possible solutions to omitted variable bias, when the omitted variable is not observed, include the following with the exception of
A. panel data estimation.
B. nonlinear least squares estimation.
C. use of instrumental variables regressions.
D. use of randomized controlled experiments.
A possible solution to errors-in-variables bias is to
A. use log-log specifications.
B. choose different functional forms.
C. use the square root of that variable since the error becomes smaller.
D. mitigate the problem through instrumental variables regression.
You try to explain the number of IBM shares traded in the stock market per day in 2005. As an independent variable you choose the closing price of the share. This is an example of
A. simultaneous causality.
B. invalid inference due to a small sample size.
C. sample selection bias since you should analyze more than one stock.
D. a situation where homoskedasticity-only standard errors should be used since you only analyze one company.
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