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.
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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.
Threats to in internal validity lead to
A. perfect multicollinearity
B. the inability to transfer data sets into your statistical package
C. failures of one or more of the least squares assumptions
D. a false generalization to the population of interest
The true causal effect might not be the same in the population studied and the population of interest because
A. of differences in characteristics of the population
B. of geographical differences
C. the study is out of date
D. all of the above
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