Sample selection bias
A. occurs when a selection process influences the availability of data and that process is related to the dependent variable.
B. is only important for finite sample results.
C. results in the OLS estimator being biased, although it is still consistent.
D. is more important for nonlinear least squares estimation than for OLS.
Simultaneous causality bias
A. is also called sample selection bias.
B. happens in complicated systems of equations called block recursive systems.
C. results in biased estimators if there is heteroskedasticity in the error term.
D. arises in a regression of Y on X when, in addition to the causal link of interest from X to Y, there is a causal link from Y to X.
Panel data estimation can sometimes be used
A. to avoid the problems associated with misspecified functional forms.
B. in case the sum of residuals is not zero.
C. in the case of omitted variable bias when data on the omitted variable is not available.
D. to counter sample selection bias.
Misspecification of functional form of the regression function
A. is overcome by adding the squares of all explanatory variables.
B. is more serious in the case of homoskedasticity-only standard error.
C. results in a type of omitted variable bias.
D. requires alternative estimation methods such as maximum likelihood.
Errors-in-variables bias
A. is only a problem in small samples.
B. arises from error in the measurement of the independent variable.
C. becomes larger as the variance in the explanatory variable increases relative to the error variance.
D. is particularly severe when the source is an error in the measurement of the dependent variable.
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