By including another variable in the regression, you will
A. decrease the regression R2 if that variable is important.
B. eliminate the possibility of omitted variable bias from excluding that variable.
C. look at the t-statistic of the coefficient of that variable and include the variable only if the coefficient is statistically significant at the 1% level.
D. decrease the variance of the estimator of the coefficients of interest.
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Errors-in-variables bias
A. is present when the probability limit of the OLS estimator is given by ![]()
.
B. arises when an independent variable is measured imprecisely.
C. arises when the dependent variable is measured imprecisely.
D. always occurs in economics since economic data is never precisely measured.
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.
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