If you had a two regressor regression model, then omitting one variable which is relevant
A. will have no effect on the coefficient of the included variable if the correlation between the excluded and the included variable is negative.
B. will always bias the coefficient of the included variable upwards.
C. can result in a negative value for the coefficient of the included variable, even though the coefficient will have a significant positive effect on Y if the omitted variable were included.
D. makes the sum of the product between the included variable and the residuals different from 0.
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In a two regressor regression model, if you exclude one of the relevant variables then
A. it is no longer reasonable to assume that the errors are homoskedastic.
B. OLS is no longer unbiased, but still consistent.
C. you are no longer controlling for the influence of the other variable.
D. the OLS estimator no longer exists.
Omitted variable bias
A. will always be present as long as the regression R2 < 1.
B. is always there but is negligible in almost all economic examples.
C. exists if the omitted variable is correlated with the included regressor but is not a determinant of the dependent variable.
D. exists if the omitted variable is correlated with the included regressor and is a determinant of the dependent variable.
The following OLS assumption is most likely violated by omitted variables bias:
A. E(ui ∣![]()
Xi) = 0
B. (Xi, Yi) i=1,..., n are i.i.d draws from their joint distribution
C. there are no outliers for Xi, ui
D. there is heteroskedasticity
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