Exclusion of a relevant variable from a multiple linear regression model leads to the problem of _____.
A. misspecification of the model
B. multicollinearity
C. perfect collinearity
D. homoskedasticity
When there are omitted variables in the regression, which are determinants of the dependent variable, then
A. you cannot measure the effect of the omitted variable, but the estimator of your included variable(s) is (are) unaffected.
B. this has no effect on the estimator of your included variable because the other variable is not included.
C. this will always bias the OLS estimator of the included variable.
D. the OLS estimator is biased if the omitted variable is correlated with the included variable.
When you have an omitted variable problem, the assumption that E(ui ∣Xi) = 0 is violated. This implies that
A. the sum of the residuals is no longer zero.
B. there is another estimator called weighted least squares, which is BLUE.
C. the sum of the residuals times any of the explanatory variables is no longer zero.
D. the OLS estimator is no longer consistent.
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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