超星 - 随堂测试6-1

单选题

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

单选题

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.

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单选题

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.

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单选题

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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单选题

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.

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单选题

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.

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单选题

The following OLS assumption is most likely violated by omitted variables bias:

A. E(ui ∣<img>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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