Question

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

Answer 1

The OLS assumption violated by omitted variables bias is E(ui | Xi) = 0, which is crucial for ensuring that OLS estimators are unbiased and consistent.

Step-by-step explanation:

The OLS assumption most likely violated by omitted variables bias is option a: E(ui | Xi) = 0. This assumption means that the error term ui has an expected value of zero given any value of Xi. When there is omitted variable bias, an important predictor is not included in the model, which typically leads to the violation of this assumption because those omitted factors are often correlated with the included predictors, thus biasing the regression coefficients.

Omitted variable bias affects the unbiasedness and consistency of the OLS estimators, making it a serious issue in regression analysis. It is crucial to include all relevant variables in the regression model to satisfy this assumption for OLS to provide the Best Linear Unbiased Estimators (BLUE). When variables are omitted, the remaining variables in the model may incorrectly capture the effects of the omitted variables.