Under the least squares assumptions for the multiple regression problem (zero conditional mean for the error term, all $X _ { i } \text { and } Y _ { i }$ being i.i.d., all $X _ { i } \text { and } u _ { i }$ having finite fourth moments, no perfect multicollinearity), the OLS estimators for the slopes and intercept
A) have an exact normal distribution for n25 .
B) are BLUE.
C) have a normal distribution in small samples as long as the errors are homoskedastic.
D) are unbiased and consistent.

Question

Under the least squares assumptions for the multiple regression problem (zero conditional mean for the error term, all $X _ { i } \text { and } Y _ { i }$ being i.i.d., all $X _ { i } \text { and } u _ { i }$ having finite fourth moments, no perfect multicollinearity), the OLS estimators for the slopes and intercept
A) have an exact normal distribution for  n>25 .
B) are BLUE.
C) have a normal distribution in small samples as long as the errors are homoskedastic.
D) are unbiased and consistent.

Quizplus · Accepted Answer

The Answer of Under the least squares assumptions for the multiple regression problem (zero conditional mean for the error term, all $X _ { i } \text { and } Y _ { i }$ being i.i.d., all $X _ { i } \text { and } u _ { i }$ having finite fourth moments, no perfect multicollinearity), the OLS estimators for the slopes and intercept
A) have an exact normal distribution for  n>25 .
B) are BLUE.
C) have a normal distribution in small samples as long as the errors are homoskedastic.
D) are unbiased and consistent.

Under the Least Squares Assumptions for the Multiple Regression Problem Xi and YiX _ { i } \text { and } Y _ { i }Xi​ and Yi​

Under the Least Squares Assumptions for the Multiple Regression Problem $X _ { i } \text { and } Y _ { i }$