The Extended Least Squares Assumptions in the Multiple Regression Model $\left( u _ { i } \text { has conditional mean zero; } \left( \boldsymbol { X } _ { i } , Y _ { j } \right) , i = 1 , \ldots , n \right.$

The extended least squares assumptions in the multiple regression model include four assumptions from Chapter 6 $\left( u _ { i } \text { has conditional mean zero; } \left( \boldsymbol { X } _ { i } , Y _ { j } \right) , i = 1 , \ldots , n \right.$ are i.i.d. draws from their joint distribution; $\boldsymbol { X } _ { i } \text {and } u _ { i }$ have nonzero finite fourth moments; there is no perfect multicollinearity) . In addition, there are two further assumptions, one of which is

A) heteroskedasticity of the error term.
B) serial correlation of the error term.
C) homoskedasticity of the error term.
D) invertibility of the matrix of regressors.