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

question 21

Multiple Choice

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.

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