One implication of the extended least squares assumptions in the multiple regression model is that a. feasible GLS should be used for estimation.
b. $E ( \boldsymbol { U } \mid \boldsymbol { X } ) = \boldsymbol { I } _ { n }$.
c. $\boldsymbol { X } ^ { \prime } \boldsymbol { X }$ is singular.
d. the conditional distribution of $\boldsymbol { U }$ given $\boldsymbol { X }$ is $N \left( \mathbf { 0 } _ { n } , \sigma _ { u } ^ { 2 } \boldsymbol { I } _ { n } \right)$.

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The Answer of One implication of the extended least squares assumptions in the multiple regression model is that a. feasible GLS should be used for estimation.
b. $E ( \boldsymbol { U } \mid \boldsymbol { X } ) = \boldsymbol { I } _ { n }$.
c. $\boldsymbol { X } ^ { \prime } \boldsymbol { X }$ is singular.
d. the conditional distribution of $\boldsymbol { U }$ given $\boldsymbol { X }$ is $N \left( \mathbf { 0 } _ { n } , \sigma _ { u } ^ { 2 } \boldsymbol { I } _ { n } \right)$.

One Implication of the Extended Least Squares Assumptions in the Multiple