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In the Case When the Errors Are Homoskedastic and Normally β^\hat { \boldsymbol { \beta } }

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In the case when the errors are homoskedastic and normally distributed, conditional on X, then a. β^\hat { \boldsymbol { \beta } } is distributed N(β,Σβ^X)N \left( \boldsymbol { \beta } , \Sigma _ { \hat { \beta } \mid X } \right) , where Σβ^X=σu2I(k+1)\Sigma _ { \hat { \beta } \mid X } = \sigma _ { u } ^ { 2 } \boldsymbol { I } _ { ( \mathrm { k } + 1 ) } .
b. β^\hat { \boldsymbol { \beta } } is distributed N(β,Σβ^)\mathrm { N } \left( \boldsymbol { \beta } , \Sigma _ { \hat { \beta } } \right) , where Σβ^=Σn(β˙β)/n=QX1ΣVQX1/n\Sigma _ { \hat { \beta } } = \Sigma _ { \sqrt { n } ( \dot { \beta } - \beta ) } / n = \boldsymbol { Q } _ { X } ^ { - 1 } \Sigma _ { V } \boldsymbol { Q } _ { X } ^ { - 1 } / n .
c. β^\hat { \beta } is distributed N(β,Σβ˙X)N \left( \boldsymbol { \beta } , \Sigma _ { \dot { \beta } \mid X } \right) , where Σβ˙X=σu2(XX)1\Sigma _ { \dot { \beta } \mid X } = \sigma _ { u } ^ { 2 } ( \boldsymbol { X } \boldsymbol { X } ) ^ { - 1 } .
d. U^=PXY\hat { U } = \boldsymbol { P } _ { \boldsymbol { X } } \boldsymbol { Y } where PX=X(XX)1X\boldsymbol { P } _ { \boldsymbol { X } } = \boldsymbol { X } \left( \boldsymbol { X } ^ { \prime } \boldsymbol { X } \right) ^ { - 1 } \boldsymbol { X } ^ { \prime } .

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