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Define the GLS Estimator and Discuss Its Properties When Ω\Omega Is Known Why Is This Estimator Sometimes Called Infeasible GLS? What

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Define the GLS estimator and discuss its properties when Ω\Omega is known. Why is this estimator sometimes called infeasible GLS? What happens when Ω\Omega is unknown? What would the Ω\Omega matrix look like for the case of independent sampling with heteroskedastic errors, where var(uiXi)=ch(Xi)=σ2X1i2?\operatorname { var } \left( u _ { i } \mid X _ { i } \right) = \operatorname { ch } \left( X _ { i } \right) = \sigma ^ { 2 } X _ { 1 i } ^ { 2 } ?
Since the inverse of the error variancecovariance matrix is needed to compute the GLS estimator, find Ω1\Omega ^ { - 1 } . The textbook shows that the original model Y=Xβ+U\mathbf { Y } = \mathbf { X } \beta + \mathbf { U }
will be transformed into Y~=X~β+U~\tilde { \boldsymbol { Y } } = \tilde { \boldsymbol { X } } \boldsymbol { \beta } + \tilde { \boldsymbol { U } } where Y~=FY,X~=FX, and U~=FU, and FF=Ω1\tilde { \boldsymbol { Y } } = \boldsymbol { F } \boldsymbol { Y } , \tilde { \boldsymbol { X } } = \boldsymbol { F } \boldsymbol { X } \text {, and } \tilde { \boldsymbol { U } } = \boldsymbol { F } \boldsymbol { U } \text {, and } \boldsymbol { \boldsymbol { F } ^ { \prime } \boldsymbol { F } } = \boldsymbol { \Omega } ^ { -1 }
Find FF in the above case, and describe what effect the transformation has on the original data.

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