Proving efficiency of OLS over GLS I'm trying to prove the efficiency of OLS over GLS when the covariance matrix of the error $\varepsilon$ is mistakenly assumed to be $\sigma^2\Sigma$ instead of $\sigma^2 I$. After deriving the variances, what I have so far is: $Var(\beta^{ols}) = \sigma^2(X'X)^{-1}$ and 
$Var(\beta^{gls}) = \sigma^2(X'\Sigma^{-1} X)^{-1}$.
Want to show $Var(\beta^{gls}) - Var(\beta^{ols})$ is psd.
$$\implies \sigma^2(X'\Sigma^{-1} X)^{-1} - \sigma^2(X'X)^{-1} \geq 0\\
\implies (X'\Sigma^{-1} X)^{-1} - (X'X)^{-1} \geq 0 \\
\iff X'X - X'\Sigma^{-1} X \geq 0
$$
But this is where I don't know how to proceed. First I tried $X'(I-\Sigma^{-1})X \geq 0$ but got stuck. Any hints on how to continue?
Update:
After one of the comments, I want to double check the variance of $\beta^{gls}$:
$$Var(\beta^{gls}) = Var[(X'\Sigma^{-1}X)^{-1} X'\Sigma^{-1}\varepsilon]\\
=(X'\Sigma^{-1}X)^{-1} X'\Sigma^{-1} Var(\varepsilon) \Sigma^{-1} X(X'\Sigma^{-1}X)^{-1}
$$
Here's is where I think I made the mistake, I replaced the variance of $\varepsilon$ as the one that we think is "right" (i.e. $\sigma^{2}\Sigma) $. Should I replace it with the real one (i.e. $\sigma^2I$)?
 A: This is what I ended up doing:
$$Var(\beta^{gls}) = Var[(X'\Sigma^{-1}X)^{-1} X'\Sigma^{-1}\varepsilon]\\
=(X'\Sigma^{-1}X)^{-1} X'\Sigma^{-1} Var(\varepsilon) \Sigma^{-1} X(X'\Sigma^{-1}X)^{-1}\\
= \sigma^2 (X'\Sigma^{-1}X)^{-1} X'\Sigma^{-1} \Sigma^{-1} X(X'\Sigma^{-1}X)^{-1}\\
$$
Want to show $Var(\beta^{gls}) - Var(\beta^{ols})$ is psd.
$$\implies \sigma^2 (X'\Sigma^{-1}X)^{-1} X'\Sigma^{-1} \Sigma^{-1} X(X'\Sigma^{-1}X)^{-1} - \sigma^2(X'X)^{-1} \geq 0\\
\iff X'X - X'\Sigma^{-1}X( X'\Sigma^{-1} \Sigma^{-1} X)^{-1}X'\Sigma^{-1}X \geq 0\\
\implies X'(I-\Sigma^{-1}X( X'\Sigma^{-1} \Sigma^{-1} X)^{-1}X'\Sigma^{-1})X \geq 0\\
\implies X'MX\geq0
$$
Where $M$ is the residual maker matrix, and since $M$ is psd, the statement holds. 
A: It is known that $V(\beta^{gls}-\beta^{ols}|X)$ is positive semidefinite. Here it turns out that $$V(\beta^{gls}-\beta^{ols}|X)=V(\beta^{gls}|X)-V(\beta^{ols}|X),$$ hence the conclusion that $\beta^{ols}$ is relatively more efficient than $\beta^{gls}$ if $V(\varepsilon|X)=\sigma^2I_N$.
