# Proof of MSE is unbiased estimator in Regression

I am trying to prove that in multivariate linear regression $MSE = (n-2)\sigma^2$

Here is my approach:

Under the usual notation,

$$Y = X\beta + \epsilon \\$$ $$\hat Y = X\hat\beta \\$$ $$\hat\beta = (X'X)^{-1}X'Y \\ \\ \implies \hat\beta' = Y'X(X'X)^{-1}$$

Now, \begin{align} \Sigma (Y_i - \hat Y_i)^2 & = (Y_i - \hat Y_i)'(Y_i - \hat Y_i) \\ & = (X(\beta - \hat \beta) + \epsilon)' (X(\beta - \hat \beta) + \epsilon)\\ & = \underbrace {(\beta - \hat \beta)'X'X(\beta - \hat \beta)}_{term1} + \underbrace {\epsilon'X (\beta - \hat \beta)}_{term2}\\ & + \underbrace {(\beta - \hat \beta)'X'\epsilon}_{term3} + \epsilon'\epsilon \\ \end{align}

Simplifying the individual terms

Term 1: \begin{align} (\beta - \hat \beta)'X'X(\beta - \hat \beta) &= (\beta - (X'X)^{-1}X'Y)'X'X(\beta - (X'X)^{-1}X'Y)\\ & = (\beta' - Y'X(X'X)^{-1})X'X(\beta - (X'X)^{-1}X'Y) \\ & = \beta'X'X\beta - Y'X\beta - \beta'(X'X)(X'X)^{-1}X'Y + Y'X(X'X)^{-1}X'Y \\ & = \beta'X'X\beta - (\beta'X' + \epsilon')X\beta - \beta'(X'X)(X'X)^{-1}X'Y + \\ & (\beta'X' + \epsilon')X(X'X)^{-1}X'Y \quad \text{(substituting the value of }Y') \\ & = - \epsilon'X\beta + \epsilon'X(X'X)^{-1}X'Y \quad \text {some terms get cancelled} \\ & = - \epsilon'X\beta + \epsilon'X(X'X)^{-1}X'( X\beta + \epsilon) \quad \text {substituting the value of } Y \\ & = \epsilon'X(X'X)^{-1}X'\epsilon \end{align}

Term 2 : \begin{align} \epsilon'X (\beta - \hat \beta) &= \epsilon'X(\beta - (X'X)^{-1}X'Y)\\ & = \epsilon'X(\beta - (X'X)^{-1}X'X\beta)\quad \text {substituting the value of } Y \\\\ & = 0 \end{align}

As Term 3 is transpose of Term 2, Term 3 = 0

\begin{align} \Sigma (Y_i - \hat Y_i)^2 & = \epsilon'X(X'X)^{-1}X'\epsilon + \epsilon'\epsilon \\ E(\Sigma (Y_i - \hat Y_i)^2) & = E(\epsilon'X(X'X)^{-1}X'\epsilon + \epsilon'\epsilon) \\ \end{align} I'm stuck here, unable to make any further simplifications. Can someone please help.

What further baffles me is the RHS term is greater than $n\sigma^2$ as $E(\epsilon'\epsilon) = n*\sigma$

• I'd imagine that those 3 terms should be (together) negative since $\hat{\beta}$ minimizes the $\sum(Y_i-\hat{Y_i})^2$ and should thus be smaller or (at least equal) then the case $\hat{\beta}=\beta$ for which the 3 terms become zero. I wonder how you get to the term 2 and 3 being zero? another note: you might like these two links. Especially the proofs for the sample variance as unbiased estimator. I imagine it can be done analogous for the multivariate case (note: use n-p instead of n-2). en.wikipedia.org/wiki/Studentized_residual en.wikipedia.org/wiki/Bias_of_an_estimator Sep 11, 2017 at 22:24
• In the simplification of term 2 you substitute $Y$ with $X \beta$ instead of $X \beta + \epsilon$. So instead you got $\beta - \hat{\beta} = (X^\prime X)^{-1} X^\prime \epsilon$, and $X(\beta - \hat{\beta})$ is the projection of $\epsilon$ onto the span of $X$. Sep 11, 2017 at 22:53

Martijn Weterings's commnet is very useful. Your derivation of term 2 is wrong.

$\epsilon'X (\beta - \hat \beta) \\= \epsilon'X(\beta - (X'X)^{-1}X'Y) \\=\epsilon'X\left\{\beta - (X'X)^{-1}X'(X\beta+\epsilon)\right\}\\=\epsilon'X \left\{\beta-(X'X)^{-1}X'X\beta -(X'X)^{-1}X'\epsilon\right\}\\=-\epsilon'X(X'X)^{-1}X'\epsilon$

Now

$\Sigma (Y_i - \hat Y_i)^2\\=\epsilon'X(X'X)^{-1}X'\epsilon-\epsilon'X(X'X)^{-1}X'\epsilon-\epsilon'X(X'X)^{-1}X'\epsilon+\epsilon'\epsilon\\=\epsilon'\epsilon-\epsilon'X(X'X)^{-1}X'\epsilon\\=\epsilon'\epsilon-\epsilon'P\epsilon$

$P$ is the projection matrix which is symmetric and idempotent

Now calculate the expectation.

$E[\Sigma (Y_i - \hat Y_i)^2]\\=E(\epsilon'\epsilon-\epsilon'P\epsilon)\\=E(\epsilon'\epsilon)-E(\epsilon'P\epsilon)\\=n\sigma^2-\sigma^2trace(P) \\\text{(Suppose tarace(P)}=k)$

$=(n-k)\sigma^2$

$\therefore \frac{\Sigma (Y_i - \hat Y_i)^2}{n-k}$ is the unbiased estimator of $\sigma^2$, $k$ is the number of parameters you want to estimate,such as you want to estimate $\beta_0$ for intercept and $\beta_1$ for one predictor, the $k$ will be equal to 2.

• Question: why does $E(\epsilon'P\epsilon)=\sigma^2trace(P)$? I understand that $E(\epsilon' \epsilon) = \sigma^2$. But why did E(P) = trace(P)? Jan 9, 2019 at 18:53

A less computationally intensive method would be \begin{aligned} e&=y-\hat{y}\\ \Sigma(y[k]-\hat{y}[k])^2&=e^Te\\ y-\hat{y}&=\phi\theta-\phi\hat{\theta}+\epsilon\\ &=\phi\theta-\phi(\phi^T\phi)^{-1}\phi^Ty+\epsilon\\ &=\phi\theta-\phi(\phi^T\phi)^{-1}\phi^T(\phi\theta+\epsilon)+\epsilon\\ &=-\phi(\phi^T\phi)^{-1}\phi^T\epsilon+\epsilon\\ e&=(I-P)\epsilon\\ e^Te&=\epsilon^T(I-P^T-P+P^TP),\;\{P=P^T=P^TP\}\\ &=\epsilon^T\epsilon-\epsilon^TP\epsilon \end{aligned} This is an easier method to get to the necessary step. You can then proceed further as explained by the previous answer.