Consider the following linear model: $Y = X\beta + u$ .

If Gauss-Markov assumptions hold, I'm trying to prove that $\hat{\sigma}^2 = \frac{\hat{u}'\hat{u}}{N-K-1}$ is an unbiased estimator for the error variance $\sigma^2$. How can I prove this?

My attempt:

I tried with:

$E(\hat{\sigma}^2) = \frac{E(\hat{u}'\hat{u})}{N-K-1} =\frac{E[(Y- X\hat{\beta})'(Y- X\hat{\beta})]}{N-K-1} = \frac{E(Y'Y) - E(Y'X \hat{\beta}) -E(\hat{\beta}X'Y + E(\hat{\beta}X'X \hat{\beta})}{N-K-1}$.

How can I calculate those expectations?

  • 1
    $\begingroup$ Pls, see the updated question above $\endgroup$
    – Mark
    Dec 22, 2023 at 10:10

1 Answer 1


To prove the unbiasedness of $\hat{\sigma}^2$ when conditioned on $X$, that is, $\mathbb{E}(\hat{\sigma}^2|X)=\sigma^2$, consider $\hat{u}= y- X \hat{\beta}=y-X(X'X)^{-1} X'y = [I_n -X(X'X)^{-1} X'X]y = My$, and using $y=X\beta +u$, write $\hat{u}=Mu$, since $MX=0$.

Just a remark about the $M$ matrix: $M$ is a matrix that is both symmetric ($M=M'$) and idempotent ($M=M^2$). The matrix $M$, named the residual maker, can be interpreted as a matrix producing the (column) vector of least squares residuals in the regression of $y$ on $X$ when it premultiplies any vector $y$.

Then, \begin{equation*} \hat{u}' \hat{u} = u'M' M u = u' M u \end{equation*} Thus, $u' M u$ is a scalar and equals its trace that we denote as $tr(u' M u)$.

Then, recall that tr(ABC)=tr(BCA), and under the homoskedasticity assumption $\mathbb{E}(uu'|X)=\sigma^2$, \begin{align*} \mathbb{E}(u'M u |X) = & \mathbb{E}(tr(u'Mu|X)) = \mathbb{E}[tr(Muu')|X] \\ = & tr[M \mathbb{E}(u u'|X)] = tr(M \sigma^2 I_n) \\ = & \sigma^2 tr(M)= \sigma^2 (n-k-1) \end{align*} The last equality comes from the fact that $tr(M)=tr(I_n)-tr[X(X'X)^{-1} X'] = n - tr[(X'X)^{-1}X'X]$, and this yelds $n-tr(I_{k+1})=n-k-1$. Eventually, \begin{equation} \mathbb{E}(\hat{\sigma}^2|X)= \mathbb{E}(u'Mu|X)/(n-k-1)=\sigma^2 \end{equation}

To find references where the properties of trace in linear algebra are discussed in a simple manner, consult the appendix of "Introductory Econometrics: A Modern Approach" by Wooldridge (latest edition)


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