I often see that the "Spherical Error" assumption is invoked for Gauss Markov. One of the parts of the assumption is that the variance is constant given $X$. The other is usually that the errors are uncorrelated with each other, given $X$. For example, on a regression of $Y \sim X$ on $i \in \{1,\ldots,n\}$:

$$ E(\epsilon_i\epsilon_j \mid X)=0 \qquad i\neq j $$

I am failing to see where this assumption factors into the proof that the least squares estimator is BLUE. Is it necessary? If not, why is it assumed?


2 Answers 2


Glen_b has articulated correctly.

The assumptions

\begin{align} \mathbb{Var}[\varepsilon_i|\mathbf X] &= \sigma^2~\forall~i\in\{1, 2,\ldots, n\}, \\ \mathbb{Cov}[\varepsilon_i\varepsilon_j|\mathbf X] &= 0~\forall~i\ne j \end{align}

effectively can be summarised as

\begin{align} \mathbb{E}[\boldsymbol\varepsilon \boldsymbol\varepsilon^\mathsf T |\mathbf X] &= \begin{pmatrix} \mathbb{E}[\varepsilon_1\varepsilon_1|\mathbf X] & \mathbb{E}[\varepsilon_1\varepsilon_2|\mathbf X] & \ldots &\mathbb{E}[\varepsilon_1\varepsilon_n|\mathbf X]\\ \mathbb{E}[\varepsilon_2\varepsilon_1|\mathbf X] & \mathbb{E}[\varepsilon_2\varepsilon_2|\mathbf X] & \ldots &\mathbb{E}[\varepsilon_2\varepsilon_n|\mathbf X]\\ \vdots & \vdots & \ldots & \vdots\\ \mathbb{E}[\varepsilon_n\varepsilon_1|\mathbf X] & \mathbb{E}[\varepsilon_n\varepsilon_2|\mathbf X] & \ldots &\mathbb{E}[\varepsilon_n\varepsilon_n|\mathbf X] \end{pmatrix}\\[1em]&= \mathrm{Diag}(\sigma^2)\\[1em]&= \sigma^2\mathbb I_n. \end{align}

Gauss-Markov tells that any estimable function has a unique unbiased linear estimate which has minimum variance in the class of all unbiased linear estimates.

The proof, as devised by Scheffe, can be outlined as taking the unbiased estimator $\mathbf a^\mathsf T\mathbf y$ of, say $\psi, $ and decomposing $\mathbf a = \mathbf a^\star + \mathbf b, ~\mathbf a^\star \in \mathbf V_r \subset \mathbf V_n, ~ \mathbf b \perp \mathbf V_r; $ here $\bf a^\star$ is the projection of $\bf a$ on $\mathbf V_r.$ Then $$\psi = \mathbb E(\mathbf {a^\star}^\mathsf T\mathbf y) .$$

Coming to the variance part and where the assumption has actually been utilised,

\begin{align} \|\mathbf a\|^2 &= \|\mathbf a^\star\|^2 + \|\mathbf a-\mathbf a^\star\|^2, \\[1em] \mathbb{Var}(\mathbf a^\mathsf T\mathbf y) &= \sigma^2\|\mathbf a\|^2 \\[3pt]&= \underbrace{\sigma^2\|\mathbf a^\star\|^2}_{{\mathbb{Var}(\mathbf {a^\star}^\mathsf T\mathbf y) }} + \sigma^2\|\mathbf a-\mathbf a^\star\|^2; \end{align}

The rest follows. One can readily notice the necessity of the assumption.


The Analysis of Variance, Henry Scheffe, John Wiley & Sons, 1959.


Where exactly it's used depends on the proof you're looking at, but in this one it is used when going from the 2nd to the 3rd line of the $\text{Var}(\tilde{\beta})$ derivation.

  • $\begingroup$ Are you referring to the 2nd to 3rd line where $Var(y) = \sigma^2$? If so, does that actually depend on $E(\epsilon_i\epsilon_j \mid X)=0 \qquad i\neq j$? $\endgroup$
    – user321627
    Commented Aug 15, 2022 at 4:24
  • $\begingroup$ Note that $y$ is a vector, so $\text{Var}(y) = \sigma^{2}I$. So yes, it does. $\endgroup$
    – Glen_b
    Commented Aug 15, 2022 at 4:42
  • $\begingroup$ Ah, so you are saying that if the condition didn't hold true we would not have the diagonal on the identity matrix, and instead there would be non-zero entries in the off-diagonals? Thank you. $\endgroup$
    – user321627
    Commented Aug 15, 2022 at 4:49

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