[proofplan] The proof rewrites the ordinary least squares estimation error as a fixed linear transformation of the noise, conditional on the design matrix $X$. Since $X$ has full column rank, $X^\top X$ is invertible and the matrix $A=(X^\top X)^{-1}X^\top$ is determined by $X$. We then compute the conditional covariance of $A\varepsilon$ componentwise and substitute the hypothesis $\operatorname{Var}(\varepsilon\mid X)=\sigma^2 I_n$. [/proofplan] [step:Express the estimation error as a conditional linear transformation of the noise] Let $(\Omega,\mathcal F,\mathbb P)$ denote the underlying probability space, and let $\sigma(X)\subset\mathcal F$ denote the sigma-algebra generated by the random matrix $X:\Omega\to\mathbb R^{n\times p}$. Define the full-measure event \begin{align*} E:=\{\omega\in\Omega:\operatorname{rank}X(\omega)=p\}. \end{align*} For every $\omega\in E$, the matrix $X(\omega)^\top X(\omega)$ is invertible. Define the random matrix \begin{align*} A:\Omega &\to \mathbb R^{p\times n}, \\ \omega &\mapsto \begin{cases} (X(\omega)^\top X(\omega))^{-1}X(\omega)^\top, & \omega\in E, \\ 0, & \omega\notin E. \end{cases} \end{align*} The event $E$ is $\sigma(X)$-measurable, and inversion is a Borel map on the [open set](/page/Open%20Set) of invertible $p\times p$ matrices, so $A$ is $\sigma(X)$-measurable. On $E$, using $y=X\beta+\varepsilon$, we compute \begin{align*} \hat{\beta} &= (X^\top X)^{-1}X^\top (X\beta+\varepsilon) \\ &= (X^\top X)^{-1}X^\top X\beta + (X^\top X)^{-1}X^\top \varepsilon \\ &= \beta + A\varepsilon. \end{align*} Therefore \begin{align*} \hat{\beta}-\beta = A\varepsilon \end{align*} almost surely. [guided] The first point is that the ordinary least squares matrix is well-defined on a full-measure event. Let $(\Omega,\mathcal F,\mathbb P)$ be the underlying probability space, and let $\sigma(X)\subset\mathcal F$ denote the sigma-algebra generated by the random matrix $X:\Omega\to\mathbb R^{n\times p}$. Define \begin{align*} E:=\{\omega\in\Omega:\operatorname{rank}X(\omega)=p\}. \end{align*} The hypothesis $\operatorname{rank}X=p$ almost surely says $\mathbb P(E)=1$. For every $\omega\in E$, the columns of $X(\omega)$ are linearly independent, so $X(\omega)^\top X(\omega)$ is a positive definite $p\times p$ matrix and hence invertible. Define \begin{align*} A:\Omega &\to \mathbb R^{p\times n}, \\ \omega &\mapsto \begin{cases} (X(\omega)^\top X(\omega))^{-1}X(\omega)^\top, & \omega\in E, \\ 0, & \omega\notin E. \end{cases} \end{align*} This definition assigns an arbitrary value on the null set $\Omega\setminus E$, which does not affect any almost-sure identity. The event $E$ is determined by $X$, and the matrix operations used in the formula are Borel on the set where the inverse exists, so $A$ is $\sigma(X)$-measurable. On $E$, substituting the model equation $y=X\beta+\varepsilon$ into the definition of $\hat{\beta}$ gives \begin{align*} \hat{\beta} &= (X^\top X)^{-1}X^\top y \\ &= (X^\top X)^{-1}X^\top (X\beta+\varepsilon) \\ &= (X^\top X)^{-1}X^\top X\beta + (X^\top X)^{-1}X^\top \varepsilon \\ &= \beta + A\varepsilon. \end{align*} Thus the estimation error is exactly \begin{align*} \hat{\beta}-\beta = A\varepsilon \end{align*} on the event $E$, and hence almost surely. This identity is the algebraic core of the proof: conditional on $X$, the randomness in $\hat{\beta}$ comes only from $\varepsilon$, transformed by the $\sigma(X)$-measurable matrix $A$. [/guided] [/step] [step:Compute the conditional covariance of the transformed noise] Let \begin{align*} m:\Omega &\to \mathbb R^n, & m &= \mathbb E[\varepsilon\mid X]. \end{align*} Since $A$ is $\sigma(X)$-measurable and finite almost surely, and since $\varepsilon$ has finite second conditional moments given $X$ by the hypothesis that $\operatorname{Var}(\varepsilon\mid X)$ exists, the product $A\varepsilon$ has finite conditional second moments given $X$. Therefore \begin{align*} \mathbb E[\hat{\beta}\mid X] &= \mathbb E[\beta+A\varepsilon\mid X] \\ &= \beta + A\,\mathbb E[\varepsilon\mid X] \\ &= \beta + A m. \end{align*} Hence \begin{align*} \hat{\beta}-\mathbb E[\hat{\beta}\mid X] &= A(\varepsilon-m). \end{align*} Using the definition of conditional variance for vector-valued random variables, \begin{align*} \operatorname{Var}(\hat{\beta}\mid X) &= \mathbb E\left[ (\hat{\beta}-\mathbb E[\hat{\beta}\mid X]) (\hat{\beta}-\mathbb E[\hat{\beta}\mid X])^\top \mid X \right] \\ &= \mathbb E\left[ A(\varepsilon-m)(\varepsilon-m)^\top A^\top \mid X \right]. \end{align*} Because $A$ and $A^\top$ are $\sigma(X)$-measurable, they factor out of the conditional expectation: \begin{align*} \operatorname{Var}(\hat{\beta}\mid X) &= A\,\mathbb E\left[ (\varepsilon-m)(\varepsilon-m)^\top \mid X \right]A^\top \\ &= A\,\operatorname{Var}(\varepsilon\mid X)\,A^\top. \end{align*} [guided] We now compute the conditional covariance, taking care to center both random vectors correctly. Define the conditional mean of the noise by \begin{align*} m:\Omega &\to \mathbb R^n, & m &= \mathbb E[\varepsilon\mid X]. \end{align*} The matrix $A$ is $\sigma(X)$-measurable and finite almost surely, so once we condition on $X$, it behaves as a known matrix. Because $\operatorname{Var}(\varepsilon\mid X)$ exists, the noise vector $\varepsilon$ has finite second conditional moments; multiplying by the finite matrix $A$ preserves finite conditional second moments for $A\varepsilon$. Therefore conditional linearity gives \begin{align*} \mathbb E[\hat{\beta}\mid X] &= \mathbb E[\beta+A\varepsilon\mid X] \\ &= \beta + A\,\mathbb E[\varepsilon\mid X] \\ &= \beta + A m. \end{align*} Subtracting this conditional mean from $\hat{\beta}=\beta+A\varepsilon$ gives \begin{align*} \hat{\beta}-\mathbb E[\hat{\beta}\mid X] &= \beta+A\varepsilon-(\beta+Am) \\ &= A(\varepsilon-m). \end{align*} By definition, the conditional variance matrix of the $\mathbb R^p$-valued random vector $\hat{\beta}$ is \begin{align*} \operatorname{Var}(\hat{\beta}\mid X) &= \mathbb E\left[ (\hat{\beta}-\mathbb E[\hat{\beta}\mid X]) (\hat{\beta}-\mathbb E[\hat{\beta}\mid X])^\top \mid X \right]. \end{align*} Substituting the centered identity just obtained, \begin{align*} \operatorname{Var}(\hat{\beta}\mid X) &= \mathbb E\left[ A(\varepsilon-m)(\varepsilon-m)^\top A^\top \mid X \right]. \end{align*} Since $A$ and $A^\top$ are functions of $X$, they are $\sigma(X)$-measurable. The conditional expectation therefore factors them outside the conditional expectation, giving \begin{align*} \operatorname{Var}(\hat{\beta}\mid X) &= A\,\mathbb E\left[ (\varepsilon-m)(\varepsilon-m)^\top \mid X \right]A^\top \\ &= A\,\operatorname{Var}(\varepsilon\mid X)\,A^\top. \end{align*} This is the conditional covariance transformation rule, derived directly from the definition. [/guided] [/step] [step:Substitute the conditional homoskedasticity hypothesis and simplify the matrix product] By hypothesis, \begin{align*} \operatorname{Var}(\varepsilon\mid X)=\sigma^2 I_n \end{align*} almost surely. Therefore \begin{align*} \operatorname{Var}(\hat{\beta}\mid X) &= A(\sigma^2 I_n)A^\top \\ &= \sigma^2 AA^\top. \end{align*} Using $A=(X^\top X)^{-1}X^\top$ and the symmetry of $X^\top X$, we have \begin{align*} A^\top &= X(X^\top X)^{-1}. \end{align*} Thus \begin{align*} AA^\top &= (X^\top X)^{-1}X^\top X(X^\top X)^{-1} \\ &= (X^\top X)^{-1}. \end{align*} Combining these identities yields \begin{align*} \operatorname{Var}(\hat{\beta}\mid X) = \sigma^2 (X^\top X)^{-1} \end{align*} almost surely, which is the desired formula. [/step]