[proofplan] We first separate the residual covariance matrix into the infeasible covariance matrix using the true errors and an error term caused by replacing $u_i$ with $\hat{u}_i$. The infeasible matrix converges to $\Omega$ by the [weak law of large numbers](/theorems/1127), while the residual replacement term is negligible because $\hat{\beta}_n \xrightarrow{\mathbb{P}} \beta_0$ and the fourth-moment assumptions control all fixed-dimensional coordinate averages. We then combine this convergence with the convergence of $(n^{-1}X^\top X)^{-1}$ to $Q^{-1}$. The HC1 result follows because its degrees-of-freedom multiplier converges to $1$ when $p$ is fixed. [/proofplan] [step:Express the residuals through the estimation error] Define the estimation error vector $\Delta_n \in \mathbb{R}^p$ by \begin{align*} \Delta_n := \hat{\beta}_n - \beta_0. \end{align*} For each $i \in \{1,\dots,n\}$, using $Y_i = X_i^\top \beta_0 + u_i$ gives \begin{align*} \hat{u}_i = Y_i - X_i^\top \hat{\beta}_n = u_i - X_i^\top \Delta_n. \end{align*} Hence \begin{align*} \hat{u}_i^2 - u_i^2 = -2u_i X_i^\top \Delta_n + (X_i^\top \Delta_n)^2. \end{align*} Therefore \begin{align*} \frac{1}{n}\sum_{i=1}^{n}\hat{u}_i^2 X_iX_i^\top = \frac{1}{n}\sum_{i=1}^{n}u_i^2X_iX_i^\top + R_n, \end{align*} where the residual replacement matrix $R_n \in \mathbb{R}^{p \times p}$ is \begin{align*} R_n := \frac{1}{n}\sum_{i=1}^{n} (\hat{u}_i^2-u_i^2)X_iX_i^\top. \end{align*} [guided] The first task is to isolate exactly where the feasible estimator differs from the infeasible one. Define \begin{align*} \Delta_n := \hat{\beta}_n - \beta_0. \end{align*} This is a random vector in $\mathbb{R}^p$. Since the model is $Y_i = X_i^\top \beta_0 + u_i$, the residual computed with $\hat{\beta}_n$ satisfies \begin{align*} \hat{u}_i = Y_i - X_i^\top \hat{\beta}_n = X_i^\top \beta_0 + u_i - X_i^\top \hat{\beta}_n = u_i - X_i^\top(\hat{\beta}_n-\beta_0) = u_i - X_i^\top\Delta_n. \end{align*} Squaring gives \begin{align*} \hat{u}_i^2 = u_i^2 - 2u_iX_i^\top\Delta_n + (X_i^\top\Delta_n)^2, \end{align*} so \begin{align*} \hat{u}_i^2-u_i^2 = -2u_iX_i^\top\Delta_n + (X_i^\top\Delta_n)^2. \end{align*} Thus the feasible middle matrix decomposes as \begin{align*} \frac{1}{n}\sum_{i=1}^{n}\hat{u}_i^2 X_iX_i^\top = \frac{1}{n}\sum_{i=1}^{n}u_i^2X_iX_i^\top + \frac{1}{n}\sum_{i=1}^{n}(\hat{u}_i^2-u_i^2)X_iX_i^\top. \end{align*} We call the second term $R_n$. The rest of the proof shows that the first term converges to $\Omega$ and $R_n$ converges to the zero matrix in probability. [/guided] [/step] [step:Show the infeasible covariance matrix converges to $\Omega$] For $j,k \in \{1,\dots,p\}$, define the real-valued random variable \begin{align*} Z_{i,jk} := u_i^2 X_{ij}X_{ik}. \end{align*} By Cauchy-Schwarz, \begin{align*} \mathbb{E}[|Z_{i,jk}|] \leq \left(\mathbb{E}[u_i^4]\right)^{1/2} \left(\mathbb{E}[X_{ij}^2X_{ik}^2]\right)^{1/2}. \end{align*} Since $|X_{ij}| \leq |X_i|$ and $|X_{ik}| \leq |X_i|$, \begin{align*} X_{ij}^2X_{ik}^2 \leq |X_i|^4, \end{align*} and therefore $\mathbb{E}[|Z_{i,jk}|] < \infty$. By the [weak law of large numbers](/theorems/1851) for i.i.d. integrable random variables (citing a result not yet in the wiki: weak law of large numbers), \begin{align*} \frac{1}{n}\sum_{i=1}^{n} Z_{i,jk} \xrightarrow{\mathbb{P}} \mathbb{E}[Z_{i,jk}] = \Omega_{jk}. \end{align*} Because $p$ is fixed, entrywise convergence over the finitely many pairs $(j,k)$ implies \begin{align*} \frac{1}{n}\sum_{i=1}^{n}u_i^2X_iX_i^\top \xrightarrow{\mathbb{P}} \Omega. \end{align*} [guided] We prove convergence entry by entry. Fix indices $j,k \in \{1,\dots,p\}$ and define \begin{align*} Z_{i,jk} := u_i^2 X_{ij}X_{ik}. \end{align*} This is the $(j,k)$ entry of the matrix $u_i^2X_iX_i^\top$. To apply the weak law of large numbers, we need integrability of $Z_{i,jk}$. The fourth-moment assumptions give it. Indeed, by Cauchy-Schwarz applied to $|u_i|^2$ and $|X_{ij}X_{ik}|$, \begin{align*} \mathbb{E}[|Z_{i,jk}|] = \mathbb{E}[u_i^2|X_{ij}X_{ik}|] \leq \left(\mathbb{E}[u_i^4]\right)^{1/2} \left(\mathbb{E}[X_{ij}^2X_{ik}^2]\right)^{1/2}. \end{align*} Since each coordinate is bounded in absolute value by the Euclidean norm, \begin{align*} X_{ij}^2X_{ik}^2 \leq |X_i|^4. \end{align*} Thus $\mathbb{E}[X_{ij}^2X_{ik}^2] < \infty$, and hence $\mathbb{E}[|Z_{i,jk}|] < \infty$. The random variables $Z_{1,jk}, Z_{2,jk}, \dots$ are i.i.d. because the observations are i.i.d. Therefore the weak law of large numbers for i.i.d. integrable random variables gives \begin{align*} \frac{1}{n}\sum_{i=1}^{n} Z_{i,jk} \xrightarrow{\mathbb{P}} \mathbb{E}[Z_{i,jk}] = \mathbb{E}[u_i^2X_{ij}X_{ik}] = \Omega_{jk}. \end{align*} There are only $p^2$ entries, and $p$ is fixed. A finite union bound therefore upgrades entrywise convergence to convergence of the whole matrix: \begin{align*} \frac{1}{n}\sum_{i=1}^{n}u_i^2X_iX_i^\top \xrightarrow{\mathbb{P}} \Omega. \end{align*} [/guided] [/step] [step:Bound the residual replacement matrix by fixed-dimensional moment averages] For $j,k \in \{1,\dots,p\}$, the $(j,k)$ entry of $R_n$ satisfies \begin{align*} |(R_n)_{jk}| &\leq 2\sum_{\ell=1}^{p}|\Delta_{n,\ell}| \left(\frac{1}{n}\sum_{i=1}^{n}|u_iX_{i\ell}X_{ij}X_{ik}|\right) \\ &\quad+ \sum_{\ell=1}^{p}\sum_{m=1}^{p} |\Delta_{n,\ell}\Delta_{n,m}| \left(\frac{1}{n}\sum_{i=1}^{n}|X_{i\ell}X_{im}X_{ij}X_{ik}|\right). \end{align*} For each fixed quadruple of indices, Hölder's inequality with four exponents all equal to $4$ gives \begin{align*} \mathbb{E}[|u_iX_{i\ell}X_{ij}X_{ik}|] \leq \left(\mathbb{E}[u_i^4]\right)^{1/4} \left(\mathbb{E}[X_{i\ell}^4]\right)^{1/4} \left(\mathbb{E}[X_{ij}^4]\right)^{1/4} \left(\mathbb{E}[X_{ik}^4]\right)^{1/4} <\infty, \end{align*} and similarly \begin{align*} \mathbb{E}[|X_{i\ell}X_{im}X_{ij}X_{ik}|] \leq \prod_{r \in \{\ell,m,j,k\}} \left(\mathbb{E}[X_{ir}^4]\right)^{1/4} <\infty. \end{align*} The weak law of large numbers therefore implies that every average appearing in the displayed bound is $O_{\mathbb{P}}(1)$. Since $p$ is fixed and $\Delta_n \xrightarrow{\mathbb{P}} 0$, it follows that $(R_n)_{jk} \xrightarrow{\mathbb{P}} 0$ for every $j,k$. Hence \begin{align*} R_n \xrightarrow{\mathbb{P}} 0. \end{align*} [guided] We now control the cost of replacing $u_i$ by $\hat{u}_i$. Fix $j,k \in \{1,\dots,p\}$. From the residual expansion, \begin{align*} (R_n)_{jk} = \frac{1}{n}\sum_{i=1}^{n} \left[-2u_iX_i^\top\Delta_n+(X_i^\top\Delta_n)^2\right]X_{ij}X_{ik}. \end{align*} Write the inner product in coordinates: \begin{align*} X_i^\top\Delta_n = \sum_{\ell=1}^{p}X_{i\ell}\Delta_{n,\ell}. \end{align*} Taking absolute values and using the triangle inequality gives \begin{align*} |(R_n)_{jk}| &\leq 2\sum_{\ell=1}^{p}|\Delta_{n,\ell}| \left(\frac{1}{n}\sum_{i=1}^{n}|u_iX_{i\ell}X_{ij}X_{ik}|\right) \\ &\quad+ \sum_{\ell=1}^{p}\sum_{m=1}^{p} |\Delta_{n,\ell}\Delta_{n,m}| \left(\frac{1}{n}\sum_{i=1}^{n}|X_{i\ell}X_{im}X_{ij}X_{ik}|\right). \end{align*} The important point is that every sample average in parentheses has a finite expectation. For the terms containing $u_i$, Hölder's inequality with exponents $4,4,4,4$ gives \begin{align*} \mathbb{E}[|u_iX_{i\ell}X_{ij}X_{ik}|] \leq \left(\mathbb{E}[u_i^4]\right)^{1/4} \left(\mathbb{E}[X_{i\ell}^4]\right)^{1/4} \left(\mathbb{E}[X_{ij}^4]\right)^{1/4} \left(\mathbb{E}[X_{ik}^4]\right)^{1/4}. \end{align*} Each coordinate fourth moment is finite because $|X_{ir}| \leq |X_i|$ for every coordinate $r$, and $\mathbb{E}[|X_i|^4] < \infty$. For the terms without $u_i$, the same form of Hölder's inequality gives \begin{align*} \mathbb{E}[|X_{i\ell}X_{im}X_{ij}X_{ik}|] \leq \left(\mathbb{E}[X_{i\ell}^4]\right)^{1/4} \left(\mathbb{E}[X_{im}^4]\right)^{1/4} \left(\mathbb{E}[X_{ij}^4]\right)^{1/4} \left(\mathbb{E}[X_{ik}^4]\right)^{1/4} <\infty. \end{align*} Thus every average in the bound is $O_{\mathbb{P}}(1)$ by the weak law of large numbers. Now use consistency. Since the asymptotic normality assumptions imply $\hat{\beta}_n \xrightarrow{\mathbb{P}}\beta_0$, we have \begin{align*} \Delta_n \xrightarrow{\mathbb{P}} 0. \end{align*} Because $p$ is fixed, the finite sums over $\ell$ and $m$ do not grow with $n$. Hence the first group of terms is a finite sum of $o_{\mathbb{P}}(1)O_{\mathbb{P}}(1)$ terms, and the second group is a finite sum of $o_{\mathbb{P}}(1)^2O_{\mathbb{P}}(1)$ terms. Therefore \begin{align*} (R_n)_{jk} \xrightarrow{\mathbb{P}} 0. \end{align*} Since this holds for all finitely many entries, the whole matrix satisfies \begin{align*} R_n \xrightarrow{\mathbb{P}} 0. \end{align*} [/guided] [/step] [step:Conclude consistency of the residual covariance matrix] Combining the decomposition from the first step with the two convergence results gives \begin{align*} \frac{1}{n}\sum_{i=1}^{n}\hat{u}_i^2X_iX_i^\top = \frac{1}{n}\sum_{i=1}^{n}u_i^2X_iX_i^\top + R_n \xrightarrow{\mathbb{P}} \Omega + 0 = \Omega. \end{align*} This proves the first asserted convergence. [/step] [step:Invert the empirical second-moment matrix] Define the empirical second-moment matrix $Q_n \in \mathbb{R}^{p \times p}$ by \begin{align*} Q_n := \frac{1}{n}X^\top X = \frac{1}{n}\sum_{i=1}^{n}X_iX_i^\top. \end{align*} For each $j,k$, the random variable $X_{ij}X_{ik}$ is integrable because \begin{align*} \mathbb{E}[|X_{ij}X_{ik}|] \leq \left(\mathbb{E}[X_{ij}^2]\right)^{1/2} \left(\mathbb{E}[X_{ik}^2]\right)^{1/2} <\infty. \end{align*} Thus the weak law of large numbers gives \begin{align*} Q_n \xrightarrow{\mathbb{P}} Q. \end{align*} Since $\lambda_{\min}(Q)>0$, the matrix $Q$ is invertible. Let $\|\cdot\|_{\mathrm{op}}$ denote the operator norm on $\mathbb{R}^{p \times p}$. On the event \begin{align*} \|Q_n-Q\|_{\mathrm{op}} < \frac{1}{\|Q^{-1}\|_{\mathrm{op}}}, \end{align*} the Neumann series argument gives invertibility of $Q_n$ and \begin{align*} Q_n^{-1}-Q^{-1} = Q_n^{-1}(Q-Q_n)Q^{-1}. \end{align*} Equivalently, continuity of inversion at the invertible matrix $Q$ yields \begin{align*} Q_n^{-1} \xrightarrow{\mathbb{P}} Q^{-1}. \end{align*} [guided] We need the two outer factors in the sandwich estimator. Define \begin{align*} Q_n := \frac{1}{n}X^\top X = \frac{1}{n}\sum_{i=1}^{n}X_iX_i^\top. \end{align*} For fixed $j,k$, the $(j,k)$ entry is the sample average of $X_{ij}X_{ik}$. This variable is integrable because Cauchy-Schwarz gives \begin{align*} \mathbb{E}[|X_{ij}X_{ik}|] \leq \left(\mathbb{E}[X_{ij}^2]\right)^{1/2} \left(\mathbb{E}[X_{ik}^2]\right)^{1/2}, \end{align*} and the second moments are finite since $\mathbb{E}[|X_i|^4] < \infty$. Therefore, by the weak law of large numbers, \begin{align*} Q_n \xrightarrow{\mathbb{P}} Q. \end{align*} It remains to justify inversion. The assumption $\lambda_{\min}(Q)>0$ says that $Q$ is positive definite, hence invertible. Matrix inversion is continuous at invertible matrices. A direct verification is as follows. If \begin{align*} \|Q_n-Q\|_{\mathrm{op}} < \frac{1}{\|Q^{-1}\|_{\mathrm{op}}}, \end{align*} then \begin{align*} Q_n = Q\left[I+Q^{-1}(Q_n-Q)\right], \end{align*} and the matrix $I+Q^{-1}(Q_n-Q)$ is invertible by the Neumann series because its perturbation has operator norm less than $1$. Thus $Q_n$ is invertible on an event whose probability tends to $1$. On that event, \begin{align*} Q_n^{-1}-Q^{-1} = Q_n^{-1}(Q-Q_n)Q^{-1}. \end{align*} Since $Q_n \to Q$ in probability and inversion is continuous at $Q$, this yields \begin{align*} Q_n^{-1} \xrightarrow{\mathbb{P}} Q^{-1}. \end{align*} [/guided] [/step] [step:Multiply the convergent sandwich factors] By definition of HC0, \begin{align*} n\widehat{\operatorname{Var}}_{\mathrm{HC0}}(\hat{\beta}_n) &= n(X^\top X)^{-1} \left(\sum_{i=1}^{n}\hat{u}_i^2X_iX_i^\top\right) (X^\top X)^{-1} \\ &= Q_n^{-1} \left(\frac{1}{n}\sum_{i=1}^{n}\hat{u}_i^2X_iX_i^\top\right) Q_n^{-1}. \end{align*} The matrix product map \begin{align*} (A,B,C) \mapsto ABC \end{align*} from $\mathbb{R}^{p \times p}\times\mathbb{R}^{p \times p}\times\mathbb{R}^{p \times p}$ to $\mathbb{R}^{p \times p}$ is continuous. Combining \begin{align*} Q_n^{-1}\xrightarrow{\mathbb{P}}Q^{-1}, \qquad \frac{1}{n}\sum_{i=1}^{n}\hat{u}_i^2X_iX_i^\top\xrightarrow{\mathbb{P}}\Omega, \end{align*} gives \begin{align*} n\widehat{\operatorname{Var}}_{\mathrm{HC0}}(\hat{\beta}_n) \xrightarrow{\mathbb{P}} Q^{-1}\Omega Q^{-1}. \end{align*} [/step] [step:Apply the fixed-dimensional HC1 multiplier] For HC1, \begin{align*} n\widehat{\operatorname{Var}}_{\mathrm{HC1}}(\hat{\beta}_n) = \frac{n}{n-p} n\widehat{\operatorname{Var}}_{\mathrm{HC0}}(\hat{\beta}_n). \end{align*} Since $p$ is fixed, \begin{align*} \frac{n}{n-p} = \frac{1}{1-p/n} \to 1. \end{align*} Multiplying the HC0 convergence by this scalar sequence gives \begin{align*} n\widehat{\operatorname{Var}}_{\mathrm{HC1}}(\hat{\beta}_n) \xrightarrow{\mathbb{P}} Q^{-1}\Omega Q^{-1}. \end{align*} This completes the proof. [/step]