Answer: The White test first estimates the model and compute the residuals. It then regresses the squared residuals on a constant, all explanatory variables, their squares, and their cross-product.

## Explanation The White test procedure consists of two main steps: **Step 1:** Estimate the original regression model and compute the residuals: $$\hat{\epsilon}_i = Y_i - \hat{Y}_i$$ **Step 2:** Regress the **squared residuals** on: - A constant term - All original explanatory variables - The squares of all explanatory variables - All cross-products of the explanatory variables **Why Option D is correct:** - It correctly specifies that we regress the **squared residuals** (not the residuals themselves) - It includes all the necessary components: constant, original variables, their squares, and cross-products - This complete specification allows the test to detect various forms of heteroskedasticity **Why other options are incorrect:** - **Option A:** Regresses residuals (not squared residuals) and misses squares and cross-products - **Option B:** Regresses residuals (not squared residuals) though it includes the correct regressors - **Option C:** Correctly uses squared residuals but misses the squares and cross-products of explanatory variables After performing this auxiliary regression, the test statistic is computed as $nR^2$ from this regression, which follows a chi-square distribution under the null hypothesis of homoskedasticity.

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