Answer: $\hat{\epsilon}_i^2 = \gamma_0 + \gamma_1 X_{1i} + \gamma_2 X_{1i}^2 + \eta_i$

## Explanation The White test is used to detect heteroskedasticity in regression models. The test involves two main steps: **Step 1:** Estimate the original regression model and compute the squared residuals. **Step 2:** Regress the squared residuals on: - A constant term - All original explanatory variables - All cross-products (squares and interactions) of the explanatory variables For a model with one explanatory variable (X₁), the correct specification for the second step is: $$ \hat{\epsilon}_i^2 = \gamma_0 + \gamma_1 X_{1i} + \gamma_2 X_{1i}^2 + \eta_i $$ **Why Option A is correct:** - Includes the constant term (γ₀) - Includes the original explanatory variable (X₁i) - Includes the squared term of the explanatory variable (X₁i²) **Why other options are incorrect:** - **Option B:** Missing the constant term - **Option C:** Missing the squared term (cross-product) - **Option D:** Missing the original explanatory variable, only includes the constant and squared term The test statistic is then computed from this auxiliary regression to determine if heteroskedasticity is present.

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