1. Jack use OLS method to estimate the parameters of linear model with intercept:
$y_t = \alpha + \beta x_t + \varepsilon_t.$
If the heteroskedasticity ($Var(\varepsilon_t | x_t) = \sigma^2_x x_t$) exits in the errors but other Gauss-Markov conditions satisfy, which statement is true?      (1)      
(A). The estimated parameter, \( \hat{\beta} \), is biased, and variance of \( \hat{\beta}_1 \) obtained by OLS method is incorrect.
(B). The estimated parameter, \( \hat{\beta} \), is biased, and variance of \( \hat{\beta}_1 \) obtained by OLS method is accurate.
(C). The estimated parameter, \( \hat{\beta} \), is unbiased, and variance of \( \hat{\beta}_1 \) obtained by White heteroskedasticity-robust method is accurate.
(D). The estimated parameter, \( \hat{\beta}_1 \), is unbiased and efficiency.
(E). Jack cannot use OLS to estimate parameters because of inefficiency.