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.