2. Eric estimates a regression model as below:

$$y_i = \alpha + \beta_1 x_i + \beta_2 z_i + u_i,$$

Where $u_i \sim N(0, \sigma_u^2)$. If Eric regress $x_i$ on $z_i$, he find the following equation statistically significant:

$$x_i = \omega + \delta_2 z_i + \varepsilon_i.$$

Which problem he will face when he use OLS method to estimate $\hat{\beta}_1$ and $\hat{\beta}_2$?
(A) Multicollinearity
(B) Serial correlation
(C) Heteroskedasticity
(D) Nonlinear in parameter
(E) Random sample

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