Consider the following generic fixed effects model, along with a two-period (t = 1, 2), randomly sampled panel data set with dependent variable y and independent variable x: y_it = beta_0 + delta_0 d2_t + beta_1x_it + a_i + u_it where y_it = value of y for individual i at time t d2_t = binary variable equal to 1 in the second time period (t = 2), and 0 otherwise (t = l) x_it = value of x for individual i at time t a_i = unobserved (time-invariant) effect u_i = idiosyncratic error Taking the first difference of the model (that is, subtracting the regression equation for t=l from the regression equation for t=2) yields the following first-differenced equation: delta y_i = y_i2 - y_i1 = You now plan to use OLS to estimate your first-differenced equation, in order to obtain the first-differenced estimator. Suppose that delta x_i is correlated with delta u_i. True or False: The first-differenced estimator is unbiased. A. True
B. False