Suppose that a fad for oats (resulting from the announcement of the health benefits of oat bran) has made you toy with the idea of becoming a broker in the oat market. Before spending your money, you decide to build a simple model of supply and demand of the market for oats: Q_Dt = beta_0 + beta P_t + beta YD_t + u_Dt Q_ST = alpha_0 + alpha P_t + alpha W_t + u_St where Q_Dt is the quantity of oats demanded in time period t, Q_St is the quantity of oats supplied in time period t, Q_St, is the price of oats in time period t, P_t, is the average of oat-farmer wages in time period t, and YD_t is the disposable income in time period t. a. You notice that no left-hand side variable appears on the right side of either of your simultaneous equations. Does this mean that OLS estimation will encounter no simultaneity bias? Why or why not? b. You expect that when P_t goes up, Q_Dt will fall. Does this mean that if you encounter simultaneity bias in the demand equation, it will be negative instead of the positive bias we typically associate with OLS estimation of simultaneous equations? Explain your answer. c. Carefully outline how would you apply the reduced form equations to solve this system. How many equations you would have to estimate? Specify exactly which variables would be in each equation. d. Given the above model, count how many endogeneous and exogenous variables you have and decide whether your model is underidentified, identified, or overidentified. Explain. e. Suppose when estimating this model, you realized that you do not have data for Wt, so your model becomes; Q_Dt = beta_0 + beta P_t + beta YD_t + u_Dt Q_ST = alpha_0 + alpha P_t + u_St Explain how the lack of data on Wt will affect your ability to estimate the model. Is the model now is underidentified, identified, or overidentified. Explain.