Financial Econometrics: a. Uses special tools not common to statistics b. Uses statistic tools to address economic policy problems c. Uses statistic tools to address financial economics problems cl. None of the above If I buy a stock at 50, | get a dividend payout of 5 and | sell at 70 a. My raw-units gross return is {70+5)/50 b. My net return in raw units is (20+S)/50 c. My log return is ln(75/50) cl. All of the above In the contest of a simple regression case, consider the following set of data: cov(x,y) =100; le) = 10; EM = 20. a. The slope of the regression is S; b. Because of lack of data, the intercept of the regression cannot be recovered c. The intercept is 100-20*10 = 80 d. None of the above The disturbance term that we add to our linear representation in our simple regression framework can be interpreted as: a. The result of omitted variables b. Errors in measurement c. Random outside influences d. All of the above Consider a matrix X (with dimension T-by-3) and a matrix Y with dimension (T-by-l) a. The product XY is T-byl b. The product X'Y is 1-by-T c. The product X'Y is 3-by-1 d. None of the above Consider a vector X which comprises T observations of the variable x. Use 1_T to denote a vector of ones with T elements: a. Avglx) = (1_T'X)/T b. AvgleZ) = (X'X)/(T"2) 6- VlX) = ((X'X) - (1_T'Xll/T d. All of the above Consider our simple regression model in matrix format as seen in class. X is a matrix with dimension Tby-2 with 1\" column of ones (i.e., it captures the intercept). Assume that: Inle'X) = [10 0 0 100]; T: 100 and sigma"2 = 20, then a. The OLS standard error for the intercept coefficient (1St coefficient) is (100*100)*20* 100 b. The OLS squared standard error for the slope coefficient (2"d coefficient) is (1/1oo)*{1oo*1oo)*2o c. The ols standard error cannot be computed as we are missing data d. None of the above 8. Assume that you have ma ny-many-many observations and base your answer on the distribution of a normal. |fthe point estimate of our slope coefficient (denoted as beta_2) is 3 and its standard error is 1.5, we can say that: a. We cannot reject the null H0: beta_2 = 0 b. We cannot reject the null H0: beta_2 > 0 c. We cannot reject the null H0: beta_2