International Finance Assignment #3 Consider the following linear regression model: Y = XB+e, where k is the number of independent variables, X = [X1 X2 ...Xk]: Tx k, (X1 X2 ... Xk) are linearly independent, X = (11 ... 1)', and e^N(0, oIT). = 1. Derive the OLS estimator of 2. Show that the OLS estimator of is unbiased. 3. Compute the variance of . 4. Explain how to compute the residual, 5. Show that X and are orthogonal. 6. Show that = X and are orthogonal. 7. Show that '/(T k) is the unbiased estimator of ?. 8. Explain R2 9. Explain R 10. Explain why ?, not R, should be used for model comparison. 11. Explain the missing variable problem. 12. Explain the overfitting problem. 13. Explain how to conduct the simple hypothesis testing. 14. Explain how to conduct the out-of-sample forecasting. 15. Explain how to compare the out-of-sample prediction performance among competing models. International Finance Assignment #3 Consider the following linear regression model: Y = XB+e, where k is the number of independent variables, X = [X1 X2 ...Xk]: Tx k, (X1 X2 ... Xk) are linearly independent, X = (11 ... 1)', and e^N(0, oIT). = 1. Derive the OLS estimator of 2. Show that the OLS estimator of is unbiased. 3. Compute the variance of . 4. Explain how to compute the residual, 5. Show that X and are orthogonal. 6. Show that = X and are orthogonal. 7. Show that '/(T k) is the unbiased estimator of ?. 8. Explain R2 9. Explain R 10. Explain why ?, not R, should be used for model comparison. 11. Explain the missing variable problem. 12. Explain the overfitting problem. 13. Explain how to conduct the simple hypothesis testing. 14. Explain how to conduct the out-of-sample forecasting. 15. Explain how to compare the out-of-sample prediction performance among competing models