2. (20 points). The following regression results are based on the true model of CEOs salary: _cons 6.585254 .0875024 75.26 0.000 6.412739 6.757769 roe .0149443 .0043287 3.45 0.001 .0064099 .0234786 sales .0000156 3.47e-06 4.51 0.000 8.81e-06 .0000225 lsalary Coef. Std. Err. t P>|t| [95% Conf. Interval] Total 66.7221632 208 .320779631 Root MSE = .53099 Adj R-squared = 0.1210 Residual 58.0824894 206 .281953832 R-squared = 0.1295 Model 8.63967378 2 4.31983689 Prob > F = 0.0000 F( 2, 206) = 15.32 Source SS df MS Number of obs = 209 Where lsalary = CEOs salary in log; sales = sales per year (1000s of $); roe: return on equity a. Interpret the coefficient of sales in the above true model b. You use a wrong model with roe as the only independent variable: lsalary = + roe + u 0 1 What would be the consequence caused by this model misspecification in terms of the estimated coefficient 1 ? Will your estimate of 1 in this case be upward biased, downward biased or unbiased? Why? c. Suppose now you add an irrelevant variable of return on Dow Jones Industrial Market Index (rom) in your model to predict CEOs salary: lsalary = + sales + roe + rom + u 0 1 2 3 What would be the consequence caused by this model misspecification in terms of the mean and the standard error of 2 ? Explain your answer.

2. (20 points). The following regression results are based on the true model of CEO's salary: Source SS df MS 2 Model Residual 8.63967378 58.0824894 4.3198 3689 .281953832 Number of obs = FC 2, 206) = Prob > E R-squared Adj R-squared Root MSE 209 15.32 0.0000 0.1295 0.1210 .53099 206 = Total 66.7221632 208 .320779631 1salary Coef. Std. Ern. t P>t [95% Conf. Interval] sales .0000156 roe .0149443 6.585254 3.47e-06 .0043287 .0875024 4.51 3.45 75.26 0.000 0.001 0.000 8.81e-06 .0064099 6.412739 .0000225 .0234786 6.757769 cons Where lsalary = CEO's salary in log; sales = sales per year (1000's of $); roe: return on equity a. Interpret the coefficient of sales in the above true model b. You use a wrong model with roe as the only independent variable: Isalary = Be + Broe #u What would be the consequence caused by this model misspecification in terms of the estimated coefficient , ? Will your estimate of B, in this case be upward biased, downward biased or unbiased? Why? c. Suppose now you add an irrelevant variable of return on Dow Jones Industrial Market Index (rom) in your model to predict CEO's salary: lsalary = B#B, sales + B,roe B,rom Eu What would be the consequence caused by this model misspecification in terms of the mean and the standard error of B, ? Explain your answer. 2. (20 points). The following regression results are based on the true model of CEO's salary: Source SS df MS 2 Model Residual 8.63967378 58.0824894 4.3198 3689 .281953832 Number of obs = FC 2, 206) = Prob > E R-squared Adj R-squared Root MSE 209 15.32 0.0000 0.1295 0.1210 .53099 206 = Total 66.7221632 208 .320779631 1salary Coef. Std. Ern. t P>t [95% Conf. Interval] sales .0000156 roe .0149443 6.585254 3.47e-06 .0043287 .0875024 4.51 3.45 75.26 0.000 0.001 0.000 8.81e-06 .0064099 6.412739 .0000225 .0234786 6.757769 cons Where lsalary = CEO's salary in log; sales = sales per year (1000's of $); roe: return on equity a. Interpret the coefficient of sales in the above true model b. You use a wrong model with roe as the only independent variable: Isalary = Be + Broe #u What would be the consequence caused by this model misspecification in terms of the estimated coefficient , ? Will your estimate of B, in this case be upward biased, downward biased or unbiased? Why? c. Suppose now you add an irrelevant variable of return on Dow Jones Industrial Market Index (rom) in your model to predict CEO's salary: lsalary = B#B, sales + B,roe B,rom Eu What would be the consequence caused by this model misspecification in terms of the mean and the standard error of B, ? Explain your