reg EARNINGS S EXP MALE Source SS df MS 500 30.38 11241.9067 3 = 0.0000 Model Residual 3747.30223 123.36772 Number of obs F(3, 496) Prob > F R-squared Adj R-squared Root MSE 61190.3892 496 = 0.1552 = 0.1501 11.107 Total 72432.2959 499 145.154902 EARNINGS Coef. Std. Err. t P>El [95% Conf. Interval] S 1.909924 .2100768 9.09 0.000 1.497174 2.322675 EXP .6422658 .2024772 3.17 0.002 .2444471 1.040085 MALE 4.070341 1.010957 4.03 -3.86 0.000 0.000 2.084055 -23.47862 6.056626 -7.652713 cons -15.56566 4.027443 Fit a wage equation using your EAWE data set, taking EARNINGS as the dependent variable and S, EXP, and MALE as the explanatory variables, and perform a Goldfeld-Quandt test for heteroskedasticity in the S dimension. Remember to sort the observations by S first. Fit a wage equation using your EAWE data set, using the same specification as in Exercise 7.3 and perform a White test for heteroskedasticity. reg EARNINGS S EXP MALE Source SS df MS 500 30.38 11241.9067 3 = 0.0000 Model Residual 3747.30223 123.36772 Number of obs F(3, 496) Prob > F R-squared Adj R-squared Root MSE 61190.3892 496 = 0.1552 = 0.1501 11.107 Total 72432.2959 499 145.154902 EARNINGS Coef. Std. Err. t P>El [95% Conf. Interval] S 1.909924 .2100768 9.09 0.000 1.497174 2.322675 EXP .6422658 .2024772 3.17 0.002 .2444471 1.040085 MALE 4.070341 1.010957 4.03 -3.86 0.000 0.000 2.084055 -23.47862 6.056626 -7.652713 cons -15.56566 4.027443 Fit a wage equation using your EAWE data set, taking EARNINGS as the dependent variable and S, EXP, and MALE as the explanatory variables, and perform a Goldfeld-Quandt test for heteroskedasticity in the S dimension. Remember to sort the observations by S first. Fit a wage equation using your EAWE data set, using the same specification as in Exercise 7.3 and perform a White test for heteroskedasticity