Fit an earnings function using your EAEF data set, using the same specification as in Exercise 7.3 and perform a White test for heteroscedasticity.

7 .s~ The following regressions were fitted using the Shanghai school cost data introduced in Section 5.1

(standard errors in parentheses):

COST = 24,000 + 339N R2 = 0.39

(27,000) (50)

COST = 51,000 - 4,0000CC + 152N + 284NOCC R2 = 0.68,

(31,000) (41,000) (60) (76)

where COST is the annual cost of running a school, N is the number of students, , ace is a dummy variable defined to be 0 for regular schools and 1 for occupational schools, and NOCC is a slope dummy variable defined as the product of Nand OCC. There are 74 schools in the sample. With the data sorted by N, the regressions are fitted again for the 26 smallest and 26 largest schools, the residual sum of squares being as shown in the table.

First regression Second regression 26 smallest 7.8 X 1010  6.7 X 1010 26/argest 54.4 X 1010 13.8 X 1010 Perform a Goldfeld-Quandt test for heteroscedasticity for the two models and, with reference to Figure 5.5, explain why the problem of heteroscedasticity is less severe in the second model.