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Econometrics

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B. Computational Problems (Pset3)
. clear all . use pp482_data.dta (SAS creation date 23JUN04:14:26) . .

. * Question 1 . * -----------. regress earn higrade age agesq Source | SS df MS -------------+-----------------------------Model | 361750618 3 120583539 Residual | 1.2871e+10 10936 1176923.06 -------------+-----------------------------Total | 1.3233e+10 10939 1209670.09 Number of obs F( 3, 10936) Prob > F R-squared Adj R-squared Root MSE = = = = = = 10940 102.46 0.0000 0.0273 0.0271 1084.9

-----------------------------------------------------------------------------earn | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------higrade | 105.7343 6.605693 16.01 0.000 92.78593 118.6826 age | 32.24626 11.72718 2.75 0.006 9.258862 55.23365 agesq | -.3568549 .1868963 -1.91 0.056 -.7232054 .0094956 _cons | -1221.05 186.2466 -6.56 0.000 -1586.127 -855.973 ------------------------------------------------------------------------------

. * Question 2 . * -----------. gen lessHS= (higrade < 12) if higrade~=. (320 missing values generated) . . gen moreHS= (higrade > 12) if higrade~=. (320 missing values generated)

 We note that the sample size of N = 10,904 is the same as in Question 1. I have chosen to define an indicator for less than high school education and an indicator for more than high school education. Thus, the base group is those with exactly 12 years of schooling. The value of having a high school diploma versus not having one is given by a $361.78 gain in earnings. The value of having more than a diploma compared to just 12 years of education is a $94.05 gain in earnings, but this number is not statistically different from zero (at the 0.1 significance level). This model seems to provide a better fit to the data because the adjusted R-squared increases from 0.0273 to 0.0317.

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