Wage Equation

Below you will find a script that reproduces the results from the wage equation example in section 4.5.2 of POE4. In this example the log-linear model is used to measure the approximate


Подпись: Figure 4.18: The plot of the residuals from a linear model. There is some visual evidence of serial correlation, suggesting that the linear model is misspecified.
Подпись: return to another year of education. The example uses a thousand observations from the CPS monthly survey from 2008.

open "@gretldirdatapoecps4_small. gdt"

2 series l_wage = log(wage)

3 ols l_wage const educ

4 scalar lb = $coeff(educ) – 1.96 * $stderr(educ)

5 scalar ub = $coeff(educ) + 1.96 * $stderr(educ)

6 print lb ub

The regression results are:

Lwage = 1.60944 + 0.0904082 educ

(0.086423) (0.0061456)

T = 1000 R2 = 0.1774 F(1, 998) = 216.41 a = 0.52661
(standard errors in parentheses)

and the 95% confidence intervals for the slope is

Variable Coefficient 95% confidence interval educ 0.0904082 0.0783484 0.102468

That is, an additional year of education is worth between 7.8% and 10.2% wage increases annually. Sign me up!

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