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Suppose you want to estimate a model of women's earnings at age 50. You have data for a sample of employed women, provided by the alumni associations of Mills College and Smith College, on:
? A woman's salary at age 50
? Her age
? Year of graduation
? Her high school GPA
? Her college GPA
? Her college major
? Her job tenure (how many years she has been with employer)
? The fraction of her household income that she earns

Questions will be based on Classical Assumptions

1. Suppose you include both HIGH SCHOOL GPA and COLLEGE GPA in your model. Both turn out to be statistically significant (by which I mean, each coefficient is at least twice as big as its estimated standard error). Suppose that you then drop COLLEGE GPA from your model. Would you expect the standard error of the coefficient on HIGH SCHOOL GPA to become smaller or larger as a result? Explain.

2. Why is it not a good idea to add Fraction of Household Income in the regression?

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https://brainmass.com/economics/econometric-models/econometrics-estimated-models-68091

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Two questions:
I need clarification in order to understand this for future exams.

Suppose you want to estimate a model of women's earnings at age 50. You have data for a sample of employed women, provided by the alumni associations of Mills College and Smith College, on:
• A woman's salary at age 50
• Her age
• Year of graduation
• Her high school GPA
• Her college ...

Solution Summary

The estimated models of women's earnings at age 50 are determined. The solution answers the question(s) below.

$2.19
See Also This Related BrainMass Solution

Dummy variable

An Economics department at a large university keeps track of its majors'
starting salaries. We address the question of the value of taking
econometrics, based on last year's crop of 50 majors. Let SAL=$ salary,
GPA = grade point average on a 4.0 scale, METRICS=1 if student took
econometrics, METRICS = 0 otherwise, SEX=1 if student is a female,
otherwise =0.

Consider the following regression
SAL = B1 + B2GPA + B3METRICS + B4METRICS * GPA + et

(a) Based on table 1, what is the marginal effect (benefit) of taking
econometrics?

(b) What is the predicted wage difference between a student who took
econometrics and one who did not, given that their GPA =3.0?
Consider another regression

SAL = B1+B2GPA+B3METRICS+B4SEX+B5METRICS*SEX+et

(c) Based on table 2, what is the reference group in this model? What
is the estimated salary of them?

(d) Does the model suggest salary difference among gender? Justify
your answer

(e) Is the value of econometrics the same for men and women? Justify
your answer.

Source | SS df MS Number of obs = 50
-------------+------------------------------ F( 3, 46) = 45.59
Model | 275083851 3 91694616.9 Prob > F = 0.0000
Residual | 92520533 46 2011315.94 R-squared = 0.7483
-------------+------------------------------ Adj R-squared = 0.7319
Total | 367604384 49 7502130.28 Root MSE = 1418.2

------------------------------------------------------------------------------
salary | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
gpa | 1918.108 396.7813 4.83 0.000 1119.428 2716.787
metrics | 8440.214 2399.488 3.52 0.001 3610.296 13270.13
metricsgpa | -1197.261 828.1421 -1.45 0.155 -2864.224 469.7027
_cons | 23379.25 1207.748 19.36 0.000 20948.18 25810.32
------------------------------------------------------------------------------

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