Introductory Econometrics: A Modern Approach 6th Edition by Jeffrey M Wooldridge

Exercise 17

The data set NBASAL.RAW contains salary information and career statistics for 269 players in the National Basketball Association (NBA).

(i) Estimate a model relating points-per-game (points) to years in the league (exper),age, and years played in college (coll). Include a quadratic in exper; the other variables should appear in level form. Report the results in the usual way.

(ii) Holding college years and age fixed, at what value of experience does the next year of experience actually reduce points-per-game? Does this make sense?

(iii) Why do you think coll has a negative and statistically significant coefficient?

(iv) Add a quadratic in age to the equation. Is it needed? What does this appear to imply about the effects of age, once experience and education are controlled for?

(v) Now regress log(wage) on points, exper, exper2, age, and coll. Report the results in the usual format.

(vi) Test whether age and coll are jointly significant in the regression from part (v).What does this imply about whether age and education have separate effects on wage, once productivity and seniority are accounted for?

Step 1 of 8

(i)

The summary for the regression model relating points to Exper, Age, Coll, Exper² is given by

Dependent Variable:
Regressors	Coefficients	Standard Error	t Stat	P-value
Intercept	34.98355245	6.970931359	5.01849045	9.58327E-07
exper	2.337019734	0.40458044	5.776403158	2.14161E-08
age	-1.06174031	0.294404955	-3.606394159	0.000371187
coll	-1.275885467	0.449573132	-2.837993145	0.004892334
expersq	-0.075766431	0.023430158	-3.233713991	0.001377424

The estimated equation comes out to be:

.

Regression Statistics
R Square	0.139791403
Adjusted R Square	0.12675794
Standard Error	5.497033998
Observations	269