samlet årgang - Økonomisk Institut - Københavns Universitet

368
Table 2. Results of the estimation, 2 nd year courses.
NATIONALØKONOMISK TIDSSKRIFT 2005. NR. 3
Variable Course Course Course Course
212E 222 234 241
Intercept term 8.00*** 10.13** 8.50*** 8.66***
(0.49) (0.59) (0.62) (0.21)
DQ -1.31** -2.59*** -3.60*** –
(0.62) (0.79) (0.82) –
GPA*DQ 0.15*** 0.18** 0.15**
(0.05) (0.07) (0.06) –
MOTAVR*DQ – – – –
DSCAND*DQ – – 2.00*** –
(0.61)
AGEADMIT – – – –
Number of
observations 51 31 46 –
R2 0.17** 0.30*** 0.39*** –
Whites test 2.06 2.50 4.53 –
p-value 0.56 0.48 0.48 –
JB test 1.85 1.09 7.69 –
p-value 0.40 0.58 0.02** –
Overidentifying
Restrictions F 1.68 1.20 0.85
p-value 0.18 0.33 0.44
Note: * significant at the 10% level, ** significant at the 5% level, *** significant at the 1% level. Figures
in parentheses are standard errors.
the effect is negative, i.e. compared to quota-1 student a quota-2 student performs
comparatively less well in these courses. The age variable turns out to be significant
only in two courses (The Company in an International setting and Principles of Marketing)
and in both of these courses it enters with a negative effect on grades, i.e. older
students perform worse than younger students. 20
Looking now at what is important for the performance of the quota-2 students, we
notice that a good GPA upon admission has a positive effect on grades in all courses,
20. In other studies (Albæk (2001)) a variable like AGEADMIT enters the model equation non-linearly.
Whether our equations are subject to misspecification of this kind is tested generally by Ramseys RESET
test. Only in one case does this test point toward a functional misspecification of the equation at the 5%
level. At the 1% level no misspecification is detected hence we do not feel a need to include non-linear
terms in the models.