samlet årgang - Økonomisk Institut - Københavns Universitet

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NATIONALØKONOMISK TIDSSKRIFT 2005. NR. 3
Table 4. Results of testing down to the models of table 1 & 2 from the extended models.
Course F-stat for over identifying p-value of F-test Competing model
restriction towards the – if any
models found in table 1 or 2
111 0.94 0.50
112 0.79 0.61
114E 1.10 0.37
122E 0.51 0.84
123 1.09 0.37
131 2.38 0.03** Extended by Eduhome
132E 0.64 0.72 DQ2, GPA*DQ2
Livabr*DQ2
212E 0.77 0.63
222 0.76 0.64
234 0.44 0.87
241 0.79 0.64
Note: ** significant at the 5% level.
First, we extend our initial model with data for the additional point-giving activities
of a quota-2 student, in order to see whether we were right in omitting these variables
in the first place. Our additional variables are: Eduhome: months spent on additional
education in the home country; Eduout: months spent on additional education abroad;
Workhome: months spent on work in the home country; Workout: months spent on work
abroad; Livabr: months spent living abroad. As all of these variables are relevant for
quota-2 students only, they enter the regression equation multiplied by DQ (the quota
dummy) just like the GPA variable, the motivational average and the DSCAND dummy.
The result of this exercise can be found in table 4. Instead of presenting all the regressions
with the new coefficient estimates, 22 table 4 presents the results of testing whether
the simple parsimonious models of tables 1 and 2 are still appropriate. Again, if the
column showing the p-value has values larger than 0.05 this hypothesis is not rejected.
The general conclusion from this exercise is that not much is changed. For a couple of
models, however, competing equations with one or two of these variables arise. These
competing models are also appropriate compared to the extended model when tested by
an F-test. Hence the suggestion that important explanatory variables were missing from
our original equation is, in general, not supported by this sensitivity analysis.
Secondly, inspired by the result in Albæk (2001) and Andersen and Østergaard
(2005) that the high-school grade and level in Mathematics are important explanatory
factors for performance, we manually collected such data for as many of the students in
22. Appendix 3 present in detail all the regression and the derived estimates.