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This section deals with regression models for discrete data with
more than two response categories.
We will introduce the commands
mlogit for multinomial logits,
ologitfor ordered logits and
oprobit for ordered probits.
We will also use a couple of old friends:
blogit for fitting hierarchical (nested) logit models and
poisson for fitting log-linear models.
Let us introduce the data on contraceptive use by age, found in Table 6.1 of the lecture notes. We will read the 7 by 3 table as 21 observations and treat the frequencies as weights:
. input ageg cuse cases
ageg cuse cases
1. 1 1 3
2. 1 2 61
3. 1 3 232
4. 2 1 80
5. 2 2 137
6. 2 3 400
7. 3 1 216
8. 3 2 131
9. 3 3 301
10. 4 1 268
11. 4 2 76
12. 4 3 203
13. 5 1 197
14. 5 2 50
15. 5 3 188
16. 6 1 150
17. 6 2 24
18. 6 3 164
19. 7 1 91
20. 7 2 10
21. 7 3 183
22. end
. label define cuse 1 "sterilization" 2 "other method" 3 "no method"
. label values cuse cuse
Continue with 6.2 The Multinomial Logit Model
Copyright © Germán Rodríguez, 1993-2003.
Please send feedback to grodri@princeton.edu