Al,
I was thinking about a multinomial logit model. But what about the grouped
nature of the data? If this were a single proportion wouldn't I want to
use -glogit-?
For example: cat indexes the categories, year indexes time, count is the
count by year and category, pop is the sum of count by year, and pct is the
proportion (count/pop):
. li
cat year count pop pct
1. 1 1 2 10 .2
2. 2 1 3 10 .3
3. 3 1 5 10 .5
4. 1 2 3 9 .3333333
5. 2 2 3 9 .3333333
6. 3 2 3 9 .3333333
.
I thinking to do the following:
expand count
mlogit cat year
Would this give me correct standard errors for the trend and constant
terms?
I just did a simulation and got identical results from doing the above as I
do with:
mlogit cat year [fweight=count]
on the unexpanded data.
In either case I get the same # of obs (19). Is this correct for the
degrees of freedom calculations, or should it be 6 - the number of
categories*years?
Regards,
--Alex Cavallo
Lexecon
(312) 322-0208 voice
(312) 322-0218 fax
-----Original Message-----
Alex -
This sounds as if a multinomial logit model (mlogit in Stata) might work
for
you.
Al Feiveson
-----Original Message-----
From: [email protected] [mailto:[email protected]]
Sent: Wednesday, August 20, 2003 10:12 PM
To: [email protected]
Subject: st: econometric analysis of proportions
I am interested in tracking changes in proportions over time and testing
for trend effects. I have a categorical variable with 11 categories that
is measured in aggregate data annually. I want to do some tests for
positive or negative trends in the shares in the categories. I know the
count of cases in each category and year and the overall annual total.
Does anyone have suggestions on how I can model this?
Regards,
--Alex Cavallo
Lexecon
(312) 322-0208 voice
(312) 322-0218 fax
Regards,
--Alex Cavallo
Lexecon
(312) 322-0208 voice
(312) 322-0218 fax
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