# st: Re: RE: econometric analysis of proportions

 From "Scott Merryman" To Subject st: Re: RE: econometric analysis of proportions Date Thu, 21 Aug 2003 19:36:24 -0500

```----- Original Message -----
From: <ACavallo@lexecon.com>
To: <statalist@hsphsun2.harvard.edu>
Sent: Thursday, August 21, 2003 3:35 PM
Subject: st: RE: econometric analysis of proportions

>
>
>
>
> 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
>

There is FAQ "How can I do logistic regression or multinomial logistic
regression with grouped data?"

The correct number of obs is 19 - since with frequency weights you are treating
each observation as one or more real observations.  See [U]23.16.1 Frequency
Weights

Hope this helps,
Scott

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