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Re: st: Multinomial regression using panel data with case-specific regressors only


From   Arne Risa Hole <arnehole@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Multinomial regression using panel data with case-specific regressors only
Date   Tue, 29 Jan 2013 20:59:17 +0000

Hi Rohit

Both -gllamm- and -mixlogit- (both available from SSC) can be used to
estimate MNL models with case-specific variables and random effects.
See e.g. the example on pages 397-400 in
http://www.stata-journal.com/article.html?article=st0133.

Arne (author of -mixlogit-)

On 27 January 2013 12:57, Rohit Sonika <net@rsonika.me> wrote:
> Hi Statalist,
>
> I have a cross-sectional, time series dataset which has information on
> over 500 companies repeated over 10 years from 1991 to 2000. During
> the time period, firms have a choice of decisions to make:
>
> 1. Stay in their state of paying no dividends or buybacks
> 2. Pay dividend, or
> 3. Buyback shares
>
> Once a decision is made, it tends to follow the trend for the rest of
> the sample period. For instance, if firm A decides to pay dividend in
> 1994 then the decision continues to occur through 2000. The equation I
> am trying to model is the likelihood of decision 2 or 3 occurring with
> decision 1 as the base, regressed on a set of firm-level
> characteristic variables.
>
> Question 1: I understand the decision a firm has to make is
> mutinomial, but unordered. However, the regressors I am using are
> case-specific, and not alternative-specific. I have gone through the
> statalist archive to understand that models like that of -gllamm- and
> -mixlogit- require alternative-specific regressors in each decision
> group, but in my case none exist. Can you suggest how best can I
> tackle this multinomial issue with a panel data?
>
> Question 2: Would it make any sense to subsample the data and do
> separate -xtlogit- regressions in the two subsamples, comparing 2 with
> 1, and 3 with 1?
>
> I am trying to do this in Stata 12. Thank you for all your help.
>
> Best,
> Rohit
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