Dear statalist,

`I am having trouble getting my head around the following issue. It's
``not directly related to Stata but I'm hoping you are willing to help me
``out. The dataset I use is a panel of respondents (see example below).
``Some of these respondents transition into a certain state during the
``period of observation, some don't. The variable 'trans' denotes if a
``respondent has transitioned at a certain time point. In my example,
``respondents 1 and 2 have, respondent 3 hasn't. I'm trying to determine
``if and how the transition affects my dependent variable. The basic
``model I'm estimating is: 'xtmixed depvar trans || time: '. I know I can
``use xtreg, but I'm using a simplified representation of the model I'm
``estimating.
`
My issue is the following:

`I have both continuous and categorical variables that I want to use in
``my estimation. Estimating the effect of my variable 'contvar', and
``distinguishing the effect between those that have and have not
``transitioned seems straightforward:
`xtmixed depvar trans contvar trans#c.contvar || time:

`However, the categorical variable I would like to use is only
``available for those that have made the transition. Creating dummy
``variables out of this categorical variable yields three dummy
``variables, one is the reference category. The problem I'm having with
``this is that for those respondents who have not transitioned, all dummy
``variables are zero. For those that have transitioned, one of them is
``one. When estimating the model xtmixed depvar trans dummy1 dummy2 ||
``time:. From the results I want to be able to conclude that respondents
``that have transitioned where dummy1==1, are significantly different in
``the depvar from respondents that have transitioned where dummy3==1. I'm
``thinking though that the reference category here is blurred, I can be
``either dummy3, or the case where the categorical variable is zero (i.e.
``not applicable). My conclusion would be that this approach is simply
``not valid in that the coefficients do not represent what they are
``supposed to represent, but I have seen papers that use such an
``approach.
`

`My question is if people on this list think that this use of dummy
``variables described above is appropriate or not? Estimating a model
``only for those that have transitioned is simply not an option. If it is
``not appropriate, perhaps some of you have suggestions on how to
``estimate the model I would like to estimate.
`

`Another totally unrelated question I have is if it is possible to
``mean-center dummy variables over time (within respondents) to separate
``the fixed and time-varying effects of dummy variables as is possible
``with continuous variables? If so, any pointers to papers that do would
``be greatly appreciated.
`
Thanks very much for any help,
Niels Schenk
Simplified representation of my dataset:
id time trans depvar contvar catvar dummy1 dummy2 dummy3
1 1 0 4 34 0 0 0 0
1 2 0 5 55 0 0 0 0
1 3 1 6 43 1 0 1 0
1 4 1 3 34 2 1 0 0
2 1 0 7 54 0 0 0 0
2 2 1 6 23 1 0 1 0
2 3 1 5 32 2 1 0 0
2 4 1 6 34 3 0 0 1
3 1 0 3 54 0 0 0 0
3 2 0 4 43 0 0 0 0
3 3 0 7 23 0 0 0 0
3 4 0 6 23 0 0 0 0
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