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RE: st: Dependent continuous variable with bounded range

From   "Nick Cox" <>
To   <>
Subject   RE: st: Dependent continuous variable with bounded range
Date   Tue, 15 Apr 2008 15:12:43 +0100

This does not sound like an interval regression problem to me. Interval
is for when at least some individual values are known as intervals not
Here values are known but must lie in a prescribed interval. 

Divide by 10 and the problem has exactly the same form as that often met

There are correspondingly various options: 

1. Go ahead regardless. The advantage is simplicity. The disadvantage is
that there is no 
guarantee that predicted values will lie in [0,10] certainly for some
values of the predictors
and possibly for your observed data. Arguably that is quite wrong in
principle and it may be a real
nuisance for your project. There are implications all the way downstream
in terms of assessing fit. 

2. Transform your response so that is unbounded. Model in terms of that
and then back-transform. 

3. A variant of 2, and in many ways preferable, to use a link function
e.g. logit on response/10. 

4. No doubt others. 


Anders Alexandersson

See -help xtintreg-. It does random-effects interval data regression

Pavlos C. Symeou <> wrote:

>  I have panel data for 100 firms for five years and I want to examine
>  effects of various variables on "reputation". My variable
>  takes  continuous values in the range of 0-10. Namely, it can take
>  values of 1,2,3 but also of 2.5, 9.6, etc. The values that the
>  "reputation" takes in my sample range between 2.6 - 8.3. Can you
>  advise if I can still use panel OLS estimation for panel data or
>  I use a different model? In essence, my main concern is the
>  of the bounded range of my variable.

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