This does not sound like an interval regression problem to me. Interval
regression
is for when at least some individual values are known as intervals not
points.
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
for
proportions.
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.
Nick
[email protected]
Anders Alexandersson
See -help xtintreg-. It does random-effects interval data regression
models.
Pavlos C. Symeou <[email protected]> wrote:
> I have panel data for 100 firms for five years and I want to examine
the
> effects of various variables on "reputation". My variable
"reputation"
> 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
variable
> "reputation" takes in my sample range between 2.6 - 8.3. Can you
please
> advise if I can still use panel OLS estimation for panel data or
should
> I use a different model? In essence, my main concern is the
limitations
> of the bounded range of my variable.
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