Thanks for your comment
WT
----- Original Message -----
From: "Nick Cox" <[email protected]>
To: <[email protected]>
Sent: Tuesday, August 08, 2006 6:49 PM
Subject: st: RE: Filling missing depr. for panel
> I don't find it possible to advise given this information, except
> to wonder whether either approach is legitimate. It sounds
> as if you can make your best guess at the missing values
> _after_ fitting the most appropriate model you can think of.
> But doing it the other way, modelling after imputation or
> interpolation, will at best overstate the strength of relationship and at worst warp
> results by building-in dubious assumptions.
>
> Otherwise put, you are at risk of finding plums in the
> dataset that you put there yourself.
>
> Nick
> [email protected]
>
> [email protected]
>
> > I have multi Variable Panel dataset believing
> > Yit=B0it+B1*X1it+B2*X2it+B3*X3it+.. The problem I need to
> > take care is that
> > there are missing Y(every 4 years basis ) such that:
> >
> > ID YR Y X1 X2 X3
> > 1 1987 1 3 4 5
> > 1 1988 . 4 5 7
> > 1 1989 . 7 3 1
> > 1 1990 . 8 9 4
> > 1 1991 . 7 5 6
> > 1 1992 6 7 6 10
> >
> > 2 1987 3 4 7 9
> > 2 1988 . 9 10 14
> > 2 1989 . 10 8 20
> > 2 1990 . 7 10 14
> > 2 1991 . 14 12 10
> > 2 1992 2 24 10 16
> > (note that this data is made randomly for illustrated purpose)
> >
> > From the reference and statalist,I may consider "ipolate" or
> > "impute" option
> > assuming there may be a linear relationship with Ys between years
> > I am so glad if anyone give me better suggestions or more
> > concern I have to look
> > before using those options
>
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