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st: RE: Filling missing depr. for panel


From   "Nick Cox" <n.j.cox@durham.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: RE: Filling missing depr. for panel
Date   Wed, 9 Aug 2006 01:49:51 +0100

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 
n.j.cox@durham.ac.uk 

Woong.Chung@colorado.edu

> 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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