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Re: st: RE: St: Panel data imputation


From   Maarten buis <maartenbuis@yahoo.co.uk>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: RE: St: Panel data imputation
Date   Tue, 21 Sep 2010 15:13:59 +0000 (GMT)

--- On Tue, 21/9/10, David Bai wrote:
> I have many more variables that can be possibly related to
> revenue. Given what you and Maaren explained below, I guess
> using ipolate and year info only might not be an accurate
> way to predict revenue. MI might be a better approach.
> Correct me if I am wrong. Thank you.

That really depends on all gritty little details of your 
data analysis: what you want to do with your imputed data, why 
some variables have missing values, what assumptions you are 
willing to make, etc. etc. So, the not very helpful "correct"
answer is that "it depends". In general I would recommend to
just ignore missing values (default approach in Stata). Methods
like MI are great but also very sensitive and hard to diagnose,
so unless you really know what you are doing I would stay away
from those techniques.

Think of it this way: I will generaly be skeptical about results
when MI noticably changes it. This might legitemately happen, but
it more often points into the direction of an error in your 
imputation model. Now, why would I go through the effort of learning
about MI if I am only going to believe the results when the MI does
not change them?

There are specific situations where MI make perfect sense, but 
MI is not suitable as a default. The problem is, that making such a
decision depends on all the gritty little details of your project, 
your theory, your research question, your discipline, and much more.
So, this is really a decision that you will have to make on your own.

Hope this helps,
Maarten

--------------------------
Maarten L. Buis
Institut fuer Soziologie
Universitaet Tuebingen
Wilhelmstrasse 36
72074 Tuebingen
Germany

http://www.maartenbuis.nl
--------------------------


      

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