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st: RE: Panel data and sparse data


From   "Nick Cox" <n.j.cox@durham.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: RE: Panel data and sparse data
Date   Wed, 16 Jul 2008 12:27:22 +0100

In this context imputation is usually called interpolation, with a
centuries-long history to boot. And you can do it inn various ways from
linear interpolation (-ipolate-) and cubic interpolation (-cipolate-
from SSC) upwards. 

But my visceral reaction is, for your situation, Don't. Survival
analysis is in a strong sense geared to make use of the information you
have and interpolation would just be a way of kidding yourself you had
more. 

Nick
n.j.cox@durham.ac.uk 

James Nachbaur

I have a panel data set of 165 counties over 55 years with many
variables observed every 10 years or every 4 to 5 years.  I am running
a survival time model with unobserved heterogeneity.  My question for
the list is, What is a good way to impute data for the years that lack
observations?  In my research, I have seen a lot on variables missing
at random, or on data sets where only one variable has missing data,
but my situation is not like those.


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