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RE: st: Regressing with variables with missing values


From   "Nick Cox" <n.j.cox@durham.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Regressing with variables with missing values
Date   Wed, 2 Nov 2005 17:07:22 -0000

Agreed. The coolest way to approach these
problems is to apply -ice-, and also to 
compare results with those on the subset 
with all non-missing. Or go out into 
the field and fill in the missing values! 

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

Richard Williams
 
> At 10:52 AM 11/2/2005, Ramani Gunatilaka wrote:
> >Dear Statalist,
> >This may seem a stupid question for the statisticians among you but
> >I'd appreciate some help.
> >I want to run a regression on cross-section data with lots of
> >variables, some of which have missing values. When I do that, Stata
> >estimates the model using only the observations which have values for
> >all variables. I downloaded tabmiss and rmiss2 as in the relvant FAQ
> >and the commands would certainly help in enabling me to decide which
> >variables to drop. But is there any way that I could retain all the
> >variables with their missing values and make allowance for 
> the missing
> >values by including a dummy for missing variables?
> 
> The way you retain the missing values is by recoding them to a 
> non-missing value, e.g. the variable's mean.  This has all sorts of 
> problems though.  The MD dummy variable indicator that you propose 
> used to be popular but has since been discredited.  See Paul 
> Allison's Sage book "Missing Data."
> 
> For a synopsis of basic strategies and their pros and cons, see
> 
> http://www.nd.edu/~rwilliam/stats2/l12.pdf
> 
> That handout is weak in discussing more advanced methods, although it 
> does allude to them.  You might check out Royston's -ice- package, 
> which was recently updated and discussed in the Stata Journal.  Use
> 
> -findit ice-

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