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Re: st: Regressing with variables with missing values


From   Paul Millar <paul.millar@shaw.ca>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Regressing with variables with missing values
Date   Wed, 02 Nov 2005 12:15:26 -0700

You might also use the post-estimation command - listmiss - to find which variables are the main culprits and which ones have missing values that are non-random wrt the dependent variable.
ssc install listmiss

- Paul Millar

At 09:18 AM 02/11/2005, you wrote:

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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