Re: st: Regressing with variables with missing values

[Paul Millar <paul.millar@shaw.ca>]

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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