Richard and Nick,
Thanks so much for your comments and suggestions. Will follow up.
Ramani
On 03/11/05, Nick Cox <[email protected]> wrote:
> 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
> [email protected]
>
> 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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