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Re: st: Regressing with variables with missing values


From   Ramani Gunatilaka <ramani.gunatilaka@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Regressing with variables with missing values
Date   Thu, 3 Nov 2005 07:50:25 +1100

Thanks, Paul. I did download listmiss and use it. Now my dilemma is
that the main culprits appear non-random wrt the dependent variable
according to listmiss (ie. t and p values appear in yellow with
stars). That means that I can't use ice because that assumes that the
missing observations are missing at random. I'd be grateful for any
suggestions as to what I should do next.
Ramani

On 03/11/05, Paul Millar <paul.millar@shaw.ca> wrote:
> 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-
> >
> >
> >-------------------------------------------
> >Richard Williams, Notre Dame Dept of Sociology
> >OFFICE: (574)631-6668, (574)631-6463
> >FAX:    (574)288-4373
> >HOME:   (574)289-5227
> >EMAIL:  Richard.A.Williams.5@ND.Edu
> >WWW (personal):    http://www.nd.edu/~rwilliam
> >WWW (department):    http://www.nd.edu/~soc
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