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Re: st: MI estimate, data missing in grouping variable (Stata 11)

From   Clara Barata <[email protected]>
To   [email protected]
Subject   Re: st: MI estimate, data missing in grouping variable (Stata 11)
Date   Thu, 5 Nov 2009 20:21:45 -0500

Hi all,
I didn't hear from anybody in the list-serve about this issue, so I am
wondering whether 1. it's a silly question, 2. my problem is not
stated clearly; 3. the email didn't go through.
If there's anybody out there experiencing similar problems with MI,
please email me.
Thanks and I apologize for the double posting, Clara

On Wed, Nov 4, 2009 at 10:39 AM, Clara Barata
<[email protected]> wrote:
> Hi!
> I am using the new MI software in Stata 11 and I am running into some
> problems with the estimation commands. I was wondering if anybody had
> any ideas on how to get around the specific problem of imputing a
> variable with a lot of data missing which you will later use as a
> grouping variable in your estimation models.
> let me explain this better. For my analysis I fit a ton of models by
> income quintiles. However, I have a lot of data missing for income,
> and so I imputted the dataset using MI creating 40 complete datasets.
> For each of the datasets MI has substituted the missing value for
> income with a possible value. The problem is that when I fit models
> for different quintiles, Mi estimate clearly recognizes that the
> estimation sample varies from one imputed dataset to another, as
> follows:
> . mi estimate : regress outcome predictor predictor2 if income<= XXX
> estimation sample varies between m=1 and m=2
> To make the model work I asked MI to ignore the variation across
> estimation samples by using esampvaryok. However, to make matters
> worse, I then need to add fixed effects to deal with the clustering of
> my data, and then MI can't fit the model at all because different
> samples include different fixed effects.
> . mi estimate : regress outcome predictor predictor2 FE1-FE100 if income<= XXX
> mi estimate: omitted terms vary
>    The set of omitted variables or categories is not consistent
> between m=1 and m=2; this is not allowed.  To identify varying sets,
> you can use mi xeq to run the command on individual imputations
>    or you can reissue the command with mi estimate, noisily
> Any ideas on how to deal with this problem?
> Clara
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