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| From | James Bernard <jamesstatalist@gmail.com> |
| To | statalist@hsphsun2.harvard.edu |
| Subject | Re: st: Imputation of missing data in an unbalanced panel using ICE |
| Date | Sat, 26 Oct 2013 00:09:27 +0800 |
Thanks Richard! It is a relief then
On Sat, Oct 26, 2013 at 1:04 AM, Richard Williams
<richardwilliams.ndu@gmail.com> wrote:
> At 09:09 AM 10/25/2013, James Bernard wrote:
>>
>> Thanks Antonis,
>>
>> How about taking the average of the imputations for an observation.
>> Let's say we have 7 imputations (m=7). Then for a particular
>> obesrvation, we could take the average of the 7 imputed value?
>>
>> Does this work?
>
>
> When there is no clear cut statistical solution I personally am open to
> improvisation. There are plenty of things where you don't need accuracy to
> 12 decimal places. You just need to be in the ballpark. So, you might try
> one imputation, a few imputations or all the imputations. You might report,
> say, that the R^2 statistics or the BIC statistics or whatever ranged
> between this and that. Another possibility would be a diagnostic test and
> you run it on different imputations and it always leads to the same
> conclusions. If you get conflicting results or borderline results you have
> to worry more, but if it is a clear cut decision no matter what you do then
> don't worry about it too much.
>
>
>> Thanks
>>
>> James
>>
>> On Fri, Oct 25, 2013 at 9:41 PM, A Loumiotis
>> <antonis.loumiotis@gmail.com> wrote:
>> > I would first create a dummy that will be used to tell -ice- which
>> > values to impute:
>> >
>> > *****
>> > clear
>> > input str1 Firm Year X
>> > "A" 2000 .
>> > "A" 2001 10
>> > "A" 2002 6
>> > "A" 2003 .
>> >
>> > "B" 1998 3
>> > "B" 1999 .
>> > "B" 2000 .
>> > "B" 2001 4
>> > "B" 2002 6
>> > "B" 2003 2
>> > end
>> >
>> > replace X=.a if X==.
>> > reshape wide X, i(Firm) j(Year)
>> > foreach v of varlist X* {
>> > gen c`v'=`v'!=.
>> > replace `v'=0 if c`v'==0
>> > }
>> > ******
>> >
>> > I would then run -ice- using the -conditional()- option (you should
>> > fill in the remaining parts for the -ice- command:
>> > ice ..., conditional(X1998:cX1998==1, ...)
>> >
>> > I don't think it is a good idea to use only the results from the first
>> > imputation because your estimates will underestimate the true
>> > variance.
>> >
>> > Antonis
>> >
>> > On Fri, Oct 25, 2013 at 2:46 PM, James Bernard
>> > <jamesstatalist@gmail.com> wrote:
>> >> Hi all,
>> >>
>> >> I have been using imputation techniques. Stata offers a wide range of
>> >> commands to conduct imputation.
>> >>
>> >> I have a unbalanced panel data. Several variables have missing values.
>> >> To benefit from the fact that the available observation of a variable
>> >> at certain times can help estimate the missing values at other times,
>> >> I changed the format of my data from long to wide and used ICE using
>> >> the instruction from this site:
>> >> http://www.ats.ucla.edu/stat/stata/faq/mi_longitudinal.htm
>> >>
>> >> These instructions work for a balanced panel data set where all firms
>> >> are supposed to have values in all years.
>> >>
>> >> But, imagine that one firm has to have values from 2000-2003, and
>> >> another from 1998-2003. And, suppose we have a variable (X) for which
>> >> some observations across these two firms are missing
>> >>
>> >> Firm Year X
>> >> --------- --------- -------
>> >> A 2000 .
>> >> A 2001 10
>> >> A 2002 6
>> >> A 2003 .
>> >>
>> >> B 1998 3
>> >> B 1999 .
>> >> B 2000 .
>> >> B 2001 4
>> >> B 2002 6
>> >> B 2003 2
>> >>
>> >> Reshaping the data from long to wide would lead to: creation of 6 new
>> >> varibale named "X1998", "X1999",......"X2003".... and values of X1998
>> >> and X1999 will be missing for firm A
>> >>
>> >> And running the ICE, it would predict values for X1998 and X1999 for
>> >> both firm A and B.
>> >>
>> >> The next step is to get the data into long form and run the -mi-
>> >> commands to make the estimation which use Rubin rules for combining
>> >> the data on the m imputations made.
>> >>
>> >> One may argue that I can let the ICE predict the values of X1998 and
>> >> X1999 for firm A. Reshape the data into long format and remove the
>> >> values of X from firm A in 1998 and in 1999, because firm A is not
>> >> supposed to have values in 1998 and 1999.
>> >>
>> >> My question is: Does asking ICE to predict values of X1998 and X1999
>> >> for firm A affect the way it predicts the value of X2000 (which is the
>> >> main observation we have to impute)?
>> >>
>> >> Does the technique I used make sense?
>> >>
>> >> Also, how wrong is to use only the first imputation (M=1) to run the
>> >> model, instead of using all the imputations?
>> >>
>> >> Thanks,
>> >> James
>> >> *
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>> >> * http://www.ats.ucla.edu/stat/stata/
>> > *
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>> *
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>
>
> -------------------------------------------
> Richard Williams, Notre Dame Dept of Sociology
> OFFICE: (574)631-6668, (574)631-6463
> HOME: (574)289-5227
> EMAIL: Richard.A.Williams.5@ND.Edu
> WWW: http://www.nd.edu/~rwilliam
>
>
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