Re: st: Imputation of missing data in an unbalanced panel using ICE

[James Bernard <jamesstatalist@gmail.com>]

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?
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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