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

[A Loumiotis <antonis.loumiotis@gmail.com>]

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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