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

[James Bernard <jamesstatalist@gmail.com>]

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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