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From |
"Carlo Lazzaro" <[email protected]> |

To |
<[email protected]> |

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

Date |
Fri, 25 Oct 2013 17:17:13 +0200 |

```
James asked:
"Also, how wrong is to use only the first imputation (M=1) to run the model,
instead of using all the imputations?".
The approach James proposes would seem to rule out the between variance
component (that is, the variance between different M=n datasets generated
via MI), which is a qualifying features of MI.
Kind regards,
Carlo
-----Messaggio originale-----
Da: [email protected]
[mailto:[email protected]] Per conto di James Bernard
Inviato: venerdì 25 ottobre 2013 13:47
A: [email protected]
Oggetto: st: Imputation of missing data in an unbalanced panel using ICE
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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**References**:**st: Imputation of missing data in an unbalanced panel using ICE***From:*James Bernard <[email protected]>

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