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From |
"Carlo Lazzaro" <carlo.lazzaro@tiscalinet.it> |

To |
<statalist@hsphsun2.harvard.edu> |

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: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] Per conto di James Bernard Inviato: venerdì 25 ottobre 2013 13:47 A: statalist@hsphsun2.harvard.edu 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 * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: R: Imputation of missing data in an unbalanced panel using ICE***From:*Nick Cox <njcoxstata@gmail.com>

**References**:**st: Imputation of missing data in an unbalanced panel using ICE***From:*James Bernard <jamesstatalist@gmail.com>

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