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From |
Nick Cox <njcoxstata@gmail.com> |

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

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

Date |
Fri, 25 Oct 2013 17:14:18 +0100 |

Indeed. I assign a big cost to asking the wrong question or giving the wrong answer. Nick njcoxstata@gmail.com On 25 October 2013 17:08, James Bernard <jamesstatalist@gmail.com> wrote: > Thanks Nick. > > yes, I agree. using imputation, though tempting, raises other issues. > So, again, lime many thing in statistics, it is a matter of > cost-benefit analysis > > On Fri, Oct 25, 2013 at 11:32 PM, Nick Cox <njcoxstata@gmail.com> wrote: >> There is a bundle of issues here. >> >> Carlo touches on one, which is what multiple imputation does and does >> not purport to provide. >> >> Another is that the method being used here -reshape-s panel data to >> wide, imputes and then -reshape-s back. >> >> This really does raise the question of precisely what assumptions are >> needed about variations in time to make that legitimate. There's no >> white magic independently of whether tacit assumptions match the data >> generating process. I've not thought this through either -- I don't do >> this stuff -- but I want to send a Hang on there... signal of caution. >> >> No-one seems interested any more in interpolation as a rough family of >> methods of filling in gaps in time series. Rather, it is a smooth >> method of filling in gaps and raises questions of its own too. >> >> >> Nick >> njcoxstata@gmail.com >> >> >> On 25 October 2013 16:17, Carlo Lazzaro <carlo.lazzaro@tiscalinet.it> wrote: >>> 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: venerdě 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/ >> >> * >> * 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/ * * 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/

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

**st: R: Imputation of missing data in an unbalanced panel using ICE***From:*"Carlo Lazzaro" <carlo.lazzaro@tiscalinet.it>

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

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

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