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From |
Clara Barata <[email protected]> |

To |
[email protected] |

Subject |
st: MI estimate, data missing in grouping variable (Stata 11) |

Date |
Wed, 4 Nov 2009 10:39:14 -0500 |

Hi! I am using the new MI software in Stata 11 and I am running into some problems with the estimation commands. I was wondering if anybody had any ideas on how to get around the specific problem of imputing a variable with a lot of data missing which you will later use as a grouping variable in your estimation models. let me explain this better. For my analysis I fit a ton of models by income quintiles. However, I have a lot of data missing for income, and so I imputted the dataset using MI creating 40 complete datasets. For each of the datasets MI has substituted the missing value for income with a possible value. The problem is that when I fit models for different quintiles, Mi estimate clearly recognizes that the estimation sample varies from one imputed dataset to another, as follows: . mi estimate : regress outcome predictor predictor2 if income<= XXX estimation sample varies between m=1 and m=2 To make the model work I asked MI to ignore the variation across estimation samples by using esampvaryok. However, to make matters worse, I then need to add fixed effects to deal with the clustering of my data, and then MI can't fit the model at all because different samples include different fixed effects. . mi estimate : regress outcome predictor predictor2 FE1-FE100 if income<= XXX mi estimate: omitted terms vary The set of omitted variables or categories is not consistent between m=1 and m=2; this is not allowed. To identify varying sets, you can use mi xeq to run the command on individual imputations or you can reissue the command with mi estimate, noisily Any ideas on how to deal with this problem? Clara * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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