Dear Statalist, I would like to ask some advice about how to impute correctly the end date of a contract, which is around 50% of missing in my dataset. My dataset is a longitudinal one, in which each line is a contract for each person for which I have the start and end dates of the contract (see an example below). What I think to do is to use the average of the end date from those with no missing (by type of contract) to fill in the missing adding this average to the start date. My goal is to know if a person is working at a certain date of the year. However, I am not pretty sure this is a correct way to impute the date. I have seen that there are other more complex methodologies like multiple imputation (MI).

1) Do you think it has sense to impute the end date of the contract with MI (or this MI method is only for a specific type of variable)?
2) In case of using this MI method, I should account the structure of the dataset which is a kind of multilevel?
3) Do I need that the explanatory variables “always” be non-missing?
4) This imputed information will be used first in a descriptive analysis and thereafter in a regression. Is it possible to say to Stata that the imputed day cannot be below the start date of the contract?
5) Is it possible to enrich the MI model with variables not varying for the person (ex. gender)?
6) Is there a test or procedure to do after the imputation to test how good it is? I mean, something like to impute some groups of end dates with no missing in the data and then compare the imputation with the real date? And how to do it?
7) Do you think that using the average mentioned before might be a good approach for an academic research?

Thanks for the help, and sorry for such number of questions!

Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input float(id start end)
1 14575 14695
1 14625 14727
1 14656     .
1 15412 15485
1 15414 15732
1 15661     .
1 15907 15979
1 15911 15983
1 15913 15985
1 16147     .
1 16184 16265
1 16196     .
1 16197 16269
1 16239 16311
1 16245 16318
1 16253 16325
1 16280 16352
1 16364 16440
1 16406 16480
1 16409     .
1 16665     .
1 17444     .
1 17669 17861
1 17860     .
1 17950     .
1 18249 18501
1 18461 18897
1 18765 18908
1 18826 19263
1 19192 19628
1 19557 19993
1 19922 20358
1 20287 20724
1 20653 21089
1 21018 21454
1 21080 21454
1 21383 21819
1 21748 22185
2 14991     .
2 15006     .
2 15856 15958
2 15887     .
2 16257 16353
2 16361 16468
2 16535 16736
2 16667     .
2 16908 17101
2 17035     .
2 17075 17558
2 17487     .
3 13729     .
3 13925     .
3 14290     .
3 14961     .
3 14968 15040
3 14970 15042
3 14973 15225
3 15023 15302
3 15374     .
3 16933 17132
3 17738 17851
3 17920 18175
3 17920 18175
3 17983 18173
3 19973 20226
3 20174     .
3 20489     .
3 20598     .
4 15123     .
4 15382 15487
4 15493 15571
4 15500 15579
4 15508 15611
4 15767     .
4 15821     .
4 15923 16097
4 16026 16279
4 16574 16653
4 16583 16700
4 16938 17044
4 16973 17055
4 16984 17068
4 17549     .
4 17675     .
4 17949     .
4 18006 18260
4 18189     .
5 16205 16368
5 16583 16706
5 16695 17029
5 16956 17242
5 17393     .
5 17395 17649
5 17430 17683
5 17577     .
5 17613 17868
5 17798     .
5 17902 18082
5 18012 18180
5 18131 18567
end
format %td start
format %td end