st: panel data daily data, missing days, how to fill it up to full months(1-31)

[obba0001 <oben.bayrak@slu.se>]

In my panel data, some people have recorded between 1-25 days of the month.
some recorded from 3 to 31 days of the month. In addition to this, some of
them are not measured for the same month, so in some months which there are
31 days and for some months there are 30 days. what i want is to create
those missing days then i will do MI impute, but first i need to make the
data ready for imputation. 
i used tsset to give you more information about my data: 

 panel variable:  id (unbalanced) 
        time variable:  day, 18aug2005 to 06jul2008 
                delta:  1 day 

i tried tsfill with the full option but it didnt work. 
the message i got is: 
timevar (day) may not contain missing values when option full is specified 

i looked for that error but didnt understand how to modify the data in order
to make this command work. I am a newbie, so i need your help. 

further information as requested: 

The desired pattern is to measure the people for a complete month. there are
people that are measured full month such as from 1 to 31th of that month.
but there are some people that are measured between 1 to 16 or 1-25 or 1-5th
of the month. Another property of my data is that the experiment was done
between 2005 to 2008. So people were not measured at the same period.  for
example, some people recorded between 1th of september 2005 to 26th of
september 2005 and some recorded between 5th of november 2006 to 31 december
2006. 

What I want is for example for the person who have measured between 1th to
25th november, I want to create rows that have  25th,27th,28th, 29th,30th
under the date coloumn and under the id coloumn the id should be same such
as 1 for each created day, and i want to copy the demographic variables to
that produced rows. but the dependent variable should include na since there
is no data for the dependent variable for those days for the given person. 

After completing this step I will do multiple imputation to produce those
dependent variables. 

Kind regards 



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