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I am a dealing with a dataset which is as follows

   y       x1    x2    time    n
   0.12    1     0     0       18
   0.3031  2     0     1       17
   .       .     .     .       .
   .       .     .     .       . 
   1.15    3     2     23      17

where

  • outcome y (CO2, Carbon DiOxide)

    • is continuous
    • not independent because, at time = 0 , n = 18 (there were 18 pigs in the study). Then at time = 1, n=17 (one pig was lost, we dont know the reason why but the pig was not part the study at time =1). The Carbon emission was 0.12 at t=0, n=18, at t =1, carbon emission was 0.3031 it could be from the same 17 pigs in t=0 or a combination of old and new pigs we dont know. So for this reason I like to assume that y is not independent
  • regressor x

    • are time varying
  • Error

    • Hetroscedastic

Based on these assumptions, what modeling approach should I use if my goal is to predict y ? Any help would be much appreciated. Thanks.

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