In my research I have run into a problem using an offset.
Research context: I am modeling household level temporary outmigration. The outcome variable is a count variable indicating the number of temporary outmigrants (variable tmig) in a particular year per household (range 0 to 11). Households are nested within villages and some village level characteristics are the predictors of main interest – thus, I am using a two-level structure. Formerly I was using Poisson models as following:
Code: Select all
runmlwin tmig cons ///
predictor1 predictor2 predictor3 , ///
level2(Village: cons) ///
level1(Household_Anon: ) ///
discrete(distribution(poisson) offset(lgsize)) nopause
Code: Select all
runmlwin pmig cons ///
predictor1 predictor2 predictor3 , ///
level2(Village: cons) ///
level1(Household_Anon: ) ///
discrete(distribution(nbinomial) offset(lgsize)) nopause
Thus, this help file entry seems to indicate that it is also possible for negative binomial models to specify an offset. From my understanding the negative binomial models are basically Poisson models with an added dispersion parameter and thus should be able to incorporate an offset. But maybe I lack understanding of the difference between the two model types…
But here is my question: In case I can’t use an offset in the negative binomial models, has anyone an advice how to deal with this issue in an alternative way? Is it statistically sound to include the logged household size as a covariate in the negative binomial models? I highly appreciate any help with this issue! Thanks!
Best,
Raphael