I have a general question on fixed-effects and mixed-effects models for panel data. I am doing a logistic regression on panel data, with data measured on the individual level.
I understand that by using fixed-effects in my model (
xtlogit...,fe in Stata) I basically ignore or do not look at the difference between individuals, but rather use the within variance of the IVs to calculate the coefficients.
Question: But what if there's also considerable difference between the individuals that I would like to capture? Is it even possible to account for both the unobserved heterogeneity (through fixed-effects) and the variance between individuals?
I read a lot about Random Effects, Population-averaged models, between-estimator and mixed-effects but I can't bridge my intellectual gap on the question "when to use which model?".
I feel that the mixed-effects logistic regression (
xtmelogit in Stata) might apply, but the term random intercept got me thinking if that really captures the variance between individuals in panel data. Also my data is not nested as in most of the examples on mixed-effects models which speak of clusters or group levels (e.g. students in schools).