  This paper provides nonparametric identification results for random
coefficient distributions in perturbed utility models. We cover discrete and
continuous choice models. We establish identification using variation in mean
quantities, and the results apply when an analyst observes aggregate demands
but not whether goods are chosen together. We require exclusion restrictions
and independence between random slope coefficients and random intercepts. We do
not require regressors to have large supports or parametric assumptions.
