Identifiability of Finite Mixtures of Multinomial Logit Models with Varying and Fixed Effects

Unique parametrizations of models are very important for parameter interpretation and
consistency of estimators. In this paper we analyze the identifiability of a general class of
finite mixtures of multinomial logits with varying and fixed effects, which includes the popular
multinomial logit and conditional logit models. The application of the general identifiability
conditions is demonstrated on several important special cases and relations to previously
established results are discussed. The main results are illustrated with a simulation study
using artificial data and a marketing dataset of brand choices.