The Generalised Multinomial ial Logit model: Misinterpreting scale and preference heterogeneity in discrete choice models or untangling the un-untanglable?

摘要

Recently published papers dealing with issues of scale and preference heterogeneity are having asignificant impact on the choice modeling community. In particular, the generalized multinomial logitmodel (GMNL) is now being widely promoted as the model of choice in many discipline areas givenits purported ability to separately identify scale and preference heterogeneity. The purpose of thispaper is to firstly discuss a number of issues related to the estimation of the GMNL model. The secondobjective of the paper is to argue that the GMNL model does not in fact untangle scale and preferenceheterogeneity as has been reported and that the outputs derived from the model have beenmisinterpreted. We further argue that the model is not a generalised version of the mixed multinomiallogit model, but in fact is a mixed multinomial logit with more flexible, but still restrictive, mixtures ofdistributions that do not necessarily equate to an ability to capture scale heterogeneity. We finallydiscuss the only theoretical circumstance under which random scale and preference can be separatelyidentified within the logit family of models.