Mixed Logit

Mixed logit is a fully general statistical model for examining discrete choices. The motivation for the mixed logit model arises from the limitations of the standard logit model. The standard logit model has three primary limitations, which mixed logit solves: "It obviates the three limitations of standard logit by allowing for random taste variation, unrestricted substitution patterns, and correlation in unobserved factors over time." Mixed logit can also utilize any distribution for the random coefficients, unlike probit which is limited to the normal distribution. It has been shown that a mixed logit model can approximate to any degree of accuracy any true random utility model of discrete choice, given an appropriate specification of variables and distribution of coefficients." The following discussion draws from Ch. 6 of Discrete Choice Methods with Simulation, by Kenneth Train (Cambridge University Press), to which the reader is referred for more details and citations. See also the article on discrete choice for information on how the mixed logit relates to discrete choice analysis in general and to other specific types of choice models.