Taste Variation in Discrete Choice Models

This paper develops an extension of the classical multinomial logit model which approximates a class of models obtained when there is uncontrolled taste variation across agents and choices in addition to the stochastic noise inherent in the logit model. Unlike semiparametric and parametric alternatives, the extended logit model is easy to estimate even when there are many potential choices. Unlike parametric alternatives, it does not require the specification of a distribution of varying tastes. The extended logit model can give a quick indication of the impact of taste variation on estimates and it generates estimates of the covariances of the taste shifters. It can be used as an exploratory device en route to the construction of a model incorporating a particular form of random taste variation and it can be used to determine whether such effort is required at all. When the amount of taste variation is not excessive the approximate model can be adequate itself. The model nests the conventional logit model which leads to a misspecification diagnostic. A method for estimating the model using conventional logit model software is proposed, asymptotic properties of estimators are derived and an application is presented.