  In multinomial response models, idiosyncratic variations in the indirect
utility are generally modeled using Gumbel or normal distributions. This study
makes a strong case to substitute these thin-tailed distributions with a
t-distribution. First, we demonstrate that a model with a t-distributed error
kernel better estimates and predicts preferences, especially in
class-imbalanced datasets. Our proposed specification also implicitly accounts
for decision-uncertainty behavior, i.e. the degree of certainty that
decision-makers hold in their choices relative to the variation in the indirect
utility of any alternative. Second, after applying a t-distributed error kernel
in a multinomial response model for the first time, we extend this
specification to a generalized continuous-multinomial (GCM) model and derive
its full-information maximum likelihood estimator. The likelihood involves an
open-form expression of the cumulative density function of the multivariate
t-distribution, which we propose to compute using a combination of the
composite marginal likelihood method and the separation-of-variables approach.
Third, we establish finite sample properties of the GCM model with a
t-distributed error kernel (GCM-t) and highlight its superiority over the GCM
model with a normally-distributed error kernel (GCM-N) in a Monte Carlo study.
Finally, we compare GCM-t and GCM-N in an empirical setting related to
preferences for electric vehicles (EVs). We observe that accounting for
decision-uncertainty behavior in GCM-t results in lower elasticity estimates
and a higher willingness to pay for improving the EV attributes than those of
the GCM-N model. These differences are relevant in making policies to expedite
the adoption of EVs.
