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In multinomial response models, idiosyncratic variations in the indirectutility are generally modeled using Gumbel or normal distributions. This studymakes a strong case to substitute these thin-tailed distributions with at-distribution. First, we demonstrate that a model with a t-distributed errorkernel better estimates and predicts preferences, especially inclass-imbalanced datasets. Our proposed specification also implicitly accountsfor decision-uncertainty behavior, i.e. the degree of certainty thatdecision-makers hold in their choices relative to the variation in the indirectutility of any alternative. Second, after applying a t-distributed error kernelin a multinomial response model for the first time, we extend thisspecification to a generalized continuous-multinomial (GCM) model and deriveits full-information maximum likelihood estimator. The likelihood involves anopen-form expression of the cumulative density function of the multivariatet-distribution, which we propose to compute using a combination of thecomposite marginal likelihood method and the separation-of-variables approach.Third, we establish finite sample properties of the GCM model with at-distributed error kernel (GCM-t) and highlight its superiority over the GCMmodel 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 topreferences for electric vehicles (EVs). We observe that accounting fordecision-uncertainty behavior in GCM-t results in lower elasticity estimatesand a higher willingness to pay for improving the EV attributes than those ofthe GCM-N model. These differences are relevant in making policies to expeditethe adoption of EVs.

A Generalized Continuous-Multinomial Response Model with a t-distributed Error Kernel