A Generalized Continuous-Multinomial Response Model with a t-distributed Error Kernel
Ricardo A. Daziano and
Papers from arXiv.org
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.
New Economics Papers: this item is included in nep-ecm and nep-upt
Date: 2019-04, Revised 2019-04
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1904.08332
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