Subodh Dubey, Prateek Bansal, Ricardo A. Daziano, Erick Guerra
arXiv 17 Apr 2019 · Econometrics · publishedTransportation Research Part B Methodological (2020)
arXiv:1904.08332 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Bhat \ Sidharthan (2012) `A new approach to specify and estimate non-normally mixed multinomial probit models', Transportation Research Part B: Methodolo… | 0.928 | 4 | 3 | 100% |
| 2 | Liu (2004) `Robit regression: A simple robust alternative to logistic and probit regression', Applied Bayesian Modeling and Causal Inferenc… | 0.928 | 4 | 3 | 100% |
| 3 | Bhat, Dubey \ Nagel (2015) `Introducing non-normality of latent psychological constructs in choice modeling with an application to bicyclist route choice',… | 0.843 | 3 | 3 | 100% |
| 4 | Azzalini \ Arellano-Valle (2013) `Maximum penalized likelihood estimation for skew-normal and skew-t distributions', Journal of Statistical Planning and Inferenc… | 0.737 | 3 | 2 | 100% |
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| 6 | Bhat, Astroza \ Hamdi (2017) `A spatial generalized ordered-response model with skew normal kernel error terms with an application to bicycling frequency', T… | 0.644 | 2 | 2 | 100% |
| 7 | Dekker, Hess, Brouwer \ Hofkes (2016) `Decision uncertainty in multi-attribute stated preference studies', Resource and Energy Economics 43, 57–73 | 0.644 | 2 | 2 | 100% |
| 8 | Ding (2016) `On the conditional distribution of the multivariate t distribution', The American Statistician 70(3), 293–295 | 0.644 | 2 | 2 | 100% |
| 9 | Genz (1992) `Numerical computation of multivariate normal probabilities', Journal of computational and graphical statistics 1(2), 141–149 | 0.644 | 2 | 2 | 100% |
| 10 | Hajivassiliou, McFadden \ Ruud (1996) `Simulation of multivariate normal rectangle probabilities and their derivatives theoretical and computational results', Journal… | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 68 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | 2009.06383 | 0.693 | 5 | 1 |