Connor R. Forsythe, Cristian Arteaga, John P. Helveston
arXiv 31 Jan 2024 · Econometrics
arXiv:2402.00184 · PDF · DOI · OpenAlex · Extracted main text
This paper introduces the Mixed Aggregate Preference Logit (MAPL, pronounced "maple”) model, a novel class of discrete choice models that leverages machine learning to model unobserved heterogeneity in discrete choice analysis. The traditional mixed logit model (also known as "random parameters logit”) parameterizes preference heterogeneity through assumptions about feature-specific heterogeneity distributions. These parameters are also typically assumed to be linearly added in a random utility (or random regret) model. MAPL models relax these assumptions by instead directly relating model inputs to parameters of alternative-specific distributions of aggregate preference heterogeneity, with no feature-level assumptions required. MAPL models eliminate the need to make any assumption about the functional form of the latent decision model, freeing modelers from potential misspecification errors. In a simulation experiment, we demonstrate that a single MAPL model specification is capable of correctly modeling multiple different data-generating processes with different forms of utility and heterogeneity specifications. MAPL models advance machine-learning-based choice models by accounting for unobserved heterogeneity. Further, MAPL models can be leveraged by traditional choice modelers as a diagnostic tool for identifying utility and heterogeneity misspecification.
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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 | Train (2009) | 0.950 | 7 | 5 | 86% |
| 2 | Fosgerau and Mabit (2013) Easy and flexible mixture distributions | 0.941 | 6 | 5 | 83% |
| 3 | Revelt and Train (1998) Mixed logit with repeated choices: Households' choices of appliance efficiency level | 0.737 | 3 | 3 | 67% |
| 4 | McFadden and Train (2000) Mixed MNL models for discrete response | 0.737 | 3 | 2 | 100% |
| 5 | van Cranenburgh, Wang, Vij, Pereira and Walker (2022) Choice modelling in the age of machine learning - Discussion paper | 0.737 | 3 | 2 | 100% |
| 6 | Arteaga, Park, Beeramoole and Paz (2022) xlogit: An open-source Python package for GPU-accelerated estimation of Mixed Logit models | 0.644 | 2 | 2 | 100% |
| 7 | Forsythe, Gillingham, Michalek and Whitefoot (2023) Technology advancement is driving electric vehicle adoption | 0.644 | 2 | 2 | 100% |
| 8 | Guo and Zhang (2021) Understanding factors influencing shared e-scooter usage and its impact on auto mode substitution | 0.644 | 2 | 2 | 100% |
| 9 | Haghani, Bliemer and Hensher (2021) The landscape of econometric discrete choice modelling research | 0.644 | 2 | 2 | 100% |
| 10 | Helveston, Liu, Feit, Fuchs, Klampfl and Michalek (2015) Will subsidies drive electric vehicle adoption? Measuring consumer preferences in the U.S. and China | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 30 scored citations.