Nonparametric mixed logit model with market-level parameters estimated from market share data

[2309.13159] Nonparametric mixed logit model with market-level parameters estimated from market share data Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Economics > Econometrics arXiv:2309.13159 (econ) [Submitted on 22 Sep 2023 ( v1 ), last revised 19 Apr 2025 (this version, v3)] Title: Nonparametric mixed logit model with market-level parameters estimated from market share data Authors: Xiyuan Ren , Joseph Y. J. Chow , Prateek Bansal View a PDF of the paper titled Nonparametric mixed logit model with market-level parameters estimated from market share data, by Xiyuan Ren and 2 other authors View PDF Abstract: We propose a nonparametric mixed logit model that is estimated using market-level choice share data. The model treats each market as an agent and represents taste heterogeneity through market-specific parameters by solving a multiagent inverse utility maximization problem, addressing the limitations of existing market-level choice models with parametric estimation. A simulation study is conducted to evaluate the performance of our model in terms of estimation time, estimation accuracy, and out-of-sample predictive accuracy. In a real data application, we estimate the travel mode choice of 53.55 million trips made by 19.53 million residents in New York State. These trips are aggregated based on population segments and census block group-level origin-destination (OD) pairs, resulting in 120,740 markets. We benchmark our model against multinomial logit (MNL), nested logit (NL), inverse product differentiation logit (IPDL), and the BLP models. The results show that the proposed model improves the out-of-sample accuracy from 65.30% to 81.78%, with a computation time less than one-tenth of that taken to estimate the BLP model. The price elasticities and diversion ratios retrieved from our model and benchmark models exhibit similar substitution patterns. Moreover, the market-level parameters estimated by our model provide additional insights and facilitate their seamless integration into supply-side optimization models for transportation design. By measuring the compensating variation for the driving mode, we found that a $9 congestion toll would impact roughly 60 % of the total travelers. As an application of supply-demand integration, we showed that a 50% discount of transit fare could bring a maximum ridership increase of 9402 trips per day under a budget of $50,000 per day. Subjects: Econometrics (econ.EM) Cite as: arXiv:2309.13159 [econ.EM] (or arXiv:2309.13159v3 [econ.EM] for this version) https://doi.org/10.48550/arXiv.2309.13159 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Transportation Research Part B 196 (2025) 103220 Related DOI : https://doi.org/10.1016/j.trb.2025.103220 Focus to learn more DOI(s) linking to related resources Submission history From: Joseph Chow [ view email ] [v1] Fri, 22 Sep 2023 19:50:55 UTC (1,884 KB) [v2] Thu, 22 Aug 2024 15:14:19 UTC (1,678 KB) [v3] Sat, 19 Apr 2025 19:00:39 UTC (2,335 KB) Full-text links: Access Paper: View a PDF of the paper titled Nonparametric mixed logit model with market-level parameters estimated from market share data, by Xiyuan Ren and 2 other authors View PDF view license Current browse context: econ.EM < prev | next > new | recent | 2023-09 Change to browse by: econ References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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