Combine and conquer: model averaging for out-of-distribution forecasting

[2506.03693] Combine and conquer: model averaging for out-of-distribution forecasting Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Economics > Econometrics arXiv:2506.03693 (econ) [Submitted on 4 Jun 2025 ( v1 ), last revised 14 Apr 2026 (this version, v3)] Title: Combine and conquer: model averaging for out-of-distribution forecasting Authors: Stephane Hess , Sander van Cranenburgh View a PDF of the paper titled Combine and conquer: model averaging for out-of-distribution forecasting, by Stephane Hess and 1 other authors View PDF HTML (experimental) Abstract: Travel behaviour modellers have an increasingly diverse set of models at their disposal, ranging from traditional econometric structures to models from mathematical psychology and data-driven approaches from machine learning. A key question arises as to how well these different models perform in prediction, especially when considering trips of different characteristics from those used in estimation, i.e. out-of-distribution prediction, and whether better predictions can be obtained by combining insights from the different models. We focus on trip distance as a key example of a variable where the application context might go beyond the estimation data. Across two case studies, we show that while data-driven approaches excel in predicting mode choice for trips within the distance bands used in estimation, beyond that range, the picture is fuzzy. To leverage the relative advantages of the different model families and capitalise on the notion that multiple `weak' models can result in more robust models, we put forward the use of a model averaging approach that allocates weights to different model families as a function of the distance between the characteristics of the trip for which predictions are made, and those used in model estimation. Overall, we see that the model averaging approach gives larger weight to models with stronger behavioural or econometric underpinnings the more we move outside the interval of trip distances covered in estimation. Across both case studies, we show that our model averaging approach obtains improved performance both on the estimation and test data, and crucially also when predicting mode choices for trips of distances outside the range used in estimation. Subjects: Econometrics (econ.EM) Cite as: arXiv:2506.03693 [econ.EM] (or arXiv:2506.03693v3 [econ.EM] for this version) https://doi.org/10.48550/arXiv.2506.03693 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Stephane Hess [ view email ] [v1] Wed, 4 Jun 2025 08:26:07 UTC (816 KB) [v2] Mon, 20 Oct 2025 12:15:52 UTC (751 KB) [v3] Tue, 14 Apr 2026 05:38:24 UTC (945 KB) Full-text links: Access Paper: View a PDF of the paper titled Combine and conquer: model averaging for out-of-distribution forecasting, by Stephane Hess and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: econ.EM < prev | next > new | recent | 2025-06 Change to browse by: econ References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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