Stephane Hess, Sander van Cranenburgh
arXiv 10 Oct 2025 · Econometrics
arXiv:2510.09185 · PDF · DOI · OpenAlex · Extracted main text
Models allowing for random heterogeneity, such as mixed logit and latent class, are generally observed to obtain superior model fit and yield detailed insights into unobserved preference heterogeneity. Using theoretical arguments and two case studies on revealed and stated choice data, this paper highlights that these advantages do not translate into any benefits in forecasting, whether looking at prediction performance or the recovery of market shares. The only exception arises when using conditional distributions in making predictions for the same individuals included in the estimation sample, which obviously precludes any out-of-sample forecasting.
appendix boundary found by appendix_command · 69% of the source is main text. Read the extracted text to check this.
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 | Revelt, D. and Train, K (1998) Mixed Logit with repeated choices: households' choices of appliance efficiency level | 0.644 | 2 | 2 | 100% |
| 2 | Train, K (2009) Discrete Choice Methods with Simulation | 0.644 | 2 | 2 | 100% |
| 3 | Fox, J. B (2015) Temporal Transferability of Mode-Destination Choice Models | 0.644 | 2 | 2 | 100% |
| 4 | Hess, S., Lancsar, E., Mariel, P., Meyerhoff, J., Song, F., van den… (2022) The path towards herd immunity: Predicting covid-19 vaccination uptake through results from a stated choice study across six con… self | 0.511 | 2 | 1 | 100% |
| 5 | Hess, S., Train, K., and Polak, J. W (2006) On the use of a Modified Latin Hypercube Sampling (MLHS) method in the estimation of a Mixed Logit model for vehicle choice self | 0.405 | 1 | 1 | 100% |
| 6 | Daly, A (2024) Forecasting choice | 0.405 | 1 | 1 | 100% |
| 7 | Danaf, M., Becker, F., Song, X., Atasoy, B., and Ben-Akiva, M (2019) Online discrete choice models: Applications in personalized recommendations | 0.405 | 1 | 1 | 100% |
| 8 | Fox, J., Daly, A., Hess, S., and Miller, E (2014) Temporal transferability of models of mode-destination choice for the greater toronto and hamilton area self | 0.405 | 1 | 1 | 100% |
| 9 | Hess, S. and Train, K. E (2011) Recovery of inter- and intra-personal heterogeneity using mixed logit models self | 0.405 | 1 | 1 | 100% |
| 10 | Calastri, C., dit Sourd, R. C., and Hess, S (2020) We want it all: experiences from a survey seeking to capture social network structures, lifetime events and short-term travel an… | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 15 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Combine and conquer: model averaging for out-of-distribution forecasting | 0.405 | 1 | 1 |