Flexibility without foresight: the predictive limitations of mixture models

Stephane Hess and Sander van Cranenburgh

Papers from arXiv.org

Abstract: 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.

Date: 2025-10
References: Add references at CitEc
Citations:

Downloads: (external link)
http://arxiv.org/pdf/2510.09185 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2510.09185

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().