Flexibility without foresight: the predictive limitations of mixture models

2025/10/10 by Stephane Hess, Hess, Stephane, Sander van Cranenburgh +1
Business, Management and Accounting · Decision Sciences · Economics, Econometrics and Finance · #Consumer Market Behavior and Pricing #Decision-Making and Behavioral Economics #Econometrics (econ.EM) #Economic and Environmental Valuation #FOS: Economics and business

paper · pdf · doi:10.48550/arxiv.2510.09185

openalex publication_date 2025/10/10 · openalex created_date 2025/10/14 · openalex updated_date 2026/07/28

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.

Citations

• On the use of a Modified Latin Hypercube Sampling (MLHS) method in the estimation of a Mixed Logit Model for vehicle choice
• Mixed Logit with Repeated Choices: Households' Choices of Appliance Efficiency Level

Cited by

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

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