Dynamic Heterogeneity in Discrete Choice Experiments
40 Pages Posted: 22 Oct 2024 Last revised: 5 Mar 2025
Date Written: September 15, 2024
Abstract
In discrete choice experiments, respondents’ decisions are studied based on repeated tasks. Especially when conducting longer sequences of tasks to gain more information, learning or fatigue may come into play and affect the identification of the effects. These effects may be exacerbated by customer-specific heterogeneity. We introduce Bayesian multinomial logit models with heterogeneously time-varying coefficients constructed as tensor products of random effects and penalized splines to capture time variation and customer-specific heterogeneity. In an empirical application, we find evidence for the presence of such effects. Our approach outperforms benchmark models in fit and predictive accuracy, as also demonstrated through a simulation.
Keywords: Choice experiments, Functional random effects, Multinomial logit model, Penalized splines, Tensor products
Suggested Citation: Suggested Citation
Hagemann, Niklas and Guhl, Daniel and Kneib, Thomas and Möllenhoff, Kathrin and Steiner, Winfried, Dynamic Heterogeneity in Discrete Choice Experiments (September 15, 2024). Available at SSRN: https://ssrn.com/abstract=4957076 or http://dx.doi.org/10.2139/ssrn.4957076
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