Quantitative Marketing and Economics

, Volume 4, Issue 1, pp 57–81

Modeling preference evolution in discrete choice models: A Bayesian state-space approach


  • Mohamed Lachaab
    • Institut Superieur de GestionUniversity of Tunis
    • Clarkson University
  • Asim Ansari
    • Graduate School of BusinessColumbia University
  • Kamel Jedidi
    • Graduate School of BusinessColumbia University
  • Abdelwahed Trabelsi
    • Institut Superieur de GestionUniversity of Tunis

DOI: 10.1007/s11129-006-6559-x

Cite this article as:
Lachaab, M., Ansari, A., Jedidi, K. et al. Quant Market Econ (2006) 4: 57. doi:10.1007/s11129-006-6559-x


We develop discrete choice models that account for parameter driven preference dynamics. Choice model parameters may change over time because of shifting market conditions or due to changes in attribute levels over time or because of consumer learning. In this paper we show how such preference evolution can be modeled using hierarchial Bayesian state space models of discrete choice. The main feature of our approach is that it allows for the simultaneous incorporation of multiple sources of preference and choice dynamics. We show how the state space approach can include state dependence, unobserved heterogeneity, and more importantly, temporal variability in preferences using a correlated sequence of population distributions. The proposed model is very general and nests commonly used choice models in the literature as special cases.

We use Markov chain monte carlo methods for estimating model parameters and apply our methodology to a scanner data set containing household brand choices over an eight-year period. Our analysis indicates that preferences exhibit significant variation over the time-span of the data and that incorporating time-variation in parameters is crucial for appropriate inferences regarding the magnitude and evolution of choice elasticities. We also find that models that ignore time variation in parameters can yield misleading inferences about the impact of causal variables.


Preference evolutionHierarchical Bayesian state-space modelsHeterogeneityMultinomial probitChoice modelsPricingPromotions

Copyright information

© Springer Science + Business Media, LLC 2006