Logit mixture models have gained increasing interest among researchers and practitioners because of their ability to capture unobserved taste heterogeneity. Becker et al. (2018) proposed a Hierarchical Bayes (HB) estimator for logit mixtures with inter- and intra-consumer heterogeneity (defined as taste variations among different individuals and among different choices made by the same individual respectively). However, the underlying model relies on strong assumptions on the inter- and intra-consumer mixing distributions; these distributions are assumed to be normal (or log-normal), and the intra-consumer covariance matrix is assumed to be the same for all individuals. This paper presents a latent class extension to the model and the estimator proposed by Becker et al. (2018) to account for flexible, semi-parametric mixing distributions. This relaxes the normality assumptions and allows different individuals to have different intra-consumer covariance matrices. The proposed model and the HB estimator are validated using real and synthetic data sets, and the models are evaluated using goodness-of-fit statistics and out-of-sample validation. Our results show that when the data comes from two or more distinct classes (with different population means and inter- and intra-consumer covariance matrices), this model results in a better fit and predictions compared to the single class model.
- Bayesian estimation
- Flexible mixing distributions
- Intra-consumer heterogeneity
- Latent class.
- Logit mixture