Logit mixture with inter and intra-consumer heterogeneity and flexible mixing distributions

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.