Random Covariance Heterogeneity in Discrete Choice Models

In this paper, we extend the standard discrete choice modelling framework by allowing for random variation in the substitution patterns between alternatives across respondents, leading to increased model flexibility. The paper shows how such a Mixed Covariance model can be specified either with purely random variation or with a mixture between random and deterministic variation. Additionally, the model can be based on an underlying GEV or ECL structure. Finally, the model can be specified as a continuous mixture or as a discrete mixture. A brief application on simulated data shows that our proposed model structure is able to retrieve variations in the error-structure across respondents, hence avoiding a source of bias in forecasting applications.