The first main contribution so far is a model and related estimation procedures that can be used to predict the demand for differentiated products, the Inverse Product Differentiation Logit (IPDL) model. A "product" can be many different things. The main feature is that the researcher needs access to market share data and that products can be segmented along a number of dimensions. In that case, the IPDL model is available. It is estimable using simple methods, it is very fast and can handle very large models. It facilitates dealing with endogeneity or reverse causality, as occurs for example when prices are responsive to demand.
The second main contribution is the discovery that rational inattention discrete choice models can be based on generalised entropy and that generalised entropies exist such that the predicted demand of the rational inattention model may have the form of any additive random utility discrete choice model. This is important in several ways. First, the result opens for the reinterpretation of existing discrete choice models. The conventional discrete choice model treats consumers as perfectly utility maximising and having perfect information regarding all choice alternatives. These assumptions are unreasonable but needed to make progress. Now we can relax the assumptions while still using the same models. Second, we now have also the possibility of using new models that take into account the effects of inattention. This can be important for describing phenomena that are difficult to describe using conventional models. In particular, we can now better think about consideration sets in different settings and explain how these may emerge within the framework of the model.
The third main contribution is a generalised entropy model for network route choice, which can be based on very large transport networks and use travel data aggregated to the level of origin-destination pairs. It allows data to be used with very high resolution, the model can be estimated with plain linear regression in short time, it allows that most of the network is unused for any specific origin-destination pair, and it predicts substitution patterns directly generated by the network structure. These points in conjunction make it fair to consider the new route choice model a major breakthrough in the area of route choice modelling.
