Determining appropriate utility specifications for discrete choice models is time-consuming and prone to errors. With the availability of larger datasets, as number possible exponentially grows with variables under consideration, analysts need spend increasing amounts time on searching good through trial-and-error, while expert knowledge required ensure these are sound. This paper proposes an a...

In this appendix, I discuss the differences in the estimates of the model that I use in this paper (baseline model) with the estimates of a random coefficient logit model. Table 7 shows the results of the estimation of both models using the subsample of customers who always purchased Diet Coke products. The table shows that the coefficients on price and the refillable dummy are not statisticall...

We propose a framework for nonparametric identification and estimation of discrete choice models with unobserved sets. recover the joint distribution sets preferences from panel dataset on choices. assume that either latent are sparse or is sufficiently long. Sparsity requires number possible to be relatively small. It satisfied, instance, when nested, they form partition. Our procedure comp...