P.S. I also tried to make plotting easier. Here, for example, are estimates from a model with two product characteristics and correlated preferences. More to come, including docs to make it easier to dig through the "problem" objects to extract results.

Finally, we put this all in a Julia package! Trying to take seriously that these complex methods cost a lot of time and effort to implement, we tried to make it as easy as possible to use our methods for estimation, inference, and some basic counterfactuals 6/6

Third, we apply our method to real demand from 12 categories of products at a large retail food chain. Our estimates agree with our simulations — constraints matter, and imposing them generates more reasonable elasticities and counterfactuals (below) 5/

Second, we show in simulations that our method improves upon the previous state of the art for this model, both because we can fully enforce constraints and because doing so gives us more accurate estimates of price elasticities. 4/

This two-step approach not only helps us impose new constraints -- it also guarantees that our constraints are satisfied *everywhere*, which we show is often not true of the best previous approach 3/

New paper with @adam-n-smith.bsky.social
papers.ssrn.com/sol3/papers....

Our paper develops a new approach for estimating demand nonparametrically while imposing economic constraints and comes with a new package, NPDemand.jl! Some things we do in the paper 1/