Choice Models and Permutation Invariance: Deep Demand Estimation in Differentiated Products Markets

Choice modeling is at the core of understanding how changes to the competitive landscape affect consumer choices and reshape market equilibria. In this paper, we propose a fundamental characterization of choice functions that encompasses a wide variety of extant choice models. We demonstrate how nonparametric estimators like neural nets can easily approximate such functionals and overcome the curse of dimensionality that is inherent in the nonparametric estimation of choice functions. We demonstrate through extensive simulations that our proposed functionals can flexibly capture underlying consumer behavior in a completely data-driven fashion and outperform traditional parametric models. Because demand settings often exhibit endogenous features, we extend our framework to incorporate estimation under endogenous features. Further, we also describe a formal inference procedure to construct valid confidence intervals on objects of interest, like price elasticity. Finally, to assess the practical applicability of our estimator, we utilize a real-world data set, US Automobile Data (1971–1990). Our empirical analysis confirms that the estimator generates realistic and comparable own- and cross-price elasticities that are consistent with the observations reported in the existing literature.

History: Tat Chan served as the senior editor for this article.

Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mksc.2024.0724.