Choice Modeling is at the core of many economics, operations, and marketing
problems. 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 non-parametric 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. As 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 dataset from S. Berry, Levinsohn, and Pakes (1995). 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.

本文介绍了一种基于非参数估计的选择模型，该模型使用神经网络逼近各种现有选择模型，并扩展到包括对内生特征的估计，同时介绍了一种有效的置信区间构建方法，使用真实数据进行了实证分析，并验证了其在实际应用中的可行性。