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.

翻译：选择模型是许多经济学、运营和营销问题的核心。在本文中，我们提出了一个涵盖多种现有选择模型的选择函数基本表征。我们展示了非参数估计器（如神经网络）如何轻松逼近此类函数，并克服选择函数非参数估计中固有的维度灾难。通过大量模拟，我们证明了所提出的函数能够以完全数据驱动的方式灵活捕捉潜在消费者行为，并优于传统参数模型。由于需求设置通常呈现内生特征，我们将框架扩展到包含内生特征下的估计。此外，我们还描述了一种正式的推断程序，用于构建价格弹性等感兴趣对象的有效置信区间。最后，为评估估计器的实际适用性，我们使用了S. Berry、Levinsohn和Pakes (1995) 的真实世界数据集。实证分析证实，该估计器生成的现实且可比的自身价格弹性和交叉价格弹性与现有文献中报告的观测结果一致。