选择模型与置换不变性：差异化产品市场中的需求估计
Choice Models and Permutation Invariance: Demand Estimation in Differentiated Products Markets
AI总结：
本文提出一种基于置换不变性的选择函数刻画，利用神经网络克服维度灾难并处理内生特征，在模拟和真实数据中优于传统参数模型，能生成可靠的价格弹性估计。
摘要：
选择建模是理解竞争格局变化如何影响消费者选择并重塑市场均衡的核心。在本文中，我们提出了选择函数的一个基本刻画，该刻画涵盖了广泛多样的现有选择模型。我们展示了像神经网络这样的非参数估计器如何能够轻松逼近此类泛函，并克服选择函数非参数估计中固有的维度灾难。我们通过大量模拟证明，我们所提出的泛函能够以完全数据驱动的方式灵活捕捉潜在的消费者行为，并且优于传统的参数化模型。由于需求环境常常表现出内生特征，我们将框架扩展以纳入内生特征下的估计。此外，我们还描述了一个形式化推断程序，用于为价格弹性等感兴趣的对象构造有效的置信区间。最后，为了评估我们估计器的实际适用性，我们使用了来自S. Berry、Levinsohn和Pakes（1995）的真实世界数据集。我们的实证分析证实，该估计器生成了现实且可比的自身价格弹性和交叉价格弹性，这些结果与现有文献中报告的观察结果一致。
英文摘要：
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 non-parametric 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
