发表机构
University of Bristol（布里斯托大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对差异化产品需求估计中非参数方法的维数灾难，提出一种利用经济限制的可扩展非参数方法，在保持灵活性的同时大幅提升计算效率，并在美国啤酒市场应用中验证了其有效性。
AI 中文摘要
许多经济问题的答案取决于需求曲线的斜率和曲率。估计差异化产品的需求需要在灵活性和可扩展性之间进行权衡。现有的非参数方法在产品数量上面临维数灾难。我开发了一种使用市场层面数据的可扩展非参数方法，通过嵌入在许多标准模型中的经济动机限制来克服这一维数灾难。模拟实验和对美国啤酒市场的应用证明了需求斜率和曲率的灵活性，并导致了反事实价格反应的显著差异。该应用中的估计仅需数秒，而随机系数嵌套logit模型则需要数小时。
英文摘要
The answers to many economic questions depend on the slope and curvature of demand. Estimating demand for differentiated products entails a trade-off between flexibility and scalability. Existing nonparametric approaches face a curse of dimensionality in the number of products. I develop a scalable nonparametric approach using market-level data that overcomes this curse through economically motivated restrictions embedded in many standard models. Simulations and an application to the U.S. beer market demonstrate flexibility in demand slope and curvature, with consequential differences in counterfactual price responses. Estimation in the application takes seconds, versus several hours for a random coefficient nested logit model.
Comments69 pages, 1 figure, 7 tables
