Price to Compete … with Many: How to Identify Price Competition in High-Dimensional Space
研究了存在大量潜在竞争对手的市场中的价格竞争问题，提出结合工具变量和高维正则化的新方法，利用在线搜索和点击流数据识别相关竞争者，并以纽约酒店市场为例验证了方法的有效性。
We study price competition in markets with a large number (in the magnitude of hundreds or thousands) of potential competitors. We address two methodological challenges: simultaneity bias and high dimensionality. Simultaneity bias arises from joint determination of prices in competitive markets. We propose a new instrumental variable approach to address simultaneity bias in high dimensions. The novelty of the idea is to exploit online search and clickstream data to uncover customer preferences at a granular level, with sufficient variations both over time and across competitors in order to obtain valid instruments at a large scale. We then develop a methodology to identify relevant competitors in high dimensions combining the instrumental variable approach with high-dimensional l − 1 norm regularization. We apply this data-driven approach to study the patterns of hotel price competition in the New York City market. We also show that the competitive responses identified through our method can help hoteliers proactively manage their prices and promotions. The online appendix is available at https://doi.org/10.1287/mnsc.2017.2820 . This paper was accepted by Vishal Gaur, operations management.