Estimating demand for differentiated products with zeroes in market share data

• 成果类型: Article
• 署名作者: Gandhi, Amit; Lu, Zhentong; Shi, Xiaoxia
• 署名单位: Airbnb; Bank of Canada; University of Wisconsin System; University of Wisconsin Madison
• 刊物名称: QUANTITATIVE ECONOMICS
• ISSN/ISSBN: 1759-7323
• DOI: 10.3982/QE1593
• 发表日期: 2023
• 页码: 381-418
• 关键词: Demand estimation; differentiated products; measurement error; moment inequality; zero; C01; C12; L10; L81

摘要

In this paper, we introduce a new approach to estimating differentiated product demand systems that allows for products with zero sales in the data. Zeroes in demand are a common problem in differentiated product markets, but fall outside the scope of existing demand estimation techniques. We show that with a lower bound imposed on the expected sales quantities, we can construct upper and lower bounds for the conditional expectation of the inverse demand. These bounds can be translated into moment inequalities that are shown to yield consistent and asymptotically normal point estimators for demand parameters under natural conditions. In Monte Carlo simulations, we demonstrate that the new approach works well even when the fraction of zeroes is as high as 95%. We apply our estimator to supermarket scanner data and find that correcting the bias caused by zeroes has important empirical implications, for example, price elasticities become twice as large when zeroes are properly controlled.