Quantitative Economics

Journal Of The Econometric Society

Edited by: Stéphane Bonhomme • Print ISSN: 1759-7323 • Online ISSN: 1759-7331

Quantitative Economics: May, 2023, Volume 14, Issue 2

Estimating Demand for Differentiated Products with Zeroes in Market Share Data

https://doi.org/10.3982/QE1593
p. 381-418

Amit Gandhi, Zhentong Lu, Xiaoxia Shi

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.


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Supplemental Material

Supplement to "Estimating Demand for Differentiated Products with Zeroes in Market Share Data"

Amit Gandhi, Zhentong Lu, and Xiaoxia Shi

Supplement to "Estimating Demand for Differentiated Products with Zeroes in Market Share Data"

Amit Gandhi, Zhentong Lu, and Xiaoxia Shi