Demand Estimation Under Incomplete Product Availability

Incomplete product availability is an important feature of many markets; ignoring changes in availability may bias demand estimates. We study a new dataset from a wireless inventory system installed on 54 vending machines to track product availability every four hours. The data allow us to account for product availability when estimating demand, and provides a valuable source of variation for identifying substitution patterns. We develop a procedure that allows for changes in product availability even when availability is only observed periodically. We find significant differences in demand estimates, with the corrected model predicting significantly larger impacts of stock-outs on profitability