Replication data for: Demand Estimation under Incomplete Product Availability

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