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We present a discrete choice, random utility model and a new estimation technique for analyzing consumer demand for large numbers of products. We allow the consumer to purchase multiple units of any product and to purchase multiple products at once (think of a consumer selecting a bundle of goods in a supermarket). In our model each product has an associated unobservable vector of attributes from which the consumer derives utility. Our model allows for heterogeneous utility functions across consumers, complex patterns of substitution and complementarity across products, and nonlinear price effects. The dimension of the attribute space is, by assumption, much smaller than the number of products, which effectively reduces the size of the consumption space and simplifies estimation. Nonetheless, because the number of bundles available is massive, a new estimation technique, which is based on the practice of negative sampling in machine learning, is needed to sidestep an intractable likelihood function. We prove consistency of our estimator, validate the consistency result through simulation exercises, and present the latest estimates from our model using supermarket scanner data.

This event will be in HYBRID format. To join this seminar online OR in person, please register using the link below. If attending online the zoom link is included in the message at the end of the registration form and will also be posted on the entry on the INET Oxford Event page on the morning of the event.

The room capacity is 60 people. If we exceed that number you will be contacted and asked to attend virtually.


Please note, this event will not be recorded.

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Meeting ID: 813 2162 1567
Passcode: 495503



Research Themes

Research Programmes