Estimating Very Large Demand Systems

• Econometrics

Date and Location

Monday, December 4, 2023, 3:40 PM - 5:00 PM
Blue Room, 1113 Social Sciences and Humanities

Abstract

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 prod- ucts 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 estima- tor, validate the consistency result through simulation exercises, and estimate our model using supermarket scanner data.

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