Estimating Very Large Demand Systems

Joshua Lanier, Jeremy Large and John Quah

INET Oxford Working Papers from Institute for New Economic Thinking at the Oxford Martin School, University of Oxford

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 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 estimate our model using supermarket scanner data.

Keywords: discrete choice; demand estimation; negative sampling; machine learning; scanner data (search for similar items in EconPapers)
JEL-codes: C13 C34 D12 L20 L66 (search for similar items in EconPapers)
Pages: 64 pages
Date: 2022-06
New Economics Papers: this item is included in nep-big, nep-cmp, nep-com, nep-dcm, nep-ecm and nep-upt
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Working Paper: Estimating very large demand systems (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:amz:wpaper:2023-01

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