Incorporating Search and Sales Information in Demand Estimation

Ali Hortacsu, Olivia R. Natan, Hayden Parsley, Timothy Schwieg and Kevin R. Williams ()
Additional contact information
Olivia R. Natan: University of California, Berkeley
Hayden Parsley: University of Texas, Austin
Timothy Schwieg: University of Chicago, Booth
Kevin R. Williams: Cowles Foundation, Yale University, https://som.yale.edu/faculty/kevin-r-williams

No 2313, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: We propose an approach to modeling and estimating discrete choice demand that allows for a large number of zero sale observations, rich unobserved heterogeneity, and endogenous prices. We do so by modeling small market sizes through Poisson arrivals. Each of these arriving consumers then solves a standard discrete choice problem. We present a Bayesian IV estimation approach that addresses sampling error in product shares and scales well to rich data environments. The data requirements are traditional market-level data and measures of consumer search intensity. After presenting simulation studies, we consider an empirical application of air travel demand where product-level sales are sparse. We find considerable variation in demand over time. Periods of peak demand feature both larger market sizes and consumers with higher willingness to pay. This amplifies cyclicality. However, observed frequent price and capacity adjustments offset some of this compounding effect.

Keywords: Discrete Choice Modeling; Demand Estimation; Zeros; Bayesian Methods; Cyclical Demand; Airline Markets (search for similar items in EconPapers)
JEL-codes: C10 C11 C13 C18 L93 (search for similar items in EconPapers)
Pages: 60 pages
Date: 2021-11
New Economics Papers: this item is included in nep-com, nep-dcm, nep-ecm, nep-ore, nep-reg and nep-tre
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