Incorporating Search and Sales Information in Demand Estimation

Ali Hortascu, Olivia R. Natan, Hayden Parsley, Timothy Schwieg and Kevin R. Williams ()
Additional contact information
Ali Hortascu: University of Chicago and NBER
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 2313R1, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: We propose a demand estimation method 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 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 as well as a measure of market sizes or consumer arrivals. After presenting simulation studies, we demonstrate the method in an empirical application of air travel demand.

Keywords: Discrete Choice Modeling; Demand Estimation; Zero-Sale Observations; Bayesian Methods; 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, Revised 2023-03
New Economics Papers: this item is included in nep-com and nep-dcm
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