Publication Date: November 2021
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
JEL Classification Codes: C10, C11, C13, C18, L93