Abstract We study demand estimation in a large auction market. In our model, a dynamically evolving population of buyers with unit demand and heterogeneous and privately known preferences for a finite set of differentiated products compete in a sequence of auctions that occur in discrete time. We define an empirically tractable equilibrium concept in which bidders behave as though they are competing with the stationary distribution of opposing bids, characterize bidding strategies, and prove existence of equilibrium. Having developed this demand system, we prove that it is non-parametrically identified from panel data. We extend the model to consider a random coefficients demand system akin to workhorse demand models in industrial organization, and show that this too is non-parametrically identified. We apply the model to estimate demand and show how large sellers can exercise market power by using persistent reserve price policies, which induce higher bids and, therefore, revenues. Our analysis highlights the importance of both dynamic bidding strategies and panel data sample selection issues when analysing these markets.

Abstract We study patterns of behavior in bilateral bargaining situations using a rich new data set describing back-and-forth sequential bargaining occurring in over 25 million listings from eBay’s Best Offer platform. We compare observed behavior to predictions from the large theoretical bargaining literature. One-third of bargaining interactions end in immediate agreement, as predicted by complete-information models. The majority of sequences play out differently, ending in disagreement or delayed agreement, which have been rationalized by incomplete information models. We find that stronger bargaining power and better outside options improve agents’ outcomes. Robust empirical findings that existing models cannot rationalize include reciprocal (and gradual) concession behavior and delayed disagreement. Another robust pattern at odds with existing theory is that players exhibit a preference for making and accepting offers that split the difference between the two most recent offers. These observations suggest that behavioral norms, which are neither incorporated nor explained by existing theories, play an important role in the success of bargaining outcomes.

We outline a framework for the empirical analysis of signaling games based on three features: sorting, incentive compatibility, and beliefs. We apply it to document cheap-talk signaling in the use of round-number offers during negotiations. Using millions of online bargaining interactions, we show that items listed at multiples of $100 receive offers that are 8–12 percent lower but are 15–25 percent more likely to sell, demonstrating the trade-off requisite for incentive compatibility. Those same sellers are more likely to accept a similar offer, and buyers are more likely to investigate their listings, consistent with seller sorting and buyer belief updating.