We develop a demand system for a dynamic auction market with directed search. In each period, heterogeneous goods are exogenously supplied and sold by second price auction, and incumbent bidders choose which good to bid on and how much to bid. Bidder valuations are multidimensional, private and...

We consider non-parametric estimation and inference of conditional moment models in high dimensions. We show that even when the dimension D of the conditioning variable is larger than the sample size n, estimation and inference is feasible as long as the distribution of the conditioning variable...

A building block of many models in empirical industrial organization is a characteristic space, where products are modeled as a bundle of characteristics over which consumers have preferences. The ability of such models to predict counterfactual outcomes depends on how well this characteristic...

We develop a model of consumer search with spatial learning in which sampling the payoff of one product causes consumers to update their beliefs about the payoffs of other products that are nearby in attribute space. Spatial learning gives rise to path dependence, as each new search decision...

Increasingly detailed consumer information makes sophisticated price discrimination possible. At fine levels of aggregation, demand may not obey standard regularity conditions. We propose a new randomized sales mechanism for such environments. Bidders can "buy-it-now" at a posted price, or...

Online platforms invest large sums in their search technology. Motivated by this observation, we investigate how lowering search costs affects the welfare of market participants, in a model where buyers with horizontally differentiated tastes search and then compete for differentiated goods in...