Seminar Vol. 121
Title: Inference on Auctions with Weak Assumptions on Information
Speaker: Elie Tamer, Harvard University
Time: December 18th, 2018 8:30–10:00
Venue: Conference Room 106B, Zhonghui Building (IESR, JNU College of Economics)
About the speaker:
Elie Tamer is a Louis Berkman Professor of Economics at the Department of Economics, Harvard University. He is also a fellow of the Econometric Society, and a former co-editor of Econometrica and Quantitative Economics. Elie Tamer received his PhD in Economics from Northwestern University in 1999. His main research interests include econometrics, and empirical industrial organization. In particular, the econometrician focuses his research on the relationship between models that economists are interested in, and the data that is observed. Elie Tamer's work has been widely published in such journals as Econometrica, Journal of Econometrics, Review of Economic Studies, Journal of Political Economy, and many others.
Abstract:
Given a sample of bids from independent auctions, this paper examines the question of inference on auction fundamentals (e.g. valuation distributions, welfare measures) under weak assumptions on information structure. The question is important as it allows us to learn about the valuation distribution in a robust way, i.e., without assuming that a particular information structure holds across observations. We leverage the recent contributions of Bergemann and Morris [2013] in the robust mechanism design literature that exploit the link between Bayesian Correlated Equilibria and Bayesian Nash Equilibria in incomplete in- formation games to construct an econometrics framework for learning about auction fundamentals using observed data on bids. We showcase our construction of identified sets in private value and common value auctions. Our approach for constructing these sets inherits the computational simplicity of solving for correlated equilibria: checking whether a particular valuation distribution belongs to the identified set is as simple as determining whether a linear program is feasible. A similar linear program can be used to construct the identified set on various welfare measures and counterfactual objects. For inference and to summarize statistical uncertainty, we propose novel finite sample methods using tail inequalities that are used to construct confidence regions on sets. We also highlight methods based on Bayesianbootstrap and subsampling. A set of Monte Carlo experiments show adequate finite sample properties of our inference procedures. We illustrate our methods using data from OCS auctions.
