Vasilis Syrgkanis, Elie Tamer, Juba Ziani
arXiv 10 Oct 2017 · Econometrics · 21 citations (OpenAlex)
arXiv:1710.03830 · PDF · DOI · OpenAlex · Extracted main text
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 \cite{Bergemann2013} in the robust mechanism design literature that exploit the link between Bayesian Correlated Equilibria and Bayesian Nash Equilibria in incomplete information 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 {\it 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 Bayesian bootstrap 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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | D. Bergemann and S. Morris (2013) Robust predictions in games with incomplete information | 0.843 | 3 | 3 | 100% |
| 2 | D. Bergemann and S. Morris (2016) Bayes correlated equilibrium and the comparison of information structures in games | 0.811 | 4 | 2 | 100% |
| 3 | T. Li, I. Perrigne, and Q. Vuong (2002) Structural estimation of the affiliated private value auction model | 0.693 | 5 | 1 | 100% |
| 4 | D. Bergemann, B. Brooks, and S. Morris (2017) First-price auctions with general information structures: Implications for bidding and revenue | 0.644 | 4 | 2 | 50% |
| 5 | L. Magnolfi and C. Roncoroni (2016) Estimation of discrete games with weak assumptions on information | 0.644 | 2 | 2 | 100% |
| 6 | A. Maurer and M. Pontil (2009) Empirical Bernstein Bounds and Sample Variance Penalization | 0.585 | 3 | 1 | 100% |
| 7 | B. Kline and E. Tamer (2016) Bayesian inference in a class of partially identified models | 0.511 | 2 | 1 | 100% |
| 8 | V. Chernozhukov, H. Hong, and E. Tamer (2007) Estimation and confidence regions for parameter sets in econometric models | 0.511 | 2 | 1 | 100% |
| 9 | J.-Y. Audibert, R. Munos, and C. Szepesvári (1876) Exploration–exploitation tradeoff using variance estimates in multi-armed bandits | 0.405 | 1 | 1 | 100% |
| 10 | G. Chamberlain and G. W. Imbens (2003) Nonparametric applications of bayesian inference | 0.405 | 1 | 1 | 100% |
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