Foundations and Trends R in Econometrics Vol. 9, Nos. 2-4 (2018) 79–302 c 2018 M. L. Gentry, T. P. Hubbard, D. Nekipelov, and H. J. Paarsch DOI: 10.1561/0800000031 Structural Econometrics of Auctions: A Review Matthew L. Gentry London School of Economics and Political Science [email protected] Timothy P. Hubbard Denis Nekipelov Colby College University of Virginia [email protected] [email protected] Harry J. Paarsch University of Central Florida [email protected] Contents 1 IntroductionandMotivation 80 1.1 Recommended, Additional Reading concerning Auctions . 97 2 Theoretical Models 98 2.1 IPVModel.......................... 98 2.2 CVModel .......................... 106 2.3 AVModel .......................... 112 2.4 Complications ........................ 119 3 Mapping Theory to Observables 145 3.1 Deriving the pdfs of Bids, and Winning Bids . 146 3.2 EmpiricalSpecifications . 154 3.3 Complications ........................ 157 4 Identification 158 4.1 NotationandDefinitions . 159 4.2 VickreyAuctions. 161 4.3 EnglishAuctions. 162 4.4 Sealed, Pay-Your-Bid Auctions . 168 4.5 DutchAuctions ....................... 176 4.6 Complications ........................ 177 ii iii 5 Two Approaches 192 5.1 Reduced-FormApproach . 194 5.2 StructuralApproach. 200 5.3 Summary........................... 205 6 Estimation Strategies 206 6.1 NonparametricMethods. 206 6.2 Semi-NonparametricMethods. 211 6.3 ParametricMethods. 217 7 Asymmetries 227 7.1 AsymmetricIPVModel . 230 8 Dependence 244 8.1 Copulas ........................... 248 8.2 UsingCopulas ........................ 250 9 Multi-Unit and Multi-Object Auctions 253 9.1 Multi-UnitAuctions . 254 9.2 Multi-ObjectAuctions. 268 9.3 Long-Lived,DynamicAuctions . 269 10 Summary, Conclusions, and Suggestions 272 Acknowledgements 277 Appendices 278 A.1 Permanent and Order Statistics with Different Urns . 278 A.2 Extreme-ValueDistributions . 281 A.3 ApproximationMethods . 282 A.4 B-Splines........................... 284 References 286 Abstract We review the literature concerned with the structural econometrics of observational data from auctions, discussing the problems that have been solved and highlighting those that remain unsolved as well as sug- gesting areas for future research. Where appropriate, we discuss differ- ent modeling choices as well as the fragility or robustness of different methods. Keywords: auctions; structural econometrics. JEL Classifications: C57, Econometrics of Games and Auctions; D44, Auctions. M. L. Gentry, T. P. Hubbard, D. Nekipelov, and H. J. Paarsch. Structural Econometrics of Auctions: A Review. Foundations and Trends R in Econometrics, vol. 9, nos. 2-4, pp. 79–302, 2018. DOI: 10.1561/0800000031. 1 Introduction and Motivation One of the great success stories in economics during the latter half of the twentieth century was the systematic theoretical investigation of incomplete information in various economic environments—for exam- ple, moral hazard in insurance markets and adverse selection in such market institutions as auctions, to name just two. Developed in tandem with the incredible advances in game theory since the Second World War, an important by-product of this research program was the for- mulation of many incomplete information problems as the design of optimal mechanisms.1 From an econometrician’s perspective, theoretical models of in- complete information are particularly compelling because they provide richer explanations of the observed heterogeneity in data than ones re- lying on measurement error or productivity shocks. For example, when individual agents use private information strategically to make deci- sions, the equilibrium interactions of those decisions with the decisions of others can generate new explanations of observed phenomena, par- ticularly in the field of industrial organization. 1The book by Tilman Börgers [2015], with chapter by Daniel Krähmer and Roland Strausz, provides a useful introduction to the topic. 80 81 As is common in economics, however, econometric and empirical de- velopments involving models of incomplete information lagged behind those in theory. For even though using theoretical models to put struc- ture on economic data was advocated by the late Nobel laureates Tryge Haavelmo [1944] and Tjalling C. Koopmans [1947, 1949, 1953] as well as associates of Koopmans at the Cowles Foundation, Yale University, in the 1950s (for example, the late Nobel laureate Leonid Hurwicz [1950]), the approach did not become generally accepted, let alone widespread, until the last quarter of the twentieth century, particularly in response to the devastating evaluation of reduced-form methods by the Nobel laureate Robert E. Lucas, Jr. [1976], which is now commonly referred to as the Lucas critique. In this review, we summarize a subset of this literature—that de- voted to the structural econometric analysis of observational data from auctions, as opposed to data from either laboratory or field experi- ments, although we mention some studies involving the latter two in passing. A classic, canonical problem in economic theory involves a seller of an object who faces several potential buyers. Although the seller probably knows the object’s value to himself, he typically has little or no information concerning what value any one of the potential buyers places on the object. Similarly, each potential buyer may have a good notion of the object’s value to himself, but probably knows little about the seller’s valuation or the valuations of the other potential buyers. In short, the seller as well as the potential buyers face incomplete, asymmetric information. How should the seller dispose of the object? One commonly used method of sale involves announcing a take-it-or-leave-it price and then selling the object to the first who accepts that price. Another involves the seller’s engaging in pair-wise negotiations with individual potential buyers, either sequentially or simultaneously. Yet a third involves sell- ing the object at auction. In short, a set of different selling mechanisms exists, from which the seller must choose, guided by some objective—an auction being one of those potential choices. The choice of mechanism by the seller typically depends on many factors, for example, the ob- 82 Introduction and Motivation jective of the seller and transaction costs to name just two. Auctions are ubiquitous in market economies. They are also ancient, their durability suggesting that the institution serves an important al- locational role. Perhaps the most important feature defining environ- ments within which auctions are used is the existence of incomplete information of some sort—the asymmetry of information between the seller and the potential buyers. Because the potential buyers have no incentive to tell the seller or any of their competitors anything about those valuations, the auction plays an important allocative role by in- ducing the potential buyers to reveal to the seller information concern- ing their valuations of the object. The way potential buyers form their valuations remains an open question in economics. In fact, in auction theory, researchers are un- usually vague about what generates the demand structure, unlike in standard demand theory where considerable care is taken to specify the structure of preferences. Suffice it to say, however, when economic theorists come to modeling the asymmetry in information as well as the heterogeneity in valuations across agents, they employ random vari- ables. Typically, each potential buyer is assumed to demand at most one unit of the object in question. In the simplest model, for each potential buyer, the marginal utility of the one unit is assumed an in- dependent realization of a continuous random variable. By and large, the budget constraint and issues of substitution are ignored. With such an austere model, it is indeed somewhat surprising that economists can provide any practical insights concerning how to bid at auctions, let alone how to structure the institution with any purpose. In the workhorse model of auction theory, which was first developed by the late Nobel laureate William S. Vickrey [1961], each of a known number N of potential buyers draws an individual-specific random val- uation independently from the same differentiable cumulative distribu- tion function (cdf) FV (v) that has corresponding probability density function (pdf) fV (v) = dFV (v)/dv. In Vickrey’s model, he assumed that V is distributed uniformly on the unit interval: the specific value of his draw is that potential buyer’s private information; it represents the monetary value of the object to him. Economic theorists refer to 83 this as the symmetric independent private-values (IPV) model because the draws are independent and the valuations are bidder specific. Also, because the valuations are drawn from the same distribution (urn so to speak), the bidders are ex ante symmetric. Different auction formats (open-outcry versus sealed-bid) and dif- ferent pricing rules (pay-your-bid versus second-price) provide potential buyers with different incentives concerning how to bid. For example, under the pay-your-bid rule, a bidder’s action (his bid) determines what he pays should he win, whereas under the second-price rule, the action (bid) of his nearest rival determines what the winner pays. In equilibrium, different functions map the private information of potential buyers (their values) into their actions (their bids). For ex- ample, open-outcry (sometimes referred to as oral) auctions can be conducted in two different ways.