This special issue on the Econometrics of Auctions is a follow-up to the conference on the same topic 1 organized in Toulouse in May 2006 as a tribute to Jean-Jacques Laffont, who passed away on 1 May 2004. Jean-Jacques Laffont's contributions are important in many fields in economics, yet he still managed to spare some of his time to help to trigger the scientific quality uplift in the empirical analysis of auctions that took place in the last 15 years and which is reviewed, for instance, by Paarsch and Hong (2006). Papers in this special issue continue this project and use Jean-Jacques' papers in the various dimensions of his work in theory, econometric identification, estimation and empirical analysis of auctions. This is why we wish to dedicate this special issue to the memory of Jean-Jacques Laffont and to his so formidable capacity for doing and inspiring excellent research in applied and theoretical economics.
In 'Estimating risk aversion from ascending and sealed-bid auctions: the case of timber auction data', Jingfeng Lu and Isabelle Perrigne rely on multiple sources of data to identify the risk aversion of bidders. Previous research using experimental or field data had proven the importance of risk aversion albeit in a parametric way. Risk aversion is difficult to identify nonparametrically in the private-value paradigm if information consists only in observed bids (Athey and Haile, 2007). In particular, the influence of risk aversion on observed bids of different organizational forms for the auction is contrasted. In ascending auctions, it is a dominant strategy to announce each bidder's private value whatever the risk aversion is. In first-price sealed-bid auctions, risk aversion has a role to play considering the uncertainty attached to bids of the other bidders. The comparison between data obtained under these two designs should allow for the identification of risk aversion. What Jingfeng and Isabelle do is to provide a constructive proof of nonparametric identification of bidders' risk aversion and an estimation procedure that builds on it. Obviously, one needs to assume that the distribution of private values remains the same across different auction designs. Jingfeng Lu and Isabelle Perrigne show that it seems a reasonable assumption in the data they use that are drawn from timber auctions organized by the US Forest Service. They estimate risk aversion nonparametrically and show that, although imperfect, a constant relative risk aversion assumption leads to a better fit than a CARA specification.
In 'Multi-round procurement auctions with secret reserve prices: theory and evidence', Lu Ji and Tong Li tackle the estimation of a multi-round auction, which is a complex game. Some advances in the estimation of these games had been proposed by Elyakime et al. (1997). What the authors focus on is that the auctioneer can auction again later the objects that were not sold in the first auction.