@article{f591e768e14b49cfab8dd4e815391958,
title = "Semiparametric estimation of first-price auctions with risk-averse bidders",
abstract = "In view of the non-identification of the first-price auction model with risk-averse bidders, this paper proposes some parametric identifying restrictions and a semiparametric estimator for the risk aversion parameter(s) and the latent distribution of private values. Specifically, we exploit heterogeneity across auctioned objects to establish semiparametric identification under a conditional quantile restriction of the bidders' private value distribution and a parameterization of the bidders' utility function. We develop a multistep semiparametric method and we show that our semiparametric estimator of the utility function parameter(s) converges at the optimal rate, which is slower than the parametric one but independent of the dimension of the exogenous variables thereby avoiding the curse of dimensionality. We then consider various extensions including a binding reserve price, affiliation among private values, and asymmetric bidders. The method is illustrated on U.S. Forest Service timber sales, and bidders' risk neutrality is rejected.",
keywords = "Optimal rate, Private value, Risk aversion, Semiparametric estimation, Semiparametric identification, Timber auctions",
author = "Sandra Campo and Emmanuel Guerre and Isabelle Perrigne and Quang Vuong",
note = "Funding Information: Acknowledgement. We are grateful to Jingfeng Lu, Jim Powell, and Mathew Shum for their suggestions as well as to the co-editor and three referees for their constructive comments. Previous versions were presented at the Malinvaud seminar, University of Cambridge, University College London, Oxford University, University of Toulouse, University of California at Los Angeles and Berkeley, University of Rochester, University of Pittsburgh, Penn State University, Princeton University, Rutgers University, New York University, Harvard-MIT, Cornell University, the Tow Conference on Auctions at the University of Iowa, the Cowles Foundation Conference on Strategy and Decision Making at Yale University, the SITE Meeting at Stanford University, the World Congress of the Econometric Society 2000, and the European Congress of the Econometric Society 2002. We thank Phil Haile and Jason Cummins for providing the data set used in this paper. Financial support from the National Science Foundation grant SES-0452154 is gratefully acknowledged by the last two authors. Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the National Science Foundation.",
year = "2011",
month = jan,
day = "1",
doi = "10.1093/restud/rdq001",
language = "English (US)",
volume = "78",
pages = "112--147",
journal = "Review of Economic Studies",
issn = "0034-6527",
publisher = "Oxford University Press",
number = "1",
}