Identification and Inference in Ascending Auctions With Correlated Private Values

Authors

  • Andrés Aradillas-López,

    1. Dept. of Economics, University of Wisconsin, 1180 Observatory Drive, Madison, WI 53706, U.S.A; aaradill@ssc.wisc.edu
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  • Amit Gandhi,

    1. Dept. of Economics, University of Wisconsin, 1180 Observatory Drive, Madison, WI 53706, U.S.A; agandhi@ssc.wisc.edu
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  • Daniel Quint

    1. Dept. of Economics, University of Wisconsin, 1180 Observatory Drive, Madison, WI 53706, U.S.A; dquint@ssc.wisc.edu
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    • We are very thankful to a co-editor and three anonymous referees for their insightful feedback. We also thank Steven Durlauf, Elena Krasnokutskaya, Jeremy Fox, Phil Haile, Bruce Hansen, Ken Hendricks, Han Hong, Ali Hortaçsu, Harry Paarsch, Jack Porter, Rob Porter, Art Shneyerov, Chris Taber, seminar participants at Chicago, Chicago Booth, Concordia, Duke, Harvard/MIT, Michigan, Northwestern, NYU, Olin Business School, Stanford, UCL, Wisconsin and Yale, and conference attendees at IIOC 2009 and 2010, SITE 2009, Cowles Summer Conference 2010, the 2011 ASSA Winter Meetings, and CAPCP 2011 for helpful comments. Aradillas-López acknowledges the support of the NSF through Grant SES-0922062.


Abstract

We introduce and apply a new nonparametric approach to identification and inference on data from ascending auctions. We exploit variation in the number of bidders across auctions to nonparametrically identify useful bounds on seller profit and bidder surplus using a general model of correlated private values that nests the standard independent private values (IPV) model. We also translate our identified bounds into closed form and asymptotically valid confidence intervals for several economic measures of interest. Applying our methods to much studied U.S. Forest Service timber auctions, we find evidence of correlation among values after controlling for a rich vector of relevant auction covariates; this correlation causes expected profit, the profit-maximizing reserve price, and bidder surplus to be substantially lower than conventional (IPV) analysis of the data would suggest.

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