 Abstract
This paper proposes a semiparametric estimation procedure of the first-price auction model with risk averse bidders within the independent private value paradigm. We show that the model is nonidentified in general from observed
bids. We then exploit heterogeneity across auctioned objects to establish semiparametric identification under
a conditional quantile restriction and parameterization of the bidders' von Neuman Morgenstern utility function. Next we propose a semiparametric method for estimating
the corresponding auction model. This method involves several steps and allows to recover the parameters of the utility function as well as the bidders' private values and their density. We show that our semiparametric estimator of the utility function parameters converges at the optimal rate, which is slower than
the parametric one. An illustration of the method on U.S. Forest Service timber sales is presented and a test of bidders' risk neutrality is performed.
Download locations
Semiparametric estimation of first-price auction with risk-averse bidders
http://www.ccr.jussieu.fr/lsta/EGuerre/risk3.pdf
|
