Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/148130
Authors: 
Grundl, Serafin
Zhu, Yu
Year of Publication: 
2016
Series/Report no.: 
Bank of Canada Staff Working Paper 2016-23
Abstract: 
This paper shows point identification in first-price auction models with risk aversion and unobserved auction heterogeneity by exploiting multiple bids from each auction and variation in the number of bidders. The required exclusion restriction is shown to be consistent with a large class of entry models. If the exclusion restriction is violated, but weaker restrictions hold instead, the same identification strategy still yields valid bounds for the primitives. We propose a sieve maximum likelihood estimator. A series of Monte Carlo experiments illustrate that the estimator performs well in finite samples and that ignoring unobserved auction heterogeneity can lead to a significant bias in risk-aversion estimates. In an application to U.S. Forest Service timber auctions we find that the bidders are risk neutral, but we would reject risk neutrality without accounting for unobserved auction heterogeneity.
Subjects: 
Econometric and statistical methods
JEL: 
C57
C14
D44
L00
Document Type: 
Working Paper

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