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Bertil Wegmann
 licentiate thesis 
Second Price Common Value Auctions and Bayesian Inference in Ebay Auctions
Abstract
Second price common value auctions is the topic of this thesis. Estimation of such
auctions are technically challenging and equilibrium bid functions in these settings
are in general complex and not easy to analyze. In Paper 1 we derive closed form
approximations of the bid function for two empirically important models. The
approximate bid functions can be evaluated directly without time consuming numerical
integration. This is crucial for speeding up likelihood/Bayesian estimations
on auction data. In Paper 2 we explore the determinants of bidder and seller behaviour
by modelling eBay auctions as independent second price common value
auctions, and assume a similar (the same in Paper 1) hierarchical Gaussian valauation
structure as in Bajari and Hortacsu (2003). We use an efficient Bayesian
variable selection algorithm to assess the importance of the model's covariates.
The good performance of the algorithm is documented on both real and simulated
data. An important result of Paper 2 is the nearly identical inferences for the approximate
bid function in Paper 1 with the exact bid function, which gives much
faster and numerically more stable evaluations of the likelihood function. We apply
the methodology to simulated data and to a carefully collected dataset of 1000 coin
auctions at eBay. The structural estimates are reasonable, both in sign and magnitude,
and the model fits the data well. Finally, we document good outofsample
predictions from the estimated model.
Keywords: Closed form solution, Bid approximation, Normal valuations, Markov Chain Monte Carlo, Variable selection, Internet auctions.
Download Introduction and Summary of Reports >>
Download report 1: Bid function approximations of second price common value auctions >>
Download report 2: Bayesian inference and variable selection in structural second price common value auctions >>
