Bayesian Inference in Structural Second-Price Common Value Auctions
2011 (English)In: Journal of business & economic statistics, ISSN 0735-0015, E-ISSN 1537-2707, Vol. 29, no 3, 382-396 p.Article in journal (Refereed) Published
Structural econometric auction models with explicit game-theoretic modeling of bidding strategies have been quite a challenge from a methodological perspective, especially within the common value framework. We develop a Bayesian analysis of the hierarchical Gaussian common value model with stochastic entry introduced by Bajari and Hortacsu. A key component of our approach is an accurate and easily interpretable analytical approximation of the equilibrium bid function, resulting in a fast and numerically stable evaluation of the likelihood function. We extend the analysis to situations with positive valuations using a hierarchical gamma model. We use a Bayesian variable selection algorithm that simultaneously samples the posterior distribution of the model parameters and does inference on the choice of covariates. The methodology is applied to simulated data and to a newly collected dataset from eBay with bids and covariates from 1000 coin auctions. We demonstrate that the Bayesian algorithm is very efficient and that the approximation error in the bid function has virtually no effect on the model inference. Both models fit the data well, but the Gaussian model outperforms the gamma model in an out-of-sample forecasting evaluation of auction prices. This article has supplementary material online.
Place, publisher, year, edition, pages
2011. Vol. 29, no 3, 382-396 p.
Bid function approximation, eBay, Internet auctions, Likelihood inference, Markov chain Monte Carlo, Normal valuation, Variable selection
Probability Theory and Statistics
IdentifiersURN: urn:nbn:se:su:diva-66979DOI: 10.1198/jbes.2011.08289ISI: 000292316500005OAI: oai:DiVA.org:su-66979DiVA: diva2:470081
authorCount :22011-12-282011-12-222013-07-12Bibliographically approved