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Bayesian comparison of private and common values in structural second-price auctions
Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.ORCID iD: 0000-0003-2193-6003
2015 (English)In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 42, no 2, 380-397 p.Article in journal (Refereed) Published
Abstract [en]

Private and common values (CVs) are the two main competing valuation models in auction theory and empirical work. In the framework of second-price auctions, we compare the empirical performance of the independent private value (IPV) model to the CV model on a number of different dimensions, both on real data from eBay coin auctions and on simulated data. Both models fit the eBay data well with a slight edge for the CV model. However, the differences between the fit of the models seem to depend to some extent on the complexity of the models. According to log predictive score the IPV model predicts auction prices slightly better in most auctions, while the more robust CV model is much better at predicting auction prices in more unusual auctions. In terms of posterior odds, the CV model is clearly more supported by the eBay data.

Place, publisher, year, edition, pages
Taylor and Francis (Routledge): STM, Behavioural Science and Public Health Titles , 2015. Vol. 42, no 2, 380-397 p.
Keyword [en]
Markov chain Monte Carlo; private values; eBay; Bayesian variable selection; common values; Gaussian model
National Category
Mathematics
Identifiers
URN: urn:nbn:se:liu:diva-112602DOI: 10.1080/02664763.2014.951604ISI: 000344560700011OAI: oai:DiVA.org:liu-112602DiVA: diva2:770225
Note

Funding Agencies|Jan Wallander and Tom Hedelius Foundation

Available from: 2014-12-10 Created: 2014-12-05 Last updated: 2017-12-05

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Wegmann, Bertil

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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  • vancouver
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  • Other style
More styles
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  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf