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The determinants of price in online auctions: more evidence from unbalanced panel data

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  • Ji-Liang Shiu
  • Chia-Hung D. Sun

Abstract

This study provides an alternative approach that takes account of the unobserved effects of each seller under a sample selection framework while using online auction data. We use data collected from Yahoo! Kimo Auction (Taiwan) to demonstrate that earlier empirical results of online auction studies may be biased due to violating the assumption of independence of the error terms between sample observations. Empirical findings show that seller reputation is no longer as the most important factor for buyers to bid on items, while the sample data confirm the unobserved heterogeneity of sellers and sample selection problem.

Suggested Citation

  • Ji-Liang Shiu & Chia-Hung D. Sun, 2014. "The determinants of price in online auctions: more evidence from unbalanced panel data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(2), pages 382-392, February.
    • Handle: RePEc:taf:japsta:v:41:y:2014:i:2:p:382-392
    DOI: 10.1080/02664763.2013.839639
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    References listed on IDEAS

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    1. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
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    5. Ockenfels, Axel & Roth, Alvin E., 2006. "Late and multiple bidding in second price Internet auctions: Theory and evidence concerning different rules for ending an auction," Games and Economic Behavior, Elsevier, vol. 55(2), pages 297-320, May.
    6. Jeffrey A. Livingston, 2005. "How Valuable Is a Good Reputation? A Sample Selection Model of Internet Auctions," The Review of Economics and Statistics, MIT Press, vol. 87(3), pages 453-465, August.
    7. Jolliffe, Dean, 2002. "The Gender Wage Gap in Bulgaria: A Semiparametric Estimation of Discrimination," Journal of Comparative Economics, Elsevier, vol. 30(2), pages 276-295, June.
    8. Chia‐Hung Sun, 2010. "The impact of auction characteristics on prices of agricultural products traded online: evidence from cherries," Agricultural Economics, International Association of Agricultural Economists, vol. 41(6), pages 587-594, November.
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    Cited by:

    1. Michael Scholz & Markus Franz & Oliver Hinz, 2016. "The Ambiguous Identifier Clustering Technique," Electronic Markets, Springer;IIM University of St. Gallen, vol. 26(2), pages 143-156, May.
    2. Shiu, Ji-Liang & Sun, Chia-Hung D., 2014. "Modeling and estimating returns to seller reputation with unobserved heterogeneity in online auctions," Economic Modelling, Elsevier, vol. 40(C), pages 59-67.

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