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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.

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  • 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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    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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