關於網路拍賣的三篇實證研究

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黃盈棠(Ying-Tang Huang) 查詢紙本館藏

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產業經濟研究所

論文名稱

關於網路拍賣的三篇實證研究
(Three Empirical Essays on Internet Auctions)

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摘要(中)

網路拍賣近來已成為重要且普遍的交易方式。無論是理論及實證的研究，由拍賣網站上獲得大量的拍賣資料，均可重新檢視傳統拍賣理論是否會在拍賣網站上成立。本論文包含三個子議題，前二個議題 (第2章及第3章) 以Yahoo拍賣網站的行動電話拍賣資料為主，第三個議題 (第4章) 則利用eBay汽車拍賣為主要實證資料來源。其結果大致如下。
本論文在第一個議題首先將拍賣網站上可獲的訊息加以分類。其訊息大致可分為賣方評價、價格相關訊息、商品相關訊息、及其他可能訊息。另外，有3個重要的拍賣結果變數，分別為該拍賣獲得之出價次數，拍賣成功的機率，及最後拍賣成交價格的形成。實證結果可以發現不同訊息變數的確顯著影響不同拍賣結果的變數。
本文在第二個議題重新檢視賣方評價對拍賣價格可能的影響。與過去研究不同的是，利用二階段估計法，在第一階段考慮的因變數為拍賣成功的機率，而拍賣最後的交易價格則為第二階段所考量的變數。實證結果顯示：賣方評價的確會顯著影響拍賣成功的機率。而對拍賣的成交價格則不存在顯著的影響，此與過去實證文獻的結果大不相同。
最後，本文第三個議題利用eBay豐田汽車拍賣來檢定著名的拍賣理論：收益均等定理 (revenue equivalent theorem, Vickrey(1961))。利用來自四種不同拍賣型式的日資料 (從2008年2月8日至4月21日，共74天)，並且估計Ravallion (1986) 所發展的實證模型，可以檢定不同拍賣型式的拍賣價格是否會收斂。而實證結果也顯示，不同拍賣型式下的成交價格的確有收斂的情形。亦即，無論賣方採用何種拍賣形式，預期價格 (即賣方收益) 會趨於一致。

摘要(英)

Transactions through Internet auction have become more popular today. The considerable and available data on auction websites (either eBay or Yahoo) attract the economists’ attentions to review the general auction theory, which include theoretical and empirical studies. There are three main topics in the essay. The first two topics (chapter 2 and 3) empirically use mobile phone auctions from Yahoo auctions website. Further, the data from eBay motors’ auctions is applied in the third topic (chapter 4).
The focus in chapter 2 might be the research foundation in Internet auctions. The available information on auction sites are classified into four parts, which are seller’s reputation, price information, item’s information, and other possible information, and these four information categorizations are the independent variables in the estimations. Otherwise, the number of received bids, the probability of auction success, and the auction transaction price may be the three important outcomes in online auctions. Consequently, due to different types of dependent variables, three different econometric models are applied. And different information types indeed have different effect on the variables of auction outcomes.
In chapter 3, the study re-tests the effect of seller’s reputation on the transaction price. In the regression, the other control variables are still contained in the empirical model. Two-step estimation method is applied to conclude that the sellers’ reputation may have no effect on the transaction prices. However, it affects the dependent variable in the first stage, which is the probability of auction success. Hence, a different conclusion compared to the previous literatures is taken by the result of this chapter.
Finally, the study uses the data from eBay Toyota motors’ auctions to test the famous auction theorem: revenue equivalence theorem (Vickrey, 1961), and this research utilize 74 daily observations (from 2008/2/8 to 2008/4/21) in each format from eBay Toyota motors’ auctions to estimate the time series econometric model developed by Ravallion (1986). The transaction price from different auction formats would converge, while the expected sellers’ revenue of different formats would be equal.

關鍵字(中)

★ 網路拍賣
★ 訊息提供
★ 評價機制
★ 收益均等定理
★ 價格收斂

關鍵字(英)

★ price convergence
★ revenue equivalence theorem
★ reputation mechanism
★ information provisions
★ internet auctions

論文目次

CONTENTS I
LIST OF FIGURES III
LIST OF TABLES IV
CHAPTER 1 INTRODUCTION TO THE ESSAY 1
CHAPTER 2 INFORMATION PROVISION AND INTERNET AUCTIONS 5
2.1 INTRODUCTION 5
2.2 INFORMATION PROVISIONS ON INTERNET AUCTIONS 8
2.3 INFORMATION TYPES AND EMPIRICAL METHODOLOGY 12
2.3.1 Information Types and Auction Outcomes 12
2.3.2 Empirical Methodology 18
2.4 DATA DESCRIPTIONS AND EMPIRICAL RESULTS 22
2.4.1 The Data 22
2.4.2 Empirical Results and Analysis 23
2.5 CONCLUDING REMARKS 28
2.6 REFERENCE 30
CHAPTER 3 THE REPUTATION MECHANISM AND INTERNET AUCTION PRICE 36
3.1 INTRODUCTION 36
3.2 THE RELATED EXOGENOUS VARIABLES 41
3.2.1 The Role of Sellers’ Reputations in Internet Auctions 41
3.2.2 Other Visible Characteristics in Internet Auctions 43
3.3 EMPIRICAL DATA AND EMPIRICAL MODEL 47
3.3.1 The Data 47
3.3.2 Empirical Model Specifications 49
3.4 EMPIRICAL RESULTS AND ANALYSIS 52
3.5 CONCLUDING REMARKS 57
3.6 REFERENCE 60
CHAPTER 4 REVENUE EQUIVALENCE AND AUCTION FORMATS: AN EMPIRICAL EVIDENCE BY EBAY TOYOTA MOTORS’ AUCTIONS 64
4.1 INTRODUCTION 64
4.2 RAVALLION MODEL AND HYPOTHESES 69
4.3 EMPIRICAL METHODS 75
4.3.1 The Data 75
4.3.2 The Ravallion Model Estimations 77
4.4 SUMMARIZATION AND DISCUSSION 80
4.5 CONCLUDING REMARKS 82
4.6 REFERENCE 85
CHAPTER 5 CONCLUSION 94

參考文獻

Akerlof, G. (1970), “The Market for ‘Lemons’: Quality Uncertainty and the Market Mechanism.” The Quarterly Journal of Economics, 84: 3, pp. 488-500.
Amemiya, T. (1981), “Qualitative Response Models: A Survey,” Journal of Economic Literature, 19:4, pp. 1483-1536.
Bajari, P. and A. Hortacsu (2002b), “Cyberspace Auctions and Pricing Issues: a Review of Empirical Findings.” Working paper.
Bajari, P. and A. Hortacsu (2003), “Economic Insights from Internet Auctions.” NBER working paper.
Bajari, P. and A. Hortacsu (2003), “The Winner's Curse, Reserve Prices and Endogenous Entry: Empirical Insights From eBay Auctions.” RAND Journal of Economics, 34:2, pp. 329-355.
Bajari, P. and A. Hortacsu (2004), “Economic Insights from Internet Auctions,” Journal of Economic Literature, 42, pp. 457-486.
Budish, E. and L. Takeyama (2001), “Buy Prices in Online Auctions: Irrationality on the Internet,” Economics Letters, 73, pp. 325-333.
Cameron, A.C. and P.K. Trivedi (1998), Regression Analysis of Count Data, Cambridge University Press.
Coppinger, V. M., V.L. Smith, and J.A. Titus (1980), “Incentives and Behavior in English, Dutch and Sealed-Bid Auctions,” Economic Inquiry, 18, pp. 1-22.
Dickey, D. and W.A. Fuller (1979), “Distribution of the Estimates for Autoregressive Time Series with a Unit Root,” Journal of the American Statistical Association, 74, pp. 427-431.
Dickey, D. and W.A. Fuller (1981), “Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root,” Econometrica, pp. 49, pp. 1057-1072.
Dodonova, A. and Y. Khoroshilov (2004), “Anchoring and transaction utility: evidence from on-line auctions,” Applied Economics Letters, 11:5, pp. 307-310.
Eaton, D. H. (2002), “Valuing Information: Evidence from Guitar Auctions on eBay.” Working paper, Murray State University.
Enders, W. (2004), Applied Econometric Time Series. New York, John Wiley and Sons. 2nd Edition.
Gilkeson, J.H. and K.Reynolds (2003), “Determinants of Internet auction success and closing price: an exploratory study,” Psychology and Marketing, 20: 6, pp.537-566.
Gurmu, S. (1991), “Tests for Detecting Over-dispersion in the Positive Poisson Regression Regression Model,” Journal of Business and Economic Statistics, 9: 2, pp. 215-22.
Hansen, R.G. (1986), “Sealed-Bid versus Open Auctions: The Evidence,” Economic Inquiry, 24, pp. 125-142.
Hidvegi, Z., W. Wang, and A.B. Whinston (2006), “Buy-price English Auction,” Journal of Economic Theory, 129, pp. 31-56.
Houser, D. and J. Wooders (2006), “Reputation in Auctions: Theory, and Evidence from eBay.” Journal of Economics and Management Strategy, 15: 2, pp. 353-369.
Johnson, R. (1979), “Oral Auctions versus Sealed Bids: An Empirical Investigation,” Natural Resources Journal, 19, pp. 315-335.
Kagel, J.H. (1995), “Auctions: A Survey of Experimental Research,” in Kagel and Roth, eds., Handbook of Experimental Economics, Princeton: Princeton University Press, 1995, pp. 501-585.
Kirkegaard, R. and P. B. Overgaard (2003), “Buy-Out Prices in Online Auctions: Multi-Unit Demand.” Working paper, University of Aarhus.
Koh, W.T.H., R.S. Mariano, and Y.K. Tse (2007), “Open vs. Sealed-bid Auctions: Testing for Revenue Equivalence under Singapore’s Vehicle Quota System,” Applied Economics, 39, pp. 125-134.
Livingston, J. (2005), “How Valuable is a Good Reputation? A Sample Selection Model of Internet Auctions.” Review of Economics and Statistics, 87: 3, pp. 453-465.
Lucking-Reiley, D. (1999), “Using Field Experiments to Test Equivalence between Auction Formats: Magic on the Internet,” American Economic Review, 89, pp.1063-1080.
Lucking-Reiley, D. (2000), “Auctions on the Internet: What’s Being Auctioned, and How?” The Journal of Industrial Economics, 48: 3, pp. 227-252.
Lucking-Reiley, D., D. Bryan, and D. Reeves (2000), “Pennies from eBay: the Determinants of Price in Online Auctions.” Working paper, Vanderbilt University.
Maddala, G.S. (1983), Limited-Dependent and Qualitative Variables in Econometrics. Cambridge University Press.
McAfee, R.P. and J. McMillan (1987), “Auctions and Bidding,” Journal of Economic Literature, 25, pp. 699-738.
McDonald, C.G. and V.C. Slawson (2002), “Reputation in an Internet Market.” Economic Inquiry, 40: 4, pp.633-650.
Mead, W. (1964), Competition and Oligopsony in the Douglas fir Lumber Industry. Berkeley, CA: University of California.
Melnik, M. I. And J. Alm (2002), “Does a Seller’s Ecommerce Reputation Matter? Evidence From eBay Auctions.” The Journal of Industrial Economics, 50: 3, pp. 337-349.
Melnik, M.I. and J. Alm (2002), “Does a Seller's Ecommerce Reputation Matter? Evidence From eBay Auctions.” The Journal of Industrial Economics, 50: 3, pp. 337-349.
Ockenfels, A., D. Reiley and A. Sadrieh ( 2006 ), “Online Auctions,” NBER Working Paper No. 12785. Prepared as a draft chapter for the Handbook of Economics and Information Systems.
Ravallion, M. (1986), “Testing Market Integration,” American Journal of Agricultural Economics, 68, pp. 102-109.
Resnick, P. and R. Zeckhauser (2001), “Trust among Strangers in Internet Transactions: Empirical Analysis of eBay's Reputation System.” Working paper prepared for NBER workshop.
Resnick, P., R. Zeckhauser, J. Swanson, and K. Lockwood (2003), “The Value of Reputation on eBay: A Controlled Experiment,” Harvard Kennedy School working paper.
Riley, J. and W.F. Samuelson (1981), “Optimal auctions,” American Economic Review, 71, pp.381-392.
Satterthwaite, M. and A. Shneyerov (2007), “Dynamic Matching, Two-Sided Incomplete Information, and Participation Costs: Existence and Convergence to Perfect Competition,” Econometrica, 75, pp. 155-200.
Smith, V.L. (1989), “Theory, Experiment and Economics,” Journal of Economic Perspectives, 3, pp. 151-169.
Tenorio, R. (1993), “Revenue Equivalence and Bidding Behavior in a Multi-Unit Auction Market: An Empirical Analysis,” Review of Economics and Statistics, 75, pp. 302-314.
Vickrey, W. (1961), “Counterspeculation, Auctions, and Competitive Sealed Tenders,” Journal of Finance, 16, pp. 8-37.
Wooldridge, J.M. (2002), Econometric Analysis of Cross Section and Panel Data, MIT Press.

指導教授

陳忠榮(Jong-Rong Chen)

審核日期

2008-6-25

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