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Testing for Reference Dependence: An Application to the Art Market

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  • Alan Beggs
  • Kathryn Graddy

Abstract

This paper tests for reference dependence, using data from Impressionist and Contemporary Art auctions. We distinguish reference dependence based on rule of thumb learning from reference dependence based on rational learning. Furthermore, we distinguish pure reference dependence from effects due to loss aversion. Thus, we use actual market data to test essential characteristics of Kahneman and Tversky`s Prospect Theory. The main methodological innovations of this paper are firstly, that reference dependence can be identified separately from loss aversion. Secondly, we introduce a consistent non-linear estimator to deal with measurement errors problems involved in testing for loss aversion. In this dataset, we find strong reference dependence but no loss aversion.

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File URL: http://www.economics.ox.ac.uk/materials/working_papers/paper228.pdf
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Bibliographic Info

Paper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 228.

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Date of creation: 01 Mar 2005
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Handle: RePEc:oxf:wpaper:228

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Keywords: Reference Dependence; Loss Aversion; Prospect Theory; Art; Auctions;

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  1. Orley Ashenfelter & Kathryn Graddy, 2010. "Art Auctions," Working Papers 1212, Princeton University, Department of Economics, Center for Economic Policy Studies..
  2. Donald W.K. Andrews & Werner Ploberger, 1992. "Optimal Tests When a Nuisance Parameter Is Present Only Under the Alternative," Cowles Foundation Discussion Papers 1015, Cowles Foundation for Research in Economics, Yale University.
  3. Amos Tversky & Daniel Kahneman, 1979. "Prospect Theory: An Analysis of Decision under Risk," Levine's Working Paper Archive 7656, David K. Levine.
  4. Alan Beggs & Kathryn Graddy, 1997. "Declining Values and the Afternoon Effect: Evidence from Art Auctions," RAND Journal of Economics, The RAND Corporation, vol. 28(3), pages 544-565, Autumn.
  5. Susanne M. Schennach, 2004. "Estimation of Nonlinear Models with Measurement Error," Econometrica, Econometric Society, vol. 72(1), pages 33-75, 01.
  6. David Genesove & Christopher Mayer, 2001. "Loss Aversion And Seller Behavior: Evidence From The Housing Market," The Quarterly Journal of Economics, MIT Press, vol. 116(4), pages 1233-1260, November.
  7. Bruno Jullien & Bernard Salanié, 1997. "Estimating Preferences under Risk : The Case of Racetrack Bettors," Working Papers 97-39, Centre de Recherche en Economie et Statistique.
  8. Orley Ashenfelter & Kathryn Graddy, 2003. "Auctions and the Price of Art," Journal of Economic Literature, American Economic Association, vol. 41(3), pages 763-787, September.
  9. Whitney K. Newey, 2001. "Flexible Simulated Moment Estimation Of Nonlinear Errors-In-Variables Models," The Review of Economics and Statistics, MIT Press, vol. 83(4), pages 616-627, November.
  10. Victor Ginsburgh & Pierre-Michel Menger, 1996. "Economics of the arts: selected essays," ULB Institutional Repository 2013/1655, ULB -- Universite Libre de Bruxelles.
  11. Robert B. Davies, 2002. "Hypothesis testing when a nuisance parameter is present only under the alternative: Linear model case," Biometrika, Biometrika Trust, vol. 89(2), pages 484-489, June.
  12. Jianping Mei & Michael Moses, 2002. "Art as an Investment and the Underperformance of Masterpieces," American Economic Review, American Economic Association, vol. 92(5), pages 1656-1668, December.
  13. Nicholas Barberis & Ming Huang & Tano Santos, 2001. "Prospect Theory And Asset Prices," The Quarterly Journal of Economics, MIT Press, vol. 116(1), pages 1-53, February.
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Cited by:
  1. Erdos, Péter & Ormos, Mihály, 2010. "Random walk theory and the weak-form efficiency of the US art auction prices," Journal of Banking & Finance, Elsevier, vol. 34(5), pages 1062-1076, May.

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