Testing for reference dependence: an application to the art market

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...

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Main Authors: Beggs, A, Graddy, K
Format: Working paper
Published: University of Oxford 2005
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author Beggs, A
Graddy, K
author_facet Beggs, A
Graddy, K
author_sort Beggs, A
collection OXFORD
description 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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spelling oxford-uuid:ffb89a33-7b18-42f5-8b87-2eb977c1080d2022-03-27T13:47:09ZTesting for reference dependence: an application to the art marketWorking paperhttp://purl.org/coar/resource_type/c_8042uuid:ffb89a33-7b18-42f5-8b87-2eb977c1080dBulk import via SwordSymplectic ElementsUniversity of Oxford2005Beggs, AGraddy, KThis 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.
spellingShingle Beggs, A
Graddy, K
Testing for reference dependence: an application to the art market
title Testing for reference dependence: an application to the art market
title_full Testing for reference dependence: an application to the art market
title_fullStr Testing for reference dependence: an application to the art market
title_full_unstemmed Testing for reference dependence: an application to the art market
title_short Testing for reference dependence: an application to the art market
title_sort testing for reference dependence an application to the art market
work_keys_str_mv AT beggsa testingforreferencedependenceanapplicationtotheartmarket
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