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Dimension Reduction and Model Averaging for Estimation of Artists’ Age-Valuation Profiles

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  • John Galbraith

    ()

  • Douglas James Hodgson

    ()

Abstract

In hedonic regression models of the valuation of works of art, the age at which an artist produces a particular work, or an indicator variable for periods in his or her artistic career, is often found to have highly significant predictive value. Most existing results are based on regressions that pool large groups of painters. Although it is of interest to estimate such regressions for individual artists, the sample sizes are often inadequate for a model that would also include the large number of other relevant variables. We address this problem of inadequate degrees of freedom in individual artist regressions by using two statistical methods (model averaging and dimension reduction) to incorporate information from a potentially large number of predictor variables, allowing us to work with relatively small samples. We find that individual age-valuation profiles can differ substantially from general pooled profiles, suggesting that methods that are more responsive to the unique features of individual artists may provide better predictions of art valuations at auction. In hedonic regression models of the valuation of works of art, the age at which an artist produces a particular work, or an indicator variable for periods in his or her artistic career, is often found to have highly significant predictive value. Most existing results are based on regressions that pool large groups of painters. Although it is of interest to estimate such regressions for individual artists, the sample sizes are often inadequate for a model that would also include the large number of other relevant variables. We address this problem of inadequate degrees of freedom in individual artist regressions by using two statistical methods (model averaging and dimension reduction) to incorporate information from a potentially large number of predictor variables, allowing us to work with relatively small samples. We find that individual age-valuation profiles can differ substantially from general pooled profiles, suggesting that methods that are more responsive to the unique features of individual artists may provide better predictions of art valuations at auction.

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Bibliographic Info

Paper provided by CIRANO in its series CIRANO Working Papers with number 2009s-41.

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Date of creation: 01 Sep 2009
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Handle: RePEc:cir:cirwor:2009s-41

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Keywords: Dimension reduction; factor-augmented model; model averaging; réduction de dimension; modèle de facteur augmenté; moyenne de modèles;

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References

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  1. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
  2. Hellmanzik, Christiane, 2010. "Location matters: Estimating cluster premiums for prominent modern artists," European Economic Review, Elsevier, vol. 54(2), pages 199-218, February.
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  16. Douglas James Hodgson, 2011. "Age-Price Profiles for Canadian Painters at Auction," CIRANO Working Papers 2011s-15, CIRANO.
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  19. Bruce E. Hansen, 2007. "Least Squares Model Averaging," Econometrica, Econometric Society, vol. 75(4), pages 1175-1189, 07.
  20. Douglas Hodgson & Keith Vorkink, 2004. "Asset pricing theory and the valuation of Canadian paintings," Canadian Journal of Economics, Canadian Economics Association, vol. 37(3), pages 629-655, August.
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Cited by:
  1. Moral-Benito, Enrique, 2010. "Model averaging in economics," MPRA Paper 26047, University Library of Munich, Germany.
  2. Charlin, Ventura & Cifuentes, Arturo, 2013. "A new financial metric for the art market," MPRA Paper 50186, University Library of Munich, Germany.
  3. John Galbraith & Greg Tkacz, 2013. "Nowcasting GDP: Electronic Payments, Data Vintages and the Timing of Data Releases," CIRANO Working Papers 2013s-25, CIRANO.

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