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

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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