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 or period at which an artist produces a particular work is often found to have highly significant predictive value. Most existing results are based on regressions that pool many painters. Although the uniqueness of artists' career paths makes it interesting to estimate such regressions for individual artists, 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, despite 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, and may be useful more generally in hedonic valuation problems.
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