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A multilevel model with autoregressive components for the analysis of tribal art prices

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  • Lucia Modugno
  • Silvia Cagnone
  • Simone Giannerini

Abstract

In this paper, we introduce a multilevel model specification with time-series components for the analysis of prices of artworks sold at auctions. Since auction data do not constitute a panel or a time series but are composed of repeated cross-sections, they require a specification with items at the first level nested in time-points. Our approach combines the flexibility of mixed effect models together with the predicting performance of time series as it allows to model the time dynamics directly. Model estimation is obtained by means of maximum likelihood through the expectation-maximization algorithm. The model is motivated by the analysis of the first database ethnic artworks sold in the most important auctions worldwide. The results show that the proposed specification improves considerably over classical proposals both in terms of fit and prediction.

Suggested Citation

  • Lucia Modugno & Silvia Cagnone & Simone Giannerini, 2015. "A multilevel model with autoregressive components for the analysis of tribal art prices," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(10), pages 2141-2158, October.
    • Handle: RePEc:taf:japsta:v:42:y:2015:i:10:p:2141-2158
    DOI: 10.1080/02664763.2015.1021304
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    Cited by:

    1. Fikret Korhan Turan & Zeynep Tosun, 2023. "Sustainable development of art industry and a statistical analysis of the factors that influence the gallery prices of contemporary artworks," Sustainable Development, John Wiley & Sons, Ltd., vol. 31(3), pages 1790-1804, June.
    2. Petrov, Nikita & Ratnikova, Tatiana, 2017. "The price index for the paintings of Henri Matisse: The sensitivity to the method of construction and connection with stock market and art indices," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 47, pages 49-73.

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