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Forecasting the Trend of Art Market

Author

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  • Mihaela-Eugenia VASILACHE

    (Ph.D. Student, School of Advanced Studies of the Romanian Academy (SCOSAAR) - Economic, Social and Legal Sciences Department)

Abstract

The paper discusses two different methods to forecasting the global index of Art Market: a Holt-Winters type exponential smoothing method for times series with additive components (time trend and seasonal variation) and a Seasonal Autoregressive Integrated Moving Average (SARIMA) model. Both methods point out that the decline of Art Market started in 2015 will continue in 2018 and 2019, and a slight recovery will be possible by 2020. We also presented a method for combining forecasts.

Suggested Citation

  • Mihaela-Eugenia VASILACHE, 2018. "Forecasting the Trend of Art Market," Computational Methods in Social Sciences (CMSS), "Nicolae Titulescu" University of Bucharest, Faculty of Economic Sciences, vol. 6(1), pages 82-93, June.
    • Handle: RePEc:ntu:ntcmss:vol6-iss1-82-93
    as

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    References listed on IDEAS

    as
    1. Aurelio Bruzzo, 2018. "Recenti iniziative europee ed italiane per la valorizzazione del patrimonio culturale," Working Papers 2018127, University of Ferrara, Department of Economics.
    2. James Mitchell & Donald Robertson & Stephen Wright, 2019. "R2 Bounds for Predictive Models: What Univariate Properties Tell us About Multivariate Predictability," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(4), pages 681-695, October.
    3. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    4. Dominik Filipiak & Agata Filipowska, 2016. "Towards data oriented analysis of the art market: survey and outlook," "e-Finanse", University of Information Technology and Management, Institute of Financial Research and Analysis, vol. 12(1), pages 21-31, June.
    5. Daiva Jurevičienė & Božena Kostecka, 2014. "Peculiarities of selection of investment artworks," Acta Oeconomica Pragensia, Prague University of Economics and Business, vol. 2014(5), pages 71-88.
    6. Mark W. Watson & James H. Stock, 2004. "Combination forecasts of output growth in a seven-country data set," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 405-430.
    7. Dorin JULA & Nicolae-Marius JULA, 2017. "Foreign Direct Investments and Employment. Structural Analysis," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 29-44, June.
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