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Machine learning versus econometrics: prediction of box office

Author

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  • Yan Liu
  • Tian Xie

Abstract

In this note, we contrast prediction performance of nine econometric and machine learning methods, including a new hybrid method combining model averaging and machine learning, using data from the film industry and social media. The results suggest that machine learning methods have an advantage in addressing short-run noise, whereas traditional econometric methods are better at capturing long-run trend. In addition, once sample heterogeneity is controlled, the new hybrid method tends to strike a right balance in dealing with both noise and trend, leading to superior prediction efficiency.

Suggested Citation

  • Yan Liu & Tian Xie, 2019. "Machine learning versus econometrics: prediction of box office," Applied Economics Letters, Taylor & Francis Journals, vol. 26(2), pages 124-130, January.
    • Handle: RePEc:taf:apeclt:v:26:y:2019:i:2:p:124-130
    DOI: 10.1080/13504851.2018.1441499
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    Cited by:

    1. Alexander Cuntz & Alessio Muscarnera & Prince C. Oguguo & Matthias Sahli, 2023. "IP assets and film finance - a primer on standard practices in the U.S," WIPO Economic Research Working Papers 74, World Intellectual Property Organization - Economics and Statistics Division.
    2. Jordi McKenzie, 2023. "The economics of movies (revisited): A survey of recent literature," Journal of Economic Surveys, Wiley Blackwell, vol. 37(2), pages 480-525, April.
    3. Antonio Rodríguez Andrés & Voxi Heinrich S. Amavilah & Abraham Otero, 2021. "Evaluation of technology clubs by clustering: a cautionary note," Applied Economics, Taylor & Francis Journals, vol. 53(52), pages 5989-6001, November.
    4. Joshua Eklund & Jong-Min Kim, 2022. "Examining Factors That Affect Movie Gross Using Gaussian Copula Marginal Regression," Forecasting, MDPI, vol. 4(3), pages 1-14, July.
    5. Jong-Min Kim & Leixin Xia & Iksuk Kim & Seungjoo Lee & Keon-Hyung Lee, 2020. "Finding Nemo: Predicting Movie Performances by Machine Learning Methods," JRFM, MDPI, vol. 13(5), pages 1-12, May.

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