Are box office revenues equally unpredictable for all movies? Evidence from a Random forest-based model
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DOI: 10.1057/s41272-016-0072-y
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References listed on IDEAS
- Kim, Taegu & Hong, Jungsik & Kang, Pilsung, 2015. "Box office forecasting using machine learning algorithms based on SNS data," International Journal of Forecasting, Elsevier, vol. 31(2), pages 364-390.
- Jehoshua Eliashberg & Sam K. Hui & Z. John Zhang, 2007. "From Story Line to Box Office: A New Approach for Green-Lighting Movie Scripts," Management Science, INFORMS, vol. 53(6), pages 881-893, June.
- Hyndman, Rob J. & Koehler, Anne B., 2006.
"Another look at measures of forecast accuracy,"
International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
- Rob J. Hyndman & Anne B. Koehler, 2005. "Another Look at Measures of Forecast Accuracy," Monash Econometrics and Business Statistics Working Papers 13/05, Monash University, Department of Econometrics and Business Statistics.
- Flores, Benito E, 1986. "A pragmatic view of accuracy measurement in forecasting," Omega, Elsevier, vol. 14(2), pages 93-98.
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Cited by:
- Steven F. Lehrer & Tian Xie, 2022.
"The Bigger Picture: Combining Econometrics with Analytics Improves Forecasts of Movie Success,"
Management Science, INFORMS, vol. 68(1), pages 189-210, January.
- Steven F. Lehrer & Tian Xie, 2018. "The Bigger Picture: Combining Econometrics with Analytics Improve Forecasts of Movie Success," NBER Working Papers 24755, National Bureau of Economic Research, Inc.
- Steven Lehrer & Tian Xie, 2020. "The Bigger Picture: Combining Econometrics with Analytics Improve Forecasts of Movie Success," Working Paper 1449, Economics Department, Queen's University.
- 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.
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Keywords
data mining; sales forecasting; nonparametric methods; regression; neural networks; random forest;All these keywords.
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