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来源类型Working Paper
规范类型报告
DOI10.3386/w24755
来源IDWorking Paper 24755
The Bigger Picture: Combining Econometrics with Analytics Improve Forecasts of Movie Success
Steven F. Lehrer; Tian Xie
发表日期2018-06-25
出版年2018
语种英语
摘要There exists significant hype regarding how much machine learning and incorporating social media data can improve forecast accuracy in commercial applications. To assess if the hype is warranted, we use data from the film industry in simulation experiments that contrast econometric approaches with tools from the predictive analytics literature. Further, we propose new strategies that combine elements from each literature in a bid to capture richer patterns of heterogeneity in the underlying relationship governing revenue. Our results demonstrate the importance of social media data and value from hybrid strategies that combine econometrics and machine learning when conducting forecasts with new big data sources. Specifically, while both least squares support vector regression and recursive partitioning strategies greatly outperform dimension reduction strategies and traditional econometrics approaches in fore-cast accuracy, there are further significant gains from using hybrid approaches. Further, Monte Carlo experiments demonstrate that these benefits arise from the significant heterogeneity in how social media measures and other film characteristics influence box office outcomes.
主题Econometrics ; Estimation Methods
URLhttps://www.nber.org/papers/w24755
来源智库National Bureau of Economic Research (United States)
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条目标识符http://119.78.100.153/handle/2XGU8XDN/582427
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GB/T 7714
Steven F. Lehrer,Tian Xie. The Bigger Picture: Combining Econometrics with Analytics Improve Forecasts of Movie Success. 2018.
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