| 来源类型 | Working Paper
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| 规范类型 | 报告
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| DOI | 10.3386/w24755
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| 来源ID | Working Paper 24755
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| The Bigger Picture: Combining Econometrics with Analytics Improve Forecasts of Movie Success |
| Steven F. Lehrer; Tian Xie
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| 发表日期 | 2018-06-25
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| 出版年 | 2018
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| 语种 | 英语
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| 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
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| URL | https://www.nber.org/papers/w24755
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| 来源智库 | National Bureau of Economic Research (United States)
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| 引用统计 |
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| 资源类型 | 智库出版物
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| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/582427
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推荐引用方式
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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文件名：
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w24755.pdf
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格式：
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Adobe PDF
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